This diploma thesis deals with a static analysis of a highway bridge structure with respecting of construction stages. Computational models of the bridge were created using RFEM 6 software. Gradual prestressing of the structure was applied in the models. Construction stages analysis is also applied to the bridge models in combination with modal and stability analysis. This thesis also deals with time – dependent analysis, therefore influence of rheological properties of material.

The theme of the master’s thesis is detailed design bridge structure. The thesis is devoted to limit states assessment of the structure. The thesis includes a time analysis structure and a detailed solution influence of construction on its proposal.

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Warm dense matter (WDM) is an extreme state of matter induced by extreme conditions and characterized as an intermediary state between (high-pressure) condensed matter and plasma. It has sparked a lot of attention in recent years as a result of current innovations in experiments and theoretical methods for modeling such complex systems. Such conditions naturally occur in astrophysical objects such as the interiors of the planets, and in white and brown dwarfs. WDM can be created in the laboratory via various methods such as laser compression, Z-pinches and heated diamond anvil cells. This thesis describes the results obtained for many such systems across a range of conditions modeled using ab-initio simulation methods. The first testbed concerns the electronic structure and linear response of the carbon phases under high-pressure and warm dense matter conditions. The focus is on modeling inelastic x-ray scattering spectra across a range of conditions useful for the analysis and interpretation of x-ray Thomson scattering (XRTS) experiments. Another major goal is to improve the existing models to compute static properties such as the equation of state, density of states with the inclusion of highly accurate data from quantum Monte Carlo (QMC) simulations relevant at finite-temperatures. This approach improves the accuracy and is also computationally inexpensive compared to path integral Monte Carlo (PIMC) methods. Lastly, improvements in linear response theory relevant for XRTS are incorporated with the inclusion of local field corrections (LFC) and finite-temperature local field corrections (T-LFC) using data from QMC simulations.

This project investigates the failure of various CMOS circuits as a result of Time Dependent Dielectric Breakdown (TDDB) and explores design techniques to increase the mean time to failure (MTTF) of large-scale circuits. Time Dependent Dielectric Breakdown is a phenomenon where the oxide underneath the gate degrades as a result of the electric field in the material. Currently, there are few well documented design techniques that can increase lifetime, but with a tool chain I created called the MTTF Analyzing Program, or MAP, I was able to test circuits under various conditions in order to identify weak links, discover relationships, and reiterate on my design and see improvements and effects. The tool chain calculates power consumption, performance, temperature, and MTTF for a 'real life' circuit. Electric VLSI, an Electronic Design Automation tool, outputs a Spice file that yields parasitic quantities and spatial dimensions. LTspice, a high performance Spice simulator, was used to calculate the voltage and current data. Finally, I created MAP to monitor the voltage, current, and dimension data and process that in conjunction with HotSpot, a thermal modeling tool, to calculate a MTTF for each MOSFET. Analysis of the data from the software infrastructure showed that transistor sizing played a role in the MTTF. To maximize the MTTF of a transistor in a CMOS inverter, the activity of the pull-up transistor should be balanced with the transistor in the pull-down chain, ensuring the electric fields are balanced across both transistors. While it is impossible to completely balance an arbitrary CMOS circuit's activity for an arbitrary set of input signals, circuits can be intelligently skewed to help maximize the MTTF without increasing power consumption and without sacrificing circuit performance. Consequently, attaining a maximum MTTF does not come at a cost as it is possible to design a circuit with a high MTTF that performs better and uses less power than a circuit with low MTTF.

Aims: This paper extends previous studies in modeling time varying linear polarization due to axisymmetric magnetic fields in rotating stars. We use the Hanle effect to predict variations in net line polarization, and use geometric arguments to generalize these results to linear polarization due to other mechanisms. Methods: Building on the work of Lopez Ariste et al., we use simple analytic models of rotating stars that are symmetric except for an axisymmetric magnetic field to predict the polarization lightcurve due to the Hanle effect. We highlight the effects for the variable line polarization as a function of viewing inclination and field axis obliquity. Finally, we use geometric arguments to generalize our results to linear polarization from the weak transverse Zeeman effect. Results: We derive analytic expressions to demonstrate that the variable polarization lightcurve for an oblique magnetic rotator is symmetric. This holds for any axisymmetric field distribution and arbitrary viewing inclination to the rotation axis. Conclusions: For the situation under consideration, the amplitude of the polarization variation is set by the Hanle effect, but the shape of the variation in polarization with phase depends largely on geometrical projection effects. Our work generalizes the applicability of results described in Lopez Ariste et al., inasmuch as the assumptions of a spherical star and an axisymmetric field are true, and provides a strategy for separating the effects of perspective from the Hanle effect itself for interpreting polarimetric lightcurves.

