This present study assessed the overall flood hazards of Sylhet city and adjacent floodplain areas based on a 1D–2D coupled hydraulic model for the recent flood event in 2022 in the Sylhet region. The model was developed utilizing discharge, water level, cross sections, and rainfall data collected from the BWDB and digital elevation model (DEM), as well as Sentinel imagery data from the USGS and Copernicus Hub, respectively. The 1D model was calibrated and validated using the water level datasets of 2004 and 2010. Manning’s roughness coefficient was applied as a tuning parameter for model calibration, ranging from 0.013 to 0.015. In addition, the 2D model was also validated by a qualitative comparison between the simulated and remote sensing-based (Sentinel-1) flood inundation maps. From the unsteady flow simulation, the flood hazard parameters (extent of flood, depth, arrival time, velocity, and duration of the flood) were produced. The produced flood extent map shows that about 50% and 20% of the land areas of the entire modelled area and Sylhet city were inundated during that flood event, respectively. In the depth map, the maximum value for the entire modelled area and Sylhet city is greater than 5 m. The modelling results suggest that the maximum and minimum flood arrival times for a 1 m-depth flood in the entire modelled area are more than 50 days and less than 5 days, respectively. It also suggests that the maximum and minimum flood arrival times for 1 m of depth in Sylhet city are more than 20 days and less than 10 days, respectively. Furthermore, it is observed that the ranges of the maximum and minimum flood velocity for the whole modelled area and the city of Sylhet are greater than 0.4 ms^-1 and lower than 0.1 ms^-1, respectively. Finally, the flood duration map shows that the maximum and minimum flood duration times for a 1 m-deep flood in the modelled area and Sylhet city are more than 115 days and less than 60 days, respectively. Hence, it can be said that the areas with a higher depth, lower arrival time, higher velocity, and longer duration are the most flood-prone and flood-susceptible areas within the study area for the 2022 Sylhet flood event. The study’s findings are expected to aid floodplain management authorities in limiting future flood damage and fatalities.

Climate change and the alarming rate of sea level rise are pausing more threats to the exposed coastal region of Bangladesh from destructive storm surges, as well as fueling the demand for speedier and more reliable surge forecast systems that predict the storm surge with sufficient lead time. However, the currently used physics-based numerical models are computationally costly and time-consuming for surge forecasting and early warning. To address this situation, the current study presents two-layer feed-forward ANN models for 24, 18, 12, and 6-hour lead times to quickly predict the surge height along the exposed coast of Bangladesh using the time series of tropical cyclone parameters and tidal data. Seven historical tropical cyclones from 1995 to 2022, including Sidr (2007), Aila (2009), Roanu (2016), and Sitrang (2022), were chosen to construct the input layer for this study based on the availability of data. The study used tidal level (provided by BIWTA and UHSLC), longitude and latitude of the cyclone eye, central atmospheric pressure, pressure gradient, highest wind speed, and distance from the cyclone eye to the area of interest (provided by NOAA and IMD) as input components. The models were developed through a neural fitting tool in MATLAB, and the best-fitted models were determined using the optimal combination of input layer parameters and the hidden layer, or the number of hidden neurons. In the application of the models to Cox's Bazar, Chittagong, Hiron Point, and Charmontaj, it was found that the best-performing models for a 24, 18, 12, and 6-hour lead time had the best-fit sets of hidden neurons of 30, 50, 20, and 70, respectively. The RMSE of the models spans between 0.121 (for a 12-hour lead time) and 0.151 (for a 24-hour lead time), indicating high precision and accuracy. The models are also rapid, predicting the water level in less than a minute. In short, the proposed method can be adapted to develop reliable models for forecasting surge levels at any other coastal site in Bangladesh.

