Comparing the performance of geostatistical models with additional information from covariates for sewage plume characterization

In this work, kriging with covariates is used to model and map the spatial distribution of salinity measurements gathered by an autonomous underwater vehicle in a sea outfall monitoring campaign aiming to distinguish the effluent plume from the receiving waters and characterize its spatial variability in the vicinity of the discharge. Four different geostatistical linear models for salinity were assumed, where the distance to diffuser, the west-east positioning, and the south-north positioning were used as covariates. Sample variograms were fitted by the Mat`ern models using weighted least squares and maximum likelihood estimation methods as a way to detect eventual discrepancies. Typically, the maximum likelihood method estimated very low ranges which have limited the kriging process. So, at least for these data sets, weighted least squares showed to be the most appropriate estimation method for variogram fitting. The kriged maps show clearly the spatial variation of salinity, and it is possible to identify the effluent plume in the area studied. The results obtained show some guidelines for sewage monitoring if a geostatistical analysis of the data is in mind. It is important to treat properly the existence of anomalous values and to adopt a sampling strategy that includes transects parallel and perpendicular to the effluent dispersion.

Occurrence of Ancylostoma in dogs, cats and public places from Andradina city, São Paulo state, Brazil

The aim of this study was to determine the frequency and intensity of Ancylostoma spp. in 33 dogs and 52 cats by means of coproparasitological examinations and parasitological necropsy, and assess the presence of contaminated feces with eggs of that parasite in public places of Andradina Municipality, São Paulo State, Brazil. Willis-Mollay and Sedimentation methods indicated Ancylostoma spp. eggs in 87.8% (29/33) dogs and 94.2% (49/52) cats. The species A. caninum and A. braziliense were found in 63.6% (21/33) and 30.3% (10/33) of dogs, respectively. Considering cats, 67.3% (35/52) were parasitized by A. braziliense, 21.1% (11/52) by A. caninum, and 9.6% (5/52) by A. tubaeforme. Forty-two canine fecal samples were collected from public environments, including 23 squares/gardens and 19 streets/sidewalks. Positive samples for Ancylostoma spp. accounted for 64.3% (27/42); squares/gardens had 60.9% (14/23) positive samples, and streets and sidewalks, 68.4% (13/19). No association was observed between the number of Ancylostoma spp parasites and age, sex and breed of the animals and also the ratio of EPG counts and the parasitic intensity observed at necropsy (p > 0.05). Based on the high occurrence of hookworm in dogs and cats in this study, the treatment with anti helminthics are needed even in those animals with negative stool tests, besides adopting control of the number of animals in public places, in order to decrease the likelihood of environmental contamination, since this parasite represents a potential hazard to human and animal health.

SOIL CONTAMINATION IN PUBLIC SQUARES IN BELO HORIZONTE, MINAS GERAIS, BY CANINE PARASITES IN DIFFERENT DEVELOPMENTAL STAGES

SUMMARY To evaluate soil contamination by parasites in different developmental stages in public squares used as recreation and leisure areas for children in Belo Horizonte (MG, Brazil), 210 soil samples and 141 canine fecal samples were collected from 42 squares in the city. These samples were analyzed by the Caldwell and Caldwell technique and the Hoffman, Pons, and Janer technique. Of the samples analyzed, 89 (42.4%) soil samples and 104 (73.5%) fecal samples were contaminated with Ancylostoma sp., Toxocara sp., Trichuris sp., or Dipylidium sp. eggs; Giardia sp. cysts; or Isospora sp. oocysts. The commonest parasite was Ancylostoma sp., found in 85% soil and 99% fecal samples, followed by Toxocara sp., found in 43.7% soil and 30.7% fecal samples.

FIRST REPORT ON Cryptococcus neoformans IN PIGEON EXCRETA FROM PUBLIC AND RESIDENTIAL LOCATIONS IN THE METROPOLITAN AREA OF CUIABÁ, STATE OF MATO GROSSO, BRAZIL

SUMMARYCryptococcosis is a severe systemic mycosis caused by two species of Cryptococcus that affect humans and animals: C. neoformans and C. gattii. Cosmopolitan and emergent, the mycosis results from the interaction between a susceptible host and the environment. The occurrence of C. neoformanswas evaluated in 122 samples of dried pigeon excreta collected in 49 locations in the City of Cuiabá, State of Mato Grosso, Brazil, including public squares (n = 5), churches (n = 4), educational institutions (n = 3), health units (n = 8), open areas covered with asbestos (n = 4), residences (n = 23), factory (n = 1) and a prison (n = 1). Samples collected from July to December of 2010 were seeded on Niger seed agar (NSA). Dark brown colonies were identified by urease test, carbon source assimilation tests and canavanine-glycine-bromothymol blue medium. Polymerase chain reaction primer pairs specific for C. neoformans were also used for identification. Cryptococcus neoformans associated to pigeon excreta was isolated from eight (6.6%) samples corresponding to six (12.2%) locations.Cryptococcus neoformans was isolated from urban areas, predominantly in residences, constituting a risk of acquiring the disease by immunocompromised and immunocompetent individuals.

