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Impact Of Adaptive Filtering-Based Noise Reduction On The Quality Of F-C Images In Masw, Doğukan Durdağ Nov 2025

Impact Of Adaptive Filtering-Based Noise Reduction On The Quality Of F-C Images In Masw, Doğukan Durdağ

Turkish Journal of Earth Sciences

This study investigates the effects of adaptive filtering, based on the least mean square algorithm, on the quality of multichannel analysis of surface waves (MASW) data and frequency-phase velocity (f-c) images. Specifically, the adaptive filtering method aims to decrease the noise and improve the determination of fundamental modes in f-c images. Both sample and field seismic data with varying noise levels were evaluated using adaptive filtering, resulting in significant improvements in f-c image quality. The filtered data displayed a wider frequency range of f-c images, and reduced noise compared to the original noisy data. Based on these findings, adaptive filtering …

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Meshed Trees: A Framework For Resilient Network Algorithms And Protocols, Peter Willis Nov 2025

Meshed Trees: A Framework For Resilient Network Algorithms And Protocols, Peter Willis

Theses

The continued growth of computer networks in both size and complexity presents challenges for the control protocols that are tasked with maintaining optimal data-forwarding paths. These protocols commonly rely on shortest-path algorithms to determine where network data should be propagated and how it should be analyzed for inclusion in forwarding tables. However, problems arise during reconvergence, the process in which devices rediscover optimal paths following a network failure. Because control protocols were built decades ago for the networks of that era, the existing shortest-path algorithms utilized were designed for problems before the advent of modern computer networks. This resulted in …

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Deep Learning Reveals How Cells Pull, Buckle, And Navigate Fibrous Environments, Abinash Padhi, Arka Daw, Atharva Agashe, Medha Sawhney, Maahi M Talukder, Mehran M H Pour, Mohammad Jafari, Guy M Genin, Farid Alisafaei, Sohan Kale, Anuj Karpatne, Amrinder S Nain Nov 2025

Deep Learning Reveals How Cells Pull, Buckle, And Navigate Fibrous Environments, Abinash Padhi, Arka Daw, Atharva Agashe, Medha Sawhney, Maahi M Talukder, Mehran M H Pour, Mohammad Jafari, Guy M Genin, Farid Alisafaei, Sohan Kale, Anuj Karpatne, Amrinder S Nain

2020-Current year OA Pubs

Cells in tissues navigate fibrous environments fundamentally differently than they do on flat substrates, but the establishment of cell forces in physiological fibrous settings remains poorly understood. Although factors such as the stiffness of the extracellular matrix (ECM) are known to drive behaviors, including cell motility on flat nonfibrous substrates, the interplay between fiber architecture and stiffness in fibrous ECM is not known. Here, we find that in fibrous environments, the directionality of mechanical forces overrides ECM stiffness as the primary regulator of contractility in migrating cells. Using an approach combining phase microscopy with deep learning to map forces in …

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Quality Improvement Project: Improving Providers’ Self-Efficacy In Facilitating Mat Usage Through Educational Training, Joshua W. Moore Nov 2025

Quality Improvement Project: Improving Providers’ Self-Efficacy In Facilitating Mat Usage Through Educational Training, Joshua W. Moore

Doctor of Nursing Practice Projects

Opioid Use Disorder (OUD) and Alcohol Use Disorder (AUD) are common diseases treated in detox facilities around the world. Medication-assisted treatment (MAT) is the most effective treatment for OUD and AUD. Despite the need for MAT, many providers do not prescribe lifesaving medications due to a lack of knowledge and confidence. The purpose of this quality improvement project at a local detox facility is to answer the question: What effect on providers’ self-efficacy in facilitating MAT usage upon discharge does educational training have? Under the Social Cognitive Theory (SCT), self-efficacy was targeted using the Plan-Do-Study-Act (PDSA) change model. The aim …

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Biochronostratigraphy Of The Cenomanian-Paleocene Platform Margin Carbonate Successions In The Geyik Dağı Area, Central Taurides, Türkiye, Kemal Tasli, Hayati̇ Koç, Cemi̇le Solak Nov 2025

Biochronostratigraphy Of The Cenomanian-Paleocene Platform Margin Carbonate Successions In The Geyik Dağı Area, Central Taurides, Türkiye, Kemal Tasli, Hayati̇ Koç, Cemi̇le Solak

