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Abstract The research aims to explore the vulnerability of Bangladesh to drought by considering a comprehensive set of twenty-four factors, classified into four major categories: meteorological, hydrological, agricultural, and socioeconomic vulnerability. To achieve this, the study utilized a knowledge-based multi-criteria method known as the Analytic Hierarchy Process (AHP) to delineate drought vulnerability zones across the country. Weight estimation was accomplished by creating pairwise comparison matrices for factors and different types of droughts, drawing on relevant literature, field experience, and expert opinions. Additionally, online-based interviews and group discussions were conducted with 30 national and foreign professionals, researchers, and academics specializing in drought-related issues in Bangladesh. Results from overall drought vulnerability map shows that the eastern hills region displays a notably high vulnerability rate of 56.85% and an extreme low vulnerability rate of 0.03%. The north central region shows substantial vulnerability at high levels (35.85%), while the north east exhibits a significant proportion (41.68%) classified as low vulnerability. The north west region stands out with a vulnerability rate of 40.39%, emphasizing its importance for drought management strategies. The River and Estuary region displays a modest vulnerability percentage (38.44%), suggesting a balanced susceptibility distribution. The south central and south east regions show significant vulnerabilities (18.99% and 39.60%, respectively), while the south west region exhibits notable vulnerability of 41.06%. The resulting model achieved an acceptable level of performance, as indicated by an area under the curve value of 0.819. Policymakers and administrators equipped with a comprehensive vulnerability map can utilize it to develop and implement effective drought mitigation strategies, thereby minimizing the losses associated with drought.

Abstract Groundwater is a primary source of drinking water for billions worldwide. It plays a crucial role in irrigation, domestic, and industrial uses, and significantly contributes to drought resilience in various regions. However, excessive groundwater discharge has left many areas vulnerable to potable water shortages. Therefore, assessing groundwater potential zones (GWPZ) is essential for implementing sustainable management practices to ensure the availability of groundwater for present and future generations. This study aims to delineate areas with high groundwater potential in the Bankura district of West Bengal using four machine learning methods: Random Forest (RF), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), and Voting Ensemble (VE). The models used 161 data points, comprising 70% of the training dataset, to identify significant correlations between the presence and absence of groundwater in the region. Among the methods, Random Forest (RF) and Extreme Gradient Boosting (XGBoost) proved to be the most effective in mapping groundwater potential, suggesting their applicability in other regions with similar hydrogeological conditions. The performance metrics for RF are very good with a precision of 0.919, recall of 0.971, F1-score of 0.944, and accuracy of 0.943. This indicates a strong capability to accurately predict groundwater zones with minimal false positives and negatives. Adaptive Boosting (AdaBoost) demonstrated comparable performance across all metrics (precision: 0.919, recall: 0.971, F1-score: 0.944, accuracy: 0.943), highlighting its effectiveness in predicting groundwater potential areas accurately; whereas, Extreme Gradient Boosting (XGBoost) outperformed the other models slightly, with higher values in all metrics: precision (0.944), recall (0.971), F1-score (0.958), and accuracy (0.957), suggesting a more refined model performance. The Voting Ensemble (VE) approach also showed enhanced performance, mirroring XGBoost's metrics (precision: 0.944, recall: 0.971, F1-score: 0.958, accuracy: 0.957). This indicates that combining the strengths of individual models leads to better predictions. The groundwater potentiality zoning across the Bankura district varied significantly, with areas of very low potentiality accounting for 41.81% and very high potentiality at 24.35%. The uncertainty in predictions ranged from 0.0 to 0.75 across the study area, reflecting the variability in groundwater availability and the need for targeted management strategies. In summary, this study highlights the critical need for assessing and managing groundwater resources effectively using advanced machine learning techniques. The findings provide a foundation for better groundwater management practices, ensuring sustainable use and conservation in Bankura district and beyond.

Abstract Water pollution is a major concern for a decaying river. Polluted water reduces ecosystem services and human use of rivers. Therefore, the present study aims to assess the irrigation suitability of the Jalangi River water. A total of 34 pre-selected water samples were gathered from the source to the sink of the Jalangi River with an interval of 10 km and one secondary station’s data from February 2012 to January 2022 were used for this purpose. The Piper diagram exhibits that the Jalangi River water is Na+–HCO3 − types, and the alkaline earth (Ca2+ + Mg2+) outperforms alkalises (Na+ + K+) and weak acids (HCO3 − + CO3 2−) outperform strong acids (Cl− + SO4 2−). SAR values ranging from 0.35 to 0.64 show that water is suitable for irrigation and poses no sodicity risks. The %Na results show that 91.18% of water samples are good and acceptable for irrigation. RSC levels indicate a significant alkalinity hazard, with 94.12% of samples considered inappropriate for irrigation. PI findings show that 91.18% of water samples are suitable for irrigation. Apart from the spatial water samples, seasonal water samples exhibit a wide variations as per the nature of irrigation hazards. Gibbs plot demonstrates that the weathering of rocks determined the hydro-chemical evolution of Jalangi River water. This study identifies very little evaporation dominance for pre- and post-monsoon water. The analysis of variance (ANOVA) test illustrates that there are no spatial variations in water quality while seasonal variations are widely noted (p

Social media and climate change are some of the most controversial issues of the 21st century. Despite numerous studies, our understanding of current social media trends, popular hot topics, and future challenges related to climate change remains significantly limited. This research presents a systematic review of climate change and social media for the first time. Review the studies published between 2009 and 2022 in places like Google Scholar, Science Direct, Web-of-Science, Scopus, ResearchGate, and others. For this systematic review, we found 1,057 articles. Forty-five articles were the most relevant according to our goals and study design, which followed the PRISMA framework. The results of this review demonstrate that Twitter is the most popular platform. Every year, we identify rising trends in the number of publications. Past studies often focused on just one social media site, like Twitter (n = 26) or Facebook (n = 5). Although most studies focus on the United States, the study area is primarily “all over the world.” This study offers a theoretical framework by examining the relationship between social media platforms and the discourse surrounding climate change. It looked into how social media trends influence public perception, raise awareness, and spur action on climate change. In practical terms, the study focuses on important and trending topics like nonbelievers and climate change. The contribution consists of synthesizing the body of research, providing insights into the state of the digital world, and suggesting future lines of inquiry for the field of social media and climate change studies. We highlighted the studies’ quality assessment result of “moderate quality.” This systematic review provides information about how climate change is now portrayed on social media and lays the groundwork for further study in this area.

Microplastic pollution has a significant threat to marine ecosystems, yet its impact on spineless cuttlefish (Sepiella inermis) remains under-researched. This study aims to address this gap by analysing microplastic contamination in Sepiella inermis from the North-East Bay of Bengal. This species is widely consumed and transported globally as food, thus holding significant health concerns. A total of 40 adult female cuttlefish were collected from two sampling sites (18°36′31.35″N 87°48′10.63″E and 15°43′35.37″N 88°12′07.01″E) in the Bay of Bengal. Tissue samples from tentacles, gut, and nidamental glands were analysed for microplastic content, alongside sediment and surface water samples. Parameters such as microplastic abundance, size, shape, and colour were recorded. The average abundance of microplastic particles was measured at 2.003 particles per gram in tentacle tissue, 2.31 particles per gram in gut tissue, and 0.99 particles per gram in nidamental gland tissue. The gut tissue exhibited the highest abundance of microplastics per gram. Chemical characterization using FT-IR and confocal Raman spectroscopy identified 11 types of microplastic polymers. Of the 11 types of plastic polymers identified, PVC was the most prevalent, accounting for 17.64 % of the microplastics found across all tissues. PVC microplastics can cause significant harm to marine life and human health by accumulating in the food chain and releasing harmful chemicals like phthalates, which can lead to endocrine disruption. ABS, PET, PP, PE, and PA microplastic polymers are highly persistent in environment, leading to long-term pollution in oceans. When ingested by marine organisms, they can disrupt entire ecosystems. In humans, the accumulation of these microplastics can impair the immune system and contribute to chronic diseases. The Pollution Load Index (PLI) was calculated for each tissue type, revealing that gut tissue is more prone to microplastic pollution compared to the nidamental gland and tentacles. The average PLI per gram of gut tissue was 2.26, which was significantly higher than 1, indicating substantial pollution. This research highlights the urgent need for comprehensive strategies to mitigate microplastic pollution, given the potential health risks associated with the consumption of contaminated marine species.

