Physical Sciences and Mathematics Commons^™

The Importance Of Solution Studies For The Structural Characterization Of The Enterovirus 5' Cloverleaf, Morgan G. Daniels, Meagan E. Werner, Xiaobing Zuo, Steven M. Pascal

Chemistry & Biochemistry Faculty Publications

Enteroviruses initiate genomic replication via a highly conserved mechanism that is controlled by an RNA platform, also known as the 5' cloverleaf (5'CL). Here, we present a biophysical analysis of the 5'CL conformation of three enterovirus serotypes under various ionic conditions, utilizing CD spectroscopy, size-exclusion chromatography, and small-angle X-ray scattering. In general, a tendency toward a smaller monomeric hydrodynamic radius in the presence of salts was observed, but the exact structural signature of each 5'CL varied depending upon the serotype. Rhinovirus B14 (RVB14) exhibited at least two monomeric conformations and a low propensity for dimerization, while poliovirus 1 (PV1) showed …

Advancing Pedagogical And Instructional Design Through Artificial Intelligence (Ai) In Education And Training Contexts, Mohan Yang, Jewoong Moon, Tian Luo, Jinhee Kim

STEMPS Faculty Publications

[Introduction] "The only way to discover the limits of the possible is to go beyond them into the impossible." - Arthur C. Clarke

It is our pleasure and honor as guest editors for this special issue of the Journal of Applied Instructional Design (JAID) to present "Advancing Pedagogical and Instructional Design through Artificial Intelligence (AI) in Education and Training Contexts." This issue arrives at a truly timely moment, as the rapid and continuous development of generative artificial intelligence (GenAI) is fundamentally reshaping the traditional paradigms of teaching, learning, and training.

A Bibliometric Analysis Of Ai-Driven Healthcare Literature Containing Kos Keywords: Trends, Themes, And Gaps, Julaine Clunis, Eric Asare

STEMPS Faculty Publications

As artificial intelligence (AI) becomes increasingly embedded in healthcare applications, concerns have emerged around the trustworthiness, interpretability, and context-awareness of these systems. Knowledge Organization Systems (KOS) hold considerable potential to address these challenges by supporting semantic standardization, explainability, and domain alignment. This study presents a bibliometric analysis of scholarly publications referencing both AI and healthcare concepts to examine how KOS are positioned within this evolving discourse. The findings indicate that while early literature frequently and explicitly referenced KOS—such as ontologies, controlled vocabularies, and classification systems—their visibility has declined relative to newer paradigms such as machine learning and large language models. …

Motion Artifacts (Ma) At-Rest In Measured Arterial Pulse Signals: Time-Varying Amplitude In Each Harmonic And Non-Flat Harmonic-Ma Coupled Baseline, Md Mahfuzur Rahman, Mamun Hasan, Zhili Hao

Mechanical & Aerospace Engineering Faculty Publications

Motion artifacts (MA) cause great variability in a measured arterial pulse signal, and treatment of MA solely as a baseline drift (BD) fails to eliminate its effect on the measured signal. This paper presents a study on the effect of MA at rest (< 0.7 Hz) on measured arterial pulse signals using a microfluidic-based tactile sensor. By taking full account of the dynamic behavior of the transmission path from the true pulse signal in an artery to a measured pulse signal at the sensor, the tissue-contact-sensor (TCS) stack, an analytical model of MA in a measured pulse signal is developed. In this model, the TCS stack is treated as a 1DOF system for its dynamic behavior; MA is quantified as the displacement (i.e., BD) and time-varying system parameters (TVSP) of the TCS stack. The mathematical expression of MA in a measured pulse signal reveals that while BD remains as low-frequency additive noise, TVSP causes time-varying harmonics in a measured pulse signal. Further time-frequency analysis (TFA) of measured pulse signals validates the existence of TVSP and, for the first time, reveals its effect on a measured pulse signal: time-varying amplitude in each harmonic and non-flat harmonic-MA-coupled baseline.

