Sustainable Computing-Informatics & Systems最新文献

{"title":"An optimization framework to response flexible energy demand based on target market in a smart grid: A case study of greenhouses","authors":"Mehran Salehi Shahrabi","doi":"10.1016/j.suscom.2025.101163","DOIUrl":"10.1016/j.suscom.2025.101163","url":null,"abstract":"<div><div>Unlike many energy-consuming sectors, greenhouses can operate with varying energy inputs while producing crops of different qualities. Supplying greenhouse energy from the main grid faces two main challenges: fluctuating energy prices throughout the day and the risk of planned or unplanned outages. Similarly, relying solely on renewable energy resources is constrained by their intermittent availability. Consequently, this study investigates energy supply planning for greenhouses with flexible demand by leveraging renewable resources within a smart grid. In this respect, a bi-objective energy planning model is developed for greenhouses, aiming to minimize energy consumption costs and maximize crop quality. This model accounts for variable main grid energy prices, the opportunity to sell renewable electricity back to the grid, and limitations on renewable energy supply during specific hours. The extended epsilon-constraint method solves the model, generating non-dominated points that define various production modes. From these results, 9 distinct production modes are presented, allowing decision-makers to select based on preferences such as desired crop quality levels and/or the quantity of electricity sold to the grid. Furthermore, sensitivity analysis is performed under two scenarios: cost reduction and crop quality improvement. Results for the first scenario show that increasing the electricity selling price reduces production costs and increases the amount sold to the main grid. In the second scenario, a significant 25 % reduction in required energy leads to a substantial decrease in production costs, a key finding of this study.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"47 ","pages":"Article 101163"},"PeriodicalIF":3.8,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596183","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 multi-objective function derived for traffic capacity enhancement using hybrid optimization of energy-aware routing in ad hoc wireless network","authors":"S.D. Nandakumar ,&nbsp;M. Lakshmanan ,&nbsp;V.S. Divya Sundar","doi":"10.1016/j.suscom.2025.101165","DOIUrl":"10.1016/j.suscom.2025.101165","url":null,"abstract":"<div><div>Wireless Adhoc Networks (WANET) undergoes different dynamic environments and have different topologies throughout communication. Routing schemes have enlightened Adhoc communication through a selection of proper paths and increased the efficiency of the communication. With energy-aware routing, the routing path concentrates on establishing the path considering the energy factors of the node and aims to prolong the network lifetime. The shared network bandwidth creates a lot of bottleneck problems and increases the network traffic. Hence, it is necessary to implement an energy-aware routing model in consideration of improved network traffic. Therefore, an effective energy-efficient optimal routing task is implemented to improve the traffic capacity of the Adhoc wireless network. Initially, from the available resources, the necessary data attributes are gathered. Further, the energy-aware optimal routing process is carried out in the Adhoc wireless network by employing Fusion of Snow Leopard Optimization with Lotus Effect Optimization Algorithm (FSLOLEO). Here, the multi-objective functions including residual energy, congestion, link stability, routing overhead, and route quality are considered by the same FSLOLEO for route creation. With the support of this energy-aware optimal routing process, the traffic capacity and lifespan of the network are improved and also the energy consumption is set to be minimized. At last, a detailed performance validation is performed for the designed process by comparing it with the traditional algorithms to prove the designed energy-aware optimal routing mechanism’s efficacy. Here, the developed method achieves a better throughput value of 97 % to prove its optimal energy-aware routing performance in wireless Adhoc networks.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"47 ","pages":"Article 101165"},"PeriodicalIF":3.8,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144695323","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":"Maximizing solar energy harvesting efficiency: Optimal hybrid deep neural learning - based MPPT for Photovoltaic systems under complex partial shading conditions","authors":"SeyedJalal SeyedShenava,&nbsp;Peyman Zare,&nbsp;Iraj Faraji Davoudkhani","doi":"10.1016/j.suscom.2025.101159","DOIUrl":"10.1016/j.suscom.2025.101159","url":null,"abstract":"<div><div>The declining viability of fossil fuels and their adverse environmental impacts are accelerating the global transition to Renewable Energy Sources (RESs), with solar energy emerging as a key pillar due to its versatility and scalability. Photovoltaic (PV) systems enable direct solar-to-electric conversion but face challenges such as nonlinear behavior and multiple Local Maximum Power Points (LMPPs) under Complex Partial Shading Conditions (CPSCs). This study introduces an enhanced Maximum Power Point Tracking (MPPT) method based on a hybrid Artificial Neural Network–Improved Incremental Conductance (ANN-IINC) model. The ANN is trained using representative datasets capturing diverse shading