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Computation

Computation is a peer-reviewed journal of computational science and engineering published monthly online by MDPI.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, ESCI (Web of Science), CAPlus / SciFinder, Inspec, dblp, and other databases.
Journal Rank: JCR - Q2 (Mathematics, Interdisciplinary Applications) / CiteScore - Q1 (Applied Mathematics)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 14.8 days after submission; acceptance to publication is undertaken in 5.6 days (median values for papers published in this journal in the second half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Journal Cluster of Mathematics and Its Applications: AppliedMath, Axioms, Computation, Fractal and Fractional, Geometry, International Journal of Topology, Logics, Mathematics and Symmetry.

Impact Factor: 1.9 (2024); 5-Year Impact Factor: 1.9 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2079-3197

Latest Articles

24 pages, 3912 KB

Open AccessArticle

Remaining Useful Life Prediction of Fracturing Truck Valve Bodies Based on the CB²-RUL Algorithm

by Xinyue Chen, Jishun Ren, Yang Wang, Jiquan He, Xuyou Guo and Gantailai Ye

Computation 2026, 14(2), 55; https://doi.org/10.3390/computation14020055 - 23 Feb 2026

The triplex reciprocating drilling pump is a critical piece of equipment in drilling platforms, and the operational condition of its core component—the valve body—directly affects the pump’s performance and the stability of the entire system. Therefore, accurate prediction of the valve body’s Remaining [...] Read more.

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

(This article belongs to the Special Issue Neural Network and Large Model-Driven Fault Diagnosis and Intelligent Operation and Maintenance for Rotating Machinery)

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22 pages, 1780 KB

Open AccessArticle

Improving the Accuracy of Infectious Disease Forecasts Based on Comparing Neural Network Architectures

by Oleksandr Kovaliv, Yuriy Kondratenko, Ievgen Sidenko, Galyna Kondratenko and Dmytro Chumachenko

Computation 2026, 14(2), 54; https://doi.org/10.3390/computation14020054 - 21 Feb 2026

This paper aims to improve the accuracy of infectious disease forecasting using machine learning methods. The main results of this work are an analysis of infectious diseases spread in Ukraine during the time span from December 2016 to January 2024 and a performance [...] Read more.

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

(This article belongs to the Special Issue Artificial Intelligence Applications in Public Health: 2nd Edition)

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28 pages, 6865 KB

Open AccessArticle

Analysis of Internal Mechanical Friction Losses Influence on the Francis-99 Runner Using the Friction Torque Approach

by Otibh M. M. Abubkry, Yun Zeng, Juan Duan, Altyib Abdallah Mahmoud Ahmed, Hassan Babeker and Altyeb Ali Abaker Omer

Computation 2026, 14(2), 53; https://doi.org/10.3390/computation14020053 - 19 Feb 2026

Francis turbines are renowned for their high efficiency and adaptability across a wide range of head and discharge conditions. However, internal mechanical friction losses (IMFLs), resulting from rotational frictional resistance between the rotating runner and the surrounding fluid, remain a significant obstacle to [...] Read more.

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

(This article belongs to the Section Computational Engineering)

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11 pages, 1064 KB

Open AccessCommunication

TOTEMS: Histogram of Evolutionarily Conserved Amino Acids

by Michael J. Fajardo, Adam G. Marsh and John R. Jungck

Computation 2026, 14(2), 52; https://doi.org/10.3390/computation14020052 - 18 Feb 2026

We have developed a tool that allows us to easily visualize evolutionary variation via complementary multiple sequence alignments and frequency-based stacked Sequence Logos. This tool, TOTEMS (hisTogram of evOluTionarily consErved aMino acidS [...] Read more.

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

(This article belongs to the Section Computational Biology)

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28 pages, 2635 KB

Open AccessArticle

Robust Backstepping-Sliding Control of a Quadrotor UAV with Disturbance Compensation

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

Computation 2026, 14(2), 51; https://doi.org/10.3390/computation14020051 - 14 Feb 2026

Quadrotor unmanned aerial vehicles (QUAVs) are widely used in civil and defense applications, yet reliable trajectory tracking remains challenging under external disturbances and limited sensing. Conventional backstepping–sliding mode controllers ensure robustness only by selecting discontinuous gains larger than the disturbance bound, which increases [...] Read more.

