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Body type classification has a great influence on plate making and garment sizing system, and the accuracy of body type classification method will greatly affect the fit of garment production. The purpose of this paper is to use the decision tree algorithm to study body classification rules, develop a decision tree body recognition model and judge the body shape of middle-aged women in Xinjiang.

First, perform dimensionless processing on the collected data of 256 middle-aged women in Xinjiang, and the dimensionless data were used for K-means body clustering; Then, quantitatively analyze the effectiveness of different classification clusters based on the silhouette coefficients. Second, the decision tree algorithm is used to divide the classified sample data into a training set and a test set at a ratio of 70/30, and select the best node and the best branch based on the Gini coefficient to construct a classification tree. Last, the overall optimal decision tree is generated by means of hyperparameter pruning.

The body shape of middle-aged women in Xinjiang can be divided into three types: standard body, plump body and obese body. The decision tree model has an excellent effect on body classification of middle-aged women in Xinjiang (precision (macro), 95.46%; precision (micro), 95.95%; recall (macro), 95.46%; recall (micro), 95.95%; F1 (macro), 95.46%; F1 (micro), 95.95%).

For scientific research, this paper is conducive to increasing the regional body type theory and stimulating the establishment of a garment sizing subdivision system in Xinjiang. In terms of production practice, this paper not only establishes a model for judging the shape of middle-aged women in Xinjiang, but also provides reference data for intermediates of various sizes. In addition, to facilitate pattern-making and the establishment of a subdivision system for the size of middle-aged women's garments in Xinjiang, this paper provides the grading values of various body control parts of middle-aged women in Xinjiang.

The purpose of the experimental investigation was to optimize the process parameters of the fused deposition modeling (FDM) technique. The optimization of the process was performed to identify the relationship between the chosen factors and the tensile strength of acrylonitrile butadiene styrene (ABS) and carbon fiber polylactic acid (PLA) thermoplastic material, FDM printed specimens. The relationship was demonstrated by using the linear experimental model analysis, and a prediction expression was established. The developed prediction expression can be used for the prediction of tensile strength of selected thermoplastic materials at a 95% confidence level.

The Taguchi L9 experimental methodology was used to plan the total number of experiments to be performed. The process parameters were chosen as three at three working levels. The working range of chosen factors was the printing speed (60, 80 and 100mm/min), 40%, 60% and 80% as the infill density and 0.1mm, 0.2mm and 0.3mm as the layer thickness. The fused deposition modeling process parameters were optimized to get the maximum tensile strength in FDM printed ABS and carbon fiber PLA thermoplastic material specimens.

The optimum condition was achieved by the process optimization, and the desired results were obtained. The maximum desirability was achieved as 0.98 (98%) for the factors, printing speed 100mm/min, infill density 60mm and layer thickness 0.3mm. The strength of the ABS specimen was predicted to be 23.83MPa. The observed strength value was 23.66MPa. The maximum desirability was obtained as 1 (100%) for the factors, printing speed 100mm/min, infill density 60mm and layer thickness 0.2mm. The strength of the carbon fiber PLA specimen was predicted to be 26.23MPa, and the obtained value was 26.49MPa.

The research shows the useful process parameters and their suitable working conditions to print the tensile specimens of the ABS and carbon fiber PLA thermoplastics by using the fused deposition modeling technique. The process was optimized to identify the most influential factor, and the desired optimum condition was achieved at which the maximum tensile strength was reported. The produced prediction expression can be used to predict the tensile strength of ABS and carbon fiber PLA filaments.

The results obtained from the experimental investigation are useful to get an insight into the FDM process and working limits to print the parts by using the ABS and carbon fiber PLA material for various industrial and structural applications.

The results will be useful in choosing the suitable thermoplastic filament for the various prototyping and structural applications. The products that require freedom in design and are difficult to produce by most of the conventional techniques can be produced at low cost and in less time by the fused deposition modeling technique.

The process optimization shows the practical exposures to state an optimum working condition to print the ABS and carbon fiber PLA tensile specimens by using the FDM technique. The carbon fiber PLA shows better strength than ABS thermoplastic material.

The purpose of this paper is to analyse how different strengths of simmelian ties affect knowledge spirals and investigate which major factors affect the influence of simmelian ties on knowledge spirals.

