Digital Commons Network^™

An Advanced Healthcare System With An Automated Vit Model For Dermoscopic Skin Cancer Identification, Ahed Abugabah, Prashant Kumar Shukla, Suchi Mishra, Abhishek Dwivedi

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Early and reliable diagnosis of skin cancer from dermoscopic images remains challenging due to class imbalance, subtle inter-class variations, lesion boundary ambiguity, and illumination inconsistency, which can degrade the robustness of conventional convolutional neural networks (CNNs). To address these limitations, this study proposes an automated smart healthcare framework for dermoscopic skin cancer diagnosis using an Enhanced Vision Transformer (E-ViT) that improves global-context modeling through self-attention while strengthening fine-grained lesion representation learning. Unlike standard ViT configurations, the proposed architecture integrates multi-scale patch embedding and attention refinement to better capture border irregularities and color–texture heterogeneity that are critical for melanoma discrimination. Furthermore, …

Fig-Gan: Fundus Image Generation Via Deep Learning Based Generative Adversarial Network For Amd Disease Diagnosis, Kailasa Thrishul, Ahed Abugabah, Amina Salhi, Manel Ayadi, Mohamed M. Sithik, D. Jayaprakash, A. Ahilan

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Globally, age-related macular degeneration (AMD) remains a main cause of irreversible vision loss. Recently, deep learning models have primarily focused on classifying fundus images for early detection of AMD progression. However, existing models rarely address the generation of future progression-aware fundus images, particularly when complete real longitudinal follow-up scans are unavailable. This limitation makes it difficult to track retinal changes over time and highlights the need for generative models capable of producing realistic drusen-level structural variations. To address these issues, a novel deep learning-based FIG-GAN model is to generate synthetic future fundus images from baseline inputs. Multi-Attention U-Net (MAU-Net) is …

Leaflet Stress, Joseph Chibuike Chibuike Nwokeafor, Joshua D. Hofmeister, Brendan B. Yeats, Lakshmi Prasad Dasi, Charanjit S. Rihal, Juan A. Crestanello, Leigh Griffiths, Mohamad A. Alkhouli, Hoda Hatoum

BILEAFLET TRANSCATHETER MITRAL VALVE (TMV) FINITE-ELEMENT DATASET

This dataset contains numerical results associated with the open-access research article “Comparative Analysis of Leaflet Materials, Stent Materials, and Stent Cell Density for Bileaflet Transcatheter Mitral Valve Design,” published in Annals of Biomedical Engineering (2026).

The study employed finite element simulations to investigate the effects of leaflet material properties, stent materials, and stent cell density on the biomechanical performance of bileaflet transcatheter mitral valves.

The deposited data support the results reported in the publication.

Data Availability and Reuse: The datasets in this collection are publicly available for research and reuse under the Creative Commons Attribution 4.0 International (CC BY 4.0) …

Stent Geometry, Joseph Chibuike Chibuike Nwokeafor, Joshua D. Hofmeister, Brendan B. Yeats, Lakshmi Prasad Dasi, Charanjit S. Rihal, Juan A. Crestanello, Leigh Griffiths, Mohamad A. Alkhouli, Hoda Hatoum

BILEAFLET TRANSCATHETER MITRAL VALVE (TMV) FINITE-ELEMENT DATASET

This dataset contains numerical results associated with the open-access research article “Comparative Analysis of Leaflet Materials, Stent Materials, and Stent Cell Density for Bileaflet Transcatheter Mitral Valve Design,” published in Annals of Biomedical Engineering (2026).

The study employed finite element simulations to investigate the effects of leaflet material properties, stent materials, and stent cell density on the biomechanical performance of bileaflet transcatheter mitral valves.

The deposited data support the results reported in the publication.

Stent Stress, Joseph Chibuike Chibuike Nwokeafor, Joshua D. Hofmeister, Brendan B. Yeats, Lakshmi Prasad Dasi, Charanjit S. Rihal, Juan A. Crestanello, Leigh Griffiths, Mohamad A. Alkhouli, Hoda Hatoum

BILEAFLET TRANSCATHETER MITRAL VALVE (TMV) FINITE-ELEMENT DATASET

This dataset contains numerical results associated with the open-access research article “Comparative Analysis of Leaflet Materials, Stent Materials, and Stent Cell Density for Bileaflet Transcatheter Mitral Valve Design,” published in Annals of Biomedical Engineering (2026).

