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Higher Education in Macau has been benefiting from a multi-layered institutional environment under China’s One-Country-Two-Systems. This presentation introduces research and education policies and practices of Macau universities under China’s national plan of the Greater Bay Area development. It aims to demonstrate and analyze how higher education actors collaborate with local and regional governments and industrial sectors in human capital formation and research innovations.

Validation of the Teachers’ Sense of Efficacy Scale (TSES) for use with teachers in Macao (SAR) was undertaken to determine its usefulness as a measure of teacher self-efficacy for inclusive education. This paper discusses the results found by analyzing various versions of the TSES and TSES-C in a Chinese format with 200 pre-service teachers in Macao (SAR).  Psychometric analyses were undertaken to investigate the validity of the existing scales and the three and two factor solutions. The results indicated a preferred 9-item version that produced improved factor loadings and reliabilities. The use of a relatively quick and short scale to measure such a complex phenomenon as teacher self-efficacy is discussed. Issues are raised regarding generalizability of scales and the impact of culture, demographics, and edifying issues that may impact on the usefulness of such scales.

Current global shifts in education towards inclusive early childhood education are deeply engineered by the crisis of educational exclusion. In responding to exclusion, teachers have mainly utilized dominant western theories to plan and implement inclusive teaching. In this chapter, we draw on a non-western philosophy, a Nichiren Buddhist (Soka) philosophy, to provide a ‘kaleidoscopic’ lens through which to create inclusive educational learning spaces that engender full participation of all children. The Soka education philosophy is a humanist concept which can guide teachers when preparing to create inclusive education. The aims of this chapter are threefold: The first is an exploration of the Nichiren Buddhist (Soka) philosophy. The second aim is to highlight how this philosophy can enable teachers to unleash the unlimited potential of children in inclusive learning settings. Thirdly, we argue that grounding early childhood teacher education in this philosophy can help improve the effectiveness of inclusive educational experience for all children.

By using both, the weak-value formulation as well as the standard probabilistic approach, we analyze the Hardy's experiment introducing a complex and dimensionless parameter ($\epsilon$) which eliminates the assumption of complete annihilation when both, the electron and the positron departing from a common origin, cross the intersection point $P$. We then find that the paradox does not exist for all the possible values taken by the parameter. The apparent paradox only appears when $\epsilon=1$; however, even in this case we can interpret this result as a natural consequence of the fact that the particles can cross the point $P$, but at different times due to a natural consequence of the energy-time uncertainty principle.

Employees in the hospitality industry, in particular, face difficult conditions such as overwork, job security, understaffing, and work-life imbalance in today's fast-paced society. Because of that, employees in the hospitality industry experience high levels of occupational stress. Severe conditions of occupational stress may not only lead to health issues for the employee but also affect their work performance. In order to get a better grasp of how occupational stress affects the employees in the hospitality industry in Macau, the research questions are: 1) What are the sources of stress in the Macau hospitality industry? 2) How does the COVID-19 pandemic change the perception of occupational stress among employees in the Macau hospitality industry? How do the companies help the employees in the Macau hospitality industry cope with occupational stress? have been formulated. Semi-structured interviews were conducted with eleven participants working in the hospitality industry, with a median age of 39 years and a median range of 13 years. Based on these interviews, participants expressed their perceptions of occupational stress under the categories of distress and eustress, while coding schemes were applied to all semi-structured interviews. The results on distress showed that reactions from employees and their working conditions, in particular, were the two most important determinants of occupational stress, while motivation helped people grow and develop their confidence. For the changes in occupational stress due to the pandemic, mental reactions from employees were the most significant determinant of occupational stress in the Macau hospitality industry during the changes brought about by the pandemic in these years, closely followed by employee job insecurity. In order to cope with occupational stress, employees should take responsibility for coping with their stress, but employers also have a role to play in this process, like providing good communications and proactive assistance to the employees. Finally, employees' responses were described as more negative as a result of their experience. Further research could focus on more methods for employees to cope with occupational stress in both physical and mental ways

