Statistical Modeling of RPCA-FCM in Spatiotemporal Rainfall Patterns Recognition

This study was conducted to identify the spatiotemporal torrential rainfall patterns of the East Coast of Peninsular Malaysia, as it is the region most affected by the torrential rainfall of the Northeast Monsoon season. Dimension reduction, such as the classical Principal Components Analysis (PCA) coupled with the clustering approach, is often applied to reduce the dimension of the data while simultaneously performing cluster partitions. However, the classical PCA is highly insensitive to outliers, as it assigns equal weights to each set of observations. Hence, applying the classical PCA could affect the cluster partitions of the rainfall patterns. Furthermore, traditional clustering algorithms only allow each element to exclusively belong to one cluster, thus observations within overlapping clusters of the torrential rainfall datasets might not be captured effectively. In this study, a statistical model of torrential rainfall pattern recognition was proposed to alleviate these issues. Here, a Robust PCA (RPCA) based on Tukey’s biweight correlation was introduced and the optimum breakdown point to extract the number of components was identified. A breakdown point of 0.4 at 85% cumulative variance percentage efficiently extracted the number of components to avoid low-frequency variations or insignificant clusters on a spatial scale. Based on the extracted components, the rainfall patterns were further characterized based on cluster solutions attained using Fuzzy C-means clustering (FCM) to allow data elements to belong to more than one cluster, as the rainfall data structure permits this. Lastly, data generated using a Monte Carlo simulation were used to evaluate the performance of the proposed statistical modeling. It was found that the proposed RPCA-FCM performed better using RPCA-FCM compared to the classical PCA coupled with FCM in identifying the torrential rainfall patterns of Peninsular Malaysia’s East Coast.

Methicillin-Resistant Staphylococcus aureus from Peninsular Malaysian Animal Handlers: Molecular Profile, Antimicrobial Resistance, Immune Evasion Cluster and Genotypic Categorization

Staphylococcus aureus (S. aureus) infections, particularly methicillin-resistant Staphylococcus aureus (MRSA) in humans and animals, have become a significant concern globally. The present study aimed to determine the prevalence and antibiogram of S. aureus isolated from animal handlers in Peninsular Malaysia. Furthermore, the genotypic characteristics of S. aureus isolates were also investigated. Nasal and oral swab samples were collected from 423 animal handlers in Peninsular Malaysia. The antibiogram profiles of S. aureus against 18 antibiotics were established using a Kirby–Bauer test. The genotypic profile of S. aureus, including the presence of antimicrobial resistance (AMR), virulence genes and spa genotypes, was investigated using molecular techniques. The overall carriage rate of S. aureus, MRSA and MDRSA was 30.5%, 1.2% and 19.4%, respectively. S. aureus was highly resistant against penicillin (72.3%) and amoxicillin (52.3%). Meanwhile, gentamicin and linezolid were fully effective against all the isolated S. aureus from animal handlers. It was observed that animal handlers with close exposure to poultry were more likely to carry S. aureus that is resistant to tetracycline and erythromycin. S. aureus isolates harboured tetracycline resistance (tetK, tetL and tetM), erythromycin resistance (ermA, ermB, ermC and msrA) and immune evasion cluster (IEC) genes (scn, chp, sak, sea and sep). Seventeen different spa types were detected among the 30 isolates of MDRSA, with t189 (16.7%) and t4171 (16.7%) being the predominant spa type, suggesting wide genetic diversity of the MDRSA isolates. The present study demonstrated the prevalence of S. aureus strains, including MRSA and MDRSA with various antimicrobial resistance and genetic profiles from animal handlers in Peninsular Malaysia.

Identification, Mapping, and Genetic Diversity of Novel Conserved Cross-Species Epitopes of RhopH2 in Plasmodium knowlesi With Plasmodium vivax

Malaria is a major public health concern, and any tangible intervention during the pre-elimination phase can result in a significant reduction in infection rates. Recent studies have reported that antigens producing cross-protective immunity can play an important role as vaccines and halt malaria transmission in different endemic regions. In this study, we studied the genetic diversity, natural selection, and discovered novel conserved epitopes of a high molecular weight rhoptry protein 2 (RhopH2) in clinical samples of Plasmodium knowlesi and Plasmodium vivax cross-protective domains, which has been proven to produce cross-protective immunity in both species. We found low levels of nucleotide diversity (P. knowlesi; π ~ 0.0093, SNPs = 49 and P. vivax π ~ 0.0014, SNPs = 23) in P. knowlesi (n = 40) and P. vivax (n = 65) samples in the PkRhopH2 cross-protective domain. Strong purifying selection was observed for both species (P. knowlesi; dS - dN = 2.41, p < 0.009, P. vivax; dS - dN = 1.58, p < 0.050). In silico epitope prediction in P. knowlesi identified 10 potential epitopes, of which 7 epitopes were 100% conserved within clinical samples. Of these epitopes, an epitope with 10 amino acids (QNSKHFKKEK) was found to be fully conserved within all P. knowlesi and P. vivax clinical samples and 80%–90% conservation within simian malaria ortholog species, i.e., P. coatneyi and P. cynomolgi. Phylogenetic analysis of the PkRhopH2 cross-protective domain showed geographical clustering, and three subpopulations of P. knowlesi were identified of which two subpopulations originated from Sarawak, Malaysian Borneo, and one comprised only the laboratory lines from Peninsular Malaysia. This study suggests that RhopH2 could be an excellent target for cross-protective vaccine development with potential for outwitting strain as well as species-specific immunity. However, more detailed studies on genetic diversity using more clinical samples from both species as well as the functional role of antibodies specific to the novel conserved epitope identified in this study can be explored for protection against infection.

