Multiple Time Scales in Diffraction Measurements of Diffusive Surface Relaxation

We grew SrTiO3 on SrTiO3 (001) by pulsed laser deposition, using x-ray scattering to monitor the growth in real time. The time-resolved small angle scattering exhibits a well-defined length scale associated with the spacing between unit cell high surface features. This length scale imposes a discrete spectrum of Fourier components and rate constants upon the diffusion equation solution, evident in multiple exponential relaxation of the "anti-Bragg" diffracted intensity. An Arrhenius analysis of measured rate constants confirms that they originate from a single activation energy.Comment: 4 pages, 3 figure

Measurements of Surface Diffusivity and Coarsening During Pulsed Laser Deposition

Pulsed Laser Deposition (PLD) of homoepitaxial SrTiO3 was studied with in-situ x-ray specular reflectivity and surface diffuse x-ray scattering. Unlike prior reflectivity-based studies, these measurements access both the time- and the length-scales of the evolution of the surface morphology during growth. In particular, we show that this technique allows direct measurements of the diffusivity for both inter- and intra-layer transport. Our results explicitly limit the possible role of island break-up, demonstrate the key roles played by nucleation and coarsening in PLD, and place an upper bound on the Ehrlich-Schwoebel (ES) barrier for downhill diffusion

A Multi-modal Machine Learning Approach and Toolkit to Automate Recognition of Early Stages of Dementia among British Sign Language Users

The ageing population trend is correlated with an increased prevalence of acquired cognitive impairments such as dementia. Although there is no cure for dementia, a timely diagnosis helps in obtaining necessary support and appropriate medication. Researchers are working urgently to develop effective technological tools that can help doctors undertake early identification of cognitive disorder. In particular, screening for dementia in ageing Deaf signers of British Sign Language (BSL) poses additional challenges as the diagnostic process is bound up with conditions such as quality and availability of interpreters, as well as appropriate questionnaires and cognitive tests. On the other hand, deep learning based approaches for image and video analysis and understanding are promising, particularly the adoption of Convolutional Neural Network (CNN), which require large amounts of training data. In this paper, however, we demonstrate novelty in the following way: a) a multi-modal machine learning based automatic recognition toolkit for early stages of dementia among BSL users in that features from several parts of the body contributing to the sign envelope, e.g., hand-arm movements and facial expressions, are combined, b) universality in that it is possible to apply our technique to users of any sign language, since it is language independent, c) given the trade-off between complexity and accuracy of machine learning (ML) prediction models as well as the limited amount of training and testing data being available, we show that our approach is not over-fitted and has the potential to scale up

Validation and comparison of two methods to Assess Human Energy Expenditure during Free-Living Activities

Background: The measurement of activity energy expenditure (AEE) via accelerometry is the most commonly used objective method for assessing human daily physical activity and has gained increasing importance in the medical, sports and psychological science research in recent years. Objective: The purpose of this study was to determine which of the following procedures is more accurate to determine the energy cost during the most common everyday life activities; a single regression or an activity based approach. For this we used a device that utilizes single regression models (GT3X, ActiGraph Manufacturing Technology Inc., FL., USA) and a device using activity-dependent calculation models (move II, movisens GmbH, Karlsruhe, Germany). Material and Methods: Nineteen adults (11 male, 8 female; 30.469.0 years) wore the activity monitors attached to the waist and a portable indirect calorimeter (IC) as reference measure for AEE while performing several typical daily activities. The accuracy of the two devices for estimating AEE was assessed as the mean differences between their output and the reference and evaluated using Bland-Altman analysis. Results: The GT3X overestimated the AEE of walking (GT3X minus reference, 1.26 kcal/min), walking fast (1.72 kcal/min), walking up2/downhill (1.45 kcal/min) and walking upstairs (1.92 kcal/min) and underestimated the AEE of jogging (2 1.30 kcal/min) and walking upstairs (22.46 kcal/min). The errors for move II were smaller than those for GT3X for all activities. The move II overestimated AEE of walking (move II minus reference, 0.21 kcal/min), walking up2/downhill (0.06 kcal/min) and stair walking (upstairs: 0.13 kcal/min; downstairs: 0.29 kcal/min) and underestimated AEE of walking fast (20.11 kcal/min) and jogging (20.93 kcal/min). Conclusions: Our data suggest that the activity monitor using activity-dependent calculation models is more appropriate for predicting AEE in daily life than the activity monitor using a single regression model

Aging and scaling laws in β\beta-hydroquinone-clathrate

The dielectric permittivity of the orientational glass methanol(x=0.73)-$\beta$-hydroquinone-clathrate has been studied as function of temperature and waiting time using different temperature-time-protocols. We study aging, rejuvenation and memory effects in the glassy phase and discuss similarities and differences to aging in spin-glasses. We argue that the diluted methanol-clathrate, although conceptually close to its magnetic pendants, takes an intermediate character between a true spin-glass and a pure random field system

Development of coordination and muscular fitness in children and adolescents with parent-reported ADHD in the German longitudinal MoMo Study

This study examined the development of muscular fitness and coordination in children and adolescents with and without attention deficit hyperactivity disorder (ADHD) over a period of 11 years. Data was collected in three measurement waves as part of the longitudinal, representative Motorik-Modul (MoMo) study in Germany (2003–2006, 2009–2012, 2014–2017). The overall sample comprised 2988 participants (253 with ADHD, 65% males; 2735 non-ADHD, 47% males; mean age 9 years). Structural equation modeling was conducted, and the estimated models had a good fit. No differences in muscular fitness were observed between participants with and without ADHD. Participants with ADHD had a lower coordinative performance at first measurement than those without ADHD. The difference in coordinative performance persisted throughout the study period