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Nature extends future: The mediating roles of affect and subjective vitality
No full textPast research has shown that nature experiences can slow down the perception of temporal duration and shift time perspective from the past to the present and future. Little is known, however, about how nature would influence people’s perception of future time left or future time perspective (FTP). In this research, I proposed that nature could expand people’s FTP. Across four studies, I found consistent support for this hypothesis. In Study 1, a correlational analysis revealed that connectedness to nature was positively associated with FTP in both American and Chinese samples. In Studies 2 and 3, participants who viewed 14 photos of natural scenes reported more expansive FTP than those who watched 14 photos of urban scenes. Moreover, both positive (vs. negative) affect (Study 2) and subjective vitality (Study 3) mediated the effect of nature on FTP. In Study 4, I tested the link between nature and FTP at the macro level by analyzing large-scale data on 1686 U.S. counties (N = 772, 956), demonstrating that people living in greener counties believed they would live longer and perceived themselves as having a longer future. Together, these findings indicate that nature can broaden people’s subjective sense of time availability
On the Nash Problem over 3-Fold Terminal Singularities
No full textIn this dissertation, we study the Nash problem over terminal singularities in dimension 3, or 3-fold terminal singularities. In particular, for 3-fold terminal singularities, we propose two conjectures that aim to characterize their induced Nash valuations. (A) Any prime divisor with discrepancy bounded by 1 induces a Nash valuation. (B) Any prime divisor with minimal discrepancy induces a Nash valuation. We prove that Conjecture A holds for toric terminal singularities in arbitrary dimension, and Conjecture B holds for 3-fold terminal singularities of type cAx/2. In the Gorenstein cases, we provide a partial result with examples for both of the conjectures
USING SELF-REGULATED STRATEGY DEVELOPMENT TO IMPROVE THE FRACTIONS SKILLS OF STUDENTS WITH AVERAGE AND LOW MATHEMATICS ACHIEVEMENT
No full textThe study investigated the effectiveness of using Self-Regulation Strategy Development (SRSD) in teaching fraction skills using an experimental research design, specifically a switching replications design. The participants consisted of 62 eighth-grade students, identified with average and low mathematics achievement, selected from a school in Al Batinah South Governorate, Oman. The participants were selected from a sample based on their mathematics achievement levels and then randomly assigned to two conditions (treatment and control group). To collect data, screening measures, a pre-post-test on fraction skills, and a social validity questionnaire were used. The results show significant differences between groups in fraction scores after receiving the intervention, indicating the effectiveness of using SRSD in teaching fraction skills. Further, the study found that SRSD is effective in helping students in the treatment group maintain fraction skills one month after intervention. The results from the self-reported questionnaire showed that the students provided positive views about how beneficial the intervention was for them. The results were discussed in terms of the role of SRSD fractions in facilitating learning for students with different levels of academic achievement and how their differences in learning environments and pre-existing differences may affect knowledge fade. Keywords: Self-regulation, fraction skills, mathematics achievement, students’ perspective
Experimental Analysis and Optimization of Battery Thermal Management Systems
No full textBatteries have become vital to daily life, with demand increasing due to the widespread adoptionof electric vehicles. As battery usage expands, ensuring safe and efficient operation is crucial. A key challenge is the gradual degradation of cell capacity, which accelerates over repeated charge and discharge cycles. Factors such as charging rates, ambient conditions, and excessive heat generation significantly impact battery longevity. In high-power applications like EVs, rapid charging and discharging can elevate cell temperatures beyond recommended limits, leading to increased degradation and potential safety risks, making the battery pack unusable. This study investigates various cooling strategies to regulate lithium-ion cell temperatures and prevent excessive heat buildup. By charging cells at different C-rates and continuously monitoring surface temperatures in a battery pack, the effectiveness of passive and active cooling mechanisms in preventing thermal runaway and uniform cell temperature is evaluated. Additionally, the study explores water cooling and PCM-based thermal regulation, analyzing their impact on temperature stability. The results provide insights into battery thermal management optimization, enhancing both safety and longevity in EVs and other high-demand applications. Furthermore, using the experimentally collected data, a thermal model is developed to predict temperature behavior during the charge cycle of a single cell. This model serves as a foundation for future improvements in battery pack thermal modeling, allowing for more accurate simulations and optimized cooling system designs. The findings contribute to the advancement of Battery Thermal Management Systems, ensuring reliable and efficient performance in nextgeneration energy storage solutions
Exploring Autism Support Programs at Four-Year Institutions in the United States
