Shahjalal University of Science and Technology

A high recombination rate is a major limiting factor in photocatalysis. Mitigating recombination through material engineering and photocatalyst optimization is key to enhancing photocatalytic performance. In this study, a heterostructure MoS2/CdS nanocomposite was synthesized through a hydrothermal method in a Teflon-lined autoclave subjected to a temperature of 200 °C for 16 hours. The resulting photocatalysts were characterized using a variety of techniques to understand their structural, surface, and optical properties. The photocatalytic activity of the as-synthesized photocatalysts was investigated by degrading methyl orange dye under both sunlight and visible light irradiation. Regardless of its MoS2 content, the heterostructure MoS2/CdS NC exhibited enhanced degradation efficiency relative to that of pure CdS, MoS2, and commercial TiO2 P25, with 5 wt% MoS2/CdS NCs exhibiting the highest degradation performance among all the evaluated photocatalysts. This behavior was justified by improved charge separation and reduced charge recombination, which were attributed to the valence band and conduction band offsets at the MoS2/CdS interface, as evidenced by band alignment study. The enhanced charge separation and reduced charge recombination were further validated by photoluminescence (PL), electrochemical impedance spectroscopy (EIS) and linear sweep voltammetry (LSV) measurements. Furthermore, an active species trapping experiment confirmed that electron transfer to oxygen and the subsequent formation of superoxide anions (O2⁻) radical play the most significant roles in photocatalytic degradation under visible light illumination. Finally, the ability to reuse the MoS2/CdS NCs multiple times without substantial loss of activity evidenced their stability, thus paving the way for advancements in large-scale environmental remediation and other industrial applications.

This Wisdom Letter describes how communities in the coastal Sundarbans region in India and Bangladesh have dealt with climate change impacts, offering research-based insights and policy suggestions for mitigating climate change effects. We demonstrate how climate change impacts are one among many drivers of vulnerabilities, thereby, questioning climate reductionism or climatism, an ideology that interprets problems through the singular lens of climate change. We show that climate change impacts are interlinked with local economies and governance11—people cannot choose climate change impacts, but they can shape them by adopting policies that mitigate the consequences instead of exacerbating them.

The COVID-19 pandemic has had a significant influence on people’s life, affecting social and economic conditions, population dynamics, politics, and cultural activities. This study investigates how COVID-19 affects married people’s fertility preferences. It also analyzes the preferences of married persons in Bangladesh with and without COVID-19. The study hypothesizes that persons afflicted with the pandemic choose to have more children than uninfected ones. A survey questionnaire and in-depth interview questions were used to gather pertinent data via snowball sampling from 200 infected and uninfected individuals residing in Sylhet City Corporation, Bangladesh, who were between the ages of 15 and 50. The study employed a mixed method strategy, including quantitative analysis (binary logistic regression), ANOVA testing, Chi-Square testing, and thematic analysis of qualitative data to examine the association between COVID-19 and fertility decisions. The results of the study show that the pandemic significantly affects people’s reproductive preferences, in particular their desire to have or not to have children in the future. In addition, compared with people who are not infected with COVID-19, people who have been infected are more likely to want to have children in the future. The study also shows that people - married couples in particular - may think twice before choosing to have children if they suffer the adverse consequences of an unforeseen shock such as a pandemic in the future. By examining the socioeconomic and cultural context of Bangladesh, this study explores how the COVID-19 pandemic influenced fertility preferences. By addressing demographic shifts during and after the pandemic, it offers important insights for researchers, policymakers, and healthcare professionals. Additionally, this study will advance knowledge of how pandemics alter reproductive desires globally and make results available for future cross-regional comparisons.

