Digital Commons Network^™

Finding Research Datasets And Evaluating Data Quality, Ibis Anette Moreno-Lozano Phd.

Day Family Research Lab Workshop Series

No abstract provided.

A Mechanistic Model Of The Wash Shuffle And Monte Carlo Exploration Of Its Impact On Card Shuffling In Texas Hold’Em, Michael A. Alexeev, Peter B. Chi

UNLV Gaming Research & Review Journal

In casino games using a standard deck of cards, a wash shuffle is sometimes performed prior to the rest of the card shuffling procedure. Unlike other methods of shuffling, the wash shuffle has not yet been well studied. To this end, we first develop a mechanistic model of the wash shuffle based on our observation of how cards tend to move when a wash shuffle is being performed. Then, we use this model to simulate the card shuffling procedure used in casino poker rooms, and explore the resulting impact on where the cards are dealt in the context of Texas …

The Future Of West African Cacao Farming, Kathryn Lesniewski

CAFE Symposium 2026

This poster covers the detrimental impact climate change has had on the West African cacao farming industry. It also covers long and short term solutions to climate change, and how the global chocolate market has been impacted by climate change.

Improving Ground Cover Crop Fractional Vegetation Mapping Via Causality-Based Deep Representation Learning, Atif Latif, Masoumeh Hashemi, Matt Yost, Somayeh Esmaeili, Xiaojun Qi

Computer Science Student Research

Semantic segmentation and deep learning methods have rarely been applied to fractional vegetation cover (FVC) segmentation tasks due to the lack of publicly available datasets for training deep learning models. FVC is a key indicator for assessing vegetation distribution, crop density, and crop responses to water availability and fertilizer application, yet conventional field-based measurement methods are time consuming, costly, labor intensive, and may lack the accuracy required for critical applications such as drought stress evaluation and water productivity. In this paper, we introduced causality-based deep learning techniques for FVC segmentation on a publicly available RGB dataset that consists of four …

An Autonomous Robotic System For Object Retrieval And Delivery: Enhancing Independence For Users Living With Disability And Older Adults, Jincheng Li, Chenghao Lin, Amna Mazen, Youssef A. Bazzi

Michigan Tech Publications

As the global population ages, there is a growing need for assistive technologies to help older adults maintain their independence. This work presents a cost-effective autonomous socially assistive robot designed for object retrieval and delivery, enhancing accessibility in home environments. The system is built on the Robot Operating System (ROS) framework and integrates three key components: the Pioneer P3-DX mobile robot for autonomous navigation, the ReactorX-200 robotic arm for pick-and-place operations, and the Kinect v2 RGB-D camera for object detection and localization. Users interact with the robot through natural language processing by issuing voice commands to retrieve various objects. Microsoft …

Long-Range Fit: A Software Package For The Representation And Study Of Long-Range Molecular Interactions, Adrian L. Batista-Planas, Ernesto Quintas-Sánchez, Richard Dawes

Chemistry Faculty Research & Creative Works

Describing intermolecular forces is fundamental to modeling and predicting the behavior of molecular systems. In particular, long-range molecular interactions with electrostatic, induction, and dispersion as the main components play a critical role, especially for low-temperature and low-density regimes. Long-range interactions are often described through perturbation theory, representing the electronic charge distribution via a multipolar series of the moments and polarizability tensors corresponding to each molecule. However, while the theory is well established, obtaining the resulting analytical expressions (and their practical implementation) constitutes a highly complex and system-dependent task. To address this challenge, we developed long-range-fit (LRF), an interactive and user-friendly …

Discovery Of Horsfieldia Macrothyrsa Bioactives With Cytotoxic Effects On Breast Cancer Cells Through In Vitro And Docking Analyses, Megawati Megawati, Akhmad Darmawan, Agus Budiawan Naro Putra, Kartika Dyah Palupi, Marissa Angelina, Ahmad Randy, Siska Andrina Kusumastuti, Faris Hermawan, Sumi Hudiyono

Karbala International Journal of Modern Science

This study investigated the phytochemical constituents and biological activities of Horsfieldia macrothyrsa leaves to identify their bioactive compounds. Methanol extracts were fractionated using n-hexane and ethyl acetate, followed by silica gel column chromatography with a stepwise polarity gradient. From 100 g of powdered leaves, three major    compounds were successfully isolated from the ethyl acetate fraction: 1-(2,4,6-trihydroxyphenyl)dodecan-1-one (1), sesamin (2), and β-sitosterol (3). Their chemical structures were confirmed using UV, IR, LC–MS/MS, and NMR  spectroscopy. Biological activities of the fractions and isolates were evaluated through DPPH antioxidant assays, α-glucosidase inhibition for antidiabetic activity, MTT …

