Digital Commons Network^™

Reliable Eigenspace Error Estimation Using Source Error Estimators, Jay Gopalakrishnan, Gabriel Pinochet-Soto

Mathematics and Statistics Faculty Publications and Presentations

We introduce a framework for repurposing error estimators for source problems to compute an estimator for the gap between eigenspaces and their discretizations. Of interest are eigenspaces of finite clusters of eigenvalues of unbounded nonselfadjoint linear operators with compact resolvent. Eigenspaces and eigenvalues of rational functions of such operators are studied as a first step. Under an assumption of convergence of resolvent approximations in the operator norm and an assumption on global reliability of source problem error estimators, we show that the gap in eigenspace approximations can be bounded by a globally reliable and computable error estimator. Also included are …

Tle-Unet-Based Image Segmentation And Feature Extraction Of Water-Cooling-Induced Complex Cracks In High-Temperature Rock Masses, Fu Tianyu, Hu Mangu, Zhang Xiaojun, Yang Xiaobin, Lyu Fu, Lyu Xiangfeng, Peng Lei

Coal Geology & Exploration

Objective and Method Water cooling-induced macroscopic surface cracks in high-temperature rock masses are characterized by fine scales, pronounced length variations, and severe class imbalance in images, posing significant challenges for image segmentation. To address these challenges, this study proposed TLE-UNet, a semantic segmentation network for rock crack images. First, through thermal treatment and uniaxial compression tests of granite specimens, crack images under varying temperatures were acquired. Then, using a self-developed Lite Edge Fusion module, high-resolution shallow features were finely aligned with deep semantic features at varying scales. Furthermore, edge detection and channel-attention mechanisms were combined to enhance crack boundary perception. …

A Gaussian Process Regression-Based Denoising Method For Transient Electromagnetic Signals, Wang Peng, Shi Jiaxing, Lu Kai, Hu Jinru, Wen Guangyuan, Zhai Haojie

Coal Geology & Exploration

Objective The transient electromagnetic (TEM) method serves as a primary geophysical technique for groundwater detection in coalfields at present, with the detection results directly influencing the implementation of water hazard prevention and control in coal mines. Since it is difficult to avoid electromagnetic interference sources such as power lines during data acquisition, TEM signals are prone to be contaminated by electromagnetic noise. Given that primary denoising techniques such as wavelet transform and empirical mode decomposition (EMD) are yet to be further improved, this study developed a novel Gaussian process regression (GPR)-based denoising method for TEM signals. Methods First, time alignment …

Non-Stokes Deposition Velocities Observed Under Rayleigh–Bénard Turbulence, Kristin Swartz-Schult, Andrew Grace, Andrew Bragg, Jesse C. Anderson, Will Cantrell, Robert Grosz, Hamed F. Sadi, Raymond Shaw, Swafuva Varappillikudy Sulaiman, Et Al.

Michigan Tech Publications

The settling and deposition behavior of small particles significantly impacts many environmental and industrial processes. Although particle settling dynamics within turbulent environments have been extensively studied through theory and simulation, more experimental investigation is needed to test the idealized results against real-world turbulent flows. In this work, we infer the settling velocity of particles in Rayleigh–Bénard (RB) turbulence through the measurement of declines in particle concentration due to deposition within a (Formula presented.) convection chamber. Oil droplets from (Formula presented.) to (Formula presented.) and glass beads from (Formula presented.) to (Formula presented.) were used as the particle material, yielding Kolmogorov-based …

