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Objectives: In high TB burden countries like Bangladesh, research and policies tend to focus on rifampicin (RIF)-resistant TB patients, leaving RIF sensitive but isoniazid (INH) resistant (Hr-TB) patients undiagnosed. Our study aims to determine the prevalence of INH resistance among pulmonary TB (PTB) patients in selected healthcare facilities in Bangladesh., Methods: This study was conducted across nine TB Screening and Treatment Centers situated in Bangladesh. Sputum samples from 1084 Xpert-positive PTB patients were collected between April 2021 and December 2022, and cultured for drug susceptibility testing (DST). Demographic and clinical characteristics of Hr-TB and drug-susceptible TB patients were compared., Results: Among available DST results of 998 culture positive isolates, resistance rate of any INH regardless of RIF susceptibility was 6.4% (64/998, 95% CI, 4.9-8.2). The rate was significantly higher in previously treated (21.1%, 16/76, 95% CI, 12.0-34.2) compared to newly diagnosed TB patients (5.2%, 48/922, 95% CI, 3.8-6.9) (p <0.001). The rate of Hr-TB was 4.5% (45/998, 95% CI, 3.3-6.0), which was also higher among previously treated (6.6%, 5/76, 95% CI, 1.4-13.5) compared to newly diagnosed TB patients (4.3%; 40/922, 95% CI, 3.1-5.9) (p=0.350). Most importantly, the rate of Hr-TB was more than double compared to MDR-TB (4.5%, 45/998, vs 1.9%, 19/998) found in the current study., Conclusions: This study reveals a high prevalence of Hr-TB, surpassing even that of the MDR-TB in Bangladesh. This emphasizes the urgent need to adopt WHO-recommended molecular tools at the national level for rapid detection of INH resistance so that patients receive timely and appropriate treatment., (Copyright © 2024. Published by Elsevier Ltd.)

Forecasting the weather in an area characterized by erratic weather patterns and unpredictable climate change is a challenging endeavour. The weather is classified as a non-linear system since it is influenced by various factors that contribute to climate change, such as humidity, average temperature, sea level pressure, and rainfall. A reliable forecasting system is crucial in several industries, including transportation, agriculture, tourism, & development. This study showcases the effectiveness of data mining, meteorological analysis, and machine learning techniques such as RNN-LSTM, TensorFlow Decision Forest (TFDF), and model stacking (including ElasticNet, GradientBoost, KRR, and Lasso) in improving the precision and dependability of weather forecasting. The stacking model strategy entails aggregating multiple base models into a meta-model to address issues of overfitting and underfitting, hence improving the accuracy of the prediction model. To carry out the study, a comprehensive 60-year meteorological record from Bangladesh was gathered, encompassing data on rainfall, humidity, average temperature, and sea level pressure. The results of this study suggest that the stacking average model outperforms the TFDF and RNN-LSTM models in predicting average temperature. The stacking average model achieves an RMSLE of 1.3002, which is a 10.906% improvement compared to the TFDF model. It is worth noting that the TFDF model had previously outperformed the RNN-LSTM model. The performance of the individual stacking model is not as impressive as that of the average model, with the validation results being better in TFDF., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Hasan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Microplastics (MPs) are polymer-based particles commonly found in diverse foods that pose serious human health impacts throughout the food chain. Assessment of MPs in different food products is a prime measure to combat MP-related food contamination. Therefore, this study first investigated the identification, characterization, and potential risks of MPs in the commercially available milk brands (19 dry powders and 06 liquid brands) in Bangladesh. The presence of MPs in milk samples was 279.47 ± 134.26 particles/kg and 182.27 ± 55.13 particles/L for powder and liquid milk, respectively, with a significant variety. Study findings displayed miscellaneous colors, fiber shapes (powder=78 %; liquid=81 %), > 0.1 mm sizes (powder=69 %; liquid=65 %), and polyethylene (powder=48 %; liquid=44 %) dominating MPs categories. The pollution load index indicated significant pollution due to the high abundance of MPs. Further, other risk-evaluating indices including contamination factor and Nemerow pollution index represent moderate to high MP-induced pollution for both milk samples. Low to moderate polymeric risks are exhibited by powder and liquid milk samples. Children