Computation

17 pages, 493 KB

Open AccessArticle

A Generalizable Agentic AI Pipeline for Developing Chatbots Using Small Language Models: A Case Study on Thai Student Loan Fund Services

by Jakkaphong Inpun, Watcharaporn Cholamjiak, Piyada Phrueksawatnon and Kanokwatt Shiangjen

Computation 2025, 13(12), 297; https://doi.org/10.3390/computation13120297 - 18 Dec 2025

Abstract

The rising deployment of artificial intelligence in public services is constrained by computational costs and limited domain-specific data, particularly in multilingual contexts. This study proposes a generalizable Agentic AI pipeline for developing question–answer chatbot systems using small language models (SLMs), demonstrated through a [...] Read more.

The rising deployment of artificial intelligence in public services is constrained by computational costs and limited domain-specific data, particularly in multilingual contexts. This study proposes a generalizable Agentic AI pipeline for developing question–answer chatbot systems using small language models (SLMs), demonstrated through a case study on the Thai Student Loan Fund (TSLF). The pipeline integrates four stages: OCR-based document digitization using Typhoon2-3B, agentic question–answer dataset construction via a clean–check–plan–generate (CCPG) workflow, parameter-efficient fine-tuning with QLoRA on Typhoon2-1B and Typhoon2-3B models, and retrieval-augmented generation (RAG) for source-grounded responses. Evaluation using BERTScore and CondBERT confirmed high semantic consistency (F_BERT = 0.9807) and stylistic reliability (F_BERT = 0.9839) of the generated QA corpus. Fine-tuning improved the 1B model’s domain alignment (F_BERT: 0.8593 → 0.8641), while RAG integration further enhanced factual grounding (F_BERT = 0.8707) and citation transparency. Cross-validation with GPT-5 and Gemini 2.5 Pro demonstrated dataset transferability and reliability. The results establish that Agentic AI combined with SLMs offers a cost-effective, interpretable, and scalable framework for automating bilingual advisory services in resource-constrained government and educational institutions. Full article

(This article belongs to the Special Issue Generative AI in Action: Trends, Applications, and Implications)

22 pages, 2279 KB

Open AccessArticle

Ship Model Identification Using Interpretable 4-DOF Maneuverability Models for River Combat Boat

by Juan Contreras Montes, Aldo Lovo Ayala, Daniela Ospino-Balcázar, Kevin Velasquez Gutierrez, Carlos Soto Montaño, Roosvel Soto-Diaz, Javier Jiménez-Cabas, José Oñate López and José Escorcia-Gutierrez

Computation 2025, 13(12), 296; https://doi.org/10.3390/computation13120296 - 18 Dec 2025

Abstract

Ship maneuverability models are typically defined by three degrees of freedom: surge, sway, and yaw. However, patrol vessels operating in riverine environments often exhibit significant roll motion during course changes, necessitating the inclusion of this dynamic. This study develops interpretable machine learning models [...] Read more.

Ship maneuverability models are typically defined by three degrees of freedom: surge, sway, and yaw. However, patrol vessels operating in riverine environments often exhibit significant roll motion during course changes, necessitating the inclusion of this dynamic. This study develops interpretable machine learning models capable of predicting vessel behavior in four degrees of freedom (4-DoF): surge, sway, yaw, and roll. A dataset of 125 h of simulated maneuvers was employed, including 29 h of out-of-distribution (OOD) conditions to test model generalization. Four models were implemented and compared over a 15-step prediction horizon: linear regression, third-order polynomial regression, a state-space model obtained via the N4SID algorithm, and an AutoRegressive model with eXogenous inputs (ARX). Results demonstrate that all models captured the essential vessel dynamics, with the state-space model achieving the best overall performance (e.g., NMSE = 0.0246 for surge velocity on test data and 0.0499 under OOD conditions). Variable-wise, surge and sway showed the lowest errors, roll rate remained stable, and yaw rate was the most sensitive to distribution shifts. Model-wise, the ARX model achieved the lowest NMSE for surge prediction (0.0149), while regression-based models provided interpretable yet less accurate alternatives. Multi-horizon evaluation (1-, 5-, 15-, and 30-step) under OOD conditions confirmed a consistent monotonic degradation across models. These findings validate the feasibility of using interpretable machine learning models for predictive control, autonomous navigation, and combat scenario simulation in riverine operations. Full article

(This article belongs to the Section Computational Engineering)

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22 pages, 7389 KB

Open AccessArticle

Shared Nodes of Overlapping Communities in Complex Networks

by Vesa Kuikka, Kosti Koistinen and Kimmo K. Kaski

Computation 2025, 13(12), 295; https://doi.org/10.3390/computation13120295 - 17 Dec 2025

Abstract

Overlapping communities are key characteristics of the structure and function analysis of complex networks. Shared or overlapping nodes within overlapping communities can either form subcommunities or act as intersections between larger communities. Nodes at the intersections that do not form subcommunities can be [...] Read more.

