Sustainable Computing-Informatics & Systems最新文献

{"title":"Multi-objective optimization of regional energy systems with exergy efficiency and user satisfaction dynamics","authors":"Xuecheng Wu,&nbsp;Qiongbing Xiong,&nbsp;Cizhen Yu","doi":"10.1016/j.suscom.2025.101213","DOIUrl":"10.1016/j.suscom.2025.101213","url":null,"abstract":"<div><div>The evolving energy landscape is increasingly integrating diverse energy sources, electricity, gas, heat, and cooling, reflecting a strategic shift driven by smart technologies and rising renewable adoption. However, the variability of renewable supply requires enhanced flexibility in demand-side management. This study presents a novel approach to optimizing regional integrated energy systems through a two-layer closed-loop model that incorporates exergy efficiency and user satisfaction dynamics. The model addresses the limitations of traditional energy systems, which often operate within the constraints of singular energy resources and fail to fully integrate renewable energies. The proposed model optimizes energy production, conversion, transmission, and consumption by using a multi-objective framework that includes economic, environmental, and exergy efficiency considerations. The proposed optimization approach significantly improves the performance of integrated energy systems. The energy efficiency is enhanced by 8.36 %, while exergy efficiency shows a notable increase of 1.61 %. Emissions are reduced by approximately 16.3 %, demonstrating the environmental benefits of the model. Though operational costs rise slightly, the trade-off favors sustainability with substantial gains in energy and environmental outcomes. The modified Multi-Objective Particle Swarm Optimization (MOPSO) algorithm outperforms traditional methods like NSGA-II and Standard PSO, achieving a higher Hypervolume value, indicating better convergence and solution diversity. This makes MOPSO a robust tool for solving multi-objective optimization problems in energy management.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101213"},"PeriodicalIF":5.7,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158340","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":"An enhanced hybrid optimization model for renewable energy storage: Integrating GWO and WOA, with Lévy mechanisms","authors":"Ercan Erkalkan","doi":"10.1016/j.suscom.2025.101207","DOIUrl":"10.1016/j.suscom.2025.101207","url":null,"abstract":"<div><div>This study addresses renewable-energy storage scheduling — a high-dimensional, multimodal optimization task — by proposing an enhanced Grey Wolf–Whale Optimization Algorithm (EGW–WOA). The method fuses GWO’s hierarchical leadership with WOA’s spiral exploitation and augments them with Lévy flights and progress-triggered chaotic re-initialization. Across 100 Monte-Carlo trials, EGW–WOAreduced 24<!--> <!-->h operating cost to <span><math><mrow><mn>2</mn><mo>.</mo><mn>94</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>5</mn></mrow></msup><mo>±</mo><mn>7</mn><mo>.</mo><mn>97</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>4</mn></mrow></msup></mrow></math></span>, improving over WOA by 16.62%, GA by 10.15%, FPA by 63.6%, and HS by 80.76%, with a 100% feasibility rate. It achieved the lowest dispersion (Std <span><math><mrow><mo>=</mo><mn>7</mn><mo>.</mo><mn>97</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>4</mn></mrow></msup></mrow></math></span>; Max–Min spread <span><math><mrow><mo>=</mo><mn>3</mn><mo>.</mo><mn>82</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>5</mn></mrow></msup></mrow></math></span>), shaved peak-demand charges by <span><math><mo>≈</mo></math></span>9%, and limited depth-of-discharge swings to <span><math><mrow><mo>&lt;</mo><mn>35</mn></mrow></math></span>%, projecting a 12%–18% life extension. A 50-iteration run completed in 38.6<!--> <!-->s on a 3.4<!--> <!-->GHz CPU — over <span><math><mrow><mn>20</mn><mo>×</mo></mrow></math></span> faster than a comparable MILP baseline — demonstrating suitability for near-real-time PV–wind microgrid control. Within the scope of <em>Sustainable Computing: Informatics and Systems</em>, this work delivers a reproducible, open-source optimization engine with non-parametric statistical validation and edge-suitable runtimes, linking algorithmic advances to system-level sustainability metrics (LCOS, demand charges). The results show how algorithm–system co-design can lower operating cost and risk while preserving battery health in cyber–physical energy systems.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101207"},"PeriodicalIF":5.7,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158339","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":"Secured and effective task scheduling in cloud computing using Levy Flight - Secretary Bird Optimization and Hash-based Message Authentication Code – Secure Hash Authentication 256","authors":"Nida Kousar Gouse,&nbsp;Gopala Krishnan Chandra Sekaran","doi":"10.1016/j.suscom.2025.101211","DOIUrl":"10.1016/j.suscom.2025.101211","url":null,"abstract":"<div><div>Dynamic computing resources are accessible through Cloud Computing (CC), which has gained popularity as a computing technology. Effective Task Scheduling (TS) is an essential aspect of CC, crucial in optimizing task distribution over available resources for