Computer Communications最新文献

{"title":"Perspectives toward biosimilars among oncologists: A Malaysian survey.","authors":"Soon Cien Chong, Retha Rajah, Poh Lee Chow, Hsio Ching Tan, Chin Man Chong, Kar Yee Khor, Wan Ping Lee, Wan Ying Tan","doi":"10.1177/10781552221104773","DOIUrl":"10.1177/10781552221104773","url":null,"abstract":"<p><p>IntroductionBiosimilars confer significant cost-saving advantages and expand patients' access to biologic therapies in cancer care. In line with the increasing availability of antineoplastic biosimilars, it is pertinent to understand the oncologists' view on the adoption of biosimilars in their clinical practice. The study aimed to assess (i) the prevalence of biosimilar use, (ii) perception towards biosimilars, (iii) factors influencing the use of biosimilars and (iv) knowledge about biosimilars among Malaysian oncologists.MethodsA cross-sectional survey was conducted among clinical oncologists and medical oncologists in Malaysia between January 2020 and February 2021 using a structured 31-item questionnaire.ResultsAmong the 121 oncologists registered in the country, 36 responded (response rate  =  30%). A total of 64% of the respondents prescribed biosimilars either often or always. Most oncologists (72%) agreed or strongly agreed that switching will not have a significant effect on the treatment benefit, with lower percentages saying that they agreed or strongly agreed that it will not lead to the emergence of additional adverse effects (56%) or harmful immunogenicity (64%). Patients' preferences (40%) and the non-availability of biosimilars in hospitals (34%) are the major barriers cited to the prescribing of biosimilars. Cost differences and robust pharmacovigilance activities are the two most important factors that would influence the prescribing of biosimilars. The mean score of knowledge in biosimilar among respondents was 3.81 (± 0.86) out of a maximum possible score of 6.ConclusionsThe identified gap in prescribing and the use of biosimilars among Malaysian oncologists warrant educational intervention and robust pharmacovigilance activities to facilitate the prescribing of biosimilars and ultimately increase the accessibility to biologics in cancer treatment.</p>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"12 1","pages":"189-199"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75120995","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 unified utility-based framework for joint scheduling and buffer management in Delay Tolerant Networks","authors":"Tuan Le","doi":"10.1016/j.comcom.2026.108432","DOIUrl":"10.1016/j.comcom.2026.108432","url":null,"abstract":"<div><div>Data delivery in Delay Tolerant Networks (DTNs) is fundamentally constrained by two scarce resources: node buffer space and contact duration. Conventional approaches typically address message scheduling and buffer management as isolated optimization problems, resulting in suboptimal resource utilization where high-utility messages are often dropped to accommodate lower-value traffic. To bridge this gap, this paper proposes a unified, utility-based framework that jointly optimizes these decisions by formulating the contact opportunity as a Multidimensional 0/1 Knapsack Problem (MKP). We derive rigorous, closed-form marginal utility functions for three distinct objectives: maximizing delivery probability, minimizing average latency, and a composite metric balancing both. Unlike static heuristics, these metrics are derived from a deadline-constrained probabilistic model that explicitly quantifies the marginal benefit of replication relative to the message’s remaining Time-to-Live (TTL). To solve the resulting NP-hard joint allocation problem in real time, we introduce a computationally efficient greedy heuristic based on Utility Density. Trace-driven simulations using real-world vehicular mobility datasets (San Francisco and Rome) demonstrate that our unified policy outperforms state-of-the-art baselines, including ReAR and OBSBM. Beyond enabling flexible Quality of Service (QoS) enforcement via a tunable weighting coefficient, the overall analysis demonstrates that the proposed framework effectively resolves resource contention, achieving up to a 40% improvement in Delivery Ratio in sparse environments and a <span><math><mrow><mn>3</mn><mo>×</mo></mrow></math></span> reduction in Average Latency when optimized for speed compared to existing techniques.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"249 ","pages":"Article 108432"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071086","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":"Learning to reallocate: MAPPO-based spectrum and power optimization for UAV–UGV clusters with dynamic reconfiguration","authors":"Wei Wang ,&nbsp;Panfeng He ,&nbsp;Boyu Wan,&nbsp;Yong Chen,&nbsp;Yu Zhang","doi":"10.1016/j.comcom.2026.108434","DOIUrl":"10.1016/j.comcom.2026.108434","url":null,"abstract":"<div><div>This paper proposes a Multi-Agent Reinforcement Learning (MARL) framework for joint spectrum and power optimization in unmanned aerial vehicle (UAV)-unmanned ground vehicle (UGV) clusters during dynamic reconfiguration. The system consists of heterogeneous agents (communication nodes and radars) operating in scenarios with malicious jamming and dynamic inter-cluster node transfers. The joint channel selection and power control problem is formulated as a partially observable Markov decision process (POMDP), with a Multi-Agent Proximal Policy Optimization (MAPPO)-based algorithm developed to address two key challenges: For