Systems and Soft Computing最新文献

{"title":"An Arabic intelligent interface for accessing NoSQL document-oriented databases","authors":"Hanane Bais ,&nbsp;Mustapha Machkour","doi":"10.1016/j.sasc.2026.200456","DOIUrl":"10.1016/j.sasc.2026.200456","url":null,"abstract":"<div><div>NoSQL document-oriented databases are widely used to manage large volumes of unstructured and semi-structured data, especially in web applications and Big Data environments. Although flexible and scalable, these databases often require users to understand their structure and specific query language, which can be difficult for non-experts.</div><div>In this article, we propose an intelligent interface that enables users to interact with NoSQL databases. Our solution relies on query translation using advanced linguistic operations. This approach ensures domain independence, meaning that the interface does not depend on the specific vocabulary of the underlying database.</div><div>Indeed, in many large-scale databases, users do not always know the exact names of collections (tables) and attributes. Even if a query is syntactically correct, it may return no results if the user employs terms different from those defined in the database. Our solution overcomes this limitation by analyzing the semantic context of queries and dynamically mapping them to NoSQL database schemas. This allows users to express their queries more naturally without needing to know the precise internal structure of the database. Moreover, our approach integrates an adaptive machine learning mechanism that incrementally improves the knowledge base with each user interaction, allowing the system to generalize to new queries and domains. This adaptive capability reduces the need for frequent reconfiguration and ensures optimal scalability</div><div>The Experimental results show that our interface significantly reduces query formulation time and enhances the accessibility of NoSQL databases for non-specialists. By combining linguistic operations, domain independence, and adaptive learning, our solution facilitates NoSQL database utilization and optimizes the user experience.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"8 ","pages":"Article 200456"},"PeriodicalIF":3.6,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized left atrial appendage segmentation via hierarchical cross-scale consistency with gated routing for echocardiography","authors":"Hossein Ghayoumi Zadeh ,&nbsp;Khosro Rezaee ,&nbsp;Ali Fayazi ,&nbsp;Mahmood Dolatabadi-Takaabi","doi":"10.1016/j.sasc.2026.200465","DOIUrl":"10.1016/j.sasc.2026.200465","url":null,"abstract":"<div><div>Accurate left atrial appendage (LAA) delineation is vital for atrial fibrillation-related left atrial appendage occlusion (LAAO) planning and device sizing; yet transthoracic echocardiography (TTE) segmentation is challenged by speckle noise, acoustic shadowing, low or variable contrast, pronounced anatomical variability, and limited annotated data. We propose a lightweight, plug-in module, the Hierarchical Cross-Scale Consistency with Gated Routing (H-CSCA), which preserves the U-Net topology and can be integrated into existing U-Net decoders as a drop-in replacement for skip connections with minimal architectural changes and parameter overhead. Our key contribution is to couple cross-scale cycle consistency with learnable gated routing within the U-Net decoder, enabling controllable fusion for echo boundary refinement. Our model aligns the encoder and decoder features across neighboring scales and selectively routes information between them to reinforce fine boundary cues without relying on transformer-heavy backbones. Unlike prior multi-scale U-Net variants that rely on fixed skip and fusion pathways, H-CSCA explicitly enforces cross-scale agreement and adaptively selects which scales contribute to refinement via learnable routing. We evaluated our method on an anonymized dataset of LAA TTE views annotated by cardiologists, with a strict patient-level split (16 training, 5 validation, 5 test; held-out, locked test set). Compared to the MSCCA-UNet baseline, H-CSCA improved the Dice coefficient from 0.9490 ± 0.0030 to 0.9598 ± 0.0026 and IoU from 0.9040 ± 0.0040 to 0.9232 ± 0.0037, with only a marginal parameter increase (15.45M to 15.71M). Boundary quality also improved, reducing mean absolute distance from 0.8223 ± 0.0791 to 0.7918 ± 0.0613 and reducing worst-case boundary deviation (Hausdorff) by approximately 2%. Overall, H-CSCA yields sharper, more stable contours at minimal computational cost, supporting reliable LAAO planning and guidance in data-limited echocardiography workflows.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"8 ","pages":"Article 200465"},"PeriodicalIF":3.6,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generation and evaluation mechanism of digital media art pattern design scheme based on interactive genetic algorithm","authors":"Shu Ma ,&nbsp;Lei Zhang","doi":"10.1016/j.sasc.2026.200474","DOIUrl":"10.1016/j.sasc.2026.200474","url":null,"abstract":"<div><div>As digital media art evolves, traditional methods of pattern design can no longer fulfill the increasing demand for personalization and diversity. Consequently, this study introduces a mechanism for generating and evaluating digital media art pattern designs, which is based on an interactive genetic algorithm. By combining subjective user evaluations with objective optimization through genetic algorithms, this approach enables intelligent and customized pattern design. Traditional design methods often suffer from low efficiency and a lack of innovation. In contrast, interactive genetic algorithms effectively