Exact renormalization group with optimal scale and its application to cosmology

Assuming an effective gravitational action with scale dependent coupling constants, a consistency condition for the local form of the cut-off scale is derived. The approach is applied to homogeneous cosmology and running couplings with an ultraviolet fixed point. Within the given approach this allows to derive bounds on the value of the fixed point.Comment: 11 pages, 3 figure

Relationship Between Leisure-Time Physical Activity and Whole Body Bone Mineral Density, Human Growth Hormone, and Leptin in Women

The benefits of structured exercise on bone health have been well documented. However, less understood is the influence of leisure-time physical activity (LA) on BMD. PURPOSE: The purpose of this study was to examine the relationships between LA, whole body BMD, and serum levels of human growth hormone (HGH) and leptin. METHODS: One hundred two apparently healthy, premenopausal women (Mean ± SD Age: 43.1 ± 4.5 y; BMI: 26.5 ± 5.2 kg/m2; body fat: 41.6 ± 7.9 %) participated in the study. Self-reported leisure-time physical activity was quantified as total minutes of moderate to vigorous activity and then participants were separated into tertiles. Whole body BMD was determined using dual energy x-ray absoptiometry (DEXA). Serum HGH, leptin, and insulin were determined by EIA. A MANCOVA was used to evaluate differences in BMD, HGH, leptin, and LA while controlling for BMI, percentage body fat, and insulin. A multiple regression model was created with BMD as the dependant variable and HGH, leptin, LA, lean body mass (LBM), and fat mass (FM) as independent variables. RESULTS: LA was significantly different between tertiles (P \u3c 0.001). There was no significant difference between the tertiles for BMD, HGH, leptin, LBM, or FM (P = 0.167). Pearson correlation coefficient revealed a significant relationship between BMD and leptin (r = 0.229; P = 0.021), but not for HGH (r = -0.062; P = 0.535) or LA (r = -0.023; P = 0.817). Multiple regression indicated that FM had the greatest influence on BMD (beta = 0.336; P = 0.002). CONCLUSION: For these women, HGH and LA were not related to BMD and FM had the greatest influence on BMD. While mean LA was significantly different between tertiles, the nature of the activities engaged in by these women may have been insufficient to propagate differences in BMD

Filter Airflow prediction model development

The implementation of air filters on commercial swine farms has effectively reduced the frequency of airborne disease transmission. However, efficiently managing filter lifespan remains a challenge and an unknown operational cost for filtered swine facilities. Individual filter testing protocols are time consuming and expensive for producers. The objective of this study was to develop a predictive model for estimating airflow for an individual filter in situ by comparing multiple machine learning models to eliminate the need for manual, individual filter testing for filter resistance. The data set was generated from a custom Air Filter Environmental Testing Chamber that mimics on farm operational conditions with a low static pressure drop per filter and ground level installation. Model parameters were developed from a six-month long data set. The models were developed when the chamber was running with a new set of pre-filters and a set of five-month old v-bank filters. The developed models include a single input linear regression, multiple linear regression, and random forest models. A single input linear regression was not an effective method for predicting the chamber airflow, R2=0.08. The multiple linear regression moderately explained the variation in the data, R2=0.77. The random forest models performed the best for predicting the test chamber airflow with both models featuring R2= 0.98. The results and models from this study will be used to determine the feasibility of an on-farm application

Does an adequate team climate for learning predict team effectiveness and innovation potential? A psychometric validation of the Team Climate questionnaire for Learning in an organizational context

This paper reports the application and psychometric validation of a multi-dimensional measure of team climate for learning in a multinational organization. The research project aimed at extending previous findings at Aston Business School, using the English 33-item version of Brodbeck's Team Climate questionnaire for Learning to assess the factors that facilitate team learning in a business context and analyze its relationship to group performance, support for innovation and different effectiveness criteria chosen by the organization we cooperated with. Data concerning the TCL, the level of group development as a related process, and measures of group performance, innovation and effectiveness were gathered from 119 participants belonging to 18 work groups of the organization's headquarters and three subsidiaries in Germany, Switzerland and Belgium. The undertaken studies were carried out using a cross-sectional and correlated design. The assessment tool proved to have good psychometric properties, providing an adequate reliability, validity and power of prediction regarding team performance (R² = .81), support for innovation (R² = .69) and team effectiveness (e.g. R² = .59 as regards to the keeping of deadlines). Potential benefits derived from the application of the presented measure, limitations of the current research project and future perspectives are discussed

Infrared proximity measurement system development and validation for classifying sow posture

The rapidly progressing field of precision livestock farming is becoming increasingly dependent on the utilization of camera technology. Integration of camera technology involves substantial intellectual input and computational power to acquire, process, and interpret images in real-time. Further, cameras and the necessary computational power can be cost-prohibitive and subsequently, become a constraint for application in a commercial livestock and poultry production systems. The purpose of this study is to develop an infrared proximity sensor based system to serve as a substitute a camera system to perform real-time monitoring of sow posture in farrowing stalls for a potentially lower cost and computational power. Monitoring sow posture can provide producers an indicator of farrowing and aid in evaluating sow demeanor during lactation. During the development of this system the long range infrared (IR) proximity sensors were individually calibrated, a sow posture algorithm was developed, and the IR-Sow Posture Detection System (IR-SoPoDS) system was evaluated in a commercial setting to a Kinect V2® camera for a range of sow postures. Average accuracy of the sow posture algorithm on the training data was found to be 96%. The overall accuracy of the IR-SoPoDS system across the three sow frame sizes were:87% (small), 90% (medium), and 89% (large). This IR-SoPoDS system shows a strong promise for further development for sow posture and behavior detection in the farrowing stall environment