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Purpose – The study aims to ascertain whether educational and social support for entrepreneurs significantly affects university students’ intentions to become successful entrepreneurs. This study examines the mediating role of the Theory of Planned Behavior (TPB) variables (e.g. entrepreneurial personal attitude, subjective norms and entrepreneurial perceived behavioral control) and entrepreneurial self-efficacy in encouraging young entrepreneurs. Design/methodology/approach – An online survey with a structured questionnaire collected data from different university students in Bangladesh; subsequently, it was analyzed through the structural equation model. Findings – The results suggested that educational support has a direct positive relationship with the three variables of TPB. Moreover, the findings indicated that social support positively influences the variables of TPB, entrepreneurial self-efficacy and entrepreneurial intentions. The variables of TPB and entrepreneurial self-efficacy were found to have a significant direct impact on entrepreneurial intentions and also exhibited favorable mediating effects of educational and social support on entrepreneurial intentions. Research limitations/implications – First, the study is only generalized to some sectors of entrepreneurship activities because the researchers used samples from university students across Bangladesh. Second, the implicit limitation of survey-based research is that respondents need to know more ways of understanding the questionnaires accurately, and some participants need to be taught how to answer the question items. Practical implications – The main practical implication for the relationship between entrepreneurial intentions and educational support involves different entrepreneurial educational programs, which give rise to attitude, behavior, self-efficacy and intentions and enhance the student's awareness of advancing a successful entrepreneurial career. Social implications – This study demonstrated that universities and social communities should promote the improvement of innovative thoughts for entrepreneurs and offer essential information about entrepreneurship. Originality/value – Because entrepreneurial educational support is a crucial factor in entrepreneurial intentions, universities need to develop a practical education system that can help improve the skills required to start new ventures. The results will improve a new route to developing students’ entrepreneurial intentions using the variables of TPB and entrepreneurial self-efficacy. Subsequently, these research findings will help to achieve governmental goals and increase the number of startups in the future.

Background and objectives: Diabetic kidney disease (DKD) is a leading complication of diabetes, contributing significantly to global cases of end-stage renal disease (ESRD). In Bangladesh, the rising prevalence of diabetes has made DKD a growing public health concern. An estimated 21.3% of diabetic patients in Bangladesh have some form of kidney impairment. The Diabetic Association of Bangladesh (BADAS) operates a network of healthcare centers that provide diabetes management across the country. Despite these efforts, significant gaps exist in DKD screening, patient education, and the use of renoprotective medications. This study aims to evaluate DKD in BADAS-affiliated healthcare centers, focusing on screening practices, management and patient education. Materials and Methods: This cross-sectional study was conducted in 8 BADAS-affiliated healthcare centers, representing diverse regions of Bangladesh. A total of 320 type 2 diabetic patients were selected using multi-stage sampling methods. Data were collected using structured questionnaires which included socio-demographic characteristics, clinical histories, comorbidities, body mass index (BMI), glycemic control status, blood pressure levels, medication usage, and diagnostic criteria for DKD. Blood samples were obtained to determine serum creatinine and HbA1c levels, and spot urine samples were collected to measure the urine albumin-to-creatinine ratio (uACR). Results: The prevalence of DKD was found to be 34.1%, with most cases in the early stages (Stage1:33% and Stage2: 45%). Screening practices were inadequate, as 52.5% of participants had never been tested for uACR or eGFR. Only 21.1% of participants with DKD were receiving renoprotective medications like ACE inhibitors or ARBs, and 35.8% were using SGLT2 inhibitors. Glycemic and blood pressure control were also suboptimal, with 81.9% of total participants having HbA1c levels ≥7% and 69.1% having uncontrolled hypertension. Of the entire study population, only 0.3% met all six prevention targets. Conclusion: DKD is prevalent among diabetic patients in BADAS-affiliated healthcare centers, with poor screening practices and underutilization of renoprotective medications. Systematic improvements in DKD management, including enhanced screening, medication use, and patient education, are essential to prevent progression to ESRD. January 2025; Vol. 19(1):001. DOI: https://doi.org/10.55010/imcjms.19.001 *Correspondence: Wasim Md Mohosin Ul Haque, Department of Nephrology, Bangladesh Institute of Research and Rehabilitation in Diabetes, Endocrine and Metabolic Disorders (BIRDEM), 122 Kazi Nazrul Islam Avenue, Dhaka 1000, Bangladesh. Email: wmmhaque@live.com; © 2025 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0).

Abstract In this research, we investigated the mechanical properties of NiCo binary alloy both with and without grain boundaries, across various alloy compositions. We investigated the effects of edge dislocation, alloy compositions, and grain boundaries on the mechanical properties of FCC $$NiCo$$ NiCo using molecular dynamics (MD) simulations. By analyzing the influence of the grain boundaries with different alloy compositions on several mechanical properties, we were able to gain a deeper understanding of how these characteristics were modified. The elastic moduli increased with increasing grain boundaries for a specific chemical composition, indicating that $$NiCo$$ NiCo becomes more rigid. The anisotropy factor analysis showed that $$NiCo$$ NiCo have a natural tendency toward anisotropy, which is further influenced by the presence of grain boundaries. Grain boundaries have a significant effect on strength and ductility across a wide range of alloy compositions. These results indicate that a deeper comprehension of these implications can aid in designing improved binary alloy with superior mechanical characteristics.

Abstract Glioblastoma (GBM) is the most malignant brain cancer and one of the leading causes of cancer-related death globally. So, identifying potential molecular signatures and associated drug molecules are crucial for diagnosis and therapies of GBM. This study suggested GBM-causing ten key genes (ASPM, CCNB2, CDK1, AURKA, TOP2A, CHEK1, CDCA8, SMC4, MCM10, and RAD51AP1) from nine transcriptomics datasets by combining supervised and unsupervised learning results. Differential expression patterns of key genes (KGs) between GBM and control samples were verified by different independent databases. Gene regulatory network (GRN) detected some important transcriptional and post-transcriptional regulators for KGs. The KGs-set enrichment analysis unveiled some crucial GBM-causing molecular functions, biological processes, cellular components, and pathways. The DNA methylation analysis detected some hypo-methylated CpG sites that might stimulate the GBM development. From the immune infiltration analysis, we found that almost all KGs are associated with different immune cell infiltration levels. Finally, we recommended KGs-guided four repurposable drug molecules (Fluoxetine, Vatalanib, TGX221 and RO3306) against GBM through molecular docking, drug likeness, ADMET analyses and molecular dynamics simulation studies. Thus, the discoveries of this study could serve as valuable resources for wet-lab experiments in order to take a proper treatment plan against GBM.

Abstract Background It has been suggested that the association between body mass index and breast cancer risk differs between Asian women and Western women. We aimed to assess the associations between body mass index and breast cancer incidence in East Asian women. Methods Pooled analyses were performed using individual participant data of 319,189 women from 13 cohort studies in Japan, Korea, and China. Participants’ height and weight were obtained by measurement or self-reports at cohort baseline. Breast cancer was defined as code C50.0-C50.9 according to the International Classification. Using a Cox proportional hazards model, hazard ratios of breast cancer were estimated for each body mass index category, with the reference group set as the group with a body mass index of 21 to

Rapid urbanization and land use changes significantly impact environmental sustainability and resource management, particularly in developing regions. Therefore, this study examines the spatiotemporal dynamics of land use and land cover (LULC) in Rangpur, Bangladesh, from 1991 to 2021 and projects future trends to 2041. Using supervised and unsupervised classification techniques, along with cellular automata and Markov-chain models, we assessed historical LULC changes and predicted future scenarios. Results show a 38.86% increase in built-up areas (BAs) and a 49.86% decrease in vegetation land (VL) during the study period, with classification accuracy above 87%. Projections indicate a further loss of over 210 km² of VL and an increase of more than 123 km² in urban areas by 2041. Notably, urban expansion is linked to the development of road networks, with significant growth from 115.06 km2 in 2021 to 124.33 km2 by 2041 within a 15-kilometer radius around the city center. These findings offer crucial insights for urban planning, emphasizing the need for sustainable strategies to manage urban expansion and protect environmental and socio-economic resilience in Rangpur.