patterns to estimate optimal reference voltages dynamically, while the IINC module accelerates convergence with reduced oscillations. To validate the proposed method, three CPSC scenarios are simulated and compared with traditional perturb and observe and INC techniques, as well as recent metaheuristic optimization algorithms. Sensitivity and descriptive statistical analyses confirm that the ANN-IINC approach not only achieves faster convergence (81.9 ms) and higher tracking accuracy (up to 99.9096 %) but also reduces standard deviation in power output by 11.3 %–14.8 % compared to classical methods. Furthermore, confidence intervals for efficiency are narrowed by over 20 %, demonstrating improved robustness and statistical significance. The method's computational complexity is optimized, maintaining real-time applicability without sacrificing precision. A comprehensive adaptive analysis and hyperparameter sensitivity study further reinforce the superiority and practical relevance of the hybrid architecture. The study offers a scalable, stable, and efficient solution to the MPPT problem under dynamic environmental conditions. These results highlight the ANN-IINC technique’s capacity to outperform both classical and metaheuristic MPPT strategies, contributing meaningfully to the advancement of intelligent PV control under CPSCs.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"47 ","pages":"Article 101159"},"PeriodicalIF":3.8,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144580914","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":"Sustainable IoT solutions: Developing a quantum-aware circuit for improving energy efficiency based on atomic silicon","authors":"Hadi Rasmi ,&nbsp;Seyed Sajad Ahmadpour ,&nbsp;Amir Seyyedabbasi ,&nbsp;Nima Jafari Navimipour ,&nbsp;Wasiq Khan","doi":"10.1016/j.suscom.2025.101161","DOIUrl":"10.1016/j.suscom.2025.101161","url":null,"abstract":"<div><div>Internet of Things (IoT) can be described as a network of physical objects equipped with sensors, processing power, software, and any other types of technology that allows them to communicate and share data with other devices and systems. The proliferation of IoT is conditional on developing energy-saving blocks of computation with sustained connectivity and real-time information processing capabilities. Traditional technologies like CMOS and VLSI circuits face critical failures at scales below 4 nm, including excessive current leakages, high energy consumption, and thermal instability, which make them less appropriate for future micro-scale IoT chips. To overcome such limitations, a new alternative technology called Atomic Silicon Dangling Bond (ASDB) nanotechnology has been developed, leveraging atomistic accuracy in countering CMOS-related inefficiencies and supporting quantum-inspired computational processes. Since Arithmetic and Logic Unit (ALU) is a primary unit of any digital system like IoT, this work introduces the necessity of quantum-aware ALU development, taking a quantum-inspired computational mechanism and leveraging ASDB’s native quantum behavior for increased performance, accuracy, and efficiency in IoT systems. A single-bit ALU for micro-IoT blocks is developed using ASDB nanotechnology with robust computational design to guarantee operational integrity. The design is analyzed through SiQAD simulator in terms of energy consumption, logical accuracy, and area consumption. The proposed ALU in this work demonstrates a reduction in occupied area and quantum cell count, highlighting a significant step toward ultra-dense integration. Furthermore, with an energy consumption reduction of 3.19% compared to the best design, this ALU offers a sustainable and practical solution for low-power IoT applications in the future.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"47 ","pages":"Article 101161"},"PeriodicalIF":5.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722584","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":"Enhancing sustainable agriculture using attention convolutional bidirectional Gated recurrent based modified leaf in wind algorithm: Integrating AI and IoT for efficient farming","authors":"V. Sellam ,&nbsp;N. Kannan ,&nbsp;S. Senthil Pandi ,&nbsp;I. Manju","doi":"10.1016/j.suscom.2025.101160","DOIUrl":"10.1016/j.suscom.2025.101160","url":null,"abstract":"<div><div>Sustainable agriculture is essential for ensuring global food security while mitigating environmental impacts. The possibilities of using remote sensing data and artificial intelligence in agricultural practices emphasize optimizing resource use, minimizing waste, and fostering resilient farming systems to adapt to changing climate conditions in the agriculture field. Multiple studies employed in utilizing remote sensing data and AI for diagnosing disease and environmental monitoring but they face challenges due to factors such as distortions and changes in climates like huge rainfall and extreme droughts affecting the farming environment. Therefore this article develops a novel Attention Convolutional Bidirectional Gated Recurrent based Modified Leaf in Wind Algorithm for assessing the disease of the plants and environmental monitoring. The algorithm leverages diverse datasets including PlantVillage, plantDoc, Soil Type, Advanced IoT Agriculture, and IDADP, and robust data preprocessing techniques such as normalization, standardization, and imbalanced data handling are essential for refining dataset integrity and optimizing model performance. Additionally, the developed model incorporates a convolutional neural network for spatial feature extraction, bidirectional gated-recurrent units for sequential context modeling, and attention mechanisms fuse the Convolutional Neural Network and bidirectional gated-recurrent units, focused on increasing the activity of the proposed network to obtain optimal results, by applying weighting model to each time steps. Moreover, to improve feature integration and optimize model performance, the proposed algorithm incorporates Modified Leaf in Wind optimization strategies. Through experimental validation, the proposed method procures the best performance in four scenarios with a precision of 97.5 % for SC1, 98.5 % for SC2, 96.9 % for SC3 %, and 97.6 % for SC4. The proposed model empowers farmers to make data-driven decisions that enhance productivity.