While vaccines ensure individual protection against COVID-19 infection, delay in receipt or refusal of vaccines will have both individual and community impacts. The behavioral factors of vaccine ...hesitancy or refusal are a crucial dimension that need to be understood in order to design appropriate interventions. The aim of this study was to explore the behavioral determinants of COVID-19 vaccine acceptance and to provide recommendations to increase the acceptance and uptake of COVID-19 vaccines in Bangladesh. We employed a Barrier Analysis (BA) approach to examine twelve potential behavioral determinants (drawn from the Health Belief Model HBM and Theory of Reasoned Action TRA) of intended vaccine acceptance. We conducted 45 interviews with those who intended to take the vaccine (Acceptors) and another 45 interviews with those who did not have that intention (Non-acceptors). We performed data analysis to find statistically significant differences and to identify which beliefs were most highly associated with acceptance and non-acceptance with COVID-19 vaccines. The behavioral determinants associated with COVID-19 vaccine acceptance in Dhaka included perceived social norms, perceived safety of COVID-19 vaccines and trust in them, perceived risk/susceptibility, perceived self-efficacy, perceived positive and negative consequences, perceived action efficacy, perceived severity of COVID-19, access, and perceived divine will. In line with the HBM, beliefs about the disease itself were highly predictive of vaccine acceptance, and some of the strongest statistically-significant (p<0.001) predictors of vaccine acceptance in this population are beliefs around both injunctive and descriptive social norms. Specifically, Acceptors were 3.2 times more likely to say they would be very likely to get a COVID-19 vaccine if a doctor or nurse recommended it, twice as likely to say that most people they know will get a vaccine, and 1.3 times more likely to say that most close family and friends will get a vaccine. The perceived safety of vaccines was found to be important since Non-acceptors were 1.8 times more likely to say that COVID-19 vaccines are "not safe at all". Beliefs about one's risk of getting COVID-19 disease and the severity of it were predictive of being a vaccine acceptor: Acceptors were 1.4 times more likely to say that it was very likely that someone in their household would get COVID-19, 1.3 times more likely to say that they were very concerned about getting COVID-19, and 1.3 times more likely to say that it would be very serious if someone in their household contracted COVID-19. Other responses of Acceptors on what makes immunization easier may be helpful in programming to boost acceptance, such as providing vaccination through government health facilities, schools, and kiosks, and having vaccinators maintain proper COVID-19 health and safety protocols. An effective behavior change strategy for COVID-19 vaccines uptake will need to address multiple beliefs and behavioral determinants, reducing barriers and leveraging enablers identified in this study. National plans for promoting COVID-19 vaccination should address the barriers, enablers, and behavioral determinants found in this study in order to maximize the impact on COVID-19 vaccination acceptance.
Five endophytic bacterial isolates were studied to identify morphologically and biochemically, according to established protocols and further confirmed by 16S rDNA Sanger sequencing, as Priestia ...megaterium, Staphylococcus caprae, Neobacillus drentensis, Micrococcus yunnanensis, and Sphingomonas paucimobiliz, which were then tested for phytohormone, ammonia, and hydrolytic enzyme production. Antioxidant compounds total phenolic content (TPC), and total flavonoid content (TFC) were assessed by using bacterial crude extracts obtained from 24-hour shake-flask culture. Phylogenetic tree analysis of those identified isolates shared sequence similarities with the members of Bacillus, Micrococcus, Staphylococcus, and Pseudomonas species, and after GenBank submission, accession numbers for the nucleotide sequences were found to be MW494406, MW494408, MW494401, MW494402, and MZ021340, respectively. In silico analysis was performed to identify their bioactive genes and compounds in the context of bioactive secondary metabolite production with medicinal value, where nine significant bioactive compounds according to six different types of bioactive secondary metabolites were identified, and their structures, gene associations, and protein-protein networks were analyzed by different computational tools and servers, which were reported earlier with their antimicrobial, anti-infective, antioxidant, and anti-cancer capabilities. These compounds were then docked to the 3-chymotrypsin-like protease (3CL.sup.pro) of the novel SARS-COV-2. Docking scores were then compared with 3CL.sup.pro reference inhibitor (lopinavir), and docked compounds were further subjected to ADMET and drug-likeness analyses. Ligand-protein interactions showed that two compounds (microansamycin and aureusimine) interacted favorably with coronavirus 3CL.sup.pro . Besides, in silico analysis, we also performed NMR for metabolite detection whereas three metabolites (microansamycin, aureusimine, and stenothricin) were confirmed from the 1H NMR profiles. As a consequence, the metabolites found from NMR data aligned with our in-silico analysis that carries a significant outcome of this research. Finally, Endophytic bacteria collected from medicinal plants can provide new leading bioactive compounds against target proteins of SARS-COV-2, which could be an effective approach to accelerate drug innovation and development.
