Computation doi: 10.3390/computation14070162

Authors: Zaid Sawlan

This paper develops an adjoint-based framework for jointly reconstructing a space–time-dependent internal heat source and an unknown initial temperature field from sparse, noisy measurements. A single adjoint solve supplies the gradients with respect to both fields, so each iteration requires one forward and one adjoint solve. Deterministically, CGLS with discrepancy-principle stopping reaches a comparable regularized solution in about an order of magnitude fewer iterations than Landweber–Fridman. In the Bayesian formulation, Gaussian noise and Matérn priors yield an exact Gaussian posterior; prior-preconditioned conjugate gradients compute the maximum a posteriori estimate, while a low-rank approximation of the prior-preconditioned data-misfit Hessian provides pointwise credible bands. The exact discrete adjoint gives machine-precision gradients, and prior-predictive experiments verify nominal pointwise coverage. Numerical experiments compare the reconstructions and assess sensitivity to noise, discretization, and prior hyperparameters. The Bayesian reconstruction is more accurate and mesh-robust in the reported tests. A calibrated generalized-χ2 discrepancy diagnostic detects misspecification caused by an omitted initial-temperature offset and, less strongly, by discontinuous sources outside the prior model. These experiments demonstrate joint reconstruction and scalable uncertainty quantification using only forward and adjoint heat-equation solves.

Computation doi: 10.3390/computation14070161

Authors: Ali Cemal Benim

Computation (ISSN 2079-3197) is embarking on a new journey by broadening its academic scope and refining the disciplinary structure of computational science and engineering [...]

Computation doi: 10.3390/computation14070160

Authors: Shahar Shelly

Background: Speech models (wav2vec 2.0, HuBERT, Whisper), large language models (GPT, LLaMA), and conversational AI have expanded computational speech analysis from handcrafted acoustic features to dialogue-based neurological assessment. How well these approaches address clinical practice has not been evaluated. Methods: We conducted a narrative review searching PubMed, Google Scholar, and IEEE Xplore, supplemented by Interspeech and ICASSP proceedings. Findings are organized along three layers: acoustic-motor (voice quality, prosody, articulation), language-transcript (lexical, syntactic, semantic, and discourse analysis), and integrated multimodal-conversational (interactive dialogue systems). Traditional acoustic biomarkers provide background; the primary focus is on foundation models, LLMs, and conversational AI. Findings: Speech foundation models outperform handcrafted features on several classification tasks but degrade on severely impaired speech due to domain mismatch with healthy training data. LLMs classify transcripts and score cognitive tests, but operate on text alone and cannot access acoustic-motor information. Conversational AI can administer cognitive screening through naturalistic dialogue, but validation is limited to small single-centre feasibility studies. Prospective clinical validation remains limited. Cross-linguistic generalizability is untested for most methods. Interpretation: The field is moving toward integrated speech-language assessment, but the gap between technical capability and clinical utility remains wide. Closing it requires diverse multilingual datasets, standardized benchmarks, prospective validation, and ethical governance.

Computation doi: 10.3390/computation14070159

Authors: Vicente Borja-Jaimes Jarniel García-Morales Jorge Enrique Lavín-Delgado Miguel Beltrán-Escobar Jorge Salvador Valdez-Martínez Guillermo Ramírez Zúñiga Heriberto Adamas-Pérez Antonio Coronel-Escamilla

Quadrotor unmanned aerial vehicles (QUAVs) exhibit strongly coupled nonlinear dynamics and are highly sensitive to disturbances and measurement noise, which can significantly degrade trajectory tracking performance and induce chattering in sliding mode-based controllers. In this work, a fractional-order backstepping sliding mode control (FO-BSMC) strategy is proposed for QUAV trajectory tracking. In contrast to existing fractional-order sliding mode approaches, where the fractional operator is typically introduced into the sliding surface or control law, the proposed methodology incorporates fractional-order behavior directly into the QUAV dynamic model through the Caputo definition, while the Grünwald–Letnikov approximation is adopted for numerical implementation. A conventional integer-order BSMC scheme is also developed, and Lyapunov-based stability analyses are presented for both the conventional BSMC and the proposed FO-BSMC formulations. The fractional order is selected using the PSO algorithm. The performance of both controllers is evaluated under external disturbances, perturbed initial conditions, and measurement noise. Monte Carlo simulations are further conducted to assess the sensitivity of the closed-loop system to initialization uncertainties. The simulation results demonstrate that the proposed FO-BSMC achieves lower tracking errors, faster convergence, improved robustness against external disturbances and measurement noise, and smoother control actions with reduced chattering than the conventional BSMC.

