IF 4.5

Array Pub Date : 2026-03-01 Epub Date: 2026-02-04 DOI: 10.1016/j.array.2026.100700

Michael Peter , Silas Mirau , Emmanuel Sinkwembe , Christian Kasumo , Calisto Guambe

{"title":"A stacking model integrating GARCH and LSTM with feature interactions for time series volatility prediction","authors":"Michael Peter , Silas Mirau , Emmanuel Sinkwembe , Christian Kasumo , Calisto Guambe","doi":"10.1016/j.array.2026.100700","DOIUrl":"10.1016/j.array.2026.100700","url":null,"abstract":"<div><div>Volatility forecasting remains a cornerstone of quantitative finance, underpinning risk management, portfolio optimization, and regulatory oversight. This study introduces a novel stacking model that integrates the generalized autoregressive conditional heteroskedasticity (GARCH) framework with long short-term memory (LSTM) networks to capture both econometric structure and nonlinear temporal dependencies in financial time series. Unlike conventional hybrid approaches that sequentially cascade outputs, the proposed framework employs GARCH and LSTM as parallel base learners, with their predictions intelligently fused through a meta-learner that exploits feature interactions and cross-model synergies. The empirical evaluation benchmarks the stacking ensemble against state-of-the-art alternatives, including DLINEAR, CKAN, N-BEATS, and individual GARCH and LSTM specifications across multiple performance metrics. Results demonstrate consistent superiority across RMSE, MAE, accuracy, RAMP, geometric mean, Hausdorff distance, and AUC metrics, validating the synergistic benefits of integrating econometric and machine learning paradigms within a theoretically grounded architecture. The model’s superior performance stems from leveraging GARCH’s parametric efficiency in modeling volatility clustering while harnessing LSTM’s capacity to capture complex nonlinear temporal patterns. Beyond methodological contributions, the framework offers practical value for enhancing systemic risk monitoring, improving stress testing frameworks, and optimizing investment strategies across diverse market conditions. The demonstrated robustness across different market regimes underscores its potential for adoption in both routine operations and crisis contexts. This research establishes stacking-based ensemble modeling as a powerful paradigm for advancing volatility prediction and provides a foundation for next-generation financial forecasting systems.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100700"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146184694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating agentic AI and digital twins for intelligent decision-making systems 整合人工智能和数字孪生体的智能决策系统

IF 4.5

Array Pub Date : 2026-03-01 Epub Date: 2026-02-20 DOI: 10.1016/j.array.2026.100721

Agus Hasan , Dong Trong Nguyen

{"title":"Integrating agentic AI and digital twins for intelligent decision-making systems","authors":"Agus Hasan , Dong Trong Nguyen","doi":"10.1016/j.array.2026.100721","DOIUrl":"10.1016/j.array.2026.100721","url":null,"abstract":"<div><div>The integration of agentic artificial intelligence (agentic AI) and digital twins (DTs) enables decision-making systems that are intelligent, adaptive, and goal-oriented. This paper advances that convergence through a multilayer integration framework that organizes perception, knowledge and data management, LLM-based reasoning, learning, decision-making, action execution, and feedback adaptation into a cohesive structure. Within this framework, LLM-driven agents act as cognitive cores for context-aware planning and collaboration, while the digital twin provides a shared situational model that maintains real-time state, simulation capabilities, and policy constraints. The contributions are threefold: (i) a novel architecture that operationalizes continuous learning and closed-loop autonomy through tightly coupled agents and a DT; (ii) a use-case demonstration in power balancing for electrical grid management, where agents coordinate demand forecasting, distributed energy resources, and network constraints via a grid digital twin; and (iii) an analysis of key enablers and challenges, such as model synchronization, interpretability, safety, and cognitive load distribution, necessary for scalable and trustworthy deployment. The proposed integration offers a principled path to resilient, transparent, and data-efficient decision-making systems for cyber–physical infrastructure and provides a foundation for extending agentic AI-DT synergies to other safety-critical domains.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100721"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Chosen-plaintext attacks on image ciphers based on nonlinear dynamical systems 基于非线性动态系统的图像密码的选择明文攻击

IF 4.5

Array Pub Date : 2026-03-01 Epub Date: 2026-01-08 DOI: 10.1016/j.array.2025.100642

Mohammad Mazyad Hazzazi , Dawood Shah , Mansoor Alghamdi , Ibrahim S. Alkhazi , Naveed Riaz

