Pub Date : 2023-08-31DOI: 10.1080/01966324.2023.2239958
Mervat Mahdy, Dina S. El-telbany
Abstract In this paper, an extension of the linear failure rate distribution is proposed, called the weighted linear failure rate distribution. This class is a generalization of the two-parameter linear failure rate distribution as well as some other lifetime distributions. The new model has the advantage of being capable of modeling various shapes of aging and failure criteria. Different properties of this new distribution and the inference of the old parameters are discussed, the skewness parameter is examined, and some well-known lifetime distributions are introduced as special sub models. Finally, an application using various types of data is presented to demonstrate the flexibility of this distribution and for illustrative purposes.
{"title":"Weighted Linear Failure Rate Distribution: Properties, Regression Model, and Applications","authors":"Mervat Mahdy, Dina S. El-telbany","doi":"10.1080/01966324.2023.2239958","DOIUrl":"https://doi.org/10.1080/01966324.2023.2239958","url":null,"abstract":"Abstract In this paper, an extension of the linear failure rate distribution is proposed, called the weighted linear failure rate distribution. This class is a generalization of the two-parameter linear failure rate distribution as well as some other lifetime distributions. The new model has the advantage of being capable of modeling various shapes of aging and failure criteria. Different properties of this new distribution and the inference of the old parameters are discussed, the skewness parameter is examined, and some well-known lifetime distributions are introduced as special sub models. Finally, an application using various types of data is presented to demonstrate the flexibility of this distribution and for illustrative purposes.","PeriodicalId":35850,"journal":{"name":"American Journal of Mathematical and Management Sciences","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47623466","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}
Pub Date : 2023-07-03DOI: 10.1080/01966324.2023.2241089
S. Sreelakshmi, Manoj Chacko
Abstract In this paper, the problem of estimation of parameters of Gompertz distribution under constant stress accelerated life testing model using a progressively type II censored sample is considered. The maximum likelihood and Bayesian methods are used for estimating the unknown parameters. Markov chain Monte Carlo techniques are employed to carry out Bayesian estimation. A simulation study is carried out to assess and compare the different estimators discussed in this paper. Finally, real data are analyzed to illustrate the results.
{"title":"Parameter Estimation of Gompertz Distribution Under Constant Stress Accelerated Life Testing Using Progressive Type-II Censoring","authors":"S. Sreelakshmi, Manoj Chacko","doi":"10.1080/01966324.2023.2241089","DOIUrl":"https://doi.org/10.1080/01966324.2023.2241089","url":null,"abstract":"Abstract In this paper, the problem of estimation of parameters of Gompertz distribution under constant stress accelerated life testing model using a progressively type II censored sample is considered. The maximum likelihood and Bayesian methods are used for estimating the unknown parameters. Markov chain Monte Carlo techniques are employed to carry out Bayesian estimation. A simulation study is carried out to assess and compare the different estimators discussed in this paper. Finally, real data are analyzed to illustrate the results.","PeriodicalId":35850,"journal":{"name":"American Journal of Mathematical and Management Sciences","volume":"42 1","pages":"234 - 249"},"PeriodicalIF":0.0,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46474539","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}
Pub Date : 2023-07-03DOI: 10.1080/01966324.2023.2226337
N. Murugeswari, P. Jeyadurga, S. Sridevi, S. Balamurali
Abstract A skip-lot sampling plan is utilized in industries to reduce the cost and the inspection efforts in products having an excellent quality. In the economical aspect, the skip-lot sampling methodology is more advantageous in reducing the inspection cost. The main intention of this paper is to develop a sampling plan by assimilating the concept of resampling with a two-level skip-lot sampling plan (SkSP-2L.2). The new plan is designated as SkSP-2L.2-R, and a single sampling plan is used as the reference plan. The performance measures of the proposed plan are derived using the Markov chain formulation. A table is constructed to select the optimal parameters that are determined by taking into account more than a few combinations of producer quality level and consumer quality level together with respective producer and consumer risks. Execution of the proposed SkSP-2L.2-R plan is discussed with an illustrative example. It is proven by the comparative study that the proposed SkSP-2L.2-R plan has better performance in discriminating the lots with minimum inspection effort when compared to existing sampling plans.