Computation doi: 10.3390/computation14070158

Authors: Narendra Kumar Upadhyay Sudhakar Periyasamy Vinod Kumar

The advent of cloud–fog–edge computing has transformed distributed data processing by performing computation closer to end devices. Due to resource constraints at edge nodes and the dynamic nature of fog-assisted communication, secure and efficient group key distribution and batch verification in such decentralized systems remain a major challenge. Many existing protocols based on Chinese remainder theorem (CRT) use a straightforward scalar product to mask the group key and hence fail in multifactor security. Others suffer from architectural overhead since they require distinct and independent sets of moduli equations with multiple mathematical structures for different network layers, which increases computing overhead, limits scalability and delays synchronization during frequent node leave/join. To mitigate these challenges, this paper proposes a unified distributed CRT-based protocol for cloud–fog–edge environments. Our protocol introduces a two-factor modular key masking mechanism by incorporating a unique secret parameter for every edge node to strengthen group key protection and enhance the overall robustness of the key distribution mechanism. Additionally, our protocol uses a single set of moduli equations across cloud–fog–edge networks, which drastically reduces computation and storage costs at the fog layer. Our protocol achieves O (1) efficiency for rekeying. Formal security analysis using ProVerif and the ROR model demonstrates that our protocol has considerable security advantages. To prove its practicality, an ESP32-based simulation on Wokwi is used to verify the correctness of group key distribution, retrieval, and batch message verification. The performance analysis findings show that our protocol outperforms others in computation cost, communication cost, security and applicability for resource-constrained cloud–fog–edge computing networks.

Computation doi: 10.3390/computation14070157

Authors: Md Ariful Alam Shazib Ahmed Tanvir Arafat Rohan Khandakar Rabbi Ahmed Areyfin Mohammed Yoshi Belal Hossain Rakibul Islam

This paper develops and evaluates a predictive analytics framework for influencer marketing return on investment (ROI), integrating hybrid deep learning architectures with trust-aware modelling to address the dual purpose of (a) developing a rigorous evaluation framework for influencer campaign performance and (b) examining the effectiveness of influencer marketing predictors. The concept of influencer marketing has quickly grown to be one of the most effective mediums within the contemporary digital advertising landscape. Due to the growing number of brands dedicating huge amounts of budgets to social media partnerships, the importance of data-driven approaches that can predict the outcomes of campaigns and, consequently, ensure the best possible return on investment (ROI) has become urgent. This paper introduces a machine learning system that can be used to forecast the sales of products promoted by influencer marketing campaigns based on campaign-level features, including type of platform, influencer type, type of campaign, time of the year, number of engagements, estimated reach, and campaign duration. A publicly available influencer marketing ROI dataset was trained and tested on an XGBoost regression model with a coefficient of determination (R2) of 0.95 indicating high predictive power and generalization. The results show that engagement metrics and estimated reach are some of the most impactful factors in sales performance, and additional contextual factors like platform selection, type of campaign, and timing of the year also moderate results. In addition to predictive modelling, this paper explains how artificial intelligence (AI) can be strategically integrated throughout the influencer marketing lifecycle. With the inclusion of AI-based analytics, marketers will be able to leverage their intuitive decision-making processes with quantifiable and replicable measures and approaches that can lead to true consumer trust and lasting brand resonance. The framework proposed can provide practitioners and researchers with a scalable basis for implementing intelligent systems in the context of influencer marketing. Recent computer science research further demonstrates that AI-driven frameworks spanning generative content modelling, AI-powered CRM architectures for understanding consumer preferences on social media, and parasocial-trust models of influencer engagement provide strong methodological complements to the predictive approach developed here, while governance and project management considerations for deploying such systems are increasingly addressed in the literature. Concurrently, a growing body of influencer marketing research examines how platform affordances shape information-seeking and trust, how influencer attributes and social satisfaction mediate purchase intention, how influencer marketing drives sustainable consumption, and how social media measurably shapes health-related behaviours all of which motivate the predictive and trust-modelling objectives of this work.

Computation doi: 10.3390/computation14070156

Authors: Larysa Sydorchuk Ruslan Gumennyi Miroslav Škoda Andrii Sydorchuk Yana Vyklyuk Iryna Batih Sai Praveen Daruvuri Ruslan Sydorchuk Maksym Sokolenko

Postoperative wound healing complications present a major challenge in plastic and reconstructive surgery, prolonging recovery and impairing outcomes. Early risk identification is difficult due to complex interactions among clinical, laboratory, and molecular factors. This study developed and evaluated machine-learning (ML) models to predict wound healing outcomes and identify key complication predictors. Utilizing a dataset of 95 women and 76 variables (including hematological, biochemical, coagulation, and gene expression profiles), we evaluated several ML approaches, including Decision Tree, Extra Trees, Gaussian/Bernoulli Naive Bayes, Logistic Regression, and Support Vector Machine. Model performance was assessed via k-fold cross-validation, ROC analysis, and SHAP feature importance. Molecular markers (COL1A1, MMP9, MAPK1, MAPK8, IL10, and CCL2) emerged as the strongest predictors, whereas conventional clinical variables showed limited value. The models achieved high discriminative performance, with validation ROC–AUC values ranging from 0.903 to 0.913. Extra Trees and Gaussian Naive Bayes demonstrated the highest sensitivity for detecting complications (Recall = 0.820 ± 0.238 and 0.807 ± 0.246, respectively). These findings highlight the value of integrating molecular-genetic biomarkers with ML for personalized risk stratification and preventive care in reconstructive surgery.

Computation doi: 10.3390/computation14070155

Authors: Yacov Satin Rostislav Razumchik Alexander Zeifman Janos Sztrik

In this paper, consideration is given to the class of birth-and-death processes with possibly state-dependent and time-varying transition intensities and a finite state space. Several techniques are available in the literature for the computation of the long-run (limiting) time-dependent performance characteristics of such processes. Whenever a solution technique is combined with a limiting regime detection method, its efficiency may be improved. It is intuitively reasonable to expect that, if additional information about the process is available, a limiting regime detection method may allow one to save more computation effort. In this paper, we demonstrate that the logarithmic norm method, which is one of the methods with which to provide ergodicity bounds for continuous-time Markov chains with discrete state space, can be utilized in such a way. When the exact values of the transition intensities of the (ergodic) birth-and-death process are known and are such that it is clear that one group of states is visited less often than the other, the method allows one to detect the limiting regime rapidly. We illustrate numerically the results obtained within the queueing theory context by considering the activity of the total number of customers in a multi-server finite-capacity queue with periodic arrival and service intensities.

