Spatiotemporal mapping of rice acreage and productivity growth in Bangladesh.

Technological advancements have long played crucial roles in rice productivity and food security in Bangladesh. Seasonal variation over time and regional differences in rice production, however, pose a threat to agricultural sustainability but remain unexplored. We performed a spatial-temporal mapping of rice cultivation area, production, and yield from 2006-2007 to 2019-2020 using secondary data for disaggregating 64 districts in Bangladesh. Growth and multivariate approaches were employed to analyze time-series data. Results showed that Mymensingh had the highest rice cultivated area and production, while Bandarban had the lowest. The 14 years highest average rice yield was found in Gopalganj and Dhaka (3.63 tons/ha), while Patuakhali (1.73 tons/ha) had the lowest. For the Aus, Aman, and Boro, the rice cultivation area in 19 districts, 11 districts, and 13 districts declined significantly. The overall rice production increased significantly in most districts. For the Aus, Aman, and Boro seasons, the rice yield in 54, 50, and 37 districts demonstrated a significant upward trend, respectively. The adoption rate of modern varieties has risen dramatically. However, there are notable variances between regions and seasons. A significant increasing trend in Aus (0.007% to 0.521%), Aman (0.004% to 0.039%), and Boro (0.013% to 0.584%) were observed in 28, 34, and 36 districts, respectively, with an increase of 1% adaptation of HYV. Predictions revealed that rice cultivation area and production of Aus, Aman, and Boro seasons will be increased in most of the regions of Bangladesh by 2030. Based on spatiotemporal cluster analysis, the five identified cluster groupings illustrated that clusters lack spatial cohesion and vary greatly seasonally. This suggests increasing rice production by expanding cultivable land, adopting high-yielding varieties, and integrating faster technological advancement in research and extension. The findings will assist scientists in developing region-specific production technologies and policymakers in designing decentral region-specific policies to ensure the future sustainability of rice production.

Current trends in textile wastewater treatment-bibliometric review.

A bibliometric study using 1992 to 2021 database of the Science Citation Index Expanded was carried out to identify which are the current trends in textile wastewater treatment research. The study aimed to analyze the performance of scholarly scientific communications in terms of yearly publications/citations, total citations, scientific journals, and their categories in the Web of Sciences, top institutions/countries and research trends. The annual publication of scientific articles fluctuated in the first ten years, with a steady decrease for the last twenty years. An analysis of the most common terms used in the authors' keywords, publications' titles, and KeyWords Plus was carried out to predict future trends and current research priorities. Adsorbent nanomaterials would be the future of wastewater treatment for decoloration of the residual dyes in the wastewater. Membranes and electrolysis are important to demineralize textile effluent for reusing wastewater. Modern filtration techniques such as ultrafiltration and nanofiltration are advanced membrane filtration applications.

Comparative analysis of biometrical and reproductive indices, proximate composition, and hemato-biochemical variables of cuchia eel Monopterus cuchia (Hamilton, 1822) from six different localities of Bangladesh.

Cuchia eel (Monopterus cuchia) is among the most sought-after freshwater fish, owing to its exceptional nutritional profile and high consumer demand. The current research aimed to establish baseline data by comparing the proximate composition, hematological, and plasma biochemical indices of Cuchia eel populations across six different geographical locations in Bangladesh: Bogra, Haluaghat, Jamalpur, Moktagacha, Sylhet, and Tangail. By examining these parameters, we aim to gain valuable insights into the nutritional benefits, physiological responses, and potential adaptations of this species to varying environments. The statistical analysis revealed no significant (P > 0.05) variances in the whole-body proximate composition of the fish captured from distinct areas. However, it was observed that different geographical regions had remarkable impacts on the variations of the majority of the hematological parameters, except for some cases. Additionally, there was a notable (P < 0.05) increase or decrease in most of the serum biochemical contents in certain localities as compared to others in this study. Light microscopic examination of Cuchia eel blood smears exhibited lower numbers but larger sizes of RBCs. The findings of this study lead to the conclusion that different localities had significant impacts on the hematology and blood biochemical indices of Cuchia eel, even though the whole-body proximate composition showed no significant variations. This research contributes to a deeper understanding of the physiological aspects of Cuchia eel.

