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The most labor-intensive and time-consuming part of warehouse operations is order picking. This paper proposes a correlation-based storage location assignment (CBSLA) approach to minimize the travel distance of the picker in a picker-to-parts warehouse. At first, the proposed CBSLA approach forms some groups of stock-keeping units (SKUs) for different warehouse aisles. Then these groups of SKUs are assigned to the storage locations consider- ing both the correlations between SKUs in a group and the correlation between groups of SKUs for efficient order picking. The effectiveness of the proposed method is measured for various warehouse configurations using simulation and compared with other well-known storage allocation methods.

Accidents due to defective railway lines and derailments are common disasters that are observed frequently in Southeast Asian countries. It is imperative to run proper diagnosis over the detection of such faults to prevent such accidents. However, manual detection of such faults periodically can be both time-consuming and costly. In this paper, we have proposed a Deep Learning (DL)-based algorithm for automatic fault detection in railway tracks, which we termed an Ensembled Convolutional Autoencoder ResNet-based Recurrent Neural Network (ECARRNet). We compared its output with existing DL techniques in the form of several pre-trained DL models to investigate railway tracks and determine whether they are defective or not while considering commonly prevalent faults such as—defects in rails and fasteners. Moreover, we manually collected the images from different railway tracks situated in Bangladesh and made our dataset. After comparing our proposed model with the existing models, we found that our proposed architecture has produced the highest accuracy among all the previously existing state-of-the-art (SOTA) architecture, with an accuracy of 93.28% on the full dataset. Additionally, we split our dataset into two parts having two different types of faults, which are fasteners and rails. We ran the models on those two separate datasets, obtaining accuracies of 98.59% and 92.06% on rail and fastener, respectively. Model explainability techniques like Grad-CAM and LIME were used to validate the result of the models, where our proposed model ECARRNet was seen to correctly classify and detect the regions of faulty railways effectively compared to the previously existing transfer learning models.

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Abstract MAX phases and their MXene compounds have received significant attention owing to their extensive potential applications. The quality and purity of the MAX phase guarantee the desired quality of the MXene product, which is essential for a variety of applications, including energy storage, catalysis, and electrical devices. Due to the purity, quality, complex structure, and unavailable commercial pure MAX powders, it is frequently required to have sophisticated synthesis and characterization techniques for the expected MAX products. Many researchers entering this field seek a comprehensive approach to the synthesis and characterization of MAX phases. Despite this, a significant portion of existing reviews have overlooked the synthesis and characterization methods specific to MAX phases, particularly when addressing MXenes. Consequently, this review aims to offer a thorough overview of the various synthesis methods and characterization techniques that are often required for MAX phases. In this review, various synthesis techniques, including their advantages and disadvantages, have also been discussed. Characterization techniques, especially x‐ray diffraction (XRD), were found to be quite critical for new researchers. However, the integration of other techniques such as scanning electron microscopy, transmission electron microscopy, x‐ray photoelectron spectroscopy, and infrared analysis enhances and complements the findings obtained through XRD. The review also underscores the challenges associated with MAX phase synthesis and proposes potential solutions, emphasizing the assessment of their suitability across a broad spectrum of applications. Overall, this review serves as a comprehensive resource and guide for researchers engaged in the exploration and application of MAX phases, emphasizing the essential techniques of synthesis and characterization in harnessing their massive potential.

Abstract Background Contagious and economically devastating, peste des petits ruminants (PPR) is a viral disease affecting goats and sheep, causing significant losses in livestock productivity and posing a threat to food security and rural livelihoods worldwide. Objectives This study was conducted to assess the status of goat farmer's knowledge, attitude and practice (KAP) about PPR disease at Sylhet district of Bangladesh. Methods A comprehensive cross‐sectional survey, conducted over 11 weeks, targeted 130 goat owners. Following a pilot study with 20 farmers, a set of 17 validated questions on PPR KAP was validated. Data collection was performed through face‐to‐face interviews by a trained team using KOBO Toolbox, with interpretation of responses based on established thresholds for knowledge (>65%), attitude (>75%) and practice (>70%). Results Overall, 67.7% of participants demonstrated a good level of knowledge about PPR disease. Males exhibited 1.42 times higher odds of knowledge compared to females (odds ratio = 1.42). The middle age group (31–45 years) showed significantly higher knowledge levels (83.3%, p 80%). Participants whose additional income came from non‐governmental organization employment showed a higher positive practice level (71.4%), 1.46 times higher than other income sources. Conclusion Strategic interventions should prioritize female farmers, educational empowerment and collaboration with non‐governmental organizations to bolster livestock health and rural livelihoods in Bangladesh as part of national PPR control strategy to fulfil the goals of Office International des Epizooties/World Organization for Animal Health (OIE/WOAH) and Food and Agriculture Organization (FAO) PPR eradication by 2030.

Abstract Background The present study investigated the potential wound healing activity comparisons between ethanol leaf & seed extracts of Costus speciosus topical formulation using mice Excision wound models, compared to Neomycin sulphate ointment as a standard drug. To assess the efficacy of Costus speciosus ointment as a potential alternative to commercially available ointments, a study was conducted using a cohort of 16 healthy male mice, aged between 3 and 4 months. The ointment formulation was prepared utilizing ethanol extracts from both the leaves and seeds, presented in a dosage form for application. This investigation seeks to provide insights into the comparative effectiveness of Costus speciosus ointments in relation to conventional market preparations, with a focus on its potential applicability as an alternative therapeutic option. The parameter measured was wound contraction and epithelization period. Result The mice groups treated with seed & leave extracts ointment showed better wound size changes than the negative and standard groups. However, the leaf extract ointment promotes the formation of granulation in tissue, allowing the reepithelization phase to occur more rapidly than seed extract. From the 1st day to the results of the 9th day of the extract indicate that there is a significant increase (P

The agricultural sector is more vulnerable to the adverse effects of climate change and excessive pesticide application, posing a significant risk to global food security. Accurately predicting crop yields is essential for mitigating these risks and providing information on sustainable agricultural practices. This research presents a novel crop yield prediction system that utilizes a year’s worth of meteorological data, pesticide records, crop yield data, and machine learning techniques. We employed rigorous methods to gather, clean, and enhance data and then trained and evaluated three machine learning models: Gradient Boosting, K-Nearest Neighbors, and Multivariate Logistic Regression. We utilized the GridSearchCV method for hyper-parameter tweaking to identify the most suitable hyper-parameter throughout K-Fold cross-validation, aiming to improve the model’s performance by avoiding overfitting. The remarkable performance of the Gradient Boosting model, with an almost flawless coefficient of determination ( $R^{2}$ ) of 99.99%, demonstrates its promise for precise yield prediction. This research also examined the correlation between projected and actual crop yields and identified the ideal meteorological conditions. It paves the way for data-driven methods in sustainable agriculture and resource distribution, ultimately leading to a more secure future with respect to food availability and resilience to climate change.

