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As a useful tool for multivariate analysis, sufficient dimension reduction (SDR) aims to reduce the predictor dimensionality while simultaneously keeping the full regression information, or some specific aspects of the regression information, between the response and the predictor. When the goal is to retain the information about the regression mean, the target of the inference is known as the central mean space (CMS). Ordinary least squares (OLS) is a popular estimator of the CMS, but it has the limitation that it can recover at most one direction in the CMS. In this dissertation, we introduce two new estimators of the CMS: the sliced OLS and the hybrid OLS. Both estimators can estimate multiple directions in the CMS. The dissertation is organized as follows. Chapter 1 provides a literature review about basic concepts and some traditional methods in SDR. Motivated from the popular SDR method called sliced inverse regression, sliced OLS is proposed as the first extension of OLS in Chapter 2. The asymptotic properties of sliced OLS, order determination, as well as testing predictor contribution through sliced OLS are studied in Chapter 2 as well. It is well-known that slicing methods such as sliced inverse regression may lead to different results with different number of slices. Chapter 3 proposes hybrid OLS as the second extension. Hybrid OLS shares the benefit of sliced OLS and recovers multiple directions in the CMS. At the same time, hybrid OLS improves over sliced OLS by avoiding slicing. Extensive numerical results are provided to demonstrate the desirable performances of the proposed estimators. We conclude the dissertation with some discussions about the future work in Chapter 4. / Statistics

Cellulose nanocrystal (CNC) has found application in a variety of novel products due to its spectrum of properties. Notably, the CNC-polymer systems have seen numerous applications in special materials like Pickering emulsions, foams and gels etc. CNC interacts with different polymers to a different extent. These interactions include molecular level and bulk interactions. Subsequently, they modify the interfacial properties. Though vibrant, the CNC-polymer molecular interaction is still unclear. We took this void in our understanding as our motivation to explore these interactions. In this work, we tried to understand why CNC interacts differently with different polymers and what drives the adsorption of polymer on CNC. Our work can also help us to understand the configurations and origins of CNC-polymer system properties. The broad range of length and time scales covered by this physical process requires a multiscale simulation approach. In this thesis, we start with the all-atom molecular simulation and focus on the specific energetic interactions between CNC surfaces and unrealistically short polymer chains. In future work, we will build on this model and develop a multiscale modeling approach for capturing the full scope of CNC-polymer interaction, including the configuration and dynamics of realistic long polymer chains around CNCs. We propose that there are two driving forces for adsorption based on the free energy difference values obtained via PMF (potential of mean force) calculations done on eight systems with different physical components. Overall, we conclude that the balance between polymer's ability to form hydrogen bonds with the surface and their interactions with the bulk solvent control the adsorption and desorption phenomenon. A larger coarse-grained model developed from our simulations will help to understand these systems better. This presented work deals with the specific energy interactions and information which we will need for the systematic coarse-graining of these systems. / Thesis / Master of Applied Science (MASc)

As one of the kernel components of ITS technology, Travel Time Estimation (TTE) has been a high-interest topic in highway operation and management for years. Out of numerous vehicle detection technologies being applied in this project, intrusive loop detector, as the representative of spot measurement devices, is the most common. The ultimate goal of this dissertation is to seek a TTE approach based primarily on spot speed measurement and capable of successfully performing in a certain accuracy range under various traffic conditions. The provision of real-time traffic information could offer significant benefits for commuters looking to make optimum travel decisions. The proposed research effort attempts to characterize typical variability in traffic conditions using traffic volume data obtained from loop detectors on I-66 Virginia during a 3-month period. The detectors logged time-mean speed, volume, and occupancy measurements for each station and lane combination. Using these data, the study examines the spatiotemporal link and path flow variability of weekdays and weekends. The generation of path flows is made through the use of a synthetic maximum likelihood approach. Statistical Analysis of Variance (ANOVA) tests are performed on the data. The results demonstrate that in terms of link flows and total traffic demand, Mondays and Fridays are similar to core weekdays (Tuesdays, Wednesdays, and Thursdays). In terms of path flows, Fridays appear to be different from core weekdays. A common procedure for estimating roadway travel times is to use either queuing theory or shockwave analysis procedures. However, a number of studies have claimed that deterministic queuing theory and shock-wave analysis are fundamentally different, producing different delay estimates for solving bottleneck problems. Chapter 5 demonstrates the consistency in the delay estimates that are derived from both queuing theory and shock-wave analysis and highlights the common errors that are made in the literature with regards to shock-wave analysis delay estimation. Furthermore, Chapter 5 demonstrates that the area between the demand and capacity curves can represent the total delay or the total vehicle-hours of travel if the two curves are spatially offset and queuing theory has its advantages on this because of its simplicity. As the established relationship between time-mean and space-mean speed is suitable for estimating time-mean speeds from space-mean speeds in most cases, it is also desired to estimate the space-mean speeds from time-mean speeds. Consequently, Chapter 6 develops a new formulation that utilizes the variance of the time-mean speed as opposed to the variance of the space-mean speed for the estimation of space-mean speeds. This demonstrates that the space-mean speeds are estimated within a margin of error of 0 to 1 percent. Furthermore, it develops a relationship between the space- and time-mean speed variance and between the space-mean speed and the spatial travel-time variance. In addition, the paper demonstrates that both the Hall and Persaud and the Dailey formulations for estimating traffic stream speed from single loop detectors are valid. However, the differences in the derivations are attributed to the fact that the Hall and Persaud formulation computes the space-mean speed (harmonic mean) while the Dailey formulation computes the time-mean speed (arithmetic mean). Chapter 7 focuses on freeway Travel Time Estimation (TTE) algorithms that are based on spot speed measurements. Several TTE approaches are introduced including a traffic dynamics TTE algorithm that is documented in literature. This traffic dynamics algorithm is analyzed, highlighting some of its drawbacks, followed by some proposed corrections to the traffic dynamics formulation. The proposed approach estimates traffic stream density from occupancy measurements, as opposed to flow measurements, at the onset of congestion. Next, the study validates the proposed model using field data from I-880 and simulated data. Comparison of five different TTE algorithms is conducted. The comparison demonstrates that the proposed approach is superior to the TTE traffic dynamics approach. Particularly, a multi-link simulation network is built to test spot-speed-measurement TTE performance on multi links, as well as the data smoothing technique's effect on TTE accuracy. Findings further prove advantages of utilizing space-mean speed in TTE rather than time-mean speed. In summary, a feasible TTE procedure that is adaptive to various traffic conditions has been established. Since each approach would under-/over-estimate travel time depending on the concrete traffic condition, different models will be selected to ensure TTE's accuracy window. This approach has broad applications because it is based on popular loop detectors. / Ph. D.

