CAAI Transactions on Intelligence Technology最新文献

{"title":"From Ambiguous Queries to Verifiable Insights: A Task-Driven Framework for LLM-Powered SOC Analysis⋆","authors":"Huan Zhang,&nbsp;Haiyan Wang,&nbsp;Hao Tan,&nbsp;Liyi Zeng,&nbsp;Jingnan Li,&nbsp;Zhaoquan Gu","doi":"10.1049/cit2.70138","DOIUrl":"https://doi.org/10.1049/cit2.70138","url":null,"abstract":"<p>Security operations centre (SOC) analysts must investigate alerts, correlate threat intelligence and interpret heterogeneous telemetry under tight timing constraints. Although large language models (LLMs) offer strong understanding capabilities, directly applying them to SOC environments remains challenging due to semantic ambiguity in analyst queries, fragmented multisource event data, limited domain-specific reasoning and reliability concerns associated with unconstrained query generation. We present a task-driven knowledge-augmented framework designed to produce verifiable and contextually grounded responses for SOC workflows. The framework integrates four components: (i) contrastive context task recognition that mitigates semantic ambiguity by mapping analyst queries to predefined SOC task types; (ii) expert-guided knowledge augmentation that fuses dense and sparse retrieval to bridge the semantic gap; (iii) schema-aligned event retrieval combined with entity-centric evidence profiling to ensure reliable and secure access to heterogeneous telemetry and (iv) verifiable task-aware generation that constrains model outputs to retrieved knowledge and security events. To assess the framework, we construct a benchmark of 12,500 validated question–answer pairs derived through semiautomated synthesis over more than 34 million real SOC records. Experiments across multiple foundation models demonstrate consistent improvements in relevance and grounding quality. Our results indicate that the four proposed components substantially enhance LLMs' reliability in practical SOC analysis.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 3","pages":"646-666"},"PeriodicalIF":5.1,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70138","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148358272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multitype Game Optimisation: A Two-Stage Fine-Tuning Framework for Multi‑Game Optimisation With Large Language Models","authors":"Xiali Li,&nbsp;Jingshi Gu,&nbsp;Feifan He,&nbsp;Yang Xiao,&nbsp;Yuanli Jia,&nbsp;Ping Lan","doi":"10.1049/cit2.70108","DOIUrl":"https://doi.org/10.1049/cit2.70108","url":null,"abstract":"<p>Large language models (LLMs) have made remarkable advances in natural language processing, demonstrating great potential in modelling structured sequences. However, adapting these capabilities to machine gaming tasks such as Go remains challenging due to limitations in strategy generalisation and optimisation efficiency. This paper presents multitype game optimisation (MyGO), a two-stage fine-tuning framework tailored for two-player perfect information board games, exploring the applicability of LLMs to nonlinguistic decision-making domains. In the supervised fine-tuning stage, we propose a unified structural encoding method, action semantic unit (ASU), which efficiently converts heterogeneous game records into discrete token sequences compatible with LLMs. In the reinforcement learning stage, we design TA-PPO (token-level adaptive proximal policy optimisation), an enhanced PPO-based algorithm to address the issue of sparse feedback commonly encountered in game reinforcement learning. Experimental results demonstrate that the fine-tuned models achieve superior or comparable performance to traditional game-playing algorithms in terms of strategy quality, rule generalisation and inference efficiency. This work provides a scalable paradigm for fine-tuning LLMs in complex decision-making tasks and lays a foundation for future research in game AI and generalisable strategy optimisation.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 3","pages":"739-753"},"PeriodicalIF":5.1,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70108","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148358149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vertical Deformation Mapping: Steering Optimiser Toward Flat Minima","authors":"Liangming Chen,&nbsp;Longbang Wang,&nbsp;Man-Fai Leung,&nbsp;Jianfeng Li,&nbsp;Long Jin","doi":"10.1049/cit2.70137","DOIUrl":"https://doi.org/10.1049/cit2.70137","url":null,"abstract":"<p>Standard deep learning optimisation is typically conducted on shape-fixed loss surfaces. However, shape-fixed loss surfaces may impede optimisers from reaching flat regions closely associated with strong generalisation. In this work, we propose a new paradigm named deformation mapping to deform the loss surface during optimisation. Moreover, we design various vertical deformation mappings (VDMs) and further analyse their contributions to the training process. Theoretically, we prove that deforming the loss surface enhances the optimiser's ability to filter out sharp minima in deterministic settings. Furthermore, by incorporating diffusion theory, we demonstrate that VDM exponentially reduces the escape time from sharp minima under stochastic noise and momentum. Empirically, visualisations of loss landscapes demonstrate that VDMs locate significantly flatter minima compared to standard optimisation. Furthermore, integrating VDMs into the training of various deep neural networks produces consistent accuracy gains on ImageNet, CIFAR-10, and CIFAR-100, with negligible additional computation. Notably, PreResNet-20 on CIFAR-100 achieves a 1.46% increase in top-1 accuracy. These results indicate that the deformation mapping is a promising paradigm for improving optimisation and generalisation in deep learning. The code is available at https://anonymous.4open.science/r/Vertical-Deformation-Mapping-2324.