Abstract In the realm of cybersecurity, the detection and analysis of obfuscated malware remain a critical challenge, especially in the context of memory dumps. This research paper presents a novel machine learning-based framework designed to enhance the detection and analytical capabilities against such elusive threats for binary and multi type’s malware. Our approach leverages a comprehensive dataset comprising benign and malicious memory dumps, encompassing a wide array of obfuscated malware types including Spyware, Ransomware, and Trojan Horses with their sub-categories. We begin by employing rigorous data preprocessing methods, including the normalization of memory dumps and encoding of categorical data. To tackle the issue of class imbalance, a Synthetic Minority Over-sampling Technique is utilized, ensuring a balanced representation of various malware types. Feature selection is meticulously conducted through Chi-Square tests, mutual information, and correlation analyses, refining the model’s focus on the most indicative attributes of obfuscated malware. The heart of our framework lies in the deployment of an Ensemble-based Classifier, chosen for its robustness and effectiveness in handling complex data structures. The model’s performance is rigorously evaluated using a suite of metrics, including accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC) with other evaluation metrics to assess the model’s efficiency. The proposed model demonstrates a detection accuracy exceeding 99% across all cases, surpassing the performance of all existing models in the realm of malware detection.

Abstract Background Antimicrobial stewardship programs (ASPs) are pivotal components of the World Health Organization's Global Action Plan to combat antimicrobial resistance (AMR). ASPs advocate rational antibiotic usage to enhance patient-centered outcomes. However, existing evidence on ASPs and their determinants is largely limited to well-equipped hospitals in high-income nations. Objective This scoping review aimed to examine the current state of hospital-based ASPs in low- and middle-income countries (LMICs), shedding light on barriers, facilitators, prescribers’ perceptions and practices, and the impact of ASP interventions. Design Scoping review on ASP. Methods Adhering to PRISMA guidelines, we conducted electronic database searches on PubMed, Scopus, and Google Scholar, covering ASP articles published between January 2015 and October 2023. Our review focused on four key domains: barriers to ASP implementation, facilitators for establishing ASP, ASP perceptions and practices of prescribers, and the impact of ASP interventions. Three reviewers separately retrieved relevant data from the included citations using EndNote 21.0. Results Among the 7016 articles searched, 84 met the inclusion criteria, representing 34 LMICs. Notably, 58% (49/84) of these studies were published after 2020. Barriers to ASP implementation, including human-resources shortage, lack of microbiology laboratory support, absence of leadership, and limited governmental support, were reported by 26% (22/84) of the studies. Facilitators for hospital ASP implementation identified in five publications included the availability of antibiotic guidelines, ASP protocol, dedicated multidisciplinary ASP committee, and prompt laboratory support. The majority of the research (63%, 53/84) explored the impacts of ASP intervention on clinical, microbiological, and economic aspects. Key outcomes included increased antibiotic prescription appropriateness, reduced antimicrobial consumption, shorter hospital stays, decreased mortality rate, and reduced antibiotic therapy cost. Conclusions The published data underscores the imperative need for widespread antimicrobial stewardship in LMIC hospital settings. Substantial ASP success can be achieved through increasing human resources, context-specific interventions, the development of accessible antibiotic usage guidelines, and heightened awareness via training and education.

The human capital theory suggests that individuals can significantly enhance their knowledge, skills, and marketability by participating in education and training programs relevant to their job. Many professionals in Bangladesh are returning to academia, since they consider the MBA degree as an advanced managerial degree for their professional development. Enrolling in an MBA program represents a strategic investment in one's human capital. The impact of the MBA degree on the development of employability and the factors contributing to it remains undetermined. Thus, this study aims to evaluate how managerial education enhances employability and shapes attitudes in Bangladeshi professionals. An explanatory sequential approach based on mixed methods research design was followed for collecting and analyzing data. The study population consisted of the alumni of evening MBA programs conducted in public universities in Bangladesh and their employers. The analysis was based on 390 valid questionnaire responses and 10 comprehensive interviews with employers. The alumni of MBA programs acknowledged substantial improvement in managerial knowledge, skills, and attitudes, a viewpoint endorsed by their employers. These results may inspire individuals without an MBA to consider pursuing the degree, aiming to improve their understanding and abilities in management with the purpose of developing their future careers. The results present a persuasive case for employers to support their employees in pursuing managerial education. It is important to recognize that the growth of the knowledge and skills of professionals is closely linked to organizational development. An employee with proper managerial capabilities is considered as a valuable resource for any organization and also contributes to national development.

This study focuses on how remittance outflow shapes the economic growth (EG) performance in leading remittance-sending nations, considering the role of trade, ICT, and human and physical capital as control variables. It utilizes panel data from 1990 to 2021 and utilizes second-generation econometric methods. Our findings reveal a cointegration among variables and show that remittance outflow is growth-enhancing in leading remittance-paying countries. Trade, capital formation, and ICT deployment positively impact economic performance and appear to be a blessing for EG. While the role of HC is insignificant, indicating that it does not affect EG. The outcome suggests that remittance outflow is not an economic problem for the remittance-paying nations; instead, they must utilize the talent and skills of migrant labor to achieve EG. Our results suggest that policymakers should concentrate on using the talent and skills of migrant labor, consider trade as a source of growth, develop policies to improve the skills and competencies of the local workforce and deploy and utilize ICT facilities effectively to achieve sustainable EG.

Tropical forests are well-known as highly productive terrestrial ecosystems, which contribute significantly to the global carbon (C) cycle. Studies indicate a correlation between the structural diversity of forests and carbon stocks in the overall forest carbon stocks, and this relationship varies across tropical ecosystems. Understanding this connection is crucial to preventing the transfer of threats to unprotected areas when safeguarding specific forests. The present study was conducted in the undisturbed natural forests of Rema Kalenga Wildlife Sanctuary, Habigonj, Bangladesh. The study aimed to estimate C stocks within the soil-vegetation system of this forest ecosystem and elucidate relationships with stand structural diversity, focusing on vertical stratification and spatial association of tree species. Stand-level C stock models, constructed through tree-level carbon stocks using allometric equations, were based on stand variables such as basal area and mean or dominant tree height. Results identified five canopy strata, with strata two and three accounting for 60% of C stocks. The importance value index of tree species demonstrated a significant association with the C stocks contribution of tree species. The total ecosystem carbon stock ranged from 185 to 220 Mg ha−1, with aboveground 103 to 132 Mg ha−1, belowground 21 to 26 Mg ha−1, litter 2.4 to 4.4 Mg ha−1, dead wood 0.5 to 4.9 Mg ha−1 and soil 11 to 44 Mg ha−1. Species diversity indices exhibited weaker relationships with C stocks, while Pielou's evenness index demonstrated a strong negative relationship (P < 0.05) to C stocks. This comprehensive assessment provides insights into the C dynamics of the forest ecosystem. It offers valuable information for policymakers devising climate change mitigation strategies through the conservation management of tropical forests as crucial C sinks.

