This paper discusses the multilevel approach in constructing a model for estimating hierarchically structured data of students' performance. Multilevel models that take into account variation from the clustering of data in different levels are compared to regression models using least squares method. This study also estimates the contributions of gender and ethnic factors on students' performance. Performance data of866 students in a science faculty in an institution of higher learning is obtained and analyzed. This data is hierarchically structured with two levels, namely students and departments. Analysis findings show different parameter estimates for both models. Also, the multilevel model which incorporates variability from different levels and predictors from higher levels is found to provide a better fit for model explaining students' performance.
[Rencana ini membincangkan pendekatan multitahap dalam pembinaan model penganggaran pencapaian pelajar yang mempunyai struktur data hierarki. Model multitahap yang mengambil kira variasi data yang berpunca dari pengelompokan data pada tahap-tahap yang berbeza dibandingkan dengan model regresi linear yang menggunakan kaedah kuasa dua terkecil. Seterusnya kajian ini menganggar sumbangan faktor jantina dan etnik ke atas pencapaian pelajar. Data pencapaian akademik seramai 866 pelajar fakulti sains di sebuah institusi pengajian tinggi telah diperoleh dan dianalisis. Data pelajar ini berstruktur hierarki dengan dua tahap, iaitu pelajar dan jabatan. Hasil kajian menunjukkan kedua-dua kaedah memberikan penganggaran yang berbeza. Malah, didapati model multitahap yang memasukkan variasi dari tahap-tahap berlainan dan pembolehubah peramal dari tahap yang lebih tinggi memberikan padanan model lebih baik bagi menerangkan pencapaian pelajar].
Haze has been a major issue afflicting Southeast Asian countries, including Malaysia, for the past few decades. Hierarchical agglomerative cluster analysis (HACA) is commonly used to evaluate the spatial behavior between areas in which pollutants interact. Typically, using HACA, the Euclidean distance acts as the dissimilarity measure and air quality monitoring stations are grouped according to this measure, thus revealing the most polluted areas. In this study, a framework for the hybridization of the HACA technique is proposed by considering the topological similarity (Wasserstein distance) between stations to evaluate the spatial patterns of the affected areas by haze episodes. For this, a tool in the topological data analysis (TDA), namely, persistent homology, is used to extract essential topological features hidden in the dataset. The performance of the proposed method is compared with that of traditional HACA and evaluated based on its ability to categorize areas according to the exceedance level of the particulate matter (PM10). Results show that additional topological features have yielded better accuracy compared to without the case that does not consider topological features. The cluster validity indices are computed to verify the results, and the proposed method outperforms the traditional method, suggesting a practical alternative approach for assessing the similarity in air pollution behaviors based on topological characterizations.
Most human activities that use water produced sewage. As urbanization grows, the overall demand for water grows. Correspondingly, the amount of produced sewage and pollution-induced water shortage is continuously increasing worldwide. Ensuring there are sufficient and safe water supplies for everyone is becoming increasingly challenging. Sewage treatment is an essential prerequisite for water reclamation and reuse. Sewage treatment plants' (STPs) performance in terms of economic and environmental perspective is known as a critical indicator for this purpose. Here, the window-based data envelopment analysis model was applied to dynamically assess the relative annual efficiency of STPs under different window widths. A total of five STPs across Malaysia were analyzed during 2015-2019. The labor cost, utility cost, operation cost, chemical consumption cost, and removal rate of pollution, as well as greenhouse gases' (GHGs) emissions, all were integrated to interpret the eco-environmental efficiency. Moreover, the ordinary least square as a supplementary method was used to regress the efficiency drivers. The results indicated the particular window width significantly affects the average of overall efficiencies; however, it shows no influence on the ranking of STP efficiency. The labor cost was determined as the most influential parameter, involving almost 40% of the total cost incurred. Hence, higher efficiency was observed with the larger-scale plants. Meanwhile, the statistical regression analysis illustrates the significance of plant scale, inflow cBOD concentrations, and inflow total phosphorus concentrations at [Formula: see text] on the performance. Lastly, some applicable techniques were suggested in terms of GHG emission mitigation.
