Independent Variables Research Articles

Associations between school-based fluoride mouth-rinse program, medical-dental expense subsidy policy, and children's oral health in Japan: an ecological study.

Dental caries are a common non-communicable disease among children. As a public health measure at the prefectural level, school-based fluoride mouth-rinse (S-FMR) program, medical/dental expense subsidy policies, and other factors may reduce the incidence of dental caries and tooth loss. Prefectures focusing on promoting oral health policies may promote both, but the interaction effect of implementing both subsidy policies and S-FMR at the prefectural level on caries prevention has not yet been examined. We conducted an ecological study using two-wave panel data, prefecture-level aggregated data in Japan for 2016 and 2018. Coefficient and 95% confidence intervals (CI) were calculated for the dependent variables for oral health using mixed-effects linear regression analysis adjusted for possible confounders. Two dependent variables were used; the standardized claim ratio (SCR) of deciduous tooth extraction and 12-year-olds' decayed, missing, or filled permanent teeth (DMFT). Four independent variables were S-FMR, the SCR of dental sealants, prefectural income per person, and subsidy policy in three models: co-payment until children enter elementary school (n = 23), no co-payment until children enter elementary school (n = 7), and co-payment continuing beyond elementary school (n = 17). The effects of six interaction terms, each representing a unique pairing from the four independent variables, were individually calculated. S-FMR was negatively associated with the SCR of deciduous tooth extractions and DMFT (coefficient = -0.11, 95% CI -0.20; -0.01 and coefficient = -0.003, 95% CI -0.005; -0.001, respectively). No co-payment until children enter elementary school was positively associated with the SCR of deciduous tooth extraction compared to co-payment until children enter elementary school(coefficient = 11.42, 95% CI 3.29; 19.55). SCR of dental sealants was positively associated with the SCR of deciduous tooth extractions (coefficient = 0.12, 95% CI 0.06; 0.19) but negatively associated with DMFT (coefficient = -0.001, 95% CI -0.003; -0.0001). Per capita prefectural income was positively associated with the SCR of deciduous tooth extractions(coefficient = 0.01, 95% CI 0.001; 0.02). No interaction was found between S-FMR and the subsidy policy at both outcomes. High S-FMR utilization and no co-payment until children enter elementary school were associated with fewer deciduous tooth extractions. Also, S-FMR and dental sealant were associated with decreased DMFT.

Exact solutions of steady Euler equations in two dimension by functional separation

Exact solutions of two dimensional Euler equations for incompressible fluid flows are found by the functional separation of variables. More specifically, two independent variables are considered in the Cartesian coordinates, polar coordinates and also general orthogonal curvilinear coordinates which include elliptic, parabolic and hyperbolic systems. Several new solutions are obtained and other classical solutions are rediscovered.

The effect of social media marketing on voting intention; an application of multidimensional panel data

PurposeThis study examines the effect of social media marketing on voting intention applying a combination of fuzzy logic methodology and a multidimensional panel data model.Design/methodology/approachThe study adopts a multidimensional panel data method that includes several fixed effects. The dependent variable is a multifaceted construct that measures the participants’ intention to vote. The independent variables are electronic word of mouth (eWOM), customisation (CUS), entertainment (ENT), interaction (INT), trendiness (TRD), candidate’s perceived image (CPI), religious beliefs (RB), gender and age. The grouping variables that signify fixed effects are employment status, level of education, mostly used social media and religion. First, the significance of said fixed effects was tested through an ANOVA process. Then, the main model was estimated, including the significant grouping variables as fixed effects.FindingsEmployment status and level of education were significant fixed effects. Also, eWOM, ENT, INT, CPI, RB and gender significantly affected participants’ voting intention.Research limitations/implicationsBeing based on a questionnaire that asked participants about how they perceive different aspects of social media, the present study is limited to their perceptions. Therefore, further studies covering the voters’ behaviour in action could be efficient complements to the present study.Practical implicationsThe findings could guide the political parties into prioritizing the aspects of social media in forming an effective campaign resulting in being elected.Social implicationsThe findings have the potential to help the public in making better informed decisions when voting. Furthermore, the results of this study indicate applications for social media which are beyond leisure time fillers.Originality/valueFuzzy logic and multidimensional panel data estimates are this study’s novelty and originality. Structural equation modelling and crisp linguistic values have been used in previous studies on social media’s effect on voting intent. The former refines the data gathered from a questionnaire, and the latter considers the possibility of including different grouping factors to achieve a more efficient and less biased estimation.