{"title":"Chosen-plaintext attacks on image ciphers based on nonlinear dynamical systems","authors":"Mohammad Mazyad Hazzazi , Dawood Shah , Mansoor Alghamdi , Ibrahim S. Alkhazi , Naveed Riaz","doi":"10.1016/j.array.2025.100642","DOIUrl":"10.1016/j.array.2025.100642","url":null,"abstract":"<div><div>Recent advances in the fields of wireless communication networks and digital technologies have resulted in a significant increase in multimedia data sharing, especially digital images. Digital images often carry sensitive and personal information, so ensuring their security has become a critical concern. To address these challenges, the cryptography community has developed various encryption techniques, many of which rely on nonlinear dynamical systems and employ processes such as pixel permutation and diffusion to secure images. In this article, we have analyzed various security vulnerabilities in image encryption schemes available in literature that are based on pixel permutation and substitution. The study is divided into two main parts. First, we identify four types of image encryption schemes based on nonlinear dynamical systems and demonstrate their susceptibility to chosen-plaintext attacks. Secondly, we implement a substitution-permutation based image encryption scheme and show that it can be distinguished using a small number of chosen plaintext pairs in a distinguishability game. Furthermore, we prove that an attacker can recover the original plaintext by leveraging knowledge of a few chosen plaintext-ciphertext pairs, despite that the implemented scheme successfully passes all security evaluation tests proposed in the literature for assessing the cryptographic strength of image encryption schemes.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100642"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Parkinson's disease pose estimation and assisted diagnosis model by integrating DeST-GCN and multi-scale graph convolutional networks 基于DeST-GCN和多尺度图卷积网络的帕金森病位姿估计与辅助诊断模型

IF 4.5

Array Pub Date : 2026-03-01 Epub Date: 2026-01-31 DOI: 10.1016/j.array.2026.100693

Min Zuo , Jialu Li , Mingchao Chang , Qingchuan Zhang

{"title":"A Parkinson's disease pose estimation and assisted diagnosis model by integrating DeST-GCN and multi-scale graph convolutional networks","authors":"Min Zuo , Jialu Li , Mingchao Chang , Qingchuan Zhang","doi":"10.1016/j.array.2026.100693","DOIUrl":"10.1016/j.array.2026.100693","url":null,"abstract":"<div><div>Parkinson's disease (PD) is a progressive neurodegenerative disorder often associated with gait abnormalities, making gait analysis valuable for early diagnosis. However, current approaches are hindered by inaccurate pose estimation and limited model interpretability, reducing their clinical usefulness. To address these issues, we propose a two-stage framework that decouples 3D pose estimation from disease classification.In the first stage, we develop a Deformable Strided Transformer Graph Convolutional Network (DeST-GCN), which integrates deformable convolutions and graph-based modeling to extract accurate 3D skeletal sequences from gait videos. In the second stage, a Multi-Scale Deformable Graph Convolutional Network (MSDeGCN) leverages adaptive multi-scale kernels to learn hierarchical spatial-temporal features for PD classification.The framework was evaluated on both public and custom gait datasets. DeST-GCN achieves a 3D pose estimation accuracy of 0.9923 with a test loss of 0.0011. The MSDeGCN module delivers strong diagnostic performance with an F1-score of 0.905, accuracy of 0.942, precision of 0.934, and recall of 0.893. Gradient-based saliency and perturbation analyses highlight the spine, knees, and ankles as key regions influencing classification outcomes.This work introduces a novel diagnostic approach that combines deformable attention mechanisms and graph-based learning, enhancing both model accuracy and interpretability. By separating pose estimation and classification, the model improves robustness and offers clinically meaningful insights, demonstrating significant promise for early PD screening and decision support.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100693"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146184651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ZKNiS-PoW: A privacy-preserving proof of ownership scheme for secure cloud storage ZKNiS-PoW：用于安全云存储的隐私保护所有权证明方案

IF 4.5

Array Pub Date : 2026-03-01 Epub Date: 2025-12-03 DOI: 10.1016/j.array.2025.100627

Tang Zhou , Le Wang , Minxian Liang

{"title":"ZKNiS-PoW: A privacy-preserving proof of ownership scheme for secure cloud storage","authors":"Tang Zhou , Le Wang , Minxian Liang","doi":"10.1016/j.array.2025.100627","DOIUrl":"10.1016/j.array.2025.100627","url":null,"abstract":"<div><div>There is a large amount of redundant data among users of cloud storage services. Client-side deduplication helps reduce the cost for service providers by avoiding repeated uploads and storage. However, this technique brings new security risks. Malicious users may use illegally obtained deduplication tags, such as file fingerprints, to fake ownership of other users’ files. Proof of Ownership (PoW) can require users to prove they have the full file, but existing methods are inefficient. They often need multiple rounds of interaction or complex computation over the whole file. As a result, the verification time increases with file size. To solve this problem, we propose a non-interactive PoW scheme based on zk-STARK. The system selects a number of challenge blocks that meet cryptographic security. It uses arithmetic circuits to encode block selection, hash computation, and the correctness of accumulators. Users only need to generate a zero-knowledge proof on these blocks. This allows them to prove they own the full file without revealing its content. The verification time does not depend on file size and appears near-constant in practice. In tests on files from 64 MB to 1 GB, our scheme is 1.2 to 46 times faster than existing methods. Security analysis shows that only a small number of blocks need to be verified. Even if an attacker knows 90% of the file, the chance of forgery is still lower than <span><math><msup><mrow><mn>2</mn></mrow><mrow><mo>−</mo><mn>80</mn></mrow></msup></math></span>. This scheme provides an efficient and practical solution for deduplication in cloud storage with strong privacy protection.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100627"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of tissue-engineered blood vessel with ultrasound computed tomography 超声计算机断层扫描评价组织工程血管