A Probabilistic Deep Learning Framework For Retrieving Chlorophyll-A From Hyperspectral Imagery: Integrating Channel Attention And Mixture Density Networks, Wenzhao Li, Surendra Maharjan, Rejoice Thomas, Junde Chen, Hesham Morgan, Michael J. Garay, Olga V. Kalashnikova, Shahryar Fazli, Charles Ichoku, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Accurate monitoring of Chlorophyll-a (Chla) is critical for assessing aquatic ecosystem health, yet ecological complexity often leads to ambiguous spectral signatures in satellite data. Traditional deterministic models assume a one-to-one mapping between spectra and pigments, often failing to capture these high-dimensional analytical challenges. In this study, we propose a novel deep learning architecture, the Channel Attention-Mixture Density Network (CA-MDN), to retrieve Chla from the National Aeronautics and Space Administration (NASA) Earth Surface Mineral Dust Source Investigation (EMIT) hyperspectral mission. The CA-MDN integrates an attention mechanism to dynamically select ecologically relevant spectral bands and employs a probabilistic output layer to quantify …

Discrete Quantization On Spherical Geometries: Explicit Models, Computations, And Didactic Exposition, Mrinal Kanti Roychowdhury

School of Mathematical & Statistical Sciences Faculty Publications

This article presents a comprehensive and analytically explicit study of optimal discrete quantization on spherical geometries equipped with the geodesic metric. Focusing on highly symmetric configurations on the unit sphere 𝕊2, we investigate three explicit models of discrete uniform distributions and derive closed-form expressions for their optimal quantizers and corresponding mean square quantization errors. (I) For N equally spaced points on the equator, we obtain exact error formulas for both divisible and non-divisible cases 𝑛 ∤𝑁, demonstrating that optimal Voronoi cells form contiguous arcs with midpoint representatives. (II) For two antipodally symmetric small circles at latitudes ±𝜙0, each with M …

Unraveling Patch Size Effects In Vision Transformers: Adversarial Robustness In Hyperspectral Image Classification, Shashi Kiran Chandrappa, Sidike Paheding, Abel A. Reyes-Angulo

Michigan Tech Publications

Highlights: This work investigates the effect of spatial patch size on the classification accuracy and adversarial robustness of Vision Transformer-based architectures for hyperspectral image analysis. What are the main findings? Smaller patch sizes generally exhibit stronger adversarial robustness while maintaining comparable clean classification performance. Larger patch sizes tend to reduce robustness by increasing sensitivity to localized adversarial perturbations, with some dataset-dependent variations. What are the implications of the main findings? Spatial patch size is an important design consideration when applying Vision Transformers to hyperspectral image classification tasks. The findings provide practical guidance for informed patch-size selection in robust, deployment-aware transformer-based …

D. The Michael Hofmann Seismic Sections -Part A, Michael H. Hofmann

1980 Flathead Lake Seismic Survey

A description of the process for constructing the full-length seismic sections from sets of scans of the paper field records made in the early 2000s by then PhD candidate Michael Hofmann.  Images of the full seismic sections follow the narrative by Robert W. Lankston.  This set of images relates to those lines labeled on the track map in Figure 4C of the Kogan (1981) thesis.

Cerebral Documents And Algorithmic Sensemaking: Searching For Expressions In Human And Artificial Cognitive Collaborations, Rebekah L. Cowell

Proceedings from the Document Academy

Generative Artificial Intelligences (AIs) and current advanced large language models (LLMs) are algorithmically designed to generate text-based conversations as conversational agents (CAs), by replicating human language and conversational communication. Pairing human cognition with generative computationally coded cognition. We have never been here before: cerebral and artificial information collaborations and processing producing expressions that may or may not become visible as second-hand/secondary source documents.

Sensemaking or sense(un)making is a unique autonomous human drive cognitively, our information processing is sensemaking in action and expressions and articulations are evidence of the sensemaking cycle. Documentation [expressed or articulated through various mediums] are a product …

L. Dots To Data: Manufacturing Digital Seismic Traces From Scanned Images Of The Paper Sign Bit Sections From The 1970 Flathead Lake Seismic Survey, Robert W. Lankston

1970 Flathead Lake Seismic Survey

PowerPoint slides and the recorded audio of a lecture presented by Robert Lankston on October 13, 2014 in the University of Montana, Department of Geosciences Colloquium lecture series.

