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Structural, Optical, and Renewable Energy-Assisted Photocatalytic Dye Degradation Studies of ZnO, CuZnO, and CoZnO Nanostructures for Wastewater Treatment
Get PDFRenewable energy can be harnessed from wastewater, whether from municipalities or industries, but this potential is often ignored. The world generates over 900 km3 of wastewater annually, which is typically treated through energy-consuming processes, despite its potential for energy production. Environmental pollution is a most important and serious issue for all and their adulterations to the aquatic system are very toxic in very low concentrations. Photocatalysis is a prominent approach to eliminating risky elements from the environment. The present study developed Zinc oxide (ZnO), Copper-doped Zinc oxide (CuZnO), and Cobalt-doped Zinc oxide (CoZnO) nanostructures (NSs) by facile hydrothermal route. The crystalline and structural stability of the synthesized nanostructures were evident from XRD and FESEM analysis. Metal, and oxygen bond and their interaction on the surfaces and their valency were explored from XPS spectra. Optical orientations and electron movements were revealed from UV-Visible analysis. After 100 min exposure time with 1 g of catalyst concentration 60%, 70%, and 89% of dye degraded, for dye concentration (5 mg/L to 50 mg/L), the huge variation observed (70% to 22%), (80% to 16%), (94% to 10%). The highest photodegradation rate (55%, 75%, 90%) was observed on pH~12 using ZnO, CoZnO, and CuZnO respectively. Photodegradation of methylene blue confirmed the largest surface area, rate of recombination, photo-excited charge carriers, photo-sensitivity range, and radical generations of ZnO, CuZnO, and CoZnO. The present study, therefore, suggested that CuZnO would be preferred to produce nanomaterials for industrial wastewater treatment like methylene
Factors Influencing Students’ Cheating Behaviours: an Empirical Evidence from China.
Get PDFThere is an ample evidence to show how students’ cheating behaviours formed in Western countries, comparatively, few studies focused on Chinese students. The purpose of this study is to measure the influence of attitude, subjective norm (SN), perceived behavioural control (PBC), and additional variable which is moral obligation on intention among Chinese students who were studying in China and the U.K. A total of 540 useable questionnaires were collected based on web-based method for further hypotheses testing. The results show that attitude, SN, PBC and moral obligation positively influence intention to cheat respectively. The SN displays significant positive influence on attitude and moral obligation. In addition, statistically significant differences in SN, PBC and intention were obtained between gender, majors and educational level and studying places which show that males had more intention than females on cheating. Major of management students had more PBC than engineering and economics students, fresh and sophomore had more PBC than junior students, and students who were studying in U.K are more influenced by SN compared to who were studying in China. This study enriches the existing knowledge on how Chinese students’ attitude, SN, PBC and moral obligation on cheating intention based on divergent demographic characteristics
WhatsApp use in a higher education learning environment: Perspective of students of a Malaysian private university on academic performance and team effectiveness
No full textThe mobile instant messaging application, WhatsApp Messenger (WhatsApp), has become a popular form of communication among adolescents, especially university students, and it has increasingly been used as a tool in collaborative learning in higher education. The use of WhatsApp for education to facilitate ubiquitous learning has been practised worldwide due to its popularity and potential to support teaching and learning processes derived from the diffusion of mobile technology and empowered by the use of smartphones. This study investigates the impact of the use of WhatsApp in a higher education learning environment on students’ perceived academic performance and team effectiveness. A convergent parallel mixed-methods research design was adopted with data collected through a self-administered online survey and two focus group interviews with students of a private university in the Sunway City, Malaysia. The findings of this study present insights into the popularity of WhatsApp among university students and that students use it for social and educational purposes due to its perceived ease of use and usefulness in enhancing academic performance and team effectiveness. Although WhatsApp is recognised as a rich and powerful collaborative tool for students with a positive impact on academic performance, it has a limited impact on the cohesion and openness of team effectiveness
Effectiveness of Using Artificial Intelligence for Early Child Development Screening
Get PDFThis study presents a novel approach to recognizing emotions in infants using machine learning models. To address the lack of infant-specific datasets, a custom dataset of infants' faces was created by extracting images from the AffectNet dataset. The dataset was then used to train various machine learning models with different parameters. The best-performing model was evaluated on the City Infant Faces dataset. The proposed deep learning model achieved an accuracy of 94.63% in recognizing positive, negative, and neutral facial expressions. These results provide a benchmark for the performance of machine learning models in infant emotion recognition and suggest potential applications in developing emotion-sensitive technologies for infants. This study fills a gap in the literature on emotion recognition, which has largely focused on adults or children and highlights the importance of developing infant-specific datasets and evaluating different parameters to achieve accurate results
A dietary pattern of frequent plant-based foods intake reduced the associated risks for atopic dermatitis exacerbation: Insights from the Singapore/Malaysia cross-sectional genetics epidemiology cohort
No full textBackground: The prevalence of atopic dermatitis (AD) has been increasing in recent years, especially in Asia. There is growing evidence to suggest the importance of dietary patterns in the development and management of AD. Here, we seek to understand how certain dietary patterns in a Singapore/Malaysia population are associated with various risks of AD development and exacerbation.