could be exposed to 3.43 times higher MPs than adults through daily oral ingestion, which has significant health effects. This study found that powder milk was the most severely MPs induced risk than liquid milk. Consequently, this study finding established a reference point for MP contamination in milk, so special attention must be taken during production, storage, and packaging stages to reduce MP contamination., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The objective of the study is to investigate the fluid flow and heat transfer characteristics applying Artificial Neural Networks (ANN) analysis in triangular-shaped cavities for the analysis of magnetohydrodynamics (MHD) mixed convection with varying fluid velocity of water/Al2O3 nanofluid. No study has yet been conducted on this geometric configuration incorporating ANN analysis. Therefore, this study analyzes and predicts the complex interactions among fluid flow, heat transfer, and various influencing factors using ANN analysis. The process of finite element analysis was conducted, and the obtained results have been verified by previous literature. The Levenberg-Marquardt backpropagation technique was selected for ANN. Various values of the Richardson number (0.01 ≤ Ri ≤ 5), Hartmann number (0 ≤ Ha ≤ 100), Reynolds number (50 ≤ Re ≤ 200), and solid volume fraction of the nanofluid (ϕ = 1%, 3% and 4%) have been selected. The ANN model incorporates the Gauss-Newton method and the method of damped least squares, making it suitable for tackling complex problems with a high degree of non-linearity and uncertainty. The findings have been shown through the use of streamlines, isotherm plots, Nusselt numbers, and the estimated Nusselt number obtained by ANN. Increasing the solid volume fraction improves the rate of heat transmission for all situations with varying values of Ri, Re, and Ha. The Nusselt number is greater with larger values of the Ri and Re parameters, but it lessens for higher value of Ha. Furthermore, ANN demonstrates exceptional precision, as evidenced by the Mean Squared Error and R values of 1.05200e-6 and 0.999988, respectively., Competing Interests: On behalf of all authors, I declared that we have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright: © 2024 Hudha et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Helical tube heat exchangers (HTHE) are commonly used as thermal devices in various thermal engineering applications. A comparative investigation was undertaken to examine several helical tube designs in relation to their potential uses with water and nanofluids. Additionally, employing the ternary hybrid nanofluid (THNF) flow in helical-type heat exchangers to assess the heat transfer and frictional loss is a unique concept, as there is currently no research on this specific application. This study involves analyzing three different design configurations, each of which has three different inlet profiles: round, square, and oval shapes. Hence, a numerical analysis has been conducted on nine cases, each including the same pipe length, helix diameter, and pitch distance. The specified range for the Reynolds number under the water and THNF flow condition is 5000-25000. The results are acquired for both fluids, considering the Nusselt number ( Nu ), friction factor ( f ), outlet temperature ( T out ), and entropy production ( S g ). Multi-Criteria Decision Making (MCDM) is employed to provide a thorough assessment of the overall performance of the proposed designs. The results have been shown as graphical representations, streamlines and contours where Nusselt number, friction factor and entropy generation have been evaluated. The Nusselt number has a higher value for the oval cross-section, while it reaches its lowest value for the square cross-section. The highest heat transfer rate is got for Design 1 with the oval-shaped case. The friction factor for a circular cross-section HHTE is 48 % higher than the friction factor for a square cross-section profile. In addition, the square shape at a Reynolds number (Re) of 25000 exhibits 5 % less entropy formation compared to the oval shape geometry at a Reynolds number of 5000. The results of MCDM analysis indicate that Design 1, which features a square section, exhibits superior performance. Conversely, Design 2, which incorporates a circular cross-section, demonstrates poor performance. Among the six ternary hybrid nanofluids, the Al 2 O 3 +CNT+Graphene nanofluid with a water basis exhibits the greatest Nusselt number., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding The authors affirm that they did not accept any outside funds, grants, or other kinds of assistance for writing this paper., (© 2024 The Authors.)