M. Epilogue For The 1970 Flathead Lake Seismic Project Collection, Robert W. Lankston

1970 Flathead Lake Seismic Survey

Re-introduction of the 1980 Qamar-Kogan seismic survey at Flathead Lake with recommendations for building an archive for that project. Article includes a proof-of-concept for converting scanned images of seismic sections recorded in variable density to digital seismic traces.

Ai-Guided Binding Mechanisms And Molecular Dynamics For Mers-Cov, Pradyumna Kumar, Lingtao Chen, Rachel Yuanbao Chen, Yin Chen, Seyedamin Pouriyeh, Progyateg Chakma, Abdur Rahman Mohd Abul Basher, Yixin Xie

Faculty Articles

The MERS-CoV (Middle East respiratory syndrome coronavirus) is a zoonotic virus with a high mortality rate and a lack of antiviral drugs, underscoring the need for effective therapeutic methods. Viral entry depends on interactions between viral surface proteins and human receptors, with Dipeptidyl Peptidase-4 (DPP4), a transmembrane glycoprotein, acting as the receptor for MERS-CoV. We employed Molecular Dynamics (MD) Simulations to identify critical interface residues under a high-performance computing (HPC) workflow for accelerated results. Target residue pairs were identified through analysis of salt bridge and hydrogen bond occupancy. The stability of these residues was confirmed through three independent MD Simulations …

Symmetric Orthogonalization And Probabilistic Weights In Resource Quantification, Gökhan Torun

Turkish Journal of Physics

Transforming nonorthogonal bases into orthogonal ones often compromises essential properties or physical meaning in quantum systems. Here, we demonstrate that Löwdin symmetric orthogonalization (LSO) outperforms the widely used Gram–Schmidt orthogonalization (GSO) in characterizing and quantifying quantum resources, with particular emphasis on coherence and superposition. We employ LSO both to construct an orthogonal basis from a nonorthogonal one and to obtain a nonorthogonal basis from an orthogonal set, thereby mitigating ambiguity related to the basis choice in defining quantum coherence. Unlike GSO, which depends on the ordering of input states, LSO applies a symmetric transformation that treats all vectors equally and …

Protecting ‘Punks’: The Shortcomings Of The Prison Rape Elimination Act, Rachel Goldsmith

Binghamton University Undergraduate Journal

Prisoner-on-prisoner sexual abuse is widespread in US male prisons, with inmates “turning each other out” by sexually assaulting each other. While the Prison Rape Elimination Act (PREA) received unanimous support in Congress in 2003, motivated by the Human Rights Watch Report “No Escape: Male Rape in Prisons,” scholars argue that the PREA fails to protect prisoners from prison rape: for example, its standards may be ineffective or criminalize consensual sex between prisoners. This essay will examine the shortcomings of the PREA, drawing on current legal scholarship, and propose solutions to enhance the law's effectiveness in protecting prisoners. The US government …

New Measurement And Analysis For The 15N + 11B Elastic Scattering System, Nurlan Amangeldi, İsmai̇l Boztosun, Dauren Soldatkhan, Bakytbek Mauyey, Kanat Makhanov, Raimbek Akhat, Aida Anuar, Gani Yergaliuly

Turkish Journal of Physics

New experimental data on the elastic scattering of the nuclear process 15N+11B at an energy of Elab=26.25 MeV were obtained on the DC-60 cyclotron of the INP RK. Based on a semi-microscopic analysis of the interaction of an accelerated 15N ion with 1p-shell nuclei 11B the effectiveness of the new B3Y-Fetal potential was studied. . The difference between the B3Y-Fetal potential and the traditional Yukawa potentials (M3Y-Reid and M3Y-Paris) is that its density-dependent parameters are determined from the saturation condition of nuclear matter at low energy. Analysis was performed on double folding optical model (DFOM) and so no for other …