This thesis compared macrotexture measurements obtained using the volumetric method (Sand Patch) and three laser-based devices: MGPS system, ICC laser profiler, and Circular Texture Meter (CTMeter). The study used data from three sources: two controlled experiments conducted at the Virginia Smart Road, field data collected on eight newly constructed hot-mix-asphalt (HMA) roadway surfaces, and data collected on airport surfaces at the Wallops flight facility, Virginia. The data collected at the Virginia Smart Road, a controlled-access two-lane road that includes various HMA and concrete surfaces, was used for the main analysis. The other two sets of data were used for verification and validation of the model developed. The analysis of the data collected at the Virginia Smart Road showed that the CTMeter mean profile depth (MPD) has the highest correlation with the volumetric (Sand Patch) mean texture depth (MTD). Furthermore, texture convexity had a significant effect on the correlation between the measurements obtained with different devices. Two sets of models for converting the laser-based texture measurements to an estimated MTD (ETD) were developed. One set of equations considered all the data collected at the Virginia Smart Road, and the other excluded the measurements on the Open-Graded Friction Course (OGFC). The developed models were tested using measurements collected at eight roadway sections throughout Virginia and the Wallops flight facility. The model, excluding the OGFC section, was successfully applied to other sites. / Master of Science

The purpose of this thesis is to broaden the tools and knowledge available for understanding the behavior of metals under irradiation to aid in the pursuit of advanced materials for deployment in Generation IV (Gen-IV) nuclear reactor designs. A mean-field study is conducted on a body-centered cubic (BCC) A-B binary metal alloy system. The performance of the simulated metal system is measured by assessing the degree of segregation that occurs at the grain boundary (GB) in the center of the one-dimensional simulation box. This mean-field method was developed using rate theory equations to observe the diffusion of defects and solute atoms in the binary BCC alloy modeled after a section of planes in the <100> direction of α-iron. The method in this thesis is adapted from a previous radiation-induced segregation (RIS) study that was similarly validated against thermal segregation isotherms. This adapted simulation code was used to study RIS by varying the initial values and conditions across ranges relevant to Generation IV reactor designs. The simulations run with this code were centered around segregation energy and the diffusion coefficient relationships between defects and solute atoms. The most influential conditions applied to both the segregation energy and diffusion coefficient relationship test suites were the temperature and dose rate. The interplay of the various segregation energies, manipulated diffusion coefficients, temperatures, and dose rates is explored in this thesis. The code used in this thesis is presented as a modular framework for further parameter study with a clear direction for more complex alloys. / Master of Science / The growing electricity demand for more efficient, safe, reliable, and sustainable means of power generation requires research and subsequent implementation of advanced Generation IV (Gen-IV) nuclear reactor designs. These proposed designs operate under significantly more strenuous conditions from the perspective of materials used in constructing the reactor. Materials inside the reactor will experience temperatures, pressures, and radiation doses greatly exceeding those of previous generations: Gen II through III+. Metals are employed in almost every component inside a reactor and are particularly susceptible to the demanding conditions due to their tendency to lose their ductility under these stressors. This thesis presents a diffusion-based code that models a binary metal alloy under conditions similar to those expected in Gen-IV reactors. The results of the code give insight into the prevalence of a phenomenon known as radiation induced segregation (RIS) in metals under these Gen-IV relevant conditions. The values input into the code have significant effects on the resulting RIS behavior of the metal alloy. This thesis presents correlations between the initial parameters and the amount of segregation this alloy experiences. The results of this thesis allow a sort of mapping of material parameters and operating conditions so that materials can be designed for optimal performance over the lifespan of the next generation of nuclear reactors. The code in this thesis was developed with the expectation that its modularity would be expanded upon to apply to more complex alloys under a broader range of initial conditions.

Yes / This paper investigates the impact of investor sentiment on the mean-variance relationship in 14 European stock markets. Applying three approaches to define investors’ neutrality and determine high and low sentiment periods, we find that individual investors’ increased presence and trading over high-sentiment periods would undermine the risk-return tradeoff. More importantly, we report that investors’ optimism (pessimism) is more determined by their normal sentiment state, represented by the all-period average sentiment level, rather than the neutrality value set in sentiment surveys.

Yes / This paper investigates the role of institutional investor sentiment in the mean–variance relation. We find market returns are negatively (positively) related to market’s conditional volatility over bullish (bearish) periods. The evidence indicates institutional investors to be sentiment traders as well.