Computation doi: 10.3390/computation14070154

Authors: Muhammad Adil Khan Tahir Ullah Khan Maaz Khan Tareq Saeed Božidar Ivanković

This paper develops probabilistic mean-square extensions of fractional Hermite–Hadamard–Mercer (HHM) type inequalities for convex stochastic processes. By employing generalized mean-square stochastic fractional integral operators, we establish new fractional inequalities that extend deterministic HHM estimates to a stochastic framework. The stochastic inequalities are interpreted in the almost sure sense, while the associated fractional operators are considered within the mean-square setting for second-order stochastic processes. Several special cases are also discussed, showing that the obtained results reduce to known fractional and stochastic inequalities for suitable choices of the parameters. As analytical consequences, the proposed results are applied to two-variable means, modified Bessel functions and the k-Digamma function. To strengthen the applied interpretation, we also present a stochastic nonlinear conductivity distortion model in which the effective conductivity is represented by a positive convex stochastic process. The corresponding heat-conduction setting, heat-flux interpretation and Monte Carlo illustrations show how the derived bounds can be used to estimate fractional stochastic averaging errors under nonlinear conductivity distortion. The numerical plots are presented as illustrative demonstrations of the behaviour of the theoretical bounds under admissible parameters.

Computation doi: 10.3390/computation14070153

Authors: Lee-Wen Chiu Ming-Chin Chen Tzu-Hsin Liu Fu-Min Chang

This paper investigates a multi-server retrial machine repair system that incorporates orbital search and a synchronous vacation mechanism. The system features standby units and examines two potential vacation scenarios for servers, reflecting real-world situations, such as technical staff in teaching hospitals taking periodic administrative or training vacations. We formulate a mathematical model using birth-and-death processes to establish the governing equation and propose a recursive matrix method to systematically derive the steady-state probabilities. Performance measures, including system availability, the expected number of failed units in the orbit, and expected waiting times, are derived. To address the conflicting objectives of minimizing total operating costs, minimizing expected waiting times, and maximizing system availability, we construct a tri-objective optimization problem. By implementing a multi-objective genetic algorithm, we identify a set of Pareto-optimal frontiers and reveal the explicit financial and operational trade-offs among these competing criteria. Numerical experiments and sensitivity analyses demonstrate that enhancing the automated retrial rate and managing the emergency repair rate are most critical to minimizing system downtime. Furthermore, the joint optimization of server capacities and vacation schedules effectively eliminates operational redundancy, showing that near-perfect equipment availability (up to 0.999) can be achieved with only marginal increases in cost. This research provides administrators with a robust decision-making framework to optimize technical resource management while ensuring near-perfect equipment availability in real-world environments.

Computation doi: 10.3390/computation14070152

Authors: Cristian David Correa-Álvarez Mario Cesar Jarramillo-Elorza Osnamir Elias Bru-Cordero

This study examines likelihood-based estimation of the joint survival function S(t1,t2)=Pr{T(1)>t1,T(2)>t2} for systems with two competing failure modes observed under right censoring. Rather than introducing a new distributional family, the study compares established dependence mechanisms within a common observed-data framework. Exponential and Weibull margins are combined with three types of dependence: Archimedean copulas, represented by the Gumbel and Clayton families; shared gamma frailty, used to model latent measurement-level heterogeneity; and Marshall–Olkin extensions, used to represent common shocks and simultaneous failures. The same observation scheme, likelihood construction, censoring design, and performance criteria are used across models. Model performance is evaluated through Monte Carlo simulation using bias, integrated mean squared error, and empirical coverage, and the workflow is illustrated with the Device G reliability data. The results show that ignoring dependence can distort joint survival estimates, especially under moderate or high censoring. They also show that copula, frailty, and Marshall–Olkin specifications can lead to different reliability assessments because they encode different stochastic mechanisms. The estimation workflow includes multi-start optimization and diagnostics for boundary solutions, Hessian stability, and irregular likelihood behavior.

Computation doi: 10.3390/computation14070151

Authors: Hongfei Zhao Yuhang Zhen Yi Huang Ying Liu

Despite recent advances in the reconstruction of biological neural networks, the generative principles underlying the structural properties of these networks remain incompletely understood. Neurons in the brain belonging to different functional classes, such as sensory neurons, interneurons, or motor neurons, exhibit distinct connectivity asymmetry patterns. Here, we analyze the differences in connectivity patterns among these types, focusing on the asymmetry between in-degree and out-degree. Our analysis reveals that sensory neurons tend to exhibit a predominance of outgoing connections (negative asymmetry), motor neurons a predominance of incoming connections (positive asymmetry), and interneurons a more balanced connectivity profile. To capture these type-specific features, we propose an extended network growth model in which nodes are assigned to predefined functional types, each with distinct initial attractiveness for incoming and outgoing edges. Simulations demonstrate that our model can reproduce the observed asymmetry indices of different neuron types in biological neural networks and can also generate diverse degree distribution shapes. This work offers a phenomenological generative framework that links neuron type identity to connectivity asymmetry, and it provides a baseline for future studies that incorporate additional biological constraints.