{"title":"Developing and Optimal Designing of a Two-Level Skip-Lot Sampling Reinspection Plan","authors":"N. Murugeswari, P. Jeyadurga, S. Sridevi, S. Balamurali","doi":"10.1080/01966324.2023.2226337","DOIUrl":"https://doi.org/10.1080/01966324.2023.2226337","url":null,"abstract":"Abstract A skip-lot sampling plan is utilized in industries to reduce the cost and the inspection efforts in products having an excellent quality. In the economical aspect, the skip-lot sampling methodology is more advantageous in reducing the inspection cost. The main intention of this paper is to develop a sampling plan by assimilating the concept of resampling with a two-level skip-lot sampling plan (SkSP-2L.2). The new plan is designated as SkSP-2L.2-R, and a single sampling plan is used as the reference plan. The performance measures of the proposed plan are derived using the Markov chain formulation. A table is constructed to select the optimal parameters that are determined by taking into account more than a few combinations of producer quality level and consumer quality level together with respective producer and consumer risks. Execution of the proposed SkSP-2L.2-R plan is discussed with an illustrative example. It is proven by the comparative study that the proposed SkSP-2L.2-R plan has better performance in discriminating the lots with minimum inspection effort when compared to existing sampling plans.","PeriodicalId":35850,"journal":{"name":"American Journal of Mathematical and Management Sciences","volume":"42 1","pages":"216 - 233"},"PeriodicalIF":0.0,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44348849","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}
Pub Date : 2023-06-23DOI: 10.1080/01966324.2023.2213835
Thomas Xavier, Joby K. Jose, S. Bagui
Abstract This paper deals with study and estimation of stress-strength reliability for a system where strength and stress components are connected in series with cold standby redundancy at system level. It is supposed that the random stress and strength both follow Kumaraswamy half-logistic distribution. In this redundant system, we consider that there N subsystems and in each subsystem, there are M statistically independent strength components under the impact of M statistically independent stress components The problem of estimation is solved in two cases. First under the assumption that random stress and strength have common first shape parameter and different second shape parameter and second with the assumption that common shape parameter is known. The stress-strength reliability is estimated using maximum likelihood and Bayesian estimation methods. Also asymptotic and Bayesian intervals for the stress-strength reliability under both the cases are constructed. Monte Carlo simulations will be performed to compare the performance of various methods. Finally a real life data set is analyzed to demonstrate the findings.
{"title":"Stress-Strength Reliability Estimation of a Series System with Cold Standby Redundancy Based on Kumaraswamy Half-Logistic Distribution","authors":"Thomas Xavier, Joby K. Jose, S. Bagui","doi":"10.1080/01966324.2023.2213835","DOIUrl":"https://doi.org/10.1080/01966324.2023.2213835","url":null,"abstract":"Abstract This paper deals with study and estimation of stress-strength reliability for a system where strength and stress components are connected in series with cold standby redundancy at system level. It is supposed that the random stress and strength both follow Kumaraswamy half-logistic distribution. In this redundant system, we consider that there N subsystems and in each subsystem, there are M statistically independent strength components under the impact of M statistically independent stress components The problem of estimation is solved in two cases. First under the assumption that random stress and strength have common first shape parameter and different second shape parameter and second with the assumption that common shape parameter is known. The stress-strength reliability is estimated using maximum likelihood and Bayesian estimation methods. Also asymptotic and Bayesian intervals for the stress-strength reliability under both the cases are constructed. Monte Carlo simulations will be performed to compare the performance of various methods. Finally a real life data set is analyzed to demonstrate the findings.","PeriodicalId":35850,"journal":{"name":"American Journal of Mathematical and Management Sciences","volume":"42 1","pages":"183 - 201"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45639697","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}
Pub Date : 2023-06-21DOI: 10.1080/01966324.2023.2215388
N. Bansal, V. Vaidyanathan, P. Chandrasekhar
Abstract We obtain Bayes estimator and its posterior Bayes risk of traffic intensity for the queueing model under the squared error loss function (SELF), when the observed data is on independent inter-arrival times and service times. We also obtain the Bayes estimators and their posterior Bayes risk of other system performance measures under SELF. The Bayes estimators are compared against the maximum likelihood estimators through simulation. The proposed Bayesian methodology is carried out for a real data observed on an ATM service point of a bank.