Insects, particularly house fly pupae (M. domestica L.), are increasingly promoted as a rich and sustainable protein source, presenting a practical solution for organic waste management. These insects possess the capability to transform animal waste into valuable biomass, underscoring the necessity to evaluate housefly larval growth on diverse waste mixtures. The primary objective was to comprehend how different culture media influence the life history traits of this species. To address this, the research aimed to assess the growth and development performance of housefly larvae reared on various waste mixtures. Adult house flies (M. domestica) were sourced from meat and fish markets in Rajshahi city, Bangladesh, employing systematic sampling from different market sections for a representative selection. Experimental diets were formulated with combinations of cattle blood, cattle manure, rice bran, wheat bran, and kitchen waste, categorized into Diet-A (kitchen waste:cattle manure:wheat bran), Diet-B (cattle blood:rice bran), Diet-C (cattle manure:wheat bran), and Diet-D (cattle blood:kitchen waste:rice bran). Additionally, a control group comprised a mixture of waste materials, including yeast, egg white, milk powder, and wheat bran. The findings revealed that larvae, pupae, and adults reared on Diet-B, incorporating cattle blood and rice bran, demonstrated increased length compared to other diets. This varied diet also produced the heaviest larvae, pupae, and adults, exhibiting the highest morphometric parameters. The moisture content of pupae varied across the different diets, ranging from 53.23±0.17 % to 65.47±1.32 %. The pupae reared on Diet-B had the highest levels of crude protein (53.44±0.05 %, p < 0.05), while those reared on Diet-C had the highest levels of fat (18.56±0.05 %, p < 0.05 except for diet A). The findings demonstrate that housefly larvae can be reared on diets containing rice bran and cattle blood, which have different nutritional profiles and biological capacities. However, the ideal rearing strategy should depend on how M. domestica will be utilized in the future. The study highlights insects, especially housefly pupae, as a sustainable protein source and an organic waste management solution. Therefore, the results of this study contribute to the broader understanding of Musca domestica and offer practical implications for both scientific research and pest management practices.

ICT use has significantly increased over the years across the world, including Saudi Arabia. This study links ICT with economic growth (EG) in Saudi Arabia, controlling human capital (HC) and COVID-19. We employ time-series annual data from 1990 to 2021, a nonlinear Autoregressive Distributed Lag (NARDL) approach, and a cointegrating regression analysis to look at the asymmetric effects of ICT diffusion on EG. The NARDL assessment establishes a cointegrating relationship among variables; the effect of ICT negative shocks on EG is favorable and relatively larger. In contrast, the positive shocks of ICT generate a negative and somewhat less impact on EG with an overall positive impact. The findings imply that the ICT, during its expansion stage, cannot contribute positively to EG, perhaps because of a lack of required skilled human capital to administer and utilize the ICT instruments. The positive and insignificant coefficient of HC supports this conclusion. Despite Saudi Arabia’s generous efforts, physical and human capital have no significant impact on EG. COVID-19 has hindered the usual economic activities in the Kingdom and impaired EG. The cointegrating regressions authenticate the robustness of the NARDL findings. The outcomes suggest policymakers should appraise the existing ICT infrastructure and initiate raising the capability of HC through practical training and education to benefit from ICT diffusion and positively impact EG.

Distributed denial-of-service (DDoS) attacks pose a significant threat to computer networks and systems by disrupting services through the saturation of targeted systems with traffic from multiple sources. Real-time detection of these attacks has become a critical cybersecurity task. However, current DDoS attack detection methods suffer from high false positive rates and limited ability to capture the complex patterns of attack traffic. This research proposes an enhanced approach for detecting DDoS attacks using a hybrid feature selection technique in combination with an ensemble-based classifiers. The ensemble-based approach aggregates many decision trees to increase classification accuracy and reduce overfitting and model robustness. The feature selection technique uses correlation analysis, mutual information, and principal component analysis to identify the most useful characteristics for attack detection. The ensemble-based Random Forest classifier from the various ensemble-based approaches with the specified relevant features produces the best detection rates. Many datasets related to identifying DDoS attacks are used to evaluate the proposed model, and experimental findings demonstrate that it surpasses existing techniques in terms of accuracy, recall, precision, f1-score, and false positive rate, with other evaluation metrics. The proposed approach achieves almost 100 % accuracy, 100 % true positive rate, and 0 % error rate making it a promising solution for DDoS attack detection.

Abstract In the age of sophisticated cyber threats, botnet detection remains a crucial yet complex security challenge. Existing detection systems are continually outmaneuvered by the relentless advancement of botnet strategies, necessitating a more dynamic and proactive approach. Our research introduces a ground-breaking solution to the persistent botnet problem through a strategic amalgamation of Hybrid Feature Selection methods—Categorical Analysis, Mutual Information, and Principal Component Analysis—and a robust ensemble of machine learning techniques. We uniquely combine these feature selection tools to refine the input space, enhancing the detection capabilities of the ensemble learners. Extra Trees, as the ensemble technique of choice, exhibits exemplary performance, culminating in a near-perfect 99.99% accuracy rate in botnet classification across varied datasets. Our model not only surpasses previous benchmarks but also demonstrates exceptional adaptability to new botnet phenomena, ensuring persistent accuracy in a landscape of evolving threats. Detailed comparative analyses manifest our model's superiority, consistently achieving over 99% True Positive Rates and an unprecedented False Positive Rate close to 0.00%, thereby setting a new precedent for reliability in botnet detection. This research signifies a transformative step in cybersecurity, offering unprecedented precision and resilience against botnet infiltrations, and providing an indispensable blueprint for the development of next-generation security frameworks.

Antimicrobial resistance (AMR), also known as antibiotic resistance, is a global health crisis. Many people die from diseases caused by multi-drug resistant pathogens (MDR). Artificial intelligence (AI) deals with applying theory and developing computer systems to conduct tasks usually requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages to solve problems. AI has been successfully used in various fields, from single-cell study to space science. There is huge potential for using AI in medical science, including fighting against MDR to combat AMR. In this review, we have highlighted some of AI's successful uses and potentiality to combat AMR for the betterment of the human race, including the basic principles of AI, available AI resources, use and scope, advantages and limitations. [ J Adv Biotechnol Exp Ther 2023; 6(3.000): 711-727]

This paper focuses on the investigation of fresh, mechanical, and durability properties of concrete with the influence of fly ash and polypropylene fiber. In this study, cement was partially replaced by 15 % and 30 % fly ash content in weight, whereas polypropylene fiber was incorporated in concrete mixes at 0.06 %, 0.12 %, and 0.18 % by volume. Twelve concrete mix proportions were developed, and slump, density, ball penetration, and compacting factor tests were conducted to examine the fresh concrete properties. Besides, mechanical characteristics, including the uniaxial compressive and splitting tensile strength of concrete, were evaluated at 7, 28, and 90 days. Further tests of concrete durability, including rapid chloride permeability test, sorptivity, and water penetration, were performed at 90 days. The results exhibited that the incorporation of fly ash developed fresh concrete properties, while polypropylene fiber decreased the fresh characteristics of concrete. Furthermore, the combination of fly ash and polypropylene fiber in concrete was substantially attained to improve the mechanical and durability characteristics compared to the control mix. Mix proportion of 15 % fly ash and 0.12 % polypropylene fiber exhibited a pronounced influence on compressive strength, chloride permeability, sorptivity, and water penetration compared to other concrete mixtures.

