Sustainable Operations and Computers最新文献_第7页

PAPQ: Predictive analytics of product quality in industry 4.0 PAPQ:工业4.0中产品质量的预测分析

Sustainable Operations and Computers Pub Date : 2023-01-01 DOI: 10.1016/j.susoc.2023.02.001

Md．Anjar Ahsan , Khaleel Ahmad , Jameel Ahamed , Mohd Omar , Khairol Amali Bin Ahmad

{"title":"PAPQ: Predictive analytics of product quality in industry 4.0","authors":"Md．Anjar Ahsan , Khaleel Ahmad , Jameel Ahamed , Mohd Omar , Khairol Amali Bin Ahmad","doi":"10.1016/j.susoc.2023.02.001","DOIUrl":"https://doi.org/10.1016/j.susoc.2023.02.001","url":null,"abstract":"<div><p>In e-commerce, Industry 4.0 is all about combining analytics, artificial intelligence, and machine learning to simplify procedures and enable product quality review. In addition, the importance of anticipating client behavior in the context of e-commerce is growing as individuals migrate from visiting physical businesses to shopping online. By providing a more personalized purchasing experience, it can increase consumer satisfaction and sales, leading to improved conversion rates and competitive advantage. Using data from e-commerce platforms such as Flipkart and Amazon, it is possible to build models for forecasting customer behavior. This study examines machine learning techniques for product quality prediction and gives an insight into the performance differences of machine learning-based models by doing descriptive data analysis and training each model separately on three datasets viz Mobile, Health Equipments, and Book Datasets. Support Vector Machine, Nave Bayes, k-Nearest Neighbors, Random Forest, and Random Tree were the machine learning methods utilized in this work. The results indicate that a Support Vector Machine Model provides the greatest fit for the prediction task, with the best performance, reasonable latency, comprehensibility, and resilience for the first two datasets, but Random Forest provides the highest performance for the Book dataset.</p></div>","PeriodicalId":101201,"journal":{"name":"Sustainable Operations and Computers","volume":"4 ","pages":"Pages 53-61"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49730657","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}

引用次数: 0

A modified UTAUT model for the acceptance and use of digital technology for tackling COVID-19 接受和使用数字技术应对COVID-19的改进UTAUT模型

Sustainable Operations and Computers Pub Date : 2022-01-01 DOI: 10.1016/j.susoc.2021.12.001

Boluwaji A. Akinnuwesi , Faith-Michael E. Uzoka , Stephen G. Fashoto , Elliot Mbunge , Adedoyin Odumabo , Oluwaseun O. Amusa , Moses Okpeku , Olumide Owolabi

{"title":"A modified UTAUT model for the acceptance and use of digital technology for tackling COVID-19","authors":"Boluwaji A. Akinnuwesi , Faith-Michael E. Uzoka , Stephen G. Fashoto , Elliot Mbunge , Adedoyin Odumabo , Oluwaseun O. Amusa , Moses Okpeku , Olumide Owolabi","doi":"10.1016/j.susoc.2021.12.001","DOIUrl":"10.1016/j.susoc.2021.12.001","url":null,"abstract":"<div><p>COVID-19 pandemic expedites the development of digital technologies to tackle the spread of the virus. Several digital interventions have been deployed to reduce the catastrophic impact of the pandemic and observe preventive measures. However, the adoption and utilization of these technologies by the affected populace has been a daunting task. Therefore, this study carried out exploratory investigation of the factors influencing the behavioural intention (BI) of people to accept COVID-19 digital tackling technologies (CDTT) using the UTAUT (Unified Theory of Acceptance and Use of Technology) framework. The study applied principal components analysis and multiple regression analysis for hypotheses testing. The study revealed that performance expectancy (PE), facilitating conditions (FC) and social influence (SI) are the best predictors of people's BI to accept CDTT. Also, organizational</p><p>influence and benefit (OIB) and government expectancy and benefits (GEB) influence the people's BI. However, variables such as age, gender and voluntariness to use CDTT have no significance to influence BI because the CDTT is still nascent and not easily accessible. The results show that the decision-makers and regulators should consider inciting variables such as PE, FC, SI, OIB and GEB, that motivate the acceptance and use of CDTT. Furthermore, the populace must be sensitized to the availability and use of CDTT in all communities. Also, the path diagram and hypothesis testing results for CDTT acceptance and use, will help government and private organizations in planning and responding to the digitalization of COVID-19 protective measures and hence revise the COVID-19 health protection regulation.</p></div>","PeriodicalId":101201,"journal":{"name":"Sustainable Operations and Computers","volume":"3 ","pages":"Pages 118-135"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666412721000507/pdfft?md5=d753927d547d68a54037b44cf9038d61&pid=1-s2.0-S2666412721000507-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78734875","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}

