Soil CO2 efflux in central Amazonia: environmental and methodological effects

Soil respiration plays a significant role in the carbon cycle of Amazonian rainforests. Measurements of soil respiration have only been carried out in few places in the Amazon. This study investigated the effects of the method of ring insertion in the soil as well as of rainfall and spatial distribution on CO2 emission in the central Amazon region. The ring insertion effect increased the soil emission about 13-20% for sandy and loamy soils during the firsts 4-7 hours, respectively. After rainfall events below 2 mm, the soil respiration did not change, but for rainfall greater than 3 mm, after 2 hours there was a decrease in soil temperature and respiration of about 10-34% for the loamy and sand soils, with emissions returning to normal after around 15-18 hours. The size of the measurement areas and the spatial distribution of soil respiration were better estimated using the Shuttle Radar Topographic Mission (SRTM) data. The Campina reserve is a mosaic of bare soil, stunted heath forest-SHF and tall heath forest-THF. The estimated total average CO2 emissions from the area was 3.08±0.8 µmol CO2 m-2 s-1. The Cuieiras reserve is another mosaic of plateau, slope, Campinarana and riparian forests and the total average emission from the area was 3.82±0.76 µmol CO2 m-2 s-1. We also found that the main control factor of the soil respiration was soil temperature, with 90% explained by regression analysis. Automated soil respiration datasets are a good tool to improve the technique and increase the reliability of measurements to allow a better understanding of all possible factors driven by soil respiration processes.

Estudo, desenvolvimento de manuais de utilização e colocação em funcionamento da bancada SIMATIC da Siemens

Dissertação de mestrado em Engenharia Mecatrónica

Estudo, projeto e desenvolvimento de uma máquina para montar e vincar sacos de papel

Dissertação de mestrado integrado em Engenharia Mecânica

Caso de demonstração de uma framework para automatização do teste de APIs de aplicações SaaS

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques

Given the limitations of different types of remote sensing images, automated land-cover classifications of the Amazon várzea may yield poor accuracy indexes. One way to improve accuracy is through the combination of images from different sensors, by either image fusion or multi-sensor classifications. Therefore, the objective of this study was to determine which classification method is more efficient in improving land cover classification accuracies for the Amazon várzea and similar wetland environments - (a) synthetically fused optical and SAR images or (b) multi-sensor classification of paired SAR and optical images. Land cover classifications based on images from a single sensor (Landsat TM or Radarsat-2) are compared with multi-sensor and image fusion classifications. Object-based image analyses (OBIA) and the J.48 data-mining algorithm were used for automated classification, and classification accuracies were assessed using the kappa index of agreement and the recently proposed allocation and quantity disagreement measures. Overall, optical-based classifications had better accuracy than SAR-based classifications. Once both datasets were combined using the multi-sensor approach, there was a 2% decrease in allocation disagreement, as the method was able to overcome part of the limitations present in both images. Accuracy decreased when image fusion methods were used, however. We therefore concluded that the multi-sensor classification method is more appropriate for classifying land cover in the Amazon várzea.

Controlo de qualidade e inspeção visual por imagem: definição de uma aplicação para registo de alterações e avaliação de desempenho

Dissertação de mestrado em Engenharia e Gestão da Qualidade

Desenvolvimento de um mecanismo automatizado de limpeza de uma grelha de uma barragem mini-hídrica

Dissertação de mestrado integrado em Engenharia Mecânica

Development of advanced cell/tissue culture systems, based on enhanced polymeric scaffolds and sophisticated bioreactors, for tissue engineering applications

Programa Doutoral em Engenharia Biomédica

An integrated model for simulation of construction phasing of arch concrete dams

Dissertação de mestrado integrado em Civil Engineering

Implementação de metodologias BIM na Direção de Engenharia de Barragens da EDP: casos de estudo de projeto de estruturas em obras hidroelétricas

Dissertação de mestrado integrado em Engenharia Civil

Development of Janus hydrogel microcapsules as novel biomaterials-stem cells construct for 3D co-culture applications

