Colloquiam: Documents published in 2025

La Ingeniería Civil en la era digital.

Eugenio Oñate — Thu, 08 Jan 2026 09:50:34 +0100

Se presenta una panorámica de los retos y oportunidades que la nueva era digital ofrece a la Ingeniería Civil, tanto para la práctica de la profesión como para los programas de formación e investigación en universidades y organismos científicos.

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

Jesús Sánchez Pinedo — Wed, 07 Jan 2026 11:22:23 +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

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

Jesús Sánchez Pinedo — Wed, 07 Jan 2026 11:19:23 +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

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

Jesús Sánchez Pinedo — Wed, 07 Jan 2026 11:18:23 +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

Exploring the Role of DataDriven Culture and Marketing Innovation in Driving Brand Credibility and Loyalty: Evidence from Fisheries Startups-Explorando el rol de la cultura basada en datos y la innovación de marketing para impulsar la credibilidad y la lealtad de marca: evidencia de empresas emergentes del sector pesquero

chao cho — Wed, 31 Dec 2025 00:21:33 +0100

Purpose- This study investigates the structural relationships between data-driven characteristics, marketing innovation, sustainability practices, and their impact on brand credibility, customer loyalty, and business performance in fisheries startups. The research is grounded in the Resource-Based View (RBV) and Dynamic Capabilities Theory, emphasizing the strategic role of data culture and innovation in startup growth.

Design/Methodology/Approach-A structured questionnaire was developed based on validated measurement scales from existing literature, translated using forward–backward translation, and pre-tested with 40 respondents. Data were collected from 380 startup stakeholders in Iran's aquaculture sector. The instrument demonstrated high reliability (Cronbach’s alpha ≥ 0.81) and convergent validity (AVE ≥ 0.59). Exploratory and Confirmatory Factor Analyses (EFA/CFA) were conducted to validate the measurement model. Structural Equation Modeling (SEM) using SmartPLS was applied to test the hypothesized paths (see Figure 1).

Findings-The results reveal that data-driven characteristics significantly influence marketing innovation (β = 0.35, p < 0.001). In turn, marketing innovation enhances brand credibility (β = 0.48, p < 0.001), which positively affects customer loyalty (β = 0.52, p < 0.001). Loyalty and sustainability practices both significantly contribute to business performance (β = 0.43 and 0.39, respectively; p < 0.001). All proposed hypotheses were supported, and model fit indices confirmed the robustness of the structural model.
Practical

Implications- This research provides actionable insights for startup managers in emerging industries, particularly in aquaculture, emphasizing the integration of data analytics, innovative marketing, and sustainability to build resilient brand performance.

Originality/Value- This is among the first studies to empirically examine the integrated role of data culture, innovation, and sustainability in determining brand and performance outcomes within fisheries startups, combining theory-driven modeling with real-world entrepreneurial data.

Dynamic Interactions between Data-Driven Capabilities, Digital Transformation, and Sustainability Orientation: A Longitudinal Multi-Industry Study

chao cho — Tue, 30 Dec 2025 23:52:23 +0100

Directly shape customer behavioral outcomes. Despite theoretical advances in the field of digital transformation, the precise mechanisms linking data-driven capability (DDC) to demand-side value creation remain poorly understood. Drawing on resource-based perspectives, dynamic capabilities, and signaling theory, this study presents a mechanism-based model in which DDC shapes customer loyalty (LOY) through market innovation quality (MIQ) and brand credibility (BC), influenced by the level of sustainability authenticity (SA). The study used a two-way–two-source design; the first wave of data including DDC and SA was collected from 300 innovative and active companies in knowledge-based manufacturing and services industries (managers or marketing experts), and in the second wave, MIQ data was collected from the same managers and BC and LOY data from more than 900 customers (an average of three customers per company). Modeling with PLS-SEM showed that DDC has a significant and significant effect on MIQ (β=0.41) and MIQ significantly increases brand credibility (β=0.46). BC is also the strongest predictor of customer loyalty (β=0.52). Mediation results showed that BC plays a full mediating role in the path DDC → MIQ → BC → LOY. In addition, moderation analysis showed that sustainability authenticity strengthens the effect of MIQ on BC (β=0.14) and the effect of BC on LOY (β=0.11); meaning that market innovation and brand credibility are more effective in companies that implement sustainability in a real, transparent and consistent way with their claims. Overall, these findings suggest that the true value of data-driven capabilities is revealed when their output is delivered in the form of visible and credible market innovations, accompanied by genuine sustainability measures. By providing a simple, economical, and actionable model, this research adds to the literature on digital transformation, market innovation, and sustainable brands, and provides practical guidance for managers on the path to building sustainable loyalty.

A Conceptual Framework for Innovative Marketing in Technology-Based Startups in the Fisheries Sector: A Mixed Approach Based on Qualitative and Quantitative Data

chao cho — Tue, 30 Dec 2025 23:26:33 +0100

The growth of startups in the fisheries industry, alongside intensifying competition, shifting consumption patterns, and market complexities, underscores the necessity of revisiting traditional marketing approaches and moving toward creative and innovative models. Despite the importance of this issue, indigenous and systematic frameworks for explaining innovative marketing in this industry remain limited. Accordingly, the present study aims to develop and articulate a comprehensive model of innovative marketing in fisheries startups. This research employs a mixed-method approach (qualitative–quantitative). In the qualitative phase, grounded theory methodology was applied, and data were collected through semi-structured interviews with 18 academic experts and experienced managers in the fisheries industry. Participants were selected using a combination of judgmental and snowball sampling, and data collection continued until theoretical saturation was achieved. In the quantitative phase, the statistical population consisted of fisheries industry practitioners, from whom 384 valid questionnaires were collected and analyzed using convenience random sampling. Structural equation modeling was employed to test the proposed model and research hypotheses. Qualitative findings revealed that the innovative marketing model in fisheries startups comprises six main dimensions: causal conditions (e.g., weak market information, limited awareness, organizational strategy, and adoption of modern advertising methods), contextual conditions (including SWOT analysis, value creation, effective customer communication, and use of social media), intervening conditions (such as health and quality standards, data sharing and advanced analytics, global markets, and changing consumption patterns), core category (training, user experience, motivational strategies, and transformation in products and services), strategies (educational content delivery, value creation, strategic evaluation, and effective customer interaction), and outcomes (such as enhanced credibility, personalized offerings, experiential and content marketing, adoption of advanced technologies, and sustainable raw materials). Quantitative results confirmed that all research hypotheses were significant at the 0.05 level, indicating good model fit and meaningful relationships among its components. Overall, findings suggest that adopting innovative marketing can play a pivotal role in enhancing brand credibility, boosting sales, strengthening innovation, creating competitive differentiation, improving performance, and attracting new customers in fisheries startups.

Simultaneous Control of inside Air Temperature and Humidity by coupled Heating and Ventilation in a Greenhouse

Hafidha MAOUEL — Tue, 30 Dec 2025 10:11:14 +0100

Greenhouse ventilation combined with heating is required to control temperature and moisture levels for a comfort and provide CO₂ for good photosynthesis. The present study focuses on the simulation of a climate in greenhouse during winter. The mathematical model, based on the energy and the water vapor balances inside the greenhouse is used. The main equations of flow are solved with the Fluent^Ò CFD package inside and outside the greenhouse. The external conditions are those of Mediterranean climate and the greenhouse is located in Tizi-Ouzou (Algeria). The study aims at getting a better compromise between flow heating combined with the air exchange rate (a technique used for the dehumidification of the air). After describing the physical phenomena, the equations that govern these phenomena and the method of solving these equations, a program for calculating the inside temperature and humidity was developed and validated by comparison with simulation results. To simulate turbulence inside and outside greenhouse the k-e Standard turbulence model, which comes out (seems) to be more accurate than the other models, have been preferred. Analytical results are used to determinate optimal conditions of flow heating combined with ventilation rate.