high performance. Assigning tasks in cloud environments is a complex process influenced by multiple factors such as network bandwidth availability, makespan and cost considerations. This study proposes a Hash-based Message Authentication Code – Secure Hash Authentication 256 (HMAC-SHA256) and Advanced Encryption Standard (AES) to ensure enhanced security in the task scheduling process within the CC environment. The HMAC-SHA256 algorithm is utilized for key generation, providing integrity verification and data authentication. The AES algorithm is employed to encrypt task data, then the Levy Flight - Secretary Bird Optimization (LF-SBO) algorithm is implemented to schedule optimal tasks in the cloud. The proposed HMAC-SHA256 – AES and LF-SBO algorithms demand lower energy requirements of 121.6 J for 10 tasks, 180.48 J for 25 tasks, 310.21 J for 50 tasks, 400.15 J for 75 tasks, and 520.34 J for 100 tasks, outperforming existing Particle Swarm Optimization (PSO).</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101211"},"PeriodicalIF":5.7,"publicationDate":"2025-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118045","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":"Blockchain-enabled IoT framework with energy-efficient machine learning for scalable and secure smart cities","authors":"Venkata Ramana Gupta Nallagattla ,&nbsp;Amrita Rai ,&nbsp;S. Thangam ,&nbsp;G. Joel Sunny Deol ,&nbsp;Abburi Srirama Kanaka Ratnam ,&nbsp;J. Nageswara Rao ,&nbsp;A. Santhi Mary Antony ,&nbsp;K. Balasubramanian ,&nbsp;Shamimul Qamar","doi":"10.1016/j.suscom.2025.101212","DOIUrl":"10.1016/j.suscom.2025.101212","url":null,"abstract":"<div><div>In a rapidly urbanizing environment, cities have changed into complicated ecosystems requiring sophisticated technological solutions to resolve excessive traffic, energy utilization, waste management, and public safety issues. This study discusses a single architecture for IoT-enabled smart cities through the use of blockchain enabled security, energy efficient machine learning, real-time analytics, and decision-making to overcome scalability, interoperability, and security issues generally present in a smart infrastructure. The framework utilizes lightweight algorithm-based cost-effective computation, integration of heterogeneous IoT devices, real-time decision making, transparency, and involvement of stakeholders. The simulation findings show substantial advantages over traditional methods: a 35 % decrease in processing latency; a 25 % decrease in energy consumption; and a 29 % increase in an index for data security. Also, predictive analytics exhibited over 90 % identification accuracy across the different urban contexts, including traffic control for improved public safety, and environmental monitoring/management scenarios that ensured reliable forecasted events and appropriate resource allocation. The blockchain module demonstrated median transaction validation times of less than 2 ms to validate IoT data streams enabling real-time secure operations even under demanding environmental conditions. Also, we achieved resource allocation optimization with efficiencies that exceeded 85 % for designated priority supplies, including food, energy, medical resources, and reduced waste and improved disaster resilience. This model is adaptable across different urban settings and is a scalable, secure, and energy efficient framework for the next generation of smart cities contributing to sustainable urbanization and improved quality of urban life.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101212"},"PeriodicalIF":5.7,"publicationDate":"2025-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158256","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":"An efficient multi-objective task scheduling for Green cloud computing using hybrid GSCOA algorithm","authors":"Kata Vijay Kumar,&nbsp;Ganesh Reddy Karri","doi":"10.1016/j.suscom.2025.101209","DOIUrl":"10.1016/j.suscom.2025.101209","url":null,"abstract":"<div><div>With the expansion of data centres in recent years, energy-related challenges have become worse. Green cloud computing (GCC) is a new computing paradigm designed to address cloud data centre energy consumption. Even with the advancements in GCC, large-scale green cloud data centres (GCDCs) continue to confront significant obstacles in lowering carbon emissions and energy consumption, particularly in the area of task scheduling. Ineffective task distribution can result in underutilized servers and overworked servers, wasting energy. Workload fluctuations make it difficult to manage resources effectively, which frequently results in energy spikes during periods of high demand. These dynamic demands are frequently not adequately satisfied by the current scheduling techniques since they might not take into consideration changing workload patterns. Therefore, in this work, an effective hybrid Greylag Sand Cat Swarm Optimization Algorithm (GSCOA) is introduced to schedule the task effectively in GCDC. This hybrid approach makes use of the Sand Cat Swarm Optimization Algorithm's (SCSOA) exploitation skills and the Greylag Goose Optimization algorithm's (GGOA) exploring capabilities. This combination makes it possible to schedule cloud user requirements to the cloud server efficiently by minimizing energy consumption. It helps the cloud server system emit less carbon dioxide, allowing for a more environmentally friendly atmosphere. Simulation results on two real-world workloads-NASA-IPSC and HPC2N, indicate that the proposed approach significantly outperforms existing scheduling methods by reducing energy consumption and improving overall system performance.