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Gas pipelines are facing serious risk because of the factors such as long service life, complex working condition and most importantly, corrosion. As one of the main failure reasons of gas pipeline, corrosion poses a great threat to its stable operation. Therefore, it is necessary to analyze the reliability of gas pipelines with corrosion defect. This paper uses the corresponding methods to predict the residual strength and residual life of pipelines.

In this paper, ASME-B31G revised criteria and finite element numerical analysis software are used to analyze the reliability of a special dangerous section of a gas gathering pipeline, and the failure pressure and stress concentration of the pipeline under three failure criteria are obtained. Furthermore, combined with the predicted corrosion rate of the pipeline, the residual service life of the pipeline is calculated.

This paper verifies the feasibility of ASME-B31G revised criteria and finite element numerical analysis methods for reliability analysis of gas pipelines with corrosion defect. According to the calculation results, the maximum safe internal pressure of the pipeline is 9.53 Mpa, and the residual life of the pipeline under the current operating pressure is 38.41 years, meeting the requirements of safe and reliable operation.

The analysis methods and analysis results provide reference basis for the reliability analysis of corroded pipelines, which is of great practical engineering value for the safe and stable operation of natural gas pipelines.

This paper aims to investigate the temperature and wear properties of vertical ball screws and to discuss the surface design of ball screws in industrial applications.

The energy equation of the screw surface considering the frictional heat was established to verify the surface temperature of the ball screw. X-ray diffraction was used to examine the micro-contact temperature between the ball and screw. Debris size and density were examined to investigate wear properties of ball screws and to study the relationship of wear debris and temperature.

First, the main energy source for the surface temperature of high speed vertical ball screws is derived from friction force between ball and screw. Second, the temperature rise between the ball and screw has great relevance with wear debris concentration. Third, the surface temperature of the screw is higher than between the nut and ball for high speed vertical ball screws due to high convection heat transfer. The contact temperature of the nut near the flange is smaller than that of the nut away from the flange end due to the high contact load and thermal conduction. Finally, correlation of particle size and surface roughness value for vertical ball screws was established, and its effects on contact temperature were studied. The theoretical analysis and experiments will help to characterize the design and manufacture of vertical ball screws.

The surface temperature and micro-contact temperature analytical model were established to study the ball screw design. Based on the surface-particle micro-contact temperature balance, the optimal range of surface roughness was designed for vertical ball screws, considering the wear debris and micro-contact temperature.

The purpose of this paper is to develop a novel active disturbance rejection attitude controller for quadrotors and propose a controller parameters identification approach to obtain better control results.

Aiming at the problem that quadrotor is susceptible to disturbance in outdoor flight, the improved active disturbance rejection control (IADRC) is applied to design attitude controller. To overcome the difficulty that adjusting the parameters of IADRC controller manually is complex, paired coevolution pigeon-inspired optimization (PCPIO) algorithm is used to optimize the control parameters.

The IADRC, where nonlinear state error feedback control law is replaced by non-singular fast terminal sliding mode control law and a third-order tracking differentiator is designed for second derivative of the state, has higher control accuracy and better robustness than ADRC. The improved PIO algorithm based on evolutionary mechanism, named PCPIO, is proposed. The optimal parameters of ADRC controller are found by PCPIO with the optimization criterion of integral of time-weighted absolute value of the error. The effectiveness of the proposed method is verified by a series of simulation experiments.

IADRC can improve the accuracy of attitude control of quadrotor and resist external interference more effectively. The proposed PCPIO algorithm can be easily applied to practice and can help the design of the quadrotor control system.

An improved active disturbance rejection controller is designed for quadrotor attitude control, and a hybrid model of PIO and evolution mechanism is proposed for parameters identification of the controller.

The purpose of this paper is to explore how external pressures, internal capability and transaction attributes of logistics outsourcing synergically influence the extent of asset-based and non-asset-based logistics outsourcing.

Based on the data surveyed from 250 manufacturing companies in China, this study employed fuzzy-set qualitative comparative analysis (fsQCA) to deduce multiple configurations for logistics outsourcing decisions.

The results suggest that asset-based logistics outsourcing is primarily driven by external imitation pressures or internal demands for logistics technologies, while non-asset-based logistics outsourcing is mainly driven by the demands for external management-based logistics services. Asset specificity plays a positive role in promoting both asset-based and non-asset-based logistics outsourcing. The requirement for third-party logistics (3PL) management capability depends on the outsourcing types and outsourcing causes.

This study provides guidance to practitioners for them to make outsourcing decisions. It suggests that asset-based logistics outsourcing is more appropriate when there are high external imitation pressures or more internal logistics demands, while non-asset-based logistics outsourcing should be used only when a firm needs management-based logistics services. Besides, 3PL users are suggested to outsource their logistics when their 3PL providers are required to make specific investments. In addition, managers should carefully evaluate firms’ capabilities in managing outsourcing relationships.