Hybrid 3d Modelling Framework For Indoor Navigation Using Federated Learning And Internet Of Things-Enabled Edge Devices, Noopur Tyagi, Jaiteg Singh, Saravjeet Singh, Ahmad Ali Alzubi, Farman Ali, Sukhjit Singh Sehra, Babar Shah

All Works

Background There has been a recent trend towards using three-dimensional (3D) models to enhance spatial awareness and maximize resource utilization in complex environments. 3D building model can be used in various applications, such as real-time guidance and tracking the positions of individuals in multi storied buildings. Cities are now being modeled and studied in three dimensions as an improved method of urban planning. Method This study proposes an advanced indoor navigation framework that combines 3D modelling, federated learning (FL), and Internet of Things (IoT) integration to deliver reliable floor-level localization and real-time guidance. In Phase 1, highly accurate 3D models …

George Sturgeon, Metal Fluorides & Vice Chair, Mark A. Griep, George D. Sturgeon

Department of Chemistry: Faculty Interviews

Dr. George Sturgeon was a chemistry professor at the University of Nebraska -Lincoln for 39 years, from 1964 to 2003. During his time at UNL, he spent 15 years as the vice chair. Something in his youth must have sparked an interest in chemistry because he earned a bachelor’s in that subject from the University of North Dakota in Grand Forks. Then, he traveled 870 miles east to Michigan State University in East Lansing where he earned his doctorate. Immediately after graduating, he joined the University of Nebraska-Lincoln in 1964 as an assistant professor but was soon promoted to associate …

A. Introduction To The Archive Of The 1980 Flathead Lake Seismic Survey, Robert W. Lankston

1980 Flathead Lake Seismic Survey

This attempts to provide details of the hardware used during the 1980 seismic survey on Flathead Lake beyond the references in the master's thesis that described the survey and the initial use of the data.

Effects Of Varying Ph, Temperature, And Sucrose Concentration On The Activity Of Invertase Enzyme, B. Rainwater, S. Pratihar

Journal of the Arkansas Academy of Science

The purpose of this research is to study the factors that affect the enzymatic activity of invertase, extracted from baker’s yeast, on hydrolysis of sucrose in aqueous medium. Invertase, a glycoprotein, is an enzyme in the glycoside hydrolase family. It catalyzes the hydrolysis of sucrose producing equimolar mixture of a-D-glucose and b-D-fructose. Invertase shows high affinity for the substrate, and is capable of both cleaving the glycosidic linkage in sucrose and transferring ab-D-O-fructofuranoside residue to an acceptor substrate. (Toledo et al. 2019). A detailed mechanism of the action of invertase was postulated by Shall et al. (Shall et al. 1971). …

Re: Conditional Approval Letter With Comments For Butte Priority Soils Operable Unit (Bpsou) Draft Final 2026 Interim Site-Wide Surface Water Monitoring Quality Assurance Project Plan (Qapp) (Dated February 4, 2026), Emma Rott

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Satellites, Urban Heat, And Environmental Justice: Community As The Bridge Between Analysis And Action, Joshua B. Fisher, Ambar Rivera, Ava Cison, Ashley Agatep, Kainani Tacazon, Sophia Spiegleman, Alison Mckenery, Rio E. Fisher, Reginald Archer, Jason A. Douglas

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Heat waves are increasing in frequency, intensity, magnitude, and duration, causing a disproportionate impact on marginalized communities exposed to urban heat islands. Newly emerging spaceborne thermal sensing instruments, such as ECOSTRESS and Hydrosat, now have the capabilities to measure urban surface temperatures accurately at the block level (< 100 m) and with enough frequency to capture transient heat waves (daily to subweekly). Such data are critical for monitoring and informing policy and mitigation efforts, such as resurfacing, green space, cooling stations, and medical mobilization. These serve to advance environmental justice and reduce health risks—and deaths—among the most vulnerable: minority, low-income, elderly, those with physical- and mental-health preconditions, unhoused, children, and outdoor workers. While scientists have increasingly used satellite data to quantify urban heat islands and risks to communities, there remains a significant gap in action resulting from such analyses—a figurative and literal “valley of death.” Reviewing over 500 scientific publications, we identify a critical lack of engagement with the communities being analyzed (10.9%; n = 58); yet, community engagement is key to bridging such analysis with subsequent action. Here, we demonstrate how participatory community engagement directly with data and analysis leads to increased policy changes and mitigation efforts. Our framework has immediate implications for how scientists may augment their work and thought processes to achieve …

