Stochastic inversion of tracer test and electrical geophysical data to estimate hydraulic conductivities.

Quantifying the spatial configuration of hydraulic conductivity (K) in heterogeneous geological environments is essential for accurate predictions of contaminant transport, but is difficult because of the inherent limitations in resolution and coverage associated with traditional hydrological measurements. To address this issue, we consider crosshole and surface-based electrical resistivity geophysical measurements, collected in time during a saline tracer experiment. We use a Bayesian Markov-chain-Monte-Carlo (McMC) methodology to jointly invert the dynamic resistivity data, together with borehole tracer concentration data, to generate multiple posterior realizations of K that are consistent with all available information. We do this within a coupled inversion framework, whereby the geophysical and hydrological forward models are linked through an uncertain relationship between electrical resistivity and concentration. To minimize computational expense, a facies-based subsurface parameterization is developed. The Bayesian-McMC methodology allows us to explore the potential benefits of including the geophysical data into the inverse problem by examining their effect on our ability to identify fast flowpaths in the subsurface, and their impact on hydrological prediction uncertainty. Using a complex, geostatistically generated, two-dimensional numerical example representative of a fluvial environment, we demonstrate that flow model calibration is improved and prediction error is decreased when the electrical resistivity data are included. The worth of the geophysical data is found to be greatest for long spatial correlation lengths of subsurface heterogeneity with respect to wellbore separation, where flow and transport are largely controlled by highly connected flowpaths.

Robust parametric indirect estimates of the expected cost of a hospital stay with covariates and censored data.

We consider the problem of estimating the mean hospital cost of stays of a class of patients (e.g., a diagnosis-related group) as a function of patient characteristics. The statistical analysis is complicated by the asymmetry of the cost distribution, the possibility of censoring on the cost variable, and the occurrence of outliers. These problems have often been treated separately in the literature, and a method offering a joint solution to all of them is still missing. Indirect procedures have been proposed, combining an estimate of the duration distribution with an estimate of the conditional cost for a given duration. We propose a parametric version of this approach, allowing for asymmetry and censoring in the cost distribution and providing a mean cost estimator that is robust in the presence of extreme values. In addition, the new method takes covariate information into account.

Use of LIDAR-based Elevation Data for Highway Drainage Analysis: A Qualitative Assessment, 2003

US Geological Survey (USGS) based elevation data are the most commonly used data source for highway hydraulic analysis; however, due to the vertical accuracy of USGS-based elevation data, USGS data may be too “coarse” to adequately describe surface profiles of watershed areas or drainage patterns. Additionally hydraulic design requires delineation of much smaller drainage areas (watersheds) than other hydrologic applications, such as environmental, ecological, and water resource management. This research study investigated whether higher resolution LIDAR based surface models would provide better delineation of watersheds and drainage patterns as compared to surface models created from standard USGS-based elevation data. Differences in runoff values were the metric used to compare the data sets. The two data sets were compared for a pilot study area along the Iowa 1 corridor between Iowa City and Mount Vernon. Given the limited breadth of the analysis corridor, areas of particular emphasis were the location of drainage area boundaries and flow patterns parallel to and intersecting the road cross section. Traditional highway hydrology does not appear to be significantly impacted, or benefited, by the increased terrain detail that LIDAR provided for the study area. In fact, hydrologic outputs, such as streams and watersheds, may be too sensitive to the increased horizontal resolution and/or errors in the data set. However, a true comparison of LIDAR and USGS-based data sets of equal size and encompassing entire drainage areas could not be performed in this study. Differences may also result in areas with much steeper slopes or significant changes in terrain. LIDAR may provide possibly valuable detail in areas of modified terrain, such as roads. Better representations of channel and terrain detail in the vicinity of the roadway may be useful in modeling problem drainage areas and evaluating structural surety during and after significant storm events. Furthermore, LIDAR may be used to verify the intended/expected drainage patterns at newly constructed highways. LIDAR will likely provide the greatest benefit for highway projects in flood plains and areas with relatively flat terrain where slight changes in terrain may have a significant impact on drainage patterns.