Evaluation of LAMP for Fasciola hepatica detection from faecal samples of experimentally and naturally infected cattle.

Fasciola hepatica causes liver fluke disease in production animals and humans worldwide. Faecal egg counts (FEC) are the most common diagnostic tool for the diagnosis of liver fluke disease. However, FEC has low sensitivity and is often unreliable for the detection of patent infection. In this study, loop-mediated isothermal amplification (LAMP) was optimised and evaluated for the detection of Fasciola hepatica infection, with the aim of increased sensitivity and making it suitable for on-farm application. LAMP was initially conducted under laboratory conditions, optimised to enable visual detection using calcein dye. DNA extraction based on bead-beating was developed to enable on-farm application. LAMP results were compared to FEC and polymerase chain reaction (PCR). Under laboratory conditions, LAMP was conducted using two incubation methods: a conventional PCR thermocycler and a field-deployable LAMP instrument. When compared to a 'rigorous' FEC protocol consisting of multiple counts using a comparatively large volume of faeces and with infection confirmed post-mortem, LAMP was highly sensitive and specific (using silica membrane DNA extraction sensitivity 88 %, specificity 100 %; using sieving and beat-beating DNA extraction sensitivity 98.9 %, specificity 100 %). When applied on-farm, LAMP was compared to conventional FEC, which suggested high sensitivity but low specificity (sensitivity 97 %, specificity 37.5 %). However, further analysis, comparing field LAMP results to laboratory PCR, suggested that the low specificity was likely the outcome of the inability of conventional FEC to detect all true F. hepatica positive samples. Based on the high sensitivity and specificity of LAMP compared to a 'rigorous' FEC protocol and its ability to be used in field settings, the study demonstrates the potential of LAMP for diagnosing F. hepatica infection in agriculture.

Identification of influential weather parameters and seasonal drought prediction in Bangladesh using machine learning algorithm.

Droughts pose a severe environmental risk in countries that rely heavily on agriculture, resulting in heightened levels of concern regarding food security and livelihood enhancement. Bangladesh is highly susceptible to environmental hazards, with droughts further exacerbating the precarious situation for its 170 million inhabitants. Therefore, we are endeavouring to highlight the identification of the relative importance of climatic attributes and the estimation of the seasonal intensity and frequency of droughts in Bangladesh. With a period of forty years (1981-2020) of weather data, sophisticated machine learning (ML) methods were employed to classify 35 agroclimatic regions into dry or wet conditions using nine weather parameters, as determined by the Standardized Precipitation Evapotranspiration Index (SPEI). Out of 24 ML algorithms, the four best ML methods, ranger, bagEarth, support vector machine, and random forest (RF) have been identified for the prediction of multi-scale drought indices. The RF classifier and the Boruta algorithms shows that water balance, precipitation, maximum and minimum temperature have a higher influence on drought intensity and occurrence across Bangladesh. The trend of spatio-temporal analysis indicates, drought intensity has decreased over time, but return time has increased. There was significant variation in changing the spatial nature of drought intensity. Spatially, the drought intensity shifted from the northern to central and southern zones of Bangladesh, which had an adverse impact on crop production and the livelihood of rural and urban households. So, this precise study has important implications for the understanding of drought prediction and how to best mitigate its impacts. Additionally, the study emphasizes the need for better collaboration between relevant stakeholders, such as policymakers, researchers, communities, and local actors, to develop effective adaptation strategies and increase monitoring of weather conditions for the meticulous management of droughts in Bangladesh.

Inhibition of microRNA-34c reduces detrusor ROCK2 expression and urinary bladder inflammation in experimental cystitis.

