895 resultados para data types and operators
Resumo:
Soil properties that influence water movement through profiles are important for determining flow paths, reactions between soil and solute, and the ultimate destination of solutes. This is particularly important in high rainfall environments. For highly weathered deep profiles, we hypothesize that abrupt changes in the distribution of the quotient [QT = (silt + sand)/clay] reflect the boundaries between textural units or textural (TS) and hydrologic (HS) stratigraphies. As a result, QT can be used as a parameter to characterize TS and as a surrogate for HS. Secondly, we propose that if chloride distributions were correlated with QT, under non-limiting anion exchange, then chloride distributions can be used as a signature indicator of TS and HS. Soil cores to a depth of 12.5 in were taken from 16 locations in the wet tropical Johnstone River catchment of northeast Queensland, Australia. The cores belong to nine variable charge soil types and were under sugarcane (Saccharun officinarum-S) production, which included the use of potassium chloride, for several decades. The cores were segmented at I m depth increments and subsamples were analysed for chloride, pH, soil water content (theta), clay, silt and sand contents. Selected bores were capped to serve as piezometers to monitor groundwater dynamics. Depth incremented QT, theta and chloride correlated, each individually, significantly with the corresponding profile depth increments, indicating the presence of textural, hydrologic and chloride gradients in profiles. However, rapid increases in QT down the profile indicated abrupt changes in TS, suggesting that QT can be used as a parameter to characterize TS and as a surrogate for HS. Abrupt changes in chloride distributions were similar to QT, suggesting that chloride distributions can be used as a signature indicator of QT (TS) and HS. Groundwater data indicated that chloride distributions depended, at least partially, on groundwater dynamics, providing further support to our hypothesis that chloride distribution can be used as a signature indicator of HS. Copyright (c) 2005 John Wiley & Sons, Ltd.
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Sustainable management of coastal and coral reef environments requires regular collection of accurate information on recognized ecosystem health indicators. Satellite image data and derived maps of water column and substrate biophysical properties provide an opportunity to develop baseline mapping and monitoring programs for coastal and coral reef ecosystem health indicators. A significant challenge for satellite image data in coastal and coral reef water bodies is the mixture of both clear and turbid waters. A new approach is presented in this paper to enable production of water quality and substrate cover type maps, linked to a field based coastal ecosystem health indicator monitoring program, for use in turbid to clear coastal and coral reef waters. An optimized optical domain method was applied to map selected water quality (Secchi depth, Kd PAR, tripton, CDOM) and substrate cover type (seagrass, algae, sand) parameters. The approach is demonstrated using commercially available Landsat 7 Enhanced Thematic Mapper image data over a coastal embayment exhibiting the range of substrate cover types and water quality conditions commonly found in sub-tropical and tropical coastal environments. Spatially extensive and quantitative maps of selected water quality and substrate cover parameters were produced for the study site. These map products were refined by interactions with management agencies to suit the information requirements of their monitoring and management programs. (c) 2004 Elsevier Ltd. All rights reserved.
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This paper examines the complexity of collaboration between child protection and mental health services, where a parent has a mental illness and there are protection concerns for children. The paper reports on data from focused in-depth interviews with 36 child protection workers, adult mental health workers and child and youth mental health workers. Data were analysed thematically, using NVivo to facilitate data management and analysis. Two dimensions were identified. The first, the process of collaboration, relates to four factors that assisted the collaborative process: communication, knowledge, role clarity and resources. The second dimension considers the challenges presented to collaborative work when a parent has a mental illness and a child is in need of protection, and identifies issues that are inherent in cases of this kind. Two types of challenge were identified. The first related to characteristics of mental illness, and included the episodic and/or unpredictable nature of mental illness, incorporating information from psychiatric and parenting capacity assessments, and the provision of ongoing support. The second type of challenge concerned the tension between the conflicting needs of parents and their children, and how this was viewed from both the adult mental health and the child protection perspective. Implications for policy and practice are identified in relation to the need for service models that provide ongoing, flexible support that can be intensified or held back as needed.
