Sustainable Computing-Informatics & Systems最新文献

{"title":"Sustainable cybersecurity solutions for smart cities: Decentralized and resource-efficient architectures","authors":"R. Rajalakshmi ,&nbsp;Bhuvan Unhelkar ,&nbsp;Siva Shankar","doi":"10.1016/j.suscom.2025.101280","DOIUrl":"10.1016/j.suscom.2025.101280","url":null,"abstract":"<div><div>The rapid growth of smart cities, powered by Internet of Things (IoT) technologies, demands robust, energy-efficient, and scalable cybersecurity solutions. As urban-scale systems increasingly depend on massive networks of sensors and edge devices, ensuring secure and sustainable communication becomes a critical challenge. This research presents a framework for Sustainable Cybersecurity Solutions, emphasizing Decentralized and Resource-Efficient Architectures for energy-optimized security in smart environments. Research proposes an Intelligent and Secure Edge-Enabled System (ISEC) model that integrates Green IoT, edge computing, and artificial intelligence (AI) to achieve secure, low-latency data transmission. It uses the smart city IoT and edge network dataset, applying Z-score normalization during preprocessing to standardize features and improve model performance, followed by Linear Discriminant Analysis (LDA) for feature extraction to maximize discriminative information and reduce dimensionality, thereby improving detection accuracy. The framework employs deep learning-based Termite Colony Optimizer-Driven Stacked Bidirectional Long Short-Term Memory (TCO-Stacked BiLSTM) to identify optimal routes and predict potential threats, enabling real-time threat detection and mitigation across the network. Edge computing decentralizes processing closer to IoT nodes, minimizing latency and reducing energy use. The ISEC model leverages this structure to avoid centralized bottlenecks, therefore embodying a resource-efficient architecture that balances security, computation, and power consumption. Low-powered sensors are supported through optimized routing protocols and lightweight security mechanisms, which reduce processing load and communication overhead. Experimental analysis shows the proposed TCO-Stacked BiLSTM model achieves accuracy, precision, recall, and F1-score ranging between 94 % and 98 %, along with reductions in energy consumption, latency, improvements in throughput, and significant enhancements in reliability, demonstrating efficient, low-latency, and resource-conscious performance for smart city IoT networks. These results confirm the efficiency and scalability of the model. Overall, the proposed solution provides a sustainable, secure, and resource-conscious framework, well-suited to the demands of modern smart cities and future urban digital infrastructures.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"49 ","pages":"Article 101280"},"PeriodicalIF":5.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145749257","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 optimization of peer-to-peer energy exchanges in microgrids using blockchain-enabled federated learning","authors":"Raenu Kolandaisamy ,&nbsp;A. Arun Kumar ,&nbsp;A.N. Sasikumar ,&nbsp;V. Sivakumar ,&nbsp;G.K. Kamalam ,&nbsp;M.K. Mohamed Faizal ,&nbsp;Muhammad Mohzary","doi":"10.1016/j.suscom.2025.101290","DOIUrl":"10.1016/j.suscom.2025.101290","url":null,"abstract":"<div><div>The growth of the penetration of distributed renewable resources has enhanced the need to have secure, scalable, and energy-efficient P2P (peer-to-peer) energy trading in microgrid settings. Conventional centralized market systems and aggregator-based trading systems can be limited by privacy leakage, high costs of communication, and single points of failure, which restrict them in the scalability of decentralized environments. The proposed blockchain-enabling federated learning (FL) framework that is suggested in this paper will aim to optimise P2P energy exchanges in microgrids to mitigate the limitations of the existing approach. Smart meters based on the Internet-of-Things record data on generation and consumption, which is locally computed to learn federated models without revealing raw information about users. The FL module uses gradient sharing together with adaptive aggregation to optimize the exchange decisions among prosumers, and the blockchain component can guarantee trade settlement with tamper-proof and the contract enforcement through proof-of-stake consensus mechanism that is based on lightweight cryptography. Moreover, edge-level compression of data and incentive sensitive participation schemes minimize communication costs and encourage fair participation in the market. Experiments of performance measurements on a MATLAB/Simulink-TensorFlow co-simulation model with Hyperledger Fabric show trading efficiency of 95.7 % and 27.6 % energy savings improvement, throughput of 174 transactions per second and 31 % communication overhead reduction compared to centralized and non-blockchain FL schemes. The findings support the ability of the proposed system to provide privacy preserving, energy saving, and safe P2P energy transactions in next-generation decentralized microgrid ecosystems.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"49 ","pages":"Article 101290"},"PeriodicalIF":5.