Swarm and Evolutionary Computation最新文献

{"title":"A finite-element dual-level contextual informed neural network with swarm space hopping algorithm based optimal feature selection and detection for EEG-based epileptic seizure detection","authors":"Nagarjuna Tandra ,&nbsp;Nikhat Akhtar ,&nbsp;K Padmanaban ,&nbsp;L. Guganathan","doi":"10.1016/j.swevo.2025.102072","DOIUrl":"10.1016/j.swevo.2025.102072","url":null,"abstract":"<div><div>This study proposes a novel approach for Electroencephalogram (EEG) based Epileptic Seizure Detection (ESD) using a Finite-Element Dual-Level Contextual Informed Neural Network (AFi-EDLCINNet) integrated with the Swarm Space Hopping Algorithm (SSHA). The approach addresses the challenges of contextual sensitivity and computational efficiency in current ESD methods. Raw EEG signals from the Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) and Bonn datasets are preprocessed with Weighted Guided Image Filtering and Entropy Evaluation Weighting (WGIF-EEW) to eliminate noise and ensure signal clarity. Feature extraction is performed using Multi-Discrete Wavelet Transform (MDWT) to capture critical patterns. A hybrid optimization method combining the White Shark Optimizer and Brown Bear Optimization Algorithm (WSO-BBOA) is used for optimal feature selection, making certain that just the most important features are included are selected. The selected features are input into AFi-EDLCINNet for classification, which is further optimized by SSHA to improve accuracy and efficiency in detecting epileptic seizures. The proposed method achieves an impressive 99.9 % classification accuracy and a low error rate of 0.7 %, outperforming other methods. This framework offers a reliable, robust solution for early seizure detection, providing clinicians with a powerful tool for personalized treatment planning. The solution is implemented using Python, demonstrating its practicality and flexibility for real-world applications.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102072"},"PeriodicalIF":8.2,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144623481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 large-scale multi-objective algorithm integrating prisoner’s dilemma model and prospect theory with adaptive learning","authors":"Yu Sun ,&nbsp;Wenhao Cai","doi":"10.1016/j.swevo.2025.102061","DOIUrl":"10.1016/j.swevo.2025.102061","url":null,"abstract":"<div><div>Numerous multi-objective problems contain many decision variables in the real world, which are referred to as large-scale multi-objective problems (LSMOPs). The challenge of achieving a balance between convergence and diversity in large-scale multi-objective optimization is addressed in this paper, which introduces a hybrid strategy for solving LSMOPs. Initially, a two-stage adaptive learning strategy based on history is employed to update the population. Subsequently, the prisoner’s dilemma model in game theory is introduced during the offspring generation stage to balance convergence and diversity. A fuzzy evolutionary mechanism is then employed for optimization to enhance the diversity and searchability of the population. Finally, a reference vector-guided selection and a risk preference mechanism based on prospect theory are employed to perform selection during the environmental selection phase. Experimental results on benchmark problems with 100–1000 decision variables reveal that the algorithm has the best overall performance compared with state-of-the-art large-scale multi-objective evolutionary algorithms (MOEAs).</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102061"},"PeriodicalIF":8.2,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Bi-level Voronoi strategy for cooperative search and coverage","authors":"Benyamin Ebrahimi ,&nbsp;Ali Asghar Bataleblu ,&nbsp;Jafar Roshanian","doi":"10.1016/j.swevo.2025.102064","DOIUrl":"10.1016/j.swevo.2025.102064","url":null,"abstract":"<div><div>In this paper, a bi-level Voronoi-based path planning strategy is proposed to address the challenge of cooperative multi-agent search and coverage in uncertain environments. While traditional Voronoi-based coverage control is commonly utilized for optimal path planning, its limitations, such as agents' premature convergence to Voronoi centroids, leading to reduced exploration and lack of incentive to move, can hinder system efficiency. The proposed bi-level strategy provides a framework to overcome such limitations while ensuring a more balanced and adaptive allocation of the environment among agents, thereby enhancing overall performance in terms of environmental mean uncertainty reduction and target detection. This framework utilizes a primary Voronoi diagram based on agent positions for initial spatial partitioning. To enhance exploration efficiency, a secondary Voronoi tessellation is applied, integrating probabilistic information about the target’s existence. The bi-level framework enables agents to achieve purposeful coverage by employing an efficient Voronoi partition allocation that integrates both the agents' positions and the probability of target existence. To this end, a novel allocation approach is employed to assign Voronoi neighbors to agents, ensuring that common cells within each agent's region are allocated to the most deserved agent. This mechanism promotes proportional contributions to uncertainty reduction, ensuring that each agent prioritizes areas of higher uncertainty or greater target likelihood. By doing so, agents operate efficiently, effectively reducing environmental uncertainty and improving target detection. Simulation results and comparative analyses validate the proposed strategy, demonstrating its superiority over conventional methods and highlighting its significance in multi-agent cooperative missions.