Adolescent girls of reproductive age who actively seek information on maternal health often tend to have better health-seeking behaviors and maternal health outcomes. Due to scant research on reproductive aged adolescent girls’ maternal health information seeking behavior in slum, in connection with social norms, we aimed for this particular study. Adopting an explorative qualitative research approach, we collected data from purposively selected married and unmarried adolescent girls aged 15–19 of different occupation by implying 12 in-depth interviews (IDIs), 2 focus group discussions (FGDs) with the same categories employed for IDIs, and 2 key informant interviews (KIIs) with a traditional birth attendant and a drug seller. Furthermore, the data were subjected to thematic analysis. Care’s Social Norms Analysis Plot (SNAP) framework was undertaken as an interpretative tool for data that was emerging rather than serving as the foundation for the study’s conduct and design. Thematic analysis was followed to analyze primary data. Findings show that most girls rely on maternal health-related information from unverified sources, including family members, traditional birth attendants, and drug sellers, which increases health risks. The majority reported that adolescent girls need professional healthcare providers in their area who would work according to their work schedule as most of the girls are engaged in income-generating work for about 9–11 hours, and the scope of work (daily wagers) hardly supports ‘leave with pay’. Therefore, there is a critical need for professional healthcare services tailored to the girls’ work schedules. Social norms and stigma further restrict access to reliable health information, especially for unmarried girls. Socioeconomic disparities also shape health-seeking behaviors, with wealthier adolescents having greater access to formal healthcare services. Addressing these barriers is crucial for improving maternal health outcomes. The results might be useful for informed policy formulation and program design to ensure better health outcomes for marginalized adolescents.

Objectives Our objectives were to ascertain: the prevalence and socio-economic distribution of hypertension, as well as the rates of undiagnosed and untreated hypertension; the association between socioeconomic status (SES) and the occurrence of hypertension, as well as the rates of undiagnosed and untreated hypertension; and the factors influencing the poor-non-poor gap in terms of the prevalence, diagnosis, and treatment of hypertension. Design Cross-sectional nationally representative study. Methods Data from the 2017–18 Bangladesh Demographic Health Survey were used. 11,776 participants who were 18 years of age or older responded to our analysis. We used the wealth index as a proxy for SES. The prevalence of hypertension, both diagnosed and undiagnosed, as well as its untreated states, were the outcome variables. Results The age-adjusted prevalence of hypertension, undiagnosed as having hypertension, and untreated cases were 25.1%, 57.2%, and 12.3%, respectively. People in the poor SES groups had a 0.88 times (95% confidence interval [CI] 0.77–0.99) lower likelihood of having hypertension compared to those in the non-poor SES group. Individuals belonging to the poor SES group exhibited a likelihood of 1.68 and 1.53 times greater for having untreated hypertension and being undiagnosed with the condition, respectively, compared to those in the non-poor SES group. The results indicated that BMI played a role in increasing the disparity between the poor and non-poor populations concerning hypertension risk. Additionally, factors such as age, gender, and education were found to exacerbate the gap in the risk of undiagnosed hypertension between these two groups. Conclusion The results of this study suggest that appropriate policy measures be developed for ongoing care and early identification, especially for older adults, men, and individuals with low levels of education from low socioeconomic backgrounds. Additionally, efforts must be made to reduce the prevalence of overweight and obesity among people in the non-poor SES category.

Background and Aims The post‐COVID‐19 pandemic period has heightened concerns about student mental health and suicide risk in Bangladesh. While studies have explored these issues during the pandemic, post‐pandemic student suicides remain under‐researched. This study investigates the characteristics, methods, and triggering events associated with suicidal behaviors among students in Bangladesh during the post‐pandemic period (2022–2023). Methods Data were collected from 150 online newspaper portals in Bangladesh, covering student suicides from 2022 to 2023. Variables such as causes, methods, timing, location, sex, age, and education level were analyzed using Microsoft Excel, SPSS, and ArcGIS. Results A total of 984 student suicide cases were identified. Females accounted for 61% of the cases, while males represented 39%, indicating a higher vulnerability among female students. The majority of cases (72.5%) involved students aged 13–19 years, followed by those aged 20–25 years (18.4%). Secondary school students represented 44.9% of the cases, with 7.2% from madrasahs. Hanging was the most common method (79.7%). Major causes included emotional distress (28%), romantic relationship issues (19.5%), academic pressure (8.4%), family problems (8.1%), mental instability (7%), and sexual harassment (3.3%). The Dhaka division reported the highest rate (27.8%) of student suicides. Suicide rates were lowest in August 2022 (4.3%) but spiked in 2023 (12.6%). Conclusion This study highlights the significant rise in student suicides in Bangladesh after the pandemic, primarily driven by emotional distress, with females and the 13–19‐year age group being most vulnerable. These findings emphasize the urgent need for gender‐specific mental health interventions to address this growing issue.

