Regression Coefficients Research Articles

Experimental study of interception effect by submerged dam on microplastics

Submerged dam can alter microplastic (MP) transport, and act as a sink for MPs. In this paper, we investigated the interception rates of Polyvinyl chloride (PVC) and Polystyrene (PS) by an artificial submerged dam in a flow flume at first, and found that most of the un-intercepted PVC and PS particles by the dam accumulated behind it under the subcritical (Fr < 1) and turbulent (Re > 500) flows. PVC particles behind the dam mainly concentrated within two dam widths, and the concentration of PS particles decreased with the distance behind the dam lengthening. Then, we performed linear regression fitting and Redundancy Analysis (RDA) between the interception rates collected in 162 experiment tests and environmental factors, including flow velocity, distance to dam and MP concentration. The results showed that the interception rate of PVC and PS particles increased with the distance to dam lengthening, but decreased with the flow velocity and MP concentration heightening. RDA revealed that the interception rate was influenced by flow velocity, distance to dam, and MP concentration from the most to the least. Our findings are believed to contribute to understanding the mechanism of the interception effect of submerged dam on microplastics.

FORECASTING THE INNOVATIVE AND DIGITAL STRENGTH OF UKRAINE’S ECONOMY ON THE BASIS OF CORRELATION-REGRESS ANALYSIS

The purpose of the research is to present the realised forecasts of Ukraine's economic power in order to find reserves for the recovery of the national economy and opportunities for the formation of digital entrepreneurship on the basis of innovative functioning, which will lead to economic growth in the medium and long term. The object of scientific research is the expenditure on SRD and S&amp;T development by types of work in Ukraine from 2010 to 2022 and the analysis of indicators of GDP the influence of time, employment and inflation factors; expansion of digital capabilities of entrepreneurship due to the conducted R&amp;D, which will become a guarantee of the emergence of breakthrough innovations as one of the key reserves of innovative and digital development of the country during the war and post-war reconstruction of Ukraine. Methodology. A study was conducted utilising dialectical, systematic, mathematical and statistical methods to investigate the R&amp;D expenses by types of work in Ukraine from 2010 to 2022 and the GDP from 2011 to 2023. This analysis determined the prospective existing reserve of economic strength with positive dynamics and highlighted the necessity for a strategic format of digital business work based on innovation. A CRA was conducted to determine the strength of the relationship between SRD expenditures and time (i.e., a set of factors that consistently influence SRD funding and drive its growth trend). Fisher's test was calculated, and an econometric analysis was performed based on GDP indicators over 56 quarters, establishing the dependence of the GDP volume on the time factor and the cyclicality of seasonal fluctuations. Resluts. The conditions for accelerating the digitisation of business processes at domestic enterprises are, in particular, the presence of highly qualified S&amp;R personnel, innovators and researchers in the country, the development of new institutes of innovative and digital development, and the transformation and adaptation of old institutes of development to the existing conditions. The obtained data of CRA show that there is a close relationship between the GDP of Ukraine and the time factor, and the direction of the relationship is direct, i.e., linear, which in this case is a positive fact. It is determined that in pursuit of the goal of restoring the innovative potential of the national economy in the post-war period and further active development of digital entrepreneurship in Ukraine, it is necessary to continue financial support for scientific research and scientific and technical developments carried out in various sectors of the economy. Practical implications. The analysed statistical data had a positive impact on the professionalism of the forecast calculations and allowed to state that in 2027, with a probability of error of 6.29%, the volume of expenditures on research and development is projected to range from 20,202.74 to 29,201.18 million UAH. The results of the CRA show that the multiple correlation coefficient (R) is 0.94, which indicates a close overall relationship between the country's GDP and three independent variables (inflation rate, unemployment rate, time factor). The linear regression equation fits the sample data well and the model is qualitative. The results of the forecast are as follows: Ukraine has the potential for post-war recovery and can develop models for post-war economic reconstruction and changes in its structure. Government officials can develop institutional instruments to attract investment and provide effective mechanisms for the future transformation of the existing labour market and human capital institution. Value / Originality. Having conducted a thorough analysis of the statistical data on the dynamics of spending on S&amp;T development and implementation of the SRD in Ukraine in 2010-2022, the authors managed to determine the relative error of approximation – a criterion for assessing the reliability of the forecast, which amounted to 8.74% and considered the approximation to be qualitative, and the forecast for 2027 is reliable. It is determined that the regression equation is most accurate when R2 approaches its maximum value, that is, 1, and in this case, it is 0.9096, which is a good result and means that the linear regression equation fits the sample data well and the model is of high quality. And for Ukraine's economy, which is under martial law, such positive expectations for GDP and the possibility of increasing R&amp;D spending give hope that Ukraine's economic strength is real, as confirmed by the forecast calculations.

