In developing countries, acute respiratory infections (ARIs) cause a significant number of deaths among children. According to Bangladesh Demographic and Health Survey (BDHS), about 25% of the deaths in children under-five years are caused by ARI in Bangladesh every year. Low-income families frequently rely on wood, coal, and animal excrement for cooking. However, it is unclear whether using alternative fuels offers a health benefit over solid fuels. To clear this doubt, we conducted a study to investigate the effects of fuel usage on ARI in children. In this study, we used the latest BDHS 2017–18 survey data collected by the Government of Bangladesh (GoB) and estimated the effects of fuel use on ARI by constructing multivariable logistic regression models. From the analysis, we found that the crude (the only type of fuel in the model) odds ratio (OR) for ARI is 1.69 [95% confidence interval (CI): 1.06–2.71]. This suggests that children in families using contaminated fuels are 69.3% more likely to experience an ARI episode than children in households using clean fuels. After adjusting for cooking fuel, type of roof material, child's age (months), and sex of the child–the effect of solid fuels is similar to the adjusted odds ratio (AOR) for ARI (OR: 1.69, 95% CI: 1.05–2.72). This implies that an ARI occurrence is 69.2% more likely when compared to the effect of clean fuel. This study found a statistically significant association between solid fuel consumption and the occurrence of ARI in children in households. The correlation between indoor air pollution and clinical parameters of ARI requires further investigation. Our findings will also help other researchers and policymakers to take comprehensive actions by considering fuel type as a risk factor as well as taking proper steps to solve this issue.
Sesame (Sesamum indicum L.) is one of the oldest oilseed crops and important for high nutritional quality as well as medicinal value. Fifty diverse sesame genotypes were evaluated to study genetic variability. The results revealed that the genotypes were a significant variation in most of the studied characters. In all cases, the phenotypic variances were much higher than genotypic variances suggests a higher level of the environmental effect on the expression of these characters. The highest genotypic coefficient of variations (GCV) was observed in seed yield per plant while the highest heritability was exhibited by hundred seed weight followed by days to 80% maturity, pods per plant, number of branches per plant and seed yield per plant. The genotypic correlation with seed yield per plant showed a significantly strong positive with days to 50% flowering, plant height and number of pods per plant at both the genotypic and phenotypic level. The path coefficient analysis showed that pods per plant and seeds per pod were the most important contributing traits to seed yield. The 50 sesame genotypes were grouped into five clusters. The highest inter-cluster distance was observed between the cluster III and V while the lowest inter-cluster distance was observed between the cluster III and IV. Among 50 sesame genotypes G7, G36, G38 and G46 might be suggested for future hybridization program for the improvement of sesame yield.
The prime drive of this paper is to examine the factors to define students’ intention to the acceptance of online classes in Bangladesh throughout COVID19 pandemic. The paper follows qualitative research for identifying research problems and quantitative research for collecting primary data. Data have been collected through questionnaire survey and analyzed by Structure Equation Modeling by Partial Least Squared (PLS). This study is grounded on Unified Theory of Acceptance and Use of Technology (UTAUT) model invented by Venkatesh, Morris, Davis, and Davis (2003) along with six additional variables that extended the model for this study. The results revealed that Performance Expectancy (PE), Social Influence (SI), Self-Management of Learning (SML), Technology Anxiety (TA) and Complexity (COM) have a substantial impact on Students’ Behavioral Intention (BI) towards the adoption of online classes. Alternatively, Effort Expectancy (EE), Facilitating Condition (FC), Perceived Cost (PC), Relative Advantage (RA), and Resistance to Change (RTC) were found insignificant relationship. This study is conducted in a certain period. The different outcomes could be found in another time frame. The result of this study will provide the idea about factors influencing adoption of online classes which may be referred for choosing future teaching-learning techniques in compare with face-to-face learning. Taking online classes may save time, energy, money of both students and teachers along with institutions which become important concern in near future.
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