Scipedia: Documents published in 2026

Role of Human Rights in the Indian Judicial System

Indian Judicial Forum — Sat, 12 Sep 2026 15:37:13 +0200

Human rights in India derive their force not from a single code but from a constitutional architecture — Part III's fundamental rights, Part IV's directive principles, and an independent judiciary empowered under Articles 32 and 226 to enforce them. This review article examines the role that Indian courts, particularly the Supreme Court, have played in transforming a modest textual guarantee of “protection of life and personal liberty” under Article 21 into an expansive charter covering dignity, privacy, livelihood, health, a clean environment, and protection from custodial abuse. It traces the doctrinal journey from Maneka Gandhi v. Union of India (1978) through the era of public interest litigation to contemporary jurisprudence on privacy and sexual autonomy, and situates this judicial trajectory alongside the institutional architecture of the National Human Rights Commission and the State Human Rights Commissions. The article further examines the human rights dimensions of the 2023 overhaul of India's criminal statutes, the persistent challenges of custodial violence, prison overcrowding, and judicial delay, and the emerging frontier of digital-age rights. It concludes that while Indian courts have been remarkably innovative in expanding the substantive content of rights, the realisation of those rights on the ground continues to depend on institutional capacity, executive compliance, and sustained public vigilance.

Recent BNS Changes and the Transformation of the Indian Legal Ecosystem

Indian Judicial Forum — Thu, 10 Sep 2026 19:57:13 +0200

On 1 July 2024, India replaced three foundational statutes of its criminal justice architecture — the Indian Penal Code, 1860, the Code of Criminal Procedure, 1973, and the Indian Evidence Act, 1872 — with the Bharatiya Nyaya Sanhita, 2023 (“BNS”), the Bharatiya Nagarik Suraksha Sanhita, 2023 (“BNSS”), and the Bharatiya Sakshya Adhiniyam, 2023 (“BSA”). This article undertakes a comprehensive doctrinal and empirical examination of the BNS and its allied enactments, situating the reform within the broader trajectory of post-colonial legal change in India. It traces the legislative history of the codes, maps their structural reorganisation, and analyses the principal substantive innovations — including the repeal of sedition, the codification of organised crime and terrorism, gender-neutral provisions for offences against the body, the introduction of community service as a sentencing option, and the statutory recognition of electronic and digital evidence. The article further evaluates the first two years of implementation, drawing on government status reports, judicial pronouncements, and academic commentary to assess institutional preparedness, infrastructural constraints, and the emerging jurisprudence of the High Courts and the Supreme Court on transitional and retrospective questions. It concludes that while the new codes represent a genuine attempt at modernisation and victim-centric reform, their long-term success is contingent upon sustained investment in judicial and police capacity, harmonised interpretation across jurisdictions, and continued judicial vigilance in safeguarding constitutional guarantees during the transition.

RECENT SUPREME COURT JUDGEMENTS AND ORDERS ON THE CJP PROTESTS

Indian Judicial Forum — Wed, 09 Sep 2026 20:16:04 +0200

This article examines the Supreme Court of India’s recent intervention in litigation arising from the 2026 student protests associated with the Cockroach Janta Party (CJP), with particular attention to the Court’s orders of 28 July, 18 August and 1 September 2026 in Shailendra Mani Tripathi v. Union of India and connected proceedings. The litigation raises an unusually dense cluster of constitutional questions: the relationship between Articles 19(1)(a) and 19(1)(b); the State’s duty to maintain public order; limits on police crowd-control powers; the use of pellet guns, tear gas, lathis and other force; protection of minors; publication and retention of protesters’ personal data; and the exceptional remedial authority under Article 142. The paper situates the 2026 proceedings within the Supreme Court’s longer jurisprudence on protest, including Himmat Lal K. Shah, Ramlila Maidan Incident, Mazdoor Kisan Shakti Sangathan, Anita Thakur and Amit Sahni (Shaheen Bagh). It also engages with recent legal scholarship on Shaheen Bagh, public order and legal mobilisation. The central argument is that the 2026 orders should not be read as creating an unqualified right to protest or as immunising violence. Rather, they deepen an emerging constitutional model in which peaceful dissent receives affirmative protection, State restrictions must remain connected to genuine public-order concerns, police force must be necessary and proportionate, protester data requires constitutional safeguards, and remedial relief may be calibrated to protect the future of young participants without extinguishing accountability for grave criminal conduct. The 18 August order is particularly significant because it constituted a High-Powered Enquiry Committee (HPEC) and identified issues extending beyond individual criminal liability: proportionality of force, use of metallic projectiles, visible identification of police personnel, surveillance, treatment of women protesters, compensation, blanket prohibitory orders, and possible misuse of Section 152 of the Bharatiya Nyaya Sanhita, 2023. The 1 September order then used Article 142 to quash protest-related FIRs and extend closure to other FIRs concerning the same July incidents, while retaining a carve-out concerning 2,873 persons identified by Delhi Police and directing a pan-India compensation policy for families affected by NEET-UG-related student suicides. The article concludes that the litigation may become a leading contemporary authority on the constitutional governance of protest in the digital-surveillance era, but that its lasting doctrinal value will depend on the HPEC’s findings and the Court’s eventual resolution of the pending constitutional questions.

The Impact of “No-Meeting Days” on Organizational Efficiency and Optimal Weekly Work Balance

Shivendra Nandan — Wed, 09 Sep 2026 07:54:24 +0200

Meetings consume a growing share of the modern knowledge worker's calendar, with surveys indicating that more than 55% of working hours are now spent in scheduled meetings and that 41% of managers regard meetings as costly and unproductive. In response, a growing number of organizations — including Meta (Facebook), Atlassian, Slack, and Owl Labs — have introduced “no-meeting days,” structured periods during which employees are protected from all scheduled interruptions, including one-on-ones. This article synthesizes findings from a 76-company MIT Sloan Management Review study and a University of Reading (Henley Business School) analysis of meeting-free policies, alongside Microsoft Human Factors Lab research on meeting-induced cognitive stress, to quantify the organizational efficiency gains associated with no-meeting days. Across the reviewed evidence, introducing two to four no-meeting days per week is associated with meeting-volume reductions of up to 60%, cooperation gains of roughly 55%, and communication-transparency improvements of roughly 65%, alongside consistent increases in autonomy, engagement, and satisfaction and reductions in micromanagement and stress. The article concludes with an evidence-informed framework for designing an optimal weekly work balance that blends protected focus time with intentional collaboration windows.

Học IELTS Cho Người Mất Gốc Đà Nẵng: Cán Đích 7.0+

WISE ENGLISH — Mon, 07 Sep 2026 11:36:09 +0200

“Học IELTS cho người mất gốc Đà Nẵng giống như leo núi mà không có bản đồ” — đây là tâm lý chung của nhiều người học tại Đà Nẵng khi đối mặt với vô vàn từ vựng Academic và ngữ pháp phức tạp. Giữa ma trận tài liệu cùng nỗi sợ “học trước quên sau”, không ít bạn đã chấp nhận bỏ cuộc hoặc tốn nhiều chi phí nhưng vẫn giậm chân tại chỗ.

Thực chất, mất gốc không phải là rào cản vĩnh viễn; việc học hoài không vào chỉ vì bạn chưa tìm đúng phương pháp dung nạp theo cơ chế não bộ. Bài viết này sẽ mang đến giải pháp học IELTS cho người mất gốc tại Đà Nẵng từ WISE English, giúp bạn tái cấu trúc tư duy tiếng Anh và tự tin bứt phá 7.0+ dễ dàng.

Summary

Đối tượng: Người mất gốc tiếng Anh, sai phát âm, muốn đạt 5.5 – 7.0+ IELTS tại Đà Nẵng.

Thời gian học: 5 – 7 tháng (rút ngắn 80% thời gian tự học nhờ phương pháp NLP).

Phương pháp từ vựng 6 bước: (1) Loại từ ➔ (2) Phát âm ➔ (3) Nghĩa ➔ (4) Đặt câu ➔ (5) Gia đình từ ➔ (6) Từ đồng nghĩa/trái nghĩa.

Phương pháp Nghe: Chép chính tả tự nhiên (Natural Dictation) giúp phản xạ không cần dịch nhẩm.

Chính sách cam kết: Ký hợp đồng cam kết đầu ra bằng văn bản – học lại 100% miễn phí nếu không đạt target.

A Mixed-Integer Numerical Optimization Framework for Reliability-Driven Energy Storage System Configuration in Power Grids

Scipedia content — Tue, 01 Sep 2026 11:26:03 +0200

This paper introduces a numerical optimization model (mixed-integer) of reliability-based design of energy storage systems (ESS) in the highrenewable-based power grid. This is done by developing a mixed-integer nonlinear programming (MINLP) model that incorporates probabilistic reliability indices (Loss of Load Probability and Expected Energy Not Supplied) directly into the objective function and also optimizes ESS sizing, placement, and operational scheduling. An approach that is a hybrid solution strategy that incorporates outer approximation and metaheuristic enhancement is used to deal with non-convexities in reliability calculations and modelling of storage degradation. Convergence analysis demonstrates that the algorithm attains optimality gaps of less than 3.5% in 150 iterations, 18–22 times more efficient than standard solvers, computationally. It has been shown with a 320 MW peak load benchmark system based on the IEEE 118-bus topology under five operating conditions that the optimized ESS configuration saves Loss of Load Probability from 0.0214 to 0.0062 (71% improvement), as well as Expected Energy Not Supplied from 1245 to 358 MWh/year, and unnecessary ESS cycling is reduced by 29%. The framework provides system planners with a computationally manageable tool to optimally deploy ESS in a renewable-dominated grid.OPEN ACCESS Received: 12/03/2026 Accepted: 29/06/2026

El capital y la teoría del imperialismo: Escritos marxistas (1872-1919)

Manuel Loyola — Mon, 31 Aug 2026 01:21:03 +0200

El objetivo de este volumen, que consta de veintinueve fuentes primarias y un artículo analítico, es doble: por un lado, analizar la dinámica del modo de producción capitalista a través de la recepción de las obras económicas de Karl Marx -particularmente los tres tomos de El capital: Crítica de la economía política y los tres tomos de Teorías sobre el plusvalor- por sus discípulos desde su muerte hasta la Primera Guerra Mundial, y por el otro, investigar cómo dichos discípulos aplicaron las categorías económicas de Marx al análisis de la fase superior -y por ende al período actual- del capitalismo: el imperialismo.

LA HISTORIA DE HOH

Manuel Loyola — Mon, 31 Aug 2026 01:15:04 +0200

Este es un libro con ilustraciones que relata el caso de una persona con problemas de salud mental graves.

Se coloca el acento en sus derechos y su anverso, la discriminación y el estigma social. El texto se divide en dos partes: una está referida a la historia del personaje (llamado HOH) a través de ilustraciones y narrativas; la otra es un relato técnico científico, desarrollado a través de unidades educativas y esquemas didácticos. El lector hará una elección de su lectura en conexión con sus intereses. Sus autores combinan en la elaboración del texto, sus conocimientos teóricos y su experiencia en tanto profesionales de la salud mental. Esperan contribuir a través de la difusión de este texto a la desestigmatización de las personas con problemas graves de salud mental, ya que ello es constitutivo de derechos humanos que deben ser respetados, y siendo, por sobre todo, una condición clave para el tratamiento, la rehabilitación y su reinserción social.

Disability and Neurodivergence in Higher Education: Experiences and Proposals from Key Stakeholders

Manuel Loyola — Mon, 31 Aug 2026 00:56:14 +0200

This book comprises ten works, including research and essays, conducted at various Chilean universities. They form a mosaic that ranges from student activism to institutional governance, from the music room to the science lab, from theoretical essays to systematic reviews. This plurality is not anecdotal; it reflects a central conviction of the Interdisciplinary Research Center on University Inclusion and Disability at the University of Chile (NIIIUD): university inclusion cannot be a self-contained compartment, the exclusive responsibility of specialized offices or support programs, but must permeate teaching, research, outreach, and, above all, the entire organizational culture. In this sense, the book is part of a tradition of critical disability studies that, in dialogue with feminisms, decolonial thought, and science and technology studies, questions the foundational dichotomies of Western modernity—normal/abnormal, able/inable, independent/dependent, productive/unproductive—and the power structures that these dichotomies uphold.

Aprender a mirar. Manual de observación etnográfica de la arquitectura cotidiana rural

Manuel Loyola — Mon, 31 Aug 2026 00:02:23 +0200

En su concepción inicial, el presente documento fue pensado como una guía de apoyo metodológico para estudiantes que realizan trabajo de campo, particularmente orientada a fortalecer la rigurosidad del quehacer etnográfico en el proceso de registro sistemático de datos. El énfasis estaba puesto, desde un comienzo, en afinar la mirada observacional y en dotar de mayor densidad analítica a los registros empíricos, evitando descripciones superficiales o meramente ilustrativas. Para ello, se proyectó originalmente como una compilación selectiva de registros fotográficos, escogidos en función de ciertas características específicas, tales como su valor descriptivo, su capacidad de evidenciar transformaciones en el tiempo y su potencial para suscitar interpretaciones contextualizadas.

BMAG CONSULTING UNVEILS EXPLOSIVE GROWTH IN GLOBAL METAL MATRIX COMPOSITES MARKET, PROJECTING TRANSFORMATIVE IMPACT ACROSS KEY INDUSTRIES FLORIDA, United States, July 5, 2026 — FOR IMMEDIATE RELEASE BMAG Consulting, a premier global advisory firm, today announced the release of its comprehensive report, 'Metal Matrix Composites Market: Strategic Outlook & Growth Opportunities,' revealing a sector on the cusp of unprecedented expansion. The report provides critical insights into the forces driving the accelerated adoption of MMCs across high-performance industries, highlighting their pivotal role in next-generation engineering and sustainable innovation. The burgeoning demand for lightweight, high-strength materials capable of withstanding extreme conditions is the primary catalyst for the Metal Matrix Composites market. Industries such as aerospace, automotive, defense, and electronics are increasingly integrating MMCs into critical components, driven by the need for enhanced performance, fuel efficiency, and extended product lifespans. BMAG Consulting's analysis spotlights specific growth segments, including aluminum-based MMCs and magnesium-based MMCs, which are witnessing significant R&D investment and commercial deployment. Download Free Sample Report: https://bmagconsulting.com/sample/metal-matrix-composites-market Technological breakthroughs in manufacturing processes, such as stir casting, powder metallurgy, and additive manufacturing, are significantly improving the cost-effectiveness and scalability of MMC production. This innovation is democratizing access to these advanced materials, pushing them beyond niche applications into mainstream industrial use. The report delves into the competitive landscape, profiling key players and emerging innovators who are shaping the future of MMC development and application globally. THE REGULATORY PIVOT The Regulatory Pivot: Moving Beyond 'One-Size-Fits-All' Diagnostics. In the rapidly evolving Metal Matrix Composites market, regulatory frameworks are transitioning from broad, generic standards to application-specific performance diagnostics. This shift is critical as MMCs are deployed in diverse, high-stakes environments, necessitating tailored certifications and validation protocols. Regulatory bodies are increasingly focusing on material traceability, lifecycle performance, and environmental impact, driving manufacturers towards rigorous testing and compliance. BMAG Consulting emphasizes that understanding these evolving regulatory nuances is paramount for market entry, product differentiation, and achieving sustainable competitive advantage. STRATEGIC MARKET OUTLOOK Strategic Market Outlook: Demand Engines. The future growth of the Metal Matrix Composites market is intrinsically linked to several powerful 'demand engines.' Foremost among these is the electric vehicle (EV) revolution, where MMCs offer superior strength-to-weight ratios and thermal management capabilities for battery housings and motor components. Concurrently, the aerospace industry's relentless pursuit of fuel efficiency and reduced emissions fuels demand for lightweight MMC structures. Furthermore, advancements in defense applications, particularly for armor and high-performance weaponry, along with the expanding realm of industrial machinery requiring durable, high-wear components, represent significant and accelerating growth vectors. These engines collectively underscore a robust, long-term trajectory for MMC market expansion. View Full Advisory Report: https://bmagconsulting.com/report/metal-matrix-composites-market ABOUT BMAG CONSULTING BMAG Consulting is a boutique advisory firm renowned for its precision execution and deep industry expertise. We partner with global leaders and emerging innovators to navigate complex market dynamics, identify strategic opportunities, and drive sustainable growth. Our commitment to data-driven insights, customized solutions, and unparalleled client success positions us as a trusted advisor in a rapidly changing world. MEDIA CONTACT Contact: Mary Joseph, Senior Consultant Organization: BMAG Consulting Phone: +1 646 759 1187 Email: Mary.Joseph@bmagconsulting.com Website: https://bmagconsulting.com/

Oliver Hawthorne — Wed, 26 Aug 2026 08:16:26 +0200

BMAG CONSULTING UNVEILS EXPLOSIVE GROWTH IN GLOBAL METAL MATRIX COMPOSITES MARKET, PROJECTING TRANSFORMATIVE IMPACT ACROSS KEY INDUSTRIES

FLORIDA, United States, July 5, 2026 — FOR IMMEDIATE RELEASE

BMAG Consulting, a premier global advisory firm, today announced the release of its comprehensive report, 'Metal Matrix Composites Market: Strategic Outlook & Growth Opportunities,' revealing a sector on the cusp of unprecedented expansion. The report provides critical insights into the forces driving the accelerated adoption of MMCs across high-performance industries, highlighting their pivotal role in next-generation engineering and sustainable innovation.

The burgeoning demand for lightweight, high-strength materials capable of withstanding extreme conditions is the primary catalyst for the Metal Matrix Composites market. Industries such as aerospace, automotive, defense, and electronics are increasingly integrating MMCs into critical components, driven by the need for enhanced performance, fuel efficiency, and extended product lifespans. BMAG Consulting's analysis spotlights specific growth segments, including aluminum-based MMCs and magnesium-based MMCs, which are witnessing significant R&D investment and commercial deployment.

Download Free Sample Report: https://bmagconsulting.com/sample/metal-matrix-composites-market

Technological breakthroughs in manufacturing processes, such as stir casting, powder metallurgy, and additive manufacturing, are significantly improving the cost-effectiveness and scalability of MMC production. This innovation is democratizing access to these advanced materials, pushing them beyond niche applications into mainstream industrial use. The report delves into the competitive landscape, profiling key players and emerging innovators who are shaping the future of MMC development and application globally.

THE REGULATORY PIVOT

The Regulatory Pivot: Moving Beyond 'One-Size-Fits-All' Diagnostics. In the rapidly evolving Metal Matrix Composites market, regulatory frameworks are transitioning from broad, generic standards to application-specific performance diagnostics. This shift is critical as MMCs are deployed in diverse, high-stakes environments, necessitating tailored certifications and validation protocols. Regulatory bodies are increasingly focusing on material traceability, lifecycle performance, and environmental impact, driving manufacturers towards rigorous testing and compliance. BMAG Consulting emphasizes that understanding these evolving regulatory nuances is paramount for market entry, product differentiation, and achieving sustainable competitive advantage.

STRATEGIC MARKET OUTLOOK

Strategic Market Outlook: Demand Engines. The future growth of the Metal Matrix Composites market is intrinsically linked to several powerful 'demand engines.' Foremost among these is the electric vehicle (EV) revolution, where MMCs offer superior strength-to-weight ratios and thermal management capabilities for battery housings and motor components. Concurrently, the aerospace industry's relentless pursuit of fuel efficiency and reduced emissions fuels demand for lightweight MMC structures. Furthermore, advancements in defense applications, particularly for armor and high-performance weaponry, along with the expanding realm of industrial machinery requiring durable, high-wear components, represent significant and accelerating growth vectors. These engines collectively underscore a robust, long-term trajectory for MMC market expansion.

View Full Advisory Report: https://bmagconsulting.com/report/metal-matrix-composites-market

ABOUT BMAG CONSULTING

BMAG Consulting is a boutique advisory firm renowned for its precision execution and deep industry expertise. We partner with global leaders and emerging innovators to navigate complex market dynamics, identify strategic opportunities, and drive sustainable growth. Our commitment to data-driven insights, customized solutions, and unparalleled client success positions us as a trusted advisor in a rapidly changing world.

