Sustainable Computing-Informatics & Systems最新文献_第3页

Energy-efficient power marketing optimization using XGBoost for enhanced market performance 利用XGBoost优化节能电力营销，提升市场绩效

IF 5.7 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2026-01-01 Epub Date: 2025-11-01 DOI: 10.1016/j.suscom.2025.101243

Jingxian Lu, Junfeng Li, Guoyi Zhao, Kunpeng Liu, Jing Yang

{"title":"Energy-efficient power marketing optimization using XGBoost for enhanced market performance","authors":"Jingxian Lu, Junfeng Li, Guoyi Zhao, Kunpeng Liu, Jing Yang","doi":"10.1016/j.suscom.2025.101243","DOIUrl":"10.1016/j.suscom.2025.101243","url":null,"abstract":"<div><div>It is in this setting of power markets competition that any marketing efforts need to be fine-tuned to the maximal levels in achieving the highest revenue from the end-users while at the same time ensuring grid resilience and firmness. Currently used techniques exhibit poor prediction performance, are unable to optimally allocate energy and do not adapt quickly to fluctuating market parameters, thus providing less than optimal solutions. The contribution of this research is the introduction of a new approach to use of the Extreme Gradient Boosting (XGBoost) algorithm in power marketing. The work proposed herein seeks to overcome these difficulties by using the feature importance and gradient-based learning in boosting the model’s prediction capability as well as fine-tuning the price framework. The model’s performance is measured and analyzed in terms of the technical power performance parameters which consists of Energy Utilization Efficiency (EUE), Load Factor (LF), and Power Loss Reduction (PLR). The experiments demonstrated an enhancement of the EUE to 92 %, the increase in LF from 0.78 to 0.91, and the decrease in PLR by 15 % as compared to the standard algorithm. MATLAB based simulation studies are performed using real-world power market data to confirm the usefulness of our model in real, dynamic and large-scale power systems. This is a highly effective and a highly efficient approach to the improvement of market performance and operational functionality.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"49 ","pages":"Article 101243"},"PeriodicalIF":5.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145651894","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}

引用次数: 0

Retraction notice to “Energy-efficient blockchain-integrated IoT and AI framework for sustainable urban microclimate management” [Sustain. Comput.: Inf. Syst. 47 (2025) 101137] 关于“节能区块链集成物联网和人工智能框架用于可持续城市微气候管理”的撤回通知[…]第一版。[参考文献47 (2025)101137]

IF 5.7 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2026-01-01 Epub Date: 2025-12-29 DOI: 10.1016/j.suscom.2025.101227

N. Krishnaraj , Hadeel Alsolai , Fahd N. Al-Wesabi , Yahia Said , Ali Alqazzaz , S. Gayathri Priya , S. Shanmathi , B. Narmada

引用次数: 0

Multi-objective energy-efficient power system scheduling using Stochastic State Space Model and reinforcement learning 基于随机状态空间模型和强化学习的多目标节能电力系统调度

IF 5.7 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-12-01 Epub Date: 2025-10-09 DOI: 10.1016/j.suscom.2025.101224

Jiaying Wang, Xiaoqian Meng, Xuan Yang, Haibing Yin, Pingkai Fang

{"title":"Multi-objective energy-efficient power system scheduling using Stochastic State Space Model and reinforcement learning","authors":"Jiaying Wang, Xiaoqian Meng, Xuan Yang, Haibing Yin, Pingkai Fang","doi":"10.1016/j.suscom.2025.101224","DOIUrl":"10.1016/j.suscom.2025.101224","url":null,"abstract":"<div><div>The increasing complexity of modern power systems, arising from increased electricity demand and large-scale renewable energy resource integration, creates significant challenges for real-time scheduling and operational reliability. Conventional deterministic scheduling processes mostly cannot incorporate the inherent uncertainty and variability associated with the fluctuations of wind and solar generation, as well as fluctuating load demand, which leads to inefficiencies in the amount of necessary resources required and increased operational costs. This research proposes a novel multi-objective power scheduling framework that incorporates a Stochastic State-Space Model (SSSM) and Reinforcement Learning (RL) for dynamic management of generation, storage, and demand uncertainty. The SSSM takes into account the stochastic variability of renewable generation, uncertain demand profiles, and exogenous contingencies of the system. The RL agent is continuously learning the best scheduling strategies as it operates the power system to minimize operational costs while improving availability and maximizing scheduling performance. Simulation results using Monte Carlo testing over a 24-hour horizon demonstrated that the proposed method achieved a reduction of up to 20 % in operational costs, 10 % more system availability, and scheduling efficiencies of over 90 % compared to traditional methods. The proposed approach offers a feasible way forward for power systems operators to simultaneously meet the objectives of cost, reliability, and sustainability under a paradigm of uncertainty, while also having relevant application to real-time operation in smart grid systems, particularly in systems with high renewable energy.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101224"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267157","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}

