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This paper aims to present the design and fabrication of a rotary magnetostrictive energy generator, using to harvest the rotation energy of human knee joint.

A rotary magnetostrictive energy generator is presented in this paper. The harvester consists of six movable flat Terfenol-D rods, surround by the picked-up coils respective, and alternate permanent magnet (PM) array fixed in the upper cover of the stator. The harvester rotates like as a stepper motor, which has rotary electromagnetic power generating effect and impacted magnetostrictive power generating effect in its rotation. Modeling and simulation are used to validate the concept. A prototype of harvester is fabricated and subjected to the experimental characterization.

The size of proposed structure is control as 77 cm³, and its mass is about 0.21 kg. Huge induced voltage generated in the short-time impact situation, and that induced voltage in the harvester can up to 18.6 V at 0.32 s stepper rotation. Also, the presented harvester has good harvesting effects at low frequency human walking situation, which is suitable to be used for future researches of wearable knee joint applications.

A new concept of magnetostrictive harvester is presneted, which will be benefit for the application of human knee joint wearable. Also, this concept will give us more idea for collection of human movement energy.

In this fast-changing world, digitization has become crucial to organizations, allowing decision-makers to alter corporate processes. Companies with a higher corporate social responsibility (CSR) level not only help encourage employees to focus on their goals, but they also show that they take their social responsibility seriously, which is increasingly important in today’s digital economy. So, this study aims to examine the relationship between digital transformation and CSR disclosure of Chinese A-share companies. Furthermore, this research investigates the moderating impact of governance heterogeneity, including CEO power and corporate internal control (INT) mechanisms.

This study used fixed effect estimation with robust standard errors to examine the relationship between digital transformation and CSR disclosure and the moderating effect of governance heterogeneity among Chinese A-share companies from 2010 to 2020. The whole sample consists of 17,266 firms, including 5,038 state-owned enterprise (SOE) company records and 12,228 non-SOE records. The whole sample data is collected from the China Stock Market and Accounting Research, the Chinese Research Data Services and the WIND databases.

The regression results lead us to three conclusions after classifying the sample into non-SOE and SOE groups. First, Chinese A-share businesses with greater levels of digitalization have lower CSR disclosures. Both SOE and non-SOE are consistent with these findings. Second, increasing CEO authority creates a more centralized company decision-making structure (Breuer et al., 2022; Freire, 2019), which improves the negative association between digitalization and CSR disclosure. These conclusions, however, also apply to non-SOE. Finally, INT reinforces the association between corporate digitization and CSR disclosure, which is especially obvious in SOEs. These findings are robust to alternative HEXUN CSR disclosure index. Heterogeneity analysis shows that the negative relationship between corporate digitalization and CSR disclosures is more pronounced in bigger, highly levered and highly financialized firms.

Digitalization and CSR disclosure are well studied, but few have examined their interactions from a governance heterogeneity perspective in China. Practitioners and policymakers may use these insights to help business owners implement suitable digital policies for firm development from diverse business perspectives.

Digitalization is increasingly important for promoting authentic CSR practices. Firms with higher CSR levels motivate their employees to pursue their goals and demonstrate their social responsibility. However, the literature has not adequately examined how firm-level digitalization influences corporate sustainability from a governance perspective. This study aims to fill this gap by exploring how digitalization affects CSR disclosure, a key aspect of sustainability, at the firm level. Furthermore, this study also aims to investigate how governance factors, such as management power, internal control and minority shareholder pressure, moderate this effect.

This study employs a fixed effect model with robust standard errors to analyze how digitalization and CSR disclosure are related and how this relationship is moderated by governance heterogeneity among Chinese A-share companies from 2010 to 2020. The sample consists of 2,339 firms, of which 360 are SOEs and 1,979 are non-SOEs. To ensure robustness, this study has excluded the observations in 2020 to avoid the effects of COVID-19 and used an alternative measure of CSR disclosure based on the HEXUN CSR disclosure index. Furthermore, this study also explores the link in various corporate-level CSR settings.

The regression findings reveal that: First, Chinese A-share firms with higher digitalization levels disclose less CSR information. This finding holds for both SOEs and non-SOEs. Second, stronger management power has a negative moderating effect that weakens the link between digitalization and CSR disclosure, and this effect is mainly driven by SOEs. Third, internal control attenuates the negative association between firm digitalization and CSR disclosure, which is more pronounced in SOEs. Finally, minority shareholders exacerbate the negative relationship between digitalization and CSR disclosure, and this effect is more evident in non-SOEs. These results are robust to excluding the potential COVID effect and using an alternative HEXUN CSR disclosure index measure.

