Scipedia: Documents published in 2026

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.

ShinyEnet: an in-house simulation software for data-driven waste-to-energy gasiﬁcation and pyrolysis

Dejan Brkić — Mon, 25 May 2026 13:19:33 +0200

ShinyEnet is an open-source software tool for modelling waste-to-energy processes, including gasification and pyrolysis. Developed at IT4Innovations, the National Supercomputing Centre of the Czech Republic at VSB Technical University of Ostrava, it utilizes operational data from experimental facility at the Centre for Energy and Environmental Technologies—Explorer (CEETe), also part of the same university. The software models a modular, mobile, and scalable system that converts waste into gaseous or liquid fuels. ShinyEnet supports dynamic simulation, including component-failure cases, optimization, and scenario analysis. The platform thus facilitates development and assessment of compact and mobile waste-toenergy units and provides tools for addressing municipal waste-management challenges. ShinyEnet is based on real operational datasets, with continuously updated data currently available for the pyrolysis process via real-time monitoring system. The interactive web application is implemented using open-source Python libraries and employs validated historical data and machine-learning models to simulate and optimize system performance.

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.

Hybrid Fractional Coupled Systems with Generalized Caputo Derivatives

Scipedia content — Fri, 08 May 2026 09:55:14 +0200

This paper establishes a comprehensive theoretical framework for a novel coupled system of nonlinear hybrid fractional differential equations involving generalized Caputo derivatives. The system’s hybrid nature, coupled with the generality of the fractional operators, allows for modeling complex interdependent processes with memory effects that cannot be adequately captured by existing models.
Using Krasnoselskii’s fixed point theorem, we prove the existence of at least one solution under explicit coupling conditions. Under appropriate Lipschitz conditions, we establish uniqueness via Banach’s contraction principle, deriving a quantitative condition involving the fractional orders, generalization parameters, and Lipschitz constants. We also conduct a rigorous analysis of Ulam-Hyers and Ulam-Hyers-Rassias stability, obtaining explicit stability constants that provide quantitative bounds on how perturbations in the input affect the solution.
The theoretical results are validated through three carefully constructed numerical examples with explicit parameter values demonstrating existence, uniqueness, and Ulam-Hyers stability. A parameter sensitivity analysis confirms the robustness of the uniqueness condition across variations in fractional orders, generalization parameters, and interval lengths. The paper concludes with a discussion of limitations and directions for future research, including extensions to higher fractional orders, delay and impulsive effects, and numerical methods.

Integrating Vector Computation and Numerical Methods for Complex Surface Design in Engineering via MATLAB

Scipedia content — Thu, 07 May 2026 11:19:04 +0200

The teaching and practical use of vector calculus in engineering often face challenges rooted in mathematical abstraction and the limited availability of tools capable of supporting three-dimensional geometric analysis. These constraints hinder precision when designing complex structural surfaces. Addressing this gap, the present study proposes the development and implementation of an interactive computational tool—built in MATLAB App Designer that integrates vector-based formulations with numerical methods to parameterize, visualize, and compute the surface area of three-dimensional geometries, with a particular focus on sizing geomembranes for circular aquaculture ponds. The research methodology comprised theoretical, numerical, and experimental components. Exact vector parameterizations were formulated, symbolic integration and discretization algorithms were implemented, and the resulting computations were assessed through error estimation and convergence analysis. The findings demonstrate a close match between analytical and numerical solutions, with relative errors below 0.1%, stable computational behavior under moderate discretization settings, and distortion-free threedimensional visualizations. Overall, the study shows that combining exact vector modeling with adaptive numerical techniques and interactive visualization provides an efficient and low-cost framework for surface-area computation and structural design. This approach offers a practical alternative to conventional CAD platforms and delivers meaningful benefits for both engineering education and industrial applications within sustainable production systems.OPEN ACCESS Received: 10/11/2025 Accepted: 14/01/2026 Published: 16/04/2026

Drill Bit Optimization Method Using Grey Clustering and Grey Correlation Analysis

Scipedia content — Thu, 07 May 2026 11:15:04 +0200

The conventional approach to drill bit selection primarily relies on the performance records of bits used in adjacent wells, where the bestperforming bit in each formation is selected for the corresponding zone to be drilled. However, this method does not take into account the lithology and rock mechanical properties of all relevant wells, nor can it evaluate the adaptability of a particular bit type to different intervals. As a result, it fails to fully ensure an optimal match between the bit and the formation, thus exhibiting significant limitations. To address these issues, this paper proposes a bit optimization method based on grey clustering and grey correlation analysis. This method comprehensively considers the influence of rock mechanics parameters on formation drillability and quantitatively evaluates the similarity in drilling resistance between the target formation and previously drilled intervals using grey clustering. This approach breaks away from the traditional constraint of limited bit options for a specific formation grade. Instead, it screens all previously used bit types to construct a candidate bit library for the target zone. Subsequently, the grey correlation method is applied to assess the candidate bits using multiple indicators that reflect bit performance. This enables the optimization of bit types for various target zones. Field applications demonstrate that the new bit selection method effectively improves upon the conventional practices by enhancing the flexibility and scientific basis of bit selection, and has yielded favorable results in actual drilling operations.OPEN ACCESS Received: 28/07/2025 Accepted: 16/10/2025 Published: 23/01/2026

CARE-FSP: A Cost-Aware and Resource-Efficient Hybrid RSO–SFO Framework for Fog-Based IoT Service Placement

Scipedia content — Mon, 04 May 2026 11:22:04 +0200

sup>Efficient service placement is a critical challenge in large-scale Internet of Things (IoT) environments, where fog computing must balance deployment cost and resource utilization under heterogeneous and dynamic conditions. To address this challenge, this paper proposes a hybrid metaheuristic approach that combines Rat Swarm Optimization (RSO) and Sunflower Optimization (SFO), leveraging the strong global exploration capability of RSO and the efficient local exploitation behavior of SFO. The proposed RSO–SFO framework integrates both strategies within a unified fitness function designed to minimize deployment cost while ensuring efficient allocation of fog resources. Extensive simulation results demonstrate that the proposed hybrid algorithm consistently outperforms state-of-the-art optimization techniques, including Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and the standalone RSO and SFO methods. Specifically, the RSO–SFO approach achieves a fitness improvement of 45.38%, reduces deployment costs by 43.71%, and maintains a high average resource utilization of 78.83%. These results confirm the effectiveness and robustness of the proposed hybrid strategy for optimal service placement in fog-based IoT environments.

IMA: An Interpretable Machine Learning Framework Based on Large-Scale Anonymous Evaluations to Decode the Black Box of Student-Supervisor Mentorships

Scipedia content — Mon, 04 May 2026 11:21:15 +0200

Student-supervisor relationships (SSR) play a central role in postgraduate training, academic development, and research well-being. In the context of the rapid expansion of Chinese graduate education, understanding mentorship quality has become increasingly important for both educational governance and student development. However, prior SSR studies have often relied on small-scale surveys, context-specific qualitative evidence, or linear analytical approaches, which limits their ability to capture heterogeneous, non-linear, and system-level patterns across institutions and disciplines. To address this gap, we propose Interpretable Mentorship Analytics (IMA), a scalable analytical framework built on large-scale anonymous student evaluations. First, we construct a multi-platform dataset of anonymous supervisor evaluations and transform unstructured review text into structured mentorship indicators through a preprocessing and LLM-assisted feature engineering pipeline. Second, we employ gradient-boosting models, including XGBoost, LightGBM, and Gradient Boosting, to model the relationship between mentorship-related features and overall evaluation outcomes. Third, we apply explainable machine learning methods, particularly SHAP and LIME, to identify global feature importance, local decision patterns, and non-linear interactions among mentorship dimensions. The results show that IMA can effectively uncover the key drivers of mentorship satisfaction, especially the central role of teacher-student relationship quality, while also revealing substantial heterogeneity across regions, institution types, and disciplines. By combining large-scale anonymous evaluations with interpretable predictive modeling, this study provides a transparent and data-driven framework for evaluations with interpretable predictive modeling, this study provides a transparent and data-driven framework for understanding SSR and offers empirical evidence for improving postgraduate supervision and educational policy.

