Search results

The purpose of this study was to solve the problem of most woven-fabric self-lubricating bearings that find it difficult to function at temperatures above 320°C, by designing a new type of new nuclear joint bearing. The results of this study will help designers to achieve accurate stress distribution, displacement deformation, fatigue life and damage of bearings. All of these can be a guide for designing self-lubricating joint bearings.

Finite element analysis is undertaken to simulate the new design bearings. To get the most appropriate and accurate results, the room temperature simulation (Simulation A), the modulus of elasticity that changes with temperature (Simulation B) and the thermal-structure-coupled simulation (Simulation C) are compared. The fatigue simulation is conducted to verify whether the self-lubricating method is reasonable and whether the bearing can function for over 60 years in an enclosed environment.

Stress distribution and displacement deformation of joint bearing can be accurately achieved via the thermal-structure coupled simulation. Work life and damage results have been achieved via the fatigue analysis, and the suggested working loads can be calculated via safety factors.

The newly designed joint bearing in which the graphite is laid on the outside of the inner ring functions and self-lubricates at temperatures above 320°C.

This paper aims to review the literature on carbon trading from the perspective of evolution, finds out the evolution path of these literatures and gives out the future research hotspots in this field.

Uses visualization tools (CiteSpace and HistCite) to systematically categorize the literature on carbon-trading schemes in the Web of Science core collection from 1998 to 2018, comprehensively analyzes carbon-trading schemes from four dimensions, namely, discipline evolution, keyword evolution, citation cluster evolution and citation path evolution.

Research on carbon-trading schemes has a specific development and evolution path along four dimensions, namely, in the discipline dimension, the largest change lies in the mathematics pointed to by at least four different disciplines; the keyword evolution dimension shows a gradual deepening emphasis on coordinated development; citation clusters identify three major clusters – carbon prices, China’s carbon trading, carbon market and supply chain; and citation paths identify three major evolutionary paths, the most important of which shows that “What affects carbon price?” has changed to “What is the impact of carbon prices?”

Reveals the evolution path of carbon trading research studies and proposes four possible development directions for carbon-trading scheme research, which is helpful for future carbon trading-related research and serves as a reference for the promotion of and improvements in carbon-trading schemes.

The purpose of this paper is to verify the relationship between the features of social media and knowledge sharing, and to examine how ambient awareness mediates this relationship.

An experiment is designed to stimulate the knowledge work in a famous Chinese business college and 156 valid samples were obtained. AMOS was used in this paper to examine the theoretical model.

There is a correlation among features of social media, ambient awareness and knowledge sharing. Surprisingly, network translucence, which indicates individuals’ meta-knowledge of others’ connections, has no influence on knowledge sharing. Although this is inconsistent with conjecture of the existing literature, it can be well explained by the phenomenon in real life, such as privacy setting in social media.

For employees who use social media to promote knowledge sharing within organizations, this study reminds them of the importance of ambient awareness. For managers, this study can give them some suggestions to make employees take full advantage of social media to achieve optimal benefits of knowledge sharing, thus improving organizational performance and innovation. For social media designers, they can make social media more useful in knowledge work by improving its specific features.

This paper proposes that ambient awareness is the mediator of the effect path between communication and knowledge sharing. And the status perception of coworkers’ exchanging information is closely related to knowledge sharing.

Smart cities show a “booming” trend both in the academia and the industry in recent years. Scholars across the world have been investigating how new technologies are applied to develop new services to the inhabitants and cities all over the world also address the “smart cities” challenges by promoting policymaking and governance. This paper aims to conduct in-depth research on smart cities by combining the study of governance policy study and information technology study.

This paper empirically mapped the trends of smart city development, outstanding scholars and hot topics about smart cities by analyzing important references using CiteSpace. The authors visualized references and topics to analyze smart city research, based on empirical data from Web of Science. Furthermore, two most important research branches – topics from smart city governance research and those from information systems (IS) research were studied, respectively.

First, the authors mapped the development of research and divided the development into three different stages. Second, the authors explored important, influential and instructive publications and publications’ attributes including authors, institutions, journals and topics. Third, the authors found there are different characteristics between the IS group and the governance group in publication situations, influential institutions, journals and authors, although the research points of the two branches are overlapping and fragmented. Finally, the authors proposed important topics, which include “internet of things (IoT)”, “big data”, “smart city systems” and “smart city management” and the authors predicted that “IoT” and “smart city challenge” would be future trends in recent years.