Adhesion Mechanics and Detachment Dynamics of Vanishing Surface Layers on Hydrogels.

Cross-linked hydrogel surfaces exhibit reduced stiffness when polymerized against polymeric hydrophobic surfaces. As such, these layers play a critical role in contact mechanics, particularly exhibiting strong relative adhesion with colloidal probes when the contact area is small. This prevents the use of continuum models of adhesive soft contact. To connect mechanisms of stretch to the force response, depth-controlled nanoindentation experiments were conducted on polyacrylamide (pAAM) hydrogel samples using colloidal probe atomic force microscopy (AFM). The pAAM sample had a high water content of >90% and was molded against polyoxymethylene (POM) to create a more dilute surface layer with thickness ∼0.5 μm. Indentations to multiple depths between 50 nm and 1.25 μm were repeated 10 times each. First, the force drops during the unloading, and separation segments of each indentation were characterized. This described the detachment progression for increasing areas of contact, revealing that the pull-off force for a single chain was in the single-pN range. Second, the stretched polymer network was modeled as an array of parallel, linear springs. Assuming a constant areal chain density of α = 100 chains/μm2, the maximum force of adhesion was plotted versus the volume of chains stretched upward, and the average chain stiffness was calculated from a linear fit to be 22.8 × 10-6 N/m. A Weibull distribution analysis of detachment events revealed a dependence of chain stiffness on maximum indentation depth (dmax), with higher stiffness at shallower depths approaching kchain ≈ 20 × 10-6 N/m. These findings on adhesion mechanics between a vanishing hydrogel surface and probe can guide the development of multifunctional hydrogels for various biomedical applications.

Intercomparison of sensible and latent heat flux measurements from combined eddy covariance, energy balance, and Bowen ratio methods above a grassland prairie

We present a comparison of four different methods of measuring sensible (H) and latent (LE) heat fluxes for a year over a mixed grass prairie ecosystem in the Nebraska SandHills [eddy covariance (EC), energy balance/Bowen ratio (EBBR), residual energy (RES), modified Bowen ratio (MBR) methods]. Additionally, we developed a set of quality control criteria for each method and present a simplification to the traditional EBBR setup. Using EC as reference, all methods yielded similar estimates of yearly H (regression slopes (m) ~ 2% from unity; HEC > HEBBR, HRES, and HMBR). For yearly LE, EBBR and RES yielded similar estimates with EC (m ~ 2% from unity; LEEC < LEEBBR and LERES), while a larger bias was found from MBR (m ~ 8% from unity; LEEC > LEMBR). At shorter time scales (~ hourly), moderate scatter was found about linear regression fits for H between EBBR and EC (R2 = 0.81), with smaller scatter between RES and MBR, and EC (R2 = 0.91). For LE, smaller scatter was also measured between EC, and EBBR and RES (R2 = 0.89 and 0.87, respectively), with the larger scatter between EC and MBR (R2 = 0.65). This suggests methods other than EC may be well suited to longer-term applications (≥ yearly), but have larger uncertainty on individual measurements.

Estimating the Extraction Time of an Upper Third Molar: Proposal and Validation of Results.

Background: Numerous studies in the literature have aimed to evaluate the difficulty level of removing third molars. However, most of these studies have focused on the lower third molars, which can lead to complications. There is a lack of a method to determine the complexity of upper third molar extraction. Therefore, this study's objective was to develop an equation using multiple linear regression to estimate the extraction time of an upper third molar based on its complexity. Methods: This study involved patients enrolled in the Master of Oral Surgery program at the University of Seville. To determine their relationship with surgical time, the researchers analyzed various factors, such as depth, root morphology, and the need for odontosection. They then validated their findings by studying patients treated at Palmaplanas Hospital in Mallorca. Results: The cohort analysis from the University of Seville revealed significant associations between surgical time and the identified factors. A regression equation design was performed to predict the total duration of surgical intervention for wisdom teeth extraction. This equation incorporates several independent variables, represented by Xi, together with a constant term, C, and the corresponding coefficients, Bi, which weight the impact of each variable on the intervention time. The results are as follows: -0.312 (spatial relationship), 0.651 (depth), -0.443 (bone and mucosa integrity), 0.214 (roots), -0.745 (ostectomy), 0.713 (odontosection), and -0.426 (suture). Upon application of the statistical methodology to the Palmaplanas Hospital cohort, a regression coefficient of 0.770 was determined. This indicates a strong correlation between the input data and the estimated surgical time. Conclusions: In conclusion, the proposed formula demonstrates notable validity in predicting the surgical time required to extract upper third molars.