The study employed finite element simulations to investigate the effects of leaflet material properties, stent materials, and stent cell density on the biomechanical performance of bileaflet transcatheter mitral valves.

The deposited data support the results reported in the publication.

Opening Area, Joseph Chibuike Nwokeafor, Joshua D. Hofmeister, Brendan B. Yeats, Lakshmi Prasad Dasi, Charanjit D. Rihal, Juan A. Crestanello, Leigh Griffiths, Mohamad A. Alkhouli, Hoda Hatoum

BILEAFLET TRANSCATHETER MITRAL VALVE (TMV) FINITE-ELEMENT DATASET

This dataset contains numerical results associated with the open-access research article “Comparative Analysis of Leaflet Materials, Stent Materials, and Stent Cell Density for Bileaflet Transcatheter Mitral Valve Design,” published in Annals of Biomedical Engineering (2026).

The study employed finite element simulations to investigate the effects of leaflet material properties, stent materials, and stent cell density on the biomechanical performance of bileaflet transcatheter mitral valves.

The deposited data support the results reported in the publication.

Data From: Physics-Data-Hybrid Modeling And Control For Vtol Aircraft, Samantha Dawn Burton

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The skeleton code corresponding to many of the results from the dissertation "Physics-Data-Hybrid Modeling and Control for VTOL Aircraft" By Samantha Dawn Burton.

Sensors And Smart Food Packaging For Supply Chains: Bridging Expiration Dates And Actual Perishability, Muhammad Umar Azam, Jolina Rodrigues, Mohamed Hamid Salim, Sarath Haridas Kaniyamparambil, Khalid Askar, Imane Belyamani, Blaise L. Tardy, Nouha Alcheikh

All Works

The food supply chain is undergoing critical changes to minimize environmental hazards, such as those associated with packaging, and reduce food waste and loss. One of the critical components of the latter is associated with the high variance in the perishability of individual foodstuffs. Herein, we review the state of the art in smart sensor systems and emphasize the critical role they could play in addressing the mismatch between “batch” based expiry dates and individual food products’ time-dependent responses to spoilage. Following a brief overview of food shelf life and associated legislation, a subsequent section summarizes the development of sensors …

Dataset For Meteorological Data From Four Weather Stations In Colorado During The 2025-2026 Winter, Kelly Elder, Banning Starr, Coleman Kane, Jihan Dahanayaka, Hans-Peter Marshall

CryoGARS Snow Data

This dataset contains observations from four Colorado meteorological stations used as part of a US Army Cold Regions Research and Engineering Lab (CRREL) snow strength project.  The purpose of these data is to measure the meteorological variables that impact the trafficability and supportability of a seasonal snowpack and will be used as forcing data to model snow strength across time and space.  Measured data include snow depth, air temperature, relative humidity, wind speed, wind direction, soil temperature, and radiation.

Dataset For Meteorological Data From Two Idaho Weather Stations During The 2025-2026 Winter, Jihan Dahanayaka, Coleman Kane, Thomas Van Der Weide, Hans-Peter Marshall

CryoGARS Snow Data

This dataset contains observations from three Idaho meteorological stations used as part of a US Army Cold Regions Research and Engineering Lab (CRREL) snow strength project.  The purpose of these data is to measure the meteorological variables that impact the trafficability and supportability of a seasonal snowpack and will be used as forcing data to model snow strength across time and space.  Measured data include snow depth, snow water equivalent (SWE), air temperature, relative humidity, wind speed, wind direction, snow temperature, soil temperature, and radiation.

Dataset For Snow Pit And Snow Strength Data From Colorado, Idaho, Alaska, And Wyoming During The 2025–2026 Winter, Coleman Kane, Kelly Elder, Katherine Gura, Stine Pedersen, H.P. Marshall, Jihan Dahanayaka

CryoGARS Snow Data

This dataset contains snowpack and snow strength observations from three study plots in Idaho, five study plots in Colorado,  three study plots in Alaska, and four study plots in Wyoming.  These data were collected in an effort to model the trafficability and supportability of a seasonal snowpack, and will be assimilated in SnowModel to model strength across time and space.  Data collected include stratigraphy, density, permittivity, temperature, specific surface area (SSA), snow depth transects, ram penetrometer profiles, Snow Scope profiles, SnowMicroPen (SMP) profiles.