Abstract Mango, often regarded as the “king of fruits,” holds a significant position in Bangladesh’s agricultural landscape due to its popularity among the general population. However, identifying different types of mangoes, especially from mango leaves, poses a challenge for most people. While some studies have focused on mango type identification using fruit images, limited work has been done on classifying mango types based on leaf images. Early identification of mango types through leaf analysis is crucial for taking proactive steps in the cultivation process. This research introduces a novel multi-layer perceptron model called WaveVisionNet, designed to address this challenge using mango leaf datasets collected from five regions in Bangladesh. The MangoFolioBD dataset, comprising 16,646 annotated high-resolution images of mango leaves, has been curated and augmented to enhance robustness in real-world conditions. To validate the model, WaveVisionNet is evaluated on both the publicly available dataset and the MangoFolioBD dataset, achieving accuracy rates of 96.11% and 95.21%, respectively, outperforming state-of-the-art models such as Vision Transformer and transfer learning models. The model effectively combines the strengths of lightweight Convolutional Neural Networks and noise-resistant techniques, allowing for accurate analysis of mango leaf images while minimizing the impact of noise and environmental factors. The application of the WaveVisionNet model for automated mango leaf identification offers significant benefits to farmers, agricultural experts, agri-tech companies, government agencies, and consumers by enabling precise diagnosis of plant health, enhancing agricultural practices, and ultimately improving crop yields and quality.

Purpose – Online shopping around the world is growing exponentially, especially during the COVID-19 pandemic. This study aims to examine how an online customer's purchasing experience influences his/her buying intention and willingness to believe in fraud news, as well as the ripple impact of satisfaction and trust, with gender as a moderator in an emerging economy during COVID-19. Design/methodology/approach – Based on the underpinning of the stimulus-organism-behavior-consequence (SOBC) theory, the research model was developed, and collected data from 259 respondents using convenience samples technique. Next, the data were analyzed using partial least squares-based structural equation modeling (PLS-SEM), SPSS (Statistical Package for the Social Sciences) and Hayes Process Macro. Findings – The study results confirmed that the online shopping experience (OSE) has positive impact on customers' satisfaction (CS), purchase intention (PI) and customer trust (CT); CS has positive effects on trust toward online shopping and their future product PI; future product PI significantly affects customers' propensity to believe and act on fraud news (PBAFN). The finding also states that gender moderates the relationships of CS to PI, OSE to PI and PI to PBAFN, but doesn't moderate the CT to PI relationship. Originality/value – The study findings will assist policymakers and online vendors to win customers' hearts and minds' through confirming satisfaction, trust and a negative attitude toward fake news, which will lead to customer loyalty and the sustainable development of the industry. Finally, the limitations and future research directions are discussed.

Abstract The number of theoretical and empirical literature on Industry 4.0 research has seen tremendous growth over the past few years. We seek to identify and classify the popular research topics published on Industry 4.0 in business and economics literature using a novel approach. First, we performed a co-occurrence analysis and found Industry 4.0 to have the strongest connection with innovation, implying the indispensability of innovation for the fourth industrial revolution. Further, we employed LDA-based topic modelling to identify the most frequent terms and labelled eight different topics based on the quantitative value of each term. Finally, we reviewed the relevant literature for each topic identified through the topic modelling process. Our literature review reveals that the concentration of existing literature is model-focussed. This study provides quantitative evidence of frequent occurrences of popular terms. We conclude that future studies should focus more on empirical measurement of the benefits of transiting towards the fourth industrial revolution.

Abstract Mushrooms have been considered as a therapeutic and nutrient-rich foods in numerous countries for years due to their significant levels of nutritional components, vitamins and antioxidants. This study aimed to assess the nutrient content, mineral profile and antioxidant properties of eight different kinds of oyster mushrooms (Pleurotus sp.) cultivated in Bangladesh. The mushrooms were analyzed for their moisture content, carbohydrates, proteins, fats, fibers, ash, vitamin A, and vitamin C. The mineral profile included sodium, potassium, calcium, and iron, as well as heavy metals. Antioxidant activities were evaluated through various methods, including phenolic, flavonoid and tannin content, total antioxidant activity, and DPPH (2,2-diphenyl-1-picryl-hydrazyl) scavenging capacity. The findings revealed that the mushrooms under investigation were high in carbohydrate (ranges between 45.25 and 63.22 g/100 g), protein (23.48–33.16 g/100 g) and dietary fiber (42.87–52.31 g/100 g). The total ash content was ranged from 7.28 to 9.41 g/100 g and vitamin C content was 0.020–0.416 mg/100 g. Interestingly, all of the mushrooms had a lower fat content (0.91–2.6%). The mineral profile indicated the presence of varying concentrations of essential elements. Hazardous heavy metals, such as Cd and Cr were not detected in the mushroom sample, while As and Pb were detected but lower than the tolerance levels. The oyster mushrooms exhibited high levels of phenolic, flavonoid, and tannin contents, as well as significant antioxidant capacity. A methanol extract of the oyster mushrooms exhibited scavenging activity against DPPH in a dose dependent manner with the highest capacity (75.4%) obtained at a concentration of 0.2 mg/ml. Our result suggests that these studied oyster mushrooms could be a good source of nutrition and antioxidants, which could be used as functional foods.

Abstract Financial institutions and businesses face an ongoing challenge from fraudulent transactions, prompting the need for effective detection methods. Detecting credit card fraud is crucial for identifying and preventing unauthorized transactions. While credit card fraud incidents are relatively rare, they can result in substantial financial losses, particularly due to the high monetary value associated with fraudulent transactions. Timely detection of fraud enables investigators to take swift actions to mitigate further losses. However, the investigation process is often time-consuming, limiting the number of alerts that can be thoroughly examined each day. Therefore, the primary objective of a fraud detection model is to provide accurate alerts while minimizing false alarms and missed fraud cases. In this paper, we introduce a state-of-the-art hybrid ensemble (ENS) dependable machine learning (ML) model that intelligently combines multiple algorithms with proper weighted optimization using grid search, including decision tree (DT), random forest (RF), K-nearest neighbor (KNN), and multilayer perceptron (MLP), to enhance fraud identification. To address the data imbalance issue, we employ the instant hardness threshold (IHT) technique in conjunction with logistic regression (LR), surpassing conventional approaches. Our experiments are conducted on a publicly available credit card dataset comprising 284,807 transactions. The proposed model achieves impressive accuracy rates of 99.66%, 99.73%, 98.56%, and 99.79%, and a perfect 100% for the DT, RF, KNN, MLP and ENS models, respectively. The hybrid ensemble model outperforms existing works, establishing a new benchmark for detecting fraudulent transactions in high-frequency scenarios. The results highlight the effectiveness and reliability of our approach, demonstrating superior performance metrics and showcasing its exceptional potential for real-world fraud detection applications.

Abstract In recent times, automated detection of diseases from pathological images leveraging Machine Learning (ML) models has become fairly common, where the ML models learn detecting the disease by identifying biomarkers from the images. However, such an approach requires the models to be trained on a vast amount of data, and healthcare organizations often tend to limit access due to privacy concerns. Consequently, collecting data for traditional centralized training becomes challenging. These privacy concerns can be handled by Federation Learning (FL), which builds an unbiased global model from local models trained with client data while maintaining the confidentiality of local data. Using FL, this study solves the problem of centralized data collection by detecting deformations in images of Red Blood Cells (RBC) in a decentralized way. To achieve this, RBC data is used to train multiple Deep Learning (DL) models, and among the various DL models, the most efficient one is considered to be used as the global model inside the FL framework. The FL framework works by copying the global model’s weight to the client’s local models and then training the local models in client-specific devices to average the weights of the local model back to the global model. In the averaging process, direct averaging is performed and alongside, weighted averaging is also done to weigh the individual local model’s contribution according to their performance, keeping the FL framework immune to the effects of bad clients and attacks. In the process, the data of the client remains confidential during training, while the global model learns necessary information. The results of the experiments indicate that there is no significant difference in the performance of the FL method and the best-performing DL model, as the best-performing DL model reaches an accuracy of 96% and the FL environment reaches 94%-95%. This study shows that the FL technique, in comparison to the classic DL methodology, can accomplish confidentiality secured RBC deformation classification from RBC images without substantially diminishing the accuracy of the categorization. Finally, the overall validity of the classification results has been verified by employing GradCam driven Explainable AI techniques.