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

Scipedia content — Mon, 29 Dec 2025 15:38:34 +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

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

Jesús Sánchez Pinedo — Fri, 19 Dec 2025 09:59:33 +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

An Enhanced Pilot Aided Channel Estimation in XL-MIMO Communication Systems Using Bitterling Swallow Fish Optimization

Jesús Sánchez Pinedo — Fri, 19 Dec 2025 09:57:33 +0100

The continuous evolution of 6th Generation (6G) wireless networks places Extremely Large-scale Multiple-Input Multiple-Output (XL-MIMO) schemes as a crucial enabler for ultra-reliable and high data rate communication. Channel estimation in XL-MIMO is crucial here because it allows the system to precisely recognize the wireless channel conditions between the transmitter and receiver, which is vital for enhancing signal processing, and resource allocation. Traditional pilot-aided channel estimation approaches face challenges, such as high error. Hence, this work proposes an innovative model called the Deep Kronecker Network-Bitterling Swallow Fish Optimization Algorithm (DKN-BSwaFOA) for pilot-aided channel estimation in XL-MIMO systems. Initially, the system model of XL-MIMO is contemplated. The pilot insertion is done at the transmitter, and the location of the pilot symbol is optimally selected using BSwaFOA. The signal is propagated over the hybrid field channel, where both farfield and near-field components coexist. At the receiver, the channel is estimated using DKN, which is trained using the proposed BSwaFOA. The experimental outcomes demonstrated that the DKN_BSwaFOA computed the minimum Root Mean Square Error (RMSE), Bit Error Rate (BER), and Mean Square Error (MSE) of 0.030, 0.002, and 0.001.OPEN ACCESS Received: 23/09/2025 Accepted: 11/11/2025

Entropy-Guided k-Core Pruning Balancing Redundancy Reduction and Information Preservation for Efficient CNN Compression

Jesús Sánchez Pinedo — Fri, 19 Dec 2025 09:55:33 +0100

Convolutional Neural Networks (CNNs) are widely used in computer vision, but their massive computational cost and parameter redundancy hinder deployment on resource-constrained devices (e.g., edge terminals). Existing filter pruning methods often struggle to balance two critical goals: aggressive redundancy reduction and effective preservation of taskcritical information—either leading to excessive accuracy loss or insufficient compression. To address this challenge, we are the first to jointly exploit k-core decomposition and information entropy in a unified pruning criterion, and we instantiate this idea in a novel graph–entropy collaborative framework that achieves Pareto-optimal compression-accuracy trade-offs. The key steps are as follows: First, we use perceptual hashing (pHash) to calculate the similarity of output feature maps between filters, then model each filter as a node in an undirected graph—edges are established only when filter similarity exceeds a predefined threshold, forming a “redundancy graph” that quantifies inter-filter redundancy. Second, kcore decomposition is applied to this graph to identify high-order redundant substructures, which helps locate redundant filters at the structural level. Finally, information entropy is introduced to evaluate the “informational value” of each node (filter) in the k-core: only filters with low redundancy and high information content are retained, ensuring minimal loss of critical features. Extensive experiments are conducted on CIFAR10 and CIFAR-100 datasets, using representative CNN architectures (VGGNet-16, ResNet-56/110, DenseNet-40). Specifically, VGGNet-16 achieves a 65.8% reduction in floating point operations (FLOPs) and an 88.8% reduction in parameters while experiencing only a 1.24% decrease in Top-1 accuracy. ResNet-56 attains a 50.1% reduction in FLOPs with a nearly imperceptible accuracy loss of 0.03%, markedly surpassing the Fire together wire together (FTWT) method which reduces FLOPs by 54% at the cost of a 1.38% accuracy decline. DenseNet-40 accomplishes a 76.5% FLOPs reduction with a 1.55% accuracy decrease, demonstrating the method’s strong applicability for high-intensity compression of densely connected networks. Furthermore, the method’s scalability is validated on the large-scale ImageNet dataset with ResNet-50, where it achieves a 73.65% FLOPs reduction with competitive accuracy, underscoring its practicality for real-world applications. These outcomes collectively affirm the effectiveness and broad applicability of the proposed graphentropy collaborative pruning framework.

Adaptive Federated Fault Diagnosis Framework for Wind Turbine Reliability

Jesús Sánchez Pinedo — Fri, 19 Dec 2025 09:50:33 +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

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

Jesús Sánchez Pinedo — Fri, 19 Dec 2025 09:48:23 +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

Bearing Fault Diagnosis Based on AVMD and HPO-DBN

Jesús Sánchez Pinedo — Fri, 19 Dec 2025 09:45:24 +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

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

Jesús Sánchez Pinedo — Fri, 19 Dec 2025 09:44:34 +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

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

Jesús Sánchez Pinedo — Fri, 19 Dec 2025 09:41:34 +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.

Computational Study of Nozzle Configuration Effects on Heat Transfer and Flow Characteristics in Aero-Engine Swirling Anti-Icing Systems

Jesús Sánchez Pinedo — Tue, 16 Dec 2025 10:32:14 +0100

Engine inlet icing persists as a critical hazard to aviation operational safety, compromising aerodynamic performance and potentially inducing catastrophic engine failure. Aero-engine swirling anti-icing systems inject high-temperature bleed air into an annular chamber at the engine’s leading edge through tangentially positioned nozzles. This high-velocity jet entrains low-temperature air within the chamber, establishing a circulatory flow that effectively heats the lip surface to prevent ice formation. This study employs computational fluid dynamics (CFD) method to systematically evaluate the flow and heat transfer characteristics of four distinct nozzle configurations within an aero-engine anti-icing chamber: conical single-orifice, diffuser-equipped, elliptical dual-orifice, and elliptical quad-orifice nozzles. Results indicate that the conical single-orifice nozzle exhibits the highest entrainment efficiency due to its concentrated jet structure, whereas the diffuser-equipped nozzle demonstrates 16.4%– 18.1% lower efficiency, attributable to premature kinetic energy dissipation. At identical bleed air flow rates, the diffuser-equipped nozzle yields the lowest circulation velocity and pressure loss, necessitating minimal bleed air pressure. The elliptical quad-orifice nozzle optimally mitigates hot and cold spots via multi-jet energy dispersion, achieving a maximum 34.7% reduction in lip surface temperature differentials compared to the conical single-orifice design within the analyzed bleed air mass flow rate range. Nozzle configurations exert limited influence on the average Nusselt number, with a maximum relative deviation of 5.48% observed across all nozzle configurations when compared to established empirical correlations.OPEN ACCESS Received: 11/07/2025 Accepted: 21/08/2025 Published: 15/12/2025

A Picture Fuzzy Decision-Making Framework for COBOT Selection in Digital Supply Chains

