Pathways toward the sustainable improvement of food security: Adopting the household food insecurity access scale in rural farming households in Bangladesh

Achieving food security is a global concern that constitutes a major challenge, particularly for the least developed countries, such as Bangladesh. In the context of globalization, the nation continues to have ongoing food insecurity, particularly in rural areas, despite its overall economic growth and development. This has become a constraint in achieving the Sustainable Development Goals (SDGs) within the established time scale, particularly SDG2 (Zero Hunger). With this consideration in mind, the present study assesses the prevalence of household food in(security) and identifies the factors that influence this among rural farming households in Bangladesh. A sample of 350 farming households was surveyed randomly from the four villages in Mymensingh, Bangladesh. The household food insecurity access scale (HFIAS) was utilized to explore household food security. The results reveal that only 18% of rural farming households were food secure while the remainder were food insecure to some extent. Using a binomial logit regression model, we found that the household head’s educational level, as well as whether the household has a savings account, owns land, receives financial or other forms of support from household members abroad, has larger farm sizes, and practices homestead gardening significantly reduce household food insecurity, whereas a higher number of members in the household increases it. The findings of this study establish a foundational understanding of food security in rural areas by employing contemporary measurement tools and techniques. This addition to the existing knowledge base will assist in the design and implementation of a comprehensive and multifaceted policy outline not only for the rural areas of Bangladesh but also for sustainable development globally

A Forecasting Prognosis of the Monkeypox Outbreak Based on a Comprehensive Statistical and Regression Analysis

The uncommon illness known as monkeypox is brought on by the monkeypox virus. The Orthopoxvirus genus belongs to the family Poxviridae, which also contains the monkeypox virus. The variola virus, which causes smallpox; the vaccinia virus, which is used in the smallpox vaccine; and the cowpox virus are all members of the Orthopoxvirus genus. There is no relationship between chickenpox and monkeypox. After two outbreaks of a disorder resembling pox, monkeypox was first discovered in colonies of monkeys kept for research in 1958. The illness, also known as “monkeypox”, still has no known cause. However, non-human primates and African rodents can spread the disease to humans (such as monkeys). In 1970, a human was exposed to monkeypox for the first time. Several additional nations in central and western Africa currently have documented cases of monkeypox. Before the 2022 outbreak, almost all instances of monkeypox in people outside of Africa were connected to either imported animals or foreign travel to nations where the illness frequently occurs. In this work, the most recent monkeypox dataset was evaluated and the significant instances were visualized. Additionally, nine different forecasting models were also used, and the prophet model emerged as the most reliable one when compared with all nine models with an MSE value of 41,922.55, an R2 score of 0.49, a MAPE value of 16.82, an MAE value of 146.29, and an RMSE value of 204.75, which could be considerable assistance to clinicians treating monkeypox patients and government agencies monitoring the origination and current state of the disease

AQIPred: A Hybrid Model for High Precision Time Specific Forecasting of Air Quality Index with Cluster Analysis

Abstract The discipline of forecasting and prediction is witnessing a surge in the application of these techniques as a direct result of the strong empirical performance that approaches based on machine learning (ML) have shown over the past few years. Especially to predict wind direction, air and water quality, and flooding. In the context of doing this research, an MLP-LSTM Hybrid Model was developed to be able to generate predictions of this nature. An investigation into the Beijing Multi-Site Air-Quality Data Set was carried out in the context of an experiment. In this particular scenario, the model generated MSE values that came in at 0.00016, MAE values that came in at 0.00746, RMSE values that came in at 13.45, MAPE values that came in at 0.42, and R 2 values that came in at 0.95. This is an indication that the model is functioning effectively. The conventional modeling techniques for forecasting, do not give the level of performance that is required. On the other hand, the results of this study will be useful for any type of time-specific forecasting prediction that requires a high level of accuracy

