Sustainable Computing-Informatics & Systems最新文献

{"title":"Multi fault classification in electrical transmission lines using feature engineering based on autogluon framework","authors":"Merve Demirci","doi":"10.1016/j.suscom.2026.101310","DOIUrl":"10.1016/j.suscom.2026.101310","url":null,"abstract":"<div><div>With the increasing electricity demand, power transmission lines continue to grow and become increasingly complex. Because even the smallest faults occurring on growing power lines can impact the grid, rapid fault detection, classification, and subsequent repair are crucial. In this study, a new Machine Learning approach based on the Autogluon framework is proposed for rapid fault detection and high-accuracy classification of transmission line faults. Three different methods are employed within the Autogluon framework, and the results are evaluated and compared using ROC analysis. The first method uses the original dataset containing 7861 data points obtained from the open-source Kaggle platform. The second approach adds statistical properties of current and voltage values (mean, standard deviation, and range) to the original dataset. The third method uses the SMOTE algorithm to generate synthetic data and increase the data size to address the imbalance in the number of fault classes in the dataset. The proposed method demonstrates the critical role of data processing and feature engineering in optimizing classification performance and achieving the most accurate diagnosis for multi-label fault classification. The numerical results compared with the literature show that the proposed method is promising for practical applications.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"50 ","pages":"Article 101310"},"PeriodicalIF":5.7,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146122686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EAURP: An energy-efficient and trust-aware unobservable routing protocol for secure mobile Ad Hoc networks","authors":"A. Chandra ,&nbsp;A.S.N. Chakravarthy","doi":"10.1016/j.suscom.2025.101285","DOIUrl":"10.1016/j.suscom.2025.101285","url":null,"abstract":"<div><div>Mobile ad hoc wireless networks (MANETs) are decentralized, lacking fixed infrastructure, which enables dynamic and flexible communication between mobile nodes. However, these networks face challenges such as limited energy resources, frequent topology changes, and performance degradation caused by node misbehavior. Existing protocols like AODV have significant limitations, including a lack of energy awareness, an inability to detect malicious behavior, and the absence of secure transmission mechanisms. These weaknesses lead to rapid energy depletion and increased vulnerability to attacks. To address these issues, this paper proposes a novel energy-aware unobservable routing protocol. The new protocol introduces custom packet types, such as PT_NID, PT_GID, and PT_CREV, to monitor the real-time behavior of neighboring nodes and to manage route revocation. Trust evaluation is performed using packet-forwarding ratios, and false positives are detected. Additionally, the protocol checks each node's residual energy before forwarding data to the next node. To ensure data confidentiality, elliptic curve cryptography (ECC) is employed, providing robust encryption while reducing resource consumption. ECC is particularly beneficial for MANET devices with limited resources, as it offers strong security with lower overhead. Simulations conducted using NS2 software demonstrate that the proposed model outperforms the traditional AODV protocol in terms of network lifetime, packet delivery ratio, throughput, and delay, particularly under conditions of node mobility and varying node density. Overall, the proposed protocol offers a more robust, scalable, and secure solution for MANET environments compared to existing protocols.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"49 ","pages":"Article 101285"},"PeriodicalIF":5.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The future role of artificial intelligence in energy management systems for smart cities: A systematic literature review of trends, gaps, and future direction","authors":"Ubaid ur Rehman","doi":"10.1016/j.suscom.2025.101249","DOIUrl":"10.1016/j.suscom.2025.101249","url":null,"abstract":"<div><div>This systematic literature review (SLR) investigates the role of artificial intelligence (AI) in energy management systems (EMS) for smart cities, analyzing 85 studies from 2019 to 2025 using the PRISMA protocol and Biblioshiny tool for bibliometric analysis. The study uniquely identifies six thematic clusters IoT integration, renewable energy integration, energy forecasting, smart energy policies, AI optimization techniques, and blockchain-enabled systems revealing trends, gaps, and future directions. Key findings highlight AI’s transformative potential in energy optimization, demand response, and renewable integration, while pinpointing critical limitations such as scalability, computational complexity, and real-time adaptability. By proposing a novel six-step SLR methodology and actionable guidelines, this review bridges theoretical advancements with practical challenges, offering a roadmap for scalable, efficient, and resilient AI-driven EMS. This work provides researchers and practitioners with a comprehensive framework to advance sustainable urban energy systems, addressing gaps in scalability, ethical considerations, and real-world implementation.