Pub Date : 2016-08-26DOI: 10.1504/IJIDS.2016.078586
P. Zaraté, Shaofeng Liu
Knowledge-based decision support systems (KBDSS) have evolved greatly over the last few decades. The key technologies underpinning the development of KBDSS can be classified into three categories: technologies for knowledge modelling and representation, technologies for reasoning and inference and web-based technologies. In the meantime, service systems have emerged and become increasingly important to value adding activities in the current knowledge economy. This paper provides a review on the recent advances in the three types of technologies, as well as the main application domains of KBDSS as service systems. Based on the examination of literature, future research directions are recommended for the development of KBDSS in general and in particular to support decision-making in service industry.
{"title":"A new trend for knowledge-based decision support systems design","authors":"P. Zaraté, Shaofeng Liu","doi":"10.1504/IJIDS.2016.078586","DOIUrl":"https://doi.org/10.1504/IJIDS.2016.078586","url":null,"abstract":"Knowledge-based decision support systems (KBDSS) have evolved greatly over the last few decades. The key technologies underpinning the development of KBDSS can be classified into three categories: technologies for knowledge modelling and representation, technologies for reasoning and inference and web-based technologies. In the meantime, service systems have emerged and become increasingly important to value adding activities in the current knowledge economy. This paper provides a review on the recent advances in the three types of technologies, as well as the main application domains of KBDSS as service systems. Based on the examination of literature, future research directions are recommended for the development of KBDSS in general and in particular to support decision-making in service industry.","PeriodicalId":303039,"journal":{"name":"Int. J. Inf. Decis. Sci.","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127284569","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 : 2016-05-12DOI: 10.1504/IJIDS.2016.076507
A. Maalel, Lassad Mejri, H. Ghézala
Recently, an increasing number of companies and industries have undergone greatly in competition. At the same time, we are witnessing an explosion technological advances and new technologies of information and communication that companies must integrate to achieve the performance that goes far beyond those obtained by conventional practices. However, these constraints are at the origin of the birth of many risks. Sometimes we are witnessing serious and costly failures, accidents and human losses, especially when it is a highly risky area such as railroad transportation (our current case study). This paper aims at developing a decision support approach, called Adast. The approach adopted in this research is based on acquiring and reusing past accident scenarii, historically validated on other homologated transport systems. It is composed of two main parts: knowledge models described by an ontology, and a reasoning process based on case-based reasoning (CBR). In this article, we present the architecture of the approach, the case model, the key processes, and the first steps of the experimental validation through the model feasibility based on Adast.
{"title":"Adast: a decision support approach based on an ontology and CBR. Application to railroad accidents","authors":"A. Maalel, Lassad Mejri, H. Ghézala","doi":"10.1504/IJIDS.2016.076507","DOIUrl":"https://doi.org/10.1504/IJIDS.2016.076507","url":null,"abstract":"Recently, an increasing number of companies and industries have undergone greatly in competition. At the same time, we are witnessing an explosion technological advances and new technologies of information and communication that companies must integrate to achieve the performance that goes far beyond those obtained by conventional practices. However, these constraints are at the origin of the birth of many risks. Sometimes we are witnessing serious and costly failures, accidents and human losses, especially when it is a highly risky area such as railroad transportation (our current case study). This paper aims at developing a decision support approach, called Adast. The approach adopted in this research is based on acquiring and reusing past accident scenarii, historically validated on other homologated transport systems. It is composed of two main parts: knowledge models described by an ontology, and a reasoning process based on case-based reasoning (CBR). In this article, we present the architecture of the approach, the case model, the key processes, and the first steps of the experimental validation through the model feasibility based on Adast.","PeriodicalId":303039,"journal":{"name":"Int. J. Inf. Decis. Sci.","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114671750","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 : 2016-05-12DOI: 10.1504/IJIDS.2016.076517
Saurabh Manek, S. Vijay, Deepali Kamthania
Educational data mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyse educational data. In this paper, an attempt has been made to propose a system that constitutes an integrated platform for thorough analysis of student's nine years data. The ETL process (extraction, cleaning, transforming and loading) have been performed with the help customised scripted tool. The tool also helps in finding new relations and generating reports for trend analysis. This analysis can help in planning strategies to improve student's performance of the new batches joining the institute. Further data mining technique J48 algorithm has been applied on nine years data to find patterns by using WEKA.
