Physical Sciences and Mathematics Commons^™

Estimation Of 3d Facial Dynamics With Nonlinear Filters For Position Tracking, Thoa Thieu, Roderick Melnik

School of Mathematical & Statistical Sciences Faculty Publications

This study presents a comparative evaluation of three nonlinear state estimation filters, the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF), for the task of 3D facial landmark tracking. Using a publicly available dataset, we assess each filter's performance under both deterministic (noise-free) and stochastic (noisy) conditions. Metrics such as mean squared error (MSE), convergence rates of state and covariance estimates, and consistency over time are used to quantify tracking performance. Results show that the EKF consistently outperforms the UKF and PF, achieving faster convergence and lower estimation error, particularly in scenarios characterized by mild nonlinearity. …

Piezoelectric Energy Harvesting From Roadways: Challenges, Advances, And Future Directions, Heba Gaber, Mohamed Abdelraheem

Faculty Publications

As the global demand for renewable energy intensifies, piezoelectric energy harvesting from roadways has emerged as a promising avenue for sustainable power generation. This systematic literature review analyzes 61 peer-reviewed studies to assess the feasibility, performance, and potential of integrating piezoelectric systems into roadway infrastructure. While technology faces challenges, such as high installation costs, limited energy output, and a scarcity of thorough economic evaluations, findings suggest it holds considerable promise as a supplementary renewable energy source. The review analyzes the operational characteristics and efficiencies of various piezoelectric transducers, identifies key factors influencing system performance, and evaluates recent technological advances. It …

Review Of Resonance: A Sociology Of Our Relationship To The World By Hartmut Rosa, Ryan Derby-Talbot

Turning Toward Being: The Journal of Ontological Inquiry in Education

No abstract provided.

A New Goodness-Of-Fit Test For Azzalini’S Skew-T Distribution Based On The Energy Distance Framework With Applications, Joseph Njuki, Abeer M. Hasan

Mathematics and Statistics

In response to the growing need for flexible parametric models for skewed and heavy-tailed data, this paper introduces a novel goodness-of-fit test for the Skew-t distribution, a widely used flexible parametric probability distribution. Traditional methods often fail to capture the complex behavior of data in fields such as engineering, public health, and the social sciences. Our proposed test, based on energy statistics, provides practitioners with a robust and powerful tool for assessing the suitability of the Skew-t distribution for their data. We present a comprehensive methodological evaluation, including a comparative study that highlights the advantages of our approach over traditional …

Is Complexity Virtuous?, Ryan Elmore, Jack Strauss

Business Information and Analytics: Faculty Scholarship

(Kelly et al., 2024) show that increasing complexity in linear models, with potentially thousands of predictors, is "virtuous". Their work contradicts the dogma of model selection, including the Principles of Parsimony and Occam's Razor. They find that when the number of predictors far exceeds the number of observations, the bias-variance trade-off breaks down, the variance declines, and the Sharpe ratio increases. In the context of ridge regression, we find that very high complexity coupled with large penalty terms (excessive shrinkage) generate forecasts that converge to a rolling window of past returns. For example, we show the past twelve-month moving average …

Climate Change Has Increased Global Evaporative Demand Except In South Asia, Saeed Karimzadeh, Arman Ahmadi, Dennis Baldocchi, Joshua B. Fisher

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Climate change alters how strongly the atmosphere draws water from the land, yet a consistent global assessment of this evaporative demand has been lacking. Here, we analyze 45 years of climate data and global models to quantify trends in the key drivers—air temperature, humidity, radiation, wind speed, and cloud cover—that determine the atmosphere’s drying power. We find that evaporative demand has increased worldwide, indicating a stronger atmospheric thirst, except in South Asia, where it has declined. There, widespread irrigation has increased soil and air moisture, enhanced cloud formation, and reduced sunlight reaching the surface, counteracting the global signal. These contrasting …

From Digital Divide To Equity-Enhancing Diffusion: Generative Ai And Writing Quality, Rebecca Tukachinsky Forster, Kerk Kee, Gabriel Miao Li

