Intelligent Systems with Applications最新文献

{"title":"Alert correlation for intelligent threat detection and response","authors":"Bronagh Lanigan ,&nbsp;Zeinab Rezaeifar ,&nbsp;Federico Cruciani ,&nbsp;Michael Milliken ,&nbsp;Jordan Vincent ,&nbsp;Samuel Moore ,&nbsp;Muhammad Aaqib ,&nbsp;Alan Mills ,&nbsp;Pushpinder K. Chouhan ,&nbsp;Alfie Beard ,&nbsp;Chris D. Nugent ,&nbsp;Luke Chen ,&nbsp;Alex Healing","doi":"10.1016/j.iswa.2025.200606","DOIUrl":"10.1016/j.iswa.2025.200606","url":null,"abstract":"<div><div>With the increasing diversity of IoT devices, keeping IT systems secure is becoming increasingly difficult. Attackers exploit vulnerabilities within the system in order to access sensitive information, typically reaching their objective through several steps. Current Intrusion Detection Systems (IDSs) focus on low-level alerts, and tend to produce a high rate of false positives. This type of information alone is insufficient for the detection of sophisticated attack scenarios such Advanced Persistent Threats (APTs). Consequently, correlation techniques have recently been introduced to correlate alerts and reconstruct attack scenarios, however, various attack scenarios exist, with diverse characteristics. Also, different steps of the APTs scenarios may have their own characteristics. Therefore, finding a proper method that covers all cases remains a challenge. Moreover, after detecting APTs, how the system should respond to these attacks to avoid sabotage to the system remains a challenge. Thus, in this paper, first for detection of the attacks, we classify different cases, and then, a method based on different characteristics of attack patterns is proposed to detect APT scenarios. The proposed method consists of two main phases: APT detection and the intelligent hybrid response framework. In APT detection phase, similar alerts are aggregated and attack graphs are generated based on a similarity matrix. These graphs, combined with third party API data enable alert correlation and APT scenario detection. Entity graphs are then created to visualise host behaviour, and alert graphs are analysed to detect APT scenarios. In the response phase, attack graphs produced from the correlation inform the hybrid response framework, integrating knowledge and data-driven components that facilitate automated or recommended mitigation. The approach was evaluated on the ZeekData24 dataset. Obtained precision and recall on the malicious traffic was observed to be 96.65% and 87.04% respectively. The results show that our approach can effectively filter false positive alerts with a reduction of the data going from 10,063 alerts daily to 586 meta-alerts, pruned to 48 attack graphs and finally reduced to 20 suspicious attack graphs.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200606"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520065","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":"AI-predictive vaccine stability: a systems biology framework to modernize regulatory testing and cold chain equity","authors":"Sinethemba H. Yakobi,&nbsp;Uchechukwu U. Nwodo","doi":"10.1016/j.iswa.2025.200584","DOIUrl":"10.1016/j.iswa.2025.200584","url":null,"abstract":"<div><div>Vaccine instability contributes to the loss of up to 25 % of doses globally, a challenge intensified by the complexity of next-generation platforms such as mRNA–lipid nanoparticles (mRNA–LNPs), viral vectors, and protein subunits. Current regulatory frameworks (ICH Q5C, WHO TRS 1010) rely on static protocols that overlook platform-specific degradation mechanisms and real-world cold-chain variability. We introduce the Systems Biology–guided AI (SBg-AI) framework, a predictive stability platform integrating omics-derived biomarkers, real-time telemetry, and explainable machine learning. Leveraging recurrent and graph neural networks with Bayesian inference, SBg-AI forecasts degradation events with 89 % accuracy—validated in African and Southeast Asian supply chains. Federated learning ensures multi-manufacturer collaboration while preserving data privacy. In field trials, dynamic expiry predictions reduced mRNA vaccine wastage by 22 %. A phased regulatory roadmap supports transition from hybrid AI-empirical models (2024) to full AI-based stability determinations by 2030. By integrating mechanistic degradation science with real-time telemetry and regulatory-compliant AI, the SBg-AI framework transforms vaccine stability from retrospective batch testing to proactive, precision-guided assurance.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200584"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097475","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":"CTR-Net: Scalable safe reinforcement learning via neural approximations of control theoretic regulators","authors":"Ramen Ghosh","doi":"10.1016/j.iswa.2025.200597","DOIUrl":"10.1016/j.iswa.2025.200597","url":null,"abstract":"<div><div>Ensuring hard constraint satisfaction during both training and deployment is central to safety-critical reinforcement learning (RL). Control-theoretic regularization (CTR) enforces safety by filtering actions through viability- or barrier-certified safe sets, but evaluating the state-dependent regulator <span><math><mrow><mi>R</mi><mrow><mo>(</mo><mi>x</mi><mo>)</mo></mrow></mrow></math></span> online is often prohibitive in high dimensions. We propose a scalable CTR framework based on <em>neural regulator