For the exponential response η_u = α + βe^γZ_u (u = 1,2,…,N) where α and β lie on the real line (-∞,∞), and γ is a positive integer; the designs are given which minimize the bias due to the inherent inability of the approximation function ŷ_u = Σ_j=0<sup>d</sub>b_je^jZ_u to fit such a model. Transformation to η_u = α + βx_u^γ and ŷ_u = Σ_j=0<sup>d</sub>b_jx_u^j facilitates the solution for minimum bias designs. The requirements for minimum bias designs follow along lines similar to those given by Box and Draper (J. Amer. Stat. Assoc., 54, 1959, p. 622). The minimum bias designs are obtained for specific values of N with a maximum protection level, γ_d*(N), for the parameter γ and an approximation function of degree d. These designs obtained possess several degrees of freedom in the choice of the design levels of the x_u or the Z_uu , which may be used to satisfy additional design requirements. It is shown that for a given N, the same designs which minimize bias for approximation functions of degree one also minimize bias for general degree d, with a decrease in γ_d*(N) as d increases. In fact γ_d*(N) = γ₁*(N) - d + 1, but with the decrease in γ_d*(N) is a compensating decrease in the actual level of the minimum bias. Furthermore, γ_d*(N) increases monotonically with N, thereby allowing the maximum protection level on 1 to be increased as desired by increasing N. In the course of obtaining solutions, some interesting techniques are developed for determining the nature of the roots of a polynomial equation which has several known coefficients and several variable coefficients. / Ph. D.

The primary objectives this investigation are: 1. To present a discussion of known submerged arc welding variables and their general affect on weld bead appearance 2. To design an experiment to investigate the affect on weld joint strength and hardness distribution across the weldment by using (1) Four different welding currents (450, 500, 550, and 650 amps). (2) Three different steels (M 1020, SAE 1045 and SAE 1095) and (3) Two different electrodes (Hobart type C-10 and C-20) as variables in the experiment. 3. To establish the welding technique and procedure to control welding variables. 4. To collect unavailable data about the submerged arc process by experimentation and statistically interpret this data. After the preliminary investigation, the author statistically designed the testing procedure for the randomized test specimens for hardness of weld zone and hear-affected zone and applied statistical technique to determine significant effects on weld quality due to the variables. The conclusions are given in chapter I. / Master of Science

In asthma therapy, the use of theophylline to prevent bronchial spasm and glucocorticoids to decrease inflammation is widely indicated. Apart from the acute asthma attack oral theophylline is treated for chronic therapy in order to minimize inflammation and to enhance the efficiency of corticosteroids and recover steroids’ anti-inflammatory actions in COPD treatment. The preferred application route for respiratory disease treatment is by inhalation, such as dry powder inhalers (DPI) being the delivery systems of first choice. As shown recently, there is an advantageous effect if the drugs are given simultaneously which is caused by a synergistic effect at the same target cell in the lung epithelia. Therefore, it seems rational to combine both substances in one particle. This type of particle has the advantage over a combination product containing both drugs in a physical mixture which occurs rather randomly deposition leading to API segregation and non-dose-uniformity. Dry powder inhalers (DPIs) is a type of therapeutic pharmaceutical formulations usually present in the solid form. Due to the nature of the solid-state, an understanding of chemical and physical properties must be established for acquiring optimum performance of the active pharmaceutical ingredients (APIs). In recent year, generation of DPIs is a destructive procedure to meet the micron size. Such processes are inefficient and difficult to control. Moreover, according to current researches on combination APIs formulation, this type of DPIs performed a greater variability in does delivery of each active, leading to poor bioavailability and limit clinical efficient. This result suggest that combination formulations require advanced quality and functionality of particles with suitable physicochemical properties. Hence, in order to production of binary and combination DPIs products, the aim of this study was to develop the spray drying and ultrasonic process for engineering of combination drug particles that will be delivered more efficiently and independently of dose variations to the lung. Microparticles were produced by spray drying or/and ultrasonic technique. The processing parameters and addition of excipients (polymers) were optimized using a full factorial design such that microparticles were produced in a narrow size range suitable for inhalation. Employing excipients resulted in high saturation environment leading to minimized sphere particles when compared to conventional solvent. Solid state characterization of microparticles using powder x-ray diffraction and differential scanning calorimetry indicated that the particles contained crystalline but no cocrystal. The combination particles comparable to or better than micronized drug when formulated as a powder blended with lactose. It was concluded that the use of HPMC enhanced crystallinity suitable for inhalation; and combination particles improved uniform distribution on the stage of NGI.

