Measures of the efficacy of a daily fifteen minute fitness programme for intermediate school children : a thesis presented in partial fulfilment of the requirements for the degree of Masterate in Education at Massey University

This study investigates the effects of a physical fitness training programme on the fitness, reading and mathematics performance of intermediate school pupils. The role of physical education in the primary school curriculum, theories of endurance and health-related fitness, as well as the possible relationship of physical fitness to academic achievement, are discussed and a review of previous studies is made. An experimental group of 29 and a control group of 57 Form 2 boys and girls, aged from 11-13 years, were pre-tested on physical fitness, reading and mathematics performance. For the next six months the control group continued with the school's normal physical education programme while the experimental group underwent a daily, fifteen minute physical fitness programme in which the exercises were kept submaximal and as continuous as possible. Both groups worked on the same reading and mathematics programmes. At the end of six months both groups were post-tested and a Student t-test applied to assess the significance of the difference in progress between the two groups. Since this thesis is designed as a study for teachers, all tests, activities and equipment were drawn from resources that are currently available for use in classrooms. The results of this study show a significant and generalized trend towards an increase in physical fitness on the part of the experimental group, but not on the part of the control group. In terms of academic achievement, the experimental group show generalized gains in relation to the control group, some of which are large enough to show up as significant on a t-test. The major conclusions of this study are: (1) Physical fitness among intermediate school children does improve as a result of a daily fifteen minute fitness programme. (2) Children who are in a superior state of physical fitness perform better in some areas of their academic work. (3) Motivation, ideally of an intrinsic nature, is the key to success in physical fitness activities, or for that matter, in any activity. The results of this study reinforce current theories regarding the importance of physical fitness and motivation and also provide some statistically significant confirmation of the correlation between physical fitness and academic performance. The educational implications of this study are pointed out and suggestions are made for future studies

Application of One-, Three-, and Seven-Day Forecasts During Early Onset on the COVID-19 Epidemic Dataset Using Moving Average, Autoregressive, Autoregressive Moving Average, Autoregressive Integrated Moving Average, and Naïve Forecasting Methods

The coronavirus disease 2019 (COVID-19) spread rapidly across the world since its appearance in December 2019. This data set creates one-, three-, and seven-day forecasts of the COVID-19 pandemic\u27s cumulative case counts at the county, health district, and state geographic levels for the state of Virginia. Forecasts are created over the first 46 days of reported COVID-19 cases using the cumulative case count data provided by The New York Times as of April 22, 2020. From this historical data, one-, three-, seven, and all-days prior to the forecast start date are used to generate the forecasts. Forecasts are created using: (1) a Naïve approach; (2) Holt-Winters exponential smoothing (HW); (3) growth rate (Growth); (4) moving average (MA); (5) autoregressive (AR); (6) autoregressive moving average (ARMA); and (7) autoregressive integrated moving average (ARIMA). Median Absolute Error (MdAE) and Median Absolute Percentage Error (MdAPE) metrics are created with each forecast to evaluate the forecast with respect to existing historical data. These error metrics are aggregated to provide a means for assessing which combination of forecast method, forecast length, and lookback length are best fits, based on lowest aggregated error at each geographic level. The data set is comprised of an R-Project file, four R source code files, all 1,329,404 generated short-range forecasts, MdAE and MdAPE error metric data for each forecast, copies of the input files, and the generated comparison tables. All code and data files are provided to provide transparency and facilitate replicability and reproducibility. This package opens directly in RStudio through the R Project file. The R Project file removes the need to set path locations for the folders contained within the data set to simplify setup requirements. This data set provides two avenues for reproducing results: 1) Use the provided code to generate the forecasts from scratch and then run the analyses; or 2) Load the saved forecast data and run the analyses on the stored data. Code annotations provide the instructions needed to accomplish both routes. This data can be used to generate the same set of forecasts and error metrics for any US state by altering the state parameter within the source code. Users can also generate health district forecasts for any other state, by providing a file which maps each county within a state to its respective health-district. The source code can be connected to the most up-to-date version of The New York Times COVID-19 dataset allows for the generation of forecasts up to the most recently reported data to facilitate near real-time forecasting

Ice Growth Measurements from Image Data to Support Ice-Crystal and Mixed-Phase Accretion Testing

This paper describes the imaging techniques as well as the analysis methods used to measure the ice thickness and growth rate in support of ice-crystal icing tests performed at the National Research Council of Canada (NRC) Research Altitude Test Facility (RATFac). A detailed description of the camera setup, which involves both still and video cameras, as well as the analysis methods using the NASA Spotlight software, are presented. Two cases, one from two different test entries, showing significant ice growth are analyzed in detail describing the ice thickness and growth rate which is generally linear. Estimates of the bias uncertainty are presented for all measurements. Finally some of the challenges related to the imaging and analysis methods are discussed as well as methods used to overcome them

Methods for Weighting Decisions to Assist Modelers and Decision Analysts: A Review of Ratio Assignment and Approximate Techniques

Computational models and simulations often involve representations of decision-making processes. Numerous methods exist for representing decision-making at varied resolution levels based on the objectives of the simulation and the desired level of fidelity for validation. Decision making relies on the type of decision and the criteria that is appropriate for making the decision; therefore, decision makers can reach unique decisions that meet their own needs given the same information. Accounting for personalized weighting scales can help to reflect a more realistic state for a modeled system. To this end, this article reviews and summarizes eight multi-criteria decision analysis (MCDA) techniques that serve as options for reaching unique decisions based on personally and individually ranked criteria. These techniques are organized into a taxonomy of ratio assignment and approximate techniques, and the strengths and limitations of each are explored. We compare these techniques potential uses across the Agent-Based Modeling (ABM), System Dynamics (SD), and Discrete Event Simulation (DES) modeling paradigms to inform current researchers, students, and practitioners on the state-of-the-art and to enable new researchers to utilize methods for modeling multi-criteria decisions

