Coefficients Of Variables Research Articles

A Study on the Effect of Color Preference on Purchase Intention of Picture Books for 3-7 Year Old Children in China and South Korea

Abstract This paper adopts a linear regression model to study the relationship between picture book color and purchase intention. The research object is selected, the research data are obtained by distributing questionnaires, the correlation coefficient formula of the linear regression model is used to get the correlation of the research independent variables, and then the least squares method is used to conduct regression analysis of picture book color preference and purchase intention. The research data show that the R² of model 1 (control variable) is 0.015. The R² of model 2 (independent variable) is 0.625, which increases the degree of explanation by 61.00% compared with model 1. The regression coefficients of the independent variables are 0.214, 0.122, and 0.126, respectively. All of them reach a significant level, which indicates that there is a significant positive effect of the color preference of picture books of Chinese and Korean children between the ages of 3-7 on the willingness to purchase. The color preference of Chinese and Korean children’s picture books aged 3-7 has a significant positive effect on purchase intention, as indicated by this. The cross-border children’s book market can benefit from this study as a good sales strategy and suggestion to better promote children’s picture books.

The Utility of “Industry 4.0” for Economic Development and Industrial Structure Change in Germany

Abstract Germany’s “Industry 4.0” strategy is a technological revolution that accelerates full penetration into industrial fields such as manufacturing, mainly based on the Internet of Things and the rapidly developing new generation of Internet technology as a carrier. This paper examines the implementation of Germany’s “Industry 4.0” strategy, focusing on R&D expenditures, capacity utilization rates, and CNC rates of key processes in the manufacturing industry. It also lays the groundwork for Germany’s economic development and the effects of industrial restructuring. We construct a regression model to analyze the impact of “Industry 4.0” on the German economy, using economic growth as a measure of economic development. We use the automobile industry as a representative to study the industrial restructuring and upgrading of Germany under the “Industry 4.0” strategy. The results demonstrate a positive correlation between the estimated coefficients of economic growth variables in each year from 2020 to 2022 and the industry’s value-added, passing the significance test at the 0.05 level. In the industrial restructuring, Germany’s automobile industry has realized the transformation from traditional manufacturing mode to intelligent manufacturing mode, and the 1387.2 billion euros of Volkswagen’s R&D investment also shows that Germany is still pursuing the technological upgrading of the industry.

ПОВЕДЕНЧЕСКИЕ ПРЕДИКТОРЫ ОЖИДАЕМОЙ ПРОДОЛЖИТЕЛЬНОСТИ ЖИЗНИ В РОССИЙСКОЙ ФЕДЕРАЦИИ (ПО ИТОГАМ ВСЕРОССИЙСКОГО ВЫБОРОЧНОГО НАБЛЮДЕНИЯ СОСТОЯНИЯ ЗДОРОВЬЯ НАСЕЛЕНИЯ В 2019 И 2023 ГГ.)

Significance. Increasing life expectancy is the main goal of the National Demography Project to achieve the national goal of protecting the population of Russia, actualized by high rates of mortality in the country. In order to identify factors influencing life expectancy in the Russian Federation in the pre-pandemic period in 2019 and 2023, correlations of indicators characterizing a healthy lifestyle in 85 regions of the Russian Federation have been analyzed with a further construction of single-factor and multiple linear regression models to project the impact of factors on the dynamics in life expectancy. The novelty of the study is the use of some elements of econometric analysis for dynamic series for 2019 and 2023 and selection of indicators that are statistically significant in the regions of the Russian Federation. Material and methods. Significant factors for the construction of regression models were selected from the indicators included in the surveys of the All-Russian sample observation of health in 2019 and 2023 including the indicator of adherence to a healthy lifestyle according to methods of the World Health Organization. Results. A correlation and regression analysis made it possible to identify the most significant factors affecting life expectancy in the regions of the Russian Federation. Inter-regional differences in life expectancy models were estimated based on the analysis of σ-convergence of the regional indicators of life expectancy from 2000 to 2023. The study revealed a trend of divergence in the regional indicators of life expectancy in 2018 and 2021-2023 due to the male cohort of the population, including risk groups for mortality from diseases of the circulatory system. Conclusion. A healthy lifestyle can have a positive effect on life expectancy with varying degrees of correlation of the components of healthy lifestyle: among the considered linear regression single-factor models, on the example of 85 regions of the Russian Federation, excessive alcohol consumption, salt intake, daily tobacco smoking, sufficient consumption of vegetables and fruits, and self-assessment of health are statistically significant. In the multiple linear regression model, a statistically significant predictor is daily smoking with an average level of the coefficient of determination. However, in order to achieve the planned targets of the National Project “Long and active life” by 2036, a more than twofold decrease in the proportion of daily smokers is required compared to 2023. The multiple linear regression model confirmed a reliable Y-intersection coefficient, the coefficients of other variables are not statistically significant, substantiating the need for a more detailed analysis of factors at the regional level. Scope of application. The presented regression assessment approaches can be used to describe the prerequisites for developing a health-preserving behavior in order to form stable trends, as well as preventive measures of the relevant demographic policy of the state to maintain stability and progressive positive development of the demographic situation and timely prognostic assessments of trends in deviation from the positive trend.

