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Land use changes in urbanization, industrialization, and agricultural processes will continue to have negative impacts on water quality at all scales. The impact of land use changes on water quality is generally studied by analyzing the relationships between land use and water quality indicators. Therefore, the purpose of this research was to review and analyze the main relationships between land use and water quality, as well as to visualize the major sources and processes of water quality pollution in Malaysia. To achieve our goal, we evaluated the significance of both land use and water quality attributes used in the past studies and correlated them to understand their relationship from another angle of view. The results revealed that 87% of the reviewed studies indicated urban land use as a major source of water pollution, while 82% indicated agricultural land use, 77% indicated forest land use, and 44% indicated other land uses. However, the results of correlation analysis showed that agricultural and forest-related activities more affected water quality through their significant positive correlation with physical and chemical indicators of water quality, while urban development activities had a greater impact on water quality through altering hydrological processes such as runoff and erosion. These findings would provide decision-makers with useful information for managing water pollution processes rather than sources only.

Digital Terrain Models (DTMs) represent an essential source of information that can allow the behaviour of the urban floodplain, and its interactions with the drainage system, to be examined, understood and predicted. Typically, such data are obtained via Light Detection and Ranging (LiDAR).If a DTM does not contain adequate representation of urban features the results from the modelling efforts can be. This is due to the fact that urban environments contain variety of features, which can have functions of storing and/or diverting flows during flood events. The work described in this paper concerns further improvements of a LiDAR filtering algorithm which was discussed in a previous work. The key characteristics of this improved algorithm are: ability to deal with buildings, detect elevated road and represent them accordance to reality and deal with bridges and riverbanks. The algorithm was tested using a real-life data from a case study of Kuala Lumpur. The results have shown that the newly developed MPMA2 algorithm has better capabilities of identifying some of the features that are vital for urban flood modelling applications than any of the currently available algorithms and it leads to better agreement between simulated and observed flood depths and flood extents.

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The oil palm industry is vital for the Malaysian economy. However, it is threatened by the Ganoderma boninense fungus, which causes basal stem rot (BSR) disease. Foliar symptoms of the disease include the appearance of several unopened spears, flat crowns, and small crown size. The effect of this disease depends on the severity of the infection. Currently, the disease can be detected manually by analyzing the oil palm tree’s physical structure. Terrestrial laser scanning (TLS) is an active ranging method that uses laser light, which can directly represent the tree’s external structure. This study aimed to classify the healthiness levels of the BSR disease using a machine learning (ML) approach. A total of 80 oil palm trees with four different healthiness levels were pre-determined by the experts during data collection with 40 each for training and testing. The four healthiness levels are T0 (healthy), T1 (mildly infected), T2 (moderately infected), and T3 (severely infected), with 10 trees in each level. A terrestrial scanner was mounted at a height of 1 m, and each oil palm was scanned at four positions at a distance of 1.5 m around the tree. Five tree features were extracted from the TLS data: C200 (crown slice at 200 cm from the top), C850 (crown slice at 850 cm from the top), crown area (number of pixels inside the crown), frond angle, and frond number. C200 and C850 were obtained using the crown stratification method, while the other three features were obtained from the top-down image. The obtained features were then analyzed by principal component analysis (PCA) to reduce the dimensionality of the dataset and increase its interpretability while at the same time minimizing information loss. The results showed that the kernel naïve Bayes (KNB) model developed using the input parameters of the principal components (PCs) 1 and 2 had the best performance among 90 other models with a multiple level accuracy of 85% and a Kappa coefficient of 0.80. Furthermore, the combination of the two highest PC variance with the most weighted to frond number, frond angle, crown area, and C200 significantly contributed to the classification success. The model also could classify healthy and mildly infected trees with 100% accuracy. Therefore, it can be concluded that the ML approach using TLS data can be used to predict early BSR infection with high accuracy.

An assessment has been carried out to study the performance of seven different LiDAR filtering algorithms and to evaluate their suitability for urban flood modelling applications. It was found that none of these algorithms can be regarded as fully suitable to support such work in its present form. was observed between a model that uses a DTM modified by the progressive morphological filtering algorithm and the predictions of other models. The overall results suggest that incorporation of building basements within the DTM can lead to a significant difference in the model results with a tendency towards overestimation for those models which do not incorporate such a feature.

One of 13 healthy dogs used in a pharmacokinetic study of imidocarb dipropionate died due to difficulty in breathing, tachycardia, weakness and profuse diarrhoea. Autopsy findings showed marked pulmonary congestion and oedema. Kidneys were grossly enlarged and markedly congested with extensive haemorrhage in the cortex and medulla. Marked tubulonephrosis was also exhibited microscopically. Liver and spleen were moderately enlarged and congested. The adverse effects of imidocarb may be due to excessive acetylcholine action.