Prognostic significance of endothelial dysfunction in hypertensive patients

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Insulin resistance as determinant of cardiac hypertrophy in never treated hypertensive patients with D/D allele of ACE-gene polymorphism

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Dynamic Identification of Large Thin Shell Structures in Concrete

The paper presents the results of a recent testing campaign carried out on the vaulted structures built by Pier Luigi Nervi in Torino Esposizioni. Nervi’s halls are a spatial structure masterpiece, admired for their daring and innovative conception. The technological achievements of the 20th century have allowed conceiving unprecedented large scale and complex structures. However, the experimental nature of numerous innovative structural and spatial configurations adopted by the designers of the time have revealed over time intrinsic fragilities that, when neglected, have threatened their long-term structural integrity. In addition, 20th century’s structures were usually conceived without accounting for seismic actions, but only for static configurations, in accordance with the technical standards of the time. Therefore, it is of crucial importance to assess the dynamic behavior of these structures to understand their vulnerability and plan their correct preservation measures. Due to its complex configuration, the setup of dynamic testing campaign for Hall B built by Nervi presented many challenges, including: i) the complex optimization problems due to the spatial characters of the vaulted structure; ii) the possible effects of damage degradation or anomalies. The aims of this investigation were to investigate the behavior of historical spatial structures to seismic actions; and to detect the presence of possible structural anomalies

Modeling and predicting students' engagement behaviors using mixture Markov models

Students' engagements reflect their level of involvement in an ongoing learning process which can be estimated through their interactions with a computer-based learning or assessment system. A pre-requirement for stimulating student engagement lies in the capability to have an approximate representation model for comprehending students' varied (dis)engagement behaviors. In this paper, we utilized model-based clustering for this purpose which generates K mixture Markov models to group students' traces containing their (dis)engagement behavioral patterns. To prevent the Expectation-Maximization (EM) algorithm from getting stuck in a local maxima, we also introduced a K-means-based initialization method named as K-EM. We performed an experimental work on two real datasets using the three variants of the EM algorithm: the original EM, emEM, K-EM; and, non-mixture baseline models for both datasets. The proposed K-EM has shown very promising results and achieved significant performance difference in comparison with the other approaches particularly using the Dataset. Hence, we suggest to perform further experiments using large dataset(s) to validate our method. Additionally, visualization of the resultant clusters through first-order Markov chains reveals very useful insights about (dis)engagement behaviors depicted by the students. We conclude the paper with a discussion on the usefulness of our approach, limitations and potential extensions of this work

X-ray spectroscopy with a photon-counting SiPM-based scintillation detector

A new low-energy X-ray detector was built and operated, using a plastic scintillator coupled to a large area SiPM. The signal is amplified with a low-noise, high gain, custom circuit providing excellent photon-counting capabilities and allowing a quasi-digital measurement. The detector was tested using X-rays coming from molybdenum K lines (17.4 and 19.6 keV), and an energy resolution of 28% is obtained with 20 photoelectrons per X-ray photon on average

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

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

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