Unraveling socioeconomic determinants of health-related behavior, reception of information, and perceptions on disease disclosure at the time of the COVID-19 pandemic: did health insurance curb the disparities in the Philippines?

BackgroundThe study uncovers micro and macro socioeconomic disparities in terms of health behavior, disease perception, and reception of information. Furthermore, findings shed light on the possible role of health insurance on access to information, disease perception and the adoption of preventive behaviors in the context of a public health emergency such as the COVID-19 pandemic.MethodsThis study employed a cross-sectional design using the Philippine Demographic and Health Survey (DHS). With a total of 29,809 respondents, it evaluated the individual or household and systemwide socioeconomic determinants of four different outcomes: receipt of information, disease perception, uptake of free preventive services, and treatment-seeking behavior. In addition to logistic regression models with the socioeconomic variables as the independent variables, models for the evaluation of the moderating effect of insurance ownership were fitted. Predicted probabilities were reported for the analysis of moderating effects.ResultsFindings show that individual and householdsocioeconomic determinants affected health-behavior and access to or receipt of information pertinent to the COVID-19 pandemic. Both education and wealth affected the receipt of information such that individuals in more advantaged socioeconomic positions were at least 30% more likely to have received information on COVID-19. Wealth was also associated to treatment-seeking behavior. Regional differences were seen across all dependent variables. Moreover, the study provides evidence that ownership of insurance can close education-based gaps in the uptake of free vaccination and COVID-19 testing.ConclusionIt is imperative that targeted efforts be maximized by utilizing existing strategies and mechanisms to reach the marginalized and disadvantaged segments of the population. Health insurance may give off added benefits that increase proficiency in navigating through the healthcare system. Further research may focus on examining pathways by which health insurance or social policies may be used to leverage responses to public health or environmental emergencies.

Limit theorems for a class of processes generalizing the U-empirical process

In this paper, we develop theory and tools for studying U-processes, a natural higher-order generalization of the empirical processes. We introduce a class of random discrete U-measures that generalize the empirical U-measure. We establish a Glivenko-Cantelli and a Donsker theorem under conditions on entropy numbers prevalent in the theory of empirical processes. These results are proved under some standard structural conditions on the Vapnik-Chervonenkis classes of functions and some mild conditions on the model. The uniform limit theorems discussed in this paper are key tools for many further developments involving empirical process techniques. Our results are applied to prove the asymptotic normality of Liu's simplicial median. We conclude this paper by extending Anscombe's central limit theorem to encompass randomly stopped U-processes, building upon its application to randomly stopped sums of independent random variables.

Prolific application of green synthesised CdS nanoparticles for the sequestration of cationic dyes from aqueous solution

Synthesis of cadmium sulfide (CdS) nanoparticles using watermelon rind aqueous extract was explored for its effectiveness in eliminating Methylene Blue (MB) and Crystal Violet (CV) dyes from water. The synthesised CdS nanoparticles were characterised with XRD, TEM and EDX and found to be around 10 nm in size with spherical shape. A central composite design was developed to optimise independent variables such as pH, contact time and initial concentrations. The developed quadratic model was found to be significant with p-value <0.0001 and lack of fit >0.005 suggesting the reliability of the model. Isotherm modelling analysis revealed that Langmuir and Freundlich's isotherms provided better explanation for the equilibrium data. The loading capacities of the CdS nanoparticles were found be 227.3 and 185.1 mg g-1, respectively for MB and CV. The experimental data showed a good fit with the pseudo-second-order kinetic model, indicating that chemical reactions were the determining factor in controlling the rate-limiting step. Thermodynamic analyses indicated that the process was spontaneous and exothermic. Desorption and regeneration investigations demonstrated that CdS nanoparticles could be successfully regenerated up to 5 times and reused in the adsorption process. The results conclude that the CdS nanoparticles are capable of remediating water containing cationic dyes.
 KEY WORDS: Watermelon rind, CdS nanoparticles, Synthetic dyes, Adsorption, RSM
 Bull. Chem. Soc. Ethiop. 2024, 38(3), 631-645. 
 DOI: https://dx.doi.org/10.4314/bcse.v38i3.7