The east coast of India, especially the coastal region of Odisha, is highly threatened by tropical cyclones. This study develops a detailed risk map for tropical cyclones in the coastal districts of Odisha at the micro level, focusing on the assessment of risk factors at the block level. Using a multi-criteria decision making (MCDM) approach, the study considers four primary risk components: Exposure, vulnerability, susceptibility, and mitigation options. The Explainable Artificial Intelligence (XAI) framework, which uses the XGBoost model in conjunction with SHAP values, is applied to identify and elucidate the factors influencing risk levels in 69 blocks. Results indicate that about 65% of the area is at high risk to tropical cyclone, especially in the northeastern and central regions. In particular, 32 blocks are classified as high to very high-risk zones. The study shows a contrast in risk levels, with blocks in the northeast and southeast at higher risk, while blocks in the southern regions such as Ganjam and Puri and in the central parts of Kendrapara and Baleswar districts are at lower risk. The findings from this study are crucial for local authorities to identify vulnerable areas and improve cyclone preparedness and risk management strategies in Odisha.

Abstract The aim of the study was to estimate future groundwater potential zones based on machine learning algorithms and climate change scenarios. Fourteen parameters (i.e., curvature, drainage density, slope, roughness, rainfall, temperature, relative humidity, lineament density, land use and land cover, general soil types, geology, geomorphology, topographic position index (TPI), topographic wetness index (TWI)) were used in developing machine learning algorithms. Three machine learning algorithms (i.e., artificial neural network (ANN), logistic model tree (LMT), and logistic regression (LR)) were applied to identify groundwater potential zones. The best-fit model was selected based on the ROC curve. Representative concentration pathways (RCP) of 2.5, 4.5, 6.0, and 8.5 climate scenarios of precipitation were used for modeling future climate change. Finally, future groundwater potential zones were identified for 2025, 2030, 2035, and 2040 based on the best machine learning model and future RCP models. According to findings, ANN shows better accuracy than the other two models (AUC: 0.875). The ANN model predicted that 23.10 percent of the land was in very high groundwater potential zones, whereas 33.50 percent was in extremely high groundwater potential zones. The study forecasts precipitation values under different climate change scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) for 2025, 2030, 2035, and 2040 using an ANN model and shows spatial distribution maps for each scenario. Finally, sixteen scenarios were generated for future groundwater potential zones. Government officials may utilize the study’s results to inform evidence-based choices on water management and planning at the national level.

Climate change has substantially increased both the occurrence and intensity of flood events, particularly in the Indian subcontinent, exacerbating threats to human populations and economic infrastructure. The present research employed novel ML models—LR, SVM, RF, XGBoost, DNN, and Stacking Ensemble—developed in the Python environment and leveraged 18 flood-influencing factors to delineate flood-prone areas with precision. A comprehensive flood inventory, obtained from Sentinel-1 Synthetic Aperture Radar (SAR) data using the Google Earth Engine (GEE) platform, provided empirical data for entire model training and validation. Model performance was assessed using precision, recall, F1-score, accuracy, and ROC-AUC metrics. The results highlighted Stacking Ensemble’s superior predictive ability (0.965), followed closely by, XGBoost (0.934), DNN (0.929), RF (0.925), LR (0.921), and SVM (0.920) respectively, establishing the feasibility of ML applications in disaster management. The maps depicting susceptibility to flooding generated by the current research provide actionable insights for decision-makers, city planners, and authorities responsible for disaster management, guiding infrastructural and community resilience enhancements against flood risks.

Despite the many fatalities and injuries, Bangladesh lacks holistic individual flood preparedness studies. This study evaluates flood preparedness in the flood-prone rural region of Dowarabazar Upazila, Sunamganj District, Bangladesh. The field survey had 596 respondents. As required, we implemented Spearman’s rank correlation and multiple linear regression. Flooding affected the study population, which made people feel extremely unsafe. Most respondents warned that floods might affect their homes. Many participants reported receiving flood-related information from locals. Most participants reported not being notified of flood forecasts and warnings in their area. The majority indicated that they sought refuge in flood shelters. A significant portion of the participants in the study were unaware of flood preparedness. Loss minimization and adaptability are deficiencies. Concerning flood preparedness, gender, age, occupation, monthly income, multiple sources of income, and house type are all significant determinants. According to field research, females are less prepared for flooding than males. Individuals with pucca housing are better equipped than those with unsafe housing (kacha). Authorities should thus take these factors into account while enhancing flood preparedness. In addition to implementing measures such as improving livelihood patterns and conducting educational campaigns, it is imperative to construct holistic flood preparedness.

Background Emergency Department (ED) crowding is a global public health issue with low-acuity ED attendances (LAA) being widely identified as contributors to the issue. A primary care partnership programme, GPFirst, was developed to encourage low-acuity patients to visit their General Practitioners (GP) first, instead of the ED. We utilised a qualitative approach to gain an understanding of participants’ perceptions of GPFirst, their health-seeking behaviours, and explore recommendations for enhancing GPFirst. Methods A qualitative study design, underpinned by an interpretivist approach, was employed. Purposive sampling was used to recruit 12 GPFirst patients from a single tertiary care centre for semi-structured interviews. These interviews were analysed using Braun and Clarke’s reflexive thematic analysis. Results Participants’ age ranged from 25 to 63 years old. Four themes were identified. Firstly, “ perceptions of GPs” , described the variations in participants’ perceptions about GPs. Secondly, “patients’ expectations of GPFirst” , noted the ED fee subsidy and prioritisation of ED care as key expectations of GPFirst. Thirdly, “facilitators and barriers to utilising GPFirst” highlighted the facilitators and barriers that influenced participants’ health-seeking behaviours and finally “recommendations for programme improvement” offered strategies for improvements. Conclusion This is the first qualitative study to examine participants’ perceptions of GPFirst. Recommendations may inform future programme redesign to optimise patient experience and facilitate programme uptake, contributing to overall efforts to reduce ED crowding locally. Future research may consider exploring GPFirst providers’ perspectives to elicit facilitators and barriers in implementation.

Abstract In recent years groundwater contamination through nitrate contamination has increased rapidly in the managementof water research. In our study, fourteen nitrate conditioning factors were used, and multi-collinearity analysis is done. Among all variables, pH is crucial and ranked one, with a value of 0.77, which controls the nitrate concentration in the coastal aquifer in South 24 Parganas. The second important factor is Cl−, the value of which is 0.71. Other factors like—As, F−, EC and Mg2+ ranked third, fourth and fifth position, and their value are 0.69, 0.69, 0.67 and 0.55, respectively. Due to contaminated water, people of this district are suffering from several diseases like kidney damage (around 60%), liver (about 40%), low pressure due to salinity, fever, and headache. The applied method is for other regions to determine the nitrate concentration predictions and for the justifiable alterationof some management strategies.