Repetibilidade e número de colheitas para seleção de genótipos de castanheira-da-amazônia em sistema agroflorestal de cultivo

Abstract The objective of this work was to estimate the repeatability coefficients of variables related to fruit and nut production, the minimum number of crops required for genotype selection, and the permanent phenotypic correlation coefficients between variables in a cultivated population of Amazon nut. A total of 40 genotypes from seeds, belonging to an agroforestry system implemented in 1995, in the state of Roraima, Brazil, were evaluated during ten years for six variables related to fruit and nut production. The genetic-statistical analyses were performed using the maximum restricted likelihood and best linear unbiased prediction (REML/BLUP) method, based on the basic model of repeatability, without an experimental design, of the Selegen-REML/BLUP software. Genetic variability between genotypes and a moderate regularity in the repetition of the studied variables between different harvests were detected. The number of harvests required to reach a coefficient of determination of 80% ranged from seven, for the variables average nut weight per fruit and number of fruits per plant, to nine, for average nut weight. The correlation results indicate the possibility of indirect selection for nut production through the evaluation of the number and weight of fruits per plant.

Factors Affecting Postoperative Early-term Mortality and Anastomotic Leakage in Geriatric Colorectal Cancer Patients.

In terms of early-term mortality, there may be variability in terms of factors belonging to age groups. While some risk factors apply to all patients undergoing colorectal cancer surgery, some factors may come to the fore in terms of age. There have been very few studies on factors that increase the risk of early-term mortality, especially for geriatric patients. It was aimed to compare factors influencing prognosis and mortality within the first 30 postoperative days between geriatric patients and those <65 years of age, and to identify factors that increase the risk of anastomotic leakage and early-term mortality, particularly in geriatric patients. Clinical, laboratory, and pathology findings from 341 patients (186 geriatric) who underwent surgery for colorectal cancer between January 2016 and December 2019 were collected and analyzed. In terms of categorical variables, comparisons between groups were made with Pearson's Chi Square test and Fisher's Exact Test. Risk coefficients of variables in terms of anastomotic leakage and early-term mortality were determined by logistic regression analysis. The results were evaluated within the 95% Confidence interval, and p < 0.05 values were considered significant. Anastomotic leakage was detected in 7% of patients, and 6.2% of the patients died within the first 30 postoperative days. The 30-day postoperative mortality rate was significantly higher in geriatric patients with hypertension (p = 0.003), those undergoing emergency surgery (p = 0.007), those with stage 4 tumors (p < 0.001), those with ostomy-related complications (p = 0.042), those who developed intraabdominal abscess or peritonitis (p < 0.001), those with respiratory failure (p = 0.009), and those with perforation (p = 0.001). In patients <65 years of age, groups stratified by these variables did not differ significantly in terms of early-term mortality rate (p > 0.05 for each). These findings show that lack of bowel preparation and development of intraabdominal abscess/peritonitis significantly increase early-term mortality rates in both <65 and geriatric patients. Additionally, hypertension, emergency surgery, advanced tumor stage, development of ostomy-related complications, respiratory failure, and perforation significantly increase early-term mortality solely in geriatric patients.

