Decision Analytics Journal最新文献

{"title":"A cost-effective seed selection model for multi-constraint influence maximization in social networks","authors":"Tarun Kumer Biswas ,&nbsp;Alireza Abbasi ,&nbsp;Ripon Kumar Chakrabortty","doi":"10.1016/j.dajour.2024.100474","DOIUrl":"https://doi.org/10.1016/j.dajour.2024.100474","url":null,"abstract":"<div><p>The Influence Maximization (IM) problem aims to maximize the diffusion of information or adoption of products among users within a social network by identifying and activating a set of initial users. It is not unrealistic to have a higher activation cost for more influential users in real-life applications. However, existing works on IM focus solely on finding the most influential users as the seed set, without considering either the activation costs of individual nodes and the total budget or the size of the seed set. This oversight may lead to infeasible solutions, particularly from financial and managerial perspectives, respectively. To address these issues, we propose a more realistic and generalized formulation called Multi-Constraint Influence Maximization (MCIM) to achieve a cost-effective solution under both budgetary and cardinality constraints. The MCIM model allows for variable-length solutions, necessitating the exclusion of seed nodes from the influence spread estimation. Consequently, unlike existing IM formulations, the spread function under the MCIM model is no longer monotonic but submodular. As it has also been proven to be an NP-hard problem, we propose a Simple Additive Weighting (SAW)-assisted Differential Evolution (DE) algorithm for solving large-size real-world IM problems. Experimental results on four datasets demonstrate the effectiveness of the proposed formulation and algorithm in finding optimal and cost-effective solutions.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100474"},"PeriodicalIF":0.0,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277266222400078X/pdfft?md5=4ed4d3754257d98ed86eed9502984de3&pid=1-s2.0-S277266222400078X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140906268","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 novel three-phase expansion algorithm for uncovering communities in social networks using local influence and similarity in embedding space","authors":"Meriem Adraoui ,&nbsp;Elyazid Akachar ,&nbsp;Yahya Bougteb ,&nbsp;Brahim Ouhbi ,&nbsp;Bouchra Frikh ,&nbsp;Asmaa Retbi ,&nbsp;Samir Bennani","doi":"10.1016/j.dajour.2024.100472","DOIUrl":"https://doi.org/10.1016/j.dajour.2024.100472","url":null,"abstract":"<div><p>Community detection can help uncover and understand complex networks’ underlying patterns and structures. It involves identifying cohesive groups with similar entities while being separated from other groups. Social networks are a prime example of an area where community detection is particularly relevant, as it can provide insight into the behaviors of individuals. Several community detection methods have been proposed, each addressing the problem from different perspectives. However, the rise of vast and intricate networks from diverse domains has necessitated the development of community detection methods that can effectively handle large-scale graphs. This study introduces a novel three-phase expansion algorithm for community discovery based on nodes’ local information and similarity in embedding space. The proposed model consists of three steps. In the first stage, we generate an embedding space in which nodes are represented as vectors, and we extract nodes that greatly influence others and have an extraordinary ability to create communities using degree centrality measures. Then, based on cosine similarity in the embedding space, we group the most similar nodes to the influential ones in the same community and create an initial community structure. In the last phase, we extract the weak communities from the initial community structure generated in the second phase and merge them with the strong ones. We conduct extensive experiments on both real-world and synthetic networks to demonstrate the effectiveness of our proposal. The experimental results show that the proposed algorithm performs better than other widely used algorithms and is highly reliable and efficient in large-scale graphs.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100472"},"PeriodicalIF":0.0,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000766/pdfft?md5=fb96382feb13bbd6c1de0cd9f703afbd&pid=1-s2.0-S2772662224000766-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140818665","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 machine learning model for sentiment analysis and satisfaction assessment with Turkish universities using Twitter data","authors":"Abdulfattah Ba Alawi,&nbsp;Ferhat Bozkurt","doi":"10.1016/j.dajour.2024.100473","DOIUrl":"https://doi.org/10.1016/j.dajour.2024.100473","url":null,"abstract":"<div><p>Social media platforms, such as X (Twitter), contain enormous amounts of data, and collecting valuable information from that data assists in making more informed decisions. In recent years, governments and institutions have begun to explore the possibilities of utilizing social networks as a platform to supply and enhance the quality of their services. Consequently, there is an increased demand to estimate people’s satisfaction with Turkish Universities written in Turkish wherever it is challenging and requires more effort to cope with its rich