Evaluation of an average numerator relationship matrix model and a Bayesian hierarchical model for growth traits in Nellore cattle with uncertain paternity

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

Generation of Nutrients and Detoxification: Possible Roles of Yeasts in Leaf-Cutting Ant Nests.

The possible roles played by yeasts in attine ant nests are mostly unknown. Here we present our investigations on the plant polysaccharide degradation profile of 82 yeasts isolated from fungus gardens of Atta and Acromyrmex species to demonstrate that yeasts found in ant nests may play the role of making nutrients readily available throughout the garden and detoxification of compounds that may be deleterious to the ants and their fungal cultivar. Among the yeasts screened, 65% exhibited cellulolytic enzymes, 44% exhibited pectinolytic activity while 27% and 17% possess enzyme systems for the degradation of protease and amylase, respectively. Galacturonic acid, which had been reported in previous work to be poorly assimilated by the ant fungus and also to have a negative effect on ants' survival, was assimilated by 64% and 79% of yeasts isolated from nests of A. texana and Acromyrmex respectively. Our results suggest that yeasts found in ant nests may participate in generation of nutrients and removal of potentially toxic compounds, thereby contributing to the stability of the complex microbiota found in the leaf-cutting ant nests.

Singular value analyses of voltage stability on power system considering wind generation variability

Pós-graduação em Engenharia Elétrica - FEIS

Complementary mass spectrometric techniques for the characterization of the organic fraction in atmospheric aerosols

Aerosol particles are strongly related to climate, air quality, visibility and human health issues. They contribute the largest uncertainty in the assessment of the Earth´s radiative budget, directly by scattering or absorbing solar radiation or indirectly by nucleating cloud droplets. The influence of aerosol particles on cloud related climatic effects essentially depends upon their number concentration, size and chemical composition. A major part of submicron aerosol consists of secondary organic aerosol (SOA) that is formed in the atmosphere by the oxidation of volatile organic compounds. SOA can comprise a highly diverse spectrum of compounds that undergo continuous chemical transformations in the atmosphere.rnThe aim of this work was to obtain insights into the complexity of ambient SOA by the application of advanced mass spectrometric techniques. Therefore, an atmospheric pressure chemical ionization ion trap mass spectrometer (APCI-IT-MS) was applied in the field, facilitating the measurement of ions of the intact molecular organic species. Furthermore, the high measurement frequency provided insights into SOA composition and chemical transformation processes on a high temporal resolution. Within different comprehensive field campaigns, online measurements of particular biogenic organic acids were achieved by combining an online aerosol concentrator with the APCI-IT-MS. A holistic picture of the ambient organic aerosol was obtained through the co-located application of other complementary MS techniques, such as aerosol mass spectrometry (AMS) or filter sampling for the analysis by liquid chromatography / ultrahigh resolution mass spectrometry (LC/UHRMS).rnIn particular, during a summertime field study at the pristine boreal forest station in Hyytiälä, Finland, the partitioning of organic acids between gas and particle phase was quantified, based on the online APCI-IT-MS and AMS measurements. It was found that low volatile compounds reside to a large extent in the gas phase. This observation can be interpreted as a consequence of large aerosol equilibration timescales, which build up due to the continuous production of low volatile compounds in the gas phase and/or a semi-solid phase state of the ambient aerosol. Furthermore, in-situ structural informations of particular compounds were achieved by using the MS/MS mode of the ion trap. The comparison to MS/MS spectra from laboratory generated SOA of specific monoterpene precursors indicated that laboratory SOA barely depicts the complexity of ambient SOA. Moreover, it was shown that the mass spectra of the laboratory SOA more closely resemble the ambient gas phase composition, indicating that the oxidation state of the ambient organic compounds in the particle phase is underestimated by the comparison to laboratory ozonolysis. These observations suggest that the micro-scale processes, such as the chemistry of aerosol aging or the gas-to-particle partitioning, need to be better understood in order to predict SOA concentrations more reliably.rnDuring a field study at the Mt. Kleiner Feldberg, Germany, a slightly different aerosol concentrator / APCI-IT-MS setup made the online analysis of new particle formation possible. During a particular nucleation event, the online mass spectra indicated that organic compounds of approximately 300 Da are main constituents of the bulk aerosol during ambient new particle formation. Co-located filter analysis by LC/UHRMS analysis supported these findings and furthermore allowed to determine the molecular formulas of the involved organic compounds. The unambiguous identification of several oxidized C 15 compounds indicated that oxidation products of sesquiterpenes can be important compounds for the initial formation and subsequent growth of atmospheric nanoparticles.rnThe LC/UHRMS analysis furthermore revealed that considerable amounts of organosulfates and nitrooxy organosulfates were detected on the filter samples. Indeed, it was found that several nitrooxy organosulfate related APCI-IT-MS mass traces were simultaneously enhanced. Concurrent particle phase ion chromatography and AMS measurements indicated a strong bias between inorganic sulfate and total sulfate concentrations, supporting the assumption that substantial amounts of sulfate was bonded to organic molecules.rnFinally, the comprehensive chemical analysis of the aerosol composition was compared to the hygroscopicity parameter kappa, which was derived from cloud condensation nuclei (CCN) measurements. Simultaneously, organic aerosol aging was observed by the evolution of a ratio between a second and a first generation biogenic oxidation product. It was found that this aging proxy positively correlates with increasing hygroscopicity. Moreover, it was observed that the bonding of sulfate to organic molecules leads to a significant reduction of kappa, compared to an internal mixture of the same mass fractions of purely inorganic sulfate and organic molecules. Concluding, it has been shown within this thesis that the application of modern mass spectrometric techniques allows for detailed insights into chemical and physico-chemical processes of atmospheric aerosols.rn

