Avaliação do desempenho de fundações em edificação no DF com a utilização de técnicas de interação solo-estrutura

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Civil e Ambiental, 2015.

Una reflexión prospectiva sobre técnicas sostenibles para controlar la reproducción en mamíferos domésticos

El control de la reproducción de los animales de granja fue una de las herramientas esenciales en la domesticación. Esto es aún de gran interés para la mejora genética, el ajuste de la producción a la disponibilidad de alimentos y del mercado y finalmente, para la reducción de los períodos improductivos. La detección del comportamiento del estro, la sincronización de las concepciones y el incremento de la potencialidad de difusión de los padres de alta genética son tres objetivos comunes a la producción de todas las especies. Los variados sistemas reproductivos entre los diversos sistemas de producción difieren a causa de las propiedades intrínsecas de las especies y de los diferentes grados de intensificación de estos sistemas. Tres tendencias claras son el continuo incremento de la productividad por medio de las mejoras de la eficiencia reproductiva, el desarrollo de técnicas nuevas y sostenibles sin el uso de hormonas y el continuo desarrollo de la inseminación artificial y de las biotécnicas reproductivas. Las futuras áreas de inversión en investigación podrían ser: las bases fisiológicas y etológicas de las interrelaciones socio-sexuales entre animales, el control genético de parámetros reproductivos, el incremento en la eficiencia de biotecnologías clásicas y nuevas y la ingeniería de técnicas reproductivas nuevas e innovativas para ser utilizadas a nivel de granja. Estas técnicas reproductivas deberían ser desarrolladas respetando los tres pilares de la sostenibilidad: el ambiente, la economía y la sociedad. Por ello deberían estar incluidas dentro de los sistemas de producción en los cuales se supone que serán aplicadas y donde deberían ser evaluadas en su sostenibilidad.

Estudo de inibidores de corrosão em concreto armado, visando a melhoria na sua durabilidade

This thesis has as objective presents a methodology to evaluate the behavior of the corrosion inhibitors sodium nitrite, sodium dichromate and sodium molybdate, as well as your mixture, the corrosion process for the built-in steel in the reinforced concrete, through different techniques electrochemical, as well as the mechanical properties of that concrete non conventional. The addition of the inhibitors was studied in the concrete in the proportions from 0.5 to 3.5 % regarding the cement mass, isolated or in the mixture, with concrete mixture proportions of 1.0:1.5:2.5 (cement, fine aggregate and coarse aggregate), superplasticizers 2.0 % and 0.40 water/cement ratio. In the modified concrete resistance rehearsals they were accomplished to the compression, consistence and the absorption of water, while to analyze the built-in steel in the concrete the rehearsals of polarization curves they were made. They were also execute, rehearsals of corrosion potential and polarization resistance with intention of diagnose the beginning of the corrosion of the armors inserted in body-of-proof submitted to an accelerated exhibition in immersion cycle and drying to the air. It was concluded, that among the studied inhibitors sodium nitrite , in the proportion of 2.0 % in relation to the mass of the cement, presented the best capacity of protection of the steel through all the studied techniques and that the methodology and the monitoring techniques used in this work, they were shown appropriate to evaluate the behavior and the efficiency of the inhibitors

Conservation of komodo dragons varanus komodoensis in the Wae Wuul nature reserve, Flores, Indonesia: a multidisciplinary approach

Multidisciplinary conservation initiatives are increasingly advocated as best practice for recovering endangered species. The Komodo dragon Varanus komodoensis is the world's largest lizard, of prominent conservation value as an umbrella species for protection of south-east Indonesian ecosystems. Komodo dragons have faced multiple human-related threat processes in the past 30 years and are listed on Appendix I of the Convention on International Trade in Endangered Species of Wild Fauna and Flora, and considered Vulnerable according to the International Union for Conservation of Nature Red List. We report on a protection programme conducted from 2005 to 2012 in the Wae Wuul nature reserve, on the island of Flores, Indonesia. The Wae Wuul ranger post was completely rebuilt, and community awareness and involvement of local people in habitat-protection schemes were regularly and successfully implemented. Local rangers were trained in wildlife-monitoring techniques. Monitoring results indicated that Komodo dragon densities were lower in Wae Wuul than in the adjacent Komodo National Park; however, a relatively high level of genetic diversity was recorded for this population. Ungulate prey showed a relatively stable prey population density. Community-based initiatives and regular wildlife monitoring are crucial to ensure the persistence of Komodo dragons on Flores. The Wae Wuul protection programme is providing several sustainability indicators by which informed management plans can be designed for long-term conservation of Komodo dragons.

