Factors of Renewable Energy Deployment and Empirical Studies of United States Wind Energy

Considered essential for countries\u27 development, energy demand is growing worldwide. Unlike conventional sources, the use of renewable energy sources has multiple benefits, including increased energy security, sustainable economic growth, and pollution reduction, in particular greenhouse gas emissions. Nevertheless, there is a considerable difference in the share of renewable energy sources in national energy portfolios. This dissertation contains a series of studies to provide an outlook on the existing renewable energy deployment literature and empirically identify the factors of wind energy generation capacity and wind energy policy diffusion in the U.S. The dissertation begins with a systematic literature review to identify drivers and barriers which could help in understanding the diverging paths of renewable energy deployment for countries. In the analysis, economic, environmental, and social factors are found to be drivers, whereas political, regulatory, technical potential and technological factors are not classified as either a driver or a barrier (i.e., undetermined). Each main category contains several subcategories, among which only national income is found to have a positive impact, whereas all other subcategories are considered undetermined. No significant barriers to the deployment of renewable energy sources are found over the analyzed period. Wind energy deployment within the states related to environmental and economic factors was seldom discussed in the literature. The second study of the dissertation is thus focused on the wind energy deployment in the United States. Wind energy is among the most promising clean energy sources and the United States has led the world in per capita newly installed generation capacity since 2000. In the second study, using a fixed-effects panel data regression analysis, the significance of a number of economic and environmental factors are investigated for 39 states from 2000 to 2015. The results suggested that the increase in economic factors is related to a significant increase in the installed wind energy capacity, whereas, the increase in environmental factors is related to a significant decrease in the installed wind capacity. The final study explores the factors of diffusion of state- and local-level wind energy support policies which are considered fundamental factors of the continuum and development of wind power in the United States. To reveal the internal determinants of state\u27s wind energy policy diffusion, we further narrow the scope and control for the geographical region in the final study. We limit our analysis to seven neighboring Midwestern states, which are located in the center of United States wind energy corridor. Using data from 2008 to 2015, the study investigates the significance of the following internal factors: wind power potential, per capita gross state product, unemployment rate, per capita value of the agriculture sector, number of establishments in agricultural sector, and state government control. Through the addition of interaction terms, the study also considers the behavioral differences in the explanatory variables under Republican and non-Republican state governance. Our findings suggest that the economic development potential and related environmental benefits were the common motivation for state- and local-level policy makers. Lastly, technical terms and agricultural sector presence provides additional motives for the state level diffusion of wind energy policies. The findings of this dissertation are expected to contribute to the understanding of how countries and states might best stimulate and support renewable energy, and in particular wind energy, deployment

Enhancing the Monte Carlo Tree Search Algorithm for Video Game Testing

In this paper, we study the effects of several Monte Carlo Tree Search (MCTS) modifications for video game testing. Although MCTS modifications are highly studied in game playing, their impacts on finding bugs are blank. We focused on bug finding in our previous study where we introduced synthetic and human-like test goals and we used these test goals in Sarsa and MCTS agents to find bugs. In this study, we extend the MCTS agent with several modifications for game testing purposes. Furthermore, we present a novel tree reuse strategy. We experiment with these modifications by testing them on three testbed games, four levels each, that contain 45 bugs in total. We use the General Video Game Artificial Intelligence (GVG-AI) framework to create the testbed games and collect 427 human tester trajectories using the GVG-AI framework. We analyze the proposed modifications in three parts: we evaluate their effects on bug finding performances of agents, we measure their success under two different computational budgets, and we assess their effects on human-likeness of the human-like agent. Our results show that MCTS modifications improve the bug finding performance of the agents