approximators</em> <span><math><mrow><msub><mrow><mover><mrow><mi>R</mi></mrow><mrow><mo>ˆ</mo></mrow></mover></mrow><mrow><mi>θ</mi></mrow></msub><mrow><mo>(</mo><mi>x</mi><mo>)</mo></mrow></mrow></math></span>—differentiable surrogates of <span><math><mrow><mi>R</mi><mrow><mo>(</mo><mi>x</mi><mo>)</mo></mrow></mrow></math></span> that enable fast projection or rejection-sampling filters within standard RL loops. We formalize a learning-theoretic analysis for approximate safety filtering and prove probably approximately correct (PAC)-style guarantees: if the set approximation error is bounded by <span><math><mi>ɛ</mi></math></span> with confidence <span><math><mrow><mn>1</mn><mo>−</mo><mi>δ</mi></mrow></math></span>, then the probability of constraint violation along a length–<span><math><mi>T</mi></math></span> rollout is bounded by a term that scales linearly in <span><math><mi>T</mi></math></span> and <span><math><mi>ɛ</mi></math></span> (plus <span><math><mi>δ</mi></math></span>). We further show that the performance suboptimality of the filtered policy is controlled analytically by the same approximation envelope, yielding an explicit, provably quantified safety-versus-optimality tradeoff (PAC bounds linear in <span><math><mi>T</mi></math></span> and the envelope), complemented by empirical ablations; see also the calculus-of-variations view of constrained tradeoffs (Younis, 2023). The resulting method, <strong>CTR-Net</strong>, is architecture-agnostic and supports real-time execution via fast, differentiable safety layers. Empirical evaluations on high-dimensional continuous-control benchmarks — including safe locomotion and constrained multi-joint manipulation — demonstrate reliable constraint satisfaction during learning and deployment, robustness under modeling uncertainty and substantial computational gains relative to exact viability/barrier baselines. By coupling operator-free neural safety sets with CTR guarantees, CTR-Net bridges theoretical safety certificates and scalable implementation, advancing practical, real-time safe RL for complex intelligent systems.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200597"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416658","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":"End-to-end semantically aware tactile generation","authors":"Mohammad Mahdi Heydari Dastjerdi,&nbsp;Abbas Akkasi,&nbsp;Hilaire Djani,&nbsp;Aatreyi Pranavbhai Mehta,&nbsp;Majid Komeili","doi":"10.1016/j.iswa.2025.200594","DOIUrl":"10.1016/j.iswa.2025.200594","url":null,"abstract":"<div><div>Tactile graphics are an essential tool for conveying visual information to visually impaired individuals. However, translating 2D plots, such as B’ezier curves, polygons, and bar charts, into an effective tactile format remains a challenge. This paper presents a novel, two-stage deep learning pipeline for automating this conversion process. Our method leverages a Pix2Pix architecture, employing a U-Net++ generator network for robust image generation. To improve the perceptual quality of the tactile representations, we incorporate an adversarial perceptual loss function alongside a gradient penalty. The pipeline operates in a sequential manner: firstly, converting the source plot into a grayscale tactile representation, followed by a transformation into a channel-wise equivalent. We evaluate the performance of our model on a comprehensive synthetic dataset consisting of 20,000 source-target pairs encompassing various 2D plot types. To quantify performance, we utilize fuzzy versions of established metrics like pixel accuracy, Dice coefficient, and Jaccard index. Additionally, a human study is conducted to assess the visual quality of the generated tactile graphics. The proposed approach demonstrates promising results, significantly streamlining the conversion of 2D plots into tactile graphics. This paves the way for the development of fully automated systems, enhancing accessibility of visual information for visually impaired individuals.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200594"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416791","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":"Mimicking human attention in driving scenarios for enhanced Visual Question Answering: Insights from eye-tracking and the human attention filter","authors":"Kaavya Rekanar ,&nbsp;Martin J. Hayes ,&nbsp;Ciarán Eising","doi":"10.1016/j.iswa.2025.200578","DOIUrl":"10.1016/j.iswa.2025.200578","url":null,"abstract":"<div><div>Visual Question Answering (VQA) models serve a critical role in interpreting visual data and responding to textual queries, particularly within the domain of autonomous driving. These models enhance situational awareness and enable naturalistic interaction between passengers and vehicle systems. However, existing VQA architectures often underperform in driving contexts due to their generic design and lack of alignment with domain-specific perceptual cues. This study introduces a targeted enhancement strategy based on the integration of human visual attention patterns into VQA systems. The proposed approach investigates visual subjectivity by analysing human responses and gaze behaviours captured through an eye-tracking experiment conducted in a realistic driving scenario. This method enables the direct observation of authentic attention patterns and mitigates the limitations introduced by subjective self-reporting. From these findings, a Human Attention Filter (HAF) is constructed to selectively preserve task-relevant features while