Scipedia: Documents published in 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

Study on Basic Machine Learning Techniques to Detect and Classify Cardiovascular Diseases

Scipedia content — Mon, 23 Mar 2026 10:44:33 +0100

The heart is an essential organ required to maintain the general health of individuals. Cardiovascular diseases (CVDs) have become the leading cause of death globally, replacing cancer and diabetes. Computer-based techniques have made it easier for physicians to diagnose various cardiac conditions, including heart failure. We are currently in the “information age,” a period characterized by the generation of millions of bytes of data every day. By applying ML algorithms’ techniques, such as Random Forest (RF), XGBoost, KNN, and GaussianNB, we can evaluate and compare the performance of machine learning classifiers for heart disease prediction and transform these data into information for the estimation of heart disease. The World Health Organization has estimated that in 2019, cardiac disease was responsible for 32% of all deaths worldwide. In this paper, we use a public data set (Indicators of Heart Disease) and hyperparameters to develop four classifiers—the Random Forest, XGBoost, KNN, and GaussianNB—and compare their performance. Based on the trial data, XGBoost was the best, with an accuracy of 91.63%, a precision of 94.40%, a recall of 88.50%, an F1-score of 91.36%, a specificity of 94.75%, and an AUC score of 97.39%. This study showcases the accuracy of machine learning systems in predicting cardiac conditions and can serve as a foundation for developing a decision-support tool aimed at detecting and preventing heart disease in its early stages.OPEN ACCESS Received: 09/10/2025 Accepted: 20/01/2026

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

Scipedia content — Mon, 23 Mar 2026 10:25:26 +0100

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

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 — Mon, 23 Mar 2026 10:23:35 +0100

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

An Improved ORB-Based Multi-View Stereo Matching Algorithm for Accurate Visual SLAM

Scipedia content — Mon, 23 Mar 2026 10:22:43 +0100

This study addresses image matching in visual Simultaneous Localization and Mapping (SLAM) to enhance synchronous positioning and mapping using a multi-view stereo matching algorithm. Utilizing a vision odometer and a binocular camera, multi-view stereo images are captured. An improved ORB (Oriented FAST and Rotated BRIEF) algorithm extracts feature points and establishes binary descriptors, with similarity computed using Euclidean distance to enable stereo matching. Mismatches are filtered using parallax constraints and corrected through triangulation. Loopback detection minimizes cumulative drift in camera position, improving spatial perception. The back-end optimization employs graph optimization theory to refine the position and pose of the binocular camera and landmarks, addressing random noise and errors. Experimental results indicate effective feature extraction, with mismatches limited to three, overall positioning error under 100 mm, and directional error within 2°.OPEN ACCESS Received: 11/08/2025 Accepted: 14/01/2026

Analytical Solution for the Internal Forces of the Nuclear Power Plant’s Water Intake and Outlet Tunnel under P Waves

Scipedia content — Thu, 19 Mar 2026 10:58:34 +0100

Analytical solutions provide an efficient means to evaluate the internal forces of tunnels and elucidate the relationships among key influencing parameters. This paper presents an analytical solution for the internal forces of the nuclear power plant’s water intake and outlet tunnel under P waves. In order to consider the slippage effect of the ground-tunnel and lining-lining interfaces, a spring-type tangential stiffness coefficient K iis introduced. Moreover, the proposed analytical solution can accommodate tunnels with an arbitrary number of lining layers and treat the linings as thick-walled cylinders, providing higher analytical accuracy. The validity of the proposed analytical solution is demonstrated through a comparative analysis with the traditional analytical solution and finite element simulations. The comparison results confirm the accuracy and superiority of the proposed method. A comprehensive parametric investigation is conducted to examine how the tangential stiffness coefficient K i, the flexibility ratio F, and the characteristics of the isolation layer influence the tunnel’s seismic behavior.OPEN ACCESS Received: 25/12/2025 Accepted: 28/01/2026 Published: 20/03/2026

Confidence Level-Based Interval-Valued p, q, r-Spherical Fuzzy Sets with Application to Industrial Waste Management

Scipedia content — Thu, 19 Mar 2026 10:52:58 +0100

Industrialization plays a substantial role in a country’s development and economic growth. As a developing nation, Pakistan heavily relies on its steel industry to drive economic progress. Pakistan Steel Mills Corporation (PkMC) is the largest steel producer in the country, manufacturing thousands of tonnes of steel annually. However, this high steel production has led to a significant increase in industrial solid waste. While various methods are available for managing industrial waste, the selection of the appropriate technology is a complex process due to the wide range of strategies and the multiple factors involved in the decisionmaking process. Existing research in the interval-valued p, q, r-spherical fuzzy sets (IVp, q, r-SFS) environment assumes 100% confidence-level from decision makers in evaluating scenarios, but real-world situations often differ from ideal situations. To address this limitation, this study incorporates confidence-level with IVp, q, r-SFS, to identify effective and sustainable waste management strategies for PkMC. Decision-makers can evaluate alternatives by assigning corresponding confidence values using this model to better capture uncertainty. This study develops and analyzes basic operational laws, averaging, and geometric operators, outlining their desirable properties. This study presents a step-by-step algorithm for the proposed MCDM methodology, demonstrating its reliability and efficiency through an industrial waste management example. Model results indicate energy recovery as the most suitable alternative. Comparative analysis further validates the effectiveness of the proposed model. This research offers helpful tips for improving decision-making in waste management and points out the importance of a robust methodological framework in tackling complex, uncertainty-driven challenges.OPEN ACCESS Received: 13/11/2025 Accepted: 19/12/2025 Published: 20/03/2026

Deep Learning and XAI for Carbon Dioxide Emissions Prediction: Integrating MLP with SHAP and Multi-Policy Scenario Analysis

Scipedia content — Thu, 19 Mar 2026 10:51:34 +0100

This study is conducted in response to the increasingly prominent climate crisis in contemporary society. It aims to contribute to the growing body of research on the application of deep learning (DL) in environmental sciences and to provide practical guidance for model selection in similar predictive tasks. To this end, the study focuses on carbon dioxide (CO2) emissions prediction, employing Multilayer Perceptron (MLP) models to analyze multi-country panel data. By integrating MLP with explainable artificial intelligence (XAI) techniques, this research not only investigates the underlying mechanisms of various factors influencing CO2 emissions but also quantifies and visualizes the contribution of different driving factors to the prediction outcomes, providing decision support for climate governance strategies. Through an analysis of global panel data, we construct a model incorporating 14 driving factors spanning multiple dimensions, including economic, social, environmental, energy, and technology aspects. To optimize the MLP model, we employ a fivedimensional hyperparameter space comprising hidden layer structure, learning rate, batch size, dropout rate, and training epochs and apply Grid Search for parameter tuning. Experimental results indicate that the MLP model achieves R2 of 0.9951, demonstrating its strong capability in highprecision nonlinear fitting under complex policy scenarios. To further enhance the interpretability of neural networks in CO2 emissions prediction, we introduce SHapley Additive exPlanations (SHAP) to quantify the marginal contributions of different driving factors. This analysis reveals that energy-related features play a dominant role in emission predictions, laying the foundation for scenario analysis and emission reduction policy evaluation. Furthermore, this study incorporates scenario analysis to simulate potential trajectories of CO2 emissions under different policy scenarios, providing a quantitative reference for future emission reduction strategies and environmental governance policies.

Comparative Analysis of Advanced Machine Learning Methods for Heat Transfer and Thermal Efficiency in a Non-Newtonian Nanofluid with Joule Heating and Lorentz Forces: Dual Solutions and Stability Analysis

Scipedia content — Wed, 18 Mar 2026 11:54:34 +0100

This research explores the unsteady stagnation point flow of modified second-grade fluid by incorporating magnetized cobalt ferrite (CoFe2O4) nanoparticles across a heated movable plate. In addition, the radiation and Joule heating are also provoked. Since the cobalt ferrite particles are very important in biosensing, drug delivery, magnetic purification/separation, etc. The leading equations are changed into ordinary differential equations by employing similarity factors and then utilizing the bvp4c solver to obtain dual numerical solutions. Stability analysis confirms that the upper branch solution is stable and physically reliable. In addition, this research is further analyzed through advanced machine learning by employing artificial neural networks in conjunction with Levenberg- Marquardt. Moreover, this research deals with an important query by computing the problem through Gaussian Process Regression (GPR).The substantial outcomes indicate the velocity increases while the temperature declines due to the viscosity factor for the upper solution. In parallel, the machine learning outcomes show that the GPR gets an excellent R2 of 1 and, for the Nusselt number prediction, delivers a predictive error within the same order of magnitude as the ANN-LM benchmark. This confirms GPR as a high-fidelity tool capable of achieving near-perfect accuracy,making it a powerful choice where both precision and predictive confidence are essential.

