47325 research outputs found
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UNF VS Embry-Riddle [Neg# 18A]
No full textRoll of B+W Film scanned, UNF VS Embry-Riddle, Undatedhttps://digitalcommons.unf.edu/spinnaker-images/1161/thumbnail.jp
UNF VS Embry-Riddle [Neg# 33A]
No full textRoll of B+W Film scanned, UNF VS Embry-Riddle, Undatedhttps://digitalcommons.unf.edu/spinnaker-images/1175/thumbnail.jp
UNF VS Embry-Riddle_Roll2 [PROOF SHEET]
No full textRoll of B+W Film scanned, UNF VS Embry-Riddle, Undatedhttps://digitalcommons.unf.edu/spinnaker-images/1178/thumbnail.jp
The impact of a pediatric interdisciplinary outpatient feeding clinic on preterm infant weight gain and caregiver compliance with feeding recommendations
No full textEarly nutrition intervention along with interdisciplinary care is important for premature infants because malnutrition often leads to poor growth in this population.1,2 Poor growth in preterm infants results in subsequent neurocognitive development as there is an association between deficient postnatal growth and poor neurologic outcome up to age 19.1,2It is important to focus on an infant’s nutrition, feeding, and any gastroenterology concerns after hospital discharge to prevent poor growth. Most importantly, it is essential to have an interdisciplinary care monitoring infant care and outcomes in an outpatient setting; however, this service is lacking in Northeast Florida. The newly developed pediatric interdisciplinary feeding clinic (within Nemours Children’s Health and Wolfson’s Rehab) aims to provide prompt care to preterm infants. To determine the impact of the feeding clinic, the study plans to review preterm infants’ nutritional status in the feeding clinic group (e.g., change in z-score of weight-for-length and change in z-score of weight-for-age) and compare to retrospective data of a similar study population, preterm infants who are in the current setting (multidisciplinary care) and not enrolled in the feeding clinic in six months period. In turn, it is hypothesized that infants enrolled in the newly developed pediatric interdisciplinary outpatient feeding clinic will have greater improvements in markers of nutritional status when compared to those infants not enrolled in the feeding clinic. This study will also evaluate the incidence of malnutrition within the study population by categorizing it into malnutrition and non-malnutrition groups. This study will utilize a retrospective-prospective before-after study design. In addition, this study will evaluate caregiver compliance with health care provider recommendations between the groups. Ultimately, this study aims to determine the benefits of a newly developed pediatric interdisciplinary outpatient feeding clinic composed of a Gastroenterology Nurse Practitioner, Registered Dietitian, and Feeding Pathologist. The results from the study will hopefully provide a glimpse of the nutrition-related benefits of an infant feeding clinic, which may lead to potential funding for future expansion.
1. Goldberg DL, Becker PJ, Brigham K, et al. Identifying Malnutrition in Preterm and Neonatal Populations: Recommended Indicators. Journal of the Academy of Nutrition and Dietetics. 2018;118(9):1571. https://search.ebscohost.com/login.aspx?direct=true&AuthType=shib&db=edselp&AN=S2212267217316295&site=eds-live&scope=site&custid=s6281220. doi:10.1016/j.jand.2017.10.006.
2. Guellec I, M.D., Lapillonne, Alexandre,M.D., PhD., Marret, Stephane,M.D., PhD., et al. Effect of Intra- and Extrauterine Growth on Long-Term Neurologic Outcomes of Very Preterm Infants. J Pediatr. 2016;175:93-99.e1. doi:10.1016/j.jpeds.2016.05.027
Large language models for multimodal user interaction in a virtual environment
Get PDFVirtual Reality (VR) is increasingly popular, but many barriers exist for individuals with little experience in coding, 3D modeling, or creating their own virtual experiences. The current tools used for content creation are often viewed as complex or frustrating, and they exhibit a steep learning curve. This problem presents an opportunity to develop tools incorporating Natural User Interfaces that better support end users. One such possible tool to assist users is Large Language Models (LLM), which can, extract a user\u27s intention through speech or text. We posit how using LLMs can better support novice and expert developers alike, and using a human-in-the-loop approach, we can foster a Human-AI cocreative process. Towards the realization of that goal, we created a multimodal tool in which users can use a virtual reality system that incorporates a large language model, as well as direct manipulation, menus, eye gaze, and speech, to facilitate a more natural VR authoring experience. We created a template in Unity3D that can be customized for various tasks, including the construction of a 3D environment and the creation of commands.
In this thesis, we describe a summative research study with 22 participants to determine the usability and future of our tool. Our participants were tasked with authoring a predefined environment, and we got a system usability scale score (SUS) of 57\% which is between Ok and Good which was expected due to the flexibility and high range of freedom within the system. All users indicated some degree of ease of use when using the system. However, most users also highlighted on how the mechanisms were difficult, highlighting the learning curve of the tool itself.
Our results indicate that our multimodal approach, combining a large language model with other 3D user interface modalities, can provide a more intuitive and accessible interface for users. Future research and development will focus on fine-tuning these interactions and expanding the capabilities to better support the user
Robotic gas source localization and distribution mapping via deep reinforcement learning
Get PDFThis research aims to advance the fields of Gas Source Localization (GSL) and Gas Distribution Mapping (GDM) by developing deep reinforcement learning (DRL) methodologies suitable for complex, real-world environments. GSL and GDM are crucial for applications such as environmental monitoring, hazardous material detection, and search-and-rescue missions, where safe and efficient exploration is essential. Traditional methods often fall short in dynamic settings influenced by factors like wind and obstacles. To address these limitations, this study proposes novel neural network architectures and learning frameworks for adaptive exploration and mapping, integrating Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) layers, and Deep Q-Networks (DQN). The research explores the use of DRL in single robot scenarios to enhance real-time decision-making and operational efficiency. In GSL, the study focuses on accurately tracing gas sources by leveraging temporal and spatial data to guide the robot through complex environments. For GDM, it introduces a hybrid framework combining Gaussian Process Regression (GPR) and DRL to estimate gas concentrations from sparse samples, thereby reducing computational overhead during real-time deployment. Through extensive experimentation, the research demonstrates that the proposed methods can outperform traditional greedy and random walk-based strategies. Ultimately, this work seeks to contribute robust, adaptive exploration strategies for autonomous robotic systems in dynamic, hazardous, and computationally constrained environments
Unidentified Woman
No full textPhotograph: Portrait of unidentified woman. Hand tinted photograph. Undated.https://digitalcommons.unf.edu/eartha_images/2000/thumbnail.jp
Unidentified Woman
No full textPhotograph: Portrait of unidentified woman wearing gown with earring and necklace set. Undated.https://digitalcommons.unf.edu/eartha_images/1998/thumbnail.jp
Unidentified Woman
No full textPostcard: Portrait of unidentified woman wearing a lace collar. Photograph printed as postcard. Undated.https://digitalcommons.unf.edu/eartha_images/1990/thumbnail.jp
Unidentified Woman With Chickens
No full textPhotograph: Unidentified woman standing in yard with chickens. Undated.https://digitalcommons.unf.edu/eartha_images/1985/thumbnail.jp