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The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

This study aims to facilitate consistency of information in building information modelling (BIM) and address the current BIM gaps through the perspectives of the productization concept and product structure (PS).

The study follows a conceptual research approach in conjunction with a single case study. First, the previous studies on BIM implementation, productization and PS are reviewed. Further, a case study is used to analyse the current state of productization in the construction sector and develop a functional PS for construction.

A Part-Phase-Elements Matrix is proposed as a construction-specific PS to facilitate consistency in information and to enhance BIM. The proposed matrix provides new avenues to facilitate consistent information exchange through the interconnection between conceptual PS and standard building objects library, and encourage collaborative communication between stakeholders.

This study explores the core of the productization concept and PS as means to facilitate consistency of information and thus address the current gaps in BIM. This as building projects progressively move towards systematic modular and prefabricated construction where the flow of reliable information about product and construction offerings becomes increasingly important.

Research on grey systems is becoming more sophisticated, and grey relational and prediction analyses are receiving close review worldwide. Particularly, the application of grey systems in marine economics is gaining importance. The purpose of this paper is to summarize and review literature on grey models, providing new directions in their application in the marine economy.

This paper organized seminal studies on grey systems published by Chinese core journal database – CNKI, Web of Science and Elsevier from 1982 to 2018. After searching the aforementioned database for the said duration, the authors used the CiteSpace visualization tools to analyze them.

The authors sorted the studies according to their countries/regions, institutions, keywords and categories using the CiteSpace tool; analyzed current research characteristics on grey models; and discussed their possible applications in marine businesses, economy, scientific research and education, marine environment and disasters. Finally, the authors pointed out the development trend of grey models.

Although researches are combining grey theory with fractals, neural networks, fuzzy theory and other methods, the applications, in terms of scope, have still not met the demand. With the increasingly in-depth research in marine economics and management, international marine economic research has entered a new period of development. Grey theory will certainly attract scholars’ attention, and its role in marine economy and management will gain considerable significance.

The purpose of this stud is to analyze the financialization effect on oil prices.

This study applied the technique of multibreak point analysis with Bai and Perron test plus VAR methodology.

Findings revealed that there was no effect on oil prices.

To the best of the author’s knowledge, this is the first paper combining the multibreakpoint analysis with VAR for the period analyzed in the present work.

The General Society Survey(GSS) is a kind of government-funded survey which aims at examining the Socio-economic status, quality of life, and structure of contemporary society. GSS data set is regarded as one of the authoritative source for the government and organization practitioners to make data-driven policies. The previous analytic approaches for GSS data set are designed by combining expert knowledges and simple statistics. By utilizing the emerging data mining algorithms, we proposed a comprehensive data management and data mining approach for GSS data sets.

The approach are designed to be operated in a two-phase manner: a data management phase which can improve the quality of GSS data by performing attribute pre-processing and filter-based attribute selection; a data mining phase which can extract hidden knowledge from the data set by performing data mining analysis including prediction analysis, classification analysis, association analysis and clustering analysis.

According to experimental evaluation results, the paper have the following findings: Performing attribute selection on GSS data set can increase the performance of both classification analysis and clustering analysis; all the data mining analysis can effectively extract hidden knowledge from the GSS data set; the knowledge generated by different data mining analysis can somehow cross-validate each other.

By leveraging the power of data mining techniques, the proposed approach can explore knowledge in a fine-grained manner with minimum human interference. Experiments on Chinese General Social Survey data set are conducted at the end to evaluate the performance of our approach.

This study aims to propose a non-linear model to describe the effect of macroeconomic shocks on delinquency rates of three kinds of bank loans. Indeed, a wealth of literature has recognized significant evidence of the linkage between macro conditions and credit vulnerability, perceiving the importance of the high amount of bad loans for economic stagnation and financial vulnerability.

Generally, this linkage was represented by linear relationships, but the strong dependence of bank loan default on the economic cycle, subject to changes in regime, could suggest non-linear models as more appropriate. Indeed, macroeconomic variables affect the performance of bank’s portfolio loan, but such a relationship is subject to changes disturbing the stability of parameters along the time. This study is an attempt to model three different kinds of bank loan defaults and to forecast them in the case of the USA, detecting non-linear and asymmetric behaviors by the adoption of a Markov-switching (MS) approach.

