Comparison of supercritical and near-critical carbon dioxide extraction of carotenoid enriched wheat bran oil

Supercritical and near-critical carbon dioxide (CO2) extraction were carried out to extract oil from wheat bran. The extraction temperatures for supercritical and near-critical CO2 were 35 - 45°C and 25 - 30°C, respectively. The applied pressure was ranging from 10 to 30 MPa for both supercritical and near-critical CO2 extraction. The extraction was performed in a semi batch process with a CO2 flow rate of 26.81 g/min for 1.5 h. The oil obtained from wheat bran at different extraction conditions was quantitatively measured to  investigate the solubility of oil at supercritical and near-critical CO2. The highest solubility was found at near-critical condition. The fatty acid compositions of wheat bran oil were measured by gas chromatography (GC). Linoleic, palmitic, oleic and γ-linolenic acid were the major fatty acids of wheat bran oil. Total carotenoid was measured spectrophotometerically. Highest yield of total carotenoid was found at 45°C and 30 MPa.Key words: Supercritical and near-critical carbon dioxide, wheat bran oil, total carotenoid

e-ESAS: Evolution of a Participatory Design-based Solution for Breast Cancer (BC) Patients in Rural Bangladesh

Healthcare facility is scarce for rural women in the developing world. The situation is worse for patients who are suffering from diseases that require long-term feedback-oriented monitoring such as breast cancer. Lack of motivation to go to the health centers on patients’ side due to sociocultural barriers, financial restrictions and transportation hazards results in inadequate data for proper assessment. Fortunately, mobile phones have penetrated the masses even in rural communities of the developing countries. In this scenario, a mobile phone-based remote symptom monitoring system (RSMS) with inspirational videos can serve the purpose of both patients and doctors. Here, we present the findings of our field study conducted on 39 breast cancer patients in rural Bangladesh. Based on the results of extensive field studies, we have categorized the challenges faced by patients in different phases of the treatment process. As a solution, we have designed, developed and deployed e-ESAS—the first mobile-based RSMS in rural context. Along with the detail need assessment of such a system, we describe the evolution of e-ESAS and the deployment results. We have included the unique and useful design lessons that we learned as e-ESAS evolved through participatory design process. The findings show how e-ESAS addresses several challenges faced by patients and doctors and positively impact their lives

Attributes of modal choice in an industrial-based urban area: A case study on Savar Paurashava

Road transportation does an excellent job of connecting Savar's inner and outer areas, but it might be difficult for commuters to decide which mode of transportation to employ because residents of Savar come from a wide range of socioeconomic backgrounds. It is necessary to do in-depth research on the characteristics of people in the study region who choose different modes of transportation to achieve the goal of selecting a certain mode for their travel. Where a person lives can have a significant impact on their typical patterns of travel behavior. People who live closer to the center of things have access to a wider variety of amenities than those who live further away, and as a consequence, their travel patterns are distinct from one another. The study's findings indicate that several factors, including income, age, gender, and vocational qualities and goals, influence a commuter's preferred method of transportation. When it comes to selecting a mode of transportation, the most predictable factors to take into account are trip time and cost. After conducting research, it was found that most excursions are made from this place to Motijheel, Tejgaon, and Savar Export Processing Zone for their services, businesses, and jobs, respectively

The use of medicinal plants in health care practices by Rohingya refugees in a degraded forest and conservation area of Bangladesh

People in developing countries traditionally rely on plants for their primary healthcare. This dependence is relatively higher in forests in remote areas due to the lack of access to modern health facilities and easy availability of the plant products.We carried out an ethno-medicinal survey in Teknaf Game Reserve (TGR), a heavily degraded forest and conservation area in southern Bangladesh, to explore the diversity of plants used by Rohingya refugees for treating various ailments. The study also documented the traditional utilization, collection and perceptions of medicinal plants by the Rohingyas residing on the edges of this conservation area. We collected primary information through direct observation and by interviewing older respondents using a semi-structured questionnaire. A total of 34 plant species in 28 families were frequently used by the Rohingyas to treat 45 ailments, ranging from simple headaches to highly complex eye and heart diseases. For medicinal preparations and treating various ailments, aboveground plant parts were used more than belowground parts. The collection of medicinal plants was mostly from the TGR. © 2009 Taylor & Francis