Abstract Background Mastalgia, or breast pain, is a prevalent and distressing condition experienced by women, impacting their daily lives and causing complications. It is common among women of reproductive age, with associations found with premenstrual syndrome, fibrocystic breast disease, psychological distress, and, in rare cases, breast cancer. While Western societies have high frequencies of Mastalgia, it is less diagnosed in Asian cultures including Bangladesh. This study aimed to investigate the prevalence and factors associated with Mastalgia among early reproductive-aged women in Bangladesh. Methods A cross-sectional survey was conducted, and data were collected from different regions of the country. A convenience sampling method was used to take 1,214 participants for the study. A pre-tested semi-structured questionnaire was used to collect data. Bivariate and multivariate analyses were conducted to ascertain factors that were significantly associated with Mastalgia. The data were analyzed using the SPSS software 26 version. Results All the participants were female (mean age: 22.87 ± 2.64 years; age ranges: 18–35 years). The prevalence of Mastalgia was found to be 35.5%. The study was conducted using a self-reported semi-structured questionnaire. Participants with a higher education level and higher income were more likely to experience Mastalgia. A family history of breast cancer and severe abdominal pain during menstruation were also identified as associated factors for Mastalgia (p

Abstract Supply chain management relies on accurate backorder prediction for optimizing inventory control, reducing costs, and enhancing customer satisfaction. Traditional machine-learning models struggle with large-scale datasets and complex relationships. This research introduces a novel methodological framework for supply chain backorder prediction, addressing the challenge of collecting large real-world datasets with 90% accuracy. Our proposed model demonstrates remarkable accuracy in predicting backorders on short and imbalanced datasets. We capture intricate patterns and dependencies by leveraging quantum-inspired techniques within the quantum-classical neural network QAmplifyNet. Experimental evaluations on a benchmark dataset establish QAmplifyNet’s superiority over eight classical models, three classically stacked quantum ensembles, five quantum neural networks, and a deep reinforcement learning model. Its ability to handle short, imbalanced datasets makes it ideal for supply chain management. We evaluate seven preprocessing techniques, selecting the best one based on logistic regression’s performance on each preprocessed dataset. The model’s interpretability is enhanced using Explainable artificial intelligence techniques. Practical implications include improved inventory control, reduced backorders, and enhanced operational efficiency. QAmplifyNet also achieved the highest F1-score of 94% for predicting “Not Backorder” and 75% for predicting “backorder,” outperforming all other models. It also exhibited the highest AUC-ROC score of 79.85%, further validating its superior predictive capabilities. QAmplifyNet seamlessly integrates into real-world supply chain management systems, empowering proactive decision-making and efficient resource allocation. Future work involves exploring additional quantum-inspired techniques, expanding the dataset, and investigating other supply chain applications. This research unlocks the potential of quantum computing in supply chain optimization and paves the way for further exploration of quantum-inspired machine learning models in supply chain management. Our framework and QAmplifyNet model offer a breakthrough approach to supply chain backorder prediction, offering superior performance and opening new avenues for leveraging quantum-inspired techniques in supply chain management.

Climatic change and environmental degradation have become a worldwide discourse. Green innovation is commonly viewed as a means of lowering environmental pollution in the era of climate change. Considering this, the primary purpose of this study is to investigate the effects of environmental technology (ET) on CO2 emissions by controlling Saudi Arabia’s ICT use, energy use, energy intensity, and financial development. It uses a quantile-based multiple-threshold “nonlinear autoregressive distributed lag (NARDL)” estimation utilizing data from 1990 to 2020. It also conducts the ARDL and NARDL estimation techniques simultaneously for comparative outcomes. The Toda–Yamamoto (T-Y) causality assessment also crosschecks the primary multiple-threshold NARDL estimates. The outcomes reveal that ET promotes environmental pollution due to its low scale compared to the Kingdom’s technological base. ICT improves environmental quality, and energy consumption deteriorates it. All three estimation techniques confirm these findings. The multiple-threshold NARDL estimation appears robust and reveals damaging impacts of energy intensity and financial development on emissions. The T-Y causality assessment also authenticates the primary estimation outcomes. The outcomes have important implications for policymakers to focus on enhancing patents for ET, raising ICT diffusion, reducing energy intensity through generating more renewable energies, expanding financial support for ICT and green investments, and ensuring a sustainable environment.

Migration of intrauterine fetal death (IUD) products from the uterus to different nearby intra abdominal organ of body especially to urinary bladder is a rare clinical situation to report. Here we are presenting a case , where a woman of 54 years of age presented to us with the history of IUD at 27 weeks of gestation 17 years back with history of passage of hard brittle bony fragment like material with urine and an abdominal lump. Laparotomy was done, some parts of IUD product was found to migrate to urinary bladder cavity, some parts to incompletely invade the intestinal loops & some parts towards the abdominal wall to form the lump. Hysterectomy was done, product was removed and the entire involved organ was repaired successfully and patient cure was uneventful.

Crinum asiaticum L. (Amaryllidaceae) is a perennial bulbous herb, locally utilized for possessing multifaceted pharmacological properties including anticancer, immune-stimulating, analgesic, antiviral, antimalarial, antibacterial and antifungal, in addition to its popularity as an aesthetic plant. Separation of MeOH extract of C. asiaticum leaves yielded three known compounds as cycloneolitsol (1), hippeastrine (2) and β-sitosterol (3). Among these, compounds 1 and 2 were subjected to the cytotoxic assay and found that they induced mild effect against HCT116, Huh7 and DU145 cell lines with the IC50 values from 73.76 to 132.53 μM. When tested for TRAIL-resistance abrogating activity, 1 (100 μM) along with TRAIL (100 ng/mL) showed moderate activity in AGS cells producing 25 % more inhibition than the agent alone. Whereas 2 (20 and 30 μM) in combination with TRAIL (100 ng/mL) exhibited strong activity in abrogating TRAIL-resistance and caused 34 % and 36 % more inhibition in AGS cells, respectively. The in-silico studies of compound 2 revealed high docking hits with the TRAIL-associated anti-apoptotic proteins which give a justification for the regulatory interactions to induce such abrogating activity. It is still recommended to conduct further investigations to understand their exact molecular mechanism.

The world is adopting the Industry 5.0 paradigm to increase human centricity, sustainability, and resilience in efficient, optimized, and profitable manufacturing systems. With benefits, however, come increased risks of economic and physical loss, driving the need for continuous improvement of Industry 5.0 cybersecurity. Implementation and advancement of adequate cybersecurity have created challenges that have been identified in the literature. In this study, key Industry 5.0 cybersecurity challenges and related sub-challenges are highlighted based on a literature review. Graph Theory and Matrix Approach (GTMA) is employed to analyze the challenges and determine relative importance based on permanent values of the variable permanent matrix (VPM). The results identify the most important Industry 5.0 cybersecurity challenges and reveal Industry 5.0 firms should primarily concentrate on supply chain vulnerabilities to decrease data loss and hacking in the organization's supply chain network. This study also recommends that executives and lawmakers acquire knowledge regarding cybersecurity challenges and prepare to deal with them. Addressing these and other subsequently prioritized challenges—the top five rounded out with emergent cybersecurity trends, non-availability of cybersecurity curriculum in education, embedded technical constraints, and absence of skilled employees and training—will lead the methodical development of holistic, robust cybersecurity programs. Firms accepting of this reality may implement such programs to mitigate evolving cyber-risk towards harnessing and sustaining the benefits of novel Industry 5.0 technologies.