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938426","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 cooperative multi-objective evolutionary and deep reinforcement learning framework for sustainable distributed scheduling with preventive maintenance","authors":"Yanan Wang,&nbsp;Yuyan Han ,&nbsp;Yuting Wang ,&nbsp;Biao Zhang ,&nbsp;Leilei Meng","doi":"10.1016/j.suscom.2025.101266","DOIUrl":"10.1016/j.suscom.2025.101266","url":null,"abstract":"<div><div>Driven by global competition and energy efficiency demands, intelligent scheduling is critical for sustainable distributed manufacturing. This paper addresses the distributed group scheduling problem with preventive maintenance (DFGSP_PM) by establishing a threshold-based maintenance-triggered mathematical model. To effectively solve this problem, a novel Deep Q-network-based Cooperative Multi-objective Optimization Evolutionary algorithm (DQN-CMOEA) is proposed. The innovations lie in a DQN-driven adaptive strategy selection mechanism, a multi-population co-evolution framework for enhanced exploration, a maintenance-aware multi-phase energy-saving strategy for reducing idle-time energy waste, and a composite convergence-diversity indicator for promoting a well-distributed and high-quality Pareto front. Extensive experiments on 405 benchmark instances show that DQN-CMOEA significantly outperforms four state-of-the-art algorithms across multiple metrics, demonstrating its effectiveness and robustness in solving complex distributed scheduling problems.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"49 ","pages":"Article 101266"},"PeriodicalIF":5.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145749255","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":"Balancing carbon footprint and algorithm performance in recommender systems: A comprehensive benchmark","authors":"Giuseppe Spillo ,&nbsp;Allegra De Filippo ,&nbsp;Cataldo Musto ,&nbsp;Michela Milano ,&nbsp;Giovanni Semeraro","doi":"10.1016/j.suscom.2025.101286","DOIUrl":"10.1016/j.suscom.2025.101286","url":null,"abstract":"<div><div>In this paper, we present a reproducible pipeline to benchmark the trade-off between carbon emissions and recommendation performance across 14 algorithms and three publicly available datasets. In particular, we contribute: <em>(a)</em> a standardized protocol to account for carbon emissions of recommendation algorithms; <em>(b)</em> an empirical quantification of the carbon cost of hyperparameter tuning, and <em>(c)</em> an evaluation of data-reduction strategies as a low-cost approach to reduce emissions while improving certain non-accuracy metrics. Unlike previous literature, which mainly focused on the trade-off between performance and emissions, our benchmark reveals the cost of hyperparameter tuning. It examines the impact of data reduction techniques on the path toward sustainability-aware recommender systems. Our results show that simpler algorithms often deliver competitive accuracy at significantly lower emissions, and that exhaustive tuning can dramatically increase carbon costs with limited accuracy gains. Generally speaking, this study aims to discuss the challenges of energy consumption in recommender systems and to develop a new generation of algorithms that prioritize sustainability. All code and experiment traces are publicly released for reproducibility on Github.<span><span>¹</span></span></div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"49 ","pages":"Article 101286"},"PeriodicalIF":5.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884052","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":"AquaSense-AMC: An adaptive modulation-control model for energy-efficient communication in underwater IoT networks","authors":"S.R. Janani ,&nbsp;Poornima. S ,&nbsp;Aruna R ,&nbsp;Chandru Vignesh C","doi":"10.1016/j.suscom.2025.101263","DOIUrl":"10.1016/j.suscom.2025.101263","url":null,"abstract":"<div><div>The Internet of Underwater Things (IoUT) is revolutionizing maritime research, environmental monitoring and intelligent aquatic applications with real-time sensing and communication. Energy-efficient communication is the biggest challenge because of the constraint of bandwidth, long delay of propagation and highly dynamic water channels. To combat these issues, AquaSense-AMC is introduced a new Adaptive Modulation-Control (AMC) Model for underwater IoT networks. The framework provides three novel components: Channel-Aware Modulation Switching (CAMS), dynamically switching between modulation depth and symbol rate based on channel fluctuations; Energy-Constrained Control Mechanism (ECCM), energy-optimizing transmission power with predictive energy management to maximize node lifetime; and Hybrid Acoustic-Optical Relay (HAOR), a two-mode relay scheme that integrates acoustic links for extreme distance reliability with optical links for high-rate near-distance data transfer. Experimental assessments prove that AquaSense-AMC saves energy by 28 %, enhances packet delivery ratio by 35 % and increases network lifetime by 22 % relative to baseline methods. The model implements a sustainable and adaptive communication system and making IoUT operation reliable and energy-efficient in intricate underwater environments.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"49 ","pages":"Article 101263"},"PeriodicalIF":5.