Communication Faculty Articles and Research

This study investigates whether generative AI can narrow the gap between stronger and developing writers and explores the mechanisms underlying these effects. In a within-subject experiment, students wrote two essays, with and without AI assistance. Computer-aided analysis of the writing quality confirmed that while all students benefited from AI, that less skillful writers gained more. There was also no evidence of skillful writers using AI in more sophisticated and beneficial ways. The study contributes to theorizing the digital divide and offers insights into maximizing the benefits of AI tools. Theoretically, we situate generative-AI use within Diffusion of Innovations, treating ChatGPT …

Benchmarking Dna Foundation Models For Genomic And Genetic Tasks, Haonan Feng, Lang Wu, Bingxin Zhao, Chad Huff, Jianjun Zhang, Jia Wu, Lifeng Lin, Peng Wei, Chong Wu

School of Medicine Faculty Publications

The rapid evolution of DNA foundation models promises to revolutionize genomics, yet comprehensive evaluations are lacking. Here, we present a comprehensive, unbiased benchmark of five models (DNABERT-2, Nucleotide Transformer V2, HyenaDNA, Caduceus-Ph, and GROVER) across diverse genomic and genetic tasks including sequence classification, gene expression prediction, variant effect quantification, and topologically associating domain (TAD) region recognition, using zero-shot embeddings. Our analysis reveals that mean token embedding consistently and significantly improves sequence classification performance, outperforming other pooling strategies. Model performance varies among tasks and datasets; while general purpose DNA foundation models showed competitive performance in pathogenic variant identification, they were less …

Development Of A Digestion Procedure Using Fe2+ Ions For Electrochemical Detection Of Mno2 Particles In Drinking Water, Kayla Elliott, Sarah Jane Payne, Zhe She

Journal of Electrochemistry

Developing methods for detection contaminants in drinking water is essential to ensuring that safe and acceptable quality drinking water is delivered to consumers. While manganese (Mn) was previously known only as a mere aesthetic issue, recent epidemiological data has shown to have negative neurological effects on humans, especially on children, prompting new health-based guidelines by Health Canada and the World Health Organization. In drinking water, Mn exists predominantly as Mn(II) and Mn(IV), and is regulated based on total Mn levels. Interestingly, measurement of Mn particulate using electroanalytical methods has not yet been reported in the literature. Herein, a digestion procedure …

High Variability In Flood Discharge And Stage Accelerates River Mobility, Chenliang Wu, Wonsuck Kim, Shuo Yang, Frank Tsai, Jeffrey A. Nittrouer, Tian Y. Dong, Duhwan Keum, Kyle M. Straub

School of Earth, Environmental, & Marine Sciences Faculty Publications

Lateral channel migration is a fundamental process in natural alluvial rivers; however, the factors that control the rate of migration remain unclear. Despite its importance in shaping river morphology, the impact of water discharge on river mobility is still largely unexplored. Here, we leverage a dataset of 64 rivers across the globe to show that higher variability in river discharge and stage promotes higher rates of river migration. To reveal the physical processes behind this relationship, we focused analyses on the lowermost 500 kilometers of the Mississippi River, where a pronounced gradient in water stage variability and migration rate exists. …

Improving Glycemic Control Among Indonesian Urban Adults: A Digital And Behavioral Extension Of The Information–Motivation–Behavioral Skills Model, Imelda Appulembang

Kesmas

Management of type 2 diabetes mellitus (T2DM) in Indonesia continues to face challenges due to behavioral, informational, and technological gaps among patients. This study analyzed the influence of self-regulatory competence and information, motivation, family support, and digital health literacy on glycemic control behavior. A cross-sectional survey was conducted from February to April 2025 among 587 adults aged >30 years with T2DM enrolled in the Chronic Disease Management Program at primary health care in six major cities: Jakarta, Surabaya, Yogyakarta, Medan, Makassar, and Banjarmasin. Data were collected through structured questionnaires and analyzed using partial least squares structural equation modeling. The findings …