Evapotranspiration (ETo) plays a crucial role in managing water resources and agricultural water consumption. It is also commonly used to quantify the total amount of water lost through a number of important processes that occur among the land and atmosphere. In this research, four deep learning algorithms—CNN, DNN, BiLSTM, and GRU—were applied to predict evapotranspiration based on 14 years of daily data from Victoria, a state in southeastern Australia. The data sample was split into two periods: nine years (2010–2019) for training and four years (2020–2023) for testing. Deep learning algorithms have good performance for predicting evapotranspiration. The results showed that the GRU and DNN models were slightly better than the other two models. In the testing phases, the GRU models found R-Square, RSME, MSE, and MAE values, 0.989, 0.1794, 0.0322, and 0.1417, respectively, while the DNN models performed 0.980, 0.185, 0.0345, and 0.1507 value of R-Square, RMSE, MSE, and MAE, respectively, which indicated the GRU model perform better than other models. The CNN model achieved an R² of 0.958, with an RMSE of 0.364 and an MSE of 0.1330, indicating less precise estimations. Similarly, the BiLSTM model performed better than CNN but still lagged behind GRU and DNN, with an R² of 0.969 and an MSE of 0.0988. Moreover, deep learning models perform well, the GRU model has comparatively excellent performance than other DL models. It has been suggested that the most accurate model to improve future studies on evapotranspiration estimations is the GRU model, which could improve irrigation efficiency and boost crop productivity.

Multidrug-resistant bacteria, particularly extended-spectrum-beta-lactamase-producing (ESBL) bacteria, pose a significant global public health challenge. Klebsiella pneumoniae (KPN) is frequently implicated in cases of this resistance. This study aimed to investigate the presence of drug and metal resistance genes in clinical K. pneumoniae isolate Kp04 and comparative genomics of clinical KPN isolates characterized from Bangladesh. A total of 12 isolates were collected. Disk-diffusion assay showed that all five isolates were resistant to 14 out of 21 tested antibiotics and sensitive to only three—tigecycline, imipenem, and meropenem. KPN Kp04 was positive for both blaSHV and blaCTX-M ESBL genes in PCR. All five isolates produced PCR amplicons of the correct size for ampicillin (ampC), tetracycline (tetC), fluoroquinolone (qnrS), and aminoglycoside (aadA) resistance genes. The whole genome of Kp04 was sequenced using the MiSeq Platform (V3 kit, 2 × 300 cycles). We utilized different databases to detect Antibiotic-Resistant Genes (ARGs), virulence factor genes (VFGs), and genomic functional features of the Kp04 strain. Whole-genome sequencing identified 75 ESBL, virulence, and multiple drug-resistant (MDR) genes including blaSHV, tetA, oqxA, oqxB, aadA, sul1-5, and mphA in KPN Kp04 isolate. Pan-genomic analysis of 43 Bangladeshi KPN isolates showed similarities between Dhaka and Chattogram isolates regarding virulence and antibiotic-resistant genes. Our results indicate the transmission of similar virulent KPN strains in Dhaka and Chattogram. This study would provide valuable information about drug sensitivity, antibiotic, and metal resistance features of K. pneumoniae circulated among hospitalized patients in Bangladeshi megacities.

Rapid urbanization and infrastructure development around the world have led to the overexploitation of natural aggregates and increased industrial waste disposal, posing significant environmental challenges. Utilizing daily-generated, non-recyclable waste materials in concrete production can address these issues while promoting sustainability. Therefore, this paper focuses on adapting ceramic tile waste and stone dust in concrete as partial replacements for natural aggregates, since they are both cost-effective and environmentally friendly. In this study, coarse aggregate was replaced with ceramic tile aggregate at 10%, 20%, and 30% levels, while fine aggregate was substituted with stone dust at 30%, 40%, and 50% levels. The mechanical properties of concrete at 28 days, such as compressive strength, split tensile strength, and bond strength, were experimentally investigated for all mixes. Afterward, finite element models were developed using ABAQUS, and comparisons were made between experimental and numerical results. The test results indicated that replacing aggregates with tile waste and stone dust increased the overall strength of the concrete compared to conventional concrete, with the maximum compressive, tensile, and bond strengths achieved in the 30% ceramic tile aggregate and 30% stone dust mix. The numerical results showed that the proposed finite element models in this study agreed well with the experimental results.