Abstract RNA 5-methyluridine (m5U) sites play a significant role in understanding RNA modifications, which influence numerous biological processes such as gene expression and cellular functioning. Consequently, the identification of m5U sites can play a vital role in the integrity, structure, and function of RNA molecules. Therefore, this study introduces GRUpred-m5U, a novel deep learning-based framework based on a gated recurrent unit in mature RNA and full transcript RNA datasets. We used three descriptor groups: nucleic acid composition, pseudo nucleic acid composition, and physicochemical properties, which include five feature extraction methods ENAC, Kmer, DPCP, DPCP type 2, and PseDNC. Initially, we aggregated all the feature extraction methods and created a new merged set. Three hybrid models were developed employing deep-learning methods and evaluated through 10-fold cross-validation with seven evaluation metrics. After a comprehensive evaluation, the GRUpred-m5U model outperformed the other applied models, obtaining 98.41% and 96.70% accuracy on the two datasets, respectively. To our knowledge, the proposed model outperformed all the existing state-of-the-art technology. The proposed supervised machine learning model was evaluated using unsupervised machine learning techniques such as principal component analysis (PCA), and it was observed that the proposed method provided a valid performance for identifying m5U. Considering its multi-layered construction, the GRUpred-m5U model has tremendous potential for future applications in the biological industry. The model, which consisted of neurons processing complicated input, excelled at pattern recognition and produced reliable results. Despite its greater size, the model obtained accurate results, essential in detecting m5U.

Abstract Background Koala retrovirus (KoRV), a major pathogen of koalas, exists in both endogenous (KoRV-A) and exogenous forms (KoRV-A to I and K to M) and causes multiple disease phenotypes, including carcinomas and immunosuppression. However, the direct association between the different KoRV subtypes and carcinogenesis remains unknown. Differentially expressed gene (DEG) analysis of peripheral blood mononuclear cells (PBMCs) of koalas carrying both endogenous (KoRV-A) and exogenous (KoRV-A, B, and C) subtypes was performed using a high-throughput RNA-seq approach. PBMCs were obtained from three healthy koalas: one infected with endogenous (KoRV-A; Group I) and two infected with exogenous (KoRV-B and/or KoRV-C; Group II) subtypes. Additionally, spleen samples (n = 6) from six KoRV-infected deceased koalas (K1- K6) and blood samples (n = 1) from a live koala (K7) were collected and examined to validate the findings. Results All koalas were positive for the endogenous KoRV-A subtype, and eight koalas were positive for KoRV-B and/or KoRV-C. Transcription of KoRV gag, pol, and env genes was detected in all koalas. Upregulation of cytokine and immunosuppressive genes was observed in koalas infected with KoRV-B or KoRV-B and -C subtypes, compared to koalas infected with only KoRV-A. We found 550 DEG signatures with significant (absolute p 1.5) dysregulation, out of which 77.6% and 22.4% DEGs were upregulated (log2FC > 1.5) and downregulated (log2FC

Abstract Cloud computing offers cost-effective IT solutions but is susceptible to security threats, particularly the Economic Denial of Sustainability (EDoS) attack. EDoS exploits cloud elasticity and the pay-per-use billing model, forcing users to incur unnecessary costs. This research introduces the Integrated Model Prediction and Feature Selection (I-MPaFS) framework to address EDoS attacks. I-MPaFS framework enhances an existing dataset to improve performance, using the generated data to build a Random Forest model for EDoS detection. Our investigation employs the UNSW-NB15, CSE-CIC-IDS18 and NSL-KDD datasets, demonstrating the proposed method’s superiority over existing techniques. The model achieved recall scores of 99.45% on the UNSW-NB15 dataset, 98.19% on the CSE-CIC-IDS18 dataset, and 99.82% on the NSL-KDD dataset, highlighting its reliability and efficacy in safeguarding cloud users from financial exploitation. This study contributes to the field by evaluating current EDoS detection methods, introducing the I-MPaFS framework, validating its performance with benchmark datasets, and comparing its effectiveness against state-of-the-art techniques. The findings affirm the significant potential of I-MPaFS in enhancing cloud security and protecting users from EDoS attacks.

Mango (Mangifera indica) is extremely perishable with a short shelf life that limits its marketability. Chitosan may extend mango storage by preventing moisture loss and gaseous exchange while preserving the nutritional quality. Therefore, the current study was designed to assess the effect of chitosan on the shelf life and quality of fresh-cut mangoes. Manually cubed mango was dipped into 0 (control), 10, 15, 20, 30, and 50 ppm chitosan solution in airtight jar and stored at ambient (25-28 °C) and refrigeration (4 °C) condition. Changes in various microbial, physical, and chemical characteristics were documented to evaluate the effectiveness of treatments in prolonging and sustaining the freshness and quality of mango. Treated mangoes significantly retarded growth of total mold and bacterial counts compared to the control sample in both storage conditions and found it lower under refrigeration. Likewise, chitosan also preserved various fruit quality attributes to a significant extent by retaining vitamin C, fat, titratable acidity, soluble sugar, and protein. However, the refrigerator stored mangoes have better ability to retard moisture loss and drop in sensory quality. Among the other solutions, 10 ppm chitosan solution exhibited better performance in reducing perishability of mango while maintaining prolonged shelf life and quality attributes. Overall, the findings revealed that chitosan solution at low temperature effectively preserves mango quality during storage and offers promising approach for the successful commercialisation of chitosan as a natural preserver for mango sellers and consumers to prolong shelf life.

Abstract Sentiment analysis is a pivotal tool in understanding public opinion, consumer behavior, and social trends, underpinning applications ranging from market research to political analysis. However, existing sentiment analysis models frequently encounter challenges related to linguistic diversity, model generalizability, explainability, and limited availability of labeled datasets. To address these shortcomings, we propose the Transformer and Attention-based Bidirectional LSTM for Sentiment Analysis (TRABSA) model, a novel hybrid sentiment analysis framework that integrates transformer-based architecture, attention mechanism, and recurrent neural networks like BiLSTM. The TRABSA model leverages the powerful RoBERTa-based transformer model for initial feature extraction, capturing complex linguistic nuances from a vast corpus of tweets. This is followed by an attention mechanism that highlights the most informative parts of the text, enhancing the model’s focus on critical sentiment-bearing elements. Finally, the BiLSTM networks process these refined features, capturing temporal dependencies and improving the overall sentiment classification into positive, neutral, and negative classes. Leveraging the latest RoBERTa-based transformer model trained on a vast corpus of 124M tweets, our research bridges existing gaps in sentiment analysis benchmarks, ensuring state-of-the-art accuracy and relevance. Furthermore, we contribute to data diversity by augmenting existing datasets with 411,885 tweets from 32 English-speaking countries and 7,500 tweets from various US states. This study also compares six word-embedding techniques, identifying the most robust preprocessing and embedding methodologies crucial for accurate sentiment analysis and model performance. We meticulously label tweets into positive, neutral, and negative classes using three distinct lexicon-based approaches and select the best one, ensuring optimal sentiment analysis outcomes and model efficacy. Here, we demonstrate that the TRABSA model outperforms the current seven traditional machine learning models, four stacking models, and four hybrid deep learning models, yielding notable gain in accuracy (94%) and effectiveness with a macro average precision of 94%, recall of 93%, and F1-score of 94%. Our further evaluation involves two extended and four external datasets, demonstrating the model’s consistent superiority, robustness, and generalizability across diverse contexts and datasets. Finally, by conducting a thorough study with SHAP and LIME explainable visualization approaches, we offer insights into the interpretability of the TRABSA model, improving comprehension and confidence in the model’s predictions. Our study results make it easier to analyze how citizens respond to resources and events during pandemics since they are integrated into a decision-support system. Applications of this system provide essential assistance for efficient pandemic management, such as resource planning, crowd control, policy formation, vaccination tactics, and quick reaction programs.

