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The aim of this study was to develop a scale to measure students' blended learning course experience. A total of 792 undergraduate students from Malaysia participated in this study. Exploratory factor analysis (EFA) was employed to evaluate the factor structure of the scale. As a result of EFA, three factors with 19 items that explained 68.06% of the total variance were obtained. The three factors are: course design, learning experience, and personal factors. Confirmatory factor analysis (CFA) was used to establish the structural validity of the scale, and item discrimination was conducted to validate the scale. The Cronbach's coefficients (α) for the factors ranged from.71 to.95. The overall Cronbach's coefficient (α) of the scale was.92, demonstrating a satisfactory level of reliability. The results showed that the developed scale is reliable and valid. Future implications for course designers and instructors are discussed. [ABSTRACT FROM AUTHOR]

The 2021–2022 flood is one of the most serious flood events in Malaysian history, with approximately 70,000 victims evacuated daily, 54 killed and total losses up to MYR 6.1 billion. From this devastating event, we realized the lack of extreme precipitation and flood inundation information, which is a common problem in tropical regions. Therefore, we developed a Rapid Extreme TRopicAl preCipitation and flood inundation mapping framEwork (RETRACE) by utilizing: (1) a cloud computing platform, the Google Earth Engine (GEE); (2) open-source satellite images from missions such as Global Precipitation Measurement (GPM), Sentinel-1 SAR and Sentinel-2 optical satellites; and (3) flood victim information. The framework was demonstrated with the 2021–2022 Malaysia flood. The preliminary results were satisfactory with an optimal threshold of five for flood inundation mapping using the Sentinel-1 SAR data, as the accuracy of inundated floods was up to 70%. Extreme daily precipitation of up to 230 mm/day was observed and resulted in an inundated area of 77.43 km2 in Peninsular Malaysia. This framework can act as a useful tool for local authorities and scientists to retrace the extreme precipitation and flood information in a relatively short period for flood management and mitigation strategy development. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents Gene-Expression Programming (GEP), which is an extension to the genetic programming (GP) approach to predict the total bed material load for three Malaysian rivers. The GEP is employed without any restriction to an extensive database compiled from measurements in the Muda, Langat, and Kurau rivers. The GEP approach demonstrated a superior performance compared to other traditional sediment load methods. The coefficient of determination, R 2 (=0.97) and the mean square error, MSE (=0.057) of the GEP method are higher than those of the traditional method. The performance of the GEP method demonstrates its predictive capability and the possibility of the generalization of the model to nonlinear problems for river engineering applications. [ABSTRACT FROM AUTHOR]