Jesús Sánchez Pinedo — Tue, 16 Dec 2025 10:31:33 +0100

Driven by digitalization in supply chains, the use of Collaborative Robots (COBOTs) has become increasingly widespread in recent years. They significantly contribute to process efficiency by working in place of, or in collaboration with, humans in a variety of operations, including welding, painting, assembly and disassembly, transportation, packaging, and palletizing. However, when uncertainty and different criteria are taken into account, decision support systems that compare practical robots based on their suitability for specific needs are inadequate. This study presents a comprehensive multi-criteria decision-making (MCDM) framework for prioritizing COBOTs with different features used in digital supply chain processes. Based on in-depth research in the literature and the opinions of experts working in companies that use relevant robots in the industry, the criteria to be evaluated when selecting COBOT types are identified. The importance of these criteria was determined using the Picture Fuzzy Step-wise Weight Assessment Ratio Analysis (PiF-SWARA) method, which effectively captures the uncertainty in experts’ decisionmaking processes. Subsequently, alternative COBOT types were ranked using the Picture Fuzzy Combinative Distance-Based Assessment (PiFCODAS) approach. This case study, which evaluates the PiF-SWARACODAS concept, reveals that according to expert assessments, cost is the most important criterion in COBOT selection, followed by process quality and space utilization. The findings about the selection of types emphasize that high-efficiency articulated robots operating at high speeds under mass production conditions are the primary priority. These robots are followed by humanoid robots. The third most important are power and force-limiting robots. The fourth and fifth types of COBOTs are hand-guided and safety-monitored stop robots. Validation and sensitivity analyses confirmed the robustness of the results. Overall, the proposed framework not only clarifies the key priorities for manufacturing facilities but also provides a validated decision support tool to align digitalization strategies with the most appropriate COBOT investments.OPEN ACCESS Received: 25/08/2025 Accepted: 15/10/2025 Published: 15/12/2025 An extensive literature review was conducted to examine previous research on COBOT applications in the supply chain, to identify evaluation criteria and alternatives for this study, and to highlight the originality of this study. The concept of COBOT was included in books, journals and conference proceedings accessible through the SCOPUS database. However, the literature search with the related keyword found too many studies. Therefore, the scope was further customized to include different types of COBOT models that can be used throughout the supply chain. To make the literature review process more systematic, the PRISMA methodology, which stands for “Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)”, was used. The PRISMA approach, designed by Moher et al. [10], has become widely used among academics in recent years. Sources are analyzed and evaluated according to the determined eligibility criteria. Then, appropriate studies are selected. Table 1lists the keywords used to perform a PRISMA method literature review, along with the number of studies found using these Table 1: Literature search with A total of 1179 studies were found in SCOPUS after searching with the were found. The results were duplicated because some

Q-Learning-Based Lightweight Task Orchestrator: A Lightweight Q-Learning-Based Scheduler for Service Level Agreement-Aware Container Placement in Heterogeneous Clusters

Jesús Sánchez Pinedo — Tue, 16 Dec 2025 10:28:48 +0100

Efficient management of workloads in diverse container clusters requires maintaining a balance between Service Level Agreement (SLA) compliance, Quality of Service (QoS), energy efficiency, and security, despite differences in resources and architectures. This research introduces Federated Q-Learning–based Lightweight Task Orchestrator (F-Q-LiTO), a compact and intelligent orchestration framework that combines predictive modeling, approximate data structures, and security filtering to enable adaptive task placement across distributed environments. Unlike complex deep reinforcement learning models such as Deep Reinforcement Model (DeepRM) or traditional heuristic schedulers like Kubernetes BinPacking, F-Q-LiTO uses tabular Q-learning enhanced with federated aggregation, which significantly reduces computational and communication overhead, making it ideal for edge computing environments with limited resources. The framework incorporates a Long Short-Term Memory (LSTM)–based predictor for proactive resource forecasting, a Count-Min Sketch for scalable resource utilization estimation, and an XOR filter for efficient and lightweight security enforcement. Experimental results demonstrate that F-Q-LiTO achieves 98.6% task completion, 96.8% SLA satisfaction, and reduces energy consumption to 180.5 kilowatt-hours (kWh). It outperforms DeepRM and Kubernetes by achieving 34% fewer missed deadlines and up to 30% lower energy imbalance. The system converges quickly—by Episode 6—and maintains cluster fairness (Jain’s Fairness Index= 0.98) along with priority-aware placement accuracy of 93.2%. Security analysis shows that F-Q-LiTO successfully blocks 98.5% of unauthorized task placements while using only 0.3 megabytes (MB) of memory. Overall, F-Q-LiTO demonstrates that a federated and lightweight reinforcement learning approach can deliver scalable, secure, and QoS-aware orchestration for modern edge and multi-cloud computing environments without compromising performance or efficiency.OPEN ACCESS Received: 24/07/2025 Accepted: 14/10/2025 Published: 15/12/2025

Study on the Mechanical Characteristics of Concrete Pavement Corner Detachment and Grouting Rein forcement Using the FEMDEM Coupling Approach

Jesús Sánchez Pinedo — Tue, 16 Dec 2025 10:27:24 +0100

Cement concrete pavements have significant advantages in the construction of transportation infrastructure. However, the disease of slab bottom voids affects their performance and service life. In this study, the Finite Element Method-Discrete Element Method (FEM-DEM) coupling method was used to deeply explore the mechanical characteristics of concrete pavement corner voids and grouting reinforcement. First, a discrete element model for corner voids was constructed, and uniaxial compression simulations were carried out to calibrate the mesoscopic parameters of the concrete surface layer and the base course. Subsequently, an FEM-DEM coupling model was established to simulate the mechanical responses of the pavement slab under different working conditions. The research found that when there is a corner void, the cracking load is 75 kN, and the peak load is 96.8 kN. After exceeding the peak load, the cracks expand rapidly. When the strain reaches 0.2, the crack growth slows down. The displacement expands in a triangular shape, and the failure mode is shear failure. After grouting reinforcement, the peak strength increases by 53% to 150 kN, the cracking pressure is 75 kN, the cracks expand rapidly first and then stabilize, and the failure form is rectangular. In addition, the load transfer, crack distribution, force chain distribution, etc., show different laws in the void and reinforced states. The FEMDEM model overcomes traditional numerical limits, precisely simulating structure-void interactions and reinforcing mechanics. It fills a mesomacro research gap, offering new insights for pavement engineering and supporting corner void grouting treatments.OPEN ACCESS Received: 20/05/2025 Accepted: 18/07/2025 Published: 15/12/2025

