Applied AI letters最新文献

{"title":"Improved YOLOv5 for Efficient Elimination of Unwanted Video Frames From High-Speed Video Arrays Capturing Bat's Kinematics","authors":"D. P. Jayathunga,&nbsp;M. Ramashini,&nbsp;Juliana Zaini,&nbsp;R. Müller,&nbsp;Liyanage C. De Silva","doi":"10.1002/ail2.70014","DOIUrl":"https://doi.org/10.1002/ail2.70014","url":null,"abstract":"<p>This research aims to investigate the flying kinematics of bats, drawing inspiration from nature to benefit humans in achieving vision-independent flights. The project uses 50 cameras mounted inside a specially designed tunnel to analyze bat flying behavior. However, the simultaneous recording of all cameras generates numerous unnecessary image frames, prompting the need for a highly accurate filter. The study focuses on developing this filter using deep learning techniques to classify images based on the bat's location within each frame. We employed widely used one-stage object detection algorithms: YOLOv4, YOLOv4-tiny, and YOLOv5. Notably, YOLOv4, even though it is the older version we adapted, fulfills the intended objective of the study with better accuracy, for two different datasets, along with a higher learning process. Then, we closely looked at the tunable hyperparameters and augmentation techniques of the YOLOv4 model and adapted them in hyperparameter tuning on YOLOv5. Then we examined the impact of these hyperparameters and data augmentation techniques on the performance of YOLOv5L. Based on the result, we adapted the hyperparameters and data augmentation techniques, which have a positive impact on the YOLOv5 model. Improved YOLOv5L achieved better performance with a mean average precision (mAP) of 99.3% where the average precision (AP) of each classification scored more than 99% along with an auto anchor detection mechanism.</p>","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.70014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145626476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Memory-Augmented Whale Optimization Algorithm (DMA-WOA) as Feature Descriptor for Polycystic Ovary Syndrome Detection","authors":"Daniel Kwame Amissah,&nbsp;Leonard Mensah Boante,&nbsp;Solomon Mensah,&nbsp;Ebenezer Owusu,&nbsp;Justice Kwame Appati","doi":"10.1002/ail2.70016","DOIUrl":"https://doi.org/10.1002/ail2.70016","url":null,"abstract":"<p>This study introduces a dynamically memory-adjusted whale optimization algorithm (DMA-WOA) for feature selection in polycystic ovary syndrome (PCOS) diagnosis. To overcome the standard WOA's limitations in balancing exploration and exploitation, DMA-WOA incorporated adaptive memory control to improve convergence stability and computational efficiency. In DMA-WOA adaptive control dynamics adjusted memory size and influence based on population diversity and fitness change, enabling consistent convergence in high-dimensional clinical data. The framework was evaluated on the only publicly available PCOS electronic health records dataset using diverse classifiers, including SVM, RF, LR, MLP, RNN, LSTM, GRU, TabTransformer, and TabNet. Results showed that DMA-WOA achieved superior accuracy, generalization, and runtime efficiency compared to baseline and standard WOA approaches, while comparative analysis with existing metaheuristics confirmed its enhanced optimization robustness and diagnostic reliability.</p>","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.70016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145625968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated AI-Based Lung Disease Classification Using Point-of-Care Ultrasound","authors":"Nixson Okila,&nbsp;Andrew Katumba,&nbsp;Joyce Nakatumba-Nabende,&nbsp;Sudi Murindanyi,&nbsp;Jonathan Serugunda,&nbsp;Cosmas Mwikirize,&nbsp;Samuel Bugeza,&nbsp;Anthony Oriekot,&nbsp;Juliet Bosa,&nbsp;Eva Nabawanuka","doi":"10.1002/ail2.70012","DOIUrl":"https://doi.org/10.1002/ail2.70012","url":null,"abstract":"<p>Timely and accurate diagnosis of lung diseases is critical for reducing related morbidity and mortality. Lung ultrasound (LUS) has emerged as a useful point-of-care tool for evaluating various lung conditions. However, interpreting LUS images remains challenging due to operator-dependent variability, low image quality, and limited availability of experts in many regions. In this study, we present a lightweight and efficient deep learning model, ParSE-CNN, alongside fine-tuned versions of VGG-16, InceptionV3, Xception, and Vision Transformer architectures, to classify LUS images into three categories: COVID-19, other lung pathology, and healthy lung. Models were trained using data from public sources and Ugandan healthcare facilities, and evaluated on a held-out Ugandan dataset. Fine-tuned VGG-16 achieved the highest classification performance with 98% accuracy, 97% precision, 98% recall, and a 97% F1-score. ParSE-CNN yielded a competitive accuracy of 95%, precision of 94%, recall of 95%, and F1-score of 97% while offering a 58.3% faster inference time (0.006 s vs. 0.014 s) and a lower parameter count (5.18 M vs. 10.30 M) than VGG-16. To enhance input quality, we developed a preprocessing pipeline, and to improve interpretability, we employed Grad-CAM heatmaps, which showed high alignment with radiologically relevant features. Finally, ParSE-CNN was integrated into a mobile LUS workflow with a PC backend, enabling real-time AI-assisted diagnosis at the point of care in low-resource settings.