Stepwise Multiple Regression Model Research Articles

English

Inter-urban travel forms one of the main spatial and economic features of any nation’s economic landscape. Understanding and forecasting inter-urban movement is a crucial input for urban and regional transportation planning. This study therefore was carried out to develop trip generation model of selected cities in Niger State. Questionnaire survey was carried in three main urban centres in the state namely; Minna, Bida and Suleja. Four (400) copies of questionnaires were administered at the three major public motor parks where travelers usually board vehicles for inter-city travels Niger state.  Using multiple stepwise regression model analysis, six independent variables namely; travel distance (TD), travel time (TT), population (PP), public institution (PI) and fare charged (FC) were regressed against the dependent variable (Y) which is inter urban travel flow (ITF). However, only 2 independent variables; that is population (PP) and fare charged (FC) were good and significant for explaining the variation in the flow of inter urban trips in the state. Two variables record R2 of 1.00 which implies 100% contribution to explanation of the variation in the inter-urban travel in Niger State. The study also discovered that Minna-Suleja route constitutes the heaviest traffic corridors in the state. In view of these findings, it was recommended that adequate public transport provision should be made to meet inter urban travel needs within the state by NSTA and a more comprehensive regional transportation planning for the entire state is recommended to cover other important towns that could not be covered in this study.   Key words: Inter urban, travel, pattern, flow, generation, attraction.

Assessment of health benefit of PM2.5 reduction during COVID-19 lockdown in China and separating contributions from anthropogenic emissions and meteorology

The national lockdown policies have drastically disrupted socioeconomic activities during the COVID-19 pandemic in China, which provides a unique opportunity to investigate the air quality response to such anthropogenic disruptions. And it is meaningful to evaluate the potential health impacts of air quality changes during the lockdown, especially for PM2.5 with adverse health effects. In this study, by using PM2.5 observations from 1388 monitoring stations nationwide in China, we examine the PM2.5 variations between the COVID-19 lockdown (February and March in 2020) and the same period in 2015–2019, and find that the national average of PM2.5 decreases by 18 μg/m3, and mean PM2.5 for most sites (about 75%) decrease by 30%–60%. The anthropogenic and meteorological contributions to these PM2.5 variations are also determined by using a stepwise multiple linear regression (MLR) model combined with the Kolmogorov–Zurbenko filter. Our results show that the change of anthropogenic emissions is a leading contributor to those widespread PM2.5 reductions, and meteorological conditions have the negative influence on PM2.5 reductions for some regions, such as Beijing–Tianjin–Hebei (BTH). Additionally, the avoided premature death due to PM2.5 reduction is estimated as a predicted number based on a log-linear concentration-response function. The total avoided premature death is 9952 in China, with dominant contribution (94%) from anthropogenic emission changes. For BTH, Yangtze River Delta, Pearl River Delta and Hubei regions, the reductions of PM2.5 are 24.1, 24.3, 13.5 and 29.5 μg/m3, with the avoided premature deaths of 1066, 1963, 454 and 583, respectively.

Explosive Quadriceps Strength Symmetry and Landing Mechanics Limb Symmetry After Anterior Cruciate Ligament Reconstruction in Females.

Emerging evidence suggests that a lower quadriceps rate of torque development (RTD) after anterior cruciate ligament (ACL) reconstruction (ACLR) may be associated with altered landing mechanics. However, the influence of quadriceps RTD magnitude and limb symmetry on landing mechanics limb symmetry remains unknown. To assess the influence of quadriceps RTD magnitude and limb symmetry on limb symmetry in sagittal-plane landing mechanics during functional landing tasks in females with or without ACLR. Cross-sectional study. Laboratory. A total of 19 females with ACLR (age = 19.21 ± 1.81 years, height = 164.12 ± 6.97 cm, mass = 63.79 ± 7.59 kg, time after surgery = 20.05 ± 9.50 months) and 19 females serving as controls (age = 21.11 ± 3.28 years, height = 167.26 ± 7.26 cm, mass = 67.28 ± 9.25 kg). Landing mechanics were assessed during a double-legged (DL) jump-landing task, a single-legged jump-landing task, and a side-cutting task. Quadriceps RTD was collected during isometric muscle contractions. Separate stepwise multiple linear regression models were used to determine the variance in limb symmetry in the sagittal-plane knee moment at initial contact, peak vertical ground reaction force, and loading rate that could be explained by quadriceps RTD magnitude or limb symmetry, group (ACLR or control), and their interaction. In the ACLR group, greater limb symmetry in quadriceps RTD was associated with greater symmetry in sagittal-plane knee moment at initial contact during the DL task (P = .004). Peak vertical ground reaction force and loading rate could not be predicted by quadriceps RTD magnitude or limb symmetry, group, or their interaction during any task. Developing greater symmetry but not magnitude in quadriceps RTD likely enabled more symmetric sagittal-plane knee landing mechanics during the DL task in the ACLR group and thus may reduce the risk of a second ACL injury. Such a protective effect was not found during the single-legged or side-cutting tasks, which may indicate that these tasks do not allow for the compensatory landing mechanism of shifting load to the uninvolved limb that was possible during the DL task.

