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A Self-Help Intervention for Caregivers of People Diagnosed With an Eating Disorder
Get PDFThe purpose of this evidence-based practice project was to support caregivers of patients with eating disorders by delivering an evidence-based intervention designed to decrease caregiving burden and illness maintaining behaviors at a regional eating disorder and weight management center (EDWMC). Based on the Cognitive Interpersonal Maintenance Model, the intervention developed by Treasure and colleagues (2006) was shown to improve caregivers? sense of self-efficacy, interaction with their loved ones, and their loved ones? outcomes. After receiving the training materials, caregivers of previous studies reported decreases in caregiving burden and illness maintaining behaviors. Furthermore, patients with caregivers who received the intervention also improved their body mass indexes (BMI) in a previous study.
For this project, caregivers were referred to the co-investigator by clinical psychologists. After screening for eligibility and providing consent, recruited caregivers were provided with the training materials (online videos and a self-help manual) and instructed to review them at their own pace for six weeks. A reminder email was sent to them weekly to complete the intervention. In addition to completing the validated questionnaires at baseline and post-intervention, caregivers virtually participated in an exit interview for evaluation. The Burden Assessment Scale, Accommodation and Enabling Scale for Eating Disorders, and Family Questionnaire were utilized to evaluate caregiving burden, accommodating and enabling behaviors, and levels of expressed emotions, respectively. The project data analysis indicated that implementing the intervention decreased the average scores for caregiving burden, accommodating and enabling behaviors, and expressed emotions. Furthermore, caregivers were satisfied with the intervention and provided positive feedback about the project. During the exit interviews, caregivers indicated that they desired a support group for future projects.
Based on an in-depth literature review and project findings, continuing intervention implementation is recommended for the EDWMC. In the future, healthcare providers, in collaboration with clinical psychologists, may consider offering these training materials developed by Treasure and colleagues to caregivers of patients with eating disorders. Furthermore, clinics may consider offering a support group for emotional support and skill practice. Healthcare providers may improve the outcomes of both caregivers and patients by addressing the needs of the caregivers and approaching illnesses holistically
Implementing Adult Waist Circumference Measurements in Primary Care
Get PDFOverweight and obesity negatively affect multiple acute and chronic disease conditions (USDHHS & ODPHP, n.d.). Two of the most valid and reliable measurements of overweight and obesity are body mass index (BMI) and waist circumference (WC) (NHLBI, n.d.). Despite evidence that waist circumference measurement (WCM) can aid in stratifying risk, the predominant measurement of obesity is BMI alone (Ross et al., 2020).
The purpose of the practice improvement project involved creating a workflow to measure adult WC at wellness visits, increase the frequency of adult WCM, and documentation of WCM in the electronic health record (EHR). The project included increasing provider and nursing awareness and knowledge about the morbidity-associated risks of an elevated WCM through an educational session. Questions posed to participants during post-education debriefing, and post-implementation elicited feedback on the effectiveness of the educational session, anticipated and encountered barriers to WCM, anticipated patient response to WCM, encountered benefits, and perceived sustainability of WCM. Patients also received information about the health risks associated with an elevated WCM through readily accessible educational materials.