Various MRI techniques, including myelin water imaging, T1w/T2w ratio mapping and diffusion-based imaging can be used to characterize tissue microstructure. However, surprisingly few studies have ...examined the degree to which these MRI measures are related within and between various brain regions. Therefore, whole-brain MRI scans were acquired from 31 neurologically-healthy participants to empirically measure and compare myelin water fraction (MWF), T1w/T2w ratio, fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) in 25 bilateral (10 grey matter; 15 white matter) regions-of-interest (ROIs). Except for RD vs. T1w/T2w, MD vs. T1w/T2w, moderately significant to highly significant correlations (p < 0.001) were found between each of the other measures across all 25 brain structures T1w/T2w vs. MWF (Pearson r = 0.33, Spearman ρ = 0.31), FA vs. MWF (r = 0.73, ρ = 0.75), FA vs. T1w/T2w (r = 0.25, ρ = 0.22), MD vs. AD (r = 0.57, ρ = 0.58), MD vs. RD (r = 0.64, ρ = 0.61), AD vs. MWF (r = 0.43, ρ = 0.36), RD vs. MWF (r = -0.49, ρ = -0.62), MD vs. MWF (r = -0.22, ρ = -0.29), RD vs. FA (r = -0.62, ρ = -0.75) and MD vs. FA (r = -0.22, ρ = -0.18). However, while all six MRI measures were correlated with each other across all structures, there were large intra-ROI and inter-ROI differences (i.e., with no one measure consistently producing the highest or lowest values). This suggests that each quantitative MRI measure provides unique, and potentially complimentary, information about underlying brain tissues - with each metric offering unique sensitivity/specificity tradeoffs to different microstructural properties (e.g., myelin content, tissue density, etc.).
This work is concerned with the design and photovoltaic performance study of organic semiconducting materials-based solar cells using a SCAPS simulator. Organic solar cells are one form that absorbs ...light and produces electricity. They exhibit high power conversion efficiency, low manufacturing costs, lightweight, and flexibility which makes them a promising technology for renewable energy. We developed and designed organic solar cell devices with the structure ITO/PEDOT: PSS/P3HT: PCBM/PFN-Br/Al for bulk-heterojunction structure and ITO/PEDOT: PSS/P3HT: PCBM/PFN-Br/Al for bilayer structure where PEDOT: PSS and PFN-Br were used as hole and electron transporting layers (HTL and ETL) respectively. The short circuit current density versus voltage (J-V) characteristics as well as photovoltaic parameters namely open circuit voltage (VOC), short circuit current density (JSC), fill factor (FF), and power conversion efficiency (PCE) have been examined. In addition, the quantum efficiency (QE) of the two structures has been also studied. The effectiveness of the photovoltaic simulated systems has also been attempted to be enhanced by varying the absorber layer's thickness for both architectures. Later, this turned out that variations in the band gap of the absorber layer had an impact on the factors that determined how well solar cells performed. It has been found that the simulated bulk-heterojunction device exhibits 20.43% PCE whereas 3.52% PCE has been calculated from the simulated bilayer device.