Dynamic Variational Autoencoder Regressor for Soft Sensor Modeling

Scipedia content — Mon, 04 May 2026 11:20:14 +0200

Due to the dynamics of industrial production, it is a great challenge to learn robust feature representations from industrial process data for building an accurate soft sensor model. The traditional variational autoencoder (VAE) can learn robust features that can adapt to the dynamic process better, but cannot be directly applied in soft sensor modeling. Therefore, a novel regression-based dynamic VAE (REGDVAE) is introduced in this paper. Firstly, the encoder of the DVAE, which is constructed by the graph attention and the convolutional neural networks, is utilized to obtain the robust spatio-temporal features. The decoder of the DVAE is responsible for reconstructing the input data via transposed convolution. Secondly, the Transformer is employed to capture the dynamic associations between the robust spatio-temporal features and corresponding outputs. Moreover, with the purpose of guaranteeing the confidence of the proposed method, the Gaussian loss function is used as the optimization target of the REG-DVAE to enhance the confidence of each predicted value. The proposed REG-DVAE is implemented in a real-world melt index modeling of the polypropylene production process. The results show that compared with other baseline methods, the REG-DVAE not only can achieve the best performance, but also can provide a confidence interval, which greatly enhances the credibility of the prediction results.

Flexible Power Supply and Distribution Based on FDN for Photovoltaic Tobacco Logistics Warehouses

Scipedia content — Mon, 04 May 2026 11:19:04 +0200

Industrial plants should implement sophisticated renewable-energy-based distribution systems to guarantee a continuous and reliable power supply and enhanced operational efficiency under varying loading conditions. A Flexible Power Supply and Distribution System (FPSDS) utilize the concept of Flexible Distribution Networks (FDN) for photovoltaic-contracted tobacco warehouse facilities. The integrated power system connects PV panels to a multi-port bidirectional DC-DC converter, which operates with a BESS and a full-bridge PWM inverter to provide bi-directional energy management and increased power reliability. The system achieves enhancement by integrating three control mechanisms, which include DLD for real-time energy distribution, IPR for loss minimization and Multi-Agent Deep Reinforcement Learning (MADRL) for adaptive system optimisation. The system manages dynamic voltage and power regimes and current movements through predictive behaviour prediction, which utilizes Model Predictive Control (MPC). Simulation results demonstrate enhanced electric power management that leads to better voltage systems alongside lower transmission losses and superior peak demand control. Such a solution enables both real-time capabilities and scalability benefits while enhancing operational durability and suits warehouses with energetic systems that function dynamically. The research creates a smart solution to incorporate renewable power sources within logistics operations that builds sustainable energy frameworks for decentralizing power systems. The proposed system introduces an integrated control framework combining Dynamic Load Distribution, Intelligent Power Routing, and Multi-Agent Deep Reinforcement Learning within an FDN for photovoltaic-integrated warehouses. The approach demonstrates a 15–20% reduction in transmission losses, a 12–18% increase in overall energy efficiency, and improved voltage stability by maintaining deviations within 5%. These results confirm the novelty of this research in achieving adaptive, intelligent, and scalable power distribution for logistics facilities.

Hierarchical Convolutional Neural Network for Emotion Recognition Using EEG and Facial Expressions

Scipedia content — Mon, 04 May 2026 11:18:13 +0200

Emotion recognition is crucial for advancing human–computer interaction (HCI) by enabling systems to interpret complex affective states. While Electroencephalogram (EEG) signals provide direct insights into neural activity, facial expressions offer external emotional cues. However, unimodal systems often struggle with robustness and generalization across diverse subjects. This study presents a Hierarchical Convolutional Neural Network (HCNN) framework that integrates EEG and facial expressions through multi-level convolutional feature extraction and featurelevel fusion. The proposed model combines deep hierarchical representations with handcrafted temporal–frequency and texture-based descriptors to form a unified feature vector. Experiments on the MAHNOB-HCI and DEAP datasets show that the HCNN achieves accuracies of 91.40% and 88.09%, outperforming CNN-, LSTM-, and SVM-based methods. The results demonstrate the model’s ability to effectively capture complementary cross-modal correlations while reducing feature redundancy and computational complexity. The HCNN framework shows great promise for real-time emotion recognition applications, offering a scalable, interpretable, and data-efficient solution for multimodal emotion recognition in next-generation HCI systems.

Computational Study of Thermal Performance of Shape-Dependent CNT/ Titanium Dioxide/Aluminum OxideSodium Alginate Hybrid Nanofluids for Solar Annular Collectors

Scipedia content — Mon, 04 May 2026 10:50:03 +0200

Solar direct absorber collectors are increasingly deploying more complex working nanofluids to enhance thermal performance. Hybrid nanofluids with non-Newtonian base fluids offer great promise in this regard. Motivated by these developments, the present article examines theoretically and numerically the thermal convection in a ternary hybrid nanofluid comprising an incompressible non-Newtonian sodium alginate base fluid in the annular gap between a pair of infinite concentric cylinders, as a model of a solar annular collector system. Sodium alginate (C6H9NaO6)n exhibits distinctive thermophysical characteristics, including enhanced versatility and viscoelasticity, rendering it appropriate for solar energy applications when integrated with hybrid nanoparticles. The Reiner-Rivlin third-grade viscoelastic model is therefore deployed to simulate the non-Newtonian characteristics. Three categories of nanoparticles are featured in the hybrid nanofluid: Carbon nanotubes (CNTs), Titanium dioxide (TiO2), and Aluminum oxide (Al2O3). The nanoparticles are categorized by their specific shapes: Carbon nanotubes exhibit a cylindrical form, Titanium dioxide exhibits a spherical configuration, and Aluminum oxide takes on a platelet shape. The presence of hybrid nanofluids influences both the internal transport characteristics and the heat flux towards the curved boundary and the behavior of nanoparticles with varying shapes is also a critical factor. Both linear and quadratic convection, along with viscous dissipation and heat generation/absorption, are also taken into account. The transformed boundary value problem is solved numerically with a finite difference method. Validation of the computational scheme with previous studies is included. Graphical results are provided for the impact of all emerging parameters on transport characteristics. Nusselt number is observed to be elevated with higher Grashof number (thermal buoyancy parameter) and heat generation parameter, whereas this trend is reversed with larger volume fractions of nanoparticles (CNTs, TiO2, Al2O3) and greater values of the quadratic convection parameter. The larger volume fraction of CNTs, TiO2, and Al2O3 nanoparticles strongly modifies viscosity and suppresses velocity magnitudes. The skin friction profile shows an increasing trend, which is greatly influenced by the Grash of number, heat generation parameter, and third-grade fluid parameter. In contrast, the quadratic convection parameter and the introduction of nanoparticles (CNTs, TiO2, Al2O3) tend to reduce skin friction magnitudes

