This study aims to explore the perceptions of the Scholarship of Teaching and Learning (SoTL) of primary and secondary school teachers in C City, China, as well as the challenges they face in developing these abilities. Through narrative inquiry involving five current teachers, the research collected their personal experiences in the development of teaching and academic abilities, with data gathered through semi-structured interviews. The findings reveal that teachers are primarily driven by external forces, professional identity, personal growth, and the need to improve teaching quality in their efforts to enhance teaching and academic abilities. However, they also encounter challenges such as teaching pressures, time management difficulties, insufficient school support, and declining energy. To overcome these obstacles, teachers have adopted strategies such as time management, task allocation, and cognitive enhancement. The study concludes by recommending that through the combined efforts of teachers, schools, and society, a strong professional belief system should be established, and a supportive environment should be created to collaboratively promote the development of teaching and academic abilities among primary and secondary school teachers, thereby fostering their professional growth.

The failure to achieve sustainable development in South Africa is due to the inability to deliver quality and adequate health services that would lead to the achievement of sustainable human security. As we live in an era of digital technology, Machine Learning (ML) has not yet permeated the healthcare sector in South Africa. Its effects on promoting quality health services for sustainable human security have not attracted much academic attention in South Africa and across the African continent. Hospitals still face numerous challenges that have hindered achieving adequate health services. For this reason, the healthcare sector in South Africa continues to suffer from numerous challenges, including inadequate finances, poor governance, long waiting times, shortages of medical staff, and poor medical record keeping. These challenges have affected health services provision and thus pose threats to the achievement of sustainable security. The paper found that ML technology enables adequate health services that alleviate disease burden and thus lead to sustainable human security. It speeds up medical treatment, enabling medical workers to deliver health services accurately and reducing the financial cost of medical treatments. ML assists in the prevention of pandemic outbreaks and as well as monitoring their potential epidemic outbreaks. It protects and keeps medical records and makes them readily available when patients visit any hospital. The paper used a qualitative research design that used an exploratory approach to collect and analyse data.

Introduction: With the adoption of the rural rehabilitation strategy in recent years, China’s rural tourist industry has entered a golden age of growth. Due to the lack of management and decision-support systems, many rural tourist attractions in China experience a “tourist overload” problem during minor holidays or Golden Week, an extended vacation of seven or more consecutive days in mainland China formed by transferring holidays during a specific holiday period. This poses a severe challenge to tourist attractions and relevant management departments. Objective: This study aims to summarize the elements influencing passenger flow by examining the features of rural tourist attractions outside China’s largest cities. Additionally, the study will investigate the variations in the flow of tourists. Method: Grey Model (1,1) is a first-order, single-variable differential equation model used for forecasting trends in data with exponential growth or decline, particularly when dealing with small and incomplete datasets. Four prediction algorithms—the conventional GM(1,1) model, residual time series GM(1,1) model, single-element input BP neural network model, and multi-element input BP network model—were used to anticipate and assess the passenger flow of scenic sites. Result: The multi-input BP neural network model and residual time series GM(1,1) model have significantly higher prediction accuracy than the conventional GM(1,1) model and unit-input BP neural network model. A multi-input BP neural network model and the residual time series GM(1,1) model were used in tandem to develop a short-term passenger flow warning model for rural tourism in China’s outskirts. Conclusion: This model can guide tourists to staggered trips and alleviate the problem of uneven allocation of tourism resources.

This paper aims to understand the local authorities’ reaction to green environment activities towards clean cities in Malaysia and how they respond to cleanliness awareness among the community. Four (4) cities, such as Melaka, Ipoh, and Muar dan Kuala Terengganu, were selected, and this study embarks on a qualitative research approach involving a semi-structured interview with top personnel from four local authorities. From the reaction point of view, some local authorities reacted positively towards the green environment and cleanliness of the city. Four (4) themes have been produced, such as awareness, which focuses on the daily routine of local authorities. Secondly, enforcement from the local government, with some warning and advice, really contributes to the changes in society’s attitude. Thirdly, support by local authority efforts, including awareness campaigns from electronic and printed media, does have a good impact. Lastly, active involvement from the local authorities regulated many communities in residential areas and had direct links with local communities and NGOs that annually organized green program activities. This study urged the Local Government Act 1976, which the local authorities are responsible for the enforcement activities such as the 3Rs (Reduce, Reuse, Recycle) activities and so on. Local authorities, state governments, and local communities should also help monitor and maintain environmental issues towards a clean city in Malaysia.