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101209"},"PeriodicalIF":5.7,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096417","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":"MEDALS: A sustainable AI framework for energy-efficient routing in 5G vehicular networks","authors":"G. Balram ,&nbsp;KDV Prasad ,&nbsp;Kamalakar Ramineni ,&nbsp;Rahul Divgan ,&nbsp;K. Ashok ,&nbsp;N.V. Phani Sai Kumar","doi":"10.1016/j.suscom.2025.101210","DOIUrl":"10.1016/j.suscom.2025.101210","url":null,"abstract":"<div><div>Intelligent transportation systems require routing protocols that optimize both performance and environmental impact simultaneously in 5G-enabled Vehicular Ad Hoc Networks (VANETs). Existing solutions often treat sustainability as a secondary constraint, which limits their effectiveness in addressing climate change goals. This study presents MEDALS (Metaheuristic-Enhanced Deep Adaptive Learning System), a hybrid framework that integrates deep reinforcement learning with metaheuristic optimization to achieve both superior performance and environmental sustainability. The system introduces the Green Performance Index (GPI), the first comprehensive metric combining energy efficiency, carbon footprint, latency, and reliability. Through extensive evaluation using industry-standard simulators, MEDALS demonstrates statistically significant improvements: MEDALS achieves 96.8 % energy efficiency (+11.6 %), 0.73 ms latency (-91.6 %), 99.7 % reliability, and 42.3 % carbon reduction while scaling to 1000 + vehicles with linear computational complexity. This will allow its practical implementation in smart cities and towards fulfillment of the sustainable development goals. This complexity augmentation of 3.3x times in the network size handling is attributed to the hybrid intelligence architecture of the framework, the adaptive deep reinforcement learning with the dual metaheuristic optimisation in intelligent fusion mechanism, and the empirically quantified O(N log N) complexity.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101210"},"PeriodicalIF":5.7,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158338","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 smart grid operations through dynamic digital twin models and deep learning","authors":"Guilin He,&nbsp;Min Lei,&nbsp;Lei Han,&nbsp;Peifa Shan,&nbsp;Ruipeng Chen","doi":"10.1016/j.suscom.2025.101200","DOIUrl":"10.1016/j.suscom.2025.101200","url":null,"abstract":"<div><div>Adopting the dynamic digital twin (DDT) model in smart grid distribution networks is a revolutionary breakthrough toward advanced dynamic energy management and control. However, even the most advanced systems fail to describe static architectural configuration adequately or they do not offer sufficient automation in this process, they are unable to handle dynamic interactions or topological hierarchy. To overcome such restrictions, this research presents a new framework for building DDT models based on Graph Neural Networks (GNNs). GNNs outperform other deep learning models when it comes to modeling graph-structured data which has application in modeling nodes and edges of smart grids. The adopted model expands the critical technical parameters' achievements and indicates a high Voltage Regulation Efficiency of 92 % and Network Efficiency belonging to 95 %; therefore, the distribution of power and operation reliability is considered optimal. The advantage of these findings is also echoed by the Voltage Profile Deviation of 0.015 p.u. and the Power Loss Reduction of 18.3 % which suggest that the proposed method offers better voltage profile stability and less energy losses than existing static models. The usefulness and applicability of the framework can be shown by performing experiments in MATLAB Simulink and Python-based libraries such as PyTorch Geometric. This study provides a novel approach to address issues in applied research and provides the basis for further advancements in realistic digital twin applications concerning smart grids.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101200"},"PeriodicalIF":5.7,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096416","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 communication in WSNs using ABCP: An Aurora and quantum tunneling approach","authors":"Salim El khediri ,&nbsp;Pascal Lorenz","doi":"10.1016/j.suscom.2025.101202","DOIUrl":"10.1016/j.suscom.2025.101202","url":null,"abstract":"<div><div>Cluster-based routing has been effective for facing the unique problems of Wireless Sensor Networks such as handling energy consumption and forwarding data in large, limited resource environments. Based on how the Aurora Borealis changes over time, this paper proposes the Aurora-Based Clustering Protocol which relies on virtual electrical drift and quantum tunneling to select flexible clusters and their heads. According to ABCP, a sensor node is represented by a charged particle and its virtual charge is measured by considering remaining energy and nearby data amounts. Nodes in the network are linked by streamlines created with magnetic-inspired methods and cluster heads are selected randomly using a fitness model that aims for both balance and central locations. It offers support for changing network arrangements and arranges paths so that communication is efficient wherever and whenever users move. ABCP was tested by running multiple simulations with a network of 300 nodes which reflects how a WSN might be used in real life. Against standard approaches such as LEACH, BeeCluster, iABC and PSO-based schemes, ABCP saves up to 28.7% more energy and adds at least 17.4% to the network’s lifetime under varying and densely packed node conditions.