Multivariate analysis for animal selection in experimental research

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Non-destructive linear model for leaf area estimation in Vernonia ferruginea Less

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Multivariate analysis for selecting animals for experimental research

Background Several researchers seek methods for the selection of homogeneous groups of animals in experimental studies, a fact justified because homogeneity is an indispensable prerequisite for casualization of treatments. The lack of robust methods that comply with statistical and biological principles is the reason why researchers use empirical or subjective methods, influencing their results. Objective To develop a multivariate statistical model for the selection of a homogeneous group of animals for experimental research and to elaborate a computational package to use it. Methods The set of echocardiographic data of 115 male Wistar rats with supravalvular aortic stenosis (AoS) was used as an example of model development. Initially, the data were standardized, and became dimensionless. Then, the variance matrix of the set was submitted to principal components analysis (PCA), aiming at reducing the parametric space and at retaining the relevant variability. That technique established a new Cartesian system into which the animals were allocated, and finally the confidence region (ellipsoid) was built for the profile of the animals’ homogeneous responses. The animals located inside the ellipsoid were considered as belonging to the homogeneous batch; those outside the ellipsoid were considered spurious. Results The PCA established eight descriptive axes that represented the accumulated variance of the data set in 88.71%. The allocation of the animals in the new system and the construction of the confidence region revealed six spurious animals as compared to the homogeneous batch of 109 animals. Conclusion The biometric criterion presented proved to be effective, because it considers the animal as a whole, analyzing jointly all parameters measured, in addition to having a small discard rate.

Estimativa da irradiação solar global pelo método de Angstrom-Prescott e técnicas de aprendizado de máquinas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling

Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since ecology is known for its complexity. Measurement and/or process errors are often the only sources of uncertainty modeled when addressing complex ecological problems, yet analyses should also account for uncertainty in sampling design, in model specification, in parameters governing the specified model, and in initial and boundary conditions. Only then can we be confident in the scientific inferences and forecasts made from an analysis. Probability and statistics provide a framework that accounts for multiple sources of uncertainty. Given the complexities of ecological studies, the hierarchical statistical model is an invaluable tool. This approach is not new in ecology, and there are many examples (both Bayesian and non-Bayesian) in the literature illustrating the benefits of this approach. In this article, we provide a baseline for concepts, notation, and methods, from which discussion on hierarchical statistical modeling in ecology can proceed. We have also planted some seeds for discussion and tried to show where the practical difficulties lie. Our thesis is that hierarchical statistical modeling is a powerful way of approaching ecological analysis in the presence of inevitable but quantifiable uncertainties, even if practical issues sometimes require pragmatic compromises.

Analysis of the impact of rainfall assimilation during LBA atmospheric mesoscale missions in Southwest Amazon

The assimilation of satellite estimated precipitation data can be used as an efficient tool to improve the analysis of rainfall generated by numerical models of weather forecast. The system of data assimilation used in this study is cumulus parameterization inversion based on the Kuo scheme. Reanalysis were performed using the field experiment data of the LBA Project (WETAMC and DRYtoWET-AMC), where it was possible to verify an improvement in the simulations results, since the data assimilation corrects the position and the intensity of rainfall in the numerical model. (C) 2012 Elsevier B.V. All rights reserved.

Statistical methods for biomedical signal analysis and processing

Statistical modelling and statistical learning theory are two powerful analytical frameworks for analyzing signals and developing efficient processing and classification algorithms. In this thesis, these frameworks are applied for modelling and processing biomedical signals in two different contexts: ultrasound medical imaging systems and primate neural activity analysis and modelling. In the context of ultrasound medical imaging, two main applications are explored: deconvolution of signals measured from a ultrasonic transducer and automatic image segmentation and classification of prostate ultrasound scans. In the former application a stochastic model of the radio frequency signal measured from a ultrasonic transducer is derived. This model is then employed for developing in a statistical framework a regularized deconvolution procedure, for enhancing signal resolution. In the latter application, different statistical models are used to characterize images of prostate tissues, extracting different features. These features are then uses to segment the images in region of interests by means of an automatic procedure based on a statistical model of the extracted features. Finally, machine learning techniques are used for automatic classification of the different region of interests. In the context of neural activity signals, an example of bio-inspired dynamical network was developed to help in studies of motor-related processes in the brain of primate monkeys. The presented model aims to mimic the abstract functionality of a cell population in 7a parietal region of primate monkeys, during the execution of learned behavioural tasks.