Numerical Investigation of the Seismic Response of Trapezoidal Corrugated Web Steel Frame Structures

Scipedia content — Wed, 18 Mar 2026 10:06:44 +0100

Using the ABAQUS finite element analysis platform, this study established numerical models of H-section steel members with trapezoidal corrugated webs and a three-story corrugated web steel frame. The dynamic characteristics of the frame structure were investigated via modal analysis and elasto-plastic time-history analysis. Employing the control variable method, the study first determined the key structural parameters of the corrugated web steel frame: specifically, a web thickness of 2 mm, a corrugation angle of 45°, a wavelength of 240 mm, and a wave height of 30 mm. Subsequently, a three-story frame model was constructed based on these parameters, and the structure’s seismic performance under the El Centro and Taft seismic waves was analyzed. The results demonstrate that the three-story corrugated web steel frame system exhibits excellent hysteretic energy dissipation capacity under low-cycle reversed loading. Structural deformation is dominated by lateral displacement, with the peak interstory displacement reaching 3.76 times the longitudinal displacement. The second floor is identified as the structural weak link. Compared with the Taft wave, the El Centro wave exerts a more significant influence on the structure’s dynamic response and induces more pronounced displacement and stress responses under the same seismic intensity.OPEN ACCESS Received: 15/10/2025 Accepted: 12/12/2025 Published: 20/03/2026

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

Scipedia content — Wed, 18 Mar 2026 10:05:23 +0100

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.

Efficient Cyclic Primes: Efficient Prime Numbers Generate a Cyclic Group of Prime-Order

Scipedia content — Wed, 18 Mar 2026 10:01:53 +0100

In the realm of mathematics and digital communication, cyclic groups of prime order have emerged as both a foundational and transformative concept. Historically, prime numbers have been pivotal in underpinning numerous cryptographic systems, with their unique properties making them integral for robust security mechanisms. Our in-depth research introduces the novel concept of Efficient Cyclic Primes, a specific subset of primes that demonstrate a heightened capability to generate diverse cyclic groups. Notably, certain prime numbers inherently foster a richer array of cyclic groups compared to others. The genesis and understanding of these efficient cyclic primes are intricately linked to the Euler number, further combined with the base number essential for cyclic group generation. We have proposed a new prime calculation algorithm that not only elucidates the process of identifying efficient cyclic primes but also lists the first 250 efficient cyclic primes that have been computed. By leveraging these primes, we chart a promising trajectory toward conceiving more potent, advanced cryptographic methodologies for the future.OPEN ACCESS Received: 27/09/2025 Accepted: 03/12/2025 Published: 20/03/2026

Deep Learning Approaches for Carbon Pricing Mechanism Design in Green Transportation Supply Chains

Scipedia content — Wed, 18 Mar 2026 10:00:19 +0100

This study uniquely integrates Long Short-Term Memory networks (LSTM) and Graph Convolutional Networks (GCN) with a multi-head attention mechanism to address dynamic carbon pricing optimization in green transportation supply chains, overcoming the limitations of traditional static models. As global climate change issues become increasingly severe, the design of carbon pricing mechanisms for green transportation supply chains has become a key factor in promoting sustainable development. We construct a hybrid deep learning model that simultaneously captures temporal dependencies in carbon emission data and spatial relationships in supply chain network structures. Traditional carbon pricing methods often rely on static models and simplified assumptions, making it difficult to adapt to complex and dynamic supply chain environments. Experimental results show that the proposed deep learning method improves carbon price prediction accuracy by 23.7% compared to traditional methods and achieves 18.5% improvement in supply chain cost optimization. Furthermore, the method achieved an average 21.6% carbon emission reduction and 15.5% cost reduction in three real green transportation supply chain cases, demonstrating its effectiveness in practical applications. The multi-objective optimization framework successfully balances the trade-off between economic and environmental benefits through organic integration of genetic algorithms and deep learning models. Ablation experiments validated the importance of each model component, and sensitivity analysis confirmed the rationality of parameter settings. This method provides strong technical support for formulating more precise and dynamic carbon pricing policies, offering significant theoretical value and practical significance for promoting sustainable development of green transportation supply chains.OPEN ACCESS Received: 17/09/2025 Accepted: 24/11/2025 Published: 20/03/2026

Intelligent Sustainable Development Management in Circular Economy Supply Chains: A Deep Learning and Multi-Objective Optimization Framework

Scipedia content — Wed, 18 Mar 2026 09:59:35 +0100

As global environmental challenges intensify, circular economy (CE) has emerged as a critical pathway for sustainable development. This study proposes a deep learning-based CE supply chain network design framework that optimizes resource allocation, reduces waste, and improves sustainability. The framework employs graph convolutional networks, long shortterm memory networks, and multi-head attention mechanisms to capture topological, temporal, and multi-dimensional supply chain features. An improved NSGA-III algorithm achieves coordinated balance among economic, environmental, social, and circularity objectives. A comprehensive sustainability evaluation system provides quantitative assessment tools. Experimental validation using real data from 15 enterprises across five industries shows the deep learning model achieves 89.2% prediction accuracy on test sets, representing 16.1% improvement over baselines and 67.9% improvement in computational efficiency. The optimized network achieves 32.4% waste reduction, 28.7% resource efficiency improvement, 25.3% cost reduction, 68.5% material circulation rate, and 89.2% network efficiency. This research contributes to theoretical understanding and provides practical guidance for manufacturing enterprises’ transition to CE, supporting sustainable development goals.OPEN ACCESS Received: 27/09/2025 Accepted: 17/11/2025 Published: 20/03/2026

Semi-Supervised Distillation Network Toward Noise-Resistant Medical Image Classification

Scipedia content — Wed, 18 Mar 2026 09:58:44 +0100

Deep learning (DL)-based models have demonstrated significant advancements in medical image classification. However, the scarcity of accurately labeled training data and the prevalence of label noise remain critical obstacles to further performance improvements. Although semi-supervised learning and learning with noisy labels (LNL) methods each offer partial remedies, their independent application often leads to suboptimal outcomes. To address this, we propose a unified framework termed the Semi-supervised Adaptive Distillation Network (SADNet), which synergistically integrates semi-supervised training with noise-robust distillation. SAD-Net consists of three core components. First, a semi-supervised learning framework is employed to generate pseudo-labels from unlabeled data, thereby augmenting the training set. Subsequently, a Noise Filtering Module (NF-Module) is introduced, which combines a Convolutional Neural Network (CNN) with an Improved Fuzzy C-Means (IFCM) algorithm using a weighted average distance metric. This module produces weighted soft labels from both models and filters out noisy samples based on a confidence threshold. Finally, an Adaptive Weighted Distillation Module (AWD-Module) is designed, incorporating the IFCM along with two CNN architectures. It processes the high-confidence samples selected by the NF-Module and performs classification via dynamically weighted soft labels derived from all three models. Extensive experiments on two medical image datasets show that SAD-Net achieves superior performance compared to state-of-the-art semi-supervised methods, attaining the highest scores in accuracy, sensitivity, specificity, and F1-score. Moreover, it outperforms leading LNL approaches across all evaluated metrics. These results validate the efficacy of the proposed SAD-Net in simultaneously mitigating the problems of limited labeled data and noisy labels in medical image classification.OPEN ACCESS Received: 23/09/2025 Accepted: 03/12/2025 Published: 20/03/2026

The Effect of Rocoto Pepper at Varying Concentrations on Parkinson’s Disease in C. elegans Models

Albjona Zhuka — Fri, 13 Mar 2026 17:29:33 +0100

The increasing incidence of Parkinson’s disease (PD) represents a significant public health challenge, highlighting the urgent need for more accessible and innovative treatments. Rocoto pepper (Capsicum pubescens), a South American chili pepper, has a unique composition of capsaicin, dihydrocapsaicin, antioxidants, and vitamins A and C. While previous studies have investigated the neuroprotective properties of several pepper species, the effects of Rocoto pepper on PD remain largely unexplored. The present study examines the effects of Rocoto pepper extract at varying concentrations on Parkinson’s disease using Caenorhabditis elegans (C. elegans) as a model organism. C. elegans were exposed to four different concentrations of Rocoto extract (0%, 4%, 8% and 16%) applied onto the surface of Nematode Growth Medium (NGM) and fed E. coli OP50. Over the course of one week, behavioral assays were conducted to monitor locomotion and touch sensitivity. By exploring an overlooked yet culturally rich crop, this study calls attention to a broader and more inclusive approach in the search for sustainable medical solutions. Future research would test the individual compounds found in the pepper to determine which component has the greatest neuroprotective effects and the mechanisms it underwent in dopaminergic pathways. Expanding the range of tested concentrations, increasing the sample size, and replicating trials would strengthen the reliability of the findings.

Constructability‑based multi‑objective optimization with machine learning‑enhanced meta‑heuristics for reinforcing bar design in rectangular concrete columns

Luis Fernando Verduzco Martínez — Sat, 07 Mar 2026 15:55:23 +0100

Optimization of reinforcing bar (rebar) design represents a preponderant factor in reducing material usage and wastes for reinforced concrete (RC) structures. The assessment of constructability of such rebar designs is crucial to improve their practicality and reduce construction costs, which makes the problem multi-objective (MO). However, when applying optimization methods for the design of rebar in RC structures, little attention has been paid on columns, in comparison to beams and slabs. Meta-heuristic algorithms (MA) have been the ones mostly deployed for these types of elements, which have proven to be of high computational cost. Additionally, an existing gap in the literature as to how to relate the design and construction stage of rebar in RC structures through constructability analysis is evident. In this regard, research has been focused mainly at the building level but not at the element level. This works presents a novel algorithmic framework using Machine Learning (ML)-enhanced meta-heuristics for the optimal design of rebar on rectangular RC columns. To assess the constructability of the resulting rebar layouts a Buildability Score (BS) model at the element level is proposed. The complexity analysis of rebar design under the constructability restrictions, through combinatorial optimization (CO), is used to assess the global time efficiency of the framework. The Non-Sorting Genetic Algorithm II (NSGA-II) was deployed for showcase and five different ML algorithms were used to enhance it, namely the k-NN classifier, SVM regression, ANN, Gauss Process (GP) regression, and Tree Ensembles (TE), where the latter three showed the best performance.