引用次数: 0

A novel ultra-low power post quantum approach using artificial intelligence based key generation for cyber physical system in Internet of things 一种基于人工智能的物联网网络物理系统密钥生成超低功耗后量子方法

IF 5.7 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-12-01 Epub Date: 2025-11-11 DOI: 10.1016/j.suscom.2025.101242

Ankita Sarkar , Mansi Jhamb

{"title":"A novel ultra-low power post quantum approach using artificial intelligence based key generation for cyber physical system in Internet of things","authors":"Ankita Sarkar , Mansi Jhamb","doi":"10.1016/j.suscom.2025.101242","DOIUrl":"10.1016/j.suscom.2025.101242","url":null,"abstract":"<div><div>The expansion of Internet of Things (IoT) devices has revolutionized various industries, particularly healthcare, where the Internet of Medical Things (IoMT) enables real-time data collection, analysis, and secure transmission of sensitive patient information. However, these resource-constrained devices face significant security challenges, particularly with the advent of quantum computing. This work introduces an intelligent cryptographic framework tailored to address these challenges, integrating lightweight cryptographic primitives, chaotic systems, and quantum-resistant techniques. Performance evaluation using image metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM) demonstrates the framework's effectiveness. The results indicate an average MSE of 5590.816, an MAE of 83.909, a PSNR of 8.044 dB, and an SSIM of 0.0224, showcasing strong encryption and minimal data distortion. Furthermore, this hybrid cryptographic system ensures diffusion, nonlinearity, randomness, and strong key dependency while demonstrating resistance to cryptanalytic and quantum attacks. The proposed framework is computationally competent, making it particularly well-suited for resource-constrained IoMT devices with a minimum energy consumption of 3.536 µJ.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101242"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520120","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}

引用次数: 0

Toward a secure and scalable IoT: A survey of IOTA-based distributed ledger technologies 迈向安全和可扩展的物联网：基于iota的分布式账本技术调查

IF 5.7 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-12-01 Epub Date: 2025-10-14 DOI: 10.1016/j.suscom.2025.101225

Tariq Alsboui , Hussain Al-Aqrabi , Ahmad Manasrah , Mahmoud Artemi

{"title":"Toward a secure and scalable IoT: A survey of IOTA-based distributed ledger technologies","authors":"Tariq Alsboui , Hussain Al-Aqrabi , Ahmad Manasrah , Mahmoud Artemi","doi":"10.1016/j.suscom.2025.101225","DOIUrl":"10.1016/j.suscom.2025.101225","url":null,"abstract":"<div><div>The increasing adoption of Internet of Things (IoT) systems demands secure, energy-efficient, and scalable solutions capable of supporting mission-critical operations. Traditional blockchain-based Distributed Ledger Technologies (DLTs), however, face limitations such as high energy consumption, poor scalability, and transaction fees, making them less ideal for IoT environments. This paper presents a structured review of IOTA’s Tangle, a lightweight, feeless, and scalable DLT designed specifically for decentralized IoT architectures. The study categorizes recent IOTA-based approaches into four key domains: security, privacy, scalability, and energy efficiency. The surveyed literature is systematically classified and analyzed, highlighting the core challenges addressed by each approach. Comparative evaluation reveals the strengths and limitations of current methods in meeting IoT requirements. The findings suggest that while IOTA offers several advantages over traditional blockchains, integrating hybrid and comprehensive solutions remains a promising direction for future research. The paper concludes by outlining open challenges and opportunities for advancing IOTA-enabled IoT systems.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101225"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362782","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}

引用次数: 0

Power management for smart grids integrating renewable energy sources using Greylag goose optimization and anti-interference dynamic integral neural network 基于灰雁优化和抗干扰动态积分神经网络的可再生能源集成智能电网电源管理

IF 5.7 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-12-01 Epub Date: 2025-09-02 DOI: 10.1016/j.suscom.2025.101199

G.K. Jabash Samuel , P. Rajendran , Papana Venkata Prasad , Chinthalacheruvu Venkata Krishna Reddy