Purpose: This study aims to clarify the meaning of sport analysis, explore the contributions derived from sporting event analysts, and highlights the importance of responsible sport gambling. It also investigates how sustainable practices can be integrated into sports analysis to enhance social well-being. Design/methodology/approach: Secondary text data from government documents, news articles, and website information were extracted by searching keywords such as sports lottery and sports analysis in traditional Chinese, and then analyzed to establish the research framework and scope. Subsequently, 18 interviews were conducted with stakeholders to gain deeper insights. Findings: The content analyses reveals that sport analysis tends to be sport data science. Sporting event analysts may contribute to improving the performance of players or a team, enhancing spectator sports, and increasing sports lottery revenues. In the leisure aspect, the professionalism of sporting event analysts not only increases epistemic and entertainment values in spectator sports but also boosts engagement with sport lotteries. To ensure these enhancements remain beneficial, it is vital to emphasize responsible sport gambling and sustainable practices that protect vulnerable groups and promote long-term health benefits for those involved in sports. The integration of sustainable practices in sport analysis and the expertise of sporting event analysts can significantly advance economic and social development by generating funds through sport lottery industry for athlete programs, sports infrastructure, and educational initiatives, aligning with multiple Sustainable Development Goals. Additionally, the professionalism of these analysts may enhance public understanding and engagement of sports, promoting increased participation in sports, reducing healthcare costs, and contributing to the development of a healthier and more resilient society. Originality: Emphasizing responsible sports gambling is essential to the sustainability of sports lotteries and the role of sporting event analysts.

The idea of emotions that is concealed in human language gives rise to metaphor. It is challenging to compute and develop a framework for emotions in people because of its detachment and diversity. Nonetheless, machine translation heavily relies on the modeling and computation of emotions. When emotion metaphors are calculated into machine translation, the language is significantly more colorful and satisfies translating criteria such as truthfulness, creativity and beauty. Emotional metaphor computation often uses artificial intelligence (AI) and the detection of patterns and it needs massive, superior samples in the emotion metaphor collection. To facilitate data-driven emotion metaphor processing through machine translation, the study constructs a bi-lingual database in both Chinese and English that contains extensive emotion metaphors. The fundamental steps involved in generating the emotion metaphor collection are demonstrated, comprising the basis of theory, design concepts, acquiring data, annotating information and index management. This study examines how well the emotion metaphor corpus functions in machine translation by proposing and testing a novel earthworm swarm-tunsed recurrent network (ES-RN) architecture in a Python tool. Additionally, the comparison study is carried out using machine translation datasets that already exist. The findings of this study demonstrated that emotion metaphors might be expressed in machine translation using the emotion metaphor database developed in this research.

The Human Development Index, which accounts for both net foreign income and the total value of goods and services generated domestically, illustrates how income becomes less significant as Gross National Income (GNI) rises by using the logarithm of income. South Africa ranks 109th out of 189 countries in the Human Development Index (HDI) within the Brazil, Russia, India, China and South Africa (BRICS) economic bloc, raising long-term sustainability concerns. The study explores the relationship between economic, demography, policy indicators and human development in South Africa. South Africa’s unique status as a developing country within the BRICS economic group, alongside its lengthy history of racial discrimination, calls for a sophisticated approach to understanding Human Development. Existing research considered economic, demography, policy indicators independently; the gap of understanding their interconnection and long-term effects in the South African contexts exists. The study addresses the gap by using Autoregressive-Distributed Lag (ARDL) approach to investigate the short-term and the long-term relationship between economic, demography, policy indicators and human development in South Africa. By discovering these links, the study hopes to provide useful insights for policymakers seeking to promote sustainable human development in South Africa. The findings indicate that growth in GDP is a key factor in the HDI since it shows that there are more financial resources available for human development. By discovering these links, the study hopes to provide useful insights for policymakers seeking to promote sustainable human development in South Africa.

This study investigates the impact of Foreign Direct Investments (FDIs) on wage dynamics in Slovakia and Slovenia, with a particular emphasis on gender-specific effects in post-Communist emerging markets. By analyzing wage outcomes for male and female workers separately, the research reveals potential disparities in FDIs-driven wage growth. Employing econometric techniques and longitudinal data, the study explores the nuanced relationship between FDIs, wage policies, and economic development over time. A temporal lag in FDIs analysis suggests that Slovakia and Slovenia have experienced differing impacts from past foreign capital flows. In Slovakia, significant correlations indicate persistent FDIs influence and a pronounced effect on gender wage disparities. In Slovenia, more moderate correlations and FDIs volatility suggest a less stable relationship between external investment and wage dynamics. The originality of this research lies in its comparative approach, examining two distinct post-Communist nations and identifying unique country-specific patterns and trends. This study contributes to a deeper understanding of FDI’s role in labor market management and its implications for gender equality in two European emerging economies.