THE CORRELATION BETWEEN SCHOOL CLIMATE DIMENSIONS AND TEACHER WELL-BEING IN MALAYSIAN INDIGENOUS SCHOOLS

Background and Purpose: Teaching at indigenous schools located in rural and outskirt areas is no small feat. Therefore, the teachers at these schools require a consistent and supportive school climate to enhance teacher well-being. As such, this study examines the relationship between the dimensions of school climate and well-being of teachers. It also discusses the application of five dimensions of school climate, namely collaboration, student relations, school resources, decision-making, and instructional innovation. Methodology: Data for this quantitative study was gathered via a set of questionnaires. A total of 291 teachers from indigenous schools along the east coast states of Peninsular Malaysia, which are Kelantan, Terengganu, and Pahang participated in this study. A descriptive analysis of the findings was performed using the Statistical Package for the Social Sciences (SPSS), while an inferential analysis was conducted using PLS-SEM. Findings: The empirical results show a significantly positive correlation between the five previously mentioned dimensions of school climate and teacher well-being. Contributions: The findings of this study affirm the relative importance of school climate and its impact on teacher well-being. This study is significant for the Ministry of Education, indigenous school administrators, teachers, as well as policymakers in developing suitable strategies to improve the school climate and teacher well-being in Malaysian indigenous schools. Keywords: Teacher well-being, collaboration, student relations, school resources, decision-making, instructional innovation. Cite as: Kamarudin, N. A., Ahmad, A., Abdul Halim, A. S., Abdullah, R., & Kamalrulzaman, N. I. (2022). The correlation between school climate dimensions and teacher well-being in Malaysian indigenous schools. Journal of Nusantara Studies, 7(1), 292-315. http://dx.doi.org/10.24200/jonus.vol7iss1pp292-315

MALAY DIALECT VARIANTS IN LANGKAWI: A DIACHRONIC PERSPECTIVE ANALYSIS

Background and Purpose: Dialectical studies scrutinised the closeness, kinship or genealogy of dialects derived from a language. Previous scholars divide Malay dialects in Peninsular Malaysia into four main clusters. The first cluster are the Johor-Melaka-Selangor and Central Perak. The second cluster is a group that spreads from the Patani region covering the Patani-Kelantan-Terengganu dialect. The third is the Negeri Sembilan cluster, and the fourth is the Kedah dialect. Generally, the Kedah dialect is divided into seven groups, covering Kedah (including Langkawi), Perlis, Penang and northern Perak Taiping. This study aims to investigate the Malay dialect variants in Langkawi Island via the Historical Linguistics approach and qualitative comparison research design. Methodology: This study utilised the qualitative research method. A total of 23 native speakers of Langkawi Malay dialect participated in this study. These informants were selected using NORM (an acronym for non-mobile, older, rural and males). Data collection mainly used several techniques such as interviews, recordings, and observations. The data were analysed based on the scope of comparative linguistics. Findings: The findings show that there are five characteristics of separated innovation between the Kuah and Air Hangat variants in one group and the Kedawang variant in other groups. Furthermore, there are nine innovations that separate the Langkawi Malay dialect from the Proto Malayik language. This study indicates that the Kedawang variant is an earlier variant apart from the Kuah and Air Hangat variants. Contributions: In addition to further strengthen the research on the various dialects spoken in Malaysia, this study attempts to highlight the complete description of the Kedah dialect varieties. This study also contributes to the science of Malay language variants and Malay Dialectology studies. Keywords: Historical linguistics, reconstruction, innovation, Langkawi Island, Malay dialect. Cite as: Ab. Hamid, N., Shahidi, A. H., Aman, R., Ramli, N., Zakaria, Z., & Iswary, E. (2022). Malay dialect variants in Langkawi: A diachronic perspective analysis. Journal of Nusantara Studies, 7(1), 142-167. http://dx.doi.org/10.24200/jonus.vol7iss1pp142-167