{"title":"Power management for smart grids integrating renewable energy sources using Greylag goose optimization and anti-interference dynamic integral neural network","authors":"G.K. Jabash Samuel , P. Rajendran , Papana Venkata Prasad , Chinthalacheruvu Venkata Krishna Reddy","doi":"10.1016/j.suscom.2025.101199","DOIUrl":"10.1016/j.suscom.2025.101199","url":null,"abstract":"<div><div>This paper proposes a hybrid power management strategy for smart grids (SGs) that integrates renewable energy sources (RESs), such as battery energy storage systems (BESS), fuel cells (FCs), wind turbines (WT), and solar photovoltaic (PV). The GGO-AIDINN approach integrates Greylag Goose Optimization (GGO) and an Anti-Interference Dynamic Integral Neural Network (AIDINN) to address high emissions during low renewable energy (RE) availability and rising operational costs from advanced infrastructure. The GGO optimizes resource allocation and energy distribution, maximizing the use of available RE. Meanwhile, AIDINN predicts energy consumption patterns based on weather conditions, improving overall system performance. The proposed GGO-AIDINN model is implemented on MATLAB and evaluated against several existing methods, including Fuzzy Logic Control (FLC), Non-dominated Sorting Genetic Algorithm (NSGA-II), and others. Results show the hybrid method achieves significant improvements, with an operational cost of $1328 per MW, emissions of 13.76 kg per MW, and an efficiency of 98.7 %. These outcomes demonstrate that GGO-AIDINN outperforms traditional techniques, offering lower costs, reduced emissions, and enhanced system efficiency. This makes it a superior solution for sustainable power management in SGs incorporating RESs and BESS.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101199"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048493","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}

引用次数: 0

Energy-efficient resource scheduling scheme using modified load adaptive sequence arrangement (M-LASA) with FILO polling for optical access network 基于FILO轮询的改进负载自适应序列调度（M-LASA）节能光接入网资源调度方案

IF 5.7 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-12-01 Epub Date: 2025-10-04 DOI: 10.1016/j.suscom.2025.101223

Mohan V , Senthil Kumar T , Chitrakala G

{"title":"Energy-efficient resource scheduling scheme using modified load adaptive sequence arrangement (M-LASA) with FILO polling for optical access network","authors":"Mohan V , Senthil Kumar T , Chitrakala G","doi":"10.1016/j.suscom.2025.101223","DOIUrl":"10.1016/j.suscom.2025.101223","url":null,"abstract":"<div><div>Power conservation gains more attention in passive optical networks for enhanced performance. Optical networks have pooling sequences that schedule the resources based on traffic load to attain better energy efficiency. Various sequence schemes have been introduced by the research community; however, the load adaptive sequence arrangement (LASA) suits well for optical access networks. This research proposes a Modified LASA (M-LASA) model that improves energy efficiency in Optical Access Networks (OAN) by integrating a First-In-Last-Out (FILO) polling sequence. The proposed scheme increases the optical network units’ (ONUs) idle time, thereby reducing power consumption significantly compared to traditional scheduling strategies. Simulation results reveal that the proposed M-LASA-FILO scheme outperforms existing methods—such as fixed polling DFB, fixed polling VCSEL, LASA, FILO-DFB, and FILO-VCSEL—in terms of reduced power consumption, improved energy savings, higher sleep count, lower delay, and minimized polling cycle time. For instance, the proposed model achieves maximum energy savings and lower delay even at increased idle time and higher traffic load, confirming its efficiency and robustness in dynamic network conditions.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101223"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266645","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}

引用次数: 0

A resilient IoT-enabled framework using hybrid decision tree and wavelet transform for secure and sustainable photovoltaic energy management 采用混合决策树和小波变换的弹性物联网框架，实现安全和可持续的光伏能源管理

IF 5.7 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-12-01 Epub Date: 2025-10-01 DOI: 10.1016/j.suscom.2025.101221

Mahmoud Elsisi , Mohammed Amer , Mahmoud N. Ali , Chun-Lien Su

{"title":"A resilient IoT-enabled framework using hybrid decision tree and wavelet transform for secure and sustainable photovoltaic energy management","authors":"Mahmoud Elsisi , Mohammed Amer , Mahmoud N. Ali , Chun-Lien Su","doi":"10.1016/j.suscom.2025.101221","DOIUrl":"10.1016/j.suscom.2025.101221","url":null,"abstract":"<div><div>The increasing integration of photovoltaic (PV) systems into smart grids necessitates resilient and secure monitoring frameworks to mitigate the impact of cyber threats such as false data injection (FDI) attacks. This study presents an Internet of Things (IoT)-enabled architecture that leverages a hybrid decision tree model combined with continuous wavelet transform (DT-CWT) for real-time anomaly detection and performance monitoring in PV systems. The CWT is used for time-frequency decomposition and feeding the extracted scalograms into a lightweight DT model. Designed with computational efficiency and low memory overhead, the proposed framework is optimized for deployment in resource-constrained edge environments. Experimental results demonstrate that the DT-CWT-based hybrid model significantly enhances detection accuracy by 97.89 % with a processing latency of 1.32 ms on edge devices and operational resilience, outperforming traditional machine learning baselines (e.g., Linear Discriminant Analysis (LDA), Gaussian Naïve Bayes (GNB), Support Vector Classifier (SVC), and Random Forest (RF), and DT) under adversarial conditions. This approach ensures data integrity, strengthens cybersecurity, and supports intelligent energy management, contributing to the realization of resilient and sustainable power grids aligned with Industry 4.0 and global sustainability goals.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101221"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266646","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}