The Evaluation of using Learning Videos for High School Mathematics Curriculum Subjects with Flipped Classroom Models on Learning Achievement in the IVET University Semarang

This research deals with the development of learning video media for high school mathematics curricula using the ADDIE stage model. This model, as the name implies, consists of five main phases or stages, namely (A) analysis, (D) design, (D) development, (I) implementation, and (E) evaluation. The implementation and evaluation phase was carried out in this second year, with an expanded test carried out in the mathematics education study program at IVET University Semarang in a fun and enjoyable way, with a posttest and a user response questionnaire with the following results: Based on research at IVET University Semarang, the results of the posttest were processed using The t-test obtained a result of 0% < 5%, then H0 was rejected. This means that we accept H1, which means that the results of learning mathematics with learning video media for high school mathematics curricula are better than conventional learning models, looking at the average learning achievement in the mean column, the group statistics table, it is obtained that the average of the experimental class was 85.50 while the average of the control class was 70.42. These results show that the learning outcomes of the experimental class were better than those of the control class. R Square value is 0.825 = 82.5%. This value means that the effect of virtual lab media on learning achievement is 82.5%. In comparison, 17.5% of learning achievement was influenced by other variables outside of the independent variables in this study.
 Keywords: the evaluation, learning videos, high school mathematics curriculum; flipped classroom models

Insights from Turkey's big data: unraveling the preventability, pathogenesis, and risk management of Alzheimer's disease (AD)

Extensive research into dementia has more recently honed in on several key areas. These areas include the advancement of techniques such as the accumulation of amyloid-β and tau proteins, the monitoring of cerebral hypometabolism rates etc. The primary objective of this study is to explore the intricate interplay between Alzheimer's disease (AD)—other dementias (D) and various chronic illnesses in terms of time, intensity, and connectivity. In this context, we retrospectively examined data of 149,786 individuals aged 65 and above who received diagnoses of AD and D in the year 2020. At first, logistic regression (LR) analysis has been made with “sex”, “age” and “foreigner” (citizenship status) independent variables for AD and D. The LR models shows that while “sex” and “age” variables have a small rate on the risk of developing AD/D, it is detected that being a foreigner increase the risk of AD and D as 69.8% and 88.5% respectively. Besides, the LR models have middle-level success prediction rate for both of the two dependent variables. Additionally, we used the parallel coordinates graphs method within the R Studio to visualize their relationships and connections. The findings of this investigation strongly suggest that AD/D don’t stand as isolated conditions, but rather stem from intricate interactions and progressive processes involving diverse chronic diseases over time. Notably, ailments including hypertension, coronary artery disease, diabetes, hyperlipidemia, and psychological disorders, contribute substantially to the emergence of both AD and D. This study highlights that the fight against AD/D can only be possible with next-generation prophylactic interventions that can predict and manage risks. Such an approach holds the potential to potentially lower AD and dementia to levels that are amenable to treatment.

