Appraisal of surface water quality for irrigation collected from Sadar upazila of Jamalpur district, Bangladesh

An attempt was made to assess surface water quality for irrigation collected from Sadar upazila of Jamalpur district, Bangladesh. Total 22 water samples were collected from the study area and analyzed for various physicochemical parameters following standard protocols at the Department of Agricultural Chemistry, Bangladesh Agricultural University, Mymensingh. Major cation and anion chemistry showed their dominance in order of Ca > K > Na > Mg and HCO3 > Cl > SO4 > BO3 > PO4 > CO3, respectively. The study revealed that 18, 14 and 6 samples were unsuitable for irrigation in respect of HCO3, K and BO3 contents in water, respectively. Among the heavy metals, the concentration of Pb, Mn, Cd and Cu in water were comparatively higher than the standard limits, which makes 22, 14, 10 and 3 samples problematic for long term irrigation in the study area. Electrical conductivity (EC) and sodium adsorption ratio (SAR) reflected that surface water samples were low to very high salinity (C1-C4) and low alkalinity (S1) hazards classes. As regards to hardness, out of 22 water samples, 2 were very hard, 8 were hard, 11 were moderately hard and only one was soft in quality. The study results concluded that HCO3, BO3, K, Pb, Mn, Cd and Cu were the major contaminants in the surface water of Sadar upazila of Jamalpur district, Bangladesh. Finally, the study suggested that the surface water in this area needs to treat to minimize the amount of contaminants before use for irrigation

A review on deep-learning-based cyberbullying detection

Bullying is described as an undesirable behavior by others that harms an individual physically, mentally, or socially. Cyberbullying is a virtual form (e.g., textual or image) of bullying or harassment, also known as online bullying. Cyberbullying detection is a pressing need in today’s world, as the prevalence of cyberbullying is continually growing, resulting in mental health issues. Conventional machine learning models were previously used to identify cyberbullying. However, current research demonstrates that deep learning surpasses traditional machine learning algorithms in identifying cyberbullying for several reasons, including handling extensive data, efficiently classifying text and images, extracting features automatically through hidden layers, and many others. This paper reviews the existing surveys and identifies the gaps in those studies. We also present a deep-learning-based defense ecosystem for cyberbullying detection, including data representation techniques and different deep-learning-based models and frameworks. We have critically analyzed the existing DL-based cyberbullying detection techniques and identified their significant contributions and the future research directions they have presented. We have also summarized the datasets being used, including the DL architecture being used and the tasks that are accomplished for each dataset. Finally, several challenges faced by the existing researchers and the open issues to be addressed in the future have been presented

A Clinical Study on Management of Incomplete Abortion by Manual Vacuum Aspiration (MVA)

Background: Abortion is an important social and public health issue. In Bangladesh complication from unsafe abortion is one of the leading causes of maternal mortality. It is a serious health problem. World Health Organisation estimates that 14% of maternal deaths which occur every year in the countries of South Asia including Bangladesh are due to abortion. Study shows manual vacuum aspiration procedure is safe and effective in incomplete abortion. Very few clinical trials were carried out in Bangladesh to assess the safety and effectivity of manual vacuum aspiration in managing incomplete abortion. Objective: To find out the outcome of manual vacuum aspiration in the management of patients of incomplete abortion. Materials and Methods: This observational descriptive study was conducted in the department of Obstetrics & Gynaecology, Dhaka Medical College & Hospital from June to December, 2004. One hundred cases of diagnosed incomplete abortion up to 12 weeks of gestation were managed by manual vacuum aspiration during this period. A data recording sheet was designed for this purpose. Haemodynamically stable patients with no history of induced abortion and fever were enrolled. Results: Procedure time of manual vacuum aspiration was short, average duration was 7 minutes. Bleeding was minimum (20-30 mL) in 67% cases and weighted mean was 29.80 mL. Eighty three percent patients were stable during the procedure and only 3% needed blood transfusion. Nonnarcotic analgesics were used in 59% cases and 33% needed only proper counselling. Average duration of hospital stay was 2 hours. Effectiveness of the procedure was about 98% with very low post procedure complication rate (2%). Conclusion: MVA procedure is a safe and effective technique of uterine evacuation in incomplete abortion. It is quick, less expensive, effective and less painful. Hospital stay and chance of perforation of uterus is less. So this procedure should be considered by health care system in Bangladesh for improving treatment of incomplete abortion to reduce both maternal morbidity and mortality

Automatic classification of textile visual pollutants using deep learning networks

Urban pollution is a massive global problem, especially in industrialized and developing nations. Visual pollution is an issue concerned with the external noticeable appearance of the modern urban areas causing human health disorders, emotional distress, driving distraction, environmental hazards, etc. Amidst the plethora of different forms of environmental pollution, visual pollution deteriorates the aesthetics of an urban environment, endorsing the importance of research and assessing it from different dimensions. The main objective of this study is to initialize a new concept of automatic identification and classification of visible contaminants related to textile industries using computer vision techniques. In this work, deep learning techniques have been applied for the automatic detection and classification of three categories of textile-based visual pollutants, i.e., cloth garbage, advertising billboards and signages, and textile dyeing waste materials. Initially, 1,709 visual pollutants images were obtained through web crawling of search engines. Additionally, 954 images were collected from two local garments factories, roadside vendors and shopping malls of Bangladesh. Next, the dataset was manually annotated by an open-source labeling tool. Finally, various deep learning techniques, Faster R-CNN, YOLOv5, and EfficientDet, have been used to classify the obtained dataset automatically. The EfficientDet framework achieved the best performance with 97% and 93% training and test accuracies, respectively. The YOLOv5 approach exhibits acceptable precision with a considerably lower number of epochs. The proposed automated classification system is expected to create future visual pollution ratings for the textile industries. Consequently, the corresponding stakeholders (industry owners, government authorities, factory workers, etc.) can introduce regulatory frameworks and control the proliferation of visual pollution. The open-source images obtained by web crawling, locally collected visual pollutants dataset and implementation code of this work are available at: https://github.com/SadiaAfrin163/Textile-Visual-Pollutants-Dataset