Computation doi: 10.3390/computation14070150

Authors: Nebojša Zdravković Dragan S. Džunić Živana Jovanovic Pešić Dalibor Nikolić

Friction stir processing (FSP) is an advanced solid-state surface modification technique for biomedical titanium alloys. This study presents a computational investigation of FSP applied to Ti-6Al-4V alloy through three-dimensional finite element modeling and Taguchi-based statistical optimization. A Taguchi L9 orthogonal array evaluated rotational speed (400–1000 rpm), traverse speed (50–100 mm/min), shoulder diameter (6–18 mm), and pin diameter (2–6 mm), reducing the required simulations from 81 (full factorial) to nine (88.9% reduction). A calibrated friction model (μ = 0.35/0.25/0.20 for 400/800/1000 rpm, F = 6000 N) yielded maximum temperatures of 870–1384 °C; all predicted temperatures remained below the melting point of Ti-6Al-4V (1660 °C). These values are consistent with experimentally reported ranges for FSW/FSP of Ti-6Al-4V. Traverse speed is the dominant parameter (ANOVA contribution: 63.1%, F = 10.44), followed by rotational speed (26.7%) and shoulder diameter (4.1%). Simulation 3 (400 rpm, 100 mm/min, Ds = 18 mm, T_max = 870 °C) appears to be the most promising thermal condition for preserving the fine-grained α + β microstructure, as it remains below the β-transus temperature (980 °C) throughout the processed zone.

Computation doi: 10.3390/computation14070149

Authors: Delei Nie Jingjing Hu Xin Wang Yu Li Jiangxing Wu Farrukh Hanif Renhai Feng

Existing PLC security solutions face a fundamental conflict between stringent real-time requirements and robust protection: traditional IT security mechanisms (e.g., encryption, authentication) introduce unacceptable latency, while software-based redundancy schemes operate at millisecond scale and remain vulnerable to common-cause failures. To bridge this gap, this study proposes MimicPLC v1.0, a dynamic defense mechanism based on a heterogeneous multi-core architecture that integrates threat perception, dynamic fault tolerance, and rapid recovery within a single chip, thereby reconciling real-time determinism with proactive security in industrial control systems. The architecture integrates three distinct CPU cores (MIPS, ARM, and RISC-V) within a single system-on-chip (ESC0830), coordinated by a dedicated hardware-based mimic scheduling subsystem. This subsystem performs real-time, loosely coupled, transaction-level consistency checks on the AHB-Lite bus operations of the heterogeneous processors, achieving nanosecond-scale arbitration latency for threat detection. We evaluate the proposed design using an industrial-strength testbed, incorporating a custom development board and the Synopsys Verdi simulation environment, under critical attack scenarios including Denial-of-Service (DoS), replay, code injection, and parameter overwrite attacks. The system maintains continuous operation through adaptive redundancy, demonstrating attack perception within 73 clock cycles and leveraging instruction-set asymmetry for effective threat containment. Rigorous validation, including 100 consecutive parameter override attacks, confirms a 100% interception rate within our tested attack scenarios, with zero false positives observed. The design complies with the IEC 61131-3 real-time standard, exhibiting a worst-case recovery duration of 9.3 ms and a 95% confidence interval for recovery latency of [4.0354, 4.0363] ms. This work pioneers a paradigm of rapid-detection endogenous security with nanosecond-scale arbitration for next-generation industrial control systems.

Computation doi: 10.3390/computation14070148

Authors: Xueyi Li Chaolun Wang Jiannan Dong Zhilin Dong Tianyang Wang

Bearing fault diagnosis methods based on single sensors often suffer from reduced accuracy due to limited information. Although multi-sensor systems provide richer vibration information, the high dimensionality and complexity of these signals still pose challenges for effective feature extraction and fusion. In addition, many existing deep learning-based fusion methods rely on a single analysis domain or simple feature concatenation, making it difficult to fully exploit the complementarity among raw temporal signals, time-domain statistical features, and frequency-domain characteristics. To address these issues, this paper proposes a multi-view graph-based fault diagnosis framework with adaptive fusion, termed MDEGCN, for bearing condition identification. Specifically, non-overlapping vibration windows are treated as graph nodes, and three graph views are constructed to capture temporal proximity, time-domain similarity, and frequency-domain correlation, respectively. Each graph view is processed by an enhanced graph neural network branch to learn view-specific representations, and an adaptive, differentiable fusion mechanism is introduced to integrate complementary information from different views for final fault classification. Experiments on the Northeast Forestry University and Politecnico di Torino bearing datasets were conducted under a purged blocked split protocol to reduce potential information leakage between adjacent windows. Additional hard settings with a low training ratio further evaluate the robustness of the proposed framework under limited labelled data. The experimental results demonstrate that MDEGCN achieves competitive diagnostic performance and provides an effective multi-view representation learning strategy for bearing fault diagnosis.