</p>","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.70012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145469949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Few-Shot Learning in Remote Sensing: Fusing Vision and Vision-Language Models","authors":"Jia Yun Chua,&nbsp;Argyrios Zolotas,&nbsp;Miguel Arana-Catania","doi":"10.1002/ail2.70010","DOIUrl":"https://doi.org/10.1002/ail2.70010","url":null,"abstract":"<p>Remote sensing has become a vital tool across sectors such as urban planning, environmental monitoring, and disaster response. Although the volume of data generated has increased significantly, traditional vision models are often constrained by the requirement for extensive domain-specific labelled data and their limited ability to understand the context within complex environments. Vision Language Models offer a complementary approach by integrating visual and textual data; however, their application to remote sensing remains underexplored, particularly given their generalist nature. This work investigates the combination of vision models and VLMs to enhance image analysis in remote sensing, with a focus on aircraft detection and scene understanding. The integration of YOLO with VLMs such as LLaVA, ChatGPT, and Gemini aims to achieve more accurate and contextually aware image interpretation. Performance is evaluated on both labelled and unlabelled remote sensing data, as well as degraded image scenarios that are crucial for remote sensing. The findings show an average MAE improvement of 48.46% across models in the accuracy of aircraft detection and counting, especially in challenging conditions, in both raw and degraded scenarios. A 6.17% improvement in CLIPScore for comprehensive understanding of remote sensing images is obtained. The proposed approach combining traditional vision models and VLMs paves the way for more advanced and efficient remote sensing image analysis, especially in few-shot learning scenarios.</p>","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.70010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145469550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Explainable and Lightweight CNN Framework for Robust Potato Leaf Disease Classification Using Grad-CAM Visualization","authors":"MD Jiabul Hoque,&nbsp;Md. Saiful Islam","doi":"10.1002/ail2.70011","DOIUrl":"https://doi.org/10.1002/ail2.70011","url":null,"abstract":"<p>For identifying foliar diseases in crops at an early stage, accurate detection is necessary in maintaining food security, minimizing economic losses, and cultivating sustainable agriculture. In staple crops, potato is highly vulnerable to lethal diseases like Early Blight and Late Blight that can drastically affect both the quality and the quantity of the yield. Conventional diagnostic procedures using visual observation and/or laboratory examinations are frequently tedious, time-consuming, and susceptible to error. To address these problems, in this research, we propose a novel deep learning architecture using a customized convolutional neural network (CNN) for classifying potato leaf images into three distinct classes, namely Early Blight, Late Blight and Healthy. The model is trained on a selective and heavily augmented subset of the PlantVillage dataset containing 11,593 images and further optimized using regularization techniques like dropout and batch normalization. The system architecture is intended to keep the tradeoff between performance and computational efficiency, so as to fit real-world agricultural scenarios. To increase interpretability and improve trust, we use the Gradient-weighted Class Activation Mapping (Grad-CAM) to visualize the regions in space of the leaves that most contribute to the prediction of the model. The experimental results show superior performance and the proposed model reaches 99.14% accuracy and close-to-perfect precision, recall and F1-scores in all of the classes. Grad-CAM visualizations validate that the model is robust in attending to biologically meaningful regions for the disease symptoms. In addition, we perform comparative analyses against recent state-of-the-art models, and demonstrate that the proposed approach outperforms the others in accuracy and interpretability.</p>","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.70011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145385007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence","authors":"Ross O'Driscoll,&nbsp;Claudia Hagen,&nbsp;Joe Bater,&nbsp;James Adams","doi":"10.1002/ail2.70007","DOIUrl":"https://doi.org/10.1002/ail2.70007","url":null,"abstract":"<p>Cyber-attacks pose a security threat to military command and control networks, Intelligence, Surveillance, and Reconnaissance (ISR) systems, and civilian critical national infrastructure. The use of artificial intelligence and autonomous agents in these attacks increases the scale, range, and complexity of this threat and the subsequent disruption they cause. Autonomous Cyber Defence (ACD) agents aimto mitigate this threat by responding at machine speed and at the scale required to address the problem. Additionally, they reduce the burden on the limited number of human cyber experts available to respond to an attack. Sequential decision-making algorithms such as Deep Reinforcement Learning (RL) provide a promising route to create ACD