Turkish Journal of Earth Sciences

Slope to basin carbonate successions, ranging in age from the Cenomanian to the Paleocene, are exposed in the Geyik Dağı area that is part of the Anamas-Akseki autochthon (Geyik Dağı unit) cropping out in western Central Taurides (southern Türkiye). Biochronostratigraphic and sedimentologic features of the successions have been recently described from 3 stratigraphic sections. They are structurally separated into 2 successions. The lower succession is unconformably overlain by Eocene turbiditic sandstones. The upper succession is in a tectonic sheet between the Eocene sandstones and the Aladağ unit. Stratigraphic bases of the successions are not exposed in the study area. The …

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A Comprehensive Analysis Of Türkiye’S Beaches: Spatial Distribution, Morphometry, And Human Impacts, Muhammed Zeynel Öztürk, Berkay Yilmaz, Abdullah Soykan Nov 2025

A Comprehensive Analysis Of Türkiye’S Beaches: Spatial Distribution, Morphometry, And Human Impacts, Muhammed Zeynel Öztürk, Berkay Yilmaz, Abdullah Soykan

Turkish Journal of Earth Sciences

The classification and morphometric characteristics of beaches is crucial for understanding coastal dynamics, evaluating natural and anthropogenic pressures, and supporting effective coastal management and conservation strategies. This is particularly important for countries like Türkiye, where extensive and diverse coastlines play a key role in tourism, the economy, and ecological sustainability. This study presents the first comprehensive, nationwide inventory and characterisation of Türkiye’s beaches, encompassing its 8483 km coastline. Using satellite imagery, 6110 beaches were identified and classified according to environmental conditions, with analyses conducted on their morphological characteristics, sea boundaries, and provincial distributions. The results show that rocky coast and …

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Towards A Generalized And Optimized Apriori Approach, Artem Abdikov Nov 2025

Towards A Generalized And Optimized Apriori Approach, Artem Abdikov

Master's Theses

Apriori is a machine learning algorithm developed in 1994 by R. Agrawal and R. Srikant for association rule mining purposes. This family of algorithms takes transactional data and analyzes relationships between variables in large datasets. The typical output of such algorithms is a prediction that if users choose item X, it is highly likely that they will also choose item Y. Apriori is known to be a robust algorithm and is used by many large companies in order to analyze user tendencies and even make recommendations. Although Apriori is a powerful algorithm, its original implementation is known to have limitations, …

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Impact Of Community Factors On Naloxone Overdose Reduction In Ohio, Theophilus Oduro Frimpong Nov 2025

Impact Of Community Factors On Naloxone Overdose Reduction In Ohio, Theophilus Oduro Frimpong

Walden Dissertations and Doctoral Studies

The opioid crisis in Ohio, driven by fentanyl, persists due to inequitable access to naloxone despite its proven effectiveness. This study examined how socioeconomic status (SES), race/ethnicity, population density, and hospital availability per capita predicted naloxone overdose reversals (dependent variable) in Ohio. Social-ecological theory guided this quantitative historical correlational analysis of Ohio’s Project DAWN index data (N = 5,280). Inclusion criteria included individuals ages 18 to 65 with naloxone-related overdose reversals (dependent), naloxone distribution (moderator), and community factors (independent). Multiple linear regression results showed that SES (p >.50), race/ethnicity (% White, p = .588; % Black, p = .495; % …

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An Intelligent Vision System For Metal Surface Defect Detection, Sida Zhang Nov 2025

An Intelligent Vision System For Metal Surface Defect Detection, Sida Zhang

Dissertations (1934 -)

With the continuous growth in the need for artificial intelligence driven manufacturing, automatic detection of metal surface quality has become a key issue in forging production. Traditional manual detection methods are not only inefficient and subjective but also have low accuracy when analyzing workpieces with complex surfaces and diverse colors. To solve this problem, this dissertation proposes two methods for metal surface defect detection and classification. This dissertation begins with the background of forging and summarizes existing literature on industrial vision systems and surface defect detection methods. Basic image processing techniques, machine learning algorithms, and deep learning foundations—including artificial neural …

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Comparative Analysis Of 2d And 3d Gait Metrics From Video Using Deep Learning Techniques, Akhilesh Singh Kushwaha Nov 2025