DFT theoretical analysis, experimental approach, and RSM process to understand the congo red adsorption mechanism on Chitosan@Graphene oxide beads

The presence of Congo red in wastewater poses significant environmental and health risks due to its toxic and carcinogenic properties. As a synthetic azo dye commonly used in the textile industry, Congo red is resistant to degradation, leading to persistent contamination in water bodies. The study focused on evaluating the efficacy of novel composite beads made from chitosan and graphene oxide as adsorbents for eliminating Congo Red from water solutions. It systematically examined various experimental factors including initial concentration, pH levels, mass of adsorbent, contact duration, and temperature, aiming to optimize the adsorption process. The composite chitosan graphene oxide beads were analyzed using several techniques, including Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), and Brunauer-Emmett-Teller (BET) method for specific surface area determination. The study involved extracting chitosan from shrimp shells and synthesizing graphene oxide from graphite using the Hummers method to enhance Congo Red adsorption efficiency. Comprehensive analysis of the composite beads assessed their physical and functional properties using various techniques. X-ray diffraction and infrared analysis identified their adsorption capabilities, while SEM and BET analysis characterized the porous surface structure of the beads. Maximum adsorption efficiency of 169.49 mg/g was achieved with an adsorbent mass of 0.05 g at pH 3. Kinetic, isotherm, and thermodynamic studies indicated an endothermic process following pseudo-second-order kinetics and the Langmuir isotherm model. The high regression coefficient R 2=0.9953 indicates the strong validity of employing Response Surface Methodology combined with the Box-Behnken Design (RSM-BBD) for accurately predicting the percentage of CR removal through adsorption. Theoretical Density Functional Theory (DFT) analysis elucidated interactions between Congo Red and the reactive sites within the chitosan/graphene oxide beads, providing insights into the adsorption mechanism.

Performance and Stability for Grain Yield and Its Components of Some Rice Cultivars under Various Environments

Refine current agricultural practices considering environmental changes are crucial for finding tolerant rice varieties that can meet the demands of human consumption. To this end, stability analysis assesses a crop genotype’s ability to adapt to various conditions. Therefore, the objective of this study was to (1) examine the interaction between rice genotypes and environmental conditions; (2) evaluate the stability of twelve rice genotypes using various stability methods; (3) identify representative environments for multi-environment testing; and (4) determine superior genotypes for specific environments. The evaluated rice cultivars were Sakha 101, Sakha 104, Sakha 105, Sakha 106, Sakha 107, Sakha 108, Giza 177, Giza 178, Giza 179, Giza 182, Egyptian Yasmine, and Sakha super 300. The experiment followed a strip-plot design, with three replications. The findings revealed significant differences among the rice varieties across various environments for the majority of the assessed characteristics. The joint regression analysis of variance demonstrated highly significant differences among rice cultivars for all the studied traits in terms of genotype-by-environment interaction (G × E). The statistical significance of the interaction between genetic and environmental factors was evident for all variables demonstrating heritable variation among the rice cultivars, specifically Sakha 108, Sakha 104, Giza 177, and Giza 178, concerning grain yield per feddan. These rice cultivars exhibited stability parameters that were not significantly different from unity for the regression coefficient (bi) and from zero for the deviations from regression (S2di) for those traits. Overall, stability criteria are essential for ensuring reliable rice production, meeting human consumption, advancing genetic improvement, and promoting environmental sustainability in agriculture.

Systematically missing data in distributed data networks: multiple imputation when data cannot be pooled

Systematically missing data in distributed data networks presents practical and methodological challenges. Failure to handle it appropriately can bias statistical inference. Multiple imputations can be used to address systematic missingness. However, when data from different study sites cannot be pooled into a unified file, conventional imputation approaches become unavailable due to the absence of a basis for imputation. To address such challenges, we introduce an imputation method based on conditional quantiles – conditional quantile imputation (CQI) – which involves four steps: (i) estimating 99 quantiles for the systematically missing variable in studies with observed data; (ii) deriving a weighted average of regression coefficients across studies and transmitting it to sites with systematically missing data; (iii) imputing the systematically missing values based on observed data and the set of regression coefficients from step ii; and (iv) combining estimates of the substantive outcome model across imputations using Rubin's rules. We evaluate CQI in different simulation scenarios and illustrate it with an applied data example. We conclude that CQI can be a suitable approach for the imputation of systematically missing data when data from multiple studies cannot be pooled.