Calibrazione di modelli idrologici con algoritmi multiobiettivo

Traditional procedures for rainfall-runoff model calibration are generally based on the fit of the individual values of simulated and observed hydrographs. It is used here an alternative option that is carried out by matching, in the optimisation process, a set of statistics of the river flow. Such approach has the additional, significant advantage to allow also a straightforward regional calibration of the model parameters, based on the regionalisation of the selected statistics. The minimisation of the set of objective functions is carried out by using the AMALGAM algorithm, leading to the identification of behavioural parameter sets. The procedure is applied to a set of river basins located in central Italy: the basins are treated alternatively as gauged and ungauged and, as a term of comparison, the results obtained with a traditional time-domain calibration is also presented. The results show that a suitable choice of the statistics to be optimised leads to interesting results in real world case studies as far as the reproduction of the different flow regimes is concerned.

Stima dei parametri di modelli idrologici mediante ottimizzazione dell’utilità

Tradizionalmente, l'obiettivo della calibrazione di un modello afflussi-deflussi è sempre stato quello di ottenere un set di parametri (o una distribuzione di probabilità dei parametri) che massimizzasse l'adattamento dei dati simulati alla realtà osservata, trattando parzialmente le finalità applicative del modello. Nel lavoro di tesi viene proposta una metodologia di calibrazione che trae spunto dell'evidenza che non sempre la corrispondenza tra dati osservati e simulati rappresenti il criterio più appropriato per calibrare un modello idrologico. Ai fini applicativi infatti, può risultare maggiormente utile una miglior rappresentazione di un determinato aspetto dell'idrogramma piuttosto che un altro. Il metodo di calibrazione che viene proposto mira a valutare le prestazioni del modello stimandone l'utilità nell'applicazione prevista. Tramite l'utilizzo di opportune funzioni, ad ogni passo temporale viene valutata l'utilità della simulazione ottenuta. La calibrazione viene quindi eseguita attraverso la massimizzazione di una funzione obiettivo costituita dalla somma delle utilità stimate nei singoli passi temporali. Le analisi mostrano come attraverso l'impiego di tali funzioni obiettivo sia possibile migliorare le prestazioni del modello laddove ritenute di maggior interesse per per le finalità applicative previste.

Estimation of Quasi-Rational DSGE Models

Small-scale dynamic stochastic general equilibrium have been treated as the benchmark of much of the monetary policy literature, given their ability to explain the impact of monetary policy on output, inflation and financial markets. One cause of the empirical failure of New Keynesian models is partially due to the Rational Expectations (RE) paradigm, which entails a tight structure on the dynamics of the system. Under this hypothesis, the agents are assumed to know the data genereting process. In this paper, we propose the econometric analysis of New Keynesian DSGE models under an alternative expectations generating paradigm, which can be regarded as an intermediate position between rational expectations and learning, nameley an adapted version of the "Quasi-Rational" Expectatations (QRE) hypothesis. Given the agents' statistical model, we build a pseudo-structural form from the baseline system of Euler equations, imposing that the length of the reduced form is the same as in the `best' statistical model.

Sustainable improvement of water networks : to achieve best decisions for rehabilitation plans using advanced GIS and statistical techniques

