Societal risk classification is the fundamental issue for online societal risk monitoring. To show the challenge and feasibility of societal risk classification toward BBS posts, an empirical analysis is implemented in this paper. Through effectiveness analysis, Support Vector Machine based on Bag-Of-Words (BOW-SVM) is adopted for challenge validation, and the distributed document embeddings of BBS posts generated by Paragraph Vector are applied to feasibility study. Based on BOW-SVM, cross-validations of BBS posts labeled by different groups and annotators are conducted. The big fluctuation of cross-validation results indicates the differences of individual risk perceptions, which brings more challenges to societal risk classification. Furthermore, based on the distributed document embeddings of BBS posts, the pairwise similarities of more than 300 thousands BBS posts from different societal risk categories are compared. The higher similarities of BBS posts in the same societal risk category reveal that BBS posts in the same societal risk category share more features than BBS posts in different categories, which manifests the feasibility of societal risk classification of BBS posts, and also reflects the possibility to improve the performance of societal risk monitoring.

This paper intends to understand the form of implementations of Enterprise Information Systems (EISs). EISs are usually provided as packaged software products. Due to the diversities of implementations, EISs are often characterized by their system architectures. A conceptual framework is proposed to delineate the diversity and dynamics of EIS implementations. This framework is constituted of three components, EIS strategy, variety, and process-level performance (SVP). In particular, the variety of implementations is defined by two constructs, application scope and application depth. A Partial Least Squares structural equation modeling approach is applied to test the hypotheses according to the survey data from 223 project reports of EIS implementations in China. The results show that the EIS strategy and variety can both affect the performance of the implemented EISs. Specifically, application depth has an important mediating effect on the relationship between EIS strategy and performance. EIS strategy and application depth are breakthrough points to improve the performance of the implemented EISs. These findings suggest that the variety plays a central and effective role in the analysis of EIS implementations. This SVP framework highlights the interconnections among its components and captures the form of EIS implementations.

Literature review indicates that sample size, attribute variance and within-sample choice distribution of alternatives are important considerations in the estimation of multinomial logit (MNL) models, but their impacts on the estimation accuracy have not been systematically studied. Therefore, the objective of this paper is to provide an empirical examination to the above issues through a set of simulated discrete choice preference and rank ordered preference datasets. In this paper, the utility coefficients, alternative specific constants (ASCs), and the mean and standard deviation of the four attributes for a set of seven hypothetical alternatives are specified as a priori. Then, synthetic datasets, with varying sample size, attribute variance and within-sample choice distribution are simulated. Based on these datasets, the utility coefficients and ASCs of the specified MNLs are re-estimated and compared with the original values specified as the priori. It is found that (1) the estimation accuracy of utility parameters increases as the sample size increases; (2) the utility coefficients can be re-estimated with reasonable accuracy, but the estimates of the ASCs are confronted with much larger errors; (3) as the variances of the alternative attributes increase, the estimation accuracy improves significantly; and (4) as the distribution of chosen choices becomes more balanced across alternatives within sample datasets, the hit-ratio decreases. The results indicate that (a) under a similar setting presented in this paper, a large sample consisting of a few thousand observations (3000–4000) may be needed in order to provide reasonable estimates for utility coefficients, particularly for ASCs; (b) a larger, but realistic attribute space is preferred in the stated preference survey design; and (c) choice datasets with unbalanced “chosen” choice frequency distribution is preferred, in order to better capture the elasticity between the “perceived utility” associated with alternative’s attributes.

Graduate students who are beginning academic research want to learn how to create and verify new knowledge in their research. Their supervisors, on the other hand, are seeking appropriate research environments including effective research guidance methods. In order to meet these demands, this paper proposes a knowledge creation model that supports the objectives of both graduate students and their supervisors. This is an academic knowledge creation model for individuals supported by a group and its origin can be traced back to a famous organizational knowledge creation model. Since this type of model is constructed from empirical knowledge, it is not easy to prove its objective significance. But, this paper tries to show the effectiveness of the proposed model as an initial stage of model validation based on a questionnaire survey of students in a graduate school in China.

This paper presents and discusses a simulation method for analyzing and evaluating system performance on a rail line from the perspective of speed profile. Dynamic analysis for train motions is introduced, and a discrete time-operation graph is proposed to represent the relation between speed profile and energy consumption. Based on them, an analytical model is formulated to provide a quick insight into the system performance. The discrete-time simulation (DTS) method is then implemented to study the system in detail. Compared to the existing simulations, two innovations are included in the DTS: (1) the analytical lookup tables that can simplify the dynamic computation and, (2) the speed profile adjustment process that forecasts and avoids future conflicts based on practical constraints. The numerical results show that the DTS speed profile has advantages over existing methods. Finally, the DTS method is used to analyze and evaluate the system performance of the current timetable on Beijing Yizhuang Metro Line. The results suggest that the current timetable is not robust enough, and thus possible improvements are discussed at both scheduling and operating stages. The proposed method is verified to be effective and reliable for practical uses.