Characterization of Graphite and ZrO2 Reinforced Aluminium Metal Matrix Composite For Enhanced Mechanical Properties in Electronics Applications

Abstract The objective of this work is to examine the wear properties of a hybrid Metal Matrix Composite (MMC) consisting of 92 weight percent (wt. %) Aluminium Alloy 6061 (AA6061), 2 wt. % Graphite (C), and 6 wt. % Zirconium Dioxide (ZrO2). It was previously determined that this composition was ideal for improved metallurgical and mechanical properties. Nine specimens of the same composition were fabricated for this continuation work and put through wear tests with different applied load, sliding velocity, and sliding distance settings. To evaluate the performance of the composite, measurements were made of the wear rate and Coefficient of Friction (COF). According to the results, the wear rate typically increased as the applied load and sliding velocity increased. At the lowest load (10 N) and velocity (2 m/s), the lowest wear rate (50.90 µg) and COF (0.197) were noted. Specimens with lower wear rates had smoother surfaces and less wear, as revealed by SEM analysis of the worn surfaces; specimens with higher wear rates showed more significant surface damage, such as deeper cuts and serrations. In specimens with less wear, XRD analysis verified the presence of crystalline aluminium, indicating a strong correlation between wear resistance and the crystalline structure of the composite. Linear regression equations were created to predict wear behavior under different conditions. According to the results, the composite's wear characteristics are greatly influenced by the sliding velocity and applied load. ZrO2 and C together have lubricating and strengthening properties that, under certain circumstances, reduce wear. The composite exhibits potential for usage in electronics applications, especially in components where wear resistance and mechanical durability are critical due to its favorable wear characteristics and mechanical properties.

Alcohol consumption in cancer patients receiving psycho-oncologic care analysis of socio-demographic, health-related and cancer-related factors.

The carcinogenic effects of alcoholic beverages and the negative impact of alcohol consumption on cancer progression and treatment outcomes are well established in oncology research. Many cancer patients experience significant psychological distress, often manifesting as elevated levels of depression and anxiety. In the general population, alcohol consumption is commonly used as a coping mechanism for such distress. For cancer patients facing substantial psychological challenges, psycho-oncology care is available to help manage their symptoms and the overall impact of their condition. However, there is limited understanding of the alcohol consumption patterns in this particularly vulnerable group of patients, as well as the disease-related factors that may influence their drinking behavior. This study aims to examine the prevalence of potentially risky alcohol consumption in cancer patients receiving psycho-oncology care and to identify sociodemographic, health-related, and psychosocial factors associated with alcohol consumption after cancer diagnosis. By understanding drinking patterns and the factors associated with them, we aim to promote healthier behaviors and enhance treatment outcomes for cancer patients receiving psycho-oncology care. A consecutive sample of 1.225 patients from the psycho-oncology outpatient clinic of the University Medical Center Hamburg Eppendorf (UKE) was analyzed with regard to their alcohol consumption behavior using the Alcohol Use Disorders Identification Test-Consumption Items (AUDIT-C). Self-report questionnaires were employed to assess sociodemographic, health-related, and cancer-specific information. For statistical analysis, multiple linear regression was utilized. In the sample of cancer patients receiving psycho-oncology support one in six of both female and male patients showed risky alcohol drinking behavior (e.g. AUDIT-C above three for women and four for men). In the analysis (R-Square: 0.056) male gender (regression coefficient B 0.686, 95% CI: 0.453-0.919) and patientsreporting a higher physical health-related quality of life (SF8-PCS) (B 0.021, 95% CI: 0.011-0.032) were associated with higher levels of alcohol consumption, whereas having children (B -0.481, 95%CI: -0.700- -0.262) was associated with lower alcohol consumption. With regard to cancer type, a single-factor analysis of variance with gender as the centered covariate showed that patients with gastrointestinal cancer had had lower average consumption levels compared to the groups of patients with breast cancer, melanoma, genitourinary cancer, and lymphoma. Also, patients with sarcoma had lower average consumption levels than patients with lymphoma. The results allow to identify patient characteristics and cancer types associated with higher or lower alcohol consumption levels and higher likelihood of risky consumption behavior in this sample of cancer patients receiving psycho-oncological support. Cancer patients are particularly susceptible to the hazardous effects of alcohol consumption. The results of this study help to identify cancer patients at risk of worsening prognosis due to alcohol consumption and to develop intervention programs to minimize alcohol consumption in this group.

