Scipedia: Documents published in 2026

Development of a new ring model for flexible protection networks

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

An Optimization-based Approach to Shell Morphing

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

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

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

Testing of welded ETFE-foils for quality assurance

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

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

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

Failure Process of ETFE Foil

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

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

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

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

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

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

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

Picture This: A New Perspective on Tensile Membrane Degradation

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

Stabilizing cable domes

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

Multi Purpose Membrane Structure in Abano Terme

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

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

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

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

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

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

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

The Avicii Arena retractable ceiling - Innovative design and installation

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

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

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

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

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

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

A data-driven model of waste gasi pyrolysis: One tailored approach for an experimental facility from the Czech Republic

Dejan Brkić — Fri, 30 Jan 2026 23:05:13 +0100

The increasing demand for sustainable energy production necessitates the development of innovative technologies for converting municipal waste into valuable energy offering a viable alternative to fossil fuels. This study presents aﬂexible, portable, and expandable waste-to-energy concept that integrates gasiﬁcation and pyrolysis processes production of combustible gases and liquid fuels. Particular emphasis is placed on the use of transparent and interpretable modelling approaches to support system optimization and future scalability. The proposed methodology is demonstrated on two experimental systems currently operated at CEET Explorer, VSB– Technical University of Ostrava, Czech Republic: (i) A primary gasiﬁcation facility equipped with a plasma torch, reactor, hydrogen separator and tank, fuel cells, and renewable grid connections; and (ii) a secondary pyrolysis unit designed to maximize pyrolysis oil production. Both systems are modelled and simulated using in-house software developed in Python, employing

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Adaptive Federated Fault Diagnosis Framework for Wind Turbine Reliability

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

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

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

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

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

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

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

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

Simulation of Hybrid and Porous Spur Gears with FEA

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

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

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

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

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

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

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

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

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

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

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

Analytical LPBF Melt Pool Simulation and Experimentation for Metamaterial Lattices

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

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

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

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

Numerical Analysis of Gyroid Structure performances in heat sink environment

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

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

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

Automatised Optimisation of Material Joints for Additive Manufactured Multimaterial Components

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

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

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

Topology Optimization with Limits on Joint Loads

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

Enhancing Engine Mount Design through Topology Optimization for Additive Manufacturing

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

Analysis on Spatter Behaviour in Powder Bed Fusion Using Solid-gas Multiphase Flow Analysis

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

Shape Optimization of Thin-Wall Geometries for Layer-Based Direct Deposition Additive Manufacturing

Scipedia content — Tue, 27 Jan 2026 10:25:51 +0100

Innovative AM Design Strategies for Integrating Carbon Fibre Composites with Metallic Structures to Support Lightweight Manufacturing

Scipedia content — Tue, 27 Jan 2026 10:23:57 +0100

Multi-scale thermal modelling and variable scan parameter optimization framework for homogeneous and predictable PBF-LB aerospace components

Scipedia content — Tue, 27 Jan 2026 10:22:19 +0100

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

Jesús Sánchez Pinedo — Tue, 27 Jan 2026 10:16:23 +0100

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

Investigation of Compensated Foundation SettlementMechanism Based on FEM-DEM Coupling Method

Jesús Sánchez Pinedo — Tue, 27 Jan 2026 10:15:23 +0100

The application of compensated foundations is common in engineering, but significant settlements often occur during the application process. A foundation pit model supported by a rigid retaining wall was established based on the Finite Element Method-Discrete Element Method (FEMDEM), and a cantilevered layer-by-layer excavation process was simulated. A rolling resistance linear model was used to simulate the sand, while a variable particle size method was adopted to establish the foundation model. It is shown that during the excavation process of the foundation pit, the stress of the sand at the pit bottom gradually decreases, and the displacement of the soil changes gradually fromsettlement to uplift as one moves frombehind the retaining wall to the bottomof the foundation pit. Moreover, theporosityof the sandat thepit bottomgradually increases.As a result, the strength of the uplifted sand at the pit bottomdecreases due to over-excavation during the foundation pit excavation stage. The uplifted sand is finally excavated during the site leveling stage, resulting in a further decrease in the strength of the sand at the pit bottom. Finally, an inverted arch bottom plate structure is proposed to mitigate the over-excavation settlement of the compensated foundation.

A New Scalable Hybrid Model Approach of High-Dimensional Time Series Forecasting Applications

Jesús Sánchez Pinedo — Tue, 27 Jan 2026 10:13:35 +0100

This study introduces a Lasso–Prophet hybrid framework developed to deal with the limitations and gap of Facebook’s Prophet model. The approach begins with Prophet’s decomposition of a time series into its fundamental components trend, seasonality, and holiday effects, and then applies Lasso regression to the residuals to capture additional structural patterns that fail to capture by the base model. This layered methodology boosts predictive accuracy by enabling the model to learn both systematic and irregular variations within temporal data by incorporating Lasso’s feature selection capability, the framework efficiently handles highdimensional datasets, retaining only the most informative predictors. The outcome is a hybrid model which achieves an optimal balance among interpretability, scalability, sparsity, and forecasting precision. Validation on simulated high-feature datasets and real-world electricity consumption data demonstrates that the Lasso–Prophet hybrid consistently outperforms the Prophet and other baseline models.OPEN ACCESS Received: 31/08/2025 Accepted: 17/11/2025 Published: 23/01/2026

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

Jesús Sánchez Pinedo — Tue, 27 Jan 2026 10:12:24 +0100

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

Solving the Sine-Gordon Equation: A Novel Numerical Approach Using Cubic B-Splines and the Method of Lines

Jesús Sánchez Pinedo — Tue, 27 Jan 2026 10:12:11 +0100

This work explores a numerical approach to solving the sine-Gordon equation using the method of lines combined with cubic B-spline interpolation. The sine-Gordon equation, a nonlinear partial differential equation, arises in various fields of physics and engineering, describing phenomena such as solitons in non-linear optics and magnetic flux lines in superconductors. In our approach the method of lines is used to discretize the spatial derivatives, thereby converting the partial differential equation into a system of ordinary differential equations. These ordinary differential equations are then solved numerically using standard techniques, specifically the Runge-Kutta method of order 4. Cubic B-spline interpolation is employed to approximate the spatial derivative, ensuring efficient and precise computation of the solution. A comprehensive stability analysis reveals that our scheme requires the time step conditiont1.53 h for numerical stability. Theoretical convergence analysis demonstrates that the method achieves O(h2)spatial convergence and O( t4)temporal convergence, resulting in an overall error bound of O(h2+ t4 ). These theoretical predictions are strongly supported by numerical experiments, where empirical convergence rates closely match the theoretical values. To validate the numerical scheme, the results are compared with existing solutions. Our findings demonstrate the accuracy and computational efficiency of the proposed method, highlighting its potential as a valuable tool for studying the dynamics and behavior of systems governed by the sine-Gordon equation.OPEN ACCESS Received: 29/08/2025 Accepted: 05/11/2025 Published: 23/01/2026

Combining Copula Theory and Machine Learning for Prediction of Ground Vibrations Induced by Tunnel Blasting

Jesús Sánchez Pinedo — Tue, 27 Jan 2026 10:10:47 +0100

To effectively predict and control the peak particle velocity (PPV) induced by tunnel blasting, this study investigates the Qinhuangdao Jiaoshan Tunnel as a case study. This study first proposes an Improved Particle Swarm Optimization (IPSO) algorithm through theoretical derivation. Building upon IPSO, a further enhanced algorithm, termed CIPSO, is developed by integrating a dependency model derived from Copula theory. The CIPSO algorithm is then employed to optimize a Support Vector Regression (SVR) model, establishing the final CIPSO-SVR prediction framework. Copula theory was employed to quantify the correlation between PPV and surface cumulative settlement (S). A regularization term incorporating Kullback-Leibler (KL) divergence was then embedded into the SVR objective function. The Hyperparameters of the CIPSO-SVR model were optimized using fixed-step rolling cross-validation. The model’s predictive performance was rigorously compared against CIPSO-optimized Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models, as well as against SVR, CNN, and LSTM models optimized by the Grey Wolf Optimizer (GWO) and Moth-Flame Optimization (MFO) algorithms. The results show that the CIPSO-SVR model achieves superior accuracy and robustness on the test set (R2= 0.9569) in predicting PPV compared to the alternative models. Crucially, the model effectively captures the inherent nonlinear relationships of complex engineering problems, even with small-sample data.OPEN ACCESS Received: 18/08/2025 Accepted: 17/10/2025 Published: 23/01/2026