The aim of this paper is to design a novel compact and wideband feed for monopulse antenna using the Orbital Angular Momentum (OAM) modes of 0 and 1. The OAM mode 0 produce the sum pattern and the OAM mode 1 generate the difference pattern. Based on the correspondence between the OAM mode 0 and the circular polarized (CP) TE11 mode in circular waveguide, as well as between the OAM mode 1 and the circular polarized TE21 mode, the feed structure consists of two circular waveguides with different radius. In the waveguides with smaller and larger radius, the CP TE11 mode and TE21 mode are excited respectively, using the novel proposed mode coupler. Each mode coupler has two pairs of arms, one of which excites the TEi1 mode (i = 1,2) and the other the orthogonal ones (TE $^{\bot }$ i1, i = 1,2) with 90-phase difference inside the circular waveguide. The frequency bandwidth of the proposed feed is from 9.65-10.85 GHz (11.7%) and the isolation between TE11 and TE21 mode couplers is -30 dB. The frequency bandwidth of the proposed structure is very higher than the state of the art references. Moreover, it has low side lobe levels as well as compact structure with high power handling. These specifications prove the ability and capability of the proposed structure in radar applications.

Abstract Determining the degree of high groundwater arsenic (As) and fluoride (F−) risk is crucial for successful groundwater management and protection of public health, as elevated contamination in groundwater poses a risk to the environment and human health. It is a fact that several non-point sources of pollutants contaminate the groundwater of the multi-aquifers of the Ganges delta. This study used logistic regression (LR), random forest (RF) and artificial neural network (ANN) machine learning algorithm to evaluate groundwater vulnerability in the Holocene multi-layered aquifers of Ganges delta, which is part of the Indo-Bangladesh region. Fifteen hydro-chemical data were used for modelling purposes and sophisticated statistical tests were carried out to check the dataset regarding their dependent relationships. ANN performed best with an AUC of 0.902 in the validation dataset and prepared a groundwater vulnerability map accordingly. The spatial distribution of the vulnerability map indicates that eastern and some isolated south-eastern and central middle portions are very vulnerable in terms of As and F− concentration. The overall prediction demonstrates that 29% of the areal coverage of the Ganges delta is very vulnerable to As and F− contents. Finally, this study discusses major contamination categories, rising security issues, and problems related to groundwater quality globally. Henceforth, groundwater quality monitoring must be significantly improved to successfully detect and reduce hazards to groundwater from past, present, and future contamination.

Dereje Tegene, Tesfalem Assefa, Abdurahman Edris Department of Obstetrics and Gynecology, Adama Hospital Medical College, Adama, EthiopiaCorrespondence: Dereje Tegene, Tel +251913909665, Email fekadutd79@gmail.comBackground: Hydrometrocolpos is a rare clinical condition characterized by the accumulation of fluid or mucus secretions in the vagina and uterus due to distal vaginal obstruction. Imperforate hymen is the most common cause of congenital hydrometrocolpos. The diagnosis of congenital hydrometrocolpos can be made prenatally or postnatally. Imperforate hymen is generally managed by hymenotomy or hymenectomy.Case Presentation: This is a 26-day-old female neonate who was born from a 29-year-old Para − 3 mother after 9 months of amenorrhea. ANC follow-up was uneventful except her obstetric ultrasound scan at 36+3 weeks found a fetal lower intra-abdominal mass. Without proper postnatal evaluation, the neonate was discharged home with the mother after 12 hours of vaginal delivery. Currently, the neonate was referred to our hospital with the diagnosis of Acute Urinary retention, 2o Hydrometrocolpos, after having frequent crying, abdominal distension, and failure to pass urine of 2 days duration. On physical examination, there was a tender, cystic, and mobile mass palpable in the lower abdomen, and up to opening the labia there was a curved membrane covering the vaginal opening. Abdomino-pelvic ultrasound and Magnetic Resonance Imaging confirmed the hydrometrocolpos and imperforate hymen. After obtaining informed consent from the family, a vertical incision and annular hymenotomy were done to drain 200mL of milky fluid. A follow-up ultrasound was done to confirm the resolution of HMC and neonate discharge on the 4th day with improvement.Conclusion: A high index of suspicion and immediate postnatal evaluation of neonates presented with prenatal U/S showing intra-abdominal mass will result in early diagnosis and treatment of neonatal hydrometrocolpos before the development of complications.Keywords: Adama, imperforate hymen, neonatal hydrometrocolpos

Abstract Soil salinity is a pressing issue for sustainable food security in coastal regions. However, the coupling of machine learning and remote sensing was seldom employed for soil salinity mapping in the coastal areas of Bangladesh. The research aims to estimate the soil salinity level in a southwestern coastal region of Bangladesh. Using the Landsat OLI images, 13 soil salinity indicators were calculated, and 241 samples of soil salinity data were collected from a secondary source. This study applied three distinct machine learning models (namely, random forest, bagging with random forest, and artificial neural network) to estimate soil salinity. The best model was subsequently used to categorize soil salinity zones into five distinct groups. According to the findings, the artificial neural network model has the highest area under the curve (0.921), indicating that it has the most potential to predict and detect soil salinity zones. The high soil salinity zone covers an area of 977.94 km2 or roughly 413.51% of the total study area. According to additional data, a moderate soil salinity zone (686.92 km2) covers 30.56% of Satkhira, while a low soil salinity zone (582.73 km2) covers 25.93% of the area. Since increased soil salinity adversely affects human health, agricultural production, etc., the study's findings will be an effective tool for policymakers in integrated coastal zone management in the southwestern coastal area of Bangladesh.

The crashworthiness of electric vehicles depends on the response of lithium-ion cells to significant deformation and high strain rates. This study thoroughly explores the mechanical behavior due to damage of lithium-ion battery (LIB) cells, focusing on Lithium Nickel Manganese Cobalt Oxide (NMC) and Lithium Iron Phosphate (LFP) types during both quasi-static indentation and dynamic high-velocity penetration tests. Employing a novel approach, a hemispherical indenter addresses gaps in stress–strain data for pouch cells, considering crucial factors like strain rate/load rate and battery cell type. In the finite element method (FEM) analysis, the mechanical response is investigated in two stages. First, a viscoplastic model is developed in Abaqus/Standard to predict the indentation test. Subsequently, a thermomechanical model is formulated to predict the high-speed-impact penetration test. Considering the high plastic strain rate of the LIB cell, adiabatic heating effects are incorporated into this model, eliminating heat conduction between elements. Addressing a notable discrepancy from prior research, this work explores the substantial reduction in force observed when transitioning from a single cell to a stack of two cells. The study aims to unveil the underlying reasons and provide insights into the mechanical behavior of stacked cells.

Abstract Spatiotemporal rainfall trend analysis as an indicator of climatic change provides critical information for improved water resource planning. However, the spatiotemporal changing behavior of rainfall is much less understood in a tropical monsoon-dominated country like Bangladesh. To this end, this research aims to analyze spatiotemporal variations in rainfall for the period 1980–2020 over Bangladesh at seasonal and monthly scales using MAKESENS, the Pettitt test, and innovative trend analysis. Multilayer Perception (MLP) neural network was used to predict the next 8 years' rainfall changes nationally in Bangladesh. To investigate the spatial pattern of rainfall trends, the inverse distance weighting model was adopted within the ArcGIS environment. Results show that mean annual rainfall is 2432.6 mm, of which 57.6% was recorded from July to August. The Mann–Kendall trend test reveals that 77% of stations are declining, and 23% have a rising trend in the monthly rainfall. More than 80% of stations face a declining trend from November to March and August. There is a declining trend for seasonal rainfall at 82% of stations during the pre-monsoon, 75% during the monsoon, and 100% during the post-monsoon. A significant decline trend was identified in the north-center during the pre-monsoon, the northern part during the monsoon, and the southern and northwestern portions during the post-monsoon season. Predicted rainfall by MLP till 2030 suggests that there will be little rain from November to February, and the maximum fluctuating rainfall will occur in 2025 and 2027–2029. The ECMWF ERA5 reanalysis data findings suggested that changing rainfall patterns in Bangladesh may have been driven by rising or reducing convective precipitation rates, low cloud cover, and inadequate vertically integrated moisture divergence. Given the shortage of water resources and the anticipated rise in water demand, the study's findings have some implications for managing water resources in Bangladesh.