Integration of Renewable Energy Sources into Electrical Power Systems and Its Impact on Grid Stability

Scipedia content — Mon, 04 May 2026 10:45:04 +0200

The integration of renewable energy sources (RES) into electrical power systems introduces critical challenges to grid stability, including frequency deviation, voltage fluctuation, and reduced transient performance. This study investigates the impact of increasing RES penetration (15%, 30%, 45%, and 60%) on grid stability using the IEEE 39-bus benchmark system. A coordinated mitigation framework integrating battery energy storage systems (BESS), synthetic inertia, and advanced inverter-based controls is proposed and evaluated. Simulation results demonstrate that at 60% RES penetration without mitigation, the frequency nadir declines to 49.32 Hz, the rate of change of frequency (RoCoF) increases to 1.82 Hz/s, voltage deviations exceed 9.3%, and the critical clearing time (CCT) reduces to 180 ms, indicating significant stability deterioration. The proposed mitigation strategy improves these metrics to 49.76 Hz, 0.94 Hz/s, 4.6%, and 260 ms, respectively, representing improvements of+0.44 Hz, –48.4%, –50.5%, and+44.4%. Benchmarking against recent literature confirms the superior performance of the coordinated approach. These findings provide quantitative guidance for grid planners and operators to maintain reliable operation under high renewable penetration scenarios.OPEN ACCESS Received: 19/01/2026 Accepted: 28/02/2026

Robust Adaptive Consensus Control of Fractional-Order Multi-Agent Systems Using BBO-Optimized Type-2 Fuzzy Neural Networks

Scipedia content — Mon, 04 May 2026 10:44:03 +0200

The consensus control problem to be discussed in this paper is concerned with nonlinear multi-agent systems with unknown dynamics, unknown fractions, time-varying delays, and input saturation limits. In order to address these difficulties, an adaptive control system is established, incorporating a recurrent general type-2 fuzzy neural network (RGT2FNN) with a biogeography-based optimization (BBO) algorithm. The RGT2FNN is used to model nonlinear functions that are not known offline, and the BBO algorithm also optimizes the parameters of the fuzzy network and performs offline identification of the fractional order by minimizing multi-step model prediction error. In order to make the model more resistant to modeling uncertainties, time-varying delays, and actuator saturation effects, a LMI-based compensator is proposed to ensure the stability of the closed-loop. Lyapunov analysis guarantees the boundedness of consensus errors. The simulation findings prove that the suggested methodology can attain an accurate consensus tracking and strong performance when the uncertainties are harsh, the delays may change with time, and the limits of input saturation are taken into account.OPEN ACCESS Received: 06/01/2026 Accepted: 16/03/2026

Mechanical Properties and Constitutive Modelling of UHPC with Ultra Fine Waste Glass Powder (UFWGP), Micro Silica, Fly Ash and Steel and Polypropylene Fibres

Scipedia content — Mon, 04 May 2026 10:43:04 +0200

Ultra-high-performance concrete (UHPC) offers superior mechanical performance but remains limited by high cement content, sustainability concerns, and the lack of reliable constitutive models capable of capturing its full stress–strain response including pre-peak and post-peak behaviour. This study presents an integrated experimental and constitutive modelling investigation of UHPC incorporating ultra-fine waste glass powder, micro silica, fly ash, and different fibre reinforcement. Two UHPC mix designs with a water-to-cementitious material ratio of 0.20 were developed, yielding 24 independent mixtures. Cement was partially replaced with 20% ultra-fine waste glass powder and combined with either micro silica or fly ash. Fibre reinforcement was introduced independently using 2% straight steel fibres and 1.5% crimped polypropylene fibres to evaluate flowability, viscosity, uniaxial compressive strength, modulus of elasticity, splitting tensile strength, flexural strength and drying shrinkage behaviour. Experimental results demonstrate that UHPC with micro silica, UFWGP and steel fibres, prepared following the ACI 239 R-18 mix design, achieved the highest 28 days compressive, splitting tensile and flexural strength of 144.8, 12.8 and 36.1 MPa, respectively. Steel fibre-reinforced mixes exhibited up to a 42% increase in compressive strength and significantly enhanced post-peak ductility compared to the control mix (without fibre). Crimped Polypropylene fibre-reinforced UHPC attained compressive strength up to 129.7 MPa, provided improved strain capacity, and effectively reduced drying shrinkage, highlighting their suitability for crack control and deformation mitigation. The incorporation of UFWGP consistently improved flowability, reduced viscosity, and enhanced mechanical performance, while fly ash improved rheological behaviour but resulted in lower earlyage strength compared to micro silica-based mixes. To capture the compressive response of UHPC, an elasto-damage model previously proposed by Khan and Zahra was reformulated by recalibrating the compression damage parameter (β) using experimentally derived and literature based compressive strength and elastic modulus. The proposed model reproduces both pre-peak and post-peak stress–strain behaviour, with prediction errors generally within ±7% of experimental results ranging from 72.4 to 148.5 MPa. The findings provide robust experimental evidence and a validated constitutive framework for the sustainable design and structural application of UHPC

Research on Heat Transfer Law of Drilling Fluid Mixed with Low Temperature Phase Change Materials

Scipedia content — Mon, 04 May 2026 10:42:08 +0200

During the drilling process of deep and ultra-deep wells, the hightemperature wellbore environment seriously affects drilling safety and efficiency. Traditional circulating cooling methods are limited by insufficient heat exchange capacity. Therefore, this paper proposes a new active wellbore temperature control technology for drilling fluids based on the endothermic mechanism of ice crystal phase change, and builds a physical simulation experimental system to conduct a study on the influence of multiple parameters. In the experiment, pre-cooled ice crystals were added to the drilling fluid, and the influence of factors such as ice crystal size, concentration, flow rate, initial temperature of the hot fluid, viscosity of the drilling fluid, and initial temperature on the cooling performance was systematically investigated. The results show that increasing the ice crystal size and concentration can significantly improve the heat exchange effect; appropriately increasing the flow rate is beneficial to enhancing heat transfer; the higher the temperature of the hot fluid, the greater the absolute cooling amplitude, but the increase in viscosity and the initial temperature of the drilling fluid weaken the cooling effect. This study provides an experimental basis and technical support for the design and engineering application of ice crystal-type functionalized drilling fluids.OPEN ACCESS Received: 23/12/2025 Accepted: 12/03/2026

Advanced Numerical Treatment of Newell Whitehead Segel Equation Using the Method of Lines with Third-Order Finite Difference Approximations

Scipedia content — Mon, 04 May 2026 10:41:03 +0200

The Newell-Whitehead-Segel equation (NWSE) is a foundational nonlinear model for understanding pattern development and bifurcation in a variety of physical and engineering systems, such as Rayleigh-Bénard convection, material microstructure evolution, and nanostructure selfassembly. This study proposes a strong high-order numerical technique for solving the NWSE that combines the Method of Lines with thirdorder finite difference approximations for spatial derivatives. The spatial discretization transforms the governing partial differential equation into a system of ordinary differential equations, which are then integrated in time using the standard fourth-order Runge-Kutta technique. A thorough stability and convergence analysis is carried out to determine the theoretical validity of the proposed method. Numerous numerical studies show that the approach is highly accurate, stable, and computationally advantageous across a number of examples of testing. This work makes a novel contribution by constructing third-order one-sided finite-difference stencils at the boundaries, which ensure high-order accuracy while successfully implementing Dirichlet boundary conditions and avoiding precision loss near domain boundaries. The suggested numerical framework is a reliable and effective tool for describing challenging pattern-forming systems, as well as precisely parametric studies for design and control applications in engineering and scientific studies.OPEN ACCESS Received: 01/01/2026 Accepted: 27/02/2026

