Rigid pavements distresses - pavement condition index evaluation

Pavements require maintenance in order to provide good service levels during their life period. Because of the significant costs of this operation and the importance of a proper planning, a pavement evaluation methodology, named Pavement Condition Index (PCI), was created by the U.S. Army Corps of Engineers. This methodology allows for the evaluation of the pavement condition along the life period, generally yearly, with minimum costs and, in this way, it is possible to plan the maintenance action and to adopt adequate measures, minimising the rehabilitation costs. The PCI methodology provides an evaluation based on visual inspection, namely on the distresses observed on the pavement. This condition index of the pavement is classified from 0 to 100, where 0 it is the worst possible condition and 100 the best possible condition. This methodology of pavement assessment represents a significant tool for management methods such as airport pavement management system (APMS) and life-cycle costs analysis (LCCA). Nevertheless, it has some limitations which can jeopardize the correct evaluation of the pavement behavior. Therefore the objective of this dissertation is to help reducing its limitations and make it easier and faster to use. Thus, an automated process of PCI calculation was developed, avoiding the abaci consultation, and consequently, minimizing the human error. To facilitate also the visual inspection a Tablet application was developed to replace the common inspection data sheet and thus making the survey easier to be undertaken. Following, an airport pavement condition was study accordingly with the methodology described at Standard Test Method for Airport Pavement Condition Index Surveys D5340, 2011 where its original condition level is compared with the condition level after iterate possible erroneous considered distresses as well as possible rehabilitations. Afterwards, the results obtained were analyzed and the main conclusions presented together with some future developments.

Improving the early diagnostic of prostate cancer by multiple biomarker detection with new biosensing devices

Prostate cancer (PCa) is the most common form of cancer in men, in Europe (World Health Organization data). The most recent statistics, in Portuguese territory, confirm this scenario, which states that about 50% of Portuguese men may suffer from prostate cancer and 15% of these will die from this condition. Its early detection is therefore fundamental. This is currently being done by Prostate Specific Antigen (PSA) screening in urine but false positive and negative results are quite often obtained and many patients are sent to unnecessary biopsy procedures. This early detection protocol may be improved, by the development of point-of-care cancer detection devices, not only to PSA but also to other biomarkers recently identified. Thus, the present work aims to screen several biomarkers in cultured human prostate cell lines, serum and urine samples, developing low cost sensors based on new synthetic biomaterials. Biomarkers considered in this study are the following: prostate specific antigen (PSA), annexin A3 (ANXA3), microseminoprotein-beta (MSMB) and sarcosine (SAR). The biomarker recognition may occurs by means of molecularly imprinted polymers (MIP), which are a kind of plastic antibodies, and enzymatic approaches. The growth of a rigid polymer, chemically stable, using the biomarker as a template allows the synthesis of the plastic antibody. MIPs show high sensitivity/selectivity and present much longer stability and much lower price than natural antibodies. This nanostructured material was prepared on a carbon solid. The interaction between the biomarker and the sensing-material produces electrical signals generating quantitative or semi-quantitative data. These devices allow inexpensive and portable detection in point-of-care testing.

The reasons behind the progression in PISA scores: An education production function approach using semi-parametric techniques

Research Masters