Abstract The elevated concentrations of heavy metals in soil considerably threaten ecological and human health. To this end, the present study assesses metals pollution and its threat to ecology from the mid-channel bar’s (char) agricultural soil in the Damodar River basin, India. For this, the contamination factor (CF), enrichment factor (EF), geoaccumulation index (Igeo), pollution index, and ecological risk index (RI) were measured on 60 soil samples at 30 stations (2 from each station, i.e., surface and sub-surface) in different parts of the mid-channel bar. The CF and EF indicate that both levels of char soil have low contamination and hence portray a higher potential for future enrichment by heavy metals. Moreover, Igeo portrays that soil samples are uncontaminated to moderately contaminated. Further, pollution indices indicate that all the samples (both levels) are unpolluted with a mean of 0.062 for surface soils and 0.048 for sub-surface soils. Both levels of the char have a low potentiality for ecological risk with an average RI of 0.20 for the surface soils and 0.19 for the sub-surface soils. Moreover, Technique for order preference by similarity to ideal solution (TOPSIS) indicates that the sub-surface soils have lower pollution than the surface soils. The geostatistical modeling reveals that the simple kriging technique was estimated as the most appropriate interpolation model. The present investigation exhibits that reduced heavy metal pollution is due to the sandy nature of soils and frequent flooding. However, the limited pollution is revealed due to the intensive agricultural practices on riverine chars. Therefore, this would be helpful to regional planners, agricultural engineers, and stakeholders in a basin area.

The research aimed to determine the best components of Malaysia-Counsellor Performance Indicator in measuring the counsellor’s performance in Malaysia. This is the first development phase of the M-CPI. This study involved two type of research designs; quantitative and qualitative approach (Mixed Method). The quantitative data has been obtained from 102 respondents and interview with eight (8) counsellors from different settings. Stratified random sampling technique was utilized to select the respondent and proportional stratification was used to determine the sample size of each stratum. A Need Assessment questionnaire has been developed by the researchers as well as the protocol interview. These two instruments were developed based on the literature reviews of previous instruments that have been invented from the western perspective to measure the performance and competency of counsellors. The results of the study were analysed using the descriptive analysis and thematic analysis. Findings have shown that majority counsellors possessed knowledge and skills in conducting counselling session. Most counsellors in the study demonstrated good interpersonal relationship, interaction, multicultural and religiosity and ethics and professionalism. Through this study, to measure the performance of counsellors, the researchers have found that they must equip themselves with knowledge, skill, interpersonal relationship, interaction, multicultural and religiosity and ethics and professionalism aspects. Based on the interview data, there were new components that have been identified to be added in the Malaysia Counsellor Performance Indicator (M-CPI) which include knowledge (theoretical and knowledge transfer), skills (case management, practical skills and academic/professional writing), interpersonal relationship and interaction, cultural and religiosity, professional roles and expertise, ethics and legality, attitudes and personality, referral and articulate philosophy of profession. In future, research should also focus on the validity and reliability of the components listed in the M-CPI.

Background: Blood transfusion is often necessary during and after lung transplantation surgery. Point-of-care guided bleeding strategies, such as rotational thromboelastometry (ROTEM), can reduce blood transfusion in cardiovascular surgery. This study aimed to assess the effect of ROTEM-guided bleeding management on the need for allogenic blood transfusion, prohemostatic medication, and clinical outcomes in lung transplantation patients. Methods: This single-center retrospective cohort study compared patients receiving bilateral lung transplantation between 2010-2014 and 2017-2020. The first cohort was treated with a clinically guided bleeding strategy and the second cohort with a ROTEM-guided bleeding strategy. Multivariable regression analyses were performed to determine the effects on primary outcomes. Results: A total of 167 (66 clinically guided vs 101 ROTEM-guided) patients were included for analysis. Baseline, intraoperative, and postoperative characteristics were mostly similar, but differed regarding the number of patients with cystic fibrosis, use of cardiopulmonary bypass, and surgical technique. The ROTEM-guided group received significantly less median red blood cells (7 [3; 13] vs 4 [1; 9] units, p

The slum is the shadow of urbanization, representing a lack of essential services, substandard housing, a congested, unhygienic environment, and poverty. Slum morphology depicts the structural arrangement of a slum area, including the road network, building arrangement, cultural artefacts, etc. Slums in megacities like Mumbai, Kolkata, Delhi, etc, have developed naturally or as a result of urbanization. However, apart from megacities, only some areas or few towns, particularly those serving as headquarters of the districts, have a considerable impact on their hinterlands. Purulia town, as the district headquarters, acts as a pull factor, which stimulates the growth of the slum area of its hinterland. Hence, Purulia was selected as a study area. The focus of this study is the morphological dynamicity of slums in Purulia, therefore the temporal analysis is necessary. Additionally, it aims to apprehend the trend and pattern of slum expansion over time in Purulia municipality. This study uses satellite images and secondary data sources (DPR for BLC under Housing for All in slums, Purulia Municipality, 2015–2016) to analyze the dynamicity of slum morphology. Structural appurtenance of the area was measured using spatial variables including, the number of buildings, and the size of the building, building orientation, building density, and heterogeneity. This spatiotemporal analysis depicts inter and intra-slum variation and detects distinct but predominantly high morphological transformation throughout the eight slums. A compact building structure with a particular heterogeneous pattern is shown in many slums. However, some slums remain almost static, experiencing minimal but haphazard morphological transformation. Street patterns, including lane-to-lane, road-to-lane, house-to-house connections, water bodies' presence, and urban planning regulations such as zoning restrictions on high-rise construction etc., influence the slum growth and transform the morphological dynamicity. Apart from structural morphology, it also reflects solid social bonding and cohesion among slum communities. Overall, this research emphasizes the intricate interplay of factors driving morphological dynamicity within the slum area, encompassing socio-economic conditions, population dynamics, neighborhood characteristics, and governmental policies. It emphasizes the need for extensive policy planning for slum upgrading and creating strategies for dwellers to secure land ownership. It also implements a systematic approach to constant monitoring of poor slum areas to capture morphological dynamicity using very high-resolution Earth observation data. This may provide a better understanding of morphological transformations over time and guide policy-making decisions.

Heat waves significantly impact people's lives and livelihoods and are becoming very alarming and recognized as hot topics worldwide, including in Bangladesh. However, much less is understood regarding recent hotspots, the frequency of heat waves over time, and their underlying causes in Bangladesh. The objective of the study is to explore the current scenario and frequency of heat waves and their possible causes across Bangladesh. The Mann-Kendall and Sen's slope techniques were used to determine seasonal and annual temperature trend patterns of heat wave frequencies. Daily maximum temperature datasets collected from the Bangladesh Meteorological Department (BMD) during 1991-2021 are applied. The frequency of days with Tmax≥ 36°C as the threshold was used to compute different types of heat waves based on the BMD's operational definition. The results show that the mild heat wave (MHW) days followed the subsequent hotspot order: Rajshahi (103) > Chuadanga (79), Ishurdi (60), and Jessore (58), respectively. The frequency of days with Tmax≥36°C was persistence for many days in 2014, especially in the western part of Bangladesh compared to other parts. Similarly, the heat waves condition shown its deadliest event by increasing more days in 2021. The highest increasing trend was identified at the Patuakhali site, with a rate of 0.516 days/year, while the highest decreasing trend was noticed at the Chuadanga site, with a rate of -0.588 days/year. The frequency of days (Tmax≥36°C) is an increasing trend in the south-western part of Bangladesh. The synoptic condition in and around Bangladesh demonstrates that the entrance of heat waves in Bangladesh is due to the advection of higher temperatures from the south/southwest of the Bay of Bengal. The outcomes will guide the national appraisal of heatwave effects, shedding light on the primary causes of definite heatwave phenomena, which are crucial for developing practical adaptation tools.