morphological structure to perform a Sentiment Analysis task. This study proposes a Turkish text sentiment analysis methodology for estimating people’s satisfaction with Turkish Universities by employing nine conventional machine learning models, deep learning techniques, and BERT-based transformers upon an original manually annotated dataset consisting of 17,793 tweets in Turkish. An innovative hybrid architecture, named BERT-BiLSTM-CNN, integrates Bidirectional Encoder Representations from Transformers (BERT), Bidirectional Long Short-Term Memory (BiLSTM), and a triple parallel Convolutional Neural Network (CNN) branch, achieving exceptional accuracy in sentiment analysis of Turkish tweets. During testing, the proposed architecture revealed an impressive accuracy rate of over 0.9101, an F1 Score of 0.8801, and a Receiver Operating Characteristic (ROC) of 0.9632 for analyzing sentiment, demonstrating that the model outperformed the state-of-the-art models, and it is capable of coping with the linguistic complexities of Turkish sentiment analysis. This work provides new insights into sentiment analysis by proposing a hybrid model that combines several computational methodologies to improve the understanding of attitudes.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100473"},"PeriodicalIF":0.0,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000778/pdfft?md5=1a3bb3730d164f65bda14895f575f5c7&pid=1-s2.0-S2772662224000778-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140822863","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 improved dynamic window approach algorithm for dynamic obstacle avoidance in mobile robot formation","authors":"Yanjie Cao,&nbsp;Norzalilah Mohamad Nor","doi":"10.1016/j.dajour.2024.100471","DOIUrl":"https://doi.org/10.1016/j.dajour.2024.100471","url":null,"abstract":"<div><p>Dynamic Window Approach (DWA) algorithm is a commonly used choice in dynamic obstacle avoidance. However, the traditional DWA algorithm evaluation function is poorly adapted to dynamic obstacle avoidance and has defects in efficiency and safety. In this paper, we consider the speed and heading of the mobile robot formation and the speed and heading of the obstacles and design the speed-varying obstacle avoidance and safety distance evaluation coefficients. The objective is to design an improved DWA algorithm to improve the obstacle avoidance ability of mobile robot formations when encountering dynamic obstacle interference. First, the obstacle environment in which the mobile robot formation is located is analyzed to determine the obstacles that threaten the formation and those that are not. Second, the obstacle velocity space is evaluated and analyzed using the velocity change evaluation coefficient so that the robot formation’s obstacle avoidance behavior after the combination of angular and linear travel velocities has high robustness in the face of dynamic obstacle interference. Finally, the evaluation coefficient of safe obstacle avoidance distance is designed to accurately judge the positional relationship between the robot formation and dynamic obstacles at the moment of obstacle avoidance, which maximizes the safety of the robot formation traveling. The experimental results show that the improved algorithm shortens the obstacle avoidance time by 37.3% and saves 16.8% of the distance traveled than the traditional algorithm. It also achieves good results in terms of robustness and safety.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100471"},"PeriodicalIF":0.0,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000754/pdfft?md5=9771efe177a10c8ebb96d9ed52f7b44b&pid=1-s2.0-S2772662224000754-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140901051","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 systematic review of hyperparameter optimization techniques in Convolutional Neural Networks","authors":"Mohaimenul Azam Khan Raiaan ,&nbsp;Sadman Sakib ,&nbsp;Nur Mohammad Fahad ,&nbsp;Abdullah Al Mamun ,&nbsp;Md. Anisur Rahman ,&nbsp;Swakkhar Shatabda ,&nbsp;Md. Saddam Hossain Mukta","doi":"10.1016/j.dajour.2024.100470","DOIUrl":"10.1016/j.dajour.2024.100470","url":null,"abstract":"<div><p>Convolutional Neural Network (CNN) is a prevalent topic in deep learning (DL) research for their architectural advantages. CNN relies heavily on hyperparameter configurations, and manually tuning these hyperparameters can be time-consuming for researchers, therefore we need efficient optimization techniques. In this systematic review, we explore a range of well used algorithms, including metaheuristic, statistical, sequential, and numerical approaches, to fine-tune CNN hyperparameters. Our research offers an exhaustive categorization of these hyperparameter optimization (HPO) algorithms and investigates the fundamental concepts of CNN, explaining the role of hyperparameters and their variants. Furthermore, an exhaustive literature review of HPO algorithms in CNN employing the above mentioned algorithms is undertaken. A comparative analysis is conducted based on their HPO strategies, error evaluation approaches, and accuracy results across various datasets to assess the efficacy of these methods. In addition to addressing current challenges in HPO, our research illuminates unresolved issues in the field. By providing insightful evaluations of the merits and demerits of various HPO algorithms, our objective is to assist researchers in determining a suitable method for a particular problem and dataset. By highlighting future research directions and synthesizing diversified knowledge, our survey contributes significantly to the ongoing development of CNN hyperparameter optimization.