Digital Commons Network^™

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 …

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). …

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 …

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 …

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 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 …

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 …

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.

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 …

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 …

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 …

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

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 …

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 …

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 …

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 …

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 …

Building The Next Cybersecurity Workforce: A Grades 7–12 Curriculum To Close The Cyber Talent Gap, Mohammed A. Salam, Iqbal Shareef, Rich P. Manprisio

Journal of Cybersecurity Education, Research and Practice

In today’s rapidly evolving technological landscape, cyberattacks pose increasing threats, yet a global shortage of cybersecurity and digital forensics professionals leaves industries vulnerable, similar to having too few law enforcement officers in a densely populated city. The judicial system faces rising digital crimes and fraud cases, further strained by the lack of experts to analyze and extract digital evidence. Despite high demand, millions of positions remain unfilled. This paper identifies the root causes of the cybersecurity workforce shortage and proposes a targeted solution: a curriculum for Grades 7–12 designed to foster cybersecurity awareness and interest. The methodology included a comprehensive …

Contemporary Cybersecurity Challenges In Emerging Technologies: A Systematic Literature Analysis, Faztudo Languisse Prof

Journal of Cybersecurity Education, Research and Practice

The accelerating convergence of artificial intelligence (AI), the Internet of Things (IoT), cloud computing, blockchain, and quantum computing has fundamentally transformed the global threat landscape, introducing cybersecurity challenges of unprecedented complexity and scale. This systematic literature review synthesizes findings from peer-reviewed publications, institutional reports, and regulatory documents published primarily between 2020 and 2025 to provide an integrated analysis of contemporary cybersecurity challenges across five key emerging technology domains. The review identifies critical vulnerabilities inherent to each domain, documents the evolution of threat actors and attack methodologies — including AI-powered ransomware, adversarial machine learning, and harvest-now-decrypt-later quantum attacks — and evaluates …

Cybersecurity Governance Of Industrial Iot In Sub-Saharan Africa: Policy Gaps, Threat Landscape, And Lessons From Comparative African Contexts, Faztudo Languisse Eng.

Journal of Cybersecurity Education, Research and Practice

The rapid deployment of Industrial Internet of Things (IIoT) systems across Sub-Saharan Africa's extractive, energy, logistics, and agro-industrial sectors has introduced a cybersecurity challenge of growing urgency: industrial networks that were designed for operational efficiency are increasingly exposed to cyber threats for which neither the organizations nor the regulatory frameworks are adequately prepared. This article examines the cybersecurity governance of IIoT systems in a developing African economy, using Mozambique as a primary case study and drawing comparative lessons from South Africa, Rwanda, and Kenya. Through an integrative literature review and documentary analysis of national digital, cybersecurity, and industrial policies, the …

Human-Driven, Autonomous, Or Hybrid? The Optimal Fleet Configurations For Ride-Hailing Platforms, Wenjing Li, Yali Zhang, Jun Sun, Zhaojun Yang

Information Systems Faculty Publications

The growing commercialization of autonomous vehicles (AVs) is reshaping consumer service preferences and prompting ride-hailing platforms to redesign fleet structures that accommodate the coexistence of human-driven vehicles (HVs) and AVs. This article develops a queueing game framework that incorporates vehicle heterogeneity and consumer preference differences to systematically compare three fleet configuration strategies: the pure HV (PHV) strategy (HVs only), the pure AV (PAV) strategy (AVs only), and the hybrid strategy (both HVs and AVs). The analysis highlights how consumer mismatch losses, AV operating costs, and service rates jointly shape equilibrium outcomes. Results show that when consumer mismatch losses are moderate, …

Capturing An Electron In The Virtual State, Alok Nath Singh, Bibek Bhandari, Rafael Sánchez, Andrew N. Jordan

Mathematics, Physics, and Computer Science Faculty Articles and Research

We address a foundational question in quantum mechanics: can a particle be directly found in a classically-forbidden virtual state? We instantiate this conceptual question by investigating the traversal of electrons through a tunnel barrier, which we define in a triple quantum dot system where the occupation of the central dot is energetically avoided. The motivation behind this setup is to answer whether the central dot is occupied or not during a virtual transition when it is being explicitly monitored. We investigate this problem in two different limits of continuous measurements: the stochastic quantum diffusion and the quantum jump. We find …