Psychosocial health of school-going adolescents during the COVID-19 pandemic: Findings from a nationwide survey in Bangladesh

From PLOS via Jisc Publications RouterHistory: received 2022-03-11, collection 2023, accepted 2023-03-08, epub 2023-03-27Acknowledgements: The authors would like to express the most profound gratitude to the authorities, teachers, parents, and students under the Konnect platform of a2i who contributed in the study. icddr,b is grateful to the governments of Bangladesh, Canada, Sweden and the UK for providing unrestricted support.Publication status: PublishedDaniel Reidpath - ORCID: 0000-0002-8796-0420 https://orcid.org/0000-0002-8796-0420Background: Common psychosocial health problems (PHPs) have become more prevalent among adolescents globally during the COVID-19 pandemic. However, the psychosocial health of school-going adolescents has remained unexplored in Bangladesh due to limited research during the pandemic. The present study aimed to estimate the prevalence of PHPs (i.e., depression and anxiety) and assess associated lifestyle and behavioral factors among school-going adolescents in Bangladesh during the COVID-19 pandemic. Methods: A nationwide cross-sectional survey was conducted among 3,571 school-going adolescents (male: 57.4%, mean age: 14.9±1.8 years; age range: 10–19 years) covering all divisions, including 63 districts in Bangladesh. A semi-structured e-questionnaire, including informed consent and questions related to socio-demographics, lifestyle, academics, pandemic and PHPs, was used to collect data between May and July 2021. Results: The prevalence of moderate to severe depression and anxiety were 37.3% and 21.7%, respectively, ranging from 24.7% in the Sylhet Division to 47.5% in the Rajshahi Division for depression, and from 13.4% in the Sylhet Division to 30.3% in the Rajshahi Division for anxiety. Depression and anxiety were associated with older age, reports of poor teacher cooperation in online classes, worries due to academic delays, parental comparison of academic performance with other classmates, difficulties coping with quarantine situations, changes in eating habits, weight gain, physical inactivity and having experienced cyberbullying. Moreover, being female was associated with higher odds of depression. Conclusions: Adolescent psychosocial problems represent a public health problem. The findings suggest a need for generating improved empirically supported school-based psychosocial support programs involving parents and teachers to ensure the well-being of adolescents in Bangladesh. School-based prevention of psychosocial problems that promote environmental and policy changes related to lifestyle practices and active living should be developed, tested, and implemented.pubpu

Contribution of selected factors on farmers’ work performance towards fertilizer application in rice of Bangladesh

There is enormous possibility to increase rice yield in Bangladesh. Inefficient and often imbalanced fertilizer use impedes farmers from achieving expected yields. It is evident from past research that farmers have resorted to applying fertilizers at inappropriate rates that do not match well with the nutrient requirement of certain crops. Therefore, this study explores the contribution of selected factors that influence farmers’ work performance and determine the highest contributing factors on farmers’ work performance towards fertilizer application in rice. This research used a multistage simple random sampling method to select 355 farmers from twenty-one rice production areas of Bangladesh. Data, collected using a structured questionnaire, were subjected to multiple linear regression analysis to explore the contribution of selected factors and identify the highest contributing factors towards farmers’ work performance. Results revealed that all the factors explained 56.1% of the variance in farmers’ work performance. Motivation of farmers was found to be the highest contributing factor, followed by knowledge that influences farmers’ work performance. The study concludes that farmers need to be equipped with essential knowledge and motivation crucial to strengthening their work performance as this will subsequently increase rice production

Impact of chitosan composites and chitosan nanoparticle composites on various drug delivery systems: a review

Chitosan is a promising biopolymer for drug delivery systems. Because of its beneficial properties, chitosan is widely used in biomedical and pharmaceutical fields. In this review, we summarize the physicochemical and drug delivery properties of chitosan, selected studies on utilization of chitosan and chitosan-based nanoparticle composites in various drug delivery systems, and selected studies on the application of chitosan films in both drug delivery and wound healing. Chitosan is considered the most important polysaccharide for various drug delivery purposes because of its cationic character and primary amino groups, which are responsible for its many properties such as mucoadhesion, controlled drug release, transfection, in situ gelation, and efflux pump inhibitory properties and permeation enhancement. This review can enhance our understanding of drug delivery systems particularly in cases where chitosan drug-loaded nanoparticles are applied