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100470"},"PeriodicalIF":0.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000742/pdfft?md5=bcf7d25d36917a6db2d67b9e06bf7bec&pid=1-s2.0-S2772662224000742-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140778845","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 integrated structural equation modeling and fuzzy qualitative comparative analysis model for examining green procurement adoption drivers","authors":"Maulana Abdul Hafish,&nbsp;Ilyas Masudin,&nbsp;Fien Zulfikarijah,&nbsp;Tsiqatun Nasyiah,&nbsp;Dian Palupi Restuputri","doi":"10.1016/j.dajour.2024.100469","DOIUrl":"https://doi.org/10.1016/j.dajour.2024.100469","url":null,"abstract":"<div><p>This research examines the factors that influence the implementation of green procurement in small and medium enterprises (SMEs), namely top management commitment, regulations, costs, pressure from consumers, and transformational leadership as moderation. The research employs a quantitative approach with 206 samples from the Indonesian SME industry. It utilizes Structural Equation Modeling (SEM) and Fuzzy-set Qualitative Comparative Analysis (FsQCA). The data set includes factors such as top management commitment, regulations, costs, consumer pressure, and transformational leadership as moderation variables. The results show that management commitment, regulations, costs, and customer pressure influence green procurement. The role of the moderating variable shows that transformational leadership only strengthens the influence of costs on green procurement and regulation on green procurement. Meanwhile, the influence of top management commitment to green procurement and customer pressure on green procurement shows that transformational leadership does not strengthen these variables. The research’s novelty lies in combining SEM and FsQCA to analyze the causal relationships among variables influencing green procurement in SMEs. It highlights the importance of understanding and effectively managing these relationships for organizations adopting green procurement practices.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100469"},"PeriodicalIF":0.0,"publicationDate":"2024-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000730/pdfft?md5=909e2b8b728f351c30159947315948dd&pid=1-s2.0-S2772662224000730-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140643591","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 efficient artificial intelligence approach for early detection of cross-site scripting attacks","authors":"Faizan Younas ,&nbsp;Ali Raza ,&nbsp;Nisrean Thalji ,&nbsp;Laith Abualigah ,&nbsp;Raed Abu Zitar ,&nbsp;Heming Jia","doi":"10.1016/j.dajour.2024.100466","DOIUrl":"https://doi.org/10.1016/j.dajour.2024.100466","url":null,"abstract":"<div><p>Cross-Site Scripting (XSS) attacks continue to pose a significant threat to web applications, compromising the security and integrity of user data. XSS is a web application vulnerability where malicious scripts are injected into websites, allowing attackers to execute arbitrary code in the victim’s browser. The consequences of XSS attacks can be severe, ranging from financial losses to compromising sensitive user information. XSS attacks enable attackers to deface websites, distribute malware, or launch phishing campaigns, compromising the trust and reputation of affected organizations. This study proposes an efficient artificial intelligence approach for the early detection of XSS attacks, utilizing machine learning and deep learning approaches, including Long Short-Term Memory (LSTM). Additionally, advanced feature engineering techniques, such as the Term Frequency-Inverse Document Frequency (TFIDF), are applied and compared to evaluate results. We introduce a novel approach named LSTM-TFIDF (LSTF) for feature extraction, which combines temporal and TFIDF features from the cross-site scripting dataset, resulting in a new feature set. Extensive research experiments demonstrate that the random forest method achieved a high performance of 0.99, outperforming state-of-the-art approaches using the proposed features. A k-fold cross-validation mechanism is utilized to validate the performance of applied methods, and hyperparameter tuning further enhances the performance of XSS attack detection. We have applied Explainable Artificial Intelligence (XAI) to understand the interpretability and transparency of the proposed model in detecting XSS attacks. This study makes a valuable contribution to the growing body of knowledge on XSS attacks and provides an efficient model for developers and security practitioners to enhance the security of web applications.