Cardiometabolic index is associated with increased bone mineral density: a population-based cross-sectional study.

Osteoporosis is a multifactorial bone disease in which lipid metabolism plays an important role. Bone Mineral Density (BMD) measured by Dual-energy X-ray Absorptiometry (DXA) is a critical indicator for diagnosing osteoporosis. The cardiometabolic index (CMI) is a novel metric that combines two quantitative indicators of blood lipids-triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C). This study explores the association between CMI and BMD and seeks to elucidate the role of lipid metabolism in the context of bone health. Based on the data of the National Health and Nutrition Examination Survey (NHANES) 2017-March 2020-pre-pandemic, weighted multiple linear regression and smooth curve fitting were used to study the relationship between CMI and femoral BMD. Stratified analyses were also conducted for age, gender, BMI, ethnicity, diabetes and hypertension status. And, the saturation threshold effect of CMI was further analyzed using a two-stage linear regression model. This study enrolled a total of 1,650 participants (48.7% males), with an average age of 63.0 ± 8.6 years. After adjusting for multiple confounding factors, CMI was positively correlated with total femur BMD, trochanter BMD, and intertrochanter BMD, while the correlation with femur neck BMD was not statistically significant. In the fully adjusted model, each unit increase in CMI was associated with a 0.026 (g/cm2) increase in total femur BMD, a 0.022 (g/cm2) increase in trochanter BMD, and a 0.034 (g/cm2) increase in intertrochanter BMD. Subjects in the highest quartile of CMI had a 0.034 (g/cm2) increase in total femur BMD, a 0.035 (g/cm2) increase in trochanter BMD, and a 0.039 (g/cm2) increase in intertrochanter BMD in the fully-adjusted model compared to those in the lowest quartile. In addition, saturation was observed between CMI and total femur BMD, trochanter BMD and intertrochanter BMD, with saturation thresholds of 1.073, 1.431 and 1.073, respectively. CMI is strongly associated with BMD, indicating its potential relevance in bone metabolism. However, the role of CMI in the context of bone health, especially regarding osteoporosis risk, requires further investigation in large-scale prospective studies.

The association between the triglyceride-glucose index and bone turnover markers in osteoporotic fractures patients aged 50 and above who are hospitalized for surgical intervention: a retrospective cross-sectional study.

To evaluate the correlation between the triglyceride-glucose (TyG) index and bone turnover markers (BTMs) in osteoporotic fractures (OPFs) patients hospitalized for surgical intervention. A retrospective cross-sectional study was conducted on 3558 OPFs patients hospitalized for surgical intervention between January 2017 and July 2022. The study obtained baseline values for various biomarkers and covariates, including fasting blood glucose, β-C-terminal telopeptide of type I collagen (β-CTX), procollagen type 1 N-terminal propeptide (P1NP), triglycerides, age, sex, body mass index, smoking, drinking, low-density lipoprotein, high-density lipoprotein, aspartate aminotransferase, uric acid, the score of American society of anesthesiologists, homocysteine, parathyroid hormone, apolipoprotein B, apolipoprotein A, magnesium, phosphorus and calcium. Multiple linear regression, curve fitting, threshold effects, and subgroup analyses were also applied. After adjusting for covariates in the regression analysis, the results revealed a negative correlation between β-CTX and P1NP levels and the baseline TyG index. Specifically, a one-unit increase in the TyG index was associated with a reduction in β-CTX levels of -0.06 (95% CI: -0.10, -0.01; P-value = 0.012) and a reduction in P1NP levels of -4.70 (95% CI: -9.30, -0.09; P-value = 0.046). Additionally, the inflection points for the nonlinear correlation between the TyG index and β-CTX and P1NP were found to be K = 6.31 and K = 6.63, respectively. The study demonstrated a negative, non-linear relationship among the TyG index, β-CTX and P1NP in OPFs patients hospitalized for surgical intervention. These findings suggest that elevated TyG index levels may adversely affect bone turnover, potentially contributing to the progression of OP.