Study on Reconstruction of Condominium in the Metropolitan Area Using Real Estate Data

In Japan, there is concern about the impact of the growing stock of older condominiums on the buildings themselves and the surrounding environment, and immediate action is needed. While in other countries, repair, elimination, and retirement of aged condominiums are the mainstream methods, in Japan, reconstruction is sometimes used. This study proposes a new decision criterion using discriminant analysis to address the problem of the difficulty and long time required to build consensus for rebuilding older condominiums. Discriminant analysis used building attributes and other variables to differentiate and predict condominium properties that have been successfully reconstructed from those that were not. Simple and clear answers will enable non-expert judgment and aid in consensus building. Multiple discriminant formulas were obtained and compared for Tokyo and neighbouring cities, as well as by subdividing and adding up these subdivisions. At the same time, coefficients of variables were changed or added to increase the reliability of the equations. As a result, a new modified discriminant equation with a high degree of reliability that advances previous studies was obtained. In addition, through the process of creating the formula, we discovered a new option for determining whether or not to rebuild based on whether or not there is an excess floor-area ratio, which is generally considered to be the solution in Japan, by increasing the site area.

Impact of Digitalization on Global Sustainable Development Across Countries

This study develops an economic development index, a social development index, an environmental sustainability index, and an information and communication technology index to explore the comparison of 34 countries in global sustainable development (GSD) and its promoting indicators, and digitalization. The composite Z-score method is used to create the above-mentioned indexes between 2000 - 2021. The statistical values of these indexes are used to explicate the performance of these countries in all components of sustainable development and digitalization. The correlation coefficients among the mentioned indicators are also estimated to measure associations among them. The regression coefficients of explanatory variables with GSD and digitalization are also estimated using a log-linear regression model. There is reported a high diversity in digitalization, economic development (ED), social development (SD), environmental sustainability (ES), and GSD across countries. GSD is not possible without ED, SD, and ES. Digitalization has a positive impact on sustainable, social, and economic development. Digital technology helps to increase GSD, while GSD seems favorable to increasing digitalization. Digitalization improves as ED, SD, and GSD increase. While digitalization has a negative impact on ES. Green innovation, green and appropriate technologies, green entrepreneurship, and technological advancement would be supportive to increase digitalization and GSD.

Modernization of Intellectual Property Discipline Governance Based on Multivariate Statistical Analysis Towards the Construction of a Powerful Intellectual Property Country

Abstract In this paper, after using the TF-IDF algorithm to calculate the weights of intellectual property keywords of digital content, the weights of fused word multi-features are improved to obtain the proportion of intellectual property keywords. Based on intellectual property keywords, combining adversarial fingerprint generation and intellectual property authentication, an intellectual property protection model based on RCNN is constructed. From the perspective of strong nation-building, the research on intellectual property affecting the artificial intelligence industry is designed, and multivariate statistical analysis of scientific governance of intellectual property in the context of strong nation-building is carried out. The results show that the SSIM values obtained by the method of this paper are much better than those obtained by the comparison method in terms of model analysis. Except for the anti-quantization attack, the rest of the indicators are all greater than 0.9. It confirms that the method presented in this paper has excellent robustness in protecting intellectual property. In the empirical analysis of the impact of intellectual property rights on the artificial intelligence industry, except for RDF, which is significant at the 5% level, the regression coefficients of the other explanatory variables are all significant at the 1% level, 2 R amounting to 0.860771 and with a DW value of 1.667387 in the range of 1.5-2.5, that is, it indicates that the level of China’s intellectual property rights protection and the competitiveness of the AI industry are positively correlated. It is positively correlated. This study promotes the improvement of China’s innovation ability and the formation of innovation mechanisms from the aspect of intellectual property protection so as to realize the comprehensive catching up of technology and economy of late-developed countries.

On the Nuisance of Control Variables in Causal Regression Analysis

Control variables are included in regression analyses to estimate the causal effect of a treatment on an outcome. In this article, we argue that the estimated effect sizes of controls are unlikely to have a causal interpretation themselves, though. This is because even valid controls are possibly endogenous and represent a combination of several different causal mechanisms operating jointly on the outcome, which is hard to interpret theoretically. Therefore, we recommend refraining from interpreting the marginal effects of controls and focusing on the main variables of interest, for which a plausible identification argument can be established. To prevent erroneous managerial or policy implications, coefficients of control variables should be clearly marked as not having a causal interpretation or omitted from regression tables altogether. Moreover, we advise against using control variable estimates for subsequent theory building and meta-analyses.