suppressing visually distracting but semantically irrelevant content. Three VQA models – LXMERT, ViLBERT, and ViLT – are evaluated to demonstrate the adaptability and impact of HAF across different visual representation strategies, including region-based and patch-based architectures. Case studies involving LXMERT and ViLBERT are conducted to assess the integration of the HAF within region-based multimodal pipelines, showing measurable improvements in performance and alignment with human-like attention. Quantitative analysis reveals statistically significant performance trends correlated with driving experience, highlighting cognitive variability among human participants and informing model interpretability. In addition, failure cases are examined to identify potential limitations introduced by attention filtering, offering critical insight into the boundaries of gaze-guided model alignment.The findings validate the effectiveness of human-informed filtering for improving both accuracy and transparency in autonomous VQA systems, and present HAF as a sustainable, cognitively aligned strategy for advancing trustworthy AI in real-world environments.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200578"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061040","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":"Comprehensive analysis on laser spots adversarial attacks using genetic algorithm","authors":"Youssef Mansour ,&nbsp;Ayad Turky ,&nbsp;Ibrahim Abaker Hashem ,&nbsp;Imad Afyouni ,&nbsp;Ali Bou Nassif ,&nbsp;Ismail Shahin ,&nbsp;Ashraf Elnagar","doi":"10.1016/j.iswa.2025.200598","DOIUrl":"10.1016/j.iswa.2025.200598","url":null,"abstract":"<div><div>Deep Neural Networks (DNNs) are highly vulnerable to disruptions caused by minimal noise, yet research on physical attacks leveraging light-based methods remains scarce. Light-based physical attacks are exceptionally stealthy, posing substantial security threats to vision-dependent applications such as autonomous driving. This paper enhances a state-of-the-art light-based physical attack that employs a genetic algorithm to optimize laser spot placement for maximum effectiveness. We expand the algorithm by introducing additional hyperparameters and systematically optimizing them to establish the most efficient workflow for this problem. To our knowledge, this is the first light-based attack capable of reliably performing physical attacks during daylight conditions, making it the most effective and robust approach of its kind. Extensive experiments conducted in a digital environment demonstrate the superiority of the genetic algorithm over random-location methods. By identifying optimal hyperparameter values, we achieve significant improvements in both performance and efficiency. Specifically, we managed to achieve an Attack Success Rate (ASR) of 89.7%, with an Average Query (AQ) of only 109.4, demonstrating a highly efficient and effective approach. The results reveal that laser spots can severely interfere with advanced DNNs, highlighting the critical security risks associated with this technique.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200598"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465829","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":"Optimizing printing processes with MCTS","authors":"Kadri Kukk ,&nbsp;Ants Torim ,&nbsp;Erki Eessaar ,&nbsp;Tarmo Kadak","doi":"10.1016/j.iswa.2025.200602","DOIUrl":"10.1016/j.iswa.2025.200602","url":null,"abstract":"<div><div>The printing industry benefits from digitalizing workflows such as customer quoting. Intelligent printing process planning is essential to determine the near-optimal price for automated quoting. This paper addresses the automation of sheet imposition, a critical and computationally intensive step in optimizing the printing process that belongs to the general class of cutting and packing problems. We propose a simple recursive sheet imposition representation as the basis for our algorithms. The Brute Force algorithm for optimizing sheet imposition guarantees the cheapest solution but is computationally infeasible for complex tasks. As alternatives, we investigate heuristic algorithms, specifically Monte Carlo Tree Search (MCTS) and Simulated Annealing (SA). Our findings show that while Brute Force is prohibitively slow, MCTS strikes a robust balance between computational performance and solution quality, consistently finding solutions within a 5% margin of optimal price. Although SA can occasionally find superior solutions, MCTS provides a more reliable and efficient approach by consistently delivering results close to the optimal price.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200602"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520071","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":"Towards efficient wafer visual inspection: Exploring novel lightweight approaches for anomaly detection and defect segmentation","authors":"Ivo Façoco,&nbsp;Rafaela Carvalho,&nbsp;Luís Rosado","doi":"10.1016/j.iswa.2025.200576","DOIUrl":"10.1016/j.iswa.2025.200576","url":null,"abstract":"<div><div>The rapid advancement of both wafer manufacturing and AI technologies is reshaping the semiconductor industry. As chip features become smaller and more intricate, the variety and complexity of defects continue to grow, making defect detection increasingly challenging. Meanwhile, AI has made significant strides in unsupervised anomaly detection and supervised defect segmentation, yet