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"49 ","pages":"Article 101249"},"PeriodicalIF":5.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Day-ahead energy management in smart combined cooling, heating and power (CCHP) grid considering optimal consumption and local self-generation","authors":"Mohamad Reza Zargar Shoshtari,&nbsp;Seyed Mehdi Hakimi,&nbsp;Ghasem Derakhshan","doi":"10.1016/j.suscom.2026.101291","DOIUrl":"10.1016/j.suscom.2026.101291","url":null,"abstract":"<div><div>With growing global energy demand, ensuring a reliable energy supply is critical for all nations. The modern Energy services in residential buildings, especially those using combined cooling, heating, and power (CCHP) systems, are particularly important in meeting these demands. Accordingly, this study focuses on day-ahead energy management in a smart CCHP grid with the participation of hybrid energy storage systems and optimal energy consumption by consumers in smart residential buildings. The energy management is modeled by a multi-level and multi-objective optimization approach considering demand response strategies (DRSs). The DRSs include electrical demand shifting of power consumption, and self-generation of power, and gas storage systems. The electrical demand shifting strategy is implemented in the first level optimization, subject to electricity pricing traffic to minimize consumers’ bills. Also, minimizing consumers’ bills in the second level optimization is done by power and gas storage systems via the local self-generation (LS-G) strategy, subject to electricity and gas prices in the energy market. In the third level optimization, multi-objective functions like minimizing operational costs, maximizing flexibility and minimizing power losses are implemented. In the proposed optimization approach, optimized energy consumption in the first and second levels is considered in the third level optimization. The proposed optimization approach for all levels is solved by using General Algebraic Modeling System (GAMS) software. In the following, solving multi-objective optimization approach in the third level is carried out by enhanced epsilon-constraint method. Also, Shannon Entropy decision making method is proposed for determining optimal solution in third level for multi-objective functions and Pareto front solutions. Finally, the findings show the optimal results of the objectives at each level and highlight consumer involvement through a comparative analysis via various case studies. The participation of DRSs leads to a 11.63 % reduction in operational costs and 18.75 % reduction in power losses, while also enhancing flexibility by 2.6 % in the CCHP grid.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"49 ","pages":"Article 101291"},"PeriodicalIF":5.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable and counterfactual lasso regression for resilient micro gas turbine power prediction in smart grids","authors":"Sheikh Muhammad Saqib ,&nbsp;Muhammad Amir khan ,&nbsp;Tariq Shahzad ,&nbsp;Muhammad Usman Tariq ,&nbsp;Tehseen Mazhar ,&nbsp;Habib Hamam","doi":"10.1016/j.suscom.2025.101284","DOIUrl":"10.1016/j.suscom.2025.101284","url":null,"abstract":"<div><div>Accurate prediction of electrical power output from micro gas turbines is essential for optimizing performance in microgrids and distributed power systems. This study introduces a novel and interpretable machine learning framework using Lasso regression applied to a newly published dataset titled Micro Gas Turbine Electrical Energy Prediction, available on Kaggle. The dataset captures time-series relationships between input control voltage and electrical power output, enabling effective modeling of micro turbine behavior. The proposed model relies on only two features, input voltage and time, ensuring computational efficiency while maintaining predictive performance. To support decision-making and model transparency, the framework incorporates Explainable AI (XAI) techniques such as SHAP and LIME, which reveal the influence of input features on predictions. Additionally, counterfactual analysis is integrated to explore how changes in inputs affect predicted outcomes. This allows users to define a minimum and maximum range for desired power outputs, providing actionable insight. The approach demonstrates high accuracy, with over 87 % of predictions falling into the low or category. By enabling interpretable and resource-efficient forecasting of local energy generation, the proposed framework contributes to the development of resilient and sustainable smart grid infrastructures. Most importantly, the proposed system is highly relevant for smart grid and microgrid operations, where transparent, accurate, and adaptive prediction of local generation units like micro gas turbines plays a critical role in maintaining system stability, load balancing, and energy efficiency.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"49 ","pages":"Article 101284"},"PeriodicalIF":5.