This study is an innovative research of its category because it visualized the development of smart city research, analyzed both governance and technology branches of smart city research synthetically using CiteSpace and forecasted future trends of smart city research by topics analysis and visualization of evolution.

This paper aims to explore the effect of fairness concerns on supply chain members' optimal decisions and profits, to compare their profits under different policies, and to investigate the impact of each policy on members, consumers, and the environment with fairness concerns.

Considering government policies and fairness concerns in recycling management, this paper develops five recycling and remanufacturing decision models (anarchy policy model, reward-penalty mechanism model, recycling investment subsidies model, government tax model, and fund subsidy system model). In each model, the manufacturer and the online platform form the Stackelberg game. This research further discusses comprehensive environmental benefits and consumer surplus under five scenarios.

First, the fairness concerns of the online platform inhibit the recovery rate and supply chain members' profit while increasing the platform's utility. Second, fairness concerns increase the profit gap between the manufacturer and online platform, and the higher the degree of fairness concerns, the greater the profit gap; however, the four policies reduce the profit gap. Finally, when there are fairness concerns, environmental taxes damage the interests of supply chain members and consumers, but are most beneficial to the environment; recycling investment subsidies are on the contrary; the fund subsidy system depends on the relative size of the treatment fund and the subsidy fund.

This paper provides useful insights on how to regulate government policy to improve supply chain management with fairness concerns.

This research aims to build a three-tiered driver system that entices contractor rule violations and explores the importance and the relationships among these drivers, hence providing theoretical support for the contractor rule violations governance.

A literature review based on fraud diamond theory identified drivers from Pressure, Opportunity, Rationalization and Capability that drive contractor rule violations. In the Chinese context, through feedback, discussion and analysis of semistructured interviews with ten experts, an improved three-tiered driver system was drafted. Based on this system, a survey was conducted and scored with experts to provide the data for this research. The decision-making trial and evaluation laboratory (DEMATEL) method was used to determine relationships and influences between factors, and the DEMATEL-based analytic network process method was used to weigh these factors.

This paper systematically studied the drivers of contractor rule violations, specifically, the results showed that pressure had an important driving effect across the driver system, and those five factors – poor cultural atmosphere, weak internal control, prior experience, moral disengagement and information asymmetry – had the most influence on contractor rule violations. The results also indicated the strong effect pressure has on enticing rule violations and revealed that culture atmosphere and internal company governance played crucial roles in the occurrence of rule violations.

This study provided construction practitioners with a robust tool to analyze the drivers of contractor rule violations. The rule violation drivers in the construction practice scenes identified in this study can provide more direct and effective violation-related guidance for contractors, regulators and the industry.

Based on the new perspective of fraud diamond, this paper systematically bulit a three-tiered driver system combining theory with practice. This study contributed to understand the driver mechanism of contractor rule violations especially the importance of internal factors of contractors, which provided theory reference for compliance governance of construction industry.

When a railway emergency occurs, it often leads to unexpected consequences, especially for trains of higher speed and larger passenger flow. Therefore, the railway emergency plan, a pre-established plan to deal with emergencies, plays an important role in reducing injuries and losses. However, the existing railway emergency plans remain as plain-text documents, requiring lots of manual work to capture the important regulations. This paper aims to propose a visualized, formal and digital railway emergency plan modeling method based on hierarchical timed Petri net (HTPN), which is also of better interpretability.

First, the general railway emergency plan was analyzed. Second, the HTPN-based framework model for the general railway emergency plan was proposed. Then, the instantiated model of electric multiple units rescue emergency plan was built by ExSpect, a Petri net simulation tool.

The experiments show that the proposed model is more digital and of better readability, visualization and performability, and, meanwhile, can generally conform to the practice well, offering a promising reference for future analysis of the optimization of railway emergency plans.

This study offers a promising reference for future analysis of the optimization of railway emergency plans.