Coloración en gráficas de mapas en la Tierra y mapas en la Luna

Ana Teresa Calderón Juárez — Mon, 15 Dec 2025 06:34:53 +0100

La ''coloración de mapas'' es un problema clásico en la ''Teoría de Grafos'', donde cada país se modela como un vértice y las fronteras entre países como aristas. El ''Teorema de los Cuatro Colores'' establece que cualquier mapa plano puede colorearse con cuatro colores sin que dos regiones adyacentes compartan el mismo color. En este artículo, exploramos la generalización del problema de coloración de mapas al caso de la Tierra y la Luna, conocido como el '''Earth Moon Problem''', propuesto por Ringel. Este problema busca determinar el número mínimo de colores necesarios para colorear un mapa donde cada país en la Tierra y su colonia lunar deben recibir el mismo color, respetando la restricción de que las regiones adyacentes en cualquiera de los dos cuerpos celestes deben tener colores distintos. Nuestro principal aporte es demostrar que el problema de la ''3-coloración'' de la Tierra-Luna es ''NP-completo'', mediante una reducción desde ''3-SAT'', lo que implica que no existe un algoritmo eficiente para resolverlo en general (suponiendo P ≠ NP). Además, complementamos demostraciones previas que aparecían incompletas en la literatura y modelamos el problema como un ''problema de satisfacción de restricciones'' (CSP), lo que permite un análisis más profundo de su complejidad computacional. Este trabajo no solo aporta una nueva demostración de que el problema de coloración de la Tierra-Luna con 3 colores es NP-completo, sino que también abre la puerta a futuros estudios sobre su dificultad para diferentes números de colores. Por último, describir el problema de coloración de la Tierra-Luna a través de grafos, un caso abierto en la coloración de grafos que extiende el problema de la coloración de mapas planos. En términos de grafos, esto se puede reformular como la búsqueda del '''número cromático máximo''' de un grafo G que es la unión de dos grafos planares (sobre el mismo conjunto de vértices). Se demuestra mediante inducción que G es 12-coloreable, como observó Heawood. Ringel conjeturó que el Problema de la Tierra-Luna era 8-coloreable pero Sulanke reportó un ejemplo que requiere 9 colores, aún no se conoce si existen configuraciones que requieran 10, 11 o 12 colores.

Youth and Rural Innovation: A Competitiveness Platform for Agricultural Development in Sampués and Toluviejo

JOSE GUILLERMO ROJAS BARRETO — Thu, 11 Dec 2025 04:06:16 +0100

El proyecto Juventud e Innovación Rural: Una Plataforma de Competitividad para el Desarrollo Local en Sampués y Toluviejo busca fortalecer la competitividad rural mediante la capacitación tecnológica, la innovación aplicada y el desarrollo de soluciones prácticas lideradas por jóvenes en comunidades rurales de Sucre, Colombia.
Mediante formación práctica en robótica, programación, biotecnología y desarrollo de prototipos, los jóvenes rurales adquieren las habilidades necesarias para identificar los desafíos locales y transformarlos en soluciones innovadoras y de bajo costo para la agricultura, la producción artesanal y otras cadenas de valor locales.
La iniciativa busca reducir las brechas tecnológicas, promover el emprendimiento juvenil y mejorar la productividad en entornos rurales mediante la integración de tecnologías modernas en los procesos tradicionales. Este proyecto destaca el papel crucial de los jóvenes innovadores como impulsores del desarrollo sostenible y competitivo en el campo colombiano.

Turismo Rural Sostenible en Toluviejo, Sucre: Revisión Crítica, Oportunidades y Retos

MARIA JOSE LARA VILORIA — Wed, 10 Dec 2025 23:10:24 +0100

Toluviejo (Montes de María) consolida una oferta de turismo rural y de naturaleza con activos como el mirador de La Piche, arroyos, cavernas, agroecosistemas y memoria cultural. Esta revisión sintetiza el estado del turismo rural sostenible en el municipio, identifica brechas en sostenibilidad ambiental, sociocultural, económica y de gobernanza, y propone líneas de acción con enfoque comunitario y de largo plazo.

MARIA JOSE LARA VILORIA — Wed, 10 Dec 2025 22:23:24 +0100

Hacia un Método de Tractografía Basado en Información Microestructura por Medio de Optimización Convexa

Ramón Aranda — Tue, 09 Dec 2025 17:26:43 +0100

This work presents a method to estimate the structure of white matter (axon bundles) by integrating microstructural information through convex optimization. The approach locally validates each segment using a physical diffusion model that assigns weights to possible trajectories, reducing spurious connections from the early stages of the process. The method is evaluated against classical algorithms using metrics such as LiFE, connectivity correlation, and the area under the ROC curve. The results show greater structural coherence and a reduction in false positives, with robust performance under noise. The study demonstrates the feasibility of incorporating microstructural information into the estimations, although it also reveals a higher number of false negatives and a high computational demand.

Evaluating the unavailability of interconnected power and communication networks with open-source tools on a petascale cluster

Dejan Brkić — Tue, 09 Dec 2025 16:40:24 +0100

In reliability engineering, unavailability is deﬁned as the probability that a system is not operational at a given point in time, typically due to failure or maintenance. A critical gap in reliability analysis by systematically evaluating the time-dependent unavailability of real interconnected power and communication networks in the Czech Republic is addressed in this work. These networks are modelled as acyclic graphs using open-source R packages. Unlike previous studies relying on commercial tools, the research presented here offers a novel, reproducible, and scalable framework. The main contribution lies in the innovative application and benchmarking of ftaproxim, an R package based on proxel simulation, which models ageing components during their entire life using various probabilistic distributions. This approach contrasts with traditional tools such as the FaultTree package, which are limited to asymptotic unavailability analysis. Here presented work evaluates both R packages on a real infrastructure model and compares their performance and computational efﬁciency on the Barbora supercomputer cluster against commercial software (Matlab). It is demonstrated how ftaproxim’s tolerance and time-step parameters can be tuned for robust computational efﬁciency and accuracy, an aspect previously unexplored. The results of the presented study show that unavailability computations can be completed in approximately 5 h under optimal settings, with absolute errors ranging from 1.0× 10−4to 9.6× 10−4when compared to commercial solutions. This integrated approach, combining open-source tools, high-

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

Jesús Sánchez Pinedo — Tue, 09 Dec 2025 10:06:13 +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

Study on the Annular Pressure Prediction Method for Ultra-Deep Well Hydraulic Lift Dual-Gradient Drilling

Jesús Sánchez Pinedo — Tue, 09 Dec 2025 10:05:44 +0100

With the ongoing advancement of oil and gas exploration into deep and ultra-deep formations in China, precise wellbore pressure control under complex geological conditions has become a critical technical challenge for safe drilling operations. To overcome the limitations of existing dual-gradient drilling (DGD) technologies—particularly their poor applicability and limited pressure regulation capability in land-based drilling—this study introduces an innovative hydraulic-lift dual-gradient drilling annular flow model, tailored for ultra-deep vertical wells. The model accounts for solid–liquid phase separation flow characteristics and the hydraulic-lift effect of downhole dual-gradient pumps. The Stability Enhancing Two-Step (SETS) method is employed to solve the strongly nonlinear, coupled governing equations, significantly improving computational stability and efficiency. Experimental validation reveals that the model’s predicted pressure distribution closely matches measured data, with a maximum average error of only 16.4%, confirming the model’s accuracy and applicability. Additionally, this study systematically analyzes the impact of key parameters—such as drilling fluid flow rate, viscosity, lift pump speed, and the number of pump sections—on bottomhole pressure regulation, providing valuable insights into their influence on annular pressure behavior. The findings offer a solid theoretical foundation for optimizing drilling parameters and ensuring safe, efficient drilling in ultra-deep wells under challenging geological conditions.OPEN ACCESS Received: 16/07/2025 Accepted: 19/08/2025

Cuantificación de incertidumbre sobre parámetros en modelos no lineales

Gerardo Tinoco-Guerrero — Tue, 09 Dec 2025 03:21:24 +0100

En este trabajo se estudia la cuantificación de incertidumbre en parámetros de modelos no lineales mediante el enfoque bayesiano. Se parte del planteamiento clásico de problemas inversos, en los cuales los parámetros del modelo deben inferirse a partir de observaciones ruidosas y de un modelo directo formulado como un sistema de ecuaciones diferenciales. Dado que estos problemas suelen estar mal planteados, se introduce la inferencia bayesiana como estrategia de regularización, permitiendo incorporar información a priori y actualizarla con datos mediante la distribución a posteriori. Se presentan los fundamentos teóricos del enfoque bayesiano, así como su aplicación al caso particular del modelo de crecimiento logístico, destacando el uso de métodos computacionales para aproximar las distribuciones resultantes de los parámetros del modelo.