AMPAr GluA1 Phosphorylation at Serine 845 in Limbic System Is Associated with Cardiac Autonomic Tone.

The central autonomic network, which is connected to the limbic system structures including the amygdala (AMY) and anterior hippocampus (aHIP), regulates the sympathetic and parasympathetic modulation of visceromotor, neuroendocrine, pain, and behavior manifestations during stress responses. Heart rate variability (HRV) is useful to estimate the cardiac autonomic tone. The levels of phosphorylation on the Ser831 and Ser845 sites of the GluA1 subunit of the AMPAr (P-GluA1-Ser845 and P-GluA1-Ser831) are useful markers of synaptic plasticity. The relation between synaptic plasticity in the human limbic system structures and autonomic regulation in humans is unknown. This study investigated the association between HRV and neurochemistry biomarkers of synaptic plasticity in AMY and aHIP. HRV indices were obtained from the resting state electrocardiogram of patients with drug-resistant mesial temporal lobe epilepsy (MTLE, n = 18) and the levels of P-GluA1-Ser845 and P-GluA1-Ser831 in the AMY and aHIP resected during the epilepsy surgery. A backward stepwise multiple linear regression models were used to analyze the association between HRV and synaptic plasticity biomarkers controlling for imbalances in the distribution of sociodemographic, clinical, neuroimaging, and neurosurgical variables. P-GluA1-Ser845 levels in AMY show a negative association (p < 0.05) with the 3 investigated parasympathetic autonomic HRV indices (SDNN, rMSSD, and HF) predicting 24 to 40% of their variation. The final multiple linear regression models include disease duration and levels of P-GluA1-Ser845 and predict 24 to 56% of cardiac autonomic tone variation (p < 0.01). P-GluA1-Ser845 levels in AMY and aHIP are negatively associated with the resting HRV in MTLE-HS indicating that increased synaptic efficiency in amygdala is associated with a parasympathetic cardiac autonomic tone impairment. The results suggest that specific changes in synaptic plasticity may be involved in the brain-heart axis regulation by the limbic system.

Drivers of information sources use pattern by the farm households of Bihar

Agriculture is the main occupation of a large majority of households in Bihar. The differences in demographics and availability and accessibility of various information sources determine the information sources use pattern. Information is one of the basic inputs required prior to, during and after performing any agriculture practices. Present study was conducted in Muzaffarpur and Madhubani districts of Bihar that included a random sample of 120 farmers, 60 each from both the districts. The farmers of Muzaffarpur and Madhubani districts were having varied perceptions on information needs for most of the aspects in agriculture. The step wise multiple regression models revealed that mass media use, education, family size and household expenditure together have explained 53 per cent variation in farmers’ perception on information needs. While, mass media use, education, annual income, family size and personal localite sources use together have determined 61 per cent variation in fulfillment of information needs with respect to various aspects of agriculture. The farmers’ attributes like mass media use, education, annual income, family size and personal localite sources use together have determined 63 per cent variation in perceived effectiveness of the communication sources.