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 3","pages":"835-846"},"PeriodicalIF":5.1,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70137","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148358250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cost-Optimal Building Energy System Scheduling Integrating Solar Irradiance Forecasting via LSTM-Attention-TCN Model","authors":"Zhengtian Wu,&nbsp;Jianyu Li,&nbsp;Yang Gao,&nbsp;Chuangyin Dang,&nbsp;Chao Tang,&nbsp;Yuansheng Li,&nbsp;Xinmiao Wang,&nbsp;Jinpeng Chen,&nbsp;Hongbo Gao,&nbsp;Xinyin Xu","doi":"10.1049/cit2.70128","DOIUrl":"https://doi.org/10.1049/cit2.70128","url":null,"abstract":"<p>Building energy systems integrating multiple energy sources can effectively reduce energy consumption and facilitate renewable energy integration. Integrating electrical energy storage (EES) into these systems helps accommodate the increasing share of renewables; however, the stochastic and intermittent nature of solar power still poses challenges to supply reliability. This study proposes a photovoltaic (PV)-oriented storage scheduling strategy, in which short-term PV generation forecasts are applied to guide the operation of a building power supply network consisting of photovoltaic panels, the grid, and energy storage systems. The forecasting approach employs a hybrid framework combining a Long Short-Term Memory (LSTM) network to capture temporal dependencies, an attention mechanism to emphasise critical time steps, and a Temporal Convolutional Network (TCN) to map the enhanced features to PV outputs. Experimental evaluation using historical datasets under multiple weather conditions and time periods shows that the proposed LSTM-Attention-TCN model achieves a mean absolute error (MAE) of 20.45 W/m² and a Nash–Sutcliffe efficiency (NSE) of 0.94, outperforming both standalone LSTM and TCN models as well as their hybrid variants in terms of accuracy and robustness. By providing high-accuracy solar irradiance forecasts to guide energy storage operation and grid interaction, the proposed model enables more efficient and economical scheduling of building energy systems. Compared with an uncontrolled scenario, the LSTM-Attention-TCN-based scheduling reduces the total operating cost by approximately 52.1%, and achieves an additional 16.5% reduction compared to a conventional strategy without predictive coordination. In addition, compared to other hybrid forecasting models such as LSTM-TCN and TCN-Attention, the proposed model achieves the lowest total cost of CNY 14.83 and demonstrates superior scheduling efficiency, thereby enhancing the stability and flexibility of building energy utilization.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 3","pages":"695-708"},"PeriodicalIF":5.1,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70128","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148358306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"M3LoRA: Flexible Task Adaptation via Multiple Low-Rank Matrices With Mixture-of-Subspaces and Minor Singular Components Initialization","authors":"Xu Luo,&nbsp;Yongbin Liu,&nbsp;Chunping Ouyang,&nbsp;Ying Yu,&nbsp;Yang Yang","doi":"10.1049/cit2.70144","DOIUrl":"https://doi.org/10.1049/cit2.70144","url":null,"abstract":"<p>Parameter-efficient fine-tuning (PEFT) has become a crucial paradigm for domain adaptation, achieving strong performance by updating only a small fraction of model parameters. Among various PEFT methods, low-rank adaptation (LoRA) is widely adopted due to its structural simplicity and computational efficiency. However, in multitask scenarios, LoRA often suffers from performance degradation due to task interference. Recent extensions, such as mixture-of-experts (MoE) and asymmetric LoRA variants, attempt to mitigate these issues; however, their reliance on fixed subspace-mixing strategies limits flexibility and makes the models more sensitive to input noise and data sparsity. Moreover, vanilla LoRA typically initialises low-rank matrices with Gaussian noise or zeros and optimises them in unconstrained subspaces, which may disrupt the structured representations learnt by pretrained models. In this article, we propose M3LoRA, a novel PEFT framework that leverages multiple low-rank matrices with mixture-of-subspaces and minor singular components initialisation. M3LoRA utilises multiple low-rank matrices to minimise interference between task-specific subspaces while maintaining representational capacity. Furthermore, it employs a learnable mixing matrix positioned between the down-projection and up-projection matrices to dynamically combine their subspaces, thereby enabling adaptive task-specific combination mechanisms. Additionally, it initialises these low-rank matrices within a subspace orthogonal to the principal singular components of pretrained weights—termed the minor singular components—thereby leveraging directions unexplored during pretraining to better capture task-specific features from labelled data. Extensive experiments on a wide range of benchmark datasets demonstrate that M3LoRA achieves substantial improvements over existing PEFT baselines, particularly in multi-task scenarios where task interference often degrades conventional LoRA performance.