引用次数: 26

Evaluation of deep learning models for detecting breast cancer using histopathological mammograms Images 利用组织病理学乳房x线照片图像检测乳腺癌的深度学习模型的评估

Sustainable Operations and Computers Pub Date : 2022-01-01 DOI: 10.1016/j.susoc.2022.06.001

Subasish Mohapatra , Sarmistha Muduly , Subhadarshini Mohanty , J V R Ravindra , Sachi Nandan Mohanty

{"title":"Evaluation of deep learning models for detecting breast cancer using histopathological mammograms Images","authors":"Subasish Mohapatra , Sarmistha Muduly , Subhadarshini Mohanty , J V R Ravindra , Sachi Nandan Mohanty","doi":"10.1016/j.susoc.2022.06.001","DOIUrl":"10.1016/j.susoc.2022.06.001","url":null,"abstract":"<div><p>Breast cancer detection based on the deep learning approach has gained much interest among other conventional-based CAD systems as the conventional based CAD system's accuracy results seems to be inadequate. The convolution neural network, a deep learning approach, has emerged as the most promising technique for detecting cancer in mammograms. In this paper we delve into some of the CNN classifiers used to detect breast cancer by classifying mammogram images into benign, cancer, or normal class. Our study evaluated the performance of various CNN architectures such as AlexNet, VGG16, and ResNet50 by training some of them from scratch and some using transfer learning with pre-trained weights. The above model classifiers are trained and tested using mammogram images from the mini-DDSM dataset which is publicly available. The medical dataset contains limited samples of data due to low patient volume; this can lead to overfitting issue, so to overcome this limitation data augmentation process is applied. Rotation and zooming techniques are applied to increase the data volume. The validation strategy used here is 90:10 ratio. AlexNet showed an accuracy of 65 percent, whereas VGG16 and ResNet50 showed an accuracy of 65% and 61%, respectively when fine-tuned with pre-trained weights. VGG16 performed significantly worse when trained from scratch, whereas AlexNet outperformed others. VGG16 and ResNet50 performed well when transfer learning was applied.</p></div>","PeriodicalId":101201,"journal":{"name":"Sustainable Operations and Computers","volume":"3 ","pages":"Pages 296-302"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666412722000162/pdfft?md5=d32bcf78b99c1e9f896f9e838891bb24&pid=1-s2.0-S2666412722000162-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81950320","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}

引用次数: 7

Impact of artificial intelligent and industry 4.0 based products on consumer behaviour characteristics: A meta-analysis-based review 基于人工智能和工业4.0的产品对消费者行为特征的影响:一项基于元分析的综述

Sustainable Operations and Computers Pub Date : 2022-01-01 DOI: 10.1016/j.susoc.2022.01.009