AbstractGroundwater is an essential natural resource that sustains numerous ecological systems and human lifestyles. The Jhargram district is facing persistent groundwater development issues, requiring comprehensive monitoring and planning, as most farmers heavily rely on groundwater for crop production. The groundwater potential zone of the Jhargram district was classified into five classes, viz., very high (5.82%), high (50.81%), moderate (30.33%), low (13.01%), and very low (0.03%), zones, respectively. The results were assessed for validity using ROC curves, which demonstrated an accuracy rate of 80.4%. The calculation of the AUC was performed in order to assess the overall accuracy of the predicted GWPZ. The GWPZ map is crucial for implementing artificial recharge structures like percolation ponds, bunds and trenching in semi-arid regions. It aids in developing sustainable groundwater management policies, mitigating drought, climate change, and water scarcity, and aiding farmers, regional planners, policy-makers, climate change experts, and local governments.

AbstractThe increasing soil erosion (SE) and the associated problems for society, economy, and environment sparked a lot of interest in estimating and mapping SE at different basin scales. The estimation of SE exhibits that SE ranges from 10 to 50 t ha−1 yr−1, with a mean SE of 20 t ha−1 yr−1. The very steep slopes account for 54.21% of total soil loss. The SRB areas where soil loss rates are >10 t ha−1 yr−1 are considered the target areas which account for 27% of the study area and 96% of the soil loss). The high SY is concentrated only in the first-order basins located in a higher slope zone in the northern part of the river. Besides, basin morphometry (basin shape, relative relief) and anthropogenic activities (agricultural land) are retained in the PSLR model as significant factors contributing to SY in the entire river basin.

Bangladesh has successfully reduced the number of deaths caused by tropical cyclones associated with storm surges in its territory. The factors responsible for this success include significant improvements in satellite-based cyclone detection, early warning systems, and emergency evacuation. However, during such events, some residents prefer to not evacuate the region. Based on the first-hand data generated through in-depth interviews, participant observations, focus group discussions with residents aged over 59 years, field visits, and investigations, in this study, we provide information on the shelter-seeking places in Bangladesh during the latest cyclones and analyse the reasons why some residents do not evacuate the affected regions post cyclone warning. The findings suggest that in Bangladesh, community trust in warnings increased from 56% during the 1991 Cyclone Gorky to 96% during the 2020 Cyclone Amphan. We identified 33 reasons for the preference of the population in Bangladesh living along the coast to not move to a cyclone shelter during emergencies; these reasons can be subdivided into six categories: 1) resilient housing, or the residents are living adjacent to strong buildings; 2) reasons associated with public cyclone shelters; 3) to protect livelihoods, properties, and personal belongings; 4) transportation problems and practical issues during the warning period; 5) interrelated community beliefs and perceptions; and 6) reasons directly associated with cyclone warnings. We identified the foundational reasons for the non-evacuation behaviour of the residents and analysed why they preferred to remain home or take shelter in adjacent strong building infrastructures. Notably, our study provides valuable insights into the factors that affect effective evacuation planning and cyclone disaster risk management along the Bangladesh coast.

Since 2000, landslides in southeast Bangladesh have resulted in over 700 deaths, most of which were in informal settlements. For instance, the 2007 landslides in informal settlements in Chittagong and the 2017 landslides in Rangamati took 127 and 150 lives, respectively. Although the government of Bangladesh claims to implement risk reduction, the number of deaths associated with landslides is increasing. Hence, this study investigated the ways to enhance landslide risk reduction interventions by addressing several scientific and technical issues related to disaster risk reduction. This study conducted focus group discussions, key informant interviews and expert interviews with key stakeholders in landslide risk management to collect qualitative data. Moreover, the historical accounts of settlements, media reporting, institutional action plans and policies were reviewed. Contribution: By integrating primary and secondary data, this study found that several political–economic aspects are major anthropogenic contributors to the recent increase in landslides in the region. The contributing factors included the following: policy and action plans to raise regional population; land management; illegal deforestation; plans to establish hydroelectricity in hilly areas; ownership of settlements; manipulation of water, gas and electricity supply to illegal settlements; commercial plantations; lack of risk governance; unplanned development activities; natural population rise; increased settlement along hill slopes. This study identified and discussed lessons learned from previous landslide disasters, the weakness of early warning systems and their dissemination and ways to improve evacuation, rescue, relief and risk reduction. Finally, this study formulated recommendations for the effective implementation of landslide risk reduction in southeast Bangladesh.

AbstractFloods are a recurrent natural calamity that presents substantial hazards to human lives and infrastructure. The study indicates that a significant proportion of the study area, specifically 27.05%, is classified as a moderate flood risk zone (FRZ), while 20.78% is designated as high or very high FRZ. The region’s low and very low FRZ are classified at 52.17%. The GIS-based AHP model demonstrated exceptional predictive precision, achieving a score of 0.749 (74.90%) as determined by the AUC-ROC, a widely used statistical evaluation tool. The current study has identified areas with high FRZ in the affected CD blocks, which are situated in low-lying flood plains, regions with gentle slopes, high drainage density, high TWI, low NDVI, high MNDWI, areas with high population density, intensive agricultural land. The findings of this research offer significant perspectives for decision-makers, city planners, and emergency management agencies in devising efficient measures to mitigate flood risks.

A study of the extended desertification due to anthropogenic causes under climate change (CC) associated with its impact is presented here. Desertification, the main environmental issue, severely impacts agricultural output, causing poverty and economic instability in a nation like India. The regional distribution of desertification was determined using the RF and MaxEnt models. The western, central, and southern portions of the nation are very high, high, and moderately susceptible to desertification, respectively, according to the RF model. The MaxEnt model indicates that the western, central, and southern parts of the country exhibit a significant susceptibility to desertification, with the eastern parts also showing a moderate level of vulnerability. The remaining portion of this region, mainly in the north, east, and northeast, is particularly resistant to desertification. The outcome demonstrated that the country's desertification process had expanded from the west to the south. However, there are some spatial differences associated with the mentioned part of the country. This relevant information is crucial for decision maker of this country to take suitable remedies in regard to the reduction of the intensity of desertification.

AbstractThis research aims to investigate the future changes in summer monsoon rainfall and temperature in Bangladesh. The study revealed that INM-CM5-0 is the best model for projecting temperature, while BCC-CSM2-MR is the best model for projecting rainfall over Bangladesh. Using data from a large ensemble of 27 models from CMIP6, the study examined the rainfall and temperature change projections of Bangladesh during the twenty first century relative to the reference period (1981–2014) under SSP2–4.5 and SSP5–8.5. Under SSP2-4.5 and SSP5-8.5, the multi-model ensemble monsoon mean rainfall over Bangladesh will fluctuate between 40 and 260 mm and 100 and 900 mm, respectively. In most parts of the country’s north, northeastern, and western regions, the projected changes in spatial patterns of monsoon rainfall indicate an increase in rainfall. The projected temperature indicated that Bangladesh’s northwest and west-central areas could face the most significant rise in temperatures, surpassing 3.8 °C under SSP5-8.5.