Carbon credit trading supports to keep the emissions of carbon dioxide within a certain limit and it encourages using green technology. Bangladesh is one of the climate change vulnerable countries of the world. This country emits a minimal amount of carbon in the atmosphere but it is suffering much due to climate change induced problems. Bangladesh has huge potentiality of selling carbon credit to others and it has sold credits in the past. The revenue generated from the sale of carbon credits is used to support the development of additional greenhouse gas reduction projects, finance climate change adaptation measures, or offset the costs of regulatory compliance. Bangladesh's carbon credit trade potential is huge but the country cannot avail it because of various limitations such as absence of a structured framework in this regard, capacity of the relevant stakeholders, insufficient funding, shortage of trained manpower and lack of awareness. Governmental organizations and non-governmental organizations, with the supports of international agencies, can work together to materialize the benefits from this sector. The present paper aims to demonstrate the carbon credit business prospect of Bangladesh. For the present study, data and information were collected from various secondary sources. There are some challenges in realization of carbon credit sales for both buyers and sellers. By considering the pros and cons of carbon credit business, Bangladesh can materialize this issue to tackle the climate change related problems.

Riverbank Erosion an endemic and recurrent natural hazard in Bangladesh in addition to potential threats of global warming related sea level rise problem. The majority of the affected people perceive riverbank erosion as a natural phenomenon. Uttor Anderchar of Sahebrampur Union is a traditional area formed on the banks of Arial Khan river in Kalikini upazila of Madaripur district. The Arial Khan river is a distributary of the Padma river. The river maintains a meander channel throughout its course and it is erosional in nature. It has disastrous socio-economic effects. The degree of economic loss and vulnerability of population due to river bank erosion of Arial Khan river has dramatically increased in recent years. The impact of land loss involves primarily the loss of homestead land, housing structures, crops, cattle, trees and household utensils. Loss of homesteads forces people to move to new places without any option and puts them in disastrous situations. River bank erosion affects people, irrespective of farm sizes. Riverbank erosion causes setback for village agriculture. Along with homestead settlements, it erodes farmland, infrastructure and the communication system. It affects the crop income of vulnerable groups

This research presents a case study on the health and healthcare access of residents living in a remote island, Nijhum Dwip of Bangladesh. The study used multi-strategy research methodology and applied various methods: household survey, FGD, KII and observation to collect data from 10 villages of the Nijhum Dwip. Data analysis suggests that inhabitant's healthcare access is in poor condition, and this situation gets worse during cyclones. The majority of Nijhum Dwip residents (70.4%) purchase medicines from the drug stores (70.4%) without visiting a doctor. Just 25.5% of the residents accesses government or private healthcare facilities, and 4.1 % of them visit Kabiraj. Majority of the child deliveries occur at home with the presence of unskilled midwives (84.7%).The major reasons which work behind this scenario are lack of healthcare facility, high travel cost due to poor communication networks and poverty. In Nijhum Dwip, 79.4% of the population lives below the poverty line, making it difficult for them to pay for medical care (which costs more that 13 USD) and expensive travel (each trip costs more than 26 USD). During the emergency periods, the severity of these problems increases. Only 66.3% of the residents mentioned that they can obtain any medical care during cyclones, of those only 18.1 % visit healthcare centre and 81.9% purchase medications. However, 33% of the resident is left without access to medical care or medications for their injuries or illness in times of calamity. The study identifies two major influencing factors of healthcare access in Nijhum Dwip: spatial inequalities and disasters, both of which require specific attention to improve inhabitants' health and wellbeing and to reach the Sustainable Development Goals 3 and 10 by 2030.