The impact of rural revitalization policies on the diversification benefits of listed agricultural companies

This paper investigates the effect of the implementation of rural revitalization policy on the diversification of listed agricultural companies, using panel quantile model with OLS regression model, and examines the heterogeneity of tax incentives on the above effect. And the manuscript find that the regression coefficient of the explanatory variables of tax incentives is 0.3367, which is significant at 1 % level. It indicates that tax incentives have a significant positive effect on the business performance of agricultural listed companies, which in turn promotes the transformation of agricultural listed companies to modern agriculture.

Research on the Optimal Output Power of Proton‐Exchange Membrane Fuel Cell Based on Improved Model Predictive Control Strategy

To achieve optimal power output of proton‐exchange membrane fuel cell (PEMFC) under load variation and noise disturbance, this article proposes a recursive subspace model predictive control based on Laguerre function (RSMPCL) strategy. First, a fractional PEMFC identification model is established, and an optimal power setter is introduced to provide an optimal power value corresponding to the current load. Second, during the predictive process, a subspace predictor is updated in real time where a Householder transformer is applied to decrease the computation time of the frequent QR decomposition, and a variable forgetting factor is introduced to enhance the sensitivity and accuracy of predictive control. In the control process, a Laguerre function is employed to transform the problem of computing the input increment at the next step into calculating the coefficients of the input variables. This transformation reduces the computation time of the control quantities. Finally, load and disturbance tests conducted on a dSPACE semiphysical simulation platform indicate that compared with constrained subspace model predictive control, the proposed RSMPCL strategy can help the fuel cells achieve the optimal power point with control accuracy, robustness, and calculation speed.

Fire, Rain and CO2: Potential Drivers of Tropical Savanna Vegetation Change, with Implications for Carbon Crediting

A global trend of increasing tree cover in savannas has been observed and ascribed to a range of possible causes, including CO2 levels, changing rainfall and fire frequency. We tested these explanations in the Australian tropical savanna, taking 96 savanna ‘cool burning’ projects from Australia’s emissions offset scheme as case studies. We obtained readings of tree cover and explanatory variables from published remote sensing or spatial data sources. These were analysed using time-series linear regression to obtain coefficients for the influence of severe fire occurrence, annual rainfall and prior percentage tree cover. Although statistically significant coefficients for the key variables were found in only half (severe fire) or one quarter (rainfall) of the individual project models, when comparing all the model coefficients across the rainfall gradient, ecologically coherent explanations emerge. No residual trend was observed, suggesting rising CO2 levels have not influenced tree cover over the study period. Our approach models tree cover change by separating ecological drivers from human-controlled factors such as fire management. This is an essential design feature of national emissions inventories and emissions offsets programs, where crediting must be additional to the expected baseline, and arise from human activity.

GENERALIZED MATRIX DECOMPOSITION REGRESSION: ESTIMATION AND INFERENCE FOR TWO-WAY STRUCTURED DATA.

Motivated by emerging applications in ecology, microbiology, and neuroscience, this paper studies high-dimensional regression with two-way structured data. To estimate the high-dimensional coefficient vector, we propose the generalized matrix decomposition regression (GMDR) to efficiently leverage auxiliary information on row and column structures. GMDR extends the principal component regression (PCR) to two-way structured data, but unlike PCR, GMDR selects the components that are most predictive of the outcome, leading to more accurate prediction. For inference on regression coefficients of individual variables, we propose the generalized matrix decomposition inference (GMDI), a general high-dimensional inferential framework for a large family of estimators that include the proposed GMDR estimator. GMDI provides more flexibility for incorporating relevant auxiliary row and column structures. As a result, GMDI does not require the true regression coefficients to be sparse, but constrains the coordinate system representing the regression coefficients according to the column structure. GMDI also allows dependent and heteroscedastic observations. We study the theoretical properties of GMDI in terms of both the type-I error rate and power and demonstrate the effectiveness of GMDR and GMDI in simulation studies and an application to human microbiome data.

