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Adolescence is a transition period during which individuals have increasing autonomy in decision-making for themselves (Casey, Jones, and Hare, 2008), often choosing among options about which they have little knowledge and experience. This process of individuation and independence is reflected as real-world risk taking behaviors (Silveri et al., 2004), including higher motor accidents, unwanted pregnancies, sexually transmitted diseases, drug addictions, and death (Casey et al., 2008). The extent to which adolescents continue to display increased behaviors with negative consequences during this period of life depends critically on their ability to explore and learn potential consequences of actions within novel environments. This learning is not limited to the value of the outcome associated with making choices, but extends to the levels of risk taken in making those choices. While the existing adolescence literature has focused on neural substrates of risk preferences, how adolescents behaviorally and neurally learn about risks remain unknown. Success or failure to learn the potential variability of these consequences, or the risks involved, in ambiguous decisions is hypothesized to be a crucial process to allow the individuals to make decisions based on their risk preferences. The alternative in which adolescents fail to learn about the risks involved in their decisions leaves the adolescent in a state of continued exploration of the ambiguity, reflected as continued risk-taking behavior. This dissertation comprises 2 papers. The first paper is a perspective paper outlining a paradigm that risk taking behavior observed during adolescents may be a product of each adolescent's abilities to learn about risk. The second paper builds on the hypothesis of the perspective paper by first examining neural correlates of risk learning and quantifying individual risk learning abilities and then examining longitudinal risk learning developmental trajectories in relation to real-world risk-trajectories in adolescent individuals. / Doctor of Philosophy / Adolescence is a transition period during which individuals have increasing autonomy in decision-making for themselves, often choosing among options about which they have little knowledge and experience. This process of individuation and independence begins with the adolescent exploring their world and those options they are ignorant of. This is reflected as real-world risk-taking behaviors, including higher motor accidents, unwanted pregnancies, sexually transmitted diseases, drug addictions, and death. We hypothesized and tested the premise that whether adolescents who succeeded or fail to learn about the negative consequences of their actions while exploring will continue to partake in behaviors with negative consequences. This learning is not limited to the value of the outcome associated with making choices, but extends to the range of possible outcomes of the choices or the risks involved. Indeed, the failure to learn the risks involved in decisions with no known information show continued and greater risk-taking behavior, perhaps remaining in a state of continued exploration of the unknown.

Previous research presents a paradox in relation to the value of exerting personal control such that personal control is generally reinforcing, but its value may also be limited in some individuals and under certain circumstances. Across two studies, this dissertation takes a step towards exploring the limitations of perceived control at the process-level by manipulating perceived control via the provision of choice. Manuscript 1 examined limitations of perceived control in the context of effort costs and found that actual control, but not illusory control, may be necessary to enhance motivation in the context of physical effort, suggesting that perceived control may be limited in the context of effort. Manuscript 2 examined limitations of perceived control in relation to self-reported symptoms of anhedonia and found that responsivity to personal control was diminished in those with higher levels of anhedonia. Together these studies examined factors associated with limitations in appetitive personal control and suggest avenues for future research exploring perceived control processes and how they may interface with reward processes, which has potential implications for developing interventions to alleviate reward-related deficits found in anhedonia. / Doctor of Philosophy / Past research has shown that exerting personal control (actively influencing things in your life) is generally desired and motivating, but for some individuals and in some circumstances personal control may be less desirable or motivating (sometime people do not want to be in control). Across two studies, this dissertation explored why perceived control (the belief that one has influence over outcomes in one's life) might not be desired or motivating. In both studies, participants experienced perceived control during experiments when they were given choices within computerized games, believing themselves to have control over outcomes in the game. Manuscript 1 examined how perceived control may be less desirable when people must exert physical effort and found that people may be less inclined to believe they have control when their choice leads to a physical effort requirement. Manuscript 2 examined whether people want to be in control when they are experiencing anhedonia, a set of psychiatric symptoms that includes diminished motivation and reduced responses to reward (for example, paying less attention to rewards in the environment). This study found that people with anhedonia symptoms did not seem to want to be in control as much as psychologically healthy people. During the computerized game, people with anhedonia did not try to make their own choices when they had an opportunity to. Together these studies examined different factors associated with people not wanting to be in control or finding personal control less motivating. This research has implications for developing therapies for people with anhedonia, particularly symptoms related to not actively taking control.