Augmenting Bottom-Up Metamodels with Predicates

Metamodeling refers to modeling a model. There are two metamodeling approaches for ABMs: (1) top-down and (2) bottom-up. The top down approach enables users to decompose high-level mental models into behaviors and interactions of agents. In contrast, the bottom-up approach constructs a relatively small, simple model that approximates the structure and outcomes of a dataset gathered fromthe runs of an ABM. The bottom-up metamodel makes behavior of the ABM comprehensible and exploratory analyses feasible. Formost users the construction of a bottom-up metamodel entails: (1) creating an experimental design, (2) running the simulation for all cases specified by the design, (3) collecting the inputs and output in a dataset and (4) applying first-order regression analysis to find a model that effectively estimates the output. Unfortunately, the sums of input variables employed by first-order regression analysis give the impression that one can compensate for one component of the system by improving some other component even if such substitution is inadequate or invalid. As a result the metamodel can be misleading. We address these deficiencies with an approach that: (1) automatically generates Boolean conditions that highlight when substitutions and tradeoffs among variables are valid and (2) augments the bottom-up metamodel with the conditions to improve validity and accuracy. We evaluate our approach using several established agent-based simulations

A Content Analysis-Based Approach to Explore Simulation Verification and Identify Its Current Challenges

Verification is a crucial process to facilitate the identification and removal of errors within simulations. This study explores semantic changes to the concept of simulation verification over the past six decades using a data-supported, automated content analysis approach. We collect and utilize a corpus of 4,047 peer-reviewed Modeling and Simulation (M&S) publications dealing with a wide range of studies of simulation verification from 1963 to 2015. We group the selected papers by decade of publication to provide insights and explore the corpus from four perspectives: (i) the positioning of prominent concepts across the corpus as a whole; (ii) a comparison of the prominence of verification, validation, and Verification and Validation (V&V) as separate concepts; (iii) the positioning of the concepts specifically associated with verification; and (iv) an evaluation of verification\u27s defining characteristics within each decade. Our analysis reveals unique characterizations of verification in each decade. The insights gathered helped to identify and discuss three categories of verification challenges as avenues of future research, awareness, and understanding for researchers, students, and practitioners. These categories include conveying confidence and maintaining ease of use; techniques\u27 coverage abilities for handling increasing simulation complexities; and new ways to provide error feedback to model users

A Double Blind, Placebo-Controlled, Randomized Crossover Study of the Acute Metabolic Effects of Olanzapine in Healthy Volunteers

Atypical antipsychotics exhibit metabolic side effects including diabetes mellitus and obesity. The adverse events are preceded by acute worsening of oral glucose tolerance (oGTT) along with reduced plasma free fatty acids (FFA) and leptin in animal models. It is unclear whether the same acute effects occur in humans.A double blind, randomized, placebo-controlled crossover trial was conducted to examine the potential metabolic effects of olanzapine in healthy volunteers. Participants included male (8) and female (7) subjects [18-30 years old, BMI 18.5-25]. Subjects received placebo or olanzapine (10 mg/day) for three days prior to oGTT testing. Primary endpoints included measurement of plasma leptin, oral glucose tolerance, and plasma free fatty acids (FFA). Secondary metabolic endpoints included: triglycerides, total cholesterol, high- and low-density lipoprotein cholesterol, heart rate, blood pressure, body weight and BMI. Olanzapine increased glucose Area Under the Curve (AUC) by 42% (2808±474 vs. 3984±444 mg/dl·min; P = 0.0105) during an oGTT. Fasting plasma leptin and triglycerides were elevated 24% (Leptin: 6.8±1.3 vs. 8.4±1.7 ng/ml; P = 0.0203) and 22% (Triglycerides: 88.9±10.1 vs. 108.2±11.6 mg/dl; P = 0.0170), whereas FFA and HDL declined by 32% (FFA: 0.38±0.06 vs. 0.26±0.04 mM; P = 0.0166) and 11% (54.2±4.7 vs. 48.9±4.3 mg/dl; P = 0.0184), respectively after olanzapine. Other measures were unchanged.Olanzapine exerts some but not all of the early endocrine/metabolic changes observed in rodent models of the metabolic side effects, and this suggest that antipsychotic effects are not limited to perturbations in glucose metabolism alone. Future prospective clinical studies should focus on identifying which reliable metabolic alterations might be useful as potential screening tools in assessing patient susceptibility to weight gain and diabetes caused by atypical antipsychotics.ClinicalTrials.gov NCT00741026

Formation of the high-affinity agonist state of the α1-adrenergic receptor at cold temperatures does not require a G-protein

AbstractTwo methods were employed to uncouple hepatic α1-adrenergic receptors from their associated G-protein (termed Gp) in order to determine wether locking of the α1-receptor in a high-affinity agonist state at cold temperatures (2°C) represents formation of a ternary complex. Uncoupling is defined as the inability to observe the GppNHp-sensitive, high-affinity agonist state of the receptor in [3H]prazosin competition binding studies performed at 25°C. The first method for achieving uncoupling involved brief alkalinization and resulted in greater 95% loss of several G-proteins. The second method involved proteolytic cleavage of either part or all of the α1-receptor coupling domain from the binding domain. Following either treatment, receptors were converted to the high-affinity agonist state at 2°C. Thus, while formation of the high-affinity state of the receptor at higher temperatures may require Gp, formation of this state at 2°C does not require Gp or even the entire α1-adregenic receptor