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884065","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":"Cybersecurity meets carbon neutrality: Strategies for sustainable data center security operations","authors":"Thomas Samraj Lawrence ,&nbsp;Martin Margala ,&nbsp;Siva Shankar S ,&nbsp;Prasun Chakrabarti ,&nbsp;Nagarajan G","doi":"10.1016/j.suscom.2025.101281","DOIUrl":"10.1016/j.suscom.2025.101281","url":null,"abstract":"<div><div>Data center security is enhanced with the rapid deployment of AI and ML in cybersecurity, yet energy use and carbon emissions have also increased with this development. In large-scale data center operations, this dual issue emphasizes the need for techniques that advance carbon neutrality while ensuring strong security. This research aims to design a sustainable cybersecurity framework that enhances computational performance while reducing environmental impact through energy-efficient modeling and optimization. This hybrid approach proposed an Oneclass Support Vector Based Bidirectional Snow Geese Algorithm (OSV-Bi-SGA). Carbon aware-cybersecurity traffic datasets are preprocessed through data cleaning, Z-score normalization, and categorical encoding to ensure robust input for modeling. Feature extraction is conducted using principal component analysis (PCA). The proposed <strong>OSV-Bi-SGA method is integrated with Bidirectional Long Short-Term Memory (BiLSTM), which</strong> captures temporal bidirectional dependencies in traffic sequences. <strong>Oneclass support vector machine (OSV) identifies</strong> anomalies when only normal class data is available. <strong>Snow Geese Algorithm (SGA)</strong> enhances parameter optimization, reducing energy cost while maintaining performance. The suggested OSV-Bi-SGA model achieved a high precision (99.42 %), recall (99.24 %), and F1-score (99.32 %), while reducing energy consumption and carbon footprint compared to baseline models. The research demonstrates that integrating evolutionary optimization with deep learning (DL), machine learning (ML) and anomaly detection can balance high-performance cybersecurity with reduced environmental impact. The OSV-Bi-SGA framework provides a promising pathway for sustainable and carbon-neutral data center security operations.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"49 ","pages":"Article 101281"},"PeriodicalIF":5.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145749253","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 implementation of machine learning models for predicting CO2 emission in carbon capture and storage systems","authors":"Zixiang Xu ,&nbsp;Xiaokai Zhou ,&nbsp;Yishan Wang","doi":"10.1016/j.suscom.2025.101264","DOIUrl":"10.1016/j.suscom.2025.101264","url":null,"abstract":"<div><div>Although Carbon Capture and Storage (CCS) systems are essential for lowering CO₂ emissions worldwide, their operational complexity and fluctuating performance factors make it difficult to predict emission levels within these systems. This issue is significant since it directly affects maximizing CCS efficiency, lowering environmental risks, and directing investment and policy choices related to climate mitigation. This research uses six machine learning (ML) models, Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost), all of which are optimized employing GridSearchCV and the Hybrid Kepler Optimization Algorithm (HKOA) to solve this issue and provide a thorough framework for the prediction of CO₂ emissions within CCS systems. According to the robust metrics’ results, the HKOA-optimized XGBoost model outperformed all others, achieving the lowest error rates (MAE = 22.6 train, 47.5 test; RMSE = 42.1 train, 196.0 test), R² values of 0.9994 (training) and 0.9879 (testing), and an objective function (OBJ) value of 15385.2 in testing. Moreover, it achieved a high A20 accuracy (≥72 %) and low uncertainty index (U95), underscoring its robust generalization and minimal deviation under uncertainty. \"Gas\" and \"activity\" were identified as important predictors by SHAP and feature importance analyses. These results not only show how well tree-based ensemble models predict emissions, but they also provide useful tools for practical use in CCS planning, monitoring, and optimization, which helps create more sustainable and profitable carbon management plans.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"49 ","pages":"Article 101264"},"PeriodicalIF":5.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145749254","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":"E2SRP: Energy efficient secure routing protocol for edge-assisted wireless sensor networks","authors":"T.N. Prabhu ,&nbsp;C. Ranjeeth Kumar ,&nbsp;B. Sabeena ,&nbsp;Huda Fatima","doi":"10.1016/j.suscom.2025.101287","DOIUrl":"10.1016/j.suscom.2025.101287","url":null,"abstract":"<div><div>Wireless Sensor Networks (WSNs) enable large-scale data collection through spatially distributed sensor nodes but face challenges due to limited energy, computational capacity, and security vulnerabilities. Traditional routing protocols optimized for wired networks are unsuitable for such constrained environments. This paper presents an Energy Efficient Secure Routing Protocol (E2SRP) for edge-assisted WSNs, addressing both energy consumption and security concerns. Trust values are computed using the Analytical Hierarchy Process (AHP) based on Direct, Indirect, and Witness Trust metrics to ensure reliable node selection. Optimal routing paths are identified through the Grey Wolf Optimization (GWO) algorithm, while BLAKE3 hashing performs secure data aggregation and deduplication at the edge. Additionally, Fuzzy Intelligence supports load balancing to enhance system stability. Simulation results using NS-3 demonstrate that the proposed model significantly improves Packet Delivery Ratio (PDR), reduces energy consumption and routing overhead, and strengthens overall security resilience compared with existing methods.