Constant Voltage Constant Current Control for Stable Operation of DC–DC Converters in Multilevel Inverters

Scipedia content — Mon, 04 May 2026 10:32:18 +0200

Multilevel inverters (MLIs) offer superior waveform quality, reduced harmonic distortion, and lower voltage stress compared to conventional twolevel converters. In such systems, the individual voltage levels are typically generated through DC-DC converters, where each level is produced for a specific duration by selectively connecting one or more DC sources. To generate lower voltage levels, some DC sources are temporarily disconnected, resulting in light-load or no-load conditions making the output of the DC-DC converters unstable. Most existing control strategies for DC-DC converters are designed to regulate the output voltage assuming a continuously connected load, which leads to instability when the converter operates at light or no-load conditions. To address this challenge, this paper employs a Constant Voltage Constant Current (CVCC) control method to ensure stable voltage generation for MLI voltage levels. The proposed method is benchmarked against Proportional–Integral (PI), Fuzzy Logic (FL), and Sliding Mode Control (SMC) techniques. Simulation results demonstrate that the proposed method achieves superior voltage stability, accuracy, and transient performance. The proposed method is used to generate three-level and seven-level voltage waveforms in the PLECS environment, and its real-time implementation further validates its effectiveness and practical applicability. The results confirm excellent voltage regulation, with capacitor voltage fluctuations remaining below 1 V (corresponding to only 0.33% of the nominal 300 V DC-link voltage, compared to the commonly accepted±5% tolerance).OPEN ACCESS Received: 06/12/2025 Accepted: 05/03/2026

A Novel Modified Grasshopper Algorithm for State Estimation of Power System and Design of Renewable Energy Powered Shunt Active Power Filter

Scipedia content — Mon, 04 May 2026 10:32:14 +0200

In recent times, meta-heuristic optimization techniques have become indispensable for effectively solving complex engineering problems involving multiple objectives. The Grasshopper Optimization Algorithm (GOA) is a population-based approach inspired by the foraging behaviour of grasshopper swarms. However, the standard GOA may fail to escape sub-optimal solutions, as it does not account for the predator-prey strategy. To address this limitation, this study introduces a modified version of GOA, termed MGOA, which incorporates the predator-prey concept to enhance its ability to avoid local optima and achieve globally optimal solutions. The proposed MGOA is tested on two distinct electric power system challenges. The first optimization problem focuses on the optimal design of a solar system (SS) combined with an energy storage system (ESS) connected to DC bus of the shunt active power filter (SHAPF) to supply an EV charging station and harmonic loads to select filter and gain values of PID controller to minimize THD and maintain stable DC bus voltage (DCBV) to enhance the power quality (PQ) of local distribution network. In addition, to show the superiority of the MGOA, two different test cases were selected with varying loads and partial shading conditions in the solar system. The second optimization problem involves power system state estimation (SE). The goal of SE is realized by employing weighted least square (WELS) or weighted least absolute value (WELAV) criterions where the objective function is formed by minimizing the sum of squares of weighted deviations (SSWD), sum of absolute values of weighted deviations (SAVWD) of estimated measurements from actual measurements Finally, the results highlight the superior performance of MGOA when compared to traditional optimization methods such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). However, the proposed method reduces THD to 2.19% with 98% of successes rate with a lower DCBV settling time of 0.03 s while GOA, GA and PSO give that with 3.18%, 3.45%, 3.27% THD with 91%, 88% and 91% of successes rate with higher settling.OPEN ACCESS Received: 23/11/2025 Accepted: 03/02/2026

Fault-Tolerant Control of FractionalOrder Multi-Agent Systems Using Interval Type-2 Fuzzy Logic and Adaptive Observers

Scipedia content — Mon, 04 May 2026 10:30:03 +0200

This study addresses the containment control challenge in heterogeneous, nonlinear fractional-order multi-agent systems (MASs) operating under uncertainties, actuator faults, unknown nonlinearities, and mixed timevarying delays, with the aim of ensuring stability and robust performance in realistic environments. A fully distributed adaptive observer is developed for each follower to estimate leader state information using only local neighbor data, and an interval type-2 fuzzy logic system is integrated into the adaptive control law to approximate unknown dynamics and compensate for actuator faults. Stability is established via a newly formulated fractional-order Lyapunov Krasovskii functional combined with inequality analysis, and the method’s effectiveness is verified through simulations on a fractional-order Lorenz-based MAS. Results show that the proposed approach achieves precise containment control despite actuator efficiency loss and bias faults, with an root-mean-square error (RMSE) of 0.028, a settling time of 1.2 s, and disturbance rejection within 0.8 s, outperforming classical Proportion, Integral, Differential (PID) and Type-1 fuzzy controllers. These findings demonstrate that the proposed framework not only enhances fault tolerance and tracking accuracy but is also computationally efficient, making it suitable for real-time applications such as UAV swarms, autonomous vehicles, and cooperative robotics.OPEN ACCESS Received: 12/11/2025 Accepted: 22/12/2025

Neural Network-Based Resilient Consensus Control of Nonlinear Multi-Agent Systems under Stochastic Disturbances and Cyber-Physical Attacks

Scipedia content — Mon, 04 May 2026 10:29:07 +0200

For nonlinear multi-agent systems (MASs) vulnerable to stochastic disturbances and cyber-physical attacks on both sensors and actuators, this paper proposes an adaptive self-triggered consensus control framework based on neural networks. By using a decentralized leader-follower eventtriggered strategy, the method avoids Zeno behavior and drastically reduces communication overhead by updating local state estimates only at designated triggering instants. An adaptive mechanism compensates for actuator attacks, and a neural network is integrated to approximate unknown nonlinear dynamics, thereby improving robustness against malicious attacks and uncertainties. To ensure stability, a lower bound on inter-event times is derived, and practical consensus is demonstrated using Lyapunov-Krasovskii analysis. Both homogeneous and heterogeneous MASs’ numerical simulations confirm that the technique guarantees bounded state convergence and reduces the impact of attacks.OPEN ACCESS Received: 22/11/2025 Accepted: 24/12/2025

Actuator Fault-Tolerant Containment Control for Fractional Multi-UAVs with Input Saturation and Communication Delays

