Digital Commons Network^™

Performance Analysis Of Ris-Empowered Ofdm-Im Communications Under Weibull Fading And Joint Tx/Rx I/Q Imbalance, Büşra Ceni̇kli̇oğlu, İbrahi̇m Develi̇, Ayşe Eli̇f Canbi̇len

Turkish Journal of Electrical Engineering and Computer Sciences

A modernist technique, reconfigurable intelligent surface (RIS) provides outstanding signal reflection and amplification, making it highly valuable for upcoming communication systems. Besides, a major contributor is index modulation (IM), attaining superior spectral and energy efficiency, and achieving hardware sufficiency. The primary and novel contribution of this work is the derivation of a highly accurate, closed-form approximate expression for the average bit error rate (ABER) of an orthogonal frequency division multiplexing (OFDM)-IM system operating in the complex and challenging environment characterized by joint transmitter/receiver (Tx/Rx) in-phase and quadrature phase imbalance (IQI) and Weibull fading. This essential analytical achievement is facilitated by …

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 …

Intro To Ai Literacies Discussion, Miranda Rectenwald

Generative AI Teaching Activities

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

Deformation Conditions And Quantifying Strain In The Chai-Kour Shear Zone In The Zagros Orogenic Belt Of Iran, Saeede Keshavarz, Saeed Zarei, Majid Shahpasandzadeh, Soumyajit Mukherjee

Turkish Journal of Earth Sciences

Strain analyses and kinematic studies in orogenic belts are essential for understanding their deformation mechanisms. The Chai-Kour brittle-ductile shear zone is located in the central Sanandaj-Sirjan metamorphic belt, southeast of the Zagros orogen. The present study analyzes deformed mylonitic rocks to clarify the deformation kinematics, temperature, and strain geometry of the Chai-Kour shear zone. Previously developed kinematic shear indicators such as S-C shear bands, deformed porphyroclasts, asymmetric boudins, and folds provide an overall top-to-the-southeast sense of shear in the Chai-Kour shear zone. Quartz micro-thermometry confirms that the Chai-Kour rocks were initially affected by a high-temperature (550–650 °C) deformation that was …

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 …

Regional Sustainability Gap Index (Rsgi): A White Paper, Rob Ogburn, Bill Provaznik, Keke Wu

Business & Community Services

The Regional Sustainability Gap Index (RSGI) is a new measure designed to assess whether a region is on a sustainable economic path for its residents. The index combines two components: Mi, which measures the current wage gap between a county and the most economically active county in the state, and Ri, which measures each county’s wage-growth trajectory relative to that same frontier county. Together, Mi and Ri reveal not only a region’s present wage altitude but also the direction in which that wage horizon is moving. In Washington State, Mi reflects the distance between a county’s median wages and King …

Fraud Detection And Explanation In Medical Claims Using Gnn Architectures, Reem Muhammad, Dina Tbaishat, Amril Nazir, Seif Yacoub, Mustafa Abdulrazek, Mohamed Ahmed Abo El-Enen, Ahmed T. Sahlol

All Works

This paper addresses the critical challenge of fraud detection in medical insurance claims-a pervasive issue causing significant financial losses in healthcare-using Graph Neural Networks (GNNs). Given the intricate nature of healthcare data, traditional fraud detection methods do not inherently capture the complex relationships and patterns among different entities. We explore the potential of GNNs to effectively identify fraudulent claims by modeling the interactions among various entities-such as patients, healthcare providers, diagnoses, and services-as a heterogeneous graph. We employ two state-of-the-art heterogeneous GNN architectures, HINormer (Heterogeneous Information Network Transformer) and HybridGNN, along with a modified homogeneous GNN, RE-GraphSAGE (GraphSAGE Graph Sample …

The Kaczmarz Algorithm In Hilbert C*-Modules, Daniel Alpay, Chad Berner, Eric S. Weber