AbstractOne of the most important aspects of the ‘sub-tropical’ monsoon-influenced environment is the issue of ‘soil erosion’ and its related ‘land degradation’. On the other hand, the climate in this area has become quite extreme. According to this viewpoint, it is important to research a future ‘soil erosion’ scenario in front of the probable effects of climate change and land use change. For the objective of assessing the extent of soil erosion in this area, this study took into account both the USLE and the RUSLE. Compared to the USLE that has been validated, RUSLE has a comparatively greater quantitative efficiency. In RUSLE, the ‘very high’ (>20) and ‘high’ (15–20) ‘soil erosion’ zones tend to be associated with the ‘north-western, western, south-western, and southern’ regions of the river basin. The ‘soil erosion’ that will occur in the future has been estimated by taking into account the projected rainfall, land use and land cover (LULC). ‘Soil erosion’ has increased from the previous time to the projected time. Predicted R factor values for SSP 585 range from 399.92 to 493.72. In addition, a growing erosion tendency associated with increased shared socio-economic pathways (SSPs) has been found.

Conjugated linoleic acid (CLA) has recently attracted significant attention as a health-promoting compound. CLA is a group of positional isomers of linoleic acid (LA) with a conjugated double bond naturally occurring in dairy and ruminant meat products. Microbial biosynthesis of CLA is a practical approach for commercial production due to its high safety and purity. There are some factors for the microbial CLA production such as strain type, microbial growth phase, pH, temperature and incubation time, based on which the amount and type of CLA can be controlled. Understanding the interplay of these factors is essential in optimizing the quantity and composition of microbial CLA, as discussed in the current study. Further exploration of CLA and its influences on human health remains a dynamic and evolving area of study.

We aim to explore the seasonal influences of meteorological factors on COVID-19 era over two distinct locations in Bangladesh using a generalized linear model (GLM) and wavelet analysis. GLM model findings show that summer humidity drives COVID-19 transmission to coastal and inland locations. During the summer in the coastal area, a 1 °C earth’s skin temperature increase causes a 41.9% increase in COVID (95% CL 86.32%-2.54%) transmission compared to inland. Relative humidity was recorded as the highest at 73.97% (95% CL, 99.3%, and 48.63%) for the coastal region, while wind speed and precipitation reduced confirmed cases by −38.62% and −22.15%, respectively. Wavelet analysis showed that coastal meteorological parameters were more coherent with COVID-19 than inland ones. The outcomes of this study are consistent with subtropical climate regions. Seasonality and climatic similarity should address to estimate COVID-19 trends. High societal concern and strong public health measures may decrease meteorological effect on COVID-19.

AbstractBangladesh has a high risk of earthquakes because the Dauki, Jamuna, and Chittagong-Myanmar faults are still active. However, the assessment of seismicity remains a big challenge due to the complex geologic setting of Bangladesh. This study employed the Guttenberg-Richter relationship and the spectral models to assess and analyze the earthquake conditions in Bangladesh. Besides, an instrumental earthquake catalogue, obtained from the Bangladesh Meteorological Department (BMD), covering 1985–2017, is established. The results revealed that the Guttenberg-Richter constants of a and b were 2.981 and 0.392, which propagated a strain release from 1992 to 2017. The spectral model analyses, e.g. wavelet transform (WT), short-time Fourier transformation (STFT), and multitaper model (MTM), demonstrated the magnitude and strain release anomalies of the same magnitude ranging from 4.8 to 5.7, indicating the probable precursor of an upcoming earthquake. Notably, magnitudes have been running around 4.5–5.8, which may act as a signal to major earthquakes that have not been evident before. The proposed models allowed for the completion of the Bangladesh earthquake catalogue and provided a platform for future seismicity assessment and earthquake probability analysis. These results should be considered in determining how likely earthquakes are to happen in an area or region.

BACKGROUND: In the present study, electrical stimulation of the brain from the skull using transcranial direct current stimulation (tDCS) was integrated with a stress-based stress reduction program. The aim of this study was to compare the effectiveness of mindfulness-based stress reduction training with tDCS in reducing anxiety in adolescents with cancer. METHODS: In this quasi-experimental study, the statistical population included all adolescents with cancer in Mahak Hospital in Tehran, Iran, in 2020. According to the inclusion and exclusion criteria, 40 people were selected and purposefully placed in 3 experimental groups and 1 control group. Patients were randomly assigned to experimental and control groups. The data collection tool used was the Penn State Worry Questionnaire (PSWQ). Members of the experimental group participated in sessions of a mindfulness-based stress reduction program and tDCS intervention. Members of the control group did not receive any intervention. To analyze the data, multivariate analysis of covariance (MANCOVA), repeated measures analysis of variance (ANOVA), and least significant difference post hoc tests were used (P < 0.05). RESULTS: The mindfulness-based stress reduction program combined with tDCS was more effective than the separate use of these interventions and reduced anxiety in adolescents with cancer (P < 0.05). Mindfulness-based stress reduction program is more effective than tDCS intervention (P < 0.05). Mindfulness-based stress reduction programs are more effective than tDCS intervention (P < 0.05). CONCLUSION: The use of a combination of mindfulness-based stress reduction therapy with tDCS is more effective than using each of these interventions separately and can be combined with the other standard treatment for these patients.

Jakarta, the biggest city in Indonesia, has one district that consists of hundreds of islands that face severe climate hazards called the Seribu Islands complex. This study explores the evidence of local climate trends, the potential impact, and its policy intervention on Seribu Islands, which are classified as small island states and widely recognized as being especially at risk from climate change, threatening their economic and social growth. Long-term in-situ climate data, satellite data, interviews with local stakeholders, and literature reviews were utilized to conduct an exploratory descriptive analysis. The result revealed that Seribu Island experienced a 2.2°C increase in minimum temperature from 1980 until 2021, 3.5-fold of the frequency of extreme temperature and precipitation, 4.17 mm/year of sea level rise, and 10.8 ha land expansion in the densest island. Moreover, about 67% of the inhabitant’s islands were occupied by built-up areas that cover more than 50% of the region. Further, under the worst-case SLR scenario, about 58.4% of the area will be affected, and about 29 islands will disappear. This evidence was also reinforced by every single local respondent’s viewpoint who felt that climate change is occurring in the region. Even though the region faces a severe threat of climate change, the issue of climate change adaptation has not been mainstreamed yet into their local policy. Therefore, the urgency of a real-time climate ground station, a real-time early warning system, and establishing a Regional Disaster Management Agency (BPBD) at the district level have yet to be addressed. Furthermore, the knowledge gained from such case studies is outlined, along with some scientific evidence that may assist small island states in better fostering the opportunities provided by climate change adaptation.

In Bangladesh, there are roughly 31 marine passenger ships that are in operation. These ships might be a good location for solar photovoltaic (PV) plants since solar energy is the best renewable energy to replace the fossil fuel used in the ships. A “tower rounded flower-shaped solar PV” system of PV panel arrangement—just looks like a “sunflower,” is proposed in this research. To harness maximum power, solar towers are designed in such a way that they may be freely rotated on their vertical axes and that the tilt angles of their solar panels can be adjusted from 0° to 50° on their horizontal axes freely. The “tower rounded flower-shaped solar PV” architecture of the PV array atop a maritime vessel is presented in this research along with a unique method for calculating the PV system's anticipated energy production. Finally calculated the realistic CO 2 emission reduction by using this approach for a sustainable future. Applying globalsolaratlas (for horizon and sun's path estimation); PVsyst 7.2, HOMER Pro, and NREL's PVWatt calculator (for solar radiation calculation); vesselfinder (for the number of vessels analysis); shiptraffic (for vessels path analysis), this research suggests that marine passenger vessels are one of the best places to construct a proposed “tower rounded flower-shaped solar PV” power plant. According to estimates, 17 passenger ships can produce roughly 1240 MW of electricity per year and may save approximately 325.56 tons of CO 2 gas emissions annually to the environment per year as compared to using fossil fuel-based power plants to produce electricity.