The empirical data in this paper were collected through e-mail and interview questionnaires to R&D teams in high-tech manufacturing enterprises in China. The authors obtained 132 teams' valid responses. The interval decision-making trial and evaluation laboratory (interval DEMATEL) method, differential evolution (DE) algorithm and Bayesian structural equation modelling (BSEM) were employed to test the theoretical framework developed for this paper.

The results show that strong simmelian ties have positive associations with high-performance work practices (HPWPs). Meanwhile, weak simmelian ties have positive associations with HPWPs. Furthermore, HPWPs and knowledge fermentation play a conducive role in the relationship between simmelian ties and knowledge spirals.

This paper contributes in three ways. First, it extends research on the relational antecedents of knowledge spirals. Second, this paper extends the study of social capital related to knowledge spirals. Third, this paper elucidates less familiar factors relating HPWPs to knowledge fermentation by testing the mediating role of HPWPs in knowledge fermentation.

This paper aims to explore empirically the emergence of entrepreneurs in China’s transition context since the 1980s.

This paper applies the China Private Enterprises Survey Database and historical evidence and Chi-Square test to identify the emergence patterns of China’s entrepreneurs in different historical stages.

This longitudinal study finds that factors such as working experience, education background and social connection had strong influences on entrepreneurship, but the impact of these have varied significantly along with institutional changes over the past three decades. It also identifies that the majority of emergent entrepreneurs were formerly low-level public servants and high-qualified managers in state-owned enterprises. Additionally, significant trends including an increasing number of female entrepreneurs, a decreasing impact from political networks, improving investment in innovation and diversifying financial sources are also found.

This paper is the first to apply the Chinese Private Enterprises Survey database to investigate the emergence patterns of China’s entrepreneurs in different historical stages after the 1978 reform.

This paper proposes an examination of the relationship between female participation in top management teams and firm performance in the emerging Chinese private economy. It aims to examine the direct link between female participation in top management teams and firm performance. This is examined in the context of human capital and social capital associated with female top executives to investigate the origins and the contingencies of the linkage.

Drawing on resource dependence theory, the study develops and tests a set of hypotheses regarding the key relationships, using the data of listed private‐owned companies in China's security exchanges in 2008, with critical information on financial performance, corporate governance structure and the top management team composition of the companies. Regression analyses are conducted to test the direct relationship and the moderating effects.

The empirical analysis supports a positive relationship between the degree of female participation and firm performance in Chinese privately owned companies. The positive relationship is further strengthened by female top executives' human capital and social capital, consistent with the hypotheses.

The present study gains consistent results with research conducted in the Western context, suggesting that the top management behavior of Chinese private enterprises is similar to that of their Western counterparts, possibly due to the fact that they are less influenced by direct governmental control and are more profit‐driven than state‐owned companies.

The results of the study suggest that Chinese private companies can gain competitive advantages through identifying, attracting, and developing female managerial talents. And the female executives in the new era should be ones with systematic education and strong social connections. Both factors facilitate female executives to contribute better to their companies' performance.

The contribution of the present study is twofold. First, drawing on extant literature in the Western business context, the present study is the first to examine how female participation in top management influences firm performance in the context of the Chinese private sector, which contributes to the understanding of and offers insights to Chinese managerial practices. Second, the study enriches the extant literature by examining the moderating effects of female executives' human and social capitals.

The paper aims to provide an adaptive neural network controller for permanent magnet synchronous motor (PMSM) under direct torque control (DTC) algorithm to minimize the torque ripple and EMI noise.

The design methodology is based on vector control used for electrical machines. MATLAB simulations supported with experimental study under C++ are used.

The simulated and experimental results show that considerable torque ripple as well as current ripple and EMI noise reduction can be achieved by utilizing adaptive neural switching algorithm to fire the inverter supplying the PMSM.

This research is limited to PMSM, however the research can be extended to include other AC motors as well. In addition, the following points can be studied: the effects of harmonics in control signals on the torque ripple can be analyzed; the actual mathematical relation between the torque and flux ripple can be studied to set the flux and torque bands width in reasonable value; different neural network algorithms can be applied to the system to solve the similar problems.