Comparative Analysis Of 2d And 3d Gait Metrics From Video Using Deep Learning Techniques, Akhilesh Singh Kushwaha

Electronic Theses and Dissertations 2020 - Present

This thesis presents a comparative evaluation of two deep learning-based methods for extracting 2D and 3D gait metrics from monocular video: a 2D-to-3D lifting approach combining AlphaPose for keypoint detection with MotionBERT for temporal 3D reconstruction, and a direct 3D estimation method using MeTRAbs. Videos of two healthy adults walking across a ProtoKinetics Zeno™ Walkway were captured laterally at 4K/60 fps using an iPhone 16. Ground-truth spatiotemporal gait parameters were obtained from the pressure-sensitive mat, enabling quantitative validation via Mean Absolute Error (MAE) and Pearson correlation. MeTRAbs consistently outperformed the lifting pipeline, achieving MAE below 2% for temporal metrics (e.g., …

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Fpga-Based Takagi-Sugeno Fuzzy Controller For Quadrotor Uav Stabilization And Trajectory Tracking, Hocine Khati, Mohamed Amine Nehmar, Arezki Fekik, Mohand Achour Touat, Hand Talem, Rabah Mellah Nov 2025

Fpga-Based Takagi-Sugeno Fuzzy Controller For Quadrotor Uav Stabilization And Trajectory Tracking, Hocine Khati, Mohamed Amine Nehmar, Arezki Fekik, Mohand Achour Touat, Hand Talem, Rabah Mellah

Turkish Journal of Electrical Engineering and Computer Sciences

This study presents the implementation of a fuzzy logic–based control system on a field-programmable gate array (FPGA) for a quadrotor autonomous aerial vehicle (UAV). The objective is to design and integrate six Takagi–Sugeno fuzzy controllers to regulate roll, pitch, and yaw angles, along with longitudinal, latitudinal, and altitude movements, thereby stabilizing the UAV and enabling it to follow a desired trajectory. Due to the computational complexity of the six controllers, achieving the desired performance requires considerable processing time, which can adversely affect the quadrotor’s mission. Owing to their high processing power and operating frequency, FPGAs enable the control algorithm to …

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Exploring The Link Between Emotional States And Coding Task Quality: A Pilot Study, Aquib Reshad, Valentina Nino, Maria Valero, Adriane Randolph, Yang Shi Nov 2025

Exploring The Link Between Emotional States And Coding Task Quality: A Pilot Study, Aquib Reshad, Valentina Nino, Maria Valero, Adriane Randolph, Yang Shi

Faculty Articles

Emotions play a crucial role in shaping cognitive performance, yet their influence on programing remains understudied. This pilot study investigates the relationship between emotional states and coding task quality. Ten participants completed a programing task while their brain activity was recorded using electroencephalography (EEG), with frontal alpha asymmetry (FAI) applied as a neural marker of emotional valence. Emotional self-reports were collected using the Scale of Positive and Negative Experience (SPANE), and coding quality was evaluated through a structured rubric. Preliminary findings indicate a potential association between FAI and coding performance, whereas self-reported affect showed weaker or inconsistent patterns. Given the …

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Advances In Artificial Intelligence For Glioblastoma Radiotherapy Planning And Treatment, Reid Master, Nesha Rubin, James Sampson, Kamlesh K Yadav, Shruti Pandita, Aria Sabbagh, Anika Krishnan, Patrick J Silva, Kenneth S Ramos, Vincent Gregoire, Nikos Paragios, Sunil Krishnan, Tej K Pandita Nov 2025

Advances In Artificial Intelligence For Glioblastoma Radiotherapy Planning And Treatment, Reid Master, Nesha Rubin, James Sampson, Kamlesh K Yadav, Shruti Pandita, Aria Sabbagh, Anika Krishnan, Patrick J Silva, Kenneth S Ramos, Vincent Gregoire, Nikos Paragios, Sunil Krishnan, Tej K Pandita