Steel-fiber-reinforced concrete (SFRC) has proven to be a practical and effective alternative to traditional concrete. It offers improved post-cracking performance, increased fracture resistance, and efficient stress transfer by incorporating steel fibers into the concrete mix. Machine learning (ML) techniques are widely used to accurately estimate concrete qualities, thus reducing time and costs. This research focuses on a novel hybrid machine learning model that such as Gaussian process regression with genetic algorithm (GPR-GA), Random Forest with genetic algorithm (RF-GA), and extreme gradient boosting with genetic algorithm (XGB-GA) to predict the compressive strength of SFRC. The proposed hybrid ML models demonstrate superior performance with an R² value over 0.92 at both the training and testing stages. Specifically, the R² value was found to be 0.9804 for GPR-GA, 0.9204 for RF-GA, and 0.9706 for XGB-GA. The RMSE values for GPR-GA, RF-GA, and XGB-GA were 8.38, 16.88, and 16.88, respectively. The overall GPR-GA model outperformed compare to the other two hybrid machine learning models, XGB-GA and RF-GA. Additionally, the SHAP method was utilized to assess the influence of input variables on the predicted compressive strength (CS) of SRFC. From the SHAP analysis, it was found that the temperature and diameter input parameters had the highest influence on the predicted CS compared to other input features. The proposed hybrid ML models provide builders and designers with a flexible and effective tool for analyzing characteristics and making accurate forecasts of the compressive strength of SFRC under high temperatures in building applications.

Background Pooling data from complex survey designs is increasingly used in the health and medical sciences. However, current methodological practices are not well documented in the literature while performing the pooling strategy. We aimed to review related pooling studies and evaluate the quality of pooling within the framework of specific methodological guidelines, particularly when combining complex surveys such as Demographic & Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). Methods We performed a systematic literature search focusing on studies utilizing the pooling method with DHS and MICS survey data. These studies were selected from those published between 2010 and 2021 and were retrieved from electronic databases (PubMed and Scopus) in accordance with pre-defined inclusion criteria. Then, we extracted 355 studies for the final review and evaluated the reporting quality of the pooling strategy while considering some methodological issues. Results The majority of studies (81.4%) reported using a pooled (one-stage) approach, while 11.8% used a separate (two-stage) approach, and 6.8% used both approaches. Approximately 63.3% of studies did not clearly describe their pooling strategy. Only 3.4% of the studies mentioned the variable harmonization process, while 66.9% addressed dealing with heterogeneity between surveys. All studies that used the separate (two-stage) approach conducted a meta-analytic procedure, while 38.1% of studies using the pooled approach employed a multilevel model. More than half of the studies (55.6%) mentioned the use of clustered standard errors. The Delta method, Bootstrap, and Taylor linearization were each applied in 11.1% of the studies for variance estimation. Survey weights, primary sampling unit (PSU) or cluster, and strata were used together in 30.5% of the studies. Survey weights were employed by 69.8%, PSU or cluster by 43.8%, and the strata variable by 31.7%. Sensitivity analysis was conducted in 16% of the studies. Conclusions Our study revealed that fundamental methodological issues associated with pooling complex survey databases, such as the selection of pooling procedures, data harmonization, accounting for cycle effects, quality control checks, addressing heterogeneity, selecting model effects, utilizing survey design variables, and dealing with missing values, etc., were inadequately reported in the included studies. We recommend authors, readers, reviewers, and editors examine pooling studies more attentively and utilize the customized checklist developed by our study to assess the quality of future pooling studies.

Viral nervous necrosis (VNN) poses a significant threat to the aquaculture industry, causing substantial losses and economic burdens. The disease, attributed to nervous necrosis viruses within the Betanodavirus genus, is particularly pervasive in the Mediterranean region, affecting various fish species across all production stages with mortality rates reaching 100%. Developing effective preventive measures against VNN is imperative. In this study, we employed rigorous immunoinformatics techniques to design a novel multi-epitope vaccine targeting VNN. Five RNA-directed RNA polymerases, crucial to the lifecycle of Betanodavirus, were selected as vaccine targets. The antigenicity and favorable physicochemical properties of these proteins were confirmed, and epitope mapping identified cytotoxic T lymphocyte, helper T lymphocyte, and linear B lymphocyte epitopes essential for eliciting a robust immune response. The selected epitopes, characterized by high antigenicity, non-allergenicity, and non-toxicity, were further enhanced by adding PADRE sequences and hBD adjuvants to increase immunogenicity. Two vaccine constructs were developed by linking epitopes using appropriate linkers, demonstrating high antigenicity, solubility, and stability. Molecular dynamics simulations revealed stable interactions between the vaccine constructs and Toll-like receptors (TLRs), essential for pathogen recognition and immune response activation in fish. Notably, vaccine construct V2 exhibited superior stability and binding affinity with TLR8, suggesting its potential as a promising candidate for VNN prevention. Overall, our study presents a comprehensive approach to VNN vaccine design utilizing immunoinformatics, offering safe, immunogenic, and effective solutions across multiple Betanodavirus species. Further experimental validation in model animals is recommended to fully assess the vaccine’s efficacy. This research contributes to improved vaccine development against diverse fish pathogens by addressing emerging challenges and individualized immunization requirements in aquaculture.