MEDIA CONTACT

Contact: Mary Joseph, Senior Consultant
Organization: BMAG Consulting
Phone: +1 646 759 1187
Email: Mary.Joseph@bmagconsulting.com
Website: https://bmagconsulting.com/

Dynamic Mechanisms and Multiparameter Control of Fracture-Induced Lost Circulation in Deep Wells—A Fluid-Solid Coupling Approach

Scipedia content — Tue, 25 Aug 2026 12:26:04 +0200

Deep and ultra-deep well drilling in multi-scale fractured formations frequently encounters fracture-induced lost circulation, which restricts drilling efficiency and reservoir development. To address this problem, this paper establishes a dynamic finite element numerical model based on fluid-solid coupling theory, revealing the multi-physics coupling mechanism of fracture propagation and leakage channel evolution. The model fully considers the seepage-stress coupling effect during drilling fluid circulation, and quantitatively analyzes the influences of pump rate, fluid density, fluid viscosity, fracture geometric parameters, elastic modulus, Poisson’s ratio and in-situ stress on fracture aperture and fluid loss rate. The research results show that: pump rate and fluid density are positively correlated with loss rate and fracture aperture; fluid viscosity presents two-stage regulation characteristics, which is negatively correlated with leakage parameters at the fracture initiation stage and positively correlated in steady-state leakage stage; longer fracture length increases loss rate but reduces fracture aperture; elastic modulus, Poisson’s ratio and in-situ stress mainly affect the early leakage behavior, with little influence on steady-state stage. The proposed multi-parameter coupling control model can realize dynamic prediction of multi-fracture leakage in deep wells, and provides theoretical support for on-site anti-leakage and plugging work.

A Robust Ensemble Learning Paradigm for Breast Cancer Diagnosis Based on Multi-Classifier Performance Optimization

Scipedia content — Tue, 25 Aug 2026 12:22:07 +0200

Breast cancer is among the major causes of cancer-related mortality in women, and early diagnosis is critical to improved survival. However, traditional diagnostic methods or standalone models often lack precision and have the potential to give false-negative results. This study aims to present a hyperparameter-tuned ensemble learning model for the classification of breast benign and malignant tumors based on the Wisconsin Breast Cancer (Original) dataset. Data quality was ensured through a series of preprocessing techniques: median imputation, Zscore standardization, and the Local Outlier Factor (LOF) noise reduction algorithm. The classical Machine Learning (ML) models (SVM, Random Forest (RF), XGBoost, Bagging) were compared with the Deep Learning models (VGG16 and DenseNet121). A hybridized soft-voting ensemble of the four classical classifiers (SVM, RF, XGBoost, and Bagging) was then constructed, and its hyperparameters were tuned with the OPTUNA framework using the Treestructured Parzen Estimator (TPE), a Bayesian optimization algorithm, over 30 trials. The results of the experiments demonstrated that the Ensemble + OPTUNA model performed with an accuracy of 99.25% and a recall of 1.00 for the malignant class compared to the individual models and recent state-of-the-art methods. This is an optimized ensemble for reliable and computationally stable performance. To enhance transparency and privacy-preserving validation in clinical settings, Explainable AI (XAI) will be incorporated into future research, while Federated Learning will be enhanced to better validate the model.

Regression Modeling and Risk Analysis of Asymmetric Unit-Interval Data in Environmental and Geoscience Studies for Sustainable Planning

Scipedia content — Tue, 25 Aug 2026 12:20:03 +0200

This paper introduces the New Extended Topp–Leone (NExTL) distribution, a flexible three-parameter probability model for data on the unit interval (0, 1). Developed using a type 2 exponentiation framework, it generalizes the classical Topp–Leone family, offering enhanced versatility in capturing various density shapes and hazard rate behaviors. We derive its core statistical properties, including moments, quantile and hazard functions, entropy measures, and order statistics. Parameter estimation via maximum likelihood is confirmed as consistent and reliable through simulation studies. For practical application, the NExTL distribution is integrated within the generalized additive models for location, scale, and shape (GAMLSS) framework, enabling comprehensive regression modeling of bounded responses. The model’s superior performance is empirically validated in two applications: a risk analysis of petroleum rock permeability data, where it provides the best fit and robust tailrisk quantification (Value at Risk and Tail Value at Risk), and a quantile regression analysis of behavioral ecology data, successfully identifying significant treatment effects on insect attraction. In both cases, the NExTL distribution outperforms standard competitors such as the beta and Kumaraswamy distributions, as measured by information criteria and goodness-of-fit statistics, representing a significant advancement for modeling proportional data in fields like engineering, finance, and biology.OPEN ACCESS Received: 13/02/2026 Accepted: 23/06/2026 Published: 07/08/2026

Thermal Barrier Coatings Materials Market Size, Share, Growth, and Forecast 2032 : BAMG Consulting

Oliver Hawthorne — Tue, 25 Aug 2026 09:25:01 +0200

BMAG CONSULTING REVEALS THERMAL BARRIER COATINGS MATERIALS MARKET POISED FOR UNPRECEDENTED GROWTH, EXCEEDING $25 BILLION BY 2035

FLORIDA, United States, July 5, 2026 — FOR IMMEDIATE RELEASE

BMAG Consulting, a leading global advisory firm, today announced the release of its pivotal new report, "Thermal Barrier Coatings Materials Market: Strategic Analysis & Forecast 2025-2035". This comprehensive study forecasts the global Thermal Barrier Coatings (TBC) materials market to surpass an astounding $25 billion by 2035, driven by escalating demand from aerospace, automotive, and power generation sectors. The report offers unparalleled insights into market dynamics, technological advancements, and strategic opportunities for industry stakeholders.

The analysis highlights that Thermal Barrier Coatings are critical for enhancing the performance and durability of components operating in extreme high-temperature environments. Industries such as aerospace, particularly in jet engine components, and gas turbines in power generation, are seeing exponential adoption rates as they strive for increased fuel efficiency, reduced emissions, and extended operational lifespans. The report delves into various material types, including yttria-stabilized zirconia, mullite, and other advanced ceramics, projecting their growth trajectories and technological maturity.

Download Free Sample Report: https://bmagconsulting.com/sample/thermal-barrier-coatings-materials-market

BMAG Consulting's experts emphasize that innovation in deposition techniques, such as Electron Beam Physical Vapor Deposition (EBPVD) and Atmospheric Plasma Spray (APS), along with the development of novel coating chemistries, are key factors propelling this market forward. The study provides a detailed segmentation by application, end-use industry, and geography, offering a granular view of the market's intricate ecosystem and identifying high-potential growth regions across North America, Europe, Asia Pacific, and emerging markets.

The report serves as an indispensable tool for manufacturers, suppliers, investors, and policymakers, offering actionable intelligence to navigate the complex market landscape. It presents strategic recommendations for market entry, competitive positioning, and investment decisions, empowering clients to capitalize on the robust growth opportunities within the thermal barrier coatings materials sector.

THE REGULATORY PIVOT

The regulatory landscape is profoundly reshaping the Thermal Barrier Coatings (TBC) materials market. With a global push towards stricter emissions standards and enhanced fuel efficiency across aerospace and power generation, manufacturers are compelled to adopt advanced TBC solutions. This shift moves beyond 'one-size-fits-all' diagnostics, demanding application-specific, high-performance coatings that meet precise operational and environmental mandates. Regulatory bodies are increasingly focusing on the durability, reliability, and long-term performance of TBCs, pushing for rigorous testing and certification processes. This pivot incentivizes innovation in materials science and coating technologies that can withstand extreme thermal cycling and corrosive environments while adhering to evolving safety and environmental compliance protocols. The report meticulously dissects these regulatory impacts, providing a roadmap for compliance and competitive advantage.

STRATEGIC MARKET OUTLOOK

The Strategic Market Outlook reveals several powerful 'Demand Engines' fueling the Thermal Barrier Coatings (TBC) materials market. Primary among these is the aerospace sector's unrelenting demand for lighter, more fuel-efficient engines that can operate at higher temperatures, directly driving the adoption of advanced TBCs for turbine blades and combustors. The power generation industry, particularly gas turbines, continues its pursuit of increased efficiency and reduced downtime, making TBCs essential for enhanced component life and performance. Furthermore, the automotive industry's transition towards high-performance internal combustion engines and the nascent but growing demand for thermal management in electric vehicle battery systems present significant opportunities. Other drivers include technological advancements in material synthesis (e.g., novel ceramic compositions, multi-layered structures) and sophisticated deposition techniques that improve coating adherence and integrity. These engines collectively ensure a robust and sustained growth trajectory for the TBC materials market.

View Full Advisory Report: https://bmagconsulting.com/report/thermal-barrier-coatings-materials-market

ABOUT BMAG CONSULTING

BMAG Consulting is a premier global advisory firm dedicated to delivering bespoke strategic insights and actionable intelligence to businesses navigating complex market landscapes. Specializing in boutique advisory services, BMAG Consulting prides itself on precision execution, data-driven analysis, and a commitment to fostering long-term global client success. Our expert teams leverage deep industry knowledge and proprietary methodologies to empower clients with the foresight needed to achieve sustainable growth and competitive advantage.

MEDIA CONTACT

Contact: Mary Joseph, Senior Consultant
Organization: BMAG Consulting
Phone: +1 646 759 1187
Email: Mary.Joseph@bmagconsulting.com
Website: https://bmagconsulting.com/

Anatomía de una transición: Estados Unidos y Chile (1920-1929)

Manuel Loyola — Mon, 24 Aug 2026 20:36:14 +0200

La autora reconstruye la anatomía de una transición histórica: la convergencia entre la expansión de los intereses de Estados Unidos en Chile y el ascenso de un nuevo grupo político en la década de 1920 que dio paso hacia una nueva estructura de poder marcada por la modernización estatal, la expansión económica estadounidense y la emergencia de nuevas élites políticas. A través del estudio de la concesión de patentes, la circulación de innovaciones tecnológicas y la profundización de los vínculos financieros, la obra muestra cómo estos procesos favorecieron la consolidación de nuevas formas de dependencia económica y política en Chile, un debate que sigue siendo central para comprender las dinámicas históricas de ascenso y declive en el orden internacional.

North Africa, Latin America and the Caribbean. Transatlantic Dialogues

Manuel Loyola — Mon, 24 Aug 2026 19:42:03 +0200

En el marco del proyecto Encuentros Intelectuales Sur-Sur, se gesta este libro como parte de un desafío por ocuparse de las relaciones, similitudes, contrastes, entre 2 regiones que conversan poco entre ellas, como son África del Norte y América Latina y Caribe. África del Norte y ALC han dialogado relativamente poco a lo largo de su historia, aunque siempre mucho más de lo que comúnmente conocen sus comunidades académicas y no digamos la población en general. Aunque si comparamos con otros casos percibimos la diferencia. Han conversado notoriamente menos de lo que han dialogado ALC con América del Norte y Europa Occidental, menos también de lo que ha dialogado África del Norte con la Europa del Mediterráneo o con el Oriente Medio.

Gregory X: The Pope Who Changed the Rules of Power

Agustin V. Startari — Mon, 24 Aug 2026 14:45:03 +0200

Amid one of the most turbulent periods in the history of the medieval papacy, Gregory X: The Pope Who Changed the Rules of Power reconstructs, with historical rigor and narrative clarity, the life and legacy of Teobaldo Visconti, an unexpected occupant of the throne of Saint Peter. His election after the longest papal election in history (nearly three years of internal divisions, enforced confinement, and external pressure) marked a turning point in thirteenth-century ecclesiastical politics.

From his beginnings as a cleric from Piacenza, archdeacon of Liège, and crusader in the Holy Land to his decisive role at the Second Council of Lyon, the pontificate of Gregory X (1271–1276) was brief yet profoundly reformist. This book examines his attempt at reconciliation with the Greek Orthodox Church, his promotion of the institutionalization of the conclave as the system for papal election, and his efforts to establish an equilibrium between the European powers and Rome’s spiritual authority. Combining political, ecclesiological, and diplomatic analysis, the work offers a portrait of a pontiff whose historiographical reception has been uneven and, above all, a study of a government that left enduring institutional instruments to the medieval Church.

This series brings together independent studies that explore power, ideology, legitimacy, and history from a cross-disciplinary perspective. Each volume is an autonomous work, yet all form part of a common thread: the critical analysis of how power is configured, exercised, and sustained over time. From ancient Egypt to twentieth-century totalitarianism, Working Papers presents, with academic rigor and clear prose, the historical and discursive mechanisms that shape our societies. Each installment is numbered to indicate its place within this evolving series.

The effect of Caffeine on the rhizogenesis of Asparagus seeds

Armaanjit Singh — Fri, 21 Aug 2026 22:43:24 +0200

This experiment investigated caffeine's effect on asparagus root development and the absorption of minerals. Previous studies have tested different concentrations of caffeine in a nutrient medium; thus, obtaining data on caffeine’s effect under optimal nutrient conditions becomes necessary. The influence of caffeine on plant yield and seed germination rates changes depending on different concentrations. To solidify the effects of different concentrations, this experiment has been conducted using concentrations of decaffeinated and caffeinated green tea. Given the prolonged germination period of asparagus seedlings, a pre-planting soaking procedure in water was done for 24 hours to enhance germination. After soaking the seedlings, they were placed in rockwool to germinate. Data was collected weekly on the root growth, and the fresh weight was collected after the experiment. By week 4, the root growth of 1% decaffeinated green tea exceeded threefold of what was observed in the control group. After week 4, all groups excluding 1% decaffeinated exhibited less root development than the control group. Concentrations of 2% or concentrations that contain caffeine restrict root growth and fresh weight. The fresh weight of seedlings that received caffeinated treatments is half that of seedlings treated with 1% decaffeinated solution. Notably, the sprouting ratio was most influenced by green tea concentrations rather than caffeine content, with 2% decaffeinated and caffeinated concentrations exhibiting the greatest sprouting ratio. With high root length and full sprouting ratio, asparagus seedlings treated with 1% decaffeinated green tea indicated the inefficiency of caffeine on the germination rate of asparagus seedlings.

Agricultural Water Management Technology Market Size, Share, Growth, and Forecast 2032 : BAMG Consulting

Oliver Hawthorne — Fri, 21 Aug 2026 10:28:18 +0200

GLOBAL AGRICULTURAL WATER MANAGEMENT TECHNOLOGY MARKET TO REACH STAGGERING GROWTH AMIDST MOUNTING WATER SCARCITY

FLORIDA, United States, July 5, 2026 — FOR IMMEDIATE RELEASE

BMAG Consulting, a leading global consulting firm, today announced the release of its groundbreaking report, The Agricultural Water Management Technology Market. This comprehensive analysis reveals that the market is ready for significant expansion, driven by global water scarcity, climate change effects, and the urgent need for sustainable food production.

The report offers a deep dive into market dynamics, technological innovations, and strategic opportunities to help stakeholders navigate this critical sector.

The new report highlights an increasing shift toward precision agriculture and smart irrigation solutions as key drivers. With global population growth driving higher food output and natural resources under unprecedented strain, effective water management is no more. An option but a necessity.

Download Free Sample Report: https://bmagconsulting.com/sample/agricultural-water-management-technology-market

BMAG Consulting's research indicates that advanced sensors, IoT-enabled systems, and data analytics are revolutionising the way we conserve and apply water. Agricultural practices lead to better production and reduced environmental footprints.

Key findings also underline the regional disparities and unique challenges facing different agricultural landscapes. From arid regions in the Middle East demanding robust desalination integration to water-stressed plains in North America, sophisticated remote sensing is necessary. The growth rate in the market is strongly influenced by location. Environmental factors and regulatory frameworks.

The report gives granular data on market segmentation, competition scenes, and future projections, offering valuable insights for investors, technology providers, and political decision-makers.

In addition, the analysis emphasises the financial necessity of adopting these technologies. In addition to environmental benefits, smart water management directly translates into expense savings for farmers through reduced water consumption and energy expenditure, adding to the addendum at the end of agricultural productivity and profitability. BMAG Consulting requirements. Early adopters and innovators in this space are ready to procure substantial competitive advantages in the coming decade.

THE REGULATORY PIVOT

The Regulatory Pivot: Goes beyond a "one size fits all" assessment. Regulatory bodies are recognised globally. The complexity of water management goes beyond 'one-size-fits-all' mandates towards an adaptive, incentive-based framework. This shift is advancing an environment where innovation thrives, encouraging the adoption of technologies that favour smart metering, water trading platforms, and conservation credits.

Future regulations: Data-driven and localised compliance is expected to be strongly emphasised. Ecological impact assessments, instead of pushing stakeholders towards a precisely engineered solution, will favour broad-stroke interventions.

STRATEGIC MARKET OUTLOOK

Strategic Market Outlook: Demand Engines. Basic demand engines for the agricultural water management technology market are multifaceted. They include the escalating global water crisis and its direct impact on food security and necessary efficient resource utilisation. Second, I'll focus on IoT, AI, and big data analytics, which enable unprecedented levels of precision and automation in irrigation.

Third, increase government subsidies and incentives for sustainable farming practices, gaining speed in technology adoption. Finally, the growing awareness and consumer demand for sustainably produced food is driving agricultural companies to invest. Eco-friendly water management solutions.

View Full Advisory Report: https://bmagconsulting.com/report/agricultural-water-management-technology-market

ABOUT BMAG CONSULTING

BMAG Consulting is a boutique advisory firm renowned for its precision in market intelligence and strategic foresight. We empower global enterprises with actionable insights, translating complex data into clear, executable strategies for sustainable growth and competitive advantage. Our commitment to excellence ensures clients achieve targeted outcomes with unparalleled efficiency, navigating dynamic markets with confidence and achieving lasting success through our tailored advisory services.

MEDIA CONTACT

Contact: Mary Joseph, Senior Consultant
Organisation: BMAG Consulting
Phone: +1 646 759 1187
Email: Mary.Joseph@bmagconsulting.com
Website: https://bmagconsulting.com/

Microbial Crop Protection Market Size, Share, Growth, and Forecast 2032 : BAMG Consulting

Oliver Hawthorne — Thu, 20 Aug 2026 09:20:02 +0200

GLOBAL MICROBIAL CROP PROTECTION MARKET PROJECTED FOR TRANSFORMATIVE GROWTH DRIVEN BY SUSTAINABILITY

FLORIDA, United States, July 5, 2026 — FOR IMMEDIATE RELEASE

BMAG Consulting, a leading consulting firm, today announced the release of its comprehensive new report describing the microbial crop protection market. Study predicts a substantial surge in market valuation, driven by the growing demand for sustainable agricultural practices, evolving regulatory landscapes, and advances in bioinnovation.

This pivotal research offers critical insights to help stakeholders navigate the shift to greener farming solutions, projecting the market to now be $15 billion by 2035.

The report indicates that the paradigm shift from traditional chemical pesticides to microbial alternatives is not now a niche trend but a core strategic imperative for global agriculture. Aggravating factors include consumer awareness of food safety, stricter environmental regulations that reduce the use of chemicals, and the rising incidence of pesticide resistance, which collectively accelerate the adoption of biological crop protection methods. The adoption of biological crop protection methods. BMAG Consulting's analysis illustrates how these forces give a new look at the competitive landscape and usher in a new era of agricultural innovation.

Download Free Sample Report: https://bmagconsulting.com/sample/microbial-crop-protection-market

BMAG Consulting’s A intensive dive into market dynamics reveals. Significant investment opportunities across the value chain, from R&D of novel microbial strains to advanced application technologies. Gives a report. Detailed segmentation of product type (biofungicides, biopesticides, bionematicides, etc.), crop type, and regional analysis offers a granular view of growth hotspots and new challenges.

Underlines it. That companies prioritize robust scientific validation and scalable production will be the best position to remove the advantage of. This burgeoning market.

THE REGULATORY PIVOT

The Regulatory Pivot: Moving Beyond 'One-Size-Fits-All' Diagnostics for Microbial Crop Protection is crucial. Traditional regulatory frameworks, often designed for synthetic chemicals, are evolving to accommodate the unique characteristics of biologicals. Key insights include:

Streamlined Approval Processes: Many regions are introducing faster, science-based approval pathways for biological pesticides, recognizing their lower environmental impact.
Data-Driven Efficacy: A growing emphasis on data-driven efficacy testing and risk assessment tailored to biological mechanisms is shaping new guidelines.
Global Harmonization Efforts: Initiatives to harmonize regulatory standards across different countries are vital for international market penetration, reducing complexities for innovators.
Consumer Safety Focus: Regulations continue to prioritize consumer and environmental safety, encouraging robust research into the specificity and non-target impacts of microbial agents.

This shift demands a proactive and adaptive approach from manufacturers and policymakers to foster innovation while ensuring safety.

STRATEGIC MARKET OUTLOOK

The Strategic Market Outlook: Demand engines for microbial crop protection are robust and multifaceted, pointing towards sustained growth. Key drivers include:

Escalating Demand for Organic and Sustainable Produce: A global consumer preference for residue-free and organically grown food is a primary catalyst, pushing farmers towards biological solutions.
Technological Advancements in Microbial R&D: Breakthroughs in genomics, fermentation, and formulation technologies are enhancing the efficacy, stability, and shelf-life of microbial products.
Integrated Pest Management (IPM) Adoption: Increased governmental and agricultural extension support for IPM strategies is driving the inclusion of microbial agents as foundational tools.
Addressing Chemical Resistance: The widespread development of resistance to conventional chemical pesticides mandates the urgent adoption of novel, biologically diverse control options.
Governmental Support and Subsidies: Many governments are offering incentives, subsidies, and grants to promote the research, development, and adoption of biopesticides, recognizing their ecological benefits.

