Physical Sciences and Mathematics Commons^™

Railway Track Condition Monitoring Based On Sensor Data And Artificial Neural Networks, Ivan Kots, Alina Isaeva, Mark Denisenko, Alexander Sinyukin, Andrey Kovalev

Turkish Journal of Electrical Engineering and Computer Sciences

Monitoring the condition of engineering objects is one of the urgent tasks of industry, construction, and transport infrastructure. This article describes a system for condition monitoring and diagnostics of rail tracks in real time. Compared with other similar studies, the proposed system has the advantages of compactness, usability, scalability and versatility of application. The proposed monitoring system is based on an Nvidia Jetson Nano embedded computing board and also includes inertial sensor modules, a microphone, a geolocation module, communication modules, an SSD storage device, and a battery. The prototype of the diagnostic module is a portable device that can be …

Modeling And Simulation Of Dynamic Energy Management Systems For Smart Buildings, Onur Özel, Ali̇ Rifat Boynueğri̇, Hayri̇ Yi̇ği̇t, Burak Tekgün

Turkish Journal of Electrical Engineering and Computer Sciences

This study presents a dynamic energy management system tailored for smart residential buildings, integrating thermal and electrical models to achieve both natural gas and electricity bill cost reduction. By harnessing wind and solar energy sources, the system aims to meet the diverse energy needs of modern homes. Through load shifting and thermal storage strategies, known as power-to-heat (P2H) approaches, the system ensures efficient renewable energy utilization while maintaining resident comfort. Validation of the proposed system was conducted using real-world data from the Yıldız Technical University Smart Home Laboratory, demonstrating its practical applicability and effectiveness. Results indicate significant reductions in both …

Grey Wolf Optimization Of Pi Controller For Power Management In Wind Farms: A Novel Approach, Anis Feddaoui, Lotfi Farah, Abdelouahab Benretem, Mohammed Abdeldjalil Djehaf

Turkish Journal of Electrical Engineering and Computer Sciences

This study proposes a novel power management strategy for wind farms using a grey wolf optimization (GWO)-based PI controller. The method aims to enhance active and reactive power control in systems employing dou bly fed induction generators. Three control strategies are evaluated—namely, a classical frequency-domain PI controller, an Artificial Neural Network (ANN)-based controller, and the proposed GWO-based PI controller—the last of which represents the main contribution. The classical PI and ANN controllers are included strictly for comparative bench marking. MATLAB simulations demonstrate that the GWO-beased PI controller offers superior dynamic performance, particularly in settling time and overshoot reduction. A power …

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 …

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 …

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 …

Intro To Ai Literacies Discussion, Miranda Rectenwald

Generative AI Teaching Activities

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

Simulating Realistic Lyman-𝛼 Emitters Including The Effect Of Radiative Transfer, Hasti Khoraminezhad, Shun Saito, Max Gronke, Chris Byrohl

Research Data

We present an empirical yet physically motivated simulation of realistic Lyman-𝛼 emitters (LAEs) at 𝑧 ∼ 2 − 3, crucial for ongoing and forthcoming cosmological LAE surveys. We combine an empirical UniverseMachine galaxy-halo model with a simple spherical expanding shell model for the Lyman-𝛼 radiative transfer, calibrating only three free parameters to simultaneously reproduce the observed Lyman-𝛼 luminosity function and the angular clustering. Our LAE model is further supported by its consistency with other observables such as the Lyman-𝛼 equivalent width distribution, the Lyman-𝛼 escape fraction as a function of stellar mass and dust reddening, and the systemic velocity offsets. …

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 …

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 …

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 …

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.

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 …

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 …

Grp-21155 Autotrader-Agentedge, Christopher Regan

C-Day Computing Showcase

This work presents AutoTrader-AgentEdge, a human-in-loop trading system that positions AI agents as collaborative partners rather than autonomous replacements. We demonstrate that multi-indicator consensus voting combined with human approval achieves superior risk-adjusted returns while maintaining interpretability and control. Core Contribution: A production-ready VoterAgent implementing democratic voting between MACD momentum and RSI extremes, generating transparent trading signals for human evaluation. Unlike black-box automation, our interactive CLI augments trader expertise through interpretable consensus logic. The human retains final decision authority at all critical junctures. Validated Performance: Empirical validation demonstrates multi-indicator voting superiority over single-indicator automation: Sharpe ratio 0.856 vs 0.841, max drawdown …

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 …

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 …

Grp-20194 Can Mental Health Apps Really Help Caregivers? Usability Findings From Human-In-The-Loop Nlp And Sentiment-Aware Analytics​, Syeda Umme Salma

C-Day Computing Showcase

Caregivers face distinctive emotional and logistical burdens, yet many mental-health apps overlook their needs and show usability issues. We introduce an automated pipeline that analyzes 317K app-store reviews from 9 apps, mapping them to Nielsen’s usability components and heuristics, together with sentiment. To assess reliability, we run a human–AI agreement study (N=50) where a domain expert (A2) and a non- expert (A1) label reviews. For heuristics, the pipeline achieves 66% exact agreement and moderate κ=0.579 with the expert, outperforming human–human agreement; components remain harder, revealing a need to refine the codebook (e.g., learnability vs satisfaction). Complementary clustering and sentiment analyses …

Grp-20185 Energy-Aware Operating Systems For Edge Artificial Intelligence Inference, Nursat Jahan

C-Day Computing Showcase

Edge Artificial Intelligence (AI) refers to running AI inference directly on local devices such as wearables, sensors, and mobile systems rather than relying on cloud computing. The growth of Edge AI has created strong demand for efficient inference on resource-limited devices. Edge AI devices must perform real-time inference while operating under strict battery constraints. Although significant model optimizations exist for managing power-intensive inference models, operating system (OS) level support is limited. Existing OS schedulers often neglect energy limits in edge devices as they prioritize fairness or throughput. In this research we proposed an OS level framework to bridge this gap …

Grp-1265 Optimizing Cpu Scheduling For Deep Learning And Llm Inference Using Onnx Runtime, Mohammod Akib Khan

C-Day Computing Showcase

Modern applications rely on AI models that must perform real-time predictions on resource-constrained edge devices like laptops. The default OS scheduler often increases context switching, which slows down deep learning and LLM inference. Since these models depend heavily on parallel processing, efficient CPU scheduling becomes essential. In this project, we analyzed how core pinning and thread-level parallelism improve inference performance on a Windows system. Using multiple micro-batch sizes. We compare latency, throughput, and per-sample inference time. The goal is to show how simple OS-level optimization can significantly improve real-time performance for both deep learning models and LLM.