FACTORS AFFECTING THE ACADEMIC RESULTS OF MASTER STUDENTS IN MATHEMATICS EDUCATION AT CAN THO UNIVERSITY, VIETNAM: A SURVEY STUDY

The study results were based on the survey data of 24 students  studying the master program in math education at Can Tho University, Vietnam. We used the questionnaire to find out the factors affecting students' learning outcomes: Learning time, learning conditions, learning environment, personal level, learning methods, collaborative learning, learning attitudes. The results show factors such as learning conditions, learning environment, time for leaning, qualifications, teaching methods, learning methods, cooperation in learning, attitude in learning are factors that significantly affect the learning of master students in Mathematics education. Therefore, universities with high-level training programs should have adequate facilities for students' learning; lecturers know how to use teaching methods to promote self-study and self-study for students, improve their ability to work independently, the ability to cooperate in the learning and research process of students. In other words, universities must uphold  their responsibilities when implementing intensive training programs, helping learners with necessary competencies as expected of the community and society.  Article visualizations

生態系サービス概念の環境政策への適用： ベトナム国メコンデルタにおいて

京都大学0048新制・課程博士博士(工学)甲第20689号工博第4386号新制||工||1682(附属図書館)京都大学大学院工学研究科都市環境工学専攻(主査)教授 清水 芳久, 教授 田中 宏明, 教授 米田 稔学位規則第4条第1項該当Doctor of Philosophy (Engineering)Kyoto UniversityDFA

Soil and Water Quality Indicators of Diversified Farming Systems in a Saline Region of the Mekong Delta, Vietnam

Saltwater intrusion, a consequence of climate change and decreased water levels, has been increasingly severe in the Mekong Delta region. Thanh Phu District, Ben Tre Province, Vietnam, is a coastal region where agricultural production and local livelihood have been impaired by saltwater intrusion, resulting in the adoption of multiple coping strategies, including rotations and intercropping. This study aims to measure and evaluate soil and water quality indicators of multiple farming systems in Thanh Phu district and contributes to developing suitable cropping patterns. Soil indicators were pH, electrical conductivity, and exchangeable Na+. Water quality characteristics include pH, salinity, dissolved N and P, alkalinity, H2S, and chemical oxygen demand (COD). The results indicated that water pH and salinity were at suitable levels to support the growth of prawn but were below the critical level required to grow black tiger shrimp and white-legged shrimp. Water alkalinity, dissolved N, P, and COD were not constraining for the growth of shrimps. However, a significant concentration of H2S may cause disadvantages for shrimp growth

Towards a “City in Nature”: evaluating the cultural ecosystem services approach using online public participation GIS to support urban green space management

The concept of cultural ecosystem services has been increasingly influential in both environmental research and policy decision making, such as for urban green spaces. However, its popular definitions tend to conflate “services” with “benefits”, making it challenging for planners to employ them directly to manage urban green spaces. Thus, attempts have been made to redefine cultural ecosystem services as the function of cultural activities in environmental spaces which result in people’s enjoyment of cultural ecosystem benefits. The operability of such a redefinition needs to be evaluated, which this study seeks to achieve with Bishan-Ang Mo Kio Park in Singapore presenting itself as a prime case study research area. Transdisciplinary mixed methods of a public participation geographic information system, which leverages on spatial data from public park users, and social media text mining analysis via Google reviews were used. A wealth of cultural ecosystem services and benefits were reported in the park, especially the recreational and aesthetic services and experiential benefits. Policy and methodological implications for future research and urban park developments were considered. Overall, this paper would recommend the employment of the redefined cultural ecosystem services approach to generate relational, data-driven and actionable insights to better support future urban green space management.Ministry of Education (MOE)Nanyang Technological UniversityPublished versionThe research activities are funded by the National Institute of Education at the Nanyang Technological University (SUG-NAP EP3/19) and the Ministry of Education—Singapore (#Tier1 RT06/19, #Tier1 2021-T1-001-056 and #Tier2MOE-T2EP402A20-0001) acquired by EP. This work is also jointly supported Research Initiation Grant from AIT (SET-2021-R011). Co-author HHL also expresses his appreciation to the International Foundation for Science for supporting this study through its Basic Research Grant Programme (NO. I2-W-6511-1)