Previous studies largely ignored the interaction effects of a set of factors on logistics outsourcing decisions, and to date, little research empirically examined how outsourcing is driven in terms of different types of outsourcing. Drawing on the institutional theory, dynamic capability view, and transaction cost theory and overarching under the complexity theory, this study examines how institutional, organizational and transactional factors interplay with each other to influence different types of logistics outsourcing (i.e. asset based and non-asset based). Methodologically, the configural analysis (i.e. fsQCA) is applied to explore complex causal configurations that drive logistics outsourcing.

This survey focuses on two families of nonlinear vector time series models, the family of vector threshold regression (VTR) models and that of vector smooth transition regression (VSTR) models. These two model classes contain incomplete models in the sense that strongly exogeneous variables are allowed in the equations. The emphasis is on stationary models, but the considerations also include nonstationary VTR and VSTR models with cointegrated variables. Model specification, estimation and evaluation is considered, and the use of the models illustrated by macroeconomic examples from the literature.

Based on signaling theory, this paper aims to explore the impact of supply chain transparency (SCT) on firms' bank loan (BL) and supply chain financing (SCF) in the context of voluntary disclosure of supplier and customer lists.

Based on panel data collected from Chinese-listed firms between 2012 and 2021, fixed-effect models and a series of robustness checks are used to test the predictions.

First, improving SCT by disclosing major suppliers and customers promotes BL but inhibits SCF. Specifically, customer transparency (CT) is more influential in SCF than supplier transparency (ST). Second, supplier concentration (SC) weakens SCT’s positive impact on BL while reducing its negative impact on SCF. Third, customer concentration (CC) strengthens the positive impact of SCT on BL but intensifies its negative impact on SCF. Last, these findings are basically more pronounced in highly competitive industries.

This study contributes to the SCT literature by investigating the under-explored practice of supply chain list disclosure and revealing its dual impact on firms' access to financing offerings (i.e. BL and SCF) based on signaling theory. Additionally, it expands the understanding of the boundary conditions affecting the relationship between SCT and firm financing, focusing on supply chain concentration. Moreover, it advances signaling theory by exploring how financing providers interpret the SCT signal and enriches the understanding of BL and SCF antecedents from a supply chain perspective.

It is particularly important to study the learner cognition based on information technology and change of learning environment in e-learning. Therefore, based on the combination of knowledge management and e-learning, the purpose of this paper is to discuss the basic rules and existing problems of virtual learning community (VLC).

In this study, a set of principles and basic framework of VLC was proposed, which could effectively instruct the construction of knowledge and collective wisdom development of VLC. Meantime, this paper revealed and constructed the theoretical framework and learning model for facilitating the advance of collective intelligence, described the main characteristics of collaborative knowledge building and traced the trajectory of convergence of collective intelligence and verified them through case studies and field studies.

The author analyzed the characteristics of VLC assisting knowledge construction and traced the trajectory of convergence of collective intelligence. In this way, the learners not only construct the meaning of knowledge in the process of participation from the edge to the center, but also complete the transformation from a green hand to an old hand. As a result, the learners form a sense of belonging to the community and complete the identity construction as a member of the community in the process of co-constructing the cultural tradition and value orientation of the community.

The analysis of the framework is made relatively in general terms, and lacks of the concrete, practical analysis. Therefore, it is necessary to adopt the social network analysis method to carry out systematic research on the framework, so as to make the actual operation of the VLC more practical.

It is significant to adopt the construction of the research system and theoretical framework in building VLC so as to improve the learner’s abilities of knowledge building and make full use of the advantages of the VLC.

This paper revealed and constructed the theoretical framework and learning model for facilitating the advance of collective intelligence. Besides, it provides a model reference for the follow-up construction of a new collective learning model.

The paper aims to explore the factors that influence the risk perception. The moderating roles of purchase experience and purchase involvement are also investigated.

The conceptual model is framed into the SOR (stimulus-organism-response) model, which was tested using data collected from the users of a leading group buying platform in China, with the help of LISREL.

The result indicates that both vendor- and intermediary-related factors exert negative yet significant influences on perceived risk. The relationships between vendor-related attributes and perceived risk are contingent on purchase involvement. The relationships between intermediary-related attributes and perceived risk are subject to purchase experience.

This study explores the moderating effects of purchase experience and purchase involvement based on the characteristics of group buying, demonstrating the differences from traditional online shopping.

As an important carrier of sustainable economy and social development, population is the foundation of the whole society. Scientific predictions of future population growth will bring great reference to macro-economic and social planning. For China, as a country of the biggest population, the research on its population policy is worthwhile.

Previous literatures on population growth prediction are generally based on time-series analysis. However, the new two-child policy in China provides us an opportunity to predict the population growth from the perspective of the welfare efficiency, since each family is able to determine whether to have the second child on account of the family’s utility. The welfare efficiency is calculated through the database of newborn babies, disposable income per capita, living resource per capita, and health expenditure pre capital. These are the main factors by which each family decides whether to bear additional babies. In this chapter, we perform the micro-economic analysis on a new policy and propose a Data Envelopment Analysis (DEA) method to predict the population growth. Under the condition of policy adjustment, we successfully predict the population growth with this method. We also propose some suggestions concerning the implementation of the new policy.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.