Sla Elastic Resin Curing Test Data, Richard Amesimenu, Johnson Nwogu, Haijun Gong

Datasets

3D printing via stereolithography (SLA) enables high-resolution polymer components, but as-printed parts may exhibit incomplete polymerization and reduced mechanical performance. This study investigates the effects of UV post-curing time on SLA-printed elastic photopolymer resin. Specimens were evaluated using Differential Scanning Calorimetry (DSC), uniaxial tensile testing (ASTM D412 Type C), and Durometer Type M hardness testing. Results show that increased post-curing enhances crosslink density, tensile strength, elastic modulus, and hardness, while extended curing may reduce ductility. An optimal post-curing window balancing stiffness and flexibility was identified. Testing datasets include DSC (dsc/), tensile testing (tensile/), and hardness testing (hardness/) results for uncured …

The Application Of Machine Learning And Deep Learning On Demand Forecasting Across Time-Critical Industries: A Systematic Review, Asmaa Seyam, Sujith Samuel Mathew, May El Barachi, Cheng Zhang, Jun Shen

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The applications of machine learning and deep learning in demand forecasting have attracted increasing attention, as they offer remarkable predictive capabilities that help automate forecasting processes and achieve higher accuracy. While numerous review studies have examined solutions within specific industries, there is a lack of comprehensive literature review investigating these solutions across different sectors. Therefore, this study overviews machine learning and deep learning applications in demand forecasting across time-critical industries, including power, tourism, water, transportation, and food. A two-tier classification framework is proposed to categorize demand forecasting studies by both application industry and methodological architecture. In addition, the most popular …

Ecu-Malnett V2, Matthew G. Gaber, Mohiuddin Ahmed, Michael N. Johnstone

Research Datasets

ECU-MALNETT (ECU MALware NETwork Traffic) is a real world, reproducible dataset of labeled benign and malicious network flows built from the Peekaboo execution corpus. Peekaboo runs evasive malware with dynamic binary instrumentation and records raw host-level PCAPs while granting full Internet access, yielding noisy, real-world captures with background OS activity and concurrent processes. To derive trustworthy labels from these traces, we apply Construct, a baseline aware, zero-trust labeling framework. Construct first ingests a baseline capture to establish reference sets (DNS qnames, HTTP hosts, TLS SNIs, and socket endpoints) and grows a conservative benign IP pool only via whitelisted DNS resolutions. …

Deployment-Oriented Evaluation Of Temporal And Spatial Forecasting Approaches For Electric Vehicle Charging Demand In Energy Systems, Maher Alaraj, Eyob Solomon Getachew

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Accurate short-term electric vehicle (EV) charging demand forecasting is important for charging infrastructure operation, grid management, and energy-system planning. This study presents a deployment-oriented and reproducible evaluation of temporal, spatial, and unified spatio-temporal forecasting approaches for day-ahead EV charging demand prediction. Using publicly available charging-session data aggregated at hourly resolution across ZIP-code regions, we compare persistence and ARIMA baselines, XGBoost, Long Short-Term Memory (LSTM) networks, Graph Convolutional Networks (GCNs), and a unified GCN+LSTM architecture under a consistent preprocessing pipeline, leakage-free validation protocol, and rolling-origin evaluation framework. For the Boulder ZIP-code dataset considered in this study, temporal information provided the dominant …

Dustmambanet: A Hybrid Inceptionv3–State-Space Network For Robust Solar Panel Dust Detection, Kadhim Hayawi, Sakib Shahriar

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To develop a robust, scalable vision-based model for automatic detection and quantification of dust accumulation on solar photovoltaic panels, overcoming limitations of existing convolutional and attention-based methods and supporting proactive maintenance. We propose DustMambaNet, a hybrid model that consists of a pretrained InceptionV3 convolutional neural network as a feature extractor and two selective state space sequence modules. The state space modules use gated depthwise convolutions to represent long-range spatial dependencies that are of linear complexity, after rearranging spatial features to form sequences. The network provides a binary classification of dust with a severity index (DSI) and a continuous one. All …

Data For Effect Of Simple Motor Repetition On Inverted Pendulum Control: Exploring Age-Related Variations, Kimia Kiani, Qiushi Fu

Research Data and Datasets

This dataset contains data from a human motor-control experiment investigating how prior motor repetition influences subsequent control and learning of a virtual inverted-pendulum task in young and older adults. Participants performed the inverted-pendulum task using a robotic interface and completed either force-based or movement-based repetition training between task blocks. The dataset includes measures of pendulum control performance, task progression, training condition, hand, and age group. These data support the study “Effect of Simple Motor Repetition on Inverted Pendulum Control: Exploring Age-Related Variations.”

