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Abstract Silicon photonic index sensors have received significant attention for label-free bio and gas-sensing applications, offering cost-effective and scalable solutions. Here, we introduce an ultra-compact silicon photonic refractive index sensor that leverages zero-crosstalk singularity responses enabled by subwavelength gratings. The subwavelength gratings are precisely engineered to achieve an anisotropic perturbation-led zero-crosstalk, resulting in a single transmission dip singularity in the spectrum that is independent of device length. The sensor is optimized for the transverse magnetic mode operation, where the subwavelength gratings are arranged perpendicular to the propagation direction to support a leaky-like mode and maximize the evanescent field interaction with the analyte space. Experimental results demonstrate a high wavelength sensitivity of − 410 nm/RIU and an intensity sensitivity of 395 dB/RIU, with a compact device footprint of approximately 82.8 μm2. Distinct from other resonant and interferometric sensors, our approach provides an FSR-free single-dip spectral response on a small device footprint, overcoming common challenges faced by traditional sensors, such as signal/phase ambiguity, sensitivity fading, limited detection range, and the necessity for large device footprints. This makes our sensor ideal for simplified intensity interrogation. The proposed sensor holds promise for a range of on-chip refractive index sensing applications, from gas to biochemical detection, representing a significant step towards efficient and miniaturized photonic sensing solutions. Graphical Abstract

Objective: The aim of the study is to provide a comprehensive overview of the epidemiology and evolution of dengue virus (DV) in Bangladesh from 2020 to 2023. Methods: From January 1, 2020, to December 31, 2023, we analyzed the daily press releases on the dengue situation made public by the government. The published sequences from the period were then subjected to phylogenetic analysis, amino acid substitutions, evolutionary rate, and selection pressure analysis. Results: In Bangladesh, dengue fever usually peaks during the monsoon season. However, the increase in cases began considerably earlier, at the end of April 2023 and the disease had claimed 1705 lives, marking its highest annual death toll ever. Dhaka is the city most severely affected. The phylogenetic analysis revealed that the Bangladeshi DV-1 strains clustered far from any known genotype, indicating the circulation of a novel genotype, whereas the DV-3 strains clustered within the existing circulating genotype I. The DV-1 strains had a total of 107 amino acid alterations and two deletions, while the DV-3 strains had 17 alterations. The Bangladeshi DV-1 and DV-3 strains evolve rapidly. Additionally, selection pressure analysis revealed one positive selection site in DV-1 and three in DV-3, respectively, which require further research. Conclusions: The circulation of novel genotypes, a higher mutation rate, and positive selection of DV might result in the recent dengue outbreaks in the country. The study will help in the development of strategies to control outbreaks in Bangladesh and other nations with similar conditions in the future.

There have been many academic investigations on promoting human behaviour in favour of environmental sustainability through education. Yet only a limited number of review papers are found which have summarized the key findings of this vital area. This study uses both bibliometric and text mining approaches to examine pro-environmental behaviour in education literature for the first time. Through bibliometric analysis, different networks in contemporary literature are highlighted. These networks reveal the influence of social welfare on pro-environmental behaviour, highlighting the value of human innate connection to raise environmental consciousness. Additionally, using posterior probability and Latent Dirichlet allocation (LDA), text mining identifies 12 different topic models by log-likelihood estimation, addressing a variety of topics related to environmental education and behaviour, such as how visitors and sustainability in environmental education affect pro-environmental behaviour, how education towards sustainability is provided in schools and universities, how the theory of planned behaviour is applied, how education and pro-environmental behaviour are related, and how sustainable education and travel are explored through different channels.

The Nandus nandus (Hamilton, 1822) is a highly prized food fish that possesses significant nutritional and commercial worth. Bangladesh, India, Pakistan, Malaysia, Myanmar, Thailand and Viet Nam are the countries where it is endemic. The IUCN Bangladesh has classified this species as near endangered due to its current progressive decline. Hence, immediate actions are necessary to efficiently manage the fishery in this country. To address this issue, a comprehensive understanding of its life history parameters is required. This study aimed to examine the growth characteristics, recruitment length, sexual maturity, mortality, exploitation rate, and maximum sustainable production of N. nandus in Bangladesh's southern coastal waters. Samples were taken every month from January through December of 2021. Our study indicates that N. nandus grows in an isometric manner. The estimated asymptomatic length of this species is 18.0 cm in total length (TL), growing at a rate of 1.30 year−1. The growth performance index of this species was determined to be 2.62, and the projected lifespan was calculated to be 2.31 years. The initial length at recruitment was measured to be 9.5 cm TL, whie the size at first sexual maturity was recorded to be 9.4 cm TL. With the contributions of the natural mortality rate (2.26 year−1) and the fishing mortality rate (1.86 year−1) the total mortality rate was found to be 4.12 year−1. The exploitation rate, determined to be 0.45, surpasses the upper limit of sustainable yield criteria set at 0.37. Therefore, it is imperative to implement strategies to avoid excessive fishing and guarantee the long-term control of N. nandus populations.

In leather manufacturing, ammonium-free deliming is gaining popularity because deliming with traditionally used ammonium salts contributes ammonia-nitrogen (NH3–N) into wastewater. Wastewater contains NH3–N effect on the treatment facility. In this study, an ammonia-free deliming agent-a combination of citric acid (CA), sodium hexametaphosphate (SHMP), and ethylenediaminetetraacetic acid (EDTA) is utilized against the traditional deliming agent to limit the release of NH3–N. Physicomechanical properties have been assessed in the produced effluent, delimed pelt, and crust leather. In contrast to traditional deliming, the amount of NH3–N and Total Kjeldahl Nitrogen (TKN) is estimated to have decreased by 98.1 % and 85.26 %, respectively. Deliming with CA + SHMP + EDTA can significantly reduce the amount of TKN in the deliming float. A notable reduction of biochemical oxygen demand (34.8 %), chemical oxygen demand (85.7 %), total dissolved solids (56.4 %), electrical conductivity (54.5 %), and turbidity (65.2 %) was achieved. The physical and organoleptic characteristics of crust leathers produced with the CA + SHMP + EDTA deliming process are comparable to the traditional deliming technique. Similar fiber structure is observed in SEM images of crust leather, wet blue leather, limed pelt, and delimed pelt. A pilot-scale production is carried out; the outcomes are similar to the experimental results. For all experimental and control investigations, the Pseudo Second Order kinetic model fits the reaction kinetics. Deliming with CA + SHMP + EDTA offers a simpler wastewater treatment process since it reduces nitrogen contamination in the wastewater.

Multi-functional textiles have become a growing trend among smart customers who dream of having multiple functionalities in a single product. Thus, this study aimed to develop a multi-functional textile from a common textile substrate like cotton equipped with electrically conductive, anti-bacterial, and flame-retardant properties. Herein, a bunch of compounds from various sources like petro-based poly-aniline (PANI), phosphoric acid (H3PO4), inorganic silver nanoparticles (Ag-NPs), and biomass-sourced fish scale protein (FSP) were used. The coating was prepared via in-situ polymerization of PANI with the cotton substrate, followed by the dipping in AGNPs solution, layer-by-layer deposition of FSP and sodium alginate, and finally, a dip-dry-cure technique after immersing the modified cotton substrate into the H3PO4 and citric acid solution. The key results indicated that the fabric treated with PANI/Ag-NPs/FSP/P-compound exhibited a balanced improvement in all three desired properties as the electrical resistance was reduced by 44.44 % while showing superior bacterial inhibition against gram-positive bacteria (S. aureus) and gram-negative bacteria (E. coli), and produced dense-black carbonaceous char residues, indicating its flame retardant properties as well. Thus, such amicable developments made the cotton textile substrate a multi-functional textile, which showed potential to be used in medical textiles, wearable electronics, fire-fighter suits, etc.