Microplastics (MPs) are an emerging global concern due to severe toxicological risks for ecosystems and public health. Therefore, this is the first study in Bangladesh to assess MP pollution and its associated risks for ecosystems and human health in the outdoor urban environment using machine learning and multivariate approaches. The occurrences of MPs in the urban road dust were 52.76 ± 20.24 particles/g with high diversity, where fiber shape (77%), 0.1-0.5 mm size MPs (75%), blue color (26%), and low-density polyethylene (24%) polymer was the dominating MPs category. Pollution load index value (1.28-4.42), showed severe pollution by MPs. Additionally, the contamination factor (1.00-5.02), and Nemerow pollution index (1.38-5.02), indicate moderate to severe MP pollution. The identified polymers based on calculated potential ecological risk (2248.52 ± 1792.79) and polymer hazard index (814.04 ± 346.15) showed very high and high risks, respectively. The occurrences of MPs could effectively be predicted by random forest, and support random vector machine, where EC, salinity, pH, OC, and texture classes were the influencing parameters. Considering the human health aspect, children and adults could be acutely exposed to 19259.68 and 5777.90 MP particles/ year via oral ingestion. Monte-Carlo-based polymers associated cancer risk assessment results indicate moderate risk and high risk for adults and children, respectively, where children were more vulnerable than adults for MP pollution risks. Overall assessment mentioned that Dhaka was the most polluted division among the other divisions., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Importance: Affordable and accessible tests for COVID-19 allow for timely disease treatment and pandemic management. SalivaDirect is a faster and easier method to implement than NPS sampling. Patients can self-collect saliva samples at home or in other non-clinical settings without the help of a healthcare professional. Sample processing in SalivaDirect is less complex and more adaptable than in conventional nucleic acid extraction methods. We found that SalivaDirect has good diagnostic performance and is ideal for large-scale testing in settings where supplies may be limited or trained healthcare professionals are unavailable., Competing Interests: The authors declare no conflict of interest.

Rotating cylinder movement in a cavity flow is an exciting field of study in heat transfer. Considerable research has been carried out on rotating cylinders under MHD mixed convection in various types of enclosures. However, considering partially heated square enclosure and magnetic field using CNT-water nanofluid is very limited. This study's goal is to assess the hydrothermal phenomena in a square enclosure with a rotating cylinder. Simulation has been conducted for different rotational speeds (Ω) and dimensionless times (τ) to observe the thermal and fluid flow behaviour. The Galerkin Residual based finite element method has been used to conduct numerical calculations. The results are shown as isotherms, streamlines, and average Nusselt number at the cylinder wall. Moreover, the drag force at the moving wall, and the fluid properties such as the root mean square (rms) of velocity, the temperature, the vorticity functions, and the average fluid temperature are also presented. The heat transfer rate, drag force, rms velocity, and temperature increase with the rise of rotational speed and dimensionless time rise. Maximum vorticity occurs at Ω = 8 and τ = 1. The maximum vorticity function increases 12 times with the increasing rotational speed. Higher rotational speed leads to increased average fluid temperature. The case of Ω = 8, τ = 1 shows the most temperature variance, while Ω = 1, τ = 0.1 has the least. Increasing rotational speed results in higher drag force on the cylinder's surface. At Ω = 4, the drag force is 2.8 times greater than at Ω = 2. Overall, the fluid flow and thermal performance boost up while the rotating speed of the cylinder is higher., Competing Interests: The authors declared that they have no Conflict interests. Data sharing not applicable to this article as no datasets were generated or analyzed during the current study., (© 2023 The Authors.)