Abstract As metallic nanostructures shrink towards the size of the electronic mean free path, thermal conductivity decreases due to increased electronic scattering rates. Matthiessen’s rule is commonly applied to assess changes in electron scattering rates, although this rule has not been validated experimentally at typical operating temperatures for most of the electronic systems (e.g., near room temperature). In this study, we experimentally evaluate the validity of Matthiessen’s rule in determining the thermal conductivity of thin metal films by measuring the in-plane thermal conductivity and electronic scattering rates of copper (Cu) films with varying thicknesses (27 nm — 5 µm), microstructures, and grain boundary segregation. Comparing total electron scattering rates measured with infrared ellipsometry to infrared ultrafast pump-probe measurements, we find that the electron-phonon coupling factor is independent of film thickness, whereas the total electronic scattering rate increases with decreasing film thickness. Our findings provide experimental validation of Matthiessen’s rule for electron transport in thin metal films at room temperature and also introduce an approach to discern critical heat transfer processes in thin metal interconnects, which holds significance for the advancement of future CMOS technology.

Abstract Background Among 13 endemic districts, the Chittagong Hill Tracts bear more than 90% of Bangladesh’s malaria burden. Despite the private sector’s prominence in rural healthcare, its role in malaria management remains underutilized. This study aimed to strategize leveraging the for-profit private sector, such as diagnostic and treatment centers, to bolster national malaria surveillance and control, advancing Bangladesh toward malaria elimination by 2030. Methods This mixed-method study commenced with a questionnaire-based cross-sectional survey followed by selected focused group discussions (FGDs) among the participants. Three FGDs were held with the for-profit service providers so that further insights and qualitative viewpoints of them can be utilized in situation analysis. Based on the endemicity and strategic priorities, a comprehensive mapping of private for-profit facilities from the regions comprising 15 sub-districts across 8 chosen districts (7 malaria endemic districts and the rest non-endemic districts) was created. For the non-endemic zone, the sub-districts were selected based on their proximity to an area with high malaria transmission. Results Among the 104 representative participants, majority were male (n = 92, 88.5%), had a diploma in their respective fields (n = 53, 51%) and were involved either in laboratory work (n = 49, 47.1%) or as owners/managers of health centers (n = 41, 39.4%). The selected health facilities were close to the corresponding Upazila Health Complexes (mean distance 2.8 km), but were distantly located from the designated district hospitals (mean distance 48.9 km). The main sources of RDT kits (62.3%) and anti-malarial drugs (63.2%) were local wholesale markets. A large share of the corresponding facilities neither provided malaria treatment services (81.7%) nor worked with the NMEP (93.3%). Conclusions This study highlights challenges and recommendations for engaging private for-profit health facilities in Bangladesh’s malaria elimination efforts. The identified challenges include low-quality RDTs, staff shortages, and inadequate capacity building. Recommendations emphasize effective training, stakeholder interaction, and enhanced oversight for successful malaria control efforts.

Abstract Background Bangladesh is rapidly urbanizing and approximately half of its urban population resides in deprived slums with limited access to basic needs. However, there is a dearth of information on vulnerability levels among slum dwellers. We aimed to assess the level of vulnerability within and between slums via various socioeconomic and health indicators. Methods A cross-sectional survey of 810 randomly selected households was conducted in two purposively selected slums, Korail and Shyampur, in Dhaka from November to December 2021. Data was collected on various indicators, including demographics, education, employment, access to utility, and healthcare services. Principal component factor analysis was employed to identify the key indicators to construct the socioeconomic and health vulnerability index for the urban slums of Bangladesh (SEHVI-BD). Kaiser-Meyer-Olkin, Bartlett’s test, and Cronbach’s alpha coefficient test were used to assess indicators’ suitability. The selected indicators were used to generate an index on a scale of 100, with a higher index value indicating a higher level of vulnerability. The estimated scores were used to categorize the vulnerability status into three levels: mild, moderate, and severe vulnerability. The Mann-Whitney-U test and Kruskal-Wallis test were applied between the generated index and other socioeconomic variables to validate the relationship. Results A total of 27 socioeconomic and health indicators were identified that explained 60% of the variance. The indicators were then grouped into six domains on the basis of their relevance. The prevalence of severe vulnerability in the Korail slum was approximately 9% and moderate vulnerability was 30% whereas these values were approximately 58% and 37%, respectively, in the Shyampur slum. The difference in the vulnerability level between the two slums was also evident across the domains. Households in the poorest wealth quintile, with lower education levels of household heads, and having irregular income experienced higher levels of vulnerability. Conclusions The SEHVI-BD offers a critical tool for policymakers to identify and address vulnerabilities, facilitating more targeted public health interventions in urban Bangladesh and similar low-income settings. This study further emphasizes the importance of integrating comprehensive vulnerability assessments into public health policies to reduce inequalities and improve well-being, especially for the urban marginalized slum population.

Highlights • Tobacco use was highest, followed by alcohol and marijuana use. • The prevalence of substance use was higher in male than female adolescents. • Parental homework supervision significantly lowered the odds of alcohol and tobacco use. • Parental free-time supervision significantly reduced the odds of alcohol and tobacco use. • No significant association was found between parent-child connectedness and marijuana use.

Abstract The success of any organization requires a skilled, competent, and satisfied workforce. If the workforce can be provided with the necessary components to ensure a high quality of working life, they will become permanent assets. Various factors undoubtedly affect the quality of workers' work lives. This study aims to investigate the drivers of the quality of work life in industrial labour force in a developing country, Bangladesh. It enumerated the elements that have an impact on industrial labour force’s quality of work life (QWL). A structured questionnaire was administered to 420 Bangladeshi workers across diverse industries, yielding a commendable response rate of 93.33%. The collected data underwent analysis employing the partial least squares structural equation modeling (PLS-SEM) technique. Representative industries and respondents were chosen by random selection. The results revealed that work environment, organizational culture and climate, relationships and cooperation, compensation and rewards, adequacy of resources, autonomy of work, job satisfaction, and security are directly related to the QWL. Training and development, and facilities do not significantly affect QWL. The research results can be used to improve the quality of work life for those working in the industrial sector. An industry may accomplish long-term and short-term goals by maintaining a pleasant workforce. The study's findings will provide policymakers and regulatory authorities of Bangladesh's industrial sector with strategic references and strategies to boost industrial productivity and economic growth for sustainable development by ensuring industrial employees' quality of work life that can serve as a template for Bangladesh.

The study was conducted to identify trends in the use of hormones and antibiotics, and traditional practices in cattle fattening in the southwest coastal region of Bangladesh. Data were collected from 150 farmers involved in beef cattle fattening through a survey questionnaire. The average numbers of cattle and beef cattle per household were 4.27 ± 1.94 and 3.54 ± 1.31, respectively. An average of 44.67 percent of the beef farmers selected indigenous (zebu) cattle and the rest of the farmers kept a variety of crossbred cattle for fattening. The average age of cattle at the start of fattening was 21.61 ± 8.07 months, the average duration of fattening was 9.71 ± 4.29 months, and farmers marketed cattle at an average weight of 285.50 ± 50.80 kg. The highest percentage (34.67%) of farmers ate beef once a week and 5.33 percent of farmers never ate beef. Among beef farmers, 53.33% and 62.67% applied hormones and antibiotics, respectively, to their beef cattle. The highest percentage of farmers used hormones (33.87%) and antibiotics (29.79%) once a week and the rest of the farmers used them at various intervals. The majority of farmers did not follow any withdrawal period before slaughtering beef cattle for application of hormones (85.48%) and antibiotics (83.58%), whereas the remaining farmers maintained different withdrawal periods. It can be concluded that more than half of the farmers applied hormones and about two-thirds of the farmers used antibiotics for beef fattening, and most of the farmers did not follow the recommended withdrawal periods before slaughter, which is of public health concern.

