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  • Bubbles and Biases: Demystifying Research and Sustainability

    Media thumbnail: Bubbles and Biases: Demystifying Research and Sustainability
    Type:
    Media
    Descripción/Resumen:
    Through my college journey, the terms “research” and “zero-emissions” have become central to my academic identity. Not just because they reflect both my lab experience and keen interest in sustainable tech, but because they evoke a personal question that many high school seniors and college underclassmen grapple with: what does it mean to be a scientist? I’m Trevon Collins, a mechanical engineering student at Tennessee Technological University. In this podcast episode, I’ll share the answers I’ve found through investigating age-old secrets of phase change and their implications for the green transition.
    Creador/Autor:
    Collins, Trevon L; Yasin, Ayaaz; Bellur, Kishan, and Payton, Eric J
    Peticionario:
    Eric Payton
    Fecha modificada:
    07/24/2026
    Fecha modificada:
    07/25/2026
    Fecha de creacion:
    2026-07-24
    Licencia:
    Attribution-NonCommercial 4.0 International

  • Do Not Fear Failure

    Media thumbnail: Do Not Fear Failure
    Type:
    Media
    Descripción/Resumen:
    Kennedy Whiters lives by "Do not fear failure; rather, fear not trying." She participated in the REALIZE REU when she was a junior at Albany State University. Kennedy performed research on methanogens, a type of anaerobic bacteria that consumes carbon dioxide and turns it into natural gas, which we can use as a source of energy in power plants. In this episode, she describes her experience working in the lab of Prof Annie Rowe, where she found the lab group to be a good source of positive support, like her family.
    Creador/Autor:
    Whiters, Kennedy D.; Rowe, Annette R., and Payton, Eric J.
    Peticionario:
    Eric Payton
    Fecha modificada:
    07/19/2026
    Fecha modificada:
    07/24/2026
    Fecha de creacion:
    2024-07-28
    Licencia:
    Attribution-NonCommercial 4.0 International

  • Advancements in automated nuclei segmentation for histopathology using you only look once-driven approaches

    Article thumbnail: Advancements in automated nuclei segmentation for histopathology using you only look once-driven approaches
    Type:
    Article
    Descripción/Resumen:
    Histopathology image analysis plays a pivotal role in disease diagnosis and treatment planning, relying heavily on accurate nuclei segmentation for extracting vital cellular information. In recent years, artificial intelligence (AI) and in particular deep learning models have been applied successfully in solving computational pathology image analysis tasks. The You Only Look Once (YOLO) object detection framework, which is based on a convolutional neural network (CNN) architecture has gained traction across various domains for its real-time processing capabilities. This systematic review aims to comprehensively explore and evaluate the advancements, challenges, and applications of YOLO-based methodologies in nuclei segmentation within the domain of histopathological images. The review encompasses a structured analysis of recent literature, focusing on the utilization of YOLO variants for nuclei segmentation. Key methodologies, training strategies, dataset specifics, and performance metrics are evaluated to elucidate the strengths and limitations of YOLO in this context. Additionally, the review highlights the unique characteristics of YOLO that enable efficient object detection and delineation of nuclei structures, offering a comparative analysis against traditional segmentation approaches. This systematic review underscores the promising outcomes achieved through YOLO-based architectures, emphasizing their potential for accurate and rapid nuclei segmentation. Furthermore, it identifies persistent challenges such as handling variances in nuclei appearances, optimizing model architectures for histopathological images, and improving generalization across diverse datasets. Insights derived from this review can provide a foundation for future research directions and enhancements in nuclei segmentation methodologies using YOLO within histopathology, fostering advancements in disease diagnosis and biomedical research.
    Creador/Autor:
    Debsarkar, Shyam
    Peticionario:
    Shyam Debsarkar
    Fecha modificada:
    12/28/2025
    Fecha modificada:
    12/28/2025
    Fecha de creacion:
    March 25, 2025
    Licencia:
    All rights reserved

  • Force-displacement response of porcine meniscus under tensile-fracture behavior

    Document thumbnail: Force-displacement response of porcine meniscus under tensile-fracture behavior
    Type:
    Document
    Descripción/Resumen:
    This dataset details the force-displacement response of porcine meniscus under tensile-fracture behavior. Samples are cut from the anterior, middle and posterior regions of the meniscus. Each specimen geometry dimension is included.
    Creador/Autor:
    Chia-Ying Lin; Kumar Vemaganti, and Long, Teng
    Peticionario:
    Teng Long
    Fecha modificada:
    03/03/2023
    Fecha modificada:
    03/03/2023
    Fecha de creacion:
    2020-11
    Licencia:
    Attribution 4.0 International

  • Wireless Sensor Monitoring Water Use at Fixtures

    Dataset thumbnail: Wireless Sensor Monitoring Water Use at Fixtures
    Type:
    Dataset
    Descripción/Resumen:
    The Dataset contains raw data that indicates the start and stop time of water flowing at fixtures in the Marian Spencer Hall Cafeteria restroom during hours of operation. The data were collected as part of an effort to develop and test a novel method of measuring flow to calculate the probability that the fixture is busy (fixture p-value). The fixture p-value is one of the parameters necessary to predict peak demand in buildings for pipe sizing purposes. There are two .csv files, a README file and a sample of the data collection template with contact information. The dataset also contains a MATLAB code written to accept data in the suggested format and estimate the fixture probability of use.
    Creador/Autor:
    Choudhary, Chandrashekhar ; Omaghomi, Toju; Buchberger, Steven; Wang, Tianshuo, and Tao, Li
    Peticionario:
    Toju Omaghomi
    Fecha modificada:
    12/19/2022
    Fecha modificada:
    12/19/2022
    Fecha de creacion:
    2022-12
    Licencia:
    Open Data Commons Open Database License (ODbL)

  • Force-displacement respose of porcine meniscus in no-slip uniaxial compression

    Dataset thumbnail: Force-displacement respose of porcine meniscus in no-slip uniaxial compression
    Type:
    Dataset
    Descripción/Resumen:
    This dataset details the force-displacement response of porcine meniscus in no-slip uniaxial compression. Samples are cut from the anterior, middle and posterior regions of the meniscus.
    Creador/Autor:
    Vemaganti, Kumar ; Lin, Chia-Ying, and Long, Teng
    Peticionario:
    Kumar Vemaganti
    Fecha modificada:
    02/24/2022
    Fecha modificada:
    02/24/2022
    Fecha de creacion:
    2020
    Licencia:
    Open Data Commons Open Database License (ODbL)