Regression Coefficients Research Articles

Assessment of the Levels of Pesticide Residues and Trace Metal Elements (TME) in Soils in the Cotton-Growing Region of North-West Benin

ABSTRACT The objective of the study was to evaluate the levels of pesticide residues and trace metal elements (ETM) in the soils of the cotton-growing zone in north-west Benin. The soils under cotton, maize and soybean were retained in a Complete Random Block design with 12 repetitions. 36 sites were sampled, with 12 of them being allocated to each crop. The analysis involved determining permethrin, cypermethrin, and glyphosate from soil samples taken by HPLC. The determination of Cd and Pb was carried out by mass spectrometry with inductively coupled plasma after organic matter destruction. A analysis of variance was used to compare soil types with the data obtained. Linear regression equation models were created to estimate trace metal elements values based on pesticide residue values. The results showed that the average amounts of permethrin (0.61 ± 0.03 µg/mg), cypermethrin (0.69 ± 0.07 µg/mg), glyphosate (0.82 ± 0.01 µg/kg), cadmium (Cd) (1.78 ± 0.21 µg/kg), lead (Pb) (1.04 ± 0.14 µg/kg) were higher (p < 0.05) in soils under cotton crops. As for lead, it can only be estimated using permethrin (Pb = 0.18 + 0.37 Per) or cypermethrin (Pb = 0.24 + 0.34Cy) or both with high determination coefficient R2 (R2= 82.01%).

Association of R-loop binding proteins with prognosis and anti-neoplastic drug sensitivity in lung adenocarcinoma: a bioinformatic study.

OBJECT: To investigate the association of R-loop binding proteins with prognosis and chemotherapy efficacy in lung adenocarcinoma. METHODS: The data related to R-loop regulatory genes were obtained from literature of R-loop proteomics and relevant databases. We used 403 lung adenocarcinoma cases in the Cancer Genome Atlas as training set, and two datasets GSE14814 and GSE31210 in Gene Expression Omnibus as validation sets. The weighted gene co-expression network analysis (WGCNA) was employed to identify R-loop genes with a significant impact on the clinical phenotype of lung adenocarcinoma. Least absolute shrinkage and selection operator (LASSO) regression was utilized to eliminate genes exhibiting multicollinearity. A multivariate Cox proportional hazards model was employed to scrutinize clinical variables and R-loop characteristic genes that exert independent prognostic effects on patient survival. Subsequently, a risk score model was constructed. The predictive capacity of this model for the prognosis of patients was analyzed and validated. Additionally, the performance of risk model on the anti-neoplastic drug sensitivity was assessed. The mutations of R-loop gene were analyzed by maftools. The effect of PLEC expression on anti-tumor drug sensitivity was tested on non-small cell lung adenocarcinoma H1299 and A549 cells in vitro. RESULTS: A collection of 1551 R-loop genes were obtained, and 78 genes exhibited significant effects on the clinical phenotype shown on WGCNA. The LASSO regression analysis retained 14 R-loop genes. A multivariate Cox analysis further identified 3 R-loop genes (HEXIM1, GLI2, PLEC) and a clinical variable (tumor grading) that were associated with patient prognosis. Risk prediction model was established according to the regression coefficients of each parameter. Kaplan-Meier survival analysis showed that the prognosis of high-risk group patients was significantly worse than that of low-risk group (P<0.01). The time-dependent ROC curve showed that the risk model had good predictive ability in both training and validation sets. Predictive analyses of anti-neoplastic drug sensitivity indicated a diminished responsiveness to both chemotherapy and targeted treatment drugs among high-risk patients. The expression of PLEC was strongly correlated with the sensitivity of gefitinib, a classical EGFR inhibitor. CONCLUSIONS: R-loop binding proteins have been identified as significant determinants in the prognosis and therapeutic strategies for lung adenocarcinoma. The results indicates that therapeutic interventions targeting these specific R-loop binding proteins might contribute to a better survival in lung cancer patients.

A candidate reference measurement procedure for quantification of glycocholic acid in human serum based on isotope dilution liquid chromatography-tandem mass spectrometry.