Comparison of Different Artificial Intelligence Techniques to Predict Floods in Jhelum River, Pakistan

Floods are among the major natural disasters that cause loss of life and economic damage worldwide. Floods damage homes, crops, roads, and basic infrastructure, forcing people to migrate from high flood-risk areas. However, due to a lack of information about the effective variables in forecasting, the development of an accurate flood forecasting system remains difficult. The flooding process is quite complex as it has a nonlinear relationship with various meteorological and topographic parameters. Therefore, there is always a need to develop regional models that could be used effectively for water resource management in a particular locality. This study aims to establish and evaluate various data-driven flood forecasting models in the Jhelum River, Punjab, Pakistan. The performance of Local Linear Regression (LLR), Dynamic Local Linear Regression (DLLR), Two Layer Back Propagation (TLBP), Conjugate Gradient (CG), and Broyden–Fletcher–Goldfarb–Shanno (BFGS)-based ANN models were evaluated using R2, variance, bias, RMSE and MSE. The R2, bias, and RMSE values of the best-performing LLR model were 0.908, 0.009205, and 1.018017 for training and 0.831, −0.05344, and 0.919695 for testing. Overall, the LLR model performed best for both the training and validation periods and can be used for the prediction of floods in the Jhelum River. Moreover, the model provides a baseline to develop an early warning system for floods in the study area

Comparison of different artificial intelligence techniques to predict floods in Jhelum River, Pakistan

Floods are among the major natural disasters that cause loss of life and economic damage worldwide. Floods damage homes, crops, roads, and basic infrastructure, forcing people to migrate from high flood-risk areas. However, due to a lack of information about the effective variables in forecasting, the development of an accurate flood forecasting system remains difficult. The flooding process is quite complex as it has a nonlinear relationship with various meteorological and topographic parameters. Therefore, there is always a need to develop regional models that could be used effectively for water resource management in a particular locality. This study aims to establish and evaluate various data-driven flood forecasting models in the Jhelum River, Punjab, Pakistan. The performance of Local Linear Regression (LLR), Dynamic Local Linear Regression (DLLR), Two Layer Back Propagation (TLBP), Conjugate Gradient (CG), and Broyden–Fletcher–Goldfarb–Shanno (BFGS)-based ANN models were evaluated using R2, variance, bias, RMSE and MSE. The R2, bias, and RMSE values of the best-performing LLR model were 0.908, 0.009205, and 1.018017 for training and 0.831, −0.05344, and 0.919695 for testing. Overall, the LLR model performed best for both the training and validation periods and can be used for the prediction of floods in the Jhelum River. Moreover, the model provides a baseline to develop an early warning system for floods in the study area.Ministry of Education (MOE)Published versionThis research was funded by the Ministry of Education of Singapore (#Tier1 2021-T1-001-056 and #Tier2 MOE-T2EP402A20-0001)

Long-term hydrological alterations and the agricultural landscapes in the Mekong Delta: insights from remote sensing and national statistics

The Vietnamese Mekong Delta (VMD) is one of the most important food baskets in Southeast Asia, contributing to more than half of the country's food production capacity and the majority of its rice exports. Constantly threatened by a multitude of environmental pressures, including climate change-induced sea-level rise, delta-wide land subsidence, sedimentation reduction and, more recently, riverbed mining, steps towards the sustainable development of the VMD is becoming increasingly vulnerable. In this paper, we examine the effect of hydrological alterations of agricultural landscape in the VMD, more specifically, the temporal trends of triple rice crop in the Long Xuyen Quadrangle (LXQ). Landsat satellite data was used to map active rice paddy sites across the three major rice cropping seasons and identify the temporal distribution of triple rice crop areas over the last 24 years (1995–2019). Results were interpreted alongside official statistical data on agriculture from Vietnam and corroborated with ground truth data points from the study site. Our results reveal a notable fall in Landsat-detected triple rice crop area between 2016 and 2019, corroborating with both literature and agricultural data indicating an increase in aquaculture areas. Here, we take note for the first time the underlying links between riverbed mining and agricultural shifts in the VMD, which could highlight important policy and management implications for the local government in order to ensure environmental sustainability and food security. We argue that a tighter and more effective regulation of riverbed mining practices in the region is both integral and necessary for the agricultural sustainability of the VMD.Ministry of Education (MOE)Nanyang Technological UniversityPublished versionThis study is the URECA research project of Tay Ru Hui under the supervision of Edward Park and Ho Huu Loc. The research activities are funded by the National Institute of Education at the Nanyang Technological University (SUG-NAP EP3/19) and the Ministry of Education - Singapore (#Tier1 RT06/19, #Tier1 2021-T1-001-056 and #Tier2MOE-T2EP402A20-0001). This work is also jointly supported Research Initiation Grant (SET-2021-R011) from AIT. Co-author Ho Huu Loc also expresses his appreciation to the International Foundation for Science for supporting this study through its Basic Research Grant Programme (NO.I2-W-6511-1)