干旱与湿润区流域时变水热耦合参数的归因分析

气候变化和下垫面条件的改变共同影响着流域的水热耦合状况，定量剖析其影响对研究变化环境下流域水量平衡和能量分配具有重要意义。以往多数研究只做了定性分析，对不同影响因素的定量贡献研究甚少。基于此，以干旱区无定河流域和湿润区汉江上游为研究区域，在Budyko框架下，估算了干旱与湿润区流域的时变水热耦合参数，并通过逐步多元回归模型、敏感性和贡献率分析，定量归因了水热耦合控制参数的演变，并将干旱与湿润区流域做了对比分析。结果表明：干旱与湿润区流域的水热耦合参数在1970-2013年间均呈显著上升趋势；不同气候区水热耦合参数变化的主导因子不同，干旱区无定河参数对降水和植被覆盖度（NDVI）具有较高的敏感性，且NDVI的变化主导着参数的变化（贡献率为89.5%）；湿润区汉江上游参数的变化对气温、有效灌溉面积（EIA）和NDVI更敏感，其中，EIA主导着参数的变化（贡献率为83.1%）；总体来说，下垫面的变化是无定河和汉江上游水热状况改变的驱动因素。;Under the background of global warming, the climate condition and underlying surface conditions of the Yellow River and the Yangtze River has changed significantly. Simultaneously, the intensification of human activities (e.g. urbanization, industrialization, grain for green and irrigation, etc.) has led to changes in underlying surface condition, which further changed the distribution of precipitation between evaporation, infiltration and runoff, thus affecting the hydrological cycle process. However, the change of climate change and underlying surface conditions jointly affect the watershed water-energy balance, and quantitative analysis their influence in different climatic regions is of great significance to the study of water balance and energy distribution of the basin under the changing environment. most of the previous studies only made qualitative analysis, and few studies focused on quantitative contributions. In addition, under the complex changing environment, the study of the changes of hydrological processes in different climatic regions and the influencing factors are helpful for the profound understanding of the change characteristics of hydrological processes in different climatic regions. Based on them,The Wuding River Basin in arid region and the upper Han River in humid region were selected as the study areas in the study. The time-varying Budyko parameter was estimated under the Budyko framework. In addition, the attribution of the evolution of the parameter was revealed quantitatively by the stepwise multiple regression model, sensitivity and contribution analysis. Moreover, a comparative analysis was made between the arid and humid regions. The results showed that the Budyko parameter increased significantly from 1970 to 2013 in the two selected areas with different driving factors. The dominant factors driving the variation of the parameter are different in diverse climatic regions. In the Wuding River basin, the parameter is highly sensitive to precipitation and vegetation coverage (NDVI), and the variation of the NDVI dominates its variation (the contribution rate is 89.5%). However, in the upper Han River, the variation of the parameter is more sensitive to temperature, effective irrigation area (EIA) and NDVI, and among which, the EIA dominates its variation (the contribution rate is 83.1%). In general, the change of underlying surface is the driving factor for the change of water and energy conditions in the Wuding River and the upper Han River.

PCA-ANN Contactless Multimodality Sensors for Body Temperature Estimation

The use of the infrared (IR) forehead thermometer is a convenient way to measure the body temperature; however, it exhibits the concerns of accuracy, reliability, and repeatability because of improper measurement poses such as distance and angle to forehead, and other variations of subject and ambient conditions. According to the advantages of quick response and contactless measurement of using IR thermometry, this article additionally considers the factors of distance to IR sensors, subject’s facial skin color, and forehead pose, as well as ambient temperature and light intensity, to propose an accurate regression for body temperature estimation. As a consequence, the proposed multimodality sensor (MMS) system consists of a set of thermopile IR sensors, a distance sensor, a light intensity sensor, and a web camera. Furthermore, a principal component analysis (PCA) approach combining the artificial neural network is utilized for the body temperature regression. In order to reduce the measurement bias, the digital axillary temperature data was used as the ground body temperature for the machine learning training and validation. The MMS system is set randomly close to the forehead at a distance of 2–30 cm to emulate the human’s operation bias. We have totally 30 subjects for this study, and we divided them into four groups as: training dataset (14 subjects, IDs: 1–14 with 300 cases), validation dataset (three subjects, IDs: 15–17 with 64 cases), concurrent-time test dataset (three subjects, IDs: 18–20 with 64 cases), and three-months-elapsed test dataset (ten subject, IDs: 21–30 with 240 cases). In the training process, the $k$ -fold cross-validation technique ( $k = 5$ ) was applied to reduce the model overfitting. In addition, multiple linear regression (MLR) and stepwise regression (SR) models were also developed for performance comparison. The result shows that the PCA-ANN temperature estimation model outperformed other predictive models with the lowest mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE) values, and highest $R$ values and can optimize the low-cost MLX90614 sensor accuracy. The three-months-elapsed test group achieves MAE $0.1~^{\circ }\text{C}$ with a standard deviation of the error $0.03~^{\circ }\text{C}$ when compared to the state-of-the-art with low cost and contactless IR sensors.

