Abstract
Similitude theory is a branch of engineering science concerned with establishing the necessary and sufficient conditions of similarity among phenomena, and has been applied to different fields such as structural engineering, vibration and impact problems. Testing of sub-scale models is still nowadays a valuable design tool, helping engineers to accurately predict the behavior of oversized prototypes through scaling laws applied to the obtained experimental results. In this manuscript it has been reviewed the developments in the methodologies used to create reduced scale models as a design tool, including those based in the use of: dimensional analysis, differential equations and energetic methods. Besides, given their importance, some major areas of research were reviewed apart: impacted structures, rapid prototyping of scale models and size effects. At last, some topics on which additional efforts can be undertaken are highlighted.

Abstract

Abstract
This paper presents a computational framework for the optimization of maintenance activities for infrastructure assets, with particular emphasis being placed on road network assets. This framework incorporates degradation and maintenance models for infrastructure assets along with multi-objective optimization for searching optimal maintenance schedules. Given a schedule of maintenance actions, the future performance is estimated by means of a Monte Carlo simulation that enables to account for inherent uncertainties. The design variables of optimization are the types of maintenance actions and their timing over the planning horizon. The objectives are to minimize both the asset degradation and maintenance cost. This includes satisfaction of constraints representing performance demands. The proposed framework is general and can be applied to different types of infrastructure assets. The numerical results, obtained for a road bridge managed by a highway operating agency, demonstrate the validity and usefulness of the proposed framework.

Abstract
We propose a new dynamic modeling framework for credit risk assessment that extends the prevailing credit scoring models built upon historical data static settings. The driving idea mimics the principle of films, by composing the model with a sequence of snapshots, rather than a single photograph. In doing so, the dynamic modeling consists of sequential learning from the new incoming data. A key contribution is provided by the insight that different amounts of memory can be explored concurrently. Memory refers to the amount of historic data being used for estimation. This is important in the credit risk area, which often seems to undergo shocks. During a shock, limited memory is important. Other times, a larger memory has merit. An application to a real-world financial dataset of credit cards from a financial institution in Brazil illustrates our methodology, which is able to consistently outperform the static modeling schema.

Abstract

Abstract
One of the most common knee joint disorders is known as osteoarthritis which results from the progressive degeneration of cartilage and subchondral bone over time, affecting essentially elderly adults. Current evaluation techniques are either complex, expensive, invasive or simply fails into detection of small and progressive changes that occur within the knee. Vibroarthrography appeared as a new solution where the mechanical vibratory signals arising from the knee are recorded recurring only to an accelerometer and posteriorly analyzed enabling the differentiation between a healthy and an arthritic joint. In this study, a vibration-based classification system was created using a dataset with 92 healthy and 120 arthritic segments of knee joint signals collected from 19 healthy and 20 arthritic volunteers, evaluated with k-nearest neighbors and support vector machine classifiers. The best classification was obtained using the k-nearest neighbors classifier with only 6 time-frequency features with an overall accuracy of 89.8% and with a precision, recall and f-measure of 88.3%, 92.4% and 90.1%, respectively. Preliminary results showed that vibroarthrography can be a promising, non-invasive and low cost tool that could be used for screening purposes. Despite this encouraging results, several upgrades in the data collection process and analysis can be further implemented.