The study examines the effect of rural women's participation in social media entrepreneurship on sustainable development in Bangladesh. The study collects 376 responses from the entrepreneurs of online clothing shops employing the simple random sampling technique. The data analysis was conducted using the PLS-SEM technique using Smart PLS 3.3v software. Developing the quantitative research design, the authors test the conceptual model by employing the deductive approach. The study finds a positive effect of women's participation in social media entrepreneurship on sustainable development within online clothing shops in Bangladesh. Bangladesh is capable of creating employment opportunities for rural women through ICT project offerings. Consistently this study also proves social media entrepreneurship increases women's contribution to their family income. Furthermore, this study finds that rural women's family income contribution positively affects sustainable development in Bangladesh. The study can help to achieve SDG 5: Gender Equality and Vision 2041 of Bangladesh at the earliest. Thus, the government, policymakers, and academics can use the study findings as a policy dialogue., Competing Interests: The author declares that there is no competing interest., (© 2023 The Authors.)

This study identifies the key factors accelerating the eco-innovation initiatives of manufacturing Small & Medium Enterprises (SMEs) in Bangladesh. The study further attempted to measure the impact of eco-innovation on a firm's economic, environmental, and societal performance, broadly called sustainability. Following the convenience sampling technique, data was collected from 876 respondents through a structured questionnaire considering the 7-point Likert scale. The Structural Equation Model (PLS-SEM) has been employed using Smart PLS v3.3 software to simultaneously show the constructs' relationships. Moreover, the study incorporated several tests to check the reliability and validity of the data. Furthermore, the degree to which data collected for this study fits well with the model has been tested by calculating model fit indices- NFI and SRMR. The findings reveal that all other factors Technological Capabilities (TC), Environmental Regulations (ER), Green products (GP), Competitive Pressure (CP), and Energy Price (EP) has a significant positive association with the firm's Eco-innovation initiatives except FR. Moreover, Eco-innovation (EI) greatly enhances the two domains of Sustainability- Environmental Performance (EnP) and Societal Performance (SoP) in manufacturing SMEs in Bangladesh. However, the relationship between EI and EcP was found to be insignificant. Apart from Economic Performance (EcP) andEnvironmental Performance(EnP), Societal Performance (SoP) as a sustainability domain remains unattended in the prior study, which has been incorporated in this research. Thus, the findings of this study will provide some unique implications for business managers and policymakers., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

This study examines the antecedents and effects of creative accounting practices (CAP) on organizational outcomes in Bangladesh. Thus, this research recognizes the antecedents of creative accounting, such as sustainable financial data (SFD), political connections (PC), corporate ethical values (CEV), future company orientations (FCO), and corporate governance practices (CGP). And also examine how the quality of financial reporting (QFR) and decision-making effectiveness (DME) are influenced by CAP. This study incorporates these fundamental antecedents of creative accounting practices on organizational outcomes by collecting survey data (n = 354) from publicly traded companies in the Dhaka Stock Exchange (DSE), Bangladesh. The study model has been tested through the "Partial Least Square- Structural Equation Modelling" (PLS-SEM) technique using Smart PLS v3.3 software. In addition, we pass various model fit measures, i.e., reliability, validity, factor analysis, and goodness of fit. This study finds that SFD does not work as an antecedent of creative accounting practices. But, the outcomes of the PLS-SEM confirm that PC, CEV, CFO and CGP work as an antecedent of CAP. Furthermore, the results of PLS-SEM also confirm that CAP influence the QFR in the positive direction and CAP influence the DME in the negative direction. Finally, QFR has a positive and significant impact on DME. Any study that has tested the impact of CAP on QFR & DME is yet to be found in the literature. However, policymakers, accounting bodies, regulators, and investors can consider these findings to formulate policy and investment decisions. Mainly, organizations can focus on PC, CEV, CFO and CGP to reduce CAP. But organizations need QFR and DME, which are critical components of organizational outcomes., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

Background Bangladesh saw its most severe dengue outbreak in 2019, with the highest number of deaths reported. This study investigated the clinical characteristics, severity spectrum, and potential outcomes of dengue in patients admitted to a tertiary care institution in Dhaka. Methods This prospective observational study was done between May 2019 to April 2020. A total of 478 nonstructural protein 1 (NS1) antigen positive confirmed dengue cases were finally enrolled. The dengue patient's stratification and severity grading were performed according to the World Health Organization (WHO) dengue guidelines, for diagnosis, treatment, prevention, and control (2009). In addition, in-hospital outcomes were assessed in terms of mortality. Results The patient's mean age was 33.90±15.82 (SD) years. The male-to-female ratio was 1.23:1. In addition to fever, the most common symptoms were myalgia (67.78%) and rashes (58.58%). According to WHO classification 33.90% of patients (n=162) were in group A, 49.40% (n=236) were in group B, and 16.70% (n=80) were in group C. The overall mortality was 1.23% in groups A, 2.97% in group B, and 11.25% in group C. The mortality was higher in the more severe group with statistical significance (p<0.001). The mean hospital stay time was significantly less in the surviving group (survival vs. death: 3.07±1.78 vs. 5.61±3.13 SD {days}, p<0.001). Leukopenia and thrombocytopenia were commonly seen in all of the severity groups. Conclusion Dengue epidemics are increasing in Bangladesh. Many group B and C cases are fatal. To reduce mortality and morbidity, health care providers must remain alert. This challenge requires public health interventions and hospital readiness., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2022, Islam et al.)