Exceptional Variety Of Baryte-Celestine-Series Speleothems In Lechuguilla Cave (New Mexico, Usa), Max Wisshak, Johannes Birkenstock, Andrea Schröder-Ritzrau, Norbert Frank, Hazel A. Barton, Harvey R. Duchene

International Journal of Speleology

An inventory of speleothems primarily composed of the closely related sulfate minerals baryte (BaSO₄) and celestine (SrSO₄) in Lechuguilla Cave (New Mexico, USA) has revealed an extraordinary variety. This includes subaerial stalactites, stalagmites, flowstone, coralloids, wall crusts, floor crystals, snow, frostwork, and subaqueous pool crystals. Large baryte and celestine crystals have also been documented in hydrothermal veins in the host rock, predating the cave and truncated when intersected by it. The respective carbonate mineral phases witherite (BaCO₃) and strontianite (SrCO₃), in contrast, were found to be rare in the studied speleothem samples, …

How Principals Who Use Artificial Intelligence For Innovation Create Cognitive Equity While Principals Who Use Ai For Efficiency Create Cognitive Debt, Jethro Jones

Dissertations

This dissertation in practice examined whether a targeted professional learning   intervention could shift school leaders’ use of generative artificial intelligence (AI) from efficiency-oriented tasks toward innovation-oriented strategic problem solving. AI is typically adopted to accelerate existing routines, which can deepen “cognitive debt” by reinforcing ineffective practices rather than improving systems. This study advanced a “cognitive equity” frame, positioning AI as a tool that can expand principals’ cognitive capacity to address complex problems and lead adaptive change. Using a quasi-experimental, single-group design, the study evaluated a free, full-day AI for Innovation workshop, which emphasized foundational understanding of how AI tools work …

Cybersecurity In Higher Education Institutions: Awareness, Policy, And Experience On Employee Behaviour, Abdullahi Abiodun Yusuf, Adriana A. Steyn

Journal of Cybersecurity Education, Research and Practice

The digital transformation of higher education institutions (HEIs) has introduced unprecedented connectivity and operational efficiency, but it has also heightened their exposure to cyber threats. South African HEIs, in particular, face increasing vulnerability due to their reliance on technology, openness, and diverse user communities. This study examines the influence of the institutional cybersecurity environment on employee cybersecurity-compliant behaviour (CCB), emphasising the critical roles of awareness, policy engagement, and experience. Drawing on Protection Motivation Theory and the Theory of Planned Behaviour, a conceptual model was developed to explore how cybersecurity awareness, policy familiarity, and prior experience shape employees’ attitudes, subjective norms, …

A Theoretical Investigation Of The Linear And Nonlinear Optical Responses Of Scandium- And Yttrium-Doped (Sub-Nm) Ag And Au Clusters, Munish Sharma, Mukesh Jakhar, Ravindra Pandey, Shashi P. Karna

Michigan Tech Publications

We investigate the linear and nonlinear optical properties of (sub-nm) Ag6 and Au6 clusters doped with Sc and Y using time-dependent density functional theory. Both parent clusters have D3h ground-state geometries but exhibit noticeably different electronic structures; scalar-relativistic corrections in Au6 induce significant s-d hybridization, resulting in larger HOMO-LUMO gaps and reduced one-photon absorption (OPA) cross-sections compared to Ag6. Two-photon absorption (TPA) peaks in the UV region show resonance enhancement via coupling with OPA-active states, with Ag6 having larger cross-sections than Au6. Doping with Sc and Y modifies the optical responses by breaking configurational symmetry and lifting HOMO degeneracies. ScAg5 …

Selective Reversal Of Cu-Amyloid Aggregation Monitored In Real Time By Fluorescence Anisotropy: Ni-Bme-Dach Vs Edta Benchmarks, Alyssa N. Schroeder, Eleanor K. Adams, Dane C. Frost, Erica Lopez, Jennie R. Giacomini, Marilyn R. Mackiewicz