Data collected during the project included the number of patients allowing a WCM, patients refusing WCM, educational pamphlets given to patients, WCM documented in the EHR, and number of WCM discussed between patient and provider during the clinic visit. Participant responses to debriefing questions suggested the educational session effectively increased knowledge and awareness of the morbidity-associated risk of an elevated WCM. Post-implementation question responses identified challenges with nurse staffing and documentation of WCM in the EHR as the most commonly encountered barriers. The most common benefit was that a WCM allowed an entry point for a conversation between patient and provider about health problems associated with increased central obesity. Patients allowing a WCM equaled 125, with only three patients refusing. Ninety-five percent of patients had a WCM documented in the EHR, 83% had the WCM discussed during the clinic visit, and 76% had the WCM documented in the providers? clinic notes. Over half of patients received an educational pamphlet on WC during the project. Since the project was successful, recommendations included continuing WCM at primary care wellness visits
"My 'Eh' Is Authentic"; Commodification of Language and Identity In Michigan's Upper Peninsula
Get PDFThe following paper is an investigation of the historic, economic, social, and ideological processes that have shaped dialect awareness in Michigan?s Upper and Lower peninsulas. The goal of this study is to explore dialect perceptions between ?Yoopers? and ?Trolls? with a specific focus on the tourist industry and material items. This work performs textual analysis of various commodified dialect features in Michigan?s Upper Peninsula to examine the complex relationship between language use and identity. Secondary research is used to synthesize the historical, political, and cultural circumstances resulting in present day dialect features, while textual analysis reveals that material artifacts circulate ideas around Yooper identity through the linguistic concept of ?enregisterment.? My hope is that this paper will add to the growing conversation surrounding regional dialect variation and the effects of regional stereotypes on language use and identity
An Application of Natural Language Processing to Classify What Terrorists Say They Want
Get PDFRaj Bridgelall is the program director for the Upper Great Plains Transportation Institute (UGPTI) Center for Surface Mobility Applications & Real-time Simulation environments (SMARTSeSM).Knowing what perpetrators want can inform strategies to achieve safe, secure, and sustainable societies. To help advance the body of knowledge in counterterrorism, this research applied natural language processing and machine learning techniques to a comprehensive database of terrorism events. A specially designed empirical topic modeling technique provided a machine-aided human decision process to glean six categories of perpetrator aims from the motive text narrative. Subsequently, six different machine learning models validated the aim categories based on the accuracy of their association with a different narrative field, the event summary. The ROC-AUC scores of the classification ranged from 86% to 93%. The Extreme Gradient Boosting model provided the best predictive performance. The intelligence community can use the identified aim categories to help understand the incentive structure of terrorist groups and customize strategies for dealing with them.https://www.ugpti.org/about/staff/viewbio.php?id=7
In Touch with Prairie Living, August 2022
Get PDFAugust 2022 column for North Dakota and South Dakota newspapers
Evolutionary and Ecological Processes in Conservation and Preservation of Plant Adaptive Potential
Get PDFAnthropogenetic disturbances, such as habitat loss and fragmentation, overexploitation, and climate change have diminished population sizes of many species, increasing risks of population extirpation or species extinction. Consequently, conservation of genetic variability, to preserve and maintain rare species? evolutionary potential and avoid within-population inbreeding, is a major goal of conservation biology. For plants, various approaches and guidelines have been developed to preserve species? genetic diversity ex situ (?off-site?). However, effective methods to guide conservation and management decisions without relying on the availability of genetic data or knowledge about population size and population genetic structure are lacking. With the first two chapters of my dissertation, I aimed to complement existing ex situ strategies by investigating surrogates for estimating genetic variation to optimize conservation of rare species? evolutionary potential when access to genetic data is limited. My results demonstrated that guiding population sampling using environmental and geographic distances, as opposed to randomly selecting source populations, can increase genetic diversity and differentiation captured in simulated ex situ collections. Likewise, my research showed that for species with largely heritable seed traits, morphological variation estimated from contemporary seed collections can be used as a proxy for standing genetic variation and help inform sampling efforts aiming to optimize genetic diversity preserved ex situ. Although strategies targeted to conserve rare species? evolutionary potential where genetic data may be lacking are needed, the increasing affordability of next-generation sequencing technologies is increasing access to genomic data for rare species. With my third chapter, I investigated whether inferring rare species? evolutionary history from genomic data may help inform conservation practices. My results demonstrated that teasing apart spatial and temporal effects of stochastic and deterministic processes on population genetic structure may be used to estimate past and contemporary changes in populations? evolutionary potential, as well as to evaluate risks and benefits of genetic rescue as a management strategy. Overall, my PhD research establishes tools and approaches to preserve genetic variation for rare species using different types of data. As world?s biodiversity continue to decline, tool development to accommodate species-specific data availability for preservation of genetic variation is crucial