Cancer has been found as a heterogeneous disease with various subtypes and aims to destroy the body’s normal cells abruptly. As a result, it is essential to detect and prognosis the distinct type of ...cancer since they may help cancer survivors with treatment in the early stage. It must also divide cancer patients into high- and low-risk groups. While realizing efficient detection of cancer is frequently a time-taking and exhausting task with the high possibility of pathologist errors and previous studies employed data mining and machine learning (ML) techniques to identify cancer, these strategies rely on handcrafted feature extraction techniques that result in incorrect classification. On the contrary, deep learning (DL) is robust in feature extraction and has recently been widely used for classification and detection purposes. This research implemented a novel hybrid AlexNet-gated recurrent unit (AlexNet-GRU) model for the lymph node (LN) breast cancer detection and classification. We have used a well-known Kaggle (PCam) data set to classify LN cancer samples. This study is tested and compared among three models: convolutional neural network GRU (CNN-GRU), CNN long short-term memory (CNN-LSTM), and the proposed AlexNet-GRU. The experimental results indicated that the performance metrics accuracy, precision, sensitivity, and specificity (99.50%, 98.10%, 98.90%, and 97.50) of the proposed model can reduce the pathologist errors that occur during the diagnosis process of incorrect classification and significantly better performance than CNN-GRU and CNN-LSTM models. The proposed model is compared with other recent ML/DL algorithms to analyze the model’s efficiency, which reveals that the proposed AlexNet-GRU model is computationally efficient. Also, the proposed model presents its superiority over state-of-the-art methods for LN breast cancer detection and classification.
In Bangladesh, there were 1073 lightning-related deaths reported only in May from 2010 to 2021. This figure accounts for 34% of the total lightning-related deaths in the country. A strong sea-level ...pressure ridge from the north-west and a 500 hPa geo-potential height ridge spanning across north-west to south-east Bangladesh favour frequent pre-monsoon lightning. The elongated low-pressure trough over the Gangetic plains of India towards Bangladesh is a very unique geo-characteristic for convective activity in May. Heightened pre-monsoon lightning activity is also due to a very strong temperature anomaly coupled with an associated convective precipitation system that is triggered by topographic forces from the Shillong Plateau and the Chittagong Hill Tracts. The southerly to south-westerly low-level jet assists moisture transport from the Bay of Bengal in the pre-monsoon. Further, the north to north-westerly subtropical jet stream provides conditions that are conducive to the development of frequent pre-monsoon lightning activity. Moreover, convective available potential energy (CAPE) all over the country in May destabilises the country’s atmosphere with numerous thunderstorms. Precise information of the pre-monsoon climatological anomaly and the associated atmospheric stability indices can be beneficial for the management of lightning-related deaths in Bangladesh.
•Methane cracking requires an optimum temperature range of 550–600°C for H2 yield.•At 550 and 600°C, catalyst showed longer activity for the whole test.•At 600°C, a 614.25gc/gNi of carbon was ...obtained using 30% Ni/Y zeolite catalysts.•Produced filamentous carbon has the same diameter as the metallic nickel itself.•VHSV has reverse and non-linear relevancy to the weight of Ni/Y zeolite catalyst.
The objective of this paper is to study the influences of different operating conditions on the hydrogen formation and properties of accumulated carbon from methane decomposition using zeolite Y supported 15% and 30% Ni, respectively, at a temperature range between 500 and 650°C in a pilot scale fixed bed reactor. The temperature ramp was showed a significant impact on the thermo-catalytic decomposition (TCD) of methane. An optimum temperature range of 550–600°C were required to attain the maximum amount of methane conversion and revealed that at 550 and 600°C, catalyst showed longer activity for the whole studied of experimental runs. Additionally, at 550°C, the methane decomposition is two times longer for 30% Ni/Y zeolite than that for 15% Ni/Y zeolite catalyst, whereas it is almost three times higher at 500°C. A maximum carbon yield of 614.25 and 157.54gc/gNi were reported after end of the complete reaction at 600°C with 30% and 15% Ni/Y zeolite catalyst, respectively. From BET, TPD, and XRD analysis, we had reported that how the chemistry between the TCD of methane and metal content of the catalysts could significantly affect the hydrogen production as well as carbon nano-fibers. TEM analysis ensured that the produced carbon had fishbone type structures with a hollow core and grew from crystallites of Ni anchored on the external surface of the catalysts and irrespective of the metal loadings, the whisker types of nano filaments were formed as confirmed from FESEM analysis. Nevertheless, the effect of volume hourly space velocity (VHSV) on the methane conversion was also investigated and reported that the methane conversion increased as VHSV and nickel concentration in Ni–Y catalysts increased. Additionally, the initial methane decomposition rate increases with VHSV and it has reverse and non-linear relevancy to the weight of Ni/Y zeolite catalyst.