Pile-supported geogrid-reinforced embankments are an exciting new foundation system that is utilized when sites are limited by a soft soil or clay. In this system, an embankment is supported by a bridging layer, consisting of granular fill and one or multiple layers of geogrid reinforcement. The bridging layer transfers the load to piles that have been driven into the soft soil or clay. The load from the embankment induces large deformations in the geogrid reinforcement, causing tensile forces in the ribs of the geogrid. Many of the current methods used to design geogrid reinforcement for this system simplify the approach by assuming that the reinforcement has a parabolic deformed shape. The purpose of this thesis is to thoroughly examine the behavior of the geogrid in a pile-supported embankment system, in an effort to determine the accuracy of the parabolic deformed shape, and identify the most important parameters that affect reinforcement design. The geogrid was analyzed using a three-dimensional model that included a cable net to represent the geogrid and linear springs to represent the soil underneath the geogrid. A larger pressure was applied to the geogrid regions that are directly above the pile caps so that arching effects could be considered, and the stiffness of the springs on top of the pile were stiffer to account for the thin layer of soil between the geogrid and the pile cap. A Mathematica algorithm was used to solve this model using the minimization of energy method. The results were compared to another model of this system that used a membrane to represent the geosynthetic reinforcement. Additionally, the maximum strain was compared to the strain obtained from a geosynthetic reinforcement design formula. A parametric study was performed using the Mathematica algorithm by varying the pile width, embankment pressure applied to the soil, embankment pressure applied to the pile, stiffness of the soil, stiffness of the soil on top of the pile, stiffness of the geogrid, geogrid orientation, rotational stiffness of the geogrid, and the layers of geogrid reinforcement. / Master of Science

Android is the most popular operating system and occupies close to 70% of the market share. With the growth in the usage of Android OS, the number of games also increased and the Android play store has over 500,000 games. Testing of Android games is done either manually or through some of the existing tools which automate some parts of this testing. Manual testing requires a great deal of effort and can be expensive to afford. The existing tools which automate testing do not make use of any domain knowledge. This can cause the testing to be ineffective as the game may involve complex strategies, intricate details, widgets, etc. Existing tools like Android Monkey and Time Machine generate random Android events, including gestures like touch, swipe, clicks, and other system-level events across the application. Some deep learning methods like Wuji were only created for combat-type games. These limitations make it imperative to create a testing paradigm that uses domain knowledge as well as is easy to use by a developer who doesn't have any machine or deep learning knowledge. In this work, we develop a tool called DRAG- Deep Reinforcement learning based Android Gamer - which leverages Reinforcement Learning to learn the requisite domain knowledge and play the game in a fashion like a human would. DRAG uses a unified Reinforcement Learning agent and a Unified Reinforcement Learning environment. It only customizes the action space for each game. This generalization is done in the following ways- 1) Record an 8-minute demo video of the game and capture the underlying Android action log. 2) Analyze the recorded video and the action log to generate an action space for the Reinforcement Learning Agent. The unified RL agent is trained by providing it the score and coverage as a reward and screenshots of the game as observed states. We chose a set of 19 different open-sourced games for evaluation of the created tool. These games differ in the action set required by each of them - some require tapping icons, some require swiping in random directions, and some require more complex actions which are a combination of different gestures. The evaluation of our tool outperformed state-of-the-art TimeMachine for all 19 games and outperformed Monkey in 16 of the 19 games. This strengthens the fact that Deep Reinforcement Learning can be used to test Android games and can provide better results than tools that make no use of any domain knowledge. / Master of Science / The popularity of the Android operating system has led to a significant increase in the number of available Android games, with over 500,000 games on the Android Play Store alone. However, ensuring the quality and functionality of these games has become a challenge. Traditional testing methods involve either time-consuming manual testing or the use of existing tools that lack the necessary domain knowledge to handle complex game mechanics effectively. To overcome these limitations, we propose a solution called DRAG: the Deep Reinforcement Learning-based Android Gamer. Our tool utilizes Reinforcement Learning (RL) to acquire the domain knowledge needed to play Android games in a manner similar to human players. Unlike other tools, DRAG incorporates a unified RL agent and environment that can be customized for each specific game.The process of customizing the action space involves two main steps. First, we record an 8-minute demonstration video of the game while capturing the underlying Android action log. Then, we analyze the video and action log to generate a tailored action space for the game. The unified RL agent is trained using rewards based on the game's score and coverage, while observed screenshots of the game serve as input states.\\We evaluated DRAG using a diverse set of 19 open-source games, each requiring different actions such as tapping icons, swiping in random directions, or complex combinations of gestures. Our results demonstrate that DRAG outperforms state-of-the-art tools like TimeMachine in all 19 games and outperforms Monkey in 16 of the 19 games. These findings highlight the effectiveness of Deep Reinforcement Learning for testing Android games and its ability to deliver better results compared to tools lacking domain knowledge.Our work introduces a new testing approach that combines RL and domain knowledge, providing a user-friendly solution for developers without extensive machine or deep learning expertise. By automating game testing to replicate human gameplay, DRAG offers the potential for more efficient and effective quality assurance in the Android gaming ecosystem.