DISTRIBUTION OF DERMATOPHYTES FROM SOILS OF URBAN AND RURAL AREAS OF CITIES OF PARAIBA STATE, BRAZIL

SUMMARYThe dermatophytes, keratinophilic fungi, represent important microorganisms of the soil microbiota, where there are cosmopolitan species and others with restricted geographic distribution. The aim of this study was to broaden the knowledge about the presence of dermatophytes in soils of urban (empty lots, schools, slums, squares, beaches and homes) and rural areas and about the evolution of their prevalence in soils of varying pH in cities of the four mesoregions of Paraiba State, Brazil. Soil samples were collected from 31 cities of Paraiba State. Of 212 samples, 62% showed fungal growth, particularly those from the Mata Paraibana mesoregion (43.5%), which has a tropical climate, hot and humid. Soil pH varied from 4.65 to 9.06, with 71% of the growth of dermatophytes occurring at alkaline pH (7.02 - 9.06) (ρ = 0.000). Of 131 strains isolated, 57.3% were geophilic species, particularly Trichophyton terrestre(31.3%) and Mycrosporum gypseum(21.4%). M. nanum and T. ajelloi were isolated for the first time in Paraiba State. The zoophilic species identified were T. mentagrophytes var.mentagrophytes (31.3 %) and T. verrucosum (7.6 %), and T. tonsurans was isolated as an anthropophilic species. The soils of urban areas including empty lots, schools, slums and squares of cities in the mesoregions of Paraiba State were found to be the most suitable reservoirs for almost all dermatophytes; their growth may have been influenced by environmental factors, soils with residues of human and/or animal keratin and alkaline pH.

Investigating the use of dasymetric techniques for assessing employment containment in Melbourne, Australia

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Factorization of elliptic boundary value problems by invariant embedding and application to overdetermined problems

Dissertação para obtenção do Grau de Doutor em Matemática

Three essays on the term structure of interest rates

A PhD Dissertation, presented as part of the requirements for the Degree of Doctor of Philosophy from the NOVA - School of Business and Economics

Context-based identification of energy consumption in industrial plants

Nowadays, reducing energy consumption is one of the highest priorities and biggest challenges faced worldwide and in particular in the industrial sector. Given the increasing trend of consumption and the current economical crisis, identifying cost reductions on the most energy-intensive sectors has become one of the main concerns among companies and researchers. Particularly in industrial environments, energy consumption is affected by several factors, namely production factors(e.g. equipments), human (e.g. operators experience), environmental (e.g. temperature), among others, which influence the way of how energy is used across the plant. Therefore, several approaches for identifying consumption causes have been suggested and discussed. However, the existing methods only provide guidelines for energy consumption and have shown difficulties in explaining certain energy consumption patterns due to the lack of structure to incorporate context influence, hence are not able to track down the causes of consumption to a process level, where optimization measures can actually take place. This dissertation proposes a new approach to tackle this issue, by on-line estimation of context-based energy consumption models, which are able to map operating context to consumption patterns. Context identification is performed by regression tree algorithms. Energy consumption estimation is achieved by means of a multi-model architecture using multiple RLS algorithms, locally estimated for each operating context. Lastly, the proposed approach is applied to a real cement plant grinding circuit. Experimental results prove the viability of the overall system, regarding both automatic context identification and energy consumption estimation.

