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Abstract As the population of Chinese immigrants has been growing rapidly in the United States, it has been understudied on the parenting behaviours as well as the roles parental stress and social support playing in parenting in this group. This study investigated whether parental stress was associated with parenting and whether this relationship was mediated by social support in a sample of 255 Chinese immigrant parents from the Survey of Asian American Families in New York City. Regression analyses with a rich array of control variables found that a higher level of parental stress and the presence of one or more stressors such as unemployment, low income, and low education were positively associated with the use of harsh discipline and parent?child conflicts and negatively associated with positive parenting practices. Social support functioned as a significant mediator in the relationships between parental stress and positive parenting practices but not in the relationships of parental stress with parent?child conflict or the use of harsh discipline.

The use of learning analytics (LA) in real-world educational applications is growing very fast as academic institutions realize the positive potential that is possible if LA is integrated in decision making. Education in schools on public health need to evolve in response to the new knowledge and th...

This book is a compilation of the best papers presented at the APEF 2019 conference which was held on 25th and 26th July 2019 at the Grand Copthorne Waterfront in Singapore. With a great number of submissions, it presents the latest research findings in economics and finance and discusses relevant issues in today's world. The book is a useful resource for readers who want access to economics, finance and business research focusing on the Asia-Pacific region.

Statistical methods such as multiple linear regression (MLR) and classification and regression tree (CART) analysis were used to build prediction models for the levels of pollutant concentrations in Macao using meteorological and air quality historical data to three periods: (i) from 2013 to 2016, (ii) from 2015 to 2018, and (iii) from 2013 to 2018. The variables retained by the models were identical for nitrogen dioxide (NO2), particulate matter (PM10), PM2.5, but not for ozone (O3) Air pollution data from 2019 was used for validation purposes. The model for the 2013 to 2018 period was the one that performed best in prediction of the next-day concentrations levels in 2019, with high coefficient of determination (R2), between predicted and observed daily average concentrations (between 0.78 and 0.89 for all pollutants), and low root mean square error (RMSE), mean absolute error (MAE), and biases (BIAS). To understand if the prediction model was robust to extreme variations in pollutants concentration, a test was performed under the circumstances of a high pollution episode for PM2.5 and O3 during 2019, and the low pollution episode during the period of implementation of the preventive measures for COVID-19 pandemic. Regarding the high pollution episode, the period of the Chinese National Holiday of 2019 was selected, in which high concentration levels were identified for PM2.5 and O3, with peaks of daily concentration exceeding 55 μg/m3 and 400 μg/m3, respectively. The 2013 to 2018 model successfully predicted this high pollution episode with high coefficients of determination (of 0.92 for PM2.5 and 0.82 for O3). The low pollution episode for PM2.5 and O3 was identified during the 2020 COVID-19 pandemic period, with a low record of daily concentration for PM2.5 levels at 2 μg/m3 and O3 levels at 50 μg/m3, respectively. The 2013 to 2018 model successfully predicted the low pollution episode for PM2.5 and O3 with a high coefficient of determination (0.86 and 0.84, respectively). Overall, the results demonstrate that the statistical forecast model is robust and able to correctly reproduce extreme air pollution events of both high and low concentration levels.