The problem of nitrogen disposal in the obese

Amino-N is preserved because of the scarcity and nutritional importance of protein. Excretion requires its conversion to ammonia, later incorporated into urea. Under conditions of excess dietary energy, the body cannot easily dispose of the excess amino-N against the evolutively adapted schemes that prevent its wastage; thus ammonia and glutamine formation (and urea excretion) are decreased. High lipid (and energy) availability limits the utilisation of glucose, and high glucose spares the production of ammonium from amino acids, limiting the synthesis of glutamine and its utilisation by the intestine and kidney. The amino acid composition of the diet affects the production of ammonium depending on its composition and the individual amino acid catabolic pathways. Surplus amino acids enhance protein synthesis and growth, and the synthesis of non-protein-N-containing compounds. But these outlets are not enough; consequently, less-conventional mechanisms are activated, such as increased synthesis of NO∙ followed by higher nitrite (and nitrate) excretion and changes in the microbiota. There is also a significant production of N(2) gas, through unknown mechanisms. Health consequences of amino-N surplus are difficult to fathom because of the sparse data available, but it can be speculated that the effects may be negative, largely because the fundamental N homeostasis is stretched out of normalcy, forcing the N removal through pathways unprepared for that task. The unreliable results of hyperproteic diets, and part of the dysregulation found in the metabolic syndrome may be an unwanted consequence of this N disposal conflict.

Automatic Decision-Oriented Mapping of Pollution Data

The paper deals with the development and application of the methodology for automatic mapping of pollution/contamination data. General Regression Neural Network (GRNN) is considered in detail and is proposed as an efficient tool to solve this problem. The automatic tuning of isotropic and an anisotropic GRNN model using cross-validation procedure is presented. Results are compared with k-nearest-neighbours interpolation algorithm using independent validation data set. Quality of mapping is controlled by the analysis of raw data and the residuals using variography. Maps of probabilities of exceeding a given decision level and ?thick? isoline visualization of the uncertainties are presented as examples of decision-oriented mapping. Real case study is based on mapping of radioactively contaminated territories.

Blind channel estimation and data detection using hidden Markov models theory

In this correspondence, we propose applying the hiddenMarkov models (HMM) theory to the problem of blind channel estimationand data detection. The Baum–Welch (BW) algorithm, which is able toestimate all the parameters of the model, is enriched by introducingsome linear constraints emerging from a linear FIR hypothesis on thechannel. Additionally, a version of the algorithm that is suitable for timevaryingchannels is also presented. Performance is analyzed in a GSMenvironment using standard test channels and is found to be close to thatobtained with a nonblind receiver.

Methods for the analysis of two dimensional measurement data of paper web

This work is devoted to the problem of reconstructing the basis weight structure at paper web with black{box techniques. The data that is analyzed comes from a real paper machine and is collected by an o®-line scanner. The principal mathematical tool used in this work is Autoregressive Moving Average (ARMA) modelling. When coupled with the Discrete Fourier Transform (DFT), it gives a very flexible and interesting tool for analyzing properties of the paper web. Both ARMA and DFT are independently used to represent the given signal in a simplified version of our algorithm, but the final goal is to combine the two together. Ljung-Box Q-statistic lack-of-fit test combined with the Root Mean Squared Error coefficient gives a tool to separate significant signals from noise.

A Monte Carlo approach to the inverse problem of diffuse pollution risk in agricultural catchments

The hydrological and biogeochemical processes that operate in catchments influence the ecological quality of freshwater systems through delivery of fine sediment, nutrients and organic matter. Most models that seek to characterise the delivery of diffuse pollutants from land to water are reductionist. The multitude of processes that are parameterised in such models to ensure generic applicability make them complex and difficult to test on available data. Here, we outline an alternative - data-driven - inverse approach. We apply SCIMAP, a parsimonious risk based model that has an explicit treatment of hydrological connectivity. we take a Bayesian approach to the inverse problem of determining the risk that must be assigned to different land uses in a catchment in order to explain the spatial patterns of measured in-stream nutrient concentrations. We apply the model to identify the key sources of nitrogen (N) and phosphorus (P) diffuse pollution risk in eleven UK catchments covering a range of landscapes. The model results show that: 1) some land use generates a consistently high or low risk of diffuse nutrient pollution; but 2) the risks associated with different land uses vary both between catchments and between nutrients; and 3) that the dominant sources of P and N risk in the catchment are often a function of the spatial configuration of land uses. Taken on a case-by-case basis, this type of inverse approach may be used to help prioritise the focus of interventions to reduce diffuse pollution risk for freshwater ecosystems. (C) 2012 Elsevier B.V. All rights reserved.