The Association of the Health Management Strategy with Subjective Health and Well-being Outcomes in General Population.

BackgroundPeople face many obstacles to overcoming crisis in life and proactively manage life crises. This study aimed to evaluate the association of self-management strategy with subjective health and well-being for the general South Korean population.MethodsWe recruited 1,200 respondents using an equal-probability sampling method from March to May 2018. A questionnaire including life version of the Smart Management Strategy for Health Assessment Tool (SAT-Life), the five Health Status Questionnaire, Short Form-12, McGill Quality of Life Questionnaire (MQOL), Patient Health Questionnaire-9 (PHQ-9), and Satisfaction With Life Scale (SWLS) was administered to participants.ResultsIn multiple stepwise logistic regression model adjusted with basic demographic variables (age, sex, region, education and monthly income level), core strategy was independently associated with physical, mental, social, spiritual, and general health status (adjusted odds ratios [aORs], 1.97–2.92). Preparation strategy was independently associated with physical, mental, spiritual, and general health status (aORs, 2.36–3.31). Implementation strategy was independently associated with physical, social, spiritual, and general health status (aORs, 2.22–2.42). Core strategy and implementation strategy were independently associated with higher Physical Component Score (aORs, 2.21–2.29) and higher Mental Component Score (aORs, 1.68–1.76). Core strategy and preparation strategy were independently associated with lower PHQ-9 (aORs, 2.63–3.74). Pearson's correlation coefficients between scores on SAT-Life and the other factors (MQOL social support, MQOL spiritual well-being, and SWLS) explain having significant correlations ranging from 0.41–0.43.ConclusionSelf-management strategies of health might be encouraged to manage subjective health and well-being outcomes.

Empirical Analysis on the Influencing Factors of Fiscal and Tax Revenue in Sichuan Province Based on Multiple Stepwise Regression

Tax is closely related to national economic development. Various macroeconomic variables comprehensively affect the scale of tax revenue. Therefore, analyzing the influencing factors of tax revenue is an important direction to study China's current economic recovery and promote tax growth. As an important province in Southwest China, Sichuan has important research value. Therefore, this paper collects the macroeconomic data of the province for 25 years, and establishes a multiple stepwise regression model. The results show that the added value of the secondary industry, the per capita disposable income of rural households and the total amount of import and export trade have a significant positive impact on the total tax revenue of Sichuan Province, and the internal expenditure of R&D funds for high-tech industries has a negative impact on the tax revenue of Sichuan Province. Based on the research conclusions of this paper, policy suggestions are put forward from the perspectives of industrial structure, income distribution and R&D cost investment, so as to provide a basis for promoting the steady return of the national economy of Sichuan Province.

Estimation of the Grassland Aboveground Biomass of the Inner Mongolia Plateau Using the Simulated Spectra of Sentinel-2 Images

An accurate assessment of the grassland aboveground biomass (AGB) is important for analyzing terrestrial ecosystem structures and functions, estimating grassland primary productivity, and monitoring climate change and carbon/nitrogen circulation on a global scale. Multispectral satellites with wide-width advantages, such as Sentinel-2, have become the inevitable choice for the large-scale monitoring of grassland biomass on regional and global scales. However, the spectral resolution of multispectral satellites is generally low, which limits the inversion accuracy of grassland AGB and restricts further application in large-scale grassland monitoring. For this reason, a satellite-scale simulated spectra method was proposed to enhance the spectral information of the Sentinel-2 data, and a simulated spectrum (SS) was constructed using this algorithm. Then, the raw spectrum (RS) of Sentinel-2 and the SS were used as data sources to calculate the vegetation indices (RS-VIs and SS-VIs, which represent vegetation indices calculated using RS and SS data, respectively), and the multi-granularity spectral segmentation algorithm (MGSS) was employed to extract spectral segmentation features (RS-SF and SS-SF, which represent segmentation features extracted by RS and SS data, respectively). Following this, these spectral features (RS-SF, SS-SF, RS-VIs, and SS-VIs) were used to estimate AGB by partial least-squares regression (PLSR) and multiple stepwise regression (MSR) models. Finally, the spatial distribution law and the reasons for the latitude zone of the Inner Mongolia Plateau were analyzed, based on precipitation, the average temperature, topography, etc. The conclusions are as follows. Firstly, the SS has more spectral information and its sensitivity to biomass is higher than the RS of Sentinel-2 in some bands, and the correlation between the SS-VIs and biomass is higher than that of the RS-VIs. Secondly, among the spectral features, the most accurate AGB estimation was obtained by SS-SF, which gave R2 = 0.95. The root mean square error (RMSE) was 10.86 g/m2 and the estimate accuracy (EA) was 82.84% in the MSR model. Additionally, RMSE = 10.89 g/m2 and EA = 82.78% in the PLSR model. Compared with the traditional estimation methods using RS and VI, R2 was increased by at least 0.2, RMSE was reduced by at least 14.08 g/m2, and EA was increased by 22.26%. Therefore, the simulated spectra method can help improve the estimation accuracy of AGB, and a new idea about regional and global large-scale biomass acquisition is provided.