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 3","pages":"681-694"},"PeriodicalIF":5.1,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70144","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148358055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Progressive Colour Equalisation and Detail Refinement for Underwater Image Enhancement","authors":"Songbai Liu,&nbsp;Jiacheng Huang","doi":"10.1049/cit2.70117","DOIUrl":"https://doi.org/10.1049/cit2.70117","url":null,"abstract":"<p>Underwater image enhancement remains a critical challenge in computational vision due to complex distortions caused by wavelength-dependent light absorption and scattering. This paper introduces CEDFNet, a novel two-stage framework that leverages advanced computational intelligence techniques for robust and high-fidelity underwater image restoration. The first stage integrates a Colour Equalisation Transformer (CET) to perform global colour correction by modelling long-range dependencies and mitigating dominant hue distortions. The second stage combines a Residual Texture Modulation Adaptor (RTMA) with an Enhanced Bilateral Enhancement Decoder (EBED) to refine structural details and enhance local contrast through context-aware and adaptive feature learning. Extensive evaluations on benchmark datasets including UIEBD, LSUI, and Colour-Checker7 validate the superiority of CEDFNet over existing state-of-the-art approaches. Quantitatively, CEDFNet achieves significant improvements across multiple perceptual and fidelity metrics such as PSNR, SSIM, FID, and LPIPS. Comprehensive ablation studies further confirm the complementary roles of CET, RTMA, and EBED, whereas parameter sensitivity analyses highlight the framework's robust and stable behaviour. By integrating transformer-based global correction with task-adaptive local enhancement, CEDFNet advances the frontier of underwater image restoration in the domain of computational intelligence. It generalises well across diverse imaging conditions and offers a lightweight and end-to-end solution suitable for real-world deployment in marine robotics, inspection, and visual perception systems.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 3","pages":"709-725"},"PeriodicalIF":5.1,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70117","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148358136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural Network Repair With Shapley-Guided Search","authors":"Xiaofu Du,&nbsp;Zixiong Zhang,&nbsp;Xuesong Wang,&nbsp;Linqiang Liu,&nbsp;Junyan Qian","doi":"10.1049/cit2.70147","DOIUrl":"https://doi.org/10.1049/cit2.70147","url":null,"abstract":"<p>The deployment of deep neural networks (DNNs) in safety-critical domains is critically hampered by their vulnerability to defects, which can arise from malicious attacks or low-quality data. Therefore, precisely locating the network components responsible for these defects, and subsequently repairing them without compromising overall model performance, presents a significant challenge. To address this, this paper introduces NSRepair, a framework that combines interpretable fault localisation with multi-objective optimisation. Specifically, to accurately attribute blame for a defect, we employ Shapley values to quantify the contribution of each neuron. To systematically manage the trade-off between defect correction and performance preservation, we formulate the repair task as a multi-objective optimisation problem. We conducted extensive experiments across four distinct repair tasks, validating NSRepair on diverse model architectures against seven specialised state-of-the-art methods. The results demonstrate that our unified framework effectively repairs a wide range of defects, demonstrating its potential as a versatile and practical solution for improving DNN dependability. Our code is publicly available at https://doi.org/10.5281/zenodo.17494304.