{"title":"Bayesian Estimation of Parameters and Measures of Effectiveness in an M|Er|1 Queueing Model","authors":"N. Bansal, V. Vaidyanathan, P. Chandrasekhar","doi":"10.1080/01966324.2023.2215388","DOIUrl":"https://doi.org/10.1080/01966324.2023.2215388","url":null,"abstract":"Abstract We obtain Bayes estimator and its posterior Bayes risk of traffic intensity for the queueing model under the squared error loss function (SELF), when the observed data is on independent inter-arrival times and service times. We also obtain the Bayes estimators and their posterior Bayes risk of other system performance measures under SELF. The Bayes estimators are compared against the maximum likelihood estimators through simulation. The proposed Bayesian methodology is carried out for a real data observed on an ATM service point of a bank.","PeriodicalId":35850,"journal":{"name":"American Journal of Mathematical and Management Sciences","volume":"42 1","pages":"202 - 215"},"PeriodicalIF":0.0,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44658127","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}
Pub Date : 2023-02-24DOI: 10.1080/01966324.2023.2175631
I. Ghosh, J. Tanley
Abstract The Borel-Tanner distribution, with two shape parameters, and r, has some leverage over the Poisson distribution, other mixtures of the Poisson distribution, and several other discrete distributions, as it can be applied to both under and over-dispersed count data. In this article, some structural properties are studied that have not been explored earlier including recursive relation between successive probabilities. For illustrative purposes, two real-life data sets will be considered to show the applicability of this distribution that are re-analyzed for this purpose.
{"title":"Some Structural Properties Related to the Borel-Taner Distribution and its’ Application","authors":"I. Ghosh, J. Tanley","doi":"10.1080/01966324.2023.2175631","DOIUrl":"https://doi.org/10.1080/01966324.2023.2175631","url":null,"abstract":"Abstract The Borel-Tanner distribution, with two shape parameters, and r, has some leverage over the Poisson distribution, other mixtures of the Poisson distribution, and several other discrete distributions, as it can be applied to both under and over-dispersed count data. In this article, some structural properties are studied that have not been explored earlier including recursive relation between successive probabilities. For illustrative purposes, two real-life data sets will be considered to show the applicability of this distribution that are re-analyzed for this purpose.","PeriodicalId":35850,"journal":{"name":"American Journal of Mathematical and Management Sciences","volume":"42 1","pages":"148 - 162"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49032276","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}
Pub Date : 2023-02-14DOI: 10.1080/01966324.2022.2163441
I. Kumar, K.Nagendra Kumar, I. Ghosh
Abstract The progressively first failure censored (PFFC) data have become very popular in the past decade due to its usefulness in life testing experiments and in reliability theory. The PFFC data record the number of failures and increase the efficiency of the estimators. The inverse Pareto distribution (IPD) is useful when empirical data suggest a decreasing or upside-down bathtub-shaped failure rate functions. In this article, we consider the classical and Bayesian estimation of the model parameter and the reliability characteristics of the IPD using the PFFC data. The maximum likelihood estimators, asymptotic confidence, and bootstrap confidence intervals are considered in the classical estimation. Under the Bayesian paradigm, Bayes estimators based on non-informative and the gamma informative priors under the squared error loss function using Tierney-Kadane approximation, importance sampling, and Metropolis-Hasting (M-H) algorithm are assessed. In addition, the highest probability density (HPD) intervals based on the M-H algorithm are also constructed. To investigate the efficacy of each of the estimation procedures, numerical computations are performed based on a simulation study. Finally, a real data set is re-analyzed to show the applicability of the IPD model under a censoring scheme.