Ground To Roof Snow Load Ratio (Gr) Data Release, Brennan Bean, Cooper Nelson, Jesse Wheeler, Scout Jarman, Salam Adil Al-Rubaye, Marc Maguire

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This data release provides historical ground-to-roof snow load ratio (GR) datasets used for snow load research and model development. The release includes original referenced datasets, cleaned country specific datasets, and a master dataset that combines Canadian and United States datasets into a standardized format for research and engineering applications.

Climate-Responsive Design For Sustainable Construction: Passive Strategies In Domestic Architecture Supporting Sustainability, Nahed Chakouf

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This study explores climate-responsive design in domestic architecture, focusing on the application and effectiveness of passive strategies across various climatic regions, including the UK, hot-arid, tropical, and cold climates. A qualitative comparative case study method was used to explore different passive design strategies, integration of sustainability, procurement, the coordination of stakeholders, and compliance with regulations in residential architectural projects under varying climate conditions. Drawing from a combination of historical case studies and original architectural projects, the research evaluates how spatial configuration, material selection, solar orientation, shading, ventilation, and insulation are optimized to achieve thermal comfort and energy efficiency. In temperate …

Dataset To Analyzing Energy Use In 2 & 3d Imaging Systems And Workflows, Michael J. Bennett

Published Works

Analyzing Energy Use in 2D & 3D Imaging Systems and Workflows Dataset

CONTENTS: Z-WaveReportingProfiles; SessionInput; DroneFlights; SessionsComputed; Types; ByType; GrossSummaryUnweighted; WeightingSummary; Raw Sampling History Data

Life Cycle Of Lithium-Ion Battery Recycling Systems Enable Carbon Capture, Utilization, And Storage: A Review, Nabeel Al-Qirim, Mohammad Kamrul Hasan, Hussam Al Hamadi, Akm Ahasan Habib, Shayla Islam

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The lithium-ion battery (LIB) is the primary component of electric vehicles (EVs), and its environmental consequences are significant for the eventual widespread adoption of EVs. Recycling used LIBs protects the environment and allows for the reuse of valuable materials. To achieve the goal of a circular economy, LIB recycling processes that conserve environmentally friendly resources and maintain sustainability need to be developed. The critical technology for achieving global climate goals is carbon capture, utilization, and storage (CCUS), which makes it possible to significantly lower carbon dioxide (CO2) emissions across power and industrial systems. The LIB recycling and CCUS are becoming …

A Large Language Model-Based Analysis Of Vulnerability Discovery In Windows Software, Puya Pakshad, Samson Quaye, Jamal Al-Karaki, Marwan Omar, Maurice E. Dawson

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Source code security auditing is essential before software release in order to identify programming faults that may lead to vulnerabilities and functional failures. In this paper, we present a structured security assessment of the Windows App SDK by integrating multiple static analysis tools with a context-aware and disagreement-aware Large Language Model (LLM) interpretation layer. Although static analyzers are effective in reporting potential weaknesses, their raw outputs often contain redundant alerts, limited contextual explanation, and inconsistent severity assignments. To address these limitations, the proposed LLM-based interpretation layer normalizes and de-duplicates alerts, filters context-limited or nonactionable warnings, and refines severity prioritization under …

A Machine Learning Framework For Residential District Cooling: Forecasting Consumption, Explaining Drivers, And Evaluating Decarbonization Pathways, Kadhim Hayawi, Husna Maliakkal, Neethu Venugopal, Thanveer Musthafa Hussain, Gomathi Bhavani Rajagopalan

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District Cooling Systems (DCS) in the Middle East, while energy-efficient, are significant contributors to carbon emissions. This study introduces a novel framework to decarbonize DCS operations by integrating predictive machine learning, explainable AI (XAI), and renewable energy planning, all grounded in extensive real-world data. Leveraging a unique dataset from 59 residential buildings in the UAE—including energy consumption, climate variables, and building features—we developed a high-fidelity cooling load forecasting model. Following a rigorous chronological validation methodology, the Random Forest model was identified as the most robust, achieving a strong performance (R² = 0.8256, RMSE = 11,668.31). Outdoor temperature was confirmed …

Concordia Seminary Magazine Spring 2026, Ken Ohlemeyer

Concordia Seminary Magazine

Shaped for His Service

Llms In Compiler Construction, Raffi Khatchadourian

Open Educational Resources

These lecture slides survey the use of large language models (LLMs) in compiler construction for a graduate compiler course (CSc 81010). They situate LLMs across the compiler pipeline and examine representative work: foundation models trained on LLVM IR and assembly (Meta's LLM Compiler), LLM-driven code optimization, binary decompilation (LLM4Decompile), and LLM-assisted automated refactoring—alongside the challenges of applying probabilistic models to tasks that demand correctness. The slides are a self-contained HTML (W3C Slidy) deck with editable Pandoc Markdown source. Part of a two-session unit on advanced compiler topics; see also "Deep Learning Compilers."