Bearing Fault Diagnosis Based on AVMD and HPO-DBN

Jesús Sánchez Pinedo — Tue, 27 Jan 2026 10:09:22 +0100

To overcome difficulties such as non-stationary vibrations, highdimensional feature redundancy, and mode selection issues that may arise during signal decomposition in bearing fault diagnosis. We propose an adaptive method called Adaptive Variational Mode Decomposition (AVMD) for extracting time-frequency domain characteristics from the bearing vibration displacement signals to the maximum extent possible. Next, the ReliefF algorithm is employed to select desired features, and an autoencoder is used to reduce the selected features dimensionally. Furthermore, because the Hunter-Prey Optimisation (HPO) algorithm can balance multiple objectives during the search process by utilising the concepts of hunter and prey to generate a better solution set, incorporating this algorithm into the Deep Belief Network (DBN) establishes an HPO-DBN fault diagnosis model. Subsequently, we validate the proposed method using both public datasets and field compressor data. Moreover, we compare the results with those obtained from the Support Vector Machine (SVM). The findings indicate that this approach enhances the bearing fault identification rate, thus supporting predictive maintenance of bearings.OPEN ACCESS Received: 13/08/2025 Accepted: 16/10/2025 Published: 23/01/2026

Comparative Study of Fuzzy Logic, P&O, Incremental Conductance, and Artificial Neural Network MPPT Methods in Fluctuating Irradiance

Jesús Sánchez Pinedo — Tue, 27 Jan 2026 10:07:57 +0100

Photovoltaic (PV) energy is among the renewable and clean energies which are been widely used in recent years worldwide. To ensure optimal energy extraction under dynamic irradiance and temperature conditions, improving the efficiency of PV systems requires advanced Maximum Power Point Tracking (MPPT) techniques. To identify the most suitable technique that can be implemented practically, we conduct a comparative study in this paper between MPPT algorithms, namely Incremental Conductance (INC), Perturb and Observe (P&O), Fuzzy Logic (FL), and Artificial Neural Network (ANN). Using MATLAB/Simulink, our study was conducted under the same operating conditions, with a focus on efficiency, statistical analysis of robustness, and computational complexity. Our results show that the FL controller delivered the best overall performance, whose effectiveness depends on the accuracy of the rule base and scaling factors. It is characterized by a mean efficiency of 97.17%, a rapid response of 0.0585 s, minimal steady-state oscillations, and strong adaptability to environmental variations. The ANN-based approach achieves a mean efficiency of 94.91% and exhibits high performance at medium to high irradiance levels. However, its efficiency decreases significantly at low irradiance, resulting in reduced stability and increased deviation. INC and P&O achieve mean efficiencies of 95.20% and 95.15%, respectively. Moreover, due to their low computational cost, both techniques can be easily implemented. However, under rapidly changing conditions, they exhibit slower dynamics and more pronounced oscillations around the maximum power point, resulting in less stability.OPEN ACCESS Received: 01/08/2025 Accepted: 14/10/2025 Published: 23/01/2026

Reliability Modeling Using Weibull Distribution in Partially Accelerated Life Tests with Unified Hybrid Censoring: White Organic LEDs and Micro-Droplet Applications

Jesús Sánchez Pinedo — Tue, 27 Jan 2026 10:06:23 +0100

Accelerated life testing has become essential in modern reliability assessment, particularly for high-reliability products where traditional testing is often impractical due to time constraints. This study introduces a comprehensive framework for reliability analysis using Weibull-based partially accelerated life tests under a unified hybrid censoring scheme. Assuming lifetimes follow a Weibull distribution, we provide both classical and Bayesian estimation procedures to estimate core quantities, shape, scale, and the acceleration factor, alongside reliability metrics at normal operating conditions. The inferential framework encompasses point estimation along with uncertainty quantification, using both approximate confidence intervals and Bayesian credible intervals derived from Markov Chain Monte Carlo methods. A comprehensive Monte Carlo simulation study evaluates the performance of various methods in terms of mean squared error, interval coverage, and average interval width across a range of censoring patterns. The results provide actionable insights and practical recommendations for selecting appropriate methods and designing future studies under different censoring scenarios. The proposed methodology is further illustrated through two real-world case studies: the reliability of white organic light-emitting diodes and the lifetime of micro-droplets in ambient environments. These examples highlight the method’s flexibility and practical relevance in both engineering and biomedical reliability applications.OPEN ACCESS Received: 31/07/2025 Accepted: 10/09/2025 Published: 23/01/2026

Study on the Annular Fluid Level IdentificationAlgorithmfor Ultra-Deep Wells Based on AcousticVelocityCorrection

Jesús Sánchez Pinedo — Tue, 27 Jan 2026 10:05:34 +0100

The geological conditions and pressure system of ultra-deep oil and gas wells are complex, and formation leakage conditions are prone to occur. Accurately obtaining the annular liquid level depth during leakage is crucial for treatment decisions. In the high-temperature and high-pressure wellbore environment of ultra-deep wells, the sound velocity shows a nonuniform variation trend, which seriously affects the accuracy of annular liquid level identification. Therefore, it is of significance to carry out a study on the annular fluid level identification algorithm based on acoustic velocity correction. Based on establishing a calculation model for the annular temperature and pressure field, a sound velocity calculation model in the annular air section was constructed. By combining the actual sound velocity of the clear signal segment identified through the echo signal, the basic parameters in the sound velocity calculation model can be calibrated to obtain the calibrated wellbore sound velocity distribution. Finally, tests were conducted on simulated well sites and noisy production wells to verify the accuracy of the annular liquid level identification algorithm constructed in this study. The results showed that the identification algorithm had an error of less than 2%. All in all, this study can effectively meet the demand for dynamic annular liquid level data during the leakage conditions, which is of great significance for the treatment and decisionmaking in deep and ultra-deep wells.OPEN ACCESS Received: 30/07/2025 Accepted: 25/09/2025 Published: 23/01/2026

AI-Driven Modeling of Window Opening Behavior in Kindergarten Classrooms: A Case Study during the Transitional Season in the Cold Region of China

Scipedia content — Mon, 26 Jan 2026 12:19:35 +0100

With increasing awareness of energy conservation and environmental protection, optimizing indoor air quality and energy consumption through rational control of window opening behavior (WOB) has become a crucial issue in building design and environmental management. However, existing research primarily focuses on buildings for adults, with relatively few studies on buildings for children, particularly those used by children aged 3–6. Moreover, previous studies often overlook the impact of functional differences between buildings on occupant behavior patterns. This study focuses on a kindergarten and proposes an event-based method for analyzing and modeling WOB. The results show that events such as arrival, class, and departure are associated with higher frequencies of window opening (exceeding 50%), whereas events such as dietary activity, indoor/outdoor activity, sleep, and tidying exhibit lower probabilities. WOB is more sensitive to indoor air quality during events with higher student activity (e.g., class, dietary activity, and indoor activity), resulting in more frequent ventilation. In terms of modeling, the Random Forest (RF) algorithm achieved higher prediction accuracy than Logistic Regression (LR) and Support Vector Machine (SVM). To reduce the complexity associated with multi-model integration, a stacking model was introduced, further enhancing predictive performance. Finally, the generalizability of the proposed method was validated using office building data from the ASHRAE occupant behavior database, achieving a maximum accuracy improvement of 3.87%. This study presents a novel approach for modeling WOB in functional buildings such as kindergartens and provides theoretical support for energy efficiency optimization and indoor air quality management.