Qualitative sonographic assessment of transmural ileal inflammation in Crohn's disease: a comparison with MRI activity score.

Intestinal ultrasound (IUS) is an accurate tool for monitoring Crohn's disease. To date, there is no clinically used validated quantitative ultrasonographic score for assessing disease activity. For magnetic resonance enterography (MRE), the magnetic resonance index of activity (MaRIA) is most used. The goal of this study was to devise a new quantitative IUS score for assessing Crohn's disease inflammation, by using a partial MaRIA score as a reference. This was a retrospective cohort study. The study cohort included patients with Crohn's disease followed between January 2016 and December 2018. Inclusion criteria were age >18 and <3 months between MRE and IUS. Linear/logistic regression was performed for the correlation of ultrasonographic parameters with MaRIA score. Ultrasonograpic features included: bowel wall thickness, disrupted bowel wall stratification, mesenteric fat proliferation, presence of lymph nodes, hypervascularity present on color Doppler flow, and the presence of complications (strictures, inflammatory mass, and fistula). Forty-two patients were included. A stepwise multiple regression model was constructed to predict MaRIA score using ultrasound features. Two variables were found to be independently significant: terminal ileum (TI) thickness (r = 0.68, P = 0.001) and mesenteric fat proliferation (r = 0.45, P = 0.019). A model was constructed as follows: MaRIA = 7 + 2.5 * TI US thickness (mm) + 7 * US fat proliferation (0 = no, 1 = yes). This model has an R2 of 0.51 for explaining the variability in the results. IUS measurements are significantly correlated with MaRIA score in the terminal ileum and a simple computational model can be constructed.

Catchment based estimation of pollutant event mean concentration (EMC) and implications for first flush assessment

The Event Mean Concentration (EMC) is considered as a key analytical parameter for assessing the quality of stormwater. The conventional estimation methods to determine EMC do not necessarily address the variability associated with the hydrologic characteristics. Accordingly, this study was conducted to identify the potential hydrologic variables that can influence EMC and thereby to create a mathematical model to determine EMC using the hydrologic variables while incorporating the catchment as an influential factor. This paper introduces an innovative approach to estimate EMC of a runoff event using a stepwise multiple linear regression model. The model incorporates hydrologic variables together with their two-way interaction terms. The catchment was included in the model as a dummy variable. This allows identifying the variability of EMC between catchments. Model can reasonably predict the EMC with an overall prediction error of 0.811. The regression coefficients of the model specify that, maximum rainfall intensity is the most influential variable having a coefficient of 1.008, followed by the average intensity with a coefficient −0.586. The interaction term of rainfall depth and the antecedent dry period indicates that for a relatively small rainfall event (<5 mm), an optimum value of antecedent dry period exists that maximises the EMC. Subsequently, EMC was employed to define the first flush runoff as an alternative approach to the conventional approaches for determining the first flush. The dynamic mean concentration (DMCt), was introduced as a parameter for estimating the first flush using EMC. The maximum accumulated runoff volume such that, DMCt≥EMC was defined as the first flush runoff. It was found that residential catchments generate more intense first flush compared to catchments with totally impervious surface areas and thereby a significant pollutant load is transported within a small initial fraction of the runoff.f

Influence of landscape mosaic structure on nitrate and phosphate discharges: An island-wide assessment in subtropical mountainous Taiwan

