Scipedia: Documents published in 2026

Hybrid Fractional Coupled Systems with Generalized Caputo Derivatives

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

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

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

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

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

Drill Bit Optimization Method Using Grey Clustering and Grey Correlation Analysis

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

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

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

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

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

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

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

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

Dynamic Variational Autoencoder Regressor for Soft Sensor Modeling

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

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

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

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

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

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

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

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

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

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

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

River Water Temperature Prediction Based on Boosting Algorithms and Ensemble Empirical Mode Decomposition: CatBoost, XGBoost, LightGBM versus AdaBoost

Scipedia content — Mon, 04 May 2026 10:46:13 +0200

This study presents a hybrid modelling framework for predicting river water temperature (Tw) by combining advanced boosting algorithms with signal decomposition techniques. Four models were compared: (i) categorical boosting (CatBoost), (ii) extreme gradient boosting (XGBoost), (iii) adaptive boosting (AdaBoost), and (iv) light gradient boosting machine (LightGBM). Each model was developed using air temperature (Ta) as input, both with and without signal decomposition. Three algorithms, empirical mode decomposition (EMD), ensemble EMD (EEMD), and complete EEMD with adaptive noise (CEEMDAN), were applied to decompose Ta into intrinsic mode functions (IMFs), which served as additional predictors. The models were trained and validated using data from four Polish river stations, and their performance was evaluated using root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R), and Nash–Sutcliffe efficiency (NSE). Tw was modelled as an instantaneous function of Ta, without incorporating a forecasting horizon, to assess the ability of decomposition-boosting hybrids to reproduce observed thermal dynamics at the same time step rather than perform multi-step predictions. Unlike previous single-hybrid studies, this work systematically compares multiple boosting-decomposition combinations under identical settings, isolating the decomposition effect and assessing accuracy, efficiency, and robustness. Results show that integrating signal decomposition substantially improves prediction accuracy. The best performance was achieved by XGBoost_EEMD (RMSE = 0.804, MAE = 0.598, R = 0.993, NSE = 0.987), reflecting a ∼64% improvement over the standalone model. The proposed hybrid modelling framework offers methodological innovation and practical potential for real-time, datadriven river temperature prediction in support of climate-resilient water management.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Scipedia content — Mon, 20 Apr 2026 10:56:08 +0200

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

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

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

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

Cost Optimization of Reinforced Concrete Frames Using Metaheuristic Algorithms

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

AKA – eHS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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