Abstract Background and Aim Non‐alcoholic fatty liver disease (NAFLD) is a growing concern, affecting about 45 million of the Bangladeshi population. There is a paucity of research on the economic burden of NAFLD. The study aims to estimate the cost of illness of NAFLD in Bangladesh. Methods In this prospective, cross‐sectional study, a total of 250 patients of NAFLD, non‐alcoholic steatohepatitis (NASH), and NASH cirrhosis were included from public and private hospitals. Costs of hospitalization, physician fees, investigation costs, expenditures on medical procedures, drugs; and nonmedical costs such as transport expenses and other informal payments (tips) were estimated. Results The overall cost per patient per evaluation was (16.90–46 942.00) USD. The cost in public and private hospitals was 384.76 and 1146.93 USD, respectively. The cost per patient of NAFLD was 157.91 (16.90–955.08) USD, and for NASH cirrhosis was 1783.80 (422.48–46 942) USD. The cost of illness increased to USD 281.18 for diabetics and 254.52 USD for hypertensive. If all the NAFLD patients are evaluated once in healthcare settings, the projected cost will be 7.11 billion USD. In NAFLD, cost for investigations, medicines, transportation, and consultation of physicians was 49.08%, 32.41%, 11.11%, and 6.67%, respectively. Conclusions NAFLD is causing a huge economic burden to the healthcare system. The cost of illness is increased with NASH cirrhosis. Overall, this study provides valuable insights into the economic burden of NAFLD in Bangladesh and emphasizes the several ways of intervention to reduce the cost by preventive measures and accessible healthcare for affected individuals.

Purpose – A wetland is a place of tourist attraction, and tourism values play a key role in economic development. Among various services provided by a wetland, recreational services are increasingly valuable in the tourism sector. This paper aims to unfold the potential recreational values of the Dal Lake in Jammu and Kashmir, India. Design/methodology/approach – The study uses individual travel cost methods (TCMs) and assesses its impact on regional development in terms of income and employment generation. A sample of 200 tourists is selected through an on-site survey on Dal Lake, and the demand for recreational visits and its value is estimated by employing the truncated Poisson regression model (TPRM) and un-truncated Poisson regression model (UTPRM). The consumers' surplus is estimated and tourists' benefit to visiting the wetland is explored. Findings – On average, estimated consumers' surplus per visitor is Rs 6,250 (US$96.15) and Rs 25,000 (US$384.61) from respective models. The annual total recreational value of the lake is accounted for Rs 1713m (US$ 26m). This high consumer surplus (CS) and recreational values of the lake indicate large demand for its recreational facilities. Originality/value – The study is based on primary data and thus, is original. The paper has implications for the policymakers to formulate sustainable management plans for the proper use of Dal Lake and tourism development.

Purpose: Correlation-based storage assignment approach has been intensively explored during the last three decades to improve the order picking efficiency. The purpose of this study is to present a comprehensive assessment of the literature about the state-of-the-art techniques used to solve correlated storage location assignment problems (CSLAP). Design/methodology/approach: A systematic literature review has been carried out based on content analysis to identify, select, analyze, and critically summarize all the studies available on CSLAP. This study begins with the selection of relevant keywords, and narrowing down the selected papers based on various criteria. Findings: Most correlated storage assignment problems are expressed as NP-hard integer programming models. The studies have revealed that CSLAP is evaluated with many approaches. The solution methods can be mainly categorized into heuristic approach, meta-heuristic approach, and data mining approach. With the advancement of computing power, researchers have taken up the challenge of solving more complex storage assignment problems. Furthermore, applications of the models developed are being tested on actual industry data to comprehend the efficiency of the models. Practical implications: The content of this article can be used as a guide to help practitioners and researchers to become adequately knowledgeable on CSLAP for their future work. Originality/value: Since there has been no recent state-of-the-art evaluation of CSLAP, this paper fills that need by systematizing and unifying recent work and identifying future research scopes.

Background: Feature extraction process is noteworthy in order to categorize brain tumors. Handcrafted feature extraction process consists of profound limitations. Similarly, without appropriate classifier, the promising improved results can’t be obtained. Objective: This paper proposes a hybrid model for classifying brain tumors more accurately and rapidly is a preferable choice for aggravating tasks. The main objective of this research is to classify brain tumors through Deep Convolutional Neural Network (DCNN) and Support Vector Machine (SVM)-based hybrid model. Methods: The MRI images are firstly preprocessed to improve the feature extraction process through the following steps: resize, effective noise reduction, and contrast enhancement. Noise reduction is done by anisotropic diffusion filter, and contrast enhancement is done by adaptive histogram equalization. Secondly, the implementation of augmentation enhances the data number and data variety. Thirdly, custom deep CNN is constructed for meaningful deep feature extraction. Finally, the superior machine learning classifier SVM is integrated for classification tasks. After that, this proposed hybrid model is compared with transfer learning models: AlexNet, GoogLeNet, and VGG16. Results: The proposed method uses the ‘Figshare’ dataset and obtains 96.0% accuracy, 98.0% specificity, and 95.71% sensitivity, higher than other transfer learning models. Also, the proposed model takes less time than others. Conclusion: The effectiveness of the proposed deep CNN-SVM model divulges by the performance, which manifests that it extracts features automatically without overfitting problems and improves the classification performance for hybrid structure, and is less time-consuming. Keywords: Adaptive histogram equalization, Anisotropic diffusion filter, Deep CNN, E-health, Machine learning, SVM, Transfer learning.

Abstract Flying insects routinely demonstrate coordinated flight in crowded assemblies despite strict communication and processing constraints. This study experimentally records multiple flying insects tracking a moving visual stimulus. System identification techniques are used to robustly identify the tracking dynamics, including a visuomotor delay. The population delay distributions are quantified for solo and group behaviors. An interconnected visual swarm model incorporating heterogeneous delays is developed, and bifurcation analysis and swarm simulation are applied to assess swarm stability under the delays. The experiment recorded 450 insect trajectories and quantified visual tracking delay variation. Solitary tasks showed a 30ms average delay and standard deviation of 50ms, while group behaviors show a 15ms average and 8ms standard deviation. Analysis and simulation indicate that the delay adjustments during group flight support swarm formation and center stability, and are robust to measurement noise. These results quantify the role of visuomotor delay heterogeneity in flying insects and their role in supporting swarm cohesion through implicit communication.

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

The utilization of waste fly ash and natural jute fiber has drawn attention to producing sustainable concrete. However, there is a lack of studies to analyze the synergistic effect of jute fiber and fly ash on the properties of concrete. Hence, the aim of this study is to investigate the combined effect of fly ash concentration and the different sizes of jute fiber on the fresh, hardened, and non-destructive testing properties of concrete. Concerning the purpose, the concrete mixes were prepared with 0.2 % and 0.4 % jute fiber incorporating 0 %, 5 %, 10 %, and 15 % replacement of cement by fly ash in concrete. For the assessment of properties, slump, density, and compacting factor test was conducted at the fresh state of concrete, whereas mechanical properties such as compressive, splitting tensile, and flexure strength test were conducted at 7 and 28 days. In addition, the non-destructive test (NDT) was also carried out to predict the destructive compressive strength by rebound hammer at 28 days. The microstructure property of optimum mix concrete was analyzed by scanning electron microscopy (SEM). It is observed that the incorporation of jute fiber decreased the slump, density, and compacting factor but increased the compressive, splitting tensile, and flexure strength, whereas the fly ash improved both fresh and hardened properties. Compared to the standard mix, fly ash-based JFRC mixtures have compressive strength improvements varying from 1.7 % to 25.9 %. The 10 % fly ash and 0.2 % volume of jute fiber exhibited a maximum of 11.64 % and 10.72 % splitting tensile and flexural strength enhancement at 28 days, respecting the control mix. In addition, the NDT strength assessment obtained 5.5 % less than destructive strength on average. From the SEM point of view, it is obtained that the 10 % fly ash with 0.2 % jute fiber composition matrix exhibits a better bonding interface and the cracking resistance nature of the concrete mix.