Patents in the Information and Communications Technology Sector - Development Trends, Problem Areas and Pressures for Change

The patent system was created for the purpose of promoting innovation by granting the inventors a legally defined right to exclude others in return for public disclosure. Today, patents are being applied and granted in greater numbers than ever, particularly in new areas such as biotechnology and information andcommunications technology (ICT), in which research and development (R&D) investments are also high. At the same time, the patent system has been heavily criticized. It has been claimed that it discourages rather than encourages the introduction of new products and processes, particularly in areas that develop quickly, lack one-product-one-patent correlation, and in which theemergence of patent thickets is characteristic. A further concern, which is particularly acute in the U.S., is the granting of so-called 'bad patents', i.e. patents that do not factually fulfil the patentability criteria. From the perspective of technology-intensive companies, patents could,irrespective of the above, be described as the most significant intellectual property right (IPR), having the potential of being used to protect products and processes from imitation, to limit competitors' freedom-to-operate, to provide such freedom to the company in question, and to exchange ideas with others. In fact, patents define the boundaries of ownership in relation to certain technologies. They may be sold or licensed on their ownor they may be components of all sorts of technology acquisition and licensing arrangements. Moreover, with the possibility of patenting business-method inventions in the U.S., patents are becoming increasingly important for companies basing their businesses on services. The value of patents is dependent on the value of the invention it claims, and how it is commercialized. Thus, most of them are worth very little, and most inventions are not worth patenting: it may be possible to protect them in other ways, and the costs of protection may exceed the benefits. Moreover, instead of making all inventions proprietary and seeking to appropriate as highreturns on investments as possible through patent enforcement, it is sometimes better to allow some of them to be disseminated freely in order to maximize market penetration. In fact, the ideology of openness is well established in the software sector, which has been the breeding ground for the open-source movement, for instance. Furthermore, industries, such as ICT, that benefit from network effects do not shun the idea of setting open standards or opening up their proprietary interfaces to allow everyone todesign products and services that are interoperable with theirs. The problem is that even though patents do not, strictly speaking, prevent access to protected technologies, they have the potential of doing so, and conflicts of interest are not rare. The primary aim of this dissertation is to increase understanding of the dynamics and controversies of the U.S. and European patent systems, with the focus on the ICT sector. The study consists of three parts. The first part introduces the research topic and the overall results of the dissertation. The second part comprises a publication in which academic, political, legal and business developments that concern software and business-method patents are investigated, and contentiousareas are identified. The third part examines the problems with patents and open standards both of which carry significant economic weight inthe ICT sector. Here, the focus is on so-called submarine patents, i.e. patentsthat remain unnoticed during the standardization process and then emerge after the standard has been set. The factors that contribute to the problems are documented and the practical and juridical options for alleviating them are assessed. In total, the dissertation provides a good overview of the challenges and pressures for change the patent system is facing,and of how these challenges are reflected in standard setting.

Semantic data integration in a newspaper content management system

A newspaper content management system has to deal with a very heterogeneous information space as the experience in the Diari Segre newspaper has shown us. The greatest problem is to harmonise the different ways the involved users (journalist, archivists...) structure the newspaper information space, i.e. news, topics, headlines, etc. Our approach is based on ontology and differentiated universes of discourse (UoD). Users interact with the system and, from this interaction, integration rules are derived. These rules are based on Description Logic ontological relations for subsumption and equivalence. They relate the different UoD and produce a shared conceptualisation of the newspaper information domain.

Prospective motion correction of 3D echo-planar imaging data for functional MRI using optical tracking.

We evaluated the performance of an optical camera based prospective motion correction (PMC) system in improving the quality of 3D echo-planar imaging functional MRI data. An optical camera and external marker were used to dynamically track the head movement of subjects during fMRI scanning. PMC was performed by using the motion information to dynamically update the sequence's RF excitation and gradient waveforms such that the field-of-view was realigned to match the subject's head movement. Task-free fMRI experiments on five healthy volunteers followed a 2×2×3 factorial design with the following factors: PMC on or off; 3.0mm or 1.5mm isotropic resolution; and no, slow, or fast head movements. Visual and motor fMRI experiments were additionally performed on one of the volunteers at 1.5mm resolution comparing PMC on vs PMC off for no and slow head movements. Metrics were developed to quantify the amount of motion as it occurred relative to k-space data acquisition. The motion quantification metric collapsed the very rich camera tracking data into one scalar value for each image volume that was strongly predictive of motion-induced artifacts. The PMC system did not introduce extraneous artifacts for the no motion conditions and improved the time series temporal signal-to-noise by 30% to 40% for all combinations of low/high resolution and slow/fast head movement relative to the standard acquisition with no prospective correction. The numbers of activated voxels (p<0.001, uncorrected) in both task-based experiments were comparable for the no motion cases and increased by 78% and 330%, respectively, for PMC on versus PMC off in the slow motion cases. The PMC system is a robust solution to decrease the motion sensitivity of multi-shot 3D EPI sequences and thereby overcome one of the main roadblocks to their widespread use in fMRI studies.