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102064"},"PeriodicalIF":8.2,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144613940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 reinforcement learning-enhanced multi-objective Co-evolutionary algorithm for distributed group scheduling with preventive maintenance","authors":"Yanan Wang ,&nbsp;Yuyan Han ,&nbsp;Yuting Wang ,&nbsp;Hongyan Sang ,&nbsp;Yuhang Wang","doi":"10.1016/j.swevo.2025.102066","DOIUrl":"10.1016/j.swevo.2025.102066","url":null,"abstract":"<div><div>In the context of the global impetus towards sustainable development and in response to grow market demands, there is a critical need for multi-regional, multi-objective, and flexible production models. Under this background, this article first formulates a mathematical model of a distributed group scheduling problem with preventive maintenance (DFGSP_PM), in which the machine's maintenance level drops below a preset threshold, preventive maintenance is triggered. Second, a reinforcement learning-enhanced multi-objective co-evolutionary algorithm (QCMOEA) is proposed. It incorporates a collaborative evaluation mechanism tailored to the characteristics of the coupled problems to extensively explore the solution space. To retain a balance between convergence and distribution properties, a solution selection strategy based on double-rank and cosine similarity approaches is utilized. Additionally, a Q-learning mechanism is adopted to dynamically select the optimal strategy during enhancing evolution for the group population. Furthermore, a three-stage increasing efficiency and reducing consumption strategy is designed by dynamically changing the machine speed. Finally, by conducting a comparative analysis with four existing metaheuristic algorithms across 405 test cases, the proposed algorithm demonstrates superior optimization capabilities in addressing this complex DFGSP_PM problem.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102066"},"PeriodicalIF":8.2,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 optimized multi-source feature extraction model with black-winged kite algorithm for hourly seamless PM2.5 estimation","authors":"Li Wang ,&nbsp;Lili Xu ,&nbsp;Shurui Fan ,&nbsp;Yong Zhang","doi":"10.1016/j.swevo.2025.102069","DOIUrl":"10.1016/j.swevo.2025.102069","url":null,"abstract":"<div><div>Fine particulate matter (PM_2.5) estimation is becoming of significant importance for public health protection and air quality management. However, it is difficult to extract effective data features and easily lead to data missing with single-source dataset. And the multi-source data PM_2.5 estimation method will also increase data dimensionality and nonlinear data coupling. In this paper, a novel hourly seamless PM_2.5 estimation method is proposed with leveraging multi-source data integration through attentive interpretable tabular learning network (TabNet)-based feature extraction and the categorical boosting (CatBoost) prediction model. Firstly, a multi-source dataset is constructed for the satellite and reanalysis data fusion to solve the missing spatio-temporal data problem and to realize the hourly seamless estimation. Secondly, the TabNet structural parameters are optimized by the black-winged kite algorithm (BKA), effectively extracting key feature information from the multi-source data. And then the hourly seamless PM_2.5 concentration is estimated by CatBoost with Bayesian optimization (BO), which improves the generalization performance of the whole model. Finally, the multi-source dataset of Beijing-Tianjin-Hebei (BTH) region is used for the experimental analysis and validation. The results demonstrate that BKA has superiority over other algorithms in optimizing TabNet feature extraction. The proposed method exhibits excellent performance in hourly seamless PM_2.5 estimation, with the R² of the day and night models reaching 0.91 and 0.92 and the RMSE as low as 12.22 μg/m³ and 11.70 μg/m³, which are better than other regression models. In addition, the hourly PM_2.5 estimated are generally consistent with observations and the applicability across various spatial locations is validated.