Soil sedimentation, often modified or accelerated by anthropogenic activities, is a geomorphic process that influenced the soil productivity, nutrient degradation, and the siltation occurrence in water bodies, which were the primary causes of land degradation specifically by water. This study focused on delineating soil erosion using the Revised Universal Soil Loss Equation involving GIS based technique in the Surma-Meghna River system. The specific river system covered around 58,975 km 2 , and its surrounding regions, located at the foothills of the Himalayan highlands to the coastal area, experienced considerable rainfall, which increased the nature of soil erosion dynamics. The study area was divided into 9 sub watersheds (WD1, WD2, WD3, WD4, WD5, WD6, WD7, WD8, WD9). The expected soil sedimentation was 10.81 t ha −1 y −1 on average. About 16% of the total area exhibited higher soil erosion rates, particularly in the northern and southern portions of the study area, with WD1 being the most erosion-prone spot. When soil particle distribution was considered, contamination in water shifted from sand to silt and clay which leads to a higher erodibility factor in the northern section (WD1-WD3) and a lower erodibility factor in the southern section (WD4-WD8). The Erosivity (R) factor was distributed maximally in WD1 (4048 MJ·mm·ha −1 ·h −1 ·y −1) and minimally in WD7 (1688 MJ·mm·ha −1 ·h −1 ·y −1). A lower deviation of the topographic (LS) factor occurred in 60% of the research area, making the topography relatively low-lying. The total amount of actual soil loss was estimated at 96 million t y −1 and the trend of actual sedimentation was found to decreased from WD1-WD9 (North to South). Resistance factor to soil loss known as Cover Management or C factor was lower in the region of WD1, WD2, WD3 & WD5 due to the presence of dense vegetation. Overall, lesser erosion was observed throughout the study area as most of the agricultural land (97%) was in the plain region (1°-4°), where seasonal crops were grown. As a result, 52% of the research area was not significantly affected by human induced soil loss. Actual sedimentation decreased to 1.76% of potential sediment. The degree of association between actual soil erosion and the R, K, LS and P factor was strong, with an R 2 value 0.98. From the local environment sensitivity analysis, the C and K factors were both indicated as sensitive due to the tropical environment and the predominance of floodplains in the north and south. The sediment delivery ratio (SDR) was derived as 0.023, with the highest value observed in WD1 and the lowest in WD2. Finally, it was concluded that sedimentation occurred on a much smaller scale than it does theoretically in the Surma-Meghna River system, as it transported 3.5 million t y −1 of sediment into the river. Vegetation significantly resisted soil sedimentation, indicating the need for reforestation in the elevated regions, as a balanced hypsometric curve was found in the study area.

This paper aims to understand the potential factors influencing the accessibility of early childhood education (ECE) in Bangladesh. Utilising data from the Multiple Indicator Cluster Survey (MICS) 2019, this study explored the influence of individual, household, socioeconomic as well as geographical variables on ECE enrollment. It employed a comprehensive approach, which involved using descriptive statistics for univariate analysis, chi-square and t-statistics for bivariate analysis, and the logit model for multivariate analysis. The findings revealed that a child's likelihood of attending an ECE program increases with their age, while those with functional difficulties and larger household sizes exhibit lower odds of attendance. Children with mothers possessing higher education levels demonstrated increased odds of ECE enrollment. Wealthy households and urban or Mymensingh division residency were associated with higher odds of ECE enrollment, while Barishal, Rajshahi, and Sylhet divisions indicated lower odds. To enhance ECE accessibility, the study recommends implementing a roadmap for ensuring universal early childcare and early education with an emphasis on socioeconomically underprivileged children, particularly in rural areas. Proposed strategies may include providing financial assistance to poor households and fostering public-private partnerships for ECE provision in remote regions like Barishal, Rangpur, Rajshahi, and Sylhet divisions. Moreover, it is crucial to make parents and communities aware about the significance of ECE in order to make sure that children ages 3-5 years are involved in ECE programs.