Landscape composition and configuration, which runoff flows through, have substantial effects on nutrient export of humid mountainous watersheds. The relationships between landscape metrics and annual and seasonal NO3-N and PO4-P fluxes are developed for 43 watersheds distributed island-wide in Taiwan during 2015–2016. The results show that the watersheds with higher concentrations of NO3-N and PO4-P mainly distributed on western Taiwan whereas watersheds with lower concentrations of NO3-N and PO4-P scattered across mountain regions and eastern Taiwan. Multiple stepwise regression models are used to realize the effects of landscape pattern on NO3-N and PO4-P exports at watershed level and riparian buffers of various width (100 m, 200 m, 500 m, 1000 m, 1500 m, and 2000 m). Our findings show that the average annual exports of NO3-N and PO4-P across various watersheds are 3.8–50.8 and 0.04–7.7 kg ha−1 yr−1, respectively, much higher than reported values worldwide. The class-level metrics IJI (interspersion and juxtaposition) of agricultural land and LPI (largest patch index) of buildup area obtained from 200 m buffer explain 85% of variation of NO3-N flux, and ED (edge of density) of buildup area and LPI of water body or percentage of water body at 200 m buffer combined contribute 90% of variation in PO4-P exports. The highly mixed development of urban and agriculture distributed along with the limited extent of riparian buffers within watersheds in Taiwan make the watershed susceptible to loss of a large quantity of nutrients and sediments into streams. Management practices such as diminishing fragmented agriculture and buildup area are recommended as they should help to reduce water pollution, and better designs of developed area and water body can further improve water quality.

Chinese College Students Have Higher Anxiety in New Semester of Online Learning During COVID-19: A Machine Learning Approach.

The COVID-19 pandemic has caused tremendous loss starting from early this year. This article aims to investigate the change of anxiety severity and prevalence among non-graduating undergraduate students in the new semester of online learning during COVID-19 in China and also to evaluate a machine learning model based on the XGBoost model. A total of 1172 non-graduating undergraduate students aged between 18 and 22 from 34 provincial-level administrative units and 260 cities in China were enrolled onto this study and asked to fill in a sociodemographic questionnaire and the Self-Rating Anxiety Scale (SAS) twice, respectively, during February 15 to 17, 2020, before the new semester started, and March 15 to 17, 2020, 1 month after the new semester based on online learning had started. SPSS 22.0 was used to conduct t-test and single factor analysis. XGBoost models were implemented to predict the anxiety level of students 1 month after the start of the new semester. There were 184 (15.7%, Mean = 58.45, SD = 7.81) and 221 (18.86%, Mean = 57.68, SD = 7.58) students who met the cut-off of 50 and were screened as positive for anxiety, respectively, in the two investigations. The mean SAS scores in the second test was significantly higher than those in the first test (P < 0.05). Significant differences were also found among all males, females, and students majoring in arts and sciences between the two studies (P < 0.05). The results also showed students from Hubei province, where most cases of COVID-19 were confirmed, had a higher percentage of participants meeting the cut-off of being anxious. This article applied machine learning to establish XGBoost models to successfully predict the anxiety level and changes of anxiety levels 4 weeks later based on the SAS scores of the students in the first test. It was concluded that, during COVID-19, Chinese non-graduating undergraduate students showed higher anxiety in the new semester based on online learning than before the new semester started. More students from Hubei province had a different level of anxiety than other provinces. Families, universities, and society as a whole should pay attention to the psychological health of non-graduating undergraduate students and take measures accordingly. It also confirmed that the XGBoost model had better prediction accuracy compared to the traditional multiple stepwise regression model on the anxiety status of university students.

Development of a Cadre of Evidence-Based Practice Mentors for Nurses: What Works?

Development of a critical mass of evidence-based practice (EBP) mentors for healthcare professionals is pivotal in facilitating and sustaining system-wide implementation of evidence-based care, especially for nurses. Empirical evidence shows that organizational factors are strongly associated with EBP. However, the understanding of organizational support to promote EBP competency is found lacking, especially in Mainland China. The purposes of this study were to (1) establish a baseline assessment of EBP nursing leadership and work environment support as well as EBP competency for EBP mentors of nurses in Western China, and (2) explore how nursing leadership and work environment impact the EBP competencies of mentors. A cross-sectional study was conducted. The study population was 286 EBP mentors for nurses from six urban general hospitals in Xi'an City, Shanxi Province. The EBP Nursing Leadership Scale and the EBP Work Environment Scale were used to evaluate their perceived organizational support. EBP competency was measured by the Evidence-Based Practice Questionnaire. There was insufficient time for EBP mentors of nurses to engage in EBP. Lack of experts in EBP and educational offerings about EBP were identified as the two lowest workplace organizational supports for EBP. The respondents reported themselves as not competent in EBP, especially for the knowledge/skills. EBP nursing leadership and work environment support were key predictors of the self-reported EBP competency in the stepwise multiple linear regression models (β=.211-.345, p<.01). It is imperative to provide EBP mentors for nurses with the knowledge and skills to achieve the expected level of EBP competency. Strategies for developing a cadre of EBP mentors for nurses who have competency in EBP need to be multipronged and target the cultivation of an organizational culture that supports EBP.