In this study, the waste newspaper was utilized to prepare carbon aerogel (CA) for adsorptive removal of Metformin hydrochloride (MH) (antidiabetic drug residues) from the aqueous system. The prepared CA was characterized using Field emission scanning electron microscopy (FE-SEM), X-ray photoelectron spectroscopy (XPS), and Brunauer-Emmett-Teller (BET) analyser. The MH adsorption was carried out using two types of water, one was laboratory-prepared distilled water and another was real river water. Although, optimum removal around 87 % was observed at pH 3, the removal efficiency at about 63 % estimated at neutral pH. In river water experiment, slightly lower rate of adsorption about 59 % was noticed. Nearly 61 % removal was found for 120 min and then no significant adsorption was found. The pseudo-second-order kinetics and Freundlich isotherm models presented a good interpretation of the adsorption experiment data. Physio-sorption was the primary mechanism of the MH adsorption was confirmed by Temkin and D-R isotherms studies. Regeneration studies demonstrated the removal stability of CA around 32 % until five cycles. An adsorption mechanism was also proposed for the removal of metformin hydrochloride using carbon aerogel.

BackgroundFetal macrosomia, marked by excessive birth weight, is a significant public health issue in developing countries, yet it has received less attention compared to low birth weight. This study aims to determine the prevalence of fetal macrosomia in Bangladesh and its associated factors.MethodsThe study utilized data from 4,754 women with complete birth weight information of their children from the Bangladesh Multiple Indicator Cluster Survey (MICS) −2019, defining fetal macrosomia as newborns with a birth weight ≥4,000 g regardless of gestational age. Bivariate logistic regression assessed associations between independent variables and fetal macrosomia, presenting adjusted odds ratios (AOR) and a 95% confidence interval (CI), while controlling for potential confounders such as women's age, wealth index, education, healthcare utilization, comorbidities, newborn sex, and place of residence.ResultsThe prevalence of fetal macrosomia was 11.6%. Significant associations with fetal macrosomia included higher maternal age group (30–34 years) (AOR = 1.36, 95% CI = 1.07–1.74), secondary level of mother's education (AOR = 1.95, 95% CI = 1.43–2.66), experienced physical attacks (AOR = 1.41, 95% CI = 1.06–1.88), hypertension during pregnancy (AOR = 1.54, 95% CI = 1.15–2.07), and rural residence (AOR = 1.25, 95% CI = 1.15–1.49). Female infants had 18% lower odds of being macrosomic compared to male infants (AOR = 0.82, 95% CI = 0.72–0.93).ConclusionOne in ten infants in Bangladesh are born with macrosomia, necessitating a multi-faceted approach involving improving maternal nutrition, promoting healthy lifestyles, enhancing access to quality prenatal care, and addressing socioeconomic, residential, and healthcare system challenges, underlining the importance of further community-based research to expand the study's scope.

Abstract Epilepsy is a serious neurological disorder that results in seizures. It can be diagnosed by analyzing the brain's electrical activity using an electroencephalogram (EEG). However, the detection of seizures from massive EEG datasets is a challenging task. To address this challenge, researchers have developed several machine‐learning classifiers and feature extraction techniques for detecting seizures. This paper proposes an energy‐efficient and fast field programmable gate array (FPGA) architecture for detecting epileptic seizures using minimal computational resources. The seizure detection system uses the one Hjorth parameter (mobility) and another statistical parameter (nonlinear energy) as features and employs two efficient classifiers, quadratic discriminant analysis and linear support vector machine (LSVM), for classifying signals into seizure and nonseizure categories. The feature extractor block is connected individually to each of these classifiers. Subsequently, the performance of these two proposed models is evaluated in terms of accuracy, sensitivity, power consumption, resource utilization, and other metrics. The results demonstrate that the SVM classifier‐based model achieved the highest accuracy (99.4%) and sensitivity (98.8%) while consuming minimal dynamic power (0.057 mW) and utilizing the minimum FPGA resources. Thus, the proposed hardware system offers a reliable and energy‐efficient solution for detecting seizures in clinical and real‐time applications.

Abstract Background Disability-free life expectancy (DFLE) has been used to gain a better understanding of the population’s quality of life. Objectives The authors aimed to estimate age and sex-specific disability-free life expectancy (DFLE) for urban and rural areas of Bangladesh, as well as to investigate the differences in DFLE between males and females of urban and rural areas. Methods Data from the Bangladesh Sample Vital Statistics-2016 and the Bangladesh Household Income and Expenditure Survey (HIES)-2016 were used to calculate the disability-free life expectancy (DFLE) of urban and rural males and females in Bangladesh in 2016. The DFLE was calculated using the Sullivan method. Results With only a few exceptions, rural areas have higher mortality and disability rates than urban areas. For both males and females, statistically significant differences in DFLE were reported between urban and rural areas between the ages of birth and 39 years. In comparison to rural males and females, urban males and females had a longer life expectancy (LE), a longer disability-free life expectancy, and a higher share of life without disability. Conclusion This study illuminates stark urban–rural disparities in LE and DFLE, especially among individuals aged

The diterpenoid phytol is evidently acting against anemia in experimental animals. However, the molecular mechanisms behind this issue are yet to be discovered. This study aimed to evaluate phytol's effect on methotrexate-induced folate-deficient animals through in vivo and in silico studies. For this, a total of thirty adult male Swiss albino mice were randomly divided into six different groups, namely the normal control (vehicle), the negative control (folate deficiency inducer, methotrexate 3 ​mg/kg), the standard (folic acid 1.5 ​mg/kg), two test groups comprising phytol 25 and 50 ​mg/kg, and a combined group composed of the standard and highest test doses of phytol. Except for the vehicle, all groups were treated with methotrexate for the first 3 days (once/day) to induce folate deficiency. Then followed by the respective treatment once a day for 3 days. Hemoglobin (Hb) level was measured from the peripheral blood (by tail cutting) on days 1st (before treatment), 4th (after methotrexate treatment), and 7th (after treatment). On the other hand, the computational studies were performed by PyMol, PyRex, Discovery Studio, and other complementary tools. Findings suggest that phytol significantly (p ​

In this study, effect of commonly used agricultural fertilizers is examined on arsenic leaching from sediment under aerobic conditions. The arsenic in leachates was gradually increased with increasing triple superphosphate (TSP), diammonium phosphate (DAP), urea, and muriate of potash (MOP) in suspension. Compared to DAP; TSP leached more arsenic from the sediment. The arsenic extraction capacity of agricultural fertilizers followed TSP > DAP > urea > MOP. Not only TSP but also other agricultural fertilizers enhance leaching of arsenic from contaminated sediments. Application of compost fertilizer with TSP and DAP could immobilize arsenic leaching from contaminated sediment of agricultural fertilizers.

In this study, an attempt has been made to investigate the possibility of a machine learning model, Artificial Neural Network (ANN) for seasonal prediction of the temperature of Dhaka city. Prior knowledge of temperature is essential, especially in tropical regions like Dhaka, as it aids in forecasting heatwaves and implementing effective preparedness schemes. While various machine learning models have been employed for the prediction of hot weather across the world, research specially focused on Bangladesh is limited. Additionally, the application of machine learning models needs to be curated to suit the particular weather features of any region. Therefore, this study approaches ANN method for prediction of the temperature of Dhaka exploring the underlying role of related weather variables. Using the daily data for the months of February to July collected from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data (0.25° × 0.25° global grid) for the years 2011–2020, this study focuses on finding the combination of weather variables in predicting temperatures. The densely populated city, Dhaka, has faced severe consequences due to extreme climate conditions in recent years, and this study will pave a new dimension for further research regarding the topic.