leverage user feedback to direct the design process and enhance the quality of patterns. The method developed by our research institute demonstrates significant advantages in the generation and evaluation of pattern designs. After 100 iterations of experiments, user satisfaction with pattern designs increased by 35 %, and design efficiency improved by 40 %. Comparative analysis revealed that patterns created using interactive genetic algorithms surpass traditional methods in visual appeal, innovation, and practicality. Furthermore, the incorporation of evaluation mechanisms ensures that pattern designs better meet user requirements, thereby enhancing the competitiveness of design schemes. The mechanism for generating and evaluating digital media art pattern designs, based on an interactive genetic algorithm, as proposed in this study, offers an efficient and innovative approach to the field of digital media art, with extensive application potential and promotional value.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"8 ","pages":"Article 200474"},"PeriodicalIF":3.6,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative evaluation of deep learning architectures for bioclast classification in atomic force microscopy images","authors":"Alicia Moya ,&nbsp;Adrián Inés ,&nbsp;Francisco Cedrón ,&nbsp;Carlos Fernández-Lozano ,&nbsp;Alejandro Fernández-Martínez ,&nbsp;Jónathan Heras","doi":"10.1016/j.sasc.2026.200458","DOIUrl":"10.1016/j.sasc.2026.200458","url":null,"abstract":"<div><div>The identification and classification of bioclasts in limestone are fundamental tasks in petrographic analysis, traditionally performed through optical microscopy and expert-driven interpretation. While Atomic Force Microscopy (AFM) provides high-resolution topographic information at micro- to nanoscales, the analysis of AFM images for bioclast identification remains challenging due to complex surface morphologies, multi-scale textures, and the lack of direct correspondence with optical observations. Existing approaches rely heavily on manual inspection and complementary imaging techniques, resulting in time-consuming and non-scalable workflows. In this work, we address the problem of automated bioclast classification directly from AFM topographic images by systematically evaluating eight state-of-the-art deep learning architectures under multiple input resolutions and training strategies. In particular, we investigate the impact of image resolution, progressive resizing, and multi-scale feature modeling on classification performance. Our comparative analysis reveals a strong positive correlation between input resolution and performance, with progressive resizing consistently improving model robustness. Among the evaluated architectures, HRNet-based models demonstrate superior performance in capturing hierarchical geological textures, achieving a maximum F1-score of 79.1. Furthermore, an ensemble of the three best-performing HRNet variants further enhances classification accuracy, reaching an F1-score of 82.1.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"8 ","pages":"Article 200458"},"PeriodicalIF":3.6,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent fine-tuning of convolutional neural networks using flamingo search for traditional dance classification","authors":"Jhansi Rani Challapalli ,&nbsp;Revathi Durgam ,&nbsp;Bhagya Lakshmi Nandipati ,&nbsp;Pallavi Malavath","doi":"10.1016/j.sasc.2026.200449","DOIUrl":"10.1016/j.sasc.2026.200449","url":null,"abstract":"<div><div>Developing efficient CNN architectures for image classification is computationally intensive, largely because identifying suitable hyperparameter combinations requires extensive experimentation. Traditional CNNs typically rely on fixed architectures and preset hyperparameters, reducing their ability to adapt efficiently when applied to diverse datasets. Previous studies have applied metaheuristic algorithms to tune CNN hyperparameters; however, many such methods optimize parameters independently or within rigid hierarchical schemes, limiting flexibility in overall network design. To address this issue, this research introduces a fine-tuned CNN architecture optimized through the Flamingo Search (FS) algorithm, a recent metaheuristic optimization method inspired by cooperative foraging behavior. The FS algorithm is employed to dynamically optimize the Neural Architecture Search Network (NASNet) CNN architecture, adjusting key hyperparameters such as the number of filters, activation functions, dropout rate, and optimizer settings. FS also explores alternative NASNet cell connections, enabling the model to adaptively construct an efficient architecture with improved feature extraction capability. The methodology involves two stages: (i) optimizing NASNET cell structures using FS on benchmark CIFAR-10 and CIFAR-100 datasets, and (ii) evaluating the optimized model on the Indian Classical Dance (ICD) dataset to test generalization performance. Implementation was carried out using Python and TensorFlow on an NVIDIA GTX 1060 GPU platform. Experimental results show that the proposed FS-CNN model achieves a classification accuracy of 97.5%, outperforming existing models such as HPSGW-CNN by 0.7%, while reducing computational cost to approximately 10 GPU days. These findings demonstrate that the FS-based optimization strategy provides an efficient and adaptive framework for large-scale image classification, with potential applicability to future tasks such as video classification and domain-specific recognition.