These engines collectively position the microbial crop protection market for significant expansion.

View Full Advisory Report: https://bmagconsulting.com/report/microbial-crop-protection-market

ABOUT BMAG CONSULTING

BMAG Consulting is a boutique advisory firm specializing in strategic market intelligence and precision execution across diverse industries. With a focus on delivering actionable insights and bespoke solutions, BMAG empowers global clients to navigate complex market dynamics, capitalise on emerging opportunities, and achieve sustainable competitive advantage. Our team of seasoned consultants leverages deep industry expertise and rigorous analytical methodologies to drive growth, innovation, and operational excellence.

MEDIA CONTACT

Contact: Mary Joseph, Senior Consultant
Organization: BMAG Consulting
Phone: +1 646 759 1187
Email: Mary.Joseph@bmagconsulting.com
Website: https://bmagconsulting.com/

Top Trung Tâm Tiếng Anh Cho Sinh Viên Tại Cần Thơ Uy Tín

WISE ENGLISH — Thu, 20 Aug 2026 03:22:53 +0200

Áp lực chuẩn đầu ra ngoại ngữ để xét tốt nghiệp đúng hạn tại các trường đại học, cao đẳng ở Cần Thơ (như Đại học Cần Thơ, Đại học Y Dược Cần Thơ, Đại học Nam Cần Thơ, Đại học FPT…) ngày càng gia tăng. Bên cạnh đó, một chứng chỉ tiếng Anh quốc tế cũng là lợi thế quan trọng giúp sinh viên nâng cao khả năng cạnh tranh khi ứng tuyển vào các tập đoàn đa quốc gia và các doanh nghiệp lớn.

Xem thêm : https://wiseenglish.edu.vn/trung-tam-tieng-anh-cho-sinh-vien-tai-can-tho

Tuy nhiên, việc tìm kiếm một trung tâm tiếng Anh cho sinh viên tại Cần Thơ đáp ứng đồng thời cả ba tiêu chí: chất lượng đào tạo, học phí hợp lý và lịch học linh hoạt vẫn là bài toán khiến nhiều bạn băn khoăn. Bài viết dưới đây sẽ phân tích những khó khăn phổ biến mà sinh viên thường gặp và gợi ý các trung tâm tiếng Anh phù hợp nhất hiện nay.

Summary
Nhu cầu: Sinh viên các trường đại học, cao đẳng tại Cần Thơ tìm kiếm trung tâm tiếng Anh uy tín để đáp ứng chuẩn đầu ra, lấy chứng chỉ IELTS/Cambridge và chuẩn bị hành trang xin việc.

Rào cản: Ngân sách học phí hạn chế, lịch học trên trường dày đặc, dễ rơi vào tình trạng học vẹt hoặc theo học trong thời gian dài nhưng hiệu quả không cao.

Giải pháp: WISE English Cái Răng (Cần Thơ) ứng dụng phương pháp Tư duy Não bộ (NLP), giúp rút ngắn thời gian học, cam kết đầu ra bằng hợp đồng và tối ưu chi phí cho sinh viên.

Top Trung Tâm Tiếng Anh Cho Sinh Viên Tại Cần Thơ Uy Tín

Menos hijos en la Provincia de Buenos Aires: una transformación en curso

Jose Luis Pavolink — Wed, 19 Aug 2026 00:34:13 +0200

En distintos países se observa desde hace décadas una disminución de la fecundidad. Esa tendencia

llevó a preguntar qué está ocurriendo en la Provincia de Buenos Aires. Como antecedente regional se

toma el trabajo de Rodríguez Vignoli (2003), que describía a comienzos del siglo XXI una reducción de la

fecundidad alta en América Latina y el Caribe, aunque con diferencias entre grupos sociales. El análisis

bonaerense utiliza tres clases de datos oficiales que no se confunden entre sí: la serie anual de

fecundidad de la Provincia, los nacimientos registrados y los datos del Censo 2022 sobre la cantidad de

hijos que habían tenido las mujeres próximas al final de su etapa reproductiva. La serie anual muestra una

caída marcada en las últimas décadas y especialmente después de 2015; los nacidos vivos registrados

pasaron de 288.505 en 2010 a 174.074 en 2022; y el Censo 2022 muestra que casi la mitad de las

mujeres observadas tenía uno o dos hijos. También aparecen diferencias importantes según nivel

educativo. El trabajo describe estos cambios sin atribuirles una causa única

Food Robotics Market Size, Share, Growth, and Forecast 2032 : BAMG Consulting

Oliver Hawthorne — Tue, 18 Aug 2026 12:07:09 +0200

BMAG CONSULTING FORECASTS EXPLOSIVE GROWTH IN GLOBAL FOOD ROBOTICS MARKET, PROJECTING MULTI-BILLION-DOLLAR VALUATION BY 2032

FLORIDA, United States, July 5, 2026 — FOR IMMEDIATE RELEASE

BMAG Consulting, a global leader in strategic market intelligence, announced today the release of its groundbreaking report, But the Global Food Robotics Market. A comprehensive analysis shows that an accelerating market trajectory Driven off unprecedented demand for full automation in the food industry.

The report projects the market to reach a multi-billion-dollar valuation by 2030, underlining a critical shift toward intelligent automation to address labour shortages and improve and ensure food safety standards.

The surge in the food robotics market is primarily driven by operational imperatives and technological advancements. Food manufacturers and service providers are quickly adopting. Robotic solutions, streamlining the process from production and packaging to delivery and quality control. Key drivers include growth. Labour costs, a constant decline in skilled workers, and the need for stringent hygiene protocols, especially post-pandemic operational frameworks.

Robotics offers a compelling solution: the promise of better productivity, less human error, and consistent output quality.

Download Free Sample Report: https://bmagconsulting.com/sample/food-robotics-market

BMAG Consulting's analysis Sections carefully examine the market by application, robot type, and geographical region, delivering to stakeholders granular insights into growth opportunities. The report highlights. Significant innovations in artificial intelligence, machine vision, and collaborative robots (cobots) are changing the feasibility and cost-effectiveness of robotic integration in food environments.

These advancements enable greater flexibility and adaptability to generate automation accessible to a broader spectrum of businesses, from large-scale processing plants to the artistic kitchen.

THE REGULATORY PIVOT

The food robotics sector Navigating a pivotal period regarding regulation. While food safety and quality standards (e.g., HACCP, ISO 22000) stay on top, the integration of advanced robotics necessitates One size fits all revisited diagnostic approaches.

Regulators are beginning to focus on performance-based standards for robotic systems to emphasise verifiable outcomes in hygiene, cross-contamination prevention, and operational integrity, instead of prescriptive equipment designs. This change encourages innovation while ensuring that automated solutions meet or exceed human-centric safety benchmarks.

Key areas include standard protocols for robotic calibration, powered-by-AI anomaly detection in food processing, and traceability mechanisms to improve automated workflows, consumer trust, and regulatory compliance.

STRATEGIC MARKET OUTLOOK

The future growth of the food robotics market is driven by several powerful demand engines. First, the explosion of e-commerce and food delivery creates an urgent need for automatic kitchen and logistics solutions capable of handling high volumes and rapid turnaround times. Second, persistent global labour shortages in the food sector make automation not only an efficiency gain but also a necessity for operational continuity.

Third, increasing consumer demand for convenience, customization, and more consistent product quality is forcing producers to invest. Precision robotics for personalized food production and ingredient management. Finally, the growing emphasis on sustainability and waste reduction gives a strong impetus; seamrobotic systems can be corrected. Ingredient usage and minimal destruction promote both economic and environmental benefits.

View Full Advisory Report: https://bmagconsulting.com/report/food-robotics-market

ABOUT BMAG CONSULTING

BMAG Consulting is a premier boutique advisory firm specialising in strategic market intelligence and precision execution across diverse global industries. Leveraging deep analytical rigour and an extensive network of industry experts, BMAG empowers its clients with actionable insights to navigate complex market landscapes, identify emerging opportunities, and achieve sustainable growth. Our commitment to bespoke solutions and measurable outcomes ensures unparalleled value, driving success for leading enterprises worldwide.

MEDIA CONTACT

Contact: Mary Joseph, Senior Consultant
Organisation: BMAG Consulting
Phone: +1 646 759 1187
Email: Mary.Joseph@bmagconsulting.com
Website: https://bmagconsulting.com/

Mycelium-Based Ingredients Market Size, Share, Growth, and Forecast 2032 : BAMG Consulting

Oliver Hawthorne — Fri, 14 Aug 2026 09:14:33 +0200

MYCELIUM-BASED INGREDIENTS MARKET SET FOR EXPLOSIVE GROWTH, DRIVEN BY SUSTAINABILITY AND INNOVATION

FLORIDA, United States, July 5, 2026 — FOR IMMEDIATE RELEASE

BMAG Consulting, a premier global advisory firm, today announced the release of its comprehensive report on the Mycelium-Based Ingredients Market. The study projects an unprecedented surge in market valuation, highlighting mycelium's pivotal role in transforming the food, pharmaceutical, and materials industries. With its *versatile applications* and *sustainable production advantages*, mycelium is rapidly emerging as a cornerstone for future-proof product development and supply chain resilience.

The report provides an in-depth analysis of the current market landscape, dissecting key growth drivers such as escalating consumer demand for sustainable and plant-based alternatives, coupled with significant advancements in fungal fermentation technologies. It meticulously maps out the competitive ecosystem, identifying *emerging players, strategic collaborations, and innovative product launches* that are redefining market dynamics. Investors and industry stakeholders will gain crucial insights into market segmentation, regional trends, and the inherent opportunities for strategic positioning and expansion.

Download Free Sample Report: https://bmagconsulting.com/sample/mycelium-based-ingredients-market

BMAG Consulting's analysis further illuminates the challenges facing the mycelium sector, including scalability hurdles, regulatory complexities, and consumer acceptance. However, the report also offers strategic pathways to overcome these obstacles, emphasizing the importance of *robust R&D, transparent communication, and agile business models*. Companies that can effectively navigate these challenges are poised to capture substantial market share and establish long-term leadership in this burgeoning industry.

Our findings underscore that mycelium-based ingredients are not merely a niche trend but represent a fundamental shift towards more *environmentally benign* and *resource-efficient production systems*. From alternative proteins and meat substitutes to biomaterials and nutraceuticals, the report explores the diverse applications poised for significant commercialization, offering a pragmatic roadmap for businesses seeking to capitalize on this transformative ingredient.

THE REGULATORY PIVOT

The rapid ascent of mycelium-based ingredients is challenging traditional regulatory frameworks. Our analysis in 'The Regulatory Pivot: Moving Beyond 'One-Size-Fits-All' Diagnostics' reveals a critical need for *adaptive and nuanced regulatory approaches* that acknowledge the unique characteristics and safety profiles of novel fungal biomass. The current 'one-size-fits-all' diagnostic often creates unnecessary barriers to innovation, hindering market entry for safe and sustainable products. We advocate for a shift towards *ingredient-specific evaluations* and *harmonized international standards* to foster innovation while safeguarding public health, predicting that countries adopting proactive, science-based regulatory pathways will attract significant investment and become hubs for novel ingredient development.

STRATEGIC MARKET OUTLOOK

Our 'Strategic Market Outlook: Demand Engines' identifies several powerful drivers propelling the Mycelium-Based Ingredients Market. Foremost is the exploding consumer demand for *sustainable, ethical, and healthy food choices*, with a particular emphasis on plant-based and alternative proteins. This is compounded by *technological breakthroughs* in precision fermentation and bioreactor design, making production more efficient and scalable. Furthermore, the increasing focus on *circular economy principles* and the search for novel *bio-based materials* in packaging and textiles are expanding mycelium's addressable market beyond food. We project that investments in *biomanufacturing infrastructure* and *cross-sector collaborations* will be key to unlocking mycelium's full commercial potential, driving a projected 300% growth in specific application areas over the next five years.

View Full Advisory Report: https://bmagconsulting.com/report/mycelium-based-ingredients-market

ABOUT BMAG CONSULTING

BMAG Consulting is a boutique advisory firm providing bespoke consulting services to a global clientele. With a relentless focus on precision execution and data-driven insights, BMAG helps organizations navigate complex market dynamics, optimize strategies, and achieve sustainable growth. Our experts bring unparalleled industry knowledge and a commitment to delivering actionable recommendations that drive measurable outcomes and ensure client success across diverse sectors.

MEDIA CONTACT

Contact: Mary Joseph, Senior Consultant
Organization: BMAG Consulting
Phone: +1 646 759 1187
Email: Mary.Joseph@bmagconsulting.com
Website: https://bmagconsulting.com/

AI Chips Market Size, Share, Growth, and Forecast 2032 : BAMG Consulting

Oliver Hawthorne — Wed, 12 Aug 2026 08:40:08 +0200

BMAG CONSULTING PROJECTS AI CHIPS MARKET TO REACH $100 BILLION BY 2030 AMIDST UNPRECEDENTED INNOVATION AND DEMAND SURGE

FLORIDA, United States, July 5, 2026 — FOR IMMEDIATE RELEASE

BMAG Consulting, a global leader in strategic market intelligence, today announced the release of its comprehensive new report, "The Global AI Chips Market: A Strategic Outlook 2024-2030." The report forecasts an astounding surge in the AI chips market, driven by accelerating AI adoption across industries and a relentless push for specialized hardware, with projections indicating a market valuation exceeding $100 billion by 2030. This pivotal analysis provides unparalleled insights into the forces reshaping the future of artificial intelligence, offering critical intelligence for stakeholders navigating this transformative sector.

The new report dissects the complex ecosystem of AI chips, identifying key technological advancements. Neuromorphic computing, in-memory processing, and specialised ASICs are critical accelerators. The demand is not limited to the cloud. Data is driving the proliferation of edge AI applications. In autonomous vehicles, IoT devices, and smart infrastructure, energy-saving and highly efficient solutions are required. Point of data generation.

Download Free Sample Report: https://bmagconsulting.com/sample/ai-chips-market

BMAG Consulting's analysis highlights the intense competitive landscape, where established giants and agile startups fight for market dominance through innovation in architecture, production processes, and software-hardware co-optimisation. Presents the report. Strategic recommendations: Navigating stakeholders' supply chain complexities, Intangible challenges, and the rapid pace of technological obsolescence to ensure sustained growth and market relevance in this dynamic environment.

THE REGULATORY PIVOT

The burgeoning AI chips face are complex and developing a regulatory landscape. While traditional hardware regulations are well established, the ethical implications of AI, combined with surrounding concerns about data privacy and algorithmic bias, are quickly affecting. The design and deployment of AI-specific silicon. Geopolitical factors, export controls, and the race to technological supremacy are also affecting global supply chains and R&D investments.

BMAG Consulting's report Underlines that a one-size-fits-all diagnostic approach to regulation is unsustainable; Instead, subtle, application-specific governance frameworks It will be important to promote innovation and, at the same time, reduce risk. To understand these regulatory currents is crucial for sustainable market entry and competitive advantage.

STRATEGIC MARKET OUTLOOK

The "Strategic Market Outlook: Demand Engines" section of the report identifies several critical drivers fueling the AI chips market. Key among these are the exponential growth of Generative AI applications, the widespread adoption of machine learning in enterprise solutions, and the relentless pursuit of autonomous systems across industries like automotive, robotics, and healthcare. Furthermore, the imperative for energy efficiency in AI workloads and the expansion of 5G and edge computing infrastructure are creating unprecedented demand for specialized, high-performance, and low-power AI processors. The convergence of these factors, coupled with significant investment in AI research and development, positions AI chips as the foundational technology for the next era of digital transformation.

View Full Advisory Report: https://bmagconsulting.com/report/ai-chips-market

ABOUT BMAG CONSULTING

BMAG Consulting is a boutique advisory firm renowned for delivering precision market intelligence and strategic guidance to global leaders. Specializing in actionable insights and meticulous analysis, BMAG empowers clients across diverse industries to navigate complex market dynamics, capitalize on emerging opportunities, and achieve sustainable growth. With a focus on precision execution and a proven track record of facilitating global client success, BMAG Consulting transforms data into strategic advantage, enabling informed decision-making in a rapidly evolving business landscape.

MEDIA CONTACT

Contact: Mary Joseph, Senior Consultant
Organization: BMAG Consulting
Phone: +1 646 759 1187
Email: Mary.Joseph@bmagconsulting.com
Website: https://bmagconsulting.com/

A Data-Driven Shrinkage for Efficient Estimation in the Generalized Poisson Regression Model: Simulation and Application

Scipedia content — Fri, 24 Jul 2026 15:22:23 +0200

Count data in practice rarely conform to the equidispersion assumption that the standard Poisson regression model imposes. When counts are overdispersed or underdispersed, the generalized Poisson regression model (GPRM) steps in as a more accommodating alternative, extending the classical framework to handle such variation. Like other generalized regression models, GPRM is typically fitted via maximum likelihood estimation (MLE). The problem is that MLE becomes unreliable when predictors are highly correlated, a condition known as multicollinearity, producing unstable coefficients, inflated standard errors, and predictors that appear statistically insignificant even when they are not. This paper introduces a new two-parameter estimator for GPRM specifically designed to handle multicollinearity. We establish its theoretical properties and evaluate it against existing methods through Monte Carlo simulations. We also apply the estimator to Canadian carbon dioxide emissions data. The realdata results mirror the simulation findings: the proposed estimator yields more stable parameter estimates with smaller standard errors, making it a practical tool when correlated predictors threaten inferential quality.OPEN ACCESS Received: 14/04/2026 Accepted: 03/06/2026 Published: 24/07/2026

Retrieval-Augmented Generation (RAG)-based chatbots for monitoring and supporting student academic progress in educational institutions: a systematic literature review

Aaron Palomino — Tue, 21 Jul 2026 01:38:19 +0200

Retrieval-Augmented Generation (RAG) systems promise to reduce large language model hallucinations by grounding responses in verified sources, which makes them candidates for supporting students at academic risk; however, evidence on their use for institutional risk monitoring remains scattered. This systematic review, conducted following PRISMA guidelines across eight databases, identified 135 studies published between 2020 and 2026, of which 100 met the inclusion criteria, complemented by a scientometric analysis of the scholarly output. Results reveal a recently formed corpus —two thirds of the studies belong to the 2025-2026 period— and a bifurcation between two bodies of literature that rarely cite each other: predictive academic risk analytics, active since 2020, and conversational artificial intelligence, whose RAG implementations do not appear before 2024 yet reach 38% of the corpus. Both traditions rely on disjoint metric families: no study jointly evaluates risk prediction accuracy together with chatbot reliability, and the two mitigation repertoires —class imbalance treatment and generative text control— never coexist within a single design. The review concludes that no hybrid architecture coupling risk prediction with retrieval-augmented generation exists to date, and proposes a research agenda focused on designing such architectures, building joint evaluation frameworks, and combining both mitigation repertoires under teacher oversight.

Multivalued Fixed Point Methods for Sequential Coupled Caputo-Type Fractional Differential Inclusions with Coupled Boundary Conditions

Scipedia content — Mon, 20 Jul 2026 13:29:03 +0200

We study a coupled system of sequential Caputo-type fractional differential inclusions with two-point integral boundary conditions. Using fixed-point techniques for multivalued operators, existence results are established under Carathéodory-type and Lipschitz-type assumptions on the multifunctions. The obtained results extend several known solvability criteria for fractional differential inclusions with nonlocal boundary conditions. An example is included to illustrate the applicability of the theoretical results.

