Questionnaires do not discriminate motor imagery ability of people with different motor expertise

Questionnaires are presented as reliable measure of motor imagery (MI), i.e. the ability to mentally simulate a movement in an internal perspective. Although there is some evidence that MI is domain-specific (i.e., i.e., higher scores for motor imagery may be generated by people with extensive real-world experience and practice), MI studies have typically employed fixed and generic movements as items. Thus, we investigated the content validity of the movement items of the Vividness of Movement Imagery Questionnaire-2 (VMIQ-2). Sixty participants were divided in groups of athletes (competitive and not-competitive, with an extensive motor experience) and not-athletes (with a reduced motor experience) and analysed by means of a mixed factorial MANOVA. The three MI modalities, external visual, internal visual and kinesthetic imagery, did not result in significantly different scores between the groups. We recommend caution in using MI generic questionnaires in studies that compare people with different motor experiences. Moreover, we suggest that the structure of the questionnaires should be redesigned, in order to make them adaptable to the specific needs of professionals and researchers.Los cuestionarios han sido considerados como medidas fiables y válidas de imaginación motora (IM), entendida como la habilidad de un sujeto de simular mentalmente un movimiento desde su perspectiva interna. Aunque hay evidencia que la IM es específica de dominio (e.g. puntuaciones más altas de IM se generan en aquello sujetos con mayor práctica y experiencia en el mundo real). En este estudio, hemos investigado la validez de contenido para los items de movimientos de la escala VMIQ-2 ("Vividness of Movement Imagery Questionnaire-2"). Sesenta participantes fueron divididos en 2 grupos mediante MANOVA factorial mixto: un grupo de "atletas" (con mayor experiencia motora, participación competitiva y no competitiva) y un grupo de "no-atletas" (con una experiencia motora reducida). Como esperábamos, los grupos no difirieron en ninguna de las puntuaciones de las tres modalidades de la IM (visual externa, visual interna y cinestésico). Por ello, recomendamos ser cuidadosos en la utilización e interpretación de los cuestionarios de IM en estudios que comparan personas con distintas habilidades motoras. Además, la estructura de los cuestionarios probablemente deba volve a diseñarse para hacerlos adaptables a las necesidades específicas de los profesionales e investigadores

Giuseppe Damiani Almeyda: design drawings compared

Giuseppe Damiani Almeyda is among the most important designers, architects, engineers and designers at the end of '800. He has participated in numerous architectural competitions, both at Palermo in Sicily and again in Italy. He has conducted numerous studies on the identification of significant types of construction from coffee house to the shrines cemeteries, the decorative detail to the urban study on a large scale. Of course He is remembered in our city, especially as the designer of the Politeama, an imposing structure that dominates the central square of Palermo. Very old city, whose roots, evidenced by the presence dating back to the Punic, have never found a break until the present day, offering a repertoire of great architectural prestige and international reputation. Moreover, capital, enjoys a privilege that few other cities can boast: it has two of the most important theaters in the history of Architecture: the Politeama and the Teatro Massimo. And our dear Almeyda participated in both competitions, winning one and losing the second. But what were the real reasons why he did not win the second project? Perhaps the town is terrified of being left with a certified copy of the already made Politeama? Or, participation in the race by Giovan Battista Filippo Basile unquestionably compromised the expert advice? Or perhaps, again, the second project did not meet in style, beauty and greatness, the grandeur of the former? These and more are the questions we have approached the study of two extraordinary projects proposed architectural competitions with only one signature, but hides itself, strange stylistic mechanisms but also social, historical, political

Moving up the Quality ladder? EU-China Trade Dynamics in Clothing

This paper compares European and Chinese exports in the clothing sector since the end of the Multi-Fiber Arrangement in 2005. Using detailed product-level data from UN Comtrade, we document the pattern of export prices and quantities observed for both countries, considering both exports to the rest of the world and to a particular destination market. We find that within narrowly defined product categories, European varieties typically sell for a higher price than Chinese varieties. But this price gap is narrowing. Despite rising prices, Chinese varieties are increasingly selling more than European varieties, suggesting that quality differences are narrowing. While European “core” products in clothing are stable over time, Chinese exports show strong product dynamics with exit and entry of new “core” products every year and “core” products changing rapidly. Both China and the EU export in every product category, resulting in a perfect product overlap with no products being exported by only one of the two. To make sure that our findings are not driven by a different product mix or a different destination country mix of EU versus Chinese exports, we compare EU and Chinese exports of clothing to the US and limit the comparison to HS6 product categories that are exported by both countries to the US. Again we obtain similar results as those obtained by comparing EU and Chinese exports to the rest of the world. Also, our evidence is suggestive of China exporting its high quality goods, while the EU exporting its most efficiently produced goods.

