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Introduction: Handstand performance in gymnastics varies across apparatuses, with antero-posterior stability playing a key role in maintaining balance. Objectives: This study explores the biomechanical interplay between abdominal, arm, and floor handstand torso stability in gymnastics, towards the torso stability of handstand performance on parallel bars. Methods: Twenty-five participants completed four tasks: holding a 10-kg dumbbell for 15 seconds (arm stability), maintaining a floor forearm plank for 15 seconds (abdominal stability), and performing 10-second of steady handstands on both the floor and parallel bars. Inclinometer sensors recorded sagittal-axis movement to quantify stability. Statistical analysis steps included correlation, normality testing, linear regression, bootstrapped mediation and moderation models, and path visualization. Results: Regression analysis revealed significant direct effects of abdominal and arm stability on torso stability during handstands on P-bars. Mediation analysis showed that floor torso stability significantly mediated the effect of arm stability, but not abdominal stability. Moderation analysis confirmed that floor torso stability amplified the influence of arm control, while no such interaction was found for abdominal control. Discussion: Bootstrapping validated the robustness of direct and moderated effects. The analysis revealed a range of meaningful and intriguing mediation and moderation pathways among the studied variables, highlighting the complexity of their interrelationships. Conclusion: Despite the small sample size, the study provides a theoretically grounded framework for future research in gymnastics biomechanics and wearable sensor applications. Introducción: El desempeño en parada de manos en gimnasia varía según el aparato, y la estabilidad anteroposterior juega un papel fundamental en el mantenimiento del equilibrio. Objetivos: Este estudio explora la interacción biomecánica entre la estabilidad abdominal, de brazos y del torso en parada de manos en gimnasia, con el fin de mejorar la estabilidad del torso en la parada de manos sobre barras paralelas. Métodos: Veinticinco participantes realizaron cuatro tareas: sostener una mancuerna de 10 kg durante 15 segundos (estabilidad de brazos), mantener una plancha de antebrazos en el suelo durante 15 segundos (estabilidad abdominal) y realizar 10 segundos de parada de manos estable tanto en el suelo como en las barras paralelas. Se utilizaron sensores de inclinómetro para registrar el movimiento en el eje sagital y cuantificar la estabilidad. El análisis estadístico incluyó correlación, pruebas de normalidad, regresión lineal, modelos de mediación y moderación mediante remuestreo bootstrap y visualización de trayectorias. Resultados: El análisis de regresión reveló efectos directos significativos de la estabilidad abdominal y de brazos sobre la estabilidad del torso durante la parada de manos en barras paralelas. El análisis de mediación mostró que la estabilidad del torso en el suelo medió significativamente el efecto de la estabilidad de los brazos, pero no el de la estabilidad abdominal. El análisis de moderación confirmó que la estabilidad del torso en el suelo amplificó la influencia del control de los brazos, mientras que no se encontró dicha interacción para el control abdominal. Discusión: El remuestreo bootstrap validó la robustez de los efectos directos y moderados. El análisis reveló una variedad de vías de mediación y moderación significativas e interesantes entre las variables estudiadas, lo que destaca la complejidad de sus interrelaciones. Conclusión: A pesar del tamaño reducido de la muestra, el estudio proporciona un marco teórico sólido para futuras investigaciones en biomecánica de la gimnasia y aplicaciones de sensores portátiles. Introdução: O desempenho na parada de mãos na ginástica varia de acordo com o aparelho, sendo a estabilidade antero-posterior fundamental para a manutenção do equilíbrio. Objectivos: Este estudo explora a interacção biomecânica entre a estabilidade abdominal, dos braços e do tronco na parada de mãos em ginástica, visando a estabilidade do tronco na parada de mãos em barras paralelas. Métodos: Vinte e cinco participantes realizaram quatro tarefas: segurar um haltere de 10 kg durante 15 segundos (estabilidade dos braços), manter a prancha de antebraços no chão durante 15 segundos (estabilidade abdominal) e realizar 10 segundos de parada de mãos estável tanto no chão como nas paralelas. Os sensores de inclinação registaram o movimento no eixo sagital para quantificar a estabilidade. As etapas de análise estatística incluíram correlação, teste de normalidade, regressão linear, modelos de mediação e moderação com bootstrap e visualização de trajetórias. Resultados: A análise de regressão revelou efeitos diretos significativos da estabilidade abdominal e dos braços na estabilidade do tronco durante a parada de mãos nas barras paralelas. A análise de mediação mostrou que a estabilidade do tronco no chão mediou significativamente o efeito da estabilidade dos braços, mas não da estabilidade abdominal. A análise de moderação confirmou que a estabilidade do tronco no solo amplificou a influência do controlo dos braços, enquanto não foi encontrada qualquer interação semelhante para o controlo abdominal. Discussão: O método bootstrap validou a robustez dos efeitos diretos e moderados. A análise revelou um leque de vias de mediação e moderação significativas e intrigantes entre as variáveis ​​estudadas, destacando a complexidade das suas inter-relações. Conclusão: Apesar do tamanho reduzido da amostra, o estudo fornece uma base teórica sólida para futuras pesquisas em biomecânica da ginástica e aplicações de sensores wearable.