The Brown Foundation: Institute of Molecular Medicine

Glioblastoma is an aggressive central nervous system tumor characterized by diffuse infiltration. Despite substantial advances in oncology, survival outcomes have shown little improvement over the past three decades. Radiotherapy remains a cornerstone of treatment; however, it faces several challenges, including considerable inter-observer variability in clinical target volume delineation, dose constraints associated with adjacent organs at risk, and the persistently poor prognosis of affected patients. Recent advances in artificial intelligence, particularly deep learning, have shown promise in automating radiation therapy mapping to improve consistency, accuracy, and efficiency. This narrative review explores current auto segmentation frameworks, dose mapping, and biologically informed radiotherapy …

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Organizational Leaders’ Effective Strategies For Using Employee Training To Mitigate Threats To Cybersecurity Compliance, Oluwaseye Adekunle Fadare Nov 2025

Organizational Leaders’ Effective Strategies For Using Employee Training To Mitigate Threats To Cybersecurity Compliance, Oluwaseye Adekunle Fadare

Walden Dissertations and Doctoral Studies

Organizational leaders increasingly face challenges associated with employee noncompliance with cybersecurity controls, which expose business leaders to reputational and financial risks. Addressing this business problem is critical for cybersecurity leaders and business executives who aim to strengthen digital resilience and sustain profitability. The purpose of this qualitative pragmatic inquiry was to explore effective strategies organizational leaders use to enhance employee cybersecurity compliance through training. The project was grounded in the protection motivation theory and total quality management frameworks, which provide insight into the behavioral and continuous improvement dimensions of cybersecurity management. Six cybersecurity leaders from Nigeria participated in semistructured interviews. …

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Spartan Daily, November 25, 2025, San Jose State University, School Of Journalism And Mass Communications Nov 2025

Spartan Daily, November 25, 2025, San Jose State University, School Of Journalism And Mass Communications

Spartan Daily, 2025

Volume 165, Issue 39

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Re: Conditional Approval Letter For The Final Butte Mine Waste Repository Geotechnical Investigation Work Plan (Dated November 14, 2025), Emma Rott Nov 2025

Re: Conditional Approval Letter For The Final Butte Mine Waste Repository Geotechnical Investigation Work Plan (Dated November 14, 2025), Emma Rott

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

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Intro To Ai Literacies Discussion, Miranda Rectenwald Nov 2025

Intro To Ai Literacies Discussion, Miranda Rectenwald

Generative AI Teaching Activities

Students consider and dicuss key literacies when using generative AI tools.

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Ecotoxicological Assessment Of A Sunscreen-Derived Pollutant, Benzophenone-2, On Freshwater Fish (Labeo Rohita), Mina Jamil, Sajid Abdullah, Maham Riaz, Sana Aziz Nov 2025

Ecotoxicological Assessment Of A Sunscreen-Derived Pollutant, Benzophenone-2, On Freshwater Fish (Labeo Rohita), Mina Jamil, Sajid Abdullah, Maham Riaz, Sana Aziz

Turkish Journal of Zoology

Ultraviolet filters (UVFs) are constituents of skincare products that protect skin against UV ray damage, such as suntan, sunburn, and skin tumors. Due to their ability to biotransform, UVFs are considered emerging pollutants. Benzophenone-2 (BP-2), with a yellowish crystalline appearance, is one of the widely used UVFs in skincare products. It enters the aquatic environment by washing off the skin during swimming and recreational activities and becomes hazardous to aquatic life due to its lipophilicity and bioaccumulative potential. This study was designed to evaluate the acute and chronic toxicity of BP-2 for the freshwater fish Labeo rohita. For acute toxicity, …

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Morphological Variability And Systematics Of Ponto-Caspian Cladocerans Of The Genus Cercopagis Sars, 1897 (Branchiopoda: Onychopoda: Cercopagidae), Larisa F. Litvinchuk, Spartak N. Litvinchuk Nov 2025

Morphological Variability And Systematics Of Ponto-Caspian Cladocerans Of The Genus Cercopagis Sars, 1897 (Branchiopoda: Onychopoda: Cercopagidae), Larisa F. Litvinchuk, Spartak N. Litvinchuk

Turkish Journal of Zoology

In recent decades, significant changes have occurred in the composition of zooplankton, especially in the Ponto-Caspian Basin. An important component of zooplankton in this region is cladocerans of the genus Cercopagis. However, the introduction of invasive species means that many species of this genus have probably become extinct. Because their disappearance molecular study of these organisms is no longer possible. Therefore, a comparative analysis of morphological variability has become more important. We studied the morphological variability of all 12 currently known species of the genus. Samples from the Caspian, Azov, Black, and Baltic Seas were analyzed. The number of species …