Co-cultures of two or more cell types and biodegradable biomaterials of natural origin have been successfully combined to recreate tissue microenvironments. Segregated co-cultures are preferred over conventional mixed ones in order to better control the degree of homotypic and heterotypic interactions. Hydrogel-based systems in particular, have gained much attention to mimic tissue-specific microenvironments and they can be microengineered by innovative bottom-up approaches such as microfluidics. In this study, we developed bi-compartmentalized (Janus) hydrogel microcapsules of methacrylated hyaluronic acid (MeHA)/methacrylated-chitosan (MeCht) blended with marine-origin collagen by droplet-based microfluidics co-flow. Human adipose stem cells (hASCs) and microvascular endothelial cells (hMVECs) were co-encapsulated to create platforms of study relevant for vascularized bone tissue engineering. A specially designed Janus-droplet generator chip was used to fabricate the microcapsules (<250â μm units) and Janus-gradient co-cultures of hASCs: hMVECs were generated in various ratios (90:10; 75:25; 50:50; 25:75; 10:90), through an automated microfluidic flow controller (Elveflow microfluidics system). Such monodisperse 3D co-culture systems were optimized regarding cell number and culture media specific for concomitant maintenance of both phenotypes to establish effective cell-cell (homotypic and heterotypic) and cell-materials interactions. Cellular parameters such as viability, matrix deposition, mineralization and hMVECs re-organization in tube-like structures, were enhanced by blending MeHA/MeCht with marine-origin collagen and increasing hASCs: hMVECs co-culture gradient had significant impact on it. Such Janus hybrid hydrogel microcapsules can be used as a platform to investigate biomaterials interactions with distinct combined cell populations.

Cardiopulmonary resuscitation: update, controversies and new advances

Cardiopulmonary arrest is a medical emergency in which the lapse of time between event onset and the initiation of measures of basic and advanced support, as well as the correct care based on specific protocols for each clinical situation, constitute decisive factors for a successful therapy. Cardiopulmonary arrest care cannot be restricted to the hospital setting because of its fulminant nature. This necessitates the creation of new concepts, strategies and structures, such as the concept of life chain, cardio-pulmonary resuscitation courses for professionals who work in emergency medical services, the automated external defibrillator, the implantable cardioverter-defibrillator, and mobile intensive care units, among others. New concepts, strategies and structures motivated by new advances have also modified the treatment and improved the results of cardiopulmonary resuscitation in the hospital setting. Among them, we can cite the concept of cerebral resuscitation, the application of the life chain, the creation of the universal life support algorithm, the adjustment of drug doses, new techniques - measure of the end-tidal carbon dioxide levels and of the coronary perfusion pressure - and new drugs under research.

Tapping the wealth of microbial data in high-throughput metabolic model reconstruction

Genome-scale metabolic models are valuable tools in the metabolic engineering process, based on the ability of these models to integrate diverse sources of data to produce global predictions of organism behavior. At the most basic level, these models require only a genome sequence to construct, and once built, they may be used to predict essential genes, culture conditions, pathway utilization, and the modifications required to enhance a desired organism behavior. In this chapter, we address two key challenges associated with the reconstruction of metabolic models: (a) leveraging existing knowledge of microbiology, biochemistry, and available omics data to produce the best possible model; and (b) applying available tools and data to automate the reconstruction process. We consider these challenges as we progress through the model reconstruction process, beginning with genome assembly, and culminating in the integration of constraints to capture the impact of transcriptional regulation. We divide the reconstruction process into ten distinct steps: (1) genome assembly from sequenced reads; (2) automated structural and functional annotation; (3) phylogenetic tree-based curation of genome annotations; (4) assembly and standardization of biochemistry database; (5) genome-scale metabolic reconstruction; (6) generation of core metabolic model; (7) generation of biomass composition reaction; (8) completion of draft metabolic model; (9) curation of metabolic model; and (10) integration of regulatory constraints. Each of these ten steps is documented in detail.

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/.

Application of multivariate statistical process control to fuel cell manufacturing

Univariate statistical control charts, such as the Shewhart chart, do not satisfy the requirements for process monitoring on a high volume automated fuel cell manufacturing line. This is because of the number of variables that require monitoring. The risk of elevated false alarms, due to the nature of the process being high volume, can present problems if univariate methods are used. Multivariate statistical methods are discussed as an alternative for process monitoring and control. The research presented is conducted on a manufacturing line which evaluates the performance of a fuel cell. It has three stages of production assembly that contribute to the final end product performance. The product performance is assessed by power and energy measurements, taken at various time points throughout the discharge testing of the fuel cell. The literature review performed on these multivariate techniques are evaluated using individual and batch observations. Modern techniques using multivariate control charts on Hotellings T2 are compared to other multivariate methods, such as Principal Components Analysis (PCA). The latter, PCA, was identified as the most suitable method. Control charts such as, scores, T2 and DModX charts, are constructed from the PCA model. Diagnostic procedures, using Contribution plots, for out of control points that are detected using these control charts, are also discussed. These plots enable the investigator to perform root cause analysis. Multivariate batch techniques are compared to individual observations typically seen on continuous processes. Recommendations, for the introduction of multivariate techniques that would be appropriate for most high volume processes, are also covered.