Abstract Background and Aims To reduce death rates for critical patients hospitalized in intensive care units (ICUs), coronavirus (COVID‐19) lacks proven and efficient treatment methods. This cross‐sectional study aims to evaluate how physicians treat severe and suspected COVID‐19 patients in the ICU department in the absence of an established approach, as well as assess the rational use of the medication in the ICU department. Methods Between June 16, 2021, and December 10, 2022, a total of 428 prescriptions were randomly gathered, including both suspected (yellow zone) and confirmed (red zone) COVID‐19 patients. For data management, Microsoft Excel 2021 was utilized, while STATA 17 provided statistical analysis. To find associations between patients' admission status and demographic details, exploratory and bivariate analyses were conducted. Results Of the 428 patients admitted to the ICU, 228 (53.27%) were in the yellow zone and 200 (46.73%) were in the verified COVID‐19 red zone. The majority of patients were male (54.44%), and the age range from 41 to 60 was the most common (41.82%). No significant deviation was detected to the yellow and red groups' prescription patterns. A total of 4001 medicines (mean 9.35/patient) were prescribed. Antiulcerants, antibiotics, respiratory, analgesics, anticoagulants, vitamins and minerals, steroids, cardiovascular, antidiabetic drugs, antivirals, antihistamines, muscle relaxants, and antifungal treatments were widely prescribed drugs. Enoxaparin (67.06%) appeared as the most prescribed medicine, followed by montelukast (60.51%), paracetamol (58.41%), and dexamethasone (51.64%). Conclusion The prescription patterns for the yellow and red groups were comparable and mostly included symptomatic treatment. Respiratory drugs constituted the most frequent therapeutic class. Polypharmacy should be taken under considerations. In ICU settings, the outcomes emphasize the need of correct diagnosis, cautious antibiotic usage, suitable therapy, and attentive monitoring.

Introduction: Despite being a widely used screening questionnaire, there is no consensus on the most appropriate measurement model for the Alcohol Use Disorders Identification Test (AUDIT). Furthermore, there have been limited studies on its measurement invariance across cross-cultural subgroups, genders, and sexual orientations. Aims: The present study aimed to examine the fit of different measurement models for the AUDIT and its measurement invariance across a wide range of subgroups by country, language, gender, and sexual orientation. Methods: Responses concerning past-year alcohol use from the participants of the cross-sectional International Sex Survey were considered (N = 62,943; Mage: 32.73; SD = 12.59). Confirmatory factor analysis, as well as measurement invariance tests were performed for 21 countries, 14 languages, three genders, and four sexual-orientation subgroups that met the minimum sample size requirement for inclusion in these analyses. Results: A two-factor model with factors describing ‘alcohol use’ (items 1–3) and ‘alcohol problems’ (items 4–10) showed the best model fit across countries, languages, genders, and sexual orientations. For the former two, scalar and latent mean levels of invariance were reached considering different criteria. For gender and sexual orientation, a latent mean level of invariance was reached. Conclusions: In line with the two-factor model, the calculation of separate alcohol-use and alcohol-problem scores is recommended when using the AUDIT. The high levels of measurement invariance achieved for the AUDIT support its use in cross-cultural research, capable also of meaningful comparisons among genders and sexual orientations.

Abstract Saudi Arabia is the leading economy in the Arabian Gulf region; it accounts for a significant amount of remittance outflow (RMO) to the rest of the world. The objective of the study is to investigate the impact of remittance outflow on the economic growth (EG) of Saudi Arabia by utilizing time-series data from 1985 to 2019 controlling trade, labor force, human capital, and physical capital. It employs the non-linear autoregressive distributive lag (NARDL) model, cointegrating regressions, and vector error correction (VEC) Granger causality check to accomplish the study. The outcomes of the NARDL exercise confirm a cointegrating association among variables and reveal that RMO has mixed but negative resultant impacts on the EG in the short run, while in the long run, EC growth is augmented if remittance outflows decline. Both trade and labor force positively contribute to EG, while neither human nor physical capital significantly influences the latter. The cointegrating regression outcomes precisely authenticate the NARDL findings and acknowledge their robustness. Moreover, the VEC Granger causality test also supports the NARDL outcomes. The outcome suggests that the policymakers may allure the expatriates through pragmatic labor laws, policies, and smart incentives to direct their savings to domestic consumption and investments. Moreover, the quality of education in general and tertiary education, in particular, needs to be upgraded in the real sense to develop the practical skills and efficiency of the Saudi labor force so that they can replace the expatriates. This will eventually reduce outbound remittances.

In developing countries around the globe, the number of vehicle users has exceeded the optimum threshold and is increasing rapidly due to speedy economic growth. Consequently, roads are getting more congested in the cities. This has resulted in increased difficulties in meeting the minimum parking demands. Moreover, most of the parking lots are at the underground level of buildings. Due to the lack of natural light sources, there is a need for a massive power source to light up a large quantity of lighting equipment continuously. This results in waste of energy, equipment losses and causes a tremendous financial burden on property management. However, an IoT driven parking management can significantly aid in the utilization of parking resources. Availability of a sensor based smart parking spot with its location information identified and transmitted using wireless communication technology can immensely benefit the vehicle owners as well as parking spot authorities. It is a comprehensive solution both for the user and the owner of the parking space. Besides, it can fulfill the lighting requirement of the parking lot with minimized energy. Some eminent features of such IoT based systems are online reservations, user authentication for ensuring security, parking guidance, online payment, and energy saving. This paper reviews IoT driven smart parking management systems. It gives insights into various approaches for the parking system, related technologies, widely used components, communication standards and concerned system security issues. Besides, the paper gives researchers a direction for future work as it directs towards some open issues in this area.

Automated bug report assignment is critical for large-scale software projects where reported bugs are frequent and expert developers are required to fix them on time. Finding an appropriate developer with the necessary skill sets and prior experience in fixing similar bugs is difficult and can be an expensive process, depending on the severity of the reported bug. To address this issue, researchers have proposed several machine learning and deep learning-based automated bug report assignment techniques that make use of historical data on reported bugs as well as fixer information. However, there is still room for improvement in the performance of these techniques. In this paper, we propose a novel deep learning-based approach that utilizes two sets of features from the reported bugs’ textual data, namely contextual information and the occurrence of repeating keywords. We develop convolutional neural network and artificial neural network modules to mine these features. We then fuse these two sets of extracted features to assign a bug to an appropriate developer. We conduct extensive experiments on eight benchmark datasets of open-source, real-world software projects to assess the effectiveness of our approach. The experimental results demonstrate that our information fusion-based approach outperforms previous models and improves automated bug report assignment. Furthermore, we debug the errors of our proposed model and publish all source code so that future researchers can contribute to this problem.