Non-invasive winding fault detection for induction machines based on stray flux magnetic sensors

Non-intrusive monitoring of health state of induction machines within industrial process and harsh environments poses a technical challenge. In the field, winding failures are a major fault accounting for over 45% of total machine failures. In the literature, many condition monitoring techniques based on different failure mechanisms and fault indicators have been developed where the machine current signature analysis (MCSA) is a very popular and effective method at this stage. However, it is extremely difficult to distinguish different types of failures and hard to obtain local information if a non-intrusive method is adopted. Typically, some sensors need to be installed inside the machines for collecting key information, which leads to disruption to the machine operation and additional costs. This paper presents a new non-invasive monitoring method based on GMRs to measure stray flux leaked from the machines. It is focused on the influence of potential winding failures on the stray magnetic flux in induction machines. Finite element analysis and experimental tests on a 1.5-kW machine are presented to validate the proposed method. With time-frequency spectrogram analysis, it is proven to be effective to detect several winding faults by referencing stray flux information. The novelty lies in the implement of GMR sensing and analysis of machine faults.

A transformation-based multivariate chart to monitor process dispersion

Multivariate monitoring techniques such as multivariate control charts are used to control the processes that contain more than one correlated characteristic. Although the majority of previous researches are focused on controlling only the mean vector of multivariate processes, little work has been performed to monitor the covariance matrix. In this research, a new method is presented to detect possible shifts in the covariance matrix of multivariate processes. The basis of the proposed method is to eliminate the correlation structure between the quality characteristics by transformation technique and then use an S chart for each variable. The performance of the proposed method is then compared to the ones from other existing methods and a real case is presented.

Traffic matrix prediction and estimation based on deep learning in large-scale IP backbone networks

Network traffic analysis has been one of the most crucial techniques for preserving a large-scale IP backbone network. Despite its importance, large-scale network traffic monitoring techniques suffer from some technical and mercantile issues to obtain precise network traffic data. Though the network traffic estimation method has been the most prevalent technique for acquiring network traffic, it still has a great number of problems that need solving. With the development of the scale of our networks, the level of the ill-posed property of the network traffic estimation problem is more deteriorated. Besides, the statistical features of network traffic have changed greatly in terms of current network architectures and applications. Motivated by that, in this paper, we propose a network traffic prediction and estimation method respectively. We first use a deep learning architecture to explore the dynamic properties of network traffic, and then propose a novel network traffic prediction approach based on a deep belief network. We further propose a network traffic estimation method utilizing the deep belief network via link counts and routing information. We validate the effectiveness of our methodologies by real data sets from the Abilene and GÉANT backbone networks.

Development of molecular approaches to assess the diversity and abundance of marine nematodes assemblages for assessment of Good Environmental Status of the estuarine and marine ecosystems

In Europe, the concerns with the status of marine ecosystems have increased, and the Marine Directive has as main goal the achievement of Good Environmental Status (GES) of EU marine waters by 2020. Molecular tools are seen as promising and emerging approaches to improve ecosystem monitoring, and have led ecology into a new era, representing perhaps the most source of innovation in marine monitoring techniques. Benthic nematodes are considered ideal organisms to be used as biological indicator of natural and anthropogenic disturbances in aquatic ecosystems underpinning monitoring programmes on the ecological quality of marine ecosystems, very useful to assess the GES of the marine environment. dT-RFLP (directed Terminal-Restriction Fragment Length Polymorphism) allows to assess the diversity of nematode communities, but also allows studying the functioning of the ecosystem, and combined with relative real-time PCR (qPCR), provides a high-throughput semi-quantitative characterization of nematode communities. These characteristics make the two molecular tools good descriptors for the good environmental status assessment. The main aim of this study is to develop and optimize the dT-RFLP and qPCR in Mira estuary (SW coast, Portugal). A molecular phylogenetic analysis of marine and estuarine nematodes is being performed combining morphological and molecular analysis to evaluate the diversity of free-living marine nematodes in Mira estuary. After morphological identification, barcoding of 18S rDNA and COI genes are being determined for each nematode species morphologically identified. So far we generated 40 new sequences belonging to 32 different genus and 17 families, and the study has shown a good degree of concordance between traditional morphology-based identification and DNA sequences. These results will improve the assessment of marine nematode diversity and contribute to a more robust nematode taxonomy. The DNA sequences are being used to develop the dT-RFLP with the ability to easily process large sample numbers (hundreds and thousands), rather than typical of classical taxonomic or low throughput molecular analyses. A preliminary study showed that the digest enzymes used in dT-RFLP for terrestrial assemblages separated poorly the marine nematodes at taxonomic level for functional group analysis. A new digest combination was designed using the software tool DRAT (Directed Terminal Restriction Analysis Tool) to distinguished marine nematode taxa. Several solutions were provided by DRAT and tested empirically to select the solution that cuts most efficiently. A combination of three enzymes and a single digest showed to be the best solution to separate the different clusters. Parallel to this, another tool is being developed to estimate the population size (qPCR). An improvement in qPCR estimation of gene copy number using an artificial reference is being performed for marine nematodes communities to quantify the abundance. Once developed, it is proposed to validate both methodologies by determining the spatial and temporal variability of benthic nematodes assemblages across different environments. The application of these high-throughput molecular approaches for benthic nematodes will improve sample throughput and their implementation more efficient and faster as indicator of ecological status of marine ecosystems.