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We introduce the Survey for Ionization in Neutral Gas Galaxies (SINGG), a census of star formation in H I selected galaxies. The survey consists of H alpha and R-band imaging of a sample of 468 galaxies selected from the H I Parkes All Sky Survey (HIPASS). The sample spans three decades in H I mass and is free of many of the biases that affect other star-forming galaxy samples. We present the criteria for sample selection, list the entire sample, discuss our observational techniques, and describe the data reduction and calibration methods. This paper focuses on 93 SINGG targets whose observations have been fully reduced and analyzed to date. The majority of these show a single emission line galaxy (ELG). We see multiple ELGs in 13 fields, with up to four ELGs in a single field. All of the targets in this sample are detected in H alpha, indicating that dormant (non-star-forming) galaxies with M-H I greater than or similar to 3x10(7) M-circle dot are very rare. A database of the measured global properties of the ELGs is presented. The ELG sample spans 4 orders of magnitude in luminosity (H alpha and R band), and H alpha surface brightness, nearly 3 orders of magnitude in R surface brightness and nearly 2 orders of magnitude in H alpha equivalent width (EW). The surface brightness distribution of our sample is broader than that of the Sloan Digital Sky Survey (SDSS) spectroscopic sample, the EW distribution is broader than prism-selected samples, and the morphologies found include all common types of star-forming galaxies (e.g., irregular, spiral, blue compact dwarf, starbursts, merging and colliding systems, and even residual star formation in S0 and Sa spirals). Thus, SINGG presents a superior census of star formation in the local universe suitable for further studies ranging from the analysis of H II regions to determination of the local cosmic star formation rate density.
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In many online applications, we need to maintain quantile statistics for a sliding window on a data stream. The sliding windows in natural form are defined as the most recent N data items. In this paper, we study the problem of estimating quantiles over other types of sliding windows. We present a uniform framework to process quantile queries for time constrained and filter based sliding windows. Our algorithm makes one pass on the data stream and maintains an E-approximate summary. It uses O((1)/(epsilon2) log(2) epsilonN) space where N is the number of data items in the window. We extend this framework to further process generalized constrained sliding window queries and proved that our technique is applicable for flexible window settings. Our performance study indicates that the space required in practice is much less than the given theoretical bound and the algorithm supports high speed data streams.
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Large amounts of information can be overwhelming and costly to process, especially when transmitting data over a network. A typical modern Geographical Information System (GIS) brings all types of data together based on the geographic component of the data and provides simple point-and-click query capabilities as well as complex analysis tools. Querying a Geographical Information System, however, can be prohibitively expensive due to the large amounts of data which may need to be processed. Since the use of GIS technology has grown dramatically in the past few years, there is now a need more than ever, to provide users with the fastest and least expensive query capabilities, especially since an approximated 80 % of data stored in corporate databases has a geographical component. However, not every application requires the same, high quality data for its processing. In this paper we address the issues of reducing the cost and response time of GIS queries by preaggregating data by compromising the data accuracy and precision. We present computational issues in generation of multi-level resolutions of spatial data and show that the problem of finding the best approximation for the given region and a real value function on this region, under a predictable error, in general is "NP-complete.
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Many emerging applications benefit from the extraction of geospatial data specified at different resolutions for viewing purposes. Data must also be topologically accurate and up-to-date as it often represents real-world changing phenomena. Current multiresolution schemes use complex opaque data types, which limit the capacity for in-database object manipulation. By using z-values and B+trees to support multiresolution retrieval, objects are fragmented in such a way that updates to objects or object parts are executed using standard SQL (Structured Query Language) statements as opposed to procedural functions. Our approach is compared to a current model, using complex data types indexed under a 3D (three-dimensional) R-tree, and shows better performance for retrieval over realistic window sizes and data loads. Updates with the R-tree are slower and preclude the feasibility of its use in time-critical applications whereas, predictably, projecting the issue to a one-dimensional index allows constant updates using z-values to be implemented more efficiently.
Resumo:
Electoral Rules and Leader Selection: Experimental Evidence from Ugandan Community Groups. Despite a large body of work documenting how electoral systems affect policy outcomes, less is known about their impact on leader selection. We study this by comparing two types of participatory decision making in Ugandan community groups: (i) vote by secret ballot and (ii) open discussion with consensus. Random assignment allows us to estimate the causal impact of the rules on leader types and social service delivery. Vote groups are found to elect leaders more similar to the average member while discussion group leaders are positively selected on socio-economic characteristics. Further, dropout rates are significantly higher in discussion groups, particularly for poorer members. After 3.5 years, vote groups are larger in size and their members save less and get smaller loans. We conclude that the secret ballot vote creates more inclusive groups while open discussion groups favor the already economically successful. Preparing for Genocide: Community Meetings in Rwanda. How do political elites prepare the civilian population for participation in violent conflict? We empirically investigate this question using data from the Rwandan Genocide in 1994. Every Saturday before 1994, Rwandan villagers had to meet to work on community infrastructure. The practice was highly politicized and, according to anecdotal evidence, regularly used by the political elites for spreading propaganda in the years before the genocide. This paper presents the first quantitative evidence of this abuse of the community meetings. To establish causality, we exploit cross-sectional variation in meeting intensity induced by exogenous weather fluctuations. We find that an additional rainy Saturday resulted in a five percent lower civilian participation rate in genocide violence. Selection into Borrowing: Survey Evidence from Uganda. In this paper, I study how changes to the standard credit contract affect loan demand and selection into borrowing, using a representative sample of urban micro enterprises, most with no borrowing experience. Hypothetical loan demand questions are used to test whether firm owners respond to changes in loans' contractual terms and whether take-up varies by firms' risk type and other firm owner characteristics. The results indicate that contracts with lower interest rates and less stringent collateral requirements attract less risky borrowers, suggesting that there is scope for improvement of standard financial contract terms. Credit Contract Structure and Firm Growth: Evidence from a Randomized Control Trial. We study the effects of credit contract structure on firm outcomes among small and medium sized firms. A randomized control trial was carried out to distinguish between some of the key constraints to efficient credit use connected to the firms' business environment and production function, namely (i) backloaded returns (ii) uncertain returns and (iii) indivisible fixed costs. Each firm was followed for the 1-year loan cycle. We describe the experiment and present preliminary results from the first 754 out of 2,340 firms to have completed the loan cycle. Firms offered a grace period have higher profits and higher household income than firms receiving a rebate later on as well as the control group. They also increased the number of paid employees and reduced the number of unpaid employees, an effect also found among firms that received a cash subsidy at the beginning of the loan cycle. We discuss potential mechanisms behind these effects.