CALRECOD -- A software for Computed Aided Learning of REinforced COncrete structural Design

Luis Fernando Verduzco Martínez — Sat, 07 Mar 2026 15:45:34 +0100

It is presented the development and implementation of a new computed aided learning MatLab Toolbox for the design of reinforced concrete structures named as CALRECOD for their abbreviation \textit{Computer Aided Learning of Reinforced Concrete Design}. Such development emerges as the result of a series of research works in the Autonomous University of Queretaro with the main purpose of improving the way in which the design of reinforced concrete structures is taught in high education institutions. CALRECOD uses optimization methods and algorithms to aid students in their design interaction learning so that they are able to compare their own designs and what commercial software delivers with optimal ones given certain load conditions on the elements or structures. The software consists almost entirely of MatLab functions (.m files) and the ACI 318-19 code is taken as their main design reference to make it internationally useful, although in some cases the Mexican code NTC-17 specifications are used. Besides MatLab functions, the software consists as well of ANSYS SpaceClaim script functions (.scscript files) as an additional tool for the aid in the visualization of design results in a 3D space in the software ANSYS SpaceClaim. CALRECOD has proven to be versatile, flexible and of easy use with a huge potential to increase learning outcomes for students in high education programs related with the design of reinforced concrete structures as well as to enhance the creation of efficient interactive environments for researchers and academics focused on the development of new design and analysis methods for such structures. With their optimization design functions, a solid comparison platform of designs' performance could be laid out and with its extended function design packages for structural systems, reinforced concrete design courses could be enhanced in a great deal regarding their program content's scope. The software can be found at:{https://github.com/calrecod/CALRECOD

Outlier-Resistant Neutrosophic Ratio Estimators based on Generalized M-Estimators

Scipedia content — Fri, 06 Mar 2026 10:55:14 +0100

Building efficient ratio-type estimators of population parameters, especially the mean and variance, has been a major theme in sampling theory. However, the growing frequency of dirty, inaccurate, and incomplete information still poses a threat to the credibility of the classic estimation processes, especially in environments where outliers are likely to occur. The paper derives a generalized type of neutrosophic robust ratio-type estimator and regression-type estimators that have been developed on the M-estimation platform, including Huber M-estimators and generalized M-estimators (Viz., Mallows-GM, Schweppes-GM, and SIS-GM) formulations, and also incorporating the auxiliary information of the HodgesLehmann estimator. The estimators are designed to be asymptotically efficient in clean data models and apply well to contamination and heavytailed error distributions. Through a comparative study using real-world data and a Monte Carlo simulation experiment, it is shown that the proposed estimators show better performance, numerical stability, and robustness compared to the current methods in the presence of uncertainty. The simulation results confirm their resilience to contamination and heavy-tailed distributions across varying contamination levels. An application to environmental data involving temperature measurements subject to measurement error and outliers further illustrates the practical relevance of the framework. Collectively, the empirical and simulation evidence support the applicability of the proposed methodology to industrial process analysis and environmental monitoring systems characterized by data imprecision and contamination.OPEN ACCESS Received: 16/11/2025 Accepted: 07/01/2026

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 — Fri, 06 Mar 2026 10:54:37 +0100

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

Concurrent Execution Process Modeling and Interaction Strategy among Coupled Design Tasks under EPC Mode

Scipedia content — Fri, 06 Mar 2026 10:53:49 +0100

The concurrent execution of preliminary design and construction drawing design under the Engineering Procurement Construction (EPC) mode is an important method to achieve efficient connection in technology and deep integration in management between them, but it also faces the risk of multiple rework and information transmission caused by repeated iterative coupling and frequent information interaction. This issue leads to inefficiencies and increases project risk, particularly in managing interdependencies and mitigating rework. Unlike previous studies, this research focuses on optimizing the interaction strategies to minimize these risks and improve overall project performance in EPC projects. In view of this, on the basis of planning and quantifying the parallel execution process of preliminary design and construction drawing design, and constructing the corresponding change probability function (which is a model that predicts the likelihood of design information changes), this study introduces a novel analytical framework for predicting the likelihood of information changes and calculating their impact on task durations. A parallel execution duration decision-making model for solving the optimal information interaction strategy (the method of managing communication and decision-making to reduce delays and rework) is constructed. This model offers new decision guidelines to minimize rework and reduce delays in design tasks. Subsequently, through the numerical derivation and analysis of the change probability function, seven propositions about the interaction strategy are proposed. Through the numerical solution and analysis of the parallel execution duration model, six propositions about the interaction strategy are proposed. These propositions contribute to theory by offering a model that quantifies rework and information transfer in concurrent execution, and to practice by providing actionable insights for improving EPC project management. The results provide methodological countermeasures for discussing the interaction strategy of preliminary design and construction drawing design in the concurrent execution process from the perspective of quantitative analysis, and also provide directional suggestions for the application method of concurrent engineering concept in EPC mode.OPEN ACCESS Received: 09/08/2025 Accepted: 13/01/2026

Garhy distribution with Different Estimation Methods and Applications to Engineering and Medical Data

Scipedia content — Fri, 06 Mar 2026 10:53:33 +0100

In this article, we introduce and investigate a new one-parameter mixture distribution called the “Garhy distribution”. The probability density function is very adaptable, as it may take on right skewed, unimodal, and heavy tailed patterns. In addition, the hazard rate function indicates that data with increasing shaped failure rates may be adapted by the Garhy distribution. Several fundamental statistical and mathematical properties are calculated including mode, quantile function, moments, mean, variance, skewness, kurtosis, moment-generating function, incomplete moments, inequality measures, order statistics, and extropy measures. The scale parameter of the Garhy distribution is estimated employing twelve different estimation approaches, maximum likelihood, maximum product of spacings, least-squares, weighted least-squares, Anderson darling, right-tail Anderson darling, left-tail Anderson darling, Cramér von-Misses, and the least-squares method. The effectiveness of these strategies is evaluated using a detailed simulation study. Furthermore, we used the Garhy distribution to examine two real-world data sets, demonstrating its superior performance compared to specific competitors.

Advancing Fractional Calculus: A Fixed-Point Strategy for Nonlinear Caputo-Hadamard Equations

Scipedia content — Fri, 06 Mar 2026 10:52:43 +0100

This paper investigates the existence, uniqueness, and Ulam–Hyers stability of solutions for a coupled system of nonlinear Caputo–Hadamard fractional differential equations in Banach spaces. By reformulating the boundary value problem into an equivalent integral system via the Hadamard fractional integral operator, sufficient conditions for existence and uniqueness are established using Krasnoselskii’s and Banach’s fixedpoint theorems. Within the same functional framework, Ulam–Hyers stability results are derived for the proposed system. The theoretical analysis provides a consistent and unified approach for studying nonlinear coupled fractional systems with nonlocal operators, and the validity of the assumptions is illustrated through representative examples.

List of participants

Quoc Tri Phung — Wed, 04 Mar 2026 13:14:03 +0100

WORKSHOP AGENDA

Quoc Tri Phung — Wed, 04 Mar 2026 13:13:14 +0100

Book of Abstracts

Quoc Tri Phung — Wed, 04 Mar 2026 13:04:03 +0100

Presentation to Eugenio Oñate and others of the National Research Award 2024 by the King of Spain Felipe VI.

Eugenio Oñate — Wed, 25 Feb 2026 19:13:06 +0100

Tribute of CIMNE to Eugenio Oñate on his retirement

Eugenio Oñate — Sun, 22 Feb 2026 20:08:31 +0100

Video on the Particle Finite Element method

Eugenio Oñate — Fri, 20 Feb 2026 11:59:10 +0100

The video presents the theoretical foundations of the Particle Finite Element Method (PFEM, https://cimne.com/pfem-launches-its-new-website/ ) and its applications in engineering and other applied sciences.

Video on the occasion of the 2024 Spanish National Research Award in Engineering and Architecture

Eugenio Oñate — Fri, 20 Feb 2026 10:28:38 +0100

The video contains the interview that the Ministry of Science, Innovation and Universities of the Government of Spain conducted with Eugenio Oñate on the occasion of the awarding of the 2024 National Research Prize, Torres Quevedo category in Engineering and Architecture.

Methodological Approach for Understanding Particle Transport. Case Study: Analyzing Influential Factors and Comparing Simulation Software

Scipedia content — Thu, 19 Feb 2026 09:23:16 +0100

Recent research shows that the investigation of particle
flow of small particles with diameters smaller than
10 micrometers has emerged as an important field of
interest, especially for purposes in the industrial sector.
Investigating the behavior, deposition and acting forces
of individual particles have become particularly
relevant. Understanding theses dynamics is crucial for
setting up practical simulations in terms of particle
transport drawn from industrial problems. Previous
research has identified numerous forces involved in
particle transport e.g. Sommerfeld, 2000, Crown, 2005,
Löffler & Raasch, 1992 or Crown, et al., 2011. However,
not all of the various forces have yet been implemented
in practice for different reasons.
In this case study, acting forces are examined
theoretically using a component coming directly from
the automotive industry. The examined component is a
venting tube used in headlights of vehicles. Due to its
simple geometry the component is suitable for
investigating particle-laden flow using different
simulation software. The simulation setup for this
component is less complex, which saves time and
reduces computational costs. The implementation of the
examination processes within different types of
simulation software has been analyzed too.
Simulating a particle-laden flow results in significant
computational costs. It is therefore necessary to identify
and implement boundary conditions. All results serve as
the basis for developing an appropriate simulation
model and to create a methodological approach
representing particle transport. The simulation results
have been validated using suitable experiments.
Furthermore, a transfer function is derived to determine
the filtering effect of the mentioned automotive
component. This study combines theoretical
background with a practical approach.

