Understanding the interaction between in-home and out-of-home activity participation decisions is important, particularly at a time when opportunities for out-of-home activities such as shopping, ...entertainment, and so forth are limited because of the COVID-19 pandemic. The travel restrictions imposed as a result of the pandemic have had a massive impact on out-of-home activities and have changed in-home activities as well. This study investigates in-home and out-of-home activity participation during the COVID-19 pandemic. Data comes from the COVID-19 Survey for assessing Travel impact (COST), conducted from March to May in 2020. This study uses data for the Okanagan region of British Columbia, Canada to develop the following two models: a random parameter multinomial logit (RPMNL) model for out-of-home activity participation and a hazard-based random parameter duration (HRPD) model for in-home activity participation. The model results suggest that significant interactions exist between out-of-home and in-home activities. For example, a higher frequency of out-of-home work-related travel is more likely to result in a shorter duration of in-home work activities. Similarly, a longer duration of in-home leisure activities might yield a lower likelihood for recreational travel. Health care workers are more likely to engage in work-related travel and less likely to participate in personal and household maintenance activities at home. The model confirms heterogeneity among the individuals. For instance, a shorter duration of in-home online shopping yields a higher probability for participation in out-of-home shopping activity. This variable shows significant heterogeneity with a large standard deviation, which reveals that sizable variation exists for this variable.
•A novel route of strength biocomposite production from waste biomass has developed.•CS-g-HEMA holds well-organized honeycomb like surface microstructure proved by SEM.•Maximum grafting yield & ...efficiency have found around 73.33 ± 0.03 % & 48.24 ± 0.01 %.•The grafted product revealed outstanding thermal stability up to 650oC proved by TGA.•CS-g-HEMA showed 4 times higher value of TS than non-grafted CS (20.86 to 80 MPa).
Shrimp shell is the most significant chitin source among bio-based materials since a huge quantity of crustacean wastes has been expelled out as a waste material from various shrimp processing industries/zones. The carapaces were treated by 2 M HCl for demineralization (DM), 2 N NaOH for deproteinization (DP), and 50 % NaOH for deacetylation (DA) to extract chitosan, without destroying the inherent microstructure of native shells. Then grafting was carried out by 2‑hydroxy ethyl methacrylate (HEMA) by using a redox initiator K2S2O8/FeSO4 to produce environmentally friendly strength bio-composite. Meanwhile, maximum yield was found when the concentration of monomer and initiator were 2.55 × 10−1 mol/L, 8.48 × 10−3 mol/L, at 70°C whereas the value has been recorded around 73.33±0.03 %. Besides the honeycomb microstructure of chitosan could enhance the overall properties of the fabricated biocomposites by using mechanical interlocking mechanism during compounding. The samples were characterized by fourier transform infrared (FTIR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), solid-state 13C cross-polarization magic-angle-spinning nuclear magnetic resonance (CPMAS-NMR) and tensile strength (TS) techniques. The microstructure and thermo-mechanical profile exhibited noteworthy changes on the bulk property of the CS-g-HEMA composites. Result suggested that the shrimp waste shell would be an attractive source of strength bio-composites that retains outstanding physicochemical, thermomechanical, morphological properties. Thus the grafted products would be significantly utilized in various engineering, and bulk scale industrial applications as a suitable replacement of fossil based synthetic ones for the sustainable environmental protection.
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During the 2019 novel coronavirus infectious disease (COVID-19) pandemic in 2020, limited data from several countries suggested reduced seasonal influenza viruses' circulation. This was due to ...community mitigation measures implemented to control the pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We used sentinel surveillance data to identify changes in the 2020 influenza season compared with previous seasons in Bangladesh.
We used hospital-based influenza surveillance (HBIS) data of Bangladesh that are generated year-round and are population-representative severe acute respiratory infection (SARI) data for all age groups from seven public and two private tertiary care level hospitals data from 2016 to 2019. We applied the moving epidemic method (MEM) by using R language (v4.0.3), and MEM web applications (v2.14) on influenza-positive rates of SARI cases collected weekly to estimate an average seasonal influenza curve and establish epidemic thresholds.
