Emerging tropical cities are experiencing rapid population growth and development, which can greatly affect the thermal environments. The effects of roadside trees and road orientation on the outdoor thermal environment were investigated on four different roads in Kuala Lumpur, Malaysia. Field measurements were conducted to assess outdoor thermal environments, where the selection of sites was based on different roadside tree morphological features and road orientations. Outdoor air temperature (T_a), relative humidity (RH), globe temperature (T_g), wind speed (WS), and wind direction (WD) were measured. Absolute humidity (AH) was estimated based on relative humidity and air temperature. Planting dense canopy trees with an average sky view factor (SVF) of 0.07 reduced the mean radiant temperature (T_mrt) by 35% and the physiological equivalent temperature (PET) by 25%. East–West (E–W) and Northwest–Southeast (NW–SE) oriented roads had high PET values of 41 °C and 43 °C, respectively. North–South (N–S) and Northeast–Southwest (NE–SW) orientated roads had lower PET values (37 °C), providing improved outdoor microclimate. Roadside trees provided greater cooling potential in E–W and NW–SE oriented roads. The findings are useful for urban road design in tropical cities in order to improve the outdoor thermal environment and pedestrian comfort.

Forecasting the severity of occupational injuries shall be all industries’ top priority. The use of machine learning is theoretically valuable to assist the predictive analysis, thus, this study attempts to propose a feature-optimized predictive model for anticipating occupational injury severity. A public database of 66,405 occupational injury records from OSHA is analyzed using five sets of machine learning models: Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, Decision Tree, and Random Forest. For model comparison, Random Forest outperformed other models with higher accuracy and F1-score. Therefore, it highlighted the potential of ensemble learning as a more accurate prediction model in the field of occupational injury. In constructing the model, this study also proposed the feature optimization technique that revealed the three most important features; ‘nature of injury’, ‘type of event’, and ‘affected body part’ in developing model. The accuracy of the Random Forest model was improved by 0.5% or 0.895 and 0.954 for the prediction of hospitalization and amputation, respectively by redeveloping and optimizing the model with hyperparameter tuning. The feature optimization is essential in providing insight knowledge to the Safety and Health Practitioners for future injury corrective and preventive strategies. This study has shown promising potential for smart workplace surveillance.

Living organisms require trace amounts of heavy metals, including cobalt, copper, manganese and zinc to survive. However, the excessive levels of the metal can be detrimental to the organism. Other heavy metals such as mercury, lead and cadmium have no vital on organisms, and their accumulation in long time period in the bodies can cause serious illness or death. The consumption of fish is recommended because fish is a basic and good nutritious food that has omega-3 fatty acids due to its cardio-protective effects. This present mini-review accounts for the description of heavy metal in fish and the effect of toxic metals on the human health. Besides, the acid digestion method was also discussed in order to identify the best method for applying in the laboratory analysis. The best method used can reduce the contamination error in the results.

This study investigated the effects of urban morphology on microclimate parameters in an urban university campus in Malaysia. Outdoor air temperatures (T_out) were recorded at eight different locations inside the campus for seven days. The study used three urban morphological parameters such as green cover ratio, height-to-width (H/W) ratio, and sky view factor (SVF). The relationship between urban morphological parameters and T_out obtained from in situ measurements was investigated. The results showed that, at a dense green cover ratio of 22% in a 7833 m² area where the H/W ratio was 0.2, T_out was reduced by about 1% due to a long building shadow cover (12 h) and a high range of SVF (from 0.61 to 0.68). The use of geographic information system (GIS) to generate the spatial data of Universiti Teknologi Malaysia Kuala Lumpur Campus (UTMKL), morphological features and in situ T_out distributions provided useful information of T_out variations, and proved the applicability of GIS as a useful tool in smart city urban planning.