International Journal of Cognitive Computing in Engineering最新文献

{"title":"Speech emotion recognition algorithm of intelligent robot based on ACO-SVM","authors":"Xueliang Kang","doi":"10.1016/j.ijcce.2024.11.008","DOIUrl":"10.1016/j.ijcce.2024.11.008","url":null,"abstract":"<div><div>Despite the significant advancements in computer speech emotion recognition technology, the deployment of intelligent robots in this domain continues to encounter challenges related to inefficiency and emotional ambiguity. Aiming at the shortcomings of existing algorithms in the accuracy and processing of complex emotion states, a novel emotion recognition model of intelligent robot speech based on ACO algorithm and multi-level support vector machine is proposed. This paper first identifies the efficiency and accuracy problems existing in the current emotion recognition system, and clarified the research gap, that is, how to effectively improve the recognition performance of the model under diverse emotion states. To solve this problem, genetic algorithm is used to fine-tune SVM model parameters, and multi-level SVM classification architecture is constructed to enhance the accuracy of emotion recognition. In the feature selection stage, ACO algorithm is introduced to explore the optimal combination of emotion features, aiming at improving the efficiency and robustness of emotion recognition. The experimental results showed that the average recognition rate of the multilevel SVM model optimized by genetic algorithm was as high as 81.26 %, which was 8.78 percentage points higher than that before optimization. When the ACO algorithm was combined with the optimized multi-stage SVM, the average recognition rate of German and Chinese speech emotion corpus was further improved to 85.62 %, with an improvement of 4.38 percentage points. In particular, the optimized algorithm performed significantly better than other emotion categories in recognizing complex emotions such as \"anger\" and \"sadness.\" This paper not only offers an innovative solution for the emotion recognition technology within the realm of intelligent robots, but also extends its methodology to have significant implications for related fields, thereby strongly advancing the in-depth application and development of intelligent bionic algorithms in speech emotion recognition. In addition, through this series of optimization strategies, this paper significantly improves the accuracy of emotion recognition, laying a solid foundation for intelligent robots to achieve more natural and efficient human-computer interaction.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 131-142"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139984","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":"MED-AGNeT: An attention-guided network of customized augmentation of samples based on conditional diffusion for textile defect detection","authors":"Jun Liu,&nbsp;Haolin Li,&nbsp;Hao Liu,&nbsp;Jiuzhen Liang","doi":"10.1016/j.ijcce.2025.01.002","DOIUrl":"10.1016/j.ijcce.2025.01.002","url":null,"abstract":"<div><div>Fabric defect detection plays a vital role in ensuring the production quality of the textile manufacturing industry. However, in practice, there are relatively few manually annotated defective samples, and considering both performance and parameter quantity, there is still room for optimization in the architecture of detection networks. Therefore, this paper proposes a textile defect detection method called MED-AGNet. Firstly, based on the diffusion model, a mask-embedding data augmentation method, MEDiffusion, is proposed. During the training process, a conditional term (M) that represents the shape of the defect is added, and through supervised learning, the generative model learns the correlation between the background and defects. In the generation stage, it samples from a normal distribution and relies on M guidance to gradually generate corresponding defective textiles, thereby expanding the original sample set. An attention-guided network (AGNet) is a network that utilizes attention to guide information across different scales. Its feature extraction module employs a dual-branch information residual unit (DIRU) as a substitute for the conventional convolution block, which combines the feature extraction capabilities of global pooling and max pooling, reducing the number of parameters while also achieving a certain improvement in detection results. In the feature fusion stage, it utilizes the attention-guided fusion module (AGFM), which can allow the attention information of high-level semantics to guide the low-level semantics, and simultaneously, adds a high-level semantic residual attention module (HSRA) to enhance the perception of defect shapes and improve detection effectiveness. Ultimately, AGNet’s true positive rate (TPR), positive predictive value (PPV), and f-measure exceed those of the state-of-the-art (SOTA) algorithms by 1.88%, 0.05%, and 0.77%, respectively, and with a consistent model architecture, its parameter quantity is reduced by 56%.