Algorithms and communication: A systematized literature review

Scipedia content — Thu, 04 Dec 2025 13:03:31 +0100

La influencia de los algoritmos en la sociedad es cada vez mayor a través de una presencia creciente en todos los ámbitos de la vida diaria, sin que seamos conscientes de ello y, en ocasiones, usurpando la identidad de otros actores sociales. El artículo tiene como propósito principal abordar la metainvestigación sobre el campo de la inteligencia artificial y la comunicación, desde una perspectiva holística que permita analizar el estado de la investigación académica, así como los posibles efectos en estas dos áreas y en la convivencia en un sistema democrático. Para ello se lleva a cabo una revisión sistematizada de la literatura reciente desde enfoques cuantitativos y cualitativos. La temática analizada es cambiante y novedosa; incluye el impacto y la interacción de algoritmos, bots, procesos automatizados y mecanismos de inteligencia artificial en el periodismo y la comunicación, así como su efecto en la democracia. Los resultados dibujan una producción científica en expansión, mayoritariamente en inglés, basada en la discusión teórica o centrada en la percepción de los profesionales de la comunicación. El objeto de estudio mayoritario se sitúa en el periodismo y en la democracia, con menor implicación de la ética o la educación. Los estudios señalan un gran interés sobre los efectos del uso de algoritmos sobre el periodismo y la democracia, pero las respuestas son todavía inciertas y los retos para los próximos años importantes.

Freshwater Snail Optimizer: A Bio-Inspired Optimizer for Engineering Design Problems

Jesús Sánchez Pinedo — Wed, 03 Dec 2025 10:59:24 +0100

As the scale and nonlinearity of optimization problems continue to increase, traditional deterministic solution strategies are becoming increasingly flawed in the face of the exponential growth of search space dimensions and multimodal objective functions. Metaheuristic algorithms, with their probability-driven global search capabilities and local development capabilities, have gradually become an essential tool for solving complex optimization tasks. We propose a Freshwater Snail Optimizer (FSO), inspired by the social behavior of water snails in terms of movement and collision, as a metaheuristic algorithm. FSO combines the floating of water snails’ air chambers, movement in the water, and population collisions and divides them into groups during initialization to balance exploration and development, achieving gratifying optimization results, especially in high-dimensional problems. We utilized CEC 2017 and CEC 2022 to qualitatively analyze FSO in various problems, and employed the Friedman test and Wilcoxon rank sum test for statistical testing. Experimental results show that our proposed FSO achieved 32 first-place results on 41 problems compared with 9 classic algorithms, and 27 first-place results when compared with 9 emerging algorithms that appeared in the past two years. FSO has also achieved the first comparison results in six engineering optimizations on multiple occasions, proving that FSO possesses well optimization capabilities and practicality for real-world problems. The source code accompanying this article has been released at:https://github.com/leogalaxy0603/Freshwater-SnailOptimizer(accessed on 12 October 2025).OPEN ACCESS Received: 19/08/2025 Accepted: 13/10/2025

Approximate Calculation of the Generalized Erdélyi-Kober Operator Using a Cubic Spline

Elina Shishkina — Wed, 03 Dec 2025 06:47:23 +0100

This article investigates the problem of approximating the generalized Erdélyi-Kober fractional operator (often referred to as the Lowndes operator) using cubic splines. A method based on cubic spline interpolation is proposed for approximating the operator on a non-uniform grid. The convergence rate of the proposed method is proven, and its stability is analyzed. Error bounds are established for functions in the class C4[0; b], providing a mathematical justification for the accuracy of the approximation. The efficiency of the method is validated through practical examples using test functions such as f (x)= x4.7and f (x)= cos x, with results presented in graphical and numerical forms. This approach ensures high accuracy and flexibility in computing fractional integrals, which is of significant importance for solving fractional models used in physics, engineering, and other sciences. The article also provides an overview of the role of the generalized Erdélyi-Kober operator in modern fractional calculus and its applications.OPEN ACCESS Received: 06/06/2025 Accepted: 08/09/2025 Published: 27/10/2025

Hybrid Discontinuous Galerkin method for perturbations of the modified Helmholtz equation

Gerardo Tinoco-Guerrero — Tue, 02 Dec 2025 18:01:43 +0100

The application of the Discontinuous Galerkin Method to elliptic problems usually leads to underdetermined linear systems, and penalization or suitable constraints are necessary. In this work, we address this issue for the modified Helmholtz equation. For this elliptic problem, we propose a hybrid numerical flux in the Discontinuous Galerkin method to introduce unknowns on the edges of the mesh, yielding a well-determined linear system. Performance is tested as a Poisson solver. Additionally, accurate approximations are presented for certain Helmholtz problems in Coastal Ocean Modeling.

Coupling of FEM and FVM Codes for Optimal Control

Scipedia content — Fri, 28 Nov 2025 10:15:35 +0100

Multiscale and multiphysics simulation leveraging coupling techniques and state-of-the-art codes

Scipedia content — Fri, 28 Nov 2025 10:14:00 +0100

High-Order Multidisciplinary Time Integration Towards Adaptive Time Stepping

Scipedia content — Fri, 28 Nov 2025 10:12:31 +0100

Acceleration of Extreme Scale Flow Simulations through Hierarchical Mesh Partitioning

Scipedia content — Fri, 28 Nov 2025 10:10:53 +0100

Numerical Coupling of a FVM and FEM Codes Applied to a Low-Prandtl Turbulent Square Cavity

Scipedia content — Fri, 28 Nov 2025 10:09:03 +0100

A FEM-FVM Coupling Code for Numerical Simulation of a Liquid Metal Heat Exchanger

Scipedia content — Fri, 28 Nov 2025 10:07:07 +0100

Multiscale Modelling with Data-Driven Brain Networks: Misfolded Proteins and Astrocytic Clearance in Alzheimer’s Disease

Scipedia content — Fri, 28 Nov 2025 10:04:44 +0100

Multiphysics Modelling of Fracture in Non-Isothermal Multiphase Clayey Soils with the Crack Phase-Field Approach: Preliminary Investigations

Scipedia content — Fri, 28 Nov 2025 10:02:58 +0100

Locomotion of Cardiomyocytes-Powered Swimmers: a Numerical Study Based on Fluid-Structure-Electrophysiology-Interaction

Scipedia content — Fri, 28 Nov 2025 10:01:11 +0100

Grid Deformation Challenges during Partitioned Simulation of Constrained Melting

Scipedia content — Fri, 28 Nov 2025 09:59:44 +0100

Development, Experimental Validation, and Uncertainty Quantification Analysis of a Multiphysics Digital Twin for Predicting Thermal Behavior in Automotive Lithium Batteries

Scipedia content — Fri, 28 Nov 2025 09:58:00 +0100

Construction and Orthogonality of Fractional Laguerre Functions via the Caputo Derivative

Jesús Sánchez Pinedo — Thu, 27 Nov 2025 10:43:13 +0100

This paper presents a rigorous framework for generalizing Laguerre polynomials to the fractional domain using the Caputo derivative. We solve the resulting fractional Laguerre differential equation via the power series method, deriving an explicit formfor the fractional Laguerre functions. A key contribution is the identification of a novel weight function, wα(x) = x−(2α−1)e−x, which is essential to prove the orthogonality of these functions over the interval [0,∞). Comprehensive numerical validation is provided, confirming the theoretical orthogonality across a wide range of fractional orders α and demonstrating a clean reduction to the classical polynomials when α = 2. An analysis of computational feasibility confirms the practical applicability of these functions for solving fractional differential equations and other applied problems.