In the face of global climate change it is estimated that the frequency and intensity of high temperature events during summer will increase in the future. The understanding of how affected aphid and aphid-symbiont relationships and how to increase tolerance in the ongoing global warming is a critical issue. Aphids are major agricultural pests thus it is important to evaluate the effects of high temperature stress from the viewpoint of pest management. The objectives of this study were to investigate the effects of high temperature stress on the growth and reproduction of aphids and obligate intracellular endosymbiont Buchnera, has a critical effect on aphid survival and reproduction. We evaluated the effects of heat shock on the pea aphid, Acyrthosiphon pisum Harris (Hemiptera: Aphididae), and its obligate endosymbiont Buchnera aphidicola by means of quantitative PCR in the treated aphids and their offspring after heat shock to analyze how high temperature stress affects the growth of both Buchnera and aphids. First-instar aphids received a single heat shock (35°C for 6 h), repetitive heat shocks (repeat of the single heat shock for 3 days), and control (a constant 20°C). Heat shock negatively affected aphid body length and Buchnera and EF1α densities. Heat-shocked aphids contained lower densities of Buchnera and EF1α than did control aphids when body length was kept constant. Heat-shocked aphids contained lower Buchnera densities relative to EF1α densities. Some heat-shocked aphids became sterile as their Buchnera density was lower than a threshold. The offspring of aphids subjected to a single heat shock recovered the amount of Buchnera, but repetitive heat shocks treated aphid exhibited markedly lower Buchnera and EF1α densities. Thus, heat shock negatively affects both aphid and Buchnera cell proliferation, this could reduce aphid growth, development, reproduction and possibly leads to extinction of local populations. 

The coronavirus disease 2019 (COVID-19) has developed into a global pandemic, affecting every nation and territory in the world. Machine learning-based approaches are useful when trying to understand the complexity behind the spread of the disease and how to contain its spread effectively. The unsupervised learning method could be useful to evaluate the shortcomings of health facilities in areas of increased infection as well as what strategies are necessary to prevent disease spread within or outside of the country. To contribute toward the well-being of society, this paper focuses on the implementation of machine learning techniques for identifying common prevailing public healthcare facilities and concerns related to COVID-19 as well as attitudes to infection prevention strategies held by people from different countries concerning the current pandemic situation. Regression tree, random forest, cluster analysis, and principal component machine learning techniques are used to analyze the global COVID-19 data of 133 countries obtained from the Worldometer website as of April 17, 2020. The analysis revealed that there are four major clusters among the countries. Eight countries have the highest cumulative infected cases and deaths, forming the first cluster. Seven countries, the United States, Spain, Italy, France, Germany, the United Kingdom, and Iran, play a vital role in explaining the 60% variation of the total variations by us of the first component characterized by all variables except for the rate variables. The remaining countries explain only 20% of the variation of the total variation by use of the second component characterized by only rate variables. Most strikingly, the analysis found that the variable number of tests by the country did not play a vital role in the prediction of the cumulative number of confirmed cases.

Background: The COVID-19 pandemic has highlighted the importance of a well-equipped and supported healthcare workforce, and Bangladesh still faces challenges in providing adequate and well-equipped healthcare services. This study aimed to assess the level of working environment/conditions of the clinical health workers in Bangladesh and their relative importance in delivering quality healthcare services. Some studies touch on aspects like job satisfaction, retention, and motivation in Bangladesh. However, a comprehensive overview is missing, which is crucial for enhancing care quality by addressing challenges. This research covered the clinical health workforce working environment in public health facilities, encompassing workload, pay, training, safety, layout, recognition, power supply, organizational factors, and more. It also assessed these aspects' importance for providing quality care. Results inform policymakers, healthcare managers, and researchers globally, shaping evidence-based strategies for better healthcare conditions, job satisfaction, retention, and population health.

Methods: The study adopted a cross-sectional design and analyzed primary data collected using quantitative methods from January to March 2022. A multi-stage sampling technique was utilized to select a representative clinical health workforce sample. From the Dhaka, Chattogram, Rajshahi, and Khulna divisions, four districts and eight upazilas were chosen as the study area. Interviews were conducted with 319 clinical workforces, comprising 109 physicians and 210 clinical staff. A questionnaire was developed based on 26 components of the working environment. Descriptive statistics and bivariate analysis were employed. Mean scores were calculated for each working condition component. Mean weight scores were also calculated to assess the relative importance of different components. Non-parametric tests were used to assess differences in responses among respondent categories. A psychometric analysis evaluated survey tool construct validity and reliability.

