Statistical analysis of the error associated with the simplification of the stratigraphy in geotechnical models

The uncertainties in the determination of the stratigraphic profile of natural soils is one of the main problems in geotechnics, in particular for landslide characterization and modeling. The study deals with a new approach in geotechnical modeling which relays on a stochastic generation of different soil layers distributions, following a boolean logic – the method has been thus called BoSG (Boolean Stochastic Generation). In this way, it is possible to randomize the presence of a specific material interdigitated in a uniform matrix. In the building of a geotechnical model it is generally common to discard some stratigraphic data in order to simplify the model itself, assuming that the significance of the results of the modeling procedure would not be affected. With the proposed technique it is possible to quantify the error associated with this simplification. Moreover, it could be used to determine the most significant zones where eventual further investigations and surveys would be more effective to build the geotechnical model of the slope. The commercial software FLAC was used for the 2D and 3D geotechnical model. The distribution of the materials was randomized through a specifically coded MatLab program that automatically generates text files, each of them representing a specific soil configuration. Besides, a routine was designed to automate the computation of FLAC with the different data files in order to maximize the sample number. The methodology is applied with reference to a simplified slope in 2D, a simplified slope in 3D and an actual landslide, namely the Mortisa mudslide (Cortina d’Ampezzo, BL, Italy). However, it could be extended to numerous different cases, especially for hydrogeological analysis and landslide stability assessment, in different geological and geomorphological contexts.

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

Predictability in Social Science, The statistical mechanics approach.

The subject of this work concerns the study of the immigration phenomenon, with emphasis on the aspects related to the integration of an immigrant population in a hosting one. Aim of this work is to show the forecasting ability of a recent finding where the behavior of integration quantifiers was analyzed and investigated with a mathematical model of statistical physics origins (a generalization of the monomer dimer model). After providing a detailed literature review of the model, we show that not only such a model is able to identify the social mechanism that drives a particular integration process, but it also provides correct forecast. The research reported here proves that the proposed model of integration and its forecast framework are simple and effective tools to reduce uncertainties about how integration phenomena emerge and how they are likely to develop in response to increased migration levels in the future.

Aerosol-cloud interactions studied with a chemistry-climate model

This study aims at a comprehensive understanding of the effects of aerosol-cloud interactions and their effects on cloud properties and climate using the chemistry-climate model EMAC. In this study, CCN activation is regarded as the dominant driver in aerosol-cloud feedback loops in warm clouds. The CCN activation is calculated prognostically using two different cloud droplet nucleation parameterizations, the STN and HYB CDN schemes. Both CDN schemes account for size and chemistry effects on the droplet formation based on the same aerosol properties. The calculation of the solute effect (hygroscopicity) is the main difference between the CDN schemes. The kappa-method is for the first time incorporated into Abdul-Razzak and Ghan activation scheme (ARG) to calculate hygroscopicity and critical supersaturation of aerosols (HYB), and the performance of the modied scheme is compared with the osmotic coefficient model (STN), which is the standard in the ARG scheme. Reference simulations (REF) with the prescribed cloud droplet number concentration have also been carried out in order to understand the effects of aerosol-cloud feedbacks. In addition, since the calculated cloud coverage is an important determinant of cloud radiative effects and is influencing the nucleation process two cloud cover parameterizations (i.e., a relative humidity threshold; RH-CLC and a statistical cloud cover scheme; ST-CLC) have been examined together with the CDN schemes, and their effects on the simulated cloud properties and relevant climate parameters have been investigated. The distinct cloud droplet spectra show strong sensitivity to aerosol composition effects on cloud droplet formation in all particle sizes, especially for the Aitken mode. As Aitken particles are the major component of the total aerosol number concentration and CCN, and are most sensitive to aerosol chemical composition effect (solute effect) on droplet formation, the activation of Aitken particles strongly contribute to total cloud droplet formation and thereby providing different cloud droplet spectra. These different spectra influence cloud structure, cloud properties, and climate, and show regionally varying sensitivity to meteorological and geographical condition as well as the spatiotemporal aerosol properties (i.e., particle size, number, and composition). The changes responding to different CDN schemes are more pronounced at lower altitudes than higher altitudes. Among regions, the subarctic regions show the strongest changes, as the lower surface temperature amplifies the effects of the activated aerosols; in contrast, the Sahara desert, where is an extremely dry area, is less influenced by changes in CCN number concentration. The aerosol-cloud coupling effects have been examined by comparing the prognostic CDN simulations (STN, HYB) with the reference simulation (REF). Most pronounced effects are found in the cloud droplet number concentration, cloud water distribution, and cloud radiative effect. The aerosol-cloud coupling generally increases cloud droplet number concentration; this decreases the efficiency of the formation of weak stratiform precipitation, and increases the cloud water loading. These large-scale changes lead to larger cloud cover and longer cloud lifetime, and contribute to high optical thickness and strong cloud cooling effects. This cools the Earth's surface, increases atmospheric stability, and reduces convective activity. These changes corresponding to aerosol-cloud feedbacks are also differently simulated depending on the cloud cover scheme. The ST-CLC scheme is more sensitive to aerosol-cloud coupling, since this scheme uses a tighter linkage of local dynamics and cloud water distributions in cloud formation process than the RH-CLC scheme. For the calculated total cloud cover, the RH-CLC scheme simulates relatively similar pattern to observations than the ST-CLC scheme does, but the overall properties (e.g., total cloud cover, cloud water content) in the RH simulations are overestimated, particularly over ocean. This is mainly originated from the difference in simulated skewness in each scheme: the RH simulations calculate negatively skewed distributions of cloud cover and relevant cloud water, which is similar to that of the observations, while the ST simulations yield positively skewed distributions resulting in lower mean values than the RH-CLC scheme does. The underestimation of total cloud cover over ocean, particularly over the intertropical convergence zone (ITCZ) relates to systematic defficiency of the prognostic calculation of skewness in the current set-ups of the ST-CLC scheme.rnOverall, the current EMAC model set-ups perform better over continents for all combinations of the cloud droplet nucleation and cloud cover schemes. To consider aerosol-cloud feedbacks, the HYB scheme is a better method for predicting cloud and climate parameters for both cloud cover schemes than the STN scheme. The RH-CLC scheme offers a better simulation of total cloud cover and the relevant parameters with the HYB scheme and single-moment microphysics (REF) than the ST-CLC does, but is not very sensitive to aerosol-cloud interactions.