Investigating academic dishonesty and its relationship with moral competence and professional identity of nursing students: a cross-sectional study

BackgroundAcademic dishonesty as a fundamental challenge in adherence of ethical principles in providing care to patients has caused concern to the scientific community of nurses. In addition, positive professional identity and high moral competence are important for nurses to perform multiple care roles in high quality care of patients. The purpose of the present study is of investigating academic dishonesty and its relationship with moral competence and professional identity of master’s and PhD nursing students.MethodsThis descriptive-analytical study was conducted by census method on 65 master’s and PhD nursing students in Shiraz University of Medical Sciences, Iran in 2023. Data collection tools included 4 questionnaires (Demographic information, Academic Dishonesty Scale (DAS), Nurses’ moral competence and Professional Identity Scale for Nursing Students (PISNS)). Data collection was done within 2 months. Descriptive and inferential analyzes including independent t-test, Pearson correlation coefficient and linear regression were used to analyze the data. The collected data were analyzed by SPSS.V.22 software. P-value less than 0.05 was considered significant.ResultsThe average age of 65 participants was 31.5 ± 5.61 years. The majority of the participants in this study were women 48 (73.8%). There is a significant and negative correlation between academic dishonesty and moral competence (r=-0.260, P-value = 0.036). There is no significant correlation between academic dishonesty and professional identity (r = 0.154, P-value = 0.22). Also, a significant positive correlation was found between moral competence and professional identity (r = 0.294, P-value = 0.018).ConclusionTherefore, considering that the fact that students have a great impact on patient care, Researchers suggest that educational institutions should prevent the increase of academic dishonesty among students by establishing new rules and regulations.

The association between occupational stress, sleep quality and premenstrual syndrome among clinical nurses

ObjectivePremenstrual Syndrome is also known as premenstrual tension syndrome because of the pronounced premenstrual mental and emotional anomalies. This study focuses on the association between occupational stress, sleep quality and premenstrual syndrome in clinical nurses and the mediating role of sleep quality.MethodsA cross-sectional study was conducted to measure occupational stress, sleep quality and premenstrual syndrome in 415 clinical nurses using the Chinese Nurses Stressor Scale, the Pittsburgh Sleep Quality Index Scale, and the Premenstrual Syndrome Scale. SPSS was used to explore the relationship between the variables, and AMOS was used to explore the mediating role between the variables.ResultsNurses’ occupational stress positively predicted PMS (β = 0.176, p < 0.001), and the regression coefficients for sleep quality were significantly different for both paths of nurses’ occupational stress (β = 0.665, p < 0.001) and PMS (β = 0.261, p < 0.001). The mediation effect accounts for 49.57% of the total effect.ConclusionThis study revealed a notably high incidence of PMS among clinical nurses in China, with occupational stress and sleep quality significantly correlated with PMS. Sleep quality played an intermediary role between occupational stress and PMS. Consequently, managers should prioritize addressing occupational stress, mitigating PMS symptoms, enhancing nurses’ health and nursing quality, and preventing nursing risks through mental health support and improving sleep quality.

An interactive R-based custom quantification program for semi-quantitative analysis of triacylglycerols in bovine milk.

The R programming language, RStudio, and open-source software solutions for analysis of liquid chromatography-mass spectrometry (LC-MS) data have been used with user-written R-based custom quantification programs (CQP) for semi-quantification of triacylglycerols (TAGs) in bovine milk lipid extracts. Using the peak-finding capabilities of the package "xcms" in RStudio, peaks were integrated, and retention times aligned, normalized, and then used for semi-quantitative analysis of a custom set of four extraction internal standards (EISs) and 29 TAG regioisomers using the choice of four analytical internal standards (AISs). Alternating stereospecific numbering (sn) 1,3 TAG regioisomers (standards 1, 3, and 5 of six calibration standards) and sn-1,2 TAG regioisomers (standards 2, 4, and 6 of six standards) were used to make a set of six calibration standards, which were used for quantification using a linear fit model, polynomial fit model, power fit model, level-bracketed linear fit, replicate-bracketed polynomial fit, replicate-bracketed power fit, and replicate- and level-bracketed linear fit and response factors. For example, the linear fit for EIS1 gave an unacceptable coefficient of determination (CoD), r2 = 0.9616, whereas the polynomial fit gave r2 = 0.9908 and the power fit gave r2 = 0.9928, while the double-bracketed linear fit gave CoDs of r2 = 0.9960, 0.9848, and 0.9781 for the three brackets, yet gave the least % difference to known calibration concentrations. For unparalleled transparency, the CQP produced webpages that allowed every step in the data processing and quantification sequence to be verified and reproduced, and contained interactive figures. The data are publicly available using a digital object identifier (DOI). The R code can be downloaded and used with the downloadable data to reproduce the results, to modify the code and further customize the results, or to copy and paste and adapt the code to other quantification applications.