Overlapping communities are key characteristics of the structure and function analysis of complex networks. Shared or overlapping nodes within overlapping communities can either form subcommunities or act as intersections between larger communities. Nodes at the intersections that do not form subcommunities can be identified as overlapping nodes or as part of an internal structure of nested communities. To identify overlapping nodes, we apply a threshold rule based on the number of nodes in the nested structure. As the threshold value increases, the number of selected overlapping nodes decreases. This approach allows us to analyse the roles of nodes considered overlapping according to selection criteria, for example, to reduce the effect of noise. We illustrate our method by using three small and two larger real-world network structures. In larger networks, minor disturbances can produce a multitude of slightly different solutions, but the core communities remain robust, allowing other variations to be treated as noise. While this study employs our own method for community detection, other approaches can also be applied. Exploring the properties of shared nodes in overlapping communities of complex networks is a novel area of research with diverse applications in social network analysis, cybersecurity, and other fields in network science. Full article

(This article belongs to the Special Issue Computational Social Science and Complex Systems—2nd Edition)

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23 pages, 4486 KB

Open AccessArticle

A Fast Distributed Algorithm for Uniform Price Auction with Bidding Information Protection

by John Sum, Chi-Sing Leung and Janet C. C. Chang

Computation 2025, 13(12), 294; https://doi.org/10.3390/computation13120294 - 17 Dec 2025

Abstract

In this paper, a fast distributed algorithm is proposed for solving the winners and price determination problems in a uniform price auction in which each bidder bids for multiple units out of a lot of k identical items with a per-unit price. In [...] Read more.

In this paper, a fast distributed algorithm is proposed for solving the winners and price determination problems in a uniform price auction in which each bidder bids for multiple units out of a lot of k identical items with a per-unit price. In a conventional setting, all bidders disclose their bidding information to an auctioneer and let the auctioneer allocate the items and determine the uniform price, i.e., the least winning price. In our setting, all bidders do not need to disclose their bidding information to the auctioneer. The bidders and the auctioneer collaboratively compute by the distributed algorithm to determine in a small number of steps the units allocated and the uniform price. The number of steps is independent of the number of bidders. At the end of the computing process, each bidder can only know the units allocated to him/her and the uniform price. The auctioneer can only know the units being allocated to the bidders and the uniform price. Therefore, neither the bidders nor the auctioneer are able to know the per-unit bidding prices of the bidders except the uniform price. Moreover, the auctioneer is not able to know the bidding units of the losing bidders. Bidders’ per-unit bidding prices are protected, and the bidding units of the losing bidders are protected. Bidding information privacy is preserved. Full article

(This article belongs to the Section Computational Social Science)

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21 pages, 1491 KB

Open AccessArticle

DPCA-GCN: Dual-Path Cross-Attention Graph Convolutional Networks for Skeleton-Based Action Recognition

by Khadija Lasri, Khalid El Fazazy, Adnane Mohamed Mahraz, Hamid Tairi and Jamal Riffi

Computation 2025, 13(12), 293; https://doi.org/10.3390/computation13120293 - 15 Dec 2025

Abstract

Skeleton-based action recognition has achieved remarkable advances with graph convolutional networks (GCNs). However, most existing models process spatial and temporal information within a single coupled stream, which often obscures the distinct patterns of joint configuration and motion dynamics. This paper introduces the Dual-Path [...] Read more.

Skeleton-based action recognition has achieved remarkable advances with graph convolutional networks (GCNs). However, most existing models process spatial and temporal information within a single coupled stream, which often obscures the distinct patterns of joint configuration and motion dynamics. This paper introduces the Dual-Path Cross-Attention Graph Convolutional Network (DPCA-GCN), an architecture that explicitly separates spatial and temporal modeling into two specialized pathways while maintaining rich bidirectional interaction between them. The spatial branch integrates graph convolution and spatial transformers to capture intra-frame joint relationships, whereas the temporal branch combines temporal convolution and temporal transformers to model inter-frame dependencies. A bidirectional cross-attention mechanism facilitates explicit information exchange between both paths, and an adaptive gating module balances their respective contributions according to the action context. Unlike traditional approaches that process spatial–temporal information sequentially, our dual-path design enables specialized processing while maintaining cross-modal coherence through memory-efficient chunked attention mechanisms. Extensive experiments on the NTU RGB+D 60 and NTU RGB+D 120 datasets demonstrate that DPCA-GCN achieves competitive joint-only accuracies of 88.72%/94.31% and 82.85%/83.65%, respectively, with exceptional top-5 scores of 96.97%/99.14% and 95.59%/95.96%, while maintaining significantly lower computational complexity compared to multi-modal approaches. Full article