The 2016-2019 average season started on epi week 18 (95% CI: 15-25) and lasted 12.5 weeks (95% CI: 12-14 weeks) until week 30.5. The 2020 influenza season started on epi week 36 and ended at epi week 41, lasting for only five weeks. Therefore, influenza epidemic started 18 weeks later, was 7.5 weeks shorter, and was less intense than the average epidemic of the four previous years. The 2020 influenza season started on the same week when COVID-19 control measures were halted, and 13 weeks after the measures were relaxed.
Our findings suggest that seasonal influenza circulation in Bangladesh was delayed and less intense in 2020 than in previous years. Community mitigation measures may have contributed to this reduction of seasonal influenza transmission. These findings contribute to a limited but growing body of evidence that influenza seasons were altered globally in 2020.
Several coronavirus disease (COVID-19) vaccines have already been authorized and distributed in different countries all over the world, including Bangladesh. Understanding public acceptance of such a ...novel vaccine is vital, but little is known about the topic.
This study aimed to investigate the determinants of intention to receive a COVID-19 vaccine and willingness to pay (WTP) among people in Bangladesh.
An anonymous and online-based survey of Bangladeshi people (mean age = 29.96 ± 9.15 years; age range = 18-60 years) was conducted using a self-reported questionnaire consisting of socio-demographics, COVID-19 experience, and vaccination-related information as well as the health belief model (HBM). Multivariable logistic regression was performed to determine the factors influencing COVID-19 vaccination intent and WTP.
Of the 894 participants, 38.5% reported a definite intention to receive a COVID-19 vaccine, whereas 27% had a probable intention, and among this intent group, 42.8% wanted to get vaccinated as soon as possible. Older age, feeling optimistic about the effectiveness of COVID-19 vaccination, believing that vaccination decreases worries and risk of COVID-19 infection, and being less concerned about side effects and safety of COVID-19 vaccination under the HBM construct were found to be significant factors in COVID-19 vaccination intention. Most of the participants (72.9%) were willing to pay for a COVID-19 vaccine, with a median (interquartile range IQR) amount of BDT 400/US$ 4.72 (IQR; BDT 200-600/US$ 2.36-7.07) per dose. Factors associated with higher WTP were younger age, being male, having higher education, residing in an urban area, having good self-rated health status, positivity towards COVID-19 vaccination's effectiveness, and being worried about the likelihood of getting infected with COVID-19. Participants who were COVID-19 vaccination intent preferred an imported vaccine over a domestically-made vaccine (22.9% vs. 14.8%), while 28.2% preferred a routine immunization schedule.
The findings indicate a considerable proportion of Bangladeshi people intended to get vaccinated and had WTP for the COVID-19 vaccine. However, urgent education and awareness programs are warranted to alleviate public skepticism regarding the COVID-19 vaccination.
Due to the ease of access to the internet in modern society users have become more prone to experiencing addictive behaviors online. The present study aimed to develop and investigate the ...psychometric properties of the Bangla Internet Gaming Disorder Scale-Short-Form (IGDS9-SF), Gaming Disorder Test (GDT), and Bergen Social Media Addiction Scale (BSMAS) due to a lack of existing sound psychometric tools in Bangladesh.
A cross-sectional paper-and-pencil survey was carried out among 428 school-aged adolescents who were active gamers (90.89% males; Meanage: 16.13±1.85 years; age range: 10-19 years). Participants were recruited using convenience sampling across four selected schools in Dhaka City, Bangladesh. Data collected included sociodemographic information, frequency of internet use and gaming behaviors, psychological states (i.e., Patient Health Questionnaire PHQ-9, Generalized Anxiety Disorder GAD-7), disordered gaming and social media use (i.e., IGDS9-SF, GDT, and BSMAS). Psychometric testing was conducted to examine the validity and reliability levels of the Bangla IGDS9-SF, GDT, and BSMAS.
The newly adapted Bangla IGDS9-SF, GDT, and BSMAS exhibited adequate levels of internal consistency. All total scores were significantly correlated with depression, anxiety, frequencies of internet use/online activities and gaming, supporting criterion and convergent validity. CFA indicated excellent construct validity as all instruments had a good fit to the data.
