Advances in the diagnosis of acute aortic syndromes: Role of imaging techniques.

Aortic diseases include a wide range of pathological conditions: aortic aneurysms, pseudoaneurysms, acute aortic syndromes, atherosclerotic and inflammatory conditions, genetic diseases and congenital anomalies. Acute aortic syndromes have acute onset and may be life-threatening. They include aortic dissection, intramural haematoma, penetrating aortic ulcer and traumatic aortic injury. Pain is the common denominator to all acute aortic syndromes. Pain occurs regardless of age, gender and other associated clinical conditions. In this review, we deal with the main findings in the clinical setting and the most recent indications for diagnostic imaging, which are aimed to start an appropriate treatment and improve the short- and long-term prognosis of these patients. © The Author(s) 2016

energy refurbishment of a university building in cold italian backcountry part 2 sensitivity studies and optimization

Abstract The first part of this study provided a discussion about the methodological approach for designing energy refurbishment measures of buildings. The case study is a building owned by University of Molise, in Campobasso, a cold Italian city. The reference scenario is a numerical model built after deep investigations, and thus surveys, questionnaires, documents and experimental measurements on the real building. Then, a calibrated energy model was presented. In this second part, starting from the calibrated model, some energy retrofit measures have been implemented. The obtained results allow to discuss two key points for researches in matter of energy refurbishment of buildings: a) the importance of using validated models to simulate the present performance; b) the environmental benefits and the economic implications of a deep energy refurbishment

Multidisciplinary Approach to Structural/Energy Diagnosis of Historical Buildings: A Case Study

Abstract A synergic approach for the investigations of the historical building performances - with reference to both the structural behavior and the energy performances for the space heating and cooling - is presented. The historical masonry building "Palazzo Bosco Lucarelli", located in Benevento, has been chosen as case study. The structural and energy analyses are carried out in parallel, especially during the identification of the building characteristics through tests and surveys in-situ. For the structural analysis - beyond examinations on materials - some dynamic tests have been used for better assessing a numerical Finite Element model necessary for the verification of the structure safety. Moreover, being necessary a structural refurbishment, also an energy retrofit could be realized. A rigorous evaluation procedure - aimed to guarantee the necessary reliability of numerical predictions - is performed in order to verify the technical and economical convenience of various energy retrofit solutions

energy refurbishment of a university building in cold italian backcountry part 1 audit and calibration of the numerical model

Abstract The study provides a methodological approach for designing energy refurbishment measures of buildings, enabling to understand the uncertainty of using numerical modelling and the real impacts due of adopting some energy efficiency technologies. The case study is a University building of the centre of Italy, and the reference scenario has been supported by various in-situ surveys, investigations and evaluations of the indoor comfort. Collected data, together with a comparison with energy bills, has allowed a proper calibration of a numerical model simulated by means EnergyPlus. All this phase is described in this paper, while a second part will discuss the energy retrofit and the building energy optimization

nzeb target for existing buildings case study of historical educational building in mediterranean climate

Abstract A key element of the Energy Performance of Building Directive 2010/31/EU is the introduction of nearly zero energy building (NZEB) standard for new constructions. However, considering the very low rate of new built volume, the major change for achieve the sustainable grow of the European economy, appears to be the renovation of existing building stock. But, is it possible to reach very low or nearly zero energy standard during refurbishment design? Proposed paper tries to answer this question, evaluating if the refurbishment of historic architectures to very low energy need is possible and economically feasible. With reference to a case study, this paper investigates the cost-optimal energy refurbishment of a Renaissance-style palace, located in the center of Naples, South Italy. The adopted methodology consists of various steps. Firstly, a model of the building has been accurately built and calibrated. Then, it has been used to evaluate possible interventions concerning both the envelope and the energy systems. The best solutions, chosen according to the European methodology of cost-optimality, have been combined in a last simulation. The results show that great energy savings as well as economic and environmental improvements are possible, although heritage buildings present a less flexibility in the proposal of energy efficiency measures

Application of a Machine Learning Technology in the Definition of Metabolically Healthy and Unhealthy Status: A Retrospective Study of 2567 Subjects Suffering from Obesity with or without Metabolic Syndrome.