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We study the effect of two types of noise, data noise and model noise, in an on-line gradient-descent learning scenario for general two-layer student network with an arbitrary number of hidden units. Training examples are randomly drawn input vectors labeled by a two-layer teacher network with an arbitrary number of hidden units. Data is then corrupted by Gaussian noise affecting either the output or the model itself. We examine the effect of both types of noise on the evolution of order parameters and the generalization error in various phases of the learning process.
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This paper explores the use of the optimisation procedures in SAS/OR software with application to the measurement of efficiency and productivity of decision-making units (DMUs) using data envelopment analysis (DEA) techniques. DEA was originally introduced by Charnes et al. [J. Oper. Res. 2 (1978) 429] is a linear programming method for assessing the efficiency and productivity of DMUs. Over the last two decades, DEA has gained considerable attention as a managerial tool for measuring performance of organisations and it has widely been used for assessing the efficiency of public and private sectors such as banks, airlines, hospitals, universities and manufactures. As a result, new applications with more variables and more complicated models are being introduced. Further to successive development of DEA a non-parametric productivity measure, Malmquist index, has been introduced by Fare et al. [J. Prod. Anal. 3 (1992) 85]. Employing Malmquist index, productivity growth can be decomposed into technical change and efficiency change. On the other hand, the SAS is a powerful software and it is capable of running various optimisation problems such as linear programming with all types of constraints. To facilitate the use of DEA and Malmquist index by SAS users, a SAS/MALM code was implemented in the SAS programming language. The SAS macro developed in this paper selects the chosen variables from a SAS data file and constructs sets of linear-programming models based on the selected DEA. An example is given to illustrate how one could use the code to measure the efficiency and productivity of organisations.
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This thesis presents an investigation, of synchronisation and causality, motivated by problems in computational neuroscience. The thesis addresses both theoretical and practical signal processing issues regarding the estimation of interdependence from a set of multivariate data generated by a complex underlying dynamical system. This topic is driven by a series of problems in neuroscience, which represents the principal background motive behind the material in this work. The underlying system is the human brain and the generative process of the data is based on modern electromagnetic neuroimaging methods . In this thesis, the underlying functional of the brain mechanisms are derived from the recent mathematical formalism of dynamical systems in complex networks. This is justified principally on the grounds of the complex hierarchical and multiscale nature of the brain and it offers new methods of analysis to model its emergent phenomena. A fundamental approach to study the neural activity is to investigate the connectivity pattern developed by the brain’s complex network. Three types of connectivity are important to study: 1) anatomical connectivity refering to the physical links forming the topology of the brain network; 2) effective connectivity concerning with the way the neural elements communicate with each other using the brain’s anatomical structure, through phenomena of synchronisation and information transfer; 3) functional connectivity, presenting an epistemic concept which alludes to the interdependence between data measured from the brain network. The main contribution of this thesis is to present, apply and discuss novel algorithms of functional connectivities, which are designed to extract different specific aspects of interaction between the underlying generators of the data. Firstly, a univariate statistic is developed to allow for indirect assessment of synchronisation in the local network from a single time series. This approach is useful in inferring the coupling as in a local cortical area as observed by a single measurement electrode. Secondly, different existing methods of phase synchronisation are considered from the perspective of experimental data analysis and inference of coupling from observed data. These methods are designed to address the estimation of medium to long range connectivity and their differences are particularly relevant in the context of volume conduction, that is known to produce spurious detections of connectivity. Finally, an asymmetric temporal metric is introduced in order to detect the direction of the coupling between different regions of the brain. The method developed in this thesis is based on a machine learning extensions of the well known concept of Granger causality. The thesis discussion is developed alongside examples of synthetic and experimental real data. The synthetic data are simulations of complex dynamical systems with the intention to mimic the behaviour of simple cortical neural assemblies. They are helpful to test the techniques developed in this thesis. The real datasets are provided to illustrate the problem of brain connectivity in the case of important neurological disorders such as Epilepsy and Parkinson’s disease. The methods of functional connectivity in this thesis are applied to intracranial EEG recordings in order to extract features, which characterize underlying spatiotemporal dynamics before during and after an epileptic seizure and predict seizure location and onset prior to conventional electrographic signs. The methodology is also applied to a MEG dataset containing healthy, Parkinson’s and dementia subjects with the scope of distinguishing patterns of pathological from physiological connectivity.