Monitoring mesenchymal stem cell cultures using image processing and pattern recognition techniques

Stem cells have attracted tremendous interest in recent times due to their promise in providing innovative new treatments for a great range of currently debilitating diseases. This is due to their potential ability to regenerate and repair damaged tissue, and hence restore lost body function, in a manner beyond the body's usual healing process. Bone marrow-derived mesenchymal stem cells or bone marrow stromal cells are one type of adult stem cells that are of particular interest. Since they are derived from a living human adult donor, they do not have the ethical issues associated with the use of human embryonic stem cells. They are also able to be taken from a patient or other donors with relative ease and then grown readily in the laboratory for clinical application. Despite the attractive properties of bone marrow stromal cells, there is presently no quick and easy way to determine the quality of a sample of such cells. Presently, a sample must be grown for weeks and subject to various time-consuming assays, under the direction of an expert cell biologist, to determine whether it will be useful. Hence there is a great need for innovative new ways to assess the quality of cell cultures for research and potential clinical application. The research presented in this thesis investigates the use of computerised image processing and pattern recognition techniques to provide a quicker and simpler method for the quality assessment of bone marrow stromal cell cultures. In particular, aim of this work is to find out whether it is possible, through the use of image processing and pattern recognition techniques, to predict the growth potential of a culture of human bone marrow stromal cells at early stages, before it is readily apparent to a human observer. With the above aim in mind, a computerised system was developed to classify the quality of bone marrow stromal cell cultures based on phase contrast microscopy images. Our system was trained and tested on mixed images of both healthy and unhealthy bone marrow stromal cell samples taken from three different patients. This system, when presented with 44 previously unseen bone marrow stromal cell culture images, outperformed human experts in the ability to correctly classify healthy and unhealthy cultures. The system correctly classified the health status of an image 88% of the time compared to an average of 72% of the time for human experts. Extensive training and testing of the system on a set of 139 normal sized images and 567 smaller image tiles showed an average performance of 86% and 85% correct classifications, respectively. The contributions of this thesis include demonstrating the applicability and potential of computerised image processing and pattern recognition techniques to the task of quality assessment of bone marrow stromal cell cultures. As part of this system, an image normalisation method has been suggested and a new segmentation algorithm has been developed for locating cell regions of irregularly shaped cells in phase contrast images. Importantly, we have validated the efficacy of both the normalisation and segmentation method, by demonstrating that both methods quantitatively improve the classification performance of subsequent pattern recognition algorithms, in discriminating between cell cultures of differing health status. We have shown that the quality of a cell culture of bone marrow stromal cells may be assessed without the need to either segment individual cells or to use time-lapse imaging. Finally, we have proposed a set of features, that when extracted from the cell regions of segmented input images, can be used to train current state of the art pattern recognition systems to predict the quality of bone marrow stromal cell cultures earlier and more consistently than human experts.

Petroleum well drilling monitoring through cutting image analysis and artificial intelligence techniques

Petroleum well drilling monitoring has become an important tool for detecting and preventing problems during the well drilling process. In this paper, we propose to assist the drilling process by analyzing the cutting images at the vibrating shake shaker, in which different concentrations of cuttings can indicate possible problems, such as the collapse of the well borehole walls. In such a way, we present here an innovative computer vision system composed by a real time cutting volume estimator addressed by support vector regression. As far we know, we are the first to propose the petroleum well drilling monitoring by cutting image analysis. We also applied a collection of supervised classifiers for cutting volume classification. (C) 2010 Elsevier Ltd. All rights reserved.

