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This study focused on the pattern recognition of Malaysian air quality based on the data obtained from the Malaysian Department of Environment (DOE). Eight air quality parameters in ten monitoring stations in Malaysia for 7 years (2005–2011) were gathered. Principal component analysis (PCA) in the environmetric approach was used to identify the sources of pollution in the study locations. The combination of PCA and artificial neural networks (ANN) was developed to determine its predictive ability for the air pollutant index (API). The PCA has identified that CH₄, NmHC, THC, O₃, and PM₁₀ are the most significant parameters. The PCA-ANN showed better predictive ability in the determination of API with fewer variables, with R ² and root mean square error (RMSE) values of 0.618 and 10.017, respectively. The work has demonstrated the importance of historical data in sampling plan strategies to achieve desired research objectives, as well as to highlight the possibility of determining the optimum number of sampling parameters, which in turn will reduce costs and time of sampling.

The utilization of renewable biomass resources for biofuel production has gained significant attention as an effective strategy for converting waste into valuable energy sources. Biodiesel, a promising form of biofuel, is derived from vegetable oils or animal fats through transesterification with methanol. Among the various methods employed for biodiesel production, base-catalyzed transesterification is widely recognized as the most efficient and cost-effective process. This study focuses on optimizing biodiesel production from coconut oil (CO) using a CaO/Al₂O₃ catalyst synthesized via the incipient wetness impregnation method. The optimization process was conducted using the Box-Behnken response surface methodology (RSM) in Design Expert statistical software. The optimal conditions identified for biodiesel production included a calcination temperature of 1000°C, a reaction time of 1 hour, and a catalyst loading of 7% with biodiesel yield of 72.33% and 75.63%, respectively from waste cooking oil (WCO) and fresh cooking oil (FCO). Under these optimized conditions, an experimental fatty acid methyl ester (FAME) yield of 75.63% was achieved. The statistical models used to predict biodiesel yield demonstrated a high degree of correlation with experimental results, yielding an R-value of 0.9756. Furthermore, analysis of variance (ANOVA) confirmed the statistical significance of the model, with reaction time and catalyst loading identified as the most influential factors based on F- and P- values. The physicochemical properties of the produced biodiesel were found to be within the specifications recommended by ASTM standards, further validating the suitability of the synthesized catalyst and optimized reaction conditions for efficient biodiesel production.

The research aimed to assess the influence of arbuscular mycorrhizal fungi (AMF) inoculation on the growth, tolerance, and phytoremediation capacity of Alocasia calidora (Schott) G.Don used in heavy metal/metalloid-polluted soil under field conditions. Five new, young A. calidora plants were planted in each treatment and control plot (1 m × 1 m). AMF inoculum was supplemented on days 0, 30, 60, and 90 in the treatment plot, and the experiment was conducted for 120 days. Substantial growth was observed in the roots and shoots of AMF-inoculated A. calidora . In the shoots of treated A. calidora , a high accumulation of Cu, As, Ni, Mn, and Pb was observed. The AMF-treated plant exhibited a higher accumulation of Cu, Fe, and Ni in the roots ( P  < 0.05). The removal efficiencies in the AMF-treated soil were 84.67, 74.82, 81.61, 80.77, 88.21, 92.26, 92.35, and 67.32% for As, Cr, Cu, Fe, Mn, Ni, Pb, and Zn, correspondingly, while, for control, the corresponding values were 61.24, 52.42, 62.95, 33.46, 57.89, 61.45, 71.19, and 54.98%. AMF-treated A. calidora demonstrated an increased tolerance and metal/metalloid accumulation in response to a variety of compounds, including tryptophan, S-(4-nitrobenzyl) glutathione, 5,7,2′,3′-tetrahydroxy flavone, 5,2-dihydroxy flavone, indole-acrylic acid, and various tripeptides. A. calidora proliferation, tolerance, and metal/metalloid accumulation were enhanced by the inoculated AMF. Therefore, treating soil contaminated with heavy metals/metalloids can be accomplished by combining AMF with plants.

The land use/land cover pattern of a region is an outcome of natural and socioeconomic factors and the utilisation by humans in time and space. This study aims to model the marine water quality using the relative impact of land use on marine water quality of selected river estuary between 2006-2013, Geographical Information System (GIS) and Cellular Automata (CA)-Markov method as a planning tool in evaluating Marine Water Quality Index (MWQI) were applied. The CA-Markov model revealed agricultural land use changes from 2006-2013 using land use land cover (LULC) in GIS as Setiu and Semerak River basins have 5.72% and 2.75%, respectively. The result indicated the impact of agricultural lands on MWQI, which is very low, according to projections of land use in 2020. Thus, the MWQI value in 2020 (Setiu 76.27 and Semerak 67.64) will be higher than MWQI mean value for 2006-2013.

Air pollution has been a significant issue in recent years due to rising industrialization and maritime activity around the globe, making air pollution forecasting a crucial concept in environmental study. This prompted the deployment of principal component analysis (PCA) for the source apportionment amongst the air quality parameters and the artificial neural network (ANN) for the prediction of the significant air quality parameters in ports area for this study. The study was carried out in seven federal ports across Malaysia for the period of 2009 and 2018, and 14 air quality parameters were calculated using information on air quality acquired from the Department of the Environment. The results of the study showed PCA identified NO x , NO, SO, NO 2 , CO, and PM 10 as the variables of significance with a variation of 44.31% with CO exhibiting the highest factor loading (0.968). Artificial Neural Network-Source Apportionment accurately predicted CO as the major pollutant with R 2 in training (0.7492) and validation (0.7492). This study has successfully established a connection between the source of apportionment of air pollutant parameters and the total number of ships, as well as an effective alternative tool for predicting the most significant air quality air pollutant parameters in Malaysian ports, which can be applied in other regions to comprehend ship emission trends.