fixed-topology networks, a MAPPO-based algorithm is proposed to maximum number of operational nodes while avoiding intra-cluster interference; For dynamic reconfiguration scenarios, an estimated maximum node capacity (EMNC)-based algorithm is proposed enabling rapid adaptation to topology changes. Simulation results demonstrate that the proposed approach achieves 93%–97% operational node ratios in static configurations (outperforming baseline methods by 15%–40%) while maintaining 90%–94% operational efficiency during dynamic reconfiguration events — a significant improvement over baseline methods that typically suffer 20%–30% performance degradation during topology changes. The proposed solution uniquely combines real-time decision-making with robust adaptation capabilities, offering a practical approach for resilient resource management in dynamic UAV–UGV networks where conventional methods fail to address both dynamic reconfiguration and adversarial interference simultaneously.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"249 ","pages":"Article 108434"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071112","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":"FMSformer: Frequency-aware multi-scale spatio temporal correlation model for edge workload prediction","authors":"M.F. Sun,&nbsp;X.H. Zhao,&nbsp;J.K. Li,&nbsp;W.Q. Liu,&nbsp;Q.T. Wu","doi":"10.1016/j.comcom.2026.108454","DOIUrl":"10.1016/j.comcom.2026.108454","url":null,"abstract":"<div><div>Accurate workload prediction enables better service deployment and mitigates the risk of disruptions and performance degradation caused by limited resources. Existing workload prediction methods mostly rely on how workload data varies over time, while neglecting spatial dependencies and multi-scale interactions among edge servers, which results in degraded accuracy and robustness of the workload prediction. To solve this problem, we propose FMSformer, a frequency-aware multi-scale spatial temporal correlation model, which simultaneously models multi-scale temporal dynamics and spatial dependencies among edge servers. Specially, it jointly models cross-server collaborative evolution at multiple time scales by grouping related servers to capture spatial dependencies, while simultaneously extracting multi-scale temporal correlations within each individual server. Furthermore, we extract high energy frequency components to model global dependencies. Finally, a time–frequency mixed loss is employed to avoid label dependence, thereby improving prediction accuracy. Experiments on three real datasets show that FMSformer reduces Mean Absolute Error by 17.4% and Mean Squared Error by 8.4% compared to state-of-the-art models, demonstrating the effectiveness of method for complex edge workload prediction.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"249 ","pages":"Article 108454"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174317","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 survey on CSI-based Wi-Fi sensing datasets and models with a focus on reproducibility","authors":"Idio Guarino ,&nbsp;Damiano Carra ,&nbsp;Marco Cominelli ,&nbsp;Francesco Gringoli ,&nbsp;Renato Lo Cigno","doi":"10.1016/j.comcom.2026.108431","DOIUrl":"10.1016/j.comcom.2026.108431","url":null,"abstract":"<div><div>Wi-Fi sensing based on Channel State Information (CSI) has witnessed considerable research activity in recent years. However, a critical literature analysis reveals that only a limited amount of proposals are potentially reproducible, with many works lacking essential experimental details, publicly available datasets, or accessible analysis code. This may impede the research progress and the subsequent transition of promising findings into practical applications. The objective of this work is to identify CSI-based sensing proposals that are potentially reproducible based on the published information. Our goal is to provide a focused review of resources that can serve as a concrete starting point for researchers and practitioners seeking to experiment with and advance the field of Wi-Fi sensing. We perform a comprehensive analysis of publicly available datasets (encompassing both the collection methodologies and the environmental characteristics) and existing sensing models, accompanied by their code, pre-processing steps, and evaluation procedures. Finally, we discuss what are the minimum requirements for truly verifiable contributions in this field, and outline the best practices for creating and sharing reproducible CSI-based sensing datasets and models.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"249 ","pages":"Article 108431"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174821","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":"Dynamic resource allocation for digital twin-enhanced hierarchical federated learning in sustainable internet of things","authors":"Ze Wei ,&nbsp;Rongxi He ,&nbsp;Xiaojing Chen ,&nbsp;Chengzhi Song","doi":"10.1016/j.comcom.2025.108410","DOIUrl":"10.1016/j.comcom.2025.108410","url":null,"abstract":"<div><div>This paper proposes a digital twin (DT)-enhanced hierarchical federated learning framework for sustainable Internet of Things (IoT) networks. In this framework, mobile edge computing servers coordinate collaborative training, while DTs maintain real-time physical-virtual synchronization. Our core contributions are threefold. First, to tackle device heterogeneity, we propose two mechanisms: (1) an elastic time window that dynamically adapts aggregation deadlines based on median training