Abstract Plantar fascia (PF) is the commonest causes of foot pain in the adult population. Several surgical treatments are available to treat PF. This study was aimed to investigate the clinical efficacy of three different treatments for plantar fasciitis. It was conducted among 60 patients, divided equally into three treatment groups named Needle Knife Therapy, Endoscopic Plantar Fasciotomy, and Conventional Painkillers. Descriptive and analytical analysis were done by using SPSS 25 software. VAS and AOFAS scores were analyzed. The maximum (n = 31) participants were in 41–60 year age range group with normal BMI. These differences of mean VAS and AOFAS pain scores between different treatment groups were statistically significant (One-way ANOVA, p

Abstract The mechanical, thermal, and morphological characteristics of epoxy resin composites reinforced using repurposed textiles are examined in this work. This study investigates reusing textile waste to produce composite materials using sustainable alternatives. This study shows how epoxy resin may be improved by mixing recycled materials with powdered coconut shells. 10% of the total weight, or the powdered coconut shell, was combined with fabric and epoxy resin in a 2:1 ratio with hardener. After the mixture was put into molds, it was given a 72‐h cure. Two samples were then ready to be tested for their mechanical, morphological, and thermal characteristics. Tensile, bending, impact, thermogravimetric analysis (TGA), and scanning electron microscopy (SEM) tests were used to evaluate performance. The force obtained in the tensile test was 1236.705 N, the bending test produced 86.76 N, and the impact test produced 3.33 J. The fabric and resin were found to have strong binding by SEM examination, and the TGA investigation indicated a notable heat absorption capability. The study offers insightful information on improving material performance through sustainable methods, which benefits the building, automobile, and aerospace sectors—industries where durability and environmental responsibility are critical. Highlights Used textiles enhance epoxy resin in composite materials. Tensile tests reveal structural integrity and longevity. Impact tests show resistance to dynamic loads. TGA and SEM analyses clarify thermal stability and bonding.

Abstract The utilization of core‐spun yarns with spandex has achieved significant interest over the past decade because of their exceptional stretchability, resilience, and comfort features. The main objective of this study was to evaluate the effects of changing the spandex denier and twist multiplier on the physio‐mechanical properties of 16 Ne core spun yarn and the produced denim fabric using these yarns as weft. Core‐spun yarns were produced from three different spandex deniers at various TM values, where cotton roving was used as the sheath. Fabrics were manufactured using an air‐jet weaving machine with the core spun yarn as weft and 100% ring cotton yarn as warp (12 Ne) following the identical 3/1 twill weave pattern. Results reveal that using different spandex percentages shows better results at different TM parameters. Both yarn and fabric were attained at a twist multiplier of 5.0, 5.5, and 6.0 for 40, 70, and 105 D, respectively. Additionally, it was discovered that the yarn produced using lower spandex denier gives higher tenacity for yarn and fabric. However, the yarn produced using a higher spandex denier gives higher elongation percentage for yarn, higher tear strength, recovery, and lower growth percentage fabric. Highlights Influence of spandex denier and twist multiplier on the physio‐mechanical properties of core‐spun yarn and the resulting denim fabric. The higher spandex denier exhibited greater tear strength, better recovery, and lower fabric growth percentages. Increasing the twist multiplier initially increased the tenacity and elongation percentage of yarns and fabrics, followed by a decrease. Higher spandex percentages reduced hairiness and increased with higher twist levels. Higher spandex denier indicates the need for tailored TM settings based on spandex denier to achieve desired fabric properties.

Abstract The demand for superhydrophobic fabric with self‐cleaning and antibacterial features as potential practical applications has been rising steadily with the passage of time. In this research work, the wet chemical method is used to manufacture cotton with a superhydrophobic coating. Thus, Nanoparticles of silver were produced by using an in situ method. Afterward, the fabric is treated with a solution of perfluorooctyltriethoxysilane to make it superhydrophobic. The cotton fabric performs remarkably with exceptional hydrophobicity as it had a contact angle of 157 ± 1° and less than 10° in sliding angle. Water droplet's dynamic behavior is also studied so that the superhydrophobic fabric development can be proven. The fabric shows excellent chemical durability by performing chemical tests. The antimicrobial and self‐cleaning properties are greatly enhanced by the superhydrophobic coating on cotton. Particle density on the superhydrophobic cotton before and after self‐cleaning was found 5.1 and 0.4 mg/cm2. In addition, the treated cotton fabric also exhibited superior antibacterial properties against Escherichia coli bacteria. Therefore, the developed multifunctional cotton fabric can be used in real‐life applications. Highlights Cotton fabric enables to repelling of dirt, oil, and water‐based stains more effectively. Silver nanoparticles possess potent antibacterial properties. Incorporation of PFOTS controls the release of silver into the environment. Versatile applications from healthcare and hospitality to outdoor and athletic apparel.

Abstract Cellulose, a naturally abundant biopolymer, holds great potential as a sustainable alternative to synthetic fibers. However, the limited understanding and awareness surrounding cellulose utilization, particularly from agricultural origins, have impeded the complete harnessing of this highly biodegradable resource. This study aimed to extract and characterize cellulose from jute and banana fibers. The extracted cellulose exhibits a light yellow to white color, and microscopic analysis of the fibers showed micro‐fibrils. X‐ray diffraction (XRD) characterization indicated that the extracted cellulose from biomass primarily consists of cellulose II structures, except for the treated banana fiber (M:L = 1:8), which contains both cellulose I and II. Moreover, increasing the M:L ratio of alkali treatment enhanced the percentage of cellulose‐II, as observed from the XRD data. The findings of this study carry significant implications for the efficient production of cellulose fibers, with diverse applications spanning from high‐volume products like regenerated fibers, automotive parts, packaging, absorbent products (diapers), textiles, and precast concrete, drug delivery mediums, electronics, additive manufacturing, bone and tissue scaffolding, and so on. This research opens the door to harnessing the potential of cellulose derived from jute and banana fibers in various industries. Highlights Extraction cellulose using the kraft process. Isolated cellulose shows a micron‐sized structure. Optimal extraction achieved with M:L ratio of 1:4. Applications of isolated cellulose: regenerated fibers, packaging, absorbent products (diapers), textiles, and so on.

Abstract The goal of the current study is to understand the elemental concentration, reserve assessment, mineralogical composition, and distribution of heavy minerals in the dune sands, foreshore, and backshore of Sonadia Island, Southeast Bangladesh. The study area yielded a number of drill hole composite sand samples that were used for density and magnetic separation of heavy minerals. These samples were then subjected to petrographic characterization, grain counting, mineralogical analyses using scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM–EDX), x-ray diffraction (XRD), and bulk sand geochemistry using X-ray fluorescence (XRF). The results of heavy liquid separation show that the total heavy mineral (THM) enrichments in foreshore sand (about 4.86%), backshore sand (about 9.03%), and dune sand (about 22.28%) are significantly higher. After characterizing the sand samples from Sonadia Island based on the abundance of each mineral species, the average content values relative to total economic heavy minerals (TEHM) were determined to be 38.14% ilmenite, 5.74% magnetite, 51.52% garnet, 1.01% zircon, 3.57% rutile, and 0.01% monazite. An SEM–EDX examination reveals that the elemental concentration of ZrO2 in zircon is 66.60%, while the average TiO2 content in rutile is ~ 95%, whereas TiO2 is 45% and the total Fe2O3 is 45.70% in ilmenite. Major phases including amphibole (tremolite) 45.9%, muscovite 15.6%, quartz 12.6%, garnet (almandine) 10.9%, dolomite 7.9%, ilmenite 4.1%, and kaolinite 1.3% are also identified by the XRD pattern. There are an estimated 19,794 tons of garnet in the foreshore sand, 47,324 tons in the backshore sand, and 276,868 tons in the dune sand within the reserve, making it the most prevalent heavy mineral. Ilmenite is the second most common heavy mineral, with quantities detected in the foreshore (15,016 tons), backshore (30,487 tons), and dune sand (215,092 tons).