Chemistry Faculty Publications and Presentations

Metal dyshomeostasis, particularly involving Cu2+, is increasingly recognized as a key contributor to amyloid-β (Aβ) aggregation and neurotoxicity in Alzheimer’s disease, motivating the development of chelators capable of selectively disrupting pathogenic metal-Aβ interactions without perturbing essential biological metals. Here, we employ steady-state fluorescence anisotropy as a real-time probe of TAMRA-Aβ1–42 rotational mobility to quantify metal-induced aggregation and its reversibility by two chelators with distinct selectivities: EDTA, a broad-spectrum benchmark, and Ni-bme-dach, a sulfur-rich metallodithiolate with high Cu affinity. Cu2+ induces the most significant increases in anisotropy, consistent with rapid formation of large nanoscale aggregates, while Fe3+ produces moderate aggregation and …

Early Detection Of Infectious Diseases: A Review Of Recent Advances In Pathogen Identification, Molecular Tools, And Metabolomics-Driven Biomarker Discovery, Patrica Ivy Agorsor, Michael O. Eze

Chemistry Faculty Research & Creative Works

The recent COVID-19 pandemic has heightened public interest in noninvasive methods for early diagnosis of infectious diseases. In addition, various government agencies have implemented "infectious disease preparedness" to mitigate future outbreaks. This review highlights conventional and advanced methods for infectious disease diagnosis with an emphasis on emerging mass spectrometry methods. Conventional methods for pathogen identification, such as culture-based techniques and molecular methods, have limitations with respect to sensitivity, specificity, and turnaround time. Recent advances in high-resolution mass spectrometry have revolutionized the field of infectious disease biomarker discovery. These techniques enable the comprehensive profiling of metabolites in various biological samples, identification …

Statistical Guarantees Of Deep Generative Models Involving Diverse Spaces: Generation Consistency And Robustness, Anish Chakrabarty

Doctoral Theses

Generative modeling focuses on the task of producing new data samples that closely resemble those drawn from an original, unknown distribution. Despite being well-known in statistical estimation theory, the approach has gained substantial traction in recent years, driven by groundbreaking results in areas such as image synthesis, natural language generation, and network modeling. The complexity of modern-era data domains and the ensuing adaptations that suitable models must undergo have presented new challenges. These advances raise several fundamental questions, the first of which is: When do generative models accurately approximate the true data distribution? One may also ask: How well do …

On Robust Estimation Of Multivariate Location And Scale With Applications, Soumya Chakraborty

Doctoral Theses

The principal objective of this thesis is, in a nutshell, to provide robust estimators of multivariate location and scale which have reasonable to high model efficiency but avoid high computational complexity so as to be practically useful in real problems. We utilize the minimum density power divergence (DPD) and the related philosophy to invoke robustness. There are some computational issues while minimizing the DPD in different multivariate set-ups. We will work on this problem rigorously and come up with three types of estimation procedures which are explicitly or implicitly related to the minimum DPD methodology, keeping the computational issue in …

Time And Water, Sheila Nayar

Journal of Religion & Film

This is a film review of Time and Water (2026), directed by Sara Dosa.

Developing Deep Neural Network Based Brain Computational Models From Psychophysics Data Of Some Simple Perceptual Phenomena: Visual As Well As Auditory, Chandran Keerthi S

Doctoral Theses

The thesis, consisting of nine chapters, explores the methodology of building testable brain computational models using Deep Neural Networks (DNN), which are trained by psychophysics data. Psychophysics is the quantitative study of perception of physical stimuli. In psychophysics experiments, one or more parameters associated with the stimuli are changed, and the human subject’s responses to the stimuli are recorded. This thesis encompasses both experimental psychophysics works, as well as computational models of the phenomena involved. The contributory chapters of the thesis start in Chapter 2 with the perspective building of the novel methodology followed throughout this research involving psychophysics on …

Response To Agency Comments Re: Comment Letter For The 2023 Butte Priority Soils Operable Unit (Bpsou) Groundwater Compliance Evaluation (Dated March 10, 2025) Received May 7, 2025, Josh Bryson

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

On The Misattribution Of Reassurance: A Structural Account, Griselda Poe

Publications and Research

This paper challenges the conventional assumption that empathy generates reassurance in interpersonal services. Reassurance is treated not as an emotion transmitted from the outside, but as an internal state transition that arises when a fixed and erroneous world model—a False Fantasy—undergoes collapse and the world becomes computable again. The study identifies a systematic misattribution pattern where providers and receivers treat empathy as a causal mechanism rather than a post hoc explanatory label. By introducing Base AI as an external reference—a system capable of providing structural information without emotional modulation—this paper demonstrates that reassurance is generated through operations such as distraction …

