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This study examined the experiences of recovery among African American college students participating in collegiate recovery programs (CRPs). A phenomenological qualitative approach was utilized to collect and analyze data. The purpose of this study explored the role of collegiate recovery programs in the recovery process for African American college students who identify as being in recovery from substance use disorders. The study answered the following research questions: 1. How does engagement in CRPs promote recovery for African American college students in recovery from substance use disorders? 2. How do CRPs enhance the recovery capital of African American college students in recovery from substance use disorders? 3. How does racial identity affect recovery capital for African Americans in the CRP pursuing recovery? To gain an in-depth understanding of participant recovery from substance use, data was collected through a background and demographic questionnaire (BDQ), semi-structured interviews, and a reflexive journal to gain rich, thick descriptions of their six-month recovery journey thus far. A comprehensive review of the existing literature indicated a void in the inclusion of African American college students’ lived experiences in recovery from substance use disorders (SUDs) while attending college. Thus, this study sought to fill a gap in the counseling and substance use research and utilized a recovery capital theoretical framework to examine the recovery experiences of African American college students in recovery participating in CRPs. Based on the data analysis, three themes emerged: (a) advocate for recovery in the CRPs, (b) pro-recovery supports in the CRPs, and (c) recovery barriers and resiliency factors for African Americans in recovery in the CRPs.
Convolutional Neural Network (CNN) models have become the mainstream method in Artificial Intelligence (AI) areas for computer vision tasks like image classification and image segmentation. Deep CNNs contain a large volume of convolution calculations. Training a large CNN may take days or even weeks, which is time-consuming and costly. When we need multiple runs to search for the optimal CNN hypermeter settings, it would take a couple of months with limited GPUs, which is not acceptable and hinders the development of CNNs. It is essential to train CNN faster.
We have proposed a novel Conditional Reduction (CR) module to compress a single 1×1 convolution layer. Then we have developed a novel three-layer Conditional block (C-block) to compress the CNN bottleneck or inverted bottlenecks. At last we have developed a novel Conditional Network (CRnet) based on the CR module and C-block. We have tested the CRnet on two image classification datasets: CIFAR-10 and CIFAR-100, with multiple network expansion ratios and compression ratios. The experiments verify our methods’ correctness with attention to the importance of the input-output pattern when selecting a compression strategy. The experiments show that our proposed CRnet better balances the model complexity and accuracy compared to the state-of-the-art group convolution and Ghost Module compression.
We have proposed a flat reduced random sampling training strategy and a bottleneck reduced random sampling strategy. We have proposed a three-stage training method based on the bottleneck reduced random sampling. Furthermore, we have proved the data visibility of a sample in the whole training process and the theoretical reduced time by four theorems and two corollaries. We have tested the two sampling methods on three image classification datasets: CIFAR-10, CIFAR-100 and ImageNet. The experiments show that our proposed two sampling strategies effectively reduce a significant training time percentage at a very small accuracy loss.
Researchers working in the work-family conflict (WFC) literature have a long history of focusing on how conflict between the work and family domains influences employees and their experiences inside and outside of work (Eby et al., 2005). However, missing from the literature is how a coworker’s WFC, specifically in the direction of family interfering with work (FIW), can go beyond influencing the person experiencing the FIW to impact other people in the work environment, such as other employees. The present study used a weekly diary study and multilevel modeling to investigate how fluctuations in coworker FIW are related to fluctuations in a focal employee’s helping behavior, as well as how engaging in that helping behavior may influence the relationship quality between coworkers and focal employees and may lead to increased role overload, work interfering with family (WIF), and need for recovery for the focal employee. The study also tested whether having higher levels of prosocial motivation moderates the relationship between coworker FIW and focal employee helping behavior. Results from the multilevel analyses indicated coworker FIW was not related to whether or not a focal employee will engage in helping behavior, and prosocial motivation levels did not moderate this relationship between coworker FIW and helping behavior. However, engaging in helping behavior was related to increased focal employee role overload and relationship quality, but not to WIF or need for recovery. Implications and limitations of these findings for the WFC literature are discussed.
Inadequate emotion regulation may underlie the development of psychopathology as well as worsened physical health, particularly in the context of stress. Cognitive reappraisal is typically considered an adaptive strategy to manage negative emotions. However, the extent to which reappraisal is beneficial may hinge upon contextual and individual differences. Specifically, it is unclear how the ability to reappraise effectively (i.e., reappraisal ability) and exposure to stressful life events moderates the association between habitual reappraisal and health. Using a series of questionnaires and an experimental task designed to measure the ability to effectively down-regulate sad emotions using reappraisal, this dissertation examines the interactive effects of habitual reappraisal, reappraisal ability, and exposure to stressful life events on depressive and anxiety symptoms as well as self-reported physical health. Results indicate that habitual reappraisal may protect against elevated depressive symptoms and worsened self-reported physical health for people exposed to stressful life events, and that reappraising often appears to be particularly important when people are less effective in their attempts. These findings provide novel contributions to the field of emotion regulation and health by clarifying that exposure to stressful life events is an important moderator in the association between reappraisal and health and by elucidating the important roles of both habitual reappraisal and reappraisal ability.