Summary: Background: The increasing prevalence of diet-related non-communicable diseases (NCDs) in South Asia is concerning, with type 2 diabetes projected to rise to 68%, compared to the global increase of 44%. Encouraging healthy diets requires stronger policies for healthier food environments. Methods: This study reviewed and assessed food environment policies in Bangladesh, India, Pakistan, and Sri Lanka from 2020 to 2022 using the Healthy Food Environment Policy Index (Food-EPI) and compared them with global best practices. Seven policy domains and six infrastructure support domains were considered, employing 47 good practice indicators to prevent NCDs. Stakeholders from government and non-governmental sectors in South Asia (n = 148) were invited to assess policy and infrastructure support implementation using the Delphi method. Findings: Implementation of food environment policies and infrastructure support in these countries was predominantly weak. Labelling, monitoring, and leadership policies received a moderate rating, with a focus on food safety, hygiene, and quality rather than obesity prevention. Key policy gaps prioritized for attention included front-of-pack labelling, healthy food subsidies, unhealthy food taxation, restrictions on unhealthy food promotion, and improvements in school nutrition standards to combat NCDs. Interpretation: Urgent action is required to expand food policies beyond hygiene and food security measures. Comprehensive strategies targeting NCD prevention are crucial to combat the escalating burden of NCDs in the region. Funding: This research was funded by the NIHR (16/136/68 and 132960) with aid from the UK Government for global health research. Petya Atanasova also acknowledges funding from the Economic and Social Research Council (ESRC) (ES/P000703/1). The views expressed are those of the authors and not necessarily of the NIHR, the UK government or the ESRC.

Background: Sclareol (SCL), a labdane diterpene compound found in Salvia sclarea L., exhibited therapeutic effects. This study investigated the potential interaction between SCL and diazepam (DZP) in modulating sedation in the thiopental sodium-induced sleeping animal model, supported by in-silico molecular docking analysis. Methods: The control, sclareol (5, 10 and 20 mg/kg), and the reference drugs [diazepam: 3 mg/kg and Caffeine (CAF): 10 mg/kg] were used in male albino mice. Then, sodium thiopental (40 mg/kg, i.p.) was administrated to induce sleep. The latent period, percentage of sleep incidence and modulation of latency were measured. Further, homology modeling of human γ-aminobutyric acid (GABA) was conducted examine the binding mode of GABA interaction with SCL, DZP, and CAF compounds Results: SCL (low dose) slightly increased the sleep latency, while the higher dose significantly prolonged sleep latency. DZP, a GABAA receptor agonist, exhibited strong sleep-inducing properties, reducing sleep latency, and increasing sleeping time. Caffeine (CAF) administration prolonged sleep latency and reduced sleeping time, consistent with its stimulant effects. The combination treatments involving SCL, DZP, and CAF showed mixed effects on sleep parameters. The molecular docking revealed good binding affinities of SCL, DZP, and CAF for GABAA receptor subunits A2 and A5. Conclusions: Our findings highlighted the complex interplay between SCL, DZP, and CAF in regulating sleep behaviors and provided insights into potential combination therapies for sleep disorders.

Thymol (THY), as the natural monoterpene phenol, acts against oxidative stress and inflammatory processes. This study aimed to evaluate the anti-inflammatory effects and possible molecular mechanisms of THY via formalin-induced mouse and egg albumin-induced chick models alongside molecular docking and molecular dynamic (MD) simulations. THY (7.5, 15, and 30 mg/kg) was investigated, compared to celecoxib and ketoprofen (42 mg/kg), as anti-inflammatory standards. THY dose-dependently and significantly (p < 0.05) decreased paw-licking and edema diameter parameters in formalin (phases I and II) and egg albumin-induced models. Moreover, THY (15 mg/kg) exerted better anti-inflammatory effects in combination with the standard drug ketoprofen than alone and with celecoxib. In silico studies demonstrated elevated binding affinities of THY with cyclooxygenase-2 (COX-2) than the COX-1 enzyme, and the ligand binds at a similar location where ketoprofen and celecoxib interact. The results of MD simulations confirmed the stability of the test ligand. THY exerted anti-inflammatory effects on Swiss mice and young chicks, possibly by interacting with COX-2. As a conclusion, THY might be a hopeful drug candidate for the management of inflammatory disorders.

Pigment dyeing has gained popularity because it eliminates the need for post-dyeing treatments such as rinsing, neutralization, and washing. Exhaust pigment dyeing demands cotton fiber cationization, often achieved with synthetic agents. This study explores chitosan as a sustainable alternative for cationizing cotton for pigment dyeing. Chitosan has certain advantageous properties that set it apart from synthetic cationic compounds, including biocompatibility and non-toxicity. Cotton fabric was cationized using chitosan at different concentrations and the cationized fabric was subsequently dyed at 3 % shade with pigment using the exhaust method. Cotton dyed with a commercial cationizer served as a comparison. The uniformity of pigment dyeing using chitosan remained unaffected, and comparable K/S values were observed when chitosan replaced the synthetic cationizing agent. Various physical and chemical properties of the dyed fabrics such as color fastness to washing, perspiration (acid and alkali), rubbing fastness, light fastness, stiffness and bursting strength were assessed. Additionally, Fourier Transform-Infrared, Fabric Hand, Biochemical Oxygen Demand, and Chemical Oxygen Demand evaluations were also carried out to examine the viability of using chitosan instead of a synthetic cationizing agent. This study demonstrates chitosan's potential as a sustainable and effective cationizing agent for pigment dyeing.

Abstract Designing circulator as an antenna interface device becomes a daunting task, particularly active‐quasi circulator. This article focuses on demonstrating the basic operation principle, design methods, technical parameters, and performance metrics of active quasi‐circulator. In addition, the study provides an analogy of the circuits and structures proposed by the researchers to enhance certain performance metrics. Active signal cancellation and passive signal cancellation are identified as the major design approaches. Tunable, wideband, and wideband‐tunable are the major types of circulators found in existing literature. Moreover, this article provides a performance comparison of the active‐quasi circulators available in existing literature. Several active quasi‐circulators were able to operate in high frequencies such as 60 GHz with acceptable isolation levels. On the other hand, several designs have over 30 dB isolation, which is a highly desired parameter. At last, the future design challenges associated with active‐quasi circulators have been discussed to provide insight into future research.

This review paper examines the crucial role of nanowires in the field of quantum computing, highlighting their importance as versatile platforms for qubits and vital building blocks for creating fault-tolerant and scalable quantum information processing systems. Researchers are studying many categories of nanowires, including semiconductor, superconducting, and topological nanowires, to explore their distinct quantum features that play a role in creating various qubit designs. The paper explores the interdisciplinary character of quantum computing, combining the fields of quantum physics and materials science. This text highlights the significance of quantum gate operations in manipulating qubits for computation, thus creating the toolbox of quantum algorithms. The paper emphasizes the key research areas in quantum technology, such as entanglement engineering, quantum error correction, and a wide range of applications spanning from encryption to climate change modeling. The research highlights the importance of tackling difficulties related to decoding mitigation, error correction, and hardware scalability to fully utilize the transformative potential of quantum computing in scientific, technical, and computational fields.

This study explores the utilization of Medical Waste Incineration Fly Ash (MWIFA) as a cost-effective adsorbent for removing dye from textile wastewater and synthetic aqueous solutions. Various factors were investigated in batch experiments, yielding impressive dye removal efficiencies of up to 98%. Optimal conditions were determined at 7.5 g/L adsorbent dosage, 40-min contact time, and 200 rpm agitation speed. The Freundlich isotherm model demonstrated a better fit than the Langmuir model, while Lagergren's pseudo second-order kinetic model highlighted chemisorption dominance. Laboratory-scale experiments suggest that MWIFA has the potential to be a viable and cost-effective adsorbent for treating wastewater. However, careful evaluation of environmental impacts is essential for large-scale application.