Sea level rise induced impacts on coastal areas of Bangladesh and local-led community-based adaptation

Bangladesh is as a low-lying country, susceptible to various Sea Level Rise (SLR) induced impacts. Previous studies have separately explored SLR effects on Bangladesh’s coastal ecosystems and livelihoods, across multiple spatial and temporal scales. However, empirical studies acknowl- edging local population’s perceptions on the causal factors to different SLR induced physio- graphic impacts, their effects at societal scale and ongoing adaptation to these impacts of SLR have not been able to establish a causal-linkage relationship between these impacts and their potential effects. Our study explores how SLR has already impacted the lives and livelihoods of coastal communities in Bangladesh and how these have been responded by adopting different adaptative measures. We applied a qualitative community-based multistage sampling procedure, using two Participatory Rural Appraisal (PRA) tools, namely Focus Group Discussions (FGDs) and Community Meetings (CM), to collect empirical data about SLR effects on livelihoods and implemented adaptation responses. Our study found that both man-made and natural causes are responsible for different physiographic impacts of SLR, and which seem to vary between place and context. Five major SLR induced impacts were identified by coastal communities, namely: salinity increase, rising water levels, land erosion, waterlogging and the emergence of char land. Salinity increase and land erosion are the two most severe impacts of SLR resulting in the largest economic losses to agriculture. Our results highlight how coastal communities in Bangladesh perceive the impacts of SLR and the benefits of different adaptation processes set in motion to protect them, via development projects and other local interventions.info:eu-repo/semantics/acceptedVersio

Supercritical carbon dioxide extraction of highly unsaturated oil from Phaleria macrocarpa seed

Good quality oil with high unsaturated fatty acids was found in the seed of a medicinal plant Phaleria macrocarpa (Mahkota dewa). Different parts especially fruit flesh of this plant are being traditionally used as important folk medicine whereas seed of this plant is usually neglected. In this study, the oil was extracted from P. macrocarpa seed using supercritical carbon dioxide. The extraction parameters were optimized by central composite design (CCD) of response surface methodology (RSM). Due to the non-linearity of the extraction process, artificial neural network (ANN) was also applied for predicting the oil yield. The optimum conditions obtained from RSM were 72 °C, 42 MPa and 4.5 ml/min CO2 flow rate where the oil yield was 52.9 g per 100 g of dry sample and coefficient of determination (R2) was 0.99. The ANN and RSM prediction showed similar R2 of 0.99 and ANN has lower average absolute deviation (AAD) of 0.25% compared to RSM (AAD of 0.31%). Five fatty acids were identified by gas chromatography–mass spectroscopy (GC–MS) analysis of the oil. The amount of oleic acid (18:1) was found to be highest (43.56%) among all the fatty acids. The total unsaturated fatty acid was 73.62% and saturated fatty acid was 26.38% in the P. macrocarpa seed oil

Deep Learning Models for Stock Market Forecasting: A Comprehensive Comparative Analysis

This study presents a comprehensive comparative analysis of deep learning models for stock market forecasting using data from two prominent stock exchanges, the National Stock Exchange (NSE) and the New York Stock Exchange (NYSE). Four deep neural network architectures—Multilayer Perceptron (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN)—were trained and tested on NSE data, focusing on Tata Motors in the automobile sector. The analysis included data from sectors such as Automobile, Banking, and IT for NSE and Financial and Petroleum sectors for NYSE. Results revealed that the deep neural network architectures consistently outperformed the traditional linear model, ARIMA, across both exchanges. The Mean Absolute Percentage Error (MAPE) values obtained for forecasting NSE values using ARIMA were notably higher compared to those derived from the neural networks, indicating the superior predictive capabilities of deep learning models. Notably, the CNN architecture demonstrated exceptional performance in capturing nonlinear trends, particularly in recognizing seasonal patterns within the data. Visualizations of predicted stock prices further supported the findings, showcasing the ability of deep learning models to adapt to dynamic market conditions and discern intricate patterns within financial time series data. Challenges encountered by different neural network architectures, such as difficulties in recognizing certain patterns within specific timeframes, were also analyzed, providing insights into the strengths and limitations of each model