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"47 ","pages":"Article 101160"},"PeriodicalIF":3.8,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144556903","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":"Multi-objective hybrid green anaconda skill optimization enabled energy and cache based QoS aware routing in delay tolerant–IoT network","authors":"Ashapu Bhavani ,&nbsp;Attada Venkataramana ,&nbsp;A.S.N. Chakravarthy","doi":"10.1016/j.suscom.2025.101158","DOIUrl":"10.1016/j.suscom.2025.101158","url":null,"abstract":"<div><div>Delay-Tolerant Network (DTN) is developed to overcome the challenges of environments where classical networking models fail due to unstable connectivity and high latency. The DTN offers stable connections between nodes and operates effectively in scenarios where nodes frequently experience disruptions or only sporadic communication opportunities. However, the classical techniques allowed limited data communication and did not apply to the network with reduced resources and which had low delivery rates and high delays. Therefore, this research aims to develop a Green Anaconda Skill Optimization (GASO) for an eQoS-aware routing solution for a DTN-IoT network. Initially, the DTN-IoT network is simulated by considering energy and mobility models. Then, for predicting the energy, Recurrent Radial Basis Function Networks (RRBFN) is used. After that, Cluster Head (CH) selection is executed by GASO, considering multiple objectives, like cache ratio, residual energy, predicted energy, throughput, distance, trust factors, and delay. Finally, GASO is employed for routing, and the above-mentioned multi-objectives are considered. Here, the GASO is established through the fusion of Green Anaconda Optimization (GAO) and Skill Optimization Algorithm (SOA). The evaluation results highlight that the GASO accomplished a reduced distance of 0.253 m, low energy consumption of 0.783 J, and minimal delay of 0.270 sec, with an increased throughput of 0.313 Mbps.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"47 ","pages":"Article 101158"},"PeriodicalIF":3.8,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534814","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":"eSMARTGreen (ESG): A scalable IoT-Based architecture for multi-greenhouse management","authors":"Fatima Abou-Mehdi-Hassani ,&nbsp;Atef Zaguia ,&nbsp;Darine Ameyed ,&nbsp;Hassan Ait Bouh ,&nbsp;Abdelhak Mkhida","doi":"10.1016/j.suscom.2025.101152","DOIUrl":"10.1016/j.suscom.2025.101152","url":null,"abstract":"<div><div>Concerns about agricultural productivity and sustainability have driven the need for smart greenhouse architectures. However, significant challenges remain in ensuring seamless data exchange, interoperability, and efficient management across multiple greenhouses. This paper introduces the eSMARTGreen (ESG) model, a novel IoT-based smart greenhouse architecture designed for scalable multi-greenhouse management. ESG features fault-tolerant, modular, and flexible deployment strategy, integrating a robust decision-making system and an interoperable framework aligned with ISO/IEC 30141 standards. The ESG model was validated through a simulation conducted using CPN Tools across a network of five greenhouses. Performance metrics showed low average latencies (19–25 ms) and reception rates of up to 72 %, confirming ESG’s responsiveness and communication efficiency under diverse operational conditions. By facilitating seamless coordination and automation, ESG contributes to greater efficiency and sustainability in smart farming. Future applications of ESG could include predictive maintenance, adaptive climate control, large-scale deployment in agricultural clusters, and integration with renewable energy systems to further enhance sustainability and operational efficiency.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"47 ","pages":"Article 101152"},"PeriodicalIF":3.8,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472260","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 cloud-edge collaborative model integrating digital twins and machine learning for scalable and adaptive distributed networks","authors":"Lei Han,&nbsp;Min Lei,&nbsp;Guilin He,&nbsp;Yangyang Li,&nbsp;Yaopeng Zhao","doi":"10.1016/j.suscom.2025.101157","DOIUrl":"10.1016/j.suscom.2025.101157","url":null,"abstract":"<div><div>The exponential growth of distributed networks, as seen in smart grids, IoT, and industrial automation, have added to the demands for effective and adaptive optimization systems. Traditional cloud solutions, while successful in providing global insights and scalability, often suffer from high latency and limited responsiveness, whereas edge-based models excel at instant decision making but lack global synergy and scale. In an