(This article belongs to the Section Computational Engineering)

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20 pages, 608 KB

Open AccessArticle

mDA: Evolutionary Machine Learning Algorithm for Feature Selection in Medical Domain

by Ibrahim Aljarah, Abdullah Alzaqebah, Nailah Al-Madi, Ala’ M. Al-Zoubi and Amro Saleh

Computation 2025, 13(12), 292; https://doi.org/10.3390/computation13120292 - 13 Dec 2025

Abstract

The rapid expansion of medical data, characterized by its complex high-dimensional attributes, presents numerous promising opportunities and substantial challenges in healthcare analytics. Adopting effective feature selection techniques is essential to take advantage of the potential of such data. This research presents a modified [...] Read more.

The rapid expansion of medical data, characterized by its complex high-dimensional attributes, presents numerous promising opportunities and substantial challenges in healthcare analytics. Adopting effective feature selection techniques is essential to take advantage of the potential of such data. This research presents a modified algorithm called (mDA), which is the hybrid algorithm between the Evolutionary Population Dynamics and the Dragonfly Algorithm. This method combines Evolutionary Population Dynamics’s strength with the Dragonfly Algorithm’s flexible capabilities, offering a robust evolutionary machine learning approach specifically designed for medical data analysis. By integrating the dynamic population modeling of Evolutionary Population Dynamics with the adaptive search techniques of Dragonfly Algorithm, the proposed mDA significantly improves accuracy, reduces the number of features, and obtains the minimum average of the fitness scores. Comparative experiments conducted on seven diverse medical datasets against other established algorithms confirm the superior performance of the proposed mDA, establishing it as a valuable approach in examining complex medical data. Full article

(This article belongs to the Topic Intelligent Optimization Algorithm: Theory and Applications)

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19 pages, 6143 KB

Open AccessArticle

Top-Down Optimization of a Multi-Physics TBS Model via Design-Change Propagation Network for Acoustic Levitation Devices

by Yuchao Liu, Yi Gan, Fujia Sun and Yuping Long

Computation 2025, 13(12), 291; https://doi.org/10.3390/computation13120291 - 10 Dec 2025

Abstract

To address the challenges of interdependent design parameters and reliance on empirical trial-and-error in ultrasonic cell levitation culture devices, this study proposes a top-down design framework integrating multi-physics modeling with complex network analysis. First, acoustic field simulations optimize transducer arrangement and define the [...] Read more.

To address the challenges of interdependent design parameters and reliance on empirical trial-and-error in ultrasonic cell levitation culture devices, this study proposes a top-down design framework integrating multi-physics modeling with complex network analysis. First, acoustic field simulations optimize transducer arrangement and define the cell manipulation field, establishing the Top-level Basic Structure (TBS). A skeleton model of the acoustofluidic coupled field is constructed based on the TBS. Core parameters are then determined by refining the TBS through multi-physics analysis. Second, a 24-node design change propagation network is constructed. Leveraging the TBS model coupled with multi-physics fields, a directed network model analyzes parameter interactions. The HITS algorithm is applied to prioritize the design sequence based on authority and hub scores, resolving parameter conflicts. Experimental validation demonstrates a device acoustic pressure of 1.3 × 10⁴ Pa, stable cell levitation within the focused acoustic field, and a 40% reduction in design cycle time compared to traditional methods. This framework systematically sequences parameters, effectively determines the design order, enhances design efficiency, and significantly reduces dependence on empirical trial-and-error. It provides a novel approach for developing high-throughput organoid culture equipment. Full article

(This article belongs to the Section Computational Engineering)

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17 pages, 715 KB

Open AccessArticle

Objective over Architecture: Fraud Detection Under Extreme Imbalance in Bank Account Opening

by Wenxi Sun, Qiannan Shen, Yijun Gao, Qinkai Mao, Tongsong Qi and Shuo Xu

Computation 2025, 13(12), 290; https://doi.org/10.3390/computation13120290 - 9 Dec 2025

Abstract

Fraud in financial services—especially account opening fraud—poses major operational and reputational risks. Static rules struggle to adapt to evolving tactics, missing novel patterns and generating excessive false positives. Machine learning promises adaptive detection, but deployment faces severe class imbalance: in the NeurIPS 2022 [...] Read more.