This study quantifies the impact of training vegetable farmers in integrated pest management (IPM) in Bangladesh. Data come from a random sample of 300 trained and 300 non-trained farmers producing ...either bitter gourd (Momordica charantia L.) or eggplant (Solanum melongena L.). Propensity score matching and inverse probability weighting was employed to correct for selection bias in observable characteristics. A range of outcome indicators along the impact pathway was used. The study finds that trained farmers had better knowledge about insect pests and the proper use of pesticides, adopted more IPM practices, and reduced the frequency of spraying and mixing different pesticides. For eggplant, but not for bitter gourd, trained farmers reduced the quantity of pesticide use and achieved a significantly higher crop yield and gross margin. The effect on consumptive expenditures, which we used as a proxy of income, was insignificant. We conclude that further promotion of IPM adoption among farmers is needed and that it should be a priority to increase the profitability of IPM practices for gradual reduction in synthetic pesticide misuse and a sustainable agricultural production.
•This study evaluates the impact of vegetable integrated pest management in Bangladesh.•Propensity score matching is used on a sample of 600 eggplant and bitter gourd farmers.•Trained farmers adopted more IPM practices and sprayed pesticides less often.•Trained eggplant farmers, but not bitter gourd farmers, had a higher yield and gross margin.•The effect on household consumptive expenditures was insignificant for both crops.
Integrated home garden interventions combine training in gardening practices with education about nutrition knowledge. Such interventions have been shown to improve nutrition behaviour in low income ...countries. However, to date rigorous evidence is lacking for their long-term impact. We test the impact of an integrated home garden intervention on vegetable production and consumption three years after the intervention ended. We analyse three rounds of survey data for 224 control and 395 intervention households in rural Bangladesh. Three years after the intervention, the average impact on vegetable production per household was 43 kg/year (+ 49% over baseline levels;
p
< 0.01), and the effect was not statistically different from the impact one year after the intervention, which demonstrates that impact was maintained in the long-term. The impact on the micronutrient supply for iron, zinc, folate and pro-vitamin A from home gardens was maintained in the long-term. These impacts may have been driven by the long-term improvements in women’s nutrition knowledge and gardening practices, explaining the sustainability of the behavioural nutrition change. We also identify positive impacts on women’s empowerment and women’s output market participation, highlighting how integrated programs, even if modest in scope, can be drivers of social change.
This paper aims to compare the educational attainment of a conflict region (the Deep South) and a non-conflict region (the rest of the South) of Thailand using the Socio-Economic Survey, 2015. This ...paper employs the Instrumental Variable approach and Oaxaca-Blinder decomposition in an intergenerational regression model. When controlling parental schooling, household income and size, religion, and gender, the results show that children from the Deep South obtain almost one year less schooling than children from the rest of the Southern region. Interestingly, Muslims are ahead in terms of educational attainment when compared to non-Muslims in the non-conflict region, but not in the conflict region. Females outperform males in both regions, but the coefficient of female dummy is higher in the non-conflict region. Moreover, the rate of intergenerational transmission of educational attainment is higher in the Deep South compared to that in the rest of the southern region, which may lead to long-term educational inequality in the Deep South region. The Oaxaca-Blinder decomposition confirms that the 40% schooling gap between these two regions is unexplained but might be due to the chronic social unrest. The findings of this paper show that customized educational reforms and policies to resolve the conflict in the Deep South of Thailand should be employed.
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