Abstract This study investigates mechanical properties, durability performance, non-destructive testing (NDT) characteristics, environmental impact evaluation, and advanced machine learning (ML) modelling techniques employed in the analysis of high-strength self-compacting concrete (HSSCC) incorporating varying supplementary cementitious materials (SCMs) to develop sustainable building construction. The findings from the fresh characteristics test indicate that mixes’ optimal flowability and passing qualities can be achieved using different concentrations of marble powder (MP) alongside a consistent amount of silica fume (SF) and fly ash (FA). Moreover, the incorporation of 10% MP along with 10% FA and 20% SF in HSSCC significantly improved the compressive strength by 14.68%, while the splitting tensile strength increased by 15.59% compared to the reference mix at 56 days. While random forest (RF), gradient boosting (GB), and their ensemble models exhibit strong coefficient correlation (R2) values, the GB model demonstrates more precision, indicating reliable predicted outcomes of the mechanical properties. Following subsequent testing, it has been demonstrated that incorporating SCMs improves the NDT properties of HSSCC and enhances its durability. The finer MP, SF, and FA particles enhanced microstructural performance by minimizing voids and cracks while improving the C–H–S bond. As noticed by its lower CO2-eq per MPa for SCMs, the HSSCC mix with up to 15% MP inclusion increased mechanical strength while reducing the environmental footprint, making it an eco-friendly concrete alternative.

Abstract Brain tumor, a leading cause of uncontrolled cell growth in the central nervous system, presents substantial challenges in medical diagnosis and treatment. Early and accurate detection is essential for effective intervention. This study aims to enhance the detection and classification of brain tumors in Magnetic Resonance Imaging (MRI) scans using an innovative framework combining Vision Transformer (ViT) and Gated Recurrent Unit (GRU) models. We utilized primary MRI data from Bangabandhu Sheikh Mujib Medical College Hospital (BSMMCH) in Faridpur, Bangladesh. Our hybrid ViT-GRU model extracts essential features via ViT and identifies relationships between these features using GRU, addressing class imbalance and outperforming existing diagnostic methods. We extensively processed the dataset, and then trained the model using various optimizers (SGD, Adam, AdamW) and evaluated through rigorous 10-fold cross-validation. Additionally, we incorporated Explainable Artificial Intelligence (XAI) techniques-Attention Map, SHAP, and LIME-to enhance the interpretability of the model’s predictions. For the primary dataset BrTMHD-2023, the ViT-GRU model achieved precision, recall, and F1-score metrics of 97%. The highest accuracies obtained with SGD, Adam, and AdamW optimizers were 81.66%, 96.56%, and 98.97%, respectively. Our model outperformed existing Transfer Learning models by 1.26%, as validated through comparative analysis and cross-validation. The proposed model also shows excellent performances with another Brain Tumor Kaggle Dataset outperforming the existing research done on the same dataset with 96.08% accuracy. The proposed ViT-GRU framework significantly improves the detection and classification of brain tumors in MRI scans. The integration of XAI techniques enhances the model’s transparency and reliability, fostering trust among clinicians and facilitating clinical application. Future work will expand the dataset and apply findings to real-time diagnostic devices, advancing the field.

The current study investigates the effects of thermal radiation and Lorentz’s force on heat and mass flow in a nanofluid with a magnetic field and free convection, as it passes over a vertical permeable sheet. The dominant PDEs are turned into linked nonlinear ODEs using an appropriate similarity transformation. Using the MATLAB ODE45 tool, dimensionless ODEs are numerically computed using the finite difference procedure. Four different water-based nanofluids are considered, including copper, titanium dioxide, aluminum oxide, and silver. The influence of emerging dimensionless numbers and other parameters, such as the magnetic force parameter, the radiation parameter, the suction parameter, the Prandtl number, the Schmidt number, the Dufour number, as well as for constant values of the modified local Grashof number and local Grashof number on the concentration, velocity, and temperature profiles is emphasized and mentioned. Moreover, the visual representation of the effects of a volume percentage of up to 4% copper nanoparticles on the distributions of concentration, temperature, and velocity is also shown. The temperature profile, concentration profile, and velocity profile all grow with an increase in the volume percent of copper nanoparticles between 0.00 and 0.04. Furthermore, numerous scenarios are investigated for the distributions of the local Sherwood number, the local Nusselt number, and the local skin friction coefficient. The local Nusselt number decreases, and the local skin friction coefficient and Sherwood number increase about by 30 %, 45 %, and 60 % respectively due to increasing the value of volume fraction from 0 % to 4 %. The local skin friction coefficient increases and the local Nusselt number decreases by about 15 % and 39 % respectively for rising values of the thermal radiation number from 0.5 to 3.5. In addition, for magnetic force parameter values between 0.5 and 4.0, the local skin friction coefficient drops by about 13 %.

Abstract Child marriage negatively affects women’s socio-economic empowerment, particularly in education and employment. This study aimed to explore women’ perspectives on the timing of their marriages, considering their educational and employment status at the time. It also sought to identify factors influencing early married women’s perception of their marriages as timely. We analyzed both quantitative and qualitative data. The quantitative data included a sample of 5,596 women aged 15–24 from the 2017/18 Bangladesh Demographic and Health Survey. Additionally, we collected qualitative data through six in-depth interviews, two focus group discussions, and 13 key informant interviews. We used a multilevel mixed-effects Poisson regression model to examine the relationship between women’s formal employment, education, and child marriage. Thematic analysis was employed for the qualitative data. Around 62% of the total women analysed reported their married occurred early with the mean age at marriage was 15.2 years. Approximately 55% of the total early married women believed their marriages occurred at the right time, especially among those who were employed at the time of their marriage. Among this subset, we also noticed a higher likelihood of discontinuing work and education following marriage. Qualitative findings revealed reasons behind this perception, such as escaping poverty, safety concerns, limited job prospects, and the impact of non-marital relationship and societal norms. While many early-married women perceived their marriage as timely, particularly those initially employed, this decision often coincides with a subsequent withdrawal from work and education. This underscores the pressing need for policies and programs aimed at educating women about the legal age for marriage and the negative consequences associated with early marriage while also equipping them with knowledge and resources for informed decision-making.

Abstract Background COVID-19 vaccine uptake among individuals with disabilities is crucial for safeguarding their health and well-being. However, the extent of vaccine uptake among this group remains largely unknown in low- and middle-income countries. This study aims to assess the COVID-19 vaccine uptake among persons with functional difficulty, disability and/or comorbidity in Bangladesh and their associated factors. Methods Data from 9,370 respondents extracted from the 2021 National Household Survey on Persons with Disability were analysed. The outcome variable was the uptake of at least one dose of the COVID-19 vaccine (yes, no). Key explanatory variables included the presence of disability (yes, no), comorbidity (yes, no), and both comorbidity and disability (yes, no) among persons with functional difficulty. The relationship between the outcome and explanatory variables was determined using mixed-effects multilevel logistic regressions adjusted for covariates. Results The overall uptake of at least one dose of the COVID-19 vaccine among persons with functional difficulty was 57.37%, among persons with functional difficulty and disability was 48.63% and among persons with functional difficulty and single (57.85%) or multi-comorbidity (60.37%). Compared to the respondents with functional difficulty only, the adjusted odds ratio (aOR) of not receiving any dose of the COVID-19 vaccine for individuals with both functional difficulty and disability was 1.37 (95% CI, 1.22–1.53), and for individuals with functional difficulty, disability and one or more comorbid conditions was 1.30 (95% CI, 1.15–1.47). The aOR of receiving at least one dose of the COVID-19 vaccine among individuals with functional difficulty and one or more comorbid conditions was significantly higher than among those with functional difficulty only. Conclusion In Bangladesh, COVID-19 vaccine uptake was relatively low among individuals with disabilities. The existing COVID-19 vaccine rollout programs and similar future programs should prioritise individuals with disabilities and include targeted strategies to reach them.

