Application of machine learning and multivariate approaches for assessing microplastic pollution and its associated risks in the urban outdoor environment of Bangladesh.

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.

SalivaDirect: an alternative to a conventional RNA extraction protocol for molecular detection of SARS-CoV-2 in a clinical setting.

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.

Rotational effect of a cylinder on hydro-thermal characteristics in a partially heated square enclosure using CNT-water nanofluid.

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.

The effect of social media entrepreneurship on sustainable development: Evidence from online clothing shops in Bangladesh.

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.

Determinants of eco-innovation initiatives toward sustainability in manufacturing SMEs: Evidence from Bangladesh.

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.

Antecedents and effect of creative accounting practices on organizational outcomes: Evidence from Bangladesh.

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.

Clinical Profile, Severity Spectrum, and Hospital Outcome of Dengue Patients in a Tertiary Care Hospital in Dhaka City.

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.

Structural modeling to understand the relationship among food safety knowledge, attitude, and self-reported HACCP practices in restaurant employees in Bangladesh.

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.

A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework.

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.

The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN.

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.

Antibacterial, anthelmintic, and analgesic activities of Piper sylvaticum (Roxb.) leaves and in silico molecular docking and PASS prediction studies of its isolated compounds.

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.