Sameen Khan , Sarika Tomar , Maryam Fatima , Mohd Zaheen Khan

{"title":"Impact of artificial intelligent and industry 4.0 based products on consumer behaviour characteristics: A meta-analysis-based review","authors":"Sameen Khan , Sarika Tomar , Maryam Fatima , Mohd Zaheen Khan","doi":"10.1016/j.susoc.2022.01.009","DOIUrl":"10.1016/j.susoc.2022.01.009","url":null,"abstract":"<div><p>In the modern era, computers using artificial intelligence (AI) and industry 4.0 have found acceptance since its application in renewable energy sectors thereby optimising the cost and efficiency of the equipment. Despite its importance, lack of comprehensive literature has been reported in the past highlighting its relationship with consumer behaviour (CB) in the market considering the modern women's in the sustainable energy field. Findings from 10 studies furnish that physiological, social, personal and economical aspects significantly impact women consumer behaviour when categorized on the perception for intention to buy, acceptance and need for recognition. The current review paper is the first distinguishable review highlighting the importance of stipulating the relationship between artificial intelligence and characteristics of consumer behaviour in the field of sustainable energies. The paper synthesises previous findings by developing a model with the aid of meta-analysis. The review and organization procedure were simultaneously verified. Eventually, outcomes of the review stipulated intention to buy area, which requires utmost importance in order to establish and maintain a healthy attitude of consumers towards women entrepreneurs and industry 4.0. In future, this review will establish a roadmap to researchers, thereby guiding to collect technology information and analyse the applications in sustainability and CB. This paper aims to enhance our expertise and simultaneously develop a feasible relationship between consumer behaviour and computer based renewable technologies by addressing different concerns related to implementation of robots at home and outlining the investigation programs for the future experiments.</p></div>","PeriodicalId":101201,"journal":{"name":"Sustainable Operations and Computers","volume":"3 ","pages":"Pages 218-225"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666412722000095/pdfft?md5=4b84f413d7626e1421384178b9ee0352&pid=1-s2.0-S2666412722000095-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76913673","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}

引用次数: 21

A review of the theoretical research and practical progress of carbon neutrality 碳中和理论研究与实践进展综述

Sustainable Operations and Computers Pub Date : 2022-01-01 DOI: 10.1016/j.susoc.2021.10.001

Xianhua Wu , Zhiqing Tian , Ji Guo

引用次数: 100

A modified adaptive genetic algorithm for multi-product multi-period inventory routing problem 多产品多周期库存路径问题的改进自适应遗传算法

Sustainable Operations and Computers Pub Date : 2022-01-01 DOI: 10.1016/j.susoc.2021.08.002

Meysam Mahjoob , Seyed Sajjad Fazeli , Soodabeh Milanlouei , Leyla Sadat Tavassoli , Mirpouya Mirmozaffari

{"title":"A modified adaptive genetic algorithm for multi-product multi-period inventory routing problem","authors":"Meysam Mahjoob , Seyed Sajjad Fazeli , Soodabeh Milanlouei , Leyla Sadat Tavassoli , Mirpouya Mirmozaffari","doi":"10.1016/j.susoc.2021.08.002","DOIUrl":"https://doi.org/10.1016/j.susoc.2021.08.002","url":null,"abstract":"<div><p>Recent developments in urbanization and e-commerce have pushed businesses to deploy efficient systems to decrease their supply chain cost. Vendor Managed Inventory (VMI) is one of the most widely used strategies to effectively manage supply chains with multiple parties. VMI implementation asks for solving the Inventory Routing Problem (IRP). This study considers a multi-product multi-period inventory routing problem, including a supplier, set of customers, and a fleet of heterogeneous vehicles. Due to the complex nature of the IRP, we developed a Modified Adaptive Genetic Algorithm (MAGA) to solve a variety of instances efficiently. As a benchmark, we considered the results obtained by Cplex software and an efficient heuristic from the literature. Through extensive computational experiments on a set of randomly generated instances, and using different metrics, we show that our approach distinctly outperforms the other two methods. In this way, we created a decision support and computer-based approach to assist policy and decision-makers in the pathway of constructing a sustainable society.</p></div>","PeriodicalId":101201,"journal":{"name":"Sustainable Operations and Computers","volume":"3 ","pages":"Pages 1-9"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.susoc.2021.08.002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72243600","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}

引用次数: 21

Understanding the adoption of Industry 4.0 technologies in improving environmental sustainability 了解采用工业4.0技术改善环境可持续性