Investigation of the Effect of Vaccination Status on the Prognosis of Covid-19 Patients Hospitalized in Pandemic Service

Objective: We aimed to evaluate the effect of vaccination status on the prognosis of COVID-19 patients with varying vaccine combinations during hospitalization
 Materials and Methods: The study was conducted with the data of 854 COVID-19 patients, of which 457 were female. The dependent variable in the comparisons was the need for intensive care, and the independent variables were gender, risk score, severity score, and vaccination status.
 Results: The mean age of the patients was 57.7±15.71 with standard deviation (SD); 49.2% had never been vaccinated, and 18.3% needed intensive care. Three logistic regression models were developed using different vaccine combinations. Three doses of CoronaVac or two doses of CoronaVac and one dose of Comirnaty vaccine in the first model, three doses of CoronaVac vaccine in the second model, and receiving the vaccine solely (at least one dose in the 6 months before the disease) in the last model were found to reduce the risk of intensive care unit (ICU) admission by 82%, 77%, and 42%, respectively.
 Conclusion: The effectiveness of vaccination in preventing the need for intensive care in inpatients was once again demonstrated, and it was observed that this effectiveness increased even more with booster vaccine doses.

Patient-provider communication quality: Socioeconomic disparities in smoking outcomes.

Patient-provider communication quality is instrumental for healthy outcomes in patients. The objective of this study is to examine the relationships between patient-provider communication quality and participant characteristics, perception of e-cigarette harmfulness, and smoking outcomes. A pooled cross-sectional design was used on secondary data obtained from the Health Information National Trends Survey (HINTS) 5 from Cycle 1 through Cycle 4, from 2017-2022. Our final sample contained 3511 observations. Our outcome variable was the perception of electronic cigarette smoking status. The independent variable was patient-provider communication quality (PPCQ), measured from a series of questions with responses on a 4-item Likert scale (always, usually, sometimes, never). Demographic variables such as marital status, health insurance status, occupation status, and health-related variables were used as participant characteristics. Ordinal logistic regression models were used to examine the above relationships. Compared to males, females had lower odds of being in a higher category of perception of e-cigarette harmfulness compared to other categories of e-cigarette harmfulness (AOR=0.66; 95% CI: 0.57-0.76). Respondents who were non-Hispanic Black or Hispanic had lower odds of being in a higher category of perception of e-cigarettes compared to Whites (AOR=0.52; 95% CI: 0.49-0.78, and AOR=0.51; 95% CI: 0.41-0.65, respectively). Respondents who had higher education level compared to those with less than high school had lower odds (AOR=0.30; 95% CI: 0.17-0.51), and Hispanics compared to Whites had higher odds (AOR=1.59; 95% CI: 1.05-2.40), of being former smokers rather than current smokers. Providers should invest in staff training and development to target the populations that need conversations regarding e-cigarette usage.

The Influence of Organizational Culture and Work Motivation on Employee Performance with Job Satisfaction as Mediation Variables (Case Study PT. BPR Eka Ayu Artha Bhuwana)

This research aims to analyze the influence of organizational culture and work motivation on employee performance with job satisfaction as a mediating variable. The independent variables in this research are organizational culture and work motivation. The dependent variable in this research is employee performance and the mediating variable in this research is job satisfaction. The data collection techniques used in this research were observation, interviews and questionnaires. The data used is primary data and secondary data, with a research sample of 32 people who are employees of PT. BPR Eka Ayu Artha Bhuwana. Hypothesis testing is carried out using a variance-based Structural Equation Model (SEM) or what is called Partial Least Square (PLS). The results of the research show that organizational culture has a positive but not significant effect on job satisfaction, work motivation has a significant positive effect on job satisfaction, organizational culture has a significant positive effect on employee performance, work motivation has a positive and not significant effect on employee performance, job satisfaction has a positive and not significant effect on performance employees, job satisfaction is not a mediation of the relationship between organizational culture and employee performance and job satisfaction is not a mediation of work motivation on employee performance.