In this materialistic era, a substantial number of people are likely to have their meals outside of their homes and largely depend on the restaurants' made food due to the prolonged working hours and tremendous pressure in workplace. Consequently, consumers expose themselves to risk and become vulnerable to illness caused by food. Unsafe food preparation and handling by restaurants' workers have made food safety concern for public health. The study was aimed to examine the relationships among food safety knowledge, attitude, and Hazard Analysis Critical Control Point (HACCP) practices in restaurant employees in Bangladesh. A total of 360 employees from 120 restaurants participated in a face-to-face interview through a structured questionnaire comprising four sections: demographic characteristics, food safety knowledge, attitudes and practices. The mean scores for each survey item were calculated and used in Structural Equation Modeling (SEM), designed to assess interrelationships among the three sections related to food safety. Participants obtained a correct average score of 53.32% in food safety knowledge, with the highest and lowest correct scores in Good Hygiene Practices (GHPs) and HACCP practices, respectively. The highest score in the attitude section belonged to "self-improvement", followed by "food safety concern". A negative correlation was observed between knowledge with practices, knowledge with attitudes, and a positive correlation was observed between practices with attitudes. A significant positive correlation was observed between HACCP practices with shelf improvement (r = 0.54, p < 0.05) and the knowledge toward food poisoning with GHP practices (r = 0.55, p < 0.05). Self-improvement and food safety concerns are negatively correlated with food poisoning, GHP, and HACCP practice. This study demonstrated that restaurant employees in Bangladesh often lack knowledge regarding food safety and HACCP. So, in order to improve knowledge and attitude on safe HACCP practice among the restaurants employees', proper education and interactive training sessions can be conducted., Competing Interests: The authors have declared no competing interest exist., (Copyright: © 2022 Hasan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The rice leaves related diseases often pose threats to the sustainable production of rice affecting many farmers around the world. Early diagnosis and appropriate remedy of the rice leaf infection is crucial in facilitating healthy growth of the rice plants to ensure adequate supply and food security to the rapidly increasing population. Therefore, machine-driven disease diagnosis systems could mitigate the limitations of the conventional methods for leaf disease diagnosis techniques that is often time-consuming, inaccurate, and expensive. Nowadays, computer-assisted rice leaf disease diagnosis systems are becoming very popular. However, several limitations ranging from strong image backgrounds, vague symptoms' edge, dissimilarity in the image capturing weather, lack of real field rice leaf image data, variation in symptoms from the same infection, multiple infections producing similar symptoms, and lack of efficient real-time system mar the efficacy of the system and its usage. To mitigate the aforesaid problems, a faster region-based convolutional neural network (Faster R-CNN) was employed for the real-time detection of rice leaf diseases in the present research. The Faster R-CNN algorithm introduces advanced RPN architecture that addresses the object location very precisely to generate candidate regions. The robustness of the Faster R-CNN model is enhanced by training the model with publicly available online and own real-field rice leaf datasets. The proposed deep-learning-based approach was observed to be effective in the automatic diagnosis of three discriminative rice leaf diseases including rice blast, brown spot, and hispa with an accuracy of 98.09%, 98.85%, and 99.17% respectively. Moreover, the model was able to identify a healthy rice leaf with an accuracy of 99.25%. The results obtained herein demonstrated that the Faster R-CNN model offers a high-performing rice leaf infection identification system that could diagnose the most common rice diseases more precisely in real-time., Competing Interests: The authors declare that they have no competing interests., (© 2021 Bari et al.)