Limiting theorems in statistical mechanics. The mean-field case.

The Curie-Weiss model is defined by ah Hamiltonian according to spins interact. For some particular values of the parameters, the sum of the spins normalized with square-root normalization converges or not toward Gaussian distribution. In the thesis we investigate some correlations between the behaviour of the sum and the central limit for interacting random variables.

Statistical mechanics of polymer models: rigorous results and applications

Monomer-dimer models are amongst the models in statistical mechanics which found application in many areas of science, ranging from biology to social sciences. This model describes a many-body system in which monoatomic and diatomic particles subject to hard-core interactions get deposited on a graph. In our work we provide an extension of this model to higher-order particles. The aim of our work is threefold: first we study the thermodynamic properties of the newly introduced model. We solve analytically some regular cases and find that, differently from the original, our extension admits phase transitions. Then we tackle the inverse problem, both from an analytical and numerical perspective. Finally we propose an application to aggregation phenomena in virtual messaging services.

Statistical shape modeling of pathological scoliotic vertebrae: A comparative analysis

Statistical shape models (SSMs) have been used widely as a basis for segmenting and interpreting complex anatomical structures. The robustness of these models are sensitive to the registration procedures, i.e., establishment of a dense correspondence across a training data set. In this work, two SSMs based on the same training data set of scoliotic vertebrae, and registration procedures were compared. The first model was constructed based on the original binary masks without applying any image pre- and post-processing, and the second was obtained by means of a feature preserving smoothing method applied to the original training data set, followed by a standard rasterization algorithm. The accuracies of the correspondences were assessed quantitatively by means of the maximum of the mean minimum distance (MMMD) and Hausdorf distance (H(D)). Anatomical validity of the models were quantified by means of three different criteria, i.e., compactness, specificity, and model generalization ability. The objective of this study was to compare quasi-identical models based on standard metrics. Preliminary results suggest that the MMMD distance and eigenvalues are not sensitive metrics for evaluating the performance and robustness of SSMs.

Natural Language Checking with Program Checking Tools

Written text is an important component in the process of knowledge acquisition and communication. Poorly written text fails to deliver clear ideas to the reader no matter how revolutionary and ground-breaking these ideas are. Providing text with good writing style is essential to transfer ideas smoothly. While we have sophisticated tools to check for stylistic problems in program code, we do not apply the same techniques for written text. In this paper we present TextLint, a rule-based tool to check for common style errors in natural language. TextLint provides a structural model of written text and an extensible rule-based checking mechanism.