Correlation Between HbA1c and Body Mass Index Among Patients with High Lipid Profile Attending Johns Hopkins Aramco Healthcare Hospital in Saudi Arabia

BACKGROUND AND OBJECTIVES: Dyslipidemia is a modifiable risk factor for cardiovascular diseases, diabetes, and stroke. The primary objective of this study was to examine the relationship between HbA1C and body mass index (BMI) among patients with high lipid profiles. METHODS: This retrospective, hospital-based study was conducted at Johns Hopkins Aramco Healthcare Facilities. Data were extracted from medical health records and included demographics, lipid profiles, and HBA1c measurements. The study included 2368 non-diabetic participants, and DATAtab was used to analyze the data. RESULTS: The participants were 57.05% male and 78.42% Saudi. The mean age was 41.48±12.1 years, and the mean body mass index (BMI) was 28.44±5.53. There was a statistically significant relationship between the use of lipid lowering medicine and HbA1c (P<0.001). There was a very weak positive but statistically significant relationship between HbA1c and BMI (r = 0.18, P<0.001). HbA1c and systolic blood pressure appeared to have a statistically significant positive association (r = 0.16, P< 0.001). There was no correlation between HbA1c and low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), or diastolic blood pressure. There was an inverse correlation between HbA1c and high-density lipoprotein cholesterol (HDL-C) (r = -0.11, P< 0.001). Regression was performed using a linear multivariable analysis with HbA1c as the dependent variable, and age (B 0.192, P<0.001), BMI (B 0.119, P<0.001), HDL (B -0.058, P<0.005), triglyceride (B 0.093, P<0.001), lipid-lowering medication (B 0.104, P<0.001), systolic BP (B 0.060, P< 0.003) as independent variables. CONCLUSION: The results indicated that HbA1c is linked to BMI, age, systolic blood pressure, triglycerides, and HDL-C levels. There was no correlation between HbA1c and LDL-C, TC, and diastolic blood pressure.

Prodromal symptoms of rheumatoid arthritis in a primary care database: variation by ethnicity and socioeconomic status.

To assess whether prodromal symptoms of rheumatoid arthritis (RA), as recorded in the Clinical Practice Research Datalink Aurum (CPRD) database of English primary care records, differ by ethnicity and socioeconomic status. A cross-sectional study to determine the coding of common symptoms (≥0.1% in the sample) in the 24 months preceding RA diagnosis in CPRD Aurum, recorded between January 1st 2004 to May 1st 2022. Eligible cases were adults with a code for RA diagnosis. For each symptom, a logistic regression was performed with the symptom as dependent variable, and ethnicity and socioeconomic status as independent variables. Results were adjusted for sex, age, BMI, and smoking status. White ethnicity and the highest socioeconomic quintile were comparators. In total, 70115 cases were eligible for inclusion, of which 66.4% female. Twenty-one symptoms were coded in > 0.1% of cases so were included in the analysis. Patients of South Asian ethnicity had higher frequency of codes for several symptoms, with the largest difference by odds ratio being muscle cramps (OR 1.71, 1.44-2.57) and shoulder pain (1.44, 1.25-1.66). Patients of Black ethnicity had higher prevalence of several codes including unintended weight loss (2.02, 1.25-3.28) and ankle pain (1.51, 1.02-2.23). Low socioeconomic status was associated with morning stiffness (1.74, 1.08-2.80) and falls (1.37, 2.03-1.82). There are significant differences in coded symptoms between demographic groups, which must be considered in clinical practice in diverse populations and to avoid algorithmic bias in prediction tools derived from routinely collected healthcare data.

Approximated Uncertainty Propagation of Correlated Independent Variables Using the Ordinary Least Squares Estimator.

For chemical measurements, calibration is typically conducted by regression analysis. In many cases, generalized approaches are required to account for a complex-structured variance-covariance matrix of (in)dependent variables. However, in the particular case of highly correlated independent variables, the ordinary least squares (OLS) method can play a rational role with an approximated propagation of uncertainties of the correlated independent variables into that of a calibrated value for a particular case in which standard deviation of fit residuals are close to the uncertainties along the ordinate of calibration data. This proposed method aids in bypassing an iterative solver for the minimization of the implicit form of the squared residuals. This further allows us to derive the explicit expression of budgeted uncertainties corresponding to a regression uncertainty, the measurement uncertainty of the calibration target, and correlated independent variables. Explicit analytical expressions for the calibrated value and associated uncertainties are given for straight-line and second-order polynomial fit models for the highly correlated independent variables.