Insights of Handloom Producers of Sirajganj District in Bangladesh

This study was undertaken to measure the current socioeconomic and profitability status of the traditional handloom producers living in Sirajganj district of Bangladesh. Primary data were collected from 60 randomly selected handloom owners. Two main products were considered in this study, i.e., sharee and gamcha . Descriptive statistics and cost-return analysis were performed to assess the present situation and profitability of handloom production. The Gini Coefficient and Lorenz Curve measured inequality among respondents. A log transformed multiple linear regression model was applied to explore the factors influencing handloom products production. Results revealed that most of the handloom weaving was financed through the weavers' own capital and taking loans from the bank. The undiscounted benefit-cost ratios were 1.12 and 1.20 for sharee and gamcha, respectively, indicating both enterprises were profitable but gamcha was more profitable than sharee. The Gini Coefficient of handloom weavers' income is less than 0.25. Results from the regression analysis revealed that human labour, yarn, color, and processing cost significantly impacted sharee and gamcha production. Therefore, efficient utilization of these resources in the production process of handloom products would be essential that can bring more profit for handloom production. [J Bangladesh Agril Univ 2022; 20(4.000): 433-440

GastroNet: Gastrointestinal Polyp and Abnormal Feature Detection and Classification With Deep Learning Approach

The early detection of digestive problems is essential for lowering the chance of acquiring any form of gastrointestinal cancer, including esophageal cancer. Endoscopy is the method that is used the majority of the time for the purpose of examining and taking photos of this sort of illness. The application of artificial intelligence is now proving to be very efficient in enhancing the identification of gastrointestinal polyps and other abnormal features located inside the gastrointestinal system. As a direct consequence of this development, the use of AI within this sector has seen substantial growth. In the framework of artificial intelligence, this research investigates how well various types of algorithms perform in terms of polyp and abnormal feature recognition accuracy, efficiency, and detection. And introduced a model in this work that is GastroNet. It is developed by doing hyperparameter fine-tuning on YOLOv5 in order to find specific polyps and abnormal characteristics, particularly esophagitis. In this method, a single neural network is used to do an analysis on the whole picture before it is disassembled into its component parts and the bounding boxes and probabilities for each one are calculated independently. The goal of the hyperparameter fine-tuning is to further enhance the overall optimization of the model. Two different methods of annotation were used on a collection of data that consisted of one thousand separate images that needed to be labelled. In addition to implementing the fine-tuned SSD model, this study used three distinct backbone networks: MobileNet v2, MobileNet v2 FPN Lite, and Resnet50 v1 FPN. Additionally, this study has used CSPdarknet53 to create the improved YOLOv4 model. The results of the studies demonstrate that the proposed model, GastroNet was effective in correctly recognizing polyps and aberrant characteristics, reaching a high mAP (mean Average Precision), F1 score, and precision with a value of 0.99 and recall with a value of 1.00. The findings of this research will be a great help to physicians in the proper identification and diagnosis of abnormal features and gastrointestinal polyps

Efficient Prediction of Water Quality Index (WQI) Using Machine Learning Algorithms

Abstract The quality of water has a direct influence on both human health and the environment. Water is utilized for a variety of purposes, including drinking, agriculture, and industrial use. The water quality index (WQI) is a critical indication for proper water management. The purpose of this work was to use machine learning techniques such as RF, NN, MLR, SVM, and BTM to categorize a dataset of water quality in various places across India. Water quality is dictated by features such as dissolved oxygen (DO), total coliform (TC), biological oxygen demand (BOD), Nitrate, pH, and electric conductivity (EC). These features are handled in five steps: data pre-processing using min-max normalization and missing data management using RF, feature correlation, applied machine learning classification, and model’s feature importance. The highest accuracy Kappa, Accuracy Lower, and Accuracy Upper findings in this research are 99.83, 99.17, 99.07, and 99.99, respectively. The finding showed that Nitrate, PH, conductivity, DO, TC, and BOD are the key qualities that contribute to the orderly classification of water quality, with Variable Importance values of 74.78, 36.805, 81.494, 105.770, 105.166, and 130.173, respectively