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102069"},"PeriodicalIF":8.2,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Performance optimization of Band Pass Filters and Wavelength Division Multiplexers for communication systems","authors":"Arup Kumar Ghosh ,&nbsp;Subhankar Bhattacharjee ,&nbsp;Gautam Garai","doi":"10.1016/j.swevo.2025.102049","DOIUrl":"10.1016/j.swevo.2025.102049","url":null,"abstract":"<div><div>The growing demand for compact, high-speed, and spectrally precise components in next-generation communication systems poses significant challenges in the design and optimization of photonic Band Pass Filters (BPFs) and Wavelength Division Multiplexers (WDMs). Conventional algorithms, such as Genetic and Taguchi Optimization (GTO), Particle Swarm Optimization (PSO), Grasshopper Optimization (GRO), and Bald Eagle Search Optimization (BESO), often suffer from premature convergence, spectral inaccuracies, limited flexibility, and poor scalability when applied to complex, high-dimensional photonic structures. To address these limitations, this study introduces the Advanced Distributed Dynamic Differential Evolution (AD³E) algorithm, an advanced distributed optimization technique. Applied to 1D Si_yGe_1-y–SiO₂ photonic crystals, AD³E achieves outstanding results in BPFs with 99. 9814% Transmittivity and a 0.4 nm FWHM at the center wavelength of 1550 nm and WDMs with 99. 9579% Transmittivity, a 0.4 nm FWHM and 0.6 nm channel spacing to avoid crosstalk. The dynamic parameter adaptation of AD³E significantly outperforms static settings. Further validation shows less than 8% performance degradation under <span><math><mo>±</mo></math></span>5% fabrication tolerance while Wilcoxon signed-rank testing (<span><math><mrow><mi>p</mi><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>05</mn></mrow></math></span>) confirms its statistical superiority over GTO, PSO, GRO, and BESO in 32 benchmark functions (30D and 100D). AD³E stands out as a powerful tool for next-generation photonic device optimization.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102049"},"PeriodicalIF":8.2,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 improved discrete multi-objective artificial protozoa optimizer for solving multi-objective knapsack problems","authors":"Junjie Liao ,&nbsp;Zheng-Ming Gao ,&nbsp;Syam Melethil Sethumadhavan ,&nbsp;Gaoshuai Su ,&nbsp;Juan Zhao","doi":"10.1016/j.swevo.2025.102070","DOIUrl":"10.1016/j.swevo.2025.102070","url":null,"abstract":"<div><div>The multi-objective knapsack problem (MOKP) is a challenging combinatorial optimization problem that traditional methods often fail to solve effectively. Consequently, researchers are increasingly adopting metaheuristic algorithms to address such problems within a reasonable time. This paper introduces an improved discrete multi-objective artificial protozoa optimizer (IDMOAPO) to tackle MOKP. The continuous solution space of the leaded sine cosine multi-objective artificial protozoa optimizer is discretized using two approaches, among which the modulo operation is identified as the most effective and adopted to develop a discrete multi-objective artificial protozoa optimizer (DMOAPO). An enhanced strategy is further incorporated into DMOAPO to improve solution quality, resulting in the development of the proposed IDMOAPO. The proposed IDMOAPO is evaluated across 16 MOKPs of four types and compared against seven algorithms. The performance metrics used for the evaluation are the number of Pareto solutions, generational distance, Spread, and inverted generational distance. Simulation results show that IDMOAPO significantly outperforms other comparison algorithms in most cases. These results highlight the effectiveness of IDMOAPO in obtaining superior Pareto fronts, confirming its suitability for solving MOKP.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102070"},"PeriodicalIF":8.2,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144589064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Reinforcement learning-integrated evolutionary algorithm for enhanced unmanned aerial vehicle coverage path planning","authors":"Seung Chan Choi ,&nbsp;Yohan Lee ,&nbsp;Sung Won Cho","doi":"10.1016/j.swevo.2025.102051","DOIUrl":"10.1016/j.swevo.2025.102051","url":null,"abstract":"<div><div>The rapid development of unmanned aerial vehicle (UAV) technologies has led to their increased utilization across various industries. In search and rescue (SAR) missions, UAVs play a critical role in overcoming mobility constraints in search environments, particularly in time-sensitive situations such as maritime operations. To enhance the efficiency of search missions, this study addresses the Coverage Path Planning (CPP) problem for multiple UAVs in irregularly shaped search areas. We propose a novel CPP framework consisting of two main phases. In Phase 1, a reinforcement learning-integrated evolutionary algorithm is introduced for search area decomposition, aiming to minimize the area of the grid map exceeding the search area. Specifically, proximal policy optimization-based particle swarm optimization (PPO–PSO) is employed to effectively adapt to complex and irregular shapes. In Phase 2, a Mixed Integer Linear Programming (MILP) model is formulated to minimize mission completion time while ensuring collision avoidance and efficient task allocation for multiple UAVs. The proposed methodology was validated through 15 experimental scenarios, including real-world maritime environments, and demonstrated superior performance compared to existing methods in managing irregularly shaped search areas.