P. Ajitha, T. Tamilvizhi, K. Sowjanya, R. Surendran, B. Bala
Time-series forecasting is an approach that uses historical and current data to project future values over time or at a given point in time, while forecasting and prediction are often synonymous, there is one interesting detail. In some professions, forecasting may refer to data at a specific future point in time, whereas prediction refers to future data in general. Most widely used to determine the nature of stock prices. A series of analyses and modeling by a finance committee is to guide investors, professors of legal sciences, and processes. And that is why he proposes that this series argument not include a sliding window; they were wise to back then, and they gave up everything, anticipating stock values relative to her. The system presents the (GUI) Graphical User Interface as a stand-alone application. The proposed findings demonstrate a highly predicted accurate approach for nonlinear time series models that are difficult to obtain from traditional models.
{"title":"Consumer product prediction using machine learning","authors":"P. Ajitha, T. Tamilvizhi, K. Sowjanya, R. Surendran, B. Bala","doi":"10.47974/jios-1415","DOIUrl":"https://doi.org/10.47974/jios-1415","url":null,"abstract":"Time-series forecasting is an approach that uses historical and current data to project future values over time or at a given point in time, while forecasting and prediction are often synonymous, there is one interesting detail. In some professions, forecasting may refer to data at a specific future point in time, whereas prediction refers to future data in general. Most widely used to determine the nature of stock prices. A series of analyses and modeling by a finance committee is to guide investors, professors of legal sciences, and processes. And that is why he proposes that this series argument not include a sliding window; they were wise to back then, and they gave up everything, anticipating stock values relative to her. The system presents the (GUI) Graphical User Interface as a stand-alone application. The proposed findings demonstrate a highly predicted accurate approach for nonlinear time series models that are difficult to obtain from traditional models.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":"46 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70470658","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}
One of the particular issues with linear programming is transportation. These are optimization efforts whose goal is to reduce the overall cost of moving goods or people through intricate logistical systems. To reduce the overall transit costs involved in distribution, the problem can be solved by optimizing the delivery system of the specific entity (such as any goods, a person, or a material) from sources (suppliers) to destinations (customers). When evacuating a region, transportation concerns are used to determine the best route for evacuees to take from boarding points to evacuation centers. Particular attention is paid to the travel distance and the overall cost of moving one person.Finding the right number of items to send from each warehouse to each customer while keeping costs to a minimum is the goal of this problem’s solution. In this study, a novel fuzzy number called the Triacontakaidigon Fuzzy Number and its membership function are introduced. In terms of both form and computation, the triacontakaidigon fuzzy number is more complex than the triangular and trapezoidal fuzzy numbers. A fuzzy ranking approach is an efficient tool for addressing the fuzzy transportation problem, as demonstrated by numerical examples.
{"title":"An approach to fuzzy transportation problem using Triacontakaidigon fuzzy number with alpha cut ranking technique","authors":"T. Malathi, P. Senthilkumar","doi":"10.47974/jios-1180","DOIUrl":"https://doi.org/10.47974/jios-1180","url":null,"abstract":"One of the particular issues with linear programming is transportation. These are optimization efforts whose goal is to reduce the overall cost of moving goods or people through intricate logistical systems. To reduce the overall transit costs involved in distribution, the problem can be solved by optimizing the delivery system of the specific entity (such as any goods, a person, or a material) from sources (suppliers) to destinations (customers). When evacuating a region, transportation concerns are used to determine the best route for evacuees to take from boarding points to evacuation centers. Particular attention is paid to the travel distance and the overall cost of moving one person.Finding the right number of items to send from each warehouse to each customer while keeping costs to a minimum is the goal of this problem’s solution. In this study, a novel fuzzy number called the Triacontakaidigon Fuzzy Number and its membership function are introduced. In terms of both form and computation, the triacontakaidigon fuzzy number is more complex than the triangular and trapezoidal fuzzy numbers. A fuzzy ranking approach is an efficient tool for addressing the fuzzy transportation problem, as demonstrated by numerical examples.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70469155","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}
Symmetric matrices play an important role in data science. In this paper, we present some new results on the eigenvalues, singular values, the spectral and Euclidean norms of real symmetric matrices in form A = [xi + xj]ni, j = 1.