ChatGPT has been a revolutionizing AI Chatbot, that has impacted academia significantly. Although there has been a number of studies over the influence of AI and education, a focus towards why students use it is minimal. This study explores factors affecting student use of ChatGPT through a triangulation approach, blending exploratory, qualitative, and quantitative analyses. Through exploration of factors via prior literature and analysis of narratives by the respondents of qualitative analysis to measuring their relationships quantitatively using SEM techniques, the study identifies core reasons to use ChatGPT by students of higher education. The findings highlight the significance of six out of seven studied exogenous variables, demonstrating strong positive associations with students' intention to use ChatGPT and their actual engagement with the platform. These empirical results validate the importance of these factors in shaping students' interactions with ChatGPT, also revealing a previously undiscovered factor that enriches existing knowledge. By substantiating qualitative insights and establishing a robust quantitative model, it enhances students' comprehension of ChatGPT adoption in educational contexts, contributing significantly to the field's knowledge base and works as a guide for policymakers to effectively design strategies in order to get maximum benefit from the tool's usage in academia.

Seaweeds possess considerable potential for developing and enhancing pharmaceutical drugs because of their wide variety as abundant sources of numerous bioactive secondary metabolites, making them well-suited for developing and enhancing pharmaceutical drugs. This study evaluated the antioxidant, secondary metabolites, total phenolic content, and total flavonoid content of various crude extracts (methanol, ethanol) and their fractions (50 % and 70 %) of Sargassum polycystum. Fourier transform infrared (FT‐IR) analysis was also performed to identify the active chemical composition of the crude extracts. The results showed that various extracts contained varying concentrations of terpenoid, saponin, phlobatannin, cardiac glycosides, phenolic, and flavonoid. FT-IR analysis confirmed the presence of phenols, carboxylic acid, ketones, ethers, aromatics, amides, and sulfonates. 100 % methanolic extract contained the maximum concentrations of total phenols (80.13 mg of GAE/g) and total flavonoids (38.33 mg of quercetin/g). 100 % methanol extracts exhibited higher antioxidant activity in the DPPH assay (78.32 %, IC50 = 1.59 mg/ml) and ABTS assay (69.82 %, IC50 = 2.39 mg/ml). The bio-functional activity of crude extracts depended on their extract and concentration. The present study demonstrates that the macroalgae S. polycystum contains noteworthy secondary metabolites and natural antioxidants, indicating their potential utilization in various pharmacological and functional food applications.

Ethnocentric conduct among employees is observed in multicultural work environments characterised by the presence of individuals from diverse origins. The existence of ethnocentrism inside the workplace has been observed to have repercussions for colleagues. Nevertheless, despite the significance of the aforementioned factors, there hasn't been a considerable pertinent scholarly study on how these aspects affect the localisation of human resources. This research closes the gap in the literature by integrating ethnocentrism and human resource localisation variables. We used Andrew F. Hayes PROCESS V4.0 to assess the hypothesised relationship between employees' ethnocentric behaviour and human resource localisation success. In addition, employee knowledge-sharing tendency works as a mediator, and employee cultural intelligence (CQ) is a moderated mediator. From the analysis of 361 respondents from multinational Company (MNC) workers, we found that ethnocentric behaviour reduces employee knowledge-sharing tendency among workers and, in return, reduces human resource localisation success. However, CQ moderates the mediated relationship between employee knowledge-sharing direction and localisation success. We also found that when employees have high CQ, the negative effect of ethnocentrism on localisation success weakens. Managers of multicultural organisations should reduce ethnocentrism to ensure the success of human resource localisation. MNCs can consider employing culturally intelligent individuals and giving them sensitivity training. Future research can integrate other variables, such as the firm's cultural characteristics, to continue this research domain. Further research can also consider collecting data from nations with high cultural distances; comparative studies between two countries are also encouraged.

Humans can carry various diseases, some of which are poorly understood and lack comprehensive solutions. Such a disease can exists in human eye that can affect one or both eyes is diabetic retinopathy (DR) which can impair function, vision, and eventually result in permanent blindness. It is one of those complex complexities. Therefore, early detection of DR can significantly reduce the risk of vision impairment by appropriate treatment and necessary precautions. The primary aim of this study is to leverage cutting-edge models trained on diverse image datasets and propose a CNN model that demonstrates comparable performance. Specifically, we employ transfer learning models such as DenseNet121, Xception, Resnet50, VGG16, VGG19, and InceptionV3, and machine learning models such as SVM, and neural network models like (RNN) for binary and multi-class classification. It has been shown that the proposed approach of multi-label classification with softmax functions and categorical cross-entropy works more effectively, yielding perfect accuracy, precision, and recall values. In particular, Xception achieved an impressive 82% accuracy among all the transfer learning models, setting a new benchmark for the dataset used. However, our proposed CNN model shows superior performance, achieving an accuracy of 95.27% on this dataset, surpassing the state-of-the-art Xception model. Moreover, for single-label (binary classifications), our proposed model achieved perfect accuracy as well. Through exploration of these advances, our objective is to provide a comprehensive overview of the leading methods for the early detection of DR. The aim is to discuss the challenges associated with these methods and highlight potential enhancements. In essence, this paper provides a high-level perspective on the integration of deep learning techniques and machine learning models, coupled with explainable artificial intelligence (XAI) and gradient-weighted class activation mapping (Grad-CAM). We present insights into their respective accuracy and the challenges they face. We anticipate that these insights will prove valuable to researchers and practitioners in the field. Our ambition is that this in-depth study and comparison of models will inform and inspire future research endeavors, ultimately leading to enhanced disease detection in medical imaging and thereby assisting healthcare professionals.

Software defect identification (SDI) is a key part of improving the quality of software projects and lowering the risks that along with maintenance. It does identify the software defect causes that have not been reached yet to get sufficient results. On the other hand, many researchers have recently developed several models, including NN, ML, DL, advanced CNN, and LSTM, to enhance the effectiveness of defect prediction. Due to an insufficient dataset size, repeated investigations, and no longer appropriate baseline selection, the research on the CNN model was unable to produce reliable results. In addition, XAI a well-known explainability approach creates deep models in computer vision, as well as successfully handles the software defect prediction that is easy for humans to understand. To address these issues, firstly we have used SMOTE for preprocessing which was collected from the NASA repository; categorical and numerical data. Secondly, we have experimented with software defect prediction using 1D-CNN and 2D-CNN named lightweight CNN (LCNN). Subsequently, evaluation we have employed a 100-repetition holdout validation. For the cross-validation setup, we utilized the 1D-CNN model was $20\times 1$ , and for the 2D-CNN model, it was $4\times 5 \times 1$ . After that, the results of the experiment were compared and assessed in terms of accuracy, MSE, and AUC. The result shows that 2D-CNN shows 1.36% better contrast with 1D-CNN. Thirdly, we have conducted research on the identification of software defect features via LIME and SHAP in XAI stand as state-of-the-art techniques. However, we cannot use 2D-CNN because it involves more complex relationships, making it challenging to create transparent explanations. That is why we have realized that 1D-CNN will superior result to explain the root cause of software feature identifications. Finally, LIME provides accurate visualization of software defect features in contrast with SHAP, as well as it helps the stakeholders of the software industry easily find actual root causes of software defect identification.

Rice is one of the foremost food grains that dispenses sustenance to about half of the world’s population. It is cultivated all over the world. The leaf disease detection of this crop is one of the chronic agricultural obstacles that farmers and planting experts have been struggling with for a long time. As a result of the leaf diseases, producing the amount of rice required to feed the world’s rising population has become very challenging. Hence, automatically detecting rice leaf diseases is an inevitable task to increase productivity. Numerous deep learning based methods have been proposed for rice leaf disease detection, which we found rather inefficient considering the size of the models. In this article, we introduce a lightweight deep Convolutional Neural Network (dCNN) based method for rice leaf disease detection, that outperforms contemporary state-of-the-art methods and showcases competitive performance against 21 established benchmark architectures, including AlexNet, MobileNet, ResNet50, DenseNet121, ResNeXt50, ShuffleNet, ConvNext, EfficientNet, GogoleNet, SwinTransformer, VisionTransformer, and MaxVit, to name a few, with significantly lower trainable parameters. Notably, our method achieves an accuracy score of 99.81%, a precision score of 0.99828, a recall score of 0.99826, and an f1-score of 0.99827. Moreover, we enhance the rice leaf disease dataset by merging two existing datasets and supplemented them with an additional 95 manually annotated images gathered from publicly available sources on the internet. We also develop a comprehensive crop health monitoring system for farmers, and develop an open API for the automatic annotation of new instances, benefiting the research community at large.