Scipedia content — Mon, 04 May 2026 10:23:03 +0200

In this paper, the issue of distributed containment control for multiple Unmanned Aerial Vehicles (UAVs) is discussed using a fractional-order model with convenient limitations. A new event-based finite-time slidingmode control algorithm is proposed to enable the follower UAVs to converge to the convex hull spanned by a set of dynamic leaders. The suggested approach is immune to actuator failure, input constraint, timevarying communication delays, as well as exogenous stochastic disturbances. Memory effects present in UAV dynamics are reflected in the control law since fractional calculus is involved in calculations. The eventtriggering mechanism is configured to reduce the communication burden and ensure stability and performance. The convergence is verified carefully in finite time via Lyapunov techniques and the fractional stability of systems. It is ascertained that the proposed control scheme is highly effective and robust, as demonstrated by numerical simulations. The numerical simulations demonstrate that the proposed fractional-order fault-tolerant containment controllers achieve rapid convergence, with the average follower tracking error reducing below 0.02 within 5 s, and the multi-UAV formation remaining stable under stochastic disturbances and input saturation. These results highlight the effectiveness and robustness of the proposed strategies in maintaining desired containment performance across all agents.OPEN ACCESS Received: 09/11/2025 Accepted: 04/01/2026

A Bi-Dimensional Identification Model of Seismic Vulnerability and Intelligent Sensing Capability: A Case of Seoul

Scipedia content — Mon, 04 May 2026 10:05:04 +0200

Seismic disasters pose increasingly complex risks to large metropolitan areas. Identifying spatial disparities between seismic vulnerability and governance capacity has become a critical issue for enhancing urban resilience. This study proposes a bi-dimensional identification model integrating seismic vulnerability and intelligent sensing capability, using the 25 districts of Seoul as a case. A comprehensive three-level indicator system of seismic vulnerability was developed. Simultaneously, the spatial density of S-DoT sensors, representing the city’s intelligent sensing infrastructure, was adopted as a proxy for district-level sensing capability. Methodologically, spatial data processing was conducted using ArcGIS Pro, while Z-score standardization and K-means clustering were performed in Python. To ensure the scientific rigor of the numerical model, a multi-scenario sensitivity analysis was conducted to evaluate the impact of multicollinearity among indicators. The clustering stability was further validated through the Adjusted Rand Index (ARI) and centroid shift metrics, and cross-checked with hierarchical clustering, confirming consistent typological structures. The results identified three types: (1) high vulnerability–low sensing, (2) moderate to low vulnerability–high sensing, and (3) moderate to low vulnerability–low sensing. These types exhibited distinct spatial clustering patterns and imply differentiated governance priorities and responses. The primary contribution of this research lies in introducing a spatially coupled model that integrates intelligent sensing into seismic vulnerability assessment. This approach moves beyond traditional static, one-dimensional frameworks, offering improved visual interpretability and decision support. The findings offer insights for resilienceoriented governance in Seoul and other high-density cities.OPEN ACCESS Received: 11/10/2025 Accepted: 20/01/2026

Numerical Investigation of a Berthed Engineering Ship Subjected to Narrow-Gap Resonance

Scipedia content — Mon, 04 May 2026 10:03:03 +0200

This paper investigates the hydrodynamic behavior of a berthed engineering ship using a potential model incorporating the damping lid method, which effectively mitigates the strong hydrodynamic interactions caused by narrow-gap resonance. First, the viscous correction approach is validated against experimental data. Then, the abrupt variations in the ship’s hydrodynamic characteristics at resonant frequencies are analyzed for different gap widths. Subsequently, by comparing the ship motion responses obtained from the response amplitude operators (RAOs) and the Cummins equation, the damping factors at specific frequencies are identified. Finally, under severe sea-state conditions, a safety assessment of the mooring system is performed. The results reveal that narrower gaps lead to stronger hydrodynamic interactions. Moreover, the predicted extreme load responses confirm that the mooring system is capable of ensuring the ship’s safety. This study provides useful insights for the design of berthed structures with narrow gaps.OPEN ACCESS Received: 30/09/2025 Accepted: 21/11/2025

A HyAR Algorithm for Dynamic Order Approval in China Energy Railway Freight System

Scipedia content — Mon, 04 May 2026 10:02:14 +0200

As a heavy-haul railway, China Energy Railway mainly transports coal and has the characteristics of stable supply and large freight volume. The order approval process follows a centralized and unified model. This approach suffers from prolonged approval cycles, extended intervals between order submission and actual transportation (with submissions required a year in advance), and vague planned delivery times, making it difficult to meet clients’ demands for precise delivery timelines and the flexibility required for high-value scattered cargo orders. To stabilize long-term revenue for transport enterprises and enhance client satisfaction, this study introduces a client value coefficient and a delivery-time satisfaction function to evaluate order value. A dynamic order approval model for China Energy Railway freight services is constructed and solved using a deep reinforcement learning algorithm. Given the large volume of orders and the complexity of order requirements in China Energy Railway, which involve multiple auxiliary decision variables, some discrete decision variables are adjusted to continuous variables to accelerate model training. This adjustment is combined with the HyAR algorithm to enhance the training efficiency of intelligent agents. Finally, the model’s performance is tested using freight data from China Energy Railway in March 2024. Under constrained capacity conditions, the dynamic order approval model achieves 3.1% improvement in comprehensive revenue compared to static approval methods.OPEN ACCESS Received: 23/09/2025 Accepted: 24/11/2025

Robust Adaptive Neural Network Tracking Control for Quadrotor Unmanned Aerial Vehicle via Reinforcement Learning Strategy

Scipedia content — Mon, 04 May 2026 10:00:13 +0200

This paper proposes an adaptive event-triggered trajectory and attitude tracking control framework for quadrotor unmanned aerial vehicle (QUAV) with external disturbances. To handle unknown uncertainties in QUAV control system, we propose a dual neural network (NN) architecture: combining reinforcement learning with disturbance estimation for real-time disturbance compensation. Specifically, the Actor-NN generates compensation signals to offset uncertainties, while the Critic-NN dynamically evaluates control performance to adjust the learning process. A nonlinear neural network disturbance observer (NNDO) is incorporated to estimate the lumped total disturbances in real time. By combining backstepping control approach with event-triggered mechanism, the proposed control strategy achieves rigorous closed-loop stability with guaranteed exclusion of Zeno behavior. Experimental validation on QUAV demonstrates the effectiveness of the proposed scheme in balancing computational efficiency and control performance.OPEN ACCESS Received: 30/08/2025 Accepted: 12/12/2025

A Hybrid Ensemble Feature Engineering and Self-Attention-Based BiLSTM Framework for Human Activity Recognition in Smart and Assistive Monitoring Systems

Anis Ben Ishak — Sun, 26 Apr 2026 21:46:24 +0200

Human Activity Recognition (HAR) has gained significant attention with the rapid development of artificial intelligence (AI) and Internet of Things (IoT) technologies, particularly for its potential in smart monitoring and assistive systems. While existing HAR frameworks demonstrate promising performance, they often suffer from high computational complexity and suboptimal feature representation. In this study, we propose a novel framework, termed HAMEFE-SARNN, which integrates ensemble feature engineering with a self-attention-based recurrent neural network for efficient HAR. The proposed approach first applies min–max normalization to standardize input data, followed by a hybrid feature selection strategy combining multiple filter-based methods (Variance Threshold, Mutual Information, Chi-square, ANOVA, and L1-based selection) and a wrapper-based Recursive Feature Elimination (RFE) technique to identify the most informative features. Subsequently, a bidirectional long short-term memory network with a self-attention mechanism (BiLSTM-SA) is employed to effectively capture temporal dependencies and emphasize relevant patterns in activity sequences. The model is evaluated on a benchmark HAR dataset, demonstrating improved performance compared to existing approaches. Although the proposed framework is validated on standard datasets, it provides a robust and efficient foundation that can be integrated into real-time smart monitoring systems, with potential applications in assisting vulnerable and disabled individuals. Future work will focus on validating the model in real-world environments and multimodal sensing scenarios.