Purpose – Digitalization is one of the major factors that fosters economic growth across the world. However, the level of digitalization varies significantly between developed and developing countries, with the latter often lagging behind. To bridge this gap, it is crucial to pinpoint the drivers of digitalization, specifically from the macroeconomic and country-level governance dimensions. Therefore, this study aims to investigate the determinants of digitalization, particularly for countries in Asia and the Pacific region. Design/methodology/approach – Our study utilizes unbalanced panel data from 46 Asian and Pacific countries for the period of 2001–2021. Initially, we analyzed the data using conventional econometric methods, such as pooled ordinary least squares (POLS), random-effects model (REM) and fixed-effects model (FEM). Moreover, we employed endogeneity-corrected techniques and alternative proxies to enhance the robustness and reliability of our findings. Findings – Our findings reveal that economic development progress, government expenditure relative to country size and political stability are key drivers of digitalization. In contrast, corruption at the country level emerges as a significant impediment. Notably, our results remain robust to endogeneity-corrected techniques and alternative proxies of digitalization. Overall, these insights can inform policymakers, helping them to understand the macroeconomic and governance factors shaping digitalization and guide their decision-making toward effective policy interventions. Originality/value – This study’s empirical findings add significant value to the existing literature by quantifying the impact of macroeconomic and governance factors on digitalization in selected countries. This offers valuable insights for policymakers, particularly in nations with lower levels of digitalization.

Abstract The fast fashion industry significantly impedes sustainability by its rapid production cycles, low-cost products, and heavy carbon footprint. Nowadays, circular economy (CE) has become an interesting concept, which follows the “take-make-dispose” model and allows products, materials, and resources to be in use as long as possible. There is a paucity of research in the identification of circular economy strategies (CES) and mapping them based on their interrelationships, which is very important from a managerial perspective. In this vein, this study identifies nine CES and analyzes their causal relationships using the Fuzzy Decision-Making Trial and Evaluation Laboratory method in the context of fast fashion footwear manufacture. The key contributions include providing actionable insights for implementing CE practices, enhancing the sustainability of the footwear industry, and offering a strategic roadmap for industry stakeholders. The results and implications of this study will guide footwear industry decision-makers in making a strategic map considering the cause-and-effect interrelationships among the CES for implementing CE business practices in the footwear industry.

Abstract Rapid replacement of vegetated land with impermeable land (built-up areas) is a major factor in the increase in Land Surface Temperature (LST), while increased LST worsens the temperature in cities and creates the Surface Urban Heat Island (SUHI) effect. The study aims to measure vegetation loss and Land Surface Temperature of the Gazipur district between 2000 and 2020 and explore the relationship among Normalized Difference Vegetation Index (NDVI), LST, and Urban Thermal Field Variance Index (UTFVI). The Landsat TM/OLI images with minimum cloud coverage have been used to derive different indices. The mean NDVI values are 0.21, 0.16, and 0.22 in 2000, 2010, and 2020 respectively which indicates a general improvement in the health of the vegetation. Besides, the highest LST values throughout 20 years, represent a general increasing trend. As a consequence, different land covers have experienced fluctuations in mean temperature. The result shows that the mean temperature of bare land, buildup, vegetation, and waterbody has increased by 4.77, 2.01, 2.25, and 2.23 °C respectively from 2000 to 2020. The strongest SUHI zone’s area grew by about 28% between 2000 and 2020. Additionally, the highest index value of UTFVI was 0.39 in 2000 and grew to 0.43 in 2010. It changed to 0.49 in 2020, or ten years later. Thus, the SUHI effect’s increasing intensity is visible. Also, regression analysis has been used to explore the correlation between the derived indices. Stakeholders from different sectors like urban planners and policymakers may take insights from this study to work to promote greenery for a healthy urban environment.

Abstract Trained immunity is a phenomenon in which brief exposure to an infectious agent or a vaccine can induce long-lasting changes in the host's immune system, enhancing protection against subsequent infections. The concept of trained immunity has a significant impact on the field of immunology and has the potential to revolutionize how we approach vaccination and infectious disease control. Investigations into trained immunity are rapidly advancing and have led to the development of new vaccines and immunotherapeutic strategies that harness the power of this phenomenon. While more investigations are needed to fully understand the mechanisms of trained immunity and its potential limitations, the prospects for its future application in clinical practice are promising. Here, we describe trained immunity as a biological process and explore the innate cues, epigenetic changes, and metabolic reprogramming activities that affect how trained immunity is induced.

Abstract This study investigates the potential of renewable energy sources to address the growing energy demands of developing nations while ensuring environmental sustainability. Here, a model-based analysis to assess the feasibility of utilizing rice straw pyrolysis as a renewable energy source is investigated. The thermogravimetric analysis identified the optimal temperature range for pyrolysis (230–400 °C) and the Coats–Redfern model was employed for kinetic analysis. Diffusion models, particularly D3, exhibited the best fit with an activation energy of 16–18 kJ/mol. Thermodynamic analysis confirmed the endothermic nature of the process, with positive enthalpy and negative entropy changes indicating an increase in energy and a more ordered product. These findings suggest that rice straw pyrolysis holds promise as a viable renewable energy option. This study contributes to understanding the rice straw pyrolysis mechanism which could be helpful for its effective utilization at commercial scale.

Abstract Background Post-COVID-19 conditions (PCCs), also known as long COVID, is persistently debilitating disorders that need investigation on their incidence, morbidity, and case-fatality rate. Purpose The objectives of this cohort study were to determine the incidence, characteristics, case-fatality, morbidity, and recovery of post-COVID-19 symptoms throughout a two-year period of observation. Methods This was a population-based cohort study of post-COVID-19 cases among 12,925 SARS-CoV-2 positive individuals in eight administrative districts of Bangladesh between July and December 2021–2023. PCC was diagnosed according to WHO clinical diagnostic criteria, and the screening procedure was completed through a household screening process. Results The incidence of PCC was 3.6%, the case-fatality rate was 1.92%, and the recovery rate was 9.0%. The significant predictors of PCC morbidity were geographical distribution, vaccination, comorbidities, and a longer duration of symptoms or multiple symptoms (p

Background and objectives: COVID-19 vaccination program has become a global priority to combat the worldwide pandemic. Studies claimed that severity and case fatality could be minimized by vaccination. The durability of antibodies developed after vaccination is crucial for preventing COVID-19. The purpose of this study was to investigate the dynamics of antibody responses to an inactivated SARS-CoV-2 virus vaccine over time. Materials and method: The study was conducted from November 2021 to November 2022 among young adults. A pre-tested structured questionnaire was used to record the socio-demographic and clinical data of all the participants. All the participants were vaccinated with two doses of Sinopharm COVID-19 vaccine. Blood samples were collected for estimation of IgG antibodies to SARS-CoV-2 spike S1 protein by indirect ELISA. Biochemical parameters namely random blood sugar (RBS), lipid profile, total protein, thyroid stimulating hormone (TSH), FT4 (free thyroxin) and vitamin D levels were determined in baseline samples by standard methods. Result: Total 348 adults, aged 18-28 years, were enrolled and of which 35.3% and 64.7% were male and female respectively. Out of 348 participants, 51.7% was seropositive for anti- SARS-CoV-2 antibodies before receiving vaccination. Seropositivity was not significantly (p >0.05) different in male and female participants before and after vaccination. Seropositivity at 1 month after 1st dose and 4 and 7 months after 2nd dose of vaccination increased significantly (p 0.05) different before and after vaccination in participants having adequate and insufficient/deficient vitamin D levels. Conclusion: The study revealed that a good proportion of young adults possessed anti- SARS-CoV-2 antibody before vaccination and the seropositivity increased to over 90% following vaccination with Sinopharm COVID-19 vaccine. High level of anti- SARS-CoV-2 antibody persisted 7 months after 2nd dose of vaccine. July 2024; Vol. 18(2):012. DOI:https://doi.org/10.55010/imcjms.18.024 *Correspondence: Nehlin Tomalika, Department of Community Medicine & Public Health, Ibrahim Medical College, 1/A, Ibrahim Sarani, Segunbagicha, Dhaka 1000, Bangladesh. Email: nehlintomalika@gmail.com