Despite huge improvements in automatic speech recognition (ASR) employing neural networks, ASR systems still suffer from a lack of robustness and generalizability issues due to domain shifting. This is mainly because principal corpus design criteria are often not identified and examined adequately while compiling ASR datasets. In this study, we investigate the robustness of the fully supervised convolutional neural networks (CNNs), and the state-of-the-art transfer learning approaches, namely self-supervised wav2vec 2.0 and weakly supervised Whisper for multi-domain ASR. We also demonstrate the significance of domain selection while building a corpus by assessing these models on a novel multi-domain Bangladeshi Bangla ASR evaluation benchmark—BanSpeech, which contains approximately 6.52 hours of human-annotated speech, totaling 8085 utterances, across 13 distinct domains. SUBAK.KO, a mostly read speech corpus for the morphologically rich language Bangla, has been used to train the ASR systems. Experimental evaluation reveals that self-supervised cross-lingual pre-training with wav2vec 2.0 is the best strategy compared to weak supervision and full supervision to tackle the multi-domain ASR task. Moreover, the ASR models trained on SUBAK.KO face difficulty recognizing speech from domains with mostly spontaneous speech. The BanSpeech is publicly available to meet the need for a challenging evaluation benchmark for Bangla ASR.1

The generation of high-quality images from textual descriptions is a challenging task in computer vision and natural language processing. The goal of text-to-image synthesis, a current topic of research, is to produce excellent images from written descriptions. This study proposes a hybrid approach to evaluating a dataset consisting of various text-image pairs by efficiently combining conditional generative adversarial networks (C-GAN), attention mechanisms, and contrastive learning (C-GAN+ATT+CL). We suggest a two-step method to improve image quality that starts by utilizing generative adversarial networks (GANs) with attention mechanisms to create low-resolution images and then contrastive learning to improve. Contrastive learning modules train on a separate dataset of high-resolution pictures; GANs learn on datasets of low-resolution text and image pairs. The Conditional GAN with Attention Mechanism and Contrastive Learning Method provides state-of-the-art performance in terms of image quality, diversity, and visual realism, among the several methods. The results of this study demonstrate that the proposed approach works better than all other methods, achieving an Inception Score (IS) of 35.23, a Fréchet Inception Distance (FID) of 18.2, and an R-Precision of 89.14. Our findings demonstrate that our “C-GAN+ATT+CL” approach significantly improves image quality and diversity and offers exciting paths for further study.

Abstract Introduction Tobacco use, in both smoking and smokeless forms, is highly prevalent among South Asian adults. The aims of the study were twofold: (1) describe patterns of SLT and combustible tobacco product use in four South Asian countries stratified by country and sex, and (2) assess the relationships between SLT and smoking intensity, smoking quit attempts, and smoking cessation among South Asian men. Methods Data were obtained from South Asia Biobank Study, collected between 2018 and 2022 from 148,944 men and women aged 18 years and above, living in Bangladesh, India, Pakistan, or Sri Lanka. Mixed effects multivariable logistic and linear regression were used to quantify the associations of SLT use with quit attempt, cessation, and intensity. Results Among the four South Asian countries, Bangladesh has the highest rates of current smoking (39.9% for male, 0.4% for female) and current SLT use (24.7% for male and 23.4% for female). Among male adults, ever SLT use was associated with a higher odds of smoking cessation in Bangladesh (OR, 2.88; 95% CI, 2.65, 3.13), India (OR, 2.02; 95% CI, 1.63, 2.50), and Sri Lanka (OR, 1.36; 95% CI, 1.14, 1.62). Ever SLT use and current SLT use was associated with lower smoking intensity in all countries. Conclusions In this large population-based study of South Asian adults, rates of smoking and SLT use vary widely by country and gender. Men who use SLT products are more likely to abstain from smoking compared with those who do not.

Abstract The remarkable structural, optical, and electronic characteristics of inorganic perovskite materials have generated significant enthusiasm within the field of solar technology. The material Ca3PI3 belongs to the same category as inorganic metal halide perovskites. This research utilized the first‐principles density functional theory (FP‐DFT) to examine how the optical and electronic characteristics of Ca3PI3 are impacted by strain. To accurately determine the band arrangement, we incorporated the relativistic spin‐orbit coupling (SOC) effect into our calculations. The planar Ca3PI3 molecule has a direct bandgap of 1.582 eV (PBE) at its Г(gamma)‐point, but while the relativistic SOC effect is included, the bandgap decreases to 1.329 eV. Under compressive strain, the bandgap of all structures decreases, whereas under tensile strain, it increases. The optical characteristics of Ca3PI3, including the dielectric function, absorption coefficient, and electron loss function, indicate its strong absorption capabilities in the visible range, driven by its band properties. Besides, the photon energy spectrum displays a red‐shift (blue‐shift) in the absorption coefficient and dielectric function with increasing amounts of compressive (tensile) strain. Therefore, the study of the strain‐induced optical and electronic characteristics of Ca3PI3 bears valuable implications for its potential use in the design of solar cells and optoelectronic devices.

Artificial Intelligence (AI) is rapidly integrating into various aspects of our daily lives, influencing decision-making processes in areas such as targeted advertising and matchmaking algorithms. As AI systems become increasingly sophisticated, ensuring their transparency and explainability becomes crucial. Functional transparency is a fundamental aspect of algorithmic decision-making systems, allowing stakeholders to comprehend the inner workings of these systems and enabling them to evaluate their fairness and accuracy. However, achieving functional transparency poses significant challenges that need to be addressed. In this paper, we propose a design for user-centered compliant-by-design transparency in transparent systems. We emphasize that the development of transparent and explainable AI systems is a complex and multidisciplinary endeavor, necessitating collaboration among researchers from diverse fields such as computer science, artificial intelligence, ethics, law, and social science. By providing a comprehensive understanding of the challenges associated with transparency in AI systems and proposing a user-centered design framework, we aim to facilitate the development of AI systems that are accountable, trustworthy, and aligned with societal values.

Abstract Background There is a paucity of resources focusing on the climate change experience of readymade garment (RMG) workers in developing countries such as Bangladesh. Therefore, this mixed method approach aims to understand the distinctive types of climate change experiences from a health and occupational perspective, along with the consequences of these changes among RMG workers in Bangladesh. Methods The study was conducted from January 2022 and February 2022 where the quantitative data were collected from 200 RMG workers in 10 randomly selected garments and two focus group discussions took place with 20 conveniently selected RMG workers. The key informants were relevant stakeholders in the industry. Quantitative findings were reported using descriptive methods and qualitative findings were analysed using a content analysis approach. Result A total of 200 RMG workers were included in the study of which the majority belonged to the age group of 26–30 years (44%), were male (55%), worked in a compliant factory (70%), and were machine operators (79%). Half of the respondents experienced damage from natural disasters (51%), but only approximately 37% received humanitarian help. Migration and urbanisation were among the aftermath of the damage caused by e natural disasters, and 42% were forced to shift their homes due to natural disasters. Competition in the job market increased, and the owners had the opportunity to take on employees at a reduced salary. The respondents flagged climate change as a major contributor to their disease patterns. More than three-quarters of the respondents became sick because of increased heat while working; however, only half received any treatment. Conclusion Employee participation in hazard recognition, employer preparedness, prevention through design, research, surveillance, and upholding workplace ethics and standards can be the answers to climate change problems for readymade garment workers.