No full textAs increasing numbers of autistic students pursue four-year college degrees in the United States, many institutions have responded by developing autism support programs (ASPs). However, there remains limited research exploring what these programs offer, how decisions about services are made, and how administrators evaluate the impact of their work. Using a combination of survey data (n = 52) and follow-up interviews (n = 6) with ASP administrators, this study examined the types of supports being provided, the factors influencing programmatic decision-making, and administrators’ perceptions of which practices are most and least beneficial for their students. The results showed that while academic and social supports are widely prioritized, other essential areas, such as mental health, independent living, and career development, receive less consistent attention. Additionally, most decision-making processes were informed by staff collaboration, student feedback, and professional networks, though many programs continue to face barriers related to funding, staffing, and institutional backing. This study contributes to the limited body of research on ASPs and offers practical, real-world insights that can inform future research and improve the ways in which colleges and universities support autistic students
The Design of a Test Setup for the Testing and Evaluation of Rotary Shaft Seal Failure
No full textAn experimental setup was designed and assembled to test the performance of a rotary seal stack under industry conditions. The system is composed of five subsystems: the DUT and drive system, the pressure system, the cooling system, control and data acquisition, and the frame. The setup can hold up to 3 seals pairs that can be arranged in different configurations. The main parameters recorded for the system are the pressure differential, the DUT shaft speed, the torque applied, and the temperature of the working fluid, DUT surface, and DUT interior. It is imperative that each subsystem works together to meet different requirements of the rotary seal test setup.Rotary seals are seals used in machinery with rotating or oscillating motions around a rotating shaft. Rotary seals are widely used in various industries to increase long-term performance and the lifespan of mechanical equipment. The seals are designed to create a barrier around a rotating shaft and their purpose is to withstand high temperatures, hold high flow, and prevent leakage of fluids or lubricants out of machines while also protecting internal components from contaminants such as dust or mud. There are various cross section designs for rotary seals, and they can be made from many different types of materials. The main parameters determining performance and lifespan of the rotary seals are operating speeds and heat production from the seal lip. This setup focuses on being able to evaluate both the effect of operating speeds and heat production on the rotary seals throughout experimental tests. This thesis will explore the design and manufacturing process of the test setup for rotary shaft seals. The goal of the setup was to be able to test the rotary seals at high temperature, hold high flow, and operate at high speeds until failure of the seals. An iterative design process was followed to recreate operation conditions used in industry with the requirements of being able to control the shaft speed, the pressure, and maintain operating temperature. The test setup was able to record data on pressure, operating temperature, speed, flow, leakage rate, and current data to evaluate the performance of multiple differing rotary shaft seals until failure
Personal Online Safety for College Students with Intellectual and Developmental Disabilities A Scoping Review, Program Survey, and Single-Case Experimental Design
No full textCurrently, there are 361 Postsecondary Education Programs (PSE) across the U.S. that give individuals with intellectual and developmental disabilities (IDD) the opportunity of pursuing postsecondary education (Think College, 2025). Many of these programs are housed within institutions of higher education that require and encourage students to have internet access and use technology for academics, communication, and socialization. This dissertation includes three manuscripts. The first manuscript is a scoping review based on selected 24 articles that discussed teaching personal safety, online safety, and cyberbullying prevention skills. The data collected from the articles was organized into four emerging themes: cyberbullying, privacy of information, social skills for safe online interactions, and responsible use of the internet. Only a few of the articles focused on individuals with disabilities. The second manuscript is a national survey that was sent to coordinators of PSE programs to learn about their student engagement in risky online behaviors as well as the instructional practices of the program in regards to personal online safety. The results were based on data from 42 respondents. Findings indicate that about forty percent of students with IDD attending PSE programs have engaged in risky online behaviors related to sharing personal private information online, sending money or financial information to strangers, and sending or receiving sexually explicit messages or emails. Survey results also discuss PSE program staff perceptions on the increased vulnerability of students with IDD to online victimization. Finally, the third manuscript is a single case research design to teach personal online safety to college students with IDD attending a postsecondary education program. The intervention was based on Behavior Skills Training and focused on teaching students three appropriate ways to respond to messages requesting personal private information. Overall, results indicate that most of the participants showed an increase in appropriate responses after the intervention. As a whole, the three studies point to the need for teaching personal online safety skills and the impact that an online safety intervention can have on students’ behaviors
Smart Policy Design for COVID-19: A Deep Reinforcement Learning Framework