Simulation of AC, DC, and AC-DC coupled mini-grids. In search of the most efficient system

Renewable energy hybrid systems and mini-grids for electrification of rural areas are known to be reliable and more cost efficient than grid extension or only-diesel based systems. However, there is still some uncertainty in some areas, for example, which is the most efficient way of coupling hybrid systems: AC, DC or AC-DC? With the use of Matlab/Simulink a mini-grid that connects a school, a small hospital and an ecotourism hostel has been modelled. This same mini grid has been coupled in the different possible ways and the system’s efficiency has been studied. In addition, while keeping the consumption constant, the generation sources and the consumption profile have been modified and the effect on the efficiency under each configuration has also been analysed. Finally different weather profiles have been introduced and, again, the effect on the efficiency of each system has been observed.

Effects of cross sections tables generation and optimization on rod ejection transient analyses

Best estimate analysis of rod ejection transients requires 3D kinetics core simulators. If they use cross sections libraries compiled in multidimensional tables,interpolation errors – originated when the core simulator computes the cross sections from the table values – are a source of uncertainty in k-effective calculations that should be accounted for. Those errors depend on the grid covering the domain of state variables and can be easily reduced, in contrast with other sources of uncertainties such as the ones due to nuclear data, by choosing an optimized grid distribution. The present paper assesses the impact of the grid structure on a PWR rod ejection transient analysis using the coupled neutron-kinetics/thermal-hydraulicsCOBAYA3/COBRA-TF system. Forthispurpose, the OECD/NEA PWR MOX/UO2 core transient benchmark has been chosen, as material compositions and geometries are available, allowing the use of lattice codes to generate libraries with different grid structures. Since a complete nodal cross-section library is also provided as part of the benchmark specifications, the effects of the library generation on transient behavior are also analyzed.Results showed large discrepancies when using the benchmark library and own-generated libraries when compared with benchmark participants’ solutions. The origin of the discrepancies was found to lie in the nodal cross sections provided in the benchmark.

