Towards a Characterization of Late Talkers: The Developmental Profile of Children with Late Language Emergence through a Web-Based Communicative-Language Assessment

Children acquire language naturally, but there is variation in language acquisition patterns. Indeed, different internal and external variables play a role in acquiring language. However, there are open research questions about the contribution of different variables to language development. Moreover, with societal changes and due to the pandemic situation, there has been a growing interest in testing digitalization related to indirect language acquisition assessment. In this study, a web-based assessment survey was developed to (1) describe the relation between expressive vocabulary, Socio- Conversational Skills (SCS), gender, parental education, executive functions (EFs), and pretend play; (2) determine whether the survey can detect differences between late talkers (LTs) and children with typical language development; (3) identify children with “overall high” and “overall low” communicative-language scores to test the validity of expressive vocabulary as a main indicator to detect LTs. The parents of 108 Italian children (51 males) aged 24–36 months participated in the study. The results showed that expressive vocabulary correlates with measures of SCS (assertiveness and responsiveness) and is reliable in identifying LTs (d = 2.73). Furthermore, SCS and EFs contribute to better characterizing the developmental profile of children aged 24–36 months

Neural Correlates of Direct Access Trading in a Real Stock Market: An fMRI Investigation

Background: While financial decision making has been barely explored, no study has previously investigated the neural correlates of individual decisions made by professional traders involved in real stock market negotiations, using their own financial resources. Aim: We sought to detect how different brain areas are modulated by factors like age, expertise, psychological profile (speculative risk seeking or aversion) and, eventually, size and type (Buy/Sell) of stock negotiations, made through Direct Access Trading (DAT) platforms. Subjects and methods: Twenty male traders underwent fMRI while negotiating in the Italian stock market using their own preferred trading platform. Results: At least 20 decision events were collected during each fMRI session. Risk averse traders performed a lower number of financial transactions with respect to risk seekers, with a lower average economic value, but with a higher rate of filled proposals. Activations were observed in cortical and subcortical areas traditionally involved in decision processes, including the ventrolateral and dorsolateral prefrontal cortex (vlPFC, dlPFC), the posterior parietal cortex (PPC), the nucleus accumbens (NAcc), and dorsal striatum. Regression analysis indicated an important role of age in modulating activation of left NAcc, while traders' expertise was negatively related to activation of vlPFC. High value transactions were associated with a stronger activation of the right PPC when subjects' buy rather than sell. The success of the trading activity, based on a large number of filled transactions, was related with higher activation of vlPFC and dlPFC. Independent of chronological and professional age, traders differed in their attitude to DAT, with distinct brain activity profiles being detectable during fMRI sessions. Those subjects who described themselves as very self-confident, showed a lower or absent activation of both the caudate nucleus and the dlPFC, while more reflexive traders showed greater activation of areas involved in strategic decision making. Discussion: The neural correlates in DAT are similar to those observed in other decision making contexts. Trading is handled as a well-learned automatic behavior by expert traders; for those who mostly rely on heuristics, cognitive effort decreases, and transaction speed increases, but decision efficiency lowers following a poor involvement of the dlPFC

Gender-related variability in information processing of disclosure documents

Disclosure is used worldwide as a tool to increase transparency and help investors to make their decisions, thus partially overcoming asymmetric information in financial markets. This research seeks to explore gender–related variability in visual attention allocation to the Key Investor Information Document, and in the evaluation of product financial attractiveness. We exploited the eye–tracking methodology to collect neural data, responding to the call for considering new data sources. The analysis shows that men tend to dedicate more attention to the sections Objectives and Past performance while women spend more time to scan the sections Risk–reward profile and Costs and charges; when evaluating product financial attractiveness, women, with respect to men, tend to evaluate more often products as poorly financially attractive. Results reveal the existence of gender–related variability in the visual search strategy for relevant information, which, in turn, can impact on the phase of product evaluation. These findings highlight the professional responsibility of regulators and supervisors to monitor sellers’ and marketers’ behaviours when they interact with consumers. Moreover, this study could provide support to develop financial disclosure documents considering individual differences and ensuring that adequate attention is allocated by investors to all financial information sources, thus raising the level of investor protection. Eventually, the study stimulates innovations to be embedded in the world–wide ongoing regulatory developments that aim at increasing transparency requirements

Documenting Complexity for the 20TH Century Heritage: the Enriched 3d Models of the Turin Exposition Nervi's Halls Digitization