On a Class of Hybrid Langevin Inclusions Involving Two Generalized Fractional Derivatives

Scipedia content — Wed, 18 Feb 2026 10:47:03 +0100

In this paper, we study the existence of solutions for a hybrid Langevin inclusion involving a combination of φ-Hilfer and φ-Caputo fractional derivatives. To this end, we construct a new operator derived from the integral solution of the given boundary value inclusion problem and subsequently apply the hypotheses of Dhage’s fixed point theorem to this fractional operator. Finally, to support our theoretical findings, an illustrative example is provided.OPEN ACCESS Received: 13/11/2025 Accepted: 20/01/2026

Integrability, Conservation Laws by Variational Analysis and Lump Solitons for (2+1) Fourth-Order Biharmonic Equations with Quantum Field Applications

Scipedia content — Wed, 18 Feb 2026 10:45:03 +0100

We study the integrability via conservation laws and discuss the nonlinearity of the fourth-order biharmonic equations in (2+ 1) dimensions related to quantum field models based on the potential functions h(u). Lie symmetry reduction is performed, and the forms of the invariant solutions are presented, including travelling wave solutions. Variational analysis has been performed based on the various potential functions h(u). Corresponding Euler-Lagrange equations and conservation laws are investigated by Noether’s theorem and presented in the form of conserved vectors. The obtained conserved flows define energy, momentum and flow dynamics supporting the system integrability. Furthermore, detailed lump and breather solutions are presented for each potential h(u) using Bilinear forms illustrating various localized and oscillatory field characteristics.OPEN ACCESS Received: 08/11/2025 Accepted: 17/12/2025

Monitoring Techniques for Structural Health of Roads Using Emerging Technologies: A Systematic Re-View of the Last Five Years

Scipedia content — Wed, 18 Feb 2026 10:44:03 +0100

This systematic review examines the most recent techniques used to monitor the structural health of roadways, with a special focus on emerging technologies applied over the past five years. The progressive deterioration of road networks and the limitations of traditional inspection methods have driven the development of more precise, automated, and efficient solutions. The technologies analyzed include LiDAR laser scanning, drones equipped with computer vision, visual sensors, mobile cameras, ground-penetrating radar (GPR), and unmanned aerial vehicles (UAVs). Each technique was assessed based on its accuracy and the type of pavement distress it can identify, such as cracks, potholes, and surface deformations. The findings indicate that these tools enhance the efficiency and safety of inspections, enabling real-time data collection. Additionally, there is a growing trend toward the integration of artificial intelligence algorithms to automate data analysis. However, data heterogeneity and the need for cross-domain model adaptation may affect performance and scalability in large-scale or multi-source scenarios. Overall, this study provides an updated perspective on the application of emerging technologies in road infrastructure management, contributing to the development of innovative strategies for sustainable and intelligent roadway maintenance.OPEN ACCESS Received: 09/09/2025 Accepted: 09/12/2025 The search strategy was designed based on the PICOC framework, using the following Figure 1: Most frequent and trending

Smart Sensors and Artificial Intelligence for Urban Water Networks: A Systematic Literature Review (2015–2025)

Scipedia content — Wed, 18 Feb 2026 10:43:13 +0100

Efficient management of urban drinking water networks is challenged by population growth, rising consumption, and leakage-related losses. This study presents a Systematic Literature Review (SLR) following the PRISMA protocol, covering research published between 2015 and 2025 on smart sensors and advanced techniques for leak detection and consumption optimization. From 788 initial records, 40 studies met the inclusion criteria. Findings indicate that acoustic, pressure, fiber-optic, and hybrid sensing enable real-time monitoring and accurate leak localization, with typical error margins between±1% and ±5%, depending on sensor type and hydraulic conditions. A marked shift toward artificial intelligence (AI) and machine learning is observed for optimal sensor placement, event classification, and prediction, achieving >95% accuracy. The cost analysis reveals a direct relationship between technological sophistication and required investment. Overall, integrating smart sensors with AI provides a promising pathway toward more sustainable, efficient, and resilient urban water management.OPEN ACCESS Received: 06/09/2025 Accepted: 30/10/2025 The search strategy was constructed using combinations of keywords and Boolean operators, adjusted for each database to ensure consistency and reproducibility. The searches targeted titles, abstracts, and

Applications of Artificial Intelligence in Bridge Structural Health Monitoring: A Systematic Review of the Last Decade

Scipedia content — Wed, 18 Feb 2026 10:42:26 +0100

Bridges are critical components of transportation networks whose structural integrity is often threatened by aging, environmental loads, and insufficient maintenance. Structural Health Monitoring (SHM) supported by Artificial Intelligence (AI) offers a transformative approach to early damage detection, predictive maintenance, and operational safety. This study presents a systematic literature review, conducted in accordance with PRISMA 2020 guidelines, on the application of AI algorithms to bridge SHM between 2015 and 2025. A total of 70 peer reviewed articles were analyzed, covering diverse geographic contexts, structural types, and sensing technologies. The review categorizes studies by AI technique (ANN, CNN, LSTM, SVM, hybrid models, and emerging methods such as Transformers and Graph Neural Networks), sensor architecture (accelerometers, fiber optic sensors, UAV based imaging, IoT modules), and performance metrics. Results indicate that convolutional and recurrent neural networks achieve detection accuracies above 95% and R2values exceeding 0.90 in displacement prediction, while hybrid approaches combining deep learning with traditional classifiers enhance robustness. Sensor integration with IoT and multimodal data fusion improves detection sensitivity, with correlation values above 0.99 in some cases. However, over 90% of studies lack robust cross validation, real world deployment, or standardized performance reporting, limiting replicability. This review highlights current trends, technical challenges, and research opportunities, including the need for interoperable sensor– algorithm platforms, explainable AI models, and broader implementation in developing regions. By consolidating existing knowledge, the study provides a technical reference for researchers, practitioners, and policymakers aiming to implement intelligent, predictive, and resource efficient bridge SHM systems.OPEN ACCESS Received: 27/08/2025 Accepted: 13/11/2025

The Particle Finite Element Method PFEM in engineering

Eugenio Oñate — Tue, 17 Feb 2026 09:30:08 +0100

How Can an Appropriate CFD Model be Developed for Turbulent Flow in Rough Pipes?: Evidence from Friction Factor Prediction

Scipedia content — Wed, 11 Feb 2026 14:07:25 +0100

This paper answers the question: “How can an appropriate turbulent rough pipe flow computational fluid dynamics (CFD) model be developed?” The Reynolds-averaged Navier-Stokes equations with the standard k-epsilon turbulence model and scalable wall functions were solved to obtain Fanning friction factors and mean velocity profiles in inflectional and monotonic rough pipes. CFD models with near-wall grid sizes from four dimensionless wall distances and two roughness treatment approaches were simulated. Eight roughness Reynolds numbers, covering the lower end of the transitionally rough regime through the fully rough regime, were studied for each roughness type. Appropriate roughness and turbulence model constants for turbulent rough pipe flows in the transitionally rough regime were determined. For model validation, the predicted mean axial velocity profiles for Reynolds numbers of 5 × 104and 5 × 105exhibited good agreement with the reference experimental data. A total of 208 CFD simulations (32 from our previous works and 176 from the present study) were analyzed. Finally, based on comparisons between predicted Fanning friction factors and established correlations, appropriate CFD models for turbulent flows in inflectional and monotonic rough pipes were identified. Suitable CFD models for accurately predicting mean velocity profiles at roughness Reynolds numbers below 11.225 were also obtained, although with the caution that improved mean velocity prediction may reduce Fanning friction factor accuracy. Furthermore, the present CFD work provides essential guidance for extending simulations to other rough surface types and rough-wall flow situations.OPEN ACCESS Received: 25/11/2025 Accepted: 30/12/2025

Computational Investigation of Fractal-Fractional Nonlinear Viscoelastic Fluids Using Local Radial Basis Function Method

Scipedia content — Wed, 11 Feb 2026 14:05:59 +0100

Fractal-fractional derivatives generalize both traditional and fractional differentiation approaches by integrating memory effects with fractal properties. This mathematical framework is especially valuable for describing complex systems in which conventional continuum mechanics becomes inadequate, particularly in scenarios involving porous or discontinuous structures. This research investigates the behavior of a non-linearWalter’s-B fluid subjected to time-varying thermal and concentration conditions. Beyond the extended derivative formulation, the analysis incorporates phenomena including first-order chemical reactions, radiative heat transfer, Joule heating, Soret effect, and viscous dissipation. Thesystem is also subjected to a transverse magnetic field with magnitude B0.The fluidmodel is initially formulated through traditional constitutive equations and subsequently generalized using a fractal-fractional operator. Solutions to this extendedmodel are computed employing ameshfree numerical approach utilizing localized radial basis functions (LRBF), which eliminates the requirement for structured grids and improves precision when addressing intricate geometries.The computational outcomes, displayed through graphical representations, illustrate how the fractional and fractal parameters influence the rheological characteristics of the Walter’s-B fluid. These findings establish that adjusting these parameters enables retrieval of classical, fractional, and fractal formulations as particular instances within this comprehensive mathematical structure.