Resumo:
Aims: To explore newly diagnosed Type 2 diabetes patients' views about Scottish diabetes services at a time when these services are undergoing a major reorganization. To provide recommendations to maximize opportunities brought by the devolvement of services from secondary to primary healthcare settings. Methods: Qualitative panel study with 40 patients newly diagnosed with Type 2 diabetes, recruited from hospital clinics and general practices in Lothian, Scotland. Patients were interviewed three times over 1 year. The study was informed by grounded theory, which involves concurrent data collection and analysis. Results: Patients were generally satisfied with diabetes services irrespective of the types of care received. Most wanted their future care/review to be based in general practice for reasons of convenience and accessibility, although they dis-liked it when appointments were scheduled for different days. Many said they lacked the knowledge/confidence to know how to manage their diabetes in particular situations, and needed access to healthcare professionals who could answer their questions promptly. Patients expressed a need for primary care professionals who had diabetes expertise, but who had more time and were more accessible than general practitioners. Patients who had encountered practice lead nurses for diabetes spoke particularly positively of these professionals. Conclusions: Nurses with diabetes training are particularly well placed to provide information and support to patients in primary care. Ideally, practices should run 'one-stop' diabetes clinics to provide structured care, with easily accessible dietetics, podiatry and retinopathy screening. Newly diagnosed patients may benefit from being made more aware of specific services provided by charitable organizations such as Diabetes UK. © 2005 Diabetes UK.
Resumo:
Local shell side coefficient measurements in the end conpartments of a model shell and tube heat exchanger have been made using an electrochemical technique. Limited data are also reported far the second compartment. The end compartment average coefficients have been found to be smaller than reported data for a corresponding internal conpartment. The second compartment data. have been shown to lie between those for the end compartments and the reported internal compartment data. Experimental data are reported fcr two port types and two baffle orientations. with data for the case of an inlet compartment impingement baffle also being given . Port type is shown to have a small effect on compartment coefficients, these being largely unaffected. Likewise, the outlet compartment average coefficients are slightly snaller than those for the inlet compartment, with the distribution of individual tube coefficients being similar. Baffle orientation has been shown to have no effect on average coefficients, but the distribution of the data is substantially affected. The use of an impingement baffle in the inlet compartment lessens the efect of baffle orientation on distribution . Recommendations are made for future work.
Resumo:
Analyzing geographical patterns by collocating events, objects or their attributes has a long history in surveillance and monitoring, and is particularly applied in environmental contexts, such as ecology or epidemiology. The identification of patterns or structures at some scales can be addressed using spatial statistics, particularly marked point processes methodologies. Classification and regression trees are also related to this goal of finding "patterns" by deducing the hierarchy of influence of variables on a dependent outcome. Such variable selection methods have been applied to spatial data, but, often without explicitly acknowledging the spatial dependence. Many methods routinely used in exploratory point pattern analysis are2nd-order statistics, used in a univariate context, though there is also a wide literature on modelling methods for multivariate point pattern processes. This paper proposes an exploratory approach for multivariate spatial data using higher-order statistics built from co-occurrences of events or marks given by the point processes. A spatial entropy measure, derived from these multinomial distributions of co-occurrences at a given order, constitutes the basis of the proposed exploratory methods. © 2010 Elsevier Ltd.
Resumo:
Analyzing geographical patterns by collocating events, objects or their attributes has a long history in surveillance and monitoring, and is particularly applied in environmental contexts, such as ecology or epidemiology. The identification of patterns or structures at some scales can be addressed using spatial statistics, particularly marked point processes methodologies. Classification and regression trees are also related to this goal of finding "patterns" by deducing the hierarchy of influence of variables on a dependent outcome. Such variable selection methods have been applied to spatial data, but, often without explicitly acknowledging the spatial dependence. Many methods routinely used in exploratory point pattern analysis are2nd-order statistics, used in a univariate context, though there is also a wide literature on modelling methods for multivariate point pattern processes. This paper proposes an exploratory approach for multivariate spatial data using higher-order statistics built from co-occurrences of events or marks given by the point processes. A spatial entropy measure, derived from these multinomial distributions of co-occurrences at a given order, constitutes the basis of the proposed exploratory methods. © 2010 Elsevier Ltd.