Youth with disabilities are overall less engaged in extracurricular, community, and social activities than their peers without disabilities. Across diagnoses, youth with intellectual and developmental disabilities (e.g., autism, intellectual disability) are less likely to participate in extracurricular or social activities compared to peers with other disabilities (e.g., learning disability; Lipscomb et al., 2017). Religious congregations may be one of the most prominent resources in rural and underrepresented communities (Institute for Emerging Issues, 2018; Pargament, 1998) and can be an important resource for youth with intellectual and developmental disabilities and their families (Carter, 2021; Liu et al., 2014). However, leaders of religious congregations (e.g., clergy; religious education directors) may not know how to support and include these youth in their congregation (Stewart-Ginsburg et al., 2020). This study measured the effects of asynchronous professional development on religious leaders’ knowledge and confidence implementing inclusive language and inclusive learning to support youth with intellectual and developmental disabilities. Sixty leaders participated in a mixed method study featuring a randomized control trial paired with concurrent qualitative questionnaires. Results indicated the asynchronous professional development was effective in improving religious leaders’ knowledge of and confidence in implementing inclusive language and inclusive learning in their religious congregations. Further, the professional development helped religious leaders identify opportunities, barriers, and drivers to implementing inclusive language and learning within their congregations. Limitations, suggestions for future research, and implications for policy and practice will be discussed.
More than two-thirds of adults and one-sixth of children and adolescents in the United States experience higher levels of body fat and/or obesity (Hales et al., 2020). Individuals with higher levels of body fat often experience weight-bias, prejudice, and discrimination from various sources including mental health professionals in the fields of psychology, social work, and marriage and family therapy (Cravens, et al., 2016; Davis-Coelho, et al., 2000; Pratt, et al., 2015; Young & Powell, 1985). However, little is known about the presence of weight-bias within the counseling field. Literature shows that weight-bias can negatively impact physical and mental health (Friedman & Puhl, 2012; Himmelstein et al., 2017; Puhl et al., 2017). Counselors may be exhibiting weight-bias towards clients, thus causing harm (Feister, 2012). The counseling profession has committed to developing multiculturally competent counselors, yet body weight is not included in discussions of bias, prejudice, oppression, and power (Bergen & Mollen, 2019). This study used a correlational, non-experimental research design and a standard multiple regression to explore relationships between weight-bias and race, gender, weight-bias education, multicultural competence, and personal experiences with weight-bias among licensed counselors (N= 587). Results indicated there were statistically significant relationships between weight-bias and gender, weight-bias education, and multicultural competence. The group of predictor variables explained a significant portion of the variance in weight-bias among counselors (F(5,553)=9.459, p<.001, R^2=.079, adjusted R^2=.070), which accounted for 7.9% of the variance.
Lung cancer is the second most common cancer and maintains a small survival rate (~20%). Non-Small Cell Lung Cancer (NSCLC) makes up 80-85% of all lung cancer diagnoses. Lung cancer patients routinely undergo surgical procedures, chemotherapy, and/or radiation and these therapies can drive ongoing systemic issues, greatly hindering patient welfare and recovery timelines. Importantly, chemotherapy and radiation can induce deleterious systemic side effects, particularly within skeletal muscle, that are not reversible even in remission. We conducted experiments to determine whether Metformin can reduce lung cancer tumor burden in immunocompetent mice while maintaining skeletal muscle health. Mice were given Lewis Lung Cancer, a form of NSCLC, in the left lung. Control animals received a vehicle treatment of saline and treated animals received Metformin. The cancer cells contained a bioluminescent reporter allowing tumor growth tracking throughout the study. Skeletal muscle homogenates from the cancer-bearing mice were analyzed for changes related to inflammation, muscle mass, and metabolism. Experiments with lung cancer cells in vitro were also conducted to determine how Metformin influences the oncogenic program of NSCLC. These findings led us to conclude that Metformin treatment, while exhibiting anti-neoplastic characteristics for many other cancers, may not be the best monotherapy for NSCLC tumor growth.
In this dissertation document, we describe the potential for Information Extraction, Information Retrieval, and Machine Learning methods to improve the process of analyzing medical texts and, in particular, Clinical Practice Guidelines (CPGs). We present the results of three in-depth studies consisting of dozens of experiments on finding condition-action and other conditional sentences in guideline documents. We are improving the state-of-the-art results (from 5% to 17%) and showing for the first time the applicability of domain adaptation and transfer learning to this problem.
We also present new methods for identifying inconsistencies in disagreements between medical guidelines, and for analyzing them using a combination of machine learning, information retrieval, and text mining methods. We show the need for a formal distinction between contradictions and disagreements in natural language texts to formally reason between contradictory medical guidelines.
