Digital Commons Network^™

Zero Trust Architecture And Ransomware Mitigation, Ely Johnson

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

Ransomware has become a critical threat to modern enterprises, exploiting excessive privileges and flat network architectures to spread rapidly. Traditional perimeter-based security models are insufficient, as they rely on implicit trust within internal networks. This paper examines how Zero Trust Architecture (ZTA) mitigates ransomware through least privilege access, continuous monitoring, and micro- segmentation. Experimental results show that ZTA can significantly reduce impact, limiting encryption to about 20% of targeted files while preserving most data. Continuous monitoring enables rapid detection (5.3 seconds) with high accuracy (up to 97.2%) and a 78% reduction in false positives. Micro-segmentation further restricts lateral movement, reducing …

Security Limitations Of The Can Bus And Detection Through Power Fingerprinting, Ken Broden

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

This paper examines the vulnerabilities of the Controller Area Network (CAN), the standard communication protocol used in most modern vehicles. It explains why CAN is widely adopted and outlines key security weaknesses in its design. The paper then reviews recent research efforts to detect and mitigate these vulnerabilities, with particular focus on an approach to origin authentication that relies on the unique power consumption patterns of each individual electronic control unit on a CAN bus.

Quantitative Methods In Education: A Practical Introduction To Statistics, Yukiko Maeda, John Gipson, Sheila Hurt, Katie H. Dufault

Purdue University Press Books

Educational research often involves understanding complex patterns in student achievement, teacher effectiveness, and institutional performance. Quantitative Methods in Education: A Practical Introduction to Statistics is designed to equip current and future educators and researchers with a basic comprehension of the statistical tools necessary to effectively analyze and interpret educational data. In today’s data-driven world, the ability to leverage statistical techniques is essential for making informed decisions that can enhance learning outcomes and maximize learner potential. This book provides a systematic approach to exploring these trends using both descriptive and inferential statistics, providing readers with the knowledge to conduct rigorous analyses …

Comparative Analysis Of Traditional And Deep Learning Time Series Architectures For Influenza A Infectious Disease Forecasting, Edmund Fosu Agyemang, Hansapani Rodrigo, Vincent Agbenyeavu

School of Mathematical & Statistical Sciences Faculty Publications

Influenza A remains a major cause of respiratory mortality worldwide, motivating accurate forecasting to support timely preparedness and resource allocation. This study presents a comparative evaluation of two traditional seasonal time series baselines, ARIMA and Holt–Winters exponential smoothing (ETS), and six deep learning (DL) architectures (Simple RNN, LSTM, GRU, BiLSTM, BiGRU, and a Transformer) for forecasting monthly Influenza A case counts in the United States. Data from January 2009 to December 2023 were analyzed, using January 2009 to December 2022 for training and January 2023 to December 2023 for out-of-sample testing. Models were tuned using a validation split and assessed …

Scattered Light Noise At Ligo Livingston Observatory Induced By Microseismic Ground Motion, Debasmita Nandi

LSU Doctoral Dissertations

Gravitational waves are “ripples” in the fabric of spacetime and create a minuscule change in the distance between two points. After publishing his theory of general relativity in 1915, Albert Einstein predicted the existence of gravitational waves as a consequence of the theory. Almost 100 years after the theoretical prediction, gravitational waves were detected for the first time in 2015 by the Advanced LIGO detectors in Livingston, Louisiana, and Hanford, Washington. Gravitational waves have a very small amplitude, making it very challenging to detect them. Different types of noise sources either of instrumental or environmental origin can reduce the astrophysical …

Critical Assessment On Challenges And Advances In Coal Pillar Strength Estimation Techniques: A Focus On Geological Discontinuities, Abhishek Kumar Singh, Sahendra Ram

Journal of Sustainable Mining

Accurate estimation of coal pillar strength is essential for ensuring safety and operational efficiency in underground mining. Current assessment methods often face limitations in addressing time-dependent failure mechanisms, geological discontinuities, and dynamic loading conditions. This paper identifies future research directions aimed at enhancing the reliability of pillar strength evaluations. One important focus is the development of advanced numerical models that account for time-dependent behaviours such as creep and fatigue. Incorporating multi-scale modelling techniques, which connect micro-scale material responses to macro-scale structural performance, may lead to more precise predictions. The integration of real-time monitoring systems measuring stress, deformation, and environmental factors …

Toward The Significance Of Crevasse Orientation In Low And High Crevassed Regions On The Kennicott Glacier, Alaska, Usa, Claire A. Sturm