No full textMore than 700 million people became infected and about 7 million have died from COVID-19, making it one of the deadliest pandemics in history. It triggered severe economic recession around the world that disrupted multiple industries such as agriculture, manufacturing and tourism. Governments around the world responded by implementing interventions to control disease transmission, but their impact varied from one country to another. There have been numerous studies to understand the effectiveness of the interventions but there are considerable variations in the interpretation. However, it is evident that tailoring policies for individual regions makes them more impactful. In the research, we decided to focus on regions of the United States and treat each state individually because of their diversity. We employ deep reinforcement learning to handle the dynamic nature of the disease, specifically multi-agent reinforcement learning where individual agent handles distinct states in a shared space to mimic realistic environment. The agent intervention differed from the actual intervention most of the time. Overall suggestion is to implement intervention earlier with higher intensity then gradually reduce aggressiveness and maintain a moderate level of intensity. However, this will depend on how much we want to prioritize public health over national economy
DISCOVERING TIMED SEQUENTIAL PATTERNS FROM STATIC AND DYNAMIC TIMED SEQUENCE DATABASES
No full textMining sequential patterns is one of the data mining tasks that aims to find the subsequences that frequently occur in a specific timestamp order within a sequence database. For example, a patient’s health history database is a type of sequence database that records medical events (such as diagnoses and treatments) in chronological order. Discovering sequential patterns in such a database can help predict the symptoms of an impending heart attack, assist healthcare providers with diagnosis, enable timely treatment, and facilitate early intervention in critical cases. However, clinical decision-making requires not only pattern identification but also precise temporal forecasting of when symptoms may occur. This necessitates the discovery of sequential patterns that incorporate temporal information, referred to as timed sequential patterns (TSP). For example, patients typically develop high cholesterol first; within three to four days, this progresses to high blood pressure, followed by elevated body temperature after another one to two days. Ultimately, a heart attack may occur within the following one to two months. Identifying interesting, useful, and temporally accurate patterns is valuable for many real-world applications, such as illness symptom prediction, weather forecasting, and transportation and arrival time analysis. While several approaches have been proposed for mining sequential patterns, most overlook the temporal relationships between the events in the subsequence. As a result, they fail to discover patterns that include this critical temporal information. To address this, some studies have incorporated temporal information into sequential patterns; however, challenges remain. For instance, some approaches require users to provide time-related inputs to identify temporal relations, rather than enabling algorithms to discover them automatically. Others consider only the first occurrence of a pattern when calculating temporal relations, ignoring other possible occurrences in the database. Furthermore, most existing sequential pattern mining algorithms assume static databases, despite the inherently dynamic nature of real-world data, which undergoes frequent insertions, deletions, and modifications. The dynamic nature complicates the efficient discovery of a complete set of sequential patterns without re-scanning the entire database. The problem is compounded by the accelerating growth of databases due to the widespread use of tracking devices, increased social media activity, and the general rise in stored data. Therefore, there is an increasing need for efficient and scalable algorithms that leverage parallelism to manage this rapid expansion effectively. This dissertation addresses these challenges through three novel algorithms: Minits+, Minits-AllOcc, and MinitsDays. Minits+ and Minits-AllOcc are designed to identify frequent timed sequential patterns in static sequence databases. While Minits+ does not consider all possible occurrences of a pattern, Minits-AllOcc does. MinitsDays, on the other hand, efficiently discovers frequent timed sequential patterns in dynamic sequence databases without requiring a complete database rescan. Due to the large scale of sequence data, significant computational power is required. To ensure scalability, parallelized versions, MMinits+, MMinits-AllOcc, and MMinitsDays, were developed for multi-core architectures. To evaluate the effectiveness and efficiency of the proposed algorithms, extensive theoretical and experimental evaluations conducted using both real-world and synthetic datasets under various parameters. The results demonstrate that the algorithms can efficiently discover accurate frequent timed sequential patterns. Minits+ outperformed the existing timed sequential pattern mining algorithm SID-PrefixSpan by 9% in execution time, while MinitsDays showed a 42% improvement over Minits-AllOcc. The parallel multi-core versions, MMinits+, MMinits-AllOcc, and MMinitsDays, achieved an average execution time improvement of 50% compared to their single-core counterparts
Examining the Predictors of STEM Degree Obtainment Within Social Cognitive Career Theory for Native American Undergraduate Students
No full textThis dissertation examines the predictors of STEM degree attainment among Native American undergraduate students using the Social Cognitive Career Theory (SCCT) framework. Leveraging a longitudinal dataset comprising Native American, Asian American, and White undergraduates, this study identifies the SCCT constructs most predictive of educational outcomes, including graduation with a STEM degree, graduation with a non-STEM degree, and earning no degree. Using elastic net multinomial regression, the analysis evaluates the influence of self-efficacy, outcome expectations, supports, barriers, and other SCCT components on these outcomes. The methodology incorporates measurement invariance testing to ensure the validity of cross-group comparisons of SCCT constructs. By integrating institutional records and self-reported survey data, this research addresses critical gaps in understanding the factors that facilitate or hinder STEM success for Native American students. The findings provide actionable insights for developing targeted interventions to support STEM persistence among underrepresented groups, with implications for broader educational policies and practices