Functional H∞ Filtering for Descriptor Systems with Monotone Nonlinearities

Jesús Sánchez Pinedo — Mon, 26 Jan 2026 10:33:19 +0100

This paper proposes a new framework for the design of functional H∞ filters tailored for nonlinear descriptor systems affected by disturbances. Earliermethods have some significant drawbacks: they rely on the restrictive assumption of system regularity, employ implicit descriptor-form filters that complicate implementation, and emphasize full-order filtering, which is often unnecessary and computationally expensive. To overcome these drawbacks, the proposed filter is developed in an explicit state-space form that allows simple implementation with arbitrary initial conditions. Moreover, its order is minimized by matching it to the dimension of the functional vector, which reduces computational complexity compared to conventional filters. A new set of sufficient conditions is presented for the existence of a functionalH∞ filter, expressed through a rank condition and a linear matrix inequality (LMI) formulation. These conditions guarantee the stability of the estimation error dynamics while ensuring that the L2 gain from disturbances to errors remains below a specified bound. A numerical example based on a simple constrained mechanical system is presented to illustrate the effectiveness of the proposed method.

Bayesian and Non-Bayesian Inference for Discrete Model Based on Censored Samples with Optimal Test Plan

Jesús Sánchez Pinedo — Mon, 26 Jan 2026 10:32:33 +0100

In reliability and lifetime studies, it is often impractical to observe the failure times of all units in a sample, particularly when the process is timeconsuming or expensive. Type II censoring addresses this by terminating the experiment after a predetermined number of failures from a total sample of size. The Discrete Alpha Power Extended Inverted Weibull distribution is particularly suitable for modeling such censored discrete lifetime data. Its flexible shape parameters allow it to capture a wide range of failure behaviors, including over-dispersion, which is common in censored datasets. In this context, the likelihood function and estimation procedures (maximum likelihood and Bayesian) explicitly account for the censoring, ensuring unbiased parameter estimates and reliable predictive inferences. Consequently, the Discrete Alpha Power Extended Inverted Weibull distribution provides a practical and statistically robust framework for analyzing discrete lifetimes under type II censoring.OPEN ACCESS Received: 28/07/2025 Accepted: 19/09/2025 Published: 23/01/2026

Drill Bit Optimization Method Using Grey Clustering and Grey Correlation Analysis

Jesús Sánchez Pinedo — Mon, 26 Jan 2026 10:31:54 +0100

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

Design and Implementation of a Vulkan-Based Rasterization System in the PC Environment

Jesús Sánchez Pinedo — Mon, 26 Jan 2026 10:28:40 +0100

Graphics application programming interfaces (APIs) for threedimensional (3D) rendering have been employed in computer graphics. Traditional 3D graphics APIs, such as Open Graphics Library (OpenGL), have structured the entire pipeline for the programmer’s convenience and are relatively easy to use. With the advancement of graphics hardware technology, new graphics APIs, including Vulkan, have been introduced to control specific function units of the graphics card and reveal the graphics processing power. Vulkan has many advantages, such as graphics processing power and parallel processing support. However, it also has the disadvantage of increasing the implementation costs because it requires detailed controls. This paper aims to implement a rasterization pipeline that applies a local illumination model with Vulkan. The implemented system can be used for standardized 3D graphics tasks as is and can also be used as a starting point for varying the pipeline configurations. This work designs and implements a Vulkan-based local rasterization pipeline. With this implemented platform, practical 3D rendering scenes are executed to be compared and analyzed with the rendering results from traditional OpenGL. The experimental results demonstrate the correctness and efficiency of the implementation. The implementation will be used as a testbed for various rendering experiments in the future.OPEN ACCESS Received: 27/07/2025 Accepted: 11/10/2025 Published: 23/01/2026

Advanced Reliability Analysis with Applications of Hjorth Constant-Stress Normal-Operating Setting Using Newly Progressive Censored Data

Jesús Sánchez Pinedo — Mon, 26 Jan 2026 10:28:28 +0100

Modern high-reliability products fail rarely, so researchers rely on accelerated life testing to obtain failure information within practical time limits. This study presents a useful framework integrating constant-stress accelerated life tests with an improved adaptive progressive Type-II censoring plan to estimate the reliability function under normal operating conditions of the Hjorth model. The Hjorth model is chosen because its hazard rate can be constant, increasing, decreasing, or bathtub-shaped, which reduces errors due to incorrect hazard assumptions. Stress affects lifetime through a log-linear relationship applied to the scale and the shape parameter. We derive the full likelihood for the proposed censoring plan across several stress levels, obtain maximum likelihood estimates with confidence intervals based on the observed information matrix, and develop a Bayesian analysis with informative prior distributions and sampling by Markov chain Monte Carlo technique. We then estimate reliability at normal operating conditions together with its interval estimates by both approaches. Extensive Monte Carlo simulations demonstrate the superior accuracy of the Bayesian estimators, especially when the number of observed failures is small or censoring is heavy, while maintaining interval coverage close to the nominal level. The practical utility of the proposed methodology is demonstrated through its application to real-world accelerated lifetime data sets. Applications to real-world data sets show that the proposed model fits the data well and yields reliable estimates of reliability at normal operating conditions.OPEN ACCESS Received: 26/07/2025 Accepted: 28/10/2025 Published: 23/01/2026

Research on Correction Method for Pipe-Soil p-y Curves in Submarine Silty Clay-Sand Gas Hydrate Formations

Jesús Sánchez Pinedo — Mon, 26 Jan 2026 10:27:23 +0100

The development of marine natural gas hydrates faces complex geomechanical challenges. Argillaceous silty hydrate reservoirs, due to their weak cementation and low permeability, have significantly different mechanical properties from those of general reservoirs. Based on the self-developed triaxial seepage experimental platform for hydrates, this paper systematically carried out triaxial compression experiments of argillaceous silt hydrate sediments, focusing on simulating the insitu temperature and pressure conditions of the formation, analyzing the influences of saturation, temperature and confining pressure on mechanical properties, and comparing them with the experimental results of sandy hydrate sediments. The experimental results show that due to the weak cementation effect of kaolin and methane hydrate, the failure mode of argillaceous silt hydrate is manifested as compression and dispersion, while sandy hydrate presents the traditional core compression failure characteristics. The peak strength of the stress-strain curve of argillaceous silt hydrate is lower than that of sandy hydrate, and the strain softening characteristic is more significant. The experimental results were calculated through MATLAB programming, and it was obtained that the cohesion and internal friction Angle of the argillaceous silt hydrate increased with saturation higher than those of the sandy hydrate. The pipe-soil coupling numerical simulation based on ABAQUS reveals that the initial stiffness and plastic deformation response of the p-y curve in the argillaceous silty hydrate formation are essentially different from those in the traditional API sandy soil model. By comparing the numerical simulation results of sandy properties and argillaceous silty hydrate, a two-parameter correction model for argillaceous silty strata was proposed. The cementation factor related to mass abundance and the displacement correction term were introduced. The error analysis indicated that the correction method was significantly superior to the API specification. Studies show that the mechanical properties of hydrates need to be evaluated independently, and the correction method provides a theoretical basis for the safety design of deep water well engineering.

Bifurcation Analysis and Dynamical Investigation of Nonlinear DiffusionReaction Equations with Nonlinear Convective Flux Term: Stability and Applications

Jesús Sánchez Pinedo — Mon, 26 Jan 2026 10:24:40 +0100

The aim of this work is to examine the rich dynamics of quadratic and quartic nonlinear diffusion-reaction (DR) equations with a nonlinear convective flux term. These equations are crucial for simulating a variety of biological and physical processes, such as the dynamics of species populations. The main goal is to use the modified extended simple equation method (mESEM), a generalization of the standard simple equation method that hasn’t been used in this situation before, to extend the analytical treatment of such equations. We obtain a variety of new exact solutions using this method, such as breathers, kink and anti-kink waves, multi-peak solitons, bright-dark solitons, periodic waves, and waveforms represented by hyperbolic, trigonometric, and rational functions. Analyzing the stability and physical relevance of these solutions is another major goal of this work. Modulational instability analysis verifies the robustness of the obtained waveforms, while bifurcation analysis reveals qualitative changes in system behavior under parameter variations. The various wave structures and their dynamical characteristics are further highlighted with graphic illustrations. In general, the study highlights the potential of mESEM to reveal rich wave phenomena with applications spanning fluid dynamics, plasma physics, chemical reaction processes, population biology, neuroscience, and optical fiber communication, in addition to showcasing its effectiveness and versatility in solving nonlinear DR equations.OPEN ACCESS Received: 15/07/2025 Accepted: 18/09/2025 Published: 23/01/2026