Sustainable Operations and Computers Pub Date : 2022-01-01 DOI: 10.1016/j.susoc.2022.01.008

Mohd Javaid , Abid Haleem , Ravi Pratap Singh , Rajiv Suman , Ernesto Santibañez Gonzalez

{"title":"Understanding the adoption of Industry 4.0 technologies in improving environmental sustainability","authors":"Mohd Javaid , Abid Haleem , Ravi Pratap Singh , Rajiv Suman , Ernesto Santibañez Gonzalez","doi":"10.1016/j.susoc.2022.01.008","DOIUrl":"10.1016/j.susoc.2022.01.008","url":null,"abstract":"<div><p>Industry 4.0 technologies provide critical perspectives for future innovation and business growth. Technologies like Artificial Intelligence (AI), Internet of Things (IoT), Big data, Machine Learning (ML), and other advanced upcoming technologies are being used to implement Industry 4.0. This paper explores how Industry 4.0 technologies help create a sustainable environment in manufacturing and other industries. Industry 4.0 technologies and the crucial interrelationships through advanced technologies should impact the environment positively. In the age of Industry 4.0, manufacturing is tightly interlinked with information and communication systems, making it more scalable, competitive, and knowledgeable. Industry 4.0 provides a range of principles, instructions, and technology for constructing new and existing factories, enabling consumers to choose different models at production rates with scalable robotics, information, and communications technology. This paper aims to study the significant benefits of Industry 4.0 for sustainable manufacturing and identifies tools and elements of Industry 4.0 for developing environmental sustainability. This literature review-based research is undertaken to identify how Industry 4.0 technologies can help to improve environmental sustainability. It also details the capabilities of Industry 4.0 in dealing with environmental aspects. Twenty major applications of Industry 4.0 to create a sustainable environment are identified and discussed. Thus, it gives a better understanding of the production environment, the supply chains, the delivery chains, and market results. Overall, Industry 4.0 technology seems environmentally sustainable while manufacturing goods with better efficiency and reducing resource consumption.</p></div>","PeriodicalId":101201,"journal":{"name":"Sustainable Operations and Computers","volume":"3 ","pages":"Pages 203-217"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666412722000071/pdfft?md5=896bc920e047a4944adda1b6028c6f63&pid=1-s2.0-S2666412722000071-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81732652","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}

引用次数: 91

Minimizing total absolute deviation of job completion times on a single machine with maintenance activities using a Lion Optimization Algorithm 使用Lion优化算法最小化单台机器上的作业完成时间的总绝对偏差

Sustainable Operations and Computers Pub Date : 2022-01-01 DOI: 10.1016/j.susoc.2021.08.003

Reza Yazdani , Mirpouya Mirmozaffari , Elham Shadkam , Mohammad Taleghani

{"title":"Minimizing total absolute deviation of job completion times on a single machine with maintenance activities using a Lion Optimization Algorithm","authors":"Reza Yazdani , Mirpouya Mirmozaffari , Elham Shadkam , Mohammad Taleghani","doi":"10.1016/j.susoc.2021.08.003","DOIUrl":"10.1016/j.susoc.2021.08.003","url":null,"abstract":"<div><p>Scheduling is a decision-making process that plays an important role in the service and production industries. Effective scheduling can assist companies to survive in the competitive market. Single machine scheduling is an important optimization problem in the scheduling research area. It can be found in a wide range of real-world engineering problems, from manufacturing to computer science. Due to the high complexity of single machine scheduling problems, developing approximation methods, particularly metaheuristic algorithms, for solving them have absorbed considerable attention. In this study, a Lion Optimization Algorithm (LOA) is employed to solve a single machine with maintenance activities, where the objective is to minimize the Total Absolute Deviation of Compilation Times (TADC). In the scheduling literature, TADC as an objective function has hardly been studied. To evaluate the performance of the LOA, it was compared against a set of well-known metaheuristics. Therefore, a set of problem was generated, and a comprehensive experimental analysis was conducted. The results of computational experiments indicate the superiority of the proposed optimization method.</p></div>","PeriodicalId":101201,"journal":{"name":"Sustainable Operations and Computers","volume":"3 ","pages":"Pages 10-16"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.susoc.2021.08.003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81493564","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}