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 238-254"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139989","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":"Data-driven brain emotional learning-based intelligent controller-PID control of MIMO systems based on a modified safe experimentation dynamics algorithm","authors":"Shahrizal Saat ,&nbsp;Mohd Ashraf Ahmad ,&nbsp;Mohd Riduwan Ghazali","doi":"10.1016/j.ijcce.2024.11.005","DOIUrl":"10.1016/j.ijcce.2024.11.005","url":null,"abstract":"<div><div>The increasing complexity of modern plant systems and the limitations of precise mathematical modeling have led to a shift towards data-driven control methods. These methods present an effective alternative that treats plants as black-box systems without requiring explicit models. The safe experimentation dynamics algorithm (SEDA) is one such method that optimizes controller parameters using data-driven techniques. Nonetheless, control performance can be limited by the fixed probability coefficient used in the original SEDA to disrupt design parameters, especially when balancing exploration and exploitation phases. This study proposes an enhanced version of the SEDA: the modified SEDA (MSEDA), to address this issue. The MSEDA introduces a dynamic probability coefficient that decreases with each iteration. The adjustment improves the balance between exploration and exploitation phases, which enhances control accuracy. The MSEDA was used to tune the brain emotional learning-based intelligent controller (BELBIC) together with a proportional-integral-derivative (PID) controller. The result was the BELBIC-PID controller inspired by the limbic system of the human brain, which has high precision. The effectiveness of the proposed MSEDA-BELBIC-PID was validated using simulations on multi-input multi-output (MIMO) systems, with a focus on tracking performance and computational efficiency. The statistical analysis of 30 independent trials demonstrated that the proposed MSEDA-BELBIC-PID was significantly improved over the original SEDA-BELBIC-PID. Wilcoxon's rank test yielded a fitness function <em>p</em>-value &lt; 0.05, which confirmed the robustness and effect of the proposed enhancement. The comparative results demonstrated that the MSEDA-BELBIC-PID consistently performed better than the original approach and had improved fitness function values, reduced total integral square error, and lower total integral square input. These findings underscored the MSEDA suitability as a data-driven tool for controller design parameter optimization. Furthermore, the low computational burden of MSEDA rendered it a strong alternative to heuristic multi-agent algorithms, which frequently encounter high computational costs with large controller design parameters.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 74-99"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139921","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":"Design and implementation of classical literature sentiment analysis system based on ensemble learning and graph neural network","authors":"Qianru Gao ,&nbsp;Jiachen Huang","doi":"10.1016/j.ijcce.2025.05.004","DOIUrl":"10.1016/j.ijcce.2025.05.004","url":null,"abstract":"<div><div>Classical literary works have attracted extensive attention in modern society because of their unique cultural memory and aesthetic value. However, due to the long history and evolution of language, how to accurately grasp its connotation, especially its emotional color, has always been a dilemma for researchers. This study is committed to the design and implementation of a classical literature sentiment analysis system based on ensemble learning and graph neural network, with the goal of breaking through the limitations of traditional methods and realizing the refined analysis of classical literature sentiment tendencies. By constructing a large-scale corpus covering classics from different eras, this study lays a solid data foundation for model training. Graph neural network technology is innovatively applied to sentiment analysis in classical literature, and the graph structure composed of lexical nodes and semantic edges is used to capture the deep semantic and structural connections of texts. At the same time, bagging and boosting ensemble learning strategies are introduced to optimize the performance of multiple GNN models and form a more robust decision set. Experimental results show that compared with traditional methods, the graph neural network has an accuracy of 91.5 % for sentiment classification, and the ensemble learning further reduces the false positive rate, improving the overall emotion recognition accuracy of the system to 93.7 %, providing an efficient and accurate innovative solution for sentiment analysis of classical literature.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 603-616"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144482417","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":"Automatic Speech Recognition: A survey of deep learning techniques and approaches","authors":"Harsh Ahlawat,&nbsp;Naveen Aggarwal,&nbsp;Deepti Gupta","doi":"10.1016/j.ijcce.2024.12.007","DOIUrl":"10.1016/j.ijcce.2024.12.007","url":null,"abstract":"<div><div>Significant research has been conducted during the last decade on the application of machine learning for speech processing, particularly speech recognition. However, in recent years, deep learning models have shown promising results for different speech related applications. With the emergence of end-to-end models, deep learning has revolutionized the field of Automatic Speech Recognition (ASR). A recent surge in transfer learning-based models and attention-based approaches on large datasets has further given an impetus to ASR. This paper provides a thorough review of the numerous studies conducted since 2010, as well as an extensive comparison of the state-of-the-art methods that are now being used in this research area, with a special focus on the numerous deep learning models, along with an analysis of contemporary approaches for both monolingual and multilingual models. Deep learning approaches are data dependent and their accuracy varies on different datasets. In this paper, we have also analyzed the various models on publicly accessible speech datasets to understand model performance across diverse datasets for practical deployment. This study also highlights the research findings and challenges with way forward that may be used as a beginning point for academicians interested in open-source Automatic Speech Recognition (ASR) research, particularly focusing on mitigating data dependency and generalizability across low resource languages, speaker variability, and noise conditions.