A Review of Earthquake Landslide Hazard Assessment Methods

Jesús Sánchez Pinedo — Thu, 27 Nov 2025 10:42:23 +0100

Landslides triggered by earthquakes are large in scale and wide in scope, making them one of the most serious geological disasters. Earthquake landslide hazard assessment has become an important part of disaster reduction and prevention work. Based on existing research and assessment practices, such an assessment is divided into two levels: individual landslide assessment and regional landslide assessment. The individual assessment, mainly required by specific engineering seismic issues, serves as the foundation of earthquake landslide hazard assessment. It includes two analysis methods: qualitative analysis based on causal relationships (e.g., comprehensive indicator modeling, logistic regression, neural network modeling, information quantity evaluation) and mechanical analysis based on physical-mechanical mechanisms (e.g., quasi-static method, Newmark method, dynamic time-history method). This paper summarizes the characteristics and problems of these two methods. Regional assessment caters to regional strong earthquake geological disaster rescue deployment, future earthquake defense planning, and engineering construction strategic layout. It has two strategies—“from region to individual” (earthquake-focused, coarse-to-fine) and “from individual to region” (landslide-focused, point-to-area)—which differ in observation angles and technical routes. Currently, the individual assessment can estimate landslide hazard probability by considering potential seismic source ori-entations, but the regional assessment lags, e.g., ignoring such orientations and lacking the application of the dynamic time-history method. Thus, this paper proposes establishing slope seismic resistance fields and multi-azimuth seismic impact fields, then overlaying them to determine regional earth-quake landslide distribution probability, and points out future research directions.

Classical and Bayesian Inference of Engineering and Disability Data: Using the Kavya Manoharan Power Chris-Jerry Distribution under Hybrid Censoring

Jesús Sánchez Pinedo — Thu, 27 Nov 2025 09:57:43 +0100

In this article, we study and introduce the Kavya-Manoharan power ChrisJerry distribution (KMPCJD) which is a new generation of the power Chris-Jerry distribution (PCJD) which is suitable for engineering and disability data. The probability density curves of KMPCJD demonstrate that it has practical applications in analyzing engineering and disability data in Saudi Arabia. Researchers have a lot of flexibility when developing statistical models for research on disability issues, since the hazard rate function (HRF) for KMPCJD can exhibit J-shaped, increasing, and decreasing trends. In addition, several significant KMPCJD features are calculated, including moments, reliability metrics, moment-generating function, and order statistics. Using data on engineering and disability difficulties, we estimate the parameters of KMPCJD and use classical and Bayesian techniques to assess their reliability and HRF under hybrid censored schemes. Asymptotic confidence/credible intervals are calculated. The numerical results show that when the sample size n increases while keeping other factors like r and T constant, the estimators for δ and λ show improved performance in terms of reduced Bias, mean square error (MSE), and narrower confidence intervals. Also, the Bayesian method also produces shorter credible intervals (LCCI) compared to the traditional confidence intervals (LACI) from ML and MPS methods, suggesting higher precision. To show the utility of the suggested distribution, it was tested in five datasets related to engineering and disability issues in Saudi Arabia. The KMPCJD performed better in terms of goodness of fit than a number of models, including the Kavya Manoharan Rayleigh inverted Weibull distribution, Kavya Manoharan Burr X distribution, exponentiated generalized power Lindley distribution, Weibull power Lindley distribution, power Lindley distribution, Kavya Manoharan generalized exponential distribution, power XLindley distribution, Kavya Manoharan unit exponentiated half logistic distribution, and PCJD. Due to its superior fit capabilities, the KMPCJD is suggested for data modeling in disciplines including engineering and disability difficulties.OPEN ACCESS Received: 27/08/2025 Accepted: 19/09/2025 Published: 27/11/2025

F-Q-LiTO: A Federated Q-Learning-Based Lightweight Intelligent Task Orchestrator for Multi-Tenant Container Clusters

Jesús Sánchez Pinedo — Thu, 27 Nov 2025 09:56:27 +0100

The demand for intelligent, scalable, and energy-conscious container orchestration has increased due to the growth of microservice-based designs and multi-tenant workloads. A novel federated reinforcement learning framework for adaptive task scheduling in heterogeneous container clusters, F-Q-LiTO (Federated Q-Learning-Based Lightweight Intelligent Task Orchestrator), is proposed in this research. In contrast to traditional orchestrators, F-Q-LiTO uses federated Q-learning to decentralise decision-making, guaranteeing convergence across dispersed nodes while maintaining data locality and minimising synchronisation overhead. The system has several lightweight components, including energy-conscious placement penalties, XOR filters for secure container fingerprinting, Count-Min Sketches (CMS) for constant-space resource estimation, and workload forecasting based on the Long Short-Term Model (LSTM) for proactive migration. In comparison to DeepPlace, F-Q-LiTO reduced task deadline misses by around 34% and achieved an average SLA satisfaction of 96.8% when tested on simulated multitenant workloads with over 1000 tasks. Ablation studies confirm that federated coordination and predictive migration materially improve performance. Global Q-values converged within six episodes, and SHAPbased explanations identify CPU forecast, SLA urgency, and node energy state as dominant decision factors. F-Q-LiTO demonstrates practical, interpretable, and low-latency orchestration suitable for dynamic edge– cloud deployments.OPEN ACCESS Received: 01/08/2025 Accepted: 09/10/2025 Published: 27/11/2025

AI-Enhanced and Other Load Modelling in Modern Power Systems: A Comprehensive Review of Advances, Challenges, and Future Directions

Jesús Sánchez Pinedo — Thu, 27 Nov 2025 09:55:13 +0100

Load modelling is a crucial element of power system study that significantly affects the field’s planning, operation, and control methods. With the increasing penetration of renewable energy sources, electric vehicles, demand-side management, and distributed generation (DG), the traditional static and dynamic load model approaches are being replaced. This paper reviews extensively the existing load modelling techniques, namely, component-based load modelling, measurement-based load modelling, and hybrid methods. In addition, advancements tuned by artificial intelligence (AI) and machine learning (ML) are critically reviewed, emphasizing improving the accuracy, flexibility, and real-time adaptability of load models. For instance, Long Short-Term Memory (LSTM) networks have demonstrated significant improvements in forecasting accuracy, while Reinforcement Learning (RL) techniques enable adaptive and real-time control of load dynamics. Special focus is laid on load modelling in conditions of imbalance, dynamic parameter identification, and integration with smart grids and active distribution networks (ADNs). The review also discusses the importance of uncertainty embedded in probabilistic and data-driven models, customer behaviour, and the stochastic nature of distributed energy resources (DERs). The areas of future study emphasized AI-assisted adaptive architectures, hybrid frameworks, and digital twin applications for resilient and intelligent load modelling.OPEN ACCESS Received: 01/08/2025 Accepted: 26/09/2025 Published: 27/11/2025

