Computation doi: 10.3390/computation14060133

Authors: Yun Liu Xijuan Liu Lifeng Guo

In this study, we present a discrete switching host–parasitoid model that incorporates biological and chemical control interventions within the integrated pest management (IPM) measures. The coupling of multi-tactic control measures induces rich and complex dynamical behaviors in the proposed system. We begin by systematically characterizing the existence and stability of fixed points in the control subsystem. The analysis then proceeds to demonstrate how the system undergoes multiple bifurcation routes, including period-doubling, transcritical, and Neimark–Sacker bifurcations. Building on this theoretical foundation, extensive numerical simulations are conducted, not only corroborating our analytical predictions but also revealing emergent phenomena such as cascading period-doubling routes and chaotic regimes. Finally, high-resolution two-parameter stability diagrams are employed to identify the critical dynamical transition boundaries, and the corresponding ecological implications for practical pest management decision-making are elaborated in depth.

Computation doi: 10.3390/computation14060132

Authors: Abdelhak Guendouzi Fatimah A. Almulhim

This paper investigates an M/M/1 queueing system with differentiated vacations, threshold-based interruptions, and customer impatience in the form of balking and reneging. Using recursive analytical methods, we derive closed-form steady-state probabilities and key performance metrics, including average queue length and customer loss rates. To address the practical need for cost-efficient operation, we formulate an economic cost function and determine the optimal service rate using Particle Swarm Optimization (PSO). Numerical experiments conducted in R show that the optimal service rate ranges between 2.71 and 3.48 across different cost structures, achieving minimum expected total costs between 183.23 and 199.04. The results further reveal that the cost function is convex with a clear global minimum, and that earlier vacation interruptions (smaller n1 and n2) significantly reduce both system congestion and customer loss. The proposed approach provides actionable insights for designing and managing service systems in domains such as healthcare, telecommunications, and cloud computing, where server availability is intermittent and customer patience is limited.

Computation doi: 10.3390/computation14060131

Authors: Yurii Kravchenko Serhii Stavytskyi Oleksandr Makhovych Andriy Dudnik Roman Dubik Dmytro Obidin Oleksandr Permiakov Oleksandr Shapran Yevhenii Makhno Yevhen Rudenko

This paper considers a numerically stable discrete-time representation of the three-dimensional Constant Turn (CT) motion model within the Interacting Multiple Model (IMM) framework for radar tracking of maneuvering aerial targets. Classical discrete CT models used in Kalman-filter-based tracking contain singular expressions in the vicinity of zero and near-zero turn rates, which may degrade estimation accuracy and impair numerical robustness. To address this problem, a Maclaurin-series-based discretization of the three-dimensional CT model is developed, in which the state transition matrix and the process-noise-related matrices are approximated in polynomial form. Linear, quadratic, and cubic approximations are constructed and analyzed. The proposed CT model is integrated into a three-model IMM algorithm together with the Constant Velocity (CV) and Constant Acceleration (CA) models. The study includes both an internal comparison of Maclaurin approximations of different orders and an external comparison with the classical CT discretization and a Padé-based reference discretization. Numerical experiments are performed for representative three-dimensional maneuvering scenarios under radar measurement conditions. The obtained results show that the proposed discretization eliminates singular behavior near zero turn rate while preserving the tracking capability of the IMM estimator. The comparative analysis demonstrates that the quadratic Maclaurin approximation provides the most favorable trade-off between modeling accuracy, numerical stability, and computational cost. It yields tracking performance close to higher-order approximations and competitive with the Padé-based reference approach, while remaining simpler for practical implementation in real-time radar tracking systems. These results indicate that the proposed quadratic approximation is a suitable solution for maneuvering aerial target tracking in three-dimensional radar applications.

Computation doi: 10.3390/computation14060130

Authors: Barbara D. Weiß Eva-Maria Wartha Christian Jordan Thomas Ladinek Bahram Haddadi Michael Harasek

Red liquor combustion is a crucial step in the chemical recovery process in the pulp and paper industry and has two main functions: recovering MgO and SO2 from magnesium bisulfite spent liquor and generating steam as a heat source for further usage. This research aims to analyze how different red liquor spraying characteristics affect combustion time, guiding recommendations for optimal spraying characteristics to achieve faster combustion using computational fluid dynamics (CFD). Red liquor combustion is simulated in the open-source environment OpenFOAM®, employing Eulerian–Lagrangian coupling simulations, treating red liquor droplets as Lagrangian particles. One-step devolatilization and combustion kinetics are derived from performed non-isothermal thermogravimetric analyses (TGA) and implemented into the model. An industrial red liquor combustion vessel served as a reference case. Through virtual experiments, we explore the impact of spray angle (15° and 30°), droplet size (2 mm and 3 mm), and spray type (fullcone vs. hollowcone) on combustion time. The performed simulations indicate that the combustion time can be reduced by approximately 30% by reducing the characteristic particle diameter from 3 mm to 2 mm. Furthermore, hollowcone spraying revealed faster combustion times than fullcone spraying. The fastest combustion time was achieved with a characteristic particle size of 2 mm, a spraying angle of 30°, and using a hollowcone spray type.

Computation doi: 10.3390/computation14060129

Authors: Aladeen Al Basheer

This study analyzes a modified Holling–Tanner predator–prey system with linear harvesting and supplementary food for the predator. The framework examines how harvesting interacts with predation and external resources to determine system dynamics. We derive explicit conditions for the existence and stability of all equilibria and identify a critical predation threshold separating stable coexistence from oscillatory dynamics. Harvesting acts as a control parameter that can suppress oscillations, eliminate interior equilibria, and drive the system toward a prey-free state. We establish sufficient conditions for pest eradication by linking harvesting intensity, predation rate, and the loss of coexistence equilibria. Local bifurcation analysis reveals Hopf and saddle–node bifurcations, marking transitions between steady states and periodic oscillations. For the spatially extended system, diffusion-driven instability is investigated, and conditions for Turing pattern formation are derived from the modified equilibrium structure. Numerical simulations support the analytical results and illustrate transitions between dynamical regimes under varying harvesting levels. The results provide explicit parameter thresholds governing stabilization, oscillation, and eradication in predator–prey systems with external resource support.

Computation doi: 10.3390/computation14060128

Authors: Peter D. Morley

We are interested in determining the physics bound for the detection of signals in modern digital radio frequency (RF) hardware. Classical signal theory (Kalman filters) requires that the signal-to-noise power ratio (SNR) >10, but this is not the physics bound. Instead, the physics bound is much more complicated. Because an important application is radar, we ask whether, in a time interval of 1 μs, a signal is present within the noise of the receiver baseband. For radar, this would be the pulse return reflection. For our analysis, we use the Keysight Technologies UXR_25 oscilloscope as the RF receiver that has an analogue-to-digital converter (ADC) chip of 256 billion samples per second. In 1 μs, then, 256 thousand voltage samples are taken. We want to determine if a signal is present using the 256 thousand voltage samples using random matrix theory (RMT). The answer for this particular ADC is that we can detect any signals with SNR >−20 dB, a thousand-fold increase from SNR > 10. This paper gives the physics bound of signal detection.

Computation doi: 10.3390/computation14060127

Authors: María Patricia Tzili Cruz Salvador Contreras Hernández José Martín Espínola Sánchez Raúl Hernández Medina Alma Alejandra Luna Gómez Adriana Marlene Pacheco Orozco

Sentiment analysis has broad applications in social media networks due to the high volume of user activity on diverse topics such as political debates. Transformer-based neural networks are among the technologies that achieve significant results in text classification. This study evaluates twelve pre-trained transformer-based models through fine-tuning for sentiment classification of Spanish-language political texts from the social media network X. Some of these models were originally created in Spanish, while others are multilingual models that include Spanish. The twelve models were trained to specialize in sentiment classification on political topics, using the same training and testing parameters, in order to compare them under equal conditions during fine-tuning. Good results were obtained with the precision, recall, and F1-score metrics mainly in multilingual models but also in some models originally created in Spanish. The study includes the detailed results of the evaluation in training and testing for the three metrics employed.

Computation doi: 10.3390/computation14060126

Authors: Ibragim Suleimenov Akhat Bakirov

This paper proposes a spatial analog of the compass rose, constructed using finite fields and the discrete logarithm operation. The basic idea is to match the geometric elements of regular and semiregular polyhedra with elements of Galois fields (GF), which allows for the introduction of discrete spherical coordinates defined in algebraic form. The icosahedron is considered as a basic example. It is shown that using the icosahedron faces and the GF(41) field results in a 20-directed spatial structure that can be interpreted through discrete analogs of polar and azimuthal coordinates. Next, a variant based on the icosahedron edges and the GF(31) field is investigated, in which the number of directions increases to 30 while maintaining the regularity of the construction. A further generalization to the case of a truncated icosahedron, associated with the GF(181) field, is also considered, demonstrating the possibility of increasing the angular resolution without abandoning the algebraic organization of the set of directions. The obtained results demonstrate that the spatial rose of compass points can be represented as a finite system of directions with an explicit internal structure, convenient for coding, enumeration, and algorithmic processing. The proposed approach is of interest for problems of discrete description of rotations, construction of finite coordinate systems, and development of sectoral control algorithms, including those applicable to UAVs and their groups. The proposed formalism may also be considered as a sector-level coding layer for command-and-control architectures in which it is sufficient to identify a spatial sector rather than reconstruct full continuous coordinates.

Computation doi: 10.3390/computation14060125

Authors: Chia Xin Liang Pu Tian Caitlyn Heqi Yin Yao Yua An-Hou Wei Ming Li Xinyuan Song Tianyang Wang Ziqian Bi Ming Liu Riyang Bao Pengbin Feng

This survey provides a comprehensive guide to Multimodal Large Language Models (MLLMs) with a focus on vision–language tasks, including image captioning, visual question answering, cross-modal retrieval, visual grounding, multi-image reasoning, long-video understanding, and embodied AI. We examine architectures, training pipelines, and practical applications, covering visual encoders, language model backbones, connector modules, contrastive pre-training, instruction tuning, and preference alignment. We also foreground first-principles constraints—information bottlenecks, data-processing limits, and statistical co-occurrence bias—that shape architecture, robustness, and evaluation. This survey centers on vision–language systems and does not cover audio-only models or code-generation tools without visual inputs. Through task-level analysis and system-level case studies, we examine prominent MLLM implementations while addressing key challenges in scalability, memory, energy use, inference cost, robustness, and cross-modal learning. We present a unified taxonomy of the MLLM design space, a comparative overview of representative models and evaluation benchmarks, and a discussion of open problems. Concluding with ethical considerations and responsible AI development, this survey offers theoretical frameworks and practical insights for researchers, practitioners, and students working at the intersection of natural language processing and computer vision.

Computation doi: 10.3390/computation14060124

Authors: Sixuan Chen Xiaobo Wu Tao Wang Hongli Ma Xiang Li Lisheng Yin

This study proposes an enhanced variant of the gold rush optimizer (GRO) algorithm, termed the complex-order gold rush optimizer (CoGRO) algorithm, to address two inherent theoretical limitations of the original GRO. First, GRO employs a random initialization strategy that lacks ergodicity and uniform coverage, leading to insufficient population diversity and a higher risk of premature convergence. Second, its position update mechanism relies solely on current-time information without incorporating historical search experience, which restricts the algorithm’s ability to model long-term dependencies and escape local optima in complex multimodal landscapes. To overcome these deficiencies, we introduce a chaotic LCS1 initialization to enhance population diversity through improved ergodic coverage, and we embed a complex-order derivative mechanism into the migration and collaboration updates to provide infinite memory capability. A comprehensive sensitivity analysis is conducted to examine the influence of control parameters on CoGRO’s performance, leading to the identification of an optimal parameter configuration. The effectiveness of the proposed algorithm is evaluated using the CEC2022 benchmark suite through ablation studies and comparative analyses with state-of-the-art algorithms. Experimental results on the CEC2022 benchmark suite comprising 12 test functions demonstrate that CoGRO significantly outperforms the original GRO, achieving an average solution accuracy improvement of 0.84% and an average standard deviation reduction of 67.6 across all 12 functions, with particularly notable improvements on hybrid and composition functions. Wilcoxon signed-rank tests confirm the statistical significance of these improvements (p<0.05). These results confirm the feasibility and effectiveness of CoGRO as an improved optimization method for complex engineering problems.

Computation doi: 10.3390/computation14060123

Authors: Eduardo M. Martin Alma L. Guerrero-Barrera F. Javier Avelar-Gonzalez Rogelio Salinas-Gutierrez Mario Jacques

Bacterial two-component systems (TCSs) mediate environmental sensing and adaptive responses through signal transduction between histidine kinases (HKs) and response regulators (RRs), thereby regulating biochemical processes essential for survival and, in pathogenic species, infection. How signaling specificity and insulation are maintained in organisms encoding multiple paralogous two-component systems remains an open question. Here, we investigate specificity in the Actinobacillus pleuropneumoniae TCS signaling network using an integrated computational framework that combines coevolutionary analysis, structural modeling, molecular dynamics simulations, and free-energy calculations. We show that cognate HK-RR recognition is established locally through clusters of coevolving interface residues, termed the orthologue interface specificity core (OISC), which mediate symmetric molecular recognition at individual interaction interfaces. However, interface-level recognition alone is insufficient to explain signaling fidelity across the network. Instead, system-wide specificity and pathway insulation emerge in this network from asymmetric energetic discrimination among cognate and non-cognate interactions across the ensemble of paralogous interfaces. Graded free-energy profiles reveal that broadly compatible interfaces can coexist with robust signaling insulation, reconciling interface promiscuity with stable network organization. Together, these findings support a two-tiered model for the TCS network analyzed here, in which symmetric interface constraints enable cognate recognition, while asymmetric network-level energetics govern signaling specificity. This framework may extend to other paralogous TCS networks.

Computation doi: 10.3390/computation14060122

Authors: Mai Al Shaaban Joey Takei Annamaria Palmiero Leya Dereje Sam Panitch Caixia Chen Yong Yang Yonghua Yan

Accurate characterization of coherent vortex structures in high-speed turbulent boundary layers presents a persistent challenge due to the flow’s high dimensionality and nonlinear dynamics. This study investigates an optimized decomposition framework that integrates modal decomposition techniques with a novel vortex identification strategy to extract dynamically significant features. The numerical solution from a previously conducted high-fidelity simulation of MVG-controlled supersonic flow serves as the testbed. Principal Component Decomposition and Non-negative Matrix Factorization are applied across multiple flow variables to evaluate their effectiveness in isolating coherent structures. The results show that, across the velocity-based cases, 3–4 modes capture 70% of the TKE with MSE about 0.1, while the Liutex case requires 14 modes but achieves a lower MSE of about 0.04. Overall, using the same number of modes yields similar reconstruction performance across all cases. The influence of various normalization and rescaling methods on decomposition performance is also examined. Optimization is guided by two primary criteria: the interpretability of spatial modes and MSE in reconstructing vortex structures. By employing low-rank matrix representations, this optimization study aims to enhance interpretability and reduce computational costs. This approach establishes a mathematically rigorous and efficient platform for analyzing vortex dynamics, achieving significant dimensionality reduction while preserving key features of turbulent transport.