Diffusional kurtosis imaging using a fast heuristic constrained linear least squares algorithm: a plugin for OsiriX

Diffusion Kurtosis Imaging (DKI) is a fairly new magnetic resonance imag-ing (MRI) technique that tackles the non-gaussian motion of water in biological tissues by taking into account the restrictions imposed by tissue microstructure, which are not considered in Diffusion Tensor Imaging (DTI), where the water diffusion is considered purely gaussian. As a result DKI provides more accurate information on biological structures and is able to detect important abnormalities which are not visible in standard DTI analysis. This work regards the development of a tool for DKI computation to be implemented as an OsiriX plugin. Thus, as OsiriX runs under Mac OS X, the pro-gram is written in Objective-C and also makes use of Apple’s Cocoa framework. The whole program is developed in the Xcode integrated development environ-ment (IDE). The plugin implements a fast heuristic constrained linear least squares al-gorithm (CLLS-H) for estimating the diffusion and kurtosis tensors, and offers the user the possibility to choose which maps are to be generated for not only standard DTI quantities such as Mean Diffusion (MD), Radial Diffusion (RD), Axial Diffusion (AD) and Fractional Anisotropy (FA), but also DKI metrics, Mean Kurtosis (MK), Radial Kurtosis (RK) and Axial Kurtosis (AK).The plugin was subjected to both a qualitative and a semi-quantitative analysis which yielded convincing results. A more accurate validation pro-cess is still being developed, after which, and with some few minor adjust-ments the plugin shall become a valid option for DKI computation

The use of analytical hierarchy process in spatial decision support system for land use management

Geographic information systems give us the possibility to analyze, produce, and edit geographic information. Furthermore, these systems fall short on the analysis and support of complex spatial problems. Therefore, when a spatial problem, like land use management, requires a multi-criteria perspective, multi-criteria decision analysis is placed into spatial decision support systems. The analytic hierarchy process is one of many multi-criteria decision analysis methods that can be used to support these complex problems. Using its capabilities we try to develop a spatial decision support system, to help land use management. Land use management can undertake a broad spectrum of spatial decision problems. The developed decision support system had to accept as input, various formats and types of data, raster or vector format, and the vector could be polygon line or point type. The support system was designed to perform its analysis for the Zambezi river Valley in Mozambique, the study area. The possible solutions for the emerging problems had to cover the entire region. This required the system to process large sets of data, and constantly adjust to new problems’ needs. The developed decision support system, is able to process thousands of alternatives using the analytical hierarchy process, and produce an output suitability map for the problems faced.

Development of process control strategies exploiting knowledge from systems biology: application to MDCK suspension cells

Madine Darby Canine Kidney (MDCK) cell lines have been extensively evaluated for their potential as host cells for influenza vaccine production. Recent studies allowed the cultivation of these cells in a fully defined medium and in suspension. However, reaching high cell densities in animal cell cultures still remains a challenge. To address this shortcoming, a combined methodology allied with knowledge from systems biology was reported to study the impact of the cell environment on the flux distribution. An optimization of the medium composition was proposed for both a batch and a continuous system in order to reach higher cell densities. To obtain insight into the metabolic activity of these cells, a detailed metabolic model previously developed by Wahl A. et. al was used. The experimental data of four cultivations of MDCK suspension cells, grown under different conditions and used in this work came from the Max Planck Institute, Magdeburg, Germany. Classical metabolic flux analysis (MFA) was used to estimate the intracellular flux distribution of each cultivation and then combined with partial least squares (PLS) method to establish a link between the estimated metabolic state and the cell environment. The validation of the MFA model was made and its consistency checked. The resulted PLS model explained almost 70% of the variance present in the flux distribution. The medium optimization for the continuous system and for the batch system resulted in higher biomass growth rates than the ones obtained experimentally, 0.034 h-1 and 0.030 h-1, respectively, thus reducing in almost 10 hours the duplication time. Additionally, the optimal medium obtained for the continuous system almost did not consider pyruvate. Overall the proposed methodology seems to be effective and both proposed medium optimizations seem to be promising to reach high cell densities.