Methods: A standardized questionnaire following the International Study of Asthma and Allergies in Childhood (ISAAC) guidelines was investigator-administered to a clinically and epidemiology well-defined allergic cohort of 13,561 young Chinese adults aged 19-22. Information on their sociodemographic, lifestyle, dietary habits, and personal and family medical atopic histories were obtained. Allergic sensitization was assessed by a skin prick test to mite allergens. Spearman's rank-order correlation was used to assess the correlation between the intake frequencies of 16 food types. Dietary patterns were identified using principal component analysis. Four corresponding dietary scores were derived to examine the association of identified dietary patterns with allergic sensitization and AD exacerbations through a multivariable logistic regression that controlled for age, gender, parental eczema, BMI, and lifestyle factors.
Results: The correlation is the strongest between the intake of butter and margarine (R = 0.65). We identified four dietary patterns, "high-calorie foods", "plant-based foods", "meat and rice", and "probiotics, milk and eggs", and these accounted for 47.4% of the variance in the dietary habits among the subjects. Among these patterns, moderate-to-high intake of "plant-based foods" conferred a negative association for chronic (Adjusted odds ratio (AOR): 0.706; 95% confidence interval (CI): 0.589-0.847; p < 0.001) and moderate-to-severe AD (AOR: 0.756; 95% CI: 0.638-0.897; p < 0.01). "Meat and rice" and "probiotics, milk and eggs" were not significantly associated with AD exacerbation. While frequent adherence to "high-calorie foods" increased the associated risks for ever AD and moderate-to-severe AD, having a higher adherence to "plant-based foods" diminished the overall associated risks.
Conclusions: Frequent adherence to "plant-based foods" was associated with reduced risks for AD exacerbation in young Chinese adults from Singapore/Malaysia. This provides the initial evidence to support the association between dietary factors and AD. Further research is needed to better understand the pathomechanisms underlying diet and AD exacerbations
Investigation of roasting and photo-oxidative stability of cold-pressed peanut oil: Lipid composition, quality characteristics, and antioxidant capacity
No full textIn this study, the effect of conventional roasting and the photo-oxidative stability of two cold-pressed peanut oil varieties (Virginia and Valencia) were investigated. Changes in the concentrations of the fatty acids (including trans isomers), minor components, nutritional quality, and antioxidant capacity were analyzed and compared. The evolution of the oxidation status was measured by peroxide value (PV), acid value (AV), p-anisidine value (p-AnV), UV-spectrophotometric indexes (E232 and E270), total oxidation value (TOTOX), and browning index (BI). Results showed a slight change in AV, while relevant primary and secondary lipid oxidation was detected, leading to an increase in PV, p-AnV, as well as E232 and E270 indexes during roasting and photo-oxidation. Furthermore, exposure to UV light resulted in a remarkable degradation of tocopherol (71.67–100%), while phytosterols were reduced by 0.16–6.68%. Roasting, on the other hand, resulted in a significant increase (p < 0.05) in the content of phytosterols, tocopherols, chlorophyll, and carotenoid as well as antioxidant activity in peanut oil. In addition, Maillard reaction products (estimated by BI) also increased with roasting; afterward, these compounds gradually declined with UV light exposure. As for the fatty acid profile and nutritional indicators, a noticeable difference was observed between unroasted and roasted peanut oils throughout photo-oxidation
Molecular mechanistic pathways underlying the anticancer therapeutic efficiency of romidepsin
No full textRomidepsin, also known as NSC630176, FR901228, FK-228, FR-901228, depsipeptide, or Istodax®, is a natural molecule produced by the Chromobacterium violaceum bacterium that has been approved for its anti-cancer effect. This compound is a selective histone deacetylase (HDAC) inhibitor, which modifies histones and epigenetic pathways. An imbalance between HDAC and histone acetyltransferase can lead to the down-regulation of regulatory genes, resulting in tumorigenesis. Inhibition of HDACs by romidepsin indirectly contributes to the anticancer therapeutic effect by causing the accumulation of acetylated histones, restoring normal gene expression in cancer cells, and promoting alternative pathways, including the immune response, p53/p21 signaling cascades, cleaved caspases, poly (ADP-ribose) polymerase (PARP), and other events. Secondary pathways mediate the therapeutic action of romidepsin by disrupting the endoplasmic reticulum and proteasome and/or aggresome, arresting the cell cycle, inducing intrinsic and extrinsic apoptosis, inhibiting angiogenesis, and modifying the tumor microenvironment. This review aimed to highlight the specific molecular mechanisms responsible for HDAC inhibition by romidepsin. A more detailed understanding of these mechanisms can significantly improve the understanding of cancer cell disorders and pave the way for new therapeutic approaches using targeted therapy
Not seeing the forest for the trees: Generalised linear model out-performs random forest in species distribution modelling for Southeast Asian felids
