Unleashing Modified Deep Learning Models in Efficient COVID19 Detection

The COVID19 pandemic, a unique and devastating respiratory disease outbreak, has affected global populations as the disease spreads rapidly. Recent Deep Learning breakthroughs may improve COVID19 prediction and forecasting as a tool of precise and fast detection, however, current methods are still being examined to achieve higher accuracy and precision. This study analyzed the collection contained 8055 CT image samples, 5427 of which were COVID cases and 2628 non COVID. The 9544 Xray samples included 4044 COVID patients and 5500 non COVID cases. The most accurate models are MobileNet V3 (97.872 percent), DenseNet201 (97.567 percent), and GoogleNet Inception V1 (97.643 percent). High accuracy indicates that these models can make many accurate predictions, as well as others, are also high for MobileNetV3 and DenseNet201. An extensive evaluation using accuracy, precision, and recall allows a comprehensive comparison to improve predictive models by combining loss optimization with scalable batch normalization in this study. Our analysis shows that these tactics improve model performance and resilience for advancing COVID19 prediction and detection and shows how Deep Learning can improve disease handling. The methods we suggest would strengthen healthcare systems, policymakers, and researchers to make educated decisions to reduce COVID19 and other contagious diseases. CCS CONCEPTS Covid,Deep Learning, Image Processing KEYWORDS Covid, Deep Learning, DenseNet201, MobileNet, ResNet, DenseNet, GoogleNet, Image Processing, Disease Detection

Mid storage seed hardening: a mechanical method to maintain seed viability during long term jute seed preservation

Seed plays an important role in agricultural sector for both production and consumption purpose. Availability of vigour seed is one of the major constraints for maximizing crop production. However, healthy seed can also lose its viability during seed storage by changing different physio-chemical properties. Influence of environmental factors and seed containers during storage leading to seed deterioration. In this research, mid storage seed hardening treatment was applied in different aged seeds of jute species (C. Capsularis & C. olitorius) with two types of storage bags. Seed hardening treatment showed the less moisture content with better germination percentage compared to the untreated species of jute seeds. Seed packing in polythene bags during both short and long term seed storages had higher viable seeds compared to the cloth packing seeds. The effect of seed hardening treatment on seed oil content and pattern of oil degradation is distinct in early period of storage. The faster rate of oil degradation, soluble protein and free amino acids was found in seeds of un-treated stored seeds in cloth bag. Contrary, very slow rate of oil degradation was observed in harden seed and stored in polythene bag which indicated better storability of harden seeds

Investigation on the mechanical properties of rubberized steel fiber concrete

Researchers investigated the utilization of crumb rubber aggregate recycled from waste tire in concrete to solve the problem of discarded tire and to produce a green sustainable concrete. However, a reduction in the mechanical properties due to crumb rubber inclusion occurs. Steel fiber rubberized concrete used in this study to provide a balance between the strength loss and sustainable issue. An investigation on the mechanical properties of rubberized concrete combined with hooked – end steel fiber is presented. Rubberized concrete with different replacement ratios of crumb rubber was incorporated in plain and steel fiber concrete mixes via partial replacement of fine aggregate. Four replacement ratios (17.5%, 20%, 22.5%, and 25%) were used to investigate the effect of the partial replacement of fine aggregate by crumb rubber on the mechanical properties of plain and steel fiber concrete. In both mixes, reduction in mechanical properties was observed to be proportionate with the increment of crumb rubber. Finally, a successful combination of steel fiber and crumb rubber was obtained due to improvement of strain capacity under flexural loading

Cumulative Effect of Crumb Rubber and Steel Fiber on the Flexural Toughness of Concrete

Concrete properties, such as toughness and ductility, are enhanced to resist different impacts or blast loads. Rubberized concrete, which could be considered a green material, is produced from recycled waste tires grinded into different crumb rubber particle sizes and mixed with concrete. In this study, the behavior of rubberized steel fiber-reinforced concrete is investigated. Flexural performance of concrete beams (40

Ethnomedicinal Value of Antidiabetic Plants in Bangladesh: A Comprehensive Review

The use of conventional drugs to treat metabolic disorders and the pathological consequences of diabetes further increases the complications because of the side effects, and is sometimes burdensome due to relatively higher costs and occasionally painful route of administration of these drugs. Therefore, shifting to herbal medicine may be more effective, economical, have fewer side effects and might have minimal toxicity. The present review amasses a list of ethnomedicinal plants of 143 species belonging to 61 families, from distinctive domestic survey literature, reported to have been used to treat diabetes by the ethnic and local people of Bangladesh. Leaves of the medicinal plants were found leading in terms of their use, followed by fruits, whole plants, roots, seeds, bark, stems, flowers, and rhizomes. This review provides starting information leading to the search for and use of indigenous botanical resources to discover bioactive compounds for novel hypoglycemic drug development

Prevalence and factors associated with dietary supplement use among Bangladeshi public university students: A cross-sectional study