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 201-237"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139487","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":"Optimizing breast cancer diagnosis: Harnessing the power of nature-inspired metaheuristics for feature selection with soft voting classifiers","authors":"Salsabila Benghazouani ,&nbsp;Said Nouh ,&nbsp;Abdelali Zakrani","doi":"10.1016/j.ijcce.2024.09.005","DOIUrl":"10.1016/j.ijcce.2024.09.005","url":null,"abstract":"<div><div>Breast cancer is a widespread and serious condition that poses a significant threat to women's health globally, contributing significantly to mortality rates. Machine learning tools play a critical role in both the effective management and early detection of this disease. Feature selection (FS) methods are essential for identifying the most impactful features to improve breast cancer diagnosis. These methods reduce data dimensionality, eliminate irrelevant information, enhance learning accuracy, and improve the comprehensibility of results. However, the increasing complexity and dimensionality of cancer data pose substantial challenges to many existing FS methods, thereby reducing their efficiency and effectiveness. To overcome these challenges, numerous studies have demonstrated the success of nature-inspired optimization (NIO) algorithms across various domains. These algorithms excel in mimicking natural processes and efficiently solving complex optimization problems. Building on these advancements, we propose an innovative approach that combines powerful feature selection methods based on NIO techniques with a soft voting classifier. The NIO techniques employed include the Genetic Algorithm, Cuckoo Search, Salp Swarm, Jaya, Flower Pollination, Whale Optimization, Sine Cosine, Harris Hawks, and Grey Wolf Optimization algorithms. The Soft Voting Classifier integrates various machine learning models, including Support Vector Machines, Gaussian Naive Bayes, Logistic Regression, Decision Tree, and Gradient Boosting. These are used to improve the effectiveness and accuracy of breast cancer diagnosis. The proposed approach has been empirically evaluated using a variety of evaluation measures, such as F1 score, precision, recall, accuracy and Area Under the Curve (AUC), for performance comparison with individual machine learning techniques. The results demonstrate that the soft-voting ensemble technique, particularly when combined with feature selection based on the Jaya algorithm, outperforms all individual classifiers on the breast cancer dataset. It achieves the highest scores for accuracy, precision, recall, F1-score, and AUC, with values of 99.6 %, 99.21 %, 100 %, 99.6 %, and 99.6 %, respectively. Following closely, both the Genetic Algorithm and Salp Swarm feature selection demonstrate strong performance, with scores of 99.5 %, 100 %, 99 %, 99.5 %, and 99.5 %, respectively. Furthermore, the approach's effectiveness in identifying the minimal set of relevant features has been evaluated. Given the critical importance of selecting relevant features in complex cancer datasets, the proposed method proves to be a valuable tool for achieving early detection and improved interpretability. This method can also support healthcare professionals in making well-informed decisions regarding cancer diagnosis and treatment strategies.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 1-20"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529260","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":"Exploring insights on deep learning-based photovoltaic fault detection for monofacial and bifacial modules using thermography","authors":"Eko Adhi Setiawan ,&nbsp;Muhammad Fathurrahman","doi":"10.1016/j.ijcce.2025.04.001","DOIUrl":"10.1016/j.ijcce.2025.04.001","url":null,"abstract":"<div><div>Routine maintenance of photovoltaic (PV) power plants is critical to mitigate module faults, which can result from environmental factors, reducing power output and accelerating module degradation. To effectively detect faults across the entire PV module array, aerial infrared thermography (AIRT) is employed, using unmanned aerial vehicles (UAVs) to capture thermal images via predetermined waypoints. Afterward, these images are analyzed by a deep learning (DL) model known for its objec detection accuracy, identifying modules requiring further inspection. While prior research has focused on monofacial modules, limited studies have examined bifacial modules, which are rapidly gaining market share due to their albedo characteristics that increase energy yields in high-albedo areas. Thus, research on bifacial performance and faults is essential to support the development of PV maintenance systems across diverse environments. This study tests bifacial modules under PV fault conditions using thermography, adhering to established inspection standards, which enables comparative analysis with monofacial modules. Furthermore, our PV fault detection model uses a novel dataset from thermal images of both module types to train a mask region-based convolutional neural network (Mask R-CNN). The experiment demonstrated that, under similar irradiation conditions, bifacial faults exhibit higher temperatures and show distinct surface patterns in their thermal images. Despite these variations, our model detected PV faults in both module types, achieving a mean average precision (mAP) of 84.27 %. The model's performance could be further enhanced by expanding the bifacial dataset to address challenges in detecting soiling defects, which vary in shape, size, and location.