Development and characterization of physical stability, glycemic index, flow behaviors, and antioxidant activity of finger millet-based beverage.

Millets are gaining attention as a superfood due to their higher nutritional value and cost-effectiveness. In this regard, extraction condition for the development of finger millet-based beverage was optimized using a central composite design. Soaking time (X1) and temperature (X2) in the range of 5-10h and 40-60°C, respectively, were the independent variables taken for three responses, namely, yield, total solids, and sedimentation index. The optimized conditions are best fitted in quadratic model (R2 0.91) for all the dependent variables. Accordingly, the optimized levels selected for soaking time and temperature were 10h and 60°C respectively, resulting in the yield (Y1) of 91.86%±0.94%, total solids (Y2) of 17.72%±0.56%, and sedimentation index (Y3) of 12.18%±0.06%. Further, xanthan gum (0.5%) and jaggery powder (5%) were added in the optimized beverage to improve its physicochemical and functional properties. Xanthan gum improved the physical stability and rheological properties of the beverage, whereas jaggery improved the flavor and phenolic content of the same. The optimized beverage had a good amount of phenolic content (53.70µgGAE/mL), antioxidant activity (DPPH 13.76µmol/mL), zeta potential (-19.8mV), and glycemic index (57). The flow curve of beverages was obtained using power law model, and result indicated good consistency index (k=0.7716Pas) with flow behavior (n=0.3411) depicted its pseudoplastic nature. The optimized extraction condition significantly reduced the antinutrients, tannin, and phytic content by 47% and 14%, respectively, in optimized beverage than control.

A Neoteric Paradigm to Improve Food Security: The Predictors of Women's Influence on Egocentric Networks' Food Waste Behaviors.

COVID-19, the most recent multi-dimensional global food crisis, challenged leadership and impacted individuals' personal networks. Two cross-sectional surveys were disseminated to women involved in their state's women's leadership committee to understand food waste behaviors. An egocentric network analysis was chosen as the methodology to better understand personal advice network characteristics and examine the impacts of Farm Bureau women's leadership committee members' advice networks on their food waste behavior. A multilevel model was conducted to identify factors related to respondents leading their network members toward positive food waste decisions. Independent variables included in the variables at the individual (e.g., each respondent's race, generation), dyadic (e.g., length respondent has known each member of her network), and network levels (e.g., proportion of the respondent's network that was female) were included in the model. Women were more likely to report connections with people they led to positive food waste behaviors and food security when: they had higher food waste sum scores, they were part of Generation X, the network member they led to more positive food waste behaviors was a friend, and if there were fewer women in their advice networks.

Factors affecting the intraoperative calculi excretion during flexible ureteroscopy lithotripsy: an in vitro analysis.

To explore the parameters influencing intraoperative calculi excretion (ICE) during flexible ureteroscopy lithotripsy (fURL) using in vitro simulation experiments. 3D-printed human kidney models were used to simulate the elimination of gravel during fURL. The factors influencing the ICE during fURL were analyzed by comparing the effects of different degrees of hydronephrosis (mild, moderate, and severe), surgical positions (supine and lateral position), ratios of endoscope-sheath diameter (RESD) (0.625, 0.725, and 0.825), gravel sizes (0.50-1.00mm, 0.25-0.50mm, and 0.10-0.25mm), and ureteral access sheaths (UASs) (traditional UAS and negative-pressure UAS) on ICE. The impacts of various UAS, RESD, degree of hydronephrosis, surgical positions, and gravel sizes on ICE were all significant (p < 0.05). We found no evidence of multicollinearity for all the independent variables, and the linear regression equation fitted as (R2 = 0.569). Employing negative-pressure UAS, smaller RESD, milder hydronephrosis, lateral position, and smaller gravel size contribute to improved ICE during fURL. Among them, the adoption of negative-pressure UAS had the most substantial effects.