Development of a model-based transient calibration process for diesel engine electronic control module tables – Part 1: data requirements, processing, and analysis

This is the first part of a study investigating a model-based transient calibration process for diesel engines. The motivation is to populate hundreds of parameters (which can be calibrated) in a methodical and optimum manner by using model-based optimization in conjunction with the manual process so that, relative to the manual process used by itself, a significant improvement in transient emissions and fuel consumption and a sizable reduction in calibration time and test cell requirements is achieved. Empirical transient modelling and optimization has been addressed in the second part of this work, while the required data for model training and generalization are the focus of the current work. Transient and steady-state data from a turbocharged multicylinder diesel engine have been examined from a model training perspective. A single-cylinder engine with external air-handling has been used to expand the steady-state data to encompass transient parameter space. Based on comparative model performance and differences in the non-parametric space, primarily driven by a high engine difference between exhaust and intake manifold pressures (ΔP) during transients, it has been recommended that transient emission models should be trained with transient training data. It has been shown that electronic control module (ECM) estimates of transient charge flow and the exhaust gas recirculation (EGR) fraction cannot be accurate at the high engine ΔP frequently encountered during transient operation, and that such estimates do not account for cylinder-to-cylinder variation. The effects of high engine ΔP must therefore be incorporated empirically by using transient data generated from a spectrum of transient calibrations. Specific recommendations on how to choose such calibrations, how many data to acquire, and how to specify transient segments for data acquisition have been made. Methods to process transient data to account for transport delays and sensor lags have been developed. The processed data have then been visualized using statistical means to understand transient emission formation. Two modes of transient opacity formation have been observed and described. The first mode is driven by high engine ΔP and low fresh air flowrates, while the second mode is driven by high engine ΔP and high EGR flowrates. The EGR fraction is inaccurately estimated at both modes, while EGR distribution has been shown to be present but unaccounted for by the ECM. The two modes and associated phenomena are essential to understanding why transient emission models are calibration dependent and furthermore how to choose training data that will result in good model generalization.

Development of a model-based transient calibration process for diesel engine electronic control module tables – Part 2: modelling and optimization

This is the second part of a study investigating a model-based transient calibration process for diesel engines. The first part addressed the data requirements and data processing required for empirical transient emission and torque models. The current work focuses on modelling and optimization. The unexpected result of this investigation is that when trained on transient data, simple regression models perform better than more powerful methods such as neural networks or localized regression. This result has been attributed to extrapolation over data that have estimated rather than measured transient air-handling parameters. The challenges of detecting and preventing extrapolation using statistical methods that work well with steady-state data have been explained. The concept of constraining the distribution of statistical leverage relative to the distribution of the starting solution to prevent extrapolation during the optimization process has been proposed and demonstrated. Separate from the issue of extrapolation is preventing the search from being quasi-static. Second-order linear dynamic constraint models have been proposed to prevent the search from returning solutions that are feasible if each point were run at steady state, but which are unrealistic in a transient sense. Dynamic constraint models translate commanded parameters to actually achieved parameters that then feed into the transient emission and torque models. Combined model inaccuracies have been used to adjust the optimized solutions. To frame the optimization problem within reasonable dimensionality, the coefficients of commanded surfaces that approximate engine tables are adjusted during search iterations, each of which involves simulating the entire transient cycle. The resulting strategy, different from the corresponding manual calibration strategy and resulting in lower emissions and efficiency, is intended to improve rather than replace the manual calibration process.

Proteome analysis in cardiovascular pathophysiology using Dahl rat model

Dahl salt-sensitive (DS) and salt-resistant (DR) inbred rat strains represent a well established animal model for cardiovascular research. Upon prolonged administration of high-salt-containing diet, DS rats develop systemic hypertension, and as a consequence they develop left ventricular hypertrophy, followed by heart failure. The aim of this work was to explore whether this animal model is suitable to identify biomarkers that characterize defined stages of cardiac pathophysiological conditions. The work had to be performed in two stages: in the first part proteomic differences that are attributable to the two separate rat lines (DS and DR) had to be established, and in the second part the process of development of heart failure due to feeding the rats with high-salt-containing diet has to be monitored. This work describes the results of the first stage, with the outcome of protein expression profiles of left ventricular tissues of DS and DR rats kept under low salt diet. Substantial extent of quantitative and qualitative expression differences between both strains of Dahl rats in heart tissue was detected. Using Principal Component Analysis, Linear Discriminant Analysis and other statistical means we have established sets of differentially expressed proteins, candidates for further molecular analysis of the heart failure mechanisms.