effort to overcome these constraints, this paper proposes a novel Cloud-Edge Collaborative Optimization Framework, which leverages the latest machine learning and digital twin algorithms, to scale up distribution networks. The model relies on Long Short-Term Memory (LSTM) networks at the edge layer to forecast traffic in real time and make local decisions, and Multi-Agent Reinforcement Learning (MARL) at the cloud layer to coordinate resources across the globe. Digital twins facilitate real-time flexibility, dynamic simulation and feedback for continuous improvement. This proposed model was extensively tested against actual network datasets. We noted a 50 % reduction in latency compared to cloud-only architectures, with latency on average, baselined at 35.34 ms, reduced to 17.67 ms; additionally, we noted 23 % more resource utilization compared to edge-only setups based on the average of 10 simulation runs. We had real world IoT traffic data for the experimentation with throughput of 50–100 Mbps and PDR greater than 90 % (consistently), which demonstrates that the network operated robustly under changing conditions; we averaged the results for reliability and significance. This study provides an ideal foundation for future work on digital-twin-enhanced cloud-edge architectures.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"47 ","pages":"Article 101157"},"PeriodicalIF":3.8,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472259","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 LIME-LSTSNM approach based green building sustainability prediction using BIM design","authors":"Yan Xia ,&nbsp;Yaning Li ,&nbsp;Siqin Liu","doi":"10.1016/j.suscom.2025.101155","DOIUrl":"10.1016/j.suscom.2025.101155","url":null,"abstract":"<div><div>This research presents a climate change-based parameter optimisation approach for sustainable green building design. The process begins with a Building Information Modeling (BIM)-based design, followed by a Design-Builder simulation. Climatic data is collected and pre-processed, and building parameters are optimized using SA2O, considering this data. BIM-based building parameters and the optimized data are then extracted. The simulation output, along with sensor and historical data, are fused using the Multiresolution Kalman Filter (MKF) technique. Incomplete data is handled with Penalized K-Log Euclidean Neighbor (PKLEN), followed by season-based grouping using KMA. Non-linear dynamics are analyzed, and features are extracted from both the grouped and non-linear data. The sustainability factor is predicted using Local Interpretable Model-agnostic Explanations (LIME), with Long Short-Term Skip Norm Memory (LSTSNM), and feedback is provided to optimise the building parameters for sustainable green building design. Experimental results show that this model achieved an accuracy of 98.24 %, demonstrating the effectiveness of the proposed approach in enhancing sustainability in building design while considering climate change.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"47 ","pages":"Article 101155"},"PeriodicalIF":3.8,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144501505","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":"Decentralized energy-efficient microgrid control Using Graph neural networks and LSTM-based Event-Triggered control","authors":"Xiaoqian Meng,&nbsp;Yajie Zhao,&nbsp;Sijia Zheng,&nbsp;Zi Ye,&nbsp;Heping Wang","doi":"10.1016/j.suscom.2025.101154","DOIUrl":"10.1016/j.suscom.2025.101154","url":null,"abstract":"<div><div>As microgrid systems become more complex and interconnected, traditional control strategies face significant challenges in terms of scalability, efficiency, and responsiveness. Existing models, often relying on time-triggered approaches, result in excessive communication, energy waste, and slower system responses. The main purpose of this work is to formulate a decentralized control architecture that communicates better, regulates voltage and frequency, and stabilizes the microgrids. To address these limitations, this research introduces an innovative decentralized control framework that combines Graph Neural Networks (GNNs) and Long Short-Term Memory (LSTM) networks, integrated with Event-Triggered Control to optimize microgrid operations. This methodology applies GNNs to capture the spatial dependencies among microgrid components like generators, storage, and loads. Meanwhile, the LSTMs identify the temporal dynamics associated with variations in load and generation. System control actions are then triggered only when necessary, hence reducing communication overhead considerably. The results demonstrates 55 % less communication load was reported, voltage regulation accuracy increased by 45 %, and other efficiency measures for frequency regulation improved by 35 %. Along with these, other performance metrics indicate a 30 % improvement of the Voltage Stability Index (VSI) going from 0.47 to 0.33 and lowering the Frequency Regulation Error (FRE) by 20 % from 4.5 % to 3.6 %. All of which consolidated the evidence of the efficiency of the approach suggested to control microgrid operations in a real-time adaptive energy-efficient manner. These findings highlight the powerful combination of GNNs and LSTMs for achieving adaptive, energy-efficient, and real-time control in decentralized microgrid systems.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"47 ","pages":"Article 101154"},"PeriodicalIF":3.8,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144479965","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}