Fraud in financial services—especially account opening fraud—poses major operational and reputational risks. Static rules struggle to adapt to evolving tactics, missing novel patterns and generating excessive false positives. Machine learning promises adaptive detection, but deployment faces severe class imbalance: in the NeurIPS 2022 BAF Base benchmark used here, fraud prevalence is 1.10%. Standard metrics (accuracy, f1_weighted) can look strong while doing little for the minority class. We compare Logistic Regression, SVM (RBF), Random Forest, LightGBM, and a GRU model on N = 1,000,000 accounts under a unified preprocessing pipeline. All models are trained to minimize their loss function, while configurations are selected on a stratified development set using validation-weighted F1-score f1_weighted. For the four classical models, class weighting in the loss (class_weight

\in {None, ‘ balanced ’}

) is treated as a hyperparameter and tuned. Similarly, the GRU is trained with a fixed class-weighted CrossEntropy loss that up-weights fraud cases. This ensures that both model families leverage weighted training objectives, while their final hyperparameters are consistently selected by the f1_weighted metric. Despite similar AUCs and aligned feature importance across families, the classical models converge to high-precision, low-recall solutions (1–6% fraud recall), whereas the GRU recovers 78% recall at 5% precision (AUC

= 0.8800

). Under extreme imbalance, objective choice and operating point matter at least as much as architecture. Full article

(This article belongs to the Special Issue Applications of Machine Learning and Data Science Methods in Social Sciences)

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26 pages, 6923 KB

Open AccessArticle

Parametric Study of Shock/Boundary-Layer Interaction and Swirl Metrics in Bleed-Enabled External Compression Intakes

by Muhammed Enes Ozcan and Nilay Sezer Uzol

Computation 2025, 13(12), 289; https://doi.org/10.3390/computation13120289 - 8 Dec 2025

Abstract

Flow quality at the engine face, especially total pressure recovery and swirl, is central to the performance and stability of external compression supersonic inlets. Steady-state RANS-based numerical computations are performed to quantify bleed/swirl trade-offs in a single-ramp intake. The CFD simulations were performed [...] Read more.

Flow quality at the engine face, especially total pressure recovery and swirl, is central to the performance and stability of external compression supersonic inlets. Steady-state RANS-based numerical computations are performed to quantify bleed/swirl trade-offs in a single-ramp intake. The CFD simulations were performed first without a bleed system over M_∞ = 1.4–1.9 to locate the practical onset of a bleed requirement. The deterioration in pressure recovery and swirl beyond M_∞ ≈ 1.6, which is consistent with a pre-shock strength near the turbulent separation threshold, motivated the use of a bleed system. The comparisons with and without the bleed system were performed next at M_∞ = 1.6, 1.8, and 1.9 across the operation map parameterized by the flow ratio. The CFD simulations were performed using ANSYS Fluent, with a pressure-based coupled solver with a realizable k-ε turbulence model and enhanced wall treatment. The results provide engine-face distortion metrics using a standardized ring to sector swirl ratio alongside pressure recovery. The results show that bleed removes low-momentum near-wall fluid and stabilizes the terminal–shock interaction, raising pressure recovery and lowering peak swirl and swirl intensity across the map, while extending the stable operating range to a lower flow ratio at a fixed M_∞. The analysis delivers a design-oriented linkage between shock/boundary-layer interaction control and swirl: when bleed is applied at and above M_∞ = 1.6, the separation footprints shrink and the organized swirl sectors weaken, yielding improved operability with modest bleed fractions. Full article

(This article belongs to the Special Issue Computational Heat and Mass Transfer (ICCHMT 2025))

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15 pages, 2311 KB

Open AccessArticle

Direct Cooling of Microsystems Using a Two-Phase Microfluidic Droplet

by Wenpei Lu, Abdel Illah El Abed, Rachid Bennacer and Xiaoyan Ma

Computation 2025, 13(12), 288; https://doi.org/10.3390/computation13120288 - 6 Dec 2025

Abstract

Droplet-based microfluidics offers a promising approach for enhancing heat transfer in microchannels, which is critical for the thermal management of microsystems. This study presents a two-dimensional numerical investigation of flow and heat transfer characteristics of liquid–liquid two-phase droplet flow in a rectangular flow-focusing [...] Read more.