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Nivolumab Plus Ipilimumab Induce Hyper-Progression In Renal Medullary Carcinoma: Results Of A Phase Ii Trial And Preclinical Evidence, Melinda Soeung, Xinmiao Yan, Ciro Zanca, Jing Qian, Menuka Karki, Fei Duan, Hania Khan, Li Zhang, David H Peng, Mariah Williams, Rong He, Ziheng Chen, Luigi Perelli, Jianfeng Chen, Rebecca S Tidwell, Pankaj K Chauhan, Courtney N Le, Truong N A Lam, Nirjar Bhattacharya, Rutvi Shah, I-Lin Ho, Jason P Gay, Caroline C Carrillo, Ningping Feng, Kang Le, Guang Gao, Teresa L Perry, Faika Mseeh, Yongying Jiang, Quanyun A Xu, Niki Marie Zacharias, Rahul A Sheth, Tharakeswara K Bathala, Priya Rao, Najat C Daw, Durga N Tripathi, Cheryl L Walker, Mohammad M Mohammad, Jianhua Zhang, Guangchun Han, Yanshuo Chu, Ruiping Wang, Minghao Dang, Enyu Dai, Fuduan Peng, Yunhe Liu, Akshaya Jadhav, Wenhua Lang, Claudio A Arrechedera, Leticia Campos Clemente, Edwin R Parra, Hsinyi Lu, Cara L Haymaker, Ignacio I Wistuba, Andrew Futreal, Andrea Viale, Michael J Soth, Philip Jones, Joseph R Marszalek, Timothy Heffernan, Giulio F Draetta, Nizar M Tannir, Jianjun Gao, Linghua Wang, Giannicola Genovese, Pavlos Msaouel Nov 2025

Nivolumab Plus Ipilimumab Induce Hyper-Progression In Renal Medullary Carcinoma: Results Of A Phase Ii Trial And Preclinical Evidence, Melinda Soeung, Xinmiao Yan, Ciro Zanca, Jing Qian, Menuka Karki, Fei Duan, Hania Khan, Li Zhang, David H Peng, Mariah Williams, Rong He, Ziheng Chen, Luigi Perelli, Jianfeng Chen, Rebecca S Tidwell, Pankaj K Chauhan, Courtney N Le, Truong N A Lam, Nirjar Bhattacharya, Rutvi Shah, I-Lin Ho, Jason P Gay, Caroline C Carrillo, Ningping Feng, Kang Le, Guang Gao, Teresa L Perry, Faika Mseeh, Yongying Jiang, Quanyun A Xu, Niki Marie Zacharias, Rahul A Sheth, Tharakeswara K Bathala, Priya Rao, Najat C Daw, Durga N Tripathi, Cheryl L Walker, Mohammad M Mohammad, Jianhua Zhang, Guangchun Han, Yanshuo Chu, Ruiping Wang, Minghao Dang, Enyu Dai, Fuduan Peng, Yunhe Liu, Akshaya Jadhav, Wenhua Lang, Claudio A Arrechedera, Leticia Campos Clemente, Edwin R Parra, Hsinyi Lu, Cara L Haymaker, Ignacio I Wistuba, Andrew Futreal, Andrea Viale, Michael J Soth, Philip Jones, Joseph R Marszalek, Timothy Heffernan, Giulio F Draetta, Nizar M Tannir, Jianjun Gao, Linghua Wang, Giannicola Genovese, Pavlos Msaouel

Faculty, Staff and Students Publications

Therapeutic options for patients with renal medullary carcinoma (RMC) are limited. Here we report the results of a phase II clinical trial (NCT03274258) of anti-PD1 nivolumab plus anti-CTLA4 ipilimumab in patients with RMC, with objective response rate as primary outcome. Enrollment was halted for futility at a prespecified interim analysis as all 10 treated patients experienced rapid disease progression. 5/10 met radiological criteria for hyperprogression and median progression-free survival (secondary outcome) was 1.38 months (95% confidence interval: 1.28, 1.60). In a post-hoc single-cell RNA sequencing analysis, data from patients with RMC before and after nivolumab plus ipilimumab treatment indicated that …