its application to wafer inspection remains underexplored. This work bridges these fields by investigating cutting-edge lightweight AI techniques for automated inspection of current generation of silicon wafers. Our study leverages a newly curated dataset comprising 1,055 images of 300 mm wafers, annotated with 6,861 defect labels across seven distinct types, along with PASS/FAIL decisions. From a data-centric perspective, we introduce a novel unsupervised dataset-splitting approach to ensure balanced representation of defect classes and image features. Using the DINO-ViT-S/8 model for feature extraction, our method achieves 96% coverage while maintaining the target 20% test ratio for both individual defects and PASS/FAIL classification. From a model-centric perspective, we benchmark several recent methods for unsupervised anomaly detection and supervised defect segmentation. For unsupervised anomaly detection, EfficientAD obtains the best performance for both pixel-level and image-wise metrics, with F1-scores of 75.14% and 82.35%, respectively. For supervised defect segmentation, UPerNet-Swin achieves the highest performance, with a pixel-level mDice of 47.90 and a mask-level F1-score of 57.45. To facilitate deployment in high-throughput conditions, we conduct a comparative analysis of computational efficiency. Finally, we explore a dual-stage output fusion approach that integrates the best-performing unsupervised anomaly detection and supervised segmentation models to refine PASS/FAIL decisions by incorporating defect severity.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200576"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021018","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":"Enhanced radiology report: Leveraging image enhancement and multi-label transfer learning with attention-based text generation","authors":"Hilya Tsaniya ,&nbsp;Chastine Fatichah ,&nbsp;Nanik Suciati ,&nbsp;Takashi Obi ,&nbsp;Joong-sun Lee","doi":"10.1016/j.iswa.2025.200605","DOIUrl":"10.1016/j.iswa.2025.200605","url":null,"abstract":"<div><div>Current research in radiology report generation tend to overlook the utilization of abnormalities depicted in medical images. This study introduces a novel radiology report generator that integrates a multi-label learning approach for predicting abnormality tags and employs transformer models for generating reports. Additionally, the research explores contrast-based image enhancement to mitigate noise in medical images, evaluating its impact on model performance. The multi-label learning is trained on a dataset with 180 abnormality labels and the features used as initial weights for MIMIC<img>CXR, as a visual feature extractor.Imbalance handling and ensemble methods are employed to optimize multi-label model performance for abnormality tag prediction. Multi-head attention, in conjunction with GPT-2, facilitates context building for medical report generation, utilizing BERT embeddings for text feature extraction. Evaluation metrics demonstrate that the proposed model achieves superior performance in both multi-label prediction accuracy 77 % and text generation, showing an increase in similarity 28 % in average compared to the baseline model. These findings suggest that leveraging transfer learning with an ensemble classifier, combined with a transformer for context building and decoding, effectively utilizes visual and text features. Furthermore, the incorporation of image enhancement techniques significantly impacts model performance.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200605"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520078","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":"AIOps for log anomaly detection in the era of LLMs: A systematic literature review","authors":"Miguel De la Cruz Cabello ,&nbsp;Tiago Prince Sales ,&nbsp;Marcos R. Machado","doi":"10.1016/j.iswa.2025.200608","DOIUrl":"10.1016/j.iswa.2025.200608","url":null,"abstract":"<div><div>Modern IT systems generate large volumes of log data that challenge timely and effective anomaly detection. Traditional methods often require intensive feature engineering and struggle to adapt to dynamic operational environments. This Systematic Literature Review (SLR) analyzes how Artificial Intelligence for IT Operations (AIOps) benefits from advanced language models, emphasizing Large Language Models (LLMs) for more effective log anomaly detection. By comparing state-of-art frameworks with LLM-driven methods, this study reveals that prompt engineering – the practice of designing and refining inputs to AI models to produce accurate and useful outputs – and Retrieval Augmented Generation (RAG) boost accuracy and interpretability without extensive fine-tuning. Experimental findings demonstrate that LLM-based approaches significantly outperform traditional methods across evaluation metrics that include F1-score, precision, and recall. Furthermore, the integration of LLMs with RAG techniques has shown a strong adaptability to changing environments. The applicability of these methods also extends to the military industry. Consequently, the development of specialized LLM systems with RAG tailored for the military industry represents a promising research direction to improve operational effectiveness and responsiveness of defense systems.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200608"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571740","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}