Within the Natural Language Processing (NLP) framework, Named Entity Recognition (NER) is regarded as the basis for extracting key information to understand texts in any language. As Bangla is a highly inflectional, morphologically rich, and resource-scarce language, building a balanced NER corpus with large and diverse entities is a demanding task. However, previously developed Bangla NER systems are limited to recognizing only three familiar entities: person, location, and organization. To address this significant limitation, we introduce a novel Bangla NER dataset B-NER, which was created using 22,144 manually annotated Bangla sentences collected from Bangla newspapers and Bangla Wikipedia. This dataset includes a total of 9,895 unique words which were manually categorized into eight different entity types, such as a person, organization, event, artifact, time indicator, natural phenomenon, geopolitical entity, and geographical location. Inter-annotator agreement experiments were conducted to validate the quality of annotations performed by three annotators, resulting in a Kappa score of 0.82. In this paper, we provide an outline of the annotation guideline illustrated with examples, discuss the B-NER dataset properties, and present benchmark evaluations of the dataset. To establish that B-NER is more comprehensive and balanced in comparison to other publicly accessible datasets, we conducted cross-dataset modeling and validation, i.e. trained NER model on one dataset while tested on another, and found that the model trained on B-NER performed the best in that settings. Furthermore, we performed exhaustive benchmark evaluations based on Bidirectional LSTM with fastText embeddings and sentence transformer models. Among these models, fine-tuned NR/IndicbnBERT achieved noticeable results with a Macro-F1 of 86%. This dataset and baseline results will be publicly available under a CC-BY 4.0 license in the CoNLL-2002 format to facilitate further research on Bangla NER.

Abstract Problematic use of Internet (PUI) and problematic use of Facebook (PUF) has been linked to escalating behavioral health issues among university students and has increased during the COVID-19 pandemic. This study estimated the prevalence of and explored associated factors for PUI and PUF among Bangladeshi university students during the COVID-19 pandemic. A cross-sectional online survey was undertaken among 1101 Bangladeshi university students between November and December 2020. The Internet Addiction Test and Facebook Addiction Scale were used to assess PIU and PUF, respectively. A multiple linear regression analysis was performed to adjust for confounders. Among the participants, PUI and PUF were found in 39.3% and 37.1%, respectively. The multiple linear regression model indicated PUI was significantly associated with participants residing in a village, arts majors, those unsatisfied with their major, having mediocre parental relationships, failure in romantic relationships, physical comorbidities, longer use of the Internet, using the Internet for purposes other than education, using social media, and downloading movies/TV series. PUF was significantly associated with village residence, lower income, arts majors, failure in romantic relationships, longer use of the Internet, using the Internet for purposes other than education, and downloading movies/TV series. Both PUI and PUF have been prevalent among Bangladeshi university students during the COVID-19 pandemic. Longitudinal & exploratory studies are warranted in the future to identify causal factors for PUI and PUF and appropriate interventions should be designed quickly for this population.

Intrusion detection is a critical aspect of network security to protect computer systems from unauthorized access and attacks. The capacity of traditional intrusion detection systems (IDS) to identify unknown sophisticated threats is constrained by their reliance on signature-based detection. Approaches based on machine learning have shown promising results in identifying unknown malicious attacks. No learning algorithm-based model, however, is able to accurately and consistently detect all different kinds of attacks. Besides that, the existing models are tested for a specific dataset. In this research, a novel ensemble-based machine-learning technique for intrusion detection is presented. Numerous public datasets and multiple ensemble strategies, including Random Forest, Gradient Boosting, Adaboost, Gradient XGBoost, Bagging, and Simple Stacking, will be employed to evaluate the performance of the proposed approach. The most relevant features for the detection of intrusion are selected using correlation analysis, mutual information, and principal component analysis. Our research using different ensemble methods demonstrates that the proposed approach using the Random Forest technique outperforms existing approaches in terms of accuracy and FPR, typically exceeding 99% with better evaluation metrics like Precision, Recall, F1-score, Balanced Accuracy, Cohen's Kappa, etc. This strategy may be a useful tool for strengthening the safety of computer systems and networks against emerging cyber threats.

Abstract JmjC (Jumonji-C) domain-containing 5 (JMJD5) plays important roles in circadian regulation in plants and humans and is involved in embryonic development and cell proliferation. JMJD5 is a 2-oxoglutarate (2OG) and Fe(II) dependent oxygenase of the JmjC subfamily, which includes histone Nε-methyl lysine-demethylases (KDMs) and hydroxylases catalysing formation of stable alcohol products. JMJD5 is reported to have KDM activity, but has been shown to catalyse C-3 hydroxylation of arginine residues in sequences from human regulator of chromosome condensation domain-containing protein 1 (RCCD1) and ribosomal protein S6 (RPS6) in vitro. We report crystallographic analyses of human JMJD5 complexed with 2OG analogues, including the widely used hypoxia mimic pyridine-2,4-dicarboxylate, both D- and L-enantiomers of the oncometabolite 2-hydroxyglutarate, and a cyclic N-hydroxyimide. The results support the assignment of JMJD5 as a protein hydroxylase and reveal JMJD5 has an unusually compact 2OG binding pocket suitable for exploitation in development of selective inhibitors. They will be useful in the development of chemical probes to investigate the physiologically relevant roles of JMJD5 in circadian rhythm and development and explore its potential as a medicinal chemistry target.

Plain English summary Eating disorders, maladaptive eating behaviors, and low diversified diets are major public health issues amid the COVID-19 pandemic among university students. The current study aimed to address these issues by investigating Orthorexia Nervosa (ON), food addiction (FA), and dietary diversity. Four-thousand-and-seventy-six students completed an online questionnaire. It was found that being underweight was associated with ON. On the other hand, being overweight/obese was associated with having FA. Individuals who were former smokers and who had poor social interaction were significantly associated with having Orthorexia Nervosa. 1st-year university students are more likely to have FA compared to 5th-year/master’s students. Active smokers were more likely to be food addicted. Students who had FA had less guilty feelings about violating food rules. Participants who had FA consumed more organ meats and eggs. Students who were from upper socio-economic status were more likely to have higher dietary diversity. Nutritional awareness programs focusing on DD and healthy eating habits should be implemented for students.

Abstract Background Coronavirus disease-2019 (COVID-19) has had negative physical and mental impacts on people globally. The current study examined general psychiatric symptoms (fear, anxiety, depression, and insomnia) and loneliness, and their interrelationships and correlates among Bangladeshi individuals approximately 1 year after the onset of the COVID-19 outbreak. Methods An internet-based cross-sectional survey was conducted among 1004 Bangladeshi people (51.8% male; mean age: 25.41 ± 7.80; age range: 18–60 years). Data were collected using a semi-structured e-questionnaire including informed consent, socio-demographics, lifestyle measures, and psychometric tools assessing loneliness, anxiety, depression, insomnia, and fear of COVID-19. Results Sizeable participants screened positive for loneliness (63.5%), anxiety (26.3%), depression (46.4%), and insomnia (50.7%). Considerable numbers of respondents also reported fear of COVID-19. In hierarchical regression analyses, loneliness, anxiety, depression, insomnia, and fear of COVID-19 were associated with socio-demographic and lifestyle factors. Loneliness, anxiety, depression, insomnia, and fear of COVID-19 were positively correlated with each other (p