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid fuzzy logic and deep reinforcement learning algorithm for adaptive task scheduling and resource allocation in heterogeneous Fog–Cloud environments","authors":"Setareh Moazzami ,&nbsp;Abbas Mirzaei ,&nbsp;Mehdi Aminian ,&nbsp;Ramin Karimi ,&nbsp;Nasser Mikaeilvand","doi":"10.1016/j.suscom.2025.101260","DOIUrl":"10.1016/j.suscom.2025.101260","url":null,"abstract":"<div><div>Intelligent task scheduling in distributed computing environments such as Fog–Cloud systems remains a significant challenge, particularly in the context of the Internet of Things (IoT), where multiple objectives such as minimizing delay, energy consumption, and makespan must be simultaneously addressed. This paper proposes an adaptive hybrid framework that integrates fuzzy logic with Deep Q-Network (DQN) reinforcement learning to optimize task scheduling and resource allocation in heterogeneous and dynamic environments. The model is designed to maintain service quality while remaining compatible with limited computational resources. The scheduling problem is first formulated as a multi-objective optimization model aimed at jointly minimizing delay, energy usage, and makespan. A fuzzy inference system is then employed to evaluate task attributes such as deadline, delay sensitivity, and data volume in order to assign priority levels. Based on this prioritization, the DQN agent dynamically allocates resources by interacting with the environment and learning from feedback. The proposed framework was evaluated on scenarios involving 500–2000 tasks under varying resource conditions, and its performance was benchmarked against conventional algorithms. Experimental results demonstrate that the proposed method achieves, on average, a 27.8 % reduction in execution time, a 29.6 % decrease in scheduling delay, an 18 % reduction in energy consumption, and a 21.4 % improvement in makespan. These outcomes highlight the framework’s effectiveness in balancing accuracy, responsiveness, and resource efficiency, making it well-suited for deployment in real-world, heterogeneous, and dynamically loaded computing environments.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"49 ","pages":"Article 101260"},"PeriodicalIF":5.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy efficient unified computing framework for smart grids with AI-driven communication, supercomputing, and energy perception orchestration","authors":"Wenchong Fang ,&nbsp;Zhifeng Zhou ,&nbsp;Yingchen Li ,&nbsp;Ma Guang ,&nbsp;Fei Chen","doi":"10.1016/j.suscom.2025.101289","DOIUrl":"10.1016/j.suscom.2025.101289","url":null,"abstract":"<div><div>The next-generation smart grid requires a unified computing framework that seamlessly integrates communication, high-performance computing (HPC), and AI to enable real-time energy perception, forecasting, and decision-making. Conventional architectures, which treat communication, computation, and control as independent modules, often suffer from latency, scalability limitations, and weak coordination across heterogeneous infrastructures. To overcome these constraints, this work proposes an energy-efficient unified computing framework where communication networks, HPC clusters, and AI orchestration operate as a tightly coupled ecosystem. AI modules handle deep learning–based perception of multi-source energy data and employ reinforcement learning to optimize dynamic load allocation and demand-side flexibility. Superscale HPC resources accelerate renewable forecasting, grid stability assessment, and large-scale optimization tasks. In parallel, adaptive communication units with edge-level compression and intelligent routing ensure low latency and resilience under varying network loads. The framework is evaluated through MATLAB/Simulink and Python co-simulation using HPC-enabled TensorFlow clusters and blockchain-secured IoT gateways. Experimental results demonstrate a System Orchestration Index (SOI) of 98.3 %, a Computational Efficiency Ratio (CER) of 37.5 %, a Demand Flexibility Index (DFI) of 33.8 %, and an end-to-end decision latency of 18 ms. Compared with conventional grid computing approaches, the proposed architecture achieves improvements of 9.4 % in orchestration efficiency, 7.8 % in computational efficiency, and 6.2 % in demand flexibility. These outcomes highlight the potential of an AI-driven, HPC-accelerated, and communication-adaptive unified computing paradigm for scalable and resilient smart grid operations.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"49 ","pages":"Article 101289"},"PeriodicalIF":5.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LFC of distributed power generation system under different cyberattacks utilizing mZOA based hFPD-PI+FP control strategy","authors":"Surya Narayan Sahu ,&nbsp;Rajendra Kumar Khadanga ,&nbsp;Deepa Das ,&nbsp;Yogendra Arya ,&nbsp;Sidhartha Panda ,&nbsp;Sasmita Padhy ,&nbsp;Preeti Ranjan Sahu","doi":"10.1016/j.suscom.2025.101282","DOIUrl":"10.1016/j.suscom.2025.101282","url":null,"abstract":"<div><div>The stability of electrical power load frequency control (LFC) system is threatened by frequency variations caused by violating the generation-demand balance and cyberattacks. This paper tackles the problem of LFC in a distributed power generation system (DPGS) that integrates energy storage and renewable energy sources. Using a modified Zebra Optimisation Algorithm (mZOA), a hybrid fuzzy PD-PI plus fuzzy P (hFPD-PI+FP) controller is suggested for LFC of DPGS under cyberattacks. Benchmark experiments validate the performance of the mZOA, showing that it performs better than the regular ZOA in terms of computation time and solution quality. According to simulation data, the mZOA based hFPD-PI+FP controller works better than the traditional PID controller at preserving frequency stability in the event of a cyberattack.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"49 ","pages":"Article 101282"},"PeriodicalIF":5.