Accurate measurement of serum glycocholic acid (GCA) is crucial for evaluating the activity of chronic hepatitis. Moreover, GCA is a novel identified biomarker for hepatocellular carcinoma. Although some laboratories have used theliquid chromatography-tandem mass spectrometry (LC-MS/MS) method to measure GCA in recent years, the problem of potential interference of GCA analogues has not been solved well yet. Neither reference measurement procedures nor reference materials for GCA have been listed in the Joint Committee for Traceability in Laboratory Medicine (JCTLM) database. For standardization of GCA, it is urgent to establish a candidate measurement procedure for GCA. In this study, a candidate reference measurement procedure for the quantification of GCA in human serum based on isotope dilution liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS) by a two-step sample pretreatment of protein precipitation and MAX solid-phase extraction was developed and validated. GCA can be completely separated from its structural analogues with gradient elution in 9 min compared with short time gradients published in previous literature by Huang's group. Method validation indicated perfect quantitation precision with intra-day and inter-day values that were ≤1.30% and ≤1.80%, respectively. The method showed excellent linearity with high regression coefficients (R2 > 0.999) over a range of 0.92 ng/g-38.38 μg/g and perfect recoveries at three spiked levels (99.87-100.43%). No interference, matrix effect, and carryover were observed. Moreover, the cRMP was successfully applied to measure GCA in serum samples and compared with two immunoassays in aclinical laboratory. As a candidate reference method, this method can promote aGCA standardization program.

Nanopillar array-based electrochemical aptamer sensor for STX sensitivity detection.

Saxitoxin (STX) is a cyanotoxin with high toxicity, and therefore, there is an urgent need to develop a facile detection method for STX. In this study, an ordered nanopillar array-based electrochemical aptasensor was fabricated for the high-performance detection of STX. The anti-STX aptamer with methylene blue (MB) incorporated at the 3'-end (MB-Apt) was immobilized at the surface of an Au@PAN nanopillar array electrode and used as the recognition element. The proposed aptasensor demonstrated highly sensitive and selective STX detection because of synergistic catalysis effects of MB and ordered nanopillar arrays along with the selection of MB-Apt. The nanopillar array-based electrochemical aptasensor exhibited high sensitivity over a wide linear concentration range of 1 pM-3 nM with a linear regression equation of ΔI (μA) = 28.0 + 6.9 × log[STX] (R2 = 0.98079) and 3-100 nM with a linear regression equation of ΔI (μA) = 10.7 + 43.4 × log[STX] (R2 = 0.98772), where R is the correlation coefficient. In addition, the limit of detection (LOD) was as low as 1 pM. Furthermore, the designed aptasensor demonstrated excellent selectivity toward STX, preventing interference from neo-STX, okadaic acid, and common metal ions. The presented orderly nanopillar array-based strategy to develop an electrochemical aptasensor for STX detection offers a promising method for developing high-performance electrochemical sensors, and the presented aptasensor should find useful application in the detection of shellfish poison.

The role of cardiorespiratory fitness and body composition in the association between physical activity and menopausal symptoms.

The aim of the study was to conduct exploratory analyses on the role of cardiorespiratory fitness (CRF) and body composition in the association between physical activity and menopausal symptoms. This was a cross-sectional (N = 298) study of women aged 51-59 years including a subsample of 82 women followed for 4 years. The severity of menopausal symptoms was assessed with the Menopause Rating Scale in total symptoms as well as using the somato-vegetative, psychological, and urogenital subscales. Physical activity was assessed with accelerometers and self-reports, body composition with dual-energy x-ray absorptiometry, and CRF with a custom-made prediction model based on the six-minute walking distance and spiroergometry. The associations of interest were studied using unstandardized regression coefficients derived from multiple linear regression models with the severity of menopausal symptoms as the outcome. Higher total body and fat mass (kg) were associated with more severe total symptoms (B = 0.06 [95% CI, 0.01 to 0.12] and 0.07 [0.01 to 0.14], respectively) as well as somato-vegetative (0.03 [0.01 to 0.05]; 0.04 [0.01 to 0.06]) and psychological symptoms (0.03 [0.00 to 0.05]; 0.03 [0.00 to 0.06]) in cross-sectional design. Total and lean body mass interacted with physical activity in total and psychological symptoms with stronger indirect associations being observed in participants with lower total and lean body mass. CRF was not associated with menopausal symptoms and did not interact with physical activity. Maintaining a healthy weight is associated with less severe menopausal symptoms in middle-aged women. The association between physical activity and the severity of menopausal symptoms varied based on the differences in total and lean body mass.