Computation doi: 10.3390/computation14070147

Authors: Tao Liu Tengda Xu Fei Ye Huili Bi Hongyu Chen Xijie Song Zan Zhou Zhengwei Wang

To evaluate the structural safety of variable-speed pumped-storage units under extreme transient conditions, this paper focuses on a variable-speed unit at a specific pumped-storage power plant. Based on boundary conditions measured during on-site load shedding tests, a three-dimensional, unidirectional fluid–structure interaction numerical model was established, incorporating stationary components such as the volute, base ring, top cover, and bottom ring. A numerical prediction and analysis of the dynamic stresses and deformations of key components were conducted for a hazardous scenario in which all units shed load simultaneously and the volute pressure reached its peak. The results show that during the load shedding process, the maximum static stress in the stationary components was 79.5 MPa, and the maximum displacement was 0.066 mm; both occurred 46.01 s after load shedding at the junction between the guide vane outlet edge and the top cover, and this value is far below the material’s yield strength of 490 MPa. Preliminary numerical evaluations indicate that the unit’s stationary components meet strength design requirements under this extreme transient condition. Furthermore, the study revealed the time lag mechanism between the peak hydraulic load and the peak structural stress in the top cover. The numerical prediction method established in this study can provide technical support for the structural safety assessment of transient processes in variable-speed units.

Computation doi: 10.3390/computation14070146

Authors: Yonis Gulzar Ishtiaq Ali

Fractional optimal control problems provide an effective mathematical framework for modeling dynamical systems with memory, hereditary behavior, and anomalous diffusion effects. However, the nonlocal nature of Caputo fractional operators and the reduced regularity of fractional solutions pose significant challenges for the development of accurate and efficient computational methods. In this paper, we develop a spectral-fractional Physics-Informed Neural Network (Spectral-fPINN) framework for solving fractional optimal control problems governed by Caputo fractional differential equations. The proposed methodology combines normalized shifted Legendre spectral approximations, fractional operational matrix formulations, and physics-informed optimization within a unified computational framework. Unlike conventional PINN and fPINN approaches, which directly approximate the unknown solution variables, the proposed framework predicts the spectral coefficient vectors associated with the shifted Legendre basis functions, yielding a low-dimensional global representation with improved approximation efficiency. Caputo fractional derivatives are evaluated through spectral operational matrices, while the resulting optimization problem is discretized using Gauss–Legendre quadrature and solved through gradient-based optimization. In addition, a theoretical analysis of the proposed Spectral-fPINN framework is presented, including approximation, consistency, stability, and convergence results, together with error estimates and residual control properties. Several benchmark linear and nonlinear fractional optimal control problems are investigated to validate the proposed methodology. The numerical results demonstrate excellent agreement with exact solutions, very small residual errors, and rapid spectral coefficient decay, confirming the high-order accuracy and robustness of the proposed approach. Overall, the proposed Spectral-fPINN framework provides an accurate, stable, and computationally efficient methodology for solving a broad class of fractional optimal control problems.

Computation doi: 10.3390/computation14070145

Authors: Roy Koomullil Emmanuel Ramogi Feroz Mohamed Iqbal Peter Rynes Vladimir Vantsevich Vamshi Korivi Nathan Tison

Predicting the wind field around military vehicles during extended missions is crucial to avoid detectability by infrared (IR) devices. This is a challenging task because of the geometric complexity of the vehicles and the unpredictable nature of wind direction, which can shift abruptly and have a significant impact on the flow field and heat transfer. Computational fluid dynamics (CFD) is routinely used to calculate flow fields around ground vehicles. However, this requires extensive computational time and memory, making it unsuitable for real-time analysis. To address these challenges, this paper focuses on machine learning (ML) techniques for accurate wind field prediction in real time for unseen wind directions within the sampled range. Reduced order modeling (ROM) is used for dimensionality reduction of flow field data derived from high-fidelity CFD simulations. ML models are trained using low-dimensional data from the ROM, and the predicted low-dimensional data for unseen wind directions by the trained ML model is used to reconstruct the flow field. ROM, in conjunction with ML techniques, offers a substantial reduction in analysis time while maintaining the ability to predict the flow field accurately. In this study, a neural network architecture with three output formulations trained using ROM data was used for the predictions, and the accuracy of the formulations was evaluated by comparing them with the CFD results. An optimal ML model is identified by varying the number of hidden layers and neurons within those layers. The developed ROM- and ML-based approach was able to predict the unseen flow field in less than a second, while a single CFD simulation required approximately 2.6 h per wind direction.

Computation doi: 10.3390/computation14070144

Authors: Mahmoud AlJamal Nawal Louzi Mohammad Q. Al-Jamal Luay Tahat Ala Mughaid Qasim Aljamal