Deep Learning Compilers, Raffi Khatchadourian

Open Educational Resources

These lecture slides introduce deep learning compilers for a graduate compiler-construction course (CSc 81010). Building on the classical compiler pipeline, they show how modern machine-learning systems compile tensor programs: static tensor and type analysis (illustrated by a WALA/Ariadne-based refactoring of imperative TensorFlow code to graph mode), MLIR-based end-to-end compilation with IREE, and the PyTorch 2.x stack—TorchDynamo graph capture, AOTAutograd, PrimTorch operator decomposition, and TorchInductor lowering to Triton (GPU) and C++/OpenMP (CPU). The slides are a self-contained HTML (W3C Slidy) deck with editable Pandoc Markdown source. Part of a two-session unit on advanced compiler topics; see also "LLMs in Compiler Construction."

Beyond Religious Narcissistic Identification: Agnostic And Atheistic Narcissism, Magdalena Żemojtel-Piotrowska, Jarosław Piotrowski, Bartłomiej Nowak, Vassilis Saroglou, John Maltby, Constantine Sedikides, Mladen Adamovic, Nur Amali Aminnuddin, Seth Christopher Yaw Appiah, Rahkman Ardi, Zana Hasan Babakr, Einar Baldvin Baldursson, Sergiu Bălțătescu, Muhammed Bilgehan Aytaç, Aidos Bolatov, Bruno Bonfá-Araujo, Matthias Burghart, Phatthanakit Chobthamkit, Marilyn Clark, Magali Clobert, Richard G. Cowden, Jesus Alonso Datu, Sandesh Dhakal, Sonya Dragova-Koleva, Begoña Espejo, Dmitry Grigoryev, Emanuela Gritti, Katherine Gundolf

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Agnosticism and atheism are often grouped simply as nonreligious identities, yet emerging research highlights their distinct psychological profiles and social implications. Among these distinctions, collective narcissism–characterized by strong attachment to one’s group, exceptionalism, and grievance for recognition–offers a framework for understanding identity processes in both nonreligious groups. We examined whether agnostics and atheists exhibit collective narcissism and its forms (agentic–focused on exceptional effectiveness; communal–focused on exceptional morality) similarly to believers. We explored cross-denominational variance in agentic and communal collective narcissism levels relying on data from 77 countries (N = 3,570; 1227 agnostics, 2343 atheists). Agnostics and atheists from secular countries …

Graduates List - May 2026, University Of Nebraska At Omaha

Commencement Lists

No abstract provided.

Worldview-Bench: A Benchmark For Evaluating Global Cultural Perspectives In Large Language Models, Abdullah Mushtaq, Imran Taj, Rafay Naeem, Ibrahim Ghaznavi, Junaid Qadir

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Background: Large Language Models (LLMs) are predominantly trained and aligned in ways that reinforce Westerncentric epistemologies and socio-cultural norms, leading to cultural homogenization and limiting their ability to reflect global civilizational plurality. Existing benchmarking frameworks fail to adequately capture this bias, as they rely on rigid, closed-form assessments that overlook the complexity of cultural inclusivity. Objectives: To address this cultural bias problem, we introduce WorldView-Bench, a benchmark designed to evaluate Global Cultural Inclusivity (GCI) in LLMs by analyzing their ability to accommodate diverse worldviews. Methods: Our approach is grounded in the Multiplex Worldview proposed by Senturk et al., which distinguishes …

Qualitative Dataset On Student Interpretation Of Automated Code Feedback And Programming Self-Efficacy, Mary Benjamin

Michigan Tech Research Data

This dataset supports a qualitative study examining how first-year engineering students interpret automated code feedback and how these interpretations relate to the development of programming self-efficacy.

The study focuses on student interactions with an automated code critiquer (WebTA) in a MATLAB-based introductory programming course. It explores how feedback influences students’ emotional responses, debugging behaviors, and confidence development.

Conceptual framing: Automated Feedback → Interpretation → Self-Efficacy Development