The objective of the research is to investigate how refugees’ influx has altered the carbon dynamics of different land uses and the relationship between land use specific carbon emissions and land surface temperature (LST). Two upazilais of the Cox’s Bazar district, Bangladesh (i.e., Ukhiya and Teknaf), were mostly affected by the Rohingya refugee influx and are the focus of the study. The study classified the land use land cover (LULC) into four classes (e.g., agricultural, forest, settlement, and water) for two different time periods (i.e., before and after the influx of Rohingya refugees) using an artificial neural network algorithm and sentinel satellite imagery. Carbon emissions and absorptions specific to land use were calculated using classified land use land cover and coefficients. Again, two time series of Landsat 8 imagery were applied to estimate land surface temperature shifts. The area of forests was found to have decreased by 21.19 square miles (9.58 percent) and the area of settlements to have increased by 18.24 square miles (8.25 percent) between 2017 and 2021. There was a negative net land-use based carbon emission of -5187.02 tons per year in 2017. In 2021, it was predicted that annual net emissions would total 2208.24 tons. LST during the study period has increased as a result of human activities that release greenhouse gases into the atmosphere. The findings of this research will inform policymakers’ decisions about the conservation and sustainable development of natural resources in the region experiencing an influx of Rohingya refugees.

We report a patient who was referred to the emergency room with pulmonary complaints and where a computed tomography (CT) scan showed an unexpected 12.6 cm nail in the thorax penetrating part of the left pulmonary upper lobe, the left pulmonary artery, the left main bronchus, and the descending aorta, which had been in situ for at least three days. The quickly deteriorating patient had to be transferred to a tertiary academic hospital where the nail was successfully surgically removed. The comprehensive description of this unique case with a discussion of the critical decision moments could render insights into the management of challenging trauma cases.

Abstract Background Coronavirus disease 2019 (COVID-19) became a pandemic within a matter of months. Analysing the first year of the pandemic, data and surveillance gaps have subsequently surfaced. Yet, policy decisions and public trust in their country’s strategies in combating COVID-19 rely on case numbers, death numbers and other unfamiliar metrics. There are many limitations on COVID-19 case counts internationally, which make cross-country comparisons of raw data and policy responses difficult. Purpose and conclusions This paper presents and describes steps in the testing and reporting process, with examples from a number of countries of barriers encountered in each step, all of which create an undercount of COVID-19 cases. This work raises factors to consider in COVID-19 data and provides recommendations to inform the current situation with COVID-19 as well as issues to be aware of in future pandemics.

Deforestation is the accelerating factor of climate change in developing countries. The German Watch Report 2020 had rated Pakistan number seventh most affected country due to adverse impacts of climate change. The problem of deforestation poses an existential danger to the forest-depleted country. It is of utmost importance to predict the main drivers to control deforestation. This study was conducted from October 2021 to August 2022 in dry temperate forests of the Chilas to investigate the current condition, causes of deforestation, and predicted the main drivers by using a binary regression model. Stratified random sampling techniques and fixed area plot method were used and taken ground measurements during field inventory to access current situation of deforestation. While a non-probability quota sampling technique and semi-structured questionnaire were utilized for the determination of main drivers of deforestation through respondent’s survey. The forest inventory result showed that most trees fall in immature and sub-mature (mainly in 10–20 and 20–30 cm) diameter classes while the binary logistic regression model predicted dominating four primary drivers (unsustainable fuel wood extraction, unsustainable timber extraction and urban crawling and rural expansion/habituation, and free and uncontrolled livestock grazing) and one secondary driver (wood for energy needs). To address the underlying causes of deforestation, the government must supply alternate energy sources, as well as other economic possibilities to reduce dependency on forests.

Bangladesh is vulnerable to a variety of natural hazards including frequent tropical cyclones, floods and, less commonly, earthquakes and tsunamis. The country has developed an effective response mechanism for frequently experiencing hazards. However, at the same time, the nation has not developed an effective response mechanism for earthquake as a result of limited experiences with this type of hazard in the recent past. This research constructs a new catalogue consisting of 144 earthquakes (between 810BC and 2015) occurring within Bangladesh and the adjacent region. From this new catalogue, the effects of earthquakes are available for 80 events which are used in order to construct the geography of hazard in Bangladesh. The construction of a detailed Modified Mercalli Intensity (MMI) map using the new earthquake data suggests that this will be useful for supporting future risk reduction efforts in Bangladesh. Specifically, the findings support the revision of the current seismic zoning map of Bangladesh to include Chittagong and Dhaka in the high-risk zone for effective earthquake risk reduction. Finally, this research concludes that analyses of long-term data helps to identify new hazard exposure and develop knowledge that is useful to formulate new disaster risk reduction policies and rectify existing know zones in Bangladesh.

Bangladesh's success in disaster risk management is often evidenced by referencing the reduction of deaths caused by tropical cyclones — the Cyclone Gorky 1991 caused 147,000 deaths, the Cyclone Sidr 2007 caused 4500 deaths and only 6 deaths by the Cyclone Mora in 2017. This raises questions of how deaths occurred by tropical cyclones in the past and what factors still might contribute towards deaths from cyclone hazards? This study answers these questions through face-to-face interviews with 362 residents, field visits and observations across coastal Bangladesh. The findings indicate that there have been improvements in house structures and design, warning responses and evacuation processes to public cyclone shelters and informal cyclone shelter centres. In the past, due to a lack of built infrastructure, strong residential houses and public cyclone shelters, deaths occurred whilst living in fragile houses; attempting to survive through holding trees and floating in storm surges. The top ten factors that may still cause deaths by tropical cyclones include: (1) Living adjacent to the coast without an embankment or lack of embankment, or the failure of an established embankment; (2) the repeat of a 1991-like cyclone; (3) non-evacuation following early warning; (4) poor roads in remote areas to facilitate mass movement; (5) distance to and insufficient number of public cyclone shelters; (6) lack of protective measures for the rising number of elderly and disabled people; (7) community's unawareness; (8) communication failure during the emergency period; (9) failure to evacuate people from remote locations; and (10) Poor radio signal and mobile network issues resulting in no warning information being effectively and timely communicated. This study provides several key recommendations addressing these factors of deaths, to be implemented by individual, community, private sectors, non-government organisations (NGOs) and public sectors across coastal Bangladesh.

Study region: The Damodar Fan Delta, West Bengal, India. Study focus: The depletion of groundwater resources worldwide is escalating due to its profuse demand for drinking, irrigation, domestic, and industrial uses. Overexploitation of groundwater in a subtropical fan delta region with rapid population growth like the Damodar Fan Delta in India is of great concern for sustainable mapping, monitoring, and managing water resources. During 2000–2020, the Damodar Fan Delta portrayed an increase in semi-critical community development blocks, implying a decline in groundwater level. To this end, the present study intends to show the groundwater level dynamics including its future prediction, using machine learning algorithms based on the seasonal groundwater level data from 2013‐14 to 2020‐21 for 30 wells. New hydrological insights for the region: Post-monsoon kharif and rabi depicted a higher fall rate in the groundwater level compared to the pre-monsoon and monsoon periods. Future predictions using the best-fit model indicated an increasing trend in the depth of groundwater levels in the future (2025–26). The extreme gradient boost regressor appeared to be the best model, while the decision tree regressor was the worst performer. The major controlling factors of groundwater level dynamics were decreasing rainfall and increasing groundwater abstraction due to population growth and increased demand for irrigation, domestic, and industrial water.