Fixed-Time Synchronization of Chaotic Neural Networks with Mixed Delays via Dynamic Event-Triggered Control

Scipedia content — Mon, 20 Apr 2026 10:59:03 +0200

This paper proposes a novel dynamic event-triggered control scheme to address the fixed-time synchronization problem for chaotic neural networks (NNs) with mixed delays. Firstly, an adaptive threshold mechanism is embedded into the dynamic event-triggered control, which occupies less communication resource in comparison with the periodic-triggered control and enables the exclusion of Zeno phenomena. Secondly, by combining Lyapunov stability theory with fixed-time convergence criteria, a sufficient condition for the fixed-time synchronization of such chaotic NNs is established. Particularly, an explicit upper-bound estimation of the settling time is derived, which solely depends on controller parameters and is independent of the initial condition. Theoretical analysis indicates that the error system can converge to a predefined neighborhood of the origin within a fixed time. Finally, numerical simulations further substantiate the feasibility and superiority of the proposed methods.OPEN ACCESS Received: 02/09/2025 Accepted: 22/12/2025 Published: 16/04/2026

An Environmental Risk Identification Method for Polar Drilling Based on G1-Cloud Model

Scipedia content — Mon, 20 Apr 2026 10:58:03 +0200

The polar region possesses enormous potential for oil and gas resource development, making it a focus of worldwide attention. However, the harsh climatic and geological conditions, along with the fragile ecosystems in the Arctic, impose stringent technical requirements for oil and gas extraction. Simultaneously, drilling operations in polar regions generate substantial amounts of liquid and solid waste, which can pollute and damage the vulnerable local environment. Therefore, there is a need to establish a set of environmental risk identification techniques or risk assessment methodologies suitable for polar drilling. Current evaluation methods— including the analytic hierarchy process, Bayesian networks, neural networks, and grey correlation analysis—have limitations such as computational complexity and strong subjective influence, which may compromise the accuracy and reliability of the assessment results. Moreover, the outcomes of these evaluations heavily depend on sample sources. Given the complexity of the polar environment, data reliability and the need for rapid, efficient assessment methods are crucial. Accordingly, this paper proposes a cloud model-based environmental risk identification method for polar drilling, which enables multi-source acquisition of polar environmental data. The cloud model replaces the membership function used in conventional fuzzy evaluation methods, thereby accounting for both the fuzziness and randomness of the raw data and improving the accuracy of evaluation results. This comprehensive cloud model-based approach can reveal the randomness and fuzziness of the evaluation subject, facilitate the conversion between data and conceptual understanding, and produce evaluation results that balance subjective and objective considerations. Rooted in fuzzy mathematics and probability theory, the cloud model yields objective, reasonable, reliable, and persuasive assessment outcomes. Compared to traditional methods, the proposed approach demonstrates stronger robustness in handling uncertainty and data scarcity, offering a reliable tool for environmental risk identification and control in polar drilling.

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

Scipedia content — Mon, 20 Apr 2026 10:56:08 +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.

Numerical Investigation of Overburden FailureMechanisms and Structural Evolution in Partial Gob-BackfillingMining of Steeply Inclined Coal Seams

Scipedia content — Mon, 20 Apr 2026 10:56:04 +0200

Cost Optimization of Reinforced Concrete Frames Using Metaheuristic Algorithms

Scipedia content — Mon, 20 Apr 2026 10:55:04 +0200

In structural engineering, the primary objective of design engineers is to ensure structural safety under applied loads according to codes and standards while achieving the most economical design. To enhance cost efficiency in complex structures such as reinforced concrete (RC) frames, computational techniques are employed. The aim is to perform a more rapid and accurate optimum cost design of RC frame systems subjected to vertical loads. This study applies five metaheuristic approaches: three metaheuristic methods and two hybrid techniques developed from them. The matrix displacement method is used to determine displacements and sectional forces of RC frame systems, with design rules following ACI 318-19 (Building Code Requirements for Structural Concrete and Commentary). The study models five different RC structures with varying dimensions and member counts, including symmetrical and asymmetrical configurations. Internal forces and displacement values of structures analyzed using the matrix displacement method in MATLAB are verified with SAP2000 structural analysis software, confirming solution validity. Among the methods applied for the optimum design, the TeachingLearning-Based Optimization (TLBO) technique proves particularly suitable for RC frame systems. The proposed design method and TLBO algorithm offer civil engineers a rapid and efficient approach to achieving cost-optimized designs while ensuring structural safety. The developed method helps engineers efficiently solve complex design problems while achieving optimal structural solutions in terms of both cost and safety.OPEN ACCESS Received: 14/08/2025 Accepted: 24/10/2025 Published: 16/04/2026

Design and Performance of an Ultra Gain Boost Converter for SolarPowered Electric Vehicle Applications

Scipedia content — Mon, 20 Apr 2026 10:40:04 +0200

This paper presents the design and performance analysis of an Ultra Gain Boost Converter (UGBC) intended for solar-powered electric vehicle (SPEV) applications. The proposed converter is derived from a cascaded boost–boost structure with a capacitor–diode voltage-lift cell, allowing high voltage gain without operating at extreme duty ratios. The proposed structure provides continuous input current, improved voltage boosting capability, and reduced voltage stress on semiconductor devices, making it suitable for photovoltaic based energy systems. Complete steady-state analysis, voltage-gain derivation, device stresses estimation, power-loss estimation, and small-signal dynamic modelling are carried out to evaluate the converter characteristics. The UGBC topology is validated with MATLAB/Simulink simulations for an operating condition of 24 to 72 V at a duty ratio of 0.33, indicating stable voltage regulation, low current ripple, and higher efficiencies. Additionally, system-level performance is given by interfacing the converter with a BLDC motor drive, where stable speed regulation and smooth torque response are observed. The results confirm that the proposed UGBC converter with a capacitor–diode cell is a feasible and efficient solution for high-gain Ultra Gain DC–DC conversion in solar-based electric mobility applications.OPEN ACCESS Received: 22/11/2025 Accepted: 29/01/2026 Published: 16/04/2026

LLM-Guided Multi-Agent Deep Reinforcement Learning for Distributed Energy Management in Renewable-Integrated Power Systems

Scipedia content — Mon, 20 Apr 2026 10:39:03 +0200

With the increasing penetration of renewable energy in power systems, distributed energy management faces numerous challenges including high-dimensional state spaces, multi-objective optimization, and realtime decision-making. This paper proposes a Large Language Model (LLM)-guided Multi-Agent Deep Reinforcement Learning (MADRL) framework for distributed energy management in renewable-integrated power systems. Building upon recent advances in LLM-guided reinforcement learning, we develop specialized mechanisms for power system control that leverage the semantic understanding and knowledge reasoning capabilities of LLMs to provide high-level strategic guidance and scenario-adaptive adjustments for MADRL agents. Specifically, we design a hierarchical architecture where the LLM layer is responsible for parsing grid operation states, generating optimization objective descriptions, and coordinating multi-agent behaviors, while the MADRL layer executes specific energy scheduling decisions. Experiments are conducted on real power grid datasets containing photovoltaic, wind power, energy storage systems, and flexible loads. Results demonstrate that the proposed method significantly outperforms traditional baseline methods in reducing operating costs, improving renewable energy utilization rates, and ensuring grid stability. Compared to standard MADRL, our method reduces system operating costs by 18.7%, decreases renewable energy curtailment by 23.4%, and improves convergence speed by 3.2 times. This study provides a novel approach for adaptive distributed energy management in smart grids.OPEN ACCESS Received: 15/11/2025 Accepted: 15/01/2026 Published: 16/04/2026

