Entire DC Network | Open Access Articles | Digital Commons Network™

Norris Burroughs, Mark Naison, Steven Payne

Oral Histories

No abstract provided.

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Joseph Cordero, Mark Naison, Steven Payne

Oral Histories

No abstract provided.

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Aspire Scholarship 2025 Learning Portfolio, Isabella Paluzzi Steele

Undergraduate Research and Engagement Symposium

This is my reflection on my PLSC 398 Engaged Learning Class - Internship in which I interned for the DC Office of Congresswoman Delia C. Ramirez.

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Ueof, Nick Truong, Pritam P. Karkomar, William J. Beksi

Event-Based Vision - Archive

UEOF is the first synthetic underwater event-based optical flow dataset derived from physically-based ray-traced RGBD sequences. It was constructed using a modern video-to-event pipeline applied to rendered underwater videos. It consists of realistic event data streams with dense ground-truth flow, depth, and camera motion. The dataset is composed of 12 minutes and 51 seconds of data across 13,714 RGB frames. This results in a total of 4.94 billion events across all scenes. UEOF exhibits a high dynamic range of motion with a mean flow magnitude of 6.1 px and a median of 3.6 px. The motion distribution is heavy-tailed. While …

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Obesity And The Gut Microbiota In The Middle East: A Cross-Cultural Study Of Lebanese And Emirati Adults, M. Ali Ahmad, M. Abou-Samra, E. Blaak, M. Karavetian, C. Ayoub Moubareck, K. Venema

All Works

Obesity is a growing public health concern in the Middle East and North Africa (MENA) region, yet limited research has explored how gut microbiota varies between Arab populations. This study compared the gut microbiota composition and diversity of Emirati and Lebanese adults with obesity and assessed the role of age and nationality in shaping microbial variation. A total of 43 Emirati and 30 Lebanese individuals with obesity (body mass index (BMI) ≥35 kg/m2) were recruited. Participants provided anthropometric and biochemical data, dietary records, and stool samples for 16S rRNA sequencing. The analysis revealed significantly higher BMI, weight, and fat mass …

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Hybrid 3d Modelling Framework For Indoor Navigation Using Federated Learning And Internet Of Things-Enabled Edge Devices, Noopur Tyagi, Jaiteg Singh, Saravjeet Singh, Ahmad Ali Alzubi, Farman Ali, Sukhjit Singh Sehra, Babar Shah

All Works

Background There has been a recent trend towards using three-dimensional (3D) models to enhance spatial awareness and maximize resource utilization in complex environments. 3D building model can be used in various applications, such as real-time guidance and tracking the positions of individuals in multi storied buildings. Cities are now being modeled and studied in three dimensions as an improved method of urban planning. Method This study proposes an advanced indoor navigation framework that combines 3D modelling, federated learning (FL), and Internet of Things (IoT) integration to deliver reliable floor-level localization and real-time guidance. In Phase 1, highly accurate 3D models …

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Nanobubbles For Bone-Targeted Drug Delivery: Biodistribution Imaging And Osteoporotic Therapy, Pedram Sotoudehbagha, Tristan Faze, Andres Larraza, Andres Alayon Mata, Mehdi Razavi

Research Data and Datasets

This dataset corresponds to the original data for the manuscript titled Nanobubbles for Bone-Targeted Drug Delivery: Biodistribution Imaging and Osteoporotic Therapy.

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Data And Code From: Rapid Scavenging Of Avian Carcasses In The Desert Southwest: Implications For Mortality Surveys Not Associated With Infrastructure, Thomas Huycke, Dylan M. Osterhaus, Kelley C. Boland, Fitsum Abadi, Martha J. Desmond

Fish, Wildlife, & Conservation Ecology: Datasets

No abstract provided.