Interstitial cystitis (IC), also called painful bladder syndrome (PBS), is 2 to 5 times more common in women than in men, yet its cause and pathogenesis remain unclear. In our study using the cyclophosphamide (CYP)-induced mouse model of cystitis, histological evaluation of the urinary bladder (UB) lamina propria (LP) showed immune cell infiltrations, indicating moderate to severe inflammation. In this study, we noticed a differential expression of a subset of microRNAs (miRs) in the UB cells (UBs) of CYP-induced cystitis as compared to the control. UB inflammatory scores and inflammatory signaling were also elevated in CYP-induced cystitis as compared to control. We identified eight UBs miRs that exhibited altered expression after CYP induction and are predicted to have a role in inflammation and smooth muscle function (miRs-34c-5p, -34b-3p, -212-3p, -449a-5p, -21a-3p, -376b-3p, -376b-5p and - 409-5p). Further analysis using ELISA for inflammatory markers and real-time PCR (RT-PCR) for differentially enriched miRs identified miR-34c as a potential target for the suppression of UB inflammation in cystitis. Blocking miR-34c by antagomir ex vivo reduced STAT3, TGF-ß1, and VEGF expression in the UBs, which was induced during cystitis as compared to control. Interestingly, miR-34c inhibition also downregulated ROCK2 but elevated ROCK1 expression in bladder and detrusor cells. Thus, the present study shows that targeting miR-34c can mitigate the STAT3, TGF-ß, and VEGF, inflammatory signaling in UB, and suppress ROCK2 expression in UBs to effectively suppress the inflammatory response in cystitis. This study highlights miR-34c as a potential biomarker and/or serves as the basis for new therapies for the treatment of cystitis.

Impact of mobile connectivity on students' wellbeing: Detecting learners' depression using machine learning algorithms.

Depression is a psychological state of mind that often influences a person in an unfavorable manner. While it can occur in people of all ages, students are especially vulnerable to it throughout their academic careers. Beginning in 2020, the COVID-19 epidemic caused major problems in people's lives by driving them into quarantine and forcing them to be connected continually with mobile devices, such that mobile connectivity became the new norm during the pandemic and beyond. This situation is further accelerated for students as universities move towards a blended learning mode. In these circumstances, monitoring student mental health in terms of mobile and Internet connectivity is crucial for their wellbeing. This study focuses on students attending an International University of Bangladesh to investigate their mental health due to their continual use of mobile devices (e.g., smartphones, tablets, laptops etc.). A cross-sectional survey method was employed to collect data from 444 participants. Following the exploratory data analysis, eight machine learning (ML) algorithms were used to develop an automated normal-to-extreme severe depression identification and classification system. When the automated detection was incorporated with feature selection such as Chi-square test and Recursive Feature Elimination (RFE), about 3 to 5% increase in accuracy was observed by the method. Similarly, a 5 to 15% increase in accuracy has been observed when a feature extraction method such as Principal Component Analysis (PCA) was performed. Also, the SparsePCA feature extraction technique in combination with the CatBoost classifier showed the best results in terms of accuracy, F1-score, and ROC-AUC. The data analysis revealed no sign of depression in about 44% of the total participants. About 25% of students showed mild-to-moderate and 31% of students showed severe-to-extreme signs of depression. The results suggest that ML models, incorporating a proper feature engineering method can serve adequately in multi-stage depression detection among the students. This model might be utilized in other disciplines for detecting early signs of depression among people.

Mapping the optimization of groundwater abstraction research: A bibliometric review in the context of South Asian region.

Water scarcity has increased dramatically in many parts of the world and has become a major environmental problem globally in recent years. To address this challenge, researchers are continuously exploring and investigating different water sources and the relevant abstraction method. South Asian countries are not an exception in this regard. Specifically, how optimization has been done during the water abstraction process is becoming a key research agenda in South Asian region. This study attempts to systematically review the optimization of groundwater abstraction research in the South Asian region. First, bibliometric analysis has been performed to quantitatively assess the current research trends in the optimization of groundwater abstraction research. Second, a qualitative analysis has been done to get more insights into the different abstraction methods and simulation models used in groundwater abstraction research. This study has addressed the gap and explored research streams through scientific and conceptual mapping of the optimization of groundwater abstraction research. As revealed, the year 2020 is the most productive in groundwater abstraction research. Indian Institute of Technology and India were found to be the most impactful institution and countries in this field. The most studied area in groundwater abstraction research was found to be sustainable groundwater management, the geochemical process of controlling groundwater evolution, the Spatio-temporal variability of groundwater and the supply-demand of water during dry seasons. As revealed, statistical and mathematical modelling analysis is the most used approach in these studies. The findings of this study suggested that the water scarcity problem can be fixed by improving the design and operational techniques of the groundwater abstraction method and by using the different water sources collectively. This study further provides future directions and opportunities for research in the field of the groundwater abstraction process.