{"title":"Reliability Estimation in Inverse Pareto Distribution Using Progressively First Failure Censored Data","authors":"I. Kumar, K.Nagendra Kumar, I. Ghosh","doi":"10.1080/01966324.2022.2163441","DOIUrl":"https://doi.org/10.1080/01966324.2022.2163441","url":null,"abstract":"Abstract The progressively first failure censored (PFFC) data have become very popular in the past decade due to its usefulness in life testing experiments and in reliability theory. The PFFC data record the number of failures and increase the efficiency of the estimators. The inverse Pareto distribution (IPD) is useful when empirical data suggest a decreasing or upside-down bathtub-shaped failure rate functions. In this article, we consider the classical and Bayesian estimation of the model parameter and the reliability characteristics of the IPD using the PFFC data. The maximum likelihood estimators, asymptotic confidence, and bootstrap confidence intervals are considered in the classical estimation. Under the Bayesian paradigm, Bayes estimators based on non-informative and the gamma informative priors under the squared error loss function using Tierney-Kadane approximation, importance sampling, and Metropolis-Hasting (M-H) algorithm are assessed. In addition, the highest probability density (HPD) intervals based on the M-H algorithm are also constructed. To investigate the efficacy of each of the estimation procedures, numerical computations are performed based on a simulation study. Finally, a real data set is re-analyzed to show the applicability of the IPD model under a censoring scheme.","PeriodicalId":35850,"journal":{"name":"American Journal of Mathematical and Management Sciences","volume":"42 1","pages":"126 - 147"},"PeriodicalIF":0.0,"publicationDate":"2023-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44465183","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}
Pub Date : 2023-02-09DOI: 10.1080/01966324.2023.2175632
Mahendra Saha, S. Dey
Abstract Process capability indices (PCIs) are most effective devices/techniques used in industries for determining the quality of products and performance of manufacturing processes. In this article, we consider the process capability index which is based on an asymmetric loss function (linear exponential) and is applicable to normally as well as non-normally distributed processes. In order to estimate the PCI when the process follows exponentiated exponential distribution, we have used ten classical methods of estimation and the performances of these classical estimates for the index are compared in terms of mean squared errors (MSEs) through simulation study. Also, the confidence intervals for the index are constructed based on four bootstrap confidence interval (BCIs) methods. A simulation study is performed in order to compare the performance of these four BCIs in terms of average width and coverage probabilities. We use two published data sets related to electronic and food industries to illustrate the performance of the proposed methods of estimation and BCIs. All the data sets show that width of bias-corrected accelerated bootstrap interval is the lowest among all other considered BCIs.
{"title":"Confidence Intervals of a Loss Based PCI Using Exponentiated-Exponential Distributed Quality Characteristics","authors":"Mahendra Saha, S. Dey","doi":"10.1080/01966324.2023.2175632","DOIUrl":"https://doi.org/10.1080/01966324.2023.2175632","url":null,"abstract":"Abstract Process capability indices (PCIs) are most effective devices/techniques used in industries for determining the quality of products and performance of manufacturing processes. In this article, we consider the process capability index which is based on an asymmetric loss function (linear exponential) and is applicable to normally as well as non-normally distributed processes. In order to estimate the PCI when the process follows exponentiated exponential distribution, we have used ten classical methods of estimation and the performances of these classical estimates for the index are compared in terms of mean squared errors (MSEs) through simulation study. Also, the confidence intervals for the index are constructed based on four bootstrap confidence interval (BCIs) methods. A simulation study is performed in order to compare the performance of these four BCIs in terms of average width and coverage probabilities. We use two published data sets related to electronic and food industries to illustrate the performance of the proposed methods of estimation and BCIs. All the data sets show that width of bias-corrected accelerated bootstrap interval is the lowest among all other considered BCIs.","PeriodicalId":35850,"journal":{"name":"American Journal of Mathematical and Management Sciences","volume":"42 1","pages":"163 - 181"},"PeriodicalIF":0.0,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46880038","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}
Pub Date : 2023-01-19DOI: 10.1080/01966324.2022.2163210
David Chris Raju, S. Sunoj, G. Rajesh
Abstract In the present study, we extend the cumulative Tsallis entropy in the past life to its bivariate case. We can use the proposed measure for determining the uncertainty associated with two-component systems in the past lifetime. Some important properties of these measures are studied along with the characterization results of some essential bivariate lifetime models. We also examine specific applications of the vector-valued measure based on the non-parametric methods of estimation and illustrated their performances using simulated and real data sets.