Free open source communities sustainability: Does it make a difference in software quality?

ContextFree and Open Source Software (FOSS) communities’ ability to stay viable and productive over time is pivotal for society as they maintain the building blocks that digital infrastructure, products, and services depend on. Sustainability may, however, be characterized from multiple aspects, and less is known how these aspects interplay and impact community outputs, and software quality specifically.ObjectiveThis study, therefore, aims to empirically explore how the different aspects of FOSS sustainability impact software quality.Method16 sustainability metrics across four categories were sampled and applied to a set of 217 OSS projects sourced from the Apache Software Foundation Incubator program. The impact of a decline in the sustainability metrics was analyzed against eight software quality metrics using Bayesian data analysis, which incorporates probability distributions to represent the regression coefficients and intercepts.ResultsFindings suggest that selected sustainability metrics do not significantly affect defect density or code coverage. However, a positive impact of community age was observed on specific code quality metrics, such as risk complexity, number of very large files, and code duplication percentage. Interestingly, findings show that even when communities are experiencing sustainability, certain code quality metrics are negatively impacted.ConclusionFindings imply that code quality practices are not consistently linked to sustainability, and defect management and prevention may be prioritized over the former. Results suggest that growth, resulting in a more complex and large codebase, combined with a probable lack of understanding of code quality standards, may explain the degradation in certain aspects of code quality.

Tobacco smoking and tooth loss in adults and elderly in Brazil: a populational-based cross-sectional study.

To determine the association between smoking and tooth loss in individuals aged 18 years or more living in Brazil. Secondary analysis of the 2019 Brazilian National Health Survey data. The outcome was self-reported tooth loss, and the main independent variable was tobacco smoking. Family income, schooling, sex and age were covariates. Multiple linear regression analysis determined the association between tobacco smoking and the number of missing teeth and then the average number of missing teeth was predicted according to smoking status. The mean number of missing teeth in 88,531 individuals aged 18 or more was 7.7 (95%CI: 7.6-7.8). At least one missing tooth was identified in 72.0% (95%CI: 71.4-72.6) of the population, 21.3% (95%CI: 20.9-21.7) had a non-functional dentition, 14.2% (95%CI: 13.9-14.6) had severe tooth loss and 10.3% (95%CI: 10.0-10.6) were edentulous. The adjusted regression coefficients for number of missing teeth showed that current or former smokers, individuals with low family income and schooling, older age and females exhibited higher tooth loss. Current and former smokers had 1.40 (95%CI: 1.35-1.46) and 1.13 (95%CI: 0.54-0.98) times more lost teeth than never smokers, respectively. Both tooth loss and smoking are common in Brazilians and are associated. Unfavorable socioeconomic status and demographic factors also predict tooth loss.

PENGARUH MEDIA PEMBELAJARAN ICT BERBASIS CLASSROOM DAN QUIZIZ TERHADAP MINAT BELAJAR SISWA PADA MATERI STATISTIKA

This research aims to examine the influence of classroom-based ICT learning media on students' interest in learning in statistics material for class VIII B at MTs SA Tarbiyatus Shibyan Guyangan Krucil Probolinggo. The approach applied in this research is a case study using quantitative methods. Data collection was carried out through observation, interviews, questionnaires, tests, and documentation. The researchers' findings identified that the results of the research that had been carried out showed that in terms of the influence of classroom and quiziz-based ICT learning media on students' interest in learning mathematics taken by research subjects with a total of 20 students in one class VIII B at MTs SA Tarbiyatus Shibyan with results obtained sig &lt; α (0.001 &lt; 0.05) then H0 is rejected, the regression line equation is accepted or the variable is the influence of classroom-based ICT learning media (X1) and quizzes (X2) on interest in learning (Y). And getting Fhit = 10.929 &gt; Ftab = 3.592 then H0 is rejected, the regression line equation is accepted or the variables X1 (Classroom Based ICT Learning Media) and And for the residual data normality test, it is found that sig &gt; α (0.116 &gt; 0.05) then H0 is rejected, there is a difference between the residual data and normal data, D_count= 0.215 &lt;〖 D〗_tabel= 0.29407 (5%) then H0 is accepted , there is no difference between residual data and normal data. So in this research developing ICT-based learning media based on Classroom and Quiz really influences students' interest in learning mathematics.