Integrating Vector Computation and Numerical Methods for Complex Surface Design in Engineering via MATLAB

Scipedia content — Mon, 20 Apr 2026 10:33:14 +0200

Numerical Simulation and Analysis of Annular Trapped Pressure during the Testing of Deepwater High-Temperature and High-Pressure Gas Wells

Scipedia content — Mon, 20 Apr 2026 10:12:04 +0200

During the testing of high-temperature and high-pressure (HTHP) offshore gas wells, heat transfer occurs between the high-temperature gas in the tubing and the low-temperature fluid in the annulus. This causes the annular fluid to expand, leading to an increase in annular trapped pressure, which poses a potential integrity risk. To accurately predict this pressure variation, a full-scale physical model of an offshore HTHP gas well was developed based on simplified assumptions of a constant geothermal gradient and homogeneous casing material. By integrating the Pressure–Volume–Temperature (PVT) equation of state with a transient heat transfer model of the wellbore, a section-by-section method for calculating annular temperature was proposed. A coupled prediction model for annular trapped pressure, incorporating both thermal effects and annular volume change, was then established. Using Well Y in the eastern South China Sea as a case study, numerical simulations of annular temperature and trapped pressure were performed. The results indicate that the model’s average relative error compared to experimental data is approximately 6.15%. When the production rate is 10× 104m3/d, the trapped pressures in annuli A, B, and C are 31, 25, and 21 MPa, respectively. Both temperature and pressure increase progressively from the wellhead to the bottom, with the most significant variations occurring in the shallow section. Sensitivity analysis shows that trapped pressure rises rapidly during the early stage of testing and gradually levels off over time. The annular trapped pressure is positively correlated with production rate, geothermal gradient, fluid expansion coefficient, and well depth, whereas increases in casing elastic modulus, Poisson’s ratio, and the thermal expansion coefficient of the fluid tend to reduce it. The study provides a theoretical foundation and practical support for evaluating wellbore integrity and controlling annular risks under complex conditions in deep-water HTHP oil and gas wells.OPEN ACCESS Received: 03/09/2025 Accepted: 11/11/2025 Published: 16/04/2026

Artificial Neural Network Analysis of Magnetohydrodynamic Flow and Thermal Radiation in Hybrid Nanofluid with Bio-Convection over a Stretching Sheet

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

This study investigates the flow and heat transfer of a hybrid nanofluid including alumina and titanium dioxide nanoparticles across a stretched sheet in the presence of a magnetic field, thermal radiation, and bioconvection. The problem is significant because hybrid nanofluids are increasingly employed in industrial cooling, biomedical transport, and energy systems that need accurate prediction of nonlinear thermal and solutal behavior. The governing nonlinear equations were transformed into ordinary differential form and computationally solved via a fourthorder Runge-Kutta method. In addition to this, an artificial neural network model was created to reproduce the numerical results and evaluate prediction abilities. The results show that stronger magnetic forces reduce velocity by about 18%, while higher radiation levels increase fluid temperature by nearly 22%. Increasing the Lewis number lowers nanoparticle concentration by roughly 15%, and higher bioconvection effects reduce microorganism density by about 12%. The neural network achieved excellent agreement with the numerical results, with regression values close to 1 and a mean squared error of order 10−10. The findings indicate that hybrid nano-fluids offer enhanced heat transfer and flow stability under MHD conditions, supporting potential improvements in fluid and thermal performance for applications requiring precise thermal management.OPEN ACCESS Received: 27/08/2025 Accepted: 05/11/2025 Published: 16/04/2026

AKA – eHS

Fahad Algarni — Sun, 12 Apr 2026 11:07:34 +0200

uvby-β photometry of open clusters. II. NGC 1342.

Joel Omar Yam Gamboa — Tue, 07 Apr 2026 21:21:43 +0200

uvby-β photoelectric photometry of the open cluster NGC 1342 is presented. From the analysis of photometric data reddening, distance, temperature and gravity are determined for each star, and from the distance and reddening of each, a mean distance modulus and reddening of 8.62±0.22 mag and E(b-y) = 0.297±0.112, respectively, to the cluster is assigned. An age of 4.0×108yr is determined through direct comparison with theoretical models. Five stars were identified in the direction of NGC 1342 as chemically peculiar stars of which four are cluster members.

Award to Eugenio Oñate of the Idelfons Cerda Medal 2019 by the Catalonian Branch of the Institution of Civil Engineering of Spain

Eugenio Oñate — Mon, 06 Apr 2026 21:11:56 +0200

Speech of Eugenio Oñate on the occasion of receiving the Idelfons Cerda Medal 2019 by the Catalonian Branch of the Institution of Civil Engineering of Spain

Prioritizing Barriers to Total Quality Management Implementation for Sustainable Construction Using AHP–GDM

Scipedia content — Mon, 30 Mar 2026 09:36:34 +0200

The construction industry is increasingly challenged by complex decisionmaking environments involving economic, technical, and sustainability related factors. This study proposes a structured multi-criteria decisionmaking (MCDM) framework to systematically evaluate and prioritize critical factors influencing construction project performance in Saudi Arabia. The methodology integrates objective weight determination with rankingbased MCDM techniques to assess multiple, often conflicting criteria using expert-driven and data-informed analysis. The results identify the most influential factors affecting construction efficiency and sustainability, providing a transparent and reproducible decision-support model. The main contribution of this study lies in offering a context-specific, analytically robust framework that advances MCDM applications in construction management, particularly within emerging economies. From a practical perspective, the findings support policymakers, project managers, and industry stakeholders in improving strategic planning, resource allocation, and risk-informed decision-making aligned with national development goals. Overall, the proposed framework enhances evidence-based decision-making and contributes to improving the resilience and performance of the construction industry.OPEN ACCESS Received: 13/11/2025 Accepted: 19/01/2026

Proposal and Energy Analysis of a New Design Integrating the SCPP into Electricity Pylon Using CFD Numerical Method: Towards Ensuring Electric Needs of Rural Dwellings

Scipedia content — Mon, 30 Mar 2026 09:31:03 +0200

Rural electrification is critical for increasing agricultural output, thereby reducing poverty and improving food security. Electricity pylons are steel structures that transport electricity at high voltages and are often the tallest objects in the countryside. The current study proposes a novel solar chimney power plant (SCPP) model in which the chimney is contained within the pylon’s vertical structure. The numerical methodology was carried out using Manzanares’ SCPP 3D axisymmetric CFD model, which was simulated with ANSYS Fluent software. The RNG k-ε turbulence model and a discrete ordinates non-grey radiation model were used. Following model validation, the results led to the identification of five turbine positions based on five collector radius intervals. To the best of our knowledge, this is the first time this has been observed in the SCPP system as a result of air velocity variations throughout the chimney. Equations based on collector radius and solar radiation are used to calculate the generated electric energy and the number of rural houses that could be electrified at each interval. Our findings confirmed the proposed model’s efficiency in generating electricity; for instance, one hour of 1000 W/m2 solar radiation using a collector radius of 200 m produces 14.82 kW that could supply 6.877 rural houses in India or 7.411 rural houses in China for 24 h. This study proposes a plan for electricity transmission companies to invest in infrastructure (pylons) to provide rural renewable electricity.OPEN ACCESS Received: 16/10/2025 Accepted: 27/11/2025