Quantification of geological uncertainty and risk using stochastic simulation and applications in the coal mining industry

Stochastic simulation is a recognised tool for quantifying the spatial distribution of geological uncertainty and risk in earth science and engineering. Metals mining is an area where simulation technologies are extensively used; however, applications in the coal mining industry have been limited. This is particularly due to the lack of a systematic demonstration illustrating the capabilities these techniques have in problem solving in coal mining. This paper presents two broad and technically distinct areas of applications in coal mining. The first deals with the use of simulation in the quantification of uncertainty in coal seam attributes and risk assessment to assist coal resource classification, and drillhole spacing optimisation to meet pre-specified risk levels at a required confidence. The second application presents the use of stochastic simulation in the quantification of fault risk, an area of particular interest to underground coal mining, and documents the performance of the approach. The examples presented demonstrate the advantages and positive contribution stochastic simulation approaches bring to the coal mining industry

Impact of weather dependency on planning decisions in the australian national electricity market

A long-term planning method for the electricity market is to simulate market operation into the future. Outputs from market simulation include indicators for transmission augmentation and new generation investment. A key input to market simulations is demand forecasts. For market simulation purposes, regional demand forecasts for each half-hour interval of the forecasting horizon are required, and they must accurately represent realistic demand profiles and interregional demand relationships. In this paper, a demand model is developed to accurately model these relationships. The effects of uncertainty in weather patterns and inherent correlations between regional demands on market simulation results are presented. This work signifies the advantages of probabilistic modeling of demand levels when making market-based planning decisions.

Introducing uncertainty into pattern discovery in temporal event sequences

Pattern discovery in temporal event sequences is of great importance in many application domains, such as telecommunication network fault analysis. In reality, not every type of event has an accurate timestamp. Some of them, defined as inaccurate events may only have an interval as possible time of occurrence. The existence of inaccurate events may cause uncertainty in event ordering. The traditional support model cannot deal with this uncertainty, which would cause some interesting patterns to be missing. A new concept, precise support, is introduced to evaluate the probability of a pattern contained in a sequence. Based on this new metric, we define the uncertainty model and present an algorithm to discover interesting patterns in the sequence database that has one type of inaccurate event. In our model, the number of types of inaccurate events can be extended to k readily, however, at a cost of increasing computational complexity.

Dimensionality reduction in complex models

As a part of the Managing Uncertainty in Complex Models (MUCM) project, research at Aston University will develop methods for dimensionality reduction of the input and/or output spaces of models, as seen within the emulator framework. Towards this end this report describes a framework for generating toy datasets, whose underlying structure is understood, to facilitate early investigations of dimensionality reduction methods and to gain a deeper understanding of the algorithms employed, both in terms of how effective they are for given types of models / situations, and also their speed in applications and how this scales with various factors. The framework, which allows the evaluation of both screening and projection approaches to dimensionality reduction, is described. We also describe the screening and projection methods currently under consideration and present some preliminary results. The aim of this draft of the report is to solicit feedback from the project team on the dataset generation framework, the methods we propose to use, and suggestions for extensions that should be considered.

Uncertainty and the influence of group norms in the attitude-behaviour relationship

Two studies were conducted to examine the impact of subjective uncertainty on conformity to group norms in the attitude-behaviour context. In both studies, subjective uncertainty was manipulated using a deliberative mindset manipulation (McGregor, Zanna, Holmes, & Spencer, 2001). In Study 1 (N = 106), participants were exposed to either an attitude-congruent or an attitude-incongruent in-group norm. In Study 2(N = 83), participants were exposed to either a congruent, incongruent, or an ambiguous in-group norm. Ranges of attitude-behaviour outcomes, including attitude-intention consistency and change in attitude-certainty, were assessed. In both studies, levels of group-normative behaviour varied as a function of uncertainty condition. In Study 1, conformity to group norms, as evidenced by variations in the level of attitude-intention consistency, was observed only in the high uncertainty condition. In Study 2, exposure to an ambiguous norm had different effects for those in the low and die high uncertainty conditions. In the low uncertainty condition, greatest conformity was observed in the attitude-congruent norm condition compared with an attitude-congruent or ambiguous norm. In contrast, individuals in the high uncertainty condition displayed greatest conformity when exposed to either an attitude-congruent or an ambiguous in-group norm. The implications of these results for the role of subjective uncertainty in social influence processes are discussed. © 2007 The British Psychological Society.