"Over time, the large shopping malls in Macao will require some changes to improve space utilization, resulting in renovation projects that affect indoor particulate matter (PM10 and PM2.5) concentrations. Employees work long hours in an environment where the ambient air quality is poor, directly affecting their work efficiency. Nonetheless, the concentration of PM produced by the interior renovation of shopping malls has yet to receive particular attention. Therefore, this study will investigate IAQ, in particular, PM10 and PM2.5 in large-scale shopping mall renovation projects in three different indoor locations (i.e., public, renovation, and construction areas) to understand the causes of indoor PM10 and PM2.5. This study will collect on-site PM data for analysis, examine whether the current control measures are appropriate and propose some improvements. The data collected will be compared with IAQ standards from the World Health Organization (WHO) and the Macao Meteorological and Geophysical Bureau (SMG), specifically PM concentrations. The research results can provide a reference guide for decision-makers, management, construction teams, design consultant teams and renovation teams of large-scale projects. In addition, the monitoring of IAQ can ensure a comfortable environment for employees and customers."

Road transportation is one of the main sources of air pollution in Macao. This study mainly explores four major roadside locations with high traffic flow in Macao from March to May 2022 and measures their pollutant concentrations (PM10 and PM2.5), traffic flow and their fuel type, as well as considering the meteorological parameters and pollutant concentration of SMG Macao to analyze the relationship between traffic flow and pollutants on roadside locations. Under the measuring distance between 3 and 6 meters, showing that the four locations had a good correlation with the roadside station data provided by SMG on both weekdays and weekends/holidays (PM2.5: R2 is 0.59 to 0.81 on weekdays and 0.79 to 0.88 on weekends/holidays, p<0.01; PM10: R2 is 0.33 to 0.82 on weekdays and 0.30 to 0.58 on weekends/holidays, p<0.05), the overall PM2.5 is 41 to 86% higher than that of the same period of Macao roadside station (SMG), and 68 to 186% higher than that of Taipa Ambient (SMG), indicating that it is more harmful to daily pedestrians. The overall relationship between PM concentration and traffic flow is small on the long-term scale (PM2.5: R2 is 0.01 to 0.13; PM10: R2 is 0.00 to 0.02). This study also analyzed air quality on EBL, the overall PM2.5 and PM10 decreased by 12.3% to 24.8% compared with non-EBL during the period, so that is indeed beneficial to the reduction of pollutant concentrations. In addition, narrower roads were overall higher when road widths added for comparison. Lastly, meteorological data added for comparison, except for relative humidity, it can be found that there is a significant correlation with long-term pollutants (p<0.05). While previous studies have found that single-day traffic flow is related to the increase in PM concentration, this paper is more inclined to their two-way effect when exploring their long-term relationship

" Air pollution in Macau has become a serious problem following the Pearl River Delta’s (PRD) rapid industrialization that began in the 1990s. While there has been continual improvement in recent years, harmful air pollutant concentration levels are still common, impacting Macau residents' health and creating long-term medical costs to local society. With this in mind, Macau needs an air quality forecast system that accurately predicts pollutant concentration and an early alert system instead of only daily real-time reminders. Some scholars have previously carried out studies to develop an air quality forecast for Macau by successfully using statistical models. Therefore, pursuant to the outcomes of previous studies, this dissertation aims to build upon research results and explore further possibilities of building a better ML air quality forecast model based on the time series of air pollutants concentration and meteorological data. Four different state-of-the-art ML algorithms were used to create predictive models to forecast PM2.5, PM10, and carbon monoxide (CO) concentrations for the next 24 and 48-hour. These were Support Vector Machine (SVM), Artificial Neural Networks (ANN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). In addition, Multiple Linear Regression MLR, a standard ML model, was used for this dissertation as a baseline reference for performance comparison. The daily measurements of air quality data in Macau from 2016 to 2021 were collected for this dissertation. The 2020 and 2021 datasets were used for model testing while the four-year data prior to 2020 and 2021 were used to build and train the ML models. The results showed that SVM, ANN, RF, and XGBoost were able to provide a very good performance in building up a 24-hour forecast with higher R2 and lower RMSE, MAE, and BIAS. Meanwhile, all ML models in 48-hour forecasting performance were satisfactory enough to be accepted as a two-day continuous forecast even if the R2 value was lower than the 24-hour forecast. The 48-hour forecasting model could be further improved by proper feature selection based on the 24-hour dataset, using the SHAP value test, and the adjusted R2 value of the 48-hour forecasting model."