{"title":"Bivariate Cumulative Tsallis Past Entropy: Properties and Applications","authors":"David Chris Raju, S. Sunoj, G. Rajesh","doi":"10.1080/01966324.2022.2163210","DOIUrl":"https://doi.org/10.1080/01966324.2022.2163210","url":null,"abstract":"Abstract In the present study, we extend the cumulative Tsallis entropy in the past life to its bivariate case. We can use the proposed measure for determining the uncertainty associated with two-component systems in the past lifetime. Some important properties of these measures are studied along with the characterization results of some essential bivariate lifetime models. We also examine specific applications of the vector-valued measure based on the non-parametric methods of estimation and illustrated their performances using simulated and real data sets.","PeriodicalId":35850,"journal":{"name":"American Journal of Mathematical and Management Sciences","volume":"42 1","pages":"30 - 50"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46164738","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}
Pub Date : 2023-01-16DOI: 10.1080/01966324.2022.2162465
Joby K. Jose, M. Drisya, Sebastian George
Abstract Stress-strength modeling has achieved considerable attention in recent years due to its applicability in various areas like engineering, quality control, psychology, biology, genetics, medicine etc. Phase-type distribution is a generalized class of distributions that is closed under several mathematical operations like maxima, minima, convolution, finite mixture etc and any discrete or continuous probability distributions on the positive real line can be represented as phase-type. Hence stress-strength reliability models based on phase-type distributions give a generalized structure for the stress-strength models. Moreover, matrix representation of the parameters helps in their flexible evaluation and easy manipulation. In Bayesian inference, we combine the prior knowledge with the information provided by the set of current observations to make more reliable inferences. Bayesian approach has the advantage of providing more meaningful inferences by making use of all available information. In this paper, we assume that the strength of the system and the stress imposed on the system are phase-type random variables, and the Bayesian inference of stress-strength reliability is discussed for the single-component systems and multi-component systems. The computation of the Bayes estimate of stress-strength reliability using the Markov Chain Monte Carlo method, based on continuous and discrete phase-type distributions are explained.
{"title":"Bayesian Inference of the Phase-Type Stress-Strength Reliability Models","authors":"Joby K. Jose, M. Drisya, Sebastian George","doi":"10.1080/01966324.2022.2162465","DOIUrl":"https://doi.org/10.1080/01966324.2022.2162465","url":null,"abstract":"Abstract Stress-strength modeling has achieved considerable attention in recent years due to its applicability in various areas like engineering, quality control, psychology, biology, genetics, medicine etc. Phase-type distribution is a generalized class of distributions that is closed under several mathematical operations like maxima, minima, convolution, finite mixture etc and any discrete or continuous probability distributions on the positive real line can be represented as phase-type. Hence stress-strength reliability models based on phase-type distributions give a generalized structure for the stress-strength models. Moreover, matrix representation of the parameters helps in their flexible evaluation and easy manipulation. In Bayesian inference, we combine the prior knowledge with the information provided by the set of current observations to make more reliable inferences. Bayesian approach has the advantage of providing more meaningful inferences by making use of all available information. In this paper, we assume that the strength of the system and the stress imposed on the system are phase-type random variables, and the Bayesian inference of stress-strength reliability is discussed for the single-component systems and multi-component systems. The computation of the Bayes estimate of stress-strength reliability using the Markov Chain Monte Carlo method, based on continuous and discrete phase-type distributions are explained.","PeriodicalId":35850,"journal":{"name":"American Journal of Mathematical and Management Sciences","volume":"42 1","pages":"13 - 29"},"PeriodicalIF":0.0,"publicationDate":"2023-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45003817","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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