Liquefaction can cause significant damage to the built environment; therefore, assessing the liquefaction hazard in a seismically active region is essential to minimize the risk. This study attempted to evaluate the liquefaction potential of the south-central coastal areas of Bangladesh by calculating the liquefaction potential index (LPI) considering a scenario earthquake of Mw ​= ​7.5 having a peak ground acceleration of 0.15g. For calculating LPI, both standard penetration test blow count (SPT-N) and shear wave velocity (Vs) data have been used in this study. The results show that the study area's LPI values vary from 0 to 37. A liquefaction hazard map is prepared for the area using the calculated LPI values from Vs data shows about 8% of the study area is very highly susceptible to liquefaction hazard, whereas 62% of the area falls under high hazard-prone area while about 28% and 2% area of the study have respectively low (0

Abstract Food and nutrition security are essential for accomplishing sustainable development goals, and a growing population requires various food sources to address hunger and nutrition. Indigenous vegetables are nutritious, healthy, and adaptable to local conditions, making them a potential food and nutrition security resource. Thus, this study aimed to evaluate the nutritional richness of indigenous vegetables by comparing the nutritional content of commonly grown indigenous vegetables like Chenopodium album (Bathua), Coccinia grandis (Telakucha), Amaranthus viridis (Shaknotey), Moringa oleifera (Shojne), Alternanthera philoxeroides (Malancha), and Xanthium strumarium (Ghagra) to BARI Lalshak-1 (Amaranthus tricolor). Shojne violet had the highest lightness (L*), directions (b*), and chroma (c*) of 43.50, 23.69, and 25.02, respectively and telakucha had the highest luminosity (h°) of 114.11. Shaknotey had more moisture (88.97 %) than bathua green (66.62 %) or ghagra (72.55 %). Ghagra had the most chlorophyll a (1.11 mg/g), b (0.65 mg/g), total chlorophyll (2.04 mg/g), ascorbic acid (22.0 mg/100 g), and flavonoids (50.1 mgQE/100 g). Bathua red had significant levels of anthocyanin, carotenoids, and strongest antioxidant activity (IC50 value): 42.9 µg/g, 0.24 mg/g, and 12.4 µg/mL, respectively. Shojne green and telakucha were found rich in phenolic content (136.0 mgGAE/100 g) and β-carotene (2.05 mg/100 g), respectively. Moreover, the studied indigenous vegetables were rich in minerals, potassium, calcium, magnesium, and iron, with a very high K content (49.4–79.4 mg/g). Correlation matrix and PCA showed that ghagra, shojne, and bathua species were nutritionally superior to others. Therefore, the current findings suggest that ghara, shojne and bathua can become a nutrient-rich indigenous vegetable and improve human nutrition.

Abstract 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.

To ensure food security in this age of production and supply disruption, the agricultural sectors of emerging economies are gradually adopting more smart technologies to achieve sustainability. However, literature on the challenges of adopting Agriculture 4.0-based smart farming technologies is still very limited. This research, therefore, explores the contextual interrelation among the challenges to adopting Agriculture 4.0-based smart technologies in the agricultural production system from a developing country's perspective and prioritizes the identified challenges. A case study was conducted in Bangladesh, an emerging economy, where data was collected through interviews and focus group discussion sessions. A total of 21 challenges were finalized as relevant to the country's context. The Interpretive Structural Modeling (ISM) technique was deployed to develop a hierarchical structure depicting the challenges' interrelations. The challenges were later ranked based on their relevant weight using the Best-Worst Method (BWM). This study finds technological complexity, lack of collaboration among different stakeholders, inadequate support from the government, and lack of action plans to have very high driving power. Challenges such as high initial investment and operational costs, lack of skilled workforce, and farmers' resistance were found to be dependent challenges. This study is expected to contribute by providing a deeper insight into the challenges of adopting Agriculture 4.0 in emerging economies so that practitioners can take effective mitigating measures to streamline the plant-based agricultural production systems to promote food security and sustainability.

Abstract Existing strategies for improving global access to safe drinking water have met only limited success. We consider an unglamorous and often neglected dimension of drinking water infrastructure provision: cleaning. We randomly assigned caretakers of community wells to participate in a training workshop about how to clean wells. Thirteen to seventeen months later, wells with caretakers assigned to receive training have negligible rates of contamination with Escherichia coli (13 months: 2%; 17 months: 4%), while control wells have substantial rates of E. coli contamination (13 months: 14%; 17 months: 19%). Rates of contamination with any coliform bacteria are almost halved (13 months: control 55%, treated 30%; 17 months: control 77%, treated 46%). We estimate the cost of preventing exposure to coliform bacteria in drinking water to be US$0.89 per person and that, if scaled up, each US$2376 spent on the intervention could avoid the death of a child.

Abstract Background Capsicum annuum, a significant agricultural and nutritional crop, faces production challenges due to its sensitivity to various abiotic stresses. Glyoxalase (GLY) and D-lactate dehydrogenase (D-LDH) enzymes play vital roles in mitigating these stresses by detoxifying the stress-induced cytotoxin, methylglyoxal (MG). Methods A genome-wide study was conducted to identify and characterize glyoxalase I (GLYI), glyoxalase II (GLYII), unique glyoxalase III or DJ-1 (GLYIII), and D-LDH gene candidates in Capsicum annuum. The identified members were evaluated based on their evolutionary relationships with known orthologues, as well as their gene and protein features. Their expression patterns were examined in various tissues, developmental stages, and in response to abiotic stress conditions using RNA-seq data and qRT-PCR. Results A total of 19 GLYI, 9 GLYII, 3 DJ-1, and 11 D-LDH members were identified, each featuring characteristic domains: glyoxalase, metallo-β-lactamase, DJ-1_PfpI, and FAD_binding_4, respectively. Phylogenetic analysis revealed distinct clades depending on functional diversification. Expression profiling demonstrated significant variability under stress conditions, underscoring their potential roles in stress modulation. Notably, gene-specific responses were observed with CaGLYI-2, CaGLYI-7, CaGLYII-6, CaDJ-1 A, and CaDLDH-1 showed upregulation under salinity, drought, oxidative, heat, and cold stresses, while downregulation were shown for CaGLYI-3, CaGLYII-1, CaDJ-1B, and CaDJ-1 C. Remarkably, CaGLYI-1 presented a unique expression pattern, upregulated against drought and salinity but downregulated under oxidative, heat, and cold stress. Conclusion The identified GLY and D-LDH gene families in Capsicum annuum exhibited differential expression patterns under different abiotic stresses. Specifically, CaGLYI-2, CaGLYI-7, CaGLYII-6, CaDJ-1 A, and CaDLDH-1 were upregulated in response to all five analyzed abiotic stressors, highlighting their critical role in stress modulation amidst climate change. This study enhances our understanding of plant stress physiology and opens new avenues for developing stress-resilient crop varieties, crucial for sustainable agriculture.

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.

Purpose – The main purpose of this paper is to find out the perception of different respondents' groups related to the factors that influence the online learning for higher education in Bangladesh during the COVID-19 pandemic. Design/methodology/approach – A survey through a structured questionnaire was conducted to gather qualitative information from the 250 respondents (university students, faculty members and administrative officers) in Bangladesh. A questionnaire has been used for collecting primary data, which have been selected using the justification method under the non-probability sampling technique. Findings – The findings of this study indicated that majority of the respondents told that online classes could be more challenging than the traditional classroom because of the technological constraints, digital divide, insufficient data pack to access the material to attend the class, poor connectivity, lack of device, poor learning environment, technophobia, delayed response and incapability of the teacher to handle efficiently the material and communication machineries. Research limitations/implications – Due to time restriction and the COVID-19 pandemic, the study was constrained only to Dhaka region in Bangladesh. Practical implications – The outcomes of the work can be supportive to the governing bodies and proprietors of the higher schooling organizations who are forecasting to adopt online education as a consistent movement in the future. Originality/value – At last, based on outcomes, investigators have presented some recommendations that can be taken into consideration at policy level. The study would help universities to comply with the pressing need to impart experiential learning through online education during the COVID-19 pandemic.