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

Jesús Sánchez Pinedo — Mon, 26 Jan 2026 10:23:25 +0100

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

Modeling Engineering Reliability Data Using the Modified Generalized Exponential Distribution

Jesús Sánchez Pinedo — Mon, 26 Jan 2026 10:22:34 +0100

This study introduces the modified generalized exponential (MGEx) distribution, a flexible probabilistic model designed to capture complex failure behaviors commonly encountered in engineering systems. The MGEx distribution accommodates various hazard rate shapes, including unimodal, increasing, and decreasing forms, making it suitable for modeling reliability and lifetime data. We derive the mathematical properties of the model and focus on computational techniques for parameter estimation using multiple frequentist approaches. To evaluate the numerical performance of these methods, we conduct extensive Monte Carlo simulations, comparing their accuracy and robustness through partial and overall ranking metrics. The practical utility of the MGEx model is demonstrated by applying it to two real-world engineering datasets. The results highlight the model’s potential in enhancing simulation accuracy and supporting data-driven decision-making in reliability analysis, system design, and failure prediction tasks.OPEN ACCESS Received: 05/07/2025 Accepted: 27/10/2025 Published: 23/01/2026

Evaluation and Optimization of High Collapse Resistance Casing Strings for Salt Cavern Gas Storage in Salt Formations

Jesús Sánchez Pinedo — Fri, 23 Jan 2026 11:26:24 +0100

Salt cavern gas storage imposes stringent requirements on casing performance due to salt rock creep and high in-situ stress, necessitating a balance between mechanical strength and economic viability. This study evaluates the collapse resistance of BG110V and BG140V casings under 50°C–85°C through full-scale experiments (ASTM E2948) and elastoplastic finite element simulations. Results show that BG140V achieves a collapse strength of 75.94 MPa at 85°C, surpassing BG110V (52.96 MPa) at the same temperature by 43.4%, attributed to its thick-walled design (17.50 mm vs. BG110V’s 15.88 mm) and material enhancements. Simulations reveal lower prediction errors for BG140V (5.9% in full collapse) compared to BG110V (20.6%). A multi-criteria model integrating collapse strength (0.5), temperature sensitivity (0.3), and life-cycle cost (LCC, 0.2) with Monte Carlo analysis demonstrates BG140V’s LCC advantage in deep reservoirs (creep rate > 1.2× 10−7s−1, 12% maintenance cost reduction), while BG110V suits shallow scenarios (18% lower procurement cost). Dynamic selection strategies with real-time monitoring, low-friction thread optimization, crystal plasticity simulations, and smart maintenance systems are proposed. This study provides a quantitative framework for balancing safety and economy, advancing the standardization of non-API casings.OPEN ACCESS Received: 30/06/2025 Accepted: 19/08/2025 Published: 23/01/2026

Analysis of Airflow Velocity on Microdroplets Using Weibull StressStress Reliability Index under Unified Type-I Progressive Hybrid Data

Jesús Sánchez Pinedo — Fri, 23 Jan 2026 11:21:33 +0100

This work presents a novel and comprehensive inferential framework for analyzing the stress-strength reliability parameter,R= P(Y < X), where X and Y denote independent stress and strength variables, respectively, both modeled as Weibull-distributed with a shared shape parameter but distinct scale parameters. A key innovation of this study lies in its integration of the unified Type-I progressively hybrid censoring scheme, which simultaneously accommodates time constraints and partial failure information, conditions often encountered in real-world reliability testing. To estimate R, we propose and evaluate four distinct inferential strategies: two frequentist (maximum likelihood estimation and maximum spacings estimation) and two Bayesian, each tailored to either the likelihood or spacings-based posterior formulation. The Bayesian methods employ Monte Carlo sampling to compute both Bayes point estimates and credible intervals under informative priors, offering robustness in small-sample or heavily censored contexts. An extensive simulation study is conducted to systematically compare the estimators in terms of bias, efficiency, and interval coverage. To validate the practical applicability of our framework, we further analyze two real-world microdroplet datasets, revealing critical insights into stress-tolerance behavior under experimental constraints. This study not only advances methodological tools for reliability inference under hybrid censoring but also establishes a blueprint for combining classical and Bayesian paradigms in stress-strength modeling.OPEN ACCESS Received: 01/07/2025 Accepted: 02/09/2025 Published: 23/01/2026

Classical and Bayesian Stress-Strength Reliability Estimation for Weibull Data under Unified Hybrid Censoring Scheme with LED Application

Jesús Sánchez Pinedo — Fri, 23 Jan 2026 11:18:41 +0100

The stress-strength reliability parameter is a key metric used in various fields, including engineering, medicine, and business. In engineering, it quantifies the probability that a system’s strength X exceeds the applied stress Y . In this study, we examine for the first time four estimation approaches for evaluating the stress-strength reliability parameter R= P(Y < X ), where X and Y are independent Weibull random variables with different scale parameters but a common shape parameter. The analysis is conducted under a unified hybrid censoring scheme. From the classical perspective, we employ the maximum likelihood and maximum product of spacings methods to obtain both point and interval estimates. From the Bayesian perspective, two forms of the posterior distribution, based on the likelihood and spacings functions, are derived and analyzed using Markov Chain Monte Carlo sampling techniques. The Bayes estimates of R are obtained under the symmetric squared error loss, and the corresponding Bayesian credible intervals are also computed. To compare the four point estimators and the four interval estimators, an extensive simulation study is performed using various experimental scenarios. Finally, comprehensive analyses for organic white light-emitting diode datasets mixed with three colors, namely red, green, and blue, are provided.OPEN ACCESS Received: 29/06/2025 Accepted: 16/09/2025 Published: 23/01/2026

Numerical Investigation on Condensation of Non-Condensable Gas in Large Deformation Enhanced Tube

Jesús Sánchez Pinedo — Fri, 23 Jan 2026 11:17:28 +0100

In order to improve the condensation heat transfer performance of tubelines in cooling systems, a new type of large deformation heat transfer tube was proposed. Based on the theory of diffusion, the distributions of temperature, velocity, heat transfer coefficient, and species are presented to compare with smooth tubes. In addition, the effects of relevant parameters on thermo-hydraulic performance are discussed. The numerical results show that the distributions of temperature and velocity show a periodic distribution. The unique structure of the large deformation heat transfer tube disturbs the fluid flow near the wall. The boundary layer of air concentration is destroyed, resulting in heat transfer enhancement. Within the scope of this paper, the heat transfer coefficient and friction factor of the large deformation heat transfer tube are increased by 1.01–3.47 times and 1.05–7.13 times compared with the smooth tube. The heat transfer performance increases with the rising dimple depth and declining noncondensable gas content and dimple pitch.OPEN ACCESS Received: 25/06/2025 Accepted: 29/07/2025 Published: 23/01/2026

Reconfigurable Solar-Powered Multiple Input Multiple Output Terahertz Antenna System

Jesús Sánchez Pinedo — Fri, 23 Jan 2026 10:41:33 +0100

The rapid evolution of terahertz (THz) technology has opened new frontiers in high-speed wireless communication, imaging, and sensing. However, the practical deployment of THz systems, particularly in remote or space-constrained environments, is often limited by power constraints and rigid antenna structures. A reconfigurable THz antenna system that is powered by solar energy is suggested. By leveraging real-time energy harvesting, the antenna system maintains operation without external power sources, making it ideal for deployment in remote IoT nodes, CubeSats, or energy-constrained sensor networks. Material used in antenna design is lead glass while gold is used as a radiator, the MIMO antenna loaded with graphene with chemical potential of graphene= 0 works from 2.4–2.5 THz, 3.87–3.98 THz, and 5.17–5.3 THz. The MIMO antenna with graphene chemical potential= 1 eV works from 2.54–2.63 THz, 3.96–4.1 THz, and 5.27–5.45 THz. The MIMO antenna with graphene chemical potential= 2 eV works from 2.6–2.68 THz, 4–4.2 THz, and 5.3–5.55 THz. Gain of antenna at 2.45, 3.95, and 5.3 THz maximum gain obtained at these frequencies is 6, 5 and 7 dBi, respectively. By integrating real-time solar energy harvesting with a tunable THz antenna structure, the design enables self-sustained, high-efficiency operation for remote IoT nodes, CubeSats, and energy-constrained sensor networks, merging green energy and next-generation THz communication in a compact platform. Advantages of the proposed design are self-powered operation, reconfigurable multi-band tuning, high gain, and suitability for diverse THz applications.OPEN ACCESS Received: 23/06/2025 Accepted: 17/09/2025 Published: 23/01/2026

