The collaborative development of complex products has gradually developed into a “main manufacturer-suppliers” mode, under which the manufacturing enterprises form a complex product collaborative manufacturing supply chain network. Quality risks which bring enormous hidden danger to the product quality can be propagated and accumulate along the supply chain. It is of great significance to quantify the propagation mechanism of quality risk between supply chain network nodes and identify the key quality risk factor that causes fluctuation of product quality. This study for the first time applies the SoV into the research on quality risk propagation of complex product collaborative manufacturing supply chain network. Firstly, this paper uses the CN to construct a complex product collaborative manufacturing supply chain network according to its characteristics. Secondly, on the basis of SoV, the quality risk propagation model is established. Thirdly, we put forward a method to identify the key quality risk factors of supply chain network based on the risk propagation effect. Lastly, a numerical simulation is given to verify the effectiveness of the model and its identification method. The results reveal that the quality risk propagation includes the vertical propagation within enterprises and the horizontal propagation from the lower-level enterprises to the upper-level enterprises of the supply chain. The quality risks of an enterprise are determined by its own quality risk factors and the quality risk passed by the lower-level enterprises.

Since MTB has been deemed as the business model of 21st century but its long-term sustainability depends on level of consumer satisfaction with its applicability in their everyday life. Therefore, current research aims to highlight factors which could shape consumers’ satisfaction and further trigger their intuition to recommend it in their circle of influence. Unified theory of acceptance and use of technology 2 (utaut2) was modified and extended to fit the purpose. Data were collected from prevailing MTB users and analyzed in PLS-SEM. Results reveal that utaut2 factors signify their effects on consumer satisfaction which further trigger their instinct to recommend MTB. Current research modified utaut2 with mediating role of consumer satisfaction with MTB which further stimulates their instinct to recommend it. The modified framework adds value to the theoretical horizon of utaut2 and further extends its applicability in a novel setting like MTB. The study offers insightful implications to MTB companies for effective design and implementation of future strategies. Moreover, limitations and suggestions for future researchers are also discussed.

Complex integrated circuits (ICs) have complex functions and various working modes, which have many factors affecting the performance of a single event effect. The single event effect performance of complex ICs is highly program-dependent and the single event sensitivity of a typical operating mode is generally used to represent the single event performance of the circuits. Traditional evaluation methods fail to consider the cross effects of multiple factors and the comprehensive effects of each factor on the single event soft error cross section. In order to solve this problem, a new quantitative study method of single event error cross section based on a generalized linear model for different test programs is proposed. The laser test data is divided into two groups: a training set and a validation set. The former is used for model construction and parameter estimation based on five methods, such as the generalized linear model and Ensemble, while the latter is used for quantitative evaluation and validation of a single event soft error cross section of the model. In terms of percentage error, the minimum mean estimation error on the validation set is 13.93%. Therefore, it has a high accuracy to evaluate the single event soft error cross section of circuits under different testing programs based on the generalized linear model, which provides a new idea for the evaluation of a single event effect on complex ICs.

China’s high-tech industry supply chain is confronted with multifaceted pressures from both internal and external environments in maintaining its security and stable development. This paper collects various operational data of Chinese high-tech enterprises spanning from 2012 to 2021, constructs a unique supply chain resilience index system tailored for the high-tech industry, and employs the entropy weight-TOPSIS method to conduct a comprehensive evaluation of supply chain resilience in China’s high-tech industry. Furthermore, GIS techniques are utilized to analyze the spatiotemporal evolution characteristics of supply chain resilience. The research findings indicate that technology research and development capability and open innovation capability exert the greatest influence on the resilience of China’s high-tech industry supply chain. Moreover, the resilience of the supply chain exhibits temporal instability with an overall downward trend and significant spatial disparities in its distribution pattern. Based on these findings, this paper proposes corresponding countermeasures, including strengthening core technology research and development capability, optimizing the regional development environment, and enhancing international cooperation, aiming to provide substantial theoretical and practical implications for enhancing the resilience of the high-tech industry supply chain.