Approximate Reduction in IntervalValued Ordered Target Information Systems Based on Attribute Weighting

Scipedia content — Mon, 20 Jul 2026 12:23:05 +0200

Attribute reduction is a critical preprocessing step in interval-valued ordered target information systems. However, conventional methods often inadvertently discard decision critical attributes during dimensionality reduction. This oversight severely degrades subsequent classification and decision making performance. To mitigate this issue, this paper develops a novel attribute weighted reduction framework that preserves essential attributes while compressing redundant features. We first formally introduce interval-valued ordered target information systems with attribute importance, and systematically establish the theoretical foundation of upper and lower approximation reductions. To facilitate efficient computation, we convert interval data into scalar representations by combining interval maxima and radii, constructing a transformed ordered target system that retains both numerical magnitude and inherent uncertainty. On this basis, we design a weighted discernibility matrix to characterize attribute distinguishability under varying importance levels. We further derive rigorous necessary and sufficient conditions for identifying upper and lower approximation reducts, enabling reliable reduction with explicit consideration of attribute weights. The rationality and correctness of the theoretical results are verified through illustrative examples. Extensive experiments on multiple real world datasets demonstrate that the proposed method achieves effective dimensionality reduction. It also consistently retains decision critical attributes.OPEN ACCESS Received: 02/04/2026 Accepted: 02/06/2026 Published: 21/07/2026

Computational Statistics and Analysis of Unit Real Data Using a New Bounded Model

Scipedia content — Mon, 20 Jul 2026 11:55:52 +0200

The formulation of constrained families of probability distributions is crucial for analyzing data inherently limited to the unit interval, such as proportions, rates, and probabilities. These models have extensive applications across various domains, such as epidemiology, dependability, finance, and environmental research, where adaptable and resilient statistical instruments are essential for accurately representing intricate real-world phenomena. Motivated by this, our work presents the bounded Uma distribution (BUmD), an innovative probability model obtained by transforming the Uma distribution to the unit interval (0, 1). The suggested distribution maintains the flexibility of the Uma family while broadening its applicability to data represented as proportions, probabilities, and rates. We examine the essential statistical characteristics of the BUmD, encompassing its moments, quantile function, extropy, and reliability metrics, with a focus on its capacity to represent various hazard rate behaviors. Sixteen traditional and contemporary estimation techniques are employed to estimate the model parameters, and their efficacy is assessed through comprehensive Monte Carlo simulations using criteria such as bias, mean squared error, and goodness-of-fit measures. The simulation findings indicate that the maximum likelihood and maximum product of spacings estimators are the most efficient. The practical relevance of the BUmD is evidenced by its application to epidemiological and public health datasets, where it demonstrates enhanced flexibility and fitting performance relative to conventional models. The suggested distribution provides a robust and flexible tool for modeling bounded lifetime, reliability, and proportional data in the applied sciences.OPEN ACCESS Received: 07/04/2026 Accepted: 20/05/2026 Published: 21/07/2026

Modelling and Simulation of Bounded Data Using the Unit Power Shanker Distribution with Practical Applications

Scipedia content — Mon, 20 Jul 2026 11:54:46 +0200

It is necessary to construct constrained families of probability distributions when dealing with data that is unit-limited by definition, such as rates, proportions, and probabilities. These models are widely used in fields such as ecology, epidemiology, reliability, and economics. To make sense of the complex real-world events, robust and adaptable statistical methods are required across all these fields. Unit power Shanker distribution (UPSD) is a novel bounded probability distribution that we introduce for this purpose. Key statistical properties, including the quantile function, reliability measures, moments, cumulative distribution function, and probability density function, are derived and studied in detail. Furthermore, the analytical links between the Tsallis entropy and other entropy measures, such as extropy, Shannon, Rényi, and Arimoto, are clarified. Maximum likelihood, Anderson-Darling, Cramér-von Mises, maximum product of spacings, least squares, and variants thereof are among the parameter estimation approaches that we examine. We assess the efficacy of several goodness-of-fit estimation methodologies for bias and mean squared error using a thorough Monte Carlo simulation study. The findings demonstrate that, in every case, the maximum likelihood estimation method outperforms the maximum product of spacings method. In various technical and scientific settings, the UPSD offers a robust and adaptable alternative for analyzing limited data.OPEN ACCESS Received: 07/04/2026 Accepted: 09/05/2026 Published: 21/07/2026

Dynamic Dominating Probability-Driven Fuzzy Iterative Algorithm for Identifying Positive Influence Dominating Sets

Scipedia content — Mon, 20 Jul 2026 11:48:05 +0200

The Minimum Positive Influence Dominating Set problem, an NP-hard extension of the classical dominating set, plays a crucial role in social network analysis by identifying a minimal node set that ensures majority positive influence coverage. Conventional approaches, including exact solvers, greedy heuristics, and metaheuristics, are hindered by prohibitive computational costs, proneness to local optima, or excessive parameter sensitivity. This study introduces a Dynamic Dominating ProbabilityDriven Fuzzy Iterative Algorithm to tackle these limitations. The proposed method employs a dynamic local probability metric to assess each candidate node’s contribution toward satisfying residual domination demands of its neighbors, with individual thresholds defined as half the node degree. By iteratively selecting the highest-probability node, incorporating implicit fuzzy-inspired probabilistic handling of uncertainty, and performing real-time status updates, the algorithm effectively mitigates local optima traps while achieving O(n3) time complexity. Experimental evaluations on benchmark networks demonstrate superior solution quality and faster convergence compared to standard baselines.OPEN ACCESS Received: 03/01/2026 Accepted: 12/03/2026 Published: 21/07/2026

Mother Algorithm Optimized Controller for Multi-Area Deregulated Power System Modelled with Solar/Wind/EV Sources: A Novel Approach

Scipedia content — Mon, 20 Jul 2026 11:47:15 +0200

Due to increased system uncertainty, nonlinear dynamics, and marketdriven power exchanges, modern deregulated multi-area power systems with high penetration of renewable energy sources (RES) like wind and solar, and electric vehicle (EV) charging stations present serious challenges to automatic generation control (AGC). Degraded frequency regulation and tie-line power control under deregulated environments result from the limited robustness, slow dynamic response, poor handling of stochastic RES/EV variations, and susceptibility to local optimal solutions of both conventional PI/PID controllers and recently reported optimizationbased AGC schemes. However, with a view to addressing these issues, this study focuses on reducing area control errors (ACE) during different operational shifts, such as frequency fluctuation (f) and tie line variations (Ptie). The main objective of this study is to determine the optimal gain settings for the Fractional Order Proportional-Integral-Derivative controller (FOPIDC) using the mother optimization algorithm (MOA). The proposed control strategy looks at how generators 3-AMS behave in a deregulated environment and emphasises the significance of FOPIDC optimization in preserving system stability with the goal of reducing integral time and absolute error (ITAE). Furthermore, the effectiveness of the proposed method is verified by comparing it with the Walrus Optimization Algorithm (WOA). As case studies, the effectiveness of the suggested strategy is also evaluated under Poolco, bilateral agreements, stability, and sensitivity analysis. In terms of generator outputs, tie-line power variations, and frequencies across different locations, comparative data unequivocally demonstrate that the suggested MOA-adjusted FOPIDC performs better than alternative approaches. In case 1, by implementing the MOA optimized controller, the settling time is 8.5 s, and the value of the objective ITAE for the transient responses is 0.0005292. However, these values are less than the values obtained by WOA. Similarly, in case 2, the settling time is 11.5 s, and ITAE is 0.000395 less

Isogeometric Boundary Element Analysis for Two-Dimensional Transient Heat Conduction Problems Based on the Radial Integration Method and the Application of Polynomial Chaos Expansion

Scipedia content — Mon, 20 Jul 2026 11:36:35 +0200

This study introduces a novel coupled computational framework that seamlessly fuses the Isogeometric Boundary Element Method (IGABEM) with the Polynomial Chaos Expansion (PCE) to address transient heat conduction problems under parametric uncertainty. The proposed approach transcends traditional numerical paradigms by constructing a unified formulation that intertwines geometric precision, computational efficiency, and probabilistic rigor. At the heart of the framework lie three tightly integrated representations. First, Non-Uniform Rational B-Splines (NURBS) basis functions serve a dual purpose—they simultaneously embody geometric modeling and field discretization. This duality allows for the direct transformation of ComputerAided Design (CAD)-level geometric fidelity into the numerical domain, preserving intricate boundary characteristics while discretizing the governing boundary integral equations with remarkable exactness. Second, through the introduction of Bézier extraction, the computational overhead associated with NURBS basis evaluations is dramatically reduced. This operation reformulates complex NURBS structures into more manageable Bézier entities, unlocking substantial efficiency gains without compromising the inherent smoothness or precision of the geometric representation. Third, the framework incorporates Polynomial Chaos Expansion to systematically quantify and propagate uncertainties in material parameters. By coupling this stochastic representation with an advanced radial integration scheme, the method elegantly circumvents the need for conventional domain meshing while maintaining high accuracy in evaluating domain integrals. The result is a streamlined yet robust uncertainty quantification process embedded directly within the boundary element context. A series of numerical experiments corroborates the effectiveness of the proposed hybrid strategy. The findings reveal that the method not only preserves the celebrated advantages of IGABEM—such as geometric exactness and reduced-dimensional computation—but also extends its capability into the stochastic domain, enabling a comprehensive evaluation of how parameter uncertainties influence transient thermal responses. In essence, this integrated IGABEM–PCE framework bridges the gap between deterministic modeling and probabilistic analysis, yielding a unified, highfidelity numerical tool. Its capacity to deliver both geometric accuracy and uncertainty quantification positions it as an indispensable technique for complex engineering scenarios, particularly in thermal management, energy conversion, and high-precision design systems, where reliability under uncertainty is paramount.

MAM-YOLO: A Real-Time Algorithm for Detecting Defects in Steel Surfaces Using Multi-Scale Feature Enhancement

Scipedia content — Mon, 20 Jul 2026 11:33:06 +0200

Traditional methods for detecting surface defects in steel typically rely on manual visual inspection, eddy current testing, magnetic particle inspection, and machine vision. These methods often struggle to adapt to defects of varying scales and complex shapes, resulting in insufficient feature extraction, false detections, missed detections, and low detection accuracy. To address these issues, we propose an enhanced YOLOv8n model named MAM-YOLO, which aims to optimize defect detection on steel surfaces through multi-scale feature enhancement. By redesigning the convolution modules, the model improves accuracy in identifying surface defects, crazing, scratches, and patches. Furthermore, the design of a new module enhances processing capabilities for diverse surface features. A new loss function optimization is also applied to better identify crack characteristics. Experimental results on the NEU-DET dataset demonstrate that MAM-YOLO increases mAP by 5.4% and improves accuracy by 5.9% compared to the original YOLOv8n. Additionally, the model’s generalization capability was verified on the KolektorSDD dataset, achieving performance superior to the YOLOv8n baseline. Therefore, the proposed MAM-YOLO model offers significant advantages in precision and robust feature extraction for identifying steel surface defects.OPEN ACCESS Received: 25/11/2025 Accepted: 27/01/2026 Published: 21/07/2026

CAM-YOLO: A Framework for Non-Salient Object Detection by Exploiting Contextual Interdependencies

Scipedia content — Mon, 20 Jul 2026 11:31:05 +0200

Real-time object detection requires identifying objects in video streams or consecutive images with minimal latency, yet it continues to struggle with non-salient objects—those that are small, occluded, or otherwise inconspicuous. To address this limitation, this paper proposes CAM-YOLO, an enhanced architecture based on YOLOv8. First, to mitigate the baseline model’s limited representational capacity for non-salient targets, we introduce a Multi-Scale Aggregation Module (MSAM) into the feature fusion process, enabling the backbone network to extract more discriminative fine-grained features. Second, to better capture global contextual relationships associated with such objects, we design a Contextual Association Module (CAM) that explicitly models long-range spatial dependencies. Furthermore, we integrate a Dual-Branch Attention Mechanism (DBAM) to refine the feature processing flow, thereby strengthening the contextual feature representations crucial for detecting non-salient instances. Extensive experiments on two large-scale public benchmarks, Microsoft Common Objects in Context 2017 (MS COCO 2017) and PASCAL Visual Object Classes (PASCAL VOC), demonstrate that CAM-YOLO achieves highly competitive performance compared to several widely-adopted realtime detectors.OPEN ACCESS Received: 19/11/2025 Accepted: 15/01/2026 Published: 21/07/2026

An Innovative Distribution that Incorporates Actuarial Parameters and Applications, Real Data in a Variety of Domains

Scipedia content — Mon, 20 Jul 2026 11:30:05 +0200

The complexity of data necessitates the development of novel distributions. The introduction of new models allows us to enhance this data and remain contemporary. In this paper, we proposed and explored a new flexible distribution, referred to as the extended inverse Weibull model. This study presents a novel probability model, the extended inverse Weibull (E-IW) distribution. The recently introduced distribution combines the E-X approach with the inverse Weibull distribution. The novel distribution facilitates the evaluation of real-world data due to its analytical feasibility and applicability. The suggested distribution may accommodate several types of datasets. Numerous statistical characteristics of the proposed model were acquired. The features include quantile functions, ordinary moments, order statistics, and moment-generating functions. functions. The estimated parameters of the new model are determined using several estimation techniques, including maximum likelihood, least squares, weighted least squares, maximum product spacing, and the Bayesian method under the square error loss function. The simulation analysis performed on the suggested model validated the dependability and consistency of its parameters. Furthermore, actuarial measures were computed, along with simulation study for these actuarial metrics was executed. Five real data sets were taken from several sectors to illustrate the importance and usefulness of the proposed model. Our empirical findings underscore the significance of the recommended model as a flexible and reliable tool for statistical modeling, with implications for improving data-driven analyses and integrating parametric modeling into modern applications.OPEN ACCESS Received: 10/11/2025 Accepted: 27/01/2026 Published: 21/07/2026

Bayesian and Classical Survival Inference for the Nadarajah–Haghighi Model under Unified Type-I Progressive Hybrid Censoring with Random Binomial Removals

Scipedia content — Mon, 20 Jul 2026 11:27:09 +0200

A highly adaptable censoring framework, known as the unified TypeI progressive hybrid censoring scheme, has been recently introduced as an enhancement to the unified hybrid censoring approach. However, this censoring method has a significant limitation: it assumes that the removal pattern is fixed and established prior to conducting the experiment, which may lack practical applicability. This study presents, for the first time, the unified Type-I progressive hybrid censoring scheme incorporating binomial removal, which enables the random withdrawal of surviving units following each failure, making it more suitable for survival studies. Assuming that the underlying distribution of the test units follows the Nadarajah–Haghighi distribution, we examine both point and interval estimation problems for the model parameters, binomial parameter, and two key survival measures. Maximum likelihood estimation serves as the classical methodology for obtaining point estimates and approximate confidence intervals. For Bayesian estimation, we employ the squared error loss function in conjunction with Markov Chain Monte Carlo sampling techniques. A comprehensive simulation analysis is conducted to assess the performance of the various point and interval estimators. In addition, to demonstrate the practical relevance of the proposed methodologies in survival analysis, a real-world data application involving the death times of a group of 45 gastric cancer patients is investigated.OPEN ACCESS Received: 02/11/2025 Accepted: 11/12/2025 Published: 21/07/2026

Reliability Analysis of Modified Kies– Rayleigh Distribution with Optimal Progressive First-Failure Sampling Plans with Application to Turbocharged Diesel Engines

Scipedia content — Mon, 20 Jul 2026 11:26:05 +0200

This study investigates the utility of the newly modified Kies–Rayleigh distribution for analyzing progressively first-failure censored samples. We develop both classical and Bayesian estimators for the distribution’s parameters and derived reliability measures, including the reliability function and hazard rate. Interval estimation is addressed via approximate confidence intervals and Bayesian credible intervals. Classical estimates are obtained numerically by solving the likelihood equations, whereas Bayesian inference is conducted through Markov chain Monte Carlo sampling from the posterior distribution. To assess and compare the performance of the classical and Bayesian procedures, we carry out a comprehensive simulation study under multiple experimental scenarios. We also consider the design problem of choosing an optimal progressive sampling plan and evaluate competing plans using four standard optimality criteria. We analyze a dataset comprising time-to-failure observations for turbocharged engines from a single model of diesel engine. The results highlight the flexibility of the modified Kies–Rayleigh model and provide guidance on estimation and design choices for progressively censored reliability data.OPEN ACCESS Received: 21/09/2025 Accepted: 07/11/2025 Published: 21/07/2026

Existence and Uniqueness Results for Weighted Fractional Stochastic Integral Equations

Scipedia content — Mon, 20 Jul 2026 11:22:15 +0200

This study explores the existence, uniqueness (EU), and Hyers-Ulam stability (HUS) for a class of weighted fractional Itô-Doob stochastic integral equations (WFIDSIEs). To establish these crucial properties, we utilize the Banach fixed-point theorem (BFPT) as a foundational tool, alongside a variety of essential mathematical inequalities. These inequalities offer valuable insights into the underlying structure and behavior of WFIDSIEs, particularly in the context of fractional and stochastic dynamics. By leveraging these techniques, we demonstrate the conditions under which solutions to these equations exist and are unique, as well as how small perturbations affect the stability of these solutions in the Hyers-Ulam sense. Our results contribute to a deeper understanding of the stability and solvability of WFIDSIEs, which are important in modeling complex systems exhibiting both stochasticity and memory effects. Additionally, these findings open avenues for further research into the robustness of solutions in fractional stochastic models across various applied disciplines, such as finance, engineering, and biology.OPEN ACCESS Received: 12/09/2025 Accepted: 11/11/2025 Published: 21/07/2026

Rainfall Trend Analysis and One-Dimensional Hydrodynamic Assessment of the Ganga River Basin Using HEC-RAS 6.3

Scipedia content — Thu, 09 Jul 2026 11:54:54 +0200

This study integrates long-term rainfall trend analysis with one-dimensional hydrodynamic modelling to assess hydro-climatic variability and flood vulnerability along the selected stretch of the Ganga River near Farakka Barrage, eastern India. Daily gridded rainfall data for 1991–2025 were analyzed to identify trends and potential change points. The Mann–Kendall test, Sen’s slope estimator, Bias-Corrected Pre-Whitening, and Trend-Free Pre-Whitening were applied to account for serial correlation. The rainfall series showed an overall decreasing tendency, although statistical significance varied among the applied trend tests after pre-whitening. HEC-RAS 6.3 simulations showed increasing water surface elevation, velocity, and discharge under 25-, 50-, and 100-year design-flood scenarios. The findings indicate that flood risk in the study reach is shaped by both hydro-climatic variability and local hydraulic controls.

Why Premium Chocolate Remains a Timeless Gift

Mukesh Nikam — Tue, 30 Jun 2026 08:32:53 +0200

Why Premium Chocolate Remains a Timeless Gift is an elegant editorial publication exploring the cultural, emotional and commercial significance of fine chocolate. It examines chocolate-gifting history, consumer psychology, artisan confectionery, premium packaging, British celebrations, corporate relationship building and hospitality. The guide also considers modern luxury-gifting trends and offers thoughtful advice on selecting an appropriate chocolate gift.

The Portfolio Decisions That Shape Careers Before Conversations Begin.

Lohar Studio — Wed, 24 Jun 2026 09:06:53 +0200

A surprising number of talented professionals lose opportunities before anyone evaluates their skills.

The issue is rarely experience, education, or capability. More often, it is visual presentation. A recruiter reviewing hundreds of applications, a casting director assessing submissions, a fashion brand exploring potential collaborators, or a corporate decision-maker searching for speakers and executives all make initial judgments within seconds.

Those judgments are frequently influenced by photography.

Not because photographs tell the whole story, but because they shape expectations before the story begins.

Many articles about professional portraits focus on cameras, poses, or technical details. What often gets overlooked is a more important question: what should an image communicate about a person’s future potential?

Whether someone needs headshot photography, fashion photography, executive portraits, or model portfolio photography, the goal is rarely to create a beautiful image alone. The goal is to create trust, relevance, and recognition.

That distinction changes everything.

The Real Purpose of Professional Photography

Professional photography is often treated as a marketing asset.

In reality, it functions more like a positioning tool.

A strong image helps viewers answer important questions immediately:

Can I trust this person?
Do they understand their industry?
Do they appear confident?
Would I feel comfortable hiring, representing, or collaborating with them?
Do they fit the opportunity being considered?

The best photographs reduce uncertainty.

They help people make decisions faster.

This applies equally to corporate photography, personal branding photography, and fashion campaigns.

The image itself becomes part of the decision-making process.

Why Generic Professional Photos Often Fail

One of the most common portfolio mistakes is looking professionally photographed without looking personally distinctive.

Many portfolios appear polished but interchangeable.

The clothing is safe.

The expressions are neutral.

The background is attractive.

Yet nothing communicates identity.

Hiring managers, agencies, and brands review thousands of images every year. Generic professionalism rarely stands out because it gives viewers nothing memorable to connect with.

Memorable images are not necessarily dramatic.

They simply feel specific.

A founder should not look like a fashion model.

A model should not look like a corporate executive.

An executive should not look like a social media influencer.

The strongest photographs align appearance with purpose.

Why Headshots Matter More Than Most People Realize

Headshots are often viewed as profile photos.

That perspective underestimates their influence.

A professional headshot becomes part of how people experience someone across multiple platforms.

LinkedIn profiles.

Company websites.

Conference speaker pages.

Media interviews.

Industry publications.

Professional directories.

Over time, these images create consistency.

Consistency creates familiarity.

Familiarity creates trust.

This is one reason many professionals choose to work with a headshot photographer in Delhi who understands not only portrait creation but also personal branding, executive positioning, and long-term professional visibility.

An effective headshot should feel current today and credible years from now.

Fashion Photography Is About Narrative, Not Clothing

Many people assume fashion photography exists primarily to showcase garments.

In reality, successful fashion imagery communicates identity.

A skilled fashion photographer in Delhi understands that strong fashion imagery is rarely about clothing alone. The objective is to communicate mood, personality, aspiration, and relevance within a specific audience or market.

Brands are not merely selling products.

They are selling aspiration, attitude, lifestyle, and belonging.

This is why fashion photographers are often asked to create visual stories rather than simple product-focused images.

