Exploring the Factors That Affect World Anti-Doping Code Compliance: An Analysis of Peru’s and Bolivia’s National Anti-Doping Organizations

The purpose of this dissertation was to identify and understand the factors that affect the World Anti-Doping Code compliance of the National Anti-Doping Organizations in Peru and Bolivia. By identifying and understanding these factors, this research may provide useful information on how to strengthen compliance and development strategies. This dissertation used a qualitative basic interpretive design with a sample of anti-doping experts from Peru, Bolivia, and the World Anti-Doping Agency, and a comprehensive document review process. Through coding and thematic analysis of the in-depth interview data and the development of a findings model, three main findings were identified as factors that affect compliance: (1) inadequate anti-doping legislation, resources, and structure; (2) authorities’ limited support and understanding of anti-doping and compliance; and (3) limited understanding of the cultural context, the value of relations, and the potential benefits of sanctions. The data analyzed suggested that the factors that affect compliance are generally heterogenous and context-specific, indicating that the best way to address them requires the implementation of context-based compliance and development strategies. Results indicate that compliance strategy may be strengthened by using different responsive regulatory tactics based on cultural differences and compliance motivations of the World Anti-Doping Code signatories

Gauge fields and interferometry in folded graphene

Folded graphene flakes are a natural byproduct of the micromechanical exfoliation process. In this Letter we show by a combination of analytical and numerical methods that such systems behave as intriguing interferometers due to the interplay between an externally applied magnetic field and the gauge field induced by the deformations in the region of the fold.Comment: 4 pages, 3 figure

Genetics of Schizophrenia and Smoking: An Approach to Studying their Comorbidity Based on Epidemiological Findings

The association between schizophrenia and tobacco smoking has been described in more than 1,000 articles, many with inadequate methodology. The studies on this association can focus on: (1) current smoking, ever smoking or smoking cessation; (2) non-psychiatric controls or controls with severe mental illness (e.g., bipolar disorder); and (3) higher smoking frequency or greater usage in smokers. The association with the most potential for genetic studies is that between ever daily smoking and schizophrenia; it may reflect a shared genetic vulnerability. To reduce the number of false-positive genes, we propose a three-stage approach derived from epidemiological knowledge. In the first stage, only genetic variations associated with ever daily smoking that are simultaneously significant within the non-psychiatric controls, the bipolar disorder controls and the schizophrenia cases will be selected. Only those genetic variations that are simultaneously significant in the three hypothesis tests will be tested in the second stage, where the prevalence of the genes must be significantly higher in schizophrenia than in bipolar disorder, and significantly higher in bipolar disorder than in controls. The genes simultaneously significant in the second stage will be included in a third stage where the gene variations must be significantly more frequent in schizophrenia patients who did not start smoking daily until their 20s (late start) versus those who had an early start. Any genetic approach to psychiatric disorders may fail if attention is not given to comorbidity and epidemiological studies that suggest which comorbidities are likely to be explained by genetics and which are not. Our approach, which examines the results of epidemiological studies on comorbidities and then looks for genes that simultaneously satisfy epidemiologically suggested sets of hypotheses, may also apply to the study of other major illnesses

Forecasting Long-term Electricity Demand in the Residential Sector

AbstractThis work describes a methodology for long-term electricity demand forecast in the residential sector. The methodology has been used in the power market studies of some Brazilian distribution utilities. The methodology is based on decomposition of the total electricity residential consumption in three components: average consumption per consumer unit, electrification rate and number of households. Then, the forecast for the total electricity consumption in residential sector is the product of forecasts for these three components. The prediction for the number of households is based on demographic models while the future trajectory of the electrification rate is defined by the targets for achieving the universal access to electricity. The product of these two components provides a forecast to the number of residential customers. The average consumption per unit consumer depends on the macroeconomic scenarios for GDP, average household income and income distribution. The proposed methodology provides a framework to integrate macroeconomic scenario, demographic projection and assumptions for ownership and efficiency of electric appliances in a long-term demand forecast. In order to illustrate the application of the proposed methodology, this paper presents a ten-year demand forecasts for the residential sector in Brazil

SPEA MULTIPLICATA (Mexican Spadefoot). PREDATION

El documento (pdf) incluye dos paginas, la portada de la revista y el texto de la nota cientifica que solo son 4 parrafos, es solo una cuartillawe document a new predator-prey interaction between a neonate Mexican Dusky Rattlesnake (Crotalus triseriatus) and a toad Spea multiplicata.CONACY

El control interno para la mejora de la rentabilidad empresarial en Nuevo Chimbote 2020

El presente estudio de investigación, tiene como propósito identificar Control Interno para la mejora de la Rentabilidad Empresarial en Nuevo Chimbote-Perú, Particularmente en la Empresa León & Asociados Auditores Consultores s. Civil, en el año 2020, habiendo usado la investigación descriptiva, diseño no experimental, siendo la población la institución antes citada, donde laboran personal directivo, administrativos y de servicios, quienes constituyen las unidades de análisis, en número de 35; la técnica usada fue la encuesta y el instrumento el cuestionario. Entre otros resultados alcanzados, se obtuvo que el control interno mejora la rentabilidad de la Empresa León & Asociados Auditores Consultores S. Civil De R.L., Nuevo Chimbote 2020; toda vez que se aplican lineamientos y/o directivas sobre las actividades administrativas económicas y financieras dentro de un determinado ejercicio económico, lo cual ha sido vertido por los gerentes y/o trabajadores de la empresa. Se puede observar que existe una relación positiva entre el control interno y la rentabilidad. se tiene que, el control interno no es adecuado, ni orienta a cumplir los objetivos y metas institucionales, lo cual conlleva que la rentabilidad no sea optima dentro de un ejercicio económico

A deep neural network approach to predicting clinical outcomes of neuroblastoma patients

Background The availability of high-throughput omics datasets from large patient cohorts has allowed the development of methods that aim at predicting patient clinical outcomes, such as survival and disease recurrence. Such methods are also important to better understand the biological mechanisms underlying disease etiology and development, as well as treatment responses. Recently, different predictive models, relying on distinct algorithms (including Support Vector Machines and Random Forests) have been investigated. In this context, deep learning strategies are of special interest due to their demonstrated superior performance over a wide range of problems and datasets. One of the main challenges of such strategies is the “small n large p” problem. Indeed, omics datasets typically consist of small numbers of samples and large numbers of features relative to typical deep learning datasets. Neural networks usually tackle this problem through feature selection or by including additional constraints during the learning process. Methods We propose to tackle this problem with a novel strategy that relies on a graph-based method for feature extraction, coupled with a deep neural network for clinical outcome prediction. The omics data are first represented as graphs whose nodes represent patients, and edges represent correlations between the patients’ omics profiles. Topological features, such as centralities, are then extracted from these graphs for every node. Lastly, these features are used as input to train and test various classifiers. Results We apply this strategy to four neuroblastoma datasets and observe that models based on neural networks are more accurate than state of the art models (DNN: 85%-87%, SVM/RF: 75%-82%). We explore how different parameters and configurations are selected in order to overcome the effects of the small data problem as well as the curse of dimensionality. Conclusions Our results indicate that the deep neural networks capture complex features in the data that help predicting patient clinical outcomes