A combination of assessment, operational forecast, and future perspective was thoroughly explored to provide an overview of the existing air quality problems in Macao. The levels of air pollution in Macao often exceed those recommended by the World Health Organization (WHO). In order for the population to take precautionary measures and avoid further health risks during high pollution episodes, it is important to develop a reliable air quality forecast. Statistical models based on linear multiple regression (MLR) and classification and regression trees (CART) analysis were successfully developed for Macao, to predict the next day concentrations of NO2, PM10, PM2.5, and O3. Meteorological variables were selected from an extensive list of possible variables, including geopotential height, relative humidity, atmospheric stability, and air temperature at different vertical levels. Air quality variables translate the resilience of the recent past concentrations of each pollutant and usually are maximum and/or the average of latest 24-hour levels. The models were applied in forecasting the next day average daily concentrations for NO2 and PM and maximum hourly O3 levels for five air quality monitoring stations. The results are expected to support an operational air iv quality forecast for Macao. The work involved two phases. On a first phase, the models utilized meteorological and air quality variables based on five years of historical data, from 2013 to 2017. Data from 2013 to 2016 were used to develop the statistical models and data from 2017 was used for validation purposes. All the developed models were statistically significantly valid with a 95% confidence level with high coefficients of determination (from 0.78 to 0.93) for all pollutants. On a second phase, these models were used with 2019 validation data, while a new set of models based on a more extended historical data series, from 2013 to 2018, were also validated with 2019 data. There were no significant differences in the coefficients of determination (R2) and minor improvements in root mean square errors (RMSE), mean absolute errors (MAE) and biases (BIAS) between the 2013 to 2016 and the 2013 to 2018 data models. In addition, for one air quality monitoring station (Taipa Ambient), the 2013 to 2018 model was applied for two days ahead (D2) forecast and the coefficient of determination (R2) was considerably less accurate to the one day ahead (D1) forecast, but still able to provide a reliable air quality forecast for Macao. To understand if the prediction model was robust to extreme variations in v pollutants concentration, a test was performed under the circumstances of a high pollution episode for PM2.5 and O3 during 2019, and a low pollution episode during 2020. Regarding the high pollution episode, the period of the Chinese National Holiday of 2019 was selected, in which high concentration levels were identified for PM2.5 and O3, with peaks of daily concentration for PM2.5 levels exceeding 55 μg/m3 and the maximum hourly concentration for O3 levels exceeding 400 μg/m3. For the low pollution episode, the 2020 period of implementation of the preventive measures for COVID-19 pandemic was selected, with a low record of daily concentration for PM2.5 levels at 2 μg/m3 and maximum hourly concentration for O3 levels at 50 μg/m3. The 2013 to 2018 model successfully predicted the high pollution episode with high coefficients of determination (0.92 for PM2.5 and 0.82 for O3). Likewise, the low pollution episode was also correctly predicted with high coefficients of determination (0.86 and 0.84 for PM2.5 and O3, respectively). Overall, the results demonstrate that the statistical forecast model is robust and able to correctly reproduce extreme air pollution events of both high and low concentration levels. Machine learning methods maybe adopted to provide significant improvements in combination of multiple linear regression (MLR) and classification and regression vi tree (CART) to further improve the accuracy of the statistical forecast. The developed air pollution forecasting model may be combined with other measures to mitigate the impact of air pollution in Macao. These may include the establishment of low emission zones (LEZ), as enforced in some European cities, license plate restrictions and lottery policy, as used in some Asian, tax exemptions on electric vehicles (EVs) and exclusive corridors for public transportations. Keywords: Air pollution; Particulate Matter; Ozone; Macao; Statistical air quality forecast; Pollution episodes; Chinese national holiday; COVID-19

The levels of air pollution in Macao often exceeded the levels recommended by WHO. In order for the population to take precautionary measures and avoid further health risks under high pollutant exposure, it is important to develop a reliable air quality forecast. Statistical models based on multiple regression (MR) analysis were developed successfully for Macao to predict the next day concentrations of PM10, PM2.5, and NO2. All the developed models were statistically significantly valid with a 95% confidence level with high coefficients of determination (from 0.89 to 0.92) for all pollutants. The models utilized meteorological and air quality variables based on five years of historical data, from 2013 to 2017. The data from 2013 to 2016 were used to develop the statistical models and data from 2017 were used for validation purposes. A wide range of meteorological and air quality variables were identified, and only some were selected as significant dependent variables. Meteorological variables were selected from an extensive list of variables, including geopotential height, relative humidity, atmospheric stability, and air temperature at different vertical levels. Air quality variables translate the resilience of the recent past concentrations of each pollutant and usually are maximum and/or the average of latest 24-hour levels. The models were applied in forecasting the next day average daily concentrations for PM10, PM2.5, and NO2 for the air quality monitoring stations. The results are expected to be an operational air quality forecast for Macao.