Computation doi: 10.3390/computation14060121

Authors: Narimene Ayat Fouzi Harrag Nassir Harrag Khaled Shaalan

Arabic’s complex morphological system and the optional use of short vowels (tashkīl) introduce substantial lexical ambiguity, posing significant challenges for Large Language Models (LLMs). While diacritics enhance linguistic precision, LLMs trained predominantly on undiacritized corpora often exhibit performance degradation when processing fully diacritized inputs due to representation shifts and tokenization inconsistencies. To address this limitation, we propose the Arabic Diacritic Lexical Knowledge Graph (ADL-KG), a structured framework that links diacritized and undiacritized forms through integrated lexical, morphological, and semantic knowledge. Building upon this resource, we introduce Diacritic-Aware Knowledge Graph Prompting (DA-KGP), a prompt augmentation strategy that injects explicit linguistic features into LLM inputs to facilitate robust interpretation of diacritized Arabic text. The framework is evaluated on the Arabic Reading Comprehension Dataset under zero-shot and few-shot question answering across AraGPT2-base, BLOOMZ-560M, SILMA-v1, and LLaMA 3.1-8B. Performance is assessed using Exact Match, BLEU, ROUGE-1, and BERTScore-F1. Experimental results show that fully diacritized prompts significantly degrade baseline performance, whereas DA-KGP consistently mitigates this effect by improving semantic alignment across diverse architectures. For AraGPT2-base, KG augmentation improves average BERTScore-F1 by +5.96 points. SILMA-v1 achieves the strongest lexical improvements, reaching 21.57 BLEU and 81.31% BERTScore-F1 in the KG-enhanced two-shot configuration. LLaMA 3.1-8B achieves the highest overall semantic performance with 82.54% BERTScore-F1 under KG-enhanced prompting, while BLOOMZ-560M also demonstrates statistically significant semantic gains through structured augmentation. These findings demonstrate that morphologically informed prompting and structured lexical grounding provide an effective and parameter-efficient strategy for improving the robustness and semantic fidelity of Arabic LLMs under fully diacritized input conditions.

Computation doi: 10.3390/computation14060120

Authors: Vahid Goodarzimehr Farnaz Salajegheh Ghanshyam Tejani

Developing seismic-resistant systems for steel frames presents a significant challenge in structural engineering, requiring sophisticated computational methods to achieve effective and precise outcomes. This study focuses on enhancing the Special Relativity Search (SRS) algorithm by redefining the mass (m) parameter, a critical element affecting its convergence characteristics. Traditionally, the SRS algorithm treated m as a fixed unit value. However, detailed analysis indicates that dynamically modifying m can substantially improve the algorithm’s ability to solve complex optimization problems. To address this, a novel weighted equation for m is proposed, leading to improved convergence rates and greater accuracy in solutions. The refined Weighted Special Relativity Search (WSRS) algorithm is then applied to optimize the placement of seismic-resistant systems in steel frames. Comparative evaluations demonstrate that the WSRS algorithm outperforms its predecessor, delivering enhanced precision and computational efficiency. This research contributes to the advancement of algorithmic techniques and the optimization of seismic-resistant structural designs.

Computation doi: 10.3390/computation14060119

Authors: Amir H. Shahriari Majid Monajjemi Fatemeh Mollaamin

This study presents an AI-assisted thermodynamic and computational fluid dynamics (CFD) evaluation of a β-type Stirling engine to improve its thermal efficiency and indicated power output. The engine performance was investigated using Restricted Dimensions Thermodynamics (RDT), the Schmidt thermodynamic model, and three-dimensional CFD simulations under various operating and geometric conditions. Key parameters including rotational speed, phase angle, piston diameter, displacer stroke, porosity, and charged pressure were systematically analyzed to determine their influence on engine behavior. A feed-forward artificial neural network (ANN) trained using the Levenberg–Marquardt optimization algorithm was integrated with CFD-generated datasets to predict engine performance and accelerate the optimization process. The AI-assisted optimization was coupled with the Variable Step-size Simplified Conjugate Gradient Method (VSCGM) to identify near-optimal operating conditions while reducing computational cost. Simulation results demonstrated that the optimization process improved the indicated power from 180.33 W to 185.44 W and increased thermal efficiency from 10.32% to 11.54%. The results also showed close agreement between predicted and experimental pressure–temperature profiles, confirming the reliability of the proposed methodology. Furthermore, CFD analyses revealed that increasing piston diameter and optimizing porosity enhanced heat transfer and pressure distribution within the engine chambers, resulting in improved thermodynamic performance. The proposed AI-driven framework provides a reliable and computationally efficient approach for the design and optimization of advanced β-type Stirling engines operating under realistic thermal conditions.

Computation doi: 10.3390/computation14060118

Authors: Wei Zuo Xiongwei Yao Yimin Li Qingqing Li

To address performance limitations in proton exchange membrane fuel cells (PEMFCs), this work proposes and numerically investigates a cathode gas diffusion layer (GDL) with conical frustum grooves. A systematic comparison is performed across three GDL configurations: a baseline structure without grooves, a design with cylindrical grooves, and the proposed conical frustum grooves. The results demonstrate that the conical frustum grooves effectively enhance liquid water removal, oxygen mass transport, membrane current density, and peak power density. This improvement arises as the grooves expand transport pathways for both liquid water and oxygen, facilitating more robust electrochemical reactions. A parametric analysis is further conducted to evaluate the effects of groove spacing, depth, top radius, and bottom radius. Reduced groove spacing, together with increased groove depth, top radius, and bottom radius, consistently improves water management and oxygen delivery. However, membrane current density and power density do not vary monotonically with groove depth and bottom radius. The optimal values for these two parameters are identified as 0.3 mm and 0.5 mm, respectively.

Computation doi: 10.3390/computation14060117

Authors: Durward A. Cator Martial L. Ndeffo-Mbah Ulisses M. Braga-Neto

We present a novel framework for epidemiological disease spread modeling that combines agent-based simulation with Boolean state-space representations and optimal filtering for state estimation under noisy observations. Our approach models individual agents in discrete Susceptible-Exposed-Infected-Recovered (SEIR) states using a compact 2-bit Boolean representation, with agent interactions governed by scheduled contact patterns. To address the challenge of inferring latent infection states from limited and noisy testing data, we develop two complementary inference approaches: (1) a Boolean Kalman particle filter for small populations that tracks the full joint distribution over agent states, and (2) a mean-field approximation for large populations that factorizes the posterior into independent marginal distributions, enabling scalability to realistic population sizes. Unlike continuous-state Kalman filters, our methods naturally handle the discrete nature of epidemiological states while accommodating realistic observation models where only a subset of agents are tested at each time step, with test results subject to false positive and false negative errors. We demonstrate that this framework enables accurate reconstruction of population-level infection dynamics and individual agent states from sparse, noisy observations across populations from 100 to 50,000 agents, providing a computationally tractable approach for real-time epidemic monitoring.

Computation doi: 10.3390/computation14050116

Authors: Michael Vögtle Rainer Stauch Hermann Knaus

Urban areas significantly influence atmospheric flow fields and momentum exchange processes, which are relevant for wind energy applications and meso-scale atmospheric modeling. However, meso-scale simulations typically represent urban effects using surface roughness parameterizations that neglect volumetric momentum losses within the urban canopy layer. In this study, a methodology is presented to derive a volumetric urban canopy parameterization directly from building-resolved computational fluid dynamics (CFD) simulations. A detailed micro-scale CFD simulation of a real urban region is used to evaluate the momentum balance within a control volume surrounding the urban region. Based on this analysis, two key parameters are derived: the vertical distribution of the House Area Density (HAD), representing the geometric characteristics of the urban morphology, and an effective drag coefficient describing the momentum loss induced by the built environment. These parameters are subsequently implemented as volumetric source terms in a urban canopy model formulated analogously to plant canopy parameterizations. The resulting urban canopy model is validated by comparison with the fully resolved CFD simulation. The results show good agreement in the streamwise momentum balance and pressure loss distribution, while computational cost is significantly reduced. The proposed urban canopy model provides a physically consistent framework for representing urban momentum sinks in meso-scale flow simulations.

Computation doi: 10.3390/computation14050115

Authors: Sidra Ishfaq Muhammad Abdullah Khan Ghulam Mustafa Muhammad Tanvir Afzal Isabel De la Torre Díez Mirtha Silvana Garat de Marin Eduardo Silva Alvarado

Predictive modeling for lung cancer severity often struggles with the high dimensionality and multi-domain nature of risk factors. While individual contributors like smoking are well-documented, the relative predictive weight of lifestyle, environmental, and genetic domains remains insufficiently quantified in integrated frameworks. This study proposes an explainable machine learning approach using an XGBoost classifier to evaluate these three distinct risk domains. Utilizing the UCI Machine Learning Repository Lung Cancer Dataset, we implemented a domain-wise ablation study to isolate the predictive signal of each factor group. To ensure scientific rigor and address the “black box” nature of ensemble models, we employed 5-fold stratified cross-validation and SHAP (Shapley Additive Explanations) for feature-level transparency. Our results demonstrate that the integrated model achieves a classification accuracy of 95.7% (AUC-ROC = 0.98) on this dataset. Notably, ablation analysis revealed that the Lifestyle domain retained the highest standalone predictive performance (92.9%), followed by the Genetic/Clinical domain (94.6%), while the Environmental domain showed a more pronounced performance drop (73.3%), suggesting differential information density across risk categories. SHAP analysis identified cumulative smoking exposure as the primary feature influencing model predictions within this dataset. This study presents a proof-of-concept interpretable framework for lung cancer risk stratification, demonstrating that domain-wise ablation combined with explainable AI can provide transparent, feature-level insight to support rather than replace clinical judgment in settings where comprehensive diagnostic testing may be limited.

Computation doi: 10.3390/computation14050114

Authors: Vasilina K. Tsimpouka Nikolaos T. Giannakopoulos Damianos P. Sakas

This study examines whether digital attention can serve as an engagement-based digital attention signal for fintech market performance. Using a revised panel of 70 firm-year observations from seven publicly verifiable fintech and payments firms over 2016–2025, the analysis combines financial outcomes, sector investment indicators, and digital variables related to web traffic, SEO visibility, social media presence, and app popularity. A Digital Attention Index (DAI) was constructed through arithmetic averaging and principal component analysis, with the first component explaining 82.39% of the digital-indicator variance. Fixed Effects models show that the DAI is positively and significantly associated with revenue, market capitalization, and net income, while sector investment is generally weak or insignificant. Out-of-sample validation confirms that panel Fixed Effects specifications outperform pooled OLS, Ridge, and Random Forest models. App popularity is the strongest standalone predictor for revenue and net income, while social media performs best for market capitalization. However, first-difference models weaken most relationships, and Granger tests indicate bidirectional temporal ordering, with financial performance often preceding digital attention. Overall, the findings support the DAI as a useful computational signal of fintech performance, while emphasizing that predictive and causal claims require cautious interpretation.

Computation doi: 10.3390/computation14050113

Authors: Malik K. Altaee Azhar G. Hamad Thamer H. Alhussein Yousef S. Al Rjoub Nasser Firouzi Przemysław Podulka

Functionally graded (FG) materials can deliver greater mechanical performance compared to pure isotropic and composite materials. Temperature has a significant effect on structural performance, as it can substantially reduce the stiffness parameter and induce thermal stresses in fully restrained structures. This study investigates the nonlinear free vibration of functionally graded beams under a thermal environment. First, the nonlinear formulation of a Timoshenko beam using von Kármán nonlinear strain theory is derived. Then, the effect of temperature is applied. Finally, using the generalized quadrature method, which is a mesh-free method, the nonlinear vibration of the FG beam with different boundary conditions is analyzed. To the best of the authors’ knowledge, this study distinctively contributes to the existing literature by providing a rigorous integration of the GDQM with strongly nonlinear thermal vibration of FG beams, highlighting the lack of purely mesh-free treatments incorporating such coupled physics. The results show that increasing the temperature can lead to an instability phenomenon. Specifically, temperature increments cause a thermally induced mode change, profoundly altering the dynamic response. The conducted parametric study indicates that increasing the gradient index n enhances the nonlinear vibration behavior of FG beams.

Computation doi: 10.3390/computation14050112

Authors: Wilson Gustavo Chango Mayra Barrera Daniel Maldonado-Ruiz Julio Balarezo Marcelo V. Garcia Geovanny Silva

This paper investigates the design and evaluation of compact convolutional neural networks (CNNs) for keyword spotting (KWS) and acoustic event detection under the stringent constraints of the TinyML paradigm. The research expands upon traditional binary classification approaches by addressing a multi-class acoustic scenario encompassing eight distinct categories: stop, no, go, yes, unknown, silence, noise_ambient, and noise_sudden. The primary objective is to evaluate the feasibility of deploying reliable acoustic detection systems on ultra-low-power microcontrollers for edge computing applications. To this end, five lightweight architectures were developed and benchmarked: AlexNet-Tiny, LeNet-Tiny, MobileNet-Tiny, VGG-Tiny, and CustomCNN-Tiny. The models were trained using Mel-spectrogram features and optimized through INT8 post-training quantization to facilitate embedded deployment. Hardware simulation was conducted targeting the XIAO nRF52840 Sense microcontroller (64 MHz, 256 KB RAM). Experimental results demonstrate that the Gold VGG-Tiny architecture achieves the highest classification accuracy (89.81%), while Silver MobileNet-Tiny provides the superior operational efficiency with the lowest inference latency (0.88 ms) and minimal energy consumption (14.4 µJ). Furthermore, the Bronze CustomCNN-Tiny model achieves the most reduced memory footprint (42.9 KB), highlighting its suitability for memory-constrained environments. Statistical validation using Cohen’s Kappa, Matthews Correlation Coefficient (MCC), and Area Under the Curve (AUC) confirms the robustness and reliability of the proposed models. The potential application of this system is motivated by acoustic monitoring for the early detection of high-risk situations, such as gender-based violence. Future work will focus on on-device physical validation and real-world deployment in wearable safety electronics.

Computation doi: 10.3390/computation14050111

Authors: Peyman Faraji Hossein Parvini Sani Asghar Rasouli

Nature-inspired optimization algorithms (NIOAs) have attracted enormous attention thanks to their great capabilities in solving complex problems. This paper presents the novel Actiniaria optimization algorithm (ACTOA), inspired by the behavior and biological characteristics of Actiniaria (sea anemones). Actiniaria are known to have unique abilities to survive and interact with various marine environments. Therefore, they can provide an appropriate model for designing an optimization algorithm. This study aimed to balance the exploration and exploitation phases using Actiniaria’s two biological mechanisms: hunting and spawning. The exploration phase is developed with a hunting mechanism as a normal distribution of the searching particles with a reduced standard deviation (SD) around the best searching particle. Next, the dispersal of Actiniaria’s eggs in the exploitation phase under forces such as wind and ocean waves is simulated. The performance of ACTOA is assessed using a set of optimization parameters. The advantages of the algorithm’s performance were also examined by 59 test functions, and ACTOA outperformed modern algorithms. Ultimately, optimization of the three dams of Sariyar, Shafaroud, and Pine Flat was put on the agenda and the proposed algorithm showed that optimal solutions were found by the 700th, 840th, and 985th iterations, which resulted in savings of 28.2, 30, and 3.5 percent in concrete volume, respectively.

Computation doi: 10.3390/computation14050110

Authors: Ayaz H. Khan

The Biconjugate Gradient Stabilized (BiCGStab) algorithm is a widely used iterative method for solving large, sparse, and non-symmetric linear systems in scientific and engineering applications. While efficient, its performance is constrained by high iteration costs, memory bandwidth limitations, and synchronization overheads in CPU implementations. This paper investigates GPU-based acceleration of BiCGStab, with particular emphasis on the use of CUDA streams to optimize kernel concurrency and improve resource utilization. A structured hepta-diagonal matrix format is adopted to ensure efficient memory access across both CPU and GPU executions. Performance evaluations are conducted across problem sizes ranging from 1 to 64 million unknowns, comparing single-threaded and multi-threaded CPU baselines against GPU implementations with and without CUDA streams. The results demonstrate that GPU acceleration achieves up to 30× speedup relative to single-threaded CPU execution and up to 5× compared to the best OpenMP configuration (16 threads), with CUDA streams providing an additional 10–20% performance improvement through intra-iteration kernel overlap. Scalability analysis reveals that GPU performance advantages increase with problem size, underscoring the effectiveness of CUDA streams in minimizing idle GPU time and enhancing throughput. These findings highlight the potential of stream-optimized GPU solvers for large-scale scientific simulations and provide a foundation for future extensions incorporating CUDA graphs and multi-GPU environments.