Ceramic additive manufacturing offers strong potential for fabricating geometrically complex and application-specific components, yet achieving reliable dimensional fidelity remains challenging because dimensional deviation is governed by highly coupled material, process, thermal, and environmental factors. To address this problem, this study proposes an uncertainty-aware computational framework for dimensional error prediction in ceramic 3D printing under variable material and process conditions. The contribution is positioned as a system-level integration of established learning, uncertainty estimation, calibration, and reliability-interpretation components within a ceramic additive manufacturing dimensional-error prediction workflow, rather than as a fundamental methodological breakthrough. The validation is conducted using the publicly available Ceramic 3D Printing Process Control Dataset, a 1000-sample tabular dataset, and the resulting findings are therefore interpreted as dataset-specific computational evidence rather than direct proof of industrial deployment readiness. The methodology begins with a structured data-driven preprocessing pipeline that transforms the Ceramic 3D Printing Process Control Dataset into a multi-condition feature space through data cleaning, one-hot material encoding, min–max normalization, and engineered descriptors capturing extrusion–speed balance, thermal gradients, cooling intensity, deposition density, and material-conditioned interactions. A multi-branch deep computational architecture is then developed to encode material, process, thermal-environmental, and engineered-feature streams separately, followed by adaptive cross-condition fusion to learn nonlinear dependencies across ceramic printing regimes. To improve reliability beyond deterministic regression, the framework jointly models aleatoric and epistemic uncertainty and incorporates calibration refinement to align predictive confidence with observed error behavior, thereby enabling preliminary reliability-oriented interpretation of stable and high-risk operating conditions. Experimental results demonstrate that the full model achieves the best overall within-dataset performance, with a test MAE of 0.0118, RMSE of 0.0172, R2=0.999, MAPE of 1.74%, calibration error of 0.003, PICP of 0.996, reliability score of 0.992, and a stable prediction rate of 98.7%. Although these values indicate strong predictive behavior under the current structured dataset, the exceptionally high R2 should be interpreted cautiously because external experimental validation, larger measured datasets, and cross-machine ceramic printing trials are still required. These findings show that the proposed framework provides an effective system-level computational strategy for dataset-specific reliability-aware dimensional quality prediction in ceramic additive manufacturing and offers a preliminary data-driven foundation for uncertainty-aware intelligent process optimization.

Computation doi: 10.3390/computation14070143

Authors: Carlotta Fontana Sina Zinatlou Ajabshir

Human digital twins (HDTs) are patient-specific computational models that combine medical imaging, physiological measurements and predictive algorithms. They are moving from an exciting concept to a realistic clinical opportunity. The key question is no longer whether HDTs can be built. The key question is which methods are mature enough to support clinical decisions and what is still missing for routine use. This systematic review maps the methodological landscape of HDTs and highlights practical bottlenecks that limit clinical translation. A PRISMA 2020 guided search of PubMed, Scopus, IEEE Xplore, and the Cochrane Library, covering publications from 2016 to 2026, identified 151 eligible studies. Bibliometric mapping and thematic synthesis were used to characterize research clusters, computational paradigms, and collaboration patterns. Three dominant application streams were identified: cardiovascular HDTs for hemodynamic simulation and procedural planning, musculoskeletal HDTs for biomechanics-driven orthopedic innovation, and neurological HDTs integrating neuroimaging with computational neuroscience. Across domains, the strongest technical trend is the rise in hybrid pipelines that combine physics-based simulation, including finite element and computational fluid dynamics models, with machine learning for segmentation, parameter identification, reduced-order modeling, and faster inference. However, reporting of verification, validation, uncertainty quantification, and explicit context of use remains uneven and prospective clinical evidence is still limited. Overall, the literature shows rapid progress toward clinically credible HDTs, while highlighting the need for scalable computation, standardized credibility pipelines, and workflow-integrated platforms to support safe and reproducible clinical adoption.

Computation doi: 10.3390/computation14060142

Authors: Nurgul Shazhdekeyeva Beket Kenzhegulov Kamka Uteuliyeva Gulash Kochshanova Gulmira Nigmetova Lyailya Kurmangaziyeva Raigul Tuleuova Saya Kenzhegulova Raushan Moldasheva

The proposed methodology combines analytical control laws with adaptive mechanisms and machine-learning-assisted modules based on regression trees, random forests, and extreme gradient boosting (XGBoost). Machine learning models are employed to approximate unknown nonlinear dynamics, compensate disturbances, and adjust controller parameters, while the overall control structure is constrained by Lyapunov stability conditions. This ensures that the inclusion of data-driven components does not violate the fundamental requirement of system stability. The effectiveness of the proposed approach is evaluated through simulation experiments across three operating modes with varying degrees of nonlinearity and dynamic complexity. The results show that hybrid models incorporating ensemble machine learning methods improved performance compared with the analytical and adaptive baselines examined. XGBoost-based control achieves the lowest error values and the highest level of Lyapunov stability compliance (up to 99.3%). The main contribution of this study lies in the development of a unified synthesis framework in which machine learning is not used as a standalone control strategy but as a machine-learning-assisted support mechanism integrated into a theoretically grounded control architecture. The proposed approach provides a balance between adaptability, accuracy, and rigorous stability guarantees, suggesting potential applicability to simulation-based and offline-assisted control design tasks, while real-time embedded implementation requires additional computational optimization and validation.

Computation doi: 10.3390/computation14060141

Authors: Apostolos Papanikolaou Yan Xing-Kaeding

This paper provides comprehensive numerical and experimental studies on the unsteady resistance of the world’s first battery-driven, zero-emissions high-speed catamaran, the MS Medstraum, in accelerated forward speed motion. These studies suggest that for a certain speed range of around Froude 0.50 (the so-called last hump of wave resistance), the corresponding unsteady resistance is significantly less than the originally anticipated value, namely, up to 40% less when adding to the steady resistance, the conventional added mass term. This surprising result could be explained by both experimental resistance tests and CFD calculations, as well as by inspection of the numerically generated wave patterns. Thus, care must be taken when applying the traditional approach to the unsteady resistance of a ship in accelerated or decelerated forward speed motion. As such, this positively affects the estimation of the required power capacity to accelerate the ship to full operational speed. This leads to reduced (fitted) battery weight and positively affects the ship’s displacement, allowing the vessel to achieve higher speeds. The present research finally yielded notable results of interest for seakeeping and ship maneuvering simulation studies; namely, comprehensive CFD simulations for the studied slender catamaran have shown that calculated added mass values for surge motion in real-flow conditions are up to six times higher than those initially estimated by ideal flow potential theory methods.