Brain-computer interface (BCI) is a viable alternative communication strategy for patients of neurological disorders as it facilitates the translation of human intent into device commands. The performance of BCIs primarily depends on the efficacy of the feature extraction and feature selection techniques, as well as the classification algorithms employed. More often than not, high dimensional feature set contains redundant features that may degrade a given classifier's performance. In the present investigation, an ensemble learning-based classification algorithm, namely random subspace k -nearest neighbour ( k -NN) has been proposed to classify the motor imagery (MI) data. The common spatial pattern (CSP) has been applied to extract the features from the MI response, and the effectiveness of random forest (RF)-based feature selection algorithm has also been investigated. In order to evaluate the efficacy of the proposed method, an experimental study has been implemented using four publicly available MI dataset (BCI Competition III dataset 1 (data-1), dataset IIIA (data-2), dataset IVA (data-3) and BCI Competition IV dataset II (data-4)). It was shown that the ensemble-based random subspace k -NN approach achieved the superior classification accuracy (CA) of 99.21%, 93.19%, 93.57% and 90.32% for data-1, data-2, data-3 and data-4, respectively against other models evaluated, namely linear discriminant analysis, support vector machine, random forest, Naïve Bayes and the conventional k -NN. In comparison with other classification approaches reported in the recent studies, the proposed method enhanced the accuracy by 2.09% for data-1, 1.29% for data-2, 4.95% for data-3 and 5.71% for data-4, respectively. Moreover, it is worth highlighting that the RF feature selection technique employed in the present study was able to significantly reduce the feature dimension without compromising the overall CA. The outcome from the present study implies that the proposed method may significantly enhance the accuracy of MI data classification., Competing Interests: The authors declare that they have no competing interests., (© 2021 Rashid et al.)

Background In the present study, we investigated the antibacterial, anthelmintic, and analgesic activities of methanol extract of P. sylvaticum leaves (MEPSL) in experimental models. Then, computational analysis (in silico molecular docking and PASS prediction) was performed to determine the potent phytoconstituents of total six isolated compounds of this plant for antibacterial and anthelmintic activities. Methods Qualitative and quantitative phytochemical studies were carried out by established methods. In vitro antibacterial activity was determined by disc diffusion technique and anthelmintic activity was tested against Tubifex tubifex worm whereas analgesic activity was determined by the acetic acid-induced writhing test in mice. Molecular docking study was performed using Schrödinger Maestro 10.1 and an online tool used for PASS prediction. Results Our phytochemical study revealed the presence of alkaloids, flavonoids, saponins, and also indicated a substantial amount of phenols (65.83 mg), flavonoids (102.56 mg), and condensed tannins (89.32 mg). MEPSL showed good antibacterial activity against both gram-positive and gram-negative bacteria. Our result exhibited that MEPSL has strong anthelmintic action compared to standard levamisole. In addition, the extract also showed a dose-dependent and statistically significant analgesic activity at the doses of 200 and 400 mg/kg, body weight. Docking studies showed that piperine and piperlonguminine have the best scores for the tested enzymes. PASS predicted the antibacterial and anthelmintic activity of both phytoconstituents. Conclusions This study suggests that MEPSL possess significant antibacterial, anthelmintic, and analgesic activities which could be related to the presence of several phytochemicals. The phytoconstituents, i.e. piperine and piperlonguminine were found to be most effective in computational studies.