DOMAIN-AWARE MULTIFIDELITY LEARNING FOR DESIGN OPTIMIZATION

Accurate physics-based models are essential to the design and optimization of engineering systems, to compute key performance indicators associated with alternative design solutions. The implementation of high-fidelity models in simulation-based design optimization poses significant challenges due to the relevant computational cost frequently associated with their execution. However, real world engineering systems can rely on the availability of multiple models or approximations of their physics, representations characterized by different computational complexity and accuracy. Those alternative models can be cheaper to evaluate and can thus be exploited to enhance the efficiency of the optimization task. Multifidelity methods allow to combine multiple sources of information at different levels of fidelity, potentially exploiting the affordability of low fidelity evaluations to massively explore the design space, then enriching the accuracy through a reduced number of high-fidelity queries [1]. Many multifidelity optimization methods combine data from multiple models into a probabilistic surrogate, frequently delaying the identification of promising design alternatives that could rather be more efficiently captured if domain specific expertise were also used to inform the search [2]. To address this challenge, we present original domain-aware multifidelity frameworks to accelerate design optimization and improve the quality of the solution. In particular, our strategy is based on an active learning scheme that combines data-driven and physics-informed utility functions, to include the expert knowledge about the specific physical phenomena during the search for the optimal design. This allows to tailor the selection of the physical model to evaluate and increase the efficiency of the learning process, using at best a limited amount of high-fidelity data to sensitively improve the design solution. We discuss several applications of the proposed framework for aerospace design optimization problems, considering atmospheric flight at low and high altitudes for both aeronautics and space applications. [1] Peherstorfer, B., Willcox, K., Gunzburger, M. Survey of multifidelity methods in uncertainty propagation, inference, and optimization. Siam Review (2018) 60(3): 550–591. [2] Di Fiore, F., Maggiore, P. Mainini L. Multifidelity domain-aware learning for the design of re-entry vehicles. Structural and Multidisciplinary Optimization (2021) 64: 3017–303

Damaging micromechanisms in an as cast ferritic and a ferritized ductile cast iron

Mechanical behavior and damaging micromechanisms in Ductile Cast Irons (DCIs) are strongly effected by matrix microstructure (e.g., phases volume fraction, grains size and grain distribution) and graphite nodules morphology peculiarities (e.g., nodularity level, nodule size, nodule count, etc.). The influence of the graphite nodules depends on both the matrix microstructure and the loading conditions (e.g., quasi-static, dynamic or cyclic loadings). According to the most recent results, these graphite nodules show a mechanical properties gradient inside the graphite nodules, with the graphite elements – matrix debonding as only one of the possible damaging micromechanisms. In this work, two different ferritic DCIs were investigated (a ferritic matrix obtained from as-cast condition and a ferritized matrix) focusing on the damaging micromechanisms in graphite nodules due to tensile stress. Specimens lateral surfaces were observed using a Scanning Electron Microscope (SEM) during the tests following a step by step procedure.Fil: D' Agostino, Laura. Università di Cassino e del Lazio Meridionale; ItaliaFil: Di Cocco, Vittorio. Università di Cassino e del Lazio Meridionale; ItaliaFil: Fernandino, Diego Omar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; ArgentinaFil: Lacoviello, Francesco. Università di Cassino e del Lazio Meridionale; Itali

Non-Myopic Multifidelity Bayesian Optimization

Bayesian optimization is a popular framework for the optimization of black box functions. Multifidelity methods allows to accelerate Bayesian optimization by exploiting low-fidelity representations of expensive objective functions. Popular multifidelity Bayesian strategies rely on sampling policies that account for the immediate reward obtained evaluating the objective function at a specific input, precluding greater informative gains that might be obtained looking ahead more steps. This paper proposes a non-myopic multifidelity Bayesian framework to grasp the long-term reward from future steps of the optimization. Our computational strategy comes with a two-step lookahead multifidelity acquisition function that maximizes the cumulative reward obtained measuring the improvement in the solution over two steps ahead. We demonstrate that the proposed algorithm outperforms a standard multifidelity Bayesian framework on popular benchmark optimization problems

NM-MF: Non-Myopic Multifidelity Framework for Constrained Multi-Regime Aerodynamic Optimization

The exploration and trade-off analysis of different aerodynamic design configurations requires solving optimization problems. The major bottleneck to assess the optimal design is the large number of time-consuming evaluations of high-fidelity computational fluid dynamics (CFD) models, necessary to capture the non-linear phenomena and discontinuities that occur at higher Mach number regimes. To address this limitation, we introduce an original non-myopic multifidelity Bayesian framework aimed at including expensive high-fidelity CFD simulations for the optimization of the aerodynamic design. Our scheme proposes a novel two-step lookahead policy to maximize the improvement of the solution quality considering the rewards of future steps, and combines it with utility functions informed by the fluid dynamic regime and the information extracted from data, to wisely select the aerodynamic model to interrogate. We validate the proposed framework for the case of a constrained drag coefficient optimization problem of a NACA 0012 airfoil, and compare the results to other popular multifidelity and single-fidelity optimization frameworks. The results suggest that our strategy outperforms the other approaches, allowing to significantly reduce the drag coefficient through a principled selection of limited evaluations of the high-fidelity CFD model