Scientific mapping of the research in microbial and chemical contamination of potable water in Bangladesh: A review of literature.

Drinking water contamination is one of the most pressing concerns for the people of Bangladesh as they rely on groundwater to meet their water needs. The existing water sources of Bangladesh are losing potability due to natural, anthropogenic, and geogenic factors, resulting in acute to severe health consequences. To address the issue of safe drinking water, researchers are constantly examining potential sources that cause the pollution of drinking water. Through bibliometric and systematic research, the current work seeks to review the past research on microbiological and chemical contamination of drinkable water in Bangladesh. The bibliometric review provides insights into the research trends, notable authors, countries, and institutions, whereas the systematic review unfolds the key research areas, the contamination process, and the strategies used to mitigate the contamination process. The results show that arsenic and various coliform bacteria are the most commonly reported sources of chemical and microbiological contaminants that degrade water quality. The study demonstrates that the most crucial factors influencing arsenic mobilization include microbial decomposition of organic matter (biologically available organic matter, for example, peat), arsenic adsorption by metal-oxyhydroxides, Fe-Mn oxyhydroxide, chemical fertilizers, pond excavation, and altering of groundwater hydrology. The studies also indicated the sources that contribute to the microbiological quality decline. The current work has addressed the scope of future research.

Analyzing Perceived Psychological and Social Stress of University Students: A Machine Learning Approach.

The COVID-19 pandemic has worsened the psychological and social stress levels of university students due to physical illness, enhanced dependence on mobile devices and internet, a lack of social activities, and home confinement. Therefore, early stress detection is crucial for their successful academic performance and mental well-being. The advent of machine learning (ML)-based prediction models can have a crucial impact in predicting stress at its early stages and taking necessary steps for the well-being of individuals. This study aims to develop a reliable machine learning-based prediction model for perceived stress prediction and validate the model using real-world data collected through an online survey among 444 university students from different ethnicity. The machine learning models were built using supervised machine learning algorithms. Principal Component Analysis (PCA) and the chi-squared test were employed as feature reduction techniques. Moreover, Grid Search Cross-Validation (GSCV) and Genetic Algorithm (GA) were employed for hyperparameter optimization (HPO). According to the findings, around 11.26% of individuals were identified with high levels of social stress. In comparison, approximately 24.10% of people were found to be suffering from extremely high psychological stress, which is quite alarming for students' mental health. Furthermore, the prediction results of the ML models demonstrated the most remarkable accuracy (80.5%), precision (1.000), F1 score (0.890), and recall value (0.826). The Multilayer Perceptron model was shown to have the maximum accuracy when combined with PCA as a feature reduction approach and GSCV for HPO. The convenience sampling technique used in this study only considers self-reported data, which may have biased results and lack generalizability. Future research should consider a large sample of data and focus on tracking long-term impacts with coping strategies and interventions. The results of this study can be used to develop strategies to mitigate adverse effects of the overuse of mobile devices and promote student well-being during pandemics and other stressful situations.

Performance Enhancement of an MoS2-Based Heterojunction Solar Cell with an In2Te3 Back Surface Field: A Numerical Simulation Approach.

