DATA ENVELOPMENT ANALYSIS OF BANKING SECTOR IN BANGLADESH

Banking sector of Bangladesh is flourishing and contributing to its economy. In this aspect measuring efficiency is important. Data Envelopment Analysis technique is used for this purpose. The data are collected from the annual reports of twenty four different banks in Bangladesh. Data Envelopment Analysis is mainly of two types - constant returns to scale and variable returns to scale. Since this study attempts to maximize output, so the output oriented Data Envelopment Analysis is used. The most efficient bank is one that obtains the highest efficiency score

Secondary and Higher Secondary Education Inequality in Bangladesh

Education and economic growth have an important relationship. Former education inequality studies found that between groups inequality was lower than within group inequality. This paper aims to investigate the both within group and between groups inequality and interpret inequality among schools and colleges with their performance. This study measures education inequality through analysis of Secondary School Certificate and Higher Secondary Certificate examination results of Bangladesh through Theil index. Analysis shows that inequality among schools at different board decreases over the year. Inequality among colleges shows non-uniform pattern. It is also found that urban areas have higher inequality than rural areas. However, inequality in urban-rural is declining, but the inequality gap between them is mounting. Keywords: Education inequality, Theil index, inequality within group, inequality between groups, inequality gap

Pesticide knowledge and attitude among the potato growing farmers of Bangladesh and determinant factors

The study aimed to assess the extent of pesticide use among potato-growing farmers in Bangladesh and its relationship with their knowledge, attitude, and socio-demographic characteristics. Data were collected from 553 farmers using a semi-structured questionnaire through multistage random sampling. Bivariate analysis was conducted to examine the relationship between the frequency of pesticide use and various socio-demographic factors. Results showed that out of 321 different pesticide brands reported, 50.5% were registered, while 47.7% were unregistered and 1.9% were banned. Among the registered pesticides, 5.6% were highly hazardous, 24.8% were moderately hazardous, and 6.2% were slightly hazardous as per World Health Organization category. A high percentage (96%) of farmers reported using pesticides in their fields, with 16.6% applying pesticides more than five times in a cropping season. Data revealed that majority of the farmers were aware of the negative effect of pesticides on health and environment. Most farmers used hand towels (77.9%) and ordinary shirts (70.0%) to cover their bodies to avoid pesticide exposure. Inappropriate disposal of empty pesticide containers was also observed. Negative binomial regression analysis revealed significant positive associations between the frequency of pesticide application and potato productivity, rate of fertilizer application, area of land owned by farmers, and their knowledge about the negative effects of pesticides on human health. The study suggests adopting integrated pest management practices, developing pest-resistant potato varieties, ensuring safe handling practices and disposal as well as stringent enforcement of laws to mitigate pesticide externalities and hence ensure sustainability in agriculture

Impact of gut microbiome on skin health : gut-skin axis observed through the lenses of therapeutics and skin diseases

Publisher Copyright: © 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.The human intestine hosts diverse microbial communities that play a significant role in maintaining gut-skin homeostasis. When the relationship between gut microbiome and the immune system is impaired, subsequent effects can be triggered on the skin, potentially promoting the development of skin diseases. The mechanisms through which the gut microbiome affects skin health are still unclear. Enhancing our understanding on the connection between skin and gut microbiome is needed to find novel ways to treat human skin disorders. In this review, we systematically evaluate current data regarding microbial ecology of healthy skin and gut, diet, pre- and probiotics, and antibiotics, on gut microbiome and their effects on skin health. We discuss potential mechanisms of the gut-skin axis and the link between the gut and skin-associated diseases, such as psoriasis, atopic dermatitis, acne vulgaris, rosacea, alopecia areata, and hidradenitis suppurativa. This review will increase our understanding of the impacts of gut microbiome on skin conditions to aid in finding new medications for skin-associated diseases.Peer reviewe

An Assessment of Renewable Energy in Bangladesh through ARIMA, Holt's, ARCH- GARCH Models

Abstract Forecasting of the Renewable Energy plays a major role in optimal decision formula for government and industrial sector in Bangladesh. This research is based on time series modeling with special application to solar energy data for Dhaka city. Three families of time series models namely, the autoregressive integrated moving average models, Holt's linear exponential smoothing, and the autoregressive conditional heteroscedastic (with their extensions to generalized autoregressive conditional heteroscedastic) models were fitted to the data. The goodness of fit is performed via the Akaike information criteria, Schwartz Bayesian criteria. It was established that the generalized autoregressive conditional heteroscedastic model was superior to the autoregressive integrated moving average model and Holt's linear exponential smoothing because the data was characterized by changing mean and variance

Unraveling birth weight determinants: Integrating machine learning, spatial analysis, and district-level mapping

Despite a decrease in the prevalence of low birth weight (LBW) over time, its ongoing significance as a public health concern in Bangladesh remains evident. Low birth weight is believed to be a contributing factor to infant mortality, prolonged health complications, and vulnerability to non-communicable diseases. This study utilizes nationally representative data from the Multiple Indicator Cluster Surveys (MICS) conducted in 2012-2013 and 2019 to explore factors associated with birth weight. Modeling birth weight data considers interactions among factors, clustering in data, and spatial correlation. District-level maps are generated to identify high-risk areas for LBW. The average birth weight has shown a modest increase, rising from 2.93 kg in 2012-2013 to 2.96 kg in 2019. The study employs a regression tree, a popular machine learning algorithm, to discern essential interactions among potential determinants of birth weight. Findings from various models, including fixed effect, mixed effect, and spatial dependence models, highlight the significance of factors such as maternal age, household head's education, antenatal care, and few data-driven interactions influencing birth weight. District-specific maps reveal lower average birth weights in the southwestern region and selected northern districts, persisting across the two survey periods. Accounting for hierarchical structure and spatial autocorrelation improves model performance, particularly when fitting the most recent round of survey data. The study aims to inform policy formulation and targeted interventions at the district level by utilizing a machine learning technique and regression models to identify vulnerable groups of children requiring heightened attention

ARE THE REAL GDP SERIES IN ASIAN COUNTRIES NONSTATIONARY OR NONLINEAR STATIONARY?

This paper checks whether per capita real gross domestic product (GDP) series in 16 Asian countries are nonstationary or nonlinear and globally stationary during the period from 1970 to 2009, by applying the nonlinear unit root tests developed by Kapitanios, Shin and Snell (2003). In five out of the sixteen countries that is approximately one-third of the countries, the series are found to be stationary with asymmetric or nonlinear mean reversion. Analyses depict that nonlinear unit root test are suitable for some cases compare to the commonly used unit root test, Augmented Dickey-Fuller (ADF) and Dickey-Fuller Generalized Least Square (DF-GLS) tests

Structural equation modeling: An application of broadband penetration and GDP growth in Asia

In the present era of globalization, broadband penetration which indicates access to high-speed internet has allowed the transferring of information in a way that was never observed before. With the growing world-wide investment and attention received by broadband infrastructure, this study examines the relationship between broadband penetration and economic growth with a panel data consisting of ten Asian countries over 2001-2015. This study uses a structural equation modeling approach to estimate the relationship between endogenous broadband penetration and economic growth through two-stage least squares, three-stage generalized method of moments and full-information maximum likelihood estimation. A positive and significant impact of broadband penetration on economic growth is found controlling individual effect of countries