This thesis aims to explore how learning accounting for non-accounting major students could be enhanced through a comprehensive framework connecting individual attributes and learning approaches. While accounting is considered an important and mandatory discipline for business students, it is also considered a difficult subject which deters students’ engagement in the subject. Although the issue has been widely recognized, there are relatively less discussions on possible remedies. Learning accounting is a dynamic process that go beyond the traditional one-to-many learning and teaching settings, all factors revolving the students’ learning environment and process, altogether determine and affect the learning efficiencies. A meta-analysis was first performed to understand the standings of accounting education and to identify gaps within existing literatures in the field of management, accounting, tourism and general education researches. A documentary review on the curricula of all Macau HEI programs involving compulsory accounting courses was also conducted to understand the role and significance of accounting courses for non-accounting college students in Macau. Based on the literature review, this study borrows constructs from theory of planned behavior (TPB) models in its research framework. It attempts to provide a comprehensive framework to understand how students learn accounting efficiently by delving into individual learning styles of the students and their perceived helpfulness and self-efficacy, in respective to different learning techniques. Due to its exploratory nature, a mixed method of qualitative and quantitative methodology is adopted, where both focus group discussions and in-depth interviews are performed with non-accounting students to elicit understandings on student’s difficulties in learning accounting and their preferences for different learning styles. Then, a full-scale quantitative survey is conducted in a Macau higher education institute to assess the learning efficiencies of accounting in different situations. A triangulation with feedback from faculties and administrators are also elicited to provide alternate dimensions on the issues. With no surprise, there is no one best 3 teaching style or method for all students, this thesis contributes to accounting education research by provide a comprehensive diagnosis on the all-round learning stimulus and hindrance in learning accounting. At the end, recommendations are provided for accounting educators to emphasize on the uniqueness of students and to encourage a more dynamic and open environment for learning. Limitations of the studies are also discussed