Spatial variation of premarital HIV testing and its associated factors among married women in Ethiopia: Multilevel and spatial analysis using 2016 demographic and health survey data.

Africa is the most severely affected area, accounting for more than two-thirds of the people living with HIV. In sub-Saharan Africa, more than 85% of new HIV-infected adolescents and 63% of all new HIV infections are accounted for by women. Ethiopia has achieved a 50% incidence rate reduction. However, mortality rate reduction is slow, as the estimated prevalence in 2021 is 0.8%. In sub-Saharan Africa, heterosexual transmission accounts for the majority of HIV infections, and women account for 58% of people living with HIV. Most of these transmissions took place during marriage. Thus, this study aimed to explore the spatial variation of premarital HIV testing across regions of Ethiopia and identify associated factors. A cross-sectional study design was employed. A total of 10223 weighted samples were taken from individual datasets of the 2016 Ethiopian Demographic and Health Survey. STATA version 14 and ArcGIS version 10.8 software's were used for analysis. A multilevel mixed-effect generalized linear model was fitted, and an adjusted prevalence Ratio with a 95% CI and p-value < 0.05 was used to declare significantly associated factors. Multilevel models were compared using information criteria and log-likelihood. Descriptive and spatial regression analyses (geographical weighted regression and ordinary least squares analysis) were conducted. Models were compared using AICc and adjusted R-squared. The local coefficients of spatial explanatory variables were mapped. In spatial regression analysis, secondary and above education level, richer and above wealth quintile, household media exposure, big problem of distance to health facility, having high risky sexual behaviour and knowing the place of HIV testing were significant explanatory variables for spatial variation of premarital HIV testing among married women. While in the multilevel analysis, age, education level, religion, household media exposure, wealth index, khat chewing, previous history of HIV testing,age at first sex, HIV related knowledge, HIV related stigma, distance to health facility, and community level media exposure were associated with premarital HIV testing among married women. Premarital HIV testing had a significant spatial variation across regions of Ethiopia. A statistically significant clustering of premarital HIV testing was observed at Addis Ababa, Dire Dawa, North Tigray and some parts of Afar and Amhara regions. Therefore area based prevention and interventional strategies are required at cold spot areas to halt the role of heterosexual transmission in HIV burden. Moreover, the considering the spatial explanatory variables effect in implementations of these strategies rather than random provision of service would make regional health care delivery systems more cost-effective.

A prospective comparative study on bladder volume measurement with portable ultrasound scanner and CT simulator in pelvic tumor radiotherapy.

The consistency of bladder volume is very important in pelvic tumor radiotherapy, and portable bladder scanner is a promising device to measure bladder volume. The purpose of this study was to investigate whether the bladder volume of patients with pelvic tumor treated with radiotherapy can be accurately measured using the Meike Palm Bladder Scanner PBSV3.2 manufactured in China and the accuracy of its measurement under different influencing factors. A total of 165 patients with pelvic tumor undergoing radiotherapy were prospectively collected. The bladder volume was measured with PBSV3.2 before simulated localization. CT simulated localization was performed when the bladder volume was 200-400ml. The bladder volume was measured with PBSV3.2 immediately after localization and recorded. The bladder volume was then delineated on CT simulation images and recorded. To compare the consistency of CT simulation bladder volume and bladder volume measured by PBSV3.2. To investigate the accuracy of PBSV3.2 in different sex, age, treatment purpose, and bladder volume. There was a significant positive correlation with bladder volume on CT and PBSV3.2 (r = 0.874; p < 0.001). The mean difference between CT measured values and PBSV3.2 was (-0.14 ± 50.17) ml. The results of the different variables showed that the overall mean of PBSV3.2 and CT measurements were statistically different in the age ≥ 65 years, bladder volumes > 400ml and ≤ 400ml groups (p = 0.028, 0.002, 0.001). There was no statistical significance between the remaining variables. The volume difference between PBSV3.2 measurement and CT was 12.87ml in male patients, which was larger than that in female patients 3.27ml. Pearson correlation analysis showed that the correlation coefficient was 0.473 for bladder volume greater than 400ml and 0.868 for bladder volume less than 400ml; the correlation coefficient of the other variables ranged from 0.802 to 0.893. This is the first large-sample study to evaluate the accuracy of PBSV3.2 in a pelvic tumor radiotherapy population using the convenient bladder scanner PBSV3.2 made in China. PBSV3.2 provides an acceptable indicator for monitoring bladder volume in patients with pelvic radiotherapy. It is recommended to monitor bladder volume with PBSV3.2 when the planned bladder volume is 200-400ml. For male and patients ≥ 65 years old, at least two repeat measurements are required when using a bladder scanner and the volume should be corrected by using a modified feature to improve bladder volume consistency.