Computation doi: 10.3390/computation14050109

Authors: Zihao Chen Zhentao Lin Bi Zeng Linyi Huang Jia Cai

To ensure the reliable operation of speech systems across diverse environments, noise addition methods have emerged as the standard solution. However, existing methods offer limited coverage of real-world scenes and depend on pre-existing noise libraries and scene metadata. This paper presents prompt-based Dynamic Generative Scene-based Noise Addition (DGSNA), a novel approach driven by generative language models that integrates Dynamic Generation of Scene-based Information (DGSI) with Scene-based Noise Addition for Speech (SNAS). The DGSI module, with a BET (Background, Examples, Task) prompt framework, dynamically generates logic-compliant scene-based information, including scene dimensions, sound sources, and microphone positions, thereby addressing the challenges of scene enumeration and detailed description. Complementing this, the SNAS module employs a Time–Frequency Diffusion-based (TFD) Text-to-Audio model to synthesize scene-specific noise. By integrating this noise with clean speech via Room Impulse Response (RIR) filters, the module streamlines the traditionally labor-intensive process of replicating diverse acoustic environments. Experimental results show that DGSNA significantly enhances the robustness of speech recognition and keyword spotting models, achieving relative improvements of up to 11.32%. Furthermore, DGSNA is highly compatible with existing noise addition techniques.

Computation doi: 10.3390/computation14050108

Authors: Marina Bershadsky Genady Kogan

We analyze parameter identifiability in a Marchuk-type immune-response model using longitudinal whole-blood transcriptomic signatures from the influenza challenge. Latent states are extracted from curated gene signatures derived from nine symptomatic and eight asymptomatic subjects. The governing delay differential equations are cast in a linear-in-parameters form; derivatives are estimated by smoothing splines, coefficients are fit by ridge regression, and the delay τ is selected by grid search. We find that the parameters governing viral and innate dynamics are consistently identifiable, with low relative error, and are highly determined, whereas adaptive-immunity and tissue-damage parameters are poorly constrained by transcriptomics alone. Introducing a small additive background term and tissue dependence markedly reduces residual variance and stabilizes estimates. Symptomatic patients exhibit a characteristic regulatory delay near 21 h. These results show that aggregated transcriptomic time series can reliably identify some subsystems of classical immune models, but that adaptive immunity and damage dynamics require explicit structural extensions or additional data modalities. The study provides a practical identification pipeline and concrete guidance on model extensions needed for transcriptomic-driven mechanistic inference.

Computation doi: 10.3390/computation14050107

Authors: Marina Bershadsky Božidar Ivanković Solomon Naftaliyev

Taking the Singularly Perturbed System (SPS) as a model of ODE system separation into fast and slow subsystems by an arbitrarily small parameter, we state and prove a theorem on the decomposition of an Ordinary Differential Equations (ODE) system without the aforementioned arbitrarily small parameter. In accordance with the proven theorem, we implemented an algorithm to decompose an ODE system into fast and slow subsystems by coordinate transformation. A similar algorithm is called the Singular Perturbed Vector Field (SPVF) algorithm; however, it is not justified by any stated theorem. Since we have not found any theorem to propose a similar ODE decomposition in the literature, we have tried to fill the gap with our theorem and algorithm explanations through examples. Finally, we propose our concept on Marchuk’s infectious diseases model, which allows a different analysis of the original Marchuk’s ODE system with delay.

Computation doi: 10.3390/computation14050106

Authors: Dmytro Chumachenko Sergiy Yakovlev

Artificial intelligence (AI) is assuming an increasingly important role in public health, where the scale, heterogeneity, and temporal dynamics of health-related data often exceed the capacity of conventional analytic approaches [...]

Computation doi: 10.3390/computation14050105

Authors: Najem N. Sirhan Riyad Alrousan Samar Al-Saqqa Faten Hamad Zaid Khrisat

Accurate short-horizon downlink throughput prediction is essential for automation in high-density 5G deployments (e.g., stadiums and events), where user load, scheduling decisions, and interference conditions change rapidly and produce highly variable user-perceived rates. This paper benchmarks lightweight regression models for per-user throughput prediction from readily available radio access network (RAN) key performance indicators (KPIs) and studies a risk-aware extension that augments point forecasts with calibrated uncertainty and an abstention (deferral) rule. Experiments use a strictly time-ordered train/calibration/test protocol on the Liverpool 5G High-Density Demand (L5GHDD) dataset. The target is strongly zero-inflated (about 62% of samples at 0 Mbps) and heavy-tailed, creating regimes where average-error optimization can mask rare but operationally important bursts. In the point-prediction benchmark, the best model is a tuned two-stage support vector regressor with a mean absolute error (MAE) of 0.452 Mbps, while the strongest single-stage model attains a weighted mean absolute percentage error (WMAPE) of 56.200%. For uncertainty quantification, we compare standard split conformal prediction against two input-adaptive alternatives. Constant-width split conformal attains 88.900% marginal coverage for a nominal 90% target with an average interval width of 2.288 Mbps, but width-based deferral is degenerate because all intervals have the same size. Variable-length conformal intervals preserve near-nominal coverage (91.100%) while producing informative width variation: normalized conformal reduces the average width to 1.344 Mbps, and conformalized quantile regression reduces it to 0.641 Mbps. At a deferral threshold of 1.500 Mbps, constant-width conformal defers all samples, whereas normalized conformal still acts on 61.200% of samples with selective MAE 0.219 Mbps. These results show that input-adaptive uncertainty is necessary for meaningful selective prediction in heteroscedastic 5G throughput dynamics.

Computation doi: 10.3390/computation14050104

Authors: Álvaro Gabriel Vega-De la Garza Ervin Jesús Alvarez-Sánchez Julio Fernando Zaballa-Contreras Rosario Aldana-Franco Fernando Aldana-Franco José Gustavo Leyva-Retureta Andrés López-Velázquez

Class imbalance among arrhythmia types and electrocardiogram (ECG) signal complexity present significant challenges for automated ECG-based arrhythmia detection. This research proposes an innovative approach that combines Genetic Algorithm (GA) optimization of Convolutional Neural Network (CNN) hyperparameters with morphological skeletonization of ECG images. The MIT-BIH Arrhythmia Database served as the primary data source, with the ECG signal converted to skeletonized representations emphasizing QRS complex geometry. A GA-optimized model was compared against a heuristic (manual design) baseline to determine optimal kernel and filter configurations. Evaluation emphasized not only overall accuracy but also robust metrics for minority classes. The optimized model achieved 97.26% accuracy, with macro recall improving substantially from 77.36% to 83.10% (+5.74%). These results demonstrate that evolutionary optimization enhances detection sensitivity to subtle geometric patterns, effectively mitigating class imbalance without artificial oversampling techniques.

Computation doi: 10.3390/computation14050103

Authors: Beichen Zheng Lili Wen

This work considers admissibility-enforcing reconstruction of reaction-channel subgroup levels on prescribed total-subgroup support and probabilities, a setting in which conventional exact reconstruction may produce negative reaction-channel levels. The proposed reconstruction relaxes conventional full matching by retaining selected low-order channel quantities associated with limiting dilution responses exactly, while fitting the remaining matching conditions in a constrained least-squares sense under nonnegativity. The exact-retention constraints are embedded through a null-space parametrization, which reduces the reconstruction to a convex optimization problem over the remaining degrees of freedom. Two variants are examined: a single-retention formulation, which is automatically feasible for nonnegative retained data, and a two-retention formulation, which is more restrictive and depends on compatibility with the fixed total-subgroup rule. Numerical tests for 238U capture data show that the proposed reconstruction removes the negative reaction-channel levels observed in the violating groups. Restoring admissibility entails deterioration in response accuracy relative to the unconstrained full-matching baseline, reflecting the trade-off between exact matching and nonnegativity on the fixed rule. Of the two variants considered, the single-retention formulation shows more stable overall behavior in the present comparison. In particular, for all violating cases at orders N≥10, it restores nonnegativity, with the reported 95th-percentile relative errors in the folded effective cross section not exceeding 8.90×10−7.

Computation doi: 10.3390/computation14050102

Authors: Amit Kumar Wakar Ahmad Om Pal Sunil

Modern user authentication systems increasingly need user and device-behavior-aware adaptive mechanisms to detect evolving threats beyond the traditional authentication framework of static credential verification. This paper proposes a hybrid multi-model framework for personalized user-level anomaly detection using a data-driven Hybrid Anomaly Score (HAS). The primary contribution lies in deriving the HAS using the joint integration of three adaptive attributes: dynamically computed per-user deviation thresholds conditioned on individual behavioral history, profile-age-aware baseline weights reflecting user cohort maturity, and criticality-scaled aggregation with the security impact of each detection methodology. The framework is evaluated on a large-scale real-world dataset and demonstrates strong detection performance, while achieving low inference latency suitable for real-time enterprise deployment. The ablation analysis of the framework confirms that dynamic weighting and personalized threshold substantially improve detection stability and convergence with an effective and deployable solution for large-scale authentication environments.

Computation doi: 10.3390/computation14050101

Authors: Valeriy G. Narushin Stefan T. Orszulik Michael N. Romanov Darren K. Griffin

Deriving from the Latin “ovum” (egg), the oval is a commonly used term, but does not have the status of a standard geometric figure like a circle or ellipse. Consequently, the oval lacks both a mathematical descriptive basis to attribute a set of key geometric parameters and an elegant formula to describe its contours. Herein, we consider the basis for deriving the formula of an oval for typical egg profiles. Specifically, these are round, ellipsoid, classic oval, pyriform (conical) and biconical shapes. To do this, we adhered to four basic postulates: (i) the ability to describe all possible egg shapes; (ii) a minimum set of measurable geometric parameters; (iii) the application of some universal indices (ratios of key geometric dimensions) to describe mathematical models; (iv) conformity with the “Main Axiom of the Mathematical Formula of the Bird’s Egg.” Additionally, we sought to comply with the principles of mathematical elegance. Following these theoretical assumptions and practical verification, we obtained a mathematically supported, elegant formula for this well-known but non-standardized geometric figure. The derived oval geometry equation will find use in applied problems of biology, construction, engineering and school curricula, alongside the classical figures of the circle and ellipse.

Computation doi: 10.3390/computation14050100

Authors: Yasuko Kawahata

As social infrastructure intensively developed during the high economic growth period of the 1970s faces simultaneous aging, there is an urgent need to transition from conventional reactive maintenance to preventive maintenance utilizing various data (data-driven asset management. However, the greatest barrier in practice is that inspection data is unevenly distributed in analog formats such as paper and unstructured files, and heavily relies on the subjective visual evaluation of expert engineers (e.g., discrete graded evaluations from A to D). The intervention of this “Assessor Bias” makes it difficult to ensure the robustness required for direct statistical analysis. This paper serves as a bridge between this analog expert knowledge and quantitative data science. It formulates human cognitive conflicts (true state, peer pressure, avoidance of cognitive load) using the distance-decay model of the Analytic Hierarchy Process (AHP) and the Softmax function, constructing a micro-macro link model accompanied by stochastic variations. Through large-scale multi-agent simulations (N=107) validating the model’s convergence, it was demonstrated that in long-tail distributions formed under peer pressure, macroscopic statistical distance metrics such as the Kullback-Leibler (KL) divergence ignore the fact that a small number of true signals are non-linearly suppressed, causing a statistical misinterpretation that “the error is within an acceptable range”. This implies that as long as macroscopic statistical indicators are over-trusted, signs of critical deterioration (minorities) will be structurally marginalized. Returning to the debate on “Homogeneity (Homogenität)” in German social statistics, this paper advocates that in order to realize objective “Micro-segmentation of Homogeneous Statistical Populations,” a paradigm shift from qualitative methods relying on human intuition to quantitative methods incorporating multi-criteria decision making is essential, rather than simply expanding the sample size.

Computation doi: 10.3390/computation14050099

Authors: Yining Xie Aoqi Shen Haochen Qi Jing Zhao Jianpeng Li Xichun Pan Anlong Zhang

Aero-engine defects often exhibit micro-scale and high-frequency characteristics under complex metallic textures, which makes precise segmentation difficult. Most existing pixel-level methods rely on spatial-domain modeling and lack frequency-domain decoupling. As a result, high-frequency details are easily hidden by low-frequency background information. In addition, repeated downsampling weakens the representation of fine-grained structures, leading to inaccurate boundary localization and limited robustness. To address these issues, a spectrum-driven hierarchical learning network is proposed for aero-engine defect segmentation. First, a dual-band spectral module is constructed using the discrete cosine transform to separate high-frequency and low-frequency components, providing stable and physically meaningful frequency-domain priors for the network. Second, a detail-guided module is designed where high-frequency features adaptively guide skip connections, compensating information loss during encoding and improving boundary recovery. Furthermore, a low-frequency-driven region-aware modeling module is developed. The internal defect regions, boundary areas, and background regions are modeled hierarchically. A dynamic hyper-kernel generation mechanism performs region-sensitive convolutional modeling, improving adaptation to complex structural variations. Extensive experiments on the Turbo19 and NEU-Seg datasets demonstrate that the proposed method produces accurate defect boundaries and achieves mIoU scores of 89.82% and 91.44%, improving over the second-best method by 5.22% and 4.42%, respectively.

Computation doi: 10.3390/computation14050098

Authors: Hasan Kanaker Hussam Fakhouri Nader Abdel Karim Maher Abuhamdeh Nurul Halimatul Asmak Ismail Sandi Fakhouri

Tool-using AI agents couple a language model with controller logic, memory, and external tools such as browsers, email, calendars, file systems, and transaction APIs. This architecture expands capability, but it also enlarges the security boundary: agents routinely ingest untrusted content while holding privileges that can reveal private data and trigger external side effects. The resulting failures are not limited to poor text generation; they include prompt injection, indirect injection through tool outputs, confused-deputy behavior, unauthorized actions, and misleading claims about the tool state. Because large-scale testing on deployed products is difficult, vendor-specific, and ethically sensitive, we present a transparent, theoretical simulation-based framework for evaluating user-facing risk in tool-using agents. The methodological contribution is a formal threat model that separates compromise, harm, and severity, and a Monte Carlo evaluation pipeline that maps architectural choices (permissions, retrieval, memory exposure, and approvals) and defensive controls to comparable outcome metrics. We instantiate the framework for six representative threat scenarios and nine defense configurations, reporting attack success rate (ASR), benign task success, latency overhead, and severity-weighted harm. Across scenarios, the least-privilege tool design is the strongest single broad control, human-in-the-loop approvals sharply reduce high-impact actions and exports but degrade under user error and habituation, retrieval allowlisting nearly eliminates indirect injection while leaving other channels largely unaffected, and rate limiting reduces tail severity more than ASR. These results position agent safety as an architectural and operational problem and because they arise from an assumption-explicit simulator rather than field measurements, should be read as comparative design guidance rather than incident-rate estimates for any deployed product.

Computation doi: 10.3390/computation14050097

Authors: Marwan Mansour Mo’taz Al Zobi Ahmad Marei Luay Daoud Nour Ibrahim Kurdi

Corporate tax avoidance has become a major governance and fiscal sustainability concern, particularly in developing economies where corporate tax revenues constitute a critical source of public financing. While prior research suggests that board gender diversity (BGD) enhances ethical oversight and monitoring, its effectiveness in constraining aggressive tax planning may depend on firms’ informational and technological environments. This study examines whether artificial intelligence (AI) capability strengthens the governance role of BGD in reducing corporate tax avoidance. Using a balanced panel of 1586 non-financial firms from developing economies over the period 2009–2023, the analysis employs firm FE models and dynamic two-step System GMM estimations to address unobserved heterogeneity, endogeneity, and the persistence of corporate tax behavior. The results indicate that BGD is positively associated with effective tax rates, implying lower levels of corporate tax avoidance. Furthermore, AI capability—measured using a lagged specification—significantly strengthens this relationship, suggesting that firms with higher AI adoption exhibit a stronger governance effect of gender-diverse boards on tax compliance. Additional robustness tests—including alternative tax avoidance measures, alternative BGD specifications, heterogeneity analysis, and selection-bias corrections using Heckman, propensity score matching (PSM), and instrumental variable (2SLS) approaches—confirm the stability of the findings. Overall, the results highlight the complementary role of technological capability and board diversity in strengthening corporate governance (CG) and fiscal discipline in developing economies.