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102051"},"PeriodicalIF":8.2,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Optimum settings for discrete PID control of nonlinear systems","authors":"Robert Vrabel","doi":"10.1016/j.swevo.2025.102052","DOIUrl":"10.1016/j.swevo.2025.102052","url":null,"abstract":"<div><div>This study investigates the application of piecewise affine approximation techniques for the control of nonlinear systems, focusing on the effective linearization of systems described by the <span><math><mi>k</mi></math></span>th order difference equation <span><math><mrow><mi>x</mi><mrow><mo>[</mo><mi>n</mi><mo>+</mo><mi>k</mi><mo>]</mo></mrow><mo>+</mo><mi>f</mi><mrow><mo>(</mo><mi>x</mi><mrow><mo>[</mo><mi>n</mi><mo>]</mo></mrow><mo>,</mo><mi>x</mi><mrow><mo>[</mo><mi>n</mi><mo>+</mo><mn>1</mn><mo>]</mo></mrow><mo>,</mo><mo>…</mo><mo>,</mo><mi>x</mi><mrow><mo>[</mo><mi>n</mi><mo>+</mo><mi>k</mi><mo>−</mo><mn>1</mn><mo>]</mo></mrow><mo>)</mo></mrow><mo>=</mo><mi>u</mi><mrow><mo>[</mo><mi>n</mi><mo>]</mo></mrow></mrow></math></span>. The proposed approach employs piecewise linearization by partitioning the nonlinear function <span><math><mi>f</mi></math></span> into simplices within a compact domain <span><math><mrow><mi>D</mi><mo>⊂</mo><msup><mrow><mi>R</mi></mrow><mrow><mi>k</mi></mrow></msup></mrow></math></span>. The parameter <span><math><mi>h</mi></math></span>, which determines the number of linear segments, governs the precision of the approximation. As <span><math><mi>h</mi></math></span> increases, the linearized system’s behavior converges uniformly to that of the original nonlinear system, facilitating improved control system performance.</div><div>A key advantage of the approach is that it does not require full knowledge of the nonlinear function <span><math><mi>f</mi></math></span>; only values at selected nodal points are needed. Furthermore, it is sufficient that <span><math><mi>f</mi></math></span> is twice continuously differentiable within each subdomain of the partition. If bounds on the gradient and Hessian of <span><math><mi>f</mi></math></span> are available within each cell, the total approximation error can be rigorously estimated.</div><div>In addition, the study incorporates PID controllers and leverages the Particle Swarm Optimization (PSO) algorithm to optimize controller parameters. The optimization framework is designed to minimize key performance indices, such as the Integral Time Absolute Error (ITAE) and Integral Squared Overshoot (ISO). Numerical simulations demonstrate the efficacy of the proposed method, highlighting its ability to balance computational complexity with approximation accuracy in nonlinear control system design.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102052"},"PeriodicalIF":8.2,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144571722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 difficulty of predicting behavior on stochastic local search algorithms","authors":"Daniel Loscos,&nbsp;Narciso Martí-Oliet,&nbsp;Ismael Rodríguez","doi":"10.1016/j.swevo.2025.102010","DOIUrl":"10.1016/j.swevo.2025.102010","url":null,"abstract":"<div><div>Identifying key properties of Stochastic Local Search (SLS) algorithms, such as convergence to optimal solutions, is essential. Unfortunately, due to their Turing-completeness and Rice’s theorem, their non-trivial semantic properties are generally undecidable. Therefore, most convergence results are achieved by abusing properties that ultimately depict them as simple (probabilistic) exhaustive search algorithms. We show that the general difficulty to prove properties of SLS algorithms has a strong theoretical basis: even when SLS algorithms are deterministic and their memory is linearly bounded, finding out their output from their input configuration is PSPACE-hard — and thus intractable if P<span><math><mo>≠</mo></math></span>PSPACE. This is proven by translating the PSPACE-hard DLBA-ACCEPT problem (i.e. given a Deterministic Linear Bounded Automaton and a word, checking whether the automaton accepts the word) into an instance of the tile-matching problem MPCP such that its solution denotes the configurations traversed by the DLBA during its execution. Simple SLS algorithms can obtain increasing partial solutions for these MPCP instances and provide the answer of the original DLBA-ACCEPT instances. It is also shown that finding out whether an SLS algorithm using linear memory fulfills any non-trivial semantic property is PSPACE-hard. An adaptation of Rice’s theorem dealing with computation artifacts running with linear space is introduced for that purpose. In order to provide an intuitive test of PSPACE-hardness for SLS algorithms, examples of how our criteria is applied to several heuristics, such as depth-first search and genetic algorithms, are shown.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102010"},"PeriodicalIF":8.2,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144571717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}