UNASSIGNED: Although the regular exercise population is a key metric for gaging the success of China\'s fitness-for-all activities, effective policy approaches to increase mass sports participation remain unclear. Previous research suggests that GDP, educational attainment, sports resources, and meteorological conditions could influence regular exercise participation. Therefore, this study first analyzed the macro-level correlates influencing China\'s regular exercise population.
UNASSIGNED: We utilize ordinary least squares (OLS) regression and geographical weighted regression (GWR) to theorize the relationship. The analysis encompasses data from the 31 administrative regions of Mainland China, as reported at the end of the 13th Five-Year Plan period. The log-log model enables us to quantify the marginal effect (elasticity) of the explanatory variables.
UNASSIGNED: The OLS regression showed that regional GDP and the proportion of the population with a university education were significant predictors. In the global model, the marginal effects of regional GDP and university education were 0.048 and 0.173, respectively. Furthermore, the GWR revealed a distinct geographic pattern that corresponds to the classic Hu Line.
UNASSIGNED: While regional GDP was also a significant correlate in our model, the elasticity demonstrates that university education had an asymmetric effect on China\'s regular exercise population. Therefore, this paper sheds light on a policy priority for the upcoming 15th Five-Year Plan, emphasizing the strategic importance of expanding university education to enhance mass sports participation. In turn, a better-educated populace may yield significant secondary effects on public health and contribute to the high-quality development of the Chinese path to modernization.

It is unclear whether Gross Domestic Product (GDP) and greenness have additional modifying effects on the association between air pollution and respiratory system disease. Utilizing a time-stratified case-crossover design with a distributed lag linear model, we analyzed the association between six pollutants (PM2.5, PM10, NO2, SO2, O3, and CO) and 555,498 respiratory hospital admissions in Beijing from 1st January 2016 to 31st December 2019. We employed conditional logistic regression, adjusting for meteorological conditions, holidays and influenza, to calculate percent change of hospitalization risk. Subsequently, we performed subgroup analysis to investigate potential effect modifications using a two-sample z test. Every 10 μg/m3 increase in PM2.5, PM10, NO2, SO2, and O3 led to increases of 0.26% (95%CI: 0.17%, 0.35%), 0.15% (95%CI: 0.09%, 0.22%), 0.61% (95%CI: 0.44%, 0.77%), 1.72% (95%CI: 1.24%, 2.21%), and 0.32% (95%CI: 0.20%, 0.43%) in admissions, respectively. Also, a 1 mg/m3 increase in CO levels resulted in a 2.50% (95%CI: 1.96%, 3.04%) rise in admissions. The links with NO2 (p < 0.001), SO2 (p < 0.001), O3 (during the warm season, p < 0.001), and CO (p < 0.001) were significantly weaker among patients residing in areas with higher levels of greenness. No significant modifying role of GDP was observed. Greenness can help mitigate the effects of air pollutants, while the role of GDP needs further investigation.

Economic loss estimation is critical for policymakers to craft policies that balance economic and health concerns during pandemic emergencies. However, this task is time-consuming and resource-intensive, posing challenges during emergencies.
To address this, we proposed using electricity consumption (EC) and nighttime lights (NTL) datasets to estimate the total, commercial, and industrial economic losses from COVID-19 lockdowns in the Philippines. Regression models were employed to establish the relationship of GDP with EC and NTL. Then, models using basic statistics and weather data were developed to estimate the counterfactual EC and NTL, from which counterfactual GDP was derived. The difference between the actual and the counterfactual GDP from 2020 to 2021 yielded economic loss.
This paper highlights three findings. First, the regression model results established that models based on EC (adj-R2 ≥ 0.978) were better at explaining GDP than models using NTL (adj-R2 ≥ 0.663); however, combining both EC and NTL improved the prediction (adj-R2 ≥ 0.979). Second, counterfactual EC and NTL could be estimated using models based on statistics and weather data explaining more than 81% of the pre-pandemic values. Last, the estimated total loss amounted to 2.9 trillion PhP in 2020 and 3.2 trillion PhP in 2021. More than two-thirds of the losses were in the commercial sector as it responded to both policies and the COVID-19 case surge. In contrast, the industrial sector was affected primarily by the lockdown implementation.
This method allowed monitoring of economic losses resulting from long-term and large-scale hazards such as the COVID-19 pandemic. These findings can serve as empirical evidence for advocating targeted strategies that balance public health and the economy during pandemic scenarios.