Suntan (And Other Solar Trigonometric Functions): Solutions For Fermi Questions: February 2025, John Adam

Mathematics & Statistics Faculty Publications

The article discusses the intensity of sunlight on the side of the author's face as they walk to and from their office, focusing on the solar zenith angle θ. It presents a formula for solar irradiance and explores how the intensity changes based on the angle and elevation. The solutions to the questions posed in the article provide insights into the maximum irradiance levels at different angles and elevations.

M Star Opacities: The B³Π–X³Δ Band System Of Tio, Peter F. Bernath, Manish Bhusal, Mirek R. Schmidt

Chemistry & Biochemistry Faculty Publications

The spectra of titanium monoxide (TiO) are used to classify M stars. TiO experimental cross sections based on a high-resolution emission spectrum recorded with a Fourier transform spectrometer at the National Solar Observatory are used for the analysis of the TiO B³Π–X³Δ transition (𝛾' system). The 3–2 and 4–3 vibrational bands are analyzed for the first time, and the analysis of the 2–1 band is revised. The transition dipole moment function from an ab initio calculation is used to calculate the band strengths. These band strengths are used to produce a B³Π–X³Δ line list for the vibrational levels v' ⩽ …

A Two-Phase Learning Approach Integrated With Multi-Source Features For Cloud Service Qos Prediction, Fuzan Chen, Jing Yang, Haiyang Feng, Harris Wu, Minqiang Li

Information Technology & Decision Sciences Faculty Publications

Quality of Service (QoS) is a key factor for users when choosing cloud services. However, QoS values are often unavailable due to insufficient user evaluations or provider data. To address this, we propose a new QoS prediction method, Multi-source Feature Two-phase Learning (MFTL). MFTL incorporates multiple sources of features influencing QoS and uses a two-phase learning framework to make effective use of these features. In the first phase, coarse-grained learning is performed using a neighborhood-integrated matrix factorization model, along with a strategy for selecting high-quality neighbors for target users. In the second phase, reinforcement learning through a deep neural network …

A Spoofing Speech Detection Method Combining Multi-Scale Features And Cross-Layer Identification, Hongyan Yuan, Linjuan Zhang, Baoning Niu, Xianrong Zheng

Information Technology & Decision Sciences Faculty Publications

Pre-trained self-supervised speech models can extract general acoustic features, providing feature inputs for various speech downstream tasks. Spoofing speech detection, which is a pressing issue in the age of generative AI, requires both global information and local features of speech. The multi-layer transformer structure in pre-trained speech models can effectively capture temporal information and global context in speech, but there is still room for improvement in handling local features. To address this issue, a speech spoofing detection method that integrates multi-scale features and cross-layer information is proposed. The method introduces a multi-scale feature adapter (MSFA), which enhances the model’s ability …

Optimal Control Of Queueing Systems With Error-Prone Servers, Junqi Hu, Sigrún Andradóttir, Hayriye Ayhan

Information Technology & Decision Sciences Faculty Publications

Consider a Markovian tandem line with finite intermediate buffers and an equal number of stations and servers. Servers are flexible but noncollaborative, so that a job can be processed by at most one server at any time. When a job is being processed, it can be damaged and wasted depending on the proficiency of the server. We identify the dynamic server assignment policy that maximizes the long-run average throughput of the system with two stations and two servers. We find that the optimal policy is either a single or a double threshold policy on the number of jobs in the …

Investing In The Age Of Generative Ai: A Gpt-Based Sentiment Analysis Approach, Xianrong Zheng

Information Technology & Decision Sciences Faculty Publications

Generative AI, which ushers a new age of AI, comes with huge economic potential. To capitalize the AI boom, investors are interested in trading AI stocks. AI chatbots, which can identify and classify the sentiment from financial news, can be leveraged for investment. So, this paper proposes a GPT-based sentiment analysis approach for trading AI stocks. Also, natural experiments are conducted to evaluate its effectiveness. Initial results show that the approach achieves a good rate of return.

Optimal Control And Structurally-Informed Gradient Optimization Of A Custom 4-Dof Rigid-Body, Brock Marcinczyk, Logan E. Beaver