Fishes are highly specialized in extracting ecologically relevant information from their diverse acoustic habitats since early developmental stages. The zebrafish (Danio rerio) is a valuable and well-stablished vertebrate model for investigating hearing functioning and disorders, development of the inner ear in vertebrates including humans, drug discovery, ecotoxicology assessments and behavioral research. Although the acoustic environment is known to shape the structure and sensitivity of auditory systems, there is no information on the natural soundscape of this species. Zebrafish are typically reared in large-scale artificial housing systems, which acoustic properties and potential effects on hearing remain largely unknown. Even though elevated levels of noise are widely present in most aquatic soundscapes and to an even greater extent in artificial environments, very limited information is known on how this important environmental stressor impacts species’ development and physiology, hearing capabilities and inner ear morphology, and behaviour. Considering that noise pollution is rapidly increasing in aquatic ecosystems, causing detrimental effects on survivability and growth and altering physiology and behaviour of organisms, it is of paramount importance to assess how this stressor affects wildlife, especially in early ontogeny, a critical period for development and establishment of phenotypic traits. For this thesis I aimed to 1) characterize the soundscape of both zebrafish natural habitats and laboratory captive conditions, and discuss possible impact on auditory sensitivity. Sound recordings were conducted in five distinct zebrafish habitats (Southwest India), from quieter stagnant environments with diverse biological/abiotic sounds to louder watercourses characterized by current and moving substrate, while artificial environmental characterization was conducted on three typical zebrafish housing systems. 8 In order to assess the impact of noise exposure on early development, my next goal was to 2) perform a split-brood experiment to test the effects of chronic noise exposure to increasing levels (130 and 150 dB re 1 μPa, continuous white noise) and different temporal regimes (mimicking shipping activity) on larval zebrafish in regards to general development, physiological stress, and behavioural patterns. Finally, the last objective consisted on 3) testing the effects of chronic noise exposure on auditory sensitivity measured based on inner ear saccular microphonics and acoustic-evoked startle responses (prepulse inhibition paradigm) in larval zebrafish, as well as evaluating whether sensitivity changes were paralleled by altered inner ear morphology. Based on bioacoustics methods, my first study found that zebrafish natural soundscape varied between 98 and 126 dB re 1 lPa in sound pressure levels. Sound spectra presented most energy below 3000 Hz and quieter noise windows were found in the noisiest habitats matching the species best hearing range. Contrastingly, recordings from zebrafish housing systems revealed higher sound levels (122–143 dB) and most energy below 1000Hz with more spectral peaks, which might cause significant impact such as auditory masking or even hearing loss. In my second research work, the acoustic treatments did not affect general development or hatching but increased noise levels led to a significant increase in mortality of larval zebrafish. The cardiac rate, yolk sac consumption and cortisol levels increased significantly with increasing noise level at both 3 and 5 dpf (days post fertilization). Variations in noise time presentations (different random noise periods similar to shipping activity) suggested that the presence of longer silent intervals is important to down-regulate physiological stress. Moreover, 5 dpf larvae exposed to 150 dB continuous noise regimes displayed increased dark avoidance in an anxiety-related dark/light preference test and displayed a significant 9 impairment in spontaneous alternation behaviour (SAB) a memory and sensorimotor related behaviour. Finally, in the last thesis goal, I found that noise-exposed specimens displayed significantly lower hair cell number and saccular epithelial area. This change in sensory morphology was paralleled by a significant decrease in inner ear saccular sensitivity at lower frequencies (100 to 200 Hz) in 5 dpf larvae. Sensorimotor hearing assessment revealed a hypersensitisation effect in noise-exposed group that displayed higher startle swimming velocity, but also significant decrease in sensitivity at 200 Hz. Altogether, this thesis provides an important ground for future research on the adaptation of zebrafish auditory system to the natural soundscapes, and highlights the importance of controlling noise conditions in captivity systems. Furthermore, results provide first evidence of noise-induced physiological stress, anxiety-driven behaviours and memory impairment in larval zebrafish larvae, showing that both noise amplitude and timing may negatively impact key physiological and behavioural endpoints in early ontogeny. The thesis also reports new findings on how acoustic stress may impact the structure and function of the inner ear in larval fish, which was followed by decreased sensitivity in sensorimotor responses to acoustic stimuli. My research highlights the importance of investigating how altered soundscapes and associated physiological and behavioural stress may affect important sensitive windows in development and impose new evolutionary challenges under a scenario of global change

Vehicles solely powered by electricity are a major technological innovation that combines individual transportation needs and environmental sustainability, yet their market penetration is low. Research has traditionally indicated factors such as the vehicle’s purchasing price, driving range, and charging time as the main barriers to adoption. However, the decision to adopt a technology also depends on what the technology represents to the user; therefore, other factors may be important to explain individuals’ behavior. This study is a quantitative and cross-sectional look at the behavioral intention to adopt battery electric vehicles (BEVs) technology in the context of Macau. The research builds on the unified theory of acceptance and use of technology 2 (UTAUT 2) (Venkatesh et. al., 2012) to explain the characteristics of the local consumers. Besides the addition of image and environmental concern to the theoretical model, the study also put forward and evaluate the construct of technology show-off, an original measure of the visible and experiential characteristics of a technology. A sample of 236 Macau residents was analyzed by structural equation modeling (SEM). The analysis of the data supported the explanatory and predictive power of our model and helped to describe the idiosyncrasies of local residents. The results provide insights related to individual technology acceptance that could be useful in designing more accurate strategies and fostering the uptake of BEVs in Macau or markets that share similarities