Results: The study found that the working environment of clinical health workers in primary and secondary healthcare facilities in Bangladesh was quite poor (3.4), with almost two-thirds of respondents showing negative views in 23 out of 26 indicators. The results also showed that working environments were significantly (p≤.05) higher in primary compared to secondary-level facilities. Moreover, men, younger workforce, and workforce with shorter length of service were more likely to report poor working environment than their counterparts. Lastly, receiving monthly salary in due time was top-ranked (99.15) in terms of importance for delivering quality healthcare, followed by availability of medicines (98.04), medical and surgical requisites (97.57), and adequate mentoring and support to perform duties (97.50).

Conclusion: The study highlights the poor working environment of clinical health workers in public health facilities in Bangladesh. It recommends that policymakers should prioritize improving the working environment by addressing the factors that are crucial for delivering quality healthcare. Improving the working environment will have a positive impact on the retention and motivation of workers, which will ultimately lead to better health outcomes for the population.

The complicated relationship between the Air Quality Index (AQI) and main pollutants (PM2.5, PM10, CO, NO2, SO2, and O3) in various air monitoring areas across Bangladesh is investigated in this extensive study, which spans the years 2014 to 2023. The study carefully examines the variables affecting air quality dynamics by using meteorological data such as minimum temperature, maximum temperature, relative humidity, and rainfall. The goal of the study is to clarify the intricate interactions between AQI and contaminants by illuminating historical trends and interactions between them. These contaminants and meteorological data are used to create predictive models for AQI utilizing cutting-edge machine learning techniques, providing subtle insights into spatiotemporal fluctuations of air quality in Bangladesh. This in-depth analysis makes a significant contribution to our understanding of the historical trends and relationships among AQI, pollutants, and meteorological variables, facilitating the creation of precise predictive models crucial for proactive actions to reduce air pollution and protect public health. These findings will serve as the basis for evidence-based policies and initiatives that will manage Bangladesh's air quality in a sustainable manner.

This study aims to assess earthquake-induced landslide hazard in Chittagong Metropolitan Area (CMA). The region is at high risk of landslides that have caused loss of lives and properties in the past. Due to the tectonic settings, the earthquake ground motion can act as an active contributing factor to landslides in the Tertiary soft sedimentary rocks of the hilly terrains of Bangladesh. That’s why the contribution of ground motion in causing landslides needs to be considered. Therefore, this research investigated the landslide-prone areas caused by earthquakes in the hilly areas of Chittagong Metropolitan Area (CMA) along with other contributing parameters. In this study, topographical parameters are prepared using the DEM data. The physical and engineering properties of the soils have been determined from the laboratory testing of the soil samples collected during field investigation. This study used the peak ground acceleration (PGA) value for 2% and 10% probability of exceedance (PoE) in 50 years. The pseudo-static model is used to derive the landslide hazard scenarios in the CMA by employing two distinct approaches, i. e., direct estimation and Monte-Carlo (MC) simulation. ArcMap 10.8 has been used for the direct estimation approach, while Python scripting has been used for the Monte-Carlo simulation. This work evaluates the landslide-prone areas of CMA produced by the earthquake ground motion and other triggering parameters by comparing the results from Monte-Carlo simulation and the direct estimation approaches. The research identified 37% and 35% of the hilly terrain of the CMA as landslide-prone regions for 2% and 10% PoE of PGA in the MC simulation approach, and again 28% and 26% landslide-prone regions for 2% and 10% PoE of PGA respectively in the direct estimation approach. The results of this analysis will guide engineers and designers in selecting safe locations for buildings and other infrastructures that will protect people's lives and properties from the devastating consequences of landslides.