No full textSpecies Distribution Models (SDMs) are a powerful tool to derive habitat suitability predictions relating species occurrence data with habitat features. Two of the most frequently applied algorithms to model species-habitat relationships are Generalised Linear Models (GLM) and Random Forest (RF). The former is a parametric regression model providing functional models with direct interpretability. The latter is a machine learning non-parametric algorithm, more tolerant than other approaches in its assumptions, which has often been shown to outperform parametric algorithms. Other approaches have been developed to produce robust SDMs, like training data bootstrapping and spatial scale optimisation. Using felid presence-absence data from three study regions in Southeast Asia (mainland, Borneo and Sumatra), we tested the performances of SDMs by implementing four modelling frameworks: GLM and RF with bootstrapped and non-bootstrapped training data. With Mantel and ANOVA tests we explored how the four combinations of algorithms and bootstrapping influenced SDMs and their predictive performances. Additionally, we tested how scale-optimisation responded to species' size, taxonomic associations (species and genus), study area and algorithm. We found that choice of algorithm had strong effect in determining the differences between SDMs' spatial predictions, while bootstrapping had no effect. Additionally, algorithm followed by study area and species, were the main factors driving differences in the spatial scales identified. SDMs trained with GLM showed higher predictive performance, however, ANOVA tests revealed that algorithm had significant effect only in explaining the variance observed in sensitivity and specificity and, when interacting with bootstrapping, in Percent Correctly Classified (PCC). Bootstrapping significantly explained the variance in specificity, PCC and True Skills Statistics (TSS). Our results suggest that there are systematic differences in the scales identified and in the predictions produced by GLM vs. RF, but that neither approach was consistently better than the other. The divergent predictions and inconsistent predictive abilities suggest that analysts should not assume machine learning is inherently superior and should test multiple methods. Our results have strong implications for SDM development, revealing the inconsistencies introduced by the choice of algorithm on scale optimisation, with GLM selecting broader scales than RF
A bibliometric analysis of emerging adulthood in the context of higher education institutions: A psychological perspectives
No full textIn recent years, there has been a rise in studies aimed at better understanding the needs and traits of emerging adults and the role that higher education institutions play in their development and success. Despite the relevance of higher education institutions to the emerging adulthood development, there has been scant work done to synthesise the literature on this topic. A bibliometric method was utilised to retrieve 2484 journal articles from Web of Science (WoS). Utilizing co-citation analysis and co-word analysis, we determined the most influential publications, mapped the knowledge structure, and predicted future trends. The results of the co-citation analysis indicate five clusters, while the co-word analysis indicates four. The results could be used as a roadmap for the future of research on emerging adults by a variety of interested parties, including policymakers, university administrators, funders, and academics
Validation of the general Framingham Risk Score (FRS), SCORE2, revised PCE and WHO CVD risk scores in an Asian population
No full textBackground: Cardiovascular risk prediction models incorporate myriad CVD risk factors. Current prediction models are developed from non-Asian populations, and their utility in other parts of the world is unknown. We validated and compared the performance of CVD risk prediction models in an Asian population.
Methods: Four validation groups were extracted from a longitudinal community-based study dataset of 12,573 participants aged ≥18 years to validate the Framingham Risk Score (FRS), Systematic Coronary Risk Evaluation 2 (SCORE2), Revised Pooled Cohort Equations (RPCE), and World Health Organization cardiovascular disease (WHO CVD) models. Two measures of validation are examined: discrimination and calibration. Outcome of interest was 10-year risk of CVD events (fatal and non-fatal). SCORE2 and RPCE performances were compared to SCORE and PCE, respectively.
Findings: FRS (AUC = 0.750) and RPCE (AUC = 0.752) showed good discrimination in CVD risk prediction. Although FRS and RPCE have poor calibration, FRS demonstrates smaller discordance for FRS vs. RPCE (298% vs. 733% in men, 146% vs. 391% in women). Other models had reasonable discrimination (AUC = 0.706-0.732). Only SCORE2-Low, -Moderate and -High (aged <50) had good calibration (X2 goodness-of-fit, P-value = 0.514, 0.189, 0.129, respectively). SCORE2 and RPCE showed improvements compared to SCORE (AUC = 0.755 vs. 0.747, P-value <0.001) and PCE (AUC = 0.752 vs. 0.546, P-value <0.001), respectively. Almost all risk models overestimated 10-year CVD risk by 3%-1430%.
Interpretation: In Malaysians, RPCE are evaluated be the most clinically useful to predict CVD risk. Additionally, SCORE2 and RPCE outperformed SCORE and PCE, respectively.
Funding: This work was supported by the Malaysian Ministry of Science, Technology, and Innovation (MOSTI) (Grant No: TDF03211036)