Mathematics, Physics, and Computer Science Faculty Articles and Research

The Kaczmarz algorithm in Hilbert spaces is a classical iterative method for stably recovering vectors from inner product data. In this paper, we extend the algorithm to the setting of Hilbert C*-modules and establish analogues of its effectiveness in both finite-dimensional and stationary cases. Consequently, we demonstrate that continuous families of elements in a Hilbert space can be uniformly recovered using the Kaczmarz algorithm. Additionally, we develop a normalized Cauchy transform for continuous families of measures and use it to provide sufficient conditions under which standard frames in Hilbert C(X)-modules can be generated by the Kaczmarz …

Phylogenetics Of Cyrtodactylus In West Nusa Tenggara: A New Distribution Record Of Cyrtodactylus Jatnai From Lombok Island And A Cryptic Species From Sumbawa Island, Sandra Rafika Devi, Rury Eprilurahman, Tuty Arisuryanti, Yuliadi Zamroni, Imran Sadewo, Windi Edriyanti, Maulana Faiz Ilham Kholiq, Evelyn Elvira

Turkish Journal of Zoology

Cyrtodactylus is one of the most diverse vertebrates of the Gekkonidae family, first described by Gray in 1827 and widely distributed in Asia, Northern Australia, and the Pacific Islands including Wallacea. The biodiversity in West Nusa Tenggara—a region in Wallacea—has been underestimated despite high fauna endemicity. Conservation strategies are needed to maintain the stability of Cyrtodactylus populations in their habitat. Therefore, this study aims to enhance the understanding of cryptic species and potential new distribution records of the genus Cyrtodactylus in West Nusa Tenggara. Molecular character identification was carried out by isolating the ND2 gene in liver tissue using polymerase …

The Impact Of Machine Learning Security Models On Cloud Data Security, Ali Sanad

Walden Dissertations and Doctoral Studies

Abstract Many information technology (IT) leaders face challenges in adopting machine learning (ML) models for cloud infrastructure, despite their potential to enhance data security. The extent to which IT leaders’ perceived security (PeS) and perceived privacy (PeP) influence their intent to adopt ML security models is critical for organizational success. Grounded in the technology acceptance model, the purpose of this quantitative correlational study was to examine the relationship between IT leaders’ PeS and PeP and their intent to adopt ML security models in cloud-based applications. Data were collected from 106 IT professionals in Chicago using a validated instrument. Results from …

Field Validation Of Multiple Species Distribution Models Shows Variation In Performance For Predicting Aedes Albopictus Distributions At The Invasion Edge, Anna V. Shattuck, Brandon D. Hollingsworth Ph.D., Jared Skrotzki, Scott R. Campbell, Christopher L. Romano, Courtney C. Murdock

Faculty Publications

Background

Climate and land use changes have resulted in range expansion of many species. In this shifting disease landscape, it is important to leverage tools that can predict the distributions of invading vectors to target surveillance and control efforts and identify at-risk populations. Species distribution models (SDMs) are used to predict ranges of invasive species; however, invasive species often violate assumptions of equilibrium and niche conservatism. Moreover, these studies are rarely validated using independent data.

Methods 

We use long-term surveillance data for Aedes albopictus, a highly invasive mosquito capable of transmitting several arboviruses, at its range edge to evaluate a …

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 …

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 …

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 …

Connectedness Of Uncertainty, Volatility, And Stock Market Performance, Szczepan Urjasz

Journal of Banking and Financial Economics

This study examines the dynamic connectedness and spillover effects among various financial and economic indicators, including uncertainty indices, market volatility, and stock market indices, from 3 June 2008, to 30 December 2024. This interconnectedness implies that shocks originating in one market or asset class can rapidly transmit to others, underscoring the potential for systemic risk and financial contagion. The US emerges as a significant net transmitter of influence within the global financial system, with the VIX playing a crucial role in influencing global financial conditions. Major European equity markets also transmit influence, while the Geopolitical Risk Index (GRI) and the …

Identification Of Novel Tat-I24-Related Peptides With Antiviral Activities, Hanna Harant, Siegfried Hofinger, Reingard Grabherr, Zsolt Ruzsics, Hartmut Hengel