{"title":"Educational data mining - a case study","authors":"Saurabh Manek, S. Vijay, Deepali Kamthania","doi":"10.1504/IJIDS.2016.076517","DOIUrl":"https://doi.org/10.1504/IJIDS.2016.076517","url":null,"abstract":"Educational data mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyse educational data. In this paper, an attempt has been made to propose a system that constitutes an integrated platform for thorough analysis of student's nine years data. The ETL process (extraction, cleaning, transforming and loading) have been performed with the help customised scripted tool. The tool also helps in finding new relations and generating reports for trend analysis. This analysis can help in planning strategies to improve student's performance of the new batches joining the institute. Further data mining technique J48 algorithm has been applied on nine years data to find patterns by using WEKA.","PeriodicalId":303039,"journal":{"name":"Int. J. Inf. Decis. Sci.","volume":"42 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129891002","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 : 2016-05-12DOI: 10.1504/IJIDS.2016.076518
Niraj Kumar, K. Srinathan, Vasudeva Varma
These days' discussion forums provide dependable solutions to the problems related to multiple domains and areas. However, due to the presence of huge amount of less-informative/inappropriate posts, the identification of the appropriate problem-solution pairs has become a challenging task. The emergence of a variety of topics, domains and areas has made the task of manual labelling of the problem solution-post pairs a very costly and time consuming task. To solve these issues, we concentrate on deep semantic and logical relation between terms. For this, we introduce a novel semantic correlation graph to represent the text. The proposed representation helps us in the identification of topical and semantic relation between terms at a fine grain level. Next, we apply the improved version of personalised pagerank using random walk with restarts. The main aim is to improve the rank score of terms having direct or indirect relation with terms in the given question. Finally, we introduce the use of the node overlapping version of GAAC to find the actual span of answer text. Our experimental results show that the devised system performs better than the existing unsupervised systems.
{"title":"Unsupervised deep semantic and logical analysis for identification of solution posts from community answers","authors":"Niraj Kumar, K. Srinathan, Vasudeva Varma","doi":"10.1504/IJIDS.2016.076518","DOIUrl":"https://doi.org/10.1504/IJIDS.2016.076518","url":null,"abstract":"These days' discussion forums provide dependable solutions to the problems related to multiple domains and areas. However, due to the presence of huge amount of less-informative/inappropriate posts, the identification of the appropriate problem-solution pairs has become a challenging task. The emergence of a variety of topics, domains and areas has made the task of manual labelling of the problem solution-post pairs a very costly and time consuming task. To solve these issues, we concentrate on deep semantic and logical relation between terms. For this, we introduce a novel semantic correlation graph to represent the text. The proposed representation helps us in the identification of topical and semantic relation between terms at a fine grain level. Next, we apply the improved version of personalised pagerank using random walk with restarts. The main aim is to improve the rank score of terms having direct or indirect relation with terms in the given question. Finally, we introduce the use of the node overlapping version of GAAC to find the actual span of answer text. Our experimental results show that the devised system performs better than the existing unsupervised systems.","PeriodicalId":303039,"journal":{"name":"Int. J. Inf. Decis. Sci.","volume":"517 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127613352","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 : 2016-05-12DOI: 10.1504/IJIDS.2016.076514
A. Seneviratne, R. Ratnayake
It is necessary to evaluate the risk levels in piping components of offshore production and process facilities (OP%PFs) to investigate potential failures. In an OP%PF, piping plays a vital role within the static mechanical equipment. Inspection planners make recommendations on the thickness measurement locations (TMLs) to be monitored based on: historical data, risk-based inspection (RBI) analysis results, plant inspection strategy guidance, etc. The inspection plans made by inexperienced inspection planners are of poor quality compared to an inspection plan made by an experienced inspection planner. Hence, to mitigate the problem, it is vital to develop expert systems to support inexperienced inspection planners and minimise suboptimal decisions. This manuscript illustrates the use of a fuzzy inference system (FIS) as an expert system for making optimal in-service inspection recommendations based on the current status and trends of TMLs. The proposed FIS enables the expertise of experienced inspection planners to be incorporated via membership functions (MFs) and a rule base, which will maintain the quality of an inspection programme at the intended level.