{"title":"On eigenvalue, singular value and norm of symmetric matrices","authors":"A. Ipek","doi":"10.47974/jios-1227","DOIUrl":"https://doi.org/10.47974/jios-1227","url":null,"abstract":"Symmetric matrices play an important role in data science. In this paper, we present some new results on the eigenvalues, singular values, the spectral and Euclidean norms of real symmetric matrices in form A = [xi + xj]ni, j = 1.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70469522","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}
Constrained optimization rises as a challenging issue concerning the evaluation of restrictions, objective and constraints of a model. For this purpose, various optimization algorithms are specifically generated or improved to achieve the best design. Performance of algorithms is strictly concerned with the search capability of the phenomena used. Herein, a state-of-the-art approach can provide worse results on constrained optimization while its performance is remarkable on a different type of optimization problem. Many engineering design problems are categorized as constrained and nonlinear. Decision variables, constraint functions and objective function always change from one problem to another. This condition reveals the necessity of robust optimization algorithms. With this inspiration, after seeing its remarkable performance on different areas (global optimization, continuous function optimization, hybrid classifier design, etc.), this paper examines a state-of-the-art technique named Gauss map-based chaotic particle swarm optimization (GM-CPSO) on constrained optimization of engineering design problems. GM-CPSO is firstly adapted to operate for constrained optimization. Then, penalty function method is utilized to form the fitness output of optimization algorithm. Six challenging design problems are handled that are gear train design, I-shaped beam design, tension / compression spring design, three-bar truss design, tubular column design, and car side impact design. In experiments, GM-CPSO is compared with the state-of-the-art studies handling the design problems. As a result, GM-CPSO achieves the best results recorded in the literature or enhances the optimum result on the specified design problem.
{"title":"Constrained optimization of engineering design problems: Analyses with Gauss map-based chaotic particle swarm optimization","authors":"Hasan Koyuncu","doi":"10.47974/jios-1313","DOIUrl":"https://doi.org/10.47974/jios-1313","url":null,"abstract":"Constrained optimization rises as a challenging issue concerning the evaluation of restrictions, objective and constraints of a model. For this purpose, various optimization algorithms are specifically generated or improved to achieve the best design. Performance of algorithms is strictly concerned with the search capability of the phenomena used. Herein, a state-of-the-art approach can provide worse results on constrained optimization while its performance is remarkable on a different type of optimization problem. Many engineering design problems are categorized as constrained and nonlinear. Decision variables, constraint functions and objective function always change from one problem to another. This condition reveals the necessity of robust optimization algorithms. With this inspiration, after seeing its remarkable performance on different areas (global optimization, continuous function optimization, hybrid classifier design, etc.), this paper examines a state-of-the-art technique named Gauss map-based chaotic particle swarm optimization (GM-CPSO) on constrained optimization of engineering design problems. GM-CPSO is firstly adapted to operate for constrained optimization. Then, penalty function method is utilized to form the fitness output of optimization algorithm. Six challenging design problems are handled that are gear train design, I-shaped beam design, tension / compression spring design, three-bar truss design, tubular column design, and car side impact design. In experiments, GM-CPSO is compared with the state-of-the-art studies handling the design problems. As a result, GM-CPSO achieves the best results recorded in the literature or enhances the optimum result on the specified design problem.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70469885","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}
The investment patterns adopted by various Companies include either internal or external sources of funds, to invest in diverse options within the capital market and money market instruments. This paper is to propose a mathematical analytic model that explores corporate investment patterns and their subsequent impact on profitability, borrowings, and other parameters of corporate health. Specifically, the paper intends to examine the immediate effect of investments on the company’s Profit After Tax (PAT), excluding investments in Fixed Assets. Furthermore, the study seeks to provide a mathematical comparative analysis using collected data on Retained Earnings, a critical component of profitability, and investments. By suggesting a mathematical data analytic model, this research paper also addresses the reasons why many businesses continue to rely on outdated methods and conventional choices, apart from Fixed Assets.
{"title":"Mathematical data analytic model for investment patterns assessment in stocks of selected sectors","authors":"Arun Gautam, Ruchi Goyal","doi":"10.47974/jios-1405","DOIUrl":"https://doi.org/10.47974/jios-1405","url":null,"abstract":"The investment patterns adopted by various Companies include either internal or external sources of funds, to invest in diverse options within the capital market and money market instruments. This paper is to propose a mathematical analytic model that explores corporate investment patterns and their subsequent impact on profitability, borrowings, and other parameters of corporate health. Specifically, the paper intends to examine the immediate effect of investments on the company’s Profit After Tax (PAT), excluding investments in Fixed Assets. Furthermore, the study seeks to provide a mathematical comparative analysis using collected data on Retained Earnings, a critical component of profitability, and investments. By suggesting a mathematical data analytic model, this research paper also addresses the reasons why many businesses continue to rely on outdated methods and conventional choices, apart from Fixed Assets.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70470157","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}
Lokesh Kumar Bhuranda, M. Rizwanullah, A. Sharma, Kamlesh Gautam, Yash Chawla
The Multi-Capacitated Problem is an optimization problem. To reduce the overall distance, the optimization problem seeks out vehicle routes that connect every customer to a storage facility. This article uses a saving matrix approach to propose an extended Vehicle Routing Problem that considers a stochastic environment and multiple capacitors. Stochastic customers are an essential element of the problem. A computational analysis also supports the suggested approach to obtain the best route.