Heavy metal pollution is posing significant threats to the environment and human health. Engineered biochar, derived from various biomass sources through thermochemical processes, has emerged as a promising solution for metal pollutant remediation and metal recovery. This review explores the latest advancements in the preparation, characterization, and application of engineered biochar for metal adsorption, recycling, and utilization. It begins by discussing the significance of metal adsorption and providing an overview of biochar properties. The review examines the preparation and characterization techniques, emphasizing feedstock selection, thermochemical conversion methods, and surface modifications. Mechanisms of metal adsorption, such as physical and chemical adsorption, ion exchange, and surface complexation, are critically discussed. Moreover, factors influencing metal adsorption capacity, including biochar properties, metal characteristics, and environmental conditions, are critically analyzed. The efficacy of engineered biochar in adsorbing specific metals, including heavy metals, transition metals, and rare earth elements, is reviewed with recent studies and key findings. Furthermore, the recycling and regeneration of metal-loaded biochar are discussed, focusing on recycling and repurposed application techniques alongside challenges and economic considerations. Finally, future perspectives are provided for the enlightening of future research. This review is unique in addressing the potential of metal-adsorbed biochar as a novel precursor to produce catalytical and electrochemical materials.

Introduction: D-pinitol, a naturally occurring inositol, has diverse biological activities like antioxidant, antimicrobial and anticancer activities. This study aimed to evaluate anti-inflammatory effect of d-pinitol in a chick model. Additionally, in silico studies were performed to evaluate the molecular interactions with cyclooxygenase-2 (COX-2).Methods: The tested groups received d-pinitol (12.5, 25, and 50 mg/kg) and the standard drugs celecoxib and ketoprofen (42 mg/kg) via oral gavage prior to formalin injection. Then, the number of licks was counted for the first 10 min, and the paw edema diameter was measured at 60, 90, and 120 min.Results and Discussion: The d-pinitol groups significantly (p < 0.05) reduced the number of paw licks and paw edema diameters, compared to negative control. When d-pinitol was combined with celecoxib, it reduced inflammatory parameters more effectively than the individual groups. The in silico study showed a promising binding capacity of d-pinitol with COX-2. Taken together, d-pinitol exerted anti-inflammatory effects in a dose-dependent manner, possibly through COX-2 interaction pathway.

This comprehensive review examines the immense capacity of nanowires, nanostructures characterized by unbounded dimensions, to profoundly transform the field of biomedicine. Nanowires, which are created by combining several materials using techniques such as electrospinning and vapor deposition, possess distinct mechanical, optical, and electrical properties. As a result, they are well-suited for use in nanoscale electronic devices, drug delivery systems, chemical sensors, and other applications. The utilization of techniques such as the vapor-liquid-solid (VLS) approach and template-assisted approaches enables the achievement of precision in synthesis. This precision allows for the customization of characteristics, which in turn enables the capability of intracellular sensing and accurate drug administration. Nanowires exhibit potential in biomedical imaging, neural interfacing, and tissue engineering, despite obstacles related to biocompatibility and scalable manufacturing. They possess multifunctional capabilities that have the potential to greatly influence the intersection of nanotechnology and healthcare. Surmounting present obstacles has the potential to unleash the complete capabilities of nanowires, leading to significant improvements in diagnostics, biosensing, regenerative medicine, and next-generation point-of-care medicines.

This article theoretically demonstrates an enormously efficient CdTe–FeSi2 based dual-junction tandem solar cell accompanied by slender semiconductor layers. The peak efficiency of the device has been ensured through the optimization of its various attributes of window, CdTe (bandgap 1.5 eV) top absorber, FeSi2 (bandgap 0.87 eV) bottom absorber and back surface layers. Additionally, the impacts of thickness, doping and the level of defects in different window, base and rear surface layers have been examined to observe how different layers affect the solar cell's performance. The optimized n-CdS/p-CdTe/p+-MoS2--n-CdS/p-FeSi2/p+-Cu2SnS3 dual-junction tandem solar device displays an efficiency of 43.9% with a voltage at no load, VOC of 1.928 V, current density under a closed circuit, JSC of 25.34 mA/cm2, and fill factor of 89.88%, respectively. These results disclose the high potential of the suggested solar cell based on CdTe and FeSi2 compounds.

Silicon-based lithium-ion battery negative electrodes represent one of graphite's most promising replacements. However, the enhanced capacity and unique Li+ storage method have raised the demands on the binder and other passive electrode components. For cycle stability, a sufficient carbonaceous matrix with silicon is needed. One of the most desirable anode materials for Li-ion batteries (LIBs) is Si, which has been noted for its exceptional volumetric and gravimetric qualities. Its affordability, abundance, and environmental safety stand out in particular. We assess the most recent improvements in the production of intercalation-type, conversion-type, and alloying-type anode materials in this work. After explaining the electrochemical reaction and failure, we reviewed several techniques for enhancing battery performance, including nanostructuring, alloying, building hierarchical structures, and employing the proper binders. Researchers will get the necessary information from this research work to conduct future research.

Abstract This investigation emphasizes the study of the structural, mechanical, thermophysical, thermodynamical, and optoelectronic properties of a Dion‐Jacobson Perovskite‐like Layered Structured (PLS) CsLaNb2O7 using the first‐principles. The former experimental investigation accepts the estimated lattice parameters and unit cell volume at the ground state. The material is mechanically stable, ductile, and anisotropic in nature. The phonon dispersion characteristics, phonon density of states, zero‐point energy and temperature‐dependent‐ enthalpy, entropy, and Debye temperature have been studied for the first time. The band gap is found to be direct. Different orbital contributions in electronic bonding have been visualized in the PDOS diagram. Optical anisotropic properties have also been investigated along the [100] and [001] polarizing directions. This material shows promising properties in the fabrication of multifunctional materials.

In this study, a comparative analysis of the structural, mechanical, electronic, and optical properties of cubic perovskites AGeX3 (A = Cs, K, and Rb; X = Cl, Br, and I) has been calculated by the density functional theory. The calculated values of structural parameters such as lattice parameters and unit cell volume of all compounds are in excellent agreement with earlier observations reported in the literature. The values of Poisson's and Pugh's ratios indicate that all compounds exhibit a ductile nature except for CsGeI3, for which the value of Pugh's and Poisson's ratios is shown as brittleness nature. All compounds exhibit an anisotropic nature by calculating the Zener anisotropy index and 3D contour plots also reveal the same nature and CsGeCl3 shows the maximum anisotropy nature. The KGeCl3 compound shows the highest Debye temperatures among the other compounds. Electronic band structures and the study of the density of states provide evidence for the semiconductor behavior of these substances. The I-base compounds show the maximum absorption, conductivity, and loss function than Cl and Br base compounds in the visible region, whereas the Cl group especially CsGeCl3 shows maximum absorption, conductivity, and loss function in the UV zone. A combined analysis suggests that the CsGeCl3 sample would be a promising lead-free candidate for high-performance optoelectronic device application in the UV zone.

Abstract This research aims to fill up the research gap in energy‐efficient transistor‐level wide word‐length carry circuit generator by using ripple carry (RC) and carry look‐ahead (CLA) method‐based hybrid 4‐bit carry generation process for wide word‐length carry‐select adder (CSLA). Compared to the existing 4‐bit CLA architectures, the proposed 4‐bit RC‐CLA method‐based hybrid 4‐bit carry generator showed performance improvement in terms of power and power delay product (PDP). Later, the 4‐bit carry architectures (existing and proposed) were used as a base to implement 16‐bit carry select adder (CSA) in order to investigate and compare the effect of using the proposed hybrid RC‐CLA based 4‐bit carry generator in large structures. Unlike 4‐bit operation, the proposed design displayed the best performance in power and PDP for 16‐bit CSA extension, which proves its effectiveness in wide word‐length adder structures.