Evaluation of novel candidate filtration markers from a global metabolomic discovery for glomerular filtration rate estimation

Creatinine and cystatin-C are recommended for estimating glomerular filtration rate (eGFR) but accuracy is suboptimal. Here, using untargeted metabolomics data, we sought to identify candidate filtration markers for a new targeted assay using a novel approach based on their maximal joint association with measured GFR (mGFR) and with flexibility to consider their biological properties. We analyzed metabolites measured in seven diverse studies encompasing 2,851 participants on the Metabolon H4 platform that had Pearson correlations with log mGFR and used a stepwise approach to develop models to< -0.5 estimate mGFR with and without inclusion of creatinine that enabled selection of candidate markers. In total, 456 identified metabolites were present in all studies, and 36 had correlations with mGFR< -0.5. A total of 2,225 models were developed that included these metabolites; all with lower root mean square errors and smaller coefficients for demographic variables compared to estimates using untargeted creatinine. Seventeen metabolites were chosen, including 12 new candidate filtration markers. The selected metabolites had strong associations with mGFR and little dependence on demographic factors. Candidate metabolites were identified with maximal joint association with mGFR and minimal dependence on demographic variables across many varied clinical settings. These metabolites are excreted in urine and represent diverse metabolic pathways and tubular handling. Thus, our data can be used to select metabolites for a multi-analyte eGFR determination assay using mass spectrometry that potentially offers better accuracy and is less prone to non-GFR determinants than the current eGFR biomarkers.

Association of COVID-19 information media, providers, and content with vaccine uptake among Tokyo residents

To elucidate appropriate ways to induce behavior that prevents the spread of infection, we examined the association between COVID-19 vaccination and COVID-19-related information after more than 85% of the population had been vaccinated. Nonprobability quota sampling was used to recruit 2000 Tokyo residents as participants. The association between previous vaccine uptake and how much people thought they were affected by each of nine media, seven providers, and four content types of information was assessed using an online survey form. Subjective influence was assessed, and order logistic regression analyses were performed. We further calculated standardized partial regression coefficients for the independent variables. The results showed that while people did not think they were strongly affected by any COVID-19 information, significant positive associations between 9 of 20 variables, and significant negative associations between 7 of 20 variables were observed with vaccine uptake. The regression analysis involving the interaction terms between independent variables and sex showed a significant association between vaccine uptake and only daily conversation. Simple slope analysis showed a stronger positive association for females than for males. Regression analysis with interaction terms between each independent variable and age showed a significant association between vaccine uptake and print newspapers, social networking services, prefectural governors, family/relatives, accessibility, side effects, and supply visibility. Simple slope analysis also showed that the positive association between the subjective influence of newspapers and vaccine uptake was observed only for older people (≥69 years), and that of prefectural governors was more significant for older than younger people (≤32 years). In contrast, the trustworthy information provided by family/relatives was positively associated with vaccine uptake only for younger people. These results suggest that careful consideration must be given to the differences in age and sex to provide appropriate information that motivates Tokyo residents to receive vaccination during COVID-19 pandemic.

A prediction model for high ovarian response in the GnRH antagonist protocol.