Ph. D.

As the Earth's atmosphere evolves due to human activity, today's modern industrial society relies significantly on the scientific community to foresee possible atmospheric complications such as the celebrated greenhouse effect. Scientists, in turn, rely on accurate measurements of the Earth Radiation Budget (ERB) in order to quantify changes in the atmosphere. The Thermal Radiation Group (TRG), a laboratory in the Department of Mechanical Engineering at Virginia Polytechnic Institute and State University, has been at the edge of technology designing and modeling ERB instruments. TRG is currently developing a new generation of thermoelectric detectors for ERB applications. These detectors consist of an array of thermocouple junction pairs that are based on a new thermopile technology using materials whose electro-thermophysical properties are not completely characterized. The objective of this investigation is to design experiments aimed at determining the electro-thermophysical properties of the detector materials. These properties are the thermal conductivity and diffusivity of the materials and the Seebeck coefficient of the thermocouple junctions. Knowledge of these properties will provide fundamental information needed for the development of optimally designed detectors that rigorously meet required design specifications. / Master of Science

In experiments with repeated measurements made on the same subjects, the repeated observations in time may be correlated. Therefore, the assumption of independent observations cannot be made in general. This thesis considers the experimental design with treatments in a two-way classification with a disproportionate number of subjects allocated to each treatment combination and repeated measurements made on the subjects. A procedure is shown to be applicable for computing an analysis under somewhat restrictive assumptions. It is assumed that the variances are equal for all times and the correlations in time are equal. The tests obtained are for the three-factor interaction, the two-factor interactions assuming the three-factor interaction zero, and the main effects assuming all interactions zero. The procedure requires the inverse of one matrix, some matrix multiplication, and the calculation of some standard sums of squares. / Master of Science

Cyber-Physical Systems represent a critical intersection of physical infrastructure and digital technologies. Ensuring the safety and reliability of these interconnected systems is vital for mitigating risks and enhancing overall system safety. In recent decades, the transportation domain has seen significant adoption of cyber-physical systems, such as automated vehicles. This dissertation will focus on the application of cyber-physical systems in transportation. Statistical learning techniques offer a powerful approach to analyzing complex transportation data, providing insights that enhance safety measures and operational efficiencies. This dissertation underscores the pivotal role of statistical learning in advancing safety within cyber physical transportation systems. By harnessing the power of data-driven insights, predictive modeling, and advanced analytics, this research contributes to the development of smarter, safer, and more resilient transportation systems. Chapter 2 proposes a novel stochastic jump-based model to capture the driving dynamics of safety-critical events. The identification of such events is challenging due to their complex nature and the high frequency kinematic data generated by modern data acquisition systems. This chapter addresses these challenges by developing a model that effectively represents the stochastic nature of driving behaviors and assume the happening of a jump process will lead to safety-critical situations. To tackle the issue of rarity in crash data, Chapter 3 introduces a variational inference of extremes approach based on a generalized additive neural network. This method leverages statistical learning to infer the distribution of extreme events, allowing for better generalization ability to unseen data despite the limited availability of crash events. By focusing on extreme value theory, this chapter enhances statistical learning's ability to predict and understand rare but high-impact events. Chapter 4 shifts focus to the safety validation of cyber-physical transportation systems, requiring a unique approach due to their advanced and complex nature. This chapter proposes a kernel-based method that simultaneously satisfies representativeness and criticality for safety verification. This method ensures that the safety evaluation process covers a wide range of scenarios while focusing on those most likely to lead to critical outcomes. In Chapter 5, a deep generative model is proposed to identify the boundary of safety-critical events. This model uses the encoder component to reduce high-dimensional input data into lower-dimensional latent representations, which are then utilized to generate new driving scenarios and predict their associated risks. The decoder component reconstructs the original high-dimensional case parameters, allowing for a comprehensive understanding of the factors contributing to safety-critical events. The chapter also introduces an adversarial perturbation approach to efficiently determine the boundary of risk, significantly reducing computational time while maintaining precision. Overall, this dissertation demonstrates the potential of using advanced statistical learning methods to enhance the safety and reliability of cyber-physical transportation systems. By developing innovative models and methodologies, this dissertation provides valuable tools and theoretical foundations for risk prediction, safety validation, and proactive management of transportation systems in an increasingly digital and interconnected world. / Doctor of Philosophy / Transportation is the foundation for modern society, cyber-physical systems are reshaping the future for automotive industry, holding a huge potential to make the transportation much safer and more efficient. Cyber-physical transportation systems are still in the phase of rapid development, ensuring the safety and reliability of these systems is crucial for its wide application. However, how to ensure safety for cyber-Physical Transportation System is still an open challenge. Statistical learning techniques offer a powerful way to analyze transportation data, providing insights that enhance safety. By leveraging data-driven insights, predictive modeling, and advanced analytics, this dissertation contributes to developing smarter, safer, and more resilient transportation systems. For better describing and identifying safety critical events, this dissertation propose a novel stochastic jump-based model helping to capture the dynamics of safety-critical events, a Variational Inference of Extremes approach to tackles the issue of limited crash data. Beside safety evaluation, a notable challenge for ensuring the safety of cyber-physical transportation system goes to how to test and develop robust control systems. To this end, Chapter 4 focuses on the safety validation of automated vehicles, proposing a kernel-based method that ensures both representativeness and criticality in safety verification. This approach covers a wide range of scenarios while concentrating on those most likely to lead to critical outcomes. Following the sampled cases, Chapter 5 proposes a data driven approach to identify the operational boundaries of safety-critical events. Overall, this dissertation demonstrates the potential of statistical learning to enhance transportation safety and reliability.