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent decision-making in smart grids using VANET and deep learning-based big data analysis","authors":"Feng Xie,&nbsp;Zheng Tan,&nbsp;Ying Zhang,&nbsp;Shao-lin Wang,&nbsp;Zheng Cao,&nbsp;Cai-yue Yang","doi":"10.1016/j.suscom.2025.101283","DOIUrl":"10.1016/j.suscom.2025.101283","url":null,"abstract":"<div><div>The increasing adoption of Electric Vehicles (EVs) and Renewable Energy (RE) sources in modern power systems presents challenges in maintaining grid stability, peak load management, and operational efficiency. This research developed an intelligent decision-making framework for VANET-enabled Smart Grids (SG) through Deep Learning (DL)-based big data analysis. A comprehensive framework is proposed that integrates Vehicle-to-Grid (V2G) optimization with a novel DL model, Emperor Penguins Colony-tuned Deep Belief with Attention-based Long Short-Term Memory (EPC-DB-AttLSTM). The framework gathers smart grid EV and renewable data and preprocessed it using data cleaning techniques such as K-Nearest Neighbour (kNN) imputation and normalization techniques like min-max normalization. Deep Belief (DB) Networks detects anomalies in grid operations, AttLSTM captures critical temporal patterns, Decision-making was enhanced through EPCO, EPC-DB-AttLSTM enables accurate forecasting and intelligent decisions which allocated EVs to charging stations efficiently, balanced load across the network, and maximized RE utilization. The attention mechanism highlights critical temporal patterns in EV load and grid data, improving prediction accuracy for intelligent decision-making. VANET communication enabled data exchange among EVs, charging stations, and grid controllers, supporting dynamic and scalable decision-making. The experiment was implemented using Python 3.10. The results demonstrated that the proposed framework achieved R² of 99.7, RMSE of 12.36, MAPE of 8.52, MSE of 1875.42, and MAE of 10.86. By integrating DL, big data analytics, and optimization-based decision-making, this framework provides a responsive, intelligent, and scalable SG solution, capable of accommodating high EV penetration and variable RE generation while optimizing operational strategies and ensuring sustainable energy management.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"49 ","pages":"Article 101283"},"PeriodicalIF":5.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards energy-efficient scientific computing: Reversible numerical linear algebra kernels in floating-point arithmetic","authors":"V. Dwarka","doi":"10.1016/j.suscom.2025.101261","DOIUrl":"10.1016/j.suscom.2025.101261","url":null,"abstract":"<div><div>Frontier scientific and AI workloads now reach <span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>19</mn></mrow></msup><mspace></mspace><mo>−</mo><mspace></mspace><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>25</mn></mrow></msup></mrow></math></span> fused multiply–add (FMA) operations per run (on the order of <span><math><mrow><mn>2</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>19</mn></mrow></msup><mspace></mspace><mo>−</mo><mspace></mspace><mn>2</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>25</mn></mrow></msup></mrow></math></span> FLOPs). At today’s <span><math><mrow><mo>∼</mo><mn>10</mn></mrow></math></span> <!--> <!-->pJ per FMA, this corresponds to approximately <span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>8</mn></mrow></msup><mspace></mspace><mo>−</mo><mspace></mspace><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>14</mn></mrow></msup></mrow></math></span> joules of arithmetic energy. At this scale, energy becomes the limiting resource for continued growth in computational workloads, motivating a re-evaluation of long-standing algorithmic assumptions. It is often assumed that reversible computing only matters near the Landauer limit. Building on prior physical arguments that full energy recovery is only possible when computation preserves information, we demonstrate that this same requirement governs floating-point numerical kernels: overwriting state enforces a non-zero energy floor, even under ideal recovery. Thus, eliminating this wall in practice requires that the numerical algorithm itself be injective. We therefore present the <em>first</em> reversible floating-point realizations of core dense numerical kernels—matrix multiplication, LU factorization, and conjugate-gradient iteration—that retain rounding information rather than discarding it. Implemented directly in IEEE arithmetic, they achieve machine-precision forward–reverse agreement on well- and ill-conditioned problems with minimal auxiliary state. A toggle-based model with measured switching costs and realistic recovery factors predicts <span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>3</mn></mrow></msup><mspace></mspace><mo>−</mo><mspace></mspace><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>4</mn></mrow></msup><mo>×</mo></mrow></math></span> reductions in arithmetic energy. These results establish injective floating-point kernels as a foundation for energy-recovering numerical computation, and indicate that realizing this potential will require sustained co-design across applied mathematics, computer science, and hardware engineering.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"49 ","pages":"Article 101261"},"PeriodicalIF":5.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}