This is a primer review of international migration theory and international migration from Bangladesh. We first present a review of the theory of international migration. Regarding international migration from Bangladesh, we note that by the middle of 2020, about 7.4 million people of Bangladesh origin were staying overseas, the sixth-largest worldwide and second-largest in South Asia. Yet there are concerns about illegal human trafficking and smuggling of undocumented workers. Recently there has been the COVID-19 pandemic, starting from the end of 2019 to date. Bangladesh international migration ground realities are often uncertain and challenging, with new situations emerging now and then in many different host countries. In summary, we argue that recent models of migration theory (circular, onward and return migration models) have successfully incorporated issues of international migration from large source countries, such as Bangladesh. Social Science Review, Vol. 38(2), December 2021 Page 51-69
In this paper, the use of 2D Wiener filter based matched filter detection (WMFD) is proposed and demonstrated for the extraction of temperature distributions in Brillouin optical time domain analysis (BOTDA) sensors. The experimental Brillouin gain spectra (BGSs) are obtained along a 38.2 km sensing fiber by adopting ten different numbers of BOTDA-trace averaging (NTA). These BGSs are first denoised by applying Wiener filter (WF) to enhance the signal-to-noise ratio (SNR) of the BOTDA-traces. The improvement of trace-SNR for using WF is quantified and analyzed experimentally. The matched filter detection (MFD) which is free from time-consuming iterative optimization procedure is then applied to the denoised BGSs for the ultrafast extraction of temperature distributions along the fiber. The measurement uncertainty, spatial resolution and temperature extraction speed provided by WMFD are also analyzed in detail and compared with that provided by widely-used curve fitting method (CFM). The results show that WF can improve the trace-SNR within the range from ~6.78 dB to ~8.28 dB depending on NTA. Consequently, WMFD can improve the measurement uncertainty within the range from ~48.70% to ~59.41% without sacrificing the spatial resolution as compared to CFM. Moreover, the speed in extracting temperature distributions from the experimental BGSs acquired with different NTA for using WMFD can be improved within the range from ~47.36 times to ~50.69 times as compared to that for using CFM. Thus, the proposed WMFD can be an effective approach for highly accurate and ultrafast extraction of temperature distributions along the sensing fiber. DUJASE Vol. 6 (2) 30-38, 2021 (July)
The virus responsible for COVID-19 is mutating day by day with more infectious characteristics. With the limited healthcare resources and overburdened medical practitioners, it is almost impossible to contain this virus. The automatic identification of this viral infection from chest X-ray (CXR) images is now more demanding as it is a cheaper and less time-consuming diagnosis option. To that cause, we have applied deep learning (DL) approaches for four-class classification of CXR images comprising COVID-19, normal, lung opacity, and viral pneumonia. At first, we extracted features of CXR images by applying a local binary pattern (LBP) and pre-trained convolutional neural network (CNN). Afterwards, we utilized a pattern recognition network (PRN), support vector machine (SVM), decision tree (DT), random forest (RF), and k-nearest neighbors (KNN) classifiers on the extracted features to classify aforementioned four-class CXR images. The performances of the proposed methods have been analyzed rigorously in terms of classification performance and classification speed. Among different methods applied to the four-class test images, the best method achieved classification performances with 97.41% accuracy, 94.94% precision, 94.81% recall, 98.27% specificity, and 94.86% F1 score. The results indicate that the proposed method can offer an efficient and reliable framework for COVID-19 detection from CXR images, which could be immensely conducive to the effective diagnosis of COVID-19-infected patients.
Objectives The study aims to explore the existing pattern of mental healthcare-seeking behavior among the adult population in Bangladesh, and to delineate the factors that influence the mental health patients' decision to receive mental healthcare services.Methods We used the National Mental Health Survey Bangladesh 2019 dataset with 7270 households to identify the patterns and catalysts of seeking mental health care services. A Probit model using a standard normal cumulative distribution function (CDF) with three specifications has been applied to identify the factors influencing mental healthcare-seeking behavior and the probability of seeking mental healthcare services in Bangladesh.Results We found that about 19% of the total adult population have mental health disorder in the country. Among the mental health disorder patients, only 10% seek healthcare services from different sources of mental healthcare services. Patients with addictive disorder show the lowest interest in seeking healthcare services, while patients with bipolar-related disorder receive treatment from mental healthcare sources. Findings portray that 4.8% of addictive disorder patients seek healthcare services. On the other hand, 34.4% of bipolar-related disorder patients receive treatment for mental health. In addition, the findings from the Probit model show that only the existence of other mentally disordered family members is the only statistically significant determinant among the socio-economic factors, such as gender, age, religion, education level and residential status. The marginal effects analysis shows that the existence of mentally disordered family members increases the probability of seeking mental health care services by around 6%. The other socio-economic variables considered in the study are found statistically insignificant. However, a married woman has a significantly higher likelihood of seeking treatment than an unmarried woman, while the family size is the only variable that significantly influences treatment-seeking behavior for men.Conclusion Though mental health conditions are major public health concerns in Bangladesh, the treatment-seeking behavior among the mentally diorder people is very low, implying a large treatment gap in the mental health sector. The findings indicate the urgent need to increase mental health service coverage among mental health patients.
Background Consumption of sugar-sweetened beverages (SSBs) or sugary drinks may reduce or even eliminate the household income allocation for other essential commodities. Reducing expenditure for consumption of other household commodities is known as the crowding-out effect of SSB. We aimed to determine the crowding-out effect of SSB expenditure on other household commodities. In addition, we also identified the factors influencing the household's decision to purchase of SSBs.Methods We used the logistic regression (logit and multinomial logit models) and the Seemingly Unrelated Regression (SUR) models. In order to find the probability of a given change in the socio-demographic variables, we also estimated the average marginal effects from the logistic regression. In addition, we regressed the SUR model by gender differences. We used Household Income and Expenditure Survey (HIES) 2016 data to estimate our chosen econometric models. HIES is nationally representative data on the household level across the country and is conducted using a multistage random sampling method by covering 46075 households.Results The findings from the logit model describe that the greater proportion of male members, larger household size, household heads with higher education, profession, having a refrigerator, members living outside of the house, and households with higher income positively affect the decision of purchasing SSB. However, the determinants vary with the various types of SSB. The unadjusted crowding out effect shows that expenditure on SSB or sugar-added drinks crowds out the household expenditure on food, clothing, housing, and energy items. On the other hand, the adjusted crowding out effect crowds out the spending on housing, education, transportation, and social and state responsibilities.Conclusion Although the household expenditure on beverages and sugar-added drinks is still moderate (around 2% of monthly household expenditure), the increased spending on beverages and sugar-added drinks is concerning due to the displacement of household expenditure for basic commodities such as food, clothing, housing, education, and energy. Therefore, evidence-based policies to regulate the sale and consumption of SSB are required for a healthy nation.
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