Development and Application of a Real-Time Flood Forecasting System (RTFlood System) in a Tropical Urban Area: A Case Study of Ramkhamhaeng Polder, Bangkok, Thailand

In urban areas of Thailand, and especially in Bangkok, recent flash floods have caused severe damage and prompted a renewed focus to manage their impacts. The development of a real-time warning system could provide timely information to initiate flood management protocols, thereby reducing impacts. Therefore, we developed an innovative real-time flood forecasting system (RTFlood system) and applied it to the Ramkhamhaeng polder in Bangkok, which is particularly vulnerable to flash floods. The RTFlood system consists of three modules. The first module prepared rainfall input data for subsequent use by a hydraulic model. This module used radar rainfall data measured by the Bangkok Metropolitan Administration and developed forecasts using the TITAN (Thunderstorm Identification, Tracking, Analysis, and Nowcasting) rainfall model. The second module provided a real-time task management system that controlled all processes in the RTFlood system, i.e., input data preparation, hydraulic simulation timing, and post-processing of the output data for presentation. The third module provided a model simulation applying the input data from the first and second modules to simulate flash floods. It used a dynamic, conceptual model (PCSWMM, Personal Computer version of the Stormwater Management Model) to represent the drainage systems of the target urban area and predict the inundation areas. The RTFlood system was applied to the Ramkhamhaeng polder to evaluate the system’s accuracy for 116 recent flash floods. The result showed that 61.2% of the flash floods were successfully predicted with accuracy high enough for appropriate pre-warning. Moreover, it indicated that the RTFlood system alerted inundation potential 20 min earlier than separate flood modeling using radar and local rain stations individually. The earlier alert made it possible to decide on explicit flood controls, including pump and canal gate operations

Adaptive capacity of high- and low dyke farmers to hydrological changes in the Vietnamese Mekong delta

This research assesses the adaptive capacity of farmers in the Vietnamese Mekong Delta's floodplains (VMD) with respect to hydrological changes. Currently, climate change and socio-economic developments induce extreme- and diminishing floods, which in turn increase farmers' vulnerability. This research assesses farmers' adaptive capacity to hydrological changes using two prevalent farming systems: high dykes featuring triple-crop rice farming and low dykes where fields are left fallow during the flood season. We examine (1) farmers' perceptions on a changing flood regime and their current vulnerabilities and (2) farmers' adaptive capacity through five sustainability capitals. Methods include a literature review and qualitative interviews with farmers. Results show that extreme floods are becoming less frequent and damaging, depending on arrival time, depth, residence time, and flow velocity. In extreme floods, farmers' adaptive capacity is generally strong, and only low dyke farmers experience damage. As for diminishing floods, which is an emerging phenomenon, the overall adaptive capacity of farmers is remarkably weaker and varies between high- and low dyke farmers. Financial capital is lower for low dyke farmers due to their double-crop rice system, and natural capital is low for both farmer groups due to a decrease in soil- and water quality, affecting yields and increasing investment costs. Farmers also struggle with an unstable rice market due to strong fluctuating prices for seeds, fertilizers, and other inputs. We conclude that both high- and low dyke farmers have to cope with new challenges, including fluctuating flood patterns and the depletion of natural resources. Increasing farmers resilience should focus on exploring better crop varieties, adjusting crop calendars, and shifting to less water-intensive crops