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"8 ","pages":"Article 200449"},"PeriodicalIF":3.6,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and application of garment art patterns based on visual sample generation","authors":"Juntao Zhang","doi":"10.1016/j.sasc.2026.200447","DOIUrl":"10.1016/j.sasc.2026.200447","url":null,"abstract":"<div><div>Clothing pattern design plays an important role in clothing aesthetics and market sales. However, with the advancement of technology and changes in consumer aesthetic needs, traditional hand-designed methods have been unable to meet the personalized and diversified pattern needs of contemporary young people. Therefore, it is necessary to explore and develop more efficient and innovative pattern design methods to meet the modern clothing market's demand for personalized, creative, and diversified design. To address this challenge, a f-WGRAN model based on zero-sample image classification is proposed. This model generates image features through an improved generative adversarial network and trains a classifier using the generated features to achieve clothing image classification and feature extraction. Meanwhile, the study adopts the <span>l</span>-system generation method for pattern drawing, and obtains the desired artistic pattern outline by selecting appropriate axioms, achieving personalized and beautiful clothing pattern design. The results showed that the f-WGRAN model achieved the highest classification accuracy of 69.8 % and 68.7 % on the AWA and FLO datasets, respectively, in the traditional zero-sample learning case. In the generalized zero-sample academic image classification experiments, the classification accuracy of the visible category samples of the f-WGRAN model was as high as 63.2 %, 75.3 %, 58.0 %, and 34.8 % on the AWA, FLO, CUB, and SUN datasets, respectively. The mean accuracy reached 60.5 %, 67.3 %, and 50.6 % on three datasets. The combination of f-WGRAN model and <span>l</span>-system method has strong feasibility in clothing pattern design. This method can automatically generate multiple styles of patterns according to different needs, achieving a faster design process and fully meeting the needs of young consumers for personalized and innovative patterns. This technology has promoted the intelligent and personalized development in the field of fashion design, providing effective methodological references for modern fashion design.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"8 ","pages":"Article 200447"},"PeriodicalIF":3.6,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Overcoming center-bias behavior: A metaheuristic algorithm with dual operators for optimized search and refinement","authors":"Erik Cuevas,&nbsp;Oscar A. González-Sánchez,&nbsp;Héctor Escobar,&nbsp;Ernesto Ayala,&nbsp;Daniel Zaldívar,&nbsp;Marco Pérez-Cisneros,&nbsp;Alma N. Rodríguez-Vázquez","doi":"10.1016/j.sasc.2025.200436","DOIUrl":"10.1016/j.sasc.2025.200436","url":null,"abstract":"<div><div>In most metaheuristic methods, a single operator is employed for both exploration and exploitation. While this makes the algorithm simple, this approach can introduce inefficiencies, such as inadequate coverage of areas, revisiting irrelevant spaces, and poor refinement of solutions. In this paper, a new metaheuristic algorithm using two operators designed specifically to perform exploration and exploitation tasks is introduced. During the initial phase, the exploration operator constructs a trajectory formed by the sequential interpolation of points obtained using the Latin Hypercube Sampling technique. The trajectory completely covers the areas in the search space and acts as a guide mechanism for exploring the runtime of the algorithm. As the search continues, each agent changes its position to follow this track such that the particles continue to explore the search domain and identify its most promising regions. In contrast, the exploitation operator uses a crossover operation in which an agent’s present position is modified to a new position inside a ribbon-shaped area using its position combined with the best solutions found until then, allowing the exploitation phase to concentrate on refining these promising solutions further. Together, these operators provide a balanced approach to exploring and exploiting the search space, enhancing the algorithm’s overall effectiveness. The efficacy of the proposed approach was validated by comparing the algorithm to various metaheuristic algorithms using a standard set of functions that have been shifted to accurately assess the performance of methods employing center-biased operators. The findings indicate that this method yields competitive outcomes, providing superior quality solutions and quicker convergence rates, while avoiding the drawbacks associated with algorithms reliant on center-biased operators.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"8 ","pages":"Article 200436"},"PeriodicalIF":3.6,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-fidelity video frame interpolation through context-aware temporal aggregation and recurrent propagation","authors":"Mohana Priya P,&nbsp;Ulagapriya K","doi":"10.1016/j.sasc.2025.200428","DOIUrl":"10.1016/j.sasc.2025.200428","url":null,"abstract":"<div><div>Accurate inpainting of missing middle frames in video sequences is vital for multiple applications like video restoration, enhancement and compression. This study introduces a sophisticated deep learning-based framework designed to address this challenge by utilizing adjoining sequences of preceding and following frames. Our approach integrates temporal aggregation and recurrent propagation to effectively perform frame inpainting. Temporal aggregation leverages visible content from adjacent frames to recreate