Regulating Lithium Metal Nucleation And Growth For Dendrite Suppression: From Liquid-Electrolyte To Solid-State Batteries, Ao Du, Juan Zhang, Pan Xu, Ya-Jie Li, Kang-Yu Yi, Zhen-Zhen Shen, Hui-Lin Ge, Guang-Wen Zhang, Chao-Hui Zhang, Yu-Hao Wang, Chen-Zi Zhao, Meng-Yang Xu, Yu-Lin Jie, Rui Wen, Shu-Hong Jiao, Si-Qi Shi, Qiang Zhang, Chun-Peng Yang, Yu-Guo Guo

Journal of Electrochemistry

Lithium metal anodes, with a theoretical capacity of up to 3860 mAh·g⁻¹, are regarded as the cornerstone for developing next-generation high-energy-density batteries. However, several key challenges hinder their practical applications, including dendrite formation, unstable solid electrolyte interphase (SEI), side reactions with electrolytes, and associated safety risks. This review systematically explores the mechanisms of lithium nucleation, growth, and stripping in both liquid and solid-state battery systems, analyzing critical theoretical concepts like heterogeneous nucleation thermodynamics, surface diffusion kinetics, space charge effects, and SEI-induced nucleation, which are crucial for understanding the genesis of dendrite growth. Additionally, the review discusses the electrochemical-mechanical …

Ai-Based Mapping Of Offshore Wind Energy Around The Korean Peninsula Using Sentinel-1 Sar And Numerical Weather Prediction Data, Jason Sung-Uk Joh, Son V. Nghiem, Menas Kafatos, Jay Liu, Jinsoo Kim, Seung Hee Kim

Institute for ECHO Articles and Research

Offshore wind farm projects are being promoted in the seas surrounding the Korean Peninsula to secure renewable energy. To support site selection, offshore wind resource maps were generated using deep neural networks trained on Sentinel-1 SAR imagery, numerical weather prediction data, offshore wind observations, sea surface temperature, and bathymetry. The deep neural network (DNN) framework consisted of six sub-models targeting eastward and northward wind components across three regions—the Yellow Sea, Korea Strait, and East Sea—to account for spatial heterogeneity. The proposed models outperformed existing approaches, achieving mean absolute errors (MAE) ranging from 1.31 to 1.69 m/s and correlation coefficients (CC) …

Biocompatible And Antibacterial Pcl-Tio2@Ag/Γ-Cd Mof Nanocomposite Coating For Corrosion Resistance Of Magnesium Alloy In Simulated Body Fluid, Sara Dehghan-Chenar, Hamid R. Zare, Zahra Mohammadpour, Maryam Sadat Mirbagheri-Firoozabad

Journal of Electrochemistry

Magnesium alloys are promising candidates for bio-implant applications due to their biodegradability and biocompatibility. However, their rapid corrosion remains a critical limitation. This study presents the development of a multifunctional nanocomposite coating designed to enhance the corrosion resistance and antibacterial properties of magnesium alloy implants. The coating comprised γ-cyclodextrin metal-organic frameworks (γ-CD MOFs) decorated with TiO₂@Ag core-shell nanoparticles, embedded in a polycaprolactone (PCL) matrix. Immersion tests in a simulated body fluid (SBF) revealed an initially higher corrosion rate for the PCL-TiO₂@Ag/γ-CD MOF coating compared to the coating without TiO₂@Ag nanoparticles; however, it demonstrated significant …

Evaluating Large Language Models For Automated Cv Ranking: A Hybrid Embedding Approach For Enhanced Recruitment, Sarah Mohamed Alhindaassi

Thesis/ Dissertation Defenses

Increasing numbers of applications have revealed limitations in legacy keyword-filtering-based Applicant Tracking Systems (ATS), which commonly overlook candidate potential and ignore contextual or transferable skills. Advances in Natural Language Processing (NLP) and Large Language Models (LLMs) offer an exhilarating alternative, supporting context-sensitive and human-crafted reasoning in candidate evaluation. This thesis systematically evaluates four classes of approaches, lexical models, embedding-based methods, Large Language Models (LLMs), and hybrid ensembles, for automation of Curriculum Vitae (CV) to Job Description (JD) matching without exploiting prior annotations or annotations at match time. Using a combination of publicly available datasets and real-world sample data covering three …