Computation doi: 10.3390/computation14050096

Authors: Tian Yao Yong Xu Yue Ma Hongtao Yan Haihang Xu An Wang

To address the core challenges in air-to-ground target re-identification (ReID), including network focus on invalid background information, poor adaptability to nonlinear feature distribution, and insufficient cross-domain generalization, this paper proposes a novel air-to-ground ReID framework based on Neighborhood Feature Centralization Attention (NFCA). On the basis of Coordinate Attention, the framework introduces a parameter-free Neighborhood Feature Centralization mechanism to build a lightweight attention module, which enhances cross-feature semantic interaction and suppresses background noise while retaining precise position encoding. It achieves end-to-end direct optimization of sample pair similarity through binary cross-entropy loss, eliminating the proxy task bias of traditional classification loss and adapting to the nonlinear structure of feature space. A multi-source data-driven training strategy is constructed by fusing ReID datasets and general classification datasets, which expands the coverage of feature space and narrows the distribution gap between training data and real air-to-ground scenarios without additional manual annotation. Experiments show that the proposed method achieves leading mAP values on the self-developed UAV air-to-ground dataset JC-1, the public person ReID dataset Market-1501, and the public vehicle ReID dataset VehicleID. Sufficient statistical validation, ablation experiments and cross-domain tests verify the advancement, reliability and generalization of the proposed method in complex air-to-ground scenarios.

Computation doi: 10.3390/computation14040095

Authors: Hongyan Wang Pathomthat Chiradeja Atthapol Ngaopitakkul Suntiti Yoomak

The increasing deployment of off-grid photovoltaic–battery energy storage systems (PV–BESSs) has intensified operational demands on battery energy storage, particularly when second-life lithium-ion batteries are employed. Due to aging-induced increases in internal resistance and reduced thermal margins, second-life batteries are more vulnerable to high-current operation at a low state-of-charge (SOC), which aggravates heat generation and accelerates degradation. In this study, an SOC-dependent soft current limiting strategy is proposed that reshapes the discharge current reference under low-SOC conditions while maintaining fixed SOC limits, thereby targeting current-domain protection rather than SOC-boundary adaptation for reliable off-grid operation. The proposed method introduces two SOC thresholds to gradually derate the allowable discharge current, preventing abrupt current changes near the lower SOC bound. A unified MATLAB/Simulink-based framework is developed for a 24 h representative off-grid PV–BESS scenario using a second-order equivalent circuit model coupled with a lumped thermal model. Simulation results show that the proposed current shaping reduces low-SOC current stress and associated Joule heating, leading to moderated temperature rise, while only slightly affecting the unmet load under the tested conditions. These findings indicate that SOC-dependent current shaping can provide a control-oriented means to reduce low-SOC electro-thermal stress in second-life batteries within the studied off-grid PV–BESS framework.

Computation doi: 10.3390/computation14040094

Authors: Karen Ochoa Lara Jancarlo Gomez-Vega Rafael Pacheco-Contreras Octavio Juárez-Sánchez

Understanding the intrinsic Li–H2 interaction, decoupled from substrate effects, is essential to rationalize the performance of lithium-decorated hydrogen storage materials. To address the current lack of a clean theoretical baseline, we characterized the sequential H2 adsorption on the gas-phase Li6 superatomic cluster using high-level density functional theory (DFT), complemented by Energy Decomposition Analysis (EDA), QTAIM, and NICS(0) calculations. Li6 acts as a structurally rigid platform (RMSD < 0.032 Å) where ligand-induced polarization progressively strengthens its σ-aromaticity (NICS(0) from −2.917 to −13.98 ppm) and increases the HOMO–LUMO gap up to 5.05 eV. EDA identifies the binding as field-activated physisorption, electrostatically dominated (65–67%) and mechanistically distinct from Kubas coordination, as confirmed by QTAIM closed-shell interaction parameters. Negative cooperativity governs an effective loading capacity of n = 2 molecules under cryogenic conditions (Teq = 143.76 and 114.64 K), while an entropic bottleneck renders higher loading non-spontaneous at all temperatures. These results establish Li6(H2)n as a foundational gas-phase reference, providing a systematic, contamination-free descriptor set for the intrinsic Li–H2 interaction. This framework is essential for isolating the electronic role of the lithium superatom and unambiguously identifying substrate-induced modulations in supported hydrogen storage materials.

Computation doi: 10.3390/computation14040093

Authors: Bharat Lal Abhinav Shukla Ayush Kumar Agrawal R Kanesaraj Ramasamy Parul Dubey

Accurate crop yield forecasting is essential for ensuring food security, optimizing agricultural resource allocation, and supporting climate-resilient farming systems. Recent advances in deep learning have improved yield prediction accuracy; however, most existing models provide deterministic estimates without quantifying predictive uncertainty. This limitation restricts their reliability under climatic variability, missing data, and real-world decision-making scenarios where risk awareness is critical. This study utilizes two publicly available multi-crop datasets comprising historical yield records integrated with weather and soil attributes across multiple growing seasons. An attention-based Transformer framework is proposed, augmented with uncertainty quantification through Monte Carlo Dropout, Quantile Regression, and Bayesian Attention mechanisms. The proposed approach represents an integrated uncertainty-aware Transformer framework that combines temporal self-attention with complementary uncertainty estimation strategies. The contribution of this work lies in the systematic integration and comparative evaluation of multiple uncertainty quantification mechanisms within a unified deep learning framework for multi-crop yield forecasting. Experimental results demonstrate improved predictive accuracy and calibration compared to deterministic baselines. However, these findings are bounded by the scope of the datasets, which consist of coarse tabular climatic and soil variables, and should be interpreted accordingly.

Computation doi: 10.3390/computation14040092

Authors: Paulo Castro Fernando Santos Pedro Lopes

The escalating complexity of network infrastructures and the increasing sophistication of cyber threats require increasingly robust and automated Intrusion Detection Systems (IDS). This article presents a comparative investigation of the effectiveness of various Machine Learning and Deep Learning architectures in detecting network anomalies in network logs. The methodology encompassed classic supervised and ensemble algorithms, such as Random Forest and XGBoost, to sequential Deep Learning approaches (LSTM, GRU) and unsupervised models based on latent reconstruction (VAE, DeepLog). The results demonstrate that supervised approaches significantly outperformed unsupervised methods in the analyzed context. The optimized XGBoost model established a performance benchmark, achieving a Recall of 0.96 and a Precision of 0.85, thereby offering an optimal balance between detecting rare threats and minimizing false alarms. In contrast, unsupervised models revealed critical limitations, suggesting that statistical mimicry between normal and anomalous traffic hinders detection based solely on reconstruction error. Additionally, the study documents the technical interoperability challenges when attempting to integrate state-of-the-art language models, such as BERT. In conclusion, this work validates the effectiveness of Gradient Boosting algorithms and recurrent networks as viable and scalable solutions for critical network security, providing guidelines for model selection in real monitoring environments.

Computation doi: 10.3390/computation14040091

Authors: Zhaohui Li Fang Gao Delian Liu

Multilayered particles possess exceptional optical properties and hold significant potential for applications in chemical analysis, life sciences, optical sensing, and photonic integration. In practical applications, however, it is often necessary to perform inverse design of multilayered particles with given optical characteristics to meet specific requirements, a process that remains time-consuming. To overcome this challenge, we propose a reinforcement learning-based method for the automated design of multilayered particles. Leveraging the self-learning capacity of reinforcement learning models in combination with an optical characteristics calculation model, the method iteratively determines particle parameters that fulfill the desired optical responses. This method effectively addresses the many-to-one parameter mapping problem in inverse design, eliminates the need for extensive pre-computations, and provides an innovative approach to the automated design of complex nanostructures.

Computation doi: 10.3390/computation14040090

Authors: B. Kh. Khuzhayorov F. B. Kholliev A. I. Usmonov B. Rushi Kumar K. K. Viswanathan

This study addresses a two-dimensional anomalous solute transport process within a two-zone fractal porous medium. A mathematical formulation is developed to characterise transport phenomena in a non-homogeneous porous domain. The medium consists of two interacting regions: one containing mobile fluid and the other containing immobile fluid, between which mass transfer occurs. In the mobile-fluid region, solute transport is governed by the convection–diffusion equation. In contrast, the immobile-fluid region is described using a first-order kinetic model. The problem of solute injection through a designated boundary point is formulated and numerically implemented. The effects of anomalous transport behaviour on solute migration and filtration characteristics are examined. The study further evaluates the pressure field, filtration velocity distribution, and solute concentration in both zones.

Computation doi: 10.3390/computation14040089

Authors: Aleksei Vakhnin Harri Niska Anders V. Lindfors Mikko Kolehmainen

Nowadays, solar energy is becoming one of the most popular sources of renewable energy worldwide. Traditional fossil fuels cause pollution and climate change, while solar power offers a clean and sustainable alternative. However, effective planning requires accurate prediction of the amount of solar energy that can be produced. Prediction accuracy directly depends on two factors: the model’s hyperparameters and the feature set. In this study, we use boosting models, such as LightGBM, XGBoost, and CatBoost, to forecast solar power production. The prediction horizon is 60 min, which corresponds to short-term forecasting. Model tuning is performed using the NSGA-II multi-objective optimization algorithm. In this study, NSGA-II simultaneously tunes hyperparameters and a feature set of boosting models. We aim to enhance the performance of the NSGA-II algorithm in the early stages using the proposed method to generate the initial population. The initialization is based on an ensemble of filtering methods. The proposed approach promotes faster convergence in the early stages of the algorithm compared to the traditional initialization method. The results of numerical experiments are proven by the Wilcoxon test.

Computation doi: 10.3390/computation14040088

Authors: Mohammed Alnahhal Mosab I. Tabash Samir K. Safi Mujeeb Saif Mohsen Al-Absy Zokir Mamadiyarov

Predictive maintenance plays a key role in digitalization initiatives; however, in real settings, issues related to failure prediction occur when failure instances are rare compared to normal instances, leading to class imbalance. In this study, we systematically compare five machine learning (ML) models—random forest, XGBoost, support vector machine, k-nearest neighbors, and multinomial logistic regression (MLR)—to detect multiclass rare failures using four imbalance-handling approaches (i.e., no handling, manual oversampling, selective manual oversampling, and class weighting), forming 20 configurations. Using the AI4I 2020 predictive maintenance dataset, which contains five failure types, we determined that XGBoost with no handling achieved the highest macro-averaged F1 (macro-F1) score (0.842) but obtained 0% recall for tool wear failure (TWF). MLR with selective manual oversampling achieved approximately 50% TWF recall with lower overall performance (0.636 macro-F1) than top-performing models such as XGBoost. We also found that very rare classes remain difficult to detect. Even high-performing models fail to consistently detect all five failure types. Overall, no single strategy can achieve a high detection rate across all performance measures.

Computation doi: 10.3390/computation14040087

Authors: Maxim Polyakov

CAR-T cell therapy has shown substantial efficacy in haematological malignancies, but its application to solid tumours remains limited by poor effector-cell infiltration, functional exhaustion, antigenic heterogeneity, and an immunosuppressive microenvironment. In this study, we develop a new spatiotemporal mathematical model of CAR-T therapy for solid tumours that integrates these resistance mechanisms within a single reaction–diffusion framework. The model is formulated as a system of partial differential equations describing functional and exhausted CAR-T cells, antigen-positive and antigen-low tumour subpopulations, and chemokine, immunosuppressive, and hypoxic fields. Steady-state analysis and finite-difference simulations showed that therapeutic outcome is governed by the interplay between CAR-T cell infiltration, exhaustion, and antigen escape. The model reproduces partial tumour regression followed by residual tumour persistence, therapy-driven enrichment of antigen-low cells, and reduced efficacy under stronger immunosuppressive and hypoxic conditions. In the combination therapy scenario considered here, repeated simulated CAR-T cell administration together with attenuation of the suppressive microenvironment improves tumour control. The proposed model provides a mechanistic basis for analysing resistance and for future optimisation studies of CAR-T therapy in solid tumours.

Computation doi: 10.3390/computation14040086

Authors: Daniil Tolmachev Roman Chertovskih

The development of high-performance Fortran code for large-scale scientific simulations is inherently challenging: direct Fortran implementation demands substantial expertise in numerical methods, optimization and system architecture. Manual derivation of numerical schemes is error-prone and time-consuming. This paper advocates a four-stage development methodology involving Python prototyping and symbolic derivation. Systematic validation at each step of incremental transition from symbolic specification to Fortran code produces numerically correct maintainable code faster than by direct manual implementation without sacrificing the resultant performance or code quality. Large Language Models effectively accelerate Python prototyping and boilerplate generation but require rigorous verification of the generated Fortran code. We suggest practical implementation guidelines including validation strategies. Python prototyping and symbolic code generation provide effective instruments for developing efficient production-ready Fortran implementations.

Computation doi: 10.3390/computation14040085

Authors: Gowrava Shenoy Beloor Raghuvir Pai Ballambat Kevin Amith Mathias Mohammad Zuber Manjunath Mallashetty Shivamallaiah Ravindra Prabhu Attur Dharshan Rangaswamy Prakashini Koteshwar Masaaki Tamagawa Shah Mohammed Abdul Khader

The use of computational fluid dynamics (CFD) to study hemodynamics in arteries offers significant potential for addressing complex flow problems. Due to its enhanced performance hardware and software, CFD has become an important approach for studying hemodynamics in human arteries. This approach is utilized to investigate hemodynamics and forecast risk factors for atherosclerotic lesion development and progression, including circulatory flow, and to analyze local flow fields and flow profiles resulting from geometric changes. This foundational study will aid in analyzing blood flow behavior through the abdominal aorta and the origin and courses of renal arteries, as well as investigating the causes of disorders such as atherosclerosis and hypertension. The current study investigates three idealized abdominal aorta–renal artery junction models under varying blood pressure settings. Materialise software V19 was used to extract the geometry data to create idealized 3D abdominal aorta–renal branching models. Unsteady flow simulations were performed in ANSYS Fluent, utilizing rigid walls and Newtonian and Carreau–Yasuda viscosity conditions. Oscillatory shear index (OSI) and Time-averaged wall shear stress (TAWSS) were measured to enhance understanding of atherosclerotic plaque formation and progression. Also, the effect of geometric change at the bifurcation area was explored, and it was discovered that this location causes considerable vortex forming zones. The evident velocity reduction and backflow development were seen, reducing shear stress. The findings indicate that low TAWSS < 0.4 Pa and OSI > 0.15 areas within the bifurcation region are more susceptible to atherosclerosis development.

Computation doi: 10.3390/computation14040084

Authors: Haifa A. Alanazi Abdulaziz G. Alanazi Nasser S. Albalawi

As the scale and complexity of Internet of Things (IoT) deployments increase, maintaining resilience in resource-constrained mesh networks becomes a significant challenge. Frequent node failures due to battery depletion, environmental interference, or hardware degradation can disrupt data flows and lead to operational downtime. To address this, we propose EdgeRescue, a novel lightweight AI-driven framework for self-healing in energy-constrained IoT mesh environments. EdgeRescue enables each node to perform local anomaly detection using compact 1D Convolutional Neural Networks (1D-CNNs) and initiates distributed, energy-aware routing reconfiguration when faults are detected. Unlike cloud-dependent methods, EdgeRescue operates entirely at the edge, requiring minimal computation, memory, and communication overhead. Extensive simulations on a 100-node testbed demonstrate that EdgeRescue improves packet delivery by 13.2%, reduces recovery latency by 57%, and lowers average node energy consumption by 18.8% compared to state-of-the-art baselines. These results establish EdgeRescue as a scalable and practical solution for achieving real-time resilience in next-generation IoT mesh networks.