Abstract Aim Immediate care of newborns is essential to scale back the mortality rate. This study tries to search out several aspects of newborn care practices of newborn birth from BDHS 2017-18 data. Methods Initially, bivariate analysis is employed to look at the differentials' initial newborn care practices by several selected background variables. The study used a simple and multinomial logistic regression model to identify the important determinants of initial care practices. Besides determinates of the factor, the study also compares the results with the cross-sectional survey data of 2014 and 2011. Results The percentage of employing a clean delivery kit during delivery and skin-to-skin contact are 22.8 and 13.7 respectively in Bangladesh in 2017 which is lower than the previous report in 2014. The odds of clean delivery kits and skin-to-skin are higher for educated mothers (OR = 3.30 and OR = 1.74) and in the case of delayed bathing the odds of the Rangpur division (OR = 1.90) are more likely higher compared to the reference category. Besides, the odds of a mother's age above 25 and birth order 3+ are less likely to reference the category for newborn care practices. Conclusion Factors identified in early newborn care practices will not only help policy makers undertake a series of interventions for improved newborn health but also ensure good -quality health services.

The escalating global temperatures associated with climate change are detrimental to plant growth and development, leading to significant reductions in crop yields worldwide. Our research demonstrates that salicylic acid (SA), a phytohormone known for its growth-promoting properties, is crucial in enhancing heat tolerance in cotton (Gossypium hirsutum). This enhancement is achieved through modifications in various biochemical, physiological, and growth parameters. Under heat stress, cotton plants typically show significant growth disturbances, including leaf wilting, stunted growth, and reduced biomass. However, priming cotton plants with 1 mM SA significantly mitigated these adverse effects, evidenced by increases in shoot dry mass, leaf-water content, and chlorophyll concentrations in the heat-stressed plants. Heat stress also prompted an increase in hydrogen peroxide levels—a key reactive oxygen species—resulting in heightened electrolyte leakage and elevated malondialdehyde concentrations, which indicate severe impacts on cellular membrane integrity and oxidative stress. Remarkably, SA treatment significantly reduced these oxidative stresses by enhancing the activities of critical antioxidant enzymes, such as catalase, glutathione S-transferase, and ascorbate peroxidase. Additionally, the elevated levels of total soluble sugars in SA-treated plants enhanced osmotic regulation under heat stress. Overall, our findings reveal that SA-triggered protective mechanisms not only preserve photosynthetic pigments but also ameliorate oxidative stress and boost plant resilience in the face of elevated temperatures. In conclusion, the application of 1 mM SA is highly effective in enhancing heat tolerance in cotton and is recommended for field trials before being commercially used to improve crop resilience under increasing global temperatures.

Abstract Background High blood pressure is a major public health problem in low- and middle-income countries. Low-sodium salt substitute (LSSS) is a promising population-level blood pressure-lowering intervention requiring minimal behavioral change. The optimal method of delivering LSSS to individuals, however, is currently unknown. Community health workers (CHWs) have successfully been used to implement health interventions in Bangladesh and may provide a venue for the dissemination of LSSS. Methods We aim to conduct a cluster-randomized controlled trial involving 309 households in rural Bangladesh previously identified and characterized by the BRAC James P Grant School of Public Health, BRAC University (BRAC JPGSPH). These households will be randomly assigned to three arms: (1) control, i.e., no intervention; (2) information only, i.e., community health workers will provide basic information on high blood pressure, the health consequences of excessive salt consumption, and feedback to the participant on the likely quantity of salt s/he consumes (estimated using a questionnaire); (3) free LSSS arm: the same information as in arm 2 will be provided, but participants will receive 6 months of free low-sodium salt along with education on the benefits of LSSS. One male and one female adult (age ≥ 18 years) in each household will be invited to participate, the exclusion criteria being households with members known to have high serum potassium levels, are taking medications known to elevate potassium levels (e.g., ACE inhibitors, ARBs, potassium-sparing diuretics), are already taking potassium supplements, or those who have known kidney disease or abnormal serum creatinine at baseline. The primary endpoint will be blood pressure at 6 months post-intervention. Discussion Recent large clinical trials of LSSS in China and India have shown not only blood pressure improvements, but also stroke, major cardiac event, and all-cause mortality reductions. Nevertheless, how to best translate this intervention to population-level effectiveness remains unclear. Our study would test whether a community health worker-based program could be effectively used to disseminate LSSS and achieve measurable blood pressure benefits. Trial registration ClinicalTrials.gov NCT05425030. Registered on June 21, 2022.

Abstract Groundwater and soil contaminated with arsenic (As) are significant environmental health concerns worldwide and have become a serious health issue for millions of Bangladeshis. This contaminated groundwater is commonly used for Boro rice production during the dry season in Bangladesh, leading to long-term As deposition in soils. The consequences of long-term irrigation with As-contaminated groundwater for bioaccumulation in food crops and hence dietary exposure to As and other metals are a threat to the resident. Highly arsenic (292 µg L−1) contaminated groundwater used to irrigate in the study area increases the As content (mean: 24.5 mg kg−1) in irrigated soil than non-irrigated soil (mean: 8.43 mg kg−1). The high enrichment factor (mean: 16.8) and geo-accumulation index (mean: 2.33) revealed that the soil of that particular area is strongly contaminated by As. In paddy, roots showed the maximum As concentration (mean: 139.8 mg kg−1) followed by straws (mean: 5.25 mg kg−1) and grains (mean: 1.23 mg kg−1) where 85–95% As exist in the roots of paddy, and only 0.8–1% As translocate to the harvesting part of the rice in the study area. In food samples, two rice grains (R-1, R-3) and a drumstick (FS-5) exceed the Indian standard of As concentration (As: 1.1 mg kg−1) in food. According to World Health Organization, the metals concentrations in all food samples exceeded the permissible limit except for Cu and Pb in red amaranth; Cr, Cd, and Pb in guava; and Cu and Cd in drumsticks. Higher values of bioaccumulation factor (BAF: 2.94) and the net translocation coefficient (NTC: 6.17) indicate As-accumulation in food from adjacent contaminated soil. Daily metal intake from food consumption suggests that the heavy metal(loid)s like As, Ni, and Pb represent high concentrations and exceed the daily ingestion limits (As: 3 µg kg−1 day−1, Ni: 1.3 µg kg−1 day−1, and Pb: 3.57 µg kg−1 day−1) developed by the Food and Agriculture Organization and European Food Safety Authority. Carcinogenic (> 10–6) and non-carcinogenic (HQ > 1) health risks of As, Pb, Cd, and Cu suggest that the community was at potential health risk according to the United States Environmental Protection Agency.

Abstract Background Low back pain (LBP) is a common condition contributing to impaired quality of life among professional vehicle drivers. Our study aimed to assess LBP prevalence and associated factors among professional bus drivers in Bangladesh. Methods A cross-sectional study was conducted among 368 professional bus drivers using a semi-structured questionnaire. A Nordic Musculoskeletal Questionnaire (NMQ) subscale was used to measure LBP. Multivariable logistic regression analysis was used to identify the factors associated with LBP. Results In the last month, 127 (34.51%) participants reported experiencing pain or discomfort in the lower backside. Multivariable logistic regression analysis showed that age of more than 40 years (adjusted odds ratio (aOR): 2.07, 95% confidence interval (CI): 1.14 to 3.75), the income of more than 15,000 BDT per month (aOR: 1.91, 95% CI: 1.11 to 3.26), work duration more than ten years (aOR: 2.53, 95% CI: 1.12 to 5.70), working more than 15 days per month (aOR: 1.93, 95% CI: 1.02 to 3.65), working more than 10 h per day (aOR: 2.46, 95% CI: 1.05 to 5.75), poor condition of the driving seat (aOR: 1.80, 95% CI: 1.08 to 3.02), current smoking habit (aOR: 9.71, 95% CI: 1.25 to 75.15), illicit substances use (aOR: 1.97, 95% CI: 1.11 to 3.48), and four hours or less sleeping time per day (aOR: 1.83, 95% CI: 1.09 to 3.06) were positively associated with LBP. Conclusion The high burden of LBP among the participants calls for a focus on this vulnerable group's occupational health and safety, with particular emphasis on implementing standard measures.