DePaul Discoveries

Supraglacial landforms serve as potential indicators of underlying bedrock morphology and glacier dynamics. The distribution and morphology of crevasses are closely associated with stress fields and ice-flow regimes, representing processes of stable glaciers, in contrast to their reduced occurrence in stagnant or receding ice masses. The orientation and density of crevasses influence a glacier’s ability to absorb solar energy, as they facilitate movement of meltwater down to subglacial rivers. Beyond the meltwater acting as a lubricant for basal sliding, the slope of the bedrock further impacts the local velocity and ice deformation of a glacier. The study of ice-fracturing mechanics …

Solvent-Free Ball-Milling Synthesis Of 1,2-Disubstituted Benzimidazoles, Zachary A. Jaffery, Katherine E. Christopher, Kyle A. Grice

DePaul Discoveries

The search for sustainability in chemistry has led to the development of the field of Green Chemistry, comprised by 12 guiding principles. Existing syntheses of benzimidazoles, a heterocycle widely used in medicinal chemistry, utilize methods with much room for improved sustainability. This work presents a solventless mechanochemical synthesis of 1,2-disubsituted substituted benzimidazoles from the condensation of phenylenediamines and aryl aldehydes. The effect of milling aid on yield and selectivity was investigated and montmorillonite K10 was determined to be the best milling aid. The purification protocol was optimized as well, with a heptane:EtOAc extraction and subsequent recrystallization from EtOH found to …

Healthcare Access And The Persistent Racial Disparities In Low Birth Weight, Zaheda A. Hussain, Yadanar Moe

DePaul Discoveries

Low birth weight (LBW), defined as less than 2,500 grams, is an influential factor contributing to infant mortality in the United States, with significant racial disparities between Black and White mothers. This study analyzed data from 3,054 Black and White mothers collected from the 2018 National Health Interview Survey to explore the association between access to medical care and LBW within different racial groups. The prevalence of LBW was 8.0%. Overall, mothers without a usual place of medical care had, on average, an 18% higher risk of LBW than those with a usual place (OR = 1.18, 95% CI: 0.80–1.74). …

From Landfill To Ladle: Using Open Lca To Calculate Food Diversion Impacts, Samantha Rodriguez

DePaul Discoveries

Food waste contributes to climate change because discarded food results in wasting embodied carbon from producing food. In addition to wasting embodied carbon, food that ends up in landfills emits methane, a powerful greenhouse gas that contributes to climate change. This life cycle assessment quantifies the carbon equivalent footprint of food donations from Costco to the Seton Soup Kitchen using OpenLCA. Two functional units were analyzed in this study. The first functional unit is one meal consisting of 500 grams of donated food that would otherwise be landfilled served to a Seton Soup Kitchen patron during one meal service. The …

Fish Tissue Digestion And Microplastic Polymer Identification Protocol Using Open-Source Ftir Database, Shawn L. Kissinger, Vick-Ariel Privert, Jason Bystriansky, Kyle A. Grice

DePaul Discoveries

Microplastic (MP) contamination in aquatic ecosystems poses significant concerns for environmental and public health, as seafood represents a critical food source worldwide. Despite increasing evidence of MP presence in marine organisms, efficient and accessible detection methods remain essential for characterizing contamination patterns and informing regulatory frameworks. This study extracted MPs from brain, gill, intestine, liver, and muscle tissues of gilt-head bream (Sparus aurata) and European sea bass (Dicentrarchus labrax), two commercially important species from the Gulf of Cádiz, Spain, using a NaOH–HNO₃ chemical digestion protocol. Isolated particles were characterized by Fourier Transform Infrared (FTIR) spectroscopy, with …

Crystal Structural Analysis Of Medieval Human Bones Using Wide-Angle X-Ray Scattering, Selly Altangerel, Leslie Gracia

DePaul Discoveries

Female human second metacarpal (mc2) bones from a medieval cemetery at Wharram Percy, England, were studied using high-energy Wide-Angle X-ray Scattering (WAXS) at beamline 1-ID of the Advanced Photon Source at Argonne National Laboratory. The age-at-death of 33 samples was determined from molar wear and classified into 3 groups:  18-30, 30-50, and 50+ years old.  Rietveld refinements of the WAXS data were performed to obtain lattice parameters of the hexagonal carbonated apatite phase (cAp). Individual fittings for cAp peaks 00.2, 00.4, and 31.0 were performed to obtain their widths and correlate them to collagen retention. For the three samples described …

Clearing The Air: Tracking Spatial And Temporal Pm2.5 Variability Along A Biking Transect In Chicago, Beau R. Rass