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Emerging Threats In Ai: A Detailed Review Of Misuses And Risks Across Modern Ai Technologies, Niyat Seghid, Farkhund Iqbal, Khalifa Al-Room, Áine Macdermott

All Works

The swift evolution of artificial intelligence (AI) has enabled unprecedented capabilities across domains, while simultaneously introducing critical vulnerabilities that can be maliciously exploited or cause unintended harm. Although multiple initiatives aim to govern AI-related risks, a comprehensive and systematic understanding of how AI systems are actively misused in practice remains limited. This paper presents a systematic review of AI misuse across modern AI technologies. We analyze documented incidents, attack mechanisms, and emerging threat vectors, drawing from existing AI risk repositories, prior taxonomies, and empirical case reports. These sources are synthesized into a unified analytical framework that categorizes AI misuse across …

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Shimmer: Introducing And Workshopping A New Methodology For Practice-Led Research, Lola Montgomery

Staff Scholarship - Australia & Dubai

This presentation examines a new methodological approach by introducing shimmer as an embodied epistemological state that frequently appears within creative-practice research but is rarely named, scaffolded, or assessed. Emerging at the intersection of feminist philosophy, phenomenology, affect theory, reflective practice, and practice-led research, shimmer describes the moment when insight coheres somatically before it can be articulated conceptually. Rather than positioning intuition as unrigorous or accidental, shimmer reframes embodied emergence as a legitimate site of knowledge production. The paper situates shimmer in relation to existing frameworks such as flow, reflective practice, and action research, identifying a persistent gap in the literature …

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Agent-Based Model Details For Quantifying Error In Drone-Based Animal Surveys: Animal Density, Distribution And Flight Patterns, Emma A. Schultz, Natasha Ellison-Neary, Landon R. Jones, Kristine O. Evans, Raymond B. Iglay

Research Data

The following dataset pertains to Python scripts of an agent-based model simulating various drone survey design and quantifying potential count bias of stationary animals in fixed landscapes as part of an accepted manuscript in Ecosphere. Simulated animals were distributed among five animal densities (n = 4, 9, 25, 49, 100 animals/survey area) and three distribution patterns (random, uniform, aggregated). Eight drone flight patterns surveyed a 4.16-km² landscape within constraints of real-world battery and line-of-sight limitations. The simplified landscape also provided 100% animal classification (i.e., all animals within image frame observed and identified).

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Critical Tourism Studies In Business Journals, Paolo Mura, Heather Jeffrey, Martin Sposato

All Works

Business is commonly understood as a parent discipline of tourism research, making business journals essential for epistemological inquiry. This paper reviews tourism literature in business journals, analysing the presence of Critical Tourism Studies, which addresses limitations of management-oriented approaches. Our analysis reveals limited engagement with Critical Tourism Studies in business journals, only 27% of reviewed articles aligning with its tenets. We propose a framework for Critical Tourism Business Studies that identifies knowledge gaps and highlights future directions for scholars publishing tourism-related topics in business journals. This framework aims to bridge disciplinary gaps and enhance interdisciplinary research. The article contributes to …

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Bull Shark (Carcharhinus Leucas) Jaw, Roman Devlin, Jade A. Mells, Rileigh Gonzalez, Nicholas D. Burchett, David W. Kerstetter

All Scans: Kerstetter Fisheries and Avian Ecology 3D Scan Series

No abstract provided.

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Wads 3d Object Detection Dataset (Wads-3d) - Sequence 12, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

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Wads 3d Object Detection Dataset (Wads-3d) - Sequence 16, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

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Wads 3d Object Detection Dataset (Wads-3d) - Sequence 20, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

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Wads 3d Object Detection Dataset (Wads-3d) - Sequence 34, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

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Wads 3d Object Detection Dataset (Wads-3d) - Sequence 76, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

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Wads 3d Object Detection Dataset (Wads-3d) - Sequence 11, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

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Wads 3d Object Detection Dataset (Wads-3d) - Sequence 13, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

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Wads 3d Object Detection Dataset (Wads-3d) - Sequence 14, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

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Wads 3d Object Detection Dataset (Wads-3d) - Sequence 15, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

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Wads 3d Object Detection Dataset (Wads-3d) - Sequence 17, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

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Wads 3d Object Detection Dataset (Wads-3d) - Sequence 18, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

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Wads 3d Object Detection Dataset (Wads-3d) - Sequence 23, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

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Wads 3d Object Detection Dataset (Wads-3d) - Sequence 26, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Go to article

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 28, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Go to article

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 30, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Go to article

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 35, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Go to article

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 36, Yiming Yang, Jeremy P. Bos

WADS-3D

WADS-3D is a standard KITTI-format dataset designed for analyzing 3D object detection robustness in severe winter weather. Addressing the need for granular analysis of perception failure in snow, the dataset includes 4,100 frames and over 30,000 labeled car instances captured during heavy snowfall and winter-storm conditions. Derived from the large-scale multimodal WADS dataset, WADS-3D enables researchers to move beyond qualitative weather assessments and systematically evaluate how varying heavy snowfall intensities impact a wide range of neural network detectors.