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"49 ","pages":"Article 101287"},"PeriodicalIF":5.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884064","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":"AI-driven energy management for resilient operation of renewable-powered microgrids","authors":"Zhixiang Dai,&nbsp;Feng Wang,&nbsp;Shaoxiong Zhang,&nbsp;Li Xu","doi":"10.1016/j.suscom.2025.101288","DOIUrl":"10.1016/j.suscom.2025.101288","url":null,"abstract":"<div><div>The microgrids function as distributed energy systems using renewable energy sources such as solar, wind, and energy storage. Microgrids have tremendous possibilities to improve an energy system in terms of resilience and sustainability. This paper essentially deals with describing an AI-based energy management system tailored for optimizing the operation of the renewable energy-powered microgrid. The EMS uses advanced ML techniques consisting of LSTM networks for forecasting, DQN for decision-making, and Random Forest depending upon solar energy forecasting. The models were tested on several benchmark datasets, namely, renewable, solar, wind, and a global energy dataset. According to the results, the model with LSTM gave the best forecasts, particularly for the Wind Energy Dataset (R ² = 0.9955). On the other hand, the highest performance for the Solar Energy Dataset was obtained by a random forest, which gave it a lesser predictive power radius than other techniques (R² = 0.827). The other energy datasets also witness optimal working of LSTM time-series approaches, giving much smaller errors for training and validation phases. Moreover, AI-assisted EMS, given improved energy resource optimization, forecasting, and operational resilience, will play a vital role in managing renewable microgrids under dynamically ever-changing and uncertain environments. This study also endorses the application of AI in furthering and passing good genes of efficiency, sustainability, and fault tolerance into renewable-powered microgrids.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"49 ","pages":"Article 101288"},"PeriodicalIF":5.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938421","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":"Challenges of IoT sensors in smart buildings ecosystems and integration of blockchain for enhanced security and efficiency","authors":"Syed Faisal Abbas Shah ,&nbsp;Tehseen Mazhar ,&nbsp;Muzammil Ahmad Khan ,&nbsp;Shahab Ali Khan ,&nbsp;Najeeb Ullah ,&nbsp;Wasim Ahmad ,&nbsp;Weiwei Jiang ,&nbsp;Habib Hamam","doi":"10.1016/j.suscom.2025.101279","DOIUrl":"10.1016/j.suscom.2025.101279","url":null,"abstract":"<div><div>Integrating Internet of Things (IoT) sensors into smart buildings has completely transformed the way to optimize and manage building management systems, from energy management to enhancing security systems. However, IoT sensors have several challenges in the context of smart buildings. The major challenge is that the vast quantity of data produced by the IoT sensors may outstrip the capabilities of current infrastructure, resulting in data storage, management, and analysis issues. Furthermore, the data generated by IoT sensors can also be unreliable and imprecise due to various environmental conditions, thereby impacting the performance of systems that rely on IoT technologies. Security is also a big challenge, as cyberattacks targeting IoT devices could endanger the confidentiality of building residents and premises. To address these challenges, adopting blockchain technology is a possible solution. Blockchain offers protection by using the decentralized ledger features for data collected from IoT sensors, as it guarantees permanent records are transparent and tamper-proof. This paper aims to highlight the different challenges faced by IoT sensors in smart buildings, and possible solutions are also provided, taking into consideration blockchain technologies. This review provides a detailed analysis of 104 works, synthesizing existing literature to evaluate the challenges of IoT sensors and how Blockchain-based approaches are applied to these challenges. This review addresses a specific gap: the absence of a building-specific, sensor-layer synthesis that systematically links smart-building IoT-sensor challenges to concrete blockchain design choices. Beyond surveying the field, we contribute a structured challenge mechanism mapping covering privacy/security, interoperability, scalability, real-time processing, energy, maintenance, localization, limited AI integration, and false positives to guide the design of secure and efficient building-management systems.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"49 ","pages":"Article 101279"},"PeriodicalIF":5.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145749256","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}