Digitalization and sustainability have been widely discussed at a macro level, but their relationship at a micro level has been largely overlooked. Moreover, there is hardly any evidence on how governance heterogeneity affects this relationship in emerging economies, especially China. This paper addresses these issues by providing empirical evidence on how digital transformation influences CSR disclosure in China, a context where digitalization and CSR are both rapidly evolving. The paper also offers implications for both practitioners and policymakers to design appropriate digital strategies for firm development from diverse business perspectives.

China is by far the world’s largest energy consumer and importer. Reasonably forecasting the trend of China’s total energy consumption (CTEC) is of great significance. The purpose of this paper is to propose a new-structure grey system model (NSGM (1, 1)) to forecast CTEC.

Two matrices for computing the parameters of NSGM (1, 1) were defined and the specific calculation formula was derived. Since the NSGM (1, 1) model increases the number of its background values, which improves the smoothness effect of the background value and weakens the effects of extreme values in the raw sequence on the model’s performance; hence it has better simulation and prediction performances than traditional grey models. Finally, NSGM (1, 1) was used to forecast China’s total energy consumption during 2016-2025. The forecast showed CTEC will grow rapidly in the next ten years.

Therefore, in order to meet the target of keeping CTEC under control at 4.8 billion tons of standard coal in 2020, Chinese government needs to take necessary measures such as transforming the economic development pattern and enhancing the energy utilization efficiency.

A new-structure grey forecasting model, NSGM (1, 1), is proposed in this paper, which improves the smoothness and weakens the effects of extreme values and has a better structure and performance than those of other grey models. The authors successfully employ the new model to simulate and forecast CTEC. The research findings could aid Chinese government in formulating energy policies and help energy exporters make rational energy yield plans.

The purpose of this paper is to forecast the future trend of Ghana’s total energy consumption (GTEC) using two grey models, which are GM(1,1) and the grey Verhulst model.

The paper employs the use of Even model GM(1,1) and the grey Verhulst model to forecast GTEC for the next five years. Since various models were used, the margin for error is minimal, hence resulting in a better choice for forecasting the future. The forecast reveals that the GTEC for the next five years will increase rapidly.

The results reveal that the models can be used accurately to predict the total energy consumption smoothly. This will aid the government of Ghana to take necessary measures such as transforming the economic development pattern and enhancing the energy utilization efficiency since future patterns of energy consumed can be predicted.

This research is meaningful to the government and all stakeholders in Ghana to help develop and appreciate the energy sector and its economic impact. This research is going to help government put in measures for efficient utilization of energy since results reveal an increase in energy consumption.

Research results could be used for development of the energy sector through managerial and economic decision making.

Ghana is a developing country and has great prospects in terms of boosting or resourcing its energy sector to meet future demands. The successfully explored models could aid the government of Ghana to formulate policies in the energy sector and generate future consumption plans.

Purpose. This chapter explored the effects of egalitarian gender attitudes and routine housework on subjective well-being among older adults in China.

Design/methodology/approach. Data were drawn from the Third Wave Survey on the Social Status of Women in China (2010). The sample included 1,260 older adults aged 63–95, consisted of 428 urban respondents and 832 rural respondents, among which included 638 men and 622 women. Stratified linear regression models were used to examine the effects of egalitarian gender attitudes and routine housework on subjective well-being among urban–rural and gender subsamples.

Findings. The results indicated that egalitarian gender attitudes were positively related to subjective well-being. Routine housework was still gendered work in both urban and rural places in China. Routine housework predicted better subjective well-being only among rural women. There were significant differences in egalitarian gender attitudes and the division of housework between urban and rural samples.

Originality/value. Gender egalitarian attitudes and the division of housework in China were patterned not only by genders but also by the urban–rural division. Different from previous studies, housework did not have influence on subjective well-beings, except a positive association among rural women in the sample.

In this study, we examine the influence of a firm’s environmental, social, and governance (ESG) performance on analysts’ stock recommendations and earnings forecast accuracy in the Chinese context.