Many of developing countries are facing crisis in water management due to increasing of population, water scarcity, water contaminations and effects of world economic crisis. Water distribution systems in developing countries are facing many challenges of efficient repair and rehabilitation since the information of water network is very limited, which makes the rehabilitation assessment plans very difficult. Sufficient information with high technology in developed countries makes the assessment for rehabilitation easy. Developing countries have many difficulties to assess the water network causing system failure, deterioration of mains and bad water quality in the network due to pipe corrosion and deterioration. The limited information brought into focus the urgent need to develop economical assessment for rehabilitation of water distribution systems adapted to water utilities. Gaza Strip is subject to a first case study, suffering from severe shortage in the water supply and environmental problems and contamination of underground water resources. This research focuses on improvement of water supply network to reduce the water losses in water network based on limited database using techniques of ArcGIS and commercial water network software (WaterCAD). A new approach for rehabilitation water pipes has been presented in Gaza city case study. Integrated rehabilitation assessment model has been developed for rehabilitation water pipes including three components; hydraulic assessment model, Physical assessment model and Structural assessment model. WaterCAD model has been developed with integrated in ArcGIS to produce the hydraulic assessment model for water network. The model have been designed based on pipe condition assessment with 100 score points as a maximum points for pipe condition. As results from this model, we can indicate that 40% of water pipeline have score points less than 50 points and about 10% of total pipes length have less than 30 score points. By using this model, the rehabilitation plans for each region in Gaza city can be achieved based on available budget and condition of pipes. The second case study is Kuala Lumpur Case from semi-developed countries, which has been used to develop an approach to improve the water network under crucial conditions using, advanced statistical and GIS techniques. Kuala Lumpur (KL) has water losses about 40% and high failure rate, which make severe problem. This case can represent cases in South Asia countries. Kuala Lumpur faced big challenges to reduce the water losses in water network during last 5 years. One of these challenges is high deterioration of asbestos cement (AC) pipes. They need to replace more than 6500 km of AC pipes, which need a huge budget to be achieved. Asbestos cement is subject to deterioration due to various chemical processes that either leach out the cement material or penetrate the concrete to form products that weaken the cement matrix. This case presents an approach for geo-statistical model for modelling pipe failures in a water distribution network. Database of Syabas Company (Kuala Lumpur water company) has been used in developing the model. The statistical models have been calibrated, verified and used to predict failures for both networks and individual pipes. The mathematical formulation developed for failure frequency in Kuala Lumpur was based on different pipeline characteristics, reflecting several factors such as pipe diameter, length, pressure and failure history. Generalized linear model have been applied to predict pipe failures based on District Meter Zone (DMZ) and individual pipe levels. Based on Kuala Lumpur case study, several outputs and implications have been achieved. Correlations between spatial and temporal intervals of pipe failures also have been done using ArcGIS software. Water Pipe Assessment Model (WPAM) has been developed using the analysis of historical pipe failure in Kuala Lumpur which prioritizing the pipe rehabilitation candidates based on ranking system. Frankfurt Water Network in Germany is the third main case study. This case makes an overview for Survival analysis and neural network methods used in water network. Rehabilitation strategies of water pipes have been developed for Frankfurt water network in cooperation with Mainova (Frankfurt Water Company). This thesis also presents a methodology of technical condition assessment of plastic pipes based on simple analysis. This thesis aims to make contribution to improve the prediction of pipe failures in water networks using Geographic Information System (GIS) and Decision Support System (DSS). The output from the technical condition assessment model can be used to estimate future budget needs for rehabilitation and to define pipes with high priority for replacement based on poor condition. rn

Long-term effects of watershed management on surface runoff and sediment loss in the Ethiopian Highlands

Data on rainfall, runoff and sediment loss from different land use types have been collected by the Soil Conservation Research Programme in seven small catchments (73-673 hectares) throughout the Ethiopian Highlands since the early 1980s. Monitoring was carried out on a storm-to-storm basis for extended periods of 10-20 years, and the data are analysed here to assess long-term effects of changes. Soil and water conservation technologies were introduced in the early years in the catchments in view of their capacity to reduce runoff and sediment yield. Results indicate that rainfall did not substantially change over the observation periods. Land use changes and land degradation, however, altered runoff, as shown by the data from small test plots (30 m2), which were not altered by conservation measures during the monitoring periods. Sediment delivery from the catchments may have decreased due to soil and water conservation, while runoff rates did not change significantly. Extrapolation of the results in the highlands, however, showed that expansion of cultivated and grazing land induced by population growth may have increased the overall surface runoff. Watershed management in the catchments, finally, had beneficial effects on ecosystem services by reducing soil erosion, restoring soil fertility, enhancing agricultural production, and maintaining overall runoff to the benefit of lowland areas and neighbouring countries.

