Partial least squares-discriminant analysis (PLS-DA) is a versatile algorithm that can be used for predictive and descriptive modelling as well as for discriminative variable selection. However, versatility is both a blessing and a curse and the user needs to optimize a wealth of parameters before reaching reliable and valid outcomes. Over the past two decades, PLS-DA has demonstrated great success in modelling high-dimensional datasets for diverse purposes, e.g. product authentication in food analysis, diseases classification in medical diagnosis, and evidence analysis in forensic science. Despite that, in practice, many users have yet to grasp the essence of constructing a valid and reliable PLS-DA model. As the technology progresses, across every discipline, datasets are evolving into a more complex form, i.e. multi-class, imbalanced and colossal. Indeed, the community is welcoming a new era called big data. In this context, the aim of the article is two-fold: (a) to review, outline and describe the contemporary PLS-DA modelling practice strategies, and (b) to critically discuss the respective knowledge gaps that have emerged in response to the present big data era. This work could complement other available reviews or tutorials on PLS-DA, to provide a timely and user-friendly guide to researchers, especially those working in applied research.

Over the last decades, the development of the Klang Valley (Malaysia), as an urban commercial and industrial area, has elevated the risk of atmospheric pollutions. There are several significant sources of air pollutants which vary depending on the background of the location they originate from. The aim of this study is to determine the trend and status of air quality and their correlation with the meteorological factors at different air quality monitoring stations in the Klang Valley. The data of five major air pollutants (PM10, CO, SO2, O3, NO2) were recorded at the Alam Sekitar Sdn Bhd (ASMA) monitoring stations in the Klang Valley, namely Petaling Jaya (S1), Shah Alam (S2) and Gombak (S3). The data from these three stations were compared with the data recorded at Jerantut, Pahang (B), a background station established by the Malaysian Department of Environment. Results show that the concentrations of CO, NO2 and SO2 are higher at Petaling Jaya (S1) which is due to influence of heavy traffic. The concentrations of PM10 and O3, however, are predominantly related to regional tropical factors, such as the influence of biomass burning and of ultra violet radiation from sunlight. They can, though, also be influenced by local sources. There are relatively stronger inter-pollutant correlations at the stations of Gombak and Shah Alam, and the results also suggest that heavy traffic flow induces high concentrations of PM10, CO, NO2 and SO2 at the three sampling stations. Additionally, meteorological factors, particularly the ambient temperature and wind speed, may influence the concentration of PM10 in the atmosphere.

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This study presents the spatial analysis of daily rainfall intensity and concentration index over Peninsular Malaysia. Daily rainfall data from 50 rainfall stations are used in this study. Due to the limited number of stations, the geostatistical method of ordinary kriging is used to compute the values of daily rainfall concentration and intensity and to map their spatial distribution. The resultant analysis of rainfall concentration indicated that the distribution of daily rainfall is more regular over the west, northwest and southwest regions compared to the east. Large areas of the eastern Peninsula display an irregularity in distribution of daily rainfall. In terms of number of rainy days, analysis of daily rainfall confirms that a large number of rainy days across the Peninsula arise from low-intensity events but only contribute a small percentage of total rain. On the other hand, a low frequency of rainy days with highintensity events contributes the largest percentage of total rain. The results indicated that the total rain in eastern areas is mainly contributed by the high-intensity events. This finding explains the occurrence of a large number of floods and soil erosions in these areas. Therefore, precautionary measures should be taken earlier to prevent any massive destruction of property and loss of life due to the hazards. These research findings are of considerable importance in providing enough information to water resource management, climatologists and agriculturists as well as hydrologists for planning their activities and modelling processes.

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Purpose. This study is aimed to compare the effects of two different orthodontic forces on crevicular alkaline phosphatase activity, rate of tooth movement, and root resorption. Materials and Methods. Twelve female subjects of class II division 1 malocclusion participated. Maxillary canines with bonded fixed appliances acted as the tested teeth, while their antagonists with no appliances acted as the controls. Canine retraction was performed using nickel titanium coil spring that delivered forces of 100 gm or 150 gm to either side. Crevicular fluid was analyzed for ALP activity, and study models were casted to measure tooth movements. Root resorption was assessed using periapical radiographs before and after the force application. Results. ALP activity at the mesial sites peaked at week 1 for 150 gm group with significant differences when compared with the 100 gm group. Cumulative canine movements were significantly greater in the 150 gm force (2.10 ± 0.50 mm) than in the 100 gm force (1.57 ± 0.44 mm). No root resorption was in the maxillary canines after retraction. Conclusions. A force of 150 gm produced faster tooth movements and higher ALP activity compared with the 100 gm group and had no detrimental effects such as root resorption.