The same principle applies to individual portfolios.

A model portfolio photography session is not simply documenting appearance.

It is demonstrating range.

Can the individual appear commercial?

Editorial?

Luxury-focused?

Contemporary?

Brand-friendly?

Fashion-industry professionals frequently evaluate adaptability as much as appearance.

The portfolio becomes evidence of versatility.

What Agencies and Casting Teams Actually Look For

A common misconception is that agencies want heavily stylized images.

In many cases, they prefer clarity.

Casting teams need to understand what a person genuinely looks like.

They want consistency between portfolio submissions and real-world appearances.

Excessive retouching often creates unnecessary friction.

Natural presentation tends to perform better because it establishes realistic expectations.

Models preparing agency submissions often collaborate with a fashion photographer in Delhi to create portfolios that showcase versatility while remaining authentic to their appearance and strengths.

For male model photography, kids model photography, and portfolio photography in general, authenticity frequently outperforms perfection.

An image does not need to be flawless.

It needs to be believable.

Believability creates confidence.

Confidence creates opportunities.

The Importance of Visual Consistency

Many professionals unintentionally create confusion.

Their website image looks corporate.

Their LinkedIn image looks casual.

Their speaking-event portrait looks ten years old.

Their social media images communicate something entirely different.

This fragmented presentation weakens personal branding.

Visual consistency does not mean using identical photographs everywhere.

It means maintaining a recognizable identity across platforms.

People should feel they are seeing the same professional regardless of where they encounter them.

Consistency is often more valuable than creativity.

Especially for executives, consultants, founders, and public-facing professionals.

The Relationship Between Confidence and Photography

Confidence is one of the most misunderstood aspects of portrait photography.

Many people assume confidence is something subjects bring to the session.

In practice, confidence often emerges during the process.

The right environment, preparation, direction, wardrobe choices, and image selection can significantly affect how individuals perceive themselves.

This matters because confidence becomes visible.

Viewers notice posture.

Eye contact.

Expression.

Presence.

The strongest professional portraits rarely look overly posed.

They look comfortable.

Comfort creates credibility.

Why Portfolio Selection Is More Important Than Most Shoots

A portfolio can succeed or fail based on image selection alone.

Many individuals choose photographs based on personal preference.

Industry decision-makers use different criteria.

The favorite image of a subject is not always the most effective image professionally.

Portfolio selection should focus on strategic communication.

Questions worth asking include:

What role does this image support?
What impression does it create?
Does it strengthen credibility?
Does it align with future goals?
Does it add something unique to the portfolio?

A smaller portfolio with clear direction often performs better than a large collection of inconsistent images.

Editorial Photography and Brand Perception

Editorial photography occupies a unique space between commercial objectives and creative storytelling.

It allows individuals and brands to communicate ideas rather than simply appearances.

For fashion professionals, editorial photography demonstrates creative potential.

For brands, it establishes visual authority.

For emerging talent, it helps create distinction in crowded markets.

Editorial work often becomes valuable because it reveals perspective.

Not just appearance.

And perspective is increasingly important in industries where audiences encounter thousands of images every day.

Beyond Individual Images: Building a Long-Term Visual Identity

The most successful professionals rarely think in terms of single photographs.

They think in terms of visual ecosystems.

A headshot.

Professional portraits.

Studio photography.

Personal branding imagery.

Commercial photography.

Lookbook photography.

Editorial content.

Each asset serves a different purpose while supporting the same overall identity.

This approach creates cohesion.

It also allows professionals to adapt across industries, platforms, publications, and opportunities without constantly reinventing themselves.

Visual identity becomes a long-term asset rather than a short-term marketing exercise.

Conclusion

Professional opportunities are often influenced by moments that happen long before conversations begin.

A recruiter opening a profile.

A casting director reviewing submissions.

A brand evaluating collaborators.

A client comparing portfolios.

In each case, photography helps shape perception before credentials are considered.

The most effective images do more than look impressive. They communicate confidence, consistency, and direction. Whether created by a headshot photographer in Delhi for professional positioning or a fashion photographer in Delhi for portfolio and brand development, strong visual assets continue to influence how opportunities are discovered, evaluated, and pursued.

That is why thoughtful headshot photography, fashion photography, portfolio development, and personal branding imagery remain valuable investments in long-term professional growth. Studios such as Lohar Studio are increasingly part of these conversations because photography is no longer just about documentation—it has become an essential component of how careers, brands, and reputations are perceived over time.

CO₂ Measurement as a Pedagogical Strategy to Promote an Ecological Culture in Educational Institutions of the Department of Sucre

Juan Emilio Pineda Palencia — Thu, 18 Jun 2026 00:49:23 +0200

ABSTRACT

Carbon dioxide (CO₂) monitoring has become an effective tool for assessing indoor air quality and promoting evidence-based environmental education. The aim of this study was to evaluate the impact of a pedagogical strategy based on participatory CO₂ monitoring on environmental awareness and research competencies among students from educational institutions in Sucre, Colombia.

A quasi-experimental study was conducted involving 126 students from three educational institutions. Continuous measurements of CO₂, temperature and relative humidity were obtained using low-cost sensors integrated into Micro:bit platforms. Environmental knowledge tests and perception surveys were applied before and after the intervention.

Significant differences were found among evaluated environments (ANOVA, p<0.001), with average CO₂ concentrations of 1,148±172 ppm in classrooms, 793±116 ppm in corridors and 612±85 ppm in green areas. Research competencies significantly improved after the intervention (Cohen's d=2.8). Furthermore, 92.1% of students reported increased awareness of the relationship between environmental quality and health.

Participatory CO₂ monitoring represents an innovative educational strategy capable of promoting environmental awareness and strengthening scientific competencies among school students.

Keywords: Carbon dioxide, air quality, environmental education, citizen science, STEM education.

Engineering Evidence Meets Legal Accountability: The Growing Role of Concrete NDT in Construction Arbitration

DR MANISH JHA — Wed, 17 Jun 2026 16:11:24 +0200

In major infrastructure projects, disputes worth millions are often fought over contract clauses, delays, and payment issues. Yet, in many cases, the outcome ultimately depends on one factor: technical evidence.

Among the various forms of technical evidence, Non-Destructive Testing (NDT)- particularly Ultrasonic Pulse Velocity (UPV) Testing- has emerged as a powerful tool in determining the quality and integrity of concrete structures. When allegations of defective workmanship, poor concrete quality, or structural deficiencies arise, ultrasonic testing reports can become central to arbitration proceedings.

Forensic Investigation of Structural Failures: A Technical & Legal Approach

DR MANISH JHA — Tue, 16 Jun 2026 15:47:23 +0200

Structural failures are among the most complex challenges in engineering and construction, often resulting in significant safety, financial, and legal consequences. While the visible damage may be apparent, determining the underlying cause and establishing accountability requires far more than a technical inspection. It demands a structured forensic investigation that integrates engineering analysis with contractual interpretation and evidentiary principles.

This article explores the essential stages of forensic investigation from a techno-legal perspective, highlighting how technical evidence, Non-Destructive Testing (NDT), root cause analysis, contemporaneous project records, and contractual obligations collectively contribute to establishing causation and liability. It emphasizes that while engineering identifies the mechanism of failure, a comprehensive techno-legal approach transforms technical findings into credible evidence capable of supporting dispute resolution, arbitration, litigation, and informed decision-making.

By bridging the disciplines of engineering and law, forensic investigations not only determine why structures fail but also promote accountability, enhance risk management, and contribute to safer and more resilient infrastructure.

Dynamic Multi-Objective Collaborative Optimization of Cement Combined Grinding Based on Cooperative Game and Temporal Awareness

Scipedia content — Fri, 12 Jun 2026 09:52:03 +0200

To address the multi-objective collaborative optimization of quality, energy consumption, and yield under dynamic conditions in the Portland cement combined grinding process, this paper proposes a novel algorithm, CGDS-LTL, based on cooperative game theory and temporal perception. First, a hybrid temporal model combining Linformer, TCN, and LSTM was developed to dynamically track process conditions in the Portland cement combined grinding process. Second, an optimization objective function was established, and a cooperative game theory framework was introduced to address the challenge of achieving multi-objective optimization, which could not be effectively solved with a single paretooptimal solution. Meanwhile, volatility metrics were used to quantify the adjustment range of operational variables, allowing for the dynamic optimization of decision constraints. This approach mitigated the deviation of the pareto front from current decision settings caused by high population randomness, ultimately identifying the optimal solution for the multiobjective collaborative optimization problem. Finally, experiments using real production data from a Portland cement plant demonstrated that, compared with NSGA-II and C-TAEA, the proposed method improved the hypervolume indicator by 95% and 33.3%, respectively, indicating a more uniform solution distribution and better convergence. This demonstrated the interpretability and effectiveness of the proposed framework for dynamic multi-objective optimization in Portland cement combined grinding.OPEN ACCESS Received: 14/04/2026 Accepted: 21/05/2026

Arctan Power Half-Logistic Model: A Bayesian Approach to Progressively Censored Engineering Data

Scipedia content — Fri, 12 Jun 2026 09:51:03 +0200

The motivation behind the creation of new statistical models is primarily driven by the need to accurately describe complex data and related phenomena. This article introduces the arctan power half logistic distribution, a new and more flexible extension of the power half logistic distribution. The proposed model’s hazard rate function is highly versatile, capable of displaying decreasing, J-shaped, or reversed J-shaped patterns. We derive its key statistical properties and investigate its application to progressively Type-II censored data. Parameter estimation is conducted using both maximum likelihood and Bayesian frameworks, the latter incorporating informative and non-informative priors across multiple loss functions. Given the analytical intractability of the posterior distributions, we employ Markov Chain Monte Carlo techniques for numerical approximation. Monte Carlo simulations demonstrate that Bayesian point and interval estimates generally outperform frequentist approaches, maintaining coverage probabilities near 95%. Finally, the model’s superiority is validated using a real-world engineering dataset, where it consistently outperforms several established competing distributions.OPEN ACCESS Received: 21/02/2026 Accepted: 20/04/2026

MIMTP: Mamba-Driven Interaction-Aware Multi-Modal Trajectory Prediction for Autonomous Driving

Scipedia content — Fri, 12 Jun 2026 09:50:02 +0200

Accurate prediction of future vehicle trajectories is essential for ensuring safety and reliable decision-making in autonomous driving systems. However, existing deep learning-based approaches exhibit several limitations. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) struggle to effectively model long-term temporal dependencies and complex agent interactions, while Transformer-based architectures often suffer from high computational complexity and limited efficiency. To overcome these challenges, this paper proposes an efficient Mamba-based feature extraction framework for jointly encoding vehicle trajectories and map information. By leveraging state-space modeling and a selective scanning mechanism, the proposed approach effectively captures longrange dependencies and enhances the representation of complex traffic behaviors. Specifically, raw scene data are first normalized and embedded into a unified feature space. A Mamba Encoder is then employed to extract high-level features from historical vehicle trajectories and map elements. Subsequently, Vehicle-Vehicle and Vehicle-Map interaction modules are introduced to explicitly model dynamic interactions among traffic participants and between vehicles and the surrounding map. The resulting high-dimensional features are further fused using an additional Mamba Encoder, while a Global Interaction Module is designed to capture scenelevel dependencies. Finally, a Gated Recurrent Unit (GRU) decoder generates multi-modal future trajectory predictions. Experimental results on the Argoverse 1 dataset demonstrate that the proposed method achieves superior performance in terms of minADE, minFDE, and minMR, while maintaining high computational efficiency.OPEN ACCESS Received: 28/01/2026 Accepted: 16/04/2026

Optimization of Artificial IntelligenceDriven Evolutionary Algorithms for Complex Engineering Problems

Scipedia content — Fri, 12 Jun 2026 09:49:13 +0200

Currently, technologies such as convolutional neural network (CNNs) and deep Q network (DQNs) are undergoing intensive research and rapid development, driving vigorous advancement in the field of artificial intelligence. Nevertheless, there is still room for improvement in addressing practical industrial problems and enhancing learning efficiency and accuracy. To address the core challenges of traditional evolutionary algorithms (EAs) in complex optimization problems, such as insufficient scalability, limited environmental adaptability, and low computational efficiency, this paper proposes an evolutionary algorithm optimization framework (DLRL-EAF) that fuses deep learning and reinforcement learning. To verify the effectiveness of the proposed method, six standard test functions (Sphere, Rastrigin, Griewank, etc.) and three practical engineering optimization problems (mechanical parts design, logistics path planning, photovoltaic array layout) are selected for comparison experiments. The performance of DLRL-EAF is evaluated using the standard genetic algorithm (SGA), the particle swarm optimization algorithm (PSO), and the adaptive genetic algorithm (AGA). Experimental results show that DLRLEAF improves the accuracy of optimal solutions by an average of 23.6%, accelerates iterative convergence by 31.2%, demonstrates greater stability in high-dimensional, complex problems, and improves scalability by more than 40%. At the same time, the proposed method significantly reduces the time and resource costs of problem-solving in practical engineering applications and demonstrates its practical value in industrial settings.OPEN ACCESS Received: 27/12/2025 Accepted: 26/03/2026

Modulating Early-life Risk without Breaking Weibull Structure: An Odds-Based Weibull Model for Engineering Failure-Time Data

Scipedia content — Thu, 11 Jun 2026 09:19:13 +0200

The classical Weibull distribution lacks flexibility for nonlinear or early- life failure behaviors. We present a new three- parameter generalized Weibull (NGW) distribution using a probability- based generator. The NGW preserves the monotonic Weibull hazard structure by adding a parameter that controls for early- life hazard and cumulative curvature. We derive its key properties (density, survival, hazard, quantiles, moments), estímate the parameters using maximum likelihood and Bayesian methods, and perform simulations. Application to engineering failure data shows that the NGW offers a competitive fit compared to several Weibull- type extensions, with a parsimonious and analytically tractable structure.

Carbon-Aware Last-Mile Delivery Optimization using Sparrow search algorithm and graph neural network risk assessment

Scipedia content — Thu, 11 Jun 2026 09:11:14 +0200

With the advancement of global carbon neutrality strategies and explosive growth in e-commerce, urban last-mile delivery faces multiple challenges in balancing economic benefits, environmental impact, and risk management. Traditional optimization methods struggle to simultaneously address multi-objective trade-offs, network topological dependencies, and small-sample risk prediction problems. This study proposes a hybrid intelligent framework integrating an Improved Sparrow Search Algorithm (ISSA) with a Meta-Learning Graph Convolutional Network on Prototype Space (ML-GCNPS) for carbon-aware delivery optimization and risk assessment. ISSA-NSGA-III generates high-quality initial populations through Tent chaotic mapping, designs an adaptive periodic convergence factor to dynamically balance exploration and exploitation, and enhances global search capability by integrating Lévy flight with Elite Opposition-Based Learning (EOBL), achieving multi-objective collaborative optimization through embedding in the NSGA-III framework. ML-GCNPS designs a feature extraction network to extract discriminative features from multi-modal node data, explicitly models class centers through prototype space embedding to enhance small-sample generalization, constructs an adaptive Vertex-to-Edge (V2E) network to dynamically infer edge weights and capture risk propagation paths, and employs a two-layer graph convolutional architecture for sufficient information propagation. Experiments on the Kaggle Supply Chain Management dataset and Carbon Monitor risk dataset demonstrate that compared to standard SSA, ISSA-NSGA-III improves total cost, carbon emissions, and resource utilization by 14.0%, 14.2%, and 15.4%, respectively, with Pareto front quality improved by 28.5%. ML-GCNPS achieves an AUPRC of 0.850 (standard deviation 0.005) and a Macro Fl-Score of 0.850 (standard deviation 0.005) in 5-way 1-shot scenarios, reduces the False Negative Rate (FNR) to 0.080 (standard deviation 0.003), and achieves a Weighted Average Cost (WAC) of 45, significantly outperforming baseline methods such as ProtoNG and MAML-GNN (paired t-test, p less than 0.01 for AUPRC and FNR versus the second-best baseline GAT-FSL). Ablation experiments validate the necessity of the meta-learning framework, graph convolutional structure, V2E network, and prototype space embedding, while alternative design comparisons demonstrate the superiority of the technical choices. While the individual algorithmic components (Tent chaotic mapping, Lévy flight, EOBL, prototypical networks, and GCN) are established techniques, the principal contribution of this study líes in their systematic integration into a closed-loop optimization-assessment-feedback decision framework, where graph structure serves as an information bridge connecting delivery optimization with risk prediction. This study provides a teclmical solution for smart city logistics and offers theoretical basis and practica! guidance for sustainable delivery under carbon neutrality goals. All reported improvements over baseline methods are statistically significant across all evaluation metrics (paired t-test or Wilcoxon rank-sum test, p < 0.001

AdaptiveTrack: An Environment-Aware and Confidence-Refined Framework for Online Multi-Object Tracking

Scipedia content — Wed, 10 Jun 2026 10:43:14 +0200

sup>Multi-object tracking (MOT) remains challenging in conditions involving occlusion, small objects, rapid motion, and crowding, wherein the accuracy of detection and the quality of association degrade simultaneously. We propose AdaptiveTrack, an online MOT framework featuring a closed-loop, confidence-aware association and recovery design: CSI-IoU adapts spatial overlap based on confidence and scale, EAMO refines similarity through density, velocity, and scale cues, and DCR updates detection confidence utilizing association context before NMS and assignment. A lightweight continuity module additionally preserves identities during missed detections. On MOT17/MOT20, AdaptiveTrack achieves HOTA 67.33 and 66.73, MOTA 82.55 and 78.30, and IDF1 83.20 and 82.57, operating at 23.5 FPS.

SIMSAND Implementation on the Geotechnical Particle Finite Element Method G-PFEM

David Leon-Vanegas — Mon, 08 Jun 2026 13:19:04 +0200

This paper presents a large-strain implementation of the SIMSAND on the Geotechnical Particle Finite Element Method (G-PFEM). SIMSAND is a simple, critical-state-based constitutive model, which captures the density and pressure dependent behaviour of sands. The formulation adopts a large-strain hyperelastic–plastic framework that employs the Kirchhoff stress tensor and the Hencky strain tensor instead of their small-strain counterparts. Local integration is performed using a multiplicative decomposition of the deformation gradient and an explicit integration scheme with substepping error control adapted to large strain. Global convergence is enhanced through the IMPLEX scheme and a non-local regularization to alleviate mesh dependence during strain-softening dominated simulations. The implementation is validated through drained and undrained triaxial simulations using both single-Gauss-point element tests and boundary-value problems. Comparation with experimental data for Fontainebleau sand NE34 shows that the model successfully reproduces contractive and dilative behaviour, as well as peak and post-peak softening. The resulting framework provides a robust tool for modelling large deformation geomechanical problems, with planned applications to cone penetration test and pile installation in sands.

Hybrid AI-Quantum Computational Framework for Efficient Eddy Current Repulsion Force Simulation

Scipedia content — Mon, 08 Jun 2026 10:33:14 +0200

Eddy current repulsion forces are essential to the operation of magnetic levitation, electromagnetic braking, and wireless power transfer systems, yet their accurate simulation remains computationally intensive due to the complexity of Maxwell’s equations and the need for fine-resolution meshing in transient, high-frequency domains. This paper presents a hybrid AI-Quantum computational framework that accelerates eddy current simulation by combining adaptive meshing via Physics-Informed Neural Networks for adaptive meshing, quantum-assisted solvers (HarrowHassidim-Lloyd algorithm and Variational Quantum Eigensolver) for selected finite element subdomains, and GPU-accelerated finite element methods, and reduced-order modeling based on Proper Orthogonal Decomposition and Quantum Autoencoders. The framework selectively routes well-conditioned sparse subblocks to quantum solvers while retaining classical GPU-based methods elsewhere. Validation across three representative applications—electromagnetic braking, magnetic levitation, and wireless power transfer—demonstrates that the hybrid solver maintains accuracy within 5%–8% of full FEM results while reducing computational cost by 1.3×–2× speedup in the tested scenarios. These results confirm the feasibility of integrating AI and near-term quantum computing into electromagnetic simulation workflows and provide guidance on complexity, resource requirements, and scalability.OPEN ACCESS Received: 14/10/2025 Accepted: 22/12/2025 Published: 29/05/2026

Computational Framework for AI-IoT Integration in Brain Tumor Detection: Numerical Modeling, Algorithmic Optimization and Real-Time Deployment

Scipedia content — Mon, 08 Jun 2026 10:17:03 +0200

The integration of Artificial Intelligence (AI) and the Internet of Medical Things (IoMT) presents significant computational and engineering challenges, especially in the deployment of real-time diagnostic systems. Therefore, this study proposed a numerically optimized and computationally efficient framework that combines deep learning (DL) architectures with IoMT-enabled deployment for the automated detection and classification of brain tumors using Magnetic Resonance Imaging (MRI). The methodology revolves around rigorous numerical preprocessing techniques, including normalized resizing, advanced data augmentation, and computationally efficient feature extraction via both custom and pre-trained Convolutional Neural Networks (CNNs). The key contributions of this study are tailored toward the evaluation of algorithmic performance beyond diagnostic accuracy, incorporating metrics such as model convergence, inference latency, memory footprint, and numerical stability under varied input conditions. The proposed AI-IoMT framework, known as I-BRAINDETECT, implemented as a web-based IoMT platform, demonstrates how algorithmic design and computational modeling can address the limitations of real-time medical image analysis. Performance evaluation and comparative analysis have shown that EfficientNetB0 and DenseNet121 achieved optimal performance in binary classification with 98.5 ± 0.202% accuracy, while ResNet50 excelled in multiclass classification with 95.4 ± 1.01% accuracy, both within a computationally constrained IoT environment. Validation of the trained models on an external dataset (Figshare) has shown that DenseNet121 achieved the best result with 93.99% accuracy. This work underscores the necessity of numerical robustness and algorithmic efficiency in bridging AI and IoT for scalable biomedical engineering solutions.