Go to article

Full-Text Articles in Entire DC Network

Norris Burroughs, Mark Naison, Steven Payne

Oral Histories

Joseph Cordero, Mark Naison, Steven Payne

Oral Histories

Aspire Scholarship 2025 Learning Portfolio, Isabella Paluzzi Steele

Undergraduate Research and Engagement Symposium

Ueof, Nick Truong, Pritam P. Karkomar, William J. Beksi

Event-Based Vision - Archive

Obesity And The Gut Microbiota In The Middle East: A Cross-Cultural Study Of Lebanese And Emirati Adults, M. Ali Ahmad, M. Abou-Samra, E. Blaak, M. Karavetian, C. Ayoub Moubareck, K. Venema

All Works

Hybrid 3d Modelling Framework For Indoor Navigation Using Federated Learning And Internet Of Things-Enabled Edge Devices, Noopur Tyagi, Jaiteg Singh, Saravjeet Singh, Ahmad Ali Alzubi, Farman Ali, Sukhjit Singh Sehra, Babar Shah

All Works

Nanobubbles For Bone-Targeted Drug Delivery: Biodistribution Imaging And Osteoporotic Therapy, Pedram Sotoudehbagha, Tristan Faze, Andres Larraza, Andres Alayon Mata, Mehdi Razavi

Research Data and Datasets

Data And Code From: Rapid Scavenging Of Avian Carcasses In The Desert Southwest: Implications For Mortality Surveys Not Associated With Infrastructure, Thomas Huycke, Dylan M. Osterhaus, Kelley C. Boland, Fitsum Abadi, Martha J. Desmond

Fish, Wildlife, & Conservation Ecology: Datasets

Emerging Threats In Ai: A Detailed Review Of Misuses And Risks Across Modern Ai Technologies, Niyat Seghid, Farkhund Iqbal, Khalifa Al-Room, Áine Macdermott

All Works

Shimmer: Introducing And Workshopping A New Methodology For Practice-Led Research, Lola Montgomery

Staff Scholarship - Australia & Dubai

Agent-Based Model Details For Quantifying Error In Drone-Based Animal Surveys: Animal Density, Distribution And Flight Patterns, Emma A. Schultz, Natasha Ellison-Neary, Landon R. Jones, Kristine O. Evans, Raymond B. Iglay

Research Data

Critical Tourism Studies In Business Journals, Paolo Mura, Heather Jeffrey, Martin Sposato

All Works

Bull Shark (Carcharhinus Leucas) Jaw, Roman Devlin, Jade A. Mells, Rileigh Gonzalez, Nicholas D. Burchett, David W. Kerstetter

All Scans: Kerstetter Fisheries and Avian Ecology 3D Scan Series

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 12, Yiming Yang, Jeremy P. Bos

WADS-3D

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 16, Yiming Yang, Jeremy P. Bos

WADS-3D

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 20, Yiming Yang, Jeremy P. Bos

WADS-3D

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 34, Yiming Yang, Jeremy P. Bos

WADS-3D

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 76, Yiming Yang, Jeremy P. Bos

WADS-3D

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 11, Yiming Yang, Jeremy P. Bos

WADS-3D

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 13, Yiming Yang, Jeremy P. Bos

WADS-3D

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 14, Yiming Yang, Jeremy P. Bos

WADS-3D

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 15, Yiming Yang, Jeremy P. Bos

WADS-3D

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 17, Yiming Yang, Jeremy P. Bos

WADS-3D

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 18, Yiming Yang, Jeremy P. Bos

WADS-3D

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 23, Yiming Yang, Jeremy P. Bos

WADS-3D

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 26, Yiming Yang, Jeremy P. Bos

WADS-3D

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 28, Yiming Yang, Jeremy P. Bos

WADS-3D

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 30, Yiming Yang, Jeremy P. Bos

WADS-3D

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 35, Yiming Yang, Jeremy P. Bos

WADS-3D

Wads 3d Object Detection Dataset (Wads-3d) - Sequence 36, Yiming Yang, Jeremy P. Bos

WADS-3D