Production of polyhydroxyalkanoates from oil-containing substrates

Different oil-containing substrates, namely, used cooking oil (UCO), fatty acids-byproduct from biodiesel production (FAB) and olive oil deodorizer distillate (OODD) were tested as inexpensive carbon sources for the production of polyhydroxyalkanoates (PHA) using twelve bacterial strains, in batch experiments. The OODD and FAB were exploited for the first time as alternative substrates for PHA production. Among the tested bacterial strains, Cupriavidus necator and Pseudomonas resinovorans exhibited the most promising results, producing poly-3-hydroxybutyrate, P(3HB), form UCO and OODD and mcl-PHA mainly composed of 3-hydroxyoctanoate (3HO) and 3-hydroxydecanoate (3HD) monomers from OODD, respectively. Afterwards, these bacterial strains were cultivated in bioreactor. C. necator were cultivated in bioreactor using UCO as carbon source. Different feeding strategies were tested for the bioreactor cultivation of C. necator, namely, batch, exponential feeding and DO-stat mode. The highest overall PHA productivity (12.6±0.78 g L-1 day-1) was obtained using DO-stat mode. Apparently, the different feeding regimes had no impact on polymer thermal properties. However, differences in polymer‟s molecular mass distribution were observed. C. necator was also tested in batch and fed-batch modes using a different type of oil-containing substrate, extracted from spent coffee grounds (SCG) by super critical carbon dioxide (sc-CO2). Under fed-batch mode (DO-stat), the overall PHA productivity were 4.7 g L-1 day-1 with a storage yield of 0.77 g g-1. Results showed that SCG can be a bioresource for production of PHA with interesting properties. Furthermore, P. resinovorans was cultivated using OODD as substrate in bioreactor under fed-batch mode (pulse feeding regime). The polymer was highly amorphous, as shown by its low crystallinity of 6±0.2%, with low melting and glass transition temperatures of 36±1.2 and -16±0.8 ºC, respectively. Due to its sticky behavior at room temperature, adhesiveness and mechanical properties were also studied. Its shear bond strength for wood (67±9.4 kPa) and glass (65±7.3 kPa) suggests it may be used for the development of biobased glues. Bioreactor operation and monitoring with oil-containing substrates is very challenging, since this substrate is water immiscible. Thus, near-infrared spectroscopy (NIR) was implemented for online monitoring of the C. necator cultivation with UCO, using a transflectance probe. Partial least squares (PLS) regression was applied to relate NIR spectra with biomass, UCO and PHA concentrations in the broth. The NIR predictions were compared with values obtained by offline reference methods. Prediction errors to these parameters were 1.18 g L-1, 2.37 g L-1 and 1.58 g L-1 for biomass, UCO and PHA, respectively, which indicates the suitability of the NIR spectroscopy method for online monitoring and as a method to assist bioreactor control. UCO and OODD are low cost substrates with potential to be used in PHA batch and fed-batch production. The use of NIR in this bioprocess also opened an opportunity for optimization and control of PHA production process.

Visual politics of the estado novo realized on the Alameda Dom Afonso Henriques in Lisbon – the construction of ideal citizenship?

The stylistic categorization of the Estado Novo has been intensely discussed by Portuguese art historians. The square Alameda Dom Afonso Henriques in Lisbon (Alameda) can be seen as paradigmatic for the architecture of power of the Estado Novo. The Alameda forms a gardened valley between two hills. There you find two prominent and highly propagandist buildings: The Instituto Superior Técnico (IST) and the Fonte Luminosa are dedicated to modern sciences and respectively to the harmonious contribution of nature to the city. The iconography of the Alameda as well as its incorporation into the propagandist use of urban planning in the 1930s and 1940s exemplify the visual politics during Salazarism. Urban planning programs intended to create cities that would preserve the character of a traditional catholic society and at the same time answer to the need to modernize the country and evoke the image of a progressive state. Thus, public buildings and urban squares such as the Alameda contributed to design a corporate image and to the ‘spirit’ of the regime.

Evaluation of geostatistical techniques to estimate the spatial distribution of aboveground biomass in the Amazon rainforest using high-resolution remote sensing data

ABSTRACT The spatial distribution of forest biomass in the Amazon is heterogeneous with a temporal and spatial variation, especially in relation to the different vegetation types of this biome. Biomass estimated in this region varies significantly depending on the applied approach and the data set used for modeling it. In this context, this study aimed to evaluate three different geostatistical techniques to estimate the spatial distribution of aboveground biomass (AGB). The selected techniques were: 1) ordinary least-squares regression (OLS), 2) geographically weighted regression (GWR) and, 3) geographically weighted regression - kriging (GWR-K). These techniques were applied to the same field dataset, using the same environmental variables derived from cartographic information and high-resolution remote sensing data (RapidEye). This study was developed in the Amazon rainforest from Sucumbíos - Ecuador. The results of this study showed that the GWR-K, a hybrid technique, provided statistically satisfactory estimates with the lowest prediction error compared to the other two techniques. Furthermore, we observed that 75% of the AGB was explained by the combination of remote sensing data and environmental variables, where the forest types are the most important variable for estimating AGB. It should be noted that while the use of high-resolution images significantly improves the estimation of the spatial distribution of AGB, the processing of this information requires high computational demand.