A New Green ETL Optimization Technique for Sustainable Data Warehousing Environment: A Scalable Energy Efficient Solution

Scipedia content — Mon, 08 Jun 2026 10:16:03 +0200

Energy consumption is an emerging concern in many fields, including information technology, particularly in data warehousing environments where Extract, Transform, Load (ETL) processes account for a significant portion of operational costs and resource utilization. Despite advances in hardware-level optimization, limited attention has been given to software-level energy optimization within ETL workflows. In reality, software is as important as hardware, and it is equally responsible for a decrease or increase in energy consumption. We argue that for modern applications in which energy efficiency is a priority, ETL processes should be optimally designed. This paper addresses this gap by proposing a Green ETL (GETL) approach designed to reduce energy consumption while maintaining high performance. The proposed method integrates transformation-level reuse through a shared transformation cache and adaptive parallel execution using Apache Spark, enabling efficient resource utilization and elimination of redundant computations. The proposed GETL removes unnecessary calculations and reduces both execution time and energy consumption, without requiring any modifications to the underlying data processing engines. To evaluate the effectiveness of the proposed GETL, experiments were conducted using the Transaction Processing Council Data Integration (TPC-DI) benchmark across multiple scale factors. The results demonstrate that the proposed approach achieves an average energy reduction of approximately 30%, with higher savings observed under large-scale workloads. In addition, GETL improves execution efficiency and reduces resource utilization compared to a traditional Spark-based ETL implementation.

AI-IoMT Synergy: A Real-Time Framework for Automated Urinary Tract Infections (UTI) Detection Based on Urine Sediments

Scipedia content — Mon, 08 Jun 2026 10:15:03 +0200

A Urinary Tract Infection (UTI) is characterized by an infection affecting the urinary system, including the kidneys, bladder, urethra, and ureters, with clinical presentations including pyelonephritis, cystitis, and urethritis. While conventional diagnostic methods such as urinalysis and urine culture and sensitivity (C&S) remain widely used, they are limited by subjectivity, time-intensive processing, susceptibility to contamination and risks of false-positive or false-negative results. This study proposes a comprehensive deep learning (DL) and Internet of Things (IoMT) framework to automate the real-time detection and classification of UTIs using microscopic urine sediment images. The study employed 2 datasets (A and B). Dataset A, a clinically acquired dataset, comprises of 3345 images (normal, erythrocytes, fungi and pus) and Dataset B, a publicly accessible data comprises of 5377 images and 26,419 cropped microscopic images corresponding to 7 classes (casts, crystals, erythrocytes leukocytes, epithelial cells, epithelial nuclei, mycetes). A two-stage classification approach was implemented: a binary task to distinguish urine sediments from normal cases, followed by a multiclass task (clinical data and online data) to identify the specific infection type. All images underwent pre-processing, including normalization, resizing, noise removal, and augmentation to enhance feature visibility and model generalizability. The data were partitioned into training (65%), validation (25%), and test (10%) sets. Six state-of-the-art DL architectures, including ResNet50-V2, ResNet101-V2, Inception-V3, XceptionV2, Inception-ResNet-V2, and VGG19 were fine-tuned using transfer learning and evaluated using accuracy, precision, recall, F1-score, and confusion matrices. The proposed models were uploaded to a website to enable realtime detection (accessible via this link: https://uticlassification.app/). The proposed pipeline demonstrated strong performance in both classification tasks, underscoring the potential of deep learning as a reliable, rapid, and reproducible tool for automated urine sediment identification in clinical practice.

A Multi-Strategy Mutation Differential Evolution for Mountainous Base Station Deployment

Scipedia content — Mon, 08 Jun 2026 10:14:04 +0200

The deployment of communication base stations establishes an efficient information transmission network; however, implementing deployment in mountainous areas with complex terrain remains amajor challenge. To address the issues of large topographical variations, dispersed villages, and low coverage efficiency, this study focuses on application issues, develops a mountainous deployment environment model that incorporates terrain elevation increments and village exclusion zones. On this basis, a Differential Evolution algorithm with Multiple Mutation Strategies (MSM-DE) is proposed to improve the balance between global exploration and local exploitation. The algorithm introduces a probabilistic multi-mutation mechanism that dynamically selects among several mutation strategies according to population diversity, and an adaptive parameter memory archive that guides the search toward promising regions.These modifications enhance both convergence speed and robustness in complex terrain optimization. Three objectives—coverage rate, village coverage satisfaction, and signal security—are combined into a weighted multi-objective function, and experiments are performed under two deployment scenarios (fixed and random village distributions).The results demonstrate that MSM-DE achieves significantly faster convergence and higher coverage performance than benchmark DE variants, validating that the proposed mutation synergy and adaptive parameter control effectively strengthen the algorithm’s optimization capability and stability in mountainous base station deployment.

Enhancing Few-Shot Text Classification with Parameter-Efficient Tuning of Large Language Models

Scipedia content — Mon, 08 Jun 2026 10:13:03 +0200

Traditional few-shot text classification models focus only on label prediction and cannot extract structured information such as entities or events, limiting their usefulness in real-world, semantics-driven tasks. They also rarely use external knowledge or parameter-efficient tuning, leading to shallow representations and weaker performance. To address this, this paper proposes a knowledge-aware multi-task framework that integrates few-shot classification with entity and event extraction. A single BERT encoder with IA3 adapters enables efficient tuning, while semantic triples extracted via spaCy and aligned with WordNet and ConceptNet are encoded using TransE. A BiLSTM captures sequential context and a softmax decoder performs token-level prediction. Experiments show strong results—97.97% accuracy, 98.00% precision, 97.95% recall, and 97.96% F1—surpassing state-of-the-art baselines. Ablation studies confirm the value of the knowledge-enhanced, multi-step design, demonstrating suitability for lowresource, knowledge-centric applications.

Efficient Mean Estimation Using Repeated Measurements in PPS Sampling with Scientific Applications

Scipedia content — Mon, 08 Jun 2026 10:12:03 +0200

Research studies in many scientific disciplines need efficient estimation methods for estimating the parameters of quantitative variables, including, but not limited to, tensile strength, wind speeds, air quality, and temperature, etc. Statisticians employ a sampling design to get a random sample and estimate the population means based on the observed sample. If the units of a finite population have different selection probabilities, unequal probability methods of sample selection are used. The existing unequal probability sampling methods allocate probabilities proportional to size (PPS) to the population units by using a one-time measurement approach. Memory– type estimation methods, on the other hand, use multiple measurements and provide more precise estimates than the traditional estimators. This research study introduces a novel memory-type estimator using a PPS sampling design. Various properties of the suggested mean estimator are analyzed, and the efficiency conditions are derived. A few real-world data sets have been used from previous studies to evaluate the performance of the suggested and competing mean estimators. Our comparative study suggests that the suggested memory-based estimator performs much better than the competing estimators, which means that the suggested memory-type estimator is an appropriate estimator for application in real-life sample surveys.

A Novel Logarithmic Sine-G Family of Distributions with Applications to Reliability Engineering Data

Scipedia content — Mon, 08 Jun 2026 10:10:14 +0200

In this research paper, we make a significant attempt to present a novel statistical methodological approach for generating more versatile distributions. The proposed method is developed by incorporating the Logarithmic and Sine functions and may be referred to as a novel logarithmic sine-G (NLS-G) family of distributions. A special sub-model of the NLSG family is investigated by utilizing the Weibull model as a base member. The sub-model of the NLS-G family may be callednovel logarithmic sineWeibull (NLS-Weibull) distribution. The density and hazard functions of the NLS-Weibull distribution are graphically illustrated, showing their behavior and characteristics. Some distributional properties of the NLSG family, including quantile function, median and quartile measures, moments, and moment generating function, are derived. The Maximum Likelihood Estimation (MLE) method is employed for estimating the model parameters of the NLS-G family of distributions. A comprehensive Monte Carlo simulation study of the proposed distribution is also conducted to evaluate the practical performance of its estimators. Furthermore, the usefulness of the newly proposed NLS-Weibull distribution is illustrated by investigating four real data sets from the field of the engineering sector. The first data set represents the failure time of electronic devices. The second data set is civil engineering data and represents the breaking stress of carbon fibers. The third data set represents the strengths of 1.5 cm glass fibers. The fourth data set represents single-carbon fibers. Based on four diagnostic criteria, it is observed that the NLS-Weibull distribution may be the best choice for the considered data sets.OPEN ACCESS Received: 02/12/2025 Accepted: 12/01/2026 Published: 29/05/2026

Monotonic Interval-Valued OWA Operators and Choquet Integrals under Generated Admissible Orders

Scipedia content — Mon, 08 Jun 2026 10:09:03 +0200

In the context of multi-criteria decision-making, the ordered weighted averaging (OWA) operator and Choquet Integral (CI) emerge as two pivotal weighted aggregation operators. Their core features include the linear order structure and the monotonicity. However, there is currently no unified method for constructing monotonic OWA operators and CIs under a certain admissible order within the interval-valued fuzzy framework. Based on the complete lattice structure of the space of all closed subintervals of [0, 1] under the generated admissible order, we propose a unified method for constructing the monotonic interval-valued OWA operators and CIs. Moreover, we theoretically prove rigorously that the proposed OWA operators and CIs satisfy the axiomatic definition of aggregation operator, particularly the monotonicity under a certain admissible order. Finally, we establish a multi-expert decision-making algorithm based on the proposed operators, where the overall preference of each alternative is obtained by aggregating its interval-valued evaluations across admissible orders. The effectiveness of the proposed approach is illustrated through a practical decision-making example.OPEN ACCESS Received: 27/11/2025 Accepted: 23/02/2026 Published: 29/05/2026

Job Shop Scheduling with AGV Charging Considering Travel Time and Processing Time

Scipedia content — Mon, 08 Jun 2026 10:05:13 +0200

Job Shop Scheduling Problem with Automated Guided Vehicles (JSPAGV) better aligns with the real-world workshop scenarios and has become a research hotspot. However, optimizing JSP-AGV with AGV charging remains a significant challenge. The comprehensive JSP-AGV model incorporated AGV charging is established, where charging is mandatory and interrupts the transportation task. Then, an improved genetic algorithm with a hybrid initialization strategy and problemspecific crossover and mutation operators is devised to minimize the Exit Time (ET). Extensive simulations are conducted to evaluate the model and algorithm. Furthermore, Design of Experiments (DoE) is employed to quantitatively analyze the impact of three critical system parameters, such as AGV battery capacity, AGV quantity, and travel time-to-processing time ratio. The analysis reveals that the travel time to processing time ratio determines the scheduling bottlenecks in JSP-AGV. Orthogonal experimental results indicate that for time-related metrics, including ET, machines waiting time for jobs, jobs waiting time for machines, AGVs waiting time for jobs, jobs waiting time for AGVs, the influencing factors are ranked in descending order of importance as follows: travel time to processing time ratio, AGV quantity and AGV battery capacity. In contrast, for AGV charging frequency metric, the order of the influence is AGV battery capacity, travel time to processing time ratio and AGV quantity.OPEN ACCESS Received: 13/11/2025 Accepted: 04/02/2026 Published: 29/05/2026

Study on the Seepage and Heat Transfer Characteristic of Single Rough Fracture and Intersecting Fracture in Hot-Dry Rock

Scipedia content — Mon, 08 Jun 2026 10:01:14 +0200

The efficient extraction of geothermal energy from fractured hot dry rock reservoirs requires accurate prediction of subsurface thermohydrodynamic processes. In this study, a novel simplified mid-plane fracture model was developed and validated as an approach that bridges the computational efficiency of two-dimensional approximations with the physical accuracy of fully three-dimensional simulations. Three distinct fracture representations were systematically compared: full 3D models, simplified 2D plane fractures, and the proposed rough mid-plane fractures. Discrepancies in flow dynamics and heat transfer predictions were quantified using coupled steady-state and transient numerical simulations. The influence of “X”-type and “V”-type fracture intersections on thermo-hydrodynamic processes was further examined. The results show that the rough mid-plane model accurately captures permeability and flow channeling effects inherent in realistic 3D fracture geometries, outperforming traditional plane approximations. Flow fields and velocity distributions are substantially modified by surface roughness, directly governing heat exchange efficiency. Flow and temperature fields are shown to be dramatically redistributed by variations in fracture aperture and intersection geometry, with heat extraction significantly enhanced by optimal aperture and injection rate combinations. Notably, “X”-type intersections are found to exhibit 40%–60% greater effective heat conduction areas than “V”-type configurations, highlighting their preferential heat transfer characteristics. These findings provide critical insights for the optimization of HDR reservoir performance, contributing to improved strategies for efficient geothermal energy extraction.OPEN ACCESS Received: 14/09/2025 Accepted: 21/10/2025 Published: 29/05/2026

Defence Technology

Yewuhalashet Fissha — Thu, 04 Jun 2026 07:05:14 +0200

Blast-induced ground vibration, quantified by peak particle velocity (PPV), is a crucial factor in mitigating environmental and structural risks in mining and geotechnical engineering. Accurate PPV prediction facilitates safer and more sustainable blasting operations by minimizing adverse impacts and ensuring regulatory compliance. This study presents an advanced predictive framework integrating CatBoost (CB) with nature-inspired optimization algorithms, including the Bat Algorithm (BAT), Sparrow Search Algorithm (SSA), Butterfly Optimization Algorithm (BOA), and Grasshopper Optimization Algorithm (GOA). A comprehensive dataset from the Sarcheshmeh Copper Mine in Iran was utilized to develop and evaluate these models using key performance metrics such as the Index of Agreement (IoA), Nash-Sutcliffe Efficiency (NSE), and the coefficient of determination (R2). The hybrid CB-BOA model outperformed other approaches, achieving the highest accuracy (R2 =0.989) and the lowest prediction errors. SHAP analysis identified Distance (Di) as the most influential variable affecting PPV, while uncertainty analysis confirmed CB-BOA as the most reliable model, featuring the narrowest prediction interval. These findings highlight the effectiveness of hybrid machine learning models in refining PPV predictions, contributing to improved blast design strategies, enhanced structural safety, and reduced environmental impacts in mining and geotechnical engineering.

Real-Time Digital Twin for Structural Health Monitoring of Floating Offshore Wind Turbines

Julio García-Espinosa — Mon, 01 Jun 2026 13:28:13 +0200

Digital twins (DTs) offer significant promise for condition-based maintenance of floating offshore wind turbines (FOWTs); however, existing solutions typically compromise either on physical rigor or real-time computational performance. This paper presents a real-time DT framework that resolves this trade-off by embedding a hydro-elastic reduced-order model (ROM) that accurately captures structural dynamics and fluid–structure interaction. Integrated in a cloud-ready Internet of Things architecture, the ROM reconstructs full-field displacements, von Mises stresses, and fatigue metrics with near real-time responsiveness. Validation on the 5 MW OC4-DeepCWind semi-submersible platform shows that the ROM reproduces finite-element (FEM) displacements and stresses with relative errors below 1%. A three-hour load case is solved in 0.69 min for displacements and 3.81 min for stresses on a consumer-grade NVIDIA RTX 4070 Ti GPU—over two orders of magnitude faster than the full FEM model—while one million fatigue stress histories (1000 hotspots×1000 operating scenarios) are processed in 37 min. This efficiency enables continuous structural monitoring, rapid *what-if* assessments and timely decision-making for targeted inspections and adaptive control. By effectively combining physics-based reduced-order modeling with high-throughput computation, the proposed framework overcomes key barriers to DT deployment: computational overhead, physical fidelity and scalability. Although demonstrated on a steel platform, the approach is readily extensible to composite structures and multi-turbine arrays, providing a robust foundation for cost-effective and reliable deep-water wind-energy operations.

Modal matrix reduction for fully coupled integrated load analysis of floating structures

Julio García-Espinosa — Mon, 01 Jun 2026 13:16:14 +0200

Structural elasticity of floating wind turbines in integrated load analysis (ILA) is typically addressed by modelling the substructure with simplified beam models. The main reason can be found in the computational cost of the structural solver when solving the fully coupled hydroelastic problems. In this work, a reduce order method based on modal matrix reduction is applied to reduce the computational cost of the structural solver. The main idea is to largely reduce the number of degrees of freedom of the structural system by retaining only those modes with significant energy. The seakeeping hydrodynamics is solved using the finite element framework SeaFEM. The structural particulars are introduced into this framework to fully integrate the fluid-structure interaction. The hydroelastic model is also coupled with the wind turbine solver OpenFAST, resulting in a complete aero-hydro-servo-elastic tool for the ILA analysis of floating turbines. A methodology is proposed to identify critical conditions and hotspots based on the structural energy. An application case of the present strategy is presented for a detailed structural design of the well-known OC4-DeepCwind. The consistency of the modal approximation and methodology is verified against the FE structural solution. The capabilities of the proposed ILA framework are demonstrated in a fully coupled and detailed structural analysis, instead of at component level, with a significant reduction of its computational time.

Accelerated fully coupled hydro-elastic analysis of ships using a combined full and modal-reduced FEM approach

Julio García-Espinosa — Mon, 01 Jun 2026 13:15:03 +0200

The numerical simulation of a ship’s hydroelastic structural response is typically carried out using simplified modelling approaches. The main reason can be found in the computational cost of the structural solver when solving the fully coupled hydro-elastic problems. In this work, a two-way coupled fluid-structure interaction model capable of efficiently and accurately computing the hydro-elastic response of a ship using a detailed full-length structural representation is proposed. To reduce the computational cost of the structural solver, a reduced-order method based on modal matrix reduction is applied. The main idea is to largely reduce the number of degrees of freedom of the structural system by retaining only those modes with significant energy. Furthermore, to improve the accuracy of the model, this work proposes a combined methodology in which a residual finite element (FE) solution is computed alongside the reduced model, while still achieving a reduction in the overall computational effort. The seakeeping hydrodynamics is solved using the computational framework SeaFEM. And the structural particulars are introduced into this framework to fully integrate the fluid-structure interaction. An application case of the proposed model strategy is presented for a detailed structural design of a ship. The consistency of the modal approximation and methodology is verified against the full FE structural solution. It shows the capabilities of the proposed framework to perform a fully coupled and detailed structural analysis, instead of at component level, with a significant reduction in computational time.

Accelerated fully coupled hydro-elastic analysis of ships based on modal matrix reduction

Julio García-Espinosa — Mon, 01 Jun 2026 13:07:03 +0200

Hydro-elastic effects such as springing, whipping and racking can significantly increase hull stresses and fatigue damage. However, fully coupled time-domain hydro-elastic simulations remain computationally prohibitive for practical ship design due to the high cost of the structural solver.

Objective: Develop an efficient two-way coupled hydro-elastic framework capable of capturing resonance effects with full-length detailed structural models, while drastically reducing computational cost.