Computation doi: 10.3390/computation14060140

Authors: Donia Y. Badawood Fahd M. Aldosari

High-Frequency Trading (HFT) dashboards require rapid reception, aggregation, and visualization of order book and trade update streams that may arrive at multi-million message rates. Conventional CPU-based and CPU-GPU hybrid visualization pipelines can suffer from significant delays during periods of burst due to CPU-mediated rendering, synchronization, kernel launch overhead, and copies on the host. This paper presents a visualization pipeline that is entirely resident on the graphics processor with zero-copy access to NIC accessible pinned buffers, persistent CUDA processing, fused stage execution of the parse-aggregate pipeline, and persistent CUDA OpenGL buffer interoperation. The goal is not to reach production status but rather to see whether host-to-host data movement can be decreased and whether the stages of GPU processing can be consolidated to improve latency, throughput and frame cadence in controlled HFT-style workloads. The evaluated workstation achieved a mean ingest-to-pixel latency of 6.3 ms using the proposed design compared to 29.4 ms for the current design, with sustained throughput of 10.2 million messages per second, which is 20 times greater than the current design, and a steady-state range of 185 to 192 frames per second with a burst floor of 178 frames per second for the proposed design. The improvement observed can be attributed to both the zero-copy ingestion and fused persistent kernel execution. Based on the obtained results, the proposed method of use of this technique in the implementation of real-time financial visualization under the proposed conditions is possible. More general testing is still required on other NICs, other generations of GPUs and PCIe configurations, workload traces, and actual exchange feeds.

Computation doi: 10.3390/computation14060139

Authors: Sandor I. Bernad Elena Silvia Bernad

Current evaluation of bypass graft performance relies predominantly on wall shear stress metrics, even though thrombosis and atherogenesis are fundamentally governed by particle transport and residence within disturbed flow regions. This disconnect limits the ability of conventional hemodynamic indicators to capture mechanisms directly linked to graft failure. In this study, we investigate how helical bypass geometry reorganises the flow and, consequently, modifies transport behaviour within the distal anastomosis by combining experimentally validated flow visualisation with computational fluid dynamics under pulsatile conditions. Particle transport was quantified using a controlled injection of 151 tracers, enabling direct assessment of retention and washout across the graft–anastomosis system. The straight configuration exhibited persistent recirculation structures that promoted localised particle retention and delayed clearance. In contrast, the helical geometry disrupted these structures, enhancing flow mixing and accelerating downstream transport. At late stages of the cardiac cycle, the helical configuration reduced residual particle retention by approximately 43% compared to the straight bypass. These findings demonstrate a transition from recirculation-driven retention to washout-dominated transport, providing a mechanistic basis for interpreting bypass performance beyond shear-based metrics. This transport-centred perspective provides a mechanistic link between flow organisation and particle residence, supporting the functional relevance of helical graft design while remaining distinct from direct modelling of biological thrombosis or atherogenesis.

Computation doi: 10.3390/computation14060138

Authors: Sathya Jayaraman Ramkumar Sivasakthivel Jayapriya Jayapal Balakrishnan Chinnaiyan

Thyroid tumors rank among the most frequently occurring endocrine cancers because early detection helps doctors deliver effective treatments that lead to better patient results. Ultrasound imaging enables the detection of thyroid nodules, yet medical professionals struggle to differentiate between benign and malignant nodules through their diagnostic tests. This study introduces a new medical framework that enables thyroid nodule diagnosis through ultrasound imaging. The proposed model combines advanced segmentation with feature extraction, classification, and reasoning components to create a complete system. The specialized segmentation method shows accurate results when it detects nodule boundaries, which leads to better analysis of specific regions. The Hybrid Vision Transformer (HVT) operates to capture detailed textural information together with complete environmental patterns, which boosts its ability to classify different elements. The proposed framework incorporates a Large Language Model (LLM), specifically Med-PaLM, to provide context-aware clinical reasoning and interpretation. The structured evaluation process uses Thyroid Imaging Reporting and Data System (TI-RADS)-based feature scoring to compare model results with designated clinical standards. The diagnostic process is enhanced through the use of a language model, which delivers contextual understanding and produces valuable information from features that have been extracted. The proposed model achieves excellent performance with accuracy at 98.5%, precision at 98.7%, recall at 98.4%, and F1-score at 98.5%, which demonstrates its capacity for accurate and equivalent performance across different classifications. The experimental results demonstrate that the model achieves better results than existing methods. The combination of multimodal data with clinical reasoning improves both the accuracy and the user experience of the system. The proposed framework provides an efficient, interpretable, and scalable solution for thyroid nodule diagnosis.