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 495-507"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143890669","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":"Adaptive traffic prediction model using Graph Neural Networks optimized by reinforcement learning","authors":"Mohammed Khairy ,&nbsp;Hoda M.O. Mokhtar ,&nbsp;Mohammed Abdalla","doi":"10.1016/j.ijcce.2025.02.001","DOIUrl":"10.1016/j.ijcce.2025.02.001","url":null,"abstract":"<div><div>Traffic prediction is critical for urban planning and transportation management, with significant implications for congestion reduction, resource allocation, and sustainability. Traditional statistical models struggle to capture the complex spatiotemporal dependencies in traffic data, leading to reduced accuracy. Graph Neural Networks (GNNs) have emerged as a more practical approach due to their ability to model these intricate relationships. However, GNN-based methods face challenges in hyperparameter selection, which impacts their performance across diverse traffic scenarios. To address this, this paper proposes an adaptive traffic prediction model that uses reinforcement learning to optimize GNN hyperparameters. Our model significantly reduces the need for manual tuning and improves prediction accuracy across real-world traffic datasets, achieving a 9.8% reduction in Mean Absolute Error (MAE) and 3.6% improvement in Root Mean Squared Error (RMSE) compared to state-of-the-art baselines. In conclusion, dynamic hyperparameter adaptation boosts robustness and efficiency in traffic forecasting. This approach also helps us better understand how to fine-tune GNNs, contributing to the broader knowledge of optimizing machine learning models. Our work helps make traffic prediction more automated and improves how to manage urban transportation.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 431-440"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592097","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":"Research on Collaborative Optimization Strategy of Railway Signal Nonlinear Control System Based on BBO Algorithm and Multi-objective Optimization","authors":"Xue Li ,&nbsp;Yixuan Yang ,&nbsp;Zheng Li ,&nbsp;Hui He","doi":"10.1016/j.ijcce.2025.05.005","DOIUrl":"10.1016/j.ijcce.2025.05.005","url":null,"abstract":"<div><div>This study focuses on exploring collaborative optimization strategies for a nonlinear control system of railway signals based on the BBO algorithm. Currently, the railway signal control system faces performance bottlenecks such as response lag and local optima due to parameter coupling when dealing with multi-objective optimization problems like train operating speed and signal delays. Traditional optimization methods struggle to achieve global collaborative regulation under complex operating conditions. Therefore, there is an urgent need to introduce efficient intelligent algorithms to enhance the system's real-time capabilities and reliability. The research constructs a mathematical model with multiple objective constraints, accurately identifies the adaptation shortcomings of the existing system in dynamic scenarios, and then employs a Biogeography-Based Optimization (BBO) algorithm for global optimization of control parameters. Specifically, it sets a population size of 50, a maximum number of iterations of 200, a migration rate dynamically adjusted between 0.6-0.9, and an adaptive mutation rate of 0.01-0.05, using root mean square error and response time as performance evaluation metrics for parameter optimization. Experimental data show that compared to traditional methods, this strategy can increase the average operating speed of trains by 15%, reduce signal delays by 20%, and improve system robustness indicators by 18.5%, achieving a collaborative enhancement of efficiency and safety while ensuring stable operation, thus providing an engineering-valued solution for the intelligent upgrade of railway transport.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 617-627"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144490782","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":"A strategy for network multi-layer information fusion based on multimodel in user emotional polarity analysis","authors":"Ronghua Wang ,&nbsp;Peng Zhuang","doi":"10.1016/j.ijcce.2024.11.007","DOIUrl":"10.1016/j.ijcce.2024.11.007","url":null,"abstract":"<div><div>Emotional analysis is an important research direction in natural language processing, which aims to automatically recognize and understand emotions and emotional polarity in texts. The study employed multiple emotional analysis models to analyze emotions in text data. Then the emotional analysis results of different models were integrated to improve accuracy through a hierarchical information fusion strategy. Meanwhile, graph embedding algorithms such as DeepWalk and Node2vec were utilized to process node information in graph data. The embedding representation of graph nodes was utilized for emotional analysis of nodes. In addition, based on models such as graph convolutional neural networks for message passing, contextual information of nodes was obtained to improve emotional analysis performance. These results indicated that the proportion of passive users was higher than that of active users, at 57.14 % and 39.68 %, respectively. Despite the large number of negative users, the frequency of connections between active users was significantly higher than between negative users. Multiple algorithms were utilized for emotional analysis and prediction. The AUC curve performed well, but the accuracy was only 56.69 %, which needs improvement. The study also evaluated the root mean square error of network predictions, with errors typically below 0.2 in large networks, demonstrating relatively accurate prediction results. This guides the further development of emotional analysis technology, promoting the research and improvement of more accurate and reliable emotional analysis methods.</div></div>","PeriodicalId":100694,"journal":{"name":"International Journal of Cognitive Computing in Engineering","volume":"6 ","pages":"Pages 120-130"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139982","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}