Abundant time-wavering solutions of a modified regularized long wave model using the EMSE technique

This research article presents the application of the enhanced modified simple equation integral technique to obtain abundant time-wavering solutions of the modified regularized long wave (RLW) model. The model is known for its significance in exploring wave phenomena in various fields, including shallow water dynamics, pressure waves in liquids and gas bubbles, nonlinear transverse waves in magneto-hydrodynamics, and ion-acoustic waves in plasma. By implementing the enhanced modified simple equation integral technique, we construct arbitrary time-varying wave solutions for the model, resulting in a diverse range of solitonic waveforms. These solutions include kink waves, anti-kink waves, bright and dark bell waves, double periodic waves, and combinations of solitons and periodic waves. The obtained solutions are visually presented through 2D and 3D density and contour plots. Our findings demonstrate the effectiveness and potential of the enhanced modified simple equation integral technique in identifying innovative time-varying soliton solutions within the modified RLW model

Quality evaluation and storage stability of mixed fruit leather prepared from mango, banana and papaya

This study aimed to elucidate the formulation and quality evaluation of fruit leather prepared from Mango, Banana and Papaya, and analyzes shelf life of developed samples at different storage conditions. Three samples of fruit leather (F1=50% mango pulp+20% banana pulp+19% papaya pulp, F2= 70% mango pulp+10% banana pulp+9% papaya pulp, F3=60% mango pulp+15% banana pulp+14% papaya pulp) were developed. The analysis of different composition such as moisture, ash, TSS (Total soluble solids), total sugar, acidity, crude fiber and ascorbic acid of all fresh fruit pulps and developed fruit leathers were taken place. The moisture, ash, and total sugar content of fruit leathers were in the range of 10.99 to 11.69%, 1.13-1.36%, and 54.08-55.38%, respectively. The fiber content ranged from 1.13 to 1.5% and sample F2 contained the highest amount (1.50%). The vitamin C content was highest in F2 (17.49 mg/ 100 g), while F3 gave the lowest (7mg/100g). The acidity of F3 was highest (0.21%), followed by F2 (0.16%) and F1 (0.12%). The sensory properties like color, texture, flavor and overall acceptability of mixed fruit leather of sample F2 (mango 70%, banana 10%, papaya 9%) was more acceptable than sample F1 and F3 which indicates mango rich leathers were much better than Papaya and banana rich leathers. The mixed fruit leathers were packed in sealed low-density and high-density polyethylene, and stored both at room temperature (25±1°C) and refrigerated temperature (4±1°C). Products were acceptable up to 4 months of storage and remained better in high-density polyethylene at room temperature than other conditions. [J Bangladesh Agril Univ 2022; 20(3.000): 323-332

Co-exploring the effects of COVID-19 pandemic on the livelihood of persons with disabilities in Bangladesh

Background: According to World Health Organization (WHO), vulnerable groups such as persons with disabilities are facing severe impacts of the pandemic. There has always been significant challenges and hurdles in terms of achieving adequate and equitable inclusivity of persons with disabilities in all sections of social life. Education and employment of persons with disabilities were least focused which created more marginalization for the community. The long term impact of these marginalization has also led to the lack of jobs and social security of persons with disabilities, which is very clear now given the crisis in place. In low and middle income countries like Bangladesh the situation is even worse. To better understand the conditions of persons with disabilities in this crisis situation, the present study was initiated to explore the dimensions of livelihood with respect to income and wellbeing of persons with disabilities and to generate evidence for developing policies around these issues.Methods: A qualitative study was undertaken among 30 persons with disabilities from 8 different geographical divisions of Bangladesh. The interviews were conducted through telephone calls due to the existing COVID-19 crisis and mobility restrictions. The respondents were purposively selected based on gender, type of disability, area of resident (urban, rural) and their ability to communicate, therefore most (25/30) respondents were persons with physical disability. Thematic analysis was conducted to generate the findings of the study.Findings: Study findings revealed that majority of the respondents were involved in informal jobs. Predominantly males were daily wage-earners and often the sole breadwinner of the families, very few females were involved in economic activities. Since they had no stable income, the economic shock from the COVID-19 pandemic had affected them badly even leading to household level famine. The study identifies low level of education and informal job security as the primary causes of socio-economic insecurity among persons with disabilities, resulting in challenges in ensuring a stable livelihood during crisis situations, such as COVID-19.Conclusion: Constant alienation of persons of disabilities from the formal sector results in the deterioration of their livelihood standards which even worsen during any emergency crisis such as COVID-19. The study pinpoints that only aided services are not adequate to ensure persons with disabilities' rights rather there is an urgent need of disability inclusion in formal job sector and livelihood training for persons with disabilities. To achieve the Sustainable Development Goals 2030 and to irradiate the inequality towards persons with disabilities in the society it is important for the Government and concern bodies to focus on the inclusiveness with better implementation and monitoring strategies