Transdisciplinary Perspectives On Ai: The Fourth Annual Conference Of The European Culture And Technology Laboratory, Connell Vaughan, Ioana Madalina Moldovan, Silivan Moldovan, Noel Fitzpatrick

Books/Book Chapters

The fourth annual conference of the ECT Lab+ was hosted by Technical University of Cluj-Napoca over two days in October 2024 at the Cluj Innovation Park. The conference brought together experts from the Arts, Humanities, Social Sciences, Technology, and other fields to discuss and reflect on the advent of Artificial Intelligence and how the associated technologies are transforming how we live, work and study. Under the title Transdisciplinary perspectives on AI: Alternative Histories, Current Practices and Possible Futures the conference moved beyond simplistic technophila and technophobia to consider whether we can co-evolve with these new technologies which combine machine learning …

Sign Patterns Of Certain Infinite Products, Zeyu Huang, Timothy J. Huber, James Mclaughlin, Pengjun Wang, Yan Xu, Dongxi Ye

School of Mathematical & Statistical Sciences Faculty Publications

The signs of Fourier coefficients of certain eta quotients are determined by dissecting expansions for theta functions and by applying a general dissection formula for certain classes of quintuple products. A characterization is given for the coefficient sign patterns for

(qi;qi)∞(qp;qp)∞

for integers i>1 and primes p>3. The sign analysis for this quotient addresses and extends a conjecture of Bringmann et al. for the coefficients of (q2;q2)∞(q5;q5)−1∞. The sign distribution for additional classes of eta quotients is considered. This addresses multiple conjectures posed by Bringmann et al.

Tannin Supplementation Alters Foraging Behavior And Spatial Distribution In Beef Cattle, Bashiri Iddy Muzzo, R. Douglas Ramsey, Kelvyn Bladen, Juan J. Villalba

Wildland Resources Student Research

Beef production on chemically uniform grass monocultures can limit nutrient synchrony and contribute to uneven pasture use. We evaluated whether supplementing tannins with bioactive plant secondary compounds improves foraging dynamics and landscape use by beef cattle grazing a meadow bromegrass monoculture in ways aligned with rangeland sustainability. Twenty-four Angus cow–calf pairs were allocated to six 3.6-ha paddocks (four pairs/paddock), randomly assigned to Control (Ctrl; n = 3) or Tannin treatment (TT; n = 3). Animals received 1 kg/cow/day of DDGs, with TT receiving an added 0.4% tannins (2:1 condensed:hydrolyzable). Grazing occurred during four 15-day periods (July– September) across two years. …

Introduction To Machine Learning And Machine Learning Systems, Raffi T. Khatchadourian Ph.D.

Open Educational Resources

Lecture slides introducing machine learning and machine learning systems for an undergraduate software engineering course. Topics include what machine learning is and how it differs from traditional programming, foundation models, the major types of learning (supervised, unsupervised, reinforcement, and others), and applications across domains. Using a food-delivery time-prediction case study, the deck walks through a typical ML pipeline—data collection and cleaning, feature engineering, model training, and evaluation—and covers evaluation methods (precision and recall, confusion matrices, error measures) along with underfitting versus overfitting and the realities of learning and evaluation in production. Based on "Machine Learning in Production/AI Engineering" by Christian …

Phase Equilibria Modeling Constraints On Channel Flow Of Metasedimentary Crust Beneath The Southeastern Tibetan Plateau, Govind Joshi, Gregory Dumond

Geosciences Faculty Publications and Presentations

Lower crust of the southeastern Tibetan Plateau includes 100 s of km2-scale zones of low resistivity at 20-40 km-depths indicating the presence of melt +/- fluid +/- graphite. These zones coincide with interconnected zones of low shear wave velocity. Data from central and southeastern Tibet support the occurrence of meta-sedimentary felsic granulites in the lower crust along a backarc-like geothermal gradient. Phase equilibria modeling of average metasedimentary pelite and greywacke bulk compositions constrained by these geophysical data explored the impact of partial melting on shear wave velocity for a range of fluid content. Low shear wave velocities beneath southeastern Tibet …