We introduce new representations for collections of guideline documents and an algorithm for comparing collections of documents. We use these to investigate conceptual distances between guidelines for the same conditions. Throughout this process, we prove the hypothesis that the difference in recommendations largely (by 69% to 86%) correlates with the differences in concepts used by the medical bodies authoring the guidelines.
Finally, we show the applicability of text analysis methods to practical problems of analyzing textual information in electronic health records. We achieved 83% accuracy in matching medical records with a list of pre-defined conditions in an EHR system, resulting in clinical system support changes in one of the leading US hospitals.
In the modern airspace, small unmanned aircraft systems (sUAS) such as multi-rotor aircraft, commonly referred to as "drones", are becoming increasingly popular with both amateur enthusiasts as well as professional pilots. In recognition of the necessity to integrate sUAS traffic into the national airspace system, Congress passed the FAA Modernization and Reform Act of 2012, which created the mandate for the FAA to regulate sUAS operation in United States national airspace. This legislation also created a number of obligations and duties for UAS pilots, including avoidance of restricted airspace, maximum flight levels, safe separation from aircraft (including other UAS), as well as avoiding flight over civilian human population and contact with personal property such as buildings or cars. Because of the nature of flying a drone either for pleasure or commercial purpose, it is very easy for operators to lose their situational awareness (SA) of the operating environment. A study published by the NASA Langley Research Center in 2017 found that the majority of commercial aviation accidents not attributable to aircraft systems failure involved the crew’s loss of SA of the aircraft or the environment, and that crew distraction from operation was associated with all of these accidents. If this is the case with commercial aircraft pilots inside of an enclosed aircraft cockpit in relative isolation, it is easy to imagine that the potential for distraction in the UAS environment is at least as great. This demonstrates the potential for a decreased SA state to create an unsafe environment for other pilots and bystanders and lead to fines and penalties for the UAS pilot if damage, injury, or disruption to the airspace occurs.
One mode of pathological flight phenomena in fixed-wing aircraft is that of pilot-induced oscillations (PIOs). These PIOs can occur either as a result of pilot-airframe coupling as in the case of biodynamic feedthrough or as a result of the lag between pilot observation and action and the propagation of the pilot’s actions and the control response of the aircraft under the influence of structural or environmental stimulus on the aircraft system. Under either scenario, the actions necessary to identify and resolve PIOs can quickly distract the pilot and cause a degradation of pilot SA level. This pilot distraction can lead to mission task element (MTE) failure, loss of aircraft control, or damage or destruction of the aircraft and surrounding persons and property. In the case of UAS, some PIOs can be induced as a result of a lack of direct tactile feedback and neurosensory coupling between the remote pilot and the aircraft. While some of these effects can be mitigated with the addition of haptics in the control actuators or through the use of first-person view monitor goggles, increased distance between the remote pilot and the UAS reduces the ability for the remote pilot to judge the effects of fine control inputs on UAS attitude. This can lead to the development of PIOs as the remote pilot attempts to control the UAS from a distance. Long-range, beyond line-of-sight missions rely upon autonomous flight control system to guide the UAS using global navigation satellite systems (GNSS) or more complex navigational methods such as inertial guidance, celestial navigation, or terrain-matching in communications-denied environments; however, these autonomous methods do not work well for primarily human remote pilot operations with augmented control applications such as bridge or communications tower inspection, where the UAS must be guided from a distance by a human pilot while focusing on specific tasks identified during the mission. To better enable an individual UAS operator to carry out complex mission task elements, we sought to develop a head-mounted display (HMD) equipped with see-through augmented reality (AR) capabilities. The objective of this HMD is to provide information to the pilot using low-complexity visual cues with sufficient information capacity to help improve mission performance and maintain pilot SA while minimally increasing or even decreasing pilot cognitive processing workload. We refer to this system as AviatAR. The contributions of this research include: new and additional insights into the development of PIO phenomena during rotary-wing UAS operation, the detection of PIO development in real-time during flight operation of rotary-wing UAS, and comparisons of the effects of communication of visual flight information to a pilot through the primary and peripheral visual fields using a see-through AR headset.
Si/C composite materials have attracted enormous research interest as the most promising candidates for the anodes of next-generation lithium-ion batteries, owing to their high energy density and mechanical buffering property. However, the major disadvantage of materials with ultra-high capacities, such as Si-based materials, is the significant volume change during cycling, which further leads to mechanical and electrochemical degradation. A comprehensive computational model is indispensable in the developing process of the excellent performance of anode material due to the low realizability, inconvenience, and high cost of experiments, which also provides powerful tools for fabrication guidance of novel Si/C composites designs. Hence, this study explores the multiphysics behavior of Si/C anodes material from the atomic level to cell level using DFT modeling and FEA methodology, systematically revealing the coupling mechanism among various physical fields, as well as providing efficient and powerful tools in the design, development, and evaluation of high energy density lithium-ion batteries.