Análisis hidroelástico totalmente acoplado de plataformas eólicas flotantes

Julio García-Espinosa — Mon, 01 Jun 2026 11:06:13 +0200

Structural elasticity of floating wind turbines, in integrated load analysis, are typically addressed by modelling the substructure with simplified beam models. The main reason can be found in the computational cost of the structural solver when solving the fully coupled hydroelastic problems. In this work, a reduce order method based on modal matrix reduction (MMR) is applied to reduce the computational cost. The main idea is to largely reduce the number of degrees of freedom of the structural system by retaining only those modes with significant energy. The seakeeping hydrodynamics is solved using the computational framework SeaFEM, based on the finite element method (FEM). The structural particulars are introduced into this framework to fully integrate the fluid-structure interaction. The hydroelastic model is also coupled with the wind turbine solver OpenFAST, resulting in a complete aero-hydro-servo-elastic tool for the ILA analysis of floating turbines. Moreover a methodology is proposed to identify critical conditions and hotspots based on the structural energy. An application case of the present strategy is presented for a detailed structural design of the OC4-DeepCwind. The consistency of the modal approximation and methodology are

Análisis hidroelástico totalmente acoplado de plataformas eólicas flotantes

Julio García-Espinosa — Mon, 01 Jun 2026 10:54:03 +0200

Artificial Intelligence Workflows with Intelligent Agents Applied to Educational Platforms for Academic Support and Student Performance Monitoring: A Systematic Literature Review

Aaron Palomino — Sun, 31 May 2026 02:00:16 +0200

The integration of Artificial Intelligence (AI) in education has transformed learning personalization and student performance management. This study aimed to analyze the technologies, implementation strategies, and reported outcomes in recent scientific literature regarding AI workflows and intelligent educational agents. Under the PRISMA methodology, a systematic review was conducted on 70 studies published between 2020 and 2026 in high-impact databases. The findings reveal a growing trend toward the use of learning analytics, multi-agent systems, and academic chatbots—tools that have proven effective in early risk detection and automated monitoring. However, critical barriers were identified, such as the lack of technological interoperability and ethical dilemmas in data governance. It is concluded that while these technologies are promising for the evolution of digital environments, their success depends on developing standardized architectures and conducting longitudinal studies to validate their long-term pedagogical impact.

How Long Does Ayurvedic Weight Loss Treatment Take to Show Results?

Mantra Ayurveda — Fri, 29 May 2026 09:13:28 +0200

Weight loss is a journey that requires consistency, patience, and the right approach. While many modern weight-loss programs promise rapid results, they often focus on temporary solutions that can be difficult to maintain. Ayurveda takes a different path by addressing the underlying causes of weight gain and supporting overall health alongside weight management.

One of the most common questions people ask is: How long does Ayurvedic weight loss treatment take to show results? The answer depends on several factors, including body type, metabolism, lifestyle habits, diet, and the severity of weight gain. An effective ayurvedic treatment for weight loss focuses on creating sustainable changes rather than delivering quick but short-lived results.

Understanding Weight Loss Through Ayurveda

Ayurveda views weight gain as a sign of imbalance within the body. According to Ayurvedic principles, excess weight is often linked to an aggravated Kapha dosha, weakened digestive fire (Agni), and the accumulation of toxins known as Ama.

Instead of simply targeting fat reduction, Ayurveda aims to:

Improve digestion
Enhance metabolism
Eliminate toxins
Balance the doshas
Promote healthy eating habits
Support physical and mental well-being

This holistic approach helps create a foundation for long-term weight management.

Why Results Vary from Person to Person

There is no universal timeline for weight loss because every individual has unique health conditions and lifestyle factors.

Several factors influence how quickly an ayurvedic treatment for weight loss may show results:

Body Constitution (Prakriti)

Each person has a unique Ayurvedic constitution.

Individuals with dominant Kapha characteristics may:

Gain weight more easily
Have slower metabolism
Require longer treatment duration

People with balanced doshas may experience improvements more quickly.

Current Weight and Health Status

The amount of excess weight can affect the timeline.

Factors include:

Body mass index (BMI)
Metabolic health
Hormonal balance
Existing medical conditions
Digestive efficiency

Individuals with multiple underlying health concerns may require a more gradual approach.

Lifestyle Habits

Daily habits significantly influence treatment outcomes.

Important factors include:

Physical activity level
Sleep quality
Stress management
Dietary choices
Consistency with treatment recommendations

Healthy habits often accelerate progress.

Typical Timeline for Ayurvedic Weight Loss Results

Although results vary, many individuals notice improvements in stages.

First Few Weeks (2–4 Weeks)

During the initial phase, the body begins adjusting to dietary and lifestyle changes.

Common improvements may include:

Better digestion
Reduced bloating
Increased energy levels
Improved bowel regularity
Reduced water retention

At this stage, the focus is often on restoring digestive health rather than rapid weight loss.

One to Three Months

After several weeks of consistent treatment, many individuals begin noticing measurable changes.

Possible improvements include:

Gradual weight reduction
Better appetite control
Improved metabolism
Reduced cravings
Enhanced physical activity tolerance

The body starts responding more efficiently to healthy lifestyle modifications.

Three to Six Months

With continued adherence to treatment plans, more significant results may become visible.

Potential outcomes include:

Sustainable weight loss
Improved body composition
Better energy levels
Enhanced digestive health
Greater overall wellness

This period often represents the most noticeable transformation for many individuals.

Long-Term Results

Ayurveda emphasizes long-term health rather than short-term outcomes.

Continued practice of Ayurvedic principles may help:

Maintain healthy weight
Prevent weight regain
Improve metabolic health
Support overall well-being

Consistency remains the key to lasting success.

Key Components of Ayurvedic Treatment for Weight Loss

Personalized Diet Plans

Diet is a cornerstone of every ayurvedic treatment for weight loss program.

Ayurvedic dietary recommendations typically focus on:

Fresh fruits and vegetables
Whole grains
Lean plant-based proteins
Seasonal foods
Easily digestible meals

Heavy, processed, and excessively oily foods are often minimized.

Herbal Support

Several Ayurvedic herbs have traditionally been used to support healthy metabolism and digestion.

Common examples include:

Triphala
Guggul
Amla
Ginger
Fenugreek
Cinnamon

These herbs are usually recommended based on individual needs and should be used under professional guidance.

Panchakarma Detoxification

Panchakarma is a specialized Ayurvedic detoxification therapy designed to remove accumulated toxins from the body.

Potential benefits include:

Improved digestion
Better metabolic efficiency
Enhanced energy levels
Support for healthy weight management

Panchakarma therapies should always be administered by qualified Ayurvedic practitioners.

The Role of Metabolism in Weight Loss

Ayurveda considers Agni, or digestive fire, essential for maintaining a healthy metabolism.

When Agni is strong:

Food is digested efficiently
Nutrients are absorbed properly
Toxins are minimized
Energy production improves

When Agni becomes weak, weight gain and digestive issues may develop.

Many Ayurvedic interventions focus specifically on strengthening Agni to support healthy metabolism.

Lifestyle Practices That Influence Results

Regular Physical Activity

Exercise complements Ayurvedic treatment by improving circulation and energy expenditure.

Recommended activities include:

Walking
Yoga
Swimming
Cycling
Light strength training

Consistency is often more important than intensity.

Stress Management

Chronic stress can contribute to weight gain through hormonal imbalances and emotional eating.

Ayurvedic stress-management practices include:

Meditation
Deep breathing exercises
Mindfulness techniques
Relaxation practices

Managing stress can positively influence weight-loss outcomes.

Quality Sleep

Sleep plays a critical role in weight management.

Poor sleep may:

Increase hunger hormones
Reduce energy levels
Slow metabolism
Increase cravings

Ayurveda encourages maintaining a regular sleep schedule and prioritizing restorative rest.

Common Mistakes That Delay Results

Many individuals unintentionally slow their progress by making avoidable mistakes.

Examples include:

Expecting immediate results
Skipping meals
Following restrictive diets
Inconsistent treatment adherence
Ignoring lifestyle recommendations
Lack of physical activity

Ayurvedic treatment works best when approached as a comprehensive wellness program rather than a temporary solution.

Benefits Beyond Weight Loss

One reason many people choose Ayurveda is that the benefits extend beyond the number on the scale.

Potential additional benefits include:

Better digestion
Improved energy levels
Enhanced mental clarity
Better sleep quality
Reduced bloating
Greater overall wellness

These improvements often contribute to long-term health and quality of life.

Why Patience Matters in Ayurvedic Weight Management

Unlike crash diets that may produce rapid but temporary changes, Ayurveda focuses on creating sustainable improvements.

Healthy weight loss generally occurs gradually because:

The body needs time to adapt
Metabolic processes improve progressively
Digestive health must be restored
Lifestyle habits require consistency

This gradual approach often helps reduce the likelihood of regaining lost weight.

Conclusion

The timeline for seeing results from an ayurvedic treatment for weight loss varies from person to person. While some individuals notice improved digestion and energy within a few weeks, meaningful weight reduction often becomes more apparent after several months of consistent effort.

Ayurveda emphasizes sustainable health improvements through balanced nutrition, herbal support, detoxification therapies, physical activity, stress management, and personalized care. By addressing the root causes of weight gain rather than focusing solely on calorie restriction, Ayurvedic treatment offers a holistic and long-term approach to healthy weight management.

For the best results, individuals should work with qualified Ayurvedic practitioners who can create personalized treatment plans tailored to their unique body constitution and health goals.

ELOGIO DE LA MECÁNICA

Eugenio Oñate — Wed, 27 May 2026 18:40:33 +0200

El texto es un elogio sobre la Mecánica. Sobre cómo el desarrollo de esa dicha disciplina ha influenciado el progreso de los pueblos, desde los albores de la humanidad hasta la actualidad, en el desarrollo y avance de la física en general y cómo previsiblemente seguirá teniendo un papel central en la configuración del mundo del futuro.

En las páginas del texto se explica que, brevemente, como si fuera un personaje desconocido para el lector, qué es la Mecánica y porqué merece ser elogiada. Esta introducción, innecesaria para la mayoría de los ingenieros, considero que es, sin embargo, oportuna para las personas que desarrollan su actividad en otros campos. Tras ello, sintetizo la evolución de la Mecánica desde los inicios de la vida del hombre hasta nuestros días, destacando los momentos que significaron un avance decisivo en su desarrollo y en sus aplicaciones. Finalmente, resumo la situación actual de la Mecánica en relación con la Ingeniería y otras Ciencias Aplicadas, en la era del Big Data y la IA, y las perspectivas de esta disciplina para el porvenir más inmediato.

Espero que este texto sirva para destacar el importante papel que la Mecánica ha tenido para configurar el mundo, tal y como lo conocemos hoy en día, y más concretamente, su esencial influencia para el avance de todas las ramas de la Ingeniería.

Nuevas estrategias y aplicaciones de la respuesta dinámica de buques y artefactos flotantes en el contexto operativo. Aplicación en el proyecto ML-AMAR

Rafael Molina Sánchez — Wed, 27 May 2026 17:45:13 +0200

En las jornadas Nacionales Españolas de Costas y Puertos se presenta las actividades de monitorización. En ella se expone que la operación de un buque está condicionada por su interacción continua con el medio físico, especialmente durante fases de alta exigencia como la aproximación al puerto, el acceso, el atraque, el amarre y la estancia atracado. En estas situaciones, la respuesta dinámica del buque constituye una fuente de información relevante para evaluar la seguridad, el confort, la eficiencia operativa y la exposición a condiciones meteo-oceánicas desfavorables. En el marco del proyecto ML-AMAR (Machine Learning Applications in Marine Engineering), se ha desarrollado una estrategia de monitorización continua orientada a integrar datos de navegación, respuesta dinámica y condiciones ambientales. Esta comunicación presenta su aplicación al buque Ro-Pax Ciudad de Barcelona, utilizado como demostrador en condiciones operativas reales. La metodología se basa en la instrumentación del buque mediante el sistema DeepMOTION-RTK, que permite registrar posición, velocidad, rumbo, aceleraciones y movimientos angulares mediante sensores GNSS/IMU. La campaña de monitorización cubre un periodo prolongado de operación, incluyendo navegación en ruta, aproximación, maniobras portuarias y estancia en puerto. Las señales registradas se integran con información meteo-oceánica procedente de fuentes externas, como Puertos del Estado y Copernicus, para relacionar la respuesta dinámica del buque con el oleaje, el viento y otras condiciones ambientales. Los resultados permiten estructurar la operación del buque por fases, identificar episodios de mayor solicitación dinámica y derivar indicadores trazables asociados a seguridad, confort y eficiencia. Asimismo, la información obtenida alimenta la plataforma DEEPVIEW, concebida para la visualización y análisis de los datos monitorizados, así como para la generación de métricas operativas de apoyo a la decisión. El trabajo confirma el interés de la monitorización dinámica como herramienta para mejorar la gestión operativa portuaria, validar modelos predictivos y avanzar hacia sistemas de apoyo basados en datos para la explotación de buques en condiciones reales. Palabras clave

Anonymized scientific-technical database from the ML-AMAR monitoring campaign

Rafael Molina Sánchez — Wed, 27 May 2026 11:52:42 +0200

The project ML-AMAR (Machine Learning Applications in Marine Engineering) aims to develop machine learning tools to optimize the lifecycle management of ships, from design and operation to maintenance and structural monitoring.

The repository includes two public anonymized datasets generated from the ML-AMAR monitoring campaign. Both datasets have been prepared for scientific and technical distribution by removing sensitive information such as exact coordinates, precise timestamps, vessel/sensor identifiers and individual trajectories.

The first dataset, ML_AMAR_anonimizacion_version_B_public, contains aggregated information on maritime routes, trip frequencies, departure time slots, trip duration classes, recurrent movement patterns and a non-georeferenced schematic network. It is intended for the analysis of maritime mobility and general route patterns.

The second dataset, ML_AMAR_positions_1min_metocean_public_B, is derived from the original one-minute monitoring records. It provides aggregated information on operational state, route phase, distance-to-port classes, metocean conditions and vessel dynamic response. It is intended for scientific and technical analyses of the relationship between navigation, waves, wind and vessel behaviour.

In both cases, the data are published only in aggregated and anonymized form. The repository does not include raw files, real GNSS positions, exact timestamps, operational identifiers or confidential correspondence tables between anonymized codes and real locations. Aggregation, discretization and suppression rules have been applied to prevent the reconstruction of individual trips or specific operational patterns.

The dataset ML_AMAR_anonimizacion_version_B_public includes aggregated route summaries, trip frequencies, time slot distributions, duration classes, recurrent cycles and a schematic non-georeferenced network. Its main purpose is the analysis of maritime mobility and route patterns.

The dataset ML_AMAR_positions_1min_metocean_public_B includes aggregated operational states, route phases, distance-to-port classes, vessel motion statistics and metocean conditions. Its main purpose is the scientific and technical analysis of vessel dynamics and metocean forcing.

Comparación técnico-formulativa entre jabones artesanales saponificados con aceites vegetales y jabones artesanales elaborados con base de glicerina

David Ernesto Barboza Pantoja — Sat, 23 May 2026 23:11:23 +0200

Este artículo analiza comparativamente dos rutas frecuentes de elaboración de jabones artesanales: el jabón obtenido por saponificación de aceites vegetales y mantecas, y el jabón elaborado a partir de una base de glicerina en barra. El propósito fue establecer diferencias técnicas, químicas, sensoriales y operativas entre ambas alternativas, considerando una formulación hipotética basada en aceite de coco, aceite de oliva, manteca de cacao, agua destilada, hidróxido de sodio y una base de glicerina. Se desarrolló una metodología documental-comparativa apoyada en literatura científica sobre saponificación, propiedades fisicoquímicas del jabón, función de los ácidos grasos y comportamiento humectante del glicerol. Los resultados muestran que los jabones saponificados con aceites permiten mayor control de dureza, espuma, sobreengrasado y perfil lipídico, pero exigen control estricto de cálculo de soda cáustica, curado y seguridad química. En contraste, los jabones de glicerina presentan una elaboración más simple, menor riesgo operativo y mejor transparencia, aunque ofrecen menor control sobre la matriz tensioactiva y dependen de la composición previa de la base comercial. Se concluye que ninguna ruta es universalmente superior: la elección depende del objetivo del productor, del nivel técnico disponible y de los atributos buscados en el producto final.

DISEÑO, MODELADO Y PROTOTIPADO DE UN SISTEMA MODULAR DE CAPTACIÓN DE AGUA LLUVIA PARA VIVIENDAS DE BAJO COSTO

Arnoldo Fernando Yepes Acosta — Sat, 23 May 2026 20:12:13 +0200

The purpose of this project is to design, model, and build a full-scale modular rainwater harvesting system intended to serve as a sustainable alternative for water resource management in low-cost housing. The goal is to integrate parametric design tools, 3D modeling, and additive manufacturing to develop components such as the roof, gutters, downspouts, primary water system, storage tank, structural base, and water outlet. The study incorporates principles of applied research, environmental sustainability, and technological innovation to strengthen skills in technical design, component assembly, structural optimization, and real-world problem-solving. It will be carried out using a phased methodology that includes research and planning, design and 3D modeling, prototyping, functional validation, and dissemination of results.

Speech of E. Oñate in Real Academia de Ingeniería de España

Eugenio Oñate — Sat, 23 May 2026 11:43:18 +0200

Numerical Investigation of Overburden Failure Mechanisms and Structural Evolution in Partial Gob-Backfilling Mining of Steeply Inclined Coal Seams

Scipedia content — Fri, 22 May 2026 10:21:04 +0200

In managing strong roof loading in steep-inclined longwall panels, this study adopts partial gob backfill mining along the dip direction. Four controlling factors for roof deformation are identified: working face length (L), mining depth (H), seam dip angle (α), and backfill length (a). Parametric analysis determines that L = 105 m combined with a 2/5 backfill ratio achieves optimal strata control. Physical experiments recorded dip-direction stress gradients: upper (8.65/7.79/8.45 MPa), central peak (9.86/9.15/9.86 MPa), and lower (8.82/8.41/8.83 MPa), with displacement increments of horizontal (+115.6%/+73.9%/+74.1%), vertical (+136.2%/+48.9%/+21.3%), and resultant (+80.6%/+94.8%/+39.2%). FLAC3D simulations systematically varied backfill ratios (1/5, 2/5, 3/5) and face lengths (90, 105, 120 m). Increasing the ratio from 1/5 to 2/5 reduced peak stress by 7.7% (15.65 → 14.45 MPa) and subsidence by 39.3% (1.78 → 1.08 m), while further increase to 3/5 yielded marginal gains (4.5%, 31.5%). At the optimal 2/5 ratio, extending face length from 90 to 105 m increased abutment stress by 8.9% (13.27→14.45 MPa) and subsidence by 17.4% (0.92→1.08 m), while 120mcaused disproportionate surges (5.2%, 49.1%) with plastic zone height soaring 81.9% (36.05→ 65.56 m). Under the optimal 105 m–2/5 configuration, staged advance (20–80 m) quantified progressive stress transfer: lower-end pillar stress rose 20.4% (9.22→11.10MPa), backfill stress 24.7% (8.75→10.91MPa), and roof subsidence from 302 to 688 mm, with plastic zone evolving as an asymmetric arch characterized by shear failure at the arch foot (lower pillar/backfill interface) and tensile failure at the crown. This integrated approach confirms that partial backfill effectively regulates strata behavior, providing a quantitative framework for sustainable steep-seam mining.

AGROALERT AI: Sistema Inteligente de Monitoreo y Alerta Temprana para Cultivos Vulnerables a Inundaciones y Sequías mediante CyberPi

Helmer Ahumada Hernandez — Thu, 21 May 2026 23:48:03 +0200

AgroAlert AI is an intelligent system developed using the CyberPi educational board, focused on monitoring and generating early warnings for crops vulnerable to floods and droughts. The project aims to provide a low-cost educational technological solution to support small farmers and rural communities affected by extreme climate events.

The system uses CyberPi integrated sensors, WiFi connectivity, and Python programming to collect environmental variables, analyze simulated climate behaviors, and generate visual and sound alerts in real time. Additionally, it allows data recording for statistical analysis and future applications related to Artificial Intelligence and the Internet of Things (IoT).

This research promotes STEM learning among high school students by strengthening skills in programming, electronics, data science, and problem-solving applied to real territorial challenges. AgroAlert AI represents an innovative proposal with social, environmental, and technological impact aligned with digital transformation processes in agriculture and risk management strategies.