A Detail Enhancement Method for Weakly Illuminated Images Using Weighted Guided Filtering Technology

Scipedia content — Mon, 30 Mar 2026 09:29:34 +0200

In order to optimize the visual effect of weakly illuminated images, this study designed a detail enhancement method for weakly illuminated images based on weighted guided filtering technology. Low-light images undergo a series of transformations that include, among others, the grayscale transformation and the multi-scale weighted guided filtering. Filtration improves both the basic and detailed layers. The proposed method is validated through experimental results to be capable of effectively increasing the visibility and clarity of dimly lit images without losing the delicate texture and edge information. More precisely, the output images register an average gradient of 17.547, which shows that the edges are sharper; information entropy that is always above 4.0, which means that the detail content is richer; and a peak signal-to-noise ratio of 42.85 to 48.37, which asserts that the image quality is high. The whole enhancement process is very fast, and it takes only 1 min to complete, indicating that this method effectively achieves the design expectation.OPEN ACCESS Received: 08/08/2025 Accepted: 29/01/2026

Optimized Multimodal Healthcare Image Fusion Using U2Net Restormer with Dilated Dense Encoder–Decoder and Haar-Based Feature Selection

Scipedia content — Mon, 23 Mar 2026 10:47:19 +0100

Multimodal medical imaging plays a pivotal role in clinical diagnostics by integrating complementary anatomical and functional information from modalities such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Single-Photon Emission Computed Tomography (SPECT). Despite notable progress, existing fusion approaches continue to face persistent challenges. Convolutional Neural Network (CNN)-based methods often suffer from information loss due to convolutional down-sampling, while Transformer architectures, though effective at capturing global dependencies, incur high computational costs and rely on large-scale pretraining. Generative Adversarial Network (GAN)-based fusion models can generate visually realistic outputs but are prone to training instability and limited reproducibility. In addition, prior studies frequently adopt inconsistent evaluation metrics, with insufficient emphasis on clinical interpretability and robustness, hindering real-world deployment across heterogeneous datasets and institutions. To address these limitations, this study proposes a U-shaped Nested Network – Restoration Transformer (U2Net–Restormer) framework with a Dilated Dense Encoder–Decoder architecture for robust multimodal medical image fusion. The framework integrates hierarchical multiscale representation learning with residual global contextual refinement. To enhance discriminative capability, an optimized Haar-based feature selection strategy is introduced to preserve high-gradient structural and functional details while reducing feature redundancy. Furthermore, an attention-driven fusion mechanism adaptively weights modality-specific contributions, enabling effective integration of heterogeneous information. The proposed method is evaluated on the Augmented Alzheimer’s Neuroimaging Library (AANLIB) multimodal brain imaging dataset, covering CT-MRI, PET-MRI, and SPECT-MRI fusion tasks. Experimental results demonstrate consistent performance gains over state-of-the-art CNN-, Transformer-, and GANbased methods, achieving Structural Similarity Index Measure (SSIM) up to 0.963, Peak Signal-to-Noise Ratio (PSNR) of 42.1 dB, Feature Mutual Information (FMI) of 0.86, and Edge Preservation Index (EPI) of 0.91, with improvements of at least 4%–6% across modalities. Subjective evaluations by radiologists and neurologists report Likert scores up to 4.8/5 for structural visibility, functional fidelity, and diagnostic value. Robustness analysis under Gaussian noise (σ= 15%) further confirms the method’s resilience. Overall, the proposed framework delivers high-fidelity, clinically interpretable multimodal fusion suitable for diverse imaging scenarios.

Automatic Detection Algorithm of Typical Hidden Dangers in Substation Based on Dynamic Convolutional Feature Extraction

Scipedia content — Mon, 23 Mar 2026 10:47:14 +0100

With the advancement of smart grid construction, substation equipment, being perpetually exposed to complex environments, is prone to latent hazards such as meter malfunctions and insulator cracks. Additionally, personnel violations may trigger safety incidents, directly jeopardising grid reliability. However, traditional manual inspections suffer from low efficiency and high rates of missed defects, while existing detection methods struggle to accommodate irregular defect classifications and multi-type defect characteristics, failing to meet engineering demands for real-time response and precise identification. To address these challenges, this study proposes a substation hazard detection framework (YOLOv10_DSE) based on an enhanced YOLOv10 (You Only Look Once version 10) architecture, designed to tackle multi-type hazard detection within complex substation scenarios. Firstly, a dynamic feature extraction module (C2fDSC) was designed, employing dynamic snake convolutions to enhance adaptive sampling capabilities for small targets and irregular defects. Secondly, a self-integrated attention module head (SEAMHead) was introduced to decouple localisation and classification tasks, thereby improving multi-type hazard discrimination accuracy. Finally, a bounding box regression loss function (inner_CIoU) was adopted to optimise small target localisation and irregular shape fitting. Experiments demonstrate that on a substation dataset containing 17 defect types, this method achieves mAP@0.5 and mAP@0.95 of 73.3% and 48.2%, respectively, representing improvements of 2.6% and 1.6% over the YOLOv10 baseline. This provides an efficient and reliable solution for multi-type defect detection in substations, holding significant engineering value for ensuring the secure operation of power grids.OPEN ACCESS Received: 15/10/2025 Accepted: 17/12/2025

An Innovative Approach to Extracting and Classifying Non-Functional Requirements Using Machine Learning and NLP

Scipedia content — Mon, 23 Mar 2026 10:46:14 +0100

This study presents a hybrid automated framework based on a combination of machine learning (ML) and natural language processing (NLP) approaches for the automatic categorization and extraction of nonfunctional requirements (NFRs) from free-text software development documents. Using the PROMISE dataset, this framework systematically integrates semantic representation learning, deep feature extraction, and kernel-based classification to improve the performance of NFR classification. Unlike current CNN-based approaches with end-to-end softmaxbased classification, our proposed method fundamentally decouples feature learning from decision making. The first approach is to use Word2Vec embeddings to capture semantic context, and then use Convolutional Neural Networks (CNNs) as high-level feature extractors. An Improved Support Vector Machine with a Radial Basis Function kernel (ISVM-RBF) is applied for final classification, enabling more discriminative decision boundaries to be drawn in the high-dimensional semantic feature space. We reveal a considerable performance improvement with the CNN– Word2Vec setup, achieving as high as a 90% precision, significantly outperforming standard ML classifiers. The study points to three main findings: (i) CNN-based feature extraction is an efficient approach for finding and classifying NFRs, (ii) the semantic representation provided by word embedding methods is clearly superior to other traditional methods used in NLP, and (iii) NLP preprocessing of text is crucial for enhancing classification accuracy. Finally, ISVM-RBF adapts kernel-based classification over features derived from CNN, which enhances the robustness of the model to semantic overlaps between NFR categories and alleviates challenges posed by potentially large textual datasets required to train such models. This hybrid CNN–ISVM-RBF design constitutes the methodological novelty of the proposed method and effectively distinguishes it from current state-of-the-art methods in the literature.OPEN ACCESS Received: 03/11/2025 Accepted: 22/01/2026