Multi-Objective Optimization and Performance Evaluation of Manifold-Based Cooling Systems for Battery Thermal Management Using RSM and NSGA-II

Jesús Sánchez Pinedo — Fri, 23 Jan 2026 10:37:24 +0100

Efficient thermal management is critical to the safety, performance, and longevity of lithium-ion battery (LIB) energy storage systems. In this study, a novel manifold cold plate featuring an overflow channel with a triangular ridge at the bottom is proposed for a liquid-cooled Battery Thermal Management System (BTMS). A comprehensive multi-objective optimization framework is developed by integrating Response Surface Methodology (RSM), the Non-dominated Sorting Genetic Algorithm II (NSGA-II), and the Linear Programming Technique forMultidimensional Analysis of Preference (LINMAP) decision-making method to minimize the maximum temperature difference (Tcell) and pressure drop (P) across the cooling plate.Thedesign variables include the manifold channel width ratio (λ), the height ratio (φ), the inlet velocity (u), and the triangular ridge angle (θ). Second-order polynomial regression models are constructed and validated using Analysis ofVariance (ANOVA), yielding high coefficients of determination (R2 = 0.9926 forTcell and 0.9600 forP), confirming strong predictive accuracy. Sensitivity analysis reveals that the inlet velocity and channel angle are the primary factors influencing system performance. The LINMAP-based decision-making approach identifies an optimal configuration with λ = 1.031, φ = 1.47, u = 1.671 m/s, and θ = 29.8°, achieving a Tcell of 12.61°C and a P of 6742.99 Pa, with validation errors below 3%. Transient simulations at 0.5 and 1C discharge rates show that the LINMAP-optimized design reduces the maximumcell temperature by 13.12°C and 11.77°C, respectively, compared to the natural convection baseline, and by 1.42°C and 0.76°C compared to the prototype design, while maintaining comparable hydraulic resistance. This work offers valuable guidance for designing and optimizing liquid-cooled battery

Analysis of Burr-XII Lifespan Using Adaptive Progressive Type-II Hybrid Binomial Censoring with Physical Modeling of Polyester and Carbon Fibers

Jesús Sánchez Pinedo — Fri, 23 Jan 2026 10:35:28 +0100

This study introduces advanced statistical methods, allowing for more efficient and accurate reliability testing of fibers such as polyester and carbon. Polyester ficbers are suitable for textiles and industrial use due to their wrinkle resistance and affordability, while carbon fibers offer superior strength, thermal stability, and corrosion resistance. To guarantee greater efficiency of inference methodologies and reduce overall testing time, the adaptive Type-II progressive hybrid censoring via binomial removals has gained popularity in reliability analysis and life-testing problems. The proposed scheme allows survival units to be removed at random stages according to a binomial law, thereby reducing experimental time while preserving statistical efficiency. When lifetimes are gathered using the suggested censoring technique, point and interval estimates of the unknown parameters of the Burr-XII model are obtained using both classical and Bayesian approaches. We obtain various Bayesian estimates using the squared loss function. Some numerical methods are employed to obtain the suggested estimators due to their complexity. The various Bayes estimates and related credible intervals are created using Markov chain Monte Carlo techniques. To assess estimator performance, extensive simulation studies are conducted, comparing bias, mean squared error, coverage probabilities, and interval lengths under varying censoring and removal settings. The simulation results confirm that the Bayesian framework, particularly with informative priors, provides more accurate and stable estimates than asymptotic likelihood-based methods. We examine two physics data sets representing polyester and carbon fibers to demonstrate the relevance of the suggested approaches in a real-world setting. These applications highlight the practical value of the proposed approach for material design, maintenance planning, and broader reliability engineering problems.OPEN ACCESS Received: 13/06/2025 Accepted: 12/09/2025 Published: 23/01/2026

AI-Driven Multi-Dimensional Computing Power Scheduling: Adaptive Resource Coordination via Task Demand Prediction in Heterogeneous Clusters

Jesús Sánchez Pinedo — Fri, 23 Jan 2026 10:34:03 +0100

This paper introduces an AI-driven computing power scheduling framework that innovatively integrates multidimensional resource optimization with machine-learning-based task-demand prediction to significantly enhance computational efficiency and resource utilization. Unlike prior works that primarily focus on Graphics Processing Unit (GPU) allocation, our method pioneers a holistic resource-coordination mechanism that dynamically balances GPU, memory, Central Processing Unit (CPU), and other critical resources according to their joint impact on task performance and cluster efficiency. A core innovation is our data-driven resource predictor, which autonomously analyzes historical task patterns and forecasts future demand, enabling the scheduler to adaptively scale resources so that user over-provisioning is reduced and under-allocation bottlenecks are avoided. Experimental validation demonstrates that this closed-loop prediction–scheduling paradigm achieves breakthroughs in both scale and efficiency: a 25.7% increase in concurrent task deployment, a 15.6% increase in task completion rate, and substantial relative utilization improvements of 7.5% for GPU and 8.0% for memory, outperforming conventional single-resource optimization approaches. These advancements establish a new direction for intelligent resource management in large-scale heterogeneous computing environments.OPEN ACCESS Received: 15/05/2025 Accepted: 11/10/2025 Published: 23/01/2026

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

Jesús Sánchez Pinedo — Fri, 23 Jan 2026 10:33:24 +0100

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

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

Jesús Sánchez Pinedo — Fri, 23 Jan 2026 10:32:35 +0100

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

Evolution Families and Time-Varying Processes in Modular Function Spaces

Nisar Ahmad — Fri, 23 Jan 2026 09:06:44 +0100

This paper develops a modular framework for the study of time--dependent linear evolution processes via evolution families. We consider non--autonomous abstract Cauchy problems generated by families of operators depending on time and introduce a notion of $\varrho$--strong continuity compatible with the modular topology. Under suitable uniform $\varrho$--boundedness assumptions, we establish the existence of evolution families and derive modular growth estimates formulated in terms of the associated modular growth function. To address regularity issues, a Steklov--type averaging technique is employed, allowing differentiability and domain inclusion to be treated in the modular sense. Several examples, including time--varying multiplication processes in integral and Orlicz--type modular spaces, are presented to illustrate the scope and effectiveness of the proposed approach.

A Quantitative Assessment of Supplier Service Quality Using AHP and SERVQUAL in

Mohammed Al Awadh — Mon, 19 Jan 2026 20:40:23 +0100

The selection and assessment of suppliers are essential for improving supply chain efficiency and fostering competitiveness. This study investigates the application of the Analytical Hierarchy Process (AHP) as a proficient method for assessing supplier service quality. AHP, a multi-criteria decision-making tool, provides a systematic and structured framework for evaluating the critical factors influencing supplier performance. The study employed the AHP model to evaluate supplier service quality in small and medium-sized enterprises (SMEs) to achieve this objective. The SERVQUAL framework consists of five dimensions—reliability, responsiveness, assurance, tangibles, and empathy accompanied by 20 sub-criteria, developed using the AHP technique. The analysis focused on evaluating the service quality of three distinct vendors, meticulously assessing their overall performance. The findings suggested that providers should emphasize reliability, responsiveness, certainty, and tangibles, while placing relatively less significance on empathy. Among the sub-criteria, delivering accurate services on the first attempt emerged as a significant issue for vendors. The AHP analysis indicates that Supplier A attained the highest performance ranking, followed by Supplier C and Supplier B. This study's findings offer actionable insights for decision-makers to effectively manage and enhance the aviation sector. By focusing on delivering exceptional services, suppliers can significantly improve customer satisfaction, aligning with the fundamental goal of supplier service excellence.