{"title":"Evaluation of risk levels in static mechanical equipment: a fuzzy expert system approach","authors":"A. Seneviratne, R. Ratnayake","doi":"10.1504/IJIDS.2016.076514","DOIUrl":"https://doi.org/10.1504/IJIDS.2016.076514","url":null,"abstract":"It is necessary to evaluate the risk levels in piping components of offshore production and process facilities (OP%PFs) to investigate potential failures. In an OP%PF, piping plays a vital role within the static mechanical equipment. Inspection planners make recommendations on the thickness measurement locations (TMLs) to be monitored based on: historical data, risk-based inspection (RBI) analysis results, plant inspection strategy guidance, etc. The inspection plans made by inexperienced inspection planners are of poor quality compared to an inspection plan made by an experienced inspection planner. Hence, to mitigate the problem, it is vital to develop expert systems to support inexperienced inspection planners and minimise suboptimal decisions. This manuscript illustrates the use of a fuzzy inference system (FIS) as an expert system for making optimal in-service inspection recommendations based on the current status and trends of TMLs. The proposed FIS enables the expertise of experienced inspection planners to be incorporated via membership functions (MFs) and a rule base, which will maintain the quality of an inspection programme at the intended level.","PeriodicalId":303039,"journal":{"name":"Int. J. Inf. Decis. Sci.","volume":"25 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126354497","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 : 2016-05-12DOI: 10.1504/IJIDS.2016.076510
M. Khodabakhshi, M. Rezaee, K. Aryavash
The motivation of this paper is to classify a set of decision making units (DMUs) into three classes A, B, and C using an optimistic-pessimistic approach of data envelopment analysis technique. To this end, the minimum and maximum possible efficiency scores of DMUs are estimated under the assumption that the sum of their scores equals to unity. Then, all DMUs are ranked two times. First, they are ranked according to their minimum scores, and then according to their maximum scores. Finally, the class of each DMU is ascertained according to its ranks in two rankings. We apply the proposed method for classifying the welfare funds of students for the universities in Iran.
{"title":"ABC classification using DEA: classification of Iranian universities from students welfare foundation viewpoint","authors":"M. Khodabakhshi, M. Rezaee, K. Aryavash","doi":"10.1504/IJIDS.2016.076510","DOIUrl":"https://doi.org/10.1504/IJIDS.2016.076510","url":null,"abstract":"The motivation of this paper is to classify a set of decision making units (DMUs) into three classes A, B, and C using an optimistic-pessimistic approach of data envelopment analysis technique. To this end, the minimum and maximum possible efficiency scores of DMUs are estimated under the assumption that the sum of their scores equals to unity. Then, all DMUs are ranked two times. First, they are ranked according to their minimum scores, and then according to their maximum scores. Finally, the class of each DMU is ascertained according to its ranks in two rankings. We apply the proposed method for classifying the welfare funds of students for the universities in Iran.","PeriodicalId":303039,"journal":{"name":"Int. J. Inf. Decis. Sci.","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116973124","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 : 2016-05-12DOI: 10.1504/IJIDS.2016.076509
H. Seetha, M. Murty, R. Saravanan
Nearest neighbour classifier and support vector machine (SVM) are successful classifiers that are widely used in many important application areas. But both these classifiers suffer from the curse of dimensionality. Nearest neighbour search, in high dimensional data, using Euclidean distance is questionable since all the pair wise distances seem to be almost the same. In order to overcome this problem, we propose a novel classification system based on majority voting. Firstly, we partition the features into a number of blocks and construct a classifier for each block. The majority voting is then performed across all classifiers to determine the final class label. Classification is also performed using non-negative matrix factorisation (NNMF) that embeds high dimensional data into low dimensional space. Experiments were conducted on three of the benchmark datasets and the results obtained showed that the proposed system outperformed the conventional classification using both k-nearest neighbour (k-NN) and support vector machine (SVM) classifiers. The proposed system also showed better performance when compared with the classification performance of 1NN and SVM classifier using NNMF-based dimensionally reduced data.