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

(This article belongs to the Section Computational Engineering)

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29 pages, 874 KB

Open AccessArticle

Extending Q-Learning for Economic Modelling: A Design Framework with Equilibrium Benchmarks

by Jorge Moya Velasco, Jorge Soria Ruiz-Ogarrio, Pedro Caja Meri and Silvia Álvarez-Santás

Computation 2026, 14(2), 50; https://doi.org/10.3390/computation14020050 - 14 Feb 2026

This paper proposes a methodological architecture to integrate Q-learning into economic modelling systematically. It addresses a common gap: the lack of a shared framework linking economic foundations to Reinforcement Learning components. Rather than introducing a new algorithm, it specifies and reports how preferences, [...] Read more.

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

(This article belongs to the Special Issue Modern Applications for Computational Methods in Applied Economics and Business Engineering)

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16 pages, 1467 KB

Open AccessArticle

ECG Heartbeat Classification Using Echo State Networks with Noisy Reservoirs and Variable Activation Function

by Ioannis P. Antoniades, Anastasios N. Tsiftsis, Christos K. Volos, Andreas D. Tsigopoulos, Konstantia G. Kyritsi and Hector E. Nistazakis

Computation 2026, 14(2), 49; https://doi.org/10.3390/computation14020049 - 13 Feb 2026

In this work, we use an Echo State Network (ESN) model, which is essentially a recurrent neural network (RNN) operating according to the reservoir computing (RC) paradigm, to classify individual ECG heartbeats using the MIT-BIH arrhythmia database. The aim is to evaluate the [...] Read more.

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

(This article belongs to the Special Issue Experiments/Process/System Modeling/Simulation/Optimization (IC-EPSMSO 2025))

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20 pages, 1035 KB

Open AccessArticle

Multi-Level Parallel CPU Execution Method for Accelerated Portion-Based Variant Call Format Data Processing

by Lesia Mochurad, Ivan Tsmots, Vita Mostova and Karina Kystsiv

Computation 2026, 14(2), 48; https://doi.org/10.3390/computation14020048 - 8 Feb 2026

This paper proposes and experimentally evaluates a multi-level CPU-oriented execution method for high-throughput portion-based processing of file-backed Variant Call Format (VCF) data and automated mutation classification. The approach is based on a formally defined local processing scheme and integrates three coordinated levels of [...] Read more.

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

(This article belongs to the Section Computational Engineering)

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26 pages, 5552 KB

Open AccessArticle

SOH- and Temperature-Aware Adaptive SOC Boundaries for Second-Life Li-Ion Batteries in Off-Grid PV–BESSs

by Hongyan Wang, Atthapol Ngaopitakkul and Suntiti Yoomak

Computation 2026, 14(2), 47; https://doi.org/10.3390/computation14020047 - 7 Feb 2026

In this study, an adaptive state-of-charge (SOC) boundary strategy (ASBS) is proposed that dynamically adjusts the admissible upper and lower SOC limits of second-life lithium-ion batteries in off-grid photovoltaic battery energy storage systems (PV-BESSs) based on real-time state of health (SOH) and temperature [...] Read more.

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

(This article belongs to the Section Computational Engineering)

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13 pages, 1009 KB

Open AccessArticle

Phishing Email Detection Using BERT and RoBERTa

by Mariam Ibrahim and Ruba Elhafiz

Computation 2026, 14(2), 46; https://doi.org/10.3390/computation14020046 - 7 Feb 2026

One of the most harmful and deceptive forms of cybercrime is phishing, which targets users with malicious emails and websites. In this paper, we focus on the use of natural language processing (NLP) techniques and transformer models for phishing email detection. The Nazario [...] Read more.