Genedit: A Context-Aware Equity, Diversity And Inclusion Principles Integration Tool For Software Engineering Education, Chetan Arora, Ajanie Kodagoda Bammanna Arachchige, Jinchun Du, Muhammad Aamir Cheema, Aster Cosmos, Antonette Shibani, Vasudha Malhotra, Naeem Janjua, Afaq Shah

Research outputs 2022 to 2026

Equity, diversity and inclusion (EDI) is widely acknowledged as essential in software engineering (SE), yet day-to-day integration into teaching remains uncommon due to time pressures, low instructor confidence, and fragmented resources. We present GenEDIt, a purpose-built, LLM-backed chatbot that helps educators weave EDI into SE education artefacts without altering intended learning outcomes. Unlike generic chat interfaces, GenEDIt provides a tailored UI and workflow, and embeds retrieval over a vetted EDI knowledge base. The tool supports five educator-centred modes - (1) methods for integrating EDI into current activities; (2) examples/datasets; (3) EDI-integrated assessments and rubrics; (4) reflective prompts; and (5) rapid …

Learning Programming In Informal Spaces: Using Emotion As A Lens To Understand Novice Struggles On R/Learnprogramming, Alif Al Hasan, Subarna Saha, Mia Mohammad Imran

Computer Science Faculty Research & Creative Works

Novice programmers experience emotional difficulties in informal online learning environments, where Confusion and Frustration can hinder motivation and learning outcomes. This study investigates novice programmers' emotional experiences in informal settings, identifies causes of emotional struggle, and explores design opportunities for affect-aware support systems. We manually annotated 1,500 posts from r/learnprogramming using the Learning-Centered Emotions framework, applying clustering, and axial coding. Confusion, Curiosity, and Frustration dominated emotional experiences, sometimes co-occurring and linked to early learning stages. Positive emotions were infrequent. The primary emotional triggers included ambiguous errors, unclear learning pathways, and misaligned resources. We identify five key areas where novice programmers …

Experimental And Density Functional Theory Study Of The Structural, Optical, And Electronic Properties Of La2Nimno6 And Nd2Nimno6 Double Perovskite Ceramics, Mahmoud S. Alkathy, Anibal Thiago Bezerra, Person Pereira Neves, Mariama Rebello De Sousa Dias, Alexandre José Gualdi, Marco Daldin Teodoro, Valmo R. Mastelaro, José A. Eiras

Physics Faculty Publications

Despite extensive study of La₂NiMnO₆ double perovskites, a systematic understanding of the effects of A-site rare-earth substitution on coupled structural, electronic, and optical properties remains lacking. Here, we provide the first integrated experimental-computational comparison of La₂NiMnO₆ (LaNMO) and Nd₂NiMnO₆ (NdNMO), establishing quantitative structure–property relationships. Rietveld refinement demonstrates that Nd³⁺ substitution amplifies MnO₆ and NiO₆ octahedral distortions 5-fold (Δd_Mn: 4.69 × 10^–4 to 23.9 × 10^–4; Δd_Ni: 1.18 × 10^–4 to 5.91 × 10^–4), reduces Mn–O–Ni bond …

Signal And Substance: Why Methodological Sophistication Fails To Guarantee Inference, Aamir Rashid, Rizwana Rasheed

Publications and Research

Despite increasing analytical sophistication, empirical research in organization studies continues to suffer from fragile inference, uneven theoretical accumulation, and contested credibility. This paper seeks to explain why these problems persist by shifting attention from authors’ methodological choices to the evaluative dynamics of peer review. Adopting a conceptual and theory-analytic approach, the paper theorizes peer review as an inferential gatekeeping system. Drawing on research design, theory evaluation, and philosophy-of-science literatures, this study develops an analytical framework to examine how evaluative routines shape what constitutes empirical rigor during the review process. The analysis identifies five recurring reviewer blind spots: inferential scope inflation, …