SHARPENING OF THERMAL SATELLITE IMAGERY FROM KLANG INDUSTRIAL AREA IN PENINSULAR MALAYSIA USING THE TSHARP APPROACH

Measuring high spatial/temporal industrial heat emission (IHE) is an important step in industrial climate studies. The availability of MODIS data products provides up endless possibilities for both large-area and long-term study. nevertheless, inadequate for monitoring industrial areas. Thus, Thermal sharpening is a common method for obtaining thermal images with higher spatial resolution regularly. In this study, the efficiency of the TsHARP technique for improving the low resolution of the MODIS data product was investigated using Landsat-8 TIR images over the Klang Industrial area in Peninsular Malaysia (PM). When compared to UAV TIR fine thermal images, sharpening resulted in mean absolute differences of about 25 °C, with discrepancies increasing as the difference between the ambient and target resolutions increased. To estimate IHE, the related factors (normalized) industrial area index as NDBI, NDSI, and NDVI were examined. The results indicate that IHE has a substantial positive correlation with NDBI and NDSI (R2 = 0.88 and 0.95, respectively), but IHE and NDVI have a strong negative correlation (R2 = 0.87). The results showed that MODIS LST at 1000 m resolution can be improved to 100 m with a significant correlation R2 = 0.84 and RMSE of 2.38 °C using Landsat 8 TIR images at 30 m, and MODIS LST at 1000 m resolution can still be improved to 100 m with significant correlation R2 = 0.89 and RMSE of 2.06 °C using aggregated Landsat-8 TIR at 100 m resolution. Similarly, Landsat-8 TIR at 100 m resolution was still improved to 30 m and used with aggregate UAV TIR at 5 m resolution with a significant correlation R2 = 0.92 and RMSE of 1.38 °C. Variation has been proven to have a significant impact on the accuracy of the model used. This result is consistent with earlier studies that utilized NDBI as a downscaling factor in addition to NDVI and other spectral indices and achieved lower RMSE than techniques that simply used NDVI. As a result, it is suggested that the derived IHE map is suitable for analyzing industrial thermal environments at 1:10,000 50,000 scales, and may therefore be used to assess the environmental effect.

Blood meal analysis of Anopheles vectors of simian malaria based on laboratory and field studies

Blood feeding and host-seeking behaviors of a mosquito play an imperative role in determining its vectorial capacity in transmitting pathogens. Unfortunately, limited information is available regarding blood feeding behavior of Anopheles species in Malaysia. Collection of resting Anopheles mosquitoes for blood meal analysis poses a great challenge especially for forest dwelling mosquitoes. Therefore, a laboratory-based study was conducted to evaluate the potential use of mosquitoes caught using human landing catch (HLC) for blood meal analysis, and subsequently to document blood feeding behavior of local Anopheles mosquitoes in Peninsular Malaysia. The laboratory-based experiment from this study revealed that mosquitoes caught using HLC had the potential to be used for blood meal analysis. Besides HLC, mosquitoes were also collected using manual aspirator and Mosquito Magnet. Overall, 47.4% of 321 field-caught Anopheles mosquitoes belonging to six species were positive for vertebrate host DNA in their blood meal. The most frequent blood meal source was human (45.9%) followed by wild boar (27.4%), dog (15.3%) and monkey (7.5%). Interestingly, only Anopheles cracens and Anopheles introlatus (Leucosphyrus Group) fed on monkey. This study further confirmed that members of the Leucosphyrus Group are the predominant vectors for knowlesi malaria transmission in Peninsular Malaysia mainly due to their simio-anthropophagic feeding behavior.

Predicting suspended sediment load in Peninsular Malaysia using support vector machine and deep learning algorithms

High loads of suspended sediments in rivers are known to cause detrimental effects to potable water sources, river water quality, irrigation activities, and dam or reservoir operations. For this reason, the study of suspended sediment load (SSL) prediction is important for monitoring and damage mitigation purposes. The present study tests and develops machine learning (ML) models, based on the support vector machine (SVM), artificial neural network (ANN) and long short-term memory (LSTM) algorithms, to predict SSL based on 11 different river data sets comprising of streamflow (SF) and SSL data obtained from the Malaysian Department of Irrigation and Drainage. The main objective of the present study is to propose a single model that is capable of accurately predicting SSLs for any river data set within Peninsular Malaysia. The ANN3 model, based on the ANN algorithm and input scenario 3 (inputs consisting of current-day SF, previous-day SF, and previous-day SSL), is determined as the best model in the present study as it produced the best predictive performance for 5 out of 11 of the tested data sets and obtained the highest average RM with a score of 2.64 when compared to the other tested models, indicating that it has the highest reliability to produce relatively high-accuracy SSL predictions for different data sets. Therefore, the ANN3 model is proposed as a universal model for the prediction of SSL within Peninsular Malaysia.