Droplet-based microfluidics offers a promising approach for enhancing heat transfer in microchannels, which is critical for the thermal management of microsystems. This study presents a two-dimensional numerical investigation of flow and heat transfer characteristics of liquid–liquid two-phase droplet flow in a rectangular flow-focusing microchannel. The phase-field method was employed to capture the interface dynamics between the dispersed (water) and continuous (oil) phases. The effects of total velocity and droplet size on pressure drop and heat transfer performance are systematically analyzed. The results indicate that the heat transfer of two-phase droplet flow was significantly enhanced compared to single-phase oil flow, with its maximum heat transfer coefficient being approximately three times that of single-phase oil flow. The average heat transfer coefficient increases with total velocity and exhibits a non-monotonic dependence on droplet size. These findings provide valuable insights into the design and optimization of rectangular flow-focusing droplet-based microfluidic cooling systems. Full article

(This article belongs to the Special Issue Computational Heat and Mass Transfer (ICCHMT 2025))

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22 pages, 12089 KB

Open AccessArticle

A Brain–Computer Interface for Control of a Virtual Prosthetic Hand

by Ángel del Rosario Zárate-Ruiz, Manuel Arias-Montiel and Christian Eduardo Millán-Hernández

Computation 2025, 13(12), 287; https://doi.org/10.3390/computation13120287 - 6 Dec 2025

Abstract

Brain–computer interfaces (BCIs) have emerged as an option that allows better communication between humans and some technological devices. This article presents a BCI based on the steady-state visual evoked potentials (SSVEP) paradigm and low-cost hardware to control a virtual prototype of a robotic [...] Read more.

Brain–computer interfaces (BCIs) have emerged as an option that allows better communication between humans and some technological devices. This article presents a BCI based on the steady-state visual evoked potentials (SSVEP) paradigm and low-cost hardware to control a virtual prototype of a robotic hand. A LED-based device is proposed as a visual stimulator, and the Open BCI Ultracortex Biosensing Headset is used to acquire the electroencephalographic (EEG) signals for the BCI. The processing and classification of the obtained signals are described. Classifiers based on artificial neural networks (ANNs) and support vector machines (SVMs) are compared, demonstrating that the classifiers based on SVM have superior performance to those based on ANN. The classified EEG signals are used to implement different movements in a virtual prosthetic hand using a co-simulation approach, showing the feasibility of BCI being implemented in the control of robotic hands. Full article

(This article belongs to the Special Issue Kinematics, Dynamics and Control for Rehabilitation Robotics and Prostheses)

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29 pages, 700 KB

Open AccessReview

Towards 6G: A Review of Optical Transport Challenges for Intelligent and Autonomous Communications

by Evelio Astaiza Hoyos, Héctor Fabio Bermúdez-Orozco and Jorge Alejandro Aldana-Gutierrez

Computation 2025, 13(12), 286; https://doi.org/10.3390/computation13120286 - 5 Dec 2025

Abstract

The advent of sixth-generation (6G) communications envisions a paradigm of ubiquitous intelligence and seamless physical–digital fusion, demanding unprecedented performance from the optical transport infrastructure. Achieving terabit-per-second capacities, microsecond latency, and nanosecond synchronisation precision requires a convergent, flexible, open, and AI-native x-Haul architecture that [...] Read more.

The advent of sixth-generation (6G) communications envisions a paradigm of ubiquitous intelligence and seamless physical–digital fusion, demanding unprecedented performance from the optical transport infrastructure. Achieving terabit-per-second capacities, microsecond latency, and nanosecond synchronisation precision requires a convergent, flexible, open, and AI-native x-Haul architecture that integrates communication with distributed edge computing. This study conducts a systematic literature review of recent advances, challenges, and enabling optical technologies for intelligent and autonomous 6G networks. Using the PRISMA methodology, it analyses sources from IEEE, ACM, and major international conferences, complemented by standards from ITU-T, 3GPP, and O-RAN. The review examines key optical domains including Coherent PON (CPON), Spatial Division Multiplexing (SDM), Hollow-Core Fibre (HCF), Free-Space Optics (FSO), Photonic Integrated Circuits (PICs), and reconfigurable optical switching, together with intelligent management driven by SDN, NFV, and Artificial Intelligence/Machine Learning (AI/ML). The findings reveal that achieving 6G transport targets will require synergistic integration of multiple optical technologies, AI-based orchestration, and nanosecond-level synchronisation through Precision Time Protocol (PTP) over fibre. However, challenges persist regarding scalability, cost, energy efficiency, and global standardisation. Overcoming these barriers will demand strategic R&D investment, open and programmable architectures, early AI-native integration, and sustainability-oriented network design to make optical fibre a key enabler of the intelligent and autonomous 6G ecosystem. Full article

(This article belongs to the Topic Computational Complex Networks)