The present study was designed to establish and validate a prediction model for high ovarian response (HOR) in the GnRH antagonist protocol. In this retrospective study, the data of 4160 cycles were analyzed following the in vitro fertilization (IVF) at our reproductive medical center from June 2018 to May 2022. The cycles were divided into a training cohort (n=3121) and a validation cohort (n=1039) using a random sampling method. Univariate and multivariate logistic regression analyses were used to screen out the risk factors for HOR, and the nomogram was established based on the regression coefficient of the relevant variables. The area under the receiver operating characteristic curve (AUC), the calibration curve, and the decision curve analysis were used to evaluate the performance of the prediction model. Multivariate logistic regression analysis revealed that age, body mass index (BMI), follicle-stimulating hormone (FSH), antral follicle count (AFC), and anti-mullerian hormone (AMH) were independent risk factors for HOR (all P< 0.05). The prediction model for HOR was constructed based on these factors. The AUC of the training cohort was 0.884 (95% CI: 0.869-0.899), and the AUC of the validation cohort was 0.884 (95% CI:0.863-0.905). The prediction model can predict the probability of high ovarian response prior to IVF treatment, enabling clinicians to better predict the risk of HOR and guide treatment strategies.

Hedonic real estate price estimation with the spatiotemporal geostatistical model

This study argues that the spatiotemporal geostatistical model for real estate prices, which accounts for and incorporates spatial autocorrelation, can be estimated successfully using the Bayesian Markov Chain Monte Carlo (MCMC) estimation. While this procedure often encounters difficulty in calculating probabilistic densities in the Metropolis–Hastings (MH) algorithm, this study introduces a feasible and practical estimation method, providing useful estimated parameters for the model. Using single-family house transaction data, we show that ordinary estimations of real estate prices, with respect to certain explanatory variables, may lead to the underestimation of standard errors of coefficients for explanatory variables with spatial effects unless spatial autocorrelation is controlled for. Our model also makes it possible to obtain accurate in-sample predictions and moderately improved out-of-sample predictions for real estate prices. This study further estimates a “decay rate:” a diminishing correlation between real estate prices and increasing distance, showing that geographical proximities are likely to have an important impact on real estate prices, especially at a range under 600 m.

Validation of ARIC heart failure risk score in the Asian population: The CORE-Thailand cohort study

Abstract Background The Atherosclerotic Risk in Communities (ARIC) heart failure (HF) score was developed from population in the United States to predict HF. However, it has not been validated in Asian population. Purpose To validate the ARIC HF score and develop the new ARIC-CORE score to predict HF in Asian population in the Cohort Of patients with high Risk for cardiovascular Events (CORE-Thailand). Methods The CORE-Thailand registry was a prospective study that enrolled Thai patients with high atherosclerotic risk. Patients were followed for the occurrence of cardiovascular events including HF. The ARIC HF score comprised nine variables (age, race, heart rate, systolic blood pressure, BP-lowering medication use, diabetes, coronary heart disease, smoking status, and body mass index) to predict the occurrence of HF. Original coefficients of the full ARIC HF model were used to calculate the original ARIC HF score. The new ARIC-CORE score was developed by re-estimating the coefficients of all ARIC score variables in the study population using ridge regression analysis. The polynomial function was applied to the continuous variables with non-linear association with HF, which included heart rate, systolic blood pressure, and body mass index. Discrimination of the models was determined using C-index and calibration was assessed by plotting the observed outcomes versus the predicted probabilities of the outcomes. Net reclassification index (NRI) was used to compare the prediction performance between the ARIC HF and the ARIC-CORE risk score. Clinical utility of the scores were assessed with decision curve analysis. Results From a total of 8919 patients who had no history of HF, 199 (2.23%) developed HF. The ARIC HF score provided a good discrimination at C-statistics of 0.71 (95% confidence interval (CI) 0.67-0.75) with good calibration. (Figure 1 A). The ARIC-CORE risk score had a C-statistics of 0.75 (95%CI;0.71-0.76) with good calibration. (Figure 1B) Using the ARIC-CORE was associated with an improved classification of HF risk (NRI 0.32, 95%CI:0.18-0.44) compared to the ARIC HF score. The net benefit of using the ARIC-CORE score was higher than the original ARIC HF score in the decision curve analysis (Figure 2). Conclusion The ARIC HF score had acceptable discrimination and calibration in the CORE cohort. Interestingly, the ARIC-CORE score performed better in classifying HF risk and could be a suitable model to be used in Asian populations.Calibration plotDecision curve analysis