Multisensor Fault Diagnosis Leveraging Reinforced Evidential Jensen-Alpha Divergence under Dempster-Shafer Theory

Scipedia content — Wed, 11 Feb 2026 14:04:34 +0100

Uncertainty and conflicting information are pervasive in artificial intelligence (AI)-driven engineering systems, especially in multisensor fault diagnosis. Dempster-Shafer theory (DST) has garnered significant interest across various fields as it provides a powerful framework for modeling uncertainty. However, despite its advantages, the application of Dempster’s rule can lead to paradoxical outcomes when it encounters highly conflicting evidence. To address this limitation, this paper first presents a new evidential Jensen-alpha divergence (EJ AD) to quantify the discrepancy between the evidence items based on DST. Furthermore, an advanced version, the reinforced evidential Jensen-alpha divergence (REJ AD) is developed, which takes into account the quantity of potential propositions. We demonstrate thatREJ ADcan be transformed into various divergences such as the χ2divergence, Jensen-Shannon divergence, Hellinger distance, and arithmetic-geometric divergence under certain conditions. Also, we show the key properties ofREJ AD, including non-negativity, non-degeneracy and symmetry. Additionally, we design a new multisensor fault diagnosis method utilizingREJ ADand belief entropy. The superior performance of the proposed method is tested in three distinct fault diagnosis cases, and analysis shows robust performance across a range of its key parameter α, offering a computationally feasible, scalable and interpretable solution for AI-based decision-making in real-world engineering applications.OPEN ACCESS Received: 15/10/2025 Accepted: 17/11/2025

A Hybrid Approach for Vulkan-Based Ray Tracing Implementations

Scipedia content — Wed, 11 Feb 2026 11:12:27 +0100

Three-dimensional (3D) graphics output started with traditional local shading models processed in real time. Ray-tracing techniques are actively introduced for high-quality output. These traditional local shading pipelines and ray tracing pipelines are typically provided independently. Recently, hybrid rendering methods were introduced to integrate the results of these pipelines for better real-time graphics results. However, technical problems arise with heterogeneous application programming interfaces (APIs). Vulkan was recently introduced as a new low-level graphics API in practical 3D graphics implementations. With its new ray tracing extensions, Vulkan has become one of the 3D graphics environments that simultaneously supports traditional local shading and modern ray tracing pipelines. Stand-alone ray tracing and hybrid rendering techniques are implemented to determine a practical implementation of the ray tracing technique in this latest Vulkan environment. This work reveals the completeness of both implementations and compares the graphics performance with experimental computer animation sequences and different camera configurations. The hybrid rendering techniques perform better with previous graphics processing unit models, and the core ray tracing implementation achieves faster processing speeds of 39% to 64% compared to the stand-alone ray tracing method, at least in the Vulkan environment with the latest NVIDIA graphics card. In contrast, the preprocessing of the geometry pass takes noticeable time, which reduces our overall performance improvements. The experimental results can be applied as guidelines for implementing practical real-time 3D graphics applications and as a new benchmark model for ray tracing implementations.OPEN ACCESS Received: 23/08/2025 Accepted: 28/11/2025 Published: 03/02/2026

Large Language Model-Driven Demand Forecasting and Inventory Optimization for University Physical Education Resource Supply Chain

Scipedia content — Wed, 11 Feb 2026 11:12:04 +0100

This study proposes an intelligent management system for university physical education resource supply chains based on Large Language Models (LLMs), aiming to address the problems of inaccurate demand forecasting and inefficient inventory management in traditional physical education resource allocation. By constructing a deep learning framework incorporating LLMs and combining multi-dimensional information including historical data, seasonal factors, course schedules, and student preferences, precise demand forecasting for sports equipment, facilities, and teaching resources is achieved. The research employs a pre-trained language model based on the Transformer architecture, combined with time series analysis and reinforcement learning algorithms, to develop dynamic inventory optimization strategies. Experimental results demonstrate that compared to traditional methods, this system improves demand forecasting accuracy by 23.7%, increases inventory turnover rate by 31.2%, and achieves a resource utilization rate of 89.6%. This research provides a novel solution for intelligent management of university physical education resources, offering significant theoretical value and practical implications.OPEN ACCESS Received: 24/08/2025 Accepted: 28/10/2025 Published: 03/02/2026

Prediction of Entropy Production and Heat Transfer Characteristics of Al2O3-Cu/Water Hybrid Nanofluid in Convection-Radiation Interaction Flow in a Porous Cavity byMachine Learning Approach

Scipedia content — Wed, 11 Feb 2026 11:11:13 +0100

Minimizing entropy production is critically important, particularly in nanofluid flows. Applying this principle to flows with porous cavity structures helps optimize heat transfer applications and enhance system efficiency. In this study, the entropy production and heat transfer characteristics of a hybrid nanofluid composed of Al2O3-Cu particles suspended in water were investigated using machine learning. The nanofluid was analyzed in the context of convection–radiation interaction flow within a porous cavity.An artificial neural networkmodel was developed to predict the average Nusselt number, Bejan number, and entropy production as functions of the Hartmann number and inclination parameters. The Bayesian Regularization algorithm was employed to train the multilayer perceptron network model. Prediction results obtained from the model with 10 neurons in the hidden layer were compared with the target values and showed excellent agreement.The developed artificial neural network model successfully predicted the Nusselt number, Bejan number, and entropy productionwith average deviation rates of−0.007%,−0.11%, and 0.0002%, respectively.

Truncated Modified Weighted Exponential Distribution with Different Estimation Methods and Applications

Scipedia content — Wed, 11 Feb 2026 11:10:13 +0100

This study introduces the Truncated Modified Weighted Exponential (TrMWE) Distribution, developed by extending the traditional modified weighted exponential distribution with an additional parameter and truncating its support to a finite range, enhancing its adaptability for modeling lifetime and reliability data. The statistical properties of the TrMWE, including moments, the moment-generating function, the quantile function, and order statistics, are examined. Parameter estimation is performed via maximum likelihood estimation (MLE), least squares and weighted least squares methods, maximum product of spacing method, Cramer-Von-Mises method, Anderson-Darling method, and right and left tails Anderson-Darling methods, with analysis of the asymptotic behavior of the estimators. Rényi and Tsallis entropies are also derived to assess the distribution’s uncertainty. The practicality of the TrMWE is illustrated using three real datasets and compared with existing distributions based on goodness-of-fit criteria, such as the Akaike Information Criterion and Bayesian Information Criterion. The results highlight the distribution’s flexibility and superior performance in modeling complex datasets.OPEN ACCESS Received: 14/08/2025 Accepted: 05/11/2025 Published: 03/02/2026

Theoretical Analysis of Seepage Field Distribution Induced by Pre-Excavation Dewatering Considering the Thickness of Waterproof Curtain and Delayed Responses of Water Table

Scipedia content — Wed, 11 Feb 2026 11:09:24 +0100

Dewatering during foundation pit excavation generates substantial hydraulic gradients, potentially causing significant seepage forces at the excavation bottom, which threaten structural stability. To mitigate such risks, suspended waterproof curtains have been widely employed to elongate seepage paths and reduce groundwater flow velocities. However, accurately predicting seepage field, especially under transient groundwater conditions with phreatic surfaces and varying curtain geometries, remains challenging. This study develops a theoretical model addressing transient groundwater seepage in foundation pits, explicitly considering a moving phreatic surface, curtain penetration depth and thickness. The proposed analytical solution is validated against experimental results and numerical simulations performed using COMSOL Multiphysics. Parametric analyses reveal that decreasing the vertical distance between the retaining wall base and the impermeable layer from 30 to 10 m reduces external groundwater drawdown by approximately 52%. Additionally, thicker waterproof curtains markedly decrease internal drawdown magnitudes, redirect seepage pathways, and effectively lower external groundwater depletion. Analyses on specific yield reveal delayed water release significantly moderates drawdown rates, reducing ultimate drawdown magnitudes. Furthermore, elevated internal excavation water levels intensify hydraulic head differences, substantially extending seepage-affected zones and amplifying drawdown responses both inside and outside the foundation pit. Overall, these findings provide critical theoretical insights for optimizing foundation pit design and improving dewatering practices, ensuring excavation safety and mitigating environmental impacts.OPEN ACCESS Received: 11/08/2025 Accepted: 05/11/2025 Accepted: 03/02/2026

NAS-Driven Quantitative Assessment of Overpressure Genesis in Sedimentary Basins

Scipedia content — Wed, 11 Feb 2026 11:08:04 +0100

The study of overpressure genesis mechanism is the foundation of hydrocarbon reservoir formation and pressure prediction research, and a thorough understanding of the formation and distribution patterns of hydrocarbon resources is essential for practical hydrocarbon exploration. However, the prediction of anomalous high-pressure genesis currently encounters numerous challenges, including the complexity of overpressure genesis, the superimposed effect of multiple mechanisms, and the dependence of traditional models on manual analysis, resulting in inefficiency and quantification challenges. To this end, this paper proposes a method for identifying and quantitatively evaluating stratigraphic overpressure mechanisms based on neural architecture search, to enable rapid and accurate quantitative evaluation of overpressure mechanisms. The results show that the main anomalous high-pressure genesis mechanisms in the target work area include undercompaction, fluid expansion and tectonic compression, with contribution rates of approximately 73% for undercompaction, and 9% and 18% for fluid expansion and tectonic compression, respectively. The model’s accuracy in the test set reaches 95.4%, significantly enhancing the identification accuracy of anomalous stratigraphic pressure genesis and its superposition relationships. The innovation of this paper lies in the combination of wave velocity-density rendezvous map with clustering algorithm and neural architecture search algorithm, offering an efficient approach to identify multiple overpressure genesis mechanisms and predict pore pressure through machine learning algorithms, which is of great theoretical significance and practical application value.OPEN ACCESS Received: 08/08/2025 Accepted: 14/10/2025 Published: 03/02/2026