Researchers are currently showing interest in molybdenum disulfide (MoS2)-based solar cells due to their remarkable semiconducting characteristics. The incompatibility of the band structures at the BSF/absorber and absorber/buffer interfaces, as well as carrier recombination at the rear and front metal contacts, prevents the expected result from being achieved. The main purpose of this work is to enhance the performance of the newly proposed Al/ITO/TiO2/MoS2/In2Te3/Ni solar cell and investigate the impacts of the In2Te3 BSF and TiO2 buffer layer on the performance parameters of open-circuit voltage (V OC), short-circuit current density (J SC), fill factor (FF), and power conversion efficiency (PCE). This research has been performed by utilizing SCAPS simulation software. The performance parameters such as variation of thickness, carrier concentration, the bulk defect concentration of each layer, interface defect, operating temperature, capacitance-voltage (C-V), surface recombination velocity, and front as well as rear electrodes have been analyzed to achieve a better performance. This device performs exceptionally well at lower carrier concentrations (1 × 1016 cm-3) in a thin (800 nm) MoS2 absorber layer. The PCE, V OC, J SC, and FF values of the Al/ITO/TiO2/MoS2/Ni reference cell have been estimated to be 22.30%, 0.793 V, 30.89 mA/cm2, and 80.62% respectively, while the PCE, V OC, J SC, and FF values have been determined to be 33.32%, 1.084 V, 37.22 mA/cm2, and 82.58% for the Al/ITO/TiO2/MoS2/In2Te3/Ni proposed solar cell by introducing In2Te3 between the absorber (MoS2) and the rear electrode (Ni). The proposed research may give an insight and a feasible way to realize a cost-effective MoS2-based thin-film solar cell.

Sustainable futures in agricultural heritage: Geospatial exploration and predicting groundwater-level variations in Barind tract of Bangladesh.

Groundwater resources are one of the essential aspects of achieving self-sufficiency in a country's agricultural production, poverty alleviation, and socioeconomic development, particularly in agricultural heritage management and practices. In the Barind Tract in Bangladesh, groundwater levels have steadily declined due to growing irrigation demand. Surface water sources become scarce during the dry season, and groundwater levels fall to levels that make minimum cultivation challenging. In these circumstances, determining the current status of groundwater is key to any action in the future. This study investigated the existing geospatial pattern and critical zone of groundwater level in Chapainawabganj District, a significant area of the Barind Tract of Bangladesh, and predicted future groundwater levels considering multiple factors. Kriging, a sophisticated geostatistical method, was performed to examine the geographical pattern and groundwater variations, and time series analysis was employed to determine data trends and make future projections. The current study used groundwater level data from 23 monitoring stations over 10 years (2009-2018). Exponential, Gaussian, and Spherical models were cross-matched here for the best predictor model in four fitness measures to determine groundwater concentrations (RMSE, ME, RMSS, ASE), and Box-Jenkins ARIMA (3,1,0) was found best-fit for predictions, and variance estimation. Likewise, cross-validation has been assessed for the accuracy of anticipated results across spatial scales. Although more research is needed to identify the underlying mechanisms, critical zones, and their pattern of modification, possible recharge zones and their locations have been identified. Future groundwater levels, critical zones, and recharge locations have been indicated for the research area and potential recommendations.

Instructional design with ADDIE and rapid prototyping for blended learning: validation and its acceptance in the context of TVET Bangladesh.

Following COVID-19, the global educational landscape shifted dramatically. Almost every educational institute in Bangladesh undertook a strategic move to begin offering online or blended learning courses to mitigate the challenges created by the pandemic. The TVET sector, particularly the polytechnic institute of Bangladesh, endeavored to explore the blended learning approach as an immediate and long-term solution to address the educational dislocation caused by the pandemic. This study attempts to conceptualize a pedagogical design based on the ADDIE and rapid prototyping model to make a reliable and robust instructional design to be used in the blended learning context. A content validity index (CVI) was used to validate the proposed model; a technology acceptance model (TAM) was employed to examine its acceptability to students; and finally, students' academic performances were analysed to evaluate the overall performance of the proposed instructional design. The findings reveal that the proposed instructional design can be a reliable and valid pedagogical approach to be implemented in the blended learning context for polytechnic students. The proposed instructional design may help TVET educators and course designers to create a robust blended learning environment in the TVET sector and in other similar disciplines, such as science and engineering education.

Child Feeding Practices of Childbearing Mothers and Their Household Food Insecurity in a Coastal Region of Bangladesh.