Error Correction of the RapidEye Sub-Pixel Correlation: A Case Study of the 2019 Ridgecrest Earthquake Sequence

The optical image sub-pixel correlation (SPC) technique is an important method for monitoring large-scale surface deformation. RapidEye images, distinguished by their short revisit period and high spatial resolution, are crucial data sources for monitoring surface deformation. However, few studies have comprehensively analyzed the error sources and correction methods of the deformation field obtained from RapidEye images. We used RapidEye images without surface deformation to analyze potential errors in the offset fields. We found that the errors in RapidEye offset fields primarily consist of decorrelation noise, orbit error, and attitude jitter distortions. To mitigate decorrelation noise, the careful selection of offset pairs coupled with spatial filtering is essential. Orbit error can be effectively mitigated by the polynomial fitting method. To address attitude jitter distortions, we introduced a linear fitting approach that incorporated the coherence of attitude jitter. To demonstrate the performance of the proposed methods, we utilized RapidEye images to extract the coseismic displacement field of the 2019 Ridgecrest earthquake sequence. The two-dimensional (2D) offset field contained deformation signals extracted from two earthquakes, with a maximum offset of 2.8 m in the E-W direction and 2.4 m in the N-S direction. A comparison with GNSS observations indicates that, after error correction, the mean relative precision of the offset field improved by 92% in the E-W direction and by 89% in the N-S direction. This robust enhancement underscores the effectiveness of the proposed error correction methods for RapidEye data. This study sheds light on large-scale surface deformation monitoring using RapidEye images.

Racialized experience, biomarkers of lead exposure, and later-life cognition: a mediation analysis.

We evaluated the role of the neurotoxicant lead (Pb) in mediating racial disparities in later-life cognition in 1,085 non-Hispanic Black and 2,839 non-Hispanic white participants in NHANES (1999-2002, 2011-2014) 60+ years of age. We operationalized Black race as a marker for the experience of racialization and exposure to systemic racism. We estimated patella bone Pb via predictive models using blood Pb and demographics. Concurrent cognition (processing speed, sustained attention, working memory) was measured by the Digit Symbol Substitution Test (DSST) and a global measure combining four cognitive tests. To obtain the portion mediated, we used regression coefficients (race on Pb * Pb on cognitive score)/(race on cognitive score), adjusting for age, NHANES cycle, and sample weights. Other confounder adjustment (education, poverty income ratio, smoking) was limited to the mediator-outcome (i.e., Pb-cognition) pathway because these factors do not lie upstream of race and so cannot confound associations with race. Pb was estimated to mediate 0.6% of the association between race and global cognition, and 4% of the DSST. Our results suggest that later-life cognitive health disparities may be impacted by avoidable lead exposure driven by environmental injustice, noting that a large proportion of the pathway of systemic racism harming cognition remains.

Accuracy of Instrumented Mouthguards During Direct Jaw Impacts Seen in Boxing.

Measuring head kinematics data is important to understand and develop methods and standards to mitigate head injuries in contact sports. Instrumented mouthguards (iMGs) have been developed to address coupling issues with previous sensors. Although validated with anthropomorphic test devices (ATDs), there is limited post-mortem human subjects (PMHS) data which provides more accurate soft tissue responses. This study evaluated two iMGs (Prevent Biometrics (PRE) and Diversified Technical Systems (DTS) in response to direct jaw impacts. Three unembalmed male cadaver heads were properly fitted with two different boil-and-bite iMGs and impacted with hook (4m/s) and uppercut (3m/s) punches. A reference sensor (REF) was rigidly attached to the base of the skull, impact kinematics were transformed to the head center of gravity and linear and angular kinematic data were compared to the iMGs including Peak Linear Acceleration, Peak Angular Acceleration, Peak Angular Velocity, Head Injury Criterion (HIC), HIC duration, and Brain Injury Criterion. Compared to the REF sensor, the PRE iMG underpredicted most of the kinematic data with slopes of the validation regression line between 0.72 and 1.04 and the DTS overpredicted all the kinematic data with slopes of the regression line between 1.4 and 8.7. While the PRE iMG was closer to the REF sensor compared to the DTS iMG, the results did not support the previous findings reported with use of ATDs. Hence, our study highlights the benefits of using PMHS for validating the accuracy of iMGs since they closely mimic the human body compared to any ATD's mandible.