Intelligent Internet Data Searching

In this thesis author approaches the problem of automated text classification, which is one of basic tasks for building Intelligent Internet Search Agent. The work discusses various approaches to solving sub-problems of automated text classification, such as feature extraction and machine learning on text sources. Author also describes her own multiword approach to feature extraction and pres-ents the results of testing this approach using linear discriminant analysis based classifier, and classifier combining unsupervised learning for etalon extraction with supervised learning using common backpropagation algorithm for multilevel perceptron.

Two-dimensional probabilistic inversion of plane-wave electromagnetic data: Methodology, model constraints and joint inversion with electrical resistivity data

Probabilistic inversion methods based on Markov chain Monte Carlo (MCMC) simulation are well suited to quantify parameter and model uncertainty of nonlinear inverse problems. Yet, application of such methods to CPU-intensive forward models can be a daunting task, particularly if the parameter space is high dimensional. Here, we present a 2-D pixel-based MCMC inversion of plane-wave electromagnetic (EM) data. Using synthetic data, we investigate how model parameter uncertainty depends on model structure constraints using different norms of the likelihood function and the model constraints, and study the added benefits of joint inversion of EM and electrical resistivity tomography (ERT) data. Our results demonstrate that model structure constraints are necessary to stabilize the MCMC inversion results of a highly discretized model. These constraints decrease model parameter uncertainty and facilitate model interpretation. A drawback is that these constraints may lead to posterior distributions that do not fully include the true underlying model, because some of its features exhibit a low sensitivity to the EM data, and hence are difficult to resolve. This problem can be partly mitigated if the plane-wave EM data is augmented with ERT observations. The hierarchical Bayesian inverse formulation introduced and used herein is able to successfully recover the probabilistic properties of the measurement data errors and a model regularization weight. Application of the proposed inversion methodology to field data from an aquifer demonstrates that the posterior mean model realization is very similar to that derived from a deterministic inversion with similar model constraints.

Data Collection Agent Framework

Ohjelmistoteollisuudessa pitkiä ja vaikeita kehityssyklejä voidaan helpottaa käyttämällä hyväksi ohjelmistokehyksiä (frameworks). Ohjelmistokehykset edustavat kokoelmaa luokkia, jotka tarjoavat yleisiä ratkaisuja tietyn ongelmakentän tarpeisiin vapauttaen ohjelmistokehittäjät keskittymään sovelluskohtaisiin vaatimuksiin. Hyvin suunniteltujen ohjelmistokehyksien käyttö lisää suunnitteluratkaisujen sekä lähdekoodin uudelleenkäytettävyyttä enemmän kuin mikään muu suunnittelulähestymistapa. Tietyn kohdealueen tietämys voidaan tallentaa ohjelmistokehyksiin, joista puolestaan voidaan erikoistaa viimeisteltyjä ohjelmistotuotteita. Tässä diplomityössä kuvataan ohjelmistoagentteihin (software agents) perustuvaa ohjelmistokehyksen suunnittelua toteutusta. Pääpaino työssä on vaatimusmäärittelyä vastaavan suunnitelman sekä toteutuksen kuvaaminen ohjelmistokehykselle, josta voidaan erikoistaa erilaiseen tiedonkeruuseen kykeneviä ohjelmistoja Internet ympäristöön. Työn kokeellisessa osuudessa esitellään myös esimerkkisovellus, joka perustuu työssä kehitettyyn ohjelmistokehykseen.

Alteration and Contamination of Archaeological Ceramics: the Perturbation Problem

Alteration and contamination processes modify the chemical composition of ceramic artefacts. This is not restricted solely to the affected elements, but also affects general concentrations. This is due to the compositional nature of chemical data, enclosed by the restriction of unit sum. Since it is impossible to know prior to data treatment whether the original compositions have been changed by such processes, the methodological approach used in provenance studies must be robust enough to handle materials that might have been altered or contaminated. The ability of the logratio transformation proposed by Aitchison to handle compositional data is studied and compared with that of present data treatments. The logaratio transformation appears to offer the most robust approach