Heat Transfer and Electroosmotic Flow over Stretching Sheet: A Sensitive Analysis through Response Surface Method

Scipedia content — Wed, 11 Feb 2026 11:07:39 +0100

Recent advancements in electro-osmotic surface coatings have led to significant theoretical and numerical exploration of how zeta potential influences the electroosmotic flow of viscous ionic fluids over a stretching sheet. The governing boundary layer equations are derived from the fundamental laws of mass, momentum, and energy conservation using appropriate similarity transformations and non-dimensionalization techniques. This system of equations is solved numerically using MATLAB’s bvp4c solver. The accuracy of the computational results is confirmed through comparison with previously published studies. To better understand the influence of various parameters on flow and thermal behavior, Response Surface Methodology and Factorial Plot analysis are applied. These statistical tools enable sensitivity analysis by systematically investigating the effects of zeta potential, electroosmosis parameter, electric field strength, and Prandtl number on key flow characteristics such as velocity, temperature distribution, skin friction coefficient, and Nusselt number. The results reveal that the electric field parameter plays a dominant role in enhancing axial velocity and increasing skin friction, making it a key factor in flow dynamics. The zeta potential significantly influences the boundary layer by modifying the electrical double layer and surface charge distribution, leading to noticeable deceleration. Meanwhile, the Prandtl number primarily governs thermal gradients and heat transfer rates, controlling the thermal behavior of the fluid. These physical insights, combined with the optimization capability of Response Surface Methodology, provide actionable guidelines for the design of electroosmotic coating processes and lab-on-chip biomedical devices.OPEN ACCESS Received: 07/08/2025 Accepted: 17/10/2025 Published: 03/02/2026

Investigation on Dynamical Mechanics of Rock-Backfill Composite Samples under SHPB Test

Scipedia content — Wed, 11 Feb 2026 11:06:14 +0100

In blast-induced caving mining with backfilling, understanding the interaction mechanisms and deformation evolution between rock and cemented tailing backfill (CTB) under coupled conditions is essential for ensuring stability. This study conducted dynamic uniaxial impact tests using the Split Hopkinson Pressure Bar (SHPB) system on rock-CTB composite specimens to investigate their mechanical response at high strain rates. Stress–strain relationships were recorded across a range of strain rates, and corresponding failure mechanisms were analyzed. A coupled SHPB model was also developed using GDEM software to simulate internal stress wave propagation and crack evolution within the composite specimens. Experimental results revealed that the dynamic compressive strength initially increases, then decreases, and eventually stabilizes as the average strain rate (ASR) increases from 27.45 s−1to 68.73 s−1. At strain rates below 60 s−1, the stress–strain curves exhibit a “stress drop” pattern, whereas above 60 s−1, a “stress rebound” behavior is observed. Energy absorption increases with ASR up to 55 s−1, then decreases, followed by a secondary increase. Numerical simulations validated the experimental findings, revealing the formation of both transverse and longitudinal cracks within the CTB. Greater deformation was observed near the transmission bar interface compared to the rock interface. These results offer valuable insights into the dynamic failure behavior of backfilled systems and inform improved backfill design in blast-induced mining operations.OPEN ACCESS Received: 05/08/2025 Accepted: 05/09/2025 Published: 03/02/2026

AI-Driven Multimodal Analysis of User Experience in Immersive Environments: A Case Study of The Sphere

Scipedia content — Wed, 11 Feb 2026 11:04:13 +0100

Artificial intelligence (AI) and sensing technologies are reshaping how people experience immersive environments. This study investigates how audiences perceive and emotionally respond to such environments through an AI-driven mixed-methods analysis. A dataset of 275 usergenerated YouTube videos documenting experiences with The Sphere, an AI-convergent immersive environment, totaling over 3000 min of content and 24 million cumulative views, was analyzed to extract experiential themes, dominant emotions, and their relationships with public engagement metrics. The analysis identified seven key experiential themes: Awe of the Display, Personalized Spatial Audio Experience, Full-Body Sensory Engagement, Dynamic Visual Spectacle, Joyful Human–AI Encounter, Futuristic Spatial Design Experience, and Transformative Event Environment. Sentiment analysis revealed that fear was the most dominant emotion in textual narratives (42.3%), followed by surprise, sadness, happiness, and anger, whereas video-based analysis highlighted happiness (25.8%) and sadness (24.5%) as the most salient visual emotions. This contrast suggests that linguistic expressions emphasized feelings of awe and overwhelm, while visual cues reflected affective immersion and emotional depth. Regression results showed that Awe of the Display had the strongest positive impact on engagement (views, likes, comments), while Personalized Spatial Audio Experience showed a negative effect. These findings deepen the understanding of user experience in immersive environments and demonstrate how AI-assisted multimodal analysis can reveal the dynamics between audience perception and engagement in next-generation immersive environments.OPEN ACCESS Received: 12/11/2025 Accepted: 15/12/2025 Published: 03/02/2026

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

Scipedia content — Wed, 11 Feb 2026 11:03:24 +0100

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

Parametric Study of the Mechanical Behaviour of Membrane Tensegrity

Scipedia content — Thu, 05 Feb 2026 10:55:22 +0100

Integration of parametric design and analysis tools

Scipedia content — Thu, 05 Feb 2026 10:54:10 +0100

Adaptive Parametric Modelling via Grasshopper for Simulation of the Erection Sequence in Large-Span Cable Roofs: Correlation with Data Recorded during Construction

Scipedia content — Thu, 05 Feb 2026 10:53:00 +0100

The Effect of Slits and Notches in Fabric Specimens under Biaxial Tension

Scipedia content — Thu, 05 Feb 2026 10:51:45 +0100

Evaluation of Equivalent Membrane Stiffnesses of Single-Set Rope Meshes

Scipedia content — Thu, 05 Feb 2026 10:50:32 +0100

Development of a new ring model for flexible protection networks

Scipedia content — Thu, 05 Feb 2026 10:49:01 +0100

An Optimization-based Approach to Shell Morphing

Scipedia content — Thu, 05 Feb 2026 10:47:47 +0100

Adjoint-based Stress Identification via Material Tensor Reconstruction in Membrane Structures using Displacement or Strain Measurements

Scipedia content — Thu, 05 Feb 2026 10:46:21 +0100

Testing of welded ETFE-foils for quality assurance

Scipedia content — Thu, 05 Feb 2026 10:45:02 +0100

Long-term behaviour of ETFE films - Evaluation of monoaxial creep and relaxation tests after 100,000 hours of loading

Scipedia content — Thu, 05 Feb 2026 10:43:47 +0100

Failure Process of ETFE Foil

Scipedia content — Thu, 05 Feb 2026 10:42:31 +0100

Application of nonlinear constitutive and yield models in the design of ETFE cladding elements

Scipedia content — Thu, 05 Feb 2026 10:41:18 +0100

The impact of textile shading systems in façade retrofitting: evaluating the operational phase in the LCA

Scipedia content — Thu, 05 Feb 2026 10:40:02 +0100

Post-Consumer Recycled (PCR) ETFE Foil: Recycling of the Chelsea and Westminster Hospital ETFE Roof

Scipedia content — Thu, 05 Feb 2026 10:38:48 +0100

Picture This: A New Perspective on Tensile Membrane Degradation

Scipedia content — Thu, 05 Feb 2026 10:37:37 +0100

Stabilizing cable domes

Scipedia content — Thu, 05 Feb 2026 10:36:27 +0100

Multi Purpose Membrane Structure in Abano Terme

Scipedia content — Thu, 05 Feb 2026 10:35:17 +0100

Integrated Detailing and Thermal Optimization in a Tent-Based Textile Structure

Scipedia content — Thu, 05 Feb 2026 10:34:08 +0100

Fundamental Design Principles for Tensile Membrane Structures: A Call for Structural Awareness in Architectural Practice

Scipedia content — Thu, 05 Feb 2026 10:32:59 +0100

A Second Life for PVC/PES Membrane Material: Re-Use-Membrane Roof for a Modular Timber Structure Pavillon

Scipedia content — Thu, 05 Feb 2026 10:31:44 +0100

The Avicii Arena retractable ceiling - Innovative design and installation

Scipedia content — Thu, 05 Feb 2026 10:30:34 +0100

Development of a pneumatic actuator based on bio-PU coated fabrics for architectural applications

Scipedia content — Thu, 05 Feb 2026 10:29:10 +0100

A Deep Learning Network based on Channel and Temporal Attentions for Decoding Motor Imagery EEG Signals

Jianhua Wu — Thu, 05 Feb 2026 06:12:56 +0100

(1) Background: Accurately decoding motor imagery (MI) tasks is a prerequisite for creating a MI-based brain-computer interface (BCI). However, low signal-to-noise ratio and non-stationarity of EEG signals present a huge challenge for the classification of MI-EEG signals, restricting the extensive development of the BCI industry.''' '''(2) Methods: In this paper, we propose a novel deep learning model CTANet that integrates both channel and temporal attention mechanisms into a convolutional neural network to improve the classification accuracy of the MI-BCI systems. The model is constituted first by three serially connected temporal, spatial, and temporal convolution layers to extract features from the brain signals, with an efficient channel attention module inserted between the second and the third convolutional layers to highlight useful feature channels. Subsequently, the time segment for task decoding is partitioned into several time windows, and a variance layer is employed for computing the logarithmic variance of each window. Next, a multi-head attention mechanism is adopted to extract temporal dependency of features from different windows. Finally, a fully connected layer is used for classifying MI-EEG signals. (3) Results: The performance of the proposed model was evaluated on two publicly available BCI datasets and compared with the state-of-the-art methods. The experimental results show that for dataset BCIC-IV2a, our network achieved classification accuracies of 81.17% and 84.33% in inter-session and intra-session scenarios respectively, whereas for dataset OpenBMI, our network achieved classification accuracy of 73.06% and 77.59% in inter-session and intra-session scenarios respectively. (4) Conclusions: These results outperform state-of-the-art networks, indicating significant potential of the proposed model CTANet in MI decoding.