We take a textual analysis approach to analyst research reports issued between 2010 and 2019, and differentiate between two distinct analyst categories: “sustainability analysts,” which refer to those more inclined to incorporate ESG information into their analyses, and “other analysts.”

Our evidence indicates that sustainability analysts tend to be significantly more likely than others to provide positive stock recommendations and demonstrate enhanced accuracy in forecasting earnings for companies with superior ESG performance. Our additional analyses reveal that this finding is particularly prominent for analysts who graduated from institutions emphasizing the protection of the environment, those recognized as star analysts, those affiliated with ESG-oriented brokerages, and forecasts made by analysts in the later part of the sample period. Our findings further indicate that sustainability analysts exhibit a more pronounced negative response when confronted with a negative ESG event.

In general, the evidence from this study reveals the interplay between ESG factors and analyst behavior, offering valuable implications for both financial analysts and sustainable investment strategies.

The study aims to elaborate the author’s basic viewpoints on how economics should view the Internet from four aspects.

This paper reviews the existing literature on this topic, and summarizes key findings and conclusions of these researches.

The findings include four major contradictions in economics brought by the Internet, and 12 new changes in economics that needs to be addressed.

This paper provides a clear summary of existing studies on economics’ views of the internet and a prospect for future internet economy research.

Employability is a key concept in higher education. Graduate employment rate is often used to assess the quality of university provision, despite that employability and employment are two different concepts. This paper will increase the understandings of graduate employability through interpreting its meaning and whose responsibility for graduate employability from the perspectives of four key stakeholders: higher education institutions, students, government and employers.

There are two stages to this literature review which was undertaken across bibliographic databases. The first stage builds a conceptual understanding of employability, relating to definition and how employability can be achieved and enhanced from the perspective of stakeholders. A structured search employing Boolean searches was conducted using a range of terms associated with key stakeholders. The second round of review drew on documentary analysis of official statements, declarations, documents, reports and position papers issued by key stakeholders in the UK, available online.

It reveals that responsibility for employability has been transferred by the UK government to higher education institutions, despite clear evidence that it needs to be shared by all the key stakeholders to be effective. In addition, there is a gap between employers' expectation for employability and the government's employability agenda.

This article highlights that solely using employment rate statistics as a key indicator for employability will encourage the practice of putting employers' needs above knowledge creation and the development of academic disciplines, with the consequence that higher education will become increasingly vocation driven.

In the fabric manufacturing industry, various unfavorable factors, including machine fault and yarn breakage, can easily cause fabric defects and affect product quality, begetting huge economic losses to enterprises. Thus, automatic fabric defect detection systems have become an important development direction. Herein, the most common defects in the fabric production process, like ribbon yarn, broken yarn, cotton ball, holes, yarn shedding and stains, are detected. Current fabric defect detection systems afford low detection accuracy and a high missed detection rate for small target fabric defects. Therefore, this study proposes deep learning technology for automatically detecting fabric defects by improving the YOLOv5s target detection algorithm. The improved algorithm is termed YOLOv5s-4SCK, which can effectively detect fabric defects. This study aims to discuss the aforementioned issues.

Specifically, based on the YOLOv5s algorithm, first, the structure of YOLOv5s is modified to add a small target detection layer, fully utilize deep and shallow features and reduce the missed detection rate of small target fabric defects. Second, the integration of CARAFE upsampling enables the effective retention of feature information and maintenance of a certain computational efficiency, thereby improving the detection accuracy. Finally, the K-Means++ clustering algorithm is used to analyze the position of the center point of the prior box to better obtain the anchor box and improve the average accuracy and evaluation index of detection.

The research results show that the YOLOv5s-4SCK algorithm increases the accuracy by 4.1% and the detection speed by 2 f.s-1 compared to the original YOLOv5s algorithm, and it effectively improves the original YOLOv5s problem of high missed detection rate of small targets.

The YOLOv5s-4SCK proposed in this paper can effectively reduce the missed detection rate of fabric defects, improve the detection efficiency and has certain industrial value.

The proposed algorithm can quickly identify fabric defects, effectively improving the detection rate. In the future, the proposed algorithm will be applied in the actual industry.

Automatic fabric defect detection reduces the manpower of inspectors, and the proposed YOLOv5s-4SCK algorithm is also suitable for other recognition fields.

The proposed YOLOv5s-4SCK algorithm has been tested using real cloth to ensure its accuracy, and its performance is better than the original YOLOv5s algorithm.