agents. These algorithms focus on a single objectivesuch as minimising the intrusion of red agents on the network, by using a handcrafted weighted sum of rewards. This approach removes the ability to adapt the model during inference, and fails to address the many competing objectivespresent when operating and protecting these networks. Conflicting objectives, such as restoring a machine from a back-up image, must be carefully balanced with the cost of associated down-time or the disruption to network traffic or services that might result. Instead of pursuing a Single-Objective RL (SORL) approach, here we present a simple example of a multi-objective network defense game that requires consideration of both defending the network against red-agents and maintaining the critical functionality of green-agents. Two Multi-Objective Reinforcement Learning (MORL) algorithms, namely Multi-Objective Proximal Policy Optimization (MOPPO) and Pareto-Conditioned Networks (PCN), are used to create two trained ACD agents whose performance is compared on our Multi-Objective Cyber Defense game. The benefits and limitations of MORL ACD agents in comparison to SORL ACD agents are discussed based on the investigations of this game.</p>","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"6 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.70007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classical Machine Learning Approaches for Early Hypertension Risk Prediction: A Systematic Review","authors":"Abebaw Agegne Engda,&nbsp;Ayodeji Olalekan Salau,&nbsp;Olubunmi Ajala","doi":"10.1002/ail2.70005","DOIUrl":"https://doi.org/10.1002/ail2.70005","url":null,"abstract":"<p><i>This review evaluates classical machine learning-based hypertension prediction models, emphasizing their role in addressing global health burdens</i>, particularly in low- and middle-income countries. Hypertension affects over 1.28 billion people globally and contributes to cardiovascular disease and mortality. The review compares machine-learning techniques with traditional methods, focusing on key datasets, evaluation metrics, and model development to advance early detection and effective hypertension management. The review used the PRISMA framework, using databases such as Google Scholar, PubMed, and IEEE explorer to identify studies published between 2020 and 2024 on machine learning techniques, predictive models, and early detection of hypertension based on relevance, methodological rigor, and inclusion criteria. The study analyzed hypertension prediction models across various countries, including the US, England, Korea, Japan, China, Indonesia, Thailand, India, Bangladesh, Nepal, and several African countries. The models' performance varied with AUC statistic values ranging from 0.6 to 0.9, indicating a wide range of predictive accuracy. Machine learning techniques generally reported higher performance metrics than traditional statistical methods. Risk factor heterogeneity was evident, with models like random forest, logistic regression, and gradient-boosted trees showing high predictive accuracy. Emerging techniques like <b>SMOTE</b> (Synthetic Minority Oversampling Technique) and ensemble methods improved unbalanced data set performance. The review explores the potential of machine learning-based hypertension prediction models in healthcare, highlighting their ability to accurately predict hypertension risk, tailor interventions to specific populations, and optimize healthcare resources in low- and middle-income countries. However, challenges include data quality, model explainability, and ethical considerations. Despite these, ML integration offers scalable and cost-effective solutions, especially in resource-limited settings. Future research should focus on diverse datasets, advanced feature integration, and longitudinal validations.</p>","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"6 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.70005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144915290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tsetse Fly Detection and Sex Classification Model Enrichment Employing YOLOv8 and YOLO11 Architecture","authors":"Wegene Demisie Jima,&nbsp;Serkalem Fekadu Desta,&nbsp;Tesfaye Adisu Tarekegn,&nbsp;Genet Shewangizaw Gebremedhin,&nbsp;Ashenafi Bekele Gutema,&nbsp;Taye Girma Debelee","doi":"10.1002/ail2.70004","DOIUrl":"https://doi.org/10.1002/ail2.70004","url":null,"abstract":"<p>The sterile insect technique (SIT) represents a highly effective and promising method for combating tsetse fly-related infections, which involves the release of sterilized male tsetse flies in the assigned zones. However, tsetse fly rearing poses specific challenges, particularly in the tsetse sex separation, as this process is labor-intensive and incurs significant costs. Here, we report a simple model that classifies tsetse flies by sex using an object detection model based on the YOLO algorithm. This paper also conducted a comparative analysis of YOLOv8 and YOLO11 deep learning models, focusing on their efficacy in tsetse fly detection and classification using a range of performance metrics and statistical analysis. The findings reveal that the classification accuracy of YOLO11 stands at 97.6%, whereas YOLOv8 achieves 95.6%. The classification precision of YOLO11 in identifying tsetse flies is 88.6%, while that of YOLOv8 is 