Matched MeSH terms: Water Pollution/analysis; Least-Squares Analysis
The interrelationships between air quality, land cover change, and road networks in the Lagos megacity have not been explored. Globally, there are knowledge gaps in understanding these dynamics, especially using remote sensing data. This study used multi-temporal and multi-spectral Landsat imageries at four epochs (2002, 2013, 2015, and 2020) to evaluate the aerosol optical thickness (AOT) levels in relation to land cover and road networks in the Lagos megacity. A look-up table (LUT) was generated using Py6S, a python-based 6S module, to simulate the AOT using land surface reflectance and top of atmosphere reflectance. A comparative assessment of the method against in situ measurements of particulate matter (PM) at different locations shows a strong positive correlation between the imagery-derived AOT values and the PMs. The AOT concentration across the land cover and road networks showed an increasing trend from 2002 to 2020, which could be explained by urbanization in the megacity. The higher concentration of AOT along the major roads is attributed to the high air pollutants released from vehicles, including home/office generators and industries along the road corridors. The continuous rise in pollutant values requires urgent intervention and mitigation efforts. Remote sensing-based AOT monitoring is a possible solution.
The Coronavirus Disease-2019 (COVID-19) outbreak is an unprecedented global pandemic, sparking grave public health emergencies. One of the measures to reduce COVID-19 transmissions recommended by the World Health Organization is hand hygiene, i.e., washing hands with soap and water or disinfecting them using an alcohol-based hand sanitiser (ABHS). Unfortunately, competing ABHSs with unknown quality, safety, and efficacy thrived, posing yet another risk to consumers. This study aims to develop, optimise, and validate a gas chromatography-mass spectrometry (GC-MS)-based analytical method to simultaneously identify and quantify ethanol or isopropyl alcohol as the active ingredient in ABHS, with simultaneous determination of methanol as an impurity. The GC-MS was operated in Electron Ionisation mode, and Selected Ion Monitoring was chosen as the data acquisition method for quantitation. The analytical method was validated for liquid and gel ABHSs, covering the specificity, linearity and range, accuracy, and precisions, including the limit of detection and the limit of quantitation. The specificity of each target analyte was established using the optimised chromatographic separation with unique quantifier and qualifier ions. The linearity was ascertained with a coefficient of determination (r2) of > 0.9994 over the corresponding specification range. Respectively, the accuracy and precisions were satisfactory within 98.99 to 101.09% and
Lateral flow devices (LFDs) are straightforward scientific tools that have made substantial advances in recent years. They have been used in many fields including the meat industry to detect disease markers, determine meat freshness or meat species determination. They are, therefore, significant in the research of meat adulteration by mixed animal species, because food component authenticity is a serious concern encompassing health, economic, legal, and religious issues. Pork adulteration is one of the most crucial issues in the global meat industry. In this review, we discuss the various types of LFDs and recent research on the development of LFDs as an authenticity tool for detecting pig additives in meat-based products, and how regulatory authorities could adopt LFDs for their workflows. Despite the benefits of rapidity, simplicity, low cost, high sensitivity, and specificity, researchers face challenges when using LFD as a final confirmation test. Future directions are suggested for globalising the use of LFD as a halal authentication method.
Spectroscopy in the visible and near-infrared region (Vis-NIR) region has proven to be an effective technique for quantifying the chlorophyll contents of plants, which serves as an important indicator of their photosynthetic rate and health status. However, the Vis-NIR spectroscopy analysis confronts a significant challenge concerning the existence of spectral variations and interferences induced by diverse factors. Hence, the selection of characteristic wavelengths plays a crucial role in Vis-NIR spectroscopy analysis. In this study, a novel wavelength selection approach known as the modified regression coefficient (MRC) selection method was introduced to enhance the diagnostic accuracy of chlorophyll content in sugarcane leaves. Experimental data comprising spectral reflectance measurements (220-1400 nm) were collected from sugarcane leaf samples at different growth stages, including seedling, tillering, and jointing, and the corresponding chlorophyll contents were measured. The proposed MRC method was employed to select optimal wavelengths for analysis, and subsequent partial least squares regression (PLSR) and Gaussian process regression (GPR) models were developed to establish the relationship between the selected wavelengths and the measured chlorophyll contents. In comparison to full-spectrum modelling and other commonly employed wavelength selection techniques, the proposed simplified MRC-GPR model, utilizing a subset of 291 selected wavelengths, demonstrated superior performance. The MRC-GPR model achieved higher coefficient of determination of 0.9665 and 0.8659, and lower root mean squared error of 1.7624 and 3.2029, for calibration set and prediction set, respectively. Results showed that the GPR model, a nonlinear regression approach, outperformed the PLSR model.