Linear and non-linear associations of depressive symptoms with oral health knowledge, attitudes, and practices among rural older adults in China: a cross-sectional study

BackgroundDepression affects the oral health of older adults; however, little is known about its impact on oral health among rural older adults in developing countries, which warrants further research. Taking China as an example, there is a large population base of rural older adults suffering from depression, and many rural older people also have long-term oral health problems, which have seriously affected their quality of life in later life. Therefore, this study aimed to explore linear and non-linear associations of depressive symptoms with oral health knowledge, attitudes, and practices among rural older adults in China.MethodsFrom November 2020 to December 2020, 1,902 rural community-dwelling older people aged 60 years and older were investigated, via a cross-sectional survey. The general information, depressive status, oral health knowledge, attitudes, and practices of the participants were obtained through face-to-face structured questionnaires. Among them, the Zung Depression Self-Rating Scale was used to investigate the depressive symptoms of the participants in this survey. The generalized linear model and classification and regression tree model were used, separately.ResultsBased on linear analysis results, we found that minimal to mild depressive symptoms [regression coefficient (β) = -0.345; 95% confidence interval (CI): -0.582 to -0.109, P = 0.004] and depressive symptoms (β = -1.064; 95% CI: -1.982 to -0.146, P = 0.023) were significantly correlated with oral health knowledge. A negative correlation was observed between minimal to mild depressive symptoms (β = -0.385; 95% CI: -0.600 to -0.170, P < 0.001) and oral health attitudes. In addition, while both minimal to mild depressive symptoms (β = 0.018; 95% CI: -0.312 to 0.347, P = 0.916) and depressive symptoms (β = 0.604; 95% CI: -0.675 to 1.883, P = 0.355) were associated with oral health practices. Furthermore, the non-linear analysis showed a combined effect of depressive symptoms on oral health attitudes, indicating that older people of a younger age, not living alone, and not suffering from depressive symptoms are more likely to report better oral health attitudes.ConclusionBoth the linear and non-linear analyses in our study showed that depressive symptoms are significantly correlated with the poor oral health attitudes of older adults in rural communities. Furthermore, depressive symptoms were associated with oral health knowledge in the linear analysis. However, no statistically significant difference was found between depressive symptoms and oral health practices in either analysis. This research deepens our knowledge and understanding of relevant evidence in the mental and oral health of people in later life. In addition, analyzing the factors that affect the oral health of older people from the perspective of their depressive status provides new thinking directions and scientific references for improving the oral health of older adults in practical life.

Generalized Additive Spatial Smoothing (GASS): A Multiscale Regression Framework for Modeling Neighborhood Effects Across Spatial Supports

A new technique called generalized additive spatial smoothing (GASS) is introduced for modeling neighborhood effects within a regression framework. GASS has a number of desirable features, namely that it provides a data-driven mechanism for endogenously selecting neighborhoods based on a spatial scale hyperparameter. By allowing different scale hyperparameters to be selected for different relationships in the model, the technique is inherently multiscale and allows neighborhoods to vary by relationship. In addition, GASS includes a measure of uncertainty associated with each scale hyperparameter. These characteristics make it attractive for modeling phenomena where proximity might be an important aspect of a process, especially when a clear definition of proximity is not immediately available. Through multiscale data-driven spatial smoothing, GASS conducts a form of change of support and therefore also facilitates the incorporation of data from diverse sources. Finally, the technique is flexible and can be adapted and expanded with relative ease because it builds on generalized additive modeling. After providing an overview of the methodology, including a modified backfitting algorithm for calibration, a simulation experiment is described and an empirical example modeling bike-share usage is presented. The simulation results indicate that GASS can generally produce reliable results pertaining to both the regression coefficients and scale hyperparameters, and the results from the empirical example demonstrate that the GASS approach provides a better model fit and captures relationships that might otherwise be obfuscated. Overall, these results highlight the potential of the GASS framework and the importance of measuring multiscale neighborhood effects.