The current condition of ensuring WHO-recommended newborn and young child feeding practice is becoming a challenge in many developing nations, particularly in places where family food security is threatened. Because many households in underdeveloped nations frequently face acute food shortage due to poverty, optimal child nutrition is jeopardized. The purpose of this study was to analyze the child feeding behaviors of mothers with children aged 0-24 months and to investigate their household food insecurity in a coastal region of Bangladesh, Suborno Char (one of Noakhali District's coastal neighbourhoods) was studied from October 2019 to April 2020. In this study, a cross-sectional survey with multistage sampling technique was employed, and Suborno Char of Noakhlai district was purposely sampled of 400 women and their children aged 0-24 months. Data were obtained using a standard questionnaire and were analyzed using statistical functions in SPSS 20.0.0. Exclusive breastfeeding 53.5%, timely supplemental feeding 75.5%, and feeding children meals from homemade, canned/formula/fortified, both homemade and canned, milk these 4 food groups were at 22.2%. The moms' education level was strongly associated with their child feeding practices. Mothers from food insecure homes were less likely to exclusively breastfeed than mothers from food secure households after drawing interpretation of univariate & multivariate analysis of variables crude odds ratio (COR) and adjusted odds ratio (AOR) and confidence interval (CI) (COR .233 at 95% CI 0.083, .655 and AOR .478 at 95% CI 0.133, 1.713). Similar outcomes were seen for early complementary feeding and minimal dietary diversification of children. In conclusion, this study discovered a less dietary inclusion trend of child feeding practices among moms (0-24 months childbearing) in Bangladesh's coastal region.

Exploring the Factors Affecting Behavioural Intention to Use Google Classroom: University Teachers' Perspectives in Bangladesh and Nigeria.

University teachers in Bangladesh and Nigeria, in general, are novices in using modern technologies such as Google classroom in the teaching learning process. This study aims to explore teachers' attitudes towards the use of Google classroom and investigate the factors that influence the teachers' acceptance and behavioural intentions to use it as a learning management system. A quantitative method has been used to examine the teachers' acceptance of Google classroom based on a simplified technology acceptance model (TAM) in two universities located in Bangladesh and Nigeria. The study reveals that Bangladeshi teachers have a higher positive attitude towards accepting this platform compared to Nigerian teachers. Besides, Nigerian teachers are impacted significantly by the technology challenges during its use. The findings of this study inform educators of the key aspects of Google classroom use which could enable them to effectively adopt it during and post the COVID-19 pandemic.

Balanced diet related knowledge, attitude and practices (KAP) among adolescent school girls in Noakhali district, Bangladesh: a cross sectional study.

OBJECTIVES: Adolescence, a crucial period for growth velocity including sexual maturation, is an important period of life and young adolescent girls need to have proper knowledge, positive attitudes and practice of balanced diet to achieve optimum growth and development. Considering its importance this study attempts to assess the knowledge, attitude and practice (KAP) of balanced diets among adolescent schoolgirls in Noakhali district, Bangladesh. METHODS: A descriptive cross-sectional study was conducted in three secondary schools in Noakhali district, Bangladesh. A multistage sampling techniques was used to select schools and study participants. A total of 485 adolescent school girls participated and a pre-designed, validated and structured questionnaire was used for data collection. In addition to descriptive statistics, chi-square test and logistic regression were employed for analysis. RESULTS: The results from the study revealed that more than half of the students did not have good knowledge about a balanced diet, 42.8% had a positive attitude, whereas the practice of consuming a balanced diet was rather low (only 4.5%). Students studying in government schools had better knowledge and attitudes than non-government school students. Moreover, 41.1% of students were underweight; more than 60% of respondent's mothers did not have an education higher than secondary school and were mostly unemployed housewives. Parent's education, mother's occupation, and types of school have a strong association with KAP; however, father's education and occupation showed an association of varying degrees. Being knowledgeable was related to positive attitudes, and students with good knowledge had (OR: 1.828; 95% CI: 1.270-2.631) more likely to have positive attitudes towards a balanced diet. CONCLUSIONS: Thus, proper knowledge about a balanced diet should be introduced with basic nutritional knowledge to all levels of public education. Interventions and programs can be designed to improve knowledge, attitude, and more specifically the practice at the household level so that adolescent girls can have a proper diet during their sexual maturation phase.

Factors Affecting Fast Food Consumption among College Students in South Asia: A Systematic Review.