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101202"},"PeriodicalIF":5.7,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096418","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":"Automated deep learning and Internet of Things framework for building energy management: A university case study","authors":"Deepshikha Shrivastava ,&nbsp;Prerna Goswami","doi":"10.1016/j.suscom.2025.101198","DOIUrl":"10.1016/j.suscom.2025.101198","url":null,"abstract":"<div><div>Monitoring energy consumption in buildings presents significant opportunities, especially in developing economies like India. However, current solutions often overlook cost-effective, small-scale, accurate, and open-source data-driven methodologies. Research in this area is often hindered by concerns related to security and privacy, high investment costs, and unpredictable returns. To address these challenges, we developed an automated hybrid deep learning and Internet of Things (DL-IoT) building energy management system (BEMS) aimed at conserving energy. The DL-IoT combines deep learning techniques with fuzzy logic to effectively manage uncertainty and noise in electrical properties. Our DL-IoT regression model demonstrated low mean absolute error and mean squared error, achieving a coefficient of determination of 0.99 for out-of-sample energy consumption predictions. We extracted twenty-seven electricity usage variables from raw data to train the model. Experimental results revealed a linear relationship between these characteristics and energy use. The proposed model successfully predicted features that could contribute to energy savings, such as Power Factor and Power in the Y Phase. Specifically, it estimated that a one-unit increase in Power in the Y Phase and Power Factor would result in a reduction in energy consumption. The findings of the experiment indicated that the model captured the variability of the data better than other models. The results demonstrated the superiority of the proposed model over other mainstream existing models. Through the results of this paper, a more efficient energy data management and consumption plan can be established.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101198"},"PeriodicalIF":5.7,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109549","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 low-latency and area-efficient QCA-based quantum-dot design for next-generation digital sustainable systems","authors":"Muhammad Zohaib ,&nbsp;Seyed-Sajad Ahmadpour ,&nbsp;Hadi Rasmi ,&nbsp;Angshuman Khan ,&nbsp;Nima Jafari Navimipour","doi":"10.1016/j.suscom.2025.101204","DOIUrl":"10.1016/j.suscom.2025.101204","url":null,"abstract":"<div><div>Digital sustainable system plays a vital role in the advancement of dynamic industries, including agriculture, healthcare, smart cities, Edge Artificial Intelligence (AI), and the Internet of Things (IoT), by facilitating high-speed, low-power, and highly compressed processing. These systems are based on the capabilities of real-time execution, processing, and analysis of large-scale information with extreme power and area limitations. However, traditional Arithmetic Logic Units (ALUs) based on complementary metal-oxide semiconductors (CMOS) are becoming challenging in terms of scalability, power consumption, space demand, and nanoscale fabrication. The ALU is one of the most important parts of such systems and has a direct effect on the overall computing performance, but current implementations cannot sustain the requirements of next-generation applications. To overcome these shortcomings, this paper offers an area-efficient and low-latency ALU that can be designed with the quantum-dot cellular automata (QCA) technology, with the advantage of employing area-efficient layout and simple cell design. The proposed QCA-based ALU has high performance, less delay, and less energy consumption, which makes it properly suitable for the next generation of digital sustainable systems applications. The outcome of the simulation indicates that there are considerable performance gains, such as an 82.37% decrease in energy consumption, and a 9.21% decrease in area relative to current available design. These enhancements emphasize the power of QCA technology as a scalable and low-energy consumption alternative to CMOS in the realization of critical computing components in sustainable digital systems.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101204"},"PeriodicalIF":5.7,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096419","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}