{"title":"Classification by majority voting in feature partitions","authors":"H. Seetha, M. Murty, R. Saravanan","doi":"10.1504/IJIDS.2016.076509","DOIUrl":"https://doi.org/10.1504/IJIDS.2016.076509","url":null,"abstract":"Nearest neighbour classifier and support vector machine (SVM) are successful classifiers that are widely used in many important application areas. But both these classifiers suffer from the curse of dimensionality. Nearest neighbour search, in high dimensional data, using Euclidean distance is questionable since all the pair wise distances seem to be almost the same. In order to overcome this problem, we propose a novel classification system based on majority voting. Firstly, we partition the features into a number of blocks and construct a classifier for each block. The majority voting is then performed across all classifiers to determine the final class label. Classification is also performed using non-negative matrix factorisation (NNMF) that embeds high dimensional data into low dimensional space. Experiments were conducted on three of the benchmark datasets and the results obtained showed that the proposed system outperformed the conventional classification using both k-nearest neighbour (k-NN) and support vector machine (SVM) classifiers. The proposed system also showed better performance when compared with the classification performance of 1NN and SVM classifier using NNMF-based dimensionally reduced data.","PeriodicalId":303039,"journal":{"name":"Int. J. Inf. Decis. Sci.","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131237763","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 : 2016-04-06DOI: 10.1504/IJIDS.2016.075789
Berna Tektas Sivrikaya, F. Çebi
This paper presents a modelling framework for generation capacity expansion planning (GEP) applicable to independent investor generation companies (GenCos) in the context of a hybrid electricity wholesale market. The proposed model is novel in the sense that the operations of the GenCo in bilateral contracts market (BCM) and day-ahead market (DAM) are incorporated. Also, the environmental considerations are modelled through the incorporation of carbon tax and carbon dioxide (CO2) cap regulations. At the end of existing generation units' useful life, refurbishment decisions are employed. In this way, conversion of old units to units with lower operation costs and/or green house gases emissions is modelled. The effect of uncertainties in electricity market prices, fuel costs, environmental regulations, budget, and the effect of the GenCos long-termed strategic behaviour in participating in BCM and DAM on the planning decisions are illustrated by sensitivity analysis.