Modeling and Simulation of a Novel Permanent Magnet Motor for Enhanced Vehicle Steering Performance

Scipedia content — Thu, 21 May 2026 10:33:15 +0200

This paper introduces a new design for a permanent magnet motor (PMM) tailored for automotive steering applications. By utilizing new permanent magnet topology, high-performance materials, and rotor designs, the newly proposed PMM design reduces torque ripple while boosting torque density. This design is integrated within a sophisticated steer-bywire (SBW) system, enabling seamless operation and reliable feedback, which are crucial for modern vehicles. The finite element analysis (FEA) substantiates the effectiveness of the proposed PMM, resulting in a 1.2% enhancement in magnetic flux density, a 4.65% increase in static torque, and a 4.4% reduction in torque ripple. This research addresses the issues in wheel steering technology and lays the groundwork for future automotive motor technologies. And also, it provides valuable insights into the development of automotive technologies, paving the way for enhanced performance and sustainability in the automotive sector. Furthermore, these improvements not only aim to enhance driver satisfaction but also align with the industry’s transition toward greener technologies.OPEN ACCESS Received: 05/10/2025 Accepted: 15/12/2025 Published: 16/04/2026

Least Square Support Vector Machine Framework for Meshless and Accurate Solution of Higher Order Boundary Value Problems with Comparative Analysis of Machine Learning Techniques

Scipedia content — Thu, 21 May 2026 10:30:16 +0200

This paper presents an enhanced Least Squares Support Vector Machine (LS-SVM) approach for meshless and accurate solution of higher-order boundary value problems (BVPs) that commonly arise in structural mechanics, fluid dynamics, and other engineering fields. The discussed method formulates thirdand fourth-order linear and nonlinear ordinary differential equations (ODEs) as data-driven optimization problems, eliminating the need for traditional mesh-based discretization. Leveraging a Radial Basis Function (RBF) kernel and regularization-based control of model complexity, the LS-SVM captures complex solution behaviour while maintaining stability and smoothness. The meshless nature of the model ensures geometry-independence, making it suitable for irregular or multi-point boundary conditions. A comparative analysis with established machine learning techniques, including Ridge Regression (RR), classical SVM, Random Forest (RF), and Extreme Gradient Boosting (XGB), demonstrates the competitive accuracy, robustness, and efficiency of LSSVM. The results highlight its potential as a promising solver for nonlinear and multi-point problems where meshless methods are advantageous. The results highlight its potential as a promising solver for simulation-based workflows in computational mechanics and scientific computing, where adaptability, generalization, and reliability are critical.OPEN ACCESS Received: 25/08/2025 Accepted: 21/10/2025 Published: 16/04/2026

Advanced Computational Study of Nonlinear Time-Fractional NewellWhitehead-Segel Equation with Caputo-Fabrizio Derivative Using B-Spline Techniques

Scipedia content — Thu, 21 May 2026 10:28:13 +0200

This study presents numerical solutions for the time-fractional NewellWhitehead-Segel (NWS) equation with a Caputo-Fabrizio derivative. Spatial derivatives are discretized using three B-splines-cubic, cubic trigonometric and extended cubic B-splines-while temporal discretization is handled by a finite difference scheme. The proposed schemes are rigorously analyzed for stability and convergence. Their performance is evaluated in terms of accuracy and computational efficiency. Numerical experiments confirm the effectiveness of these techniques in capturing the dynamics of the fractional NWS equation. Each B-spline variant demonstrates unique strengths, highlighting the flexibility of B-spline approaches for solving fractional differential equations with nonlocal, memory-dependent operators. These results affirm the reliability and robustness of B-spline-based methods for such problems, paving the way for future advancements in this area.OPEN ACCESS Received: 23/08/2025 Accepted: 08/12/2025 Published: 16/04/2026

Qualitative Analysis of Nonlinear Systems Involving Hadamard-Type Fractional Derivatives with Nonlocal Boundary Conditions and Stability Properties

Scipedia content — Thu, 21 May 2026 10:25:03 +0200

This paper establishes a comprehensive analysis of a coupled system of nonlinear Hadamard-type fractional differential equations subject to generalized nonlocal integral boundary conditions. The distinct logarithmic kernel of the Hadamard derivative makes this framework particularly suitable for modeling scale-invariant processes and ultraslow diffusion phenomena. The existence and uniqueness of solutions are rigorously investigated using fixed point theory: Banach’s contraction principle ensures uniqueness, while the Leray-Schauder nonlinear alternative guarantees existence under more general growth conditions. Furthermore, the system is proven to be Ulam-Hyers stable, ensuring that approximate solutions remain close to exact solutions, which is crucial for the robustness of the model in practical applications. The theoretical findings are effectively validated through two detailed numerical examples, demonstrating the applicability of the established results to different classes of nonlinearities.OPEN ACCESS Received: 22/08/2025 Accepted: 03/11/2025 Published: 23/01/2026

Coupled Thermal and Solutal Transport in Magnetic Nanofluids with Field-Dependent Viscosity in Porous Media: A Stability Perspective

Scipedia content — Thu, 21 May 2026 10:11:14 +0200

This study investigates the linear stability of double-diffusive convection in magnetic nanofluids (MNFs) within a horizontal porous medium, accounting for field–dependent viscosity (FDV). A modified Buongiorno– type model incorporates Brownian motion, thermophoresis, magnetophoresis, and Darcy resistance. The resulting eigenvalue problem is solved via a Chebyshev pseudospectral–QZ algorithm under rigid–rigid (RR), rigid–free (RF), and free–free (FF) boundary conditions for both water–based (Wb) and ester–based (Eb) MNFs. Results show that magnetic and solutal effects lower the critical Rayleigh number (Rac) from the classical Darcy–Bénard limit of ≈39.48 to as low as ≈23.8, indicating enhanced instability. In contrast, increasing the FDV coefficient (δ), Langevin parameter (αL), and nanoparticle concentration difference (�φ) raises Rac, stabilizing the system. Eb–MNFs exhibit consistently higher Rac values—by 15%–20% compared to Wb–MNFs, due to greater viscosity and lower thermal diffusivity. These findings clarify the interplay of magnetoviscous damping and solutal buoyancy, offering predictive insights for the design of magnetically tunable porous heat exchangers and thermal management systems.

Integration of Monte Carlo Simulation and Linear Programming in Multi-Product Multi-Stage Supply Chains: A Theoretical Framework for Decision-Making under Uncertainty

Scipedia content — Thu, 21 May 2026 09:47:03 +0200

This study outlines a hybrid Monte Carlo Simulation-Linear Programming framework to increase the level of operational efficiency of a multistage supply chain. This model is an integration of probabilistic simulation and deterministic optimization to take into account the effects of demand variability, lead-time variability, and capacity variability on profitability and the overall service delivery performance. The suggested framework is tested with the help of a multi-product, multi-stage supply chain case study that is implemented on the basis of a publicly available dataset containing around 9000 transactional records. Monte Carlo simulation generates random uncertainty scenarios, whereas linear programming finds the ideal decisions related to production, distribution, and inventory levels for each iteration. The results suggest that reducing the amount of demand variability and better capacity planning resulted in a good performance with an expected profit of $328,100.16, a profit variance of 3.72× 109, and a service level of 94.3%. The result of the sensitivity analysis shows that demand variability and lead times have a negative effect on profit, while optimal capacity planning enhances operational flexibility. The MCS-LP provides an advantage to the use of stochastic and deterministic methods for risk-aware decision making and has the potential to be computationally scalable and efficient for uncertaintydriven supply chain design. The general approach provides a decisionsupport tool for managers considering how to balance costs, risk, service quality, and uncertainty in ever-changing industrial contexts.OPEN ACCESS Received: 14/11/2025 Accepted: 13/01/2026 Published: 20/03/2026

AI-Driven Predictive Analytics for Demand Forecasting in Transportation Logistics to Enhance Supply Chain Agility

Scipedia content — Thu, 21 May 2026 09:46:04 +0200

The logistics and transportation sectors are struggling with major issues like demand variations, disruptions, and inefficiencies, which ultimately undermine the agility and efficiency of the entire supply chain. Most of the time, traditional forecasting models are not entirely accurate in response to life-changing factors like weather, traffic, and inventory levels. The present research intends to build an AI-powered predictive model that can seamlessly enhance not only demand forecasting and logistics but also by the integration of real-time data. The framework incorporates several Machine Learning (ML) models, which are Light GBM for demand forecasting, Random Forest for disruption prediction, Linear Regression for shipping cost estimation, and Support Vector Regression for delivery time deviation prediction. A thorough dataset containing historical demand, weather conditions, traffic, and stock levels was used for the model’s training and evaluation, and its performance was monitored using MAE, MSE, RMSE, and MAPE metrics. The findings indicate that the suggested framework is a lot better than the existing ones, with Light GBM getting the lowest MAE (0.056), MSE (0.005), RMSE (0.072), and MAPE (0.142). This means that the new system can predict much better than before, thus making it possible for the company to take the right decision at the right time and consequently improving the overall supply chain efficiency. The research paper reveals the future possibilities of AI-based solutions for optimising logistics operations and building supply chain resilience.OPEN ACCESS Received: 20/10/2025 Accepted: 25/12/2025 Published: 03/02/2026

An Integrated IVHFS and DEMATEL-ANP Framework for Competitive Intelligence Evaluation in Smart Factories

Scipedia content — Thu, 21 May 2026 09:42:03 +0200

In the era of big data, the ability to evaluate high-quality and actionable competitive intelligence (CI) has become essential for smart factories to support data-driven decision-making and maintain technological and operational advantages. However, the highly dynamic and complex nature of the smart manufacturing environment introduces considerable uncertainty, hesitation, and interdependencies among evaluation indicators, posing significant challenges to traditional decision-making frameworks. To address these issues, this study proposes an integrated framework that combines interval-valued hesitant fuzzy sets (IVHFS) with the decision-making trial and evaluation laboratory-analytic network process (DEMATEL-ANP). IVHFS is employed to capture the ambiguity and hesitation inherent in expert judgments, enabling a more flexible and realistic representation of evaluation inputs. Subsequently, the DEMATEL-ANP approach is used to uncover the causal relationships among CI indicators and to construct a network-based weighting structure that reflects their interdependencies. A case study in a smart factory is conducted to validate the practicality and effectiveness of the proposed framework, and a sensitivity analysis confirmed its stability.OPEN ACCESS Received: 04/08/2025 Accepted: 28/10/2025 Published: 23/01/2026

Thermomechanical Uncertainty Analysis of Steel Partition Walls Using Direct FE2 and Polynomial Chaos Expansion

Scipedia content — Thu, 21 May 2026 09:39:14 +0200

Steel partition walls are essential components in modern civil engineering, providing both structural support and spatial separation. These walls are frequently exposed to combined thermal and mechanical loads, particularly in specialized environments such as high-temperature workshops or fire scenarios, where their thermo-mechanical coupling behavior is critical to building safety and functionality. This study integrates the direct finite element squared (Direct FE2) method with generalized polynomial chaos expansion (PCE) to quantify the uncertainties in key material propertiesnamely, the elastic modulus and the coefficient of thermal expansionand to evaluate their effects on the thermo-mechanical performance of steel partition walls. The proposed approach enables efficient simulation of material uncertainties and their influence on structural behavior under coupled thermal-mechanical conditions. Case studies demonstrate both the accuracy and computational efficiency of the method, while sensitivity analysis highlights the most influential uncertainty factors. The integration of Direct FE2and PCE thus offers a robust framework for assessing the reliability of steel partition walls under uncertain conditions, providing valuable insights for design optimization and enhancing the safety and efficiency of building structures in practical applications.OPEN ACCESS Received: 05/07/2025 Accepted: 17/09/2025 Published: 23/01/2026

Numerical Simulation Analysis of Influence of Wind and Temperature on Deformation of Constructed Zhaidi River High Bridge Pier

Scipedia content — Thu, 21 May 2026 09:34:14 +0200

Bridges are key projects of high-grade Expressways in mountainous areas. The verticality of bridge piers with a height of more than 100 m is crucial to ensure the safety and stability of bridge projects. When a pier construction is completed but the upper beam structure has not yet been connected (socalled after construction or constructed), the verticality of the pier is most likely to vary due to some factors. Based on the Zhaidi River Bridge project of Yunnan Zhenhe Expressway, considering the natural environment of the bridge site, three wind force intensity levels (Beaufort Scale 8, 10, and 12) and two climate conditions (high temperature and high radiation in summer, and low temperature and low radiation in winter) were identified; and the wind-induced deformation, temperature-induced deformation, and wind-temperature coupled deformation of the constructed main pier of the Zhaidi River Bridge with a height of 112.60 m were simulated with ANSYS Workbench numerical simulation platform. The simulation results show that: the influence of wind on pier deformation is much greater than that of ambient temperature variation; the influence of solar radiation on temperature-induced deformation of the bridge pier is much greater than that of air temperature variation; the temperature-induced deformation of the pier body under low temperature and low radiation condition in winter is greater than that under high temperature and high radiation condition in summer; the directional effect of the superposition of wind-induced deformation and temperature-induced deformation is more significant under low temperature and low radiation condition in winter.OPEN ACCESS Received: 11/04/2025 Accepted: 16/10/2025 Published: 23/01/2026

A Deep Learning-Based Time-Depth Conversion Method for Continuous Wellbore Temperature Profile Construction Using a Micro-Measurer

Scipedia content — Mon, 18 May 2026 10:26:13 +0200

Real-time monitoring of oil-gas wellbore temperature profiles provides important support for drilling strategy adjustment and model parameter calibration. MEMS-based micro-measurers can collect fullwellbore temperature data by flowing with drilling fluid circulation, but converting their time-series records to well depth relies on idealized steady-motion assumptions that neglect wall collisions, sticking, and local flow disturbances, introducing systematic positioning errors. In this study, a deep learning-based time-depth conversion method is proposed. A gated recurrent unit (GRU)-based temporal neural network is developed to extract motion-state features from six-axis dynamic signals, and a bounded velocity correction mechanism is introduced to compensate deviations from the idealized terminal velocity. The results show that: (1) The proposed model effectively learns motion-state deviations from dynamic response data and provides stable velocity correction under complex downhole conditions. (2) The corrected time-velocity curve exhibits transient fluctuations relative to the idealized terminal velocity, accurately capturing non-ideal behaviors while preserving directional stability. (3) During model training, the loss function decreases progressively and stabilizes, indicating good convergence and training stability. (4) Comparative analysis shows that the proposed GRU-Full method achieves a mean absolute anchor-point error of 1.23 m (0.24% of the well depth), outperforming the MLP and LSTM alternatives. Ablation experiments confirm that both the physical constraints and regularization terms contribute substantially to the model accuracy. This study enhances the spatial mapping accuracy of temperature data acquired by micro-measurers under complex downhole dynamic conditions. The established physically constrained deep learning framework provides a new technical pathway for refined wellbore thermal-field characterization and intelligent drilling decision-making.

A Point Cloud Registration Method for Ship Plates Based on Density Denoising and Anderson-Accelerated Iterative Closest Point

Scipedia content — Mon, 18 May 2026 10:25:23 +0200

Point cloud registration is essential for closed-loop digital forming of ship plates, where CAD-toscan alignment is required for surface error evaluation and compensation. Industrial ship-plate point clouds acquired by structured-light or laser sensing are often corrupted by boundary-related structural outliers and are feature-sparse, causing classical ICP to be sensitive to mismatches and slow to converge. We propose a training-free, deployment-oriented pipeline that combines density-based outlier removal with an Andersonaccelerated ICP (A-ICP) formulated in the SE(3) Lie algebra. Experiments on three representative plate geometries show 37.7%–39.5% lower registration error and indicate improved convergence behavior relative to classical ICP variants. The method is further validated on an SKWB-2500 machine-in-the-loop workflow, achieving MAE of 0.68 mm (sail-shaped) and 0.38 mm (saddle-shaped), with corresponding RMSE of 0.7301 mm and 0.4141 mm. Learning-based baselines are not benchmarked due to the lack of a fair in-domain dataset and retraining protocol under proprietary sensing conditions.

Optimal FOPID Controller Design for DC Motor Speed Control Using Ant Lion Optimization Algorithm

Scipedia content — Mon, 18 May 2026 10:20:14 +0200

DC motors are frequently utilized in industrial and automation applications where accurate speed control is crucial. Although conventional Proportional-Integral-and Derivative (PID) controllers are widely utilized, their constant gain values make them less effective in managing dynamic loads and disturbances. It’s difficult to get optimal transient and steady-state performance with traditional PID tuning methods. To overcome these limitations, more adaptable and dependable control systems are needed. This study introduces a novel control strategy by optimizing a Fractional-Order PID (FOPID) controller using the Ant Lion Optimization (ALO) method. Mathematical modeling is used to determine the DC motor’s transfer function. An ALO, a metaheuristic algorithm, is then implemented to improve five FOPID parameters using Integral Timeweighted Absolute Error (ITAE). The simulation is done in MATLAB-Simulink software. According to the findings, the enhanced ALO-FOPID controller decreased settling time (0.0728 s) and rising time (0.0455 s) when compared to the PID controller, which is taken as a reference. It is noted that the proposed ALO-tuned FOPID demonstrated enhanced response over the conventional methods, demonstrating the usefulness of bioinspired algorithms for precision control applications. The comparison of the proposed methodology is also done with other studies during different operating conditions. The results show that intelligent optimizationbased control in industrial systems is feasible, which aids in the creation of reliable and flexible automation solutions.

Solvability of Quadratic Fractional Integral Equations by Family of Measures of Noncompactness in Fréchet Algebra C(R+, L1(R+))

Scipedia content — Thu, 14 May 2026 10:40:03 +0200

We define the Fréchet algebra C(R+,L1(R+)) and then define a new family of measures of noncompactness. We prove a fixed point theorem that generalizes the Darbo’s fixed point theoremin this space. By applying the technique of measures of noncompactness in conjunction with new fixed point theorem, we investigate the solvability of a certain quadratic fractional integral equations. Then, we state two examples to support our main results.

Tuning Curvature in Quadratic Regression via Caputo Fractional Derivatives: Theory and Applications

Scipedia content — Thu, 14 May 2026 10:33:03 +0200

Classical regression can only examine the relation between response and predictor variables based on integer order calculus theory. What happens when non integer order calculus is considered is a field where a vast spectrum of studies can be undertaken. The purpose of this study introduces a novel fractional-order quadratic regression model grounded in the Caputo derivative framework, addressing the limitation and the rigidity of classical polynomial regression in adapting to the intrinsic curvature of data. The core innovation is the use of the fractional order ν as a tunable parameter for curvature-sensitive optimization. Our main contributions are fourfold: First, we establish a fundamental theoretical pillar by proving that the second-order Caputo derivative preserves the curvature direction of quadratic functions, enabling a principled optimization framework. Second, we rigorously demonstrate the model’s robustness by proving the existence and uniqueness of solutions via Banach’s fixed point theorem and establishing stability bounds through a fractional Grönwall inequality. Third, we develop a practical methodology to identify an optimal fractional order ν that minimizes the error-to-explained-variation ratio (SSE/SSR). Finally, we validate the framework on four diverse real-world datasets from air quality, soil science, education, and meteorology. The proposed model consistently outperforms classical quadratic regression, achieving a reduction in the SSE/SSR ratio by up to 21% in specific cases. The proposed method yields more efficient models with either lower estimation error or higher correlation coefficients, positioning Caputo fractional quadratic regression as a powerful and theoretically sound alternative for modeling cases where quadratic regression is considered appropriate.OPEN ACCESS Received: 10/09/2025 Accepted: 05/11/2025 Published: 23/01/2026

Statistical Inference of Step Stress Partially Accelerated Life-Testing for Insulating Fluid between Electrodes under Censored Data and Different Loss Functions

Scipedia content — Thu, 14 May 2026 10:11:04 +0200

Long testing times are usually required for the life testing of very reliable products or materials. The testing process can be hastened by using accelerated life tests. The lifespan of the items that accelerated life tests inspect is reduced since they test products in more severe circumstances than those found in regular use scenarios. Data that was censored and disclosed the precise timings of failure may point to accelerated life tests where all units assigned to test are unknown, or where all units assigned to test have not failed for a few reasons, including challenges with technology, tools, costs, and schedules. The step-stress partially accelerated life test was examined in this work using the type-I progressive hybrid censoring scheme and the type-II progressive censoring scheme. The influence of the stress shift is explained using the tempered random variable model, where the failure times of the items are assumed to follow the alpha power Lomax distribution. The unknown parameters are estimated using the maximum likelihood estimation and Bayesian methods. The asymptotic theory of maximum likelihood estimation is also employed in the construction of the approximate confidence intervals. While the point estimates under two censoring schemes are compared in terms of absolute biases and root mean squared errors, approximate confidence intervals and coverage probabilities are compared in terms of their lengths and coverage probabilities. Additionally, three possible optimal test strategies are investigated using different optimal criteria. The performance of the estimators was evaluated and contrasted with two censoring techniques with various sample sizes using a simulation study. Finally, a numerical example for insulating fluid between electrodes data is presented to illustrate how the methods will work in real-world scenarios.OPEN ACCESS Received: 11/06/2025 Accepted: 29/07/2025 Published: 23/01/2026

Structural assessment of vessels by dynamic hydroelastic analysis based on modal matrix reduction

Julio García-Espinosa — Fri, 08 May 2026 12:04:15 +0200

During the design of a vessel or marine artefact, one of the main objectives is to ensure a long service life. The structure of these artefacts is essential to achieve this goal, as it is responsible for withstanding the extremely unfavourable stresses of the high seas. The aim of this paper is to present a method of detailed structural assessment by applying a dynamic hydroelastic model [1] that allows structural verification by greatly reducing the calculation time without losing accuracy. The current procedures used for this purpose require a detailed study of each loading condition, which implies many simulation hours and high computational cost. In this model, the high-fidelity structural solution is projected onto the modal basis to get the system modal matrix and extend the response amplitude operators (RAO) to the modal response amplitude operators (MRAO) of the structure. By retaining only those eigenmodes that preserve most of the structural elastic energy, the number of structural degrees of freedom can be significantly reduced. This reduced model has been implemented and coupled in the time domain with the seakeeping software SeaFEM, enabling quick analysis of a large number of load cases of the structure [2]. In this work, the first case study of a ship is presented.