A protocol for Italian validation of DEMQoL-Proxy Scale: assessing the Quality of Life of people with moderate or mild dementia

In this paper, we propose an adaptation of a protocol for a tool's validation. We have utilized this phases-theory to validate in Italian language an instrument to assess Quality of Life for people with moderate or mild dementia. We will explain the example of our Italian validation of DEMQoL-Proxy considering each De Vellis's phase. We will explain our application of De Vellis's model to Italian example described. For the first three phases, we reproduced the original validating study in which authors (Smith et al., 2005) defined what to measure, how generate a set of items and the structure of the scale. Indeed, for the last five phases we explained the adaptation of De Vellis's model to Italian validation. We hope that this model could be effective to validating goals, for researchers and in particular for all professionals who deal with caregivers and patients with moderate and mild dementia. Furthermore, the measurement of the Quality of Life makes the scale widely useful within the various professional specialties and setting. Finally, thanks to the methodological assumptions adopted following the De Vellis's eight-phase model, we can affirm that this first Italian pre-validation of the DEMQoL-Proxy seems to be an excellent forerunner for its effective validation in the Italian context

Differential Maternal Feeding Practices, Eating Self-Regulation, and Adiposity in Young Twins

Restrictive feeding is associated with childhood obesity; however, this could be due to other factors that drive children to overeat and parents to restrict (eg, child genetics). Using a twin design to better control for confounders, we tested differences in restrictive feeding within families in relation to differences in twins’ self-regulatory eating and weight status

The remarkable properties of the symbiotic star AE Circinus

We present new optical spectroscopy and photometry, 2MASS infrared observations and 24 years of combined AAVSO and AFOEV photometry of the symbiotic star candidate \ae. The long-term light curve is characterized by outbursts lasting several years and having a slow decline of $\sim 2 \times 10^{-4}$ mag/day. The whole range of variability of the star in the $V$ band is about 4 magnitudes. The periodogram of the photometric data reveals strong signals at $\sim$ 342 and 171 days. The presence of the emission feature at $\lambda$ 6830 \AA at minimum and the detection of absorption lines of a $\sim$ K5 type star confirm the symbiotic classification and suggest that AE Cir is a new member of the small group of s-type yellow symbiotic stars. We estimate a distance of 9.4 kpc. Our spectrum taken at the high state shows a much flatter spectral energy distribution, the disappearance of the $\lambda$ 6830 \AA emission feature and the weakness of the He II 4686 emission relative to the Balmer emission lines. Our observations indicate the presence of emission line flickering in time scales of minutes in 2001. The peculiar character of \ae is revealed in the visibility of the secondary star at the high and low state, the light curve resembling a dwarf nova superoutburst and the relatively short low states. The data are hard to reconciliate with standard models for symbiotic star outbursts.Comment: accepted for publication in MNRAS, 7 figure

Search for Associations Containing Young stars (SACY): I. Sample & Searching Method

We report results from a high-resolution optical spectroscopic survey aimed to search for nearby young associations and young stars among optical counterparts of ROSAT All-Sky Survey X-ray sources in the Southern Hemisphere. We selected 1953 late-type (B-V >= 0.6), potentially young, optical counterparts out of a total of 9574 1RXS sources for follow-up observations. At least one high-resolution spectrum was obtained for each of 1511 targets. This paper is the first in a series presenting the results of the SACY survey. Here we describe our sample and our observations. We describe a convergence method in the (UVW) velocity space to find associations. As an example, we discuss the validity of this method in the framework of the BetaPic Association.Comment: Accepted for publication in A&

Welding defect detection: coping with artifacts in the production line

Visual quality inspection for defect detection is one of the main processes in modern industrial production facilities. In the last decades, artificial intelligence solutions took the place of classic computer vision techniques in the production lines and specifically in tasks that, for their complexity, were usually demanded to human workers yet obtaining similar or greater performance of their counterparts. This work exploits a Deep Neural Network for a smart monitoring system capable of performing accurate quality checks to detect welding defects in fuel injectors during the production stage. The contribution focuses on a novel approach to cope with unforeseen changes in production quality introduced by the alteration of a particular machine or process. Results suggest that pre-filtering could avoid the retraining of custom-designed networks. Moreover, the introduction of a weighting strategy on the confusion matrix allows obtaining good performance estimations even in the case of small and unbalanced datasets. Concerning a specific demanding case of an imbalanced dataset with very few positive examples, the system displayed a 96.30% accuracy on defect classification

Robust Image Stitching and Reconstruction of Rolling Stocks Using a Novel Kalman Filter with a Multiple-Hypothesis Measurement Model

This work introduces a novel algorithm for the reconstruction of rolling stocks from a sequence of images. The research aims at producing an accurate and wide image model that can be used as a Digital Twin (DT) for diagnosis, fault prediction, maintenance, and other monitoring operations. When observing large surfaces with nearly constant textures, metallic reflections, and repetitive patterns, motion estimation algorithms based on whole image error minimization and feature pairing with Random Sampling and Consensus (RANSAC) or Least Median of Squares (LMedS) fail to provide appropriate associations. To overcome such an issue, we propose a custom Kalman Filter (KF) modified by adding multiple input-noise sources represented as a Gaussian mixture distribution (GM), and specific algorithms to select appropriate data and variance to use for state prediction and correction. The proposed algorithm has been tested on images of train vessels, having a high number of windows, and large metallic paintings with constant or repetitive patterns. The approach here presented showed to be robust in the presence of high environmental disturbances and a reduced number of features. A large set of rolling stocks has been collected during a six months campaign. The set was employed to demonstrate the validity of the proposed algorithm by comparing the reconstructed twin versus known data. The system showed an overall accuracy in length estimation above 99%

On Multi-Agent Cognitive Cooperation: Can virtual agents behave like humans?

Individuals tend to cooperate or collaborate to reach a common goal when the going gets tough creating a common frame of reference that is a common mental representation of the situation. Information exchange among people is fundamental for building a shared strategy through the grounding process that exploits different communication channels like vision, haptic, or voice. Indeed, human perception is typically multi-modal. This work proposes a two-fold study investigating the cognitive collaboration process both among humans and virtual agents of a multi-agent reinforcement learning (MARL) system. The experiment with humans consists of an interactive virtual shared environment that uses multi-modal channels (visual and haptics) as interaction cues. Haptic feedback is fundamental for a good sense of presence and for improving the performance in completing a task. In this manuscript, an experiment, consisting of escaping a virtual maze trying to get the best score possible, is introduced. The experiment is meant to be performed in pairs, and the perceptual information is split among the participants. A custom haptic interface has been used for the interaction with the virtual environment. The machine learning case, instead, proposes two virtual agents implemented using a tabular Q-learning paradigm to control a single avatar in a 2D labyrinth, introducing a new form of MARL setting. As it is known, it is not easy to get familiar with haptics for people that have never used it, and that if not properly transmitted, the cognitive workflow does not produce any improvements. However, the main findings of the proposed work are that haptic-driven multi-modal feedback information is a valuable means of collaboration since it allows to establish a common frame of reference between the two participants. The machine learning experiments show that even independent agents, implemented with properly designed rewards, can learn the intentions of the other participant in the same environment and collaborate to accomplish a common task