Nutritional status of under-five aged children of ready-made garment workers in Bangladesh: A cross-sectional study

Background The ready-made garment (RMG) sector is a significant contributor to the economic growth of Bangladesh, accounting for 10% of the country’s GDP and more than 80% of its foreign exchange earnings. The workforce in this sector is predominantly made up of women, with 2.5 million women working in the industry. However, these women face numerous challenges in carrying out their culturally-expected household responsibilities, including childcare, due to severe resource constraints. As a result, the children of these working women have a higher incidence of malnutrition, particularly stunted growth. This study aims to identify the factors that contribute to stunting in children under the age of five whose mothers work in the RMG sector in Bangladesh. Methods The study collected data from 267 female RMG workers in the Gazipur district of Bangladesh using a simple random sampling technique. Chi-square tests were used to determine the associations between the factors influencing child stunting, and Multinomial Logit Models were used to estimate the prevalence of these factors. Results The study found that the prevalence of moderate and severe stunting among the children of RMG workers living in the Gazipur RMG hub was 19% and 20%, respectively. The study identified several significant predictors of child stunting, including the mother’s education level, nutritional knowledge, control over resources, receipt of antenatal care, household size, sanitation facilities, and childbirth weight. The study found that improving the mother’s education level, increasing household size, and receiving antenatal care during pregnancy were important factors in reducing the likelihood of child stunting. For example, if a mother’s education level increased from no education to primary or secondary level, the child would be 0.211 (0.071–0.627) and 0.384 (0.138–1.065) times more likely to have a normal weight and height, respectively, than to be moderately stunted. Conclusion The study highlights the challenges faced by working women in the RMG sector, who often receive minimal wages and have limited access to antenatal care services. To address these challenges, the study recommends policies that support antenatal care for working-class mothers, provide daycare facilities for their children, and implement a comprehensive social safety net program that targets child nutrition. Improving the socioeconomic status of mothers is also critical to reducing child malnutrition in this population

Determination of population size.

BackgroundThe ready-made garment (RMG) sector is a significant contributor to the economic growth of Bangladesh, accounting for 10% of the country’s GDP and more than 80% of its foreign exchange earnings. The workforce in this sector is predominantly made up of women, with 2.5 million women working in the industry. However, these women face numerous challenges in carrying out their culturally-expected household responsibilities, including childcare, due to severe resource constraints. As a result, the children of these working women have a higher incidence of malnutrition, particularly stunted growth. This study aims to identify the factors that contribute to stunting in children under the age of five whose mothers work in the RMG sector in Bangladesh.MethodsThe study collected data from 267 female RMG workers in the Gazipur district of Bangladesh using a simple random sampling technique. Chi-square tests were used to determine the associations between the factors influencing child stunting, and Multinomial Logit Models were used to estimate the prevalence of these factors.ResultsThe study found that the prevalence of moderate and severe stunting among the children of RMG workers living in the Gazipur RMG hub was 19% and 20%, respectively. The study identified several significant predictors of child stunting, including the mother’s education level, nutritional knowledge, control over resources, receipt of antenatal care, household size, sanitation facilities, and childbirth weight. The study found that improving the mother’s education level, increasing household size, and receiving antenatal care during pregnancy were important factors in reducing the likelihood of child stunting. For example, if a mother’s education level increased from no education to primary or secondary level, the child would be 0.211 (0.071–0.627) and 0.384 (0.138–1.065) times more likely to have a normal weight and height, respectively, than to be moderately stunted.ConclusionThe study highlights the challenges faced by working women in the RMG sector, who often receive minimal wages and have limited access to antenatal care services. To address these challenges, the study recommends policies that support antenatal care for working-class mothers, provide daycare facilities for their children, and implement a comprehensive social safety net program that targets child nutrition. Improving the socioeconomic status of mothers is also critical to reducing child malnutrition in this population.</div