Based on existing DTC control system, it is only required to change the software switching algorithm, to provide smooth torque, given that the switching frequency of the inverter module is more than or equal to 15 MHz and the system is supplied with timers. In addition a relatively higher DC voltage may be required to achieve higher speed compared with the traditional DTC.

In this paper, the stator flux position, and errors due to deviations from reference values of the torque and stator flux are used to select two active vectors while at the same time the absolute value of the torque error and the stator flux position are used neural network structure to adapt the switching of the inverter in order to control the applied average voltage level in such a way as to minimize the torque ripple, so instead of fixed time table structure, a neural network controller is used to calculate the switching time for the selected vectors and no PI controller is used as the case in the traditional space vector modulation. This work is directed to motor drive system designers who seek highly smooth torque performance with EMI noise reduction.

The purpose of this study is to investigate whether the corporate environmental, social and governance (ESG) performance of enterprise is influenced by the enterprise digital transformation. In addition, this study explains how enterprise digital transformation affects ESG performance.

The sample covers 4,646 nonfinancial companies listed on China’s A-share market from 2009 to 2021. The study adopts the fixed-effects multiple linear regression to perform the data analysis.

The study finds that enterprise digital transformation has a significant inverted U-shaped impact on ESG performance. Moderate digital transformation can improve enterprise ESG performance, whereas excessive digital transformation will bring new organizational conflicts and increase enterprise costs, which is detrimental to ESG performance. This inverted U-shaped effect is more pronounced in industrial cities, manufacturing industries and enterprises with less financing constraints and executives with financial backgrounds. Enterprise digital transformation mainly affects ESG performance by affecting the level of internal information communication and disclosure, the level of internal control and the principal-agent cost.

The government should take multiple measures to encourage enterprises to choose appropriate digital transformation based on their own production behaviors and development strategies, encourage them to innovate and upgrade their organizational management and development models in conjunction with digital transformation and guide them to use digital technology to improve ESG performance.

This study shows that irrational digital transformation cannot effectively improve the ESG performance of enterprises and promote the sustainable development of the country. Enterprises should carry out reasonable digital transformation according to their own development needs and finally improve the green and sustainable development ability of enterprises and promote the sustainable development of society.

This study examines the relationship between enterprise digital transformation and ESG performance. Different from the linear relationship between the two in previous major studies, this study proves the inverse U-shaped relationship between enterprise digital transformation and ESG performance through mathematical theoretical model derivation and empirical test. This study also explores in detail how corporate digital transformation affects ESG performance, as well as discusses heterogeneity at the city, industry and firm levels. It is proposed that enterprises should take into account their own characteristics and carry out reasonable digital transformation according to their development needs.

This study aims to examine how digitalization affects the work efficiency of the Shariah Supervisory Board (SSB) in Islamic banks.

This study uses panel data analysis of annual report disclosures over the past 10 years. The authors have selected 79 Islamic banks for the period ranging from 2012 to 2021. The criteria for SSB efficiency used in this research are disclosure of Zakat and disclosure in the SSB report.

The econometric results show that digitalization has a positive effect on improving the work efficiency of the SSB in Islamic banks. Accordingly, the authors provide evidence that the higher the bank's digital engagement, the higher the quality of the SSB.

The findings highlight the need to improve the current understanding of SSB structures and governance mechanisms that can better assist Islamic banks in engaging in effective compliance with recent governance and accounting reforms. Moreover, Islamic banks are the most capable and appropriate to implement and activate digitalization because they are based on a vital root calling for development if there are executives believing in it, as well as legislation supporting and serving them.

This study explored how teachers change, what teachers learn and how they learn during the implementation of project-based learning through lesson study.

In this study, three university researchers, one doctoral student and six mathematics school teachers formed a lesson study team. Using a qualitative research method, this study employed a locally integrating networking strategy to combine the modified Interconnected Model of Teacher Professional Growth (IMTPG) and Bannister's framework to describe the teachers' knowledge change when participating in a lesson study on project-based learning.

The research revealed that the school teachers' knowledge about authenticity and assessment in the context of project-based learning was changed after the lesson study and how the changes were triggered.

The study demonstrates how the networking of two different theories—modified IMTPG and Bannister's framework—contributes to a better understanding of the process of teachers' collective practice, as well as the knowledge change in PjBL. This networking was done by combining the two theories, which were superimposed at the domain of practice.