The main goal of the present research was to explore the seasonal variation of deoxynivalenol (DON) in wheat, corn, and their products, collected during 2018-2019. Samples of 449 of wheat and products and 270 samples of corn and their products were examined using reverse-phase liquid chromatography with a UV detector. The findings of the present work showed that 104 (44.8%) samples of wheat and products from the summer season, and 91 (41.9%) samples from winter season were contaminated with DON (concentration limit of detections (LOD) to 2145 µg/kg and LOD to 2050 µg/kg), from summer and winter seasons, respectively. In corn and products, 87 (61.2%) samples from summer and 57 (44.5%) samples from winter season were polluted with DON with levels ranging from LOD to 2967 µg/kg and LOD to 2490 µg/kg, from the summer and winter season, respectively. The highest dietary intake of DON was determined in wheat flour 8.84 µg/kg body weight/day from the summer season, and 7.21 µg/kg body weight/day from the winter season. The findings of the work argued the need to implement stringent guidelines and create awareness among farmers, stakeholders, and traders of the harmful effect of DON. It is mostly observed that cereal crops are transported and stockpiled in jute bags, which may absorb moisture from the environment and produce favorable conditions for fungal growth. Therefore, these crops must store in polyethylene bags during transportation and storage, and moisture should be controlled. It is highly desirable to use those varieties that are more resistant to fungi attack. Humidity and moisture levels need to be controlled during storage and transportation.
Constructed wetlands (CWs) are affordable and reliable green technologies for the treatment of various types of wastewater. Compared to conventional treatment systems, CWs offer an environmentally friendly approach, are low cost, have fewer operational and maintenance requirements, and have a high potential for being applied in developing countries, particularly in small rural communities. However, the sustainable management and successful application of these systems remain a challenge. Therefore, after briefly providing basic information on wetlands and summarizing the classification and use of current CWs, this study aims to provide and inspire sustainable solutions for the performance and application of CWs by giving a comprehensive review of CWs' application and the recent development of their sustainable design, operation, and optimization for wastewater treatment. To accomplish this objective, thee design and management parameters of CWs, including macrophyte species, media types, water level, hydraulic retention time (HRT), and hydraulic loading rate (HLR), are discussed. Besides these, future research on improving the stability and sustainability of CWs are highlighted. This article provides a tool for researchers and decision-makers for using CWs to treat wastewater in a particular area. This paper presents an aid for informed analysis, decision-making, and communication. The review indicates that major advances in the design, operation, and optimization of CWs have greatly increased contaminant removal efficiencies, and the sustainable application of this treatment system has also been improved.
Matched MeSH terms: Costs and Cost Analysis; Waste Water/analysis
Climate change and tourism's interaction and vulnerability have been among the most hotly debated topics recently. In this context, the study focuses on how CO2 emissions, the primary cause of global warming and climate change, respond to changes in tourism development. In order to do so, the impact of tourism development on CO2 emissions in the most visited countries is investigated. A panel data from 2000 to 2017 for top 70 tourist countries are analysed using a spatial econometric method to investigate the spatial effect of tourism on environmental pollution. The direct, indirect, and overall impact of tourism on CO2 emissions are estimated using the most appropriate generalized nested spatial econometric (GNS) method. The findings reveal that tourism has a positive direct effect and a negative indirect effect; both are significant at the 1% level. The negative indirect effect of tourism is greater than its direct positive effect, implying an overall significantly negative impact. Further, the outcome of financial development and CO2 emissions have an inverted U-shaped and U-shaped relationship in direct and indirect impacts. Population density, trade openness, and economic growth significantly influence environmental pollution. In addition, education expenditure and infrastructure play a significant moderating role among tourism and environmental pollution. The results have important policy implications as they establish an inverted-U-shaped relationship among tourism and CO2 emissions and indicate that while a country's emissions initially rise with the tourism industry's growth, it begins declining after a limit.
Clustering texts together is an essential task in data mining and information retrieval, whose aim is to group unlabeled texts into meaningful clusters that facilitate extracting and understanding useful information from large volumes of textual data. However, clustering short texts (STC) is complex because they typically contain sparse, ambiguous, noisy, and lacking information. One of the challenges for STC is finding a proper representation for short text documents to generate cohesive clusters. However, typically, STC considers only a single-view representation to do clustering. The single-view representation is inefficient for representing text due to its inability to represent different aspects of the target text. In this paper, we propose the most suitable multi-view representation (MVR) (by finding the best combination of different single-view representations) to enhance STC. Our work will explore different types of MVR based on different sets of single-view representation combinations. The combination of the single-view representations is done by a fixed length concatenation via Principal Component analysis (PCA) technique. Three standard datasets (Twitter, Google News, and StackOverflow) are used to evaluate the performances of various sets of MVRs on STC. Based on experimental results, the best combination of single-view representation as an effective for STC was the 5-views MVR (a combination of BERT, GPT, TF-IDF, FastText, and GloVe). Based on that, we can conclude that MVR improves the performance of STC; however, the design for MVR requires selective single-view representations.