Predicting dental anxiety in young adults: classical statistical modelling approach versus machine learning approach

ObjectivesTo predict and identify the key demographic and clinical exposure factors associated with dental anxiety among young adults, and to compare if the traditional statistical modelling approach provides similar results to the machine learning (ML) approach in predicting factors for dental anxiety.MethodsA cross-sectional study of Western Illinois University students. Three survey instruments (sociodemographic questionnaire, modified dental anxiety scale (MDAS), and dental concerns assessment tool (DCA)) were distributed via email to the students using survey monkey. The dependent variable was the mean MDAS scores, while the independent variables were the sociodemographic and dental concern assessment variables. Multivariable analysis was done by comparing the classical statistical model and the machine learning model. The classical statistical modelling technique was conducted using the multiple linear regression analysis and the final model was selected based on Akaike information Criteria (AIC) using the backward stepwise technique while the machine learining modelling was performed by comparing two ML models: LASSO regression and extreme gradient boosting machine (XGBOOST) under 5-fold cross-validation using the resampling technique. All statistical analyses were performed using R version 4.1.3.ResultsThe mean MDAS was 13.73 ± 5.51. After careful consideration of all possible fitted models and their interaction terms the classical statistical approach yielded a parsimonious model with 13 predictor variables with Akaike Information Criteria (AIC) of 2376.4. For the ML approach, the Lasso regression model was the best-performing model with a mean RMSE of 0.617, R2 of 0.615, and MAE of 0.483. Comparing the variable selection of ML versus the classical statistical model, both model types identified 12 similar variables (out of 13) as the most important predictors of dental anxiety in this study population.ConclusionThere is a high burden of dental anxiety within this study population. This study contributes to reducing the knowledge gap about the impact of clinical exposure variables on dental anxiety and the role of machine learningin the prediction of dental anxiety. The predictor variables identified can be used to inform public health interventions that are geared towards eliminating the individual clinical exposure triggers of dental anxiety are recommended.

County-Level Poverty Evaluation Using Machine Learning, Nighttime Light, and Geospatial Data

The accurate and timely acquisition of poverty information within a specific region is crucial for formulating effective development policies. Nighttime light (NL) remote sensing data and geospatial information provide the means for conducting precise and timely evaluations of poverty levels. However, current assessment methods predominantly rely on NL data, and the potential of combining multi-source geospatial data for poverty identification remains underexplored. Therefore, we propose an approach that assesses poverty based on both NL and geospatial data using machine learning models. This study uses the multidimensional poverty index (MPI), derived from county-level statistical data with social, economic, and environmental dimensions, as an indicator to assess poverty levels. We extracted a total of 17 independent variables from NL and geospatial data. Machine learning models (random forest (RF), support vector machine (SVM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM)) and traditional linear regression (LR) were used to model the relationship between the MPI and independent variables. The results indicate that the RF model achieved significantly higher accuracy, with a coefficient of determination (R2) of 0.928, a mean absolute error (MAE) of 0.030, and a root mean square error (RMSE) of 0.037. The top five most important variables comprise two (NL_MAX and NL_MIN) from the NL data and three (POI_Ed, POI_Me, and POI_Ca) from the geographical spatial data, highlighting the significant roles of NL data and geographical data in MPI modeling. The MPI map that was generated by the RF model depicted the detailed spatial distribution of poverty in Fujian province. This study presents an approach to county-level poverty evaluation that integrates NL and geospatial data using a machine learning model, which can contribute to a more reliable and efficient estimate of poverty.

Density and Correlation in a Random Sequential Adsorption Model

We consider the random sequential adsorption process on the one- dimensional lattice with nearest-neighbor exclusion. In this model, each site s 2 Z starts empty and a particle will be deposited in it at time ts, where (ts)s2Z is a sequence of independent random variables uniformly distributed on the interval [0; 1]. The site will be occupied if both of its neighbors are vacant. Analytical expressions for the density of occupied sites and the pair correlation function, for all time t, are well-established and have been obtained through methods such as generating functions and differential equations. In this study, we present a method based on probabilistic arguments for the calculation of these expressions.