62970 research outputs found
Sort by
Optimizing image capture for computer vision-powered taxonomic identification and trait recognition of biodiversity specimens
Get PDF1. Biological collections house millions of specimens with digital images increasingly available through open-access platforms. However, most imaging protocols were developed for human interpretation without considering automated analysis requirements. As computer vision applications revolutionize taxonomic identification and trait extraction, a critical gap exists between current digitization practices and computational analysis needs. This review provides the first comprehensive practical framework for optimizing biological specimen imaging for computer vision applications.
2. Through interdisciplinary collaboration between taxonomists, collection managers, ecologists and computer scientists, we synthesized evidence-based recommendations addressing fundamental computer vision concepts and practical imaging considerations. We provide immediately actionable implementation guidance while identifying critical areas requiring community standards development.
3. Our framework encompasses 10 interconnected considerations for optimizing image capture for computer vision-powered taxonomic identification and trait extraction. We translate these into practical implementation checklists, equipment selection guidelines and a roadmap for community standards development, including filename conventions, pixel density requirements and cross-institutional protocols.
4. By bridging biological and computational disciplines, this approach unlocks automated analysis potential for millions of existing specimens and guides future digitization efforts towards unprecedented analytical capabilities.US National Science Foundation. Grant Number: #2118240
US National Science Foundation. Grant Number: #2330423
US National Science Foundation EPSCOR. Grant Number: #2429418
US Department of Agriculture’s National Institute of Food and Agriculture Hatch Project Award. Grant Number: #MEO-022425
Natural Sciences and Engineering Research Council of Canada. Grant Number: #58513
Community College Faculty, Staff, and LGBTQ+ Students’ Perceptions of LGBTQ+ Students’ Sense of Belonging Related to Positive Campus Experiences and Effects on Well-Being and Success
Get PDFStudies reflect college students in the United States as a population vulnerable to experiencing mental health issues that are negatively affecting their ability to be academically successful. In particular, students who identify as LGBTQ+ and students who attend community colleges are reporting greater rates of mental health issues such as anxiety and depression than their heterosexual, cisgender peers and peers attending four-year institutions. Supporting a sense of belonging has been identified as a possible strategy in improving student outcomes, including well-being and academic achievement.
The purpose of this study was to explore community college faculty, staff, and LGBTQ+ students’ perceptions of the campus influences that contribute positively to LGBTQ+ students’ sense of belonging. Strayhorn’s model of college students’ sense of belonging is utilized as a conceptual framework. Using an anti-deficit approach and social constructivist lens, research questions sought to explore community college faculty, staff, and LGBTQ+ students’ perceptions of the campus influences that contribute positively to LGBTQ+ students’ sense of belonging. Of specific interest in this study was how LGBTQ+ students perceived their sense of belonging affected overall well-being and success as a student, as well as their recommendations for institutional best practices in supporting LGBTQ+ students’ sense of belonging.
Data collection included semi-structured interviews, field notes, and data collected from the institution’s public website. A five-step data analysis spiral that included identifying open codes, axial codes, and themes was followed. Trustworthiness of the study was practiced through member checking, memoing, triangulation, audit trail, reflexive journaling, and the use of rich, thick description. Findings from this study suggest LGBTQ+ community college students’ sense of belonging may positively affect mental health issues, contribute to pride in self, be closely related to feelings of safety, and facilitate positive learning and persistence behaviors. In addition, findings indicate LGBTQ+ community college students attribute sense of belonging to a LGBTQ+-friendly climate, positive faculty relationships, and connecting with people who share experiences and stories. Findings also provide insight into community college faculty, staff, and LGBTQ+ students’ recommended best practices for supporting LGBTQ+ student belonging. Several implications and recommendations for higher education practice, particularly for community college leaders, are outlined to inform college leaders on fostering positive campus experiences that support LGBTQ+ students’ sense of belonging, well-being, and overall student success. Lastly, recommendations for future research focused on LGBTQ+ community college students’ sense of belonging are discussed