The findings suggest that the Bangla IGDS9-SF, GDT, and BSMAS are sound psychometric instruments due to their satisfactory psychometric properties including internal consistency, criterion validity, convergent validity, and construct validity.
•A new & beneficial biocomposite from ACLF was prepared by grafting of HEMA monomer.•The order of tensile property of biopolymeric samples were RF<ATF<BF< HEMA-g-ACLF.•Fabricated HEMA-g-ACLF have ...shown outstanding thermal stability even after 600 °C.•Max. grafting yield and efficiency were found around 95.79% and 54.91% respectively.•While con. of monomer & initiator were 8.18 × 10−1 & 5.5 × 10−3 Mol/L at 70 °C for 120 m.
In this study a prominent technique namely grafting of 2‑hydroxy ethyl methacrylate (known as HEMA) monomer onto modified bleached Agave cantala leaf fibers (ACLF) to fabricate bio-composite by using K2S2O8/FeSO4 as a redox initiator in aqueous media to develop mechanical tensile strength and also for sustainable environmental performances. Various investigated parameters like reaction time, monomer concentration, initiator concentration, grafting temperature have been optimized successfully to obtain maximum grafting yield. Noteworthy that the grafting percentage onto modified fibres has shown an increasing tendency while monomer concentration increases from (0.084–0.82)Mol/L and initiator concentration from (0.005–0.0074)Mol/L and thereafter it started to decrease. Meanwhile, the rate of reaction actually depends upon the reaction time and temperature. Whereas maximum graft yield was obtained exactly at 70 °C within 2 h in addition the obtained values were around 87.25% and 95.79% respectively. The modified and grafted polymeric samples were characterized by FTIR spectroscopy, SEM analysis and TGA techniques. The effect of external influences such as temperature, exposure to sunlight and biodegradability in sewage water on raw, modified fibers and HEMA-g-ACLF composites were also studied. The tensile properties and water resistance characteristics of grafted products were far better than that of extracted raw and modified α-cellulosic fibers.
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► The emission of hazardous volatiles was investigated from back diffusion gases of automobiles. ► Pollutant emission patterns vary greatly between the different driving conditions. ► Back diffusion ...can be a significant source of in-vehicle pollution to passengers.
As back diffusion gases from automobiles are significant sources of in-vehicular pollution, we investigated eight automobiles, five for back diffusion (driving) measurements and three for reference conditions (non-driving). To characterize the back diffusion emission conditions, seven volatile organic compounds (VOC) and four carbonyl compounds (CCs) were measured along with dilution-to-threshold (D/T) ratio. The data obtained from back diffusion measurements were examined after having been divided into three subcategories: (i) driving and non-driving, (ii) with and without automobile upgrading (sealing the inner line), and (iii) differences in CO emission levels. Among the VOCs, the concentrations of toluene (T) was found to be the highest (range: 13.6–155ppb), while benzene (0.19–1.47ppb) was hardly distinguishable from its ambient levels. Other VOCs (xylene, trimethylbenzene, and styrene) were generally below <1ppb. Unlike VOCs, the concentrations (ppb) of CCs were seen at fairly enhanced levels: 30.1−95 (formaldehyde), 34.6−87.2 (acetaldehyde), 4.56−34.7 (propionaldehyde), and 3.45−68.8 (butyraldehyde). The results of our study suggest that the back diffusion phenomenon, if occurring, can deteriorate in-vehicle air, especially with the most imminent health hazards from a compound such as formaldehyde in view of its exceedance pattern over common guidelines.