IF 4.5

Array Pub Date : 2026-03-01 Epub Date: 2026-01-21 DOI: 10.1016/j.array.2026.100682

Yichuan Tang , Enxhi Jaupi , Srikar Nekkanti , William G. DeMaria , Marsha W. Rolle , Haichong K. Zhang

{"title":"Evaluation of tissue-engineered blood vessel with ultrasound computed tomography","authors":"Yichuan Tang , Enxhi Jaupi , Srikar Nekkanti , William G. DeMaria , Marsha W. Rolle , Haichong K. Zhang","doi":"10.1016/j.array.2026.100682","DOIUrl":"10.1016/j.array.2026.100682","url":null,"abstract":"<div><div>Tissue-engineered blood vessels (TEBVs) represent a critical advancement in vascular medicine, offering transformative potential in drug testing, regenerative therapies, and disease modeling. Current evaluation methods, however, rely heavily on destructive techniques such as histology, which preclude further use of samples and limit real-time monitoring. Ultrasound Computed Tomography (USCT) emerges as a promising alternative, enabling non-destructive, high-resolution imaging within bioreactors. While prior work has demonstrated the feasibility of USCT for TEBV monitoring using needle and tubing phantoms, this study advances the field by imaging real TEBV samples and employing histological analysis as the ground truth for validation. This paper utilizes a prototype USCT system that achieve comprehensive 360-degree reconstructions of TEBV cross-sections. Validated through both needle-phantom studies and histology comparisons, the system demonstrates high accuracy with an average measurement error of 0.03 mm and adaptability within bioreactor environments. Our results underscore USCT’s capacity for non-destructive TEBV evaluation, paving the way for enhanced monitoring during cultivation. Future developments aim to refine image reconstruction and expand clinical applications.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100682"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146184696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep learning models for straddle carriers: Predictive maintenance 跨运车的深度学习模型：预测性维护

IF 4.5

Array Pub Date : 2026-03-01 Epub Date: 2026-02-06 DOI: 10.1016/j.array.2026.100706

Pooja Mudbhatkal , Martti Juhola , Mikko Asikainen , SantoshKumar Patel

引用次数: 0

Efficient tool path computing for Industry 5.0: Application to turning lathe machining 面向工业5.0的高效刀具轨迹计算：在车床加工中的应用

IF 4.5

Array Pub Date : 2026-03-01 Epub Date: 2026-01-31 DOI: 10.1016/j.array.2026.100701

Héctor Migallón , Antonio Jimeno-Morenilla , Eduard Duta-Costache , José-Luis Sánchez-Romero

{"title":"Efficient tool path computing for Industry 5.0: Application to turning lathe machining","authors":"Héctor Migallón , Antonio Jimeno-Morenilla , Eduard Duta-Costache , José-Luis Sánchez-Romero","doi":"10.1016/j.array.2026.100701","DOIUrl":"10.1016/j.array.2026.100701","url":null,"abstract":"<div><div>This paper presents an efficient approach to toolpath generation tailored to the needs of Industry 5.0, with a focus on turning lathe machining. The study addresses the challenge of rapidly and accurately generating helical toolpaths in personalized manufacturing, where traditional sequential methods often become computational bottlenecks. To overcome this limitation, we propose efficient parallel implementations of the Virtual Digitizing (VD) algorithm, specifically designed to accelerate the computation of machining trajectories on both multicore and manycore architectures. The multicore implementation achieves notable speedups, especially when execution is properly tuned. The manycore strategy explores both asynchronous (coarse-grained) and synchronous (fine-grained) execution models. In the asynchronous method, independent trajectory computations are assigned to separate CUDA threads, whereas the synchronous method further parallelizes the internal processing of each trajectory point, providing finer computational granularity. Experimental evaluations conducted on authentic industrial shoe last models reveal notable gains in computational efficiency. The manycore implementation achieves up to <span><math><mrow><mn>70</mn><mi>x</mi></mrow></math></span> acceleration on low-end GPUs, over <span><math><mrow><mn>80</mn><mi>x</mi></mrow></math></span> on high-range devices and over <span><math><mrow><mn>270</mn><mi>x</mi></mrow></math></span> on state-of-the-art GPU devices when compared to their respective CPU-based computations. Although the synchronous method introduces additional complexity, it delivers the best performance on powerful GPU platforms, whereas the asynchronous method is better suited for resource-constrained systems. Therefore, the study concludes that the optimal parallelization strategy depends on the available hardware.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100701"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146184693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Model-based evaluation of synthetic financial time series data: A comparative study with multi-metric validation 基于模型的综合金融时间序列数据评价：多度量验证的比较研究