The First 10 Years Of The Hawc Gamma-Ray Observatory: Science Results, A. Albert, R. Alfaro, C. Alvarez, A. Andrés, E. Anita-Rangel, M. Araya, N. Ghosh, S. Groetsch, P. Huntemeyer, N. Kelley-Hoskins, K. Leavitt, T. Lewis, M. Najafi, R. Turner

Michigan Tech Publications

The High-Altitude Water Cherenkov (HAWC) Observatory, located on the slopes of the Sierra Negra volcano in Mexico, began operations in March 2015. Over the past decade, HAWC has enabled the exploration of a broad range of topics in high-energy astrophysics and particle physics, resulting in more than 90 peer-reviewed publications. These studies have significantly advanced our understanding of several previously unexplored and poorly understood phenomena in the TeV energy regime. The present work provides an overview of the key scientific contributions of HAWC during its first ten years of operation.

A Practical Framework For Integrating Basic Separation Techniques With Molecular Quantification To Extract And Monitor Antibiotic Resistance Genes In Water Samples, Amani Jereb, Celestene Adrianne Sebag, Asmaa Sadoon, Diksha Shrestha, Mary Savin, Wen Zhang, Yong Wang

Physics Faculty Publications and Presentations

Antibiotic resistance of harmful microorganisms is recognized as a serious threat to human and animal health, which is worsened by the dissemination of antibiotic resistance genes (ARGs). However, it remains challenging to identify ARGs in water samples and to distinguish intracellular (iARGs) and extracellular ARGs (eARGs). In this work, we present a feasibility study to use simple, practical methodologies to extract and distinguish ARGs in water samples using both filtration and centrifugation, followed by quantification using qPCR. Using both synthetic water samples and lake water samples, we demonstrated that the workflow reliably detected and quantified ARGs in synthetic samples. In …

An Objective Method To Locate Shear Lines During The Northeast Monsoon Season In The Philippines, Lyndon Mark P. Olaguera, John A. Manalo, Jun Matsumoto, Faye Abigail T. Cruz, Jose Ramon T. Villarin

SOSE Affiliate: Manila Observatory

The shear line is a narrow zone of maximum horizontal wind shear, typically identified as a confluence zone of low-level wind streams. Previous studies showed that this system can trigger heavy to extreme rainfall events in the Philippines during the northeast monsoon season. However, a research gap remains in objectively identifying and locating this system. Thus, this study develops a detection method that may be used for monitoring and forecasting the location of shear lines. Results show that the gradient of meridional winds in the y-direction (∂V925hPa/∂y), the boundary layer moisture flux convergence, and the 925 hPa relative vorticity may …

A Feature-Free Deep Learning Approach For Midair Hand Gesture Recognition From Surface Electromyogram (Semg) Data, Yasir Altaf, Abdul Wahid, Mudasir Manzoor Kirmani

Turkish Journal of Electrical Engineering and Computer Sciences

Midair hand gesture recognition plays a crucial role in applications such as sign language recognition and human-computer interaction, particularly for supporting individuals with partial or complete hearing loss. However, recognizing gestures in midair remains challenging due to the rapid and complex nature of hand movements. To address this, noninvasive techniques like surface electromyography (sEMG)—which captures muscle activity through sensors placed on the skin—have gained attention. sEMG provides rich time-series data that reflect both spatial and temporal muscle dynamics. In this study, we propose a deep learning architecture that combines convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to classify …

Integrated Log Spectrogram Convolutional Neural Network (Ils-Cnn) For Robust Spoken Digit Recognition, Awais Ahmed

Turkish Journal of Electrical Engineering and Computer Sciences

Spoken digit recognition (SDR), a type of supervised automatic speech recognition, is essential for various human-machine interaction applications, including banking operations, dialing systems, price extraction, and airline reservation systems. However, designing an effective SDR system presents several challenges, such as developing labeled audio data, selecting appropriate feature extraction methods, and creating high-performance models. To overcome these challenges, a novel approach for robust spoken digit recognition using an integrated log spectrogram convolutional neural network (ILS-CNN) has been proposed. The proposed work presents an efficient SDR method by taking advantage of a log spectrogram layer directly within the neural network to enhance …

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 …

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 …

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 …