times while incorporating distance-aware resource compensation to mitigate channel degradation, and (2) a DT-enhanced weighting strategy that dynamically balances energy sustainability, channel quality, and model freshness while guaranteeing convergence through closed-loop cross-layer coordination. Second, we derive a convergence bound explicitly linked to the device participation ratio, establishing a direct theoretical connection between resource allocation and learning performance. Then, through theoretical analysis, it can be found that reducing training latency and energy consumption by jointly optimizing computing and communication resources, as well as EH duration, is key to maximizing this ratio without compromising the reliability of the gradients, thereby indirectly enhancing convergence. Third, guided by this insight, we formulate a mixed-integer nonlinear programming problem that aims to maximize the participation ratio while jointly minimizing energy consumption and training latency, by optimizing the energy harvesting time, collaboration ratio, and communication/computation resources. To solve this NP-hard problem, we propose a DT-driven decomposition framework that partitions it into two subproblems, which are then solved by three DT-driven algorithms with provable near-optimality guarantees. Experimental results validate the superiority of our approach, demonstrating significant improvements in convergence performance, latency, energy efficiency, and participant sample rate, while also advancing the sustainability of FL.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"248 ","pages":"Article 108410"},"PeriodicalIF":4.3,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929270","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":"Power prediction and energy aware placement of containers over virtual machines","authors":"Rafael Albuquerque,&nbsp;Brigitte Jaumard","doi":"10.1016/j.comcom.2025.108340","DOIUrl":"10.1016/j.comcom.2025.108340","url":null,"abstract":"<div><div>The rapid expansion of 5G and the upcoming arrival of 6G have significantly increased the demand for cloud computing resources, especially in edge cloud servers, to meet stringent connectivity and latency requirements. This surge has raised serious energy concerns as data centers now account for about 1–1.5% of global energy consumption and contribute about 1% of global CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> emissions. In response to these facts, this study proposes a novel energy-aware machine learning model, using power sensor data from physical machines (PMs) in data centers, to optimize energy consumption while managing container placement as a use case.</div><div>We conducted experiments in a testbed using realistic 5G traffic scenarios, deliberately avoiding artificial stressors such as stress-ng, which create synthetic loads that do not accurately reflect real-world resource utilization. Our machine learning model, particularly the XGBoost implementation, proved to be highly effective, achieving an <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> score of 91.2%. The model demonstrated the ability to reduce energy consumption by 3% and improve task completion times, all without the need for explicit consolidation strategies or cluster reconfiguration.</div><div>This approach highlights the power of machine learning in optimizing energy efficiency in dynamic and resource-intensive environments such as edge cloud servers, providing a scalable solution for data centers facing increasing energy demands.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"248 ","pages":"Article 108340"},"PeriodicalIF":4.3,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145903969","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":"Unmanned aerial vehicle-enabled mobile edge computing for semantic communications","authors":"Liyuan Xie ,&nbsp;Wancheng Xie ,&nbsp;Huabing Lu ,&nbsp;Helin Yang","doi":"10.1016/j.comcom.2026.108411","DOIUrl":"10.1016/j.comcom.2026.108411","url":null,"abstract":"<div><div>With the rapid evolution of wireless networks and the increasing demand for flexible communication and computing services, unmanned aerial vehicles (UAVs) have emerged as a promising solution to enhance the performance of these networks. This paper investigates a UAV-assisted mobile edge computing (MEC) system with semantic communication (SemCom) to improve the efficiency of wireless networks by transmitting only meaningful information, thereby reducing bandwidth and computational resource requirements. We propose a resource scheduling approach to minimize the weighted sum of overall latency for task processing and energy consumption under malicious jamming attacks. The approach jointly optimizes device scheduling, UAV trajectory, task offloading ratio, bandwidth allocation, and the number of transmitted SemCom symbols under different constraints. The optimization problem is complex and non-convex, involving ongoing decision-making due to constantly changing parameters. To address this challenge, we present a proximal policy optimization (PPO)-based deep reinforcement learning (DRL) algorithm for real-time resource management. The proposed PPO-based resource scheduling approach effectively schedules both communication and computing resources to minimize the cost of the UAV-enabled wireless network against jamming attacks. Simulation-based performance analysis indicates that the PPO-based SemCom scheme reduces task execution latency and energy consumption compared to baseline approaches across various network scenarios. The proposed framework provides valuable insights into the design and optimization of UAV-assisted MEC systems with SemCom for enhanced wireless network performance in the presence of adversarial jamming.