Generalized Convergence of Sequences of Fuzzy Numbers by Means of Modulus Functions

Jesús Sánchez Pinedo — Thu, 27 Nov 2025 09:54:13 +0100

In this paper, we extend the concepts of statistical convergence and strong summability for the sequences of fuzzy numbers using modulus functions. By introducing appropriate conditions on the modulus functions, we generalize and refine existing notions of convergence within the fuzzy setting. Additionally, we establish several interrelationships between these extended concepts, thereby contributing to the deeper understanding of summability and convergence behavior in the sequences of fuzzy numbers.OPEN ACCESS Received: 16/06/2025 Accepted: 20/08/2025 Published: 27/11/2025

Modeling and Thermal Analysis of a Ground Cooling System for Drilling Fluids in Ultra-Deep Wells

Jesús Sánchez Pinedo — Thu, 27 Nov 2025 09:53:33 +0100

Elevated downhole temperatures in ultra-deep wells (>8000 m) accelerate thermal degradation of drilling fluids and tools, reducing operational safety and efficiency. The reduction of wellbore temperature is an important issue. In this paper, a cooling system model for a drilling fluid was designed. Additionally, a comprehensive analysis of the heat transfer behavior within ultra-deep wells was conducted. A miniaturized heat exchange cooling device was used to simulate various conditions, including fluid media, flow rate, drilling fluid/coolant temperature, and heat exchanger structure. This analysis elucidates the impact of various factors on cooling efficiency. The experimental results show that the cooling effect is best at a medium flow rate, with a refrigerant temperature of−10°C reducing the temperature of pure water from 60°C to 32°C. The experiment also found that the higher the temperature of the pure water and the lower the temperature of the coolant, the better the heat transfer efficiency. For water-based drilling fluid, the optimum cooling flow rate is around 0.52 m/s, with an average Reynolds number of 4966, and the maximum cooling range can exceed 30°C. Furthermore, the coil heat exchanger significantly improves the cooling rate compared to the straight-tube heat exchanger, although the pressure difference also increases. The cooling rate of oil-based drilling fluid at high flow rates is greater than that of water-based drilling fluid, and the pressure difference in the coil heat exchanger for oil-based drilling fluid, which has higher viscosity, increases significantly. This research provides an experimental basis for the design and optimization of drilling fluid surface cooling systems, which is crucial for improving the safety and efficiency of deep and ultra-deep wells drilling.OPEN ACCESS Received: 10/06/2025 Accepted: 15/08/2025 Published: 27/11/2025

Multi-Dimensional Mechanical Properties Approach to Analyzing Thin UHPFRC Decks

Jesús Sánchez Pinedo — Thu, 27 Nov 2025 09:51:13 +0100

This study evaluates the flexural behavior of an Ultra high performance fiber-reinforced concrete (UHPFRC) slab through experimental and Finite Element Method (FEM) analytical investigations. A full-size U-UHPFRC bridge deck specimen serves as a reference for the research. A nonlinear FEM is put forward to link material characteristics, failure mode, and bearing capacity of U-UHPFRC decks, considering the failure behavior with different impact parameters of reinforcement ratio, thickness and side ratio. The flexural performance calculation formula for UHPFRC slabs was derived using three failure modes. The results indicate that this method can effectively predict the load transfer and distribution patterns of UHPFRC thin slabs, providing a reference range for the reinforcement ratio, thickness and long-short side ratio in UHPFRC one-way or two-way slabs. These research results can optimize the crack resistance and toughness of thin UHPFRC decks, improve durability, and appropriately reduce carbon emissions. It is suitable for bridges or special structures with higher load requirements and provides theoretical support for the full-life operation and development of UHPFRC components.OPEN ACCESS Received: 31/05/2025 Accepted: 10/07/2025 Published: 27/11/2025

Numerical Analysis of Geometry and Hole Effects on Fatigue Crack Growth and Component Lifespan

Jesús Sánchez Pinedo — Thu, 27 Nov 2025 09:50:23 +0100

Mechanical failures have caused significant damage and financial losses. However, compared to the vast number of successful designs for mechanical components and structures, mechanical failures are relatively rare. Mechanical failures involve a highly complex interaction between time, load, and the environment, with the environment comprising two factors: temperature and corrosion. The load can be uniform, steady, variable, uniaxial, or multiaxial. Undesirable effects of cyclic loads lead to crack initiation and growth, ultimately resulting in component failure. In this study, the focus is on increasing the life of components under cyclic loading by investigating crack behavior and development in the presence of stop holes. The investigation was conducted on aluminum 7075. Initially, the arrangement of circular holes in the standard CT sample was obtained using Design Expert software to assess this effect. By performing simulations in Abaqus software, the life of each sample was determined. In the next stage, based on these results, the samples are subjected to examination in the presence of key-shaped notches.OPEN ACCESS Received: 22/03/2025 Accepted: 27/05/2025 Published: 27/11/2025

Fondazioni profonde di dighe, ture e diaframmi

Alessandro Calvi — Sun, 23 Nov 2025 09:42:43 +0100

Alessandro Calvi — Sun, 23 Nov 2025 09:35:41 +0100

Alessandro Calvi — Sun, 23 Nov 2025 09:35:23 +0100

Alessandro Calvi — Sun, 23 Nov 2025 09:35:07 +0100

Alessandro Calvi — Sun, 23 Nov 2025 09:33:43 +0100

Alessandro Calvi — Sun, 23 Nov 2025 09:33:17 +0100

Alessandro Calvi — Sun, 23 Nov 2025 09:32:34 +0100

Alessandro Calvi — Sun, 23 Nov 2025 09:31:53 +0100

DE LA RESPONSABILITÉ DES ENTREPRENEURS ET DES INGÉNIEURS-ARCHITECTES EN MATIÈRE DE CONSTRUCTION D'USINES, DE BARRAGES ET D'INSTALLATIONS

Alessandro Calvi — Sat, 22 Nov 2025 21:22:34 +0100

Les nouveaux groupes de la centrale prototype de Castet

Alessandro Calvi — Sat, 22 Nov 2025 21:04:33 +0100

Technical Due Diligence Report – Anderson Dam & Hydropower Plant (California, USA)

Alessandro Calvi — Sat, 22 Nov 2025 19:50:23 +0100

This study represents the technical due diligence of the hydroelectric plant consisting of the
Anderson Dam (Leroy Anderson) and its power station, located in California, USA.
This technical contribution is structured analyzing the hydrogeological aspects of the dam site's
geographical context, including its structural behavior in the event of significant seismic events, and
then going more in detail on the energy assessments that take into account the temporal distribution
of rainfall and flow rates, as well as the efficiency of the hydraulic machinery installed.