Comparing the Performance of Deep Learning Algorithms for Vehicle Detection and Classification
Get PDFThe rapid pace of developments in Artificial Intelligence (AI) provides unprecedented opportunities to enhance the performance of Intelligent Transportation Systems. Automating vehicle detection and classification using computer vision methods can complement traditional sensors or serve as a cost-effective and environmentally friendly substitute for conventional sensors. This study investigates the robustness of existing deep learning models for vehicle identification and classification using a heterogenous dataset. The dataset is grouped into six distinct classes based on the Federal Highway Administration (FHWA) vehicle classification scheme. This study uses three different versions of You Only Look Once (YOLO) single-stage object detection models, namely YOLOv7, YOLOv5m, and YOLOv5s. The comparative evaluation will depend on four performance metrics: recall, precision, F1-score and mean average precision (MAP). The results show that for this case study, YOLOv7 outperformed the other models with 84.7% precision, 89.4% recall, 86.1% F1-score and 93% MAP at 0.5, and 82.4% MAP at 0.95
The Effect of Rate-Setting Methods on U.S. Commercial Airports? Credit Ratings
Get PDFU.S. airports are owned and operated by the government, and a major source of their external funding comes from issuing municipal bonds. Credit rating agencies (CRAs) evaluate the bonds using multiple factors, but the judgments behind the ratings are not well understood. One of those factors considered is the airport agreement with airlines, which follows one of the three rate-setting mechanisms known as residual method, compensatory method, and hybrid method.Using a set of unbalanced panel data for 58 medium and large airports from 2010 to 2019, I estimated a probit model to examine the effect of the rate-setting methods, airport?s financial performance, and airport characteristics on airport credit ratings. My results show that compensatory airports consistently receive a very high credit rating from Fitch and the determinant that has the single largest effect on credit ratings is the status of serving as a hub of a legacy airline
A Study on Deep Learning for Prognostics and Health Management Applications: An Evolutionary Convolutional Long Short-Term Memory Deep Neural Network Data-Driven Model for Prognostics of Aircraft Gas Turbine
Get PDFThe fundamental concept of prognostics and health management (PHM) within the scope of Condition-Based Maintenance (CBM) is to find an approach to evaluate the system health and predict its remaining useful life (RUL). Many methods and algorithms have been proposed for PHM modeling, most of which have been proven to perform relatively well. One of the leading algorithms in the current data-driven technology era is a deep learning approach, which is based on the concept of multiple hidden layers in a neural network. RUL prediction is an important part of PHM, which is the science that is aimed at increasing the reliability of the system and, in turn, reducing the maintenance cost and potential failure. The majority of the PHM models proposed during the past few years have shown a significant increase in the number systems that are data-driven. While more complex data-driven models are often associated with higher accuracy, there is a corresponding need to reduce model complexity. One possible approach is to reduce the complexity of the model is to use the features (attributes or variables) selection and dimensionality reduction methods before the model training process. In this work, the effectiveness of multiple search-based methods that seek for the best features set to perform model training, which included, filter and wrapper feature selection methods (correlation analysis, relief forward/backward selection, and others), along with Principal Component Analysis (PCA) as a dimensionality reduction method, was investigated. A basic algorithm of deep learning, Feedforward Artificial Neural Network (FFNN), was used as a benchmark modeling algorithm. It is believed that all of those approaches can also be applied to the prognostics of an aircraft engine. The aircraft engine data from NASA Ames prognostics data repository was used to test the effectiveness of the filter and wrapper feature selection methods. The findings show that applying feature selection methods helps to improve overall model accuracy by 3% to 5% compared to other existing works and significantly reduces the complexity by using 7 out of 21 less input nodes for the deep learning type of models
The Effects of Variation in Temperature and Parental Behavior on Offspring Body Mass, Telomeres and Survival Are Context-Dependent in Free Living House Sparrows (Passer domesticus)
Get PDFAlthough developing birds are vulnerable to extreme and erratic temperature conditions associated with climate change, parents have some ability to buffer these effects via incubation and postnatal behavior. However, parents are constrained by their own physiology and ecology. In this thesis, I sought to determine which factors (seasonal thermal profile, consistency of ambient temperature and/or parental behavior) drove traits linked to fitness across ontogeny in free-living house sparrow nestlings (Passer domesticus). I found that the effects of these factors were context-dependent; seasonal thermal profile and average temperature were important in shaping body size across ontogeny, but variance in nest temperature and female postnatal visits better predicted hatching and day 10 survival, respectively. Future studies should seek to answer these questions in other populations and explore hypotheses surrounding interactions between developmental environments to better our understanding of climate change and thermoregulation in response to increasingly warm and erratic global temperatures