Artificial Intelligence In Nuclear Cardiology: Technical Perspectives, Strategic Directions, And Recommendations From An Iaea Expert Working Group, Christiane Wiefels, Luis Eduardo Juárez-Orozco, Pietro Selemo Craviolatti, Oleksandr Diahiliev, Amir Eskander, Raffaele Giubbini, Weihua Zhou, Et Al.

Michigan Tech Publications

Artificial intelligence (AI) is increasingly permeating nuclear cardiology and offers the possibility to enhance diagnostic accuracy, prognostic stratification, and operational efficiency. AI is demonstrating applicability across the imaging workflow—from individualized patient selection and adaptive image reconstruction to denoising of low-dose datasets, automated attenuation and motion correction, calcium scoring, and the integration of imaging with clinical and functional variables for enhanced diagnosis and comprehensive risk assessment. But the translational trajectory of AI in nuclear cardiology is challenged by the lag in fundamental AI knowledge among researchers and clinicians, the quality of the target data regarding heterogeneity in acquisition protocols, scanner platforms, …

A Virtual Community Math Circle, Skona Brittain, Sayonita Ghosh Hajra, Steve Heller, Daniel Hodgins, Peter Petto, Gabriella Pinter, Lauren Rose, A. Gwinn Royal, Asmita Sodhi

Journal of Math Circles

The Julia Robinson Mathematics Festival (JRMF) Community Math Circle is free, volunteer-run, and online. We collaborate with JRMF and use their activities in our events. Started during the pandemic, the Community Math Circle continues to thrive. We have about 40 participants attending every month, typically kids aged 6 to 13, teachers, facilitators, and other adults. This article describes how our event is organized, planned, and executed, and how we train facilitators. We will also offer reflections on our successes and challenges.

Pediatric Inflammatory Bowel Disease Tissue Classification From Pathology Slide Images: Detecting Phenotypes Using Computer Vision, Chloe Martin-King, Ali Nael, Louis Ehwerhemuepha, Blake Calvo, Quinn Gates, Jamie Janchoi, Elisa Ornelas, Melissa Perez, Andrea Venderby, John Miklavcic, Peter Chang, Aaron Sassoon, Brian Rubio, Ghislaine Barrigan, Kenneth Grant

Food Science Faculty Articles and Research

Background and Aims

Methods

Results …

Anfis And Rbfnn Efficacy And Timescale Dependence In Spei-Based Drought Prediction Using Meteorological Inputs, Alisa Afendi, Muhamad Usman Tariq, Shuhaida Ismail, Azizul Azhar Ramli

Journal of Informatics and Web Engineering

Drought is a slow-onset natural disaster that has far-reaching effects on agriculture, water security, and socio-economic systems, especially in climate-vulnerable countries such as Malaysia. It is imperative to predict droughts for prompt mitigation efforts. In this paper, the influence of temporal scale on drought modelling has been put into discussion by analyzing a comparison between two machine learning (ML) models: Adaptive Neuro-Fuzzy Inference System (ANFIS); Radial Basis Function Neural Network (RBFNN) based on Standardized Precipitation Evapotranspiration Index (SPEI) as depicting drought. The SPEI of four timescale categories (SPEI-3, SPEI-6, SPEI-9, and SPEI-12) were calculated weekly and monthly (two different temporal …

Vehicle Re-Identification System Using Residual Network With Instance-Batch Normalization, Wei Jie Low, Kah-Ong Michael Goh, Check-Yee Law, Connie Tee, Yong-Wee Sek, Md Ismail Hossen