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

Jesús Sánchez Pinedo — Wed, 28 Jan 2026 12:03:33 +0100

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

Adaptive Power Splitting Strategies for Smart Microgrids with Enhancing Energy Efficiency and Resilience through Dynamic Load Management

Jesús Sánchez Pinedo — Wed, 28 Jan 2026 12:02:43 +0100

The integration of a renewable energy distributed generation into microgrids poses a significant constraint in the way power is managed, further so due to the natural variability in renewable generation and the variability in the load demands. To address these issues, this paper introduces a novel approach to the Spider Swarm Optimization (SSO) algorithm, the Dynamic LoadAdaptive Power Splitting (DLAPS) strategy, to enable real-time adaptive power sharing and enhance system resilience. Unlike the classical methods of power allocation that are static, according to which the power is divided between sources of renewable energy and storage systems, and between these sources and critical loads, the DLAPS-SSO applies the idea of a machine learning based predictive model to predict the power and dynamically optimize power allocation between the sources of renewable energy and storage systems and the sources and the critical loads. The model provides a multi-objective optimization framework that aims to minimize power losses and grid frequency variations, and to maximize the system’s resilience to disturbances, including disconnection from the grid, component malfunctions, and the availability of renewable energy sources. The comparison of simulation results with those of the Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) methods shows that the energy efficiency of the DLAPS-SSO increases by 15%–20%, and the amount of power losses across various load profiles decreases by 30%–35%. Moreover, the proposed solution offers 60% faster recovery time in case of grid disconnection, maintains 65.9% of the critical load in case of component failure, and provides 40%–50% less resilience than state-of-the-art techniques. The analysis of seasons and real data shows that there is stability of the behavior with the increase of efficiency (18%–22% during winter, and 23%–25% during summer), and the ability of the suggested approach to be robust when changing plant configuration/operation. Integration of optimization of dynamic load management and adaptive power splitting will spur microgrid control strategies and offer a viable strategy to stabilize the grid, reduce operation costs, and enable sustainable changes in energy transformations. The results demonstrate the essential role of bio-inspired optimization and reactivity in the next generation of smart grids.

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

Jesús Sánchez Pinedo — Wed, 28 Jan 2026 10:49:43 +0100

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

Biological Solitons in Biomembranes: Analytical Solutions of a Boussinesq-Type Equation with Amplitude-Dependent Nonlinearity

Jesús Sánchez Pinedo — Wed, 28 Jan 2026 10:47:33 +0100

Boussinesq-type equations (BTE) emerge in various fields of fluid and solid mechanics, particularly where nonlinearities and dispersion are considered. Boussinesq-type equations are used to model wave effects in biomembranes, particularly longitudinal waves. They can account for nonlinear and dispersive effects that are important for characteristic wave behavior in biomembranes, composed of lipids, with distinct nonlinear effects. This provides a realistic description of longitudinal mechanical waves in nerve membranes. In this research, we investigate the Boussinesq-type equation that describes the waves in biomembranes with amplitude-dependent nonlinearities, using the Khater method (KM) and the Jacobi elliptic function method (JEFM). In addition to producing generic biological answers, the proposed methods allow the analysis of single wave solutions. These methods make it easier to derive solutions for solitary waves, which occur in a variety of forms, including bell, antibell, periodic, anti-kink and kink solitons. Each of these waves has a wide range of possible applications in biomathematics. Some of the findings are displayed as contour, 2D, and 3D graphics with particular parameter values applied under the specified conditions in order to highlight the important propagation properties. To the best of our knowledge, the biological solitons of the considered model have not been reported by using the proposed techniques in the literature. These results provide new theoretical insights into wave phenomena in biomembranes and may contribute to biological physics and nonlinear science.OPEN ACCESS Received: 04/11/2025 Accepted: 15/12/2025 Published: 23/01/2026

Fractional-Order Resilient Control for UAV–USV Cooperation under Actuator Constraints, Signal Attacks, and Wind Gusts

Jesús Sánchez Pinedo — Wed, 28 Jan 2026 10:42:58 +0100

The paper presents a resilient dynamic adaptive event-triggered sliding mode control (DAET–SMC) framework for fractional-order delayed multi-agent systems under actuator saturation, stochastic disturbances, and cyber-attacks. Existing methods often fail to ensure containment and formation stability when multiple practical constraints coexist. The proposed approach leverages Riemann–Liouville fractional dynamics to capture system memory effects and integrates adaptive compensation to mitigate actuator faults, measurement attacks, and communication delays. Numerical simulations on a 16-agent network with one leader and fifteen followers show that all followers achieve containment within 20 s, with formation errors below 10−2m, while maintaining bounded control effort. Compared with conventional non-adaptive controllers, the proposed method demonstrates faster convergence, superior robustness, and resilience under combined disturbances, achieving up to 35% faster error convergence and maintaining control input within saturation limits. These results confirm the effectiveness of the DAET–SMC strategy for practical multi-agent coordination in uncertain and constrained environments.OPEN ACCESS Received: 30/10/2025 Accepted: 26/11/2025 Published: 23/01/2026

Robust Neutrosophic Ratio-Type Estimators Using REWLSE: A Simulation-Based Approach for Efficient Mean Estimation under Outlier-Contaminated Data

Jesús Sánchez Pinedo — Wed, 28 Jan 2026 10:42:23 +0100

The exact estimations of population mean under the influence of indeterminacy and data contamination are a long-standing issue in survey sampling. Traditional ratio-type estimators are highly sensitive to influential observations, and the neutrosophic methods that are currently used do not effectively describe robustness in the face of uncertainty. The current research constructs a generalized family of neutrosophic robust ratio-type estimators that are developed in the context of Robust and Efficient Weighted Least Square Estimation (REWLSE) framework. Bias and mean square error (MSE) expressions are analytically derived for Ordinary Least Squares (OLS) and REWLSE frameworks in order to allow extensive comparisons between theory and efficiency. Monte Carlo simulations on neutrosophic data are systematically used to study the finitesample behavior of proposed estimators, and an empirical evaluation of these estimators is done using actual temperature data. The simulation and empirical evidence have repeatedly shown that suggested REWLSEbased neutrosophic estimators have significant efficiencies, they remain highly resistant to outliers, and perform better than OLS-based ones. These results support the effectiveness of the suggested framework and highlight its potential to become a powerful and trustworthy alternative to population mean estimation in uncertain, imprecise, and contaminated data environments.OPEN ACCESS Received: 13/10/2025 Accepted: 12/11/2025 Published: 23/01/2026

Robust Finite Population Mean Estimation under Outlier Contamination Using Adaptive UK’s Redescending M-Estimation

Jesús Sánchez Pinedo — Wed, 28 Jan 2026 10:41:23 +0100

The efficient estimation of population parameters under non-ideal data conditions remains a critical challenge in survey sampling. Traditional estimators based on ordinary least squares (OLS) often yield unreliable results when datasets contain outliers or deviate from normality. This study introduces a new class of ratio-type estimators that incorporate population parameters such as the median and decile mean and are developed under both OLS and UK’s redescending M-estimation frameworks. To further enhance robustness, an adaptive variant of the UK’s redescending M-estimator is proposed, which automatically adjusts its tuning constant based on the degree of contamination. Analytical derivations of bias and mean square error (MSE) confirm the superiority of the proposed estimators over their OLS counterparts. Empirical validation using realworld socio-economic survey data and extensive simulation studies across varying sample sizes, outlier rates, and distributional forms demonstrate that the adaptive UK’s redescending estimator achieves substantial efficiency gains and reduced bias, even under high contamination levels. The results establish the adaptive redescending M-estimation approach as a robust and computationally efficient alternative for finite population mean estimation in the presence of outliers.OPEN ACCESS Received: 10/10/2025 Accepted: 03/11/2025 Published: 23/01/2026

Enhancing Wind Turbine Reliability: A Hybrid State-Space and Generative Approach to SCADA-Based Fault Detection