Computation doi: 10.3390/computation14040083

Authors: Altyib Abdallah Mahmoud Ahmed Juan Wang Meihong Liu Aboubaker I. B. Idriss Abdelgalal O. I. Abaker

Brush seals represent the most effective sealing technology, offering 5 to 10 times lower leakage flow rates, resulting in an 80% to 90% increase in sealing efficiency. However, key challenges remain in optimizing brush seal performance, including managing high frictional heat, maintaining consistent leakage flow, and preventing mechanical deformation failures within the bristle pack. This study uses a fluid–mechanical coupling method to establish and refine numerical investigation procedures. Using porous media and local thermal non-equilibrium (LTNE) approaches, the effects of the pressure ratio on seal performance are analyzed. The results reveal that the difference between the maximum directional and total deformations is 0.9108 mm, with the total deformation being approximately 79,666% larger than the directional deformation. These findings highlight that the bristle pack must be designed with primary consideration of total deformation to enhance performance and efficiency. The proposed methodologies enable more robust comparative evaluations of alternative brush seal configurations, including two-stage bristle packs and inline structural models. This facilitates the identification of optimized structures that minimize leakage, enhance energy dissipation, and improve the overall seal performance, thereby advancing the porous media model from a general approximation to a design-optimized tool.

Computation doi: 10.3390/computation14040082

Authors: Zhushun Su Bi Zeng Pengfei Wei Keyun Wang Zhentao Lin

With the continuous emergence of exam question types, accurate classification of knowledge points is crucial for intelligent exam analysis. Existing methods focus on text or text–image fusion but largely ignore spatial layout. To address this limitation, we propose a heterogeneous layout-aware cross-modal framework for knowledge point classification. The architecture begins with an encoding module where independent text and layout encoders extract semantic content and spatial configurations, respectively. We then design a layout-aware enhancing module consisting of two parallel cross-modal blocks, namely a Layout-Aware Text-Enhancing block and a Context-Aware Layout-Enhancing block. This module supports the bidirectional fusion of text and layout features and generates a comprehensive representation that integrates both semantic and spatial information. Furthermore, a dynamic router with top-k expert selection is introduced to dynamically adapt to question-specific knowledge distributions and focus on core knowledge points for precise classification. Experimental results demonstrate that our method effectively integrates text and layout information, significantly enhancing performance on the proposed QType-EDU dataset. The approach achieves 91.56% accuracy for coarse-grained classification and 80.58% for fine-grained classification, with an overall F1-score of 91.39%, surpassing all baseline models.

Computation doi: 10.3390/computation14040081

Authors: Wilson Gustavo Chango-Sailema Homero Velasteguí-Izurieta William Paul Pazuña-Naranjo Joffre Stalin Monar Rebeca Mariana Moposita-Lasso Santiago Israel Logroño-Naranjo Carlos Roberto López-Paredes Jacqueline Elizabeth Ponce Geovanny Euclides Silva-Peñafiel Angel Patricio Flores-Orozco Cindy Johanna Choez-Calderón Marcelo Vladimir Garcia

This study analyzes the factors associated with vehicle technology classification in Ecuador, using fuel category (electric, hybrid, and internal combustion) as the dependent variable under an Explainable Artificial Intelligence (XAI) approach. Following the CRISP-DM methodology, we compared the performance of XGBoost and LightGBM algorithms using a dataset of 482,754 administrative records from the Internal Revenue Service (SRI). Both models achieved outstanding predictive performance with a Macro F1-score of 0.987, demonstrating robustness despite the severe class imbalance (electric vehicles represent only 1.3% of the total). The integration of SHAP (SHapley Additive exPlanations) values identified tax appraisal and engine displacement as the most influential features in the model predictions in the adoption of electric vehicles. In contrast, territorial factors exert a more significant influence on the acquisition of hybrid vehicles. Finally, the findings demonstrate that boosting models, combined with XAI techniques, provide transparent analytical tools that can support evidence-based transport decarbonization strategies in emerging economies.

Computation doi: 10.3390/computation14040080

Authors: Jingyang Li Zhenqiu (Laura) Lu

Classification methods such as exploratory factor analysis (EFA) and network community detection (NCD) are widely used to identify latent item groupings in multidimensional psychological assessments. However, direct comparisons between these approaches remain limited. In addition, evaluations of clustering methods often rely on overall classification metrics, which may obscure systematic differences in how well distinct types of items are recovered. Item characteristics—such as core–peripheral positions and loading patterns—may influence classification outcomes, yet few studies have examined how these item types interact with clustering methods. The present study addresses these gaps by comparing EFA and NCD within a unified machine-learning evaluation framework that varies sample size, latent structure, preprocessing strategy, and machine-learning classifier choice (Random Forests vs. Support Vector Machines). Results show that the performance of both EFA and NCD is influenced by sample size, item type, latent structure, and classifier choice. Moreover, the downstream classifier moderates how sensitive each method is to differences among item types. These findings highlight the importance of considering item-type heterogeneity when evaluating clustering methods and demonstrate the value of machine-learning-based frameworks for advancing psychometric classification approaches.

Computation doi: 10.3390/computation14040079

Authors: Myriam E. Bruno Alessandro Nobile Paolo Oresta

Rayleigh–Bénard convection (RBC) provides a benchmark for studying buoyancy-driven instabilities and heat transport in confined fluids. Heat transfer scaling in cylindrical geometries is well established, whereas the role of the anisotropy induced by the domain geometry, such as elliptical shapes, has not fully explored. This study presents direct numerical simulations of RBC in two domains of equal height, H=0.0124 m, and different cross-sections: a circular cylinder with radius R=3.11×10−3 m and an elliptical cylinder with semi-axes equal to Rmax=3.11×10−3 m, Rmin=1.55×10−3 m, respectively. The simulations, performed at Rayleigh number Ra=2×106 and Prandtl number Pr=1.68 (for water) under the Boussinesq approximation, reveal that (i) the average Nusselt number is comparable in both cases (⟨Nu⟩≈38.23 for the circular case and ⟨Nu⟩≈39.22 for the elliptical one) and (ii) the different domain geometries influence the thermal transport mechanism and flow organization. Specifically, in the cylindrical cell, heat transfer is regulated by a large-scale circulation roll, whereas in the case of the elliptical shape, the domain is populated by thermal plumes driving the convective dynamics. The latter phenomenon is evidenced by larger Nusselt number fluctuations at the lower and upper plates, with a standard deviation increasing from σ≈2.21 in the circular cylinder to σ≈4.57 in the elliptical domain. These results highlight that the geometric anisotropy modifies the coupling between boundary layers and the core flow dynamics, leading to enhanced intermittency without affecting the magnitude of the heat flux. Therefore, the elliptical domain is suitable for applications characterized by enhanced mixing.

Computation doi: 10.3390/computation14040078

Authors: Jaime Sayago-Heredia Tatiana Elizabeth Landivar Roberto Vásconez Wilson Chango-Sailema

This study develops a multiregional forecasting framework for road traffic accidents in Ecuador, addressing a critical limitation in existing predictive approaches that rely predominantly on point error metrics without validating the statistical assumptions underlying forecast uncertainty. Although the analysis is conducted at the provincial level, the spatial dimension is used primarily for cross-regional comparison and risk classification rather than for explicit spatial interaction modeling. Using a dataset of 27,648 monthly observations covering all 24 provinces from 2014 to 2025, the study applies the Prophet model within a Design Science Research paradigm and a CRISP-DM implementation cycle. Separate provincial models are estimated with a 24-month forecasting horizon, and methodological rigor is ensured through systematic residual diagnostics using the Shapiro–Wilk test for normality and the Ljung–Box test for temporal independence. Empirical results indicate that the Prophet-based artifact outperforms a naïve seasonal benchmark in 70.8% of the provinces, demonstrating excellent predictive accuracy in structurally stable regions such as Tungurahua (MAPE = 10.9%). At the same time, the framework enables the identification of critical emerging risks in provinces such as Santo Domingo and Cotopaxi, where projected increases exceed 49% despite acceptable point forecasts. The findings confirm that point accuracy alone does not guarantee the validity of confidence intervals and that residual validation is essential for trustworthy uncertainty quantification. Overall, the proposed approach provides a robust foundation for a predictive surveillance system capable of supporting differentiated, evidence-based road safety policies in territorially heterogeneous contexts.

Computation doi: 10.3390/computation14040077

Authors: Abhilash Hebbandi Ningappa S. M. Abdul Khader Harishkumar Kamat Masaaki Tamagawa Ganesh Kamath Raghuvir Pai B. Prakashini Koteswar Irfan Anjum Badruddin Mohammad Zuber Kevin Amith Mathias Gowrava Shenoy Baloor

Understanding the hemodynamics of the carotid artery is essential for assessing atherosclerotic disease progression and identifying regions vulnerable to plaque formation. Background: Disturbed flow patterns and abnormal shear stresses, particularly near the carotid bifurcation, are known to influence endothelial dysfunction; therefore, this study aims to quantify the impact of patient-specific carotid artery geometry on key hemodynamic parameters associated with atherosclerotic risk. Methods: Four patient-specific carotid artery geometries were reconstructed from medical imaging data, processed using MIMICS, and analyzed using computational fluid dynamics in ANSYS Fluent, with blood modeled as an incompressible non-Newtonian fluid using the Carreau–Yasuda viscosity model under pulsatile flow conditions; velocity streamlines, pressure distribution, time-averaged wall shear stress (TAWSS), and oscillatory shear index (OSI) were evaluated at early systole, peak systole, and peak diastole. Results: The simulations revealed complex flow behaviour, including flow reversal, pressure build-up, and low-shear regions concentrated near the carotid bulb and bifurcation, with TAWSS consistently identifying low-shear zones (<1 Pa) across all geometries and OSI exhibiting pronounced directional oscillations in models with increased curvature and wider bifurcation angles. Conclusions: These findings demonstrate that geometric characteristics such as bifurcation angle, vessel tortuosity, and asymmetry play a critical role in shaping local haemodynamics, underscoring the utility of patient-specific CFD analysis as a diagnostic and predictive tool for atherosclerotic risk assessment and supporting more informed, personalized clinical decision-making.

Computation doi: 10.3390/computation14040076

Authors: Marta Torres-Polo Eduardo Guzmán Ortíz

Construction and demolition waste (CDW) represents a critical environmental challenge in the building sector, with global generation exceeding 3.57 billion tonnes annually. The circular economy (CE) framework offers a transformative pathway through selective deconstruction and material recovery, yet implementation faces significant barriers including information asymmetry, supply chain fragmentation, and regulatory uncertainty. This study conducts a systematic literature review using the Context–Mechanism–Outcome (CMO) framework to analyze how computational methods, specifically Digital Twins (DT), Building Information Modeling (BIM), Internet of Things (IoT), blockchain, artificial intelligence, and robotics, act as enablers for resilience in CDW management. Following PRISMA 2020 guidelines and realist synthesis principles, we analyzed 42 high-quality empirical studies from Web of Science and Scopus (2015–2025). Our analysis identifies seven primary mechanisms: traceability (M1), simulation (M2), classification (M3), tracking (M4), collaboration (M5), analytics (M6) and robotics (M7). These mechanisms interact with four critical contexts (information asymmetry, supply chain fragmentation, economic uncertainty, operational risks) to generate outcomes at two levels: resilience capabilities (visibility, monitoring, collaboration, flexibility, anticipation) and performance indicators (recovery rates, cost reduction, CO2 emissions mitigation, occupational safety). Key findings from the CMO analysis reveal that blockchain-enabled traceability increases material recovery rates by 15–25%, DT simulation reduces deconstruction costs by 20–30%, and computer vision automation improves sorting accuracy to 85–95%. The study contributes middle-range theories explaining how digital technologies enable circular transitions under specific contextual conditions, offering actionable strategic implications for researchers, project managers, technology developers, and policymakers committed to advancing computational economics in sustainable construction.

Computation doi: 10.3390/computation14040075

Authors: Andrey Rozhkov Andrey Kozelkov Vadim Kurulin Maxim Shishlenin

In this article, we explore the possibility of using reinforcement learning to create convective flow approximation schemes that maintain accuracy and stability at high Courant-Friedrichs-Lewy (CFL) numbers in the finite-volume discretization of advection equations. Unlike most existing data-driven discretization methods, which primarily concentrate on spatial grid refinement, this work emphasizes increasing the allowable time step without compromising solution accuracy. This approach reduces the total number of time integration steps, thereby enabling faster computation. A neural network is used as a surrogate model for reconstructing the convective flow, which takes as input local information about the flow, scalars, and geometry and predicts scalar values at node points. Reinforcement learning is used for training and is formulated as a policy optimization problem, where the long-term reward is defined as the difference between the numerical and reference solutions over the entire simulation period. Both the genetic algorithm and the Deep Deterministic Policy Gradient (DDPG) method are investigated. The effectiveness of the approach is evaluated using a one-dimensional nonlinear advection problem with a constant velocity field. Despite the simplicity of the test case, the results demonstrate that the trained convective flux approximation scheme achieves accuracy comparable to or better than the classical second-order linear upwind (LUD) scheme, while operating at CFL numbers 2–50 times higher than the optimal CFL for LUD, thereby reducing the simulation time by the same factor. This allows for a wider range of stability and accuracy in the finite-volume method and the use of larger time steps without compromising the quality of the solution. The study is intentionally limited to a single spatial dimension and serves as a basic analysis of the method’s applicability. The results demonstrate that reinforcement learning can successfully find more convective flow approximation schemes that improve efficiency at high CFL numbers than conventional explicit second-order schemes, establishing a framework that is subsequently extended in our follow-up work to improve training methods and three-dimensional complex transport problems. The proposed method improves the spatial discretization of convective fluxes, which is independent of the choice of time integration scheme. Therefore, the neural reconstruction can in principle be used in both explicit and implicit finite-volume solvers.

Computation doi: 10.3390/computation14030074

Authors: Andrei I. Malinouski Oscar S. Rabinovich Heorhi U. Barakhouski

Calculation of heat transfer in granular materials is an important task for many applications, from thermal management in electronics to exploring celestial soils. Usually, an effective thermal-conductivity model is employed to predict heat flux in unstructured granular media, such as a packed bed. However, a more advanced approach, the discrete element method (DEM), can capture the complex effects of mechanical loading and material mixtures on thermal transport coefficients, which traditional models struggle with. Pivotal for this approach is knowing the heat transfer coefficient between two adjacent particles. Currently, in most DEM-capable software, only particles in direct surface contact are considered to have non-zero heat conduction. We propose considering particles that are close to each other but don’t have a contact area with a non-zero surface area. We perform numerical modeling of the conductive heat transfer coefficient between equal spherical particles separated by media, assuming the fluid’s thermal conductivity is at least an order of magnitude lower. We use numerical solutions of differential equations to account for both thermal resistance within particles and through the gap between them. We found a simple generalized correlation for the heat transfer coefficient between particles and a general formula for the angular distribution of heat flux density across the particle surface. By employing a non-dimensional approach, the obtained formulas are constructed using non-dimensional parameters: the ratio of the particle’s thermal conductivity to that of the medium, and the ratio of the gap width between particles to their radius. The resulting formula is simple and convenient for DEM heat transfer calculations in packed and fluidized beds.

Computation doi: 10.3390/computation14030073

Authors: Giannis Christoforidis Apostolos N. Papadopoulos

In recent years, social networks have shown a great influx of new users and traffic. As their popularity grows, so does the interest in researching ways to process the information available, in order to produce useful knowledge. One direction is making personalized recommendations based on users’ preferences and on their social behavior and related characteristics in general. Static recommendations, however, are proven to be highly inaccurate, since as time progresses, people tend to change their preferences, making different decisions than the ones predicted previously. This calls for an adaptive algorithm that shifts according to the changes in preferences and habits of the users. Handling the stream of information is challenging, as the new data can severely change the recommendations to many users. In this work, we propose a novel streaming Point-of-Interest recommendation algorithm that explicitly incorporates location-aware features into its dynamic update mechanism, enabling continuous adaptation to newly arriving data. The proposed approach is experimentally evaluated based on real-life data sets containing the network structure as well as check-in information. The results demonstrate high accuracy, achieving at the same time significant performance gains with respect to runtime costs compared to conventional approaches.