ABSTRACT: Background: Colibacillosis, caused by avian pathogenic Escherichia coli (APEC), is one of the most significant infectious diseases affecting poultry worldwide. Objectives: This study aimed to determine the genomic diversity, virulence factor genes (VFGs), and antimicrobial resistance genes (ARGs) in the APEC MTR_BAU02 strain isolated from a layer chicken using whole-genome sequencing (WGS). Methods: Paired-end (2 × 250) WGS was performed using Illumina MiSeq sequencer (Illumina, San Diego, CA) and de novo assembly was performed using SPAdes. Core genome multilocus sequence typing (cgMLST) analysis between APEC MTR_BAU02 and all of the ST1196 E. coli strains retrieved from the National Center for Biotechnology Information (NCBI) GenBank database was performed using the BacWGSTdb 2.0 server. We utilized different databases to detect ARGs, VFGs, and genomic functional features of the APEC MTR_BAU02 strain. Results: The complete genome of APEC MTR_BAU02 consists of 94 contigs comprising 4,924,680 bp (51.1% guanine-cytosine [GC] content), including 4681 protein-coding sequences, one chromosome, and one plasmid, and was assigned to ST1196. The closest relatives of APEC MTR_BAU02 were four isolates originating from human clinical specimens (diarrhetic stool) in Bangladesh and two clinical isolates originating from chicken in India, which differed by 694 core genome multilocus sequence typing (cgMLST) alleles. One hundred and twenty-two ARGs and 92 VFGs were identified in the APEC MTR_BAU02 genome. Metabolic functional annotations detected 380 SEED subsystems including genes coding for carbohydrate metabolism, protein metabolism, cofactors, vitamins, prosthetic groups and pigments, respiration, membrane transport, stress response, motility and chemotaxis, and virulence, disease, and defense. Conclusion: This study reports the genome sequence of a multidrug-resistant APEC strain isolated from layer birds in Bangladesh. The ARGs and VFGs, widespread in APEC MTR_BAU02, are similar to those found in human isolates, and highlight the growing threat of antimicrobial resistance in both poultry and humans.

Background: Infection prevention and control (IPC) is a critical feature of preventing the spread of healthcare-associated infections (HAIs) in hospitals. IPC practices are particularly important in resource-constrained and crowded hospital settings. The successful implementation of infection prevention measures depends on healthcare worker (HCW) knowledge of, attitude toward, and practice (KAP) of IPC. In this project, we assessed the KAP of HCWs and identified factors associated with IPC compliance at tertiary-care hospitals in Bangladesh. Methods: From September 2020 to January 2021, we conducted this hospital-based cross-sectional assessment at 11 tertiary-care hospitals. A semistructured questionnaire was used to conduct face-to-face interviews with physicians, nurses, and cleaning staff who were directly involved in patient care. Based on >75% of the total score, each KAP component was divided into adequate knowledge, favorable attitude, and safe practice. We performed descriptive analysis and multivariate logistic regression to determine the KAP score and associated factors influencing IPC compliance in hospital settings. Results: In total, 1,728 HCWs were interviewed; 76.8% of the participants had adequate knowledge on IPC and 54.6% reported safe practices. However, only 16.2% of HCWs had a favorable attitude toward IPC. Among the 3 HCW groups, nurses had the highest KAP scores (76.07±12.7) followed by physicians (69.8±16.2), and cleaning staff (34.4±27.3). Only 29.2% of HCWs reported having received IPC training, and they cited heavy workload as a barrier to IPC guideline adherence. HCWs having adequate knowledge showed 9 times higher odds of safe IPC practice (AOR, 9.36; 95% CI, 5.47–16.04). HCWs who had a favorable attitude toward IPC were 16 times as likely to perform safe practice toward IPC activities (AOR, 15.5; 95% CI, 10.27–23.42). Conclusions: Knowledge of safe practices and having a favorable attitude toward IPC are key components of a successful IPC program. Significant improvements are required among all levels of HCWs in Bangladesh tertiary-care hospitals, especially cleaning staff. Educational interventions to train on IPC guidelines, plus monitoring, could improve HCW safe practices.

Extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli has been linked to both life-threatening hospital- and community-acquired infections across the globe. Here, we conducted a systematic review and meta-analysis to evaluate the prevalence of ESBL in E. coli isolated from humans, animals, and environments in Bangladesh. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, the current systematic review and meta-analysis was taken into account for studies published between 2010 and 2021 in peer-reviewed journals. The meta-analysis was performed on “R” version 4.2.2. A total of 36 studies were included in this systematic review and meta-analysis; among them, 22 were human, seven were animal, four were environmental, and three were multidisciplinary studies. The meta-analysis revealed that the pooled prevalence of ESBL-producing E. coli in Bangladesh was 21% (95% CI: 15%–27%). On the sample basis, the pooled prevalence of ESBL-producing E. coli in humans, animals, and environments was 17% (95% CI: 11%–23%), 22% (95% CI: 9%–34%), and 39% (95% CI: 16%–62%), respectively. All the pooled prevalence of ESBL-producing E. coli showed substantial heterogeneity (I2 > 75%; p

BackgroundIntense academic pressure and unhealthy competition have turned the university entrance exam scenario in Bangladesh into a Pandora's Box, which might cause mental health difficulties among young students. However, there is a severe scarcity of studies concerning such issues of university entrance examination-seeking students in Bangladesh.MethodsThis study aimed to assess the prevalence and associated factors of depression symptoms, anxiety, and stress among undergraduate entrance admission-seeking students in Bangladesh. A cross-sectional study design was followed using an online tool including socio-demographic questions, and the 21-item Bangla Depression, Anxiety and Stress Scale (BDASS-21). The survey form was completed by 452 Bangladeshi students who passed the higher secondary certificate (HSC) examination in 2020 and were planning to get admission to the undergraduate level during the data collection.ResultsThe prevalence of mild to extremely severe levels of depression symptoms, anxiety, and stress was 57.7%, 61.4%, and 44.6%, respectively. Females were more likely to have depression, anxiety, and stress symptoms than males. The students from science backgrounds were at higher risk of developing depression and stress symptoms when compared with students from business studies backgrounds. Besides, students with a previous history of mental illness, a preference for getting admitted into the public university, and less monthly family income (

Abstract Background The Posttraumatic Stress Disorder Checklist (PCL-5) is the most widely used screening tool in assessing posttraumatic stress disorder symptoms, based on the Diagnostic and Statistical Manual of Mental disorders (DSM-5) criteria. This study aimed to evaluate the psychometric properties of the newly translated Bangla PCL-5. Methods A cross-sectional survey was carried out among 10,605 individuals (61.0% male; mean age: 23.6 ± 5.5 [13–71 years]) during May and June 2020, several months after the onset of the COVID-19 outbreak in Bangladesh. The survey included the Bangla PCL-5 and the PHQ-9 depression scale. We used confirmatory factor analysis to test the four-factor DSM-5 model, the six-factor Anhedonia model, and the seven-factor hybrid model. Results The Bangla PCL-5 displayed adequate internal consistency (Cronbach’s alpha = 0.90). The Bangla PCL-5 score was significantly correlated with scores of the PHQ-9 depression scale, confirming strong convergent validity. Confirmatory factor analyses indicated the models had a good fit to the data, including the four-factor DSM-5 model, the six-factor Anhedonia model, and the seven-factor hybrid model. Overall, the seven-factor hybrid model exhibited the best fit to the data. Conclusions The Bangla PCL-5 appears to be a valid and reliable psychometric screening tool that may be employed in the prospective evaluation of posttraumatic stress disorder in Bangladesh.