AI Uses How Much Water? Navigating Regulation of AI Data Centers' Water Footprint Post-Watershed Loper Bright Decision Comments
Get PDFAI Uses How Much Water? Navigating Regulation of AI Data Centers’ Water Footprint Post-Loper Bright Decision examines the growing water demands of artificial intelligence data centers and the regulatory challenges they present. It explains how the Supreme Court’s "Loper Bright" decision reshapes administrative deference and affects agency authority over water-use regulation. The discussion situates AI data centers within existing federal and state water management frameworks, highlighting gaps exposed by evolving judicial standards. It also considers the implications for environmental oversight when agencies must justify water-related regulations without broad judicial deference. The abstract concludes by identifying emerging regulatory pathways for addressing the water footprint of AI infrastructure in a post-"Loper Bright" landscape
Mapping the Infodemic: Geolocating Reddit Users and Unsupervised Topic Modeling of COVID-19-Related Misinformation
Get PDFThe problem of geolocating Reddit users without access to the author information API is tackled in this study. Using subreddit data, we analyzed and identified user location based on their interactions within location-specific subreddits. Using unsupervised learning methods such as Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF) algorithms, we examined conversations about COVID-19 and immunization across the U.S., focusing on COVID-19 vaccination. Our topic modeling identifies four themes: humor and sarcasm (e.g., jokes about microchips), conspiracy theories (e.g., tracking devices and microchips in the COVID-19 vaccine), public skepticism (e.g., debates over vaccine safety and freedom), and vaccine brand concerns (e.g., Pfizer, Moderna, and booster shots). Our geolocation analysis shows that regions with lower vaccination rates often exhibit a higher prevalence of misinformation-labeled comments. For example, counties such as Ada County (Idaho), Newton County (Missouri), and Flathead County (Montana) showed both a low vaccine uptake and a high rate of false information. This study provides useful information on the many different examples of misinformation that are disseminated online. It gives us a better understanding of how people in different parts of the U.S. think about getting a COVID-19 vaccine
Assessing the Social and Environmental Impact of a Clothing Reuse Business Model: The Case of Circular Thrift—An Innovative, Community-Based Startup
Get PDFTo contribute to the emerging knowledge on the sustainability impacts of small, circular clothing reuse businesses in the US, we employed a case study research methodology to empirically test the case of Circular Thrift, an innovative, community-based startup business model with potential to create a circular fashion ecosystem on the firm level. Primary data on circular activities were collected on site within the first year of business operation. The Life Cycle Assessment methodology was conducted to assess environmental impact avoidance. The social impact of reused products was assessed to contribute to a more comprehensive understanding of the benefits of born circular business models. Tangible environmental benefits accounted for the collection of 10,772 apparel units and resulted in the diversion of 2311.05 kg (approximately 5095 pounds) of clothing from the local landfill. Social impact accounted for 45.86% of the collected items that were given back to the local community. Empirical testing of the environmental benefits of a Circular Thrift business model makes a strong case for scaling up reusable efforts as a means to address post-consumer textile waste at the local community level within the US, where formal and government-regulated resource collection and recovery systems still do not exist
Stroke Prevalence and Risk Factors in Rural Communities Within a Resource-Constrained South Asian Setting: Population-Based Study of 1.3 Million Individuals
Get PDFBackground: Stroke is a leading cause of death and long-term disability worldwide, with an estimated 6.2 million deaths each year. In Bangladesh, data on stroke prevalence and risk factors in rural areas are limited, making it difficult to develop effective early prevention and intervention programs.
Objective: This study aimed (1) to present the prevalence of stroke in a rural community in Bangladesh and (2) to identify and associate various stroke risk factors.
Methods: Data collection was done by community health workers, as a part of the “Enriched Sastho” program of the Palli Karma Sahayak Foundation, Bangladesh. Community health workers received 2 weeks of training to ensure data quality. The presence of stroke was determined by a binary survey question, with a history of stroke=1 and absence=0. The prevalence of stroke per 1000 people was examined along with the 95% CI. In addition, the association of stroke risk predictors was calculated using multivariate logistic regression and presented in crude odds ratio (OR) and adjusted OR along with 95% CI.