Matched MeSH terms: Cluster Analysis; Principal Component Analysis*
The elemental profiles of six honey samples from Malaysia had been constructed using the data obtained from both ICP-AES and ICP-MS. Potassium and sodium were the most abundant minerals covering from 69.3-78.6% and 14.1-28.7%, respectively. The ratio of potassium to sodium was more than one. Even though the minerals and trace elements composition varied dependent on the type of honey samples, there was no statistically significant difference between the analysed honey samples, namely tualang, gelam, acacia and a few forest honeys based on two-factor ANOVA and cluster analysis. The total element content of honey samples were strongly correlated with the electrical conductivity, but only have moderate correlation with the ash content and honey colour based on the regression analysis. PCA result on the available elemental data from worldwide honeys, including honey samples from Malaysia revealed that potassium and sodium were the mineral markers to distinguish honey origin. Both tualang and gelam honey samples from Malaysia have close mineral profile with sesame honeys from Egypt and multifloral honeys from India, whereas forest honeys Malaysia were near to avocado honeys from Spain and multifloral honeys from India.
Chocolate contains a wide range of antioxidants that includes soluble phenolic compounds (phenolic acids, catechin, epicatechin, and proanthocyanidins), insoluble polymeric phenolics and methylxanthines. The objective of this study was to determine phenolic and theobromine contents in dark (DC), milk (MC), and white (WC) chocolates commonly found in the Malaysian marketplace. Total phenolic and flavonoids were determined by means of a spectrometric assay, while catechin, epicatechin and theobromine were quantified using a reverse-phase HPLC method. Dark chocolates exhibited the highest phenolics and flavonoids contents, followed by milk and white chocolates. Catechin and epicatechin were major flavonoids detected in dark chocolates. Theobromine was detected in dark and milk chocolates, but not in white chocolates. A high correlation (r= 0.93) between total phenolic and flavonoid contents, indicating that the major phenolic compounds in dark chocolates belong to the flavonoid class. When nutrition and health promotion are of concern, dark chocolates would be recommended over milk and white chocolates owing to their higher contents of antioxidant phenolic compounds.
Total lipid contents and fatty acid composition of 13 marine fish species namely, "jenahak" (Lutianus agentimaculatus), "kebasi" (Anadontostoma chacunda), "duri" (Arius cumatranus), "tenggiri batang" (Scomberomorus commersoni), "kembong" (Rastrelliger kanagurta), "kintan" or "sebalah" (Psettodes crumei), "kerisi" (Pristipomodes typus), "kerapu" (Epinephelus sexfasciatus), "gelama kling" (Sciaena dussumieri), "malong" (Congresax talabon), "laban" (Cynoglossus lingua), "yu 9" (Scolidon sorrakowah) and "bagi" (Aacnthurs nigrosis) commonly found in Pulau Tuba, one of the islands surrounding the popular tourist destination Langkawi in Malaysia were determined. All fish showed a considerable amount of unsaturated fatty acids particularly those with 4, 5 and 6 double bonds. Two physiologically important n-3 polyunsaturated fatty acids (PUFAs), i.e. eicosapentaenoic acid (EPA) and docasahaexaenoic acid (DHA), made up of more than 50% of the total PUFAs. For saturated fatty acids, palmitic was found to be the major one in all types of fish studied. Based on DHA, EPA and arachidonic acid (AA) contents, "gelama kling" was found to be the best source (23, 11 and 7%, respectively) followed by "kerapu" (21, 10, 9%) and "sebalah" (19, 14, 4%).