DePaul Discoveries

Fine particulate matter (PM₂.₅) poses significant risks to human health and disproportionately affects marginalized communities in urban environments (World Health Organization [WHO], 2021; Tessum et al., 2021). Using low-cost mobile sensors, this study explored spatiotemporal PM₂.₅ concentration patterns along a north–south transect of Halsted Street in Chicago. A 2B Technologies Portable Aerosol Monitor (PAM) mounted on a bicycle was used to continuously record PM₂.₅ concentrations with associated GPS coordinates at approximately two-second intervals during 16 sampling events between 08-12-2025 and 10-05-2025. PM₂.₅ concentrations did not differ significantly among the South Side, West Loop, and North Side regions, contrary to the …

Depaul Discoveries Volume 15 Cover Art, Dina Khdair

DePaul Discoveries

No abstract provided.

A Holistic Approach For Workforce Scheduling And Routing, Kerem Can Manalp, Ansel Kaplan Erol, Kutluhan Erol, Cem Evrendi̇lek

Turkish Journal of Electrical Engineering and Computer Sciences

The workforce scheduling and routing problem (WSRP) involves assigning tasks across multiple locations while accounting for varying travel times, service durations, time windows, and skill requirements in a wide range of industries, from healthcare to telecommunications. This paper presents a mixed-integer programming model for the WSRP that balances the trade-off between cost and customer satisfaction using a score-generation function and subsequently evaluates the trade-off between solution quality and computation time for several algorithms on well-known datasets. We demonstrate that our model effectively balances cost, service-level agreement satisfaction, and task priorities while providing high-quality solutions in a timely manner. Observing that …

Efficient Edge Implementation Of Midasnet For Real-Time Depth Estimation In Indoor Robotics, Muhammed Yasi̇n Adiyaman, İsmai̇l Fai̇k Başkaya

Turkish Journal of Electrical Engineering and Computer Sciences

Real-time depth estimation is crucial in many vision-related tasks, including autonomous driving, 3D reconstruction, robotics, and simultaneous localization and mapping. In recent years, many methods have been proposed to solve depth maps from images by utilizing different modality setups like monocular vision, binocular vision, or sensor fusion. However, for real-time deployment on edge devices, complex methods are not suitable due to latency constraints and limited computation capacity. For edge implementation, models should be simple, minimal in size, and hardware-friendly. Considering these factors, we implemented MiDaSNet, which works on the simplest setup of monocular vision and utilizes hardware-friendly convolutional neural network-based …

Oregon State Rank Assessment For Steens Mountain Cushion Buckwheat (Eriogonum Ovalifolium Var. Rubidum), Lindsey K. Wise

Institute for Natural Resources Publications

Oregon state conservation status assessment for Steens Mountain Cushion Buckwheat (Eriogonum ovalifolium var. rubidum) using NatureServe methodology, 2026.

Llm-As-A-Judge For Infection Prevention And Control And Antimicrobial Resistance Impact: Comparing Three Main Llms Vs. Human Experts' Assessment, Marcello Di Pumpo, Leonardo Villani, Maria Rosaria Gualano, Danilo Buonsenso, Francesca Raffaelli, Daniele Donà, Patrizia Laurenti, Vittorio Maio, Stefania Boccia, Walter Ricciardi

College of Population Health Faculty Papers

BACKGROUND: Large language models (LLMs) are increasingly used to generate health information, yet their reliability as evaluators remains unclear. This study investigated the feasibility of an LLM-as-a-judge methodology in the context of infection prevention and antimicrobial resistance (AMR), comparing automated ratings with human expert benchmarks.

METHODS: We performed a secondary analysis of an expert-annotated dataset of health messages. Three leading LLMs (ChatGPT, Claude, Gemini) independently evaluated the same messages using an adapted DISCERN tool across five domains: information reliability, quality, AMR impact, persuasiveness, and overall score. We utilized descriptive statistics, intra-rater reliability tests, and mixed-effects ordinal regression to analyze divergence …

A Significant Moment In The Colorado River Water Supply Crisis, Jack Schmidt, Anne Castle, John Fleck, Eric Kuhn, Kathryn Sorensen, Katherine Tara

The Traveling Wilburys of the Colorado River

On Sunday, July 12, the surface of Lake Powell was 3524.32 feet above sea level, and the surface of Lake Mead was at 1042.77 ft.7 These elevations equated to 5,505,869 and 7,169,640 acre feet (af), respectively, of live storage in the two reservoirs. The combined total live storage of these reservoirs was 12,675,509 af. The last time the combined total live storage in Lake Powell and Lake Mead was this small was May 23, 1957, during construction of Glen Canyon Dam when the entire amount of 12,668,000 af was stored in Lake Mead.