Results:
The study analyzed data from 1,341,589 individuals, with an average (SD) age of 29.23 (19.05) years. The overall stroke prevalence was found to be 1.07 per 1000 people, with a higher prevalence in male participants and increasing with age. The highest stroke prevalence was observed in the Khulna division (OR 1.881, 95% CI 1.671‐2.117), and the least in the Rangpur division (OR 0.677, 95% CI 0.576‐0.795). Individuals aged 65‐79 years were at a higher risk of having a stroke than other age groups (crude OR 9.883 and adjusted OR 9.728 [adjusted for sex]). In addition, male participants were at greater risk of having a stroke than female participants were (crude OR 1.565 and adjusted OR 1.469 [adjusted for age]).
Conclusions: The study emphasizes the need for early prevention and intervention programs for stroke in rural Bangladesh and the importance of managing hypertension and diabetes to reduce stroke risk.The research project was funded by Institute of Advanced Research (IAR), United International University under project title: iCRP: An intelligent cardiovascular diseases risk profiling for early identification of myocardial infraction (MI) and stroke in Bangladesh (project code: UIU-IAR-02-2022-SE-10). The research publication was funded by Independent University, Bangladesh
Faculty prefer clear guidelines and individual interactions in a mentorship program
Get PDFObjective: To assess the thoughts and feelings of faculty regarding a new mentoring program.
Methods: An online survey was developed and sent to faculty members through institutional email at the beginning (September) and end of the school year (May) of initiation of a faculty-student mentorship program.
Results: In September and May, faculty felt confident in their ability to provide mentorship for students. Faculty believe that individual meetings are more effective than group meetings for mentoring students.
Conclusions: Overall, faculty confidence in mentoring increased over time, but there were concerns about the program’s structure, engagement, and effectiveness. Faculty valued individual mentoring more than group mentoring and expressed the need for better administrative support and clearer guidelines.
Clinical Relevance
Providing faculty with clear objectives and practical guidance may improve faculty confidence, engagement, and feelings of efficacy in veterinary student mentoring programs. Effective mentoring can lead to better-prepared graduates who are more confident, skilled, and capable of handling the complexities of their future clinical roles. This, in turn, can improve patient care and outcomes
AI Uses How Much Water? Navigating Regulation of AI Data Centers' Water Footprint Post-Watershed Loper Bright Decision
Get PDFThe article argues that the rapid growth of AI data centers in the United States, driven by technological innovation, is leading to significant environmental impacts, particularly concerning water consumption. It emphasizes the urgent need for federal legislation to regulate these data centers, ensuring transparency and accountability while balancing the benefits of AI with sustainability. The proposed Sustainable AI Data Center Water Consumption Act aims to provide a uniform regulatory framework, empowering federal agencies like the EPA to enforce standards and mitigate the environmental harms associated with AI development
The State-Created Danger Doctrine: A Viable Federal Claim for Student Victims of Peer Sexual Abuse
Get PDFThe State-Created Danger Doctrine: A Viable Federal Claim for Student Victims of Peer Sexual Abuse examines the potential use of the state-created danger doctrine as a federal constitutional remedy for students harmed by peer sexual abuse. The article explains the limitations of existing legal frameworks, including Title IX and § 1983 claims based on special relationships, in addressing such harms. It analyzes federal appellate case law applying the state-created danger doctrine in school settings and identifies doctrinal barriers to successful claims. The discussion explores how affirmative actions or policies by school officials may increase students’ vulnerability to abuse. The article concludes by assessing the circumstances under which the doctrine may provide a viable path to relief for student victims
Forecasting Inland Waterway Container on Barge Volume: A Machine Learning Approach Using Economic Features
Get PDFThis study presents a machine learning approach to predict Container-on-Barge (COB) volume in Inland Waterway Transportation (IWT) systems, focusing exclusively on using economic features as predictors. Five machine learning models were trained using European economic features to forecast COB volume, while historical COB volume was used solely for validation and hyperparameter tuning. Among these models, the convolutional neural network combined with long short-term memory (CNN-LSTM) exhibited superior performance, achieving a mean absolute percentage error (MAPE) of 1.08% when forecasting eight consecutive quarters of COB volume in Europe. The results demonstrate the feasibility of accurately forecasting COB volume using economic features. This research develops an alternative method to forecast COB volume and provides a foundation for developing transfer learning models to predict COB volume in other emerging markets where historical COB volume data is limited. The findings are expected to assist in strategic planning and infrastructure investment for efficient and sustainable COB IWT systems.This work was supported by the U.S. Department of Transportation under Grant Award Number [69A3551747130]