Abstract Background The global outbreak of COVID-19 has created unprecedented havoc among health care workers, resulting in significant psychological strains like insomnia. This study aimed to analyze insomnia prevalence and job stressors among Bangladeshi health care workers in COVID-19 units. Methodology We conducted this cross-sectional study to assess insomnia severity from January to March 2021 among 454 health care workers working in multiple hospitals in Dhaka city with active COVID-dedicated units. We selected 25 hospitals conveniently. We used a structured questionnaire for face-to-face interviews containing sociodemographic variables and job stressors. The severity of insomnia was measured by the Insomnia Severity Scale (ISS). The scale has seven items to evaluate the rate of insomnia, which was categorized as the absence of Insomnia (0–7); sub-threshold Insomnia (8–14); moderate clinical Insomnia (15–21); and severe clinical Insomnia (22–28). To identify clinical insomnia, a cut-off value of 15 was decided primarily. A cut-off score of 15 was initially proposed for identifying clinical insomnia. We performed a chi-square test and adjusted logistic regression to explore the association of different independent variables with clinically significant insomnia using the software SPSS version 25.0. Results 61.5% of our study participants were females. 44.9% were doctors, 33.9% were nurses, and 21.1% were other health care workers. Insomnia was more dominant among doctors and nurses (16.2% and 13.6%, respectively) than others (4.2%). We found clinically significant insomnia was associated with several job stressors (p

ABSTRACT Lactiplantibacillus plantarum ATCC 202195 has probiotic properties that may be beneficial in early infancy. Since the effects of probiotics are often strain and dose specific, tools are needed to identify and enumerate L. plantarum ATCC 202195 with high sensitivity and specificity. Herein, a probe-based real-time quantitative PCR (qPCR) assay was developed to detect and enumerate L. plantarum ATCC 202195. The probe sequence contained a single nucleotide polymorphism within gene FEE41_08190 that was unique to L. plantarum ATCC 202195 at the time of the assay’s design. The linear dynamic range of the assay spanned five orders of magnitude with an R 2 that did not deviate from linearity and an amplification efficiency that ranged from 97% to 110% across six independent plates. At 95% confidence, the limit of detection in the assay was estimated at roughly 15 cells. No amplification was observed for reactions containing gDNA from seven other Lactobacillus species. This is the first report of a qPCR assay that detects and quantifies L. plantarum ATCC 202195. The assay is intended to be used as a resource for future clinical trials and commercial manufacturing settings. IMPORTANCE When administered for seven consecutive days shortly after birth, the probiotic bacterium Lactiplantibacillus plantarum ATCC 202195 plus fructooligosaccharide (FOS) was reported to reduce sepsis and lower respiratory tract infection events during early infancy in a randomized trial in India. Since probiotic effects are often strain specific, strain-level detection and quantification by routine molecular methods enables the monitoring of safety outcomes, such as probiotic-associated bacteremia, and allows for the quality of probiotic interventions to be assessed and monitored (i.e., verify strain identity and enumerate). Despite the potential clinical applications of L. plantarum ATCC 202195, an assay to detect and quantify this strain has not previously been described. Herein, we report the design of primer and probe sequences to detect L. plantarum ATCC 202195 and the development and optimization of a real-time PCR assay to detect and quantify the strain with high specificity and high sensitivity.

Municipal solid waste management (MSWM) poses a considerable challenge to developing countries like Bangladesh because of the rising waste generation rates and lack of effective management practices such as illegal open dumping and informal waste collection. One of the crucial factors in the successful management of MSW is to select the appropriate technology which is a complex multi-criteria and laborious process. Despite the global emphasis on the importance of MSWM in the literature, there is a lack of studies conducted in developing countries that effectively identify and analyze the critical performance criteria for appropriate technology selection. This research aims to address this shortcoming by identifying, and prioritizing the selection criteria and finally investigating the inter-relationship between them and the degree to which they affect or are affected by one another. First, a thorough literature review and expert consultation were employed to determine a set of 21 key criteria using the Fuzzy Delphi method (FDM). Later, taking into account the imprecise and subjective nature of the DEMATEL method on human judgements, the Fuzzy DEMATEL technique was employed to investigate the cause-effect relationships among the identified criteria. The findings of the study demonstrated that 14 criteria were categorized as causal elements that have the most significant influence on the MSWM technology selection process and 7 criteria were categorized as effect. The selection of MSWM technology demands greater consideration of the top three ranked criteria, namely T4- Access to Technology (AT), T8- Feasibility (F), and the Ec6-Infrastructure requirements (IR). By identifying the pertinent criteria, structures and interrelationships, the outcome of the study can facilitate a better understanding of causal relationships among the criteria that require specific consideration from the decision-makers and allow them to select appropriate MSW management technology.

The Fourier theorem states that any time-series can be decomposed into a set of sinusoidal frequencies, each with its own phase and amplitude. The literature suggests that some frequencies are important to reproduce key qualities of eye-movements (“signal”) and some of frequencies are not important (“noise”). To investigate what is signal and what is noise, we analyzed our dataset in three ways: (1) visual inspection of plots of saccade, microsaccade and smooth pursuit exemplars; (2) analysis of the percentage of variance accounted for (PVAF) in 1,033 unfiltered saccade trajectories by each frequency band; (3) analyzing the main sequence relationship between saccade peak velocity and amplitude, based on a power law fit. Visual inspection suggested that frequencies up to 75 Hz are required to represent microsaccades. Our PVAF analysis indicated that signals in the 0-25 Hz band account for nearly 100% of the variance in saccade trajectories. Power law coefficients (a, b) return to unfiltered levels for signals low-pass filtered at 75 Hz or higher. We conclude that to maintain eye- movement signal and reduce noise, a cutoff frequency of 75 Hz is appropriate. We explain why, given this finding, a minimum sampling rate of 750 Hz is suggested.

This study evaluated the effects of sclareol (SCL) with or without caffeine (CAF) and quercetin (QUR) using in-vivo and in-silico studies. For this, 5-day-old chicks weighing between 45 and 48 g were randomly divided into five groups and treated accordingly. The chicks were monitored to compare the occurrence, latency, and duration of sleep as well as the loss and gain of righting reflex in response to SCL-10 mg/kg, CAF-10 mg/kg, and QUR-50 mg/kg using a thiopental sodium (TS)-induced sleeping model. Data were analyzed by one-way ANOVA followed by t-Student–Newman-Keuls' as a posthoc test at 95% confidence intervals with multiple comparisons. An in-silico study was also performed to investigate the possible antidepressant mechanisms of the test and/or standard drugs with different subunits of GABAA receptors. In comparison to the SCL, CAF, and QUR individual groups, SCL+CAF+QUR significantly increased the latency while decreasing the length of sleep. The incidence of loss and gain of the righting reflex was also modulated in the combination group. SCL showed better interaction with GABAA (α2 and α5) subunits than QUR with α2, α3, and α5. All these compounds showed stronger interactions with the GABAA receptor subunits than the standard CAF. Taken together, SCL, CAF, and QUR reduced the TS-induced righting reflex and sleeping time in the combination group more than in the individual treatments. SCL may show its antidepressant effects, possibly through interactions with GABAA receptor subunits.