No description available.

The statistical problems of intercropping experiments (which involve the growing of two or more crops together) are investigated in this study. Measures of combined yield are discussed; the Land Equivalent Ratio (LER) is shown to be the 'best' index for intercropping. Problems that arise in the standardization of LER are investigated, and use of a single pair of divisors is recommended. The use of systematic designs are advocated for yield-density studies, to reduce the number of guard rows. A 3-way systematic design is proposed and methods of analysis are suggested. A regression model is employed for the combined yield data (LER), from which estimates of the optimum densities can be calculated. The study also deals with varietal trials in intercropping. Methods for reducing the large number of possible varietal combinations to be tested in the field and ways of reducing the block size are given. The field layout is discussed, and illustrated by examples. Stability measures that can be used in intercropping are derived and it is shown how they can be used in evaluating stable varietal combinations. It is also shown how information about the contribution to stability of each crop can be obtained. The best proportions of the component crops in the intercropping mixture is also investigated. Design and analysis for an experiment on proportions in conjunction with varying densities is given. / Ph. D. / incomplete_metadata

The aim of this dissertation is to study the role of attention in two important domains – news consumption and advertising. The World Economic Forum, in its Global Risks Report, has identified a “deteriorating global outlook” for the next decade. The top three contributors to this negative outlook are misinformation, climate change, and societal polarization. Specifically, the report predicts that as the technological landscape changes and polarization grows, “the truth will come under pressure” and “environmental risks could hit the point of no return.” (World Economic Forum, 2024). Therefore, the two most important imperatives facing the world today are combatting polarization through misinformation and promoting organizational social responsibility (by promoting organizations that work toward socially desirable outcomes like combatting climate change and ensuring social equity). This dissertation addresses both these issues through the lens of attention.Across 4 studies, this dissertation shows that while increased attention does help in spotting individual false claims, increasing consumers’ attention to news stories may not be a silver-bullet solution to combatting fake news narratives in longer-than-headline contexts. When people consume news stories, their impression of the story as a whole is an important determinant of how they perceive claims within that story and whether they are likely to share them. Importantly, the current work shows that greater attention might exacerbate the viral spread of false claims because people often rely on their heuristic judgments of the news stories in which they first encountered a claim to determine sharing intentions. This result underscores the importance of revisiting regulatory and organizational strategies to combat misinformation. The current dissertation outlines how biometrics can be used as a robust method to identify news stories that are likely to give rise to viral claims (fake or otherwise), thereby enabling organizations to direct their fact-checking resources better. This dissertation also shows, across five studies, how brands and NPOs that are actively contributing to improving societal outcomes can better advertise their efforts. I study the role of attention in CSR (Corporate Social Responsibility) advertising. While normative reasoning suggests that providing consumers with more information about organizational efforts is better for improving consumer attitudes and behavior, we show that this is not always the case. Specifically, the current work explicates that while it is beneficial for brands to communicate their concrete resource contributions to a social cause in their CSR advertising, it is not always beneficial for NPOs to do so. The difference arises because when brands reveal a signal of resource commitment to the cause in a CSR ad, people notice this signal, and it makes people believe that the brand is more honest and sincere. On the other hand, when NPOs- often the ones working closest to the social causes on the ground - reveal their resource contributions to a cause in a CSR ad, people pay less attention to these signals in the ad. Consequently, they are less likely to infer any additional sincerity on the part of the NPO. / Business Administration/Marketing