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This study aims to explore the reasons why some Chinese private entrepreneurs are reluctant to make charitable donations, with a focus on the perspective of “original sin” suspicion. The objective of this paper is to examine the challenges faced by these entrepreneurs, especially those suspected of “original sin,” when making charitable donations, and to provide recommendations for addressing these challenges.

Using data from the Chinese Private Enterprises Survey Database for the years 2008, 2010, 2012 and 2014, this study used ordinary least squares regression to examine the relationship between “original sin” suspicion and charitable donations from private enterprises.

This study examined the impact of “original sin” suspicion on charitable donations and found that it significantly reduces the donations of privatized enterprises. The negative impact of “original sin” suspicion on charitable donations is especially pronounced in small and medium-sized enterprises (SMEs), as well as those that have experienced changes in local leadership.

While previous research focused on the motivations of private enterprises that donated, they failed to identify which types of enterprises were reluctant to donate and why. By focusing on the “original sin” suspicion surrounding entrepreneurs in privatized enterprises and the political costs they face, this study sheds light on the challenges they encounter in charitable donations and explains why privatized enterprises, especially SMEs, are unwilling to make charitable donations.

Given the regional heterogeneity of economic development, electricity consumption in various regions exhibits a discrepant growth pattern. The purpose of this study is to construct a grey system forecasting model with intelligent parameters for predicting provincial electricity consumption in China.

First, parameter optimization and structural expansion are simultaneously integrated into a unified grey system prediction framework, enhancing its adaptive capabilities. Second, by setting the minimum simulation percentage error as the optimization goal, the authors apply the particle swarm optimization (PSO) algorithm to search for the optimal grey generation order and background value coefficient. Third, to assess the performance across diverse power consumption systems, the authors use two electricity consumption cases and select eight other benchmark models to analyze the simulation and prediction errors. Further, the authors conduct simulations and trend predictions using data from all 31 provinces in China, analyzing and predicting the development trends in electricity consumption for each province from 2021 to 2026.

The study identifies significant heterogeneity in the development trends of electricity consumption systems among diverse provinces in China. The grey prediction model, optimized with multiple intelligent parameters, demonstrates superior adaptability and dynamic adjustment capabilities compared to traditional fixed-parameter models. Outperforming benchmark models across various evaluation indicators such as root mean square error (RMSE), average percentage error and Theil’s index, the new model establishes its robustness in predicting electricity system behavior.

Acknowledging the limitations of traditional grey prediction models in capturing diverse growth patterns under fixed-generation orders, single structures and unadjustable background values, this study proposes a fractional grey intelligent prediction model with multiple parameter optimization. By incorporating multiple parameter optimizations and structure expansion, it substantiates the model’s superiority in forecasting provincial electricity consumption.

This paper aims to propose an elementary approach toward the identification of assembly defects of a cam curved groove mechanism.

A numerical analysis method for identifying the assembly defects of the cam curved groove mechanism is proposed by resorting to Hertz contact theory. A general mathematical model is established to analyze the kinematic and dynamic characteristics with an interference fit between the main roller and cam curved groove, including the contact points of the external and internal ring.

The analysis method of the contact point characteristics of the cam curved groove mechanism is given in this paper, and the kinematic and dynamic characteristics of the main roller can be analyzed. The numerical examples presented in this paper are implemented in MATLAB, feasibility and validity of the above algorithm are verified by the finite element method.

Regarding the defects of the interference fit, the findings of this paper can serve as a reference for researchers in reducing the defects in the design process of the cam mechanism.

Vehicle lightweight design has positive implications for reducing energy consumption and abating greenhouse gas emissions. The traditional trailer axle design mainly focuses on the overall performance of the trailer axle. Only when the local performance does not meet the requirements will local performance optimization be done, such as local heat treatment to improve local strength. Such a design results in an uneven distribution of axle performance and excess performance in some local structures. The purpose of this study is to investigate the weight reduction on the premise of ensuring the structural dimensions of the outer surface of the axle remain unchanged and the reliability of the axle.

The axle is parameterized by computer aided design, and the optimized axle finite element model based on computer aided engineering is established and verified by taking the eight dimensions of the axle cavity structure which affect the performance as parameters. A genetic algorithm is used to optimize the axle cavity structure size and axle weight based on multiobjective optimization, and eight optimized size parameters of axle cavity structure are obtained.

The total weight of the optimized axle of TM1314 is reduced by 10.2 kg, and the weight reduction ratio reaches 10.7%. According to the optimized structural size of the axle, the specimen was trial-manufactured, and the bench tests of stiffness, strength and fatigue life were carried out according to the test requirements of the trailer axle standard (JT/T 475-2002). The test results show that the maximum deformation of the specimen is 2.46 mm, the strength safety factor of the specimen body and the steel plate spring seat are 6.71 and 6.86 and bear the alternating load more than 1.05 × 106 times, which meets the standard of the trailer axle and is better than the original design requirements of the trailer axle.

In this study, the multiobjective optimization model of the axle is established, the response surface is constructed by the Latin hypercube sampling design method and the optimal solution set is obtained by the multiobjective genetic algorithm. It has been verified by bench tests that it can achieve a weight reduction of 10.7% under the premise of the same structure and size of the outer surface of the axle. The lightweight method based on multiobjective optimization proposed in this paper can provide a reference for the lightweight design of other key vehicle components.

In the era of big data, there is doubt about the significance of causal inference as a paramount scientific task in the social sciences. Meanwhile, data-mining techniques rooted in big data and artificial intelligence (AI) have infiltrated numerous aspects of social science research. This study aims to expound the criticality of discerning causal relationships – beyond mere correlations – and scrutinizes the ramifications of big data and AI in the identification of causality.

This study discusses the challenges and opportunities for causality identification in the era of big data under the framework of potential outcomes model and structural causal model.

First, even in the age of big data, correlations that lack interpretability, robustness and feasibility cannot substitute causality. Second, the richness of the sample size does not help solve the problem of systematic bias in the process of causal inference. Furthermore, current AI research targets correlations rather than causality, thus creating difficulties in advancing from observations to counterfactuals.

This study provides insights into the impact of big data era on causal inference in the social sciences, with a view toward enhancing the pool of theoretical concepts available to researchers in relevant fields and accurately guiding the direction of scientific research in these fields.

Perhaps the most significant development in the global business arena in the post-war period has been the emergence of the Asia-Pacific rim countries as a significant economic force.

Many manufacturing sites require precision assembly. Particularly, similar to cell phones, assembly at the sub-mm scale is not easy, even for humans. In addition, the system should assemble each part with adequate force and avoid breaking the circuits with excessive force. The purpose of this study is to assemble high precision components with relatively reasonable vision devices compared to previous studies.

This paper presents a vision-force guided precise assembly system using a force sensor and two charge coupled device (CCD) cameras without an expensive 3-dimensional (3D) sensor or computer-aided design model. The system accurately estimates 6 degrees-of-freedom (DOF) poses from a 2D image in real time and assembles parts with the proper force.

In this experiment, three connectors are assembled on a printed circuit board. This system obtains high accuracy under 1 mm and 1 degree error, which shows that this system is effective.

This is a new method for sub-mm assembly using only two CCD cameras and one force sensor.