85.9%. Additionally, YOLO11 demonstrates an inference speed of 13.0 ms, slightly faster than YOLOv8's 13.4 ms in tsetse sex detection. Moreover, YOLO11 outperformed YOLOv8 in both F1 score and [email protected]–0.9, a success attributed to its enhanced architectural design. However, statistical tests indicate there is no significant difference between the two models, achieving <i>p</i> values ≥ 0.05 for all metrics. This study adds value to tsetse rearing and fly-based disease control by offering automated tsetse sex detection insights into its practical uses in real-world contexts. Furthermore, this research enriches the understanding of the two models with tsetse flies as the focal point and recommends a more effective and accurate detection approach. Finally, integrating the model with the mobile object detection Android app will reduce tsetse sex sorting dependency on experienced technical experts and enhance tsetse rearing productivity.</p>","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"6 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.70004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144897662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Hybrid AI and Fuzzy MCDM Approach for Retailer Evaluation: Leveraging Sentiment Analysis and Expert Insights","authors":"Adem Pinar","doi":"10.1002/ail2.70006","DOIUrl":"https://doi.org/10.1002/ail2.70006","url":null,"abstract":"<p>This study proposes a hybrid methodology for evaluating leading retail companies based on customer perspectives, combining Artificial Intelligence (AI)-driven sentiment analysis with fuzzy multiple criteria decision-making (MCDM). The framework integrates large-scale customer review analysis with expert decision-making to provide a comprehensive assessment of retail performance. The process begins with AI-based text mining to collect and analyze customer reviews, extracting emotional tones and identifying frequently mentioned criteria. Expert judgment is then applied to refine, organize, and assign importance to these criteria. The q-rung orthopair fuzzy set MCDM methodology is employed to address uncertainty, conflicting objectives, and qualitative expert opinions by translating them into a structured quantitative evaluation. This hybrid approach offers a balanced assessment that combines subjective and objective dimensions. As a case study, 2000 customer reviews from each of four major U.S. retailers—Amazon, Walmart, Costco, and Target—were analyzed to derive key evaluation criteria based on user feedback. The proposed method distinguishes itself through its unique integration of sentiment analysis and decision-makers' expert evaluations, enabling a holistic and robust evaluation of alternatives. By bridging customer perceptions with expert analysis, this methodology provides a deeper, more nuanced understanding of retailer performance, contributing to improved supplier selection and business decision-making processes. A second analysis, enabled by this methodology, also highlighted key performance differences among the retailers in areas such as customer service, delivery experience, and return/refund processes. Among the findings, Target and Amazon showed the strongest overall sentiment performance, while Costco excelled in return policies and Walmart exhibited weaker results in customer service and delivery. As a result, this hybrid methodology offers valuable insights for both decision-makers aiming to optimize supplier selection and customers seeking better shopping experiences.</p>","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"6 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.70006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144894251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thematic Analysis of Expert Opinions on the Use of Large Language Models in Software Development","authors":"Sargam Yadav,&nbsp;Abhishek Kaushik,&nbsp;Asifa Mehmood Qureshi","doi":"10.1002/ail2.127","DOIUrl":"https://doi.org/10.1002/ail2.127","url":null,"abstract":"<p>Large Language Models (LLMs) have gained popularity in recent years due to their ability to generate human-like text and conduct context-aware conversations in natural languages. This ability can be greatly beneficial for fields like software development, where LLMs can assist with tasks such as code generation, code review, and debugging. In this paper, thematic analysis has been performed on unstructured opinions obtained from 11 experts about the integration of LLMs in the field of software development to understand their benefits and limitations using two natural language processing (NLP) techniques: sentiment analysis and keyword extraction and analysis. Sentiment analysis suggests that most experts were optimistic and pragmatic about the use of generative artificial intelligence in software development, although some experts engaged in critical reflection. Keyword extraction and analysis mapped several keywords to pre-defined themes, which highlighted benefits of LLMs such as improved code quality and enhanced developer productivity, as well as challenges such as the risk of over-reliance, and privacy and security concerns.</p>","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"6 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.127","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144814825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}