The rising demand for alternative fuels due to the depletion of diesel oil has accelerated biodiesel production, primarily synthesized through base-catalyzed transesterification. In this study, recycled cooking oil was used as the feedstock, with an alumina-supported calcium oxide catalyst doped with magnesium (Ca/Mg/Al 2 O 3 ), synthesized via the wet impregnation method, serving as the heterogeneous base catalyst. The catalyst optimization involved adjusting calcination temperatures, the dopant-to-base ratio, and reaction times. The optimal conditions were attained with a 10% Mg loading, calcined at 800°C, and a reaction time of one hour. Under these parameters, biodiesel yield reached 48.30%. XRD analysis revealed CaCO 3 and MgAl 2 O 4 as the active species on the catalyst surface, while FESEM analysis showed the catalyst’s irregular shape and particle aggregation, contributing to the yield. These findings suggest that the Ca/Mg (90:10)/Al 2 O 3 catalyst is a promising heterogeneous base catalyst with significant potential for biodiesel production.

This study provided a step-by-step procedure to investigate the distribution of 17 amino acids (AAs) in 50 fish, 50 bovine and 54 porcine gelatines using Ultra-High-Performance Liquid Chromatography Diode-Array Detector (UHPLC–DAD) with the incorporation of principal component analysis (PCA). Dataset pre-processing step, including outlier removal, analysis of variance (ANOVA), dataset adequacy test, dataset transformation and correlation test was performed before the PCA. The method rendered linearity range of 37.5–1000 pmol/µL and accuracy of 85–111% recovery. The bovine and porcine gelatines showed a similar ranking while the l -Alanine (Ala), l -Arginine (Arg) and l -Glutamic acid (Glu) concentrations had differed the fish gelatine from the bovine and porcine gelatines. The PCA, which explained 77.013% cumulative variability at eigenvalue of 5.436, showed AAs with strong FL in PC1 had polar and nonpolar side chains while AAs with strong FL in PC2 had polar side chain. The AAs with moderate and weak FL in PC1 had a nonpolar side chain. The AAs with strong FL of in PC1 were also the same AAs with 7, 6 and 5 strong CMs as determined in the correlation test. The second PCA showed that the l -Serine (Ser), Arg, Glycine (Gly), l -Threonine (Thr), l -Methionine (Met), l -Histidine (His) and L-Hydroxyproline (Hyp) were significant in fish gelatine; Hyp, Met, Thr, Ser, His, Gly, and Arg in bovine gelatine; and l -Proline (Pro), l -Tyrosine (Tyr), l -Valine (Val), l -Leucine (Leu), and l -Phenylalanine (Phe) in porcine gelatine. The 100% fish, bovine and porcine gelatines accommodated grouping 1, 2 and 3, respectively, which proved that AAs with strong FL (Hyp, His, Ser, Arg, Gly, Thr, Pro, Tyr, Met, Val, Leu and Phe) were the significant AAs and becomes the biomarkers to identify the gelatine source. From this study, the PCA was a useful tool to analyse a multivariate dataset that could provide an in-depth understanding of AA distributions as compared to ANOVA and correlation test.

Hydrogeomorphologically, the study of river meandering provides information on the tendency of rivers to reach and form a state of equilibrium. The process of meander changes is important in order to identify the environment-related causes that occur naturally or vice versa. Sedimentation, erosion, flood, and water quality problems usually are being specifically studied, but in a broad view, changes in the platform of the river affect all the problems that occur. This article discusses the effects of the meanders evolution changes from land use and climate change in the upstream of Sungai Pahang in over 61 years from 1932 to 1993. Based on Geographical Information System (GIS), the topographic maps, scaled to 1:50,000 in geo-reference, were overlaid and digitalized. The main alignments of the upstream reach from those years were superimposed, and the changes were identified based on sinuosity index. In this task, the study areas were divided into two major plots for river plan classification. The results indicated that the average of alignment on the sinuosity index is 1.24 to 1.48 in plot A, while in plot B, the results are not stable. Based on historical results, a very significant change of meander was identified in the subplot Ua3 in plot A, where 21.2 % segments were recorded with high changes. This could be associated with significant exploration at hilly areas in the Cameron Highlands. Large-scale changes in land use pattern are coupled with global climate change where total rainfall recorded was at 2,760 mm in plot A on the year 1993. While for the plot B segment, the percentage of meander changes is 41.5 % versus plot A which is 86.7 %. This is due to the fact that plot B is the forest reserve and national park, areas with natural environment, possessing lithosols characteristic soils in the upper plot B area, and the trend of land use change (forested areas) is substantially lower than in plot A, with a 10 % difference. The aim of this study is to understand the impact of the land use changes due to climatic conditions on the meander evolution changes at the upstream reach of the Pahang River and suggest a number of solutions to mitigate or adaptation strategies to cope with those changes in the future.

The purpose of this study is to identify the needs for the development of communication skills based on the learning style module for vocational college students. This study employed a quantitative approach of the survey study design. The respondents consisted of 109 communication skills lecturers at vocational colleges throughout Malaysia. Analysis of the study showed that the overall mean value for all three constructs; the problem (M=3.8441), knowledge (M=4.2058), and need (4.5811), were high. Meanwhile, the results of t-test showed no significant difference for all three constructs; problem [t (109) = 0.279; p = 0.718, (p> 0.05)], knowledge [t (109) = 1.222; p = 0.224, (p> 0.05)], and need [t (109) = 0.812; p = 0.419, (p> 0.05)] for gender. In conclusion, the development of this Communication Skills Based on Learning Style Module is proposed as a teaching aid to improve students' mastery of communication skills.