Computation doi: 10.3390/computation14030072

Authors: Pedro Aguilar-Encarnacion Pedro Peñafiel-Arcos Marcos Barahona Morales Wilson Chango

The management of municipal solid waste in intermediate cities exhibits high daily variability and source heterogeneity, which hinders operational sizing and material recovery. Reliable predictions are required from heterogeneous and often-scarce data. However, studies that compare multiple machine learning algorithms with temporal validation on short time series in intermediate cities are still limited. This study compares fourteen machine learning algorithms to predict the daily generation of organic and inorganic waste in La Joya de los Sachas, Ecuador, formulating the problem as a multi-output regression problem. An adapted CRISP-DM design was employed, using primary data from a waste characterization campaign, temporal feature engineering, variable encoding, and an expanding-window backtesting protocol against lag-7 persistence and ARIMA. Tree-based ensembles achieved the best performance. AdaBoost provided the best organic forecasts (R2=0.985, RMSE =0.081, MAE=0.061 in rate space), while Random Forest was best for inorganic (R2=0.965, RMSE =0.049, MAE=0.040). Linear models were stable but slightly inferior, and other approaches (SVR, KNN, MLP, Lasso, ElasticNet) showed lower generalization capacity. The study provides a multi-output regression protocol with temporal validation for municipal contexts with short time series, comparative evidence across fourteen algorithms, and a conversion from rates to kilograms for operational use.

Computation doi: 10.3390/computation14030071

Authors: Dimitris Tsamatsoulis

This study performs a sensitivity analysis of CO2 emissions from clinker and cement production using life cycle assessment (LCA). Both local and global sensitivity analyses (LSA and GSA) are conducted. LSA uses outputs from the GCCA EPD tool—developed by the Global Cement and Concrete Association to facilitate Environmental Product Declarations—and examines correlations between perturbed input variables and the resulting output changes. For GSA, we present an analytical derivation of Sobol’ indices. We derive quantitative relationships between alternative materials and fuels and key technical indices, while preserving clinker and cement quality throughout the sensitivity analysis. Increasing the share of the alternative fuels (AFs) categories and of recycled concrete produces a negative percentage change in CO2 emitted from the clinker (CO2/CL). The largest CO2/CL reductions arise from high-biomass fuels, followed by alternative solid fuels and refuse-derived fuels, shredded tires, and, lastly, recycled concrete. The clinker-to-cement ratio (CL/CEM) dominates the CO2 emitted in cement production (1% change → 0.926–0.956% change), while clinker-level CO2 reductions transmit to cement with only minor variation, confirmed by Sobol’ indices. Aside from reducing CO2/CL by increasing alternative materials and fuels, the two principal approaches to lowering CO2/CEM are: (i) minimizing clinker content in cement where permitted by applicable standards while maintaining the same performance, and (ii) designing new cement types that deliver equivalent performance with lower clinker content.

Computation doi: 10.3390/computation14030070

Authors: Roaa Soloh Bilal Nakhal Abdallah El Chakik

Precise segmentation of brain tumors from multimodal MRI scans is essential for accurate neuro-oncological diagnosis and treatment planning. To address this challenge, we propose a label-free optimization-driven segmentation framework based on the α-expansion graph cut algorithm, offering improved computational efficiency and interpretability compared to deep learning alternatives. The method relies on structured optimization and handcrafted features, including local intensity patches, entropy-based texture descriptors, and statistical moments, to compute voxel-wise unary potentials via gradient-boosted decision trees (XGBoost). These are integrated with spatially adaptive pairwise terms within a graph model optimized through α-expansion. Evaluation on 146 BraTS validation volumes demonstrates reliable whole-tumor overlap, with a mean Dice score of 0.855 ± 0.184 and a 95% Hausdorff distance of 18.66 mm. Bootstrap analysis confirms the statistical stability of these results. The low computational overhead and modular design make the method particularly suitable for transparent and resource-constrained clinical deployment scenarios.

Computation doi: 10.3390/computation14030069

Authors: Lingling Ma

In this paper, a hybrid model reduction method for solving flows in fractured media is proposed. The approach integrates the Generalized Multiscale Finite Element Method (GMsFEM) with a novel variable-separation (VS) technique. Compared with many widely used variable-separation methods, the proposed model reduction method shares their merits but has lower computation complexity and higher efficiency. Within this framework, we can get the low-rank variable-separation expansion of dual-continuum model solutions in a systematic enrichment manner. No iteration is performed at each enrichment step. The expansion is constructed using two sets of basis functions: stochastic basis functions and deterministic physical basis functions, both derived from offline, model-oriented computations. To efficiently construct the stochastic basis functions, the original model is used to learn stochastic information. Meanwhile, the deterministic physical basis functions are trained using solutions obtained by applying an uncoupled GMsFEM to the dual-continuum system at a select number of optimal samples. Once these bases are established, the online evaluation for each new random sample becomes highly efficient, allowing for the computation of a large number of stochastic realizations at minimal cost. To demonstrate the performance of the proposed method, two numerical examples for dual-continuum models with random inputs are presented. The results confirm that the hybrid model reduction method is both efficient and achieves high approximation accuracy.

Computation doi: 10.3390/computation14030068

Authors: Myung Soon Song Amber Kulp Yun Lu Francis J. Vasko

The partial coverage weighted set covering problem (PCWSCP) allows for less than 100% of the rows to be satisfied in a weighted set covering problem (WSCP). This paper does not claim to contribute to operations research (OR) theory or methodology. Instead, it demonstrates that a large number of PCWSCPs based on WSCPs from the OR literature can be efficiently solved using the software Gurobi 12 with default parameter settings on a standard PC. This is an important practical result because it indicates what types of PCWSCPs can be solved optimally using commercial software without resorting to customized algorithms that do not guarantee optimums or even bounds on their solutions. Specifically, using 105 WSCP instances from the literature, 420 PCWSCP instances are generated with 105 instances at 80%, 85%, 90%, and 95% coverage respectively. It is shown that using Gurobi on a standard PC, optimal solutions could be obtained within 300 s (average of 17 s) for instances with up to 800 rows by 8000 columns by 2% density. This is about 86% of the 420 instances. As expected, in general, the execution time decreases as the row coverage decreases. Furthermore, it is shown that initializing (“warm-starting”) Gurobi with solutions from either a greedy, carousel greedy, or local branching algorithm results in no statistically significant difference in performance compared to Gurobi’s cold start. Hence, there is no advantage to “warm-starting” Gurobi with one of these common heuristic approaches when solving PCWSCPs. Finally, this is the first time the weighted version of the partial coverage set covering problem is discussed in the literature. All previous discussions dealt only with solution approaches specifically developed for the unit-cost version of the problem.

Computation doi: 10.3390/computation14030067

Authors: Pablo Martínez Otero Alberto Tellaeche Mar Hernández Melero

This paper evaluates the current maturity of automatic code-generation workflows for deploying modern CNN-based object detectors on embedded GPU platforms. We compare a native pipeline against a code generation pipeline through a Model-Based Engineering (MBE) approach, using YOLOv8/YOLOv9 inference on NVIDIA Jetson Orin Nano and Jetson AGX Orin as representative edge-GPU workloads. We report detection-quality metrics (mAP, PR curves) and system-level metrics (latency distribution and initialization overhead) under a controlled single-class scenario based on a CARLA-generated sequence with frame-level annotations. Absolute accuracy and latency values are scenario-dependent and may vary under different camera optics, illumination, motion blur, sensor noise, occlusion patterns, and multi-class scene. Results quantify the performance gap between code generation and native pipelines and show that, for the evaluated workloads, the automated pipeline remains less competitive in both latency and accuracy. We discuss the implications of this gap for deployment workflows in safety-oriented domains, and we outline bottlenecks that should be addressed. The study is intended as a controlled traffic-light detection micro-benchmark and does not aim to validate full ADAS perception stacks.

Computation doi: 10.3390/computation14030066

Authors: Chih-Chiang Hong

The thermal vibrations of a thick-walled functionally graded material (FGM) plate–cylindrical shells in unsteady supersonic flow with a Navier–Stokes equation analytical–numerical flow model and third-order shear deformation theory (TSDT) displacement models are investigated. The aerodynamic pressure load can be provided by using the Navier–Stokes equation analytical–numerical flow model. The data regarding the effect of the aerodynamic pressure load and TSDT model of the motion equation on the thermal stress and displacement of the FGM plate–cylindrical shells in unsteady supersonic flow are calculated with the generalized differential quadrature (GDQ) method. The Navier–Stokes equation analytical–numerical flow model, TSDT model, and advanced shear correction coefficient provide an additional effect on the values of displacement and stress.

Computation doi: 10.3390/computation14030065

Authors: Glory Kawira Mutua Musyoka Kinyili Dominic Makaa Kitavi

This study formulates a deterministic model to assess the effect of a family-based control and management (FBCM) strategy against the transmission of Helicobacter pylori infection and its consequent development of gastric ulcers and gastric cancer. The model includes nine epidemiological compartments to model disease transmission and contact epidemiology between susceptible and infected individuals. In the model analysis, we compute positivity, the invariant region, equilibria, stabilities, and bifurcation analysis. We calculate the control reproduction number R0 and demonstrate that the model has a unique disease-free equilibrium (DFE) and an endemic equilibrium point (EEP) that are locally and globally stable for R0<1 and R0>1, respectively. We perform a thorough mathematical analysis and validate the model by fitting it to real data on gastric cancer cases recorded at Meru Teaching and Referral Hospital, Kenya. The best numerical results are achieved when we combine both preventive measures (sensitization and a family-based approach) and curative measures (prompt treatment and adherence), resulting in the greatest decrease in gastric ulcer and gastric cancer cases compared with a single intervention. This study shows that integrated household-level interventions can reduce transmission and prevent mild-to-severe disease progression through effective sensitization campaigns, high FBCM efficacy, effective gastric ulcer treatment, and adherence to drug protocols. The use of such strategies offers an effective means of reducing Helicobacter pylori-related gastric ulcers and gastric cancer outcomes, with important implications for public health control program design.

Computation doi: 10.3390/computation14030064

Authors: Raigul Tuleuova Nurgul Shazhdekeyeva Sharbat Nurzhanova Aigul Myrzasheva Saltanat Sharmukhanbet Maxot Rakhmetov Makhatova Valentina Lyailya Kurmangaziyeva

Linear integrators are traditionally used in motion control systems to compensate for static effects and suppress low-frequency disturbances. However, their use is inevitably accompanied by phase delays that limit the performance and robustness of control systems, especially in conditions of parametric uncertainty. In this regard, nonlinear integrators have been considered for several decades as a promising alternative that can weaken phase constraints and improve the quality of transients. In this paper, the concept of nonlinear integrators is reinterpreted in the context of self-organizing motion control of precision stages. In contrast to traditional approaches focused primarily on frequency analysis and the method of describing the function, a method is proposed for the synthesis of a self-organizing control system for nonlinear SISO objects based on catastrophe theory, namely in the class of elliptical dynamics with the property of structural stability. The control action is formed in such a way that transitions between stable modes occur due to bifurcation-conditioned self-organization, without using external switching logic. To ensure strict analytical guarantees of stability, the Lyapunov gradient-velocity vector function method is used, which guarantees aperiodic robust stability, suppression of oscillatory and chaotic modes, as well as monotonic convergence of trajectories under conditions of parameter uncertainty. The parameters of the nonlinear integrator are adapted using Self-Organizing Maps (SOM), while any parameter changes are allowed only within the regions that meet the conditions of Lyapunov stability. This approach ensures the alignment of analytical and data-oriented methods without violating the structural stability of the system. The results of numerical experiments demonstrate the superiority of the proposed method in comparison with classical linear and adaptive regulators in problems of controlling the movement of stages, especially near bifurcation boundaries and with significant parametric uncertainty. The results obtained confirm that the integration of nonlinear integrators with catastrophe theory and self-organization mechanisms forms a promising basis for the creation of robust and high-precision motion control systems of a new generation.

Computation doi: 10.3390/computation14030063

Authors: Himanshu Rana Adnan Ibrahimbegovic

Prediction of fatigue failure in concrete structures remains a major challenge due to progressive material degradation. Reliable prediction, therefore, requires modeling the 3D heterogeneous microstructure of concrete to explain the underlying mechanisms governing fatigue failure. While such mesoscale models can reliably predict the fatigue-induced fracture mechanisms, the identification of the associated material parameters remains a significant challenge due to the high-dimensional parameter space introduced by the model. The key challenge addressed in this study is to capture microcrack initiation and coalescence under fatigue loading, using a model capable of representing fracture process: crack initiation, crack propagation, and final failure. Firstly, concrete domain is discretized into Voronoi cells, enabling explicit representation of aggregates and mortar by randomly assigning cohesive links connecting Voronoi cells as aggregates and mortar. After this, mortar links are modeled as coupled damage–plasticity 3D Timoshenko beam elements with nonlinear kinematic hardening and isotropic softening introduced using embedded discontinuity formulation, enabling fracture Modes I–III, whereas aggregate links are modeled as elastic 3D Timoshenko beam elements. The model efficiency is additionally reinforced by using surrogate model approach, with corresponding material parameter identification carried out by multi-objective Bayesian optimization framework to reproduce experimental results. The performance of the proposed model is illustrated by reproducing experimental results obtained from concrete cube compression test and three-point bending test under low-cycle fatigue loading, where the errors between experimental and numerical results are reduced by 82% (stress) and 88% (energy) for the cube test and by 86% (force) and 93% (energy) for the bending test, relative to the initial dataset error.

Computation doi: 10.3390/computation14030062

Authors: Arda Yunianta

Accurate segmentation of breast cancer regions in histopathological images is critical for advancing computer-aided diagnostic systems, yet challenges persist due to heterogeneous tissue structures, staining variations, and the need to capture features across multiple scales. This study introduces MSB-UNet, a novel Multi-Scale Bifurcated U-Net architecture designed to address these challenges through a dual-pathway encoder–decoder framework that processes images at multiple resolutions simultaneously. By integrating a bifurcated encoder with a Feature Fusion Module, MSB-UNet effectively captures fine-grained cellular details and broader tissue-level patterns. MSB-UNet is formulated as a binary segmentation framework (tumor vs. outside region of interest), producing a two-channel probability map via a channel-wise Softmax output. Evaluated on a publicly available breast cancer histopathology dataset, MSB-UNet achieves a Dice Similarity Coefficient (DSC) of 91.3% and a mean Intersection over Union (mIoU) of 84.4%, outperforming state-of-the-art segmentation models. The architecture demonstrates better results compared to other baseline methods and has the potential to enhance automated diagnostic tools for breast cancer histopathology.

Computation doi: 10.3390/computation14030061

Authors: Manuel J. Pasion-Fuentes Mauricio P. Galvez-Legua Diego E. Galvez-Aranda

Recent advances in energy storage systems and in increasingly efficient, safe, and energy-dense cell chemistries have driven the need for commercial Battery Management System (BMS) architectures with greater control, data acquisition, and communication capabilities, primarily oriented towards customization. This demand introduces a significant change in how electrical systems are modeled and simulated when they integrate active electrochemical elements such as lithium-ion cells. This work presents the development and modeling of a BMS for critical and high-efficiency applications, based on active balancing techniques and incorporating an additional safety stage to respond to failures when charging LiFePO4 cells. The electrochemical model was built using an equivalent RLC circuit and RC pairs to represent the Thevenin response of the cell. For the simulation of active balancers, LTspice was employed, while charging and discharging processes and their effects on state of charge (SOC) and state of health (SOH) were complemented through analysis in MATLAB R2024a.The proposed approach offers an efficient tool for evaluating cell dynamics and validating battery management strategies in demanding scenarios. While the current approach prioritizes the individual modeling of electrical conversion systems, our framework presents an innovative multisystem macromodel, where not only is the electrical behavior simulated but also the control, efficiency, and safety of the system are determined, prioritizing reproducibility through SPICE tools.