{"title":"Stochastic optimization of multi-capacitated vehicle routing problem with pickup and delivery using saving matrix algorithm","authors":"Lokesh Kumar Bhuranda, M. Rizwanullah, A. Sharma, Kamlesh Gautam, Yash Chawla","doi":"10.47974/jios-1413","DOIUrl":"https://doi.org/10.47974/jios-1413","url":null,"abstract":"The Multi-Capacitated Problem is an optimization problem. To reduce the overall distance, the optimization problem seeks out vehicle routes that connect every customer to a storage facility. This article uses a saving matrix approach to propose an extended Vehicle Routing Problem that considers a stochastic environment and multiple capacitors. Stochastic customers are an essential element of the problem. A computational analysis also supports the suggested approach to obtain the best route.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70470419","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}
Poonam Katyare, Shubhalaxmi S. Joshi, Sheetal Rajapurkar
The Internet of Things (IoT) plays a vital role in the automation of Construction Industry. The real time data of the construction equipment is monitored using IoT devices. An integral approach of IoT based sensing data and Machine Learning (ML) models helps to predict the fuel consumed by the equipment. This paper presents the real time data modeling to estimate the fuel consumption for a trip travelled by the construction equipment using IoT enabled remote data along with machine learning algorithms. The Random Forest, Extreme Gradient Boosting (XGBoost) ensemble methods and Lasso Cross Validation (LassoCV), Support Vector Machines Regression models are used in this study. These models are fitted on dataset and splits the data into training and testing data. Based on the comparative analysis of coefficient of determination, LassoCV technique produces more accurate results along with the other models using Models’ accuracy measures. This study would help the decision makers for cost estimation of the construction project which includes fuel consumption as major component of cost.
{"title":"Real time data modeling for forecasting fuel consumption of construction equipment using integral approach of IoT and ML techniques","authors":"Poonam Katyare, Shubhalaxmi S. Joshi, Sheetal Rajapurkar","doi":"10.47974/jios-1363","DOIUrl":"https://doi.org/10.47974/jios-1363","url":null,"abstract":"The Internet of Things (IoT) plays a vital role in the automation of Construction Industry. The real time data of the construction equipment is monitored using IoT devices. An integral approach of IoT based sensing data and Machine Learning (ML) models helps to predict the fuel consumed by the equipment. This paper presents the real time data modeling to estimate the fuel consumption for a trip travelled by the construction equipment using IoT enabled remote data along with machine learning algorithms. The Random Forest, Extreme Gradient Boosting (XGBoost) ensemble methods and Lasso Cross Validation (LassoCV), Support Vector Machines Regression models are used in this study. These models are fitted on dataset and splits the data into training and testing data. Based on the comparative analysis of coefficient of determination, LassoCV technique produces more accurate results along with the other models using Models’ accuracy measures. This study would help the decision makers for cost estimation of the construction project which includes fuel consumption as major component of cost.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70470456","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}
In this research study, the authors examine the impact of Entrepreneurial Orientation (EO) on hotel performance, specifically in the context of Jaipur, Rajasthan, India. A quantitative approach was taken, surveying 88 hotel managers who rated the structures on a 7-point scale. The dimensionality of the scales was evaluated using factorial analysis, and the authors investigated the effect of absorptive abilities on entrepreneurial propensity using factorial scores generated from Exploratory Factor Analysis. The results suggest a direct relationship between EO, Business Performance (BP), and absorption capacity (ACAP*EO) on BP. However, there was no direct relationship found between ACAP and BP, indicating that the association discovered in this study is a moderator-only effect. The interaction regression coefficient was negative, indicating that absorptive capacity reduced the effects of EO on beach hotel performance, likely due to their competitive aggressiveness. The study indentify EO attributes of “innovation, risk-taking, proactivity, and autonomy” positively impacted performance. These findings suggest that entrepreneurial strategies can significantly impact hotel performance, and that absorptive capacity plays a moderating role in this relationship.
