In-plane and out-of plane experimental characterization of rc masonry infilled frames

Seismic investigations of typical south European masonry infilled frames were performed by testing two reduced scale specimens: one in the in-plane direction and another in the out-ofplane direction. Information about geometry and reinforcement scheme of those structures constructed in 1980s were obtained by [1]. The specimen to be tested in the in-plane direction was constructed as double leaf masonry while the specimen for testing in the out-of-plane direction is constructed with only its exterior leaf since the recent earthquakes have highlighted the vulnerability of the external leaf of the infills in out-of-plane direction [2]. The tests were performed by applying the pre-defined values of displacements in the in-plane and out-of-plane directions in the control points. For in-plane testing it was done by hydraulic actuator and for out-of-plane testing through the application of an airbag. Input and output air in the airbag was controlled by using a software to apply a specific displacement in the control point of the infill wall. Mid-point of the infill was assumed as a control point for outof- plane testing. Deformation and crack patterns of the infill confirm the formation of two-way arching mechanism of the masonry infill until collapse of the upper horizontal interface between infill and frame which is known as weakest interface due to difficulties in filling the mortar between bricks of last row and upper beam. This results in the crack opening through a welldefined path and the consequent collapse of the infill.

Analysis of output properties of the Peroxiredoxin/Thioredoxin/Thioredoxin reductase system

Dissertação de mestrado em Bioinformática

Os efeitos do processo de insolvência nas relações patrimoniais entre os cônjuges

Dissertação de mestrado em Direito Judiciário

Real-Time decision support using data mining to predict blood pressure critical events in intensive medicine patients

Patient blood pressure is an important vital signal to the physicians take a decision and to better understand the patient condition. In Intensive Care Units is possible monitoring the blood pressure due the fact of the patient being in continuous monitoring through bedside monitors and the use of sensors. The intensivist only have access to vital signs values when they look to the monitor or consult the values hourly collected. Most important is the sequence of the values collected, i.e., a set of highest or lowest values can signify a critical event and bring future complications to a patient as is Hypotension or Hypertension. This complications can leverage a set of dangerous diseases and side-effects. The main goal of this work is to predict the probability of a patient has a blood pressure critical event in the next hours by combining a set of patient data collected in real-time and using Data Mining classification techniques. As output the models indicate the probability (%) of a patient has a Blood Pressure Critical Event in the next hour. The achieved results showed to be very promising, presenting sensitivity around of 95%.