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22 pages, 4820 KB

Open AccessArticle

Theoretical Insight into Non-Covalent Complexes of Closo-Borate Anions [B_nH_n−1X]^y− with Glycine

by Ilya N. Klyukin, Anastasia V. Kolbunova, Alexander S. Novikov, Alexandra A. Klyukina, Konstantin Y. Zhizhin and Nikolay T. Kuznetsov

Computation 2025, 13(12), 285; https://doi.org/10.3390/computation13120285 - 5 Dec 2025

Abstract

Non-covalent contacts play a significant role in binding between fragments in supramolecular assemblies. Understanding the non-covalent binding capabilities of closo-borate anions and their derivatives is a significant research challenge, due to their ability to interact with biomolecules. The present work was focused [...] Read more.

Non-covalent contacts play a significant role in binding between fragments in supramolecular assemblies. Understanding the non-covalent binding capabilities of closo-borate anions and their derivatives is a significant research challenge, due to their ability to interact with biomolecules. The present work was focused on the theoretical study of non-covalent complexes between glycine and closo-borate anions [B_nH_n−1X]^y− (X = H, NH₃, OH, SH, F; n = 10, 12; y = 1, 2). The main binding patterns between glycine and cluster systems were defined, and the effect of the exo-polyhedral substituent on the stability of non-covalent complexes was analysed. Complexes based on ammonium and hydroxy derivatives of closo-borate anions [B_nH_n−1X]^y− (X = NH₃, OH; n = 10, 12; y = 1, 2) were the most stable among all the derivatives considered. The findings of this work can be applied to the design of non-covalent complexes of closo-borate systems with biomolecules. Full article

(This article belongs to the Special Issue Feature Papers in Computational Chemistry—2nd Edition)

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27 pages, 4928 KB

Open AccessArticle

A Visual Representation–Based Computational Approach for Student Dropout Analysis: A Case Study in Colombia

by Juan-Carlos Briñez-De-León, Alejandra-Estefanía Patiño-Hoyos, Farley-Albeiro Restrepo-Loaiza and Gabriel-Jaime Cardona-Osorio

Computation 2025, 13(12), 284; https://doi.org/10.3390/computation13120284 - 3 Dec 2025

Abstract

Academic dropout is a persistent challenge in higher education, particularly in contexts with socio-economic disparities and diverse learning conditions. Traditional predictive models often fail to capture the complex, non-linear interactions underlying student trajectories due to their reliance on low-dimensional and linear representations. This [...] Read more.

Academic dropout is a persistent challenge in higher education, particularly in contexts with socio-economic disparities and diverse learning conditions. Traditional predictive models often fail to capture the complex, non-linear interactions underlying student trajectories due to their reliance on low-dimensional and linear representations. This study introduces a visual representation–based computational approach for a student dropout analysis, applied to a real institutional dataset from Colombia. The methodology transforms structured student records into enriched visual encodings that map variable magnitudes, correlations, and latent relationships into spatial and textural patterns. These image-based representations allow convolutional neural networks (CNNs) to exploit hierarchical feature extraction, uncovering hidden dependencies not accessible through conventional classifiers. Experimental results demonstrate that a Convolutional Neural Network (CNN) trained from scratch outperforms both baseline machine learning models and transfer learning architectures across all evaluation metrics. Beyond predictive accuracy, the approach enhances data expressiveness, interpretability, and generalization, offering a visual-analytical perspective for understanding dropout dynamics. The Colombian case study confirms the feasibility and potential of this strategy in real educational settings, supporting early identification of at-risk students and contributing to the development of robust, explainable models in educational data mining and learning analytics. Full article

(This article belongs to the Section Computational Engineering)

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32 pages, 1616 KB

Open AccessArticle

Convergence Analysis and Optimisation of Privacy-Preserving Federated Learning for Hierarchical Graph Neural Networks in Distributed Cloud Anomaly Detection

by Comfort Lawal, Olatayo M. Olaniyan, Kennedy Okokpujie and Emmanuel Adetiba

Computation 2025, 13(12), 283; https://doi.org/10.3390/computation13120283 - 2 Dec 2025

Abstract

Distributed cloud networks spanning multiple jurisdictions face significant challenges in anomaly detection due to privacy constraints, regulatory requirements, and communication limitations. This paper presents a mathematically rigorous framework for privacy-preserving federated learning on hierarchical graph neural networks, providing theoretical convergence guarantees and optimisation [...] Read more.