Journal of Informatics and Web Engineering

Vehicle Re-identification (Re-ID) has become extremely important due to the increasing number of vehicles on the road and its potential to address traffic-related challenges. As a result, there is also a growing need for efficient methods to track and identify vehicles across multiple traffic cameras. One of the biggest challenges of this task is the variations in vehicle appearances across different camera angles. This is because vehicles can appear significantly different when captured from various camera angles and viewpoints. Furthermore, the current vehicle Re-ID solutions typically require extensive coding knowledge, making it inaccessible to many potential users. Therefore, we focus …

Robust Medical Image Prediction Via Adaptive Reconstruction: Bridging The Gap In Low-Quality Data, Prateek Singhal, Madan Singh

Journal of Informatics and Web Engineering

Medical image prediction plays a very significant role in clinical decision-making and early detection and diagnosis of different diseases. However, the quality of medical images has a huge impact on the predictive models' accuracy. Poor-quality data usually occurs due to problems like noise, artifacts, and low resolution and poses a major challenge for reliable medical image prediction. Framework advances medical image analysis through three novel contributions firstly, A hybrid architecture combining wavelet-based denoising with deep learning (DL) enhancement (unlike existing single-approach methods). Secondly, Cross-modality robustness validated on low-quality CT/MRI/X-rays from real clinics (versus modality-specific solutions), and lastly, A closed-loop system …

Hybrid Sentiment Analysis Model For Customer Feedback Interpretation Using Lexicon, Machine Learning And Deep Learning Techniques, Jou Jia Yi, Lew Sook Ling, Tan Li Tao

Journal of Informatics and Web Engineering

Customer feedback is pivotal in enhancing service quality and user satisfaction across digital platforms. However, traditional sentiment analysis methods often struggle with informal languages, contextual nuances, and aspect-specific opinions. In this paper, a hybrid sentiment analysis framework is proposed, utilizing lexicon-based (VADER), machine learning (Support Vector Machine and Random Forest), and deep learning (BERT) techniques to achieve improved sentiment classification accuracy and interpretability compared to previous studies. The framework incorporates advanced preprocessing techniques, such as emoji normalization, handling of negation, and detection of intensifiers, to better capture emotional information in user-generated content. The objectives of this study are to develop …

An Empirical Evaluation Of Machine Learning Methods And Text Classifiers For Sentiment Analysis Of Online Consumer Reviews, Pei Qin Lo, Sew Lai Ng, Li-Xian Jiao

Journal of Informatics and Web Engineering

This study aims to identify the best predictive model for analysing online product reviews (OPRs) in the electronics industry, with a secondary focus on leveraging unstructured customer feedback to support product improvement. Using a dataset of 9,675 Oppo mobile phone reviews, this study employs three classification models—Random Forest, Support Vector Machine (SVM) and Logistic Regression–paired with Term Frequency-Inverse Document Frequency (TF-IDF) or bidirectional encoder representation transformer (BERT) as the embedding models to analyse customer sentiment and derive actionable insights. The methodology features a comprehensive analysis pipeline that includes text preprocessing with the Natural Language Toolkit (NLTK), feature extraction using) vectorization …

A Conceptual Framework On Development Of Sign Language Chatbot For E-Commerce, Salma Jahan Nisha, Nabhan Salih, Wan-Noorshahida Mohd-Isa

Journal of Informatics and Web Engineering

This study proposes and validates a conceptual framework using a sign language (SL) chatbot in the e-commerce domain to improve accessibility. Currently, the use of SL is a less-explored area in online platforms. It is difficult for any communication-challenged person to interact online for buying products and services, using a chatbot, particularly for people using SL. The objective of this study is to introduce a novel hybrid architecture using SL Recognition with a conversational-based chatbot agent via a custom Application Programming Interface (API) in e-commerce platforms. Our work proposes a combined hybrid chatbot framework model using Convolutional Neural Network (CNN) …