引用次数: 7

A review on sustainable supply chain network design: Dimensions, paradigms, concepts, framework and future directions 可持续供应链网络设计综述:维度、范式、概念、框架和未来方向

Sustainable Operations and Computers Pub Date : 2022-01-01 DOI: 10.1016/j.susoc.2022.01.001

Sidharath Joshi

{"title":"A review on sustainable supply chain network design: Dimensions, paradigms, concepts, framework and future directions","authors":"Sidharath Joshi","doi":"10.1016/j.susoc.2022.01.001","DOIUrl":"10.1016/j.susoc.2022.01.001","url":null,"abstract":"<div><p>Supply chains are getting more and more complex with addition of new sustainability paradigms in highly fragile and vulnerable environments as the world is transforming faster and faster due to the acceleration of activities, operations and new technologies. To date, few efforts have been made to systematically explore the status of sustainable supply chains networks models as a few research includes sustainable development as a main attribute of the problem considered. This review is the outcome of several papers under the year frame from 2010 to 2021 delivering the role of sustainability in supply chain network with identification of strategies and various methodologies used by the academicians. A new framework of sustainable supply chain network design dimensions with inclusion of indicators and the parameters have been introduced. Moreover, future paths and research directions are provided for researchers and practitioners to explore the concepts of sustainability and new avenues of research to include sustainability aspects more effectively.</p></div>","PeriodicalId":101201,"journal":{"name":"Sustainable Operations and Computers","volume":"3 ","pages":"Pages 136-148"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666412722000010/pdfft?md5=a2a543cd5607e8e3160abf915fdf5b76&pid=1-s2.0-S2666412722000010-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86424794","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}

引用次数: 22

Prediction of recommendations for employment utilizing machine learning procedures and geo-area based recommender framework 利用机器学习程序和基于地理区域的推荐框架预测就业建议

Sustainable Operations and Computers Pub Date : 2022-01-01 DOI: 10.1016/j.susoc.2021.11.001

Binny Parida, Prashanta KumarPatra, Sthitapragyan Mohanty

{"title":"Prediction of recommendations for employment utilizing machine learning procedures and geo-area based recommender framework","authors":"Binny Parida, Prashanta KumarPatra, Sthitapragyan Mohanty","doi":"10.1016/j.susoc.2021.11.001","DOIUrl":"10.1016/j.susoc.2021.11.001","url":null,"abstract":"<div><p>With increment in the utilization of Internet, the pace of increment of social networks is getting ubiquitous in recent years. This paper focuses on the job portal websites. The research objective of this paper is that the recommender framework takes the abilities from the website and makes suggestion to the candidates with the jobs whose descriptions are coordinating with their profiles the most. This paper additionally presents a short presentation on recommender framework and talks about different categories of this framework. From the start, information is cleaned by expelling the filthy information as extra space and duplicates. Then the job recommendations are made to the target applicants on the basis of their preferences. It utilizes different Machine Learning procedures which results show that Random Forest Classifier (RFC) gives the most noteworthy expectation accuracy when contrasted with different procedures. Finally, the optimization technique is utilized to get the most exact outcome. The advantage of recommender framework in career orientation is expressed. Geo-area based recommendation framework is utilized to find the organization's position which can assist the ideal applicants with reaching their destination. This examination shows that the utilization of job recommender system can assist with improving the recommendation of appropriate employment for work searchers.</p></div>","PeriodicalId":101201,"journal":{"name":"Sustainable Operations and Computers","volume":"3 ","pages":"Pages 83-92"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666412721000489/pdfft?md5=346b8c5896b1d4d183a2bb54df41ac9b&pid=1-s2.0-S2666412721000489-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85612011","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}

引用次数: 7