missing frames, ensuring high spatial fidelity and feature conservation. Optical flow estimation, utilizing methods such as Farneback Optical Flow, estimates displacement between frames and provides motion vectors that guide the interpolation process, enabling accurate alignment and blending of frames. Recurrent propagation is accomplished through Long Short-Term Memory (LSTM) networks that maintains temporal coherence by embedding and propagating information from preceding frames, thus ensuring smooth transitions and consistency across the video sequence. To further enhance performance, our model includes a context-aware feature extraction mechanism that adapts to various motion patterns and occlusions, optimizing the reconstruction quality. Framework has been evaluated on MSU Video Frame Interpolation (VFI) Benchmark Dataset, which provides diverse and challenging scenarios for interpolation, as well as the YouTube-8 M dataset, which contains a wide range of real-world video content. The experimental results demonstrate the robustness of the proposed model: a PSNR of 32.00 and an SSIM score of 0.905 indicate its superior reconstruction quality and structural similarity compared to baseline models. These results underscore the framework’s effectiveness in handling complex motion dynamics and occlusions, making it well suited for advanced video restoration, enhancement and compression tasks.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"8 ","pages":"Article 200428"},"PeriodicalIF":3.6,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SEED-APR: A closed-loop self-evolving framework for automated program repair","authors":"Senlin Jiang,&nbsp;Wenjian Zhang,&nbsp;Hao Pan","doi":"10.1016/j.sasc.2026.200463","DOIUrl":"10.1016/j.sasc.2026.200463","url":null,"abstract":"<div><div>Current automated program repair (APR) methods that employ large language models frequently encounter issues such as patch overfitting, constrained semantic generalization, and stagnant evolution due to static test-case evaluation and single-path generation. In order to address these issues, a self-evolving patch generation framework, termed SEED-APR, is proposed. The integration of runtime feedback with semantic diffusion is a key feature of this framework. The approach employed in this study involves the utilization of a Code Semantic Diffusion Model (CodeSDM) to generate diverse, semantically equivalent patches via denoising diffusion in latent space. The Runtime Feedback Score (RF-score) is derived from lightweight sandbox execution and is used to evaluate patches against real-world behavioral constraints. The model autonomously refines its repair strategy through LoRA-based self-evolution, using contrastive learning to convert runtime feedback into gradient updates. This process establishes a closed loop of generation, evaluation and optimization. Experiments on Defects4J v2.0 and Bugs.jar demonstrate that SEED-APR attains repair accuracy of 58.7 % and 55.2 %, respectively, accompanied by 56.8 % zero-shot cross-project accuracy and 0.78 semantic diversity. This performance has been demonstrated to be significantly superior to that of existing methods, including GPT-4-Coder. A comparison of SEED-APR with state-of-the-art methods such as GPT-4-Coder and CodeT5+ reveals significant improvements in terms of repair accuracy, cross-project generalization, and semantic diversity.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"8 ","pages":"Article 200463"},"PeriodicalIF":3.6,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Log anomaly detection in AIOps: A real-world implementation using Large Language Models","authors":"Miguel De la Cruz Cabello ,&nbsp;Tiago Prince Sales ,&nbsp;Marcos R. Machado","doi":"10.1016/j.sasc.2026.200475","DOIUrl":"10.1016/j.sasc.2026.200475","url":null,"abstract":"<div><div>This study investigates the application of Large Language Models (LLMs) for log anomaly detection within the emerging field of AIOps, where large-scale operational logs are increasingly used to support reliability engineering and automated incident response. However, deploying LLM-based anomaly detection in military environments raises practical constraints, including strict data confidentiality, limited data sharing, and frequent shifts in operational conditions and log formats. To address these challenges, we design and implement a self-supervised anomaly detection framework based on LogBERT, trained only on normal Linux syslog sequences, and deploy it locally to avoid external dependencies. We explore critical parameters, including the minimum number of tokens per log sequence, sliding window intervals, and mask ratios while attempting to detect log anomaly. In controlled experiments, a 15-second sliding window with a 10-second overlap provided the best trade-off between detection effectiveness and inference latency, supporting real-time monitoring requirements. Quantitative evaluation demonstrates that shorter sliding windows, despite capturing less context, resulted in slightly higher detection performance of anomalous logs. The model achieved high accuracy in distinguishing normal from abnormal log sequences, where sequences were classified as anomalous if more than 10% of masked tokens were incorrectly predicted. A qualitative assessment with domain experts further validated the operational usefulness of the approach, indicating reduced manual monitoring effort and suitability for integration into AIOps pipelines under confidentiality constraints.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"8 ","pages":"Article 200475"},"PeriodicalIF":3.6,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}