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Grp-0176 A Comparative Analysis Of Traditional Virtual Machines And Micro Virtual Machines, Tasnim Akter Onisha Nov 2025

Grp-0176 A Comparative Analysis Of Traditional Virtual Machines And Micro Virtual Machines, Tasnim Akter Onisha

C-Day Computing Showcase

Virtualization technologies form the backbone of modern cloud and serverless platforms, but the balance between performance efficiency and strong isolation remains a key research challenge. Using a comparative literature based methodology, benchmark data from empirical studies are synthesized and normalized across four metrics: startup latency, memory footprint, isolation strength, and scalability. The findings show that traditional VMs such as KVM and Xen provide robust, formally verified isolation but incur higher boot times and memory usage, whereas Micro VMs like Firecracker and Kata Containers achieve lower latency (often < 125 ms) and smaller memory footprints (5–30 MB) while preserving VM-level isolation. Overall, Micro VMs deliver near container responsiveness with VM-grade security, making them suited for serverless and edge environments. As a future direction, this study highlights the need for scalable, formally verified Micro VM architectures for next-generation cloud systems.

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Grp-0165 Reinforcement Learning For Latency-Aware Priority Boosting In Linux Completely Fair Scheduler, Joseph Natter Nov 2025

Grp-0165 Reinforcement Learning For Latency-Aware Priority Boosting In Linux Completely Fair Scheduler, Joseph Natter

C-Day Computing Showcase

Tail latency remains a persistent challenge in Linux’s Completely Fair Scheduler (CFS), particularly when short, latency-sensitive jobs compete with longer ones. Traditional boosting heuristics improve tail latency but require manual tuning and generalize poorly across workloads. This project evaluates whether a reinforcement-learning (RL) controller can dynamically apply priority boosts more effectively than fixed heuristics. Using a discrete-event Python simulator modeled after CFS, this project compares baseline CFS, heuristic boosting, and PPO-based learned boosting under mixed workloads. Results show that while heuristics achieve the lowest absolute latency, RL achieves competitive tail-latency reduction with significantly better fairness and adaptability. A state-space study …

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Grp-21186 A Safe Vision-Guided Robotic Injection Control Framework Based On Reinforcement Learning, Zhiguo Liu Nov 2025

Grp-21186 A Safe Vision-Guided Robotic Injection Control Framework Based On Reinforcement Learning, Zhiguo Liu

C-Day Computing Showcase

This project presents a modular vision-guided robotic framework for safe deltoid intramuscular injection. An external YOLO-based detector localizes the deltoid region and outputs a 3D injection point in the robot base frame. In NVIDIA Isaac Sim, a simulated myCobot 280 arm receives this point, and a deep reinforcement learning policy generates a safe approach pose under kinematic and safety constraints, while a deterministic controller executes the final straight-line insertion and withdrawal. A safety supervisor monitors target validity, joint limits and distance to a simplified arm model, triggering immediate stop and retraction when unsafe conditions arise.

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Grp-20236 Zero-Day Host-Based Intrusion Detection Via Hybrid Deep Sequence Modeling Of Systemcalls​ ​, Syeda Umme Salma Nov 2025

Grp-20236 Zero-Day Host-Based Intrusion Detection Via Hybrid Deep Sequence Modeling Of Systemcalls​ ​, Syeda Umme Salma

C-Day Computing Showcase

Protecting endpoints has become increasingly challenging, as adversaries have been effective in bypassing defenses. Traditional signature-based Host-based IDS performs quite well at recognizing known patterns but often struggles with previously unseen activity. This study incorporates deep neural sequence modeling with classic OS telemetry to flag novel behavior from Linux system-cell traces. This study design is paired with a sequence encoder over syscall streams, incorporating lightweight statistical signals that are derived from process activity. We believe that our hybrid neural network approach will outperform conventional baselines and boost recall on unknown attacks while maintaining low false-positive rates, providing a practical and …

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Grp-20219 Continuous Monitoring Of Cardiovascular Risk From Smartwatch Data Using A Knowledge Distillation Framework, Nursat Jahan Nov 2025

Grp-20219 Continuous Monitoring Of Cardiovascular Risk From Smartwatch Data Using A Knowledge Distillation Framework, Nursat Jahan