Cerebral Infarction in an Elderly Patient with Coronavirus Disease

N/

Automated Video Game Testing Using Synthetic and Human-Like Agents

In this paper, we present a new methodology that employs tester agents to automate video game testing. We introduce two types of agents -synthetic and human-like- and two distinct approaches to create them. Our agents are derived from Reinforcement Learning (RL) and Monte Carlo Tree Search (MCTS) agents, but focus on finding defects. The synthetic agent uses test goals generated from game scenarios, and these goals are further modified to examine the effects of unintended game transitions. The human-like agent uses test goals extracted by our proposed multiple greedy-policy inverse reinforcement learning (MGP-IRL) algorithm from tester trajectories. MGPIRL captures multiple policies executed by human testers. These testers' aims are finding defects while interacting with the game to break it, which is considerably different from game playing. We present interaction states to model such interactions. We use our agents to produce test sequences, run the game with these sequences, and check the game for each run with an automated test oracle. We analyze the proposed method in two parts: we compare the success of human-like and synthetic agents in bug finding, and we evaluate the similarity between humanlike agents and human testers. We collected 427 trajectories from human testers using the General Video Game Artificial Intelligence (GVG-AI) framework and created three games with 12 levels that contain 45 bugs. Our experiments reveal that human-like and synthetic agents compete with human testers' bug finding performances. Moreover, we show that MGP-IRL increases the human-likeness of agents while improving the bug finding performance

The Industrial and Economic Impact of End-of-Life Telecommunications Products: A Comparative Analysis

This study analyzes the impact of obsolete telecommunication devices in particular, cellular phones on both industry and in economy. A background study will be conducted to depict the growing significance of the accumulating waste and potential material resources. Following this, the product structure of various cellular phones will be compared for their bill-of-materials and materials. The study will then propose an efficient end-of-life solution methodology that would provide economic and environmental gain. Related literature review and the gaps in the end-of-life processing solutions will also be included in the study. Solution for design-for-environment including other lessons learned from the research will be provided as a guideline for further progress. This study provides crucial information about calculating in-stock metals, aids in recycling decisions, aids in prioritizing disassembly, bill -of-materials of a cell phone for disassembly sequence, legislation in the Unites Estates, and how changing design will affect the industry

survey among thesis supervisors at a large German university hospital

Objectives: To identify underlying causes for failure of medical thesis projects and the constantly high drop-out rate in Germany from the supervisors' perspective and to compare the results with the students' perspective. Setting: Cross-sectional survey. Online questionnaire for survey of medical thesis supervisors among the staff of Charité—Universitätsmedizin Berlin, Germany. Published, earlier longitudinal survey among students for comparison. Participants: 1069 thesis supervisors participated. Data extraction and synthesis: Data are presented using descriptive statistics, and the χ2 test served to compare the results among supervisors with the earlier data from the longitudinal survey of doctoral students. Primary and secondary outcomes: Not applicable. This survey is an observational study. Results: Of 3653 potential participants, 1069 (29.3%) supervising 3744 doctoral candidates participated in the study. Supervisors considered themselves to be highly motivated and to offer adequate supervision. On the other hand, 87% stated that they did not feel well prepared for thesis supervision. Supervisors gave lack of timeliness of doctoral students and personal differences (p=0.024 and p=0.001) as the main reasons for terminating thesis projects. Doctoral students predominantly mentioned methodological problems and difficult subjects as critical issues (p=0.001 and p<0.001). Specifically, students felt ill prepared for the statistical part of their research—49.5% stated that they never received statistical assistance, whereas 97% of supervisors claimed to help their students with statistical analysis. Conclusions: The authors found that both thesis supervisors and medical students feel ill prepared for their roles in the process of a medical dissertation. Contradictory reasons for terminating medical thesis projects based on supervisors' and students' self- assessment suggest a lack of communication and true scientific collaboration between supervisors and doctoral students as the major underlying issue that requires resolution

A Statistical Approach for the Continuous Improvement of the Energy Utilization in the Technology Building Laboratories

This project focuses on improving the electricity energy utilization in the Technology Building Laboratories. School of Engineering has spent a significant amount of capital irrigating the computers in the labs. In our study, we found that the electricity expense on each student is $115.95 per year, and the total electricity expense on students is$25,736 annually. Since number of students going to the labs is normally distributed, we try to explore methods of increasing number of students utilizing the computers to decrease the per capita electricity expense, and thus an optimized energy utilization will be achieved