Computation doi: 10.3390/computation14030057

Authors: Sara Nassar Rachid Hedjam Samir Brahim Belhaouari

Projection-based variants of optimal transport, such as the Sliced Wasserstein (SW) and its extensions, have become popular alternatives to classical Wasserstein distances due to their scalability and analytical tractability. However, most of these methods rely on independently sampled random projections, which often fail to capture semantically meaningful directions, leading to inefficiencies and limited expressiveness, especially in high-dimensional settings. In this work, we propose the Hybrid Merging Projection Wasserstein (HW) distance, a novel and efficient alternative that addresses these limitations by combining data-driven and random projections in a principled way. At the core of HW is the Linear Merging Projection (LMP), a new projection technique designed to minimize between-class variance, thereby promoting smooth alignment between distributions. HW incorporates random directions as well to achieve a balance between structural awareness and projection diversity. We evaluate HW across a range of synthetic and real-world benchmarks, including color transfer and distribution alignment tasks, to demonstrate the favorable performance of the proposed HW.

Computation doi: 10.3390/computation14030060

Authors: Ion Mălăel

With the growth of urban zones and the increasing need for energy, the use of renewable energy solutions in the built environment becomes a must. Due to their small size and the ability to capture wind from any direction, vertical-axis wind turbines are an alternative to conventional wind energy generators. However, the use of these turbines in the built environment faces difficulties due to performance inefficiencies, particularly because of the intricate aerodynamic characteristics of the blades. This work investigates a method for increasing the efficiency of VAWTs by addressing blade-to-blade interactions using Computational Fluid Dynamics simulations. The research aims to improve turbine design for urban locations, which motivates the application context of the study. The present numerical model employs a uniform inflow to isolate blade–blade interaction mechanisms under controlled conditions. The paper presents a design that minimizes aerodynamic losses, decreases turbulence-induced drag, and increases overall energy capture efficiency by modeling different blade configurations and their interactions. The performance of four asymmetric configurations of blade chord and radius was numerically studied and compared to a symmetric configuration.

Computation doi: 10.3390/computation14030059

Authors: Chad A. Davis Caimiao Liu

Accurate estimation of network density is central to egocentric social network analysis, yet existing survey-based methods require researchers to balance accuracy against participant burden and systematic recall bias. Traditional approaches, such as fixed-list name generators, tend to overrepresent salient ties. Although the more recent random sampling method yields better accuracy, it relies on exhaustive free recall, which can be cognitively demanding and impractical for researchers. In this study, we introduce and evaluate an alternative approach—incremental recall—that structures alter nomination across relationship categories to improve coverage of differing tie strengths while reducing respondent burden. Using a large-scale Monte Carlo simulation encompassing over 9 million egocentric networks, we compare incremental recall against traditional fixed-list recall and random sampling across a wide range of network sizes, compositions, and recall bias assumptions. Results show that the incremental recall method consistently outperforms traditional fixed-list recall and performs comparably to or better than random sampling under unbiased and moderately biased recall conditions. Performance advantages persist even when respondents are unable to provide the full number of alters specified by design. We further validate these findings using empirical egocentric network data from 103 participants. Treating observed networks as proxy ground truths, empirical results closely mirror the simulation patterns, confirming the robustness of incremental recall under real-world reporting conditions. These findings demonstrate that incremental recall addresses a central practical challenge in egocentric social network research: balancing feasibility and accuracy in density estimation. The proposed method maintains strong performance while substantially reducing respondent burden and simplifying administration for applied studies. For researchers conducting large scale surveys where network density is one of several measures, incremental recall provides a practical and validated alternative to exhaustive recall that maintains robustness to realistic reporting biases.

Computation doi: 10.3390/computation14030058

Authors: Nurlan Temirbekov Gayaz Khakimzyanov Ainur Kerimakyn

In this paper, the coordinate transformation method is applied to the Navier–Stokes equations expressed in terms of the stream function and vorticity formulation. An elliptical grid generator is used to construct an orthogonal curvilinear grid within an irregular domain of complex geometry, mapping the physical region onto a computational square domain. The developed algorithm is capable of generating both orthogonal and general curvilinear grids. The finite-difference scheme of the Navier–Stokes system in arbitrary orthogonal curvilinear coordinates is then solved numerically on this grid using the alternating direction method. Numerical simulations of the Roach problem are conducted at low Reynolds numbers and on grids of varying resolutions. The obtained results are compared with the reference studies of Napolitano and Orlandi, showing satisfactory agreement with the data reported by 16 other research groups. Overall, the proposed method enables efficient numerical simulation of laminar flows in domains with complex geometry. The developed approach provides high accuracy and stability and can be effectively used for the numerical analysis of applied fluid dynamics problems. Furthermore, the methodology described in this work may serve as a foundation for future studies focused on improving computational efficiency and expanding the applicability of curvilinear grid techniques in modern fluid dynamics.

Computation doi: 10.3390/computation14030056

Authors: Rabelani Innocent Nthangeni Caston Sigauke Thakhani Ravele Thinawanga Hangwani Tshisikhawe

This paper presents probabilistic wind energy forecasting using quantile regression averaging combined with a conformal prediction modelling framework. The study uses data from Eskom, South Africa’s power utility company. The data is from April 2019 to November 2023. A partial linear additive quantile regression (PLAQR) averaging method is used to combine forecasts from two competing forecasting models: eXtreme Gradient Boosting (XGBoost) and Principal Component Regression (PCR). To compare the predictive abilities of the models, two data splits are used: 80%, 10% and 10% for the first set, and 85%, 10% and 5% for the second set, for training, validation and testing, respectively. Empirical results suggest that the combined predictions from PLAQR perform better than the individual models, significantly improving calibration and accuracy. The proposed combination has the smallest root mean square error (RMSE) and the highest probability of change in direction (POCID). The combination captures nonlinearities and produces well-calibrated probabilistic results. Probability integral transform histograms validate this. This performance gain reflected the importance of data volume. This is reinforced by the fact that the PLAQR model, which combines the benefits of tree-based approaches and linear models, is a robust modelling approach for reliable renewable energy forecasting. Future research directions should consider more varied ensembles.

Computation doi: 10.3390/computation14020055

Authors: Xinyue Chen Jishun Ren Yang Wang Jiquan He Xuyou Guo Gantailai Ye

The triplex reciprocating drilling pump is a critical piece of equipment in drilling platforms, and the operational condition of its core component—the valve body—directly affects the pump’s performance and the stability of the entire system. Therefore, accurate prediction of the valve body’s Remaining Useful Life (RUL) is of great significance for ensuring the safe operation of drilling pumps and enabling predictive maintenance. However, achieving this goal involves two major challenges: (1) The complex degradation process of the valve body, which involves strong impact loads, nonlinear wear, and coupling effects between fluid and mechanical systems, makes it difficult to establish a stable degradation model and achieve accurate RUL prediction. (2) There is a lack of publicly available real-world datasets for research purposes. To address these challenges, we propose CEEMDAN-BWO-optimized Bidirectional LSTM for Remaining Useful Life prediction (CB2-RUL). The method first applies Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to the raw vibration signals for decomposition and denoising, thereby improving signal stationarity and enhancing feature representation. Next, the Black Widow Optimization (BWO) algorithm is employed to automatically tune key hyperparameters of a Bidirectional Long Short-Term Memory (BiLSTM) network. Finally, the optimized BiLSTM captures the temporal evolution patterns of valve-body degradation and produces high-accuracy RUL estimates. Finally, to verify the effectiveness of the proposed approach, we constructed a real-world dataset named VB-Lifecycle, which comprises ten valve bodies from different positions within the equipment and spans the complete lifecycle from pristine condition to failure. Extensive experiments conducted on the VB-Lifecycle dataset demonstrate that the proposed method provides accurate RUL prediction for valve bodies.

Computation doi: 10.3390/computation14020054

Authors: Oleksandr Kovaliv Yuriy Kondratenko Ievgen Sidenko Galyna Kondratenko Dmytro Chumachenko

This paper aims to improve the accuracy of infectious disease forecasting using machine learning methods. The main results of this work are an analysis of infectious diseases spread in Ukraine during the time span from December 2016 to January 2024 and a performance comparison of different neural network architectures in the scope of time series forecasting. The following steps were taken: analysis of current forecasting methods, selection of neural network architectures, dataset preprocessing, and model testing. The developed system can be an effective tool for rational management decisions to ensure the epidemiological well-being and biosecurity of the population.

Computation doi: 10.3390/computation14020053

Authors: Otibh M. M. Abubkry Yun Zeng Juan Duan Altyib Abdallah Mahmoud Ahmed Hassan Babeker Altyeb Ali Abaker Omer

Francis turbines are renowned for their high efficiency and adaptability across a wide range of head and discharge conditions. However, internal mechanical friction losses (IMFLs), resulting from rotational frictional resistance between the rotating runner and the surrounding fluid, remain a significant obstacle to further performance optimisation. This study introduced a CFD-derived integral friction torque framework, validated through theoretical analysis, that enables the spatially resolved quantification of IMFLs in Francis turbine runners. Building on this framework, a comprehensive computational approach was established to quantify IMFLs in a Francis turbine runner using a CFD-derived integral torque method combined with a theoretical verification model. Three runner configurations were analysed: the original runner model (ORM), a modified runner (RM1) with selective exit height reduction, and a modified runner (RM2) with uniform exit height reduction. Transient simulations were conducted at the best efficiency point (BEP) using the shear stress transport (SST) k–ω turbulence model and a sliding mesh approach. The numerical results were verified using the theoretical model and systematically evaluated to assess IMFL mechanisms and runner performance. The findings demonstrate that variations in runner geometry significantly influence internal frictional resistance and turbine efficiency. Compared with ORM, both RM1 and RM2 reduced the rotational friction torque, with RM2 exhibiting the greatest improvement: a 2.83% reduction in total friction resistance torque, a 14.74% reduction in total power losses, and a 1% absolute increase in efficiency. These improvements are primarily attributed to reduced wall shear stress and a more uniform pressure distribution across the runner surface.

Computation doi: 10.3390/computation14020052

Authors: Michael J. Fajardo Adam G. Marsh John R. Jungck

We have developed a tool that allows us to easily visualize evolutionary variation via complementary multiple sequence alignments and frequency-based stacked Sequence Logos. This tool, TOTEMS (hisTogram of evOluTionarily consErved aMino acidS), visualizes conserved regions in a multiple sequence alignment within regions of a three-dimensional structure that share similar degrees of evolutionary conservation as revealed in ConSurf output data. Unlike Sequence Logos that illustrate the relative frequency of individual amino acid residues (as in MSAViewer), or moving window averages that focus on properties such as hydrophobicity or electrical charge (as in CATH), TOTEMS can help users discriminate degrees of evolutionary conservation in adjacent positions within a three-dimensional structure. Thus, we offer a tool that serves to complement pre-existing visualization applications such as ConSurf, MSAViewer, and CATH. TOTEMS and its source code are freely available.

Computation doi: 10.3390/computation14020051

Authors: Vicente Borja-Jaimes Jorge Salvador Valdez-Martínez Miguel Beltrán-Escobar Guillermo Ramírez-Zúñiga Adriana Reyes-Mayer Manuela Calixto-Rodríguez

Quadrotor unmanned aerial vehicles (QUAVs) are widely used in civil and defense applications, yet reliable trajectory tracking remains challenging under external disturbances and limited sensing. Conventional backstepping–sliding mode controllers ensure robustness only by selecting discontinuous gains larger than the disturbance bound, which increases chattering and limits the use of smooth switching functions. This paper addresses these limitations by integrating explicit disturbance compensation into a backstepping–sliding framework through a super-twisting observer (STO). The STO reconstructs matched disturbances acting on the translational and rotational dynamics in real time, and the estimated signals are directly injected into the control law. This approach enables effective disturbance rejection beyond the nominal sliding gain while preserving robustness under smooth control actions. Simulation results under single- and multi-frequency perturbations demonstrate accurate disturbance reconstruction (FIT indices above 95%), improved tracking performance, and a significant reduction in chattering. The proposed strategy provides a robust control solution for QUAVs operating in uncertain environments.

Computation doi: 10.3390/computation14020050

Authors: Jorge Moya Velasco Jorge Soria Ruiz-Ogarrio Pedro Caja Meri Silvia Álvarez-Santás

This paper proposes a methodological architecture to integrate Q-learning into economic modelling systematically. It addresses a common gap: the lack of a shared framework linking economic foundations to Reinforcement Learning components. Rather than introducing a new algorithm, it specifies and reports how preferences, frictions, information structures, and time horizons map to the reward function, discount factor, and learning environment design. Equilibrium outcomes serve as benchmarks for comparing learned policies, not as imposed axioms. This approach interprets learning dynamics through standard economic categories and enables comparability across studies. The architecture organizes models along explicit dimensions: behavioural preferences, institutional frictions, economic environment class, information structure, learning and exploration mechanisms, and evaluation metrics. A simulation illustrates how variations in frictions, risk attitudes, and intertemporal preferences affect learned policies, their stability, and their relationship to static benchmarks. The paper aims to promote the cumulative use of Reinforcement Learning in applied economics by providing a general specification that improves interpretability, comparability, and reproducibility, turning deviations from theoretical equilibria into measurable diagnostics that refine economic fundamentals.

Computation doi: 10.3390/computation14020049

Authors: Ioannis P. Antoniades Anastasios N. Tsiftsis Christos K. Volos Andreas D. Tsigopoulos Konstantia G. Kyritsi Hector E. Nistazakis

In this work, we use an Echo State Network (ESN) model, which is essentially a recurrent neural network (RNN) operating according to the reservoir computing (RC) paradigm, to classify individual ECG heartbeats using the MIT-BIH arrhythmia database. The aim is to evaluate the performance of ESN in a challenging task that involves classification of complex, unprocessed one-dimensional signals, distributed into five classes. Moreover, we investigate the performance of the ESN in the presence of (i) noise in the dynamics of the internal variables of the hidden (reservoir) layer and (ii) random variability in the activation functions of the hidden layer cells (neurons). The overall accuracy of the best-performing ESN, without noise and variability, exceeded 96% with per-class accuracies ranging from 90.2% to 99.1%, which is higher than previous studies using CNNs and more complex machine learning approaches. The top-performing ESN required only 40 min of training on a CPU (Intel i5-1235U@1.3 GHz) HP laptop. Notably, an alternative ESN configuration that matched the accuracy of a prior CNN-based study (93.4%) required only 6 min of training, whereas a CNN would typically require an estimated training time of 2–3 days. Surprisingly, ESN performance proved to be very robust when Gaussian noise was added to the dynamics of the reservoir hidden variables, even for high noise amplitudes. Moreover, the success rates remained essentially the same when random variability was imposed in the activation functions of the hidden layer cells. The stability of ESN performance under noisy conditions and random variability in the hidden layer (reservoir) cells demonstrates the potential of analog hardware implementations of ESNs to be robust in time-series classification tasks.

