This paper investigate to use of information technology, i.e. machine learning algorithms for water assessment in Timor-Leste. It is essential to access clean water to ensure the safety for humans and others livings in this world. The Water Quality Index (WQI) is the standard tool for assessing water quality, which can be calculated from physicochemical and microbiological parameters. However, in developing countries, it is continuing need to bring water and energy for the most disadvantaged, make it necessary to find new solutions. In such case, missing-value imputation and machine learning models are useful for classifying water samples into suitable or unsuitable with significant accuracy. Some imputation methods were tested, and four machine learning algorithms were explored: logistic regression, support vector machine, random forest, and Gaussian naïve Bayes. We obtained a dataset with 368 observations from 26 groundwater sampling points in Dili city of Timor-Leste. According to experimental results, it is found that 64% of the water samples are suitable for human consumption. We also found k-NN imputation and random forest method were the clear winners, achieving 96% accuracy with three-fold cross validation. The analysis revealed that some parameters significantly affected the classification results.
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Objective: In myocardial perfusion single-photon emission computed tomography (SPECT), abdominal activity often interferes with the evaluation of perfusion in the inferior wall, especially after pharmacological stress. In this randomized study, we examined the effect of carbonated water intake versus still water intake on the quality of images obtained during myocardial perfusion images (MPI) studies. Methods: A total of 467 MIBI studies were randomized into a carbonated water group and a water group. The presence of intestinal activity adjacent to the inferior wall was evaluated by two observers. Furthermore, a semiquantitative analysis was performed in the adenosine subgroup,using a count ratio of the inferior myocardial wall and adjacent abdominal activity. Results: The need for repeated SPECT in the adenosine studies was 5.3 % in the carbonated water group versus 19.4 % in the still water group (p = 0.019). The inferior wall-to-abdomen count ratio was significantly higher in the carbonated water group compared to the still water group (2.11 ± 1.00 vs. 1.72 ± 0.73, p\0.001). The effect of carbonated water during rest and after exercise was not significant. Conclusions: This randomized study showed that carbonated water significantly reduced the interference of extra-cardiac activity in adenosine SPECT MPI. Keywords: Extra-cardiac radioactivity, Myocardial SPECT, Image quality enhancement, Carbonated water
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Small urban water bodies, like ponds or canals, are often assumed to cool their surroundings during hot periods, when water bodies remain cooler than air during daytime. However, during the night they may be warmer. Sufficient fetch is required for thermal effects to reach a height of 1–2 m, relevant for humans. In the ‘Really cooling water bodies in cities’ (REALCOOL) project thermal effects of typical Dutch urban water bodies were explored, using ENVI-met 4.1.3. This model version enables users to specify intensity of turbulent mixing and light absorption of the water, offering improved water temperature simulations. Local thermal effects near individual water bodies were assessed as differences in air temperature and Physiological Equivalent Temperature (PET). The simulations suggest that local thermal effects of small water bodies can be considered negligible in design practice. Afternoon air temperatures in surrounding spaces were reduced by typically 0.2 °C and the maximum cooling effect was 0.6 °C. Typical PET reduction was 0.6 °C, with a maximum of 1.9 °C. Night-time warming effects are even smaller. However, the immediate surroundings of small water bodies can become cooler by means of shading from trees, fountains or water mists, and natural ventilation. Such interventions induce favorable changes in daytime PET.
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