A geotechnical composite foundation system that has become increasingly popular over the years is a column-supported, geosynthetic-reinforced embankment. This system consists of strong columns or piles placed in soft clay, a bridging layer of sand or sand and gravel, and one or more layers of geosynthetic reinforcement. It is often used in soft ground situations where there is a need for faster construction and/or where there are adjacent structures that would be affected by settlement caused by the new embankment. The geosynthetic reinforcement is placed in the bridging layer to help transfer the load to the columns and decrease the total and differential settlements. Current methods of analysis for this material are extremely simplified, and do not thoroughly model the behavior of the system. Therefore, a more comprehensive analysis needs to be conducted that will better predict the true effect of the geosynthetic layer or layers. In this thesis, one geosynthetic layer was considered. Models were developed using two different computer programs: Mathematica and ABAQUS. In Mathematica, the Rayleigh-Ritz method was used to approximate the deflections and tensile forces in the membrane. This method considered the geosynthetic reinforcement as a plate and minimized the total energy of the system. In ABAQUS, a finite element modeling program, the membrane was analyzed as a shell, and results were compared with some results from Mathematica. A parametric study was completed in Mathematica to determine the effects of different parameters. The parameters varied involved the geogrid properties (Poisson's ratio, modulus of elasticity, and thickness), the vertical load, the soil stiffness above the piles, the soil stiffness between the piles, the size of the piles, and the distance between the piles. / Master of Science

A number of theories attempt to explain the elements of the decision making process when one is faced with an ethical dilemma. Trevino's model (1986)posited a main effect of cognitive moral development (CMD) on ethical behavior, moderated by reinforcement contingencies. Past research has failed to examine the full spectrum of reinforcement contingencies: rewarding ethical behavior (RE), punishing unethical behavior (PU), rewarding unethical behavior (RU), and punishing ethical behavior (PE). It was hypothesized that RE and PU would encourage ethical behavior, while RU and PE would encourage unethical behavior. An additional hypothesis that has not been examined is that reinforcement contingencies would cause individuals who are at the conventional level of CMD to regress to earlier stages of moral reasoning. Support for these hypotheses was not found. Possible explanations for the results are discussed, including the nature of the task itself. / Master of Science

Alumina particles filled Ultra-high Molecular Weight Polyethylene (UHMWPE), with Al2O3 contents 0, 1, and 2.5 wt% were milled for up to 10 hours by the mechanical alloying (MA) process performed at room temperature to produce composite powders. Compression molding was utilized to produce sheets out of the milled powders. A partial phase transformation from orthorhombic and amorphous phases to monoclinic phase was observed to occur for both the un-reinforced and reinforced UHMWPE in the solid state, which disappeared after using compression molding to produce composite sheets. The volume fraction of the monoclinic phase increased with milling time, mostly at the expense of the amorphous phase. The melting temperature decreased as a function of milling time as a result of modifications in the UHMWPE molecular structure caused by the milling. At the same time, for a given alumina composition the activation energy of melting increased with milling time. Generally, the crystallinity of the molded sheets increased with milling time, and this caused the yield strength and elastic modulus to increase with milling time for a given alumina composition. However, the tensile strength and ductility remained about the same. / Master of Science