引用次数: 0

Federated deep learning for secure and energy-efficient cyber threat mitigation in smart grid automation 联合深度学习在智能电网自动化中安全、节能的网络威胁缓解

IF 5.7 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-12-01 Epub Date: 2025-11-05 DOI: 10.1016/j.suscom.2025.101248

Mohammed Shuaib

{"title":"Federated deep learning for secure and energy-efficient cyber threat mitigation in smart grid automation","authors":"Mohammed Shuaib","doi":"10.1016/j.suscom.2025.101248","DOIUrl":"10.1016/j.suscom.2025.101248","url":null,"abstract":"<div><div>This research presents the federated deep-learning (DL) based cybersecurity platform of smart-grid automation with the focus on privacy, distributed intelligence and energy efficiency. The federated learning system allows grid-edge devices (such as substations and smart meters) to cooperate in training a threat-detection model without sharing raw data hence maintaining local confidentiality. The proposed structure is a hybrid convolutional neural network (CNN)-long short-term memory (LSTM) model, which runs locally to predict spatiotemporal threats and the synchronization of the model is done in a Federated Averaging (FedAvg) algorithm. The model achieves a Threat Detection Accuracy (TDA) of 97.2 per cent, and a False Alarm Rate of 3.6 per cent. Compared to centralized learning, communication overhead is reduced by 41 % and, hence, the control response latency is maintained. The importance of optimisation update intervals and pruning of edge models reduce energy consumption during training by 22 % of the original consumption. The resilience of the system to fake data injection and command-spoofing attacks is verified by simulation on the modified KDD 99 data set and real-grid situations in NS −3. The federated solution ensures scalable implementation of heterogeneous grid resources. In general, this study is a safe and energy-efficient approach towards the reduction of changing cyber threats within real-time smart-grid settings.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101248"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465672","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}

引用次数: 0

Chronological pufferfish optimization algorithm for task scheduling in cloud computing 云计算中任务调度的时序河豚优化算法

IF 5.7 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-12-01 Epub Date: 2025-10-30 DOI: 10.1016/j.suscom.2025.101241

K. Venkatraman , Harish Padmanaban , S. Sharanyaa

{"title":"Chronological pufferfish optimization algorithm for task scheduling in cloud computing","authors":"K. Venkatraman , Harish Padmanaban , S. Sharanyaa","doi":"10.1016/j.suscom.2025.101241","DOIUrl":"10.1016/j.suscom.2025.101241","url":null,"abstract":"<div><div>Cloud Computing is the practice of delivering computing resources like databases, servers, and storage virtually rather than relying on physical hardware and software. Cloud computing plays a vital role in providing various resources, including infrastructure and storage, as a facility on the internet. It eliminates the necessity for businesses and individuals to self-manage physical resources. Cloud computing provides scalable computing power and flexible resources. Cloud structure is heterogeneous, uncertain and dynamic in nature. Allocating resources, like memory, Central Processing Unit (CPU), and bandwidth, is processed by task scheduling. However, the problem's complexity increases with the increase in several tasks, making it a Nondeterministic Polynomial (NP)-hard problem. Hence, an efficient Chronological Pufferfish Optimization Algorithm (CPOA) is proposed, which incorporates the Pufferfish Optimization Algorithm (POA) and chronological concept, to minimize the resource utilization, reduce the makespan, increase the throughput and lower the utilization of energy in task scheduling. Multi-objective task scheduling is carried out by considering fitness parameters, including task reliability, CPU cost, makespan, energy, earliest finish time, predicted memory capacity, earliest start time, and energy. Then, energy prediction is performed by utilizing a Quasi Recurrent Neural Network (QRNN). Afterwards, task scheduling is done using the proposed CPOA for obtaining the optimal solution. Moreover, the developed CPOA attained better results with an energy of 90.67 J, resource utilization of 0.954 %, a makespan of 0.598 sec, and throughput of 90.99 mbps correspondingly.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101241"},"PeriodicalIF":5.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520136","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}

引用次数: 0