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

(This article belongs to the Special Issue Sentiment-Driven Modelling in Business, Economics, and Social Sciences)

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39 pages, 2558 KB

Open AccessArticle

An Enhanced Projection-Iterative-Methods-Based Optimizer for Complex Constrained Engineering Design Problems

by Xuemei Zhu, Han Peng, Haoyu Cai, Yu Liu, Shirong Li and Wei Peng

Computation 2026, 14(2), 45; https://doi.org/10.3390/computation14020045 - 6 Feb 2026

This paper proposes an Enhanced Projection-Iterative-Methods-based Optimizer (EPIMO) to overcome the limitations of its predecessor, the Projection-Iterative-Methods-based Optimizer (PIMO), including deterministic parameter decay, insufficient diversity maintenance, and static exploration–exploitation balance. The enhancements incorporate three core strategies: (1) an adaptive decay strategy that introduces [...] Read more.

This paper proposes an Enhanced Projection-Iterative-Methods-based Optimizer (EPIMO) to overcome the limitations of its predecessor, the Projection-Iterative-Methods-based Optimizer (PIMO), including deterministic parameter decay, insufficient diversity maintenance, and static exploration–exploitation balance. The enhancements incorporate three core strategies: (1) an adaptive decay strategy that introduces stochastic perturbations into the step-size evolution; (2) a mirror opposition-based learning strategy to actively inject structured population diversity; and (3) an adaptive adjustment mechanism for the Lévy flight parameter

β

to enable phase-sensitive optimization behavior. The effectiveness of EPIMO is validated through a multi-stage experimental framework. Systematic evaluations on the CEC 2017 and CEC 2022 benchmark suites, alongside four classical engineering optimization problems (Himmelblau function, step-cone pulley design, hydrostatic thrust bearing design, and three-bar truss design), demonstrate its comprehensive superiority. The Wilcoxon rank-sum test confirms statistically significant performance improvements over its predecessor (PIMO) and a range of state-of-the-art and classical algorithms. EPIMO exhibits exceptional performance in convergence accuracy, stability, robustness, and constraint-handling capability, establishing it as a highly reliable and efficient metaheuristic optimizer. This research contributes a systematic, adaptive enhancement framework for projection-based metaheuristics, which can be generalized to improve other swarm intelligence systems when facing complex, constrained, and high-dimensional engineering optimization tasks. Full article

(This article belongs to the Section Computational Engineering)

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12 pages, 196 KB

Open AccessEditorial

Nonlinear System Modelling and Control: Trends, Challenges, and Future Perspectives

by Chathura Wanigasekara

Computation 2026, 14(2), 44; https://doi.org/10.3390/computation14020044 - 3 Feb 2026

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

(This article belongs to the Special Issue Nonlinear System Modelling and Control)

21 pages, 2314 KB

Open AccessArticle

Methodology for Predicting Geochemical Anomalies Using Preprocessing of Input Geological Data and Dual Application of a Multilayer Perceptron

by Daulet Akhmedov, Baurzhan Bekmukhamedov, Moldir Tanashova and Zulfiya Seitmuratova

Computation 2026, 14(2), 43; https://doi.org/10.3390/computation14020043 - 3 Feb 2026

The increasing need for accurate prediction of geochemical anomalies requires methods capable of capturing complex spatial patterns that traditional approaches often fail to represent adequately. For N datasets of the form (X_i,Y_i) representing the geographic coordinates of [...] Read more.

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

(This article belongs to the Section Computational Engineering)

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32 pages, 1083 KB

Open AccessArticle

Information Inequalities for Five Random Variables

by Laszlo Csirmaz and Elod P. Csirmaz

Computation 2026, 14(2), 42; https://doi.org/10.3390/computation14020042 - 2 Feb 2026

The entropic region is formed by the collection of the Shannon entropies of all subvectors of finitely many jointly distributed discrete random variables. For four or more variables, the structure of the entropic region is mostly unknown. We utilize a variant of the [...] Read more.

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

(This article belongs to the Section Computational Engineering)

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28 pages, 2180 KB

Open AccessArticle

Modelling of Batch Fermentation Processes of Ethanol Production by Kluyveromyces marxianus

by Olympia Roeva, Anastasiya Zlatkova, Velislava Lyubenova, Maya Ignatova, Denitsa Kristeva, Gergana Roeva and Dafina Zoteva

Computation 2026, 14(2), 41; https://doi.org/10.3390/computation14020041 - 2 Feb 2026

A representative cluster-based model of the batch process of ethanol production by Kluyveromyces sp. is proposed. Experimental data from fermentation processes of 17 different strains of K. marxianus are used; each of them potentially exhibits different metabolic and kinetic behavior. Three algorithms for [...] Read more.