Predicting Structural Temperature of Ancient City Walls: A Case Study Using Ambient Temperature and Deep Learning

ABSTRACT Accurate prediction of the structural temperature field is required for the performance assessment of engineering structures and for maintaining structural safety. However, there are complex time-lag effects between ambient temperature and structural temperature. It is difficult to accurately describe and predict the structural temperature using traditional linear and nonlinear fitting methods. Hence, this study uses the advantages of gated recurrent unit (GRU) neural networks in dealing with complex nonlinear mapping issues, further considering solar radiation and developing a structural temperature prediction framework using ambient temperature and the deep learning model. The framework is applied in structural temperature prediction for an ancient city wall in China. The solar altitude angle at the location of the city wall is calculated, and the input and output structures of the deep learning neural network model are optimized. In the long-term prediction of the structural temperature of the city wall, the root mean square error (RMSE) of the GRU model is less than 0.6°C, which solves the problem of low accuracy caused by time-lag effects.

PENGARUH KECEMASAN DI PERPUSTAKAAN TERHADAP PEMENUHAN KEBUTUHAN INFORMASI PEMUSTAKA PADA PERPUSTAKAAN UIN SUNAN KALIJAGA YOGYAKARTA

This research examines how anxiety influences fulfilling users' information needs at the UIN Sunan Kalijaga Yogyakarta Library. This research uses a quantitative method using a sample of 100 active student respondents at UIN Sunan Kalijaga Yogyakarta with a purposive sampling technique. The analysis results show that the anxiety variable's coefficient of determination on meeting needs is 0.642. These results show that the influence of anxiety on fulfilling information needs in the UIN Sunan Kalijaga Yogyakarta Library is 64.2%. The nature of the impact of these two variables is positive, where the simple linear regression equation is Y = 4.203 – 0.334X. The results of hypothesis testing show a rcalculate of 13.268. These results show results that are greater than. This research concludes that library anxiety significantly and positively influences meeting information needs.

Multi-instrument spectroscopic study for authentication of curcumin content in commercial turmeric powders using machine learning algorithms

Adulteration during processing of turmeric powder not only causes health risks for the consumers but also affects its quality. There is a need for rapid and non-invasive analysis of its active ingredient, curcumin, during the supply-chain. In the present study a total six IR instruments ranging from hand-held (NIR), portable (NIR) and standalone (FTNIR and FTIR) were used to obtain spectral data of 160 different turmeric samples. The curcumin content quantified using HPLC procedure was used as the response variable for analytical model using machine learning tools. Real coded genetic algorithm (RCGA) as the variable selection procedure provided most critical variables in the sets of 10, 20, 30 and 40 variables. Sensitivity analysis has revealed the most critical fingerprint(s) in authenticating curcumin across all the instruments. The hand-held (NIR) device with only 20 spectral variables resulted in 93 % accuracy using SVM classifier, and RP (regression co-efficient of prediction) values of 0.970 and 0.997 using RF and XGBoost, respectively. In case of FTNIR and FTIR instruments 100 % classification accuracy was achieved using SVM, whereas RF and XGBoost resulted in RP values greater than 0.93. This study enables classification and quantification of curcumin content in commercial turmeric powders using non-destructive methodology.

Overcoming Time-Varying Confounding in Self-Controlled Case Series with Active Comparators: Application and Recommendations.