U.S. national and state population-based estimates of adults living with autism spectrum disorder (ASD) are nonexistent due to the lack of existing surveillance systems funded to address this need. Therefore, we estimated national and state prevalence of adults 18–84 years living with ASD using simulation in conjunction with Bayesian hierarchal models. In 2017, we estimated that approximately 2.21% (95% simulation interval (SI) 1.95%, 2.45%) or 5,437,988 U.S. adults aged 18 and older have ASD, with state prevalence ranging from 1.97% (95% SI 1.55%, 2.45%) in Louisiana to 2.42% (95% SI 1.93%, 2.99%) in Massachusetts. Prevalence and case estimates of adults living with ASD (diagnosed and undiagnosed) can help states estimate the need for diagnosing and providing services to those unidentified.

\"Written by two established experts in the field, the purpose of this handbook is to provide a practical, one-stop reference on regression analysis. The focus is on the tools that both practitioners and researchers use in real life. It is intended to be a comprehensive collection of the theory, methods, and applications of the subject matter, but it is deliberately written at an accessible level. The handbook will provide a quick and convenient reference or \"refresher\" on ideas and methods that are useful for the accurate analysis of data and its resulting interpretations. Students can use the book as an introduction to and/or summary of key concepts in regression and related course work (such as linear, nonlinear, and nonparametric regressions). Plentiful references are supplied for the more motivated readers. Theory is presented when necessary, and always supplemented by hands-on examples. Software routines are available via an author-maintained web site\"-- Provided by publisher.

Identifying interesting relationships between pairs of variables in large data sets is increasingly important. Here, we present a measure of dependence for two-variable relationships: the maximal information coefficient (MIC). MIC captures a wide range of associations both functional and not, and for functional relationships provides a score that roughly equals the coefficient of determination (R²) of the data relative to the regression function. MIC belongs to a larger class of maximal information-based nonparametric exploration (MINE) statistics for identifying and classifying relationships. We apply MIC and MINE to data sets in global health, gene expression, major-league baseball, and the human gut microbiota and identify known and novel relationships.

In prevention science and related fields, large meta-analyses are common, and these analyses often involve dependent effect size estimates. Robust variance estimation (RVE) methods provide a way to include all dependent effect sizes in a single meta-regression model, even when the exact form of the dependence is unknown. RVE uses a working model of the dependence structure, but the two currently available working models are limited to each describing a single type of dependence. Drawing on flexible tools from multilevel and multivariate meta-analysis, this paper describes an expanded range of working models, along with accompanying estimation methods, which offer potential benefits in terms of better capturing the types of data structures that occur in practice and, under some circumstances, improving the efficiency of meta-regression estimates. We describe how the methods can be implemented using existing software (the “metafor” and “clubSandwich” packages for R), illustrate the proposed approach in a meta-analysis of randomized trials on the effects of brief alcohol interventions for adolescents and young adults, and report findings from a simulation study evaluating the performance of the new methods.

This review covers the core concepts and design decisions of TensorFlow. TensorFlow, originally created by researchers at Google, is the most popular one among the plethora of deep learning libraries. In the field of deep learning, neural networks have achieved tremendous success and gained wide popularity in various areas. This family of models also has tremendous potential to promote data analysis and modeling for various problems in educational and behavioral sciences given its flexibility and scalability. We give the reader an overview of the basics of neural network models such as the multilayer perceptron, the convolutional neural network, and stochastic gradient descent, the most commonly used optimization method for neural network models. However, the implementation of these models and optimization algorithms is time-consuming and error-prone. Fortunately, TensorFlow greatly eases and accelerates the research and application of neural network models. We review several core concepts of TensorFlow such as graph construction functions, graph execution tools, and TensorFlow’s visualization tool, TensorBoard. Then, we apply these concepts to build and train a convolutional neural network model to classify handwritten digits. This review is concluded by a comparison of low- and high-level application programming interfaces and a discussion of graphical processing unit support, distributed training, and probabilistic modeling with TensorFlow Probability library.