{"title":"An empirical study for customer orientation and its impact on hotel industry","authors":"G. Shukla","doi":"10.47974/jios-1417","DOIUrl":"https://doi.org/10.47974/jios-1417","url":null,"abstract":"In this research study, the authors examine the impact of Entrepreneurial Orientation (EO) on hotel performance, specifically in the context of Jaipur, Rajasthan, India. A quantitative approach was taken, surveying 88 hotel managers who rated the structures on a 7-point scale. The dimensionality of the scales was evaluated using factorial analysis, and the authors investigated the effect of absorptive abilities on entrepreneurial propensity using factorial scores generated from Exploratory Factor Analysis. The results suggest a direct relationship between EO, Business Performance (BP), and absorption capacity (ACAP*EO) on BP. However, there was no direct relationship found between ACAP and BP, indicating that the association discovered in this study is a moderator-only effect. The interaction regression coefficient was negative, indicating that absorptive capacity reduced the effects of EO on beach hotel performance, likely due to their competitive aggressiveness. The study indentify EO attributes of “innovation, risk-taking, proactivity, and autonomy” positively impacted performance. These findings suggest that entrepreneurial strategies can significantly impact hotel performance, and that absorptive capacity plays a moderating role in this relationship.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70470805","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}
Abdelmalik Boussaad, K. Melkemi, F. Melgani, Z. Mokhtari
Wavelet analysis has shown to be an interesting tool for representing ECG signals for classification. In this paper, we present a new ECG signal representation based on the notion of non-stationary wavelets. The main difference with the construction of standard wavelets is that the multiresolution spaces are generated by scale-dependent functions in order to achieve increased flexibility and sparseness. In order to customize the non-stationary wavelet to the given ECG classification task, we resort to the fireworks optimization algorithm, thus making the proposed method general and not constrained by the choice of a particular classifier. The proposed method is validated on AAMI classes of the well-known MIT data set. Results compared to standard stationary wavelets show a significant boost in accuracy.
{"title":"Non-stationary wavelet for ECG signal classification","authors":"Abdelmalik Boussaad, K. Melkemi, F. Melgani, Z. Mokhtari","doi":"10.47974/jios-1128","DOIUrl":"https://doi.org/10.47974/jios-1128","url":null,"abstract":"Wavelet analysis has shown to be an interesting tool for representing ECG signals for classification. In this paper, we present a new ECG signal representation based on the notion of non-stationary wavelets. The main difference with the construction of standard wavelets is that the multiresolution spaces are generated by scale-dependent functions in order to achieve increased flexibility and sparseness. In order to customize the non-stationary wavelet to the given ECG classification task, we resort to the fireworks optimization algorithm, thus making the proposed method general and not constrained by the choice of a particular classifier. The proposed method is validated on AAMI classes of the well-known MIT data set. Results compared to standard stationary wavelets show a significant boost in accuracy.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70469594","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}
G. S. Kanth, Sivudu Macherla, B. Laxmikantha, K. Samatha, K. R. Kumar, Bechoo Lal
Relied on discernible or corporeal attributes, human beings are recognized by employing biometric scheme. In computer perception and design ratification domain, progressive studies are carried out in face recognition. Given the constant development in the discipline of imaging sensor, a legion of rest of the novel problems has occurred. The chief issue remains how to discover focus region more precisely for multi-focus face detection. Several studies have been proliferated in face discernment, spotting, and protection acknowledgment; the key problem remains in this is considering those images into contemplation that had “disparate dimensions” and “disparate aspect ratio” in a singular frame avoiding the progression to attain or surpass human-level accuracy in human facial aspect like noise in face pictures, defying lighting conditions and posture ratio.
{"title":"Generality imaging for optimized face classification using deep learning techniques","authors":"G. S. Kanth, Sivudu Macherla, B. Laxmikantha, K. Samatha, K. R. Kumar, Bechoo Lal","doi":"10.47974/jios-1409","DOIUrl":"https://doi.org/10.47974/jios-1409","url":null,"abstract":"Relied on discernible or corporeal attributes, human beings are recognized by employing biometric scheme. In computer perception and design ratification domain, progressive studies are carried out in face recognition. Given the constant development in the discipline of imaging sensor, a legion of rest of the novel problems has occurred. The chief issue remains how to discover focus region more precisely for multi-focus face detection. Several studies have been proliferated in face discernment, spotting, and protection acknowledgment; the key problem remains in this is considering those images into contemplation that had “disparate dimensions” and “disparate aspect ratio” in a singular frame avoiding the progression to attain or surpass human-level accuracy in human facial aspect like noise in face pictures, defying lighting conditions and posture ratio.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":"53 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70470395","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}
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