Uncertainty analysis in the Model Web

This thesis provides a set of tools for managing uncertainty in Web-based models and workflows.To support the use of these tools, this thesis firstly provides a framework for exposing models through Web services. An introduction to uncertainty management, Web service interfaces,and workflow standards and technologies is given, with a particular focus on the geospatial domain.An existing specification for exposing geospatial models and processes, theWeb Processing Service (WPS), is critically reviewed. A processing service framework is presented as a solutionto usability issues with the WPS standard. The framework implements support for Simple ObjectAccess Protocol (SOAP), Web Service Description Language (WSDL) and JavaScript Object Notation (JSON), allowing models to be consumed by a variety of tools and software. Strategies for communicating with models from Web service interfaces are discussed, demonstrating the difficultly of exposing existing models on the Web. This thesis then reviews existing mechanisms for uncertainty management, with an emphasis on emulator methods for building efficient statistical surrogate models. A tool is developed to solve accessibility issues with such methods, by providing a Web-based user interface and backend to ease the process of building and integrating emulators. These tools, plus the processing service framework, are applied to a real case study as part of the UncertWeb project. The usability of the framework is proved with the implementation of aWeb-based workflow for predicting future crop yields in the UK, also demonstrating the abilities of the tools for emulator building and integration. Future directions for the development of the tools are discussed.

Quantifying the effects of temperature and concentration on variable-density flow in numerical modeling of groundwater systems: Implications for predictive uncertainty and data collection

Groundwater systems of different densities are often mathematically modeled to understand and predict environmental behavior such as seawater intrusion or submarine groundwater discharge. Additional data collection may be justified if it will cost-effectively aid in reducing the uncertainty of a model's prediction. The collection of salinity, as well as, temperature data could aid in reducing predictive uncertainty in a variable-density model. However, before numerical models can be created, rigorous testing of the modeling code needs to be completed. This research documents the benchmark testing of a new modeling code, SEAWAT Version 4. The benchmark problems include various combinations of density-dependent flow resulting from variations in concentration and temperature. The verified code, SEAWAT, was then applied to two different hydrological analyses to explore the capacity of a variable-density model to guide data collection. ^ The first analysis tested a linear method to guide data collection by quantifying the contribution of different data types and locations toward reducing predictive uncertainty in a nonlinear variable-density flow and transport model. The relative contributions of temperature and concentration measurements, at different locations within a simulated carbonate platform, for predicting movement of the saltwater interface were assessed. Results from the method showed that concentration data had greater worth than temperature data in reducing predictive uncertainty in this case. Results also indicated that a linear method could be used to quantify data worth in a nonlinear model. ^ The second hydrological analysis utilized a model to identify the transient response of the salinity, temperature, age, and amount of submarine groundwater discharge to changes in tidal ocean stage, seasonal temperature variations, and different types of geology. The model was compared to multiple kinds of data to (1) calibrate and verify the model, and (2) explore the potential for the model to be used to guide the collection of data using techniques such as electromagnetic resistivity, thermal imagery, and seepage meters. Results indicated that the model can be used to give insight to submarine groundwater discharge and be used to guide data collection. ^

Taking risks in the face of uncertainty:an exploratory analysis of green innovation

The relationship between uncertainty and firms’ risk-taking behaviour has been a focus of investigation since early discussion of the nature of enterprise activity. Here, we focus on how firms’ perceptions of environmental uncertainty and their perceptions of the risks involved impact on their willingness to undertake green innovation. Analysis is based on a cross-sectional survey of UK food companies undertaken in 2008. The results reinforce the relationship between perceived environmental uncertainty and perceived innovation risk and emphasise the importance of macro-uncertainty in shaping firms’ willingness to undertake green innovation. The perceived (market-related) riskiness of innovation also positively influences the probability of innovating, suggesting either a proactive approach to stimulating market disruption or an opportunistic approach to innovation leadership.