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"248 ","pages":"Article 108411"},"PeriodicalIF":4.3,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980583","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":"AgriSmart: An IoT-enabled framework for agricultural resource optimization","authors":"Xu Tao ,&nbsp;Jackson Butcher ,&nbsp;Christian Cumini ,&nbsp;Mounica Talasila ,&nbsp;Salmeron Cortasa Montserrat ,&nbsp;Alessio Sacco ,&nbsp;Michael Popp ,&nbsp;Guido Marchetto ,&nbsp;Simone Silvestri","doi":"10.1016/j.comcom.2026.108416","DOIUrl":"10.1016/j.comcom.2026.108416","url":null,"abstract":"<div><div>Efficient use of farming resources (e.g., nitrogen, water, pesticides) is key to maximizing productivity and promoting sustainable agriculture. Traditional methods, such as fixed-rate applications or soil sampling, often fail to adapt to changing in-season conditions and specific nutrient demands, leading to inefficiencies and environmental harm. In this work, we propose <em>AgriSmart</em>, an IoT-enabled framework that optimizes resource application strategies to maximize crop yield while minimizing resource usage within a given budget. AgriSmart formulates an optimization problem solved periodically using an enhanced Differential Evolution (DE) algorithm that balances exploration and exploitation, following a Model Predictive Control (MPC) approach. Crop yield responses to varying application timings and rates are estimated using the process-based crop simulation model DSSAT (Decision Support System for Agrotechnology Transfer). To improve flexibility and reduce computational complexity, we introduce <em>adjustable receding horizon</em> that allows multiple actions to be applied before re-optimization, enabling adaptation to resources with different application frequencies (e.g., water vs. nitrogen). As the time horizon advances, AgriSmart dynamically adjusts the resource applications to better match crop needs at each growth stage, responding to evolving weather and field conditions. We evaluate AgriSmart in two use cases: irrigation scheduling for soybean and nitrogen management for maize. Results show that AgriSmart outperforms existing methods, achieving up to 21.4% water savings for soybean without yield loss, and increasing maize yield by 20% while reducing nitrogen use by up to 32%.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"248 ","pages":"Article 108416"},"PeriodicalIF":4.3,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980582","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":"MoCS: Modular configuration synthesis via large language models and graph neural network-augmented recommendation","authors":"Yuqi Dai ,&nbsp;Hua Zhang ,&nbsp;Jingyu Wang ,&nbsp;Jianxin Liao","doi":"10.1016/j.comcom.2026.108428","DOIUrl":"10.1016/j.comcom.2026.108428","url":null,"abstract":"<div><div>Network configuration synthesis is essential for automated configuration management in large and complex networks. However, existing synthesizers face challenges in practical applications, including limited scalability, slow synthesis speed, insufficient support for various routing protocols, and difficulty in handling mixed vendor configurations.</div><div>To address these issues, this paper proposes MoCS, a modular configuration synthesizer that integrates multiple Large Language Models (LLMs) with Graph Neural Network (GNN)-enhanced recommendations to enable protocol-agnostic and vendor-compliant configuration synthesis. MoCS decomposes the synthesis pipeline into three LLM-based modules, each following a unified prompt engineering framework with task-specific adaptations. Specifically, the Intent Translation Module (IT-Module) translates natural language intents into structured configuration tasks, while the Configuration Graph Generation Module (CG-Module) constructs a Configuration Knowledge Graph (CKG) by incorporating semantic information from network topologies, structured tasks, and vendor-specific configuration templates. These two modules collaborate to support various protocols and mixed vendor configurations via a unified graph representation. The Configuration Recommendation Module (CR-Module) utilizes a heterogeneous GNN-based model (HGAT-CR) to perform type-aware reasoning over the CKG and generate top-<span><math><mi>k</mi></math></span> candidate parameters. These candidates provide prior knowledge that narrows the search space and improves recommendation accuracy. Finally, they are refined through an LLM-guided optimization mechanism that combines formal verification feedback to produce the final configuration, ensuring maximal intent satisfaction while minimizing side effects.</div><div>Our evaluation demonstrates that MoCS outperforms existing synthesizers, including NetComplete, INCS, and ConfigReco. In large networks with complex intents, MoCS achieves a high coverage rate (88.23 ± 1.12%), low redundancy rate (7.89 ± 1.59%), perfect intent satisfaction rate (1.00 ± 0.00), and reasonable runtime (143.83 ± 21.89s). Furthermore, MoCS can synthesize mixed vendor configurations, which current synthesizers cannot handle.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"248 ","pages":"Article 108428"},"PeriodicalIF":4.3,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980586","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}