Search For Low-Mass Electron-Recoil Dark Matter Using A Single-Charge Sensitive Supercdms-Hvev Detector, M. F. Albakry, I. Alkhatib, D. Alonso-González, J. Anczarski, T. Aralis, T. Aramaki, I. Ataeelangroudy, C. Bathurst, R. Bhattacharyya, A. J. Biffl, P. L. Brink, M. Buchanan, R. Bunker, B. Cabrera, R. Calkins, R. A. Cameron, C. Cartaro, D. G. Cerdeño, Y. Y. Chang, M. Chaudhuri, J. H. Chen, R. Chen, N. Chott, J. Cooley, H. Coombes, P. Cushman, R. Cyna, S. Das

All Works

We present constraints on low-mass dark matter electron scattering and absorption interactions using a SuperCDMS high-voltage eV-resolution (HVeV) detector. Data were taken underground in the NEXUS facility located at Fermilab with an overburden of 225 meters of water equivalent. The experiment benefits from the minimizing of luminescence from the printed circuit boards in the detector holder used in all previous HVeV studies. A blind analysis of 6.1 g·days of exposure produces exclusion limits for dark matter-electron scattering cross sections for masses as low as 1 MeV/c², as well as on the photon-dark photon mixing parameter and the coupling …

Exploring Enacted Active Learning Practices In Postsecondary Chemistry Lecture And Laboratory Courses, Kendall M. Zammit

USF Tampa Graduate Theses and Dissertations

The work in this thesis begins to explore the enacted active learning practices in postsecondary chemistry lecture and laboratory courses. Chapter 1 explores the level of inquiry in postsecondary laboratories. Chapter 2 explores instructional and assessment practices at two-year institutions.

Chemistry postsecondary laboratories offer a unique opportunity to engage students in inquiry-based instruction. Inquiry-based instruction was categorized by the asking faculty members at four-institutions about six characteristics of laboratory design: choosing a problem, background research, designing procedures, analyzing data, communicating results, and interpreting results. As students have the freedom to make decisions on more characteristics of inquiry learning, the closer …

Corrigendum To “Interim Rationalizable Implementation Of Functions” (Kunimoto T, Saran R, Serrano R (2024) Mathematics Of Operations Research 49(3):1791–1824), Takashi Kunimoto, Rene Saran, Roberto Serrano

Research Collection School Of Economics

This is the brief corrigendum to “Interim rationalizable implementation of functions” [Kunimoto T, Saran R, Serrano R (2024) Interim rationalizable implementation of functions. Math. Oper Res. 49(3):1791–1824].

Communication As Layered Architecture: Core Processing And Empathic Modulation, Griselda Poe

Publications and Research

This paper proposes a structural re-description of communication by separating Core processing from its social interface. Using the developmental sequence of Large Language Models (LLMs) as an external reference point, a layered architecture is identified, consisting of a foundational Core processing layer and a subsequent Empathic modulation layer.

The investigation begins with the observation that empathic signaling can obstruct rather than facilitate interaction for certain individuals. By examining the emergence of Base AI—Core processing prior to empathic adjustment—it is demonstrated that coherent, constraint-preserving interaction is possible without affective resonance.

Through this framework, existing cognitive theories and observed "deficits" are repositioned. …

Impact Of Iron-Bearing Fillers On The Mechanical Strength And Chemical Stability Of Magnesium Potassium Phosphate Matrices Incorporating Rhenium, Sergey Sayenko, Volodymyr Shkuropatenko, Hans Conrad Zur Loye, Petr Vecernik, Monika Kiselova, Vlastislav Kašpar, Vlastimil Miller, Petr Bezdicka, Jan Šubrt, Petra Ecorchard, Natalija Murafa, Iva Milisavljevic, Scott T. Misture

Faculty Publications

We report on the study of the immobilization process of non-radioactive rhenium (Re), a chemical analogue of technetium-99 (⁹⁹Tc), in compounds based on magnesium potassium phosphate (MKP), as well as the possibility of enhancing their properties with iron-bearing additives/fillers. Powdered Re2O7 was used as the initial Re-containing source. Because of the solubility and high leachability of Tc (VII), which is also volatile at high temperatures, its immobilization for long-term storage and disposal poses a serious challenge to researchers. Taking this into account, low-temperature stabilization technology based on MKP, a cementitious material, is currently considered promising. We prepared experimental …

A Note On Sufficient Dimension Folding For Regression Mean Function With Categorical Predictors, Bilin Zeng, Akim Adekpedjou, Xuerong Meggie Wen