Computation doi: 10.3390/computation14060137

Authors: Panhapiseth Lim Priyanka Kumar Richard Zanni Timothy Lambdin

Federated learning lets organizations train a shared model without pooling private data. The standard method, Federated Averaging, requires all participants to use the same input features, a condition that fails in cross-sector phishing detection, where banks analyze URL structure and hospitals analyze email content. We present RankBridge, a system that groups participants by comparing ranked lists of SHapley Additive exPlanations (SHAP) feature importance rather than model weights or gradients. Each participant trains a local LightGBM model, extracts the top-K features by SHAP importance, and sends a 60-byte ranked list of feature indices to a central server. The server applies rank correlation and Ward’s hierarchical clustering to identify similarly threatened organizations. RankBridge operates in two modes: ModelShare, where models are also shared within each discovered group for prediction ensembling, and RankOnly, where the server returns only a group label and each participant keeps their model private. Across 32 participants in five organization types, RankBridge (ModelShare) achieves F1 =0.853 (AUC =0.926) on synthetic data and F1 =0.772 (AUC =0.812) on real phishing data, and it is the only method to outperform isolated local training on both. On real heterogeneous data the standard baselines adapted to LightGBM, including Federated Averaging, retain a moderate thresholded F1 (≈0.73) but their ranking quality collapses to near-random (AUC ≈0.59, PR-AUC ≈0.66), whereas RankBridge sustains AUC =0.812 and PR-AUC =0.819. RankBridge recovers the correct organizational groupings with Normalized Mutual Information (NMI) =0.973. The rank-based grouping channel itself transmits 60 bytes per participant per round, roughly 10,000× less than a full model upload.

Computation doi: 10.3390/computation14060136

Authors: Moustafa El-Shahed Kadi Alowais Yousef Alnafisah

This paper develops an optimal control framework for a mechanical–structural model of bone remodeling that couples osteocytes, osteoblasts, and osteoclasts with bone density, incorporating porosity-dependent feedback mechanisms. To represent clinically relevant interventions, three bounded control functions are introduced: anabolic stimulation of osteoblast activity, anti-resorptive suppression of osteoclast-mediated resorption, and structural modulation of porosity feedback. The controlled system is shown to be mathematically well-posed, and the necessary optimality conditions are derived via Pontryagin’s Maximum Principle, leading to explicit characterizations of the optimal controls. The resulting state–adjoint system is solved numerically using a forward–backward sweep method. Numerical results demonstrate that the optimal intervention effectively suppresses osteoclast activity and drives the system toward higher, more stable bone density levels than the uncontrolled dynamics. In particular, the anti-resorptive control consistently plays the dominant role in shaping the optimal strategy. A cost-effectiveness analysis based on ACER, ICER, and the efficient frontier shows that strategies involving anti-resorptive inhibition achieve the greatest therapeutic gains at moderate cost, while additional controls yield only marginal improvements. Sensitivity analysis further indicates that parameters associated with osteoclast dynamics and bone formation have the strongest influence on density-related outcomes.

Computation doi: 10.3390/computation14060135

Authors: Du’a Al-zaleq Lewa’ Alzaleq Suboh Alkhushayni

In this study, we utilize the ϕ6-model expansion method to derive a diverse set of Jacobi elliptic function solutions for the conformable resonant Nonlinear Schrödinger Equation (NLSE) with parabolic law nonlinearity. As the modulus of the Jacobi elliptic functions approaches 1 and 0, the solutions transform into hyperbolic and trigonometric functions, respectively. This methodology yields various exact traveling wave solutions, including kink solitons, singular solitons, periodic solutions, and singular periodic solutions. Notably, this work represents the first investigation into identifying Jacobi elliptic function solutions for the conformable resonant NLSE. These results enhance the understanding of the nonlinear dynamical properties intrinsic to the NLSE. We use graphical illustrations to highlight the dynamical features of the solutions. Moreover, our approach showcases versatility in addressing other nonlinear partial differential equations, offering insights applicable to nonlinear optics, fluid dynamics, and quantum physics.

Computation doi: 10.3390/computation14060134

Authors: Mulatijiang Maimaiti Ge Yan Qunyi Huang Abudureyimujiang Aosimanjiang Xiangyu Zhang

Monopile offshore wind turbines are vulnerable to excessive vibration under coupled wind, wave, and seismic loading because of their slender and flexible structural characteristics. This study investigates a single-sided vibro-impact nonlinear energy sink (SSVI NES) installed inside the nacelle of a 5 MW monopile offshore wind turbine. A reduced-order ten-degree-of-freedom dynamic model is established using the Euler-Lagrange formulation, and turbulent wind, irregular wave, and seismic inputs are generated using TurbSim, the Kaimal and JONSWAP spectra, the Morison equation, and 15 PEER ground-motion records. The proposed SSVI NES is compared with an optimized tuned mass damper (TMD) under nominal and frequency-detuned conditions. Under the nominal design condition, the optimized TMD and the representative SSVI NES reduce the RMS nacelle fore-aft displacement by approximately 55% and 50%, respectively, indicating that the SSVI NES provides near-benchmark vibration mitigation. Meanwhile, the maximum absorber stroke of the SSVI NES is reduced by approximately 40% compared with that of the optimized TMD, which is beneficial for nacelle-integrated implementation. Under frequency detuning, the response-reduction effectiveness of the TMD decreases from approximately 55% to 20%, whereas the SSVI NES retains approximately 80% of its nominal RMS-based control effectiveness. These quantified results show that the SSVI NES offers a balanced combination of competitive nominal response reduction, reduced absorber motion demand, and improved robustness against structural-frequency variations. The proposed device therefore provides a promising passive-control strategy for enhancing the serviceability and multi-hazard resilience of monopile offshore wind turbines.

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.