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 3","pages":"816-834"},"PeriodicalIF":5.1,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70147","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148358219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Resource Allocation Optimisation and Security-Resilient Control for Bandwidth-Limited Network Control Systems With Data Conflicts","authors":"Da Chen,&nbsp;Kaibo Shi,&nbsp;Bin Guo,&nbsp;Kangkang Sun,&nbsp;Huaicheng Yan,&nbsp;Xiao Cai","doi":"10.1049/cit2.70098","DOIUrl":"https://doi.org/10.1049/cit2.70098","url":null,"abstract":"<p>Networked control systems (NCSs) often suffer from performance degradation due to limited communication bandwidth, which can cause data transmission conflicts and packet loss. Existing scheduling strategies may fail to simultaneously meet the real-time requirements and the importance of multisensor data, and they are particularly vulnerable under distributed denial of service (DDoS) attacks. Firstly, to address these challenges, a greedy algorithm is proposed to optimise the data transmission process to satisfy the importance and real-time requirement of sensor data. It enables the dynamic allocation of network resources, reduces the possibility of data conflict and improves the communication efficiency. Then, an observer-based control algorithm is designed to ensure the system's stability and security resilience under bandwidth constraints and DDoS attacks. Therefore, in the face of packet loss caused by data conflict, the control algorithm can maintain efficient resource scheduling capability and improve the robustness of the system. Finally, the simulation results show that the proposed dynamic resource scheduling framework can guarantee the performance of NCSs under constrained network conditions.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 3","pages":"920-934"},"PeriodicalIF":5.1,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70098","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148358241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Neural Network Surrogate Modelling for Predicting and Optimising CO2 Conversion to Methanol Under Uncertainty","authors":"Muhammad Zulkefal,&nbsp;Iftikhar Ahmad,&nbsp;Hakan Caliskan,&nbsp;Hiki Hong,&nbsp;Farooq Ahmad","doi":"10.1049/cit2.70131","DOIUrl":"https://doi.org/10.1049/cit2.70131","url":null,"abstract":"<p>This study introduces a data-driven surrogate modelling framework that combines an artificial neural network (ANN) with particle swarm optimisation (PSO) and a genetic algorithm (GA) to optimise methanol production under uncertain conditions. A steady-state Aspen Plus model was developed and converted into dynamic mode by applying ± 5% uncertainty across 12 key process variables, generating 3880 data points that reflect realistic operational variability. The ANN model was trained and validated on the samples, achieving predictive accuracy (<i>R</i>² = 0.988, RMSE = 28.59) on unseen test data. Key features of the work include the use of the ANN as a surrogate model, its integration within PSO and GA optimisation frameworks and its application alongside Sobol and Fourier amplitude sensitivity test (FAST) methods to identify the most influential process variables affecting the methanol production rate. The proposed framework resulted in performance improvements, with PSO achieving an increase of 38.63% and GA 33.14% in methanol production. Cross-validation with the Aspen Plus model confirmed the reliability of the optimised operating conditions, with relative errors ranging from 0.07% to 2.15%. Overall, the study demonstrates the effectiveness of integrating surrogate modelling with intelligent optimisation techniques to improve the efficiency and robustness of methanol production processes under uncertainty.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 3","pages":"633-645"},"PeriodicalIF":5.1,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70131","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148358058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SVLGaussian: Single View Language Gaussian Splatting","authors":"Jiachen Wang,&nbsp;Mingyang Ding,&nbsp;Min Tan,&nbsp;Luocheng Zhang,&nbsp;Jingrui Fan,&nbsp;Wenwen Pan,&nbsp;Zhou Yu,&nbsp;Jiajun Ding","doi":"10.1049/cit2.70148","DOIUrl":"https://doi.org/10.1049/cit2.70148","url":null,"abstract":"<p>Open-vocabulary 3D querying based on 3D Gaussian splatting (3DGS) shows great promise in facilitating accurate 3D query capabilities of AI systems. These methods typically rely on pre-captured multi-view images to enable natural language interactions with 3D scenes. In practice, when embodied AI encounters unexplored scenes, it is difficult to obtain observations from different viewpoints beforehand. This challenge highlights the importance of exploring natural language-driven 3D scene querying from a single current viewpoint. This paper proposes single view language Gaussian splatting (SVLGaussian) for the novel task: Open-vocabulary 3D querying based on the input single view. By leveraging multi-round inference of multimodal large language models, SVLGaussian efficiently generates pixel-level semantic probabilities and rapidly embeds them into a 3D Gaussian field, enabling real-time language-guided semantic querying. To verify our model, we annotated three datasets: Lerf_ovs and 3D-OVS, which are tailored for open-vocabulary 3D querying, and RE10K, which is adapted for single-view 3D reconstruction. Both quantitative and qualitative results show that our method effectively supports open-vocabulary 3D querying from a single view.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 3","pages":"875-899"},"PeriodicalIF":5.1,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70148","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148358302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}