Mathematics and Statistics Faculty Research & Creative Works

Multi-dimensional arrays are referred to as tensors. Tensor-valued predictors are commonly encountered in modern biomedical applications, such as electroencephalogram (EEG), magnetic resonance imaging (MRI), functional MRI (fMRI), diffusion-weighted MRI, and longitudinal health data. In survival analysis, it is both important and challenging to integrate clinically relevant information, such as gender, age, and disease state along with medical imaging tensor data or longitudinal health data to predict disease outcomes. Most existing higher-order sufficient dimension reduction regressions for matrix- or array-valued data focus solely on tensor data, often neglecting established clinical covariates that are readily available and known to have predictive value. …

Majority Of Utahns Support Keeping Bears Ears National Monument As Originally Designated, Michaelann Nelson

Utah People and Environment Poll (UPEP)

Bears Ears National Monument in Southeastern Utah was established in 2016 by President Obama after a coalition of Native American petitioned for its designation. In 2017, during his first administration, President Trump reduced the size of the monument by 85%. It was subsequently restored to the original boundaries in 2021 by President Biden. In 2022, the Utah Legislature provided $5 million in funding for the Attorney General’s office to challenge the Bears Ears National Monument designation, and the State of Utah filed a lawsuit in August 2022 challenging the designation (1).

Responsible Ai In Teaching And Learning, Asa B. Stone, Mark C. Stone, Derek M. Heeren, Santosh Pitla

PRAIRIE: Pioneering Responsible AI for Research, Innovation, and Education

The rapid adoption of generative artificial intelligence (AI) in higher education presents both transformative opportunities and significant pedagogical risks. While AI tools are becoming embedded in academic and professional environments, their integration into teaching and learning raises critical questions about cognitive engagement, academic integrity, equity, and skill development. This white paper proposes a principled framework for the responsible integration of AI in higher education, grounded in the dual commitment to AI literacy and the cultivation of durable skills.

The framework articulates six core principles: purposefulness; transparency; integrity and attribution; critical AI literacy; equity and access; and privacy and data protection. …

Future Trends In Cybersecurity: A Meta-Review, Yara Mohammed, Manar Alsaid, Gahangir Hossain

Faculty Publications

The increasing importance of cybersecurity in protecting digital assets, data and infrastructures necessitates a reevaluation of research priorities within the discipline. As of today, numerous emerging cybersecurity topics are gaining significant importance in both academic research and industry applications. To identify recent trends in cybersecurity topics, this study extracts scholarly articles from two prestigious academic databases, the ACM Digital Library, and Google Scholar, covering the period from early 2015 to late 2024.Through a systematic identification of trends and focal points in cybersecurity research, a comprehensive analysis is facilitated, including Latent Dirichlet Allocation (LDA), Biterm Topic Modeling (BTM), keyword frequencies, and …

Fortifying The Seams Between C/C++ And Rust: Characterizing Bugs In Interop Tools, Xuemeng Cai, Jiakun Liu, Cunyang Liu, Lingfeng Bao, Yijun Yu, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Rust has become increasingly popular in recent years due to its safety and high performance. Despite these advantages, Rust projects rarely start from scratch in practice, and many Rust-based systems instead use hybrid programming, where Rust interoperates with existing C/C++ code. To reduce the manual effort involved in this interoperation (interop) process, several interop tools have been proposed to facilitate hybrid programming between Rust and C/C++. However, the challenges and limitations of these tools remain largely unexplored, leaving developers unclear about the future directions and users unclear about the appropriate usage scenarios. To fill the gap, we mined 320 bugs …

Obstructive Sleep Apnea Prediction: A Comprehensive Review And Comparative Study, Thi Khanh Chi Huynh, Amonae Dabbs-Brown, Anna Jurek-Loughrey, James Mulhall, Tuan Dung Pham, Ngoc Phu Doan, Viet Hung Tran, Zichi Zhang, Xuan Hoang Nguyen, Yimeng An, Peixin Li, Phi Hung Nguyen, Thi Linh Hoang, Xinming Shi, Hans Vandierendonck, Sebastien Bailly, Jean-Louis Pépin, Thai Son Mai

Research Collection School Of Computing and Information Systems

Obstructive Sleep Apnea (OSA) is a highly prevalent sleep disorder linked to considerable public health burdens and comorbidities. However, its heterogeneous presentation and the limited accessibility of traditional diagnostic tools such as polysomnography (PSG) lead to widespread underdiagnosis. As a result, artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL) models, have attracted attention as an alternative pathway to detection. This paper first provides a comprehensive review of AI-driven OSA diagnosis, covering different diagnosis problems, input-data types, data biases, pre-processing techniques, and model performance. We then leverage the largest clinical dataset used in OSA prediction to date, …