Cnkg: Harnessing Large Language Models For Cognitive Neuroscience Knowledge Graph Construction, Ali Sarabadani, Kheirollah Rahsepar Fard, Hamid Dalvand

Turkish Journal of Electrical Engineering and Computer Sciences

Textual resources are among the most valuable sources of information in cognitive neuroscience (CN) for understanding and investigating brain activity and cognitive processes. Extracting and constructing knowledge graphs (KGs) from these texts can facilitate medical research by providing deeper insights into neurological diseases and brain function. In recent years, the use of large language models (LLMs) in natural language processing (NLP) has become increasingly widespread, significantly enhancing the extraction of meaningful information from large volumes of text. This study proposes a novel approach for constructing and evaluating a specialized knowledge graph, termed the cognitive neuroscience knowledge graph (CNKG), from scientific …

Robust Variable-Gain Backstepping Control For Nonlinear Systems With Real-Time Application To Induction Motor, Fadi Alyoussef, İbrahi̇m Kaya, Ahmad Akrad, Rabia Sehab, Cristina Morel

Turkish Journal of Electrical Engineering and Computer Sciences

This study proposes a novel variable-gain mechanism with a minimal number of tuning parameters to enhance the performance of conventional backstepping controllers for nonlinear systems while avoiding singularity and peaking phenomena. The proposed approach is simple, computationally efficient, and well suited for real-time implementation without imposing a significant computational burden. Its effectiveness is validated through real-time experiments conducted using a dSPACE DS1104 controller board and a 7.5-kW induction motor (IM). Simulation results demonstrate that the proposed controller outperforms the conventional backstepping controller. Robustness analyses under variations in stator resistance, load inertia, and viscous friction coefficient reveal substantial reductions in the …

Swindeitvit: A Soft Voting Vision Transformer Ensemble For Accurate And Explainable Solar Panel Fault Detection, Mahe Zabin

Turkish Journal of Electrical Engineering and Computer Sciences

Solar panels are becoming very essential in providing sustainable energy but they are usually affected by defects on the surface like dust, snow, bird droppings, physical damages and electrical faults which interfere with their performance. These faults must be identified accurately and in a timely manner to enhance energy efficiency, lower the maintenance cost, and supplement the traditional manual methods of inspection which are labor-intensive, time-consuming and subject to human errors in judgment. The most common methods, such as traditional CNNs and hybrid architectures tend to be less accurate, less explainable and cannot be properly evaluated to be deployed in …

Enhancement Of Nested Hexagonal Fractal Antenna Performance For Multiband Wireless Applications, Abdelbasset Azzouz, Rachid Bouhmidi, Mohammed Chetioui, Redouane Berber, Ahmed Jamal Abdullah Al-Gburi

Turkish Journal of Electrical Engineering and Computer Sciences

This work focuses on developing a compact multiband antenna to meet the growing demand for versatile and efficient radiating structures in modern wireless communication systems. A hexagonal fractal antenna is proposed and analyzed for applications such as mobile communications, WLAN, industrial, scientific and medical (ISM) bands, Wi-Fi, satellite links, radar systems, and military communications. By iteratively modifying the antenna geometry with larger hexagonal elements, the design enhances multiband behavior and improves key performance parameters including gain, S11, voltage standing wave ratio (VSWR), and radiation characteristics. The antenna is modeled using high-frequency structure simulator (HFSS)® and fabricated on a low-cost 0.8 …

Erratum To “Sgsc-Kko-Lstm: A Deeplearning Classifier Model For Smart Grid” [Turkish Journal Of Electrical Engineering & Computer Sciences 34 (2) 2026 185-213], Samaniba Imchen, Dushmanta Kumar Das

Turkish Journal of Electrical Engineering and Computer Sciences

The first and second authors were incorrectly ordered in the article PDF due to a typesetting error. To rectify this oversight and ensure the accuracy of the published work, the author order have been corrected as follows: 1. Samaniba Imchen – First Author 2. Dushmanta Kumar Das – Second Author

A link to the original article can be found at: https://doi.org/10.55730/1300-0632.4170

Evolutionary Neural Architecture Search: A Survey, Ferda Nur Özçeli̇k, Mehmet Önder Efe

Turkish Journal of Electrical Engineering and Computer Sciences

Deep Neural Networks (DNNs) have achieved remarkable success across diverse machine learning applications, yet designing effective architectures remains a laborious, expert-driven process. Neural Architecture Search (NAS) was introduced to automate this process, with Evolutionary NAS (ENAS) emerging as one of the most effective and widely adopted NAS paradigms. This survey provides a comprehensive and systematic review of 164 ENAS studies published between 2020 and 2024, categorized according to the specific evolutionary algorithm employed as the search strategy. Unlike prior surveys—which either treat evolutionary methods at a high level or focus on general NAS pipelines—this study is, to the best of …