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BackgroundThe ready-made garment (RMG) sector is a significant contributor to the economic growth of Bangladesh, accounting for 10% of the country’s GDP and more than 80% of its foreign exchange earnings. The workforce in this sector is predominantly made up of women, with 2.5 million women working in the industry. However, these women face numerous challenges in carrying out their culturally-expected household responsibilities, including childcare, due to severe resource constraints. As a result, the children of these working women have a higher incidence of malnutrition, particularly stunted growth. This study aims to identify the factors that contribute to stunting in children under the age of five whose mothers work in the RMG sector in Bangladesh.MethodsThe study collected data from 267 female RMG workers in the Gazipur district of Bangladesh using a simple random sampling technique. Chi-square tests were used to determine the associations between the factors influencing child stunting, and Multinomial Logit Models were used to estimate the prevalence of these factors.ResultsThe study found that the prevalence of moderate and severe stunting among the children of RMG workers living in the Gazipur RMG hub was 19% and 20%, respectively. The study identified several significant predictors of child stunting, including the mother’s education level, nutritional knowledge, control over resources, receipt of antenatal care, household size, sanitation facilities, and childbirth weight. The study found that improving the mother’s education level, increasing household size, and receiving antenatal care during pregnancy were important factors in reducing the likelihood of child stunting. For example, if a mother’s education level increased from no education to primary or secondary level, the child would be 0.211 (0.071–0.627) and 0.384 (0.138–1.065) times more likely to have a normal weight and height, respectively, than to be moderately stunted.ConclusionThe study highlights the challenges faced by working women in the RMG sector, who often receive minimal wages and have limited access to antenatal care services. To address these challenges, the study recommends policies that support antenatal care for working-class mothers, provide daycare facilities for their children, and implement a comprehensive social safety net program that targets child nutrition. Improving the socioeconomic status of mothers is also critical to reducing child malnutrition in this population.</div

The extent of mothers’ nutritional knowledge.

BackgroundThe ready-made garment (RMG) sector is a significant contributor to the economic growth of Bangladesh, accounting for 10% of the country’s GDP and more than 80% of its foreign exchange earnings. The workforce in this sector is predominantly made up of women, with 2.5 million women working in the industry. However, these women face numerous challenges in carrying out their culturally-expected household responsibilities, including childcare, due to severe resource constraints. As a result, the children of these working women have a higher incidence of malnutrition, particularly stunted growth. This study aims to identify the factors that contribute to stunting in children under the age of five whose mothers work in the RMG sector in Bangladesh.MethodsThe study collected data from 267 female RMG workers in the Gazipur district of Bangladesh using a simple random sampling technique. Chi-square tests were used to determine the associations between the factors influencing child stunting, and Multinomial Logit Models were used to estimate the prevalence of these factors.ResultsThe study found that the prevalence of moderate and severe stunting among the children of RMG workers living in the Gazipur RMG hub was 19% and 20%, respectively. The study identified several significant predictors of child stunting, including the mother’s education level, nutritional knowledge, control over resources, receipt of antenatal care, household size, sanitation facilities, and childbirth weight. The study found that improving the mother’s education level, increasing household size, and receiving antenatal care during pregnancy were important factors in reducing the likelihood of child stunting. For example, if a mother’s education level increased from no education to primary or secondary level, the child would be 0.211 (0.071–0.627) and 0.384 (0.138–1.065) times more likely to have a normal weight and height, respectively, than to be moderately stunted.ConclusionThe study highlights the challenges faced by working women in the RMG sector, who often receive minimal wages and have limited access to antenatal care services. To address these challenges, the study recommends policies that support antenatal care for working-class mothers, provide daycare facilities for their children, and implement a comprehensive social safety net program that targets child nutrition. Improving the socioeconomic status of mothers is also critical to reducing child malnutrition in this population.</div