Comparing it with the classical linear model, the authors identify evidence for the presence of regimes and asymmetries, changing in correspondence of the recession periods during the span of 1987–2017.

The data are at a quarterly frequency, and more observations and more extended research periods could ameliorate the MS technique.

The good forecasting performance of this model could be applied by authorities to fine-tune their policies and deal with different types of loans and to diversify strategies during the different economic trends. In addition, bank management can refer to the performance of macroeconomic conditions to predict the performance of their bad loans.

The authors show a clear outperformance of the MS model concerning the linear one.

The authors combine options logic with transaction cost economics to explain why firms maintain, divest or buy out their international joint ventures (IJVs). It is suggested that a decline in environmental risk and higher partner-related risk makes a firm more likely to acquire an IJV but less likely to divest an IJV. The study also investigates how IJV age moderates the effects of a decline in environmental risk and higher partner-related risk.

The study employs competing risks analyses to examine the drivers of different termination outcomes using a dataset consisting of 459 IJVs in the People's Republic of China, of which 110 were either acquired or divested by their foreign parent.

The study finds that changes in environmental risk and partner-related risk affect how firms terminate their IJVs in the People's Republic of China. Specifically, the authors find that the effect of exogenous and endogenous risk are more pronounced for the acquisition of IJVs than for the divestment of IJVs.

The study contributes to international marketing research by complementing options logic with transaction cost economics to provide a theoretical explanation of the different ways in which IJVs in the People's Republic of China are terminated.

IJVs continue to be an important yet often unstable method to serve international markets. Our findings increase managers' awareness of the effect that two important sources of risk may have on the termination of IJVs in the People's Republic of China.

The study provides novel insights into the effect that changes in exogenous and endogenous risk have on a firm's choice of termination mode drawing on novel data on the different ways in which foreign firms have terminated their IJVs in the Peoples' Republic of China.

With the growing need for employees to be innovative, public-sector organizations are investing in employee training. This study aims to examine the effects of a combined Lean Six Sigma and innovation training, using action learning, on public-sector employees’ creative role identity and innovative work behavior.

The authors studied a public service agency in Singapore in which a five-day Lean Innovation Training was implemented, using a combination of Lean Six Sigma and Creative Problem-Solving tools, with a simulation on day one and subsequent team-based project coaching, spread over six months. The authors administered pre- and postintervention surveys among all the employees, and initiated group interviews and observations before, during and after the intervention.

Creative role identity and innovative work behavior had significantly improved six months after the intervention, enabled through senior management’s transformational leadership. The training induced managers to role-model innovative work behaviors while cocreating, with their employees, a renewal of their agency’s core processes. The three completed improvement projects contributed to an innovative work culture and reduced service turnaround time.

Starting with a role-playing simulation on the first day, during which leaders and followers swapped roles, the action-learning type training taught all the organizational members to use various Lean Six Sigma and Creative Problem-Solving tools. This nimble Lean Innovation Training, and subsequent team-based project coaching, exemplifies how advancing the staff’s creative role identity can have a positive impact.

The purpose of this paper is to develop a real-time trajectory planner with optimal maneuver for autonomous vehicles to deal with dynamic obstacles during parallel parking.

To deal with dynamic obstacles for autonomous vehicles during parking, a long- and short-term mixed trajectory planning algorithm is proposed in this paper. In long term, considering obstacle behavior, A-star algorithm was improved by RS curve and potential function via spatio-temporal map to obtain a safe and efficient initial trajectory. In short term, this paper proposes a nonlinear model predictive control trajectory optimizer to smooth and adjust the trajectory online based on the vehicle kinematic model. Moreover, the proposed method is simulated and verified in four common dynamic parking scenarios by ACADO Toolkit and QPOASE solver.

Compared with the spline optimization method, the results show that the proposed method can generate efficient obstacle avoidance strategies, safe parking trajectories and control parameters such as the front wheel angle and velocity in high-efficient central processing units.

It is aimed at improving the robustness of automatic parking system and providing a reference for decision-making in a dynamic environment.