Introduction: The usage of dietary supplement (DS) such as vitamins, minerals, and fish oil has expanded, but there is limited data on their use by sub-populations such as university students. The study was aimed to investigate the prevalence of DS use among Bangladeshi university students and its associated factors. Methods: A cross-sectional survey of 390 students was conducted from two public universities from Barishal Division in Bangladesh using a structured questionnaire with 72 questions divided into five sections: sociodemographic, knowledge, opinions, and attitudes, types of DS, reasons and sources for using DS, and adverse reactions after taking DS. Descriptive statistics and logistic regression were utilized to estimate the results. Results: Among all the students, 15.6% students were using DS where only 7.7% of them used DS according to physicians' recommendation. Additionally, students used DS for general health and well-being, weight gaining and as a source of energy for physical and sporting activities, etc. The use of DS was significantly associated with female sex (AOR = 5.44, 95% CI: 2.18-13.52), >= 25 years age (AOR = 0.08, 95% CI: 0.01-0.67), underweight (AOR = 5.86, 95% CI: 1.95-17.62), having major illness (AOR = 6.99, 95% CI: 1.98-24.70) and good knowledge of DS (AOR = 2.64, 95% CI: 1.23-5.64). Conclusion: This study provides new findings on DS use and its correlates in Bangladeshi students which may be used by the policymakers to improve DS usage among students. Adaptation of an appropriate program is recommended to educate students on proper and safer ways of using DS

Identifying Long-Term Deposit Customers : A Machine Learning Approach

Majority of the revenue from the banking sector is usually generated from long term deposits by customers. It is for banks to understand customer characteristics to increase product sales. To aid this, marketing strategies are employed to target potential customers and let them interact with the banks directly, generating a large amount of data on customer characteristics and demographics. In recent years, it has been discovered that various data analysis, feature selection and machine learning techniques can be employed to analyze customer characteristics as well as variables that can impact customer decision significantly. These methods can be used to identify consumers in different categories to predict whether a customer would subscribe to a long-term deposit, allowing the marketing strategy to be more successful. In this study, we have taken a R programming approach to analyze financial transaction data to gain insight into how business processes can be improved using data mining techniques to find interesting trends and make more data-driven decisions. We have used statistical analysis like Exploratory Data Analysis (EDA), Principal Component Analysis (PCA), Factor Analysis and Correlations in the given data set. Besides, the study's goal is to use at least three typical classification algorithms among Logistic Regression, Random Forest, Support Vector Machine and K-nearest neighbors, and then make predictive models around customers signing up for long term deposits. Where we have gotten best accuracy from Logistic Regression which is 90.64 % as well the sensitivity is 99.05 %. Results were analyzed using the accuracy, sensitivity, and specificity score of these algorithms.acceptedVersionPeer reviewe

In vivo anxiolytic and in vitro anti-inflammatory activities of water-soluble extract (WSE) of Nigella sativa (L.) seeds

The WSE is a highly polar, gummy and mucilaginous bioactive content of the Nigella sativa (L.) seeds. This study reports the anxiolytic and anti-inflammatory effects of WSE investigated using Elevated Plus Maze (EPM) and Hole-Board Test (HBT) in adult mice and human RBCs haemolysis inhibition and protein denaturation respectively. The oral WSE treatment (100 & 200 mg/kg b.w/day) for 72 hours has exhibited slightly better anxiolytic effect (p < 0.05) through the time span (92.33 & 93.33 s) spent in the opened arms of EPM vs. diazepam (1 mg/kg b.w i.p/day; 69.33 s). In HBT, only WSE (200 mg/kg b.w/day) has shown a promising number of mean head pokes (13.27 times/min) vs. diazepam (12.87 times/min). The WSE (62.5-500 mg/mL) exposure has exhibited 40.14-72.18% protection against lysis of RBCs vs. aspirin (57.04-71.48%) whilst 62.67-67.66% inhibition of protein denaturation vs. diclofenac sodium (43.11-80.64%). The current findings suggested WSE has promising anxiolytic and anti-inflammatory activities

Anti-inflammatory, thrombolytic and hair-growth promoting activity of the n-hexane fraction of the methanol extract of Leea indica leaves

The anti-inflammatory, thrombolytic, and hair growth-promoting activity of the n-hexane fraction from the methanol extract of Leea indica (NFLI) leaves was investigated. NFLI showed significant inhibition of hemolysis and protein denaturation, and exhibited a concentration-dependent thrombolytic activity. When applied topically to mice at concentrations of 10, 1, 0.1%, NFLI demonstrated a significant increase in average hair length (p < 0.001) compared with untreated animals. NFLI (1% concentration) exhibited the highest percentage of hair regrowth on day 7, 14 and 21 (81.24, 65.60, and 62.5%, respectively). An in silico study was further conducted to predict the binding affinity of phytochemicals previously reported in L. indica towards PGD2 synthase (PDB ID: 2VD1), an enzyme that catalyses the isomerisation of prostaglandin H2 to PGD2 which is involved in hair loss. Phthalic acid, farnesol, n-tricosane, n-tetracosane, and n-heptacosane showed the best ligand efficiencies towards PGD2 synthase and their intermolecular interactions were visualised using BIOVIA Discovery Studio Visualizer. Our results indicate that L. indica could represent a promising natural alternative to tackle alopecia

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic