Assessments of lake profiling on temperature, Total Suspended Solid (TSS) and turbidity in the Kenyir Lake, Terengganu, Malaysia

Spatial interpolation method of water quality evaluation are frequently used to estimate valuation Kenyir Lake profiling used regression analysis and Geographic Information System (GIS) to assess a few of the water quality classification at Kenyir Lake. The purpose is to investigate the relative performance of different interpolation methods in surface waters. The study archived data from the Kenyir Lake using spatial interpolation of inverse distance weighting (IDW), which incorporates output from a process-based regression model.Interpolation were performed on temperature, total suspended solid (TSS) and turbidity (TUR) based on in-situ and ex-situ analyses according to the correlation matrix and linear regression at 14 different depths for the Chomor River and Mahadir Island. The result showed outlet significantly decreased over depth caused the water quality deterioration of Kenyir Lake development.Keywords: lake profiling; inverse distance weighting; total suspended solid (TSS); Interpolation; geographic information system (GIS

A review on machine learning techniques used for students’ performance prediction

Research on predictive models has been widely used in higher educational institutions, especially in predicting students’ performance. Results that were obtained through predictive models can help lecturers in ensuring students’ achievement so that students’ failure rates can be reduced. Higher failure rates have a negative impact not only on students but also on institutions and shareholders. In this paper, thirty journals and case studies have been reviewed where the most important part highlighted is machine learning techniques that have been used in developing predictive models to predict students’ performance from the previous six years. Although the main objective of this paper is to provide an overview of machine learning techniques in predicting students’ performance, it is also important for researchers to identify the target variable used in those techniques as these two objectives are related to each other. In conclusion, a student’s final grade is the most widely used as a target variable, and the Decision Tree method is the most frequently used machine learning technique by the authors in the previous studies

Removal of zinc and lead ions by polymer-enhanced ultrafiltration using unmodified starch as novel binding polymer

The removal of zinc and lead from aqueous dilute solutions by polymer-enhanced ultrafiltration process using unmodified starch as a new binding polymer was studied. Experiments were performed to determine the effects of transmembrane pressure, pH, concentration of metal ions on the retention and permeate flux. The performance of the proposed new binding polymer was compared to that of polyethyleneimine a conventional polymer frequently used in polymer-enhanced ultrafiltration. The retention of zinc and lead ions reached 96 and 66 , respectively, using 0.05 unmodified starch at pH 7. Overall unmodified starch showed better retention for zinc ions then polyethyleneimine, whereas polyethyleneimine retention for lead ions was higher. Solution pH was found to have little effect on flux

Influence of calcination temperatures on structure and magnetic properties of calcium ferrite nanoparticles synthesized via sol-gel method

Calcium ferrite (CaFe2O4) nanoparticles using calcium nitrate and ferric nitrate as starting materials, and supplemented with citric acid as chelating agent was carried out. This mixture was synthesized through a sol-gel method and then calcined at 550 °C, 650 °C, and 750 °C. The effects of calcination temperatures on the crystalline structure, the surface morphology and the magnetic properties of CaFe2O4 NPs were observed. The orthorhombic structure of calcium ferrite NPS was analysed through an X-ray diffraction. The size of calcined samples at 550 °C, 650 °C, 750 °C were (13.59 nm), (18.9 nm), and (46.12 nm), respectively. Magnetic analysis was measured by using a vibrating sample magnetometer (VSM). The magnetic saturation (Ms) of samples calcined at 550 °C was found to possess the highest value of magnetic property; 80.33 emu/g

Impact of polyvinylpyrrolidone and quantity of silver nitrate on silver nanoparticles sizing via solvothermal method for dye-sensitized solar cells

The multiple sizing of silver nanoparticles (AgNPs) were synthesized from the miscible compound of ethylene glycol (EG), polyvinylpyrrolidone (PVP) and silver nitrate (AgNO3) via the solvothermal method. During the synthesis, the PVP-AgNO3 was contemplated as a paramount parameter. Using the simple method of solvothermal, the sizing of AgNPs was easily controlled in accord with the augmentation of PVP-AgNO3 at secured and moderate temperature. In regards to the sizing of AgNPs, the presence of minimum agglomeration, the absorption capability and chemical structures were highlighted through a series of verification includes ultraviolet–visible (UV–Vis) spectroscopy, Fourier-transform infrared (FTIR) spectroscopy and transmission electron microscopy (TEM) analysis. The effectiveness of the synthesized AgNPs was further investigated and compared with the commercial AgNPs by incorporating the AgNPs into titanium dioxide (TiO2) semiconductor film-based dye-sensitized solar cells (DSSCs). Results signified that the spherical AgNPs with produced sizing within the range of 19.6 to 45.2 nm were greatly impacting by tunable quantities of PVP-AgNO3, which was validated in the forms of linear equations. A larger size promotes a slower nucleation rate that conduces agglomeration. In opposition to this, the smallest size of AgNPs develops a faster formation rate of Ag ions into AgNPs, inducing the deterrent of agglomeration in light of notable particle dispersion. The power conversion efficiency (PCE) contributed by the incorporation of synthesized AgNPs into TiO2 is also 41.2% higher than that of the commercial AgNPs-TiO2. This is because the synthesized AgNPs provides less agglomeration which led to a better surface plasmonic effect towards the nanoparticles

The effect of particle size on physicochemical and thermal analysis of rice husk for explosion studies

The effect of rice husk particle size on physicochemical and thermal behaviour was studied for identify whether it has the potential to explode. The thermal degradation of the lignocellulosic constituent in rice husk was evaluated via thermogravimetric analysis (TGA). Rice husk morphology and elemental composition were evaluated via scanning electron microscopy with energy dispersive X-ray (SEM-EDX). Results showed that the rice husk samples were richer in cellulose than in lignin in terms of weight percent, indicating that they were combustible. Uncontrolled combustion propagation can lead to an explosion. However, the presence of ±5 wt% silicon in rice husk may reduce the explosion severity due to its low thermal conductivity. Furthermore, the smallest particle size, 71 μm recorded faster thermal degradation and more explosive as compared to 106, 160 and 250 μm. This preliminary data is very useful to improve the safety technique specifically for rice husk dust explosion protection, prevention, and mitigation

Hepatitis A virus: Declining seroprevalence in children and adolescents in Southeast Asia

Southeast Asian Journal of Tropical Medicine and Public Health292255-26

A review of Hib epidemiology in Asia

Southeast Asian Journal of Tropical Medicine and Public Health314650-65