The Design of XML-Based Model and Experiment Description Languages for Network Simulation

The Simulation Automation Framework for Experiments (SAFE) is a project created to raise the level of abstraction in network simulation tools and thereby address issues that undermine credibility. SAFE incorporates best practices in network simulationto automate the experimental process and to guide users in the development of sound scientific studies using the popular ns-3 network simulator. My contributions to the SAFE project: the design of two XML-based languages called NEDL (ns-3 Experiment Description Language) and NSTL (ns-3 Script Templating Language), which facilitate the description of experiments and network simulationmodels, respectively. The languages provide a foundation for the construction of better interfaces between the user and the ns-3 simulator. They also provide input to a mechanism which automates the execution of network simulation experiments. Additionally,this thesis demonstrates that one can develop tools to generate ns-3 scripts in Python or C++ automatically from NSTL model descriptions.

Cardiac transplantation with hearts from donors after circulatory declaration of death: haemodynamic and biochemical parameters at procurement predict recovery following cardioplegic storage in a rat model

OBJECTIVES: Donation after circulatory declaration of death (DCDD) could significantly improve the number of cardiac grafts for transplantation. Graft evaluation is particularly important in the setting of DCDD given that conditions of cardio-circulatory arrest and warm ischaemia differ, leading to variable tissue injury. The aim of this study was to identify, at the time of heart procurement, means to predict contractile recovery following cardioplegic storage and reperfusion using an isolated rat heart model. Identification of reliable approaches to evaluate cardiac grafts is key in the development of protocols for heart transplantation with DCDD. METHODS: Hearts isolated from anaesthetized male Wistar rats (n = 34) were exposed to various perfusion protocols. To simulate DCDD conditions, rats were exsanguinated and maintained at 37°C for 15-25 min (warm ischaemia). Isolated hearts were perfused with modified Krebs-Henseleit buffer for 10 min (unloaded), arrested with cardioplegia, stored for 3 h at 4°C and then reperfused for 120 min (unloaded for 60 min, then loaded for 60 min). Left ventricular (LV) function was assessed using an intraventricular micro-tip pressure catheter. Statistical significance was determined using the non-parametric Spearman rho correlation analysis. RESULTS: After 120 min of reperfusion, recovery of LV work measured as developed pressure (DP)-heart rate (HR) product ranged from 0 to 15 ± 6.1 mmHg beats min(-1) 10(-3) following warm ischaemia of 15-25 min. Several haemodynamic parameters measured during early, unloaded perfusion at the time of heart procurement, including HR and the peak systolic pressure-HR product, correlated significantly with contractile recovery after cardioplegic storage and 120 min of reperfusion (P < 0.001). Coronary flow, oxygen consumption and lactate dehydrogenase release also correlated significantly with contractile recovery following cardioplegic storage and 120 min of reperfusion (P < 0.05). CONCLUSIONS: Haemodynamic and biochemical parameters measured at the time of organ procurement could serve as predictive indicators of contractile recovery. We believe that evaluation of graft suitability is feasible prior to transplantation with DCDD, and may, consequently, increase donor heart availability.

Multimodal Integration in Statistical Learning: Evidence from the McGurk Illusion

Recent advances in the field of statistical learning have established that learners are able to track regularities of multimodal stimuli, yet it is unknown whether the statistical computations are performed on integrated representations or on separate, unimodal representations. In the present study, we investigated the ability of adults to integrate audio and visual input during statistical learning. We presented learners with a speech stream synchronized with a video of a speaker's face. In the critical condition, the visual (e.g., /gi/) and auditory (e.g., /mi/) signals were occasionally incongruent, which we predicted would produce the McGurk illusion, resulting in the perception of an audiovisual syllable (e.g., /ni/). In this way, we used the McGurk illusion to manipulate the underlying statistical structure of the speech streams, such that perception of these illusory syllables facilitated participants' ability to segment the speech stream. Our results therefore demonstrate that participants can integrate audio and visual input to perceive the McGurk illusion during statistical learning. We interpret our findings as support for modality-interactive accounts of statistical learning.