Domain-Aware Active Learning for Multifidelity Optimization

Bayesian optimization is a popular strategy for the optimization of black-box objective functions [1]. In many engineering applications, the objective can be evaluated with multiple representations at different levels of fidelity, to enhance a trade-off between cost and accuracy. Accordingly, multifidelity methods have been proposed in a Bayesian framework to efficiently combine information sources, using low-fidelity models to enable the exploration of design alternatives, and improve the accuracy of the solution through limited high-fidelity evaluations [2]. Most multifidelity methods based on active learning search the optimal design considering only the information extracted from the surrogate model. This can preclude the evaluation of promising design configurations that can be captured only including the knowledge of the particular physical phenomena involved [3]. To address this issue, this presentation discusses original domain-aware multifidelity Bayesian frameworks to accelerate design analysis and optimization performances. In particular, our strategy comes with an active learning scheme to adaptively sample the design space, combining statistical data from the surrogate model with physical information from the specific domain. Our formulation introduces physics-informed utility functions as additional contributions to the acquisition functions. This permits to enhance the active learning with a physicsbased insight and to realize a form of domain awareness which is beneficial to the efficiency and accuracy of the optimization task. The presentation will discuss several applications and implementations of the proposed approach for single discipline and multidisciplinary aerospace design optimization problems. [1] Snoek, J., Larochelle, H.. Adams, R.P. Practical bayesian optimization of machine learning algorithms. Advances in neural information processing systems. (2012) 25. [2] Peherstorfer, B., Willcox, K., Gunzburger, M. Survey of multifidelity methods in uncertainty propagation, inference, and optimization. Siam Review (2018) 60(3): 550–591. [3] Di Fiore, F., Maggiore, P. Mainini L. Multifidelity domain-aware learning for the design of re-entry vehicles. Structural and Multidisciplinary Optimization (2021

Malleability of the self: electrophysiological correlates of the enfacement illusion

Self-face representation is fundamentally important for self-identity and self-consciousness. Given its role in preserving identity over time, self-face processing is considered as a robust and stable process. Yet, recent studies indicate that simple psychophysics manipulations may change how we process our own face. Specifically, experiencing tactile facial stimulation while seeing similar synchronous stimuli delivered to the face of another individual seen as in a mirror, induces 'enfacement' illusion, i.e. the subjective experience of ownership of the other’s face and a bias in attributing to the self, facial features of the other person. Here we recorded visual Event-Related Potentials elicited by the presentation of self, other and morphed faces during a self-other discrimination task performed immediately after participants received synchronous and control asynchronous Interpersonal Multisensory Stimulation (IMS). We found that self-face presentation after synchronous as compared to asynchronous stimulation significantly reduced the late positive potential (LPP; 450-750 ms), a reliable electrophysiological marker of self-identification processes. Additionally, enfacement cancelled out the differences in LPP amplitudes produced by self- and other-face during the control condition. These findings represent the first direct neurophysiological evidence that enfacement may affect self-face processing and pave the way to novel paradigms for exploring defective self-representation and self-other interactions

IMAGE-BASED MODELING TECHNIQUES FOR ARCHITECTURAL HERITAGE 3D DIGITALIZATION: LIMITS AND POTENTIALITIES

3D reconstruction from images has undergone a revolution in the last few years. Computer vision techniques use photographs from data set collection to rapidly build detailed 3D models. The simultaneous applications of different algorithms (MVS), the different techniques of image matching, feature extracting and mesh optimization are inside an active field of research in computer vision. The results are promising: the obtained models are beginning to challenge the precision of laser-based reconstructions. Among all the possibilities we can mainly distinguish desktop and web-based packages. Those last ones offer the opportunity to exploit the power of cloud computing in order to carry out a semi-automatic data processing, thus allowing the user to fulfill other tasks on its computer; whereas desktop systems employ too much processing time and hard heavy approaches. Computer vision researchers have explored many applications to verify the visual accuracy of 3D model but the approaches to verify metric accuracy are few and no one is on Autodesk 123D Catch applied on Architectural Heritage Documentation. Our approach to this challenging problem is to compare the 3Dmodels by Autodesk 123D Catch and 3D models by terrestrial LIDAR considering different object size, from the detail (capitals, moldings, bases) to large scale buildings for practitioner purpose