Mental illness has grown to become a prevalent and global health concern that affects individuals across various demographics. Timely detection and accurate diagnosis of mental disorders are crucial ...for effective treatment and support as late diagnosis could result in suicidal, harmful behaviors and ultimately death. To this end, the present study introduces a novel pipeline for the analysis of facial expressions, leveraging both the AffectNet and 2013 Facial Emotion Recognition (FER) datasets. Consequently, this research goes beyond traditional diagnostic methods by contributing a system capable of generating a comprehensive mental disorder dataset and concurrently predicting mental disorders based on facial emotional cues. Particularly, we introduce a hybrid architecture for mental disorder detection leveraging the state-of-the-art object detection algorithm, YOLOv8 to detect and classify visual cues associated with specific mental disorders. To achieve accurate predictions, an integrated learning architecture based on the fusion of Convolution Neural Networks (CNNs) and Visual Transformer (ViT) models is developed to form an ensemble classifier that predicts the presence of mental illness (e.g., depression, anxiety, and other mental disorder). The overall accuracy is improved to about 81% using the proposed ensemble technique. To ensure transparency and interpretability, we integrate techniques such as Gradient-weighted Class Activation Mapping (Grad-CAM) and saliency maps to highlight the regions in the input image that significantly contribute to the model's predictions thus providing healthcare professionals with a clear understanding of the features influencing the system's decisions thereby enhancing trust and more informed diagnostic process.
Breast cancer is a global health issue and a leading cause of death among women. Early detection through increased awareness and knowledge on breast cancer and breast cancer screening is thus ...crucial. The aim of the present study was to assess the effect of an educational intervention program on breast cancer knowledge and the practice of breast self-examination among young female students of a university in Bangladesh.
A quasi-experimental (pre-post) study design was conducted at Jahangirnagar University in Bangladesh. Educational information on breast cancer and breast self-examination (BSE), demonstration of BSE procedure and leaflets were distributed among 400 female students after obtaining written informed consent. The stepwise procedures of BSE performance were demonstrated with images. Pre-intervention and 15 days post-intervention assessments were conducted to assess the changes in knowledge on breast cancer and practices of BSE. Mc-Nemar's tests and paired sampled t-tests were performed to investigate the differences between pre- and post-test stages.
A total of 400 female university students aged 18-26 years were included in the sample. Significant changes were found in knowledge and awareness about breast cancer and BSE practices after the educational intervention. The significant differences were measured in the mean scores of pre-test vs. post-test: breast cancer symptoms (2.99 ± 1.05 vs. 6.35 ± 1.15; p < 0.001), risk factors (3.35 ± 1.19 vs. 7.56 ± 1.04; p < 0.001), treatment (1.79 ± 0.90 vs. 4.63 ± 0.84; p < 0.001), prevention (3.82 ± 1.32 vs. 7.14 ± 1.03; p < 0.001), screening of breast cancer (1.82 ± 0.55 vs. 3.98 ± 0.71; p < 0.001) and process of BSE (1.57 ± 1.86 vs. 3.94 ± 0.93; p < 0.001). Likewise, a significant percentage of change in BSE practices was obtained between pre-test and post-test (21.3% vs. 33.8%; p < 0.001).
Study findings confirm that the study population had inadequate awareness and knowledge at baseline which was improved significantly after educational intervention. A nationwide roll-out with community-based interventions is recommended for the female population in both rural and urban areas.
Collisions involving public transit (PT) have increased significantly in the recent times. In developing countries, the scenario is much worse as the PT collision occurrence and injury severity are ...higher by manifold. This study investigates the injury severity of victims of PT involved crashes in Dhaka, Bangladesh. Victims include PT occupants and counterpart road users such as pedestrians who are more vulnerable. A latent segmentation-based ordered logit (LSOL) model is developed using police-reported crash records of Dhaka. The LSOL model addresses the ordinality of injury severity and captures unobserved heterogeneity by allocating victims into discrete latent segments. The LSOL model is estimated for two segments: segment two is high-risk segment which includes unconventional vehicle occupants and pedestrians, whereas segment one is low-risk segment. The injury severity model results suggest that person profile, environmental factors and built environment characteristics significantly influence injury severity. For example, female and elderly victims have higher likelihood to sustain severe injury. The model confirms the existence of significant heterogeneity. For instance, mid-block collisions are more likely to yield severe injury in the high-risk segment, and less likely to result in severe injury in the low-risk segment. Similar heterogeneity is confirmed for variables representing impaired driver, three-way intersection, and police-controlled intersection. The elasticity effect analysis suggests that female victims, younger and adult victims, unconventional vehicle occupants, pedestrians, and mid-block crashes have substantial positive impact on fatality. The identified influential factors and heterogeneity captured in the study need to be accommodated in road safety plans and policies.
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