Enamel development defects and oral symptoms: A hierarchical approach.

This study aimed to determine the relationship between enamel developmental defects (DDEs) and children's oral symptoms in the early and late mixed dentition in a hierarchical approach. Population-based cross-sectional study of 772 children. Calibrated dental examiners examined for DDEs, malocclusion, and dental caries. Parents answered questions related to the socioeconomic condition of the family. The Child Perception Questionnaire (CPQ₈₋₁₀) was used to identify oral symptoms as the outcome variable. Analysis was adjusted in a backward stepwise hierarchical multiple logistic regression model. Symptoms were predicted by being female, having a father with low education and having DDEs in the upper first molars (ORs = 1.42; 95% CI: 1.06-1.89; 1.46: 1.10-1.96 and 2.02: 0.99-4.05 respectively). DDEs are associated with oral symptoms in Brazilian children.

New empirical approaches for compressive strength assessment of CFRP confined rectangular concrete columns

Utilizing of CFRP for confining concrete columns has been demonstrated to improve the capacity and ductility of columns. In reason of numerous parameters that affect the compressive strength of the CFRP confined rectangular concrete columns (CRCC), developing formula is complicated. In the current study, three methods are evaluated for predicting the residual compressive strength of CFRP CRCC. Multiple regressions (MR), stepwise regression (SR), and artificial neural network (ANN) models were extended as credible methods for generating and evaluating the compressive strength of CFRP CRCC. The required data for training algorithms obtained from a reliable database. The accuracy of the developed formulae is verified using appropriate criteria. Then, a comparison was made between proposed formulae-based models to examine the accuracy degree of these methods. It is understood that despite a negligible difference between proposed models results the obtained formulae based on the SR and ANN methods give the exact results than the MR model.

In-season weather data provide reliable yield estimates of maize and soybean in the US central Corn Belt

Weather conditions regulate the growth and yield of crops, especially in rain-fed agricultural systems. This study evaluated the use and relative importance of readily available weather data to develop yield estimation models for maize and soybean in the US central Corn Belt. Total rainfall (Rain), average air temperature (Tavg), and the difference between maximum and minimum air temperature (Tdiff) at weekly, biweekly, and monthly timescales from May to August were used to estimate county-level maize and soybean grain yields for Iowa, Illinois, Indiana, and Minnesota. Step-wise multiple linear regression (MLR), general additive (GAM), and support vector machine (SVM) models were trained with Rain, Tavg, and with/without Tdiff. For the total study area and at individual state level, SVM outperformed other models at all temporal levels for both maize and soybean. For maize, Tavg and Tdiff during July and August, and Rain during June and July, were relatively more important whereas for soybean, Tavg in June and Tdiff and Rain during August were more important. The SVM model with weekly Rain and Tavg estimated the overall maize yield with a root mean square error (RMSE) of 591 kg ha−1 (4.9% nRMSE) and soybean yield with a RMSE of 205 kg ha−1 (5.5% nRMSE). Inclusion of Tdiff in the model considerably improved yield estimation for both crops; however, the magnitude of improvement varied with the model and temporal level of weather data. This study shows the relative importance of weather variables and reliable yield estimation of maize and soybean from readily available weather data to develop a decision support tool in the US central Corn Belt.