Objectives: This study intends to evaluate Dhaka city slum dwellers’ responses to Dengue fever (DF).Methods: 745 individuals participated in a KAP survey that was pre-tested. Face-to-face interviews were performed to obtain data. Python with RStudio was used for data management and analysis. The multiple regression models were applied when applicable.Results: 50% of respondents were aware of the deadly effects of DF, its common symptoms, and its infectious nature. However, many were unaware that DF could be asymptomatic, a previously infected person could have DF again, and the virus could be passed to a fetus. Individuals agreed that their families, communities, and authorities should monitor and maintain their environment to prevent Aedes mosquito breeding. However, overall 60% of the study group had inadequate preventative measures. Many participants lacked necessary practices such as taking additional measures (cleaning and covering the water storage) and monitoring potential breeding places. Education and types of media for DF information were shown to promote DF prevention practices.Conclusion: Slum dwellers lack awareness and preventative activities that put them at risk for DF. Authorities must improve dengue surveillance. The findings suggest efficient knowledge distribution, community stimulation, and ongoing monitoring of preventative efforts to reduce DF. A multidisciplinary approach is needed to alter dwellers’ behavior since DF control can be done by raising the population’s level of life. People and communities must perform competently to eliminate vector breeding sites.

Understanding spatiotemporal variability in precipitation and temperature and their future projections is critical for assessing environmental hazards and planning long-term mitigation and adaptation. In this study, 18 Global Climate Models (GCMs) from the most recent Coupled Model Intercomparison Project phase 6 (CMIP6) were employed to project the mean annual, seasonal, and monthly precipitation, maximum air temperature (Tmax), and minimum air temperature (Tmin) in Bangladesh. The GCM projections were bias-corrected using the Simple Quantile Mapping (SQM) technique. Using the Multi-Model Ensemble (MME) mean of the bias-corrected dataset, the expected changes for the four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) were evaluated for the near (2015–2044), mid (2045–2074), and far (2075–2100) futures in comparison to the historical period (1985–2014). In the far future, the anticipated average annual precipitation increased by 9.48%, 13.63%, 21.07%, and 30.90%, while the average Tmax (Tmin) rose by 1.09 (1.17), 1.60 (1.91), 2.12 (2.80), and 2.99 (3.69) °C for SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively. According to predictions for the SSP5-8.5 scenario in the distant future, there is expected to be a substantial rise in precipitation (41.98%) during the post-monsoon season. In contrast, winter precipitation was predicted to decrease most (11.12%) in the mid-future for SSP3-7.0, while to increase most (15.62%) in the far-future for SSP1-2.6. Tmax (Tmin) was predicted to rise most in the winter and least in the monsoon for all periods and scenarios. Tmin increased more rapidly than Tmax in all seasons for all SSPs. The projected changes could lead to more frequent and severe flooding, landslides, and negative impacts on human health, agriculture, and ecosystems. The study highlights the need for localized and context-specific adaptation strategies as different regions of Bangladesh will be affected differently by these changes.

Human trafficking is the third most lucrative form of trafficking in the world (following drugs and counterfeit goods). Multiple outbreaks of unrest between October 2016 and August 2017 in the Rakhine State of Myanmar triggered ~745,000 influxes of Rohingyas crossing into Bangladesh through the border boundaries at Teknaf and Ukhiya sub-districts of Cox's Bazar. In this regard, the media confirmed that over a thousand Rohingya people, particularly women and girls, were victims of human trafficking. This research aims to explore the underlying causes of human trafficking (HT) during emergency responses and seeks to understand how the knowledge and capacity of the refugee, local administration, and law enforcement agencies in Bangladesh can be improved in promoting counter-trafficking (CT) and safe migration processes. In order to achieve the objectives, this study reviews acts, rules, policies, and action plans of the Government of Bangladesh on the HT, CT, and safe migration processes. Then, a case study has been applied to present the ongoing CT and safe migration programs of an NGO called Young Power in Social Action (YPSA), which received funding and technical support from the International Organization of Migration (IOM) for this purpose. This study also evaluates the effectiveness of the program through conducting key informant interviews (KIIs) and focus group discussions (FGDs) with the beneficiary and non-beneficiary participants including refugees, law-enforcing agencies (LEAs), and NGOs in Teknaf and Ukhyia. Thus, this study identifies program-level strengths and weaknesses in relation to the CT and safe migration process and provides key directions on how they can be improved. It concludes that non-state actors have a significant role in preventing HT and promoting CT and safe migration for Rohingyas in Bangladesh.

Purpose – The small-scale artisanal fishers in coastal Bangladesh are comparatively more vulnerable to climate risks than any other communities in Bangladesh. Based on practicality, this paper aims to explain the local level climate change perception, its impact and adaptation strategies of the fisher in southeast coastal villages in Bangladesh. Design/methodology/approach – To achieve the above objective, this study used structural, semi-structured interviews and focus group discussion in two coastal communities, namely, at Salimpur in the Sitakund coast and Sarikait Sandwip Island, Bangladesh. It reviews and applies secondary data sources to compare and contrast the findings presented in this study. Findings – Results show that the fishers perceived an increase in temperature, frequency of tropical cyclones and an increase in sea level. They also perceived a decrease in monsoon rainfall. Such changes impact the decreasing amount of fish in the Bay of Bengal and the fishers’ livelihood options. Analysing seasonal calendar of fishing, findings suggest that fishers’ well-being is highly associated with the amount of fish yield, rather than climatic stress, certain non-climatic factors (such as the governmental rules, less profit, bank erosion and commercial fishing) also affected their livelihood. The major adaptation strategies undertaken include, but are not limited to, installation of tube well or rainwater harvesting plant for safe drinking water, raising plinth of the house to cope with inundation and use of solar panel/biogas for electricity. Originality/value – Despite experiencing social stress and extreme climatic events and disasters, the majority of the fishing community expressed that they would not change their profession in future. The research suggests implementing risk reduction strategies in the coastal region of Bangladesh that supports the small-scale fishers to sustain their livelihood despite climate change consequences.

In this study, a set of coupled multi-media compartments (i.e., sediment, soil, water and vegetable) was used to assess ecological and health risks from the ingestion of 11 PTEs (Pb, Cd, Cr, As, Hg, Cu, Zn, Ni, Co, Fe, and Mn) and their transportation routes in the water-soil-plant system from the coastal Bhola Island, Bangladesh. The mean concentrations of Cd, Pb, and Co for soil and Cd, Co, and As for sediment were higher than their reference values. In contrast, Cd, Pb, and Ni concentrations in water surpassed the acceptable limits set by national and international laws and were considered unsuitable for drinking purposes. Vegetables demonstrated high Pb and Cd contents, demonstrating a potential food safety risk to the inhabitants. Results of principal component analysis (PCA) revealed that Cd, Pb, Hg, Cu, Ni and Zn sources were likely to be anthropogenic, especially agro-farming inputs, whereas the Fe, As, Cr, Mn, and Co sources were similar to natural origin. So, Cd, Pb and Co are the key contaminants in the study area and pose the elevated health and ecological risks in the coastal area. Cd and Pb exhibited higher ecological risks in soils and sediments, as Pb had the highest bio-accessibility (BA; 0.02 ± 0.003) and Cd possessed a high bioaccumulation factor (BCF; 0.004 ± 0.006). The self-organizing map analysis recognized three spatial patterns which are good agreement with PCA. The average hazard index (HI) values for soil were above the permissible level (HI > 1) set by the respective agency; two times higher HI values were noticed for children than adults, suggesting children are highly susceptible to health risk. Continuous monitoring and source controls for Cd and Pb, along with agro-farming management practices, need to be implemented to reduce the risk of PTE contamination to the aquatic ecosystem and its inhabitants.