Ductile-Fragile Transition: A Novel Comparison Between Fracture Mechanics of Materials and Framed Structures_Conference Presentation

Alessandro Calvi — Sat, 22 Nov 2025 19:46:23 +0100

A Multi-Scale Approach to Progressive Collapse: Linking Material Fracture Mechanics and Hierarchical RC Frames Behavior

Alessandro Calvi — Sat, 22 Nov 2025 19:41:33 +0100

Analysis of flow past a porous net-like screen through high-fidelity simulations

Scipedia content — Fri, 21 Nov 2025 10:57:19 +0100

Numerical Simulations of Wave Breaking Flows with Air Entrainment

Scipedia content — Fri, 21 Nov 2025 10:55:57 +0100

Composite materials for wind assisted ship propulsion systems

Scipedia content — Fri, 21 Nov 2025 10:50:50 +0100

Experimental and numerical study of dynamic loads on marine propellers operating under near-surface and partially submerged conditions.

Scipedia content — Fri, 21 Nov 2025 10:49:23 +0100

Passive pitch control for improving efficiency in tidal turbines

Scipedia content — Fri, 21 Nov 2025 10:47:49 +0100

Using CFD and VR to Model and Visualize Fire in the Ship Engine Room

Scipedia content — Fri, 21 Nov 2025 10:46:17 +0100

Fluid-Structure Interaction in Maritime Applications: Towards Partitioned Coupling Simulation of Wind-Sail Interaction

Scipedia content — Fri, 21 Nov 2025 10:44:50 +0100

Effect of ship pitching on frigate flight deck aerodynamics by CFD analysis and experimental validation

Scipedia content — Fri, 21 Nov 2025 10:43:22 +0100

A Fundamental Study of Wind Loads on Vessel by CFD

Scipedia content — Fri, 21 Nov 2025 10:41:57 +0100

Multi-resolution WENO semi-Lagrangian High-order Finite Element Method for a Nonhydrostatic Ocean Model

Scipedia content — Fri, 21 Nov 2025 10:40:40 +0100

Effects of Offshore Wind Energy on Ocean Circulation and Mixing

Scipedia content — Fri, 21 Nov 2025 10:36:24 +0100

Simple and advanced numerical methods for determining the hydrodynamic properties of a TLP-type floating wind turbine

Scipedia content — Fri, 21 Nov 2025 10:34:59 +0100

Numerical Analysis of Fluid-Structure Interaction (FSI) for Semi-Submersible Floating Offshore Wind Turbines (FOWT)

Scipedia content — Fri, 21 Nov 2025 10:33:00 +0100

Toward Meshless Turbulent Flow Simulation: LES-Integrated Vortex Particle Method

Scipedia content — Fri, 21 Nov 2025 10:31:24 +0100

Impacts of Offshore Wind Farm Wakes on Regional Ocean Circulation

Scipedia content — Fri, 21 Nov 2025 10:29:40 +0100

Wave-Age Influence on Aerodynamic Loads and Wake Recovery in Offshore Wind Turbines

Scipedia content — Fri, 21 Nov 2025 10:27:51 +0100

Design Digital Twinning for Hydro-Structural Optimization: Addressing High-Dimensional Design Spaces with Parametric Model Embedding

Scipedia content — Fri, 21 Nov 2025 10:24:16 +0100

Ship Motion Digital Twinning via Dynamic Mode Decomposition Approaches

Scipedia content — Fri, 21 Nov 2025 10:22:44 +0100

Integrating Environmental Forecasts and Ship Motion Analysis for Predictive Modelling of Motion Sickness in Maritime Operations

Scipedia content — Fri, 21 Nov 2025 10:21:18 +0100

Non-Intrusive Model Order Reduction: Applications in Naval Optimization

Scipedia content — Fri, 21 Nov 2025 10:19:56 +0100

Holistic Digital Twin of the Ocean

Scipedia content — Fri, 21 Nov 2025 10:18:32 +0100

A Fully Coupled and Efficient Numerical Model for Slender Marine Vegetation in Waves

Scipedia content — Fri, 21 Nov 2025 10:12:51 +0100

Numerical Modeling of Structural Response of IMTA System to Environmental Loading of the Gulf of Mexico

Scipedia content — Fri, 21 Nov 2025 10:11:22 +0100

A Fluid-Structure Interaction (FSI) Numerical Model for Aquaculture-Related Structures in Offshore Environment

Scipedia content — Fri, 21 Nov 2025 10:09:57 +0100

Finite volume modelling of moored floating structured with fluid-mooring interaction

Scipedia content — Fri, 21 Nov 2025 10:08:36 +0100

Characterization and Modeling of an Innovative Textile Mooring Chain

Scipedia content — Fri, 21 Nov 2025 10:07:19 +0100

Numerical study of the fluid-structure interaction in submerged aquatic canopy using immersed boundary-lattice Boltzmann method

Scipedia content — Fri, 21 Nov 2025 10:05:55 +0100

Drag Loads to Aquaculture Nets and the Corresponding Flow Velocity Reduction behind

Scipedia content — Fri, 21 Nov 2025 10:04:15 +0100

FEM modelling of the mechanical phenomena at the mesoscopic scale within the cross section of a synthetic subrope for the mooring lines of floating offshore wind turbines.

Scipedia content — Fri, 21 Nov 2025 10:02:15 +0100

Comparison study of elements used in FEA of aquaculture nets

Scipedia content — Fri, 21 Nov 2025 10:00:48 +0100

A simple overview of least squares

R. Itza Balam — Thu, 20 Nov 2025 15:51:24 +0100

In this work we aim to give an overview of least squares for curve fitting. The idea is to illustrate, for a broad audience, the mathematical foundations and practical methods used to solve this simple problem. We will consider four methods: the normal equations method, the QR factorization, the singular value decomposition (SVD), as well as a new approach based on neural networks. The last approach is not as common as the others, but it is very interesting because, in modern days, it has become a very important tool in many branches of modern knowledge, like data science (DS), machine learning (ML) and artificial intelligence (AI).

Sampling the Darcy friction factor using Halton, Hammersley, Sobol, and Korobov sequences: Data points from the Colebrook relation

Dejan Brkić — Thu, 20 Nov 2025 13:39:42 +0100

When the Colebrook equation is used in its original implicit form, the unknown pipe flow friction factor can only be obtained through time-consuming and computationally demanding iterative calculations. The empirical Colebrook equation relates the unknown Darcy friction factor to a known Reynolds number and a known relative roughness of a pipe’s inner surface. It is widely used in engineering. To simplify computations, a variety of explicit approximations have been developed, the accuracy of which must be carefully evaluated. For this purpose, this Data Descriptor gives a sufficient number of pipe flow friction factor values that are computed using a highly accurate iterative algorithm to solve the implicit Colebrook equation. These values serve as reference data, spanning the range relevant to engineering applications, and provide benchmarks for evaluating the accuracy of the approximations. The sampling points within the datasets are distributed in a way that minimizes gaps in the data. In this study, a Python Version v1 script was used to generate quasi-random samples, including Halton, Hammersley, Sobol, and deterministic lattice-based Korobov samples, which produce smaller gaps than purely random samples generated for comparison purposes. Using these sequences, a total of 220= 1,048,576 data points were generated, and the corresponding datasets are provided in in the zenodo repository. When a smaller subset of points is needed, the required number of initial points from these sequences can be used directly. Dataset:https://doi.org/10.5281/zenodo.17280142

Modelling Wave-Structure Interaction of Submerged Flexible Plates

Scipedia content — Thu, 20 Nov 2025 11:01:50 +0100