Development and validation of a multifrequency bioimpedance spectroscopy equation to predict appendicular skeletal muscle mass in hemodialysis patients

The Body Composition Monitor (BCM), a multifrequency bioimpedance spectroscopy device, has been widely used to assess body composition in hemodialysis patients because its measurement is not affected by overhydration commonly seen in chronic kidney disease. We aimed to develop and validate an equation for obtaining appendicular skeletal muscle mass (ASM) from BCM taking dual-energy X-ray absorptiometry (DXA) as the reference among hemodialysis patients. A total of 322 consecutive body composition measurements with BCM and DXA in 263 hemodialysis patients were randomly divided at a ratio of 2:1 into development and validation groups. Stepwise multiple regression modeling was applied to develop the ASM prediction equation. We evaluated the model as a diagnostic tool for sarcopenia using cutoffs of ASM defined by the Asian Working Group for Sarcopenia (AWGS). We further explored the association between ASM predicted by the BCM equation and all-cause mortality in two independent cohorts: one with 326 stage 3-5 CKD patients and one with 629 hemodialysis patients. BCM yielded the following equation: ASM (kg) = -1.838 + 0.395 × total body water (L) + 0.105 × body weight (kg) + 1.231 × male sex - 0.026×age (years) (R2=0.914, standard error of estimate=1.35kg). In the validation group, Bland-Altman reliability analysis showed no significant bias of 0.098kg and limits of agreement ±2.440kg. Using the AWGS criteria, the model was found to have a sensitivity of 94.1%, a specificity of 98.8%, a positive predictive value of 84.2%, and a negative predictive value of 99.6% for the diagnosis of sarcopenia. Low ASM predicted by the BCM equation was associated with significantly worse overall survival among CKD patients but not hemodialysis patients. The new BCM equation provides a feasible and valid option for assessing ASM in hemodialysis patients.

Is insulin resistance play a role in left atrium dysfunction in young hypertensive patients?

Abstract Insulin resistance with normoglycemia could lead to subclinical inflammation, left atrium enlargement and ventricular systolic and diastolic dysfunction. The aim of our study was to analyzed associations between markers of insulin resistance and parameters of left atrium function in young hypertensive patients. Methods 965 young people were screened, among which 64 (6.6%) had high normal blood pressure and 57 (5.9%) had arterial hypertension. 121 patients with uncomplicated arterial hypertension grade 1–2 were included to the study (81% male, mean age 25.6 (7.3) years, 22 (18%) had obesity, 51 (42%) were smokers). All patients undergone clinical and laboratory evaluation, 24-h blood pressure monitoring, echocardiography. LA function were evaluated by 2D-speckle tracking echocardiography with left atrial reservoir strain (LASr), left atrial booster strain (LASb), left atrial conduit strain (LACs). Insulin level, serum glucose, was measured by using overnight fasting blood sampling between 7 am and 9 am. Insulin resistance was evaluated by HOMA-IR. Results Mean office systolic BP was 132 [122; 138]; diastolic BP – 70 [63; 76]. LAVI was 23 [19; 27] ml/m2. Mean insulin was 8.1 [5.4; 12.3] mIU/l, HOMA-IR – 1.9 [1.2; 2.8]. Correlations between LASr and body mass, waist circumference, insulin level, uric acid, HOMA-IR, E/e', LASb and insulin, HOMA-IR, uric acid, LASc and body mass, waist circumference, insulin level were revealed (p&amp;lt;0.05). Independent association LASc with BMI (B −0.22, p=0.02), LASb with insulin level (B −0.37, p=0.02), LASr with insulin (B −0.37, p=0.02) and with left ventricular (LV) global longitudinal strain (B −0.32, p=0.046) were confirmed in multiple stepwise regression model. Associations between endomyocardial LASb and insulin, HOMA-IR, endomyocardial LASr and body mass, waist circumference, insulin, uric acid, HOMA-IR, E/e', LASra and insulin and HOMA-IR, LASre with body mass, waist circumference, insulin, uric acid, HOMA-IR (p&amp;lt;0.05) were found during correlation analysis. Multiple stepwise regression showed independent associations endomyocardial LASb with HOMA-IR (B −0.44, p=0.005), endomyocardial LASr with HOMA-IR (B −0.46, p=0.003) and with LV global longitudinal strain (B −0.31, p=0.04), LASra with HOMA-IR (B −0.46, p=0.008), LASre with body mass (B −0.48, p=0.036) and with insulin (B −0.38, p=0.001). Conclusion Left atrium reservoir function is load dependent and influenced by LV function. For subclinical left atrium remodeling assessment booster function should be analized along with reservoir function. In young hypertensive patients insulin level were independently associated with left atrial dysfunction. Funding Acknowledgement Type of funding source: Public Institution(s). Main funding source(s): Project 5-100