Computation doi: 10.3390/computation14020048

Authors: Lesia Mochurad Ivan Tsmots Vita Mostova Karina Kystsiv

This paper proposes and experimentally evaluates a multi-level CPU-oriented execution method for high-throughput portion-based processing of file-backed Variant Call Format (VCF) data and automated mutation classification. The approach is based on a formally defined local processing scheme and integrates three coordinated levels of parallelism: block-based partitioning of file-backed VCF portions read sequentially into localized fragments with data-level parallel processing; task-level decomposition of feature construction into independent transformations; and execution-level specialization via JIT compilation of numerical kernels. To prevent performance degradation caused by nested parallelism, a resource-control mechanism is introduced as an execution rule that bounds effective parallelism and mitigates oversubscription, improving throughput stability on a single multi-core CPU node. Experiments on a public chromosome-17 VCF dataset for BRCA1-region pathogenicity classification demonstrate that the proposed multi-level local CPU execution (parsing/filtering, feature construction, and JIT-specialized numeric kernels) reduces runtime from 291.25 s (sequential) to 73.82 s, yielding a 3.95× speedup. When combined with resource-coordinated parallel model training, the end-to-end runtime further decreases to 51.18 s, corresponding to a 5.69× speedup, while preserving classification quality (accuracy 0.8483, precision 0.8758, recall 0.8261, F1 0.8502). A stage-wise ablation analysis quantifies the contribution of each execution level and confirms consistent scaling under resource-bounded execution.

Computation doi: 10.3390/computation14020047

Authors: Hongyan Wang Atthapol Ngaopitakkul Suntiti Yoomak

In this study, an adaptive state-of-charge (SOC) boundary strategy (ASBS) is proposed that dynamically adjusts the admissible upper and lower SOC limits of second-life lithium-ion batteries in off-grid photovoltaic battery energy storage systems (PV-BESSs) based on real-time state of health (SOH) and temperature feedback. The strategy is formulated using a unified electrical–thermal–aging model with an online state estimator and ensures both electrical safety and power feasibility while remaining fully compatible with standard energy management functions. Two representative simulations—a single-day operating profile and a continuous thirty-day sequence—demonstrate the effectiveness of the ASBS. In the twenty-four-hour case, the duration spent in high state-of-charge conditions is reduced by approximately 0.30–0.50 h, the abrupt end-of-charging transition is eliminated, and the temperature rise is slightly moderated, all without any loss of energy supply. Over thirty days, the difference between the ASBS and a fixed state-of-charge window remains effectively zero for almost all hours, with only a brief midday deviation of −4 to −5 percentage points and no cumulative drift. Indicators of electrical and thermal stress improve substantially, including an approximate 70% reduction in the root mean square charging current. These results confirm that the ASBS provides a practical and non-intrusive means of mitigating stress on second-life lithium-ion batteries while preserving full energy autonomy in off-grid photovoltaic systems.

Computation doi: 10.3390/computation14020046

Authors: Mariam Ibrahim Ruba Elhafiz

One of the most harmful and deceptive forms of cybercrime is phishing, which targets users with malicious emails and websites. In this paper, we focus on the use of natural language processing (NLP) techniques and transformer models for phishing email detection. The Nazario Phishing Corpus is preprocessed and blended with real emails from the Enron dataset to create a robustly balanced dataset. Urgency, deceptive phrasing, and structural anomalies were some of the neglected features and sociolinguistic traits of the text, which underwent tokenization, lemmatization, and noise filtration. We fine-tuned two transformer models, Bidirectional Encoder Representations from Transformers (BERT) and the Robustly Optimized BERT Pretraining Approach (RoBERTa), for binary classification. The models were evaluated on the standard metrics of accuracy, precision, recall, and F1-score. Given the context of phishing, emphasis was placed on recall to reduce the number of phishing attacks that went unnoticed. The results show that RoBERTa has more general performance and fewer false negatives than BERT and is therefore a better candidate for deployment on security-critical tasks.

Computation doi: 10.3390/computation14020045

Authors: Xuemei Zhu Han Peng Haoyu Cai Yu Liu Shirong Li Wei Peng

This paper proposes an Enhanced Projection-Iterative-Methods-based Optimizer (EPIMO) to overcome the limitations of its predecessor, the Projection-Iterative-Methods-based Optimizer (PIMO), including deterministic parameter decay, insufficient diversity maintenance, and static exploration–exploitation balance. The enhancements incorporate three core strategies: (1) an adaptive decay strategy that introduces stochastic perturbations into the step-size evolution; (2) a mirror opposition-based learning strategy to actively inject structured population diversity; and (3) an adaptive adjustment mechanism for the Lévy flight parameter β to enable phase-sensitive optimization behavior. The effectiveness of EPIMO is validated through a multi-stage experimental framework. Systematic evaluations on the CEC 2017 and CEC 2022 benchmark suites, alongside four classical engineering optimization problems (Himmelblau function, step-cone pulley design, hydrostatic thrust bearing design, and three-bar truss design), demonstrate its comprehensive superiority. The Wilcoxon rank-sum test confirms statistically significant performance improvements over its predecessor (PIMO) and a range of state-of-the-art and classical algorithms. EPIMO exhibits exceptional performance in convergence accuracy, stability, robustness, and constraint-handling capability, establishing it as a highly reliable and efficient metaheuristic optimizer. This research contributes a systematic, adaptive enhancement framework for projection-based metaheuristics, which can be generalized to improve other swarm intelligence systems when facing complex, constrained, and high-dimensional engineering optimization tasks.

Computation doi: 10.3390/computation14020044

Authors: Chathura Wanigasekara

Nonlinear systems engineering has undergone a profound transformation with the rapid development of computational tools and advanced analytical methods [...]

Computation doi: 10.3390/computation14020043

Authors: Daulet Akhmedov Baurzhan Bekmukhamedov Moldir Tanashova Zulfiya Seitmuratova

The increasing need for accurate prediction of geochemical anomalies requires methods capable of capturing complex spatial patterns that traditional approaches often fail to represent adequately. For N datasets of the form (Xi,Yi) representing the geographic coordinates of sampling points and Ci denoting the geochemical measurement, training multilayer perceptrons (MLPs) presents a challenge. The low informativeness of the input features and their weak correlation with the target variable result in excessively simplified predictions. Analysis of a baseline model trained only on geographic coordinates showed that, while the loss function converges rapidly, the resulting values become overly “compressed” and fail to reflect the actual concentration range. To address this, a preprocessing method based on anisotropy was developed to enhance the correlation between input and output variables. This approach constructs, for each prediction point, a structured informational model that incorporates the direction and magnitude of spatial variability through sectoral and radial partitioning of the nearest sampling data. The transformed features are then used in a dual-MLP architecture, where the first network produces sectoral estimates, and the second aggregates them into the final prediction. The results show that anisotropic feature transformation significantly improves neural network prediction capabilities in geochemical analysis.

Computation doi: 10.3390/computation14020042

Authors: Laszlo Csirmaz Elod P. Csirmaz

The entropic region is formed by the collection of the Shannon entropies of all subvectors of finitely many jointly distributed discrete random variables. For four or more variables, the structure of the entropic region is mostly unknown. We utilize a variant of the Maximum Entropy Method to obtain five-variable non-Shannon entropy inequalities, which delimit the five-variable entropy region. This method adds copies of some of the random variables in generations. A significant reduction in computational complexity, achieved through theoretical considerations and by harnessing the inherent symmetries, allowed us to calculate all five-variable non-Shannon inequalities provided by the first nine generations. Based on the results, we define two infinite collections of such inequalities and prove them to be entropy inequalities. We investigate downward-closed subsets of non-negative lattice points that parameterize these collections, and based on this, we develop an algorithm to enumerate all extremal inequalities. The discovered set of entropy inequalities is conjectured to characterize the applied method completely.

Computation doi: 10.3390/computation14020041

Authors: Olympia Roeva Anastasiya Zlatkova Velislava Lyubenova Maya Ignatova Denitsa Kristeva Gergana Roeva Dafina Zoteva

A representative cluster-based model of the batch process of ethanol production by Kluyveromyces sp. is proposed. Experimental data from fermentation processes of 17 different strains of K. marxianus are used; each of them potentially exhibits different metabolic and kinetic behavior. Three algorithms for clustering are applied. Two modifications of Principal Component Analysis (PCA)—hierarchical clustering and k-means clustering; and InterCriteria Analysis (ICrA) are used to simplify a large dataset into a smaller set while preserving as much information as possible. The experimental data are organized into two main clusters. As a result, the most representative fermentation processes are identified. For each of the fermentation processes in the clusters, structural and parameter identification are performed. Four different structures describing the specific substrate (glucose) consumption rate are applied. The best structure is used to derive the representative model using the data from the first cluster. Verification of the derived model is performed using experimental data of the second cluster. Model parameter identification is performed by applying an evolutionary optimization algorithm.

Computation doi: 10.3390/computation14020040

Authors: Rafael Lahoz-Beltra

The growing presence of autonomous AI systems, such as self-driving cars and humanoid robots, raises critical ethical questions about how these technologies should make moral decisions. Most existing moral machine (MM) models rely on secular, utilitarian principles, which prioritize the greatest good for the greatest number but often overlook the religious and cultural values that shape moral reasoning across different traditions. This paper explores how theological perspectives, particularly those from Christian, Islamic, and East Asian ethical frameworks, can inform and enrich algorithmic ethics in autonomous systems. By integrating these religious values, the study proposes a more inclusive approach to AI decision making that respects diverse beliefs. A key innovation of this research is the use of large language models (LLMs), such as ChatGPT (GPT-5.2), to design with human guidance MM architectures that incorporate these ethical systems. Through Python 3 scripts, the paper demonstrates how autonomous machines, e.g., vehicles and humanoid robots, can make ethically informed decisions based on different religious principles. The aim is to contribute to the development of AI systems that are not only technologically advanced but also culturally sensitive and ethically responsible, ensuring that they align with a wide range of theological values in morally complex situations.

Computation doi: 10.3390/computation14020037

Authors: Tangying Lv Wenming Yin Hengkai Yao Qingliang Liu Yitong Sun Kuan Zhao Shanliang Zhu

Physics–Informed Neural Networks (PINNs) have demonstrated efficacy in solving both forward and inverse problems for nonlinear partial differential equations (PDEs). However, they frequently struggle to accurately capture multiscale physical features, particularly in regions exhibiting sharp local variations such as shock waves and discontinuities, and often suffer from optimization difficulties in complex loss landscapes. To address these issues, we propose LocRes–PINN, a physics–informed neural network framework that integrates local awareness mechanisms with residual learning. This framework integrates a radial basis function (RBF) encoder to enhance the perception of local variations and embeds it within a residual backbone to facilitate stable gradient propagation. Furthermore, we incorporate a residual–based adaptive refinement strategy and an adaptive weighted loss scheme to dynamically focus training on high–error regions and balance multi–objective constraints. Numerical experiments on the Extended Korteweg–de Vries, Navier–Stokes, and Burgers equations demonstrate that LocRes–PINN reduces relative prediction errors by approximately 12% to 67% compared to standard benchmarks. The results also verify the model’s robustness in parameter identification and noise resilience.

Computation doi: 10.3390/computation14020039

Authors: Argyris N. Stassinakis Efstratios V. Chatzikontis Kyle R. Drexler Andreas D. Tsigopoulos Gratchia Mkrttchian Hector E. Nistazakis

Free-space optical (FSO) communication enables high-bandwidth license-free data transmission and is particularly attractive for maritime point-to-point links. However, FSO performance is strongly affected by atmospheric conditions. This work presents a semi-empirical model quantifying the impact of fine particulate matter (PM2.5) on received optical power in a maritime FSO link. The model is derived from long-term experimental measurements collected over a 2.96 km horizontal optical path above the sea surface, combining received signal strength indicator (RSSI) data with co-located PM2.5 observations. Statistical analysis reveals a strong negative correlation between PM2.5 concentration and received optical power (Pearson coefficient −0.748). Using a logarithmic attenuation formulation, the PM2.5-induced attenuation is estimated to increase by approximately 0.0026 dB/km per µg/m3 of PM2.5 concentration. A second-order semi-empirical model captures the observed nonlinear attenuation behavior with a coefficient of determination of R2 = 0.57. The proposed model provides a practical tool for link budgeting, performance forecasting, and adaptive design of maritime FSO systems operating in aerosol-rich environments.

Computation doi: 10.3390/computation14020038

Authors: Eleni Douvi Dimitra Douvi Jason Tsahalis Haralabos-Theodoros Tsahalis

Although hydrogen plays a crucial role in the EU’s strategy to reduce greenhouse gas emissions, its storage and transport are technically challenging. If ammonia is produced efficiently, it can be a promising hydrogen carrier, especially in decentralized and flexible conditions. The Horizon EU HySTrAm project addresses this problem by developing a small-scale, containerized demonstration plant consisting of (1) a short-term hydrogen storage container using novel ultraporous materials optimized through machine learning, and (2) an ammonia synthesis reactor based on an improved low-pressure Haber–Bosch process. This paper presents an initial version of a Python (v3.9)-based dashboard designed to visualize and optimize the simulation workflow of the ammonia synthesis process. Designed as a baseline for a future online, automated tool, the dashboard allows the comparison of three reactor configurations already defined through simulations and aligned with the upcoming experimental campaign: single tube, two reactors in parallel swing mode and two reactors in series. Pressures at the inlet/outlet, temperatures across the reactor, operation recipe and ammonia production over time are displayed dynamically to evaluate the performance of the reactor. Future versions will include optimization features, such as the identification of optimal operating modes, the reduction of production time, an increase of productivity, and catalyst degradation estimation.

Computation doi: 10.3390/computation14020035

Authors: Ege Erdoğan Barbaros Çetin

Orthotropic materials are increasingly employed in advanced thermal systems due to their direction-dependent heat transfer characteristics. Accurate numerical modeling of heat conduction in such media remains challenging, particularly for 3D geometries with nonlinear boundary conditions and internal heat generation. In this study, conventional boundary element method (BEM) and isogeometric boundary element method (IGABEM) formulations are developed and compared for steady-state orthotropic heat conduction problems. A coordinate transformation is adopted to map the anisotropic governing equation onto an equivalent isotropic form, enabling the use of classical Laplace fundamental solutions. Volumetric heat generation is incorporated via the radial integration method (RIM), preserving the boundary-only discretization, while nonlinear Robin boundary conditions are treated using variable condensation and a Newton–Raphson iterative scheme. The performance of both methods is evaluated using a hollow ellipsoidal benchmark problem with available analytical solutions. The results demonstrate that IGABEM provides higher accuracy and smoother convergence than conventional BEM, particularly for higher-order discretizations, which is owing to its exact geometric representation and higher continuity. Although IGABEM involves additional computational overhead due to NURBS evaluations, both methods exhibit similar quadratic scaling with respect to the degrees of freedom.

Computation doi: 10.3390/computation14020036

Authors: Biao Qiu Chaiyan Jettanasen

Most unpaved roads are subjectively classified as Class D roads. However, significant variations exist across different sites and environments (e.g., mining areas). A major challenge in the engineering field is how to quickly correct the Power Spectral Density (PSD) of the unpaved road in question using existing equipment and limited sensors. To address this issue, this study combines real-vehicle test data with a suspension dynamics simulation model. It employs time-domain reconstruction via Inverse Fast Fourier Transform (IFFT) and wavelet processing methods to construct an optimized model that fuses time-frequency domain features. With the help of a surrogate optimization method, the model achieves the best approximation of the actual road surface, corrects the PSD parameters of the unpaved road, and provides a reliable input basis for vehicle dynamics simulation, fatigue life prediction, and performance evaluation.

Computation doi: 10.3390/computation14020034

Authors: Ziqian Xu Zhiyong Zhang

With the growing accessibility of tools such as online surveys and web scraping, longitudinal social network data are more commonly collected in social science research along with non-network survey data. Such data play a critical role in helping social scientists understand how relationships develop and evolve over time. Existing dynamic network models such as the Stochastic Actor-Oriented Model and the Temporal Exponential Random Graph Model provide frameworks to analyze traits of both the networks and the external non-network covariates. However, research on the dynamic latent space model (DLSM) has focused mainly on factors intrinsic to the networks themselves. Despite some discussion, the role of non-network data such as contextual or behavioral covariates remain a topic to be further explored in the context of DLSMs. In this study, one application of the DLSM to incorporate dynamic non-network covariates collected alongside friendship networks using autoregressive processes is presented. By analyzing two friendship network datasets with different time points and psychological covariates, it is shown how external factors can contribute to a deeper understanding of social interaction dynamics over time.