Resilience Training for VA Primary Care Providers

Physician burnout syndrome is epidemic within the U.S. healthcare system. Burnout is defined by three main criteria: emotional exhaustion, depersonalization, and a low sense of personal accomplishment, and its prevalence is highest among primary care providers. The VA’s All Employee Survey (AES) demonstrates that more than 50% of physicians working for the VA Healthcare System exhibit at least one of these symptoms. The literature discusses that this syndrome can be improved by increasing physician resilience. This capstone project first analyzed the need for resiliency training among VA primary care providers. After the needs analysis, an online training that encompassed mindfulness as a way of building reliance was created. This training will be presented to the leadership at VA Boston, and it will serve as an initial attempt to decrease provider burnout among this organization’s primary care physicians

Push-Over Analysis of RC Frame with Corroded Rebar

As known, the Italian building heritage largely consists of reinforced concrete frames designed before the '80s, which are, in many cases, built in the absence of specific anti-seismic criteria. Moreover, many of them, today, are characterized by bad structural conditions. Moreover, the problem of the structural conditions of the existing buildings, and their residual strength capacity, is often linked to the deterioration induced by the corrosive phenomena, which end up having a big impact on steel rebar mechanical properties. In this work, in order to investigate the influence of corrosion-damage on seismic response of existing reinforced concrete structures, a study has been carried out by analysing the non-linear behaviour of a reinforced concrete frame. The strength deterioration and reduction of the cross-section of steel rebar have been investigated and taken into account in the numerical analysis. This work shows the way in which the corrosion levels affected the push-over response, and the numerical results have been deeply analysed

Shear Strength Degradation due to Flexural Ductility Demand in R.C. Elements

A proposal is formulated that allows to evaluate the residual shear strength of reinforced concrete columns and beams for an assigned flexural ductility demand by limiting the range of the deviation angle between the inclinations of the yield \uf071 and the crack lines. In order to take into account the degradation due to cyclic loads, the reduction of the range of the deviation angle is related to the value of cinematic ductilit

Increasing the Capacity of Existing Bridges by Using Unbonded Prestressing Technology: A Case Study

External posttensioning or unbonded prestressing was found to be a powerful tool for retrofitting and for increasing the life extension of existing structures. Since the 1950s, this technique of reinforcement was applied with success to bridge structures in many countries, and was found to provide an efficient and economic solution for a wide range of bridge types and conditions. Unbonded prestressing is defined as a system in which the post-tensioning tendons or bars are located outside the concrete crosssection and the prestressing forces are transmitted to the girder through the end anchorages, deviators, or saddles. In response to the demand for a faster and more efficient transportation system, there was a steady increase in the weight and volume of traffic throughout the world. Besides increases in legal vehicle loads, the overloading of vehicles is a common problem and it must also be considered when designing or assessing bridges. As a result, many bridges are now required to carry loads significantly greater than their original design loads; and their deck results still deteriorated by cracking of concrete, corrosion of rebars, snapping of tendons, and so forth. In the following, a case study about a railway bridge retrofitted by external posttensioning technique will be illustrated

A holistic auto-configurable ensemble machine learning strategy for financial trading

Financial markets forecasting represents a challenging task for a series of reasons, such as the irregularity, high fluctuation, noise of the involved data, and the peculiar high unpredictability of the financial domain. Moreover, literature does not offer a proper methodology to systematically identify intrinsic and hyper-parameters, input features, and base algorithms of a forecasting strategy in order to automatically adapt itself to the chosen market. To tackle these issues, this paper introduces a fully automated optimized ensemble approach, where an optimized feature selection process has been combined with an automatic ensemble machine learning strategy, created by a set of classifiers with intrinsic and hyper-parameters learned in each marked under consideration. A series of experiments performed on different real-world futures markets demonstrate the effectiveness of such an approach with regard to both to the Buy and Hold baseline strategy and to several canonical state-of-the-art solutions

Towards the definition of a European Digital Building Logbook: A survey

Both the operational phase and embodied emissions that are introduced during the construction phase through the manufacture, sourcing, and installation of the building's materials and components are significant contributors to carbon emissions from the built environment. It is essential to change the current design and (re)construction processes in order to achieve the energy-saving targets for the EU building stock and move toward a society that is net carbon neutral. This change must be made from both a technical perspective as well as from a methodological perspective. To accomplish this, the EU has suggested several regulations and legislative steps to phase out inefficient structures. The most recent of these initiatives propose the idea of a Digital Building Logbook, which serves as a central repository for all pertinent building data, including information on energy efficiency. In this work, we present a survey of the elements that have been taken into consideration for the creation of the Digital Building Logbook to give an overview of what research has been done so far

A Unified Surface Geometric Framework for Feature-Aware Denoising, Hole Filling and Context-Aware Completion

Technologies for 3D data acquisition and 3D printing have enormously developed in the past few years, and, consequently, the demand for 3D virtual twins of the original scanned objects has increased. In this context, feature-aware denoising, hole filling and context-aware completion are three essential (but far from trivial) tasks. In this work, they are integrated within a geometric framework and realized through a unified variational model aiming at recovering triangulated surfaces from scanned, damaged and possibly incomplete noisy observations. The underlying non-convex optimization problem incorporates two regularisation terms: a discrete approximation of the Willmore energy forcing local sphericity and suited for the recovery of rounded features, and an approximation of the l(0) pseudo-norm penalty favouring sparsity in the normal variation. The proposed numerical method solving the model is parameterization-free, avoids expensive implicit volumebased computations and based on the efficient use of the Alternating Direction Method of Multipliers. Experiments show how the proposed framework can provide a robust and elegant solution suited for accurate restorations even in the presence of severe random noise and large damaged areas