This study investigates the potential determinants of capital flight from BRICS countries. We use the residual method to estimate capital flight and employ GMM testing approach. We find a surge in the volume of capital flight from BRICS especially in the aftermath of global financial crisis. The empirical results suggest that, past values of the capital flight, real GDP growth rates, exchange rate depreciation, unemployment rate, business confidence index and financial stability indicator as significant factors causing resident capital outflows from BRICS nations. These findings call for an effective policy framework by the fiscal and monetary authorities.

In recent years, rising carbon dioxide (CO2) emissions and other negative environmental effects of human activity have raised concerns about the planet\'s future. Rapid industrialization, urbanization, and economic activity have shown a surge in CO2 emissions, contributing to global warming and climate change. The main purpose of this study is to examine the impact of such as gross domestic product, gross domestic product square, foreign direct investment, environmental diplomacy, environmental diplomacy security, and renewable energy consumption on environmental degradation. This study provides a new perspective on environmental diplomacy in OECD countries using panel data econometric methodologies from 1991 to 2020. It contributes to our understanding of the role of environmental and economic factors in reducing CO2 emissions. The panel data is also analyzed by CD, CIPS, FMOLS, DOLS, and PMG-ARDL tests. However, as per the findings of this research, all the factors significantly impact environmental degradation (Co2 emission). Finding data to either confirm or deny the efficacy of the Environmental Kuznets Curve theory within the framework of OECD countries is possible through this approach. This policy framework attempts to solve the issues at the connection of environmental diplomacy and economic concerns by emphasizing cooperation and sustainability and incorporating environmental considerations into economic decision-making processes in OECD countries.

Research output provides an insight into the development of the scientific capability of a country. Budget allocation for research and development (R&D) is directly proportional to the research output of a country. Bibliometric analysis of South American countries has not been done in many studies. The purpose of this paper was to analyse research outputs from South American countries on various metrics. An analysis was done for a period of 11 years from 2010 to 2020. The analysis revealed that Brazil with highest percentage of research spend has lowest Field Weighted Citation Impact (FWCI). This contrasts with Uruguay, whose FWCI is high despite comparatively lower spend on R&D and lower publication output. Although Argentina has the highest percentage of researchers per million population (1202), it has the least papers per researchers (0.3 per year) among the countries studied. A huge disparity in terms of percentage of research spent, research output, papers per researcher, and output with national and international co-authorship was observed.

Transportation energy demand has a significant impact on worldwide energy consumption and greenhouse gas emissions. Accurate transportation energy demand predictions can help policymakers develop and implement successful energy policies and strategies. In this study, a novel approach to predict transportation energy demand using the Artificial Neural Network (ANN) based on the Improved Red Fox Optimizer (IRFO) has been suggested. The proposed method utilizes the ANN model to solve the complex nonlinear relationships between transportation energy demand and its effective parameters including Gross Domestic Product (GDP), population, and vehicle numbers. Also, the IRFO algorithm was utilized to modify the ANN model\'s parameters to improve the prediction accuracy. The experimental findings demonstrate the ANN-IRFO model performs better than the other method in terms of accuracy and effectiveness. It predicts the growth of GDP, population, and vehicles number by 5.5 %, 4.8 %, and 4.2 %, respectively. The findings demonstrate that the suggested method can provide accurate forecasts for transportation energy demand, which can help decision-makers to make informed decisions and policies regarding energy management and sustainability.

