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

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Prognostic significance of endothelial dysfunction in hypertensive patients

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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

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

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

In-Operation Experimental Modal Analysis of a Three Span Open-Spandrel RC Arch Bridge

This paper presents the results of the dynamic tests conducted on a historical reinforced concrete arch bridge located in the Tuscan-Emilian Apennines, in the province of Parma (Italy). The design of the sensors location was determined in order to investigate the possible separation into bodies operated by the joints between the different spans. The ambient vibration data allowed the dynamic characterization of the 3-span arch bridge with the total length of 146 m and 18 m in width. The interpretation of the main global modes, distinctly detected through time domain identification methods, indicates that the horizontal response is governed by the deformability of the joints. The results show that the obtained modal features provide a reliable reference for the subsequent updating of the bridge FE model

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

Expanding Glucocerebrosidase Involvement in Neurodegeneration: D419H Mutation Causing Dementia with Lewy Bodies

Mutations in the glucocerebrosidase gene (GBA) are a common genetic risk factor for Dementia with Lewy Bodies (DLB). Hereby, we describe an Italian family with three DLB relatives harboring the D419H GBA variant. The pedigree analysis indicates a dominant inheritance pattern, suggesting that heterozygous GBA mutations may differently affect the risk of Parkinson-dementia syndromes. This should be taken into account for genetic counseling in relatives of patients with GBA associated Parkinson’s Disease/DLB

Trust/untrust is not the same as true/false. lessons learned and ethical questions on the application of untrustworthiness scales to judge individuals

This special paper reflects on trustworthiness and its implications for scientific medical journals and all the communities they serve: health professionals, policymakers, the public, and a specific discipline, in our case, Physical and Rehabilitation Medicine. We start from a recent episode: a paper claimed the untrustworthiness of two randomised controlled trials (RCTs) published in the European Journal of Physical and Rehabilitation Medicine based on a newly developed trustworthiness scale, used until now only in systematic reviews. This likely represents the first case of applying such a scale focusing on a single leading author. Developing a proper answer to this case led us to present some insights from the perspective of a Journal editor. We discuss the impact of false research results, why trust is needed in science and medicine, the difference between untrust and false results, the problems in judging trustworthiness, the unfortunately weak capacity of the peer review system in preventing these issues, the problems of "post-hoc" judgements and the emerging ethical issues. We conclude with some suggestions for the future based on prevention at the system level