The key factors playing a role in the pathogenesis of metabolic alterations observed in many patients with obesity have not been fully characterized. Their identification is crucial, and it would represent a fundamental step towards better management of this urgent public health issue. This aim could be accomplished by exploiting the potential of machine learning (ML) technology. In a single-centre study (n = 2567), we used an ML analysis to cluster patients with metabolically healthy (MHO) or metabolically unhealthy (MUO) obesity, based on several clinical and biochemical variables. The first model provided by ML was able to predict the presence/absence of MHO with an accuracy of 66.67% and 72.15%, respectively, and included the following parameters: HOMA-IR, upper body fat/lower body fat, glycosylated haemoglobin, red blood cells, age, alanine aminotransferase, uric acid, white blood cells, insulin-like growth factor 1 (IGF-1) and gamma-glutamyl transferase. For each of these parameters, ML provided threshold values identifying either MUO or MHO. A second model including IGF-1 zSDS, a surrogate marker of IGF-1 normalized by age and sex, was even more accurate with a 71.84% and 72.3% precision, respectively. Our results demonstrated high IGF-1 levels in MHO patients, thus highlighting a possible role of IGF-1 as a novel metabolic health parameter to effectively predict the development of MUO using ML technology

Application of regional physically-based landslide early warning model: tuning of the input parameters and validation of the results

The Aosta Valley region is located in North-West Alpine mountain chain. The geomorphology of the region is characterized by steep slopes, high climatic and altitude (ranging from 400 m a.s.l of Dora Baltea's river floodplain to 4810 m a.s.l. of Mont Blanc) variability. In the study area (zone B), located in Eastern part of Aosta Valley, heavy rainfall of about 800-900 mm per year is the main landslides trigger. These features lead to a high hydrogeological risk in all territory, as mass movements interest the 70% of the municipality areas (mainly shallow rapid landslides and rock falls). An in-depth study of the geotechnical and hydrological properties of hillslopes controlling shallow landslides formation was conducted, with the aim to improve the reliability of deterministic model, named HIRESS (HIgh REsolution Stability Simulator). In particular, two campaigns of on site measurements and laboratory experiments were performed. The data obtained have been studied in order to assess the relationships existing among the different parameters and the bedrock lithology. The analyzed soils in 12 survey points are mainly composed of sand and gravel, with highly variable contents of silt. The range of effective internal friction angle (from 25.6° to 34.3°) and effective cohesion (from 0 kPa to 9.3 kPa) measured and the median ks (10E-6 m/s) value are consistent with the average grain sizes (gravelly sand). The data collected contributes to generate input map of parameters for HIRESS (static data). More static data are: volume weight, residual water content, porosity and grain size index. In order to improve the original formulation of the model, the contribution of the root cohesion has been also taken into account based on the vegetation map and literature values. HIRESS is a physically based distributed slope stability simulator for analyzing shallow landslide triggering conditions in real time and in large areas using parallel computational techniques. The software runs in real-time by assimilating weather data and uses Monte Carlo simulation techniques to manage the geotechnical and hydrological input parameters. In this context, an assessment of the factors controlling the geotechnical and hydrological features is crucial in order to understand the occurrence of slope instability mechanisms and to provide reliable forecasting of the hydrogeological hazard occurrence, especially in relation to weather events. In particular, the model and the soil characterization were applied in back analysis, in order to assess the reliability of the model through validation of the results with landslide events that occurred during the period. The validation was performed on four past events of intense rainfall that have affected Valle d'Aosta region between 2008 and 2010 years triggering fast shallows landslides. The simulations show substantial improvement of the reliability of the results compared to the use of literature parameters. A statistical analysis of the HIRESSS outputs in terms of failure probability has been carried out in order to define reliable alert levels for regional landslide early warning systems