A system for 3-D reconstruction of a patient-specific surface model from calibrated X-ray images

This paper presents a system for 3-D reconstruction of a patient-specific surface model from calibrated X-ray images. Our system requires two X-ray images of a patient with one acquired from the anterior-posterior direction and the other from the axial direction. A custom-designed cage is utilized in our system to calibrate both images. Starting from bone contours that are interactively identified from the X-ray images, our system constructs a patient-specific surface model of the proximal femur based on a statistical model based 2D/3D reconstruction algorithm. In this paper, we present the design and validation of the system with 25 bones. An average reconstruction error of 0.95 mm was observed.

Ecosystem functions are resistant to extreme changes to rainfall regimes in a mesotrophic grassland

Major changes to rainfall regimes are predicted for the future but the effect of such changes on terrestrial ecosystem function is largely unknown. We created a rainfall manipulation experiment to investigate the effects of extreme changes in rainfall regimes on ecosystem functioning in a grassland system. We applied two rainfall regimes; a prolonged drought treatment (30 % reduction over spring and summer) and drought/downpour treatment (long periods of no rainfall interspersed with downpours), with an ambient control. Both rainfall manipulations included increased winter rainfall. We measured plant community composition, CO2 fluxes and soil nutrient availability. Plant species richness and cover were lower in the drought/downpour treatment, and showed little recovery after the treatment ceased. Ecosystem processes were less affected, possibly due to winter rainfall additions buffering reduced summer rainfall, which saw relatively small soil moisture changes. However, soil extractable P and ecosystem respiration were significantly higher in rainfall change treatments than in the control. This grassland appears fairly resistant, in the short term, to even the more extreme rainfall changes that are predicted for the region, although prolonged study is needed to measure longer-term impacts. Differences in ecosystem responses between the two treatments emphasise the variety of ecosystem responses to changes in both the size and frequency of rainfall events. Given that model predictions are inconsistent there is therefore a need to assess ecosystem function under a range of potential climate change scenarios.

THE IMPLEMENTATION OF FEDERAL FAMILY PLANNING POLICY: AN ANALYSIS OF FACTORS AFFECTING STATE EXPENDITURES, FISCAL YEARS 1976-1981 (WOMEN, BUDGET, POPULATION)

The purpose of this study was to analyze the implementation of national family planning policy in the United States, which was embedded in four separate statutes during the period of study, Fiscal Years 1976-81. The design of the study utilized a modification of the Sabatier and Mazmanian framework for policy analysis, which defined implementation as the carrying out of statutory policy. The study was divided into two phases. The first part of the study compared the implementation of family planning policy by each of the pertinent statutes. The second part of the study identified factors that were associated with implementation of federal family planning policy within the context of block grants.^ Implemention was measured here by federal dollars spent for family planning, adjusted for the size of the respective state target populations. Expenditure data were collected from the Alan Guttmacher Institute and from each of the federal agencies having administrative authority for the four pertinent statutes, respectively. Data from the former were used for most of the analysis because they were more complete and more reliable.^ The first phase of the study tested the hypothesis that the coherence of a statute is directly related to effective implementation. Equity in the distribution of funds to the states was used to operationalize effective implementation. To a large extent, the results of the analysis supported the hypothesis. In addition to their theoretical significance, these findings were also significant for policymakers insofar they demonstrated the effectiveness of categorical legislation in implementing desired health policy.^ Given the current and historically intermittent emphasis on more state and less federal decision-making in health and human serives, the second phase of the study focused on state level factors that were associated with expenditures of social service block grant funds for family planning. Using the Sabatier-Mazmanian implementation model as a framework, many factors were tested. Those factors showing the strongest conceptual and statistical relationship to the dependent variable were used to construct a statistical model. Using multivariable regression analysis, this model was applied cross-sectionally to each of the years of the study. The most striking finding here was that the dominant determinants of the state spending varied for each year of the study (Fiscal Years 1976-1981). The significance of these results was that they provided empirical support of current implementation theory, showing that the dominant determinants of implementation vary greatly over time. ^