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

(This article belongs to the Section Computational Biology)

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31 pages, 3327 KB

Open AccessArticle

Can Generative AI Co-Evolve with Human Guidance and Display Non-Utilitarian Moral Behavior?

by Rafael Lahoz-Beltra

Computation 2026, 14(2), 40; https://doi.org/10.3390/computation14020040 - 2 Feb 2026

The growing presence of autonomous AI systems, such as self-driving cars and humanoid robots, raises critical ethical questions about how these technologies should make moral decisions. Most existing moral machine (MM) models rely on secular, utilitarian principles, which prioritize the greatest good for [...] Read more.

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

(This article belongs to the Section Computational Social Science)

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Graphical abstract

15 pages, 1906 KB

Open AccessArticle

Semi-Empirical Estimation of Aerosol Particle Influence at the Performance of Terrestrial FSO Links over the Sea

by Argyris N. Stassinakis, Efstratios V. Chatzikontis, Kyle R. Drexler, Andreas D. Tsigopoulos, Gratchia Mkrttchian and Hector E. Nistazakis

Computation 2026, 14(2), 39; https://doi.org/10.3390/computation14020039 - 2 Feb 2026

Free-space optical (FSO) communication enables high-bandwidth license-free data transmission and is particularly attractive for maritime point-to-point links. However, FSO performance is strongly affected by atmospheric conditions. This work presents a semi-empirical model quantifying the impact of fine particulate matter (PM2.5) on received optical [...] Read more.

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

(This article belongs to the Section Computational Engineering)

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21 pages, 4280 KB

Open AccessArticle

Development of a Dashboard for Simulation Workflow Visualization and Optimization of an Ammonia Synthesis Reactor in the HySTrAm Project (Horizon EU)

by Eleni Douvi, Dimitra Douvi, Jason Tsahalis and Haralabos-Theodoros Tsahalis

Computation 2026, 14(2), 38; https://doi.org/10.3390/computation14020038 - 2 Feb 2026

Although hydrogen plays a crucial role in the EU’s strategy to reduce greenhouse gas emissions, its storage and transport are technically challenging. If ammonia is produced efficiently, it can be a promising hydrogen carrier, especially in decentralized and flexible conditions. The Horizon EU [...] Read more.

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

(This article belongs to the Special Issue Experiments/Process/System Modeling/Simulation/Optimization (IC-EPSMSO 2025))

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26 pages, 11426 KB

Open AccessArticle

LocRes–PINN: A Physics–Informed Neural Network with Local Awareness and Residual Learning

by Tangying Lv, Wenming Yin, Hengkai Yao, Qingliang Liu, Yitong Sun, Kuan Zhao and Shanliang Zhu

Computation 2026, 14(2), 37; https://doi.org/10.3390/computation14020037 - 2 Feb 2026

Physics–Informed Neural Networks (PINNs) have demonstrated efficacy in solving both forward and inverse problems for nonlinear partial differential equations (PDEs). However, they frequently struggle to accurately capture multiscale physical features, particularly in regions exhibiting sharp local variations such as shock waves and discontinuities, [...] Read more.

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

(This article belongs to the Special Issue Advances in Computational Methods for Fluid Flow)

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22 pages, 4463 KB

Open AccessArticle

A Method for Road Spectrum Identification in Real-Vehicle Tests by Fusing Time-Frequency Domain Features

by Biao Qiu and Chaiyan Jettanasen

Computation 2026, 14(2), 36; https://doi.org/10.3390/computation14020036 - 2 Feb 2026

Most unpaved roads are subjectively classified as Class D roads. However, significant variations exist across different sites and environments (e.g., mining areas). A major challenge in the engineering field is how to quickly correct the Power Spectral Density (PSD) of the unpaved road [...] Read more.

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

(This article belongs to the Section Computational Engineering)

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