Distributed cloud networks spanning multiple jurisdictions face significant challenges in anomaly detection due to privacy constraints, regulatory requirements, and communication limitations. This paper presents a mathematically rigorous framework for privacy-preserving federated learning on hierarchical graph neural networks, providing theoretical convergence guarantees and optimisation bounds for distributed anomaly detection. A novel layer-wise federated aggregation mechanism is introduced, featuring a proven convergence rate

O (1 / \sqrt{T})

. That preserves hierarchical structure during distributed training. The theoretical analysis establishes differential privacy guarantees of

(ε = 1.0, δ = 10^{- 5})

through layer-specific noise calibration, achieving optimal privacy–utility tradeoffs. The proposed optimisation framework incorporates: (1) convergence-guaranteed layer-wise aggregation with bounded gradient norms, (2) privacy-preserving mechanisms with formal composition analysis under the Moments Accountant framework, (3) meta-learning-based personalisation with theoretical generalisation bounds, and (4) communication-efficient protocols with a proven 93% reduction in overhead. Rigorous evaluation on the FEDGEN testbed, spanning 2780 km across Nigeria and the Democratic Republic of Congo, demonstrates superior performance with hierarchical F1-scores exceeding 95% across all regions, while maintaining theoretical guarantees. The framework’s convergence analysis shows robustness under realistic constraints, including 67% client participation, 200 ms latency, and 20 Mbps bandwidth limitations. This work advances the theoretical foundations of federated graph learning while providing practical deployment guidelines for cross-jurisdictional cloud networks. Full article

(This article belongs to the Section Computational Engineering)

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30 pages, 1663 KB

Open AccessArticle

Deep Learning-Driven Integration of Multimodal Data for Material Property Predictions

by Vítor Costa, José Manuel Oliveira and Patrícia Ramos

Computation 2025, 13(12), 282; https://doi.org/10.3390/computation13120282 - 1 Dec 2025

Abstract

Advancements in deep learning have revolutionized materials discovery by enabling predictive modeling of complex material properties. However, single-modal approaches often fail to capture the intricate interplay of compositional, structural, and morphological characteristics. This study introduces a novel multimodal deep learning framework for enhanced [...] Read more.

Advancements in deep learning have revolutionized materials discovery by enabling predictive modeling of complex material properties. However, single-modal approaches often fail to capture the intricate interplay of compositional, structural, and morphological characteristics. This study introduces a novel multimodal deep learning framework for enhanced material property prediction, integrating textual (chemical compositions), tabular (structural descriptors), and image-based (2D crystal structure visualizations) modalities. Utilizing the Alexandriadatabase, we construct a comprehensive multimodal dataset of 10,000 materials with symmetry-resolved crystallographic data. Specialized neural architectures, such as FT-Transformer for tabular data, Hugging Face Electra-based model for text, and TIMM-based MetaFormer for images, generate modality-specific embeddings, fused through a hybrid strategy into a unified latent space. The framework predicts seven critical material properties, including electronic (band gap, density of states), thermodynamic (formation energy, energy above hull, total energy), magnetic (magnetic moment per volume), and volumetric (volume per atom) features, many governed by crystallographic symmetry. Experimental results demonstrated that multimodal fusion significantly outperforms unimodal baselines. Notably, the bimodal integration of image and text data showed significant gains, reducing the Mean Absolute Error for band gap by approximately 22.7% and for volume per atom by 22.4% compared to the average unimodal models. This combination also achieved a 28.4% reduction in Root Mean Squared Error for formation energy. The full trimodal model (tabular + images + text) yielded competitive, and in several cases the lowest, error metrics, particularly for band gap, magnetic moment per volume and density of states per atom, confirming the value of integrating all three modalities. This scalable, modular framework advances materials informatics, offering a powerful tool for data-driven materials discovery and design. Full article

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23 pages, 2020 KB

Open AccessArticle

Assessment of Computational Tools for Analysing the Observability and Accessibility of Nonlinear Models

by Mahmoud Shams Falavarjani, Adriana González Vázquez and Alejandro F. Villaverde

Computation 2025, 13(12), 281; https://doi.org/10.3390/computation13120281 - 1 Dec 2025

Abstract

Accessibility and observability are two properties of dynamic models that provide insights into the structural relationships between their input, output, and state variables. They are closely related to controllability and structural local identifiability, respectively. Observability and identifiability determine, respectively, the possibility of inferring [...] Read more.

Accessibility and observability are two properties of dynamic models that provide insights into the structural relationships between their input, output, and state variables. They are closely related to controllability and structural local identifiability, respectively. Observability and identifiability determine, respectively, the possibility of inferring the unmeasured state variables and parameters of a model from output measurements; accessibility and controllability describe the possibility of driving its state by changing its input. Analysing these structural properties in nonlinear models of ordinary differential equations can be challenging, particularly when dealing with large systems. Two main approaches are currently used for their study: one based on differential geometry, which uses symbolic computation, and another one based on sensitivity calculations that uses numerical integration. These approaches are implemented in two MATLAB (R2024b) software tools: the differential geometry approach in STRIKE-GOLDD, and the sensitivity-based method in StrucID. These toolboxes differ significantly in their features and capabilities. Until now, their performance had not been thoroughly compared. In this paper we present a comprehensive comparative study of them, elucidating their differences in applicability, computational efficiency, and robustness against computational issues. Our core finding is that StrucID has a substantially lower computational cost than STRIKE-GOLDD; however, it may occasionally yield inconsistent results due to numerical issues. Full article

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

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Open AccessArticle

Numerical Investigations of Water-Lubricated Core Annular Flow (CAF) for Heavy Oil Transportation

by Salim Al Jadidi, Dadapeer Doddamani, Yahya Ubaid Al Shamsi, Ibrahim Nasser Al Siyabi and Siva Subramanian

Computation 2025, 13(12), 280; https://doi.org/10.3390/computation13120280 - 1 Dec 2025

Abstract

This study examines the flow behavior of water-lubricated heavy oil transport utilizing the core annular flow (CAF) technique. The goal is to enhance efficiency and minimize risks in pipeline operations. The flow was numerically simulated in a horizontal pipe using a Large Eddy [...] Read more.

This study examines the flow behavior of water-lubricated heavy oil transport utilizing the core annular flow (CAF) technique. The goal is to enhance efficiency and minimize risks in pipeline operations. The flow was numerically simulated in a horizontal pipe using a Large Eddy Simulation (LES) model within a commercial Computational Fluid Dynamics (CFD) framework. The Geo Reconstruct scheme is employed to accurately capture the oil–water interface, and both oil and water initialization methods were assessed against experimental data. Results show that the LES model accurately reproduces the main flow features observed experimentally, particularly for low-viscosity oil–water systems. This suggests that the model can be a reliable tool for predicting flow behaviour in similar fluid systems. Further validation with varying parameters could enhance its applicability across a broader range of conditions. In cases of heavy oil, the velocity profile remains nearly constant within the oil core, indicating rigid body-like motion surrounded by a turbulent water annulus. Turbulence intensity and oil volume fraction distributions were closely related, with higher turbulence in water and lower in oil. Although wall adhesion modelling limited fouling prediction, simulations confirmed that fouling can significantly increase pressure losses. This illustrates the value of considering both fluid dynamics and material interactions in such systems. Future studies could explore the impact of varying temperature and pressure conditions on fouling behaviour to further refine predictive models. Overall, the LES approach proved suitable for analysing turbulent CAF, offering insights for optimizing viscosity ratios, flow rates, and design parameters for safer and more efficient heavy oil transport. Full article

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

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14 pages, 283 KB

Open AccessArticle

Elasticity Coefficients as Effects Measures in Model Formulations

by Tarald O. Kvålseth

Computation 2025, 13(12), 279; https://doi.org/10.3390/computation13120279 - 1 Dec 2025

Abstract

When dealing with mathematical or statistical models involving one or more explanatory (independent) variables, one often wants to determine the effects of such variables on a response (independent) variable. In the case of linear regression models, one such effects measure is the so-called [...] Read more.

When dealing with mathematical or statistical models involving one or more explanatory (independent) variables, one often wants to determine the effects of such variables on a response (independent) variable. In the case of linear regression models, one such effects measure is the so-called standardized regression coefficients used in various statistical software packages and discussed in regression textbooks. However, since strong reservations have been expressed against the use of standardized regression coefficients because of various limitations, the objective or initial working hypothesis behind the present research was that some alternative measure without such limitations ought to be explored. Consequently, elasticity coefficients are formulated and proposed as both relative and absolute such measures. While the standardized regression coefficients lack any convenient interpretation, the proposed elasticity coefficients have the particularly desirable property of having a logical and intuitively appealing interpretation in terms of the relative change in the value of the response variable as a consequence of a relative change (of 1 percent) in the value of one or more of the explanatory variables. Those elasticity measures have the flexibility of being applicable to individual or to all explanatory variables and to individual or to all observations or data sets. A numerical example is used to illustrate the use of these new measures. Comparison between values of the standardized regression coefficients and those of the corresponding elasticity coefficients based on reported data from various sources is provided. Also, the form of the elasticity coefficients for a variety of different types of models is presented. Statistical inferences are also discussed. Full article

(This article belongs to the Section Computational Social Science)

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