Abstract. Great attention is increasingly paid to the heritage belonging to the XX century, particularly for the spatial structures made of concrete, that are a significant trait of this modern movement architecture. Since they demand today urgent conservation plans sustaining their deterioration, the multidisciplinary researches should devotes a profound investigations for tailored approaches providing a clear indication of best practices and recommendation for correct 3D documentation, information management and structural assessment and monitoring. In this framework, the Geomatics approaches are advancing the interests toward the multi-scale and multi-sensor digitization and for supporting management of complex information in enriched 3D models. The iconic halls B and C in Torino Esposizioni (Italy), designed by Pier Luigi Nervi, is the case study presented. It was recently awarded by the Getty Keeping it Modern grant. The multi-disciplinary research conducted, still in progress, focuses a particularly into the investigation of the structural analysis and consistency of ferrocement elements of the vaulted system finalized to the structural condition assessment. Here the role of multi-scale and multi-sensor 3D models is investigated, such as the development of a digital twin of the halls as a starting point to create an enriched informative system. The reconstruction of this model particularly considering the large extension and the complexity of the spaces, is addressed to works as a collector of 3D multi-sensor data and information related to the diagnostic investigation on structural health monitoring for the durability of ferrocement elements

War, human rights, and medical scientific publishing: European Journal of Physical and Rehabilitation Medicine will accept from now on only papers by authors signing the UN Declaration on Human Rights

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DOCUMENTING COMPLEXITY FOR THE 20TH CENTURY HERITAGE: THE ENRICHED 3D MODELS OF THE TURIN EXPOSITION NERVI’S HALLS DIGITIZATION

Great attention is increasingly paid to the heritage belonging to the XX century, particularly for the spatial structures made of concrete, that are a significant trait of this modern movement architecture. Since they demand today urgent conservation plans sustaining their deterioration, the multidisciplinary researches should devotes a profound investigations for tailored approaches providing a clear indication of best practices and recommendation for correct 3D documentation, information management and structural assessment and monitoring. In this framework, the Geomatics approaches are advancing the interests toward the multi-scale and multi-sensor digitization and for supporting management of complex information in enriched 3D models. The iconic halls B and C in Torino Esposizioni (Italy), designed by Pier Luigi Nervi, is the case study presented. It was recently awarded by the Getty Keeping it Modern grant. The multi-disciplinary research conducted, still in progress, focuses a particularly into the investigation of the structural analysis and consistency of ferrocement elements of the vaulted system finalized to the structural condition assessment. Here the role of multi-scale and multi-sensor 3D models is investigated, such as the development of a digital twin of the halls as a starting point to create an enriched informative system. The reconstruction of this model particularly considering the large extension and the complexity of the spaces, is addressed to works as a collector of 3D multi-sensor data and information related to the diagnostic investigation on structural health monitoring for the durability of ferrocement elements

Anchoring effect in visual information processing during financial decisions: an eye-tracking study

When reading a financial disclosure document, subjects are faced with multiple information cues and might simplify decisional complexity by relying on heuristics. This study explores whether, in an attempt to filter information from the Payment Account Fees Information Document (FID), subjects anchor their evaluation to a specific item, leading to biased financial choices. By detecting the visual search strategy in 70 subjects through eye tracking, we observed that people exhibited systematic visual anchoring to the top of the document, which corresponds to the Liquidity section that displays the Annual Fee. Moreover, data revealed that subjects sometimes fail to recognize the most advantageous products. This mainly occurs when the Annual Fee is high, even if the other charges compensate for that amount, clarifying the link between visual search strategy and financial decisions. Data also showed the role of financial literacy in modulating attention, as poorly financially literate subjects are more prone to anchoring bias. The findings contribute to the neuroeconomics literature on anchoring effect and highlight practical implications for financial regulators and managers involved in the ergonomics of documents

Building typological classification in Switzerland using deep learning methods for seismic assessment

Natural disasters, such as earthquakes, have always represented a danger to human life. Seismic risk assessment consists of the evaluation of existing buildings and their expected response in case of an earthquake; the exposure model of buildings plays a key role in risk calculations. With this respect, in recent years, advanced techniques have been developed to speed up and automatize the processes of data acquisition to data interpretation, although it is worth mentioning that the visual survey is essential to train and validate Machine Learning (ML) methods. In the present study, the identification of building types is conducted by exploiting the traditional visual survey to implement a Deep Learning (DL) classification model. As a first step, city mapping schemes are obtained by classifying buildings according to the main features (i.e., construction period and height classes). Then, Random Forest (RF), a supervised learning algorithm, is applied to classify different building types by exploiting all their attributes. The RF model is trained and tested on the cities of Neuchatel and Yverdon-Les-Bains. The decent accuracy of the results encourages the application of the method to different cities, with proper adjustments in datasets, features and algorithms