The purpose of this study is to discuss the principles and factors that influence the site selection of emergency medical facilities for public health emergencies. This paper discusses the selection of the best facilities from the available facilities, proposes the capacity of new facilities, presents a logistic regression model and establishes a site selection model for emergency medical facilities for public health emergencies in megacities.

Using Guangzhou City as the research object, seven alternative facility points and the points' capacities were preset. Nine demand points were determined, and two facility locations were selected using genetic algorithms (GAs) in MATLAB for programing simulation and operational analysis.

Comparing the results of the improved GA, the results show that the improved model has fewer evolutionary generations and a faster operation speed, and that the model outperforms the traditional P-center model. The GA provides a theoretical foundation for determining the construction location of emergency medical facilities in megacities in the event of a public health emergency.

First, in this case study, there is no scientific assessment of the establishment of the capacity of the facility point, but that is a subjective method based on the assumption of the capacity of the surrounding existing hospitals. Second, because this is a theoretical analysis, the model developed in this study does not consider the actual driving speed and driving distance, but the speed of the unified average driving distance and the driving distance to take the average of multiple distances.

The results show that the method increases the selection space of decision-makers, provides them with stable technical support, helps them quickly determine the location of emergency medical facilities to respond to disaster relief work and provides better action plans for decision makers.

The results show that the algorithm performs well, which verifies the applicability of this model. When the solution results of the improved GA are compared, the results show that the improved model has fewer evolutionary generations, faster operation speed and better model than the intermediate model GA. This model can more successfully find the optimal location decision scheme, making that more suitable for the location problem of megacities in the case of public health emergencies.

The research findings provide a theoretical and decision-making basis for the location of government emergency medical facilities, as well as guidance for enterprises constructing emergency medical facilities.

Being an efficient mechanism for the value of money, public–private partnership (PPP) is one of the most prominent approaches for infrastructure construction. Hence, many controversies about the performance effectiveness of these delivery systems have been debated. This research aims to develop a novel performance management perspective by revealing the causal effect of key performance indicators (KPIs) on PPP infrastructures.

The literature review was used in this study to extract the PPPs KPIs. Experts’ judgment and interviews, as well as questionnaires, were designed to obtain data. Copula Bayesian network (CBN) has been selected to achieve the research purpose. CBN is one of the most potent tools in statistics for analyzing the causal relationship of different elements and considering their quantitive impact on each other. By utilizing this technique and using Python as one of the best programming languages, this research used machine learning methods, SHAP and XGBoost, to optimize the network.

The sensitivity analysis of the KPIs verified the causation importance in PPPs performance management. This study determined the causal structure of KPIs in PPP projects, assessed each indicator’s priority to performance, and found 7 of them as a critical cluster to optimize the network. These KPIs include innovation for financing, feasibility study, macro-environment impact, appropriate financing option, risk identification, allocation, sharing, and transfer, finance infrastructure, and compliance with the legal and regulatory framework.

Identifying the most scenic indicators helps the private sector to allocate the limited resources more rationally and concentrate on the most influential parts of the project. It also provides the KPIs’ critical cluster that should be controlled and monitored closely by PPP project managers. Additionally, the public sector can evaluate the performance of the private sector more accurately. Finally, this research provides a comprehensive causal insight into the PPPs’ performance management that can be used to develop management systems in future research.

For the first time, this research proposes a model to determine the causal structure of KPIs in PPPs and indicate the importance of this insight. The developed innovative model identifies the KPIs’ behavior and takes a non-linear approach based on CBN and machine learning methods while providing valuable information for construction and performance managers to allocate resources more efficiently.

The paper aims to propose a clustering model for panel data. More specifically, the paper aims to construct a gray incidence model for panel data to solve the classification with multi-factors and multi-attributes.

The paper opted for a clustering theory study using gray incidence theory based on dynamic weighted function. The paper presents an example to verify the rationality of the new model, which suggests that the new model can reflect the incidence degree of panel data.

The paper provides a new gray incidence model based on a dynamic weighted function that can amplify the characteristics of the sample to some extent. The properties of the new incidence model, such as normalization, symmetry and nearness, are all satisfied. The paper also shows that the new incidence model performs very well on cluster discrimination.

The new model in this paper has supplemented and improved the gray incidence analysis theory for panel data.