Durum wheat is more susceptible to Fusarium head blight (FHB) than other types or classes of wheat. The disease is one of the most devastating in wheat; it reduces yield and end-use quality and contaminates the grain with fungal mycotoxins such as deoxynivalenol (DON). A panel of 265 Canadian and European durum wheat cultivars, as well as breeding and experimental lines, were tested in artificially inoculated field environments (2019-2022, inclusive) and two greenhouse trials (2019 and 2020). The trials were assessed for FHB severity and incidence, visual rating index, Fusarium-damaged kernels, DON accumulation, anthesis or heading date, maturity date, and plant height. In addition, yellow pigment and protein content were analyzed for the 2020 field season. To capture loci underlying FHB resistance and related traits, GWAS was performed using single-locus and several multi-locus models, employing 13,504 SNPs. Thirty-one QTL significantly associated with one or more FHB-related traits were identified, of which nine were consistent across environments and associated with multiple FHB-related traits. Although many of the QTL were identified in regions previously reported to affect FHB, the QTL QFhb-3B.2, associated with FHB severity, incidence, and DON accumulation, appears to be novel. We developed KASP markers for six FHB-associated QTL that were consistently detected across multiple environments and validated them on the Global Durum Panel (GDP). Analysis of allelic diversity and the frequencies of these revealed that the lines in the GDP harbor between zero and six resistance alleles. This study provides a comprehensive assessment of the genetic basis of FHB resistance and DON accumulation in durum wheat. Accessions with multiple favorable alleles were identified and will be useful genetic resources to improve FHB resistance in durum breeding programs through marker-assisted recurrent selection and gene stacking.

To understand the epidemiological trend of gonorrhea in China from 2004 to 2021, predict the prevalence of the disease, and provide basic theory and data support for monitoring and managing gonorrhea. Gonorrhea incidence data in China from 2004 to 2021 were collected through the China Public Health Science Data Center and National Administration of Disease Prevention and Control, and the incidence and epidemiological characteristics were analyzed. Statistical analysis was performed using Joinpoint and autoregressive integrated moving average (ARIMA) models. A linear correlation model was used to analyze the correlation between gross domestic product (GDP) and the incidence rate. From 2004 to 2021, a total of 2,289,435 cases of gonorrhea were reported in China, with an average reported incidence rate of 9.46/100,000 people and a downward followed by an upward trend. Individuals with gonorrhea were primarily 20-30 y of age, with 1,034,847 cases (53.38%) from 2004 to 2018. The trend of increasing incidence was most obvious in the 10-20 age group (5,811 cases in 2004 to 12,752 cases in 2018, AAPC = 6.1, P < .001). The incidence of gonorrhea in China was negatively correlated with GDP from 2004 to 2021 (r = -0.547, P = .019). The correlation coefficient between the average incidence growth rate of each region from 2012 to 2018 and the average growth rate of regional GDP was 0.673 (P < .01). The root mean square error (RMSE) of the ARIMA model was 4.89%, showing powerful performance. There would be 97,910 gonorrhea cases in 2023 as predicted by the model.

To evaluate the effect of vaccination/booster administration dynamics on the reduction of excess mortality during COVID-19 infection waves in European countries.
We selected twenty-nine countries from the OurWorldInData project database according to their population size of more than one million and the availability of information on dominant SARS-CoV-2 variants during COVID-19 infection waves. After selection, we categorized countries according to their \"faster\" or \"slower\" vaccination rates. The first category included countries that reached 60% of vaccinated residents by October 2021 and 70% by January 2022. The second or \"slower\" category included all other countries. In the first or \"faster\" category, two groups, \"boosters faster\'\' and \"boosters slower\" were created. Pearson correlation analysis, linear regression, and chi-square test for categorical data were used to identify the association between vaccination rate and excess mortality. We chose time intervals corresponding to the dominance of viral variants: Wuhan, Alpha, Delta, and Omicron BA.1/2.
The \"faster\" countries, as opposed to the \"slower\" ones, did better in protecting their residents from mortality during all periods of the SARS-CoV-2 pandemic and even before vaccination. Perhaps higher GDP per capita contributed to their better performance throughout the pandemic. During mass vaccination, when the Delta variant prevailed, the contrast in mortality rates between the \"faster\" and \"slower\" categories was strongest. The average excess mortality in the \"slower\" countries was nearly 5 times higher than in the \"faster\" countries, and the odds ratio (OR) was 4.9 (95% CI 4.4 to 5.4). Slower booster rates were associated with significantly higher mortality during periods dominated by Omicron BA.1 and BA.2, with an OR of 2.6 (CI 95%. 2.1 to 3.3). Among the European countries we analyzed, Denmark, Norway, and Ireland did best, with a pandemic mortality rate of 0.1% of the population or less. By comparison, Bulgaria, Serbia, and Russia had a much higher mortality rate of up to 1% of the population.
Thus, slow vaccination and booster administration was a major factor contributing to an order of magnitude higher excess mortality in \"slower\" European countries compared to more rapidly immunized countries.