Decision-making by one or more individuals to select a course of action is predicated on the values and preferences to identify, choose options, and finally select the option that is evaluated to be the “best option.” Decision theory provides the means to model and analyze both the processes and options available to the decision-makers. This dissertation assembled in three phases: 1) An effort to collect and review existing literature relating to the concept of expanding decision analysis options to provide a model of decision-making made with time-dependent factors along with uncertainty and risk. Further, adding the concept of a decision to update time-dependent decision data in a Bayesian fashion aids in modeling decision-making thought processes. This review included a total of 395 research artifacts. 2) Development of a technical approach using the information gathered in the literature review to guide planning for a decision-making simulation of individuals and organizations. The approach emphasizes creating a decision-making simulation framework with capabilities to model time-dependent factors, information processing and communication, and fuzzy-stochastic data. 3) Use of the technical approach to develop a simulation framework to simulate complex decision-making and work packages at multiple levels in an organization using time-dependent factors, information processing and communication, and fuzzy-stochastic data. Using a Discrete Event Simulation (DES) and Agent-Based Models (ABM) to simulate people and their interactions, this framework was then be used to simulate decision-making and work processes within an organization. Ultimately, the Agile Enterprise Simulation (AES) capability was created and demonstrated.

<p dir="ltr">The emergence of time-dependent data provides researchers with unparalleled opportunities to investigate disaggregated levels of safety performance on roadway infrastructures. A disaggregated crash risk analysis uses both time-dependent data (e.g., hourly traffic, speed, weather conditions and signal controls) and fixed data (e.g., geometry) to estimate hourly crash probability. Despite abundant research on crash risk analysis, coordinated signalized intersections continue to require further investigation due to both the complexity of the safety problem and the relatively small number of past studies that investigated the risk factors of coordinated signalized intersections. This dissertation aimed to develop robust crash risk prediction models to better understand the risk factors of coordinated signalized intersections and to identify practical safety countermeasures. The crashes first were categorized into three types (same-direction, opposite-direction, and right-angle) within several crash-generating scenarios. The data needed were organized in hourly observations and included the following factors: road geometric features, traffic movement volumes, speeds, weather precipitation and temperature, and signal control settings. Assembling hourly observations for modeling crash risk was achieved by synchronizing and linking data sources organized at different time resolutions. Three different non-crash sampling strategies were applied to the following three statistical models (Conditional Logit, Firth Logit, and Mixed Logit) and two machine learning models (Random Forest and Penalized Support Vector Machine). Important risk factors, such as the presence of light rain, traffic volume, speed variability, and vehicle arrival pattern of downstream, were identified. The Firth Logit model was selected for implementation to signal coordination practice. This model turned out to be most robust based on its out-of-sample prediction performance and its inclusion of important risk factors. The implementation examples of the recommended crash risk model to building daily risk profiles and to estimating the safety benefits of improved coordination plans demonstrated the model’s practicality and usefulness in improving safety at coordinated signals by practicing engineers.</p>

Indiana University-Purdue University Indianapolis (IUPUI) / Time-to-event outcomes are widely utilized in medical research. Assessing the cumulative effects of time-varying exposures on time-to-event outcomes poses challenges in statistical modeling. First, exposure status, intensity, or duration may vary over time. Second, exposure effects may be delayed over a latent period, a situation that is not considered in traditional survival models. Third, exposures that occur within a time window may cumulatively in uence an outcome. Fourth, such cumulative exposure effects may be non-linear over exposure latent period. Lastly, exposure-outcome dynamics may differ among groups defined by individuals' characteristics. These challenges have not been adequately addressed in current statistical models. The objective of this dissertation is to provide a novel approach to modeling group-specific dynamics between cumulative timevarying exposures and a time-to-event outcome. A framework of group-specific dynamic models is introduced utilizing functional time-dependent cumulative exposures within an etiologically relevant time window. Penalizedspline time-dependent Cox models are proposed to evaluate group-specific outcome-exposure dynamics through the associations of a time-to-event outcome with functional cumulative exposures and group-by-exposure interactions. Model parameter estimation is achieved by penalized partial likelihood. Hypothesis testing for comparison of group-specific exposure effects is performed by Wald type tests. These models are extended to group-specific non-linear exposure intensity-latency-outcome relationship and group-specific interaction effect from multiple exposures. Extensive simulation studies are conducted and demonstrate satisfactory model performances. The proposed methods are applied to the analyses of group-specific associations between antidepressant use and time to coronary artery disease in a depression-screening cohort using data extracted from electronic medical records.