C-Day Computing Showcase

Cardiovascular Disease (CVD) is one of the leading causes of global health concern, but current risk assessments are limited to episodic clinical visits. Most machine learning (ML) models trained on clinical data offer high accuracy but are not practical for continuous monitoring. Smartwatch-based wearables provide continuous real-time physiological data but lack clinical validation for robust risk prediction outside the clinical setting. To bridge this gap, we proposed a novel teacher-student knowledge distillation framework that transfers knowledge of complex and large EHR datasets to a small Fitbit smartwatch dataset-based prediction model. The student model achieves promising accuracy, identifying all types of …

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Uc-1152 Sustainsync, Youssef El-Shaer, Zaid Khan Nov 2025

Uc-1152 Sustainsync, Youssef El-Shaer, Zaid Khan

C-Day Computing Showcase

Sustain Sync investigates how organizations can standardize sustainability tracking and how AI can convert that data into actionable insights. The platform normalizes utility data into a consistent schema aligned with industry frameworks. It applies machine learning forecasting and a retrieval-augmented co-benefit engine to relate sustainability goals across domains such as CO₂, water, and biodiversity. Through a simple dashboard, Sustain Sync demonstrates an end-to-end data-driven approach for goal tracking, trend analysis, and AI-driven sustainability recommendations for an organization.

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Ur-1256 Realistic Paint-Like Color Mixing In Gimp, Calvin Crose, Cassidie Grogan, Dawson White, Joey Montalvo Nov 2025

Ur-1256 Realistic Paint-Like Color Mixing In Gimp, Calvin Crose, Cassidie Grogan, Dawson White, Joey Montalvo

C-Day Computing Showcase

Gimp updated its support for the MyPaint brushes, from version 1.0 to version 2.0. However, despite the added support, the application still uses legacy blending from version 1.0 of MyPaint brushes and lacks the new tools for spectral blending offered in version 2.0. A request has been made to implement the new blending so users can produce art using this feature that mirrors real-world blending.

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Ur-1163 Editing Films And Movies On Gimp, Ahmed Al Saad, William Grace, Brian Robinson Nov 2025

Ur-1163 Editing Films And Movies On Gimp, Ahmed Al Saad, William Grace, Brian Robinson

C-Day Computing Showcase

This project is to develop an import feature for Digital Picture Exchange (.DPX) files with GIMP. The DPX format is popularly used in the film and visual effects industry. DPX stores uncompressed high bit depth images. Although the project is still in progress, a working prototype is provided.

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Ur-0247 Lyric Prediction Model, Evan Gideon, Garrett Dasher, Ulrich Batanado, Drew Claerbout, Andrew Henshaw Nov 2025

Ur-0247 Lyric Prediction Model, Evan Gideon, Garrett Dasher, Ulrich Batanado, Drew Claerbout, Andrew Henshaw

C-Day Computing Showcase

Word prediction plays a central role in the development and refinement of large language models, supporting applications such as search optimization, dialect identification, and conversational AI systems like Siri as AI text generation becomes increasingly widespread, the demand for precise and contextually aware predictive capabilities continues to grow. This project presents lyric prediction model designed to generate the next lyric based on preceding words, with the ability to identity line breaks and sequential structure. Ultimately, this work aims to advance lyrical text generation by enabling the model to emulate the stylistic characteristics of specific artists or musical genre.

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Ur-0246 Quantum Ml For Science & Engineering, Dharani Shakthivel, Haoxian Tan, Justin Martin Nov 2025

Ur-0246 Quantum Ml For Science & Engineering, Dharani Shakthivel, Haoxian Tan, Justin Martin

C-Day Computing Showcase

Classical machine learning methods - including CNNs, SVMs, PCA, Logistic Regression, and Random Forests - have achieved strong performance across fields such as computer vision, malware detection, and drug discovery. However, these models face scalability limits when trained on large or high-dimensional datasets. Quantum computing introduces superposition, interference, and entanglement, enabling quantum kernels, quantum feature maps, and hybrid quantum-classical architectures that may reduce computational cost or enhance data representation. This project implements classical versions of these algorithms alongside their quantum counterparts to evaluate differences in accuracy, efficiency, and resource demands. By comparing performance across diverse scientific and engineering datasets, the …

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