Late-medieval construction site management at the Monastery of Jerónimos in Lisbon

Through the analysis of the exceptional accounting documents of 1517 related to the construction of the Monastery of Jerónimos (Lisbon), this paper discusses the main characteristics of a new model of construction site organization. In the later Middle Ages we can find, among others, two main models of constructing site organization. One, older and more widespread, consisted in a centralized and pyramidal management model. The other, apparently more recent, was based in the existence of several autonomous teams working simultaneously, each one responsible for building a specific part or section of the building. This paper describes and discusses this new organizational model as it was adopted and implemented by João de Castilho (1470–1552) for the construction of the Monastery of Jerónimos in 1517, probably for the first time in Portugal, but with some parallels in other places in Europe.

FISHji: new ImageJ macros for the quantification of fluorescence in epifluorescence images

Fluorescence in situ hybridization (FISH) is based on the use of fluorescent staining dyes, however, the signal intensity of the images obtained by microscopy is seldom quantified with accuracy by the researcher. The development of innovative digital image processing programs and tools has been trying to overcome this problem, however, the determination of fluorescent intensity in microscopy images still has issues due to the lack of precision in the results and the complexity of existing software. This work presents FISHji, a set of new ImageJ methods for automated quantification of fluorescence in images obtained by epifluorescence microscopy. To validate the methods, results obtained by FISHji were compared with results obtained by flow cytometry. The mean correlation between FISHji and flow cytometry was high and significant, showing that the imaging methods are able to accurately assess the signal intensity of fluorescence images. FISHji are available for non-commercial use at http://paginas.fe.up.pt/nazevedo/.

Tube-jack and sonic testing for the evaluation of the state of stress in historical masonry

In the investigation and diagnosis of damages to historical masonry structures, the state of stress of the masonry is an important characteristic that must be determined with as much accuracy as possible. Flat-jack testing is a traditional method used to determine the state of stress in historical masonry structures. However, when irregular masonry is tested the method can cause damage to the masonry units and the accuracy of the method is reduced. An enhanced technique, called tube-jack testing, is being developed at the University of Minho to reduce the damage caused during testing and improve the accuracy when used on irregular masonry. This method uses multiple cylindrical jacks inserted in a line of holes drilled in the mortar joints of the masonry, avoiding damage to the masonry units. Concurrently with the development of tube-jack testing, the effect of stress state on sonic testing is being studied. Sonic testing is often used to determine locations of voids and damage in masonry. The focus of these studies was to determine if the state of stress is influencing the sonic test results. In this paper the results of tube-jack testing and sonic testing on masonry walls, built for the purpose of this study in the laboratory, loaded in compression is presented. The tube-jack testing is used to estimate the state of stress in the masonry and the sonic test results are evaluated based on the effect of the applied load on the wall. Future testing and study are suggested for continued development of these test methods.

Tunnel engineering – influence of the type and the quantity of measurements in the back analysis of geomechanical parameters

The monitoring data collected during tunnel excavation can be used in inverse analysis procedures in order to identify more realistic geomechanical parameters that can increase the knowledge about the interested formations. These more realistic parameters can be used in real time to adapt the project to the real structure in situ behaviour. However, monitoring plans are normally designed for safety assessment and not especially for the purpose of inverse analysis. In fact, there is a lack of knowledge about what types and quantity of measurements are needed to succeed in identifying the parameters of interest. Also, the optimisation algorithm chosen for the identification procedure may be important for this matter. In this work, this problem is addressed using a theoretical case with which a thorough parametric study was carried out using two optimisation algorithms based on different calculation paradigms, namely a conventional gradient-based algorithm and an evolution strategy algorithm. Calculations were carried for different sets of parameters to identify several combinations of types and amount of monitoring data. The results clearly show the high importance of the available monitoring data and the chosen algorithm for the success rate of the inverse analysis process.

Understanding the dispersion of fillers in polymer matrices

Understanding the mixing process of complex composite materials is fundamental in several industrial processes. For instance, the dispersion of fillers in polymer melt matrices is commonly employed to manufacture polymer composites, using a twin-screw extruder. The effectiveness of the filler dispersion depends not only on the complex flow patterns generated, but also on the polymer melt rheological behavior. Therefore, the availability of a numerical tool able to predict mixing, taking into account both fluid and particles phases would be very useful to increase the process insight, and thus provide useful guidelines for its optimization. In this work, a new Eulerian-Lagrangian numerical solver is developed OpenFOAM® computational library, and used to better understand the mechanisms determining the dispersion of fillers in polymer matrices. Particular attention will be given to the effect of the rheological model used to represent the fluid behavior, on the level of dispersion obtained. For the Eulerian phase the averaged volume fraction governing equations (conservation of mass and linear momentum) are used to describe the fluid behavior. In the case of the Lagrangian phase, Newton’s second law of motion is used to compute the particles trajectories and velocity. To study the effect of fluid behavior on the filler dispersion, several systems are modeled considering different fluid types (generalized Newtonian or viscoelastic) and particles volume fraction and size. The results obtained are used to correlate the fluid and particle characteristics on the effectiveness of mixing and morphology obtained.

The bounds of education in the human brain connectome

Inter-individual heterogeneity is evident in aging; education level is known to contribute for this heterogeneity. Using a cross-sectional study design and network inference applied to resting-state fMRI data, we show that aging was associated with decreased functional connectivity in a large cortical network. On the other hand, education level, as measured by years of formal education, produced an opposite effect on the long-term. These results demonstrate the increased brain efficiency in individuals with higher education level that may mitigate the impact of age on brain functional connectivity.

The role of IFNγ in higher brain function: in health and under chronic stress

Tese de Doutoramento em Ciências da Saúde

The role of macroH2A1 in prostate carcinogenesis

Dissertação de mestrado em Genética Molecular

Real-Time models to predict the use of vasopressors in monitored patients

The needs of reducing human error has been growing in every field of study, and medicine is one of those. Through the implementation of technologies is possible to help in the decision making process of clinics, therefore to reduce the difficulties that are typically faced. This study focuses on easing some of those difficulties by presenting real-time data mining models capable of predicting if a monitored patient, typically admitted in intensive care, will need to take vasopressors. Data Mining models were induced using clinical variables such as vital signs, laboratory analysis, among others. The best model presented a sensitivity of 94.94%. With this model it is possible reducing the misuse of vasopressors acting as prevention. At same time it is offered a better care to patients by anticipating their treatment with vasopressors.