{"title":"Long-termed investment planning model for a generation company operating in both bilateral contract and day-ahead markets","authors":"Berna Tektas Sivrikaya, F. Çebi","doi":"10.1504/IJIDS.2016.075789","DOIUrl":"https://doi.org/10.1504/IJIDS.2016.075789","url":null,"abstract":"This paper presents a modelling framework for generation capacity expansion planning (GEP) applicable to independent investor generation companies (GenCos) in the context of a hybrid electricity wholesale market. The proposed model is novel in the sense that the operations of the GenCo in bilateral contracts market (BCM) and day-ahead market (DAM) are incorporated. Also, the environmental considerations are modelled through the incorporation of carbon tax and carbon dioxide (CO2) cap regulations. At the end of existing generation units' useful life, refurbishment decisions are employed. In this way, conversion of old units to units with lower operation costs and/or green house gases emissions is modelled. The effect of uncertainties in electricity market prices, fuel costs, environmental regulations, budget, and the effect of the GenCos long-termed strategic behaviour in participating in BCM and DAM on the planning decisions are illustrated by sensitivity analysis.","PeriodicalId":303039,"journal":{"name":"Int. J. Inf. Decis. Sci.","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129603813","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 : 2016-04-06DOI: 10.1504/IJIDS.2016.075787
M. Khoveyni, R. Eslami
Recognition of benchmarking for decision making units (DMUs) contains a key component of future planning. For this reason, in this research, we investigate an enriched form of data envelopment analysis (DEA) that is linked to managers' future planning goals. We need to mention that top managers' goals are deemed for a long time. In this study, a DEA approach is presented to find benchmark of DMUs with imposed inputs. In our proposed approach, it is assumed that some inputs have been imposed on the target decision making unit (DMU) by allocation from higher levels of management or by history. A goal programming structure is used by a new model for exploring points on the efficient frontier that they are realistically achievable by DMUs. Lastly, we apply the model to real world dataset then some conclusions are drawn and also directions for future research are suggested.
{"title":"Managerial goals directed benchmarking for organised efficiency in data envelopment analysis","authors":"M. Khoveyni, R. Eslami","doi":"10.1504/IJIDS.2016.075787","DOIUrl":"https://doi.org/10.1504/IJIDS.2016.075787","url":null,"abstract":"Recognition of benchmarking for decision making units (DMUs) contains a key component of future planning. For this reason, in this research, we investigate an enriched form of data envelopment analysis (DEA) that is linked to managers' future planning goals. We need to mention that top managers' goals are deemed for a long time. In this study, a DEA approach is presented to find benchmark of DMUs with imposed inputs. In our proposed approach, it is assumed that some inputs have been imposed on the target decision making unit (DMU) by allocation from higher levels of management or by history. A goal programming structure is used by a new model for exploring points on the efficient frontier that they are realistically achievable by DMUs. Lastly, we apply the model to real world dataset then some conclusions are drawn and also directions for future research are suggested.","PeriodicalId":303039,"journal":{"name":"Int. J. Inf. Decis. Sci.","volume":"12 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113967558","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 : 2016-04-06DOI: 10.1504/IJIDS.2016.075788
B. K. Sarkar
Designing accurate model for classification problem is a real concern in context of machine learning. The various factors such as inclusion of excellent samples in the training set, the number of samples as well as the proportion of each class type in the set (that would be sufficient for designing model) play important roles in this purpose. In this article, an investigation is introduced to address the question of what proportion of the samples should be devoted to the training set for developing a better classification model. The experimental results on several datasets, using C4.5 classifier, shows that any equidistributed data partitioning in between (20%, 80%) and (30%, 70%) may be considered as the best sample partition to build classification model irrespective to domain, size and class imbalanced.
{"title":"A case study on partitioning data for classification","authors":"B. K. Sarkar","doi":"10.1504/IJIDS.2016.075788","DOIUrl":"https://doi.org/10.1504/IJIDS.2016.075788","url":null,"abstract":"Designing accurate model for classification problem is a real concern in context of machine learning. The various factors such as inclusion of excellent samples in the training set, the number of samples as well as the proportion of each class type in the set (that would be sufficient for designing model) play important roles in this purpose. In this article, an investigation is introduced to address the question of what proportion of the samples should be devoted to the training set for developing a better classification model. The experimental results on several datasets, using C4.5 classifier, shows that any equidistributed data partitioning in between (20%, 80%) and (30%, 70%) may be considered as the best sample partition to build classification model irrespective to domain, size and class imbalanced.","PeriodicalId":303039,"journal":{"name":"Int. J. Inf. Decis. Sci.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121036198","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：