EMOTION CONTROL, OVERREACTIVE PARENTING, AND MOTHERS’ EXECUTIVE FUNCTIONS

Evidence suggests that mothers’ emotion control difficulties are associated with their self-reported and observed overreactive parenting. Specifically, mothers who have difficulties managing their negative emotions and experience more anger, are more likely to discipline harshly. In addition to this emotional process, evidence suggests that poorer cognitive executive function (EF) is also associated with mothers’ use of overreactive discipline. However, the association between EF performance and overreactive parenting is inconsistent. The purpose of this study is to assess how different EFs may moderate the association between emotion control and overreactive parenting. I hypothesized that (1) mothers’ emotion control would be negatively related to levels of overreactive parenting and (2) this relationship would be moderated by mothers’ EF abilities Specifically, I am predicting that poor executive functioning would exacerbate the impact of poor emotional control on over-reactive parenting. This socio-economically diverse sample included 57 mothers (M = 35.2 years old) of 2- to 5-year-old children. Mothers completed questionnaires and three laboratory assessments of executive function tasks. Consistent with the proposed hypothesis, mothers’ emotion control was negatively associated with levels of overreactive parenting. Contrary to our hypothesis, there was no significant moderating effect of mothers’ EF performance or their self-report of EF on this relation. However maternal EF was independently associated with overreactive parenting. The findings from this study add to the growing body of research that concerns the role of EF performance in parenting

Outcome-impact survey on the public awareness & appreciation programme and traditional knowledge documentation programme of Sarawak biodiversity centre / Voon Boo Ho... [et al.]

Outcome and impact evaluations for important service organisations such as Sarawak Biodiversity Centre is crucial in ensuring high quality policymaking and effective service operations management. A continuous and consistent emphasis on serving the targeted customer groups as well as the general public will always be necessary for excellent and sustainable biodiversity conservation and development in Sarawak. It is great to note that Sarawak Biodiversity Centre's core functions are generally wellregarded and respected towards Sarawak's biodiversity excellence. This six-month state-wide questionnaire survey which focuses on evaluating the Awareness & Appreciation Programme and Traditional Knowledge Documentation Programme of the Centre has found numerous favourable outcomes and impacts of the two programmes. Besides, there are also useful insights for further improvement. Sarawak Biodiversity Centre has been very actively involved in promoting Sarawak's biodiversity and educating the general public on the importance of Sarawak's biodiversity and the Traditional Knowledge related to Sarawak's biodiversity. The strategic programmes are developed and implemented accordingly. Specifically, the Awareness and Appreciation Programme helps to: i) provide opportunities for the general public to participate in the Centre's Awareness and Appreciation programme and enhance appreciation for the State's rich biodiversity and her biotechnology initiatives, ii) organize seminars and forums that focus on biodiversity-biotechnology topics, targeted at policy makers, key government officials, members of the academia, researchers and scientists, industry representatives and the media, and iii) collate and disseminate accurate and factual information on biodiversity-biotechnology to th

Spatio-Temporal Clustering of Sarawak Malaysia Total Protected Area Visitors

Based on data of visitors to national parks, nature reserves and wildlife sanctuaries in Sarawak, this study’s objective is to use the spatial and temporal analysis to describe the underlying trend and temporal pattern of local and foreign visitors and ultimately infer the temporal distribution of visitors to 18 different TPAs. The second aim of the study is to cluster the visitors according to the location of TPAs using Wards hierarchical clustering method. By comparing average monthly visitors’ count, we observed that the average number of monthly visitors significantly reflects the distribution concentration of visitors based on the spatial map. Findings indicate that the monthly distributions of local and foreign visitors differ according to different TPAs. The spatial and temporal analysis found that local visitors’ arrival is high at the end of the year while foreign visitors showed significant arrival during the months of July, August and September. The Wards minimum variance method was able to cluster TPAs local and foreign visitors into very high, high, medium and low visitor area. This study provides additional information that could contribute to identifying the periods of highest visitor pressure, design measures to manage the concentration of visitors and improve the overall visitors’ experience. The findings of the study are also important to respective local authorities in providing information for planning and monitoring tourism in TPAs. Consecutively, this will ensure sustainability of TPAs resources while protecting their biodiversity

Using Machine Learning to Predict Visitors to Totally Protected Areas in Sarawak, Malaysia

The machine learning approach has been widely used in many areas of studies, including the tourism sector. It can offer powerful estimation for prediction. With a growing number of tourism activities, there is a need to predict tourists’ classification for monitoring, decision making, and planning formulation. This paper aims to predict visitors to totally protected areas in Sarawak using machine learning techniques. The prediction model developed would be able to identify significant factors affecting local and foreign visitors to these areas. Several machine learning techniques such as k-NN, Naive Bayes, and Decision Tree were used to predict whether local and foreign visitors’ arrival was high, medium, or low to these totally protected areas in Sarawak, Malaysia. The data of local and foreign visitors’ arrival to eighteen totally protected areas covering national parks, nature reserves, and wildlife centers in Sarawak, Malaysia, from 2015 to 2019 were used in this study. Variables such as the age of the park, distance from the nearest city, types of the park, recreation services availability, natural characteristics availability, and types of connectivity were used in the model. Based on the accuracy measure, precision, and recall, results show Decision Tree (Gain Ratio) exhibited the best prediction performance for both local visitors (accuracy = 80.65) and foreign visitors (accuracy = 84.35%). Distance to the nearest city and size of the park were found to be the most important predictors in predicting the local tourist visitors’ park classification, while for foreign visitors, age, type of park, and the natural characteristics availability were the significant predictors in predicting the foreign tourist visitors’ parks classification. This study exemplifies that machine learning has respectable potential for the prediction of visitors’ data. Future research should consider bagging and boosting algorithms to develop a visitors’ prediction model