Confounding by indication is a key challenge for pharmacoepidemiologists. Although self-controlled study designs address time-invariant confounding, indications sometimes vary over time. For example, infection might act as a time-varying confounder in a study of antibiotics and uveitis, because it is time-limited and a direct cause both of receiving antibiotics and uveitis. Methods for incorporating active comparators in self-controlled studies to address such time-varying confounding by indication have only recently been developed. In this paper we formalize these methods, and provide a detailed description for how the active comparator rate ratio can be derived in a self-controlled case series (SCCS): either by explicitly comparing the regression coefficients for a drug of interest and an active comparator under certain circumstances using a simple ratio approach, or through the use of a nested regression model. The approaches are compared in two case studies, one examining the association between thiazolidinediones and fractures, and one examining the association between fluoroquinolones and uveitis using the UK Clinical Practice Research DataLink. Finally, we provide recommendations for the use of these methods, which we hope will support the design, execution and interpretation of SCCS using active comparators and thereby increase the robustness of pharmacoepidemiological studies.

Comparative biomonitoring of airborne potentially toxic elements using mosses (Hypnum cupressiforme, Brachythecium spp.) and lichen (Evernia prunastri) over remote areas.

The selection of the appropriate biomonitor species is a crucial criterion for biomonitoring on a broad spatial scale. Mosses Hypnum cupressiforme and Brachythecium spp. and lichen Evernia prunastri were sampled at 22 remote sites over Serbia aiming interspecies comparison of their bioconcentration capacities. The concentration of 16 potentially toxic elements (PTEs), Al, Ba, Cd, Co, Cr, Cu, Fe, Hg, Mn, Ni, P, Pb, S, Sr, V, and Zn, was measured in the samples. Between the co-located mosses, linear regression analysis (type II) showed significant determination coefficients only for a couple of the elements (Cd and S), while for H. cupressiforme vs. lichen, significant regression lines were obtained for a broader set of elements (Ba, Cd, Fe, Hg, Mn, Ni, Sr). The ratio of the PTEs in the mosses discovered higher concentrations in H. cupressiforme than in another moss at some sites and vice versa at other sites. According to the PTE ratios, H. cupressiforme accumulated much more element content than the lichen, but followed a similar spatial pattern. In addition, principal component analysis (PCA) pointed out a different grouping of the PTEs depending on the species tested. The poor correlation of the moss-moss data is perhaps because several species of the genus Brachythecium were sampled, which possibly influenced the average genus accumulation capacity. In addition, morphological features of the mosses (concave vs. flat leaflets, creeping vs. cushiony life form) presumably delegate differences in PTE accumulation. To conclude, it should be careful with using more biomonitor species, even of the same genus, within the same study.

Media Shaping Heroes: An Analysis of Chinese Public’s Media Consumption and Heroic Identification

Heroism serves as the banner showcasing the sublime spirit of the Chinese nation, and constructing a societal identity rooted in heroism is a crucial reference for the successful continuation of heroism in the new era. Moreover, media consumption plays a pivotal role in influencing public identification with heroism. From the perspective of cultivation theory, this study surveyed 511 respondents nationwide, utilizing quantitative measurements and empirical analysis through linear regression equations. The results indicate that there is a significant positive correlation between the media usage of the Chinese public and their social identification with heroism, impacting cognitive, emotional, and behavioral dimensions differently. Additionally, traditional media is the main force in shaping this identification. Concurrently, there are evident generational differences in the public’s heroic social identity, with the memory of heroes in the new media era showing a trend of "intergenerational decline." Future research should explore how to effectively use media to transmit the spirit of heroism and promote value integration. This includes optimizing media content, formats, and communication strategies to meet the needs of different audiences and enhancing the public’s deep understanding and recognition of heroism.

Relationship Between Health Literacy Level and the Adoption of Preventive Behaviors Against Coronavirus Disease