IF 4.5

Array Pub Date : 2026-03-01 Epub Date: 2026-01-30 DOI: 10.1016/j.array.2026.100684

Patrick Naivasha, George Musumba, Patrick Gikunda, John Wandeto

{"title":"Model-based evaluation of synthetic financial time series data: A comparative study with multi-metric validation","authors":"Patrick Naivasha, George Musumba, Patrick Gikunda, John Wandeto","doi":"10.1016/j.array.2026.100684","DOIUrl":"10.1016/j.array.2026.100684","url":null,"abstract":"<div><div>This research presents a behaviorally informed framework for synthesizing financial time-series data, specifically designed to emulate the complex dynamics of foreign exchange markets. Deviating from conventional generative adversarial networks (GANs) or purely statistical distribution-matching, the proposed methodology adopts a game-theoretic architecture. This framework integrates trader-interaction dynamics, stochastic strategies, and information asymmetry, treating the market as a strategic participant to reproduce authentic volatility patterns and structural dependencies. To ensure numerical stability across extensive simulations, the study introduces a uniform upward scaling procedure and controlled initialization, preventing pathological price behaviors without compromising the underlying statistical properties. The framework's analytical fidelity was rigorously evaluated against a suite of econometric and machine learning models, including ARIMA, XGBoost, LSTM, N-BEATS, and DLinear. Experimental results involving 12,960 hourly observations demonstrate that the synthetic data maintains strong alignment with empirical benchmarks. DLinear emerged as the superior model, exhibiting exceptional stability with an R<sup>2</sup> frequently exceeding 0.98 and a Mean Absolute Scaled Error (MASE) near unity. While XGBoost and N-BEATS yielded competitive results, ARIMA and LSTM showed anticipated performance degradation due to temporal noise. Comprehensive residual diagnostics, including Ljung-Box tests and stationarity assessments, confirm that the generated series are behaviorally consistent and analytically reliable. This framework thus provides a robust foundation for comparative modeling and experimental financial research.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100684"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146184708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Label-efficient sleep staging from multi-channel EEG with self-supervised contrastive learning and iterative self-distillation 基于自监督对比学习和迭代自蒸馏的多通道脑电图标签有效睡眠分期

IF 4.5

Array Pub Date : 2026-03-01 Epub Date: 2026-02-17 DOI: 10.1016/j.array.2026.100718

Jie Ouyang , Peng Xiao , Jingxue Chen , Fried-Michael Dahlweid , Yiming Chen , Yuanwang Wei , Zou Lai

{"title":"Label-efficient sleep staging from multi-channel EEG with self-supervised contrastive learning and iterative self-distillation","authors":"Jie Ouyang , Peng Xiao , Jingxue Chen , Fried-Michael Dahlweid , Yiming Chen , Yuanwang Wei , Zou Lai","doi":"10.1016/j.array.2026.100718","DOIUrl":"10.1016/j.array.2026.100718","url":null,"abstract":"<div><div>Manual sleep stage classification from polysomnography (PSG) is labor-intensive and subject to expert variability, motivating automated and deployment-oriented solutions for clinical use. We present a multi-channel <em>self-supervised learning (SSL)</em> contrastive framework combined with iterative self-distillation for accurate and <em>label-efficient</em> sleep staging. The approach employs a dual-branch convolutional network that processes electroencephalogram (EEG) channels independently and integrates complementary information via a cross-attention fusion module. During pre-training, a contrastive objective leverages temporal adjacency to form positive pairs and maintains hard negatives dynamically to learn robust representations from unlabeled data. Subsequent fine-tuning with minimal labels is enhanced by iterative self-distillation through pseudo-label refinement. On the Sleep-EDF Expanded (SleepEDF-v2) dataset, the method achieves strong performance with only 1% labeled data (accuracy 76.31%, macro-F1 66.53%), competitive against existing SSL baselines. The resulting compact model and single-site training setup align with practical constraints in hospitals and wearable scenarios, reducing annotation burden and supporting secure, scalable clinical deployment.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100718"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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