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100466"},"PeriodicalIF":0.0,"publicationDate":"2024-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000705/pdfft?md5=d8337fc3e6c7d9511262ccfa7ec0e613&pid=1-s2.0-S2772662224000705-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140631611","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 intelligent recommender system using machine learning association rules and rough set for disease prediction from incomplete symptom set","authors":"Kamakhya Narain Singh ,&nbsp;Jibendu Kumar Mantri","doi":"10.1016/j.dajour.2024.100468","DOIUrl":"https://doi.org/10.1016/j.dajour.2024.100468","url":null,"abstract":"<div><p>Digital devices are an integral component of the healthcare sector. With the advancement of modern technology with Artificial Intelligence (AI) and Machine Learning (ML), an automated diagnosis system with promising results is not a difficult task. This study aims to develop a recommender system (RS) for better diagnosis and improvement of patient care by hybridizing machine learning association rules (AR) and rough set theory (RST) to classify acute and life-threatening diseases. Initially data is preprocessed using binary, on-hot vector, and min–max scale to remove the noise. RST is used for feature selection to deal with incompleteness, inconsistency, and vagueness. We have designed an Associated Symptom Selection (ASS) algorithm to extract the mutually associated symptoms which need to be further matched in the existing database for prediction. ASS is especially helpful in detecting neurodevelopmental type diseases because the symptoms are usually not detectable by standard tests, and observations of behavioral expressions do general testing. The experiment is carried out using six popular ML classifiers such as AR, Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), Linear Support Vector Machine (LSVM), and Naive Bayes (NB) on a publicly available datasets. Performance was compared among different classifiers regarding the accuracy, precision, recall, F1-score, and J-Score value. The experimental result shows that AR performs better on clinical data with an accuracy of 94.40%, precision of 90.73%, recall of 94.45%, F1-score of 92.55%, and J-score of 95.14% and on autism with 98.7% accuracy, 98% precision, 97.8% recall, 97.9% F1-score, and 97.12% J-score respectively.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100468"},"PeriodicalIF":0.0,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000729/pdfft?md5=5d404290fff0fa44cd18e634ca426d4e&pid=1-s2.0-S2772662224000729-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140643593","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 inverse data envelopment analysis model for solving time substitution problems","authors":"Mojtaba Ghiyasi","doi":"10.1016/j.dajour.2024.100467","DOIUrl":"https://doi.org/10.1016/j.dajour.2024.100467","url":null,"abstract":"<div><p>This paper proposes new inverse data envelopment analysis (DEA) models considering input and output efficiency measures for the time substitution problem. We compare our DEA model with existing models in the literature. We show that existing models in the literature overestimate the required resources, and on the other hand, these are a special case of our models. We show that our models are unit invariant. We also describe another appealing characteristic of our model, that is, efficiency invariant, as a consequence of inverse structure. An illustrative example is provided to explain the proposed model and provide a comparison of our models and existing models in the literature. We also used our models in a real-world application, the selected economic sectors in Iran. The results show some potential improvements during the period of study.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100467"},"PeriodicalIF":0.0,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000717/pdfft?md5=347ea4b399c78c76af193a77417cd1e6&pid=1-s2.0-S2772662224000717-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140643590","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 cluster-based human resources analytics for predicting employee turnover using optimized Artificial Neural Networks and data augmentation","authors":"Mohammad Reza Shafie ,&nbsp;Hamed Khosravi ,&nbsp;Sarah Farhadpour ,&nbsp;Srinjoy Das ,&nbsp;Imtiaz Ahmed","doi":"10.1016/j.dajour.2024.100461","DOIUrl":"https://doi.org/10.1016/j.dajour.2024.100461","url":null,"abstract":"<div><p>This study presents an innovative methodology to predict employee turnover by integrating Artificial Neural Networks (ANN) with clustering techniques. We focus on hyperparameter tuning with various input parameters to obtain optimal ANN models. By segmenting data, the study identifies critical turnover predictors, allowing targeted interventions to be implemented to improve the efficiency and effectiveness of retention policies. Data augmentation using Conditional Generative Adversarial Networks (CTGAN) is performed on clusters with imbalanced data. Following this, the optimized ANN models are applied to these augmented clusters, leading to a notable improvement in their performance. We evaluate our optimized ANN models against five other ANN variants and four traditional machine learning models to demonstrate their superior accuracy and recall. The proposed approach achieves operational advantages by shifting away from generalized strategies to more focused, cluster-based policies, which can optimize resource utilization and reduce costs. Because of its practicality and enhanced ability to predict and manage employee turnover, this method, supported by empirical evidence, is a significant advancement in human resource (HR) analytics</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"11 ","pages":"Article 100461"},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224000651/pdfft?md5=097a7e1383b4406264e4df0870869d51&pid=1-s2.0-S2772662224000651-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140621042","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}