Unhealthy dietary practices have increased among college students in South Asia. Higher intakes of energy-dense foods may increase health risks such as weight gain in college students. The purpose of the study was to gather evidence on fast food consumption patterns and factors that influenced fast food consumption among college students in South Asia. Electronic databases (CINAHL, PsycINFO, PubMed, and ScienceDirect) were used to find relevant potential English-written articles. We followed the standard process of identification, screening, eligibility assessment, study quality assessment, and data extraction from the included articles in this review. In total, data were synthesized from 28 articles. Factors such as gender, being younger, higher socio-economic class, higher body mass index (being overweight or obese), study groups such as students from Humanities, low nutrition knowledge, and the internet addiction played the roles as positive determinants (all P values < 0.05) of consuming fast food frequently. Furthermore, taste, brand reputation, accessibility, location, price, ambiance, hygiene practice, variety, promotional offers, and timely service significantly (all P values < 0.05) increased intakes of fast food in college students. Engaged institutional nutrition education intervention and multisectoral approaches are necessary to reduce fast food consumption in college students along with enabling them to make healthy food choices.Key teaching pointsFrequency of fast food consumption (daily to several times a week) has increased among college students in recent years in South Asia.This systematic review shows that a number of socio-economic, physiological, and behavioral factors influence fast food consumption in college students.Commonly, male students and persons with higher body mass index consume fast food more often. Taste, price, and convenience, accessibility, availability, ambiance, and location also promote fast food consumption in college students.Nutrition interventions are needed to promote healthy dietary intake behavior among college students in South Asia.

Review of flipped learning in engineering education: Scientific mapping and research horizon.

The COVID-19 pandemic makes flipped learning more relevant to address the challenges of remote learning. Therefore, renewed attention is warranted in critically appraising the implications on which flipped learning is built. Though several studies have reviewed the flipped learning research in the past, the majority has qualitatively synthesized the flipped learning literature, thus, lacking the overall perspective provided quantitatively for appraising the existing state of affairs of flipped learning research in engineering education. This study addresses this gap by objectively mapping the conceptual, intellectual, and social structure of research development in flipped learning using a bibliometric review method. Findings reveal that flipped learning in engineering education is a relatively new field of research and in recent time it has entered into the stage of exponential growth. Findings also show the effectiveness of the flipped learning model to address the challenges of complex pedagogical applications in different fields of engineering education. This study provides a quantitative synopsis of the flipped learning literature which can be used as an anchor for future study.

Growth and trend analysis of area, production and yield of rice: A scenario of rice security in Bangladesh.

Bangladesh positioned as third rice producing country in the world. In Bangladesh, regional growth and trend in rice production determinants, disparities and similarities of rice production environments are highly desirable. In this study, the secondary time series data of area, production, and yield of rice from 1969-70 to 2019-20 were used to investigate the growth and trend by periodic, regional, seasonal and total basis. Quality checking, trend fitting, and classification analysis were performed by the Durbin-Watson test, Exponential growth model, Cochrane-Orcutt iteration method and clustering method. The production contribution to the national rice production of Boro rice is increasing at 0.97% per year, where Aus and Aman season production contribution significantly decreased by 0.48% and 0.49% per year. Among the regions, Mymensingh, Rangpur, Bogura, Jashore, Rajshahi, and Chattogram contributed the most i.e., 13.9%, 9.8%, 8.6%, 8.6%, 8.2%, and 8.0%, respectively. Nationally, the area of Aus and Aman had a decreasing trend with a -3.63% and -0.16% per year, respectively. But, in the recent period (Period III) increasing trend was observed in the most regions. The Boro cultivation area is increasing with a rate of 3.57% per year during 1984-85 to 2019-20. High yielding variety adoption rate has increased over the period and in recent years it has found 72% for Aus, 73.5% for Aman, and 98.4% for Boro season. As a result, the yield of the Aus, Aman, and Boro seasons has been found increasing growth for most of the regions. We have identified different cluster regions in different seasons, indicating high dissimilarities among the rice production regions in Bangladesh. The region-wise actionable plan should be taken to rapidly adopt new varieties, management technologies and extension activities in lower contributor regions to improve productivity. Cluster-wise, policy strategies should be implemented for top and less contributor regions to ensure rice security of Bangladesh.