Sex differences in tactile defensiveness in children with ADHD and their siblings

Contains fulltext : 71056.pdf (publisher's version ) (Closed access)Tactile defensiveness (TD) is a disturbance in sensory processing and is observed in some children with attention-deficit-hyperactivity disorder (ADHD). TD has been examined in male children with ADHD and in children with ADHD without differentiating by sex. As males and females with ADHD may differ in the clinical expression of the disorder and associated deficits, the aim of this study was to examine sex differences in TD in males and females with ADHD. Non-affected siblings were also examined to investigate familiality of TD. The Touch Inventory for Elementary-School-Aged Children was administered to 47 children with ADHD (35 males, 12 females; mean age 9y 8mo [SD 1y 11mo]), 36 non-affected siblings (21 males, 15 females; mean age 8y 10mo [SD 2y 4mo]), and 35 control children (16 males, 19 females; mean age 9y 5mo [SD 6mo]). Results indicated that females with ADHD displayed higher levels of TD than males with ADHD (who did not differ from control males). This suggests that TD is sex specific and may contribute to the identification of ADHD in females, thus improving diagnostic and therapeutic strength in this under-referred group. Non-affected siblings were unimpaired, regardless of sex, which suggests that TD is specific to the disorder and not part of a familial risk for ADHD

Zonal Safety and Particular Risk Analysis for Early Aircraft Design using Parametric Geometric Modelling

Safety assessment is paramount in aircraft design. For unconventional aircraft or aircraft with novel propulsion or system architectures or technologies, it is critical to investigate safety as early as possible in the design process to eliminate unfeasible aircraft configurations and system architectures. In this context, the Zonal Safety Analysis (ZSA) and the Particular Risk Analysis (PRA) that evaluate the safety aspects from an aircraft configuration and system placement perspective are essential to perform early. These analyses require a three-dimensional (3D) model of the aircraft and systems and substantial manual effort, limiting the ability to perform rapid iterations required to support design space exploration and, eventually, multidisciplinary design optimization. To analyze many aircraft configurations and system architectures, parametric 3D modelling, ZSA, and PRA require automation. This thesis reviews the methodologies for performing the ZSA and PRA from a systems point of view and proposes a novel methodology for semi-automated conceptual-level ZSA and PRA (CZSA and CPRA) implemented using Python and OpenVSP. As part of CZSA, automated aircraft 3D modelling, parametric zone definition, and zone-component interaction analysis methods are developed that are supported by a manually prepared database of safety-driven best practices. The CPRA involves parametric modelling of particular risk threat zones for trajectory-based PRAs and automated detection of system components in these zones. The effectiveness of the proposed approach is demonstrated with case studies for conventional and unconventional aircraft designs and novel system technologies. The presented work is a step towards integrating system safety analysis into multidisciplinary analysis and optimization environments, thus increasing conceptual design maturity and reducing development time

Essays in Bayesian Econometrics

This dissertation is comprised of three chapters. Chapter 1 is a reprint of my job market. The chapter considers the efficient estimation of opinion pools in the Bayesian paradigm and extends their application to cases where the number of competing models exceeds the number of observations. An appropriate Bayesian formulation and estimation algorithm is proposed which 1) accommodates any proper scoring rule and 2) allows the weights to shrink towards any possible combination. This flexibility makes the Bayesian opinion pool relevant for applications related to model averaging and model selection and improves stability compared to the ones estimated using scoring rules in a small sample setting. Results from a simulation study reveal that the proposed Bayesian opinion pool methodology improves prediction accuracy. An application involving the Survey of Professional Forecasters demonstrates that the Bayesian opinion pool's inflation forecast competes well with the equal-weight aggregated inflation forecast published by the Federal Bank of Philadelphia. The application showcases the usefulness of the Bayesian solution in situations where traditional opinion pools fail. Chapter two introduces a non-parametric vector autoregressive model with dynamic factor (DF-NPVAR) through a hierarchical Bayesian approach. The chapter considers the specification, identification and estimation of the DF-NPVAR model, allowing it to be efficiently fit via MCMC algorithms. The model aims at effectively capturing dynamic relationships among variables and enabling the incorporation of extensive information sets. Issues related to model comparison and extensions to settings with autocorrelated errors and qualitative variables are also considered. In an application employing post-war US data, the DF-NPVAR model successfully identifies non-linear associations between macroeconomic variables and the dynamic factor captures the business cycle component, which aligns with officially declared recession periods. Chapter three discusses a Bayesian estimation for the FAVAR models using the precision-based algorithm. The model is fully identified under the identification restrictions of \cite{bai2016estimation}. The approach increases the efficiency of the Gibbs sampler and avoids slow convergence and poor mixing (\cite{chan2009efficient}). This article then contrasts the Bayesian approach with the one-step and two-step estimation techniques proposed in \cite{bernanke2005measuring}. The simulation study finds that the Bayesian approach recovers the unobservable factor in a simple FAVAR framework compared to estimation techniques proposed in \cite{bernanke2005measuring}

Trust Dispersion and Effective Human-AI Collaboration: The Role of Psychological Safety (Short Paper)

Trust is a crucial factor in team performance for human-human and human-AI teams. While research made significant advancements in uncovering factors that affect the human decision to trust their AI teammate, it disregards the potential dynamics of trust in teams with multiple team members. To address this gap, we propose that trust in AI is an emergent state that can be differentiated on the individual and team level. We highlight the importance of considering the dispersion of trust levels in human-AI teams to understand better how trust influences team performance. Furthermore, we transfer the concept of psychological safety from human psychology literature and propose its role in buffering the potential adverse effects of dispersed trust attitudes