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Family Reading night 2024
No full texthttps://digitalcommons.imsa.edu/frn_images_2024/1045/thumbnail.jp
Family Reading night 2024
No full texthttps://digitalcommons.imsa.edu/frn_images_2024/1050/thumbnail.jp
Family Reading night 2024
No full texthttps://digitalcommons.imsa.edu/frn_images_2024/1061/thumbnail.jp
ENABLING IMPROVED HAND FUNCTION IN DISABLED INDIVIDUALS VIA INTENTION ESTIMATION AND SUPPRESSION OF DISRUPTED CENTRAL DRIVE
Get PDFRecipient of the 2024 Alumni Distinguished Leadership Award
Get PDFDr. Sara Goeking ’92
Sara is the National Program Manager for the U.S. Forest Service’s Forest Inventory and Analysis program, which provides the nation’s benchmark data for major forest research and management decisions. As the first woman to fill this leadership role, Sara strives to build inclusive teams that cross disciplines to fill gaps in our knowledge of forests. She has mentored early career forest scientists around the globe through the U.S.-sponsored Women in Forest Carbon Initiative to improve gender equity in forest and climate science. She has also conducted capacity development in Asia, Africa, and South America to help countries address the forest carbon component of international climate agreements.
Sara completed a B.S. in Environmental Sciences in 1996, an M.S. in Forest Ecology in 2003, and a Ph.D. in Watershed Sciences in 2022. Her research has leveraged large datasets from field-based studies, remote sensing, hydrology, and climate science to understand how forests respond to climate and subsequently affect our environment. The dramatic effects of climate change on forests worldwide convinced her that sound forest management decisions require robust forest monitoring data. Her work helped justify the decision to list whitebark pine as a threatened species and led to current efforts to identify climate-resilient restoration sites. Sara’s dissertation research questioned a longstanding assumption that forest disturbances result in increased water supply, unexpectedly finding that disturbances in arid watersheds reduce water supply.
Sara is motivated by awe in the natural world. She believes in the power of data to translate stories into useful expectations, and she is filled with gratitude for the love of learning instilled by the IMSA community
Experimental Paradigm for Studying Impairments in Bilateral Reaching and Grasping After Stroke
No full textAfter stroke, patients experience significant loss in performing activities of daily living (ADL) such as reach-and-grasp. The paresis mainly affects the side of the body contralateral to the lesion and slight deficits to the ipsilesional side, causing an asymmetry in impairment. However, we lack a comprehensive understanding of how functional reach-to-grasp is impaired following stroke, especially during bi-manual tasks. This project in the long term aims to determine the impact of asymmetric arm impairments due to stroke on unilateral and bilateral reach-to-grasp movements. As a first step, we present the initial development of the proposed approach and feasibility demonstrated in preliminary data from three healthy participants. Participants performed reaching and grasping movements to move a medially positioned engineered cube onto a higher-elevated platform unilaterally and bilaterally. Muscle activity was measured with electromyography (EMG) sensors, while the engineered cube provided insight into participants’ physical interactions with the object, measuring force and motion, with an IMU sensor measuring the speed and acceleration of the cube. The quantification of unilateral and bilateral reach was feasible. Trends of similar acceleration profiles and different muscle activity in lateral triceps were found in both participants between unilateral and bilateral grasp. The insights from this study will ultimately lead to the development of better training and intervention methods to mitigate the impact of stroke in performing ADL such as reach-and-grasp
Using NLP (Natural Language Processing) and Models Like TF-IDF (Term Frequency – Inverse Document Frequency), GloVe (Global Vectors for Word Representation), Open AI’s GPT, and Sentence-BERT (Bidirectional Encode Representations from Transformers) to Sort Through and Organize the Search Queries to Prevent Question Repeats in StackOverflow
No full textThis research presents an overview for search query management in StackOverflow, a popular platform for programming in which users can ask and answer questions about their code. With the use of Natural
Language Processing (NLP) techniques, and models including TF-IDF (Term Frequency – Inverse Document Frequency), GloVe (Global Vectors for Word Representation), OpenAI’s GPT, and Sentence-BERT (Bidirectional Encoder Representations from Transformers), the research aims to effectively sort and organize search queries to prevent questions from being repeated in a different way. The TF-IDF method constructs a robust document-term matrix to quantify term importance, while GloVe enhances comprehension by converting words into vector representation. OpenAI’s GPT model generates contextually coherent responses, and Sentence-BERT allows for the comparison of semantic similarities to detect duplicate questions. Through integration of these methods, the research enhances search query management, ensuring efficient information retrieval and improved user experience on StackOverflow. The evaluation findings on real-world datasets highlight the effectiveness of the proposed method in reducing duplicate questions and optimizing query resolution processes. This research enhances search features in online technical forums, providing practical tips to boost user interaction and knowledge sharing in programming communities
Deploying Sensorless V2V Communication for Enhanced Driver Awareness: A C-V2X and GNSS-based System Utilizing OBD Ports for Broad Vehicle Integration
No full textThis paper presents a Vehicle-to-Vehicle (V2V) communication system designed to enhance road safety by leveraging Cellular Vehicle-to-Everything technology and the Global Navigation Satellite System. Unlike Advanced Driver Assistance Systems (ADAS), which rely on external sensors and direct vehicle control for collision avoidance, this module focuses on boosting driver awareness through real-time auditory alerts. Utilizing the vehicle\u27s onboard diagnostic port for data access, the system employs machine learning algorithms to analyze vehicular communication data. It identifies potential hazards based on historical collision data and movements preceding collisions, resulting in auditory warnings conveyed directly through the car’s sound system. Although ADAS may achieve a higher reduction in collision rates by intervening in vehicle control, the proposed V2V module aims to significantly reduce accidents by alerting drivers to imminent dangers, thereby enhancing safety with broad accessibility and ease of retrofitting into existing vehicles. The system\u27s design emphasizes ease of integration and scalability, supported by a detailed theoretical framework, positioning it as a promising advancement in vehicular safety technology