Jesús Sánchez Pinedo — Wed, 28 Jan 2026 10:38:24 +0100

Wind turbine reliability is essential for the renewable energy sector, as failures in key parts such as gearboxes and main bearings lead to more than $10 billion in downtime and maintenance costs each year. Supervisory control and data acquisition (SCADA) systems can monitor turbines using signals such as vibration, power output, and wind speed; however, applying machine learning to this data type is challenging due to the presence of unbalanced fault types and complex time patterns. Previous research has explored physics-informed deep learning, digital twins, and contrastive learning, achieving noteable fault detection accuracy. However, challenges remain in detecting rare faults, dealing with imbalanced data, combining data sources, and model generalization. This study presents StateSpaceNetWithGen (SS-Gen), a hybrid model integrating state-space modeling for temporal dynamics with generative augmentation for class imbalance. Tested on a 35,000-sample SCADA dataset (2018–2019), SS-Gen achieved high accuracy (≈1.00) and F1-score (≈1.00) on this specific dataset, improving by 33% over baselines. To further validate the strengths of the proposed method, the methodology is validated on a second dataset with different distribution. These results support more reliable and interpretable wind turbine health monitoring and move the field toward stronger physics-informed and federated machine learning solutions.OPEN ACCESS Received: 06/10/2025 Accepted: 19/11/2025 Published: 23/01/2026

Adaptive Federated Fault Diagnosis Framework for Wind Turbine Reliability

Jesús Sánchez Pinedo — Wed, 28 Jan 2026 10:34:07 +0100

Wind turbine reliability is critical for sustainable energy production, yet fault diagnosis faces challenges due to data privacy concerns, heterogeneous operational conditions, and resource constraints in distributed wind farms. Traditional centralized Machine Learning (ML) approaches struggle with these issues, necessitating decentralized solutions. This study introduces the Adaptive Federated Fault Diagnosis (AF2D) framework, a novel Federated Learning (FL) approach for wind turbine fault diagnosis that ensures data privacy while addressing non-i.i.d. data distributions. Using a dataset of 35 uniaxial vibration recordings from six turbines at the University of Mustansiriyah, AF2D leverages two key modules: Adaptive Model Aggregation (AMA) and Lightweight Model Optimization (LMO). AMA employs Jensen-Shannon divergence and cosine similarity to adaptively aggregate local model updates, mitigating data heterogeneity, while LMO applies structured pruning (60% filter reduction) and 8bit quantization to enable deployment on resource-constrained SCADA systems. Results show AF2D achieves 91.3% accuracy (±1.2%, 95% confidence interval), a 3.5% improvement over FedAvg (87.8%± 1.4%), with statistical significance (p < 0.05), and outperforms state-of-the-art methods like Clustered FL (88.5%) and Privacy-Preserving FL (87.2%). LMO reduces inference time by 64.44% and memory usage by 53.71%, enhancing edge deployment feasibility. However, the small dataset raises overfitting risks, and scalability tests reveal a threefold communication cost increase (54.5 to 150.6 MB) for 18 clients, mitigated by proposed compression (30%–50% reduction) and asynchronous updates (20%–40% overhead reduction). Privacy is maintained with a differential privacy guarantee of= 1.0, though advanced techniques like secure multiparty computation could achieve <1. Despite limitations in severe fault detection and dataset diversity, AF2D demonstrates robust performance. Future work includes integrating multi-modal data (SCADA, vibration, environmental), testing real-time deployment, and expanding federated datasets to enhance generalizability and scalability.OPEN ACCESS Received: 11/09/2025 Accepted: 16/10/2025 Published: 23/01/2026

A Novel Sin Model SMEx with Application on COVID-19 and Precipitation Data

Jesús Sánchez Pinedo — Wed, 28 Jan 2026 10:33:23 +0100

The present study proposes a new and flexible trigonometric extension of the moment exponential distribution, termed the Sine Moment Exponential (SMEx) distribution, developed using the sine-G family of distributions. This model offers an attractive alternative to well-known lifetime distributions by providing enhanced flexibility for analyzing lifetime datasets that exhibit leptokurtic or platykurtic behavior. Several statistical properties of the SMEx distribution are derived, including its moments, quantile function, mean residual life, and order statistics. To assess its performance, five different estimation approaches are applied, including Anderson-Darling estimation, maximum likelihood estimation, Cramervon Mises estimation, ordinary least squares estimation, and weighted least squares estimation. A detailed Monte Carlo simulation study is utilized to illustrate the estimation behavior of these considered estimation procedures. In the end, two datasets associated with COVID-19 and precipitation are utilized to illustrate the applicability and flexibility of the proposed distribution. It is found that the proposed distribution efficiently analyzed these datasets as compared to competitive distributions.OPEN ACCESS Received: 06/10/2025 Accepted: 28/10/2025 Published: 23/01/2026

Enhancing Wind Power Forecasting Using Hybrid Multi-Head Attention and 1-Dimensional Convolutional Neural Networks

Jesús Sánchez Pinedo — Wed, 28 Jan 2026 10:32:43 +0100

The accurate forecasting of wind power plays a veritable part in integrating renewable energy from wind turbines into power grids. Wind power, being a highly volatile mode of energy generation owing to temporal variations and complex weather patterns, renders reliable predictions essential for energy management and grid stability. In order to tackle this, we propose a hybrid Multi-Head Attention and 1D-Convolutional Neural Network (MHA-CNN) architecture that combines attention mechanisms and convolutional layers to capture both long-term dependencies and localized features in time-series data from a Supervisory Control and Data Acquisition (SCADA) system. The model effectively improves forecasting performance by attaining an R2score of 99.42 for hour-ahead and 96.52 for day-ahead predictions on a 50,540-sample, 10-min SCADA dataset using 5-fold chronological cross-validation, outperforming traditional methods without any manual feature engineering. The proposed method is also evaluated across multiple scenarios to assess the robustness of the proposed approach.OPEN ACCESS Received: 01/10/2025 Accepted: 10/11/2025 Published: 23/01/2026

Simulation of Hybrid and Porous Spur Gears with FEA

Scipedia content — Tue, 27 Jan 2026 10:49:29 +0100

Analytical and Numerical Investigation of Sandwich Beams with Additively Manufactured Lattice Cores and Composite Facesheets

Scipedia content — Tue, 27 Jan 2026 10:47:56 +0100

Analytical Investigation of Global and Local Instability in Additively Manufactured Lattice-Core Sandwich Columns

Scipedia content — Tue, 27 Jan 2026 10:46:24 +0100

A Thermal-History-Informed Inherent Strain Framework for Efficient Distortion Prediction in PBF-LB

Scipedia content — Tue, 27 Jan 2026 10:44:42 +0100

From Computed Tomography to Finite Element Analysis: Mapping Porosity in Metal Additive Manufacturing

Scipedia content — Tue, 27 Jan 2026 10:42:53 +0100

Variations in morphology and microstructure caused by different melt pool dynamics in laser powder bed fusion of ultrathin walls

Scipedia content — Tue, 27 Jan 2026 10:40:53 +0100

Analytical LPBF Melt Pool Simulation and Experimentation for Metamaterial Lattices

Scipedia content — Tue, 27 Jan 2026 10:39:18 +0100

Research on the Mechanical Characteristics and Structural Optimization of HighPressure Diaphragm Compressors in Hydrogen Refueling Stations under Service Conditions

Scipedia content — Tue, 27 Jan 2026 10:38:24 +0100

To enhance the fatigue life and service safety of the diaphragm in high-pressure diaphragm compressors, this study investigated the realworld operating conditions of hydrogen refueling station diaphragm compressors. A refined finite element model of the gas cavity cover plate–diaphragm–oil cavity support plate assembly was established using Abaqus software. Static structural analysis, thermo-structural coupling analysis, and modal analysis were conducted to examine the stress distribution of the diaphragm assembly under extreme working conditions, the influence of bolt preload on the modal characteristics of the compressor, and the effect of diaphragm thickness on stress distribution and fatigue life. The research results indicate that air holes/passages and oil holes/passages significantly affect the stress distribution of the diaphragm. The high-stress areas of the diaphragm are mainly concentrated in the transition zone of the chamber and the overlapping area between the diaphragm and the air/oil passages. The temperature inside the diaphragm compressor’s membrane chamber significantly affects the stress level of the diaphragm. When the chamber temperature reaches 245°C, the maximum equivalent stress of the diaphragm reaches 1079 MPa. As the preload increases, the modal frequencies generally rise, with higher-order modes showing greater sensitivity to preload variations. Considering the stress level, fatigue life, and deflection performance of each diaphragm, the diaphragm thickness should be designed to be 0.4 mm. The finite element simulation model and research results proposed in this paper can provide a reference for the design improvement and selection of cavity types and diaphragms of diaphragm compressors in hydrogen refueling stations, as well as for the online health monitoring of hydrogen refueling stations.OPEN ACCESS Received: 31/07/2025 Accepted: 09/09/2025 Published: 23/01/2026

Numerical Analysis of Gyroid Structure performances in heat sink environment

Scipedia content — Tue, 27 Jan 2026 10:37:36 +0100

Energy-based form-finding of column-like lattice elements

Scipedia content — Tue, 27 Jan 2026 10:35:52 +0100

Automatised Optimisation of Material Joints for Additive Manufactured Multimaterial Components

Scipedia content — Tue, 27 Jan 2026 10:34:21 +0100

Automated workflow with simulation-driven topology optimization for the economic design of hybrid-manufactured tool components

Scipedia content — Tue, 27 Jan 2026 10:32:38 +0100

Topology Optimization with Limits on Joint Loads

Scipedia content — Tue, 27 Jan 2026 10:31:06 +0100

Enhancing Engine Mount Design through Topology Optimization for Additive Manufacturing

Scipedia content — Tue, 27 Jan 2026 10:29:14 +0100