Modeling of dangerous phenomena and innovative techniques for hazard evaluation and risk mitigation

Society is frequently exposed to and threatened by dangerous phenomena in many parts of the world. Different types of such phenomena require specific actions for proper risk management, from the stages of hazard identification to those of mitigation (including monitoring and early-warning) and/or reduction. The understanding of both predisposing factors and triggering mechanisms of a given danger and the prediction of its evolution from the source to the overall affected zone are relevant issues that must be addressed to properly evaluate a given hazard.

Listening to nature : techniques for large-scale monitoring of ecosystems using acoustics

Climate change and human activity are subjecting the environment to unprecedented rates of change. Monitoring these changes is an immense task that demands new levels of automated monitoring and analysis. We propose the use of acoustics as a proxy for the time consuming auditing of fauna, especially for determining the presence/absence of species. Acoustic monitoring is deceptively simple; seemingly all that is required is a sound recorder. However there are many major challenges if acoustics are to be used for large scale monitoring of ecosystems. Key issues are scalability and automation. This paper discusses our approach to this important research problem. Our work is being undertaken in collaboration with ecologists interested both in identifying particular species and in general ecosystem health.

Structural health monitoring of bridges using acoustic emission technology and signal processing techniques

Structural health monitoring (SHM) is the term applied to the procedure of monitoring a structure’s performance, assessing its condition and carrying out appropriate retrofitting so that it performs reliably, safely and efficiently. Bridges form an important part of a nation’s infrastructure. They deteriorate due to age and changing load patterns and hence early detection of damage helps in prolonging the lives and preventing catastrophic failures. Monitoring of bridges has been traditionally done by means of visual inspection. With recent developments in sensor technology and availability of advanced computing resources, newer techniques have emerged for SHM. Acoustic emission (AE) is one such technology that is attracting attention of engineers and researchers all around the world. This paper discusses the use of AE technology in health monitoring of bridge structures, with a special focus on analysis of recorded data. AE waves are stress waves generated by mechanical deformation of material and can be recorded by means of sensors attached to the surface of the structure. Analysis of the AE signals provides vital information regarding the nature of the source of emission. Signal processing of the AE waveform data can be carried out in several ways and is predominantly based on time and frequency domains. Short time Fourier transform and wavelet analysis have proved to be superior alternatives to traditional frequency based analysis in extracting information from recorded waveform. Some of the preliminary results of the application of these analysis tools in signal processing of recorded AE data will be presented in this paper.

Development and synthesis of innovative approaches to the surgical assessment of articular cartilage degeneration

This research has established, through ultrasound, near infrared spectroscopy and biomechanics experiments, parameters and parametric relationships that can form the framework for quantifying the integrity of the articular cartilage-on-bone laminate, and objectively distinguish between normal/healthy and abnormal/degenerated joint tissue, with a focus on articular cartilage. This has been achieved by: 1. using traditional experimental methods to produce new parameters for cartilage assessment; 2. using novel methodologies to develop new parameters; and 3. investigating the interrelationships between mechanical, structural and molec- ular properties to identify and select those parameters and methodologies that can be used in a future arthroscopic probe based on points 1 and 2. By combining the molecular, micro- and macro-structural characteristics of the tissue with its mechanical properties, we arrive at a set of critical benchmarking parameters for viable and early-stage non-viable cartilage. The interrelationships between these characteristics, examined using a multivariate analysis based on principal components analysis, multiple linear regression and general linear modeling, could then to deter- mine those parameters and relationships which have the potential to be developed into a future clinical device. Specifically, this research has found that the ultrasound and near infrared techniques can subsume the mechanical parameters and combine to characterise the tissue at the molecular, structural and mechanical levels over the full depth of the cartilage matrix. It is the opinion in this thesis that by enabling the determination of the precise area of in uence of a focal defect or disease in the joint, demarcating the boundaries of articular cartilage with dierent levels of degeneration around a focal defect, better surgical decisions that will advance the processes of joint management and treatment will be achieved. Providing the basis for a surgical tool, this research will contribute to the enhancement and quanti�cation of arthroscopic procedures, extending to post- treatment monitoring and as a research tool, will enable a robust method for evaluating developing (particularly focalised) treatments.