Matched MeSH terms: Fatty Acids, Unsaturated/analysis*; Fish Products/analysis*; Food Analysis*
Size-segregated airborne fine (PM2.1) and coarse (PM>2.1) particulates were measured in an urban environment over central Indo-Gangetic plain in between 2015 and 2018 to get insights into its nature, chemistry and sources. Mean (±1σ) concentration of PM2.1 was 98 (±76) μgm-3 with a seasonal high during winter (DJF, 162 ± 71 μgm-3) compared to pre-monsoon specific high in PM>2.1 (MAMJ, 177 ± 84 μgm-3) with an annual mean of 170 (±69) μgm-3. PM2.1 was secondary in nature with abundant secondary inorganic aerosols (20% of particulate mass) and water-soluble organic carbon (19%) against metal enriched (25%) PM>2.1, having robust signature of resuspensions from Earth's crust and road dust. Ammonium-based neutralization of particulate acidity was essentially in PM2.1 with an indication of predominant H2SO4 neutralization in bisulfate form compared to Ca2+ and Mg2+-based neutralization in PM>2.1. Molecular distribution of n-alkanes homologues (C17-C35) showed Cmax at C23 (PM2.1) and C18 (PM>2.1) with weak dominance of odd-numbered n-alkanes. Carbon preference index of n-alkanes was close to unity (PM2.1: 1.4 ± 0.3; PM>2.1: 1.3 ± 0.4). Fatty acids (C12-C26) were characterized with predominance of even carbon with Cmax at n-hexadecanoic acid (C16:0). Low to high molecular weight fatty acid ratio ranged from 2.0 (PM>2.1) to 5.6 (PM2.1) with vital signature of anthropogenic emissions. Levoglucosan was abundant in PM2.1 (758 ± 481 ngm-3) with a high ratio (11.6) against galactosan, emphasizing robust contribution from burning of hardwood and agricultural residues. Receptor model resolves secondary aerosols and biomass burning emissions (45%) as the most influential sources of PM2.1 whereas, crustal (29%) and secondary aerosols (29%) were found responsible for PM>2.1; with significant variations among the seasons.
Bioactive glasses have been used as coatings for biomedical implants because they can be formulated to promote osseointegration, antibacterial behavior, bone formation, and tissue healing through the incorporation and subsequent release of certain ions. However, shear loading on coated implants has been reported to cause the delamination and loosening of such coatings. This work uses a recently developed fracture mechanics testing methodology to quantify the critical strain energy release rate under nearly pure mode II conditions, GIIC, of a series of borate-based glass coating/Ti6Al4V alloy substrate systems. Incorporating increasing amounts of SrCO3in the glass composition was found to increase the GIICalmost twofold, from 25.3 to 46.9J/m2. The magnitude and distribution of residual stresses in the coating were quantified, and it was found that the residual stresses in all cases distributed uniformly over the cross section of the coating. The crack was driven towards, but not into, the glass/Ti6Al4V substrate interface due to the shear loading. This implied that the interface had a higher fracture toughness than the coating itself.
One of the most common types of adulteration of honey involves the addition of invert sugar syrups. A new method was developed to measure the stable isotope ratios of carbon and carbon-bound non-exchangeable (CBNE) hydrogen from specific molecular positions in fructose and glucose in honey. This was achieved through periodate oxidation of the sugars to produce formaldehyde, followed by reaction with ammonia to form hexamethylenetetramine (HMT). The preparation was simplified, optimized, and validated by isotopic analysis of replicate syntheses of HMT from fructose, glucose, sugar syrup and a representative authentic honey sample. The optimized method had a repeatability standard deviation from 1.5‰ to 3.0‰ and from 0.1‰ to 0.4‰ for δ2H and δ13C, respectively. This methodology has advantages over alternative isotopic methods, for measuring CBNE hydrogen isotope ratios in sugars, in terms of time, sensitivity and operability and offers a complementary method to differentiate authentic honey from invert sugar syrups.
The presence of harmful substances in the atmosphere poses significant risks to the environment and public health. These pollutants can come from natural sources like dust and wildfires, or from human activities such as industrial, transportation, and agricultural practices. The objective of this study was to assess air quality on the East Coast of Peninsular Malaysia by analyzing historical data from the Department of Environment, Malaysia. Daily measurements of PM10, O3, SO2, NO2, and CO were collected from eight monitoring stations over 11 years (2011-2021) and analyzed using environmetric techniques. Hierarchical agglomerative cluster analysis (HACA) classified two stations as belonging to the high pollution cluster (HPC), three stations as part of the moderate pollution cluster (MPC), and three stations as the low pollution cluster (LPC). Discriminant analysis revealed a correct assignment rate of 90.50%, indicating that all five parameters were able to differentiate pollution levels with high significance (p
Statistical analysis of heavy metal concentrations in sediment was studied to understand the interrelationship between different parameters and also to identify probable source component in order to explain the pollution status of selected estuaries. Concentrations of heavy metals (Cu, Zn, Cd, Fe, Pb, Cr, Hg and Mn) were analyzed in sediments from Juru and Jejawi Estuaries in Malaysia with ten sampling points of each estuary. The results of multivariate statistical techniques showed that the two regions have different characteristics in terms of heavy metals selected and indicates that each region receives pollution from different sources. The results also showed that Fe, Mn, Cd, Hg, and Cu are responsible for large spatial variations explaining 51.15% of the total variance, whilst Zn and Pb explain only 18.93 of the total variance. This study illustrates the usefulness of multivariate statistical techniques for evaluation and interpretation of large complex data sets to get better information about the heavy metal concentrations and design of monitoring network.