Background: Health literacy skills play a pivotal role in adopting preventive behaviors. The COVID-19 pandemic has brought renewed emphasis on health-oriented behaviors, and health literacy is considered one of the most influential indicators affecting health-centric behaviors. Objectives: This study was conducted to determine the relationship between the level of health literacy and the adoption of preventive behaviors against COVID-19 among students at Jundishapur University of Medical Sciences in Ahvaz. Methods: In this study, the research population comprised 223 newly enrolled students from seven main faculties at Jundishapur University of Medical Sciences in Ahvaz in the year 2022. Participants were randomly selected, and data collection instruments included a COVID-19 prevention questionnaire and a health literacy questionnaire for adults. The questionnaire links and study participation consent forms were sent to the participants. Inclusion criteria consisted of willingness and consent to participate in the study, and enrollment in the first year of study (either the first or second semester) in various academic levels at Ahvaz University of Medical Sciences. The exit criteria were defined as failure to respond to more than 5% of the questions. After data collection, descriptive statistics, correlation coefficient tests, and regression analysis were employed using SPSS software for data analysis. Results: The findings revealed that women, married individuals, employed participants, and those with underlying health conditions obtained higher scores in preventive behaviors against COVID-19 and health literacy. Regression coefficients indicated that a one-unit increase in health literacy scores could lead to a 0.439-unit increase in preventive behaviors against COVID-19. According to correlation analysis, a positive, direct, and significant correlation existed among all dimensions of health literacy and preventive behaviors against COVID-19 (P &lt; 0.05), with the behavioral dimension being the only significant predictor of preventive behaviors. Specifically, a one-unit increase in the behavioral dimension score could result in a 0.373-unit increase in preventive behaviors against COVID-19 (P &lt; 0.001). Conclusions: The results showed that health literacy is directly related to COVID-19 preventive behaviors. Therefore, to promote preventive behaviors against infectious diseases, special attention should be paid to health literacy and its influential dimensions. Recognizing the importance of advancing health literacy across various dimensions can significantly contribute to the control and prevention of communicable diseases.

Modelling to predict performance and emission parameters of hydrogen, additive and nano-particles blended fuel in a dual-fuel diesel engine through artificial neural network

The depleted state of fossil fuels has led to the need to find an alternate solution for the utilization of transportation vehicles. The use of hydrogen, fuel additives, and nanoparticles has been part of the research in the recent past. Additionally, the use of artificial neural network was applied to anticipate the results for the optimization of the work and output, which has also been a study of interest recently. In the current work, different blends of diesel with hydrogen, TGME as a fuel additive, and Al2O3 nanoparticles were utilized to check for the best performance and the least emissions. The best performance was seen with the inclusion of TGME in diesel along with hydrogen, while the use of nanoparticles increased CO formation. An improvement in the BTE was also found with the blends of TGME and nanoparticles in diesel along with hydrogen. The usage of TGME along with H2 performed the best in terms of BTE (15.8%) and while the least emission of NOx, HC, and CO was found with the combination of diesel and H2. The artificial neural network was applied afterwards to predict the results using the obtained data from the experimental results. It was found that the values of regression coefficients for BTE and ITE were close to 1 (0.99 for BTE and 0.98 for ITE). In the above cases, the value of the mean square error was also observed to be least. Furthermore, the results for the regression coefficient of emissions were close to 1 for NOx (0.94), HC (0.89), and CO (0.93) with minimum possible mean square errors in all the cases. The results showed that they were in line with the predicted data as well as the available literature for the anticipation of the obtained results.

Assessing the Impact of Financial Characteristics on the Performance of Ghana Club 100 Companies

After a decade of reforming policy, building and developing the multi-national market in Ghana, the Ghana Investment Promotion Centre (GIPC) introduced Ghana Club 100 to recognize the top 100 performing companies in terms of their competitiveness and contribution to economic growth. We adopted a quantitative research methodology involving correlational and casual research design to assess the impact of financial characteristics on the performance of 80 listed Ghana Club 100 firms. Financial characteristics represent the independent variables measured using liquidity, financial leverage and business activity, whiles company performance represents measured using return on asset. SPSS Pearson's correlation coefficient and multiple regression were used to analyze the data gathered from the 80 listed companies. The data analysis reveals four key findings: 1/ a statistically significant positive relationship exists between leverage and performance; 2/ no statistically significant relationship exists between liquidity and performance; 3/ a statistically significant positive relationship exists between business activity and performance; and 4/ leverage, liquidity and business activity had statistically significant positive impact on firm performance. These findings contribute to knowledge by critically explaining the casual relationship between financial characteristics and firm performance with specific reference to Ghana Club 100 companies. The findings have strategic implications for the Ghanaian businesses and economy in terms of enhancing firm’s financial performance. Due to limited access to scarce resources, the scope of the study focused on Club 100 firms which represent all the companies in Ghana. Future research can be conducted on the effect of financial characteristics on the performance of non-listed companies in Ghana.