The Effects of Trichosanthes Root Extract on Regeneration Rate, Behavior, and Movement of Dugesia tigrina

Alexander Lee — Tue, 13 Jan 2026 14:47:14 +0100

This study investigated the effects of Trichosanthes root extract on the regeneration rate of Dugesia tigrina, a freshwater planarian. Planarians were chosen for their regenerative ability and stem cell systems. Traditionally used in Chinese medicine for its anti-inflammatory and antioxidant properties, Trichosanthes root was tested for its unexplored regenerative potential. The goal was to evaluate a cost-effective, biologically based method for enhancing tissue regeneration with potential applications in regenerative medicine and wound healing. Planarians were continuously exposed to varying concentrations of the extract, with locomotor activity recorded before and after treatments. Specimens were then transversely amputated into head and tail fragments, which were observed independently. Regenerative outcomes, including body length, eyespot formation, and indicators of nervous system recovery, were measured at regular intervals. Results demonstrated that in trial 1, group D slightly decreased in head length regeneration compared to our control group A. Although group A had the smallest amount of head regeneration over the course of 11 days, there was no significant difference found between the groups in trial 1. Similarly, group C had the least regenerative ability overall over 11 days, but there was still no statistical difference found between the groups. Ultimately, Trichosanthes root was not significantly effective in the regeneration, locomotor activity, and behavior of Dugesia Tirgrina. These findings suggest that even as planarians remain valuable assets in testing biological compounds, Tricosanthes root may not have benefited tissue regeneration as hypothesized.

The Effects of GlyNAC on the Regeneration Rate and Movement of Dugesia tigrina

Alexander Lee — Fri, 02 Jan 2026 16:52:23 +0100

This study aims to investigate the effects of GlyNAC on planaria tissue regeneration and behavioral changes. There has been a focus on anti-aging in the older population due to the adverse effects that come with it such as muscle fatigue, a decrease in tissue regeneration, etc. As a result, there was an increase in research on finding an effective supplement to combat health issues and aging. GlyNAC, a combination of Glycine and N-acetylcysteine, has been shown to reverse and improve health by increasing glutathione levels. Glutathione is an antioxidant with multiple health benefits such as protecting against oxidative stress, a main factor in causing cell death. This experiment was conducted using planarians because they have a similar central nervous system, tissue regeneration, and glutathione production to humans. There were 1 control group and 3 experimental groups. The control group was fed regular ground beef while the experimental group was fed ground beef with GlyNAC. For tissue regeneration, each group would be measured and observed before and after the ground beef or GlyNAC-mixed ground beef. The planarians’ motility was recorded for 20 minutes with grid paper underneath their petri dishes. The results indicate that GlyNAC treated groups had increased regeneration and movement. With this support, our hypothesis of GlyNAC increases the regeneration and movement in planarians. Future Research can be done by having bigger experimental groups for more accurate data. Introduction GlyNAC There has been an interest in anti-aging in the modern day due to one’s fear of aging and the many symptoms that come with it, such as muscle fatigue, inflammation, a decrease in tissue

Drift Boss: Drive really far

Drift boss — Thu, 01 Jan 2026 08:53:22 +0100

Get ready to slide into the driver's seat of Drift Boss, a deceptively simple yet addictive driving game where timing is everything. Forget complex controls; this game is all about the one-button mechanic. Your mission is to navigate endless, winding tracks floating high in the sky, drifting around sharp corners with perfect precision. It sounds easy, but the procedurally generated paths demand intense focus and quick reflexes. As you collect coins and unlock unique vehicles—from taxis to fire trucks—you’ll find yourself hooked, constantly chasing that next high score. Can you stay on track, or will you plummet into the abyss?

The effect of Pomegranate juice on C. elegans under Thermal Stress

Armaanjit Singh — Thu, 01 Jan 2026 04:02:33 +0100

This experiment analyzed the effects of pomegranate juice on C. elegans under thermal stress. C. elegans, a nematode with similar genes to humans, is an effective model for studying human diseases. Thermal stress occurs when an organism is exposed to high temperatures, which results in the release of heat shock proteins. Heat shock proteins act as molecular chaperones that help restore protein homeostasis following heat stress. Despite their presence, thermal stress can still weaken antioxidant defense in organisms. However, pomegranate juice is rich in antioxidants and polyphenols, which can help strengthen antioxidant defense. In this study, pomegranate extract was dissolved in water and mixed with agar powder. Using this pomegranate mixed agar, E. coli was cultured for 24 hours. The results showed that the 5 mg/ml concentration increased survival rates of C. elegans. However, the 10 and 20 mg/ml concentrations lowered survival rates, which can be attributed to dose-dependent toxicity. Interestingly, the higher concentrations of pomegranate extract significantly increased C. elegans reproductive rates. This research expands on previous studies that examined the effect of pomegranate juice on C. elegans and distinctly focuses on its role under thermal stress. This distinction helps gain a deeper insight into pomegranate juice's effect on oxidative stress.

The Effects of Pomegranate Juice on C. elegans under Thermal Stress

Armaanjit Singh — Mon, 21 Jul 2025 20:30:43 +0200

This experiment analyzed the effects of pomegranate juice on C. elegans under thermal stress. C. elegans, a nematode with similar genes to humans, is an effective model for studying human diseases. Thermal stress occurs when an organism is exposed to high temperatures, which results in the release of heat shock proteins. Heat shock proteins act as molecular chaperones that help restore protein homeostasis following heat stress. Despite their presence, thermal stress can still weaken antioxidant defense in organisms. However, pomegranate juice is rich in antioxidants and polyphenols, which can help strengthen antioxidant defense. In this study, pomegranate extract was dissolved in water and mixed with agar powder. Using this pomegranate mixed agar, E. coli was cultured for 24 hours. The results showed that the 5 mg/ml concentration increased survival rates of C. elegans. However, the 10 and 20 mg/ml concentrations lowered survival rates, which can be attributed to dose-dependent toxicity. Interestingly, the higher concentrations of pomegranate extract significantly increased C. elegans reproductive rates. This research expands on previous studies that examined the effect of pomegranate juice on C. elegans and distinctly focuses on its role under thermal stress. This distinction helps gain a deeper insight into pomegranate juice's effect on oxidative stress.

Compensation of thermal and mechanical effects of a Lamb-Wave based SHM system in a composite aerostructure.

Jesús Sesé — Sun, 06 Jul 2025 23:08:53 +0200

Structural Health Monitoring has become one of the next challenges in the aeronautical industry in order to know the real-time status of structures and optimize their maintenance. The use of Lamb Waves for damage detection is proven; however, its effectiveness is affected by the boundary conditions in which the structure is located. To counteract this effect, it is essential to model its behavior in different environmental and operating conditions to ensure the correct detection of variations in the structure during its service life. This work presents the characterization of the Lamb wave behavior for a composite UAV supporting surface. By applying different load and temperature conditions to the structure, different conditions of the structure during flight will be simulated. The results obtained for the newly fabricated structure will be compared with the results obtained for the same structure damaged by impact. And finally, the main effects produced on the signal (arrival time, amplitude...) will be analyzed; and how they can simulate the presence of a damage that has not occurred (false positive) or camouflage the presence of existing damage (false negative).

Fracture characterization of hybrid composite laminates in modes I and II

Juan de Gracia Igelmo — Wed, 02 Jul 2025 09:27:53 +0200

In this work, the Mode I and Mode II interlaminar fracture toughness of a hybrid laminate composite consisting of carbon fiber-reinforced layers and glass fiber-reinforced layers was characterized. Unidirectional laminates were used for the tests, and the stacking sequence was chosen with the aim of achieving pure fracture modes in the tests. Mechanical characterization was carried out using three-point bending tests with different spans, taking into account the effects of indentation, shear, and support rotation. Mode I and Mode II interlaminar fracture tests were performed using the ADCB (Asymmetric Double Cantilever Beam) and AENF (Asymmetric End Notched Flexure) tests, respectively, also considering the effects of shear, local deformation, and rotations due to bending. The data were obtained using a recently published analytical model that allows the resistance curve to be found for each load-displacement data obtained from the testing machine.

Matrix cracking effect in thermoset and thermoplastic CFRP: development of an analysis tool for the design of hydrogen tanks

Mayerlin Salgado — Thu, 12 Jun 2025 14:32:44 +0200

Liquid hydrogen (LH₂) is a promising alternative to reduce carbon dioxide (CO₂) emissions in the aeronautical industry. However, current tanks do not meet their rigorous design and safety requirements. Carbon Fibre Reinforced Polymers (CFRP) offer advantages over classic metallic materials, such as higher stiffness and toughness at low temperatures, as well as lower density. Nevertheless, matrix cracking in composites can cause leaks through the tank walls, compromising their tightness, even with a low crack density. Therefore, a deeper understanding of this phenomenon could prevent permeability loss and ensure the operational safety of the tank. Currently, thermoplastic composites are gaining distinction over thermosets in the aeronautical industry due to their better range of properties and the possibility of out-of-autoclave manufacturing. In this context, CF/PEEK composite has shown greater resistance to damage propagation compared to epoxy-based materials, positioning it as a promising candidate for LH₂ storage tanks. This research evaluates the initiation and propagation of transverse cracks in the matrix in specimens with 0º and 90º layer orientations of thermoplastic (CF/PEEK) and thermoset (M21E/IMA-12K) matrices under static loads and at room temperature. Two different stacking sequences, [0/90/0₂/90₂]s and [90/0/90₂/0₂]s, have been analysed. X-ray tomography images were captured at different deformation states. From these images, crack density was calculated using a digital processing method developed by the authors, which quantifies the number of cracks in each layer across the entire width of the specimen. This method allows for a three-dimensional (3D) visualization of the inspected area, facilitating the tracking of possible paths that could compromise the tightness of the tanks. These results contribute to the design of safer and more efficient tanks for liquid hydrogen storage in aeronautical applications.

Flame retardant polyurethane nanofibers based on the Diels-Alder reaction

Arantxa Eceiza — Tue, 10 Jun 2025 18:03:24 +0200

In this study, a halogen-free flame retardant containing maleimide groups was synthesized and incorporated into a polyurethane solution containing pendant furan groups (PUF), with the aim of producing nanofibres via electrospinning. The furan groups of the polyurethane and the maleimide groups of the flame retardant readily react with each other via the Diels–Alder (DA) reaction. The resulting nanofibre membranes were characterized before and after crosslinking through DA reaction. Microscopy images of the membranes showed morphologically homogeneous and defect-free nanometric fibers. The crosslinked membranes exhibited higher solvent resistance and superior mechanical properties compared to the uncrosslinked ones. Thermal analysis showed that the flame retardant promotes the formation of a protective carbonaceous layer that gives the membranes fire-resistant properties.

Development of thermoplastic composites with energy storage capacity for aeronautical applications

Fernando Ramos Saz — Fri, 16 May 2025 20:23:22 +0200

The RE-CELL project proposes a comprehensive approach to developing multifunctional structural composites capable of energy storage for non-critical aeronautical applications. The project is rooted in the recycling and valorization of carbon fiber waste through solvolysis, promoting environmental sustainability and circular economy principles. Recovered fibers are functionalized with conductive materials such as carbonaceous particles and MXenes, enabling them to serve as both structural reinforcement and electrodes. Solid electrolytes are produced by incorporating thermally stable ionic liquids and BaTiO₃ as a dielectric enhancer into a polycarbonate matrix via extrusion. The resulting films function simultaneously as electrolytes and separators. Finally, the structural cells are assembled by compression molding and subjected to mechanical (tensile, flexural, delamination) and electrochemical (electrochemical impedance spectroscopy, cyclic voltammetry) characterization. The project aims to scale up the most promising multifunctional laminates and validate their performance through a demonstrator based on the geometry of an actual aircraft fuselage component.

Mechanical performance of continuous fiber-reinforced thermoplastic composites for structural applications

Jaime Lozano — Fri, 16 May 2025 07:47:23 +0200

Continuous fiber-reinforced thermoplastic composites are emerging as an efficient structural alternative to traditional thermoset materials, thanks to their recyclability, good impact resistance, and suitability for continuous processing. In this work, a manufacturing system was developed based on the thermoplastic pultrusion of unidirectional polypropylene (PP) and carbon fiber (CF) tapes, followed by hot compression molding. This approach enables the production of structural profiles with high fiber alignment and good consolidation. Experimental characterization included tensile, flexural, and impact tests to assess the structural applicability of the manufactured profiles. The results demonstrate a balanced combination of stiffness, mechanical strength, and impact performance, confirming the potential of this system for functional applications in sectors such as mobility or infrastructure, where lightweight, efficient processing, and sustainability are key requirements.

Development of dry fiber based on thermoplastic binders for aeronautical applications

GUILLERMO ULLDEMOLINS DE OLIVES — Thu, 15 May 2025 12:13:34 +0200

As part of the regional NEOCOMP project, funded by IVACE in the Valencian Community, AIMPLAS presents a comprehensive approach to the development of advanced composite materials by producing dry fiber through its own pilot line. This work focuses on the development of thermoplastic binders, dry fiber production, pre-consolidation and resin infusion, as well as the study of mechanical properties. The main objective is the development of thermoplastic binders and the subsequent production of dry fiber. The binders will be formulated using compounding technologies with co-rotating twin-screw extruders to ensure proper dispersion and distribution of the additives. The dry fiber will be produced using a pilot plant system designed and developed by AIMPLAS, equipped with the necessary technology for binder adhesion onto continuous reinforcement. Finally, the materials will be validated for processing with AFP (Automated Fiber Placement) equipment to achieve pre-consolidated panels. After pre-consolidation, resin infusion will be carried out to obtain high-performance structural materials. The binder materials will be characterized through powder granulometry analysis, while the dry fiber will be studied using scanning electron microscopy (SEM) to evaluate binder-to-fiber distribution and adhesion. The project concludes with a thorough tensile analysis of the infused panels, following standards for mechanical performance evaluation. The results obtained not only highlight the potential of the developed materials but also demonstrate the feasibility of this approach for the industrial production of high-performance composite materials for key sectors such as aerospace.

Lightweight, very high-speed bogie frame. CFRP in high-responsibility railway components

Diego Escobar Redondo — Fri, 25 Apr 2025 16:50:14 +0200

In recent years, the railway sector has been demanding lighter, high-responsibility components made from composites due to the incorporation of new technologies to improve passenger comfort and experience, as well as to address new environmental challenges (reducing emissions, energy consumption, increasing capacity, etc.). With this objective, Talgo introduces a high-performance composite bogie frame (CFRP) for very high-speed trains, traditionally made of metal (steel), which allows for weight reductions of over 30%. Additionally, this new bogie leverages the fatigue properties of these materials to incorporate the first stage of suspension into the frame itself. The reduction of bogie elements, as well as infusion manufacturing, improves competitiveness throughout the lifecycle compared to a traditional bogie, complying with the EN-45545-2 HL2 standards for fire, smoke, and toxicity. The project for this lightweight frame will be completed with manufacturing and bench testing in accordance with EN 13749 standards, as well as ballistic impact tests with ballast stones.

Nanocomposites based on PVDF-HFP and piezoelectric nanoparticles fabricated by solvent-casting and electrospinning techniques for sensing applications

Esther Arribas Yuste — Mon, 14 Apr 2025 23:24:13 +0200

This work deals with the development of flexible piezoelectric nanocomposites based on PVDF-HFP copolymer reinforced with ZnO nanoparticles (NPs) for biomedical sensing applications. Two fabrication techniques are analyzed: solvent casting and electrospinning. The films obtained by the solvent casting technique, characterized by FTIR-ATR and Atomic Force Microscopy (AFM) in PFM mode, show a significant increase of the piezoelectric coefficient (d₃₃) for ZnO NPs concentrations equal or higher than 12% by weight, with respect to the unreinforced polymeric matrix. FTIR analysis estimated an electroactive phase content close to 60% in the reinforced samples. On the other hand, the preliminary study of optimization of electrospinning parameters, evaluated by Scanning Electron Microscopy (SEM), demonstrates the feasibility of forming mats with high fiber content and homogeneous nanofiber diameters between 110-140 nm, whose morphology depends on parameters such as the speed of the collector. These preliminary results are promising for obtaining flexible piezoelectric fabrics by both fabrication routes.

Characterization of 3D-printed cordierite-rGO nanocomposites for aerospace applications

Maria Garcia-Martinez — Mon, 14 Apr 2025 23:00:45 +0200

One of the most critical challenges in the aerospace industry is the mismatch in the coefficient of thermal expansion (CTE) between optical components in satellites and their metallic supports, which limits system reliability and performance. Ceramic materials, due to their superior thermal properties, offer a potential solution; however, their adoption has been limited by the complexity of their geometries and conventional manufacturing constraints. Additive manufacturing has opened new opportunities for the development of advanced ceramics, including ceramic matrix composites (CMCs). Within the framework of the AERORECORD-3D project, funded by the Spanish Ministry of Science and Innovation, ceramic cordierite-based supports reinforced with reduced graphene oxide (rGO) have been developed for aerospace applications. In this study, cordierite nanocomposites with varying rGO contents were successfully fabricated via 3D printing. Their thermal, electrical, and mechanical properties were evaluated to assess their performance, exploring their potential as advanced materials for demanding space applications. This work represents a significant step toward the implementation of 3D-printed ceramic nanocomposites by combining innovative materials with advanced additive manufacturing technologies.

Caracterización de las propiedades mecánicas de estructuras kirigami impresas en 3D y sus conexiones fabricadas con materiales compuestos

Montserrat Dolz — Mon, 14 Apr 2025 22:00:24 +0200

El avance de la tecnología de impresión 3D ha permitido la creación de estructuras complejas con formas únicas, como los diseños basados en kirigami, inspirados en el arte de cortar papel para formar estructuras tridimensionales. Estas estructuras ofrecen beneficios significativos, como una construcción ligera, despliegue rápido y la capacidad de soportar cargas mecánicas y absorber energía de deformación. Sin embargo, existe una investigación limitada sobre el comportamiento de estas estructuras y sus conexiones críticas cuando se fabrican con materiales compuestos mediante técnicas de impresión 3D.

Este estudio examina las propiedades mecánicas de estructuras kirigami impresas en 3D y sus conexiones, centrándose en mejorar el comportamiento de unión y optimizar el uso del material. Se emplea el marco Space Mapping para abordar la anisotropía inherente de los compuestos impresos en 3D, transformando el comportamiento direccional complejo del material en un dominio isotrópico equivalente. Este enfoque permite aplicar modelos no lineales isotrópicos ya consolidados, mejorando la precisión de la simulación y reduciendo el coste computacional.

Se utilizan simulaciones por elementos finitos para modelar el comportamiento de las estructuras kirigami, prestando especial atención a las propiedades del material, la adhesión entre capas, la dirección de impresión y la orientación de las fibras en el material compuesto. Se llevan a cabo ensayos mecánicos para validar las simulaciones, centrándose en la rigidez y resistencia de los pliegues y uniones bajo diferentes condiciones de carga. Mediante la mejora de los mecanismos de plegado y la optimización de la distribución del material en las zonas críticas, esta investigación busca demostrar cómo se comparan las estructuras kirigami frente a los diseños sólidos tradicionales en aplicaciones que requieren tanto resistencia como adaptabilidad.

Estos resultados son especialmente relevantes para sectores donde se necesitan diseños ligeros y flexibles, como la aeronáutica, la automoción, el transporte marítimo y la ingeniería civil. Combinando análisis numérico con validación experimental, el estudio ofrece aportaciones valiosas para la optimización de los puntos de plegado y conexión en estructuras kirigami impresas en 3D, con vistas a su aplicación en la ingeniería avanzada.

Experimental characterisation of the translaminar fracture toughness of an additive manufacturing c-CFRP composite

Alex Fernández — Mon, 14 Apr 2025 13:32:24 +0200

Fibre breakeage in composite materials is usually a determining damage mechanism for its structural integrity due to the high energy associated, in comparison with matrix cracking. For this reason, the assessment of the translaminar fracture toughness is relevant for accurate numerical predictions of composite structures. However, there are scarce investigations related to this topic for additive manufactured composites reinforced with continuous fibres. In this investigation, the translaminar fracture toughness of 3D-printed continuous fibre reinforced polymer (c-CFRP) composites was characterised using double-tapered compact tension (2TCT) specimens. The 2TCT geometric dimensions were obtained through a parametric study to prevent undesired failure modes. The results show a translaminar fracture toughness of 17.4 N/mm for the tested 0/90 laminates. The fracture toughness corresponding to the tensile failure of the 0° ply was derived using a rule-of-mixtures approach. Post-mortem micrographic and X-ray analysis indicated the presence of fibre pull-outs in the crack surface and confirmed the absence of any additional damage, validating the use of 2TCT geometry for the determination of the translaminar fracture toughness in additively manufactured c-CFRP composites.

Development and integration of printed strain gauge-based sensors for structural health monitoring in composite material repairs of wind turbine blades

EDURNE TELLECHEA — Mon, 14 Apr 2025 11:00:23 +0200

Structural health monitoring is essential to ensure safety and extend the service life of critical components in renewable energy systems, such as repaired wind turbine blades. This study presents the development of strain gauges printed using advanced functional printing techniques, optimized for integration into composite materials and tailored to the mechanical characteristics of repaired areas to assess the effectiveness and durability of structural repairs. The integration of these sensors would enable real-time monitoring of key parameters, such as microstrains, during the operation of the blade after repair. The experimental work included the printing of strain gauges, evaluation of different substrates, and cyclic loading tests under controlled conditions to assess the accuracy and sensitivity of the printed gauges. Additionally, composite material coupons were characterized under tensile and compressive loads to analyse the impact of gauge integration on the mechanical properties of the composite. Preliminary results demonstrate the feasibility of this approach, although full validation is required, including aging studies, testing in real operational environments, and exposure to extreme temperature variations prior to industrial implementation.

Mechanical behavior analysis at low temperature of flax/epoxy laminates

Gabriel Enrique Sánchez Escudero — Sun, 13 Apr 2025 11:49:34 +0200

The growing environmental consciousness of recent years has driven the composite materials industry to increasingly adopt natural fibers as a sustainable alternative to synthetic ones, aiming to reduce the ecological footprint of manufacturing processes. Replacing conventional synthetic fibers with natural counterparts, such as flax, offers a significant decrease in energy consumption during laminate production, enhancing overall process sustainability. This study explores the mechanical performance of a flax fiber-reinforced epoxy laminate with a twill weave configuration. The material was tested under tensile and in-plane shear loading at both room temperature and subzero conditions (–40 °C and –70 °C). The laminate demonstrated a nonlinear stress–strain response, characterized by three distinct regions, suggesting that a trilinear model may be appropriate for its numerical simulation. The influence of temperature on the mechanical behavior was assessed in both the longitudinal and transverse tensile directions, as well as in shear. Results reveal that lower temperatures lead to increased stiffness and strength, although differences between –40 °C and –70 °C were not substantial.

Analysis of Delamination Behavior in Mode I under Thermal Environment Exposure in Adhesive Joints of Carbon-Epoxy Composite Materials

Paula Vigón — Fri, 11 Apr 2025 19:25:45 +0200

This study analyzes the delamination behavior in mode I, under static and fatigue loading, in adhesive joints on a composite material with an epoxy matrix and unidirectional carbon fiber reinforcement (CFRP). The samples were exposed in a climate chamber at 60°C and 70% relative humidity for different periods (no exposure, 1, 2, and 4 weeks). Subsequently, standardized DCB tests were performed to evaluate the effect of environmental aging on interlaminar fracture toughness and adhesive strength. After an initial static characterization, reference parameters for fatigue tests were defined, obtaining initiation (ΔG–N) and crack growth (G–da/dN) curves. The initiation data were analyzed using a Weibull probabilistic model. The results show a change in the epoxy adhesive behavior with exposure time, evidenced by a reduction in the fatigue limit and an increase in crack propagation rates.