A Revenue-Driven Approach For Enhanced Task Utilization In Vehicular Cloud Computing, Ashish Singh Saluja, Satyabrata Das, Sanjib Kumar Nayak, Sohan Kumar Pande

Turkish Journal of Electrical Engineering and Computer Sciences

Vehicular networks support intelligent transportation through vehicle-to-roadside Units (V2R) and vehicle-to-vehicle (V2V) communication but face challenges from dynamic topologies, limited RSU coverage, and bandwidth scarcity, which impact service delivery and revenue. RDA-ITU addresses these challenges by integrating V2R and V2V paradigms to maximize RSU revenue, enhance service availability, and improve system efficiency. It dynamically allocates services based on real-time network conditions and vehicle mobility, leveraging V2V relays to optimize both RSU-direct and cooperative communication. Through extensive simulations, RDA-ITU significantly outperforms four baselines: RBSM, VVMM-U, VVMM-LW, and VVMM-MA. It achieves 81.1% higher total revenue, 154.8% more completed requests, and 103.6% higher …

Dual-Stream Bilstm Framework With Histogram-Based Shape Features For Household Load Forecasting, Chang Xu, Wong Jee Keen Raymond, Hazlee Azil Illias, Hazlie Mokhlis

Turkish Journal of Electrical Engineering and Computer Sciences

This study proposes a dual-stream BiLSTM framework for household load forecasting that integrates time-series dynamics with histogram-based daily shape features. Unlike existing models relying on weather or external data, the proposed method extracts intrinsic load-shape information directly from normalized daily curves. A multihead attention module fuses temporal and shape representations, enabling adaptive weighting of informative dimensions. Experiments on three real-world datasets show consistent improvements over the baseline BiLSTM, with up to 30.12%, 24.27%, and 19.03% reductions in MAE, RMSE, and SMAPE, respectively. The results highlight the framework’s robustness and efficiency for fine-grained load forecasting without external inputs.

Class-Aligned Frequency Augmentation Using Variational Mode Decomposition Forfew-Shot Image Classification, Leila Boussaad

Turkish Journal of Electrical Engineering and Computer Sciences

Few-shot image classification benefits from data augmentation, yet most existing methods operate in pixel space with limited control over spectral semantics. We introduce a lightweight, frequency-guided augmentation strategy based on Variational Mode Decomposition (VMD). Our method constructs an offline, per-class ModeBank by decomposing downsampled luminance patches and retaining midband modes that encode class-specific texture patterns. During episodic training, VMD is never executed online: instead, for each support image, a same-class midband mode is selected and blended using PSNR-targeted scaling with a luminance energy cap, ensuring perceptual consistency. The augmentation is fast, reproducible, class-consistent, and integrates seamlessly into standard metric-based pipelines …

Parameter Optimization Of Dual-Qsg Based Pll For Real-Time Control Of Grid-Connected Ev Chargers, Gaurav Yadav, Sudhanshu Mittal, Vineet Kumar, Sombir Kundu, Praveen Bansal

Turkish Journal of Electrical Engineering and Computer Sciences

Dual-Quadrature Signal Generator (D-QSG) based Phase lock loop (PLL) has been recently proposed to handle the nonideal grid voltage conditions. However, selecting the parameter for D-QSG based controller has been a great challenge, especially for higher-order systems. Inappropriate parameter selection tends to increase settling time both in terms of amplitude as well as harmonics attenuation. Hence, in the proposed work, the main focus is on parameter selection to achieve a faster response. Here, a fourth-order Quasi-Synchronous Generator has been realized by cascading the two nonidentical second order generalized integrators (NISOGIs). Furthermore, the parameters of both the NISOGIs are selected in …

Scalefusion: Hierarchical Feature Aggregation For Unified Multiscale Object Detection, Ayşenur Yaylaci, Berru Kaya, Mehmet Kiliçarslan

Turkish Journal of Electrical Engineering and Computer Sciences

Detecting objects across a wide range of scales, particularly small ones, remains a significant challenge in computer vision. Existing methods often improve small object detection at the cost of performance on larger objects or introduce significant computational overhead through external techniques like image slicing. This paper introduces ScaleFusion, a novel, unified, end-to-end object detection architecture designed to provide robust performance across all scales within a single model. The core of our approach is a hierarchical feature aggregation strategy structured like a tree. ScaleFusion processes an image by running a shared backbone network only on fine-grained patches at the lowest level …