A qualitative analysis of the artificial neural network model and numerical solution for the nanofluid flow through an exponentially stretched surface

This article aims to analyze the two-dimensional (2D) nanofluid (Ag/C2H6O2) flow past an exponentially stretched sheet. The magnetic field impact, heat source/sink, and convection in the thermal profile are taken into account. The complexity of the problem is reduced by introducing a dimensionless group of functions. The reduced model is transformed into a system of first-order ordinary differential equations (ODEs). This system is further analyzed with the artificial neural network (ANN), which is trained using the Levenberg–Marquardt algorithm. The whole dataset is sub divided into three parts: training (70%), validation (15%), and testing (15%). The impact of nonlinear heat source/sink parameter, magnetic parameter, volume fraction of nanoparticles, and Prandtl number is displayed through graphs. The heat source, volume fraction, and the Prandtl number cause an increase in the thermal profile with its larger values. The magnetic parameter causes a decline in both the thermal and momentum boundary layers with its higher values. The analysis shows that the thermal energy profile is enhanced with the larger values of the volume fraction of silver nanoparticles and heat source. For each case study, the residual error (RE), regression line, and validation of the results are presented. The performance of the proposed methodology is numerically tabulated for the nanoparticle volume fraction shown in Table 3, where the minimum absolute error (AE) is 5.3373e−11 at ϕ=0.05. Based on this, we recommend ϕ=0.05 for better performance. The AEs for the ANN and bvp4c are computed for the state variables in Tables for the magnetic parameter M=5,10, and 15. These tables show the overall performance of the ANN and further validate the present study. We have also validated the results of the ANN through the mean squared error graphically, where the accuracy of the proposed methodology is proven.

Investigation of the Effects of Creative Imagination on Academic Outcomes in Design Programs

Imagination as the key to the creative process is one of the key components of design education that affects students' problem-solving skills and innovation. The purpose of this research is to determine the impact of creative imagination on the academic performance of students enrolled in design courses. It intends to obtain a measure of the way creative imagination affects or influences students’ performance as well as their achievements. However, there is a lack of understanding regarding the correlation between creativity and performance among university students. The study collected data on creative imagination and academic performance through a survey of 350 participants drawn from five universities. Self-administered questionnaires were distributed to 350 participants, of which 320 were 176 males and 144 females. The research included quantitative techniques such as linear regression, correlation analysis, and structural equation modeling-partial least squares (SEM-PLS). Further, the result of the analysis showed that imagination level has the most significant impact on academic outcomes in design programs with a strong positive effect (β=0.35, p-value = 0.04). Higher creative imagination scores had a positive correlation with grades and students' involvement. This underscores the importance of forecasting to enhance students’ performance and engagement in design programs. The strategies used in education should be utilized to supplement creativity to raise the performance and satisfaction of students.

Health Literacy in Preventing and Controlling Dengue Hemorrhagic Fever Among Village Health Volunteer, Pattani Province, Thailand

Background: Dengue Haemorrhagic Fever (DHF) is a mosquito-borne viral infection caused by the dengue virus (DENV). The situation of the DHF prevalence rate was still high in the community. One reason for this high rate involves community members, who had less participated in the prevention and control measures of DHF. Objectives: This descriptive study aimed to study factors associated with preventing and controlling DHF among village health volunteer, Pattani Province. Methods: Population consisted of 6,841 village health volunteers registered in Pattani Province, Thailand, in fiscal year 2020, and the sample size was calculated using Daniel formula for 215 cases, and systematic random sampling was carried out for collecting data. Data were collected by questionnaires composed of 3 parts, including characteristics, health literacy factors, and preventing and controlling DHF. In addition to check questionnaire standardization, validity was checked by three experts and try out testing reliability by Cronbach’s coefficient alpha about 0.87. Data were analysed using statistical as Pearson product moment correlation coefficient and Stepwise Multiple Regression Analysis. Results: The analysis found that health literacy factors were associated with preventing and controlling DHF among village health volunteer, Pattani Province such as access skill (r=0.310, p-value&lt;0.001) communication skill (r=0.201, p-value=0.003) media literacy skill (r=0.386, p-value&lt;0.001) decision skill (r=0.349, p-value&lt;0.001) self-management skill (r=0.480, p-value&lt;0.001), respectively. Conversely, cognitive skill had no association (r=0.117, p-value=0.086). Conclusion: The finding showed that health literacy of village health volunteer was associated with the preventing and controlling DHF. Therefore, health workers should encourage the village health volunteers to participate in sustainable prevention and control of DHF.