Michigan Tech Publications

To identify novel peptides with potential antiviral activities, a database search was performed based on the primary sequence of the peptide I24 (CLAFYACFC), the effective part of the antiviral peptide TAT-I24 consisting of peptide I24 and the cell penetrating TAT-peptide (amino-acids 48–60, GRKKRRQRRRPPQ). A Protein BLAST search identified several sequences with high similarity to I24 in diverse proteins, some of which are known to be involved in the interaction with nucleic acids. Selected sequences and newly designed variants of I24 were synthesized as TAT fusion peptides and tested for antiviral activity in two well-established models: baculovirus transduction of HEK293 cells …

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.

Towards A Holistic Model: Transition Pedagogy In High School Enabling Programs. A Practice Report, Johanna Elizabeth Nieuwoudt, Angela Jones

Research outputs 2022 to 2026

The release of the Australian Universities Accord Final Report in 2024 and call for contextually relevant pathways to higher education continues the widening participation agenda that was introduced more than 50 years ago in Australia, New Zealand and the United Kingdom. Enabling education continues to contribute to targets with the evolution of high school enabling programs. It is imperative, as the Accord suggests, that these programs support successful transition. Such programs thus require a pedagogical model for transition. This practice report adds to emerging research on high school enabling programs by exploring how an enabling transition pedagogy (ETP), an adaptation …

Turning Patients' Open-Ended Narratives Of Chronic Pain Into Quantitative Measures: Natural Language Processing Study, Raquel Norel, Jennifer Gewandter, Zhengwu Zhang, Anika Tahsin, Chadi G Abdallah, John Markman, Zhiyao Duan, Guillermo Cecchi, Paul Geha

Faculty, Staff and Students Publications

Background: Subjective report of pain remains the gold standard for assessing symptoms in patients with chronic pain and their response to analgesics. This subjectivity underscores the importance of understanding patients' personal narratives, as they offer an accurate representation of the illness experience.

Objective: In this pilot study involving 20 patients with chronic low back pain (CLBP), we applied emerging tools from natural language processing (NLP) to derive quantitative measures that captured patients' pain narratives.

Methods: Patients' narratives were collected during recorded semistructured interviews in which they spoke about their lives in general and their experiences with CLBP. Given that NLP …

Gc-0258 Safecircle:​ Ai And Micro-Radar-Based Remote Monitoring For Patients With Ad/Adrd​, Awan-Ur- Rahman, Soarov Borty, Shakib Quddus

C-Day Computing Showcase

Alzheimer's disease and related dementias (AD/ADRD) is an irreversible and degenerative neurological condition that severely impacts neurons, resulting in cognitive decline and memory loss. This study explores a mHealth system, including a SafeCircle iOS prototype, a novel solution that combines artificial intelligence with cutting-edge micro-radar technology. The platform offers a variety of features, including management of patient and caregiver profiles, real-time alerts in case of emergencies, emergency contact lists, one-touch SOS support, sharing of live locations, and recording of unusual events in video. It is a responsive and reliable care assistant that optimizes patient safety while reducing caregiver burden.

Grm-0267 Learned Heuristics For Efficient A* Search: Improving Pathfinding In Combinatorial Pathfinding Problems, Jamia Jackson

C-Day Computing Showcase

This work proposes a learned heuristic framework designed to improve planning efficiency in deterministic pathfinding tasks. Building on the classic 8-puzzle as an initial test domain, supervised models were trained to approximate heuristic values and guide node ordering during A* search. The proposed approach focuses on modifying and enhancing traditional heuristics such as Manhattan distance by incorporating learned corrections that reduce search depth and node expansions. Experimental results show that the learned heuristic consistently improves search efficiency over standard baselines. Although this evaluation began with the 8-puzzle, the framework establishes a foundation for scaling to significantly larger and more practical …

Grp-0200 Tadps: Thrashing-Aware Dynamic Priority Scheduler For Virtual Memory Systems, Nasim Ahmed

C-Day Computing Showcase

Modern operating systems use multiprogramming and virtual memory to run multiple programs efficiently. When memory demand is high, excessive page swapping causes thrashing, degrading performance. This work introduces a thrashing-aware dynamic priority scheduler (TADPS) that uses page fault frequency to guide CPU scheduling. Processes with high page faults get lower priority, allowing others to progress efficiently. Tests under different memory pressures show that this approach improves CPU use, throughput, and stability compared with baseline schedulers such as FCFS and RR. Specifically, TADPS maintained superior throughput and efficiency, and consistently lower average turnaround time than FCFS and RR.

Grp-0197 Evaluating The Ability Of Llms To Interpret, Optimize And Translate Llvm Ir, Reuven Mueller

C-Day Computing Showcase

This study investigates whether modern state-of-the-art Large Language Models (LLMs) can interpret, optimize, and translate low-level intermediate representations (IR) used in compilers and binary translation software. We evaluate LLM performance on LLVM IR across three tasks: 1) interpreting the underlying algorithmic behavior, 2) identifying missed optimization opportunities, and generating improved IR variants, 3) Translating IR between AArch64 and x86-64 targets. To ensure correctness, all LLM generated IR is checked using a validation pipeline that verifies its syntax, structural correctness, compiles it into machine code, and executes it on randomized test data. Early results show that LLMs can perform non-trivial IR …

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.

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 …

Grp-0161 Predicting The Linux Scheduler’S Next Move With Transformers, Amirmohammad Naddaf Shargh

C-Day Computing Showcase

We study whether deep learning can help the Linux CFS, which makes fair scheduling decisions without using historical behavior and may preempt tasks that are near completion. We adopt a dataset and baseline LSTM from earlier work and introduce a Transformer model to explore predictive scheduling. We train both on real scheduling traces to learn the next selected task and timing trends, and evaluate them using task classification accuracy and direction accuracy. Our results show that the LSTM remains the stronger baseline and captures CFS patterns more effectively than our Transformer model in this comparison under identical conditions for fairness.

Grp-0160 Quantum Anonymous Notification Protocol, Nitin Jha, Prateek Paudel

C-Day Computing Showcase

The scalability of current quantum networks is limited due to noisy quantum components and high implementation costs, thereby limiting the security advantages that quantum networks provide over their classical counterparts. Quantum Augmented Networks (QuANets) address this by integrating quantum components in classical network infrastructure to improve robustness and end-to-end security. To enable such integration, Quantum Anonymous Notification (QAN) is a method to anonymously inform a receiver of an incoming quantum communication. Therefore, several quantum primitives will serve as core tools, namely, quantum voting, quantum anonymous protocols, quantum secret sharing, etc. However, all current quantum protocols can be compromised in the …

Grp-21187 Adaptive Sched_Deadline On Linux For Robotic-Arm Control, Zhiguo Liu

C-Day Computing Showcase

Industrial robot arms often run 500–1000 Hz control loops on general-purpose Linux. Most cycles are on time, but rare long scheduling delays can cause visible end-effector jitter, force spikes, and unstable insertion behavior. Static tuning of priorities and budgets cannot fully remove these long-tail outliers without wasting CPU. This work asks whether a lightweight adaptive layer on top of SCHED_DEADLINE can reduce high-percentile latency while keeping good utilization.

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.

Grp-21156 Detecting Dealer Gamma Hedging Mechanics: How Llms Identify Market Structure Without Context, Christopher Regan

C-Day Computing Showcase

We introduce obfuscation testing, a novel methodology for validating whether large language models detect structural market patterns through causal reasoning rather than temporal association. Testing three dealer hedging constraint patterns (gamma positioning, stock pinning, 0DTE hedging) on 242 trading days (95.6% coverage) of S&P 500 options data, we find LLMs achieve 71.5% detection rate using unbiased prompts that provide only raw gamma exposure values without regime labels or temporal context. The WHO→WHOM→WHAT causal framework forces models to identify the economic actors (dealers), affected parties (directional traders), and structural mechanisms (forced hedging) underlying observed market dynamics. Critically, detection accuracy (91.2%) remains …