Research on the Participation of Chinese Sports Cultural Elite in Ice and Snow Sports

Governments have a responsibility to provide equal opportunities for sport and physical activity to all people of population. Chinese governments have issued many policies, such as “exhibition in the south, expansion in the West and East” of ice and snow sports to promote and stimulate the participation of the broad masses of the people. As a high-cost sport, the participants of ice and snow sports are usually socially elite groups. This study investigated the participation of cultural elite groups in ice and snow sports and investigated the social mobilization effect of ice and snow sports participation promotion policies by using binary regression and sequential regression models. The research shows that there are two different stages of one-time and continuous participation in the development of ice and snow sports in China. The one-time participation of ordinary people in ice and snow sports is mainly in response to the social mobilization of government policies. At the same time, it is positively correlated with site restrictions and knowledge of ice and snow sports. In the continuous participation group, gender, income, perception of ice and snow culture, and convenience near the site were highly positively correlated with consumption level. According to the results, low- and middle income people are less likely to participate in these activities because of their income. Therefore, this policy can increase inequalities.

Intelligent Informatization Early Warning Analysis of Agricultural Economy Based on Support Vector Sequential Regression Model

The development of science and technology has laid a solid foundation for the economic informatization of agriculture, and at the same time it brought technical guarantee for the development of agriculture, and the development of agriculture has provided an important material foundation for the development of science and technology. How to identify and deeply study agricultural economic informatization, give early warning to risk information, and ensure the steady development of the whole industry has become a key issue in the application of Internet technology in the field of agricultural development. This paper studies the present situation of agricultural economy informatization development process and applies support vector machine to forecast regional economic development level. The warning limit of agricultural economic growth rate is obtained on the basis of warning situation and warning indicator in early warning index system. The economic early warning model is established based on the support vector sequential regression method, and then the data is trained by MATLAB software to verify the rationality of the early warning model, and the accuracy and corresponding error of the model are given. Experimental results show that the prediction accuracy is 99.3%, the error is less than 0.05, and the prediction effect is relatively ideal, for agricultural economic intelligence information to provide accurate warning and agricultural economic research agricultural commercial development to provide support.

Multiple imputation with missing data indicators

Multiple imputation is a well-established general technique for analyzing data with missing values. A convenient way to implement multiple imputation is sequential regression multiple imputation, also called chained equations multiple imputation. In this approach, we impute missing values using regression models for each variable, conditional on the other variables in the data. This approach, however, assumes that the missingness mechanism is missing at random, and it is not well-justified under not-at-random missingness without additional modification. In this paper, we describe how we can generalize the sequential regression multiple imputation imputation procedure to handle missingness not at random in the setting where missingness may depend on other variables that are also missing but not on the missing variable itself, conditioning on fully observed variables. We provide algebraic justification for several generalizations of standard sequential regression multiple imputation using Taylor series and other approximations of the target imputation distribution under missingness not at random. Resulting regression model approximations include indicators for missingness, interactions, or other functions of the missingness not at random missingness model and observed data. In a simulation study, we demonstrate that the proposed sequential regression multiple imputation modifications result in reduced bias in the final analysis compared to standard sequential regression multiple imputation, with an approximation strategy involving inclusion of an offset in the imputation model performing the best overall. The method is illustrated in a breast cancer study, where the goal is to estimate the prevalence of a specific genetic pathogenic variant.

Fluid Cognitive Abilities Are Important for Learning and Retention of a New, Explicitly Learned Walking Pattern in Individuals After Stroke

Background There is significant variability in poststroke locomotor learning that is poorly understood and affects individual responses to rehabilitation interventions. Cognitive abilities relate to upper extremity motor learning in neurologically intact adults, but have not been studied in poststroke locomotor learning. Objective To understand the relationship between locomotor learning and retention and cognition after stroke. Methods Participants with chronic (>6 months) stroke participated in 3 testing sessions. During the first session, participants walked on a treadmill and learned a new walking pattern through visual feedback about their step length. During the second session, participants walked on a treadmill and 24-hour retention was assessed. Physical and cognitive tests, including the Fugl-Meyer-Lower Extremity (FM-LE), Fluid Cognition Composite Score (FCCS) from the NIH Toolbox -Cognition Battery, and Spatial Addition from the Wechsler Memory Scale-IV, were completed in the third session. Two sequential regression models were completed: one with learning and one with retention as the dependent variables. Age, physical impairment (ie, FM-LE), and cognitive measures (ie, FCCS and Spatial Addition) were the independent variables. Results Forty-nine and 34 participants were included in the learning and retention models, respectively. After accounting for age and FM-LE, cognitive measures explained a significant portion of variability in learning ( R2 = 0.17, P = .008; overall model R2 = 0.31, P = .002) and retention (Δ R2 = 0.17, P = .023; overall model R2 = 0.44, P = .002). Conclusions Cognitive abilities appear to be an important factor for understanding locomotor learning and retention after stroke. This has significant implications for incorporating locomotor learning principles into the development of personalized rehabilitation interventions after stroke.

Visible, potential, and functional land degradation assessment in southern Africa

Land degradation in drylands threatens vulnerable ecosystems and socioeconomic development. Currently, NDVI derived from remotely sensed data is the main tool used for degradation detection. Three indicators have usually been used to detect land degradation: (1) NDVI trends for visible degradation, (2) residual for human-induced potential degradation, and (3) sensitivity of vegetation to rainfall for functional degradation. However, few studies have integrated and compared these three indicators. In this study, we used Global Inventory Monitoring and Modelling System Version (GIMMS 3.1) NDVI dataset and Multiple Source Weighted-Ensemble Precipitation (MSWEP) rainfall dataset (1982-2015) and applied linear regression, Time Series Segmented and Residual Trend (TSS-RESTREND), and Sequential Regression (SeRGs) methods to detect degradation in southern Africa. The results showed that degradation was detected by these three respective indicators in 18.7%, 11.3%, and 7.1% of the study area. Degradation from any one type was found to occupy 27.21% of the total area, whereas the co-occurrence of two or more types only occupied 3.84%. These results indicate the dominant discrepancies among these indicators and the independent relationships among the degradation processes. Despite significantly greening, potential degradation and functional degradation were still observed. On the regional scale, spatial patterns of degradation were affected by different levels of aridity. On the national scale, the proportions of degradation were still influenced by increased population, inadequate policies, and other factors. This study highlights the need to detect degradation with multiple indicators and improves our understanding of degradation types and intensity.

Factors affecting the effectiveness of endovascular kidney revascularization in patients with renal arteria stenosis

The paper analyzes the results of surgical treatment of 109 patients with renal artery stenosis and manifestations of ischemic kidney disease (IBD) in the form of renovascular arterial hypertension. Blood pressure normalization was noted in 97 (89%) patients in the early and in 91 (84%) in the late postoperative periods. A sequential regression and multivariate correlation analysis of the factors of a good outcome of the operation was carried out. It was determined that significant prognostic factors are the presence of microalbuminuria and the duration of the history of arterial hypertension before surgery (correlation coefficients (HR) –0.684 and –0.695 at p = 0.0001). With these factors, the difference between the assessment of cumulative survival without clinical deterioration and the Cox proportional hazards regression model was statistically significant.

MATCH BIAS OR NONIGNORABLE NONRESPONSE? IMPROVED IMPUTATION AND ADMINISTRATIVE DATA IN THE CPS ASEC

We test an improved imputation technique, sequential regression multivariate imputation (SRMI), for the Current Population Survey Annual Social and Economic Supplement to address match bias. Furthermore, we augment the model with administrative tax data to test for nonignorable nonresponse. Using data from 2009, 2011, and 2013, we find that the current hot deck imputation used by the Census Bureau produces different distribution statistics, downward for poverty and inequality and upward for median income, relative to the SRMI model-based estimates. Our results suggest that these differences are a result of match bias, not nonignorable nonresponse. Nearly all poverty, median income, and inequality estimates are not significantly different when comparing imputation models with and without administrative data. However, there are clear efficiency gains from using administrative data.

Using CFD Modelling to Relate Pig Lying Locations to Environmental Variability in Finishing Pens

The purpose of this research was to determine which environmental factors within the pen space differ between the locations where pigs choose to lie and areas they avoid. Data on external weather conditions and the construction parameters for an existing commercial finishing pig building were input into a Dynamic Thermal (DT) model generating heat flow and surface temperature patterns in the structure and these were then input into a Computational Fluid Dynamics (CFD) model to generate data on the theoretical spatial patterns of temperature and air velocity within one room of this building on a specified day. The exact location of each pig in six selected pens within this room was taken from images from ceiling-mounted video cameras at four representative time points across the day. Using extracted air velocity and temperature data at the height of 0.64 m above the floor and a grid of approximately 600 mm to create a series of ‘cells’, the effective draught temperature (TED) was calculated from the models for each cell. Using a sequential regression model, the extent to which the actual lying locations of the pigs could be reliably predicted from the environmental conditions generated by model outputs and other pen factors was explored. The results showed that air velocity, TED and proximity to a solid pen partition (all significant at P < 0.05) had significant predictive value and collectively explained 15.55% of the total explained deviation of 17.13%. When the presence of an adjacent pig was considered, results showed that lying next to an adjacent pig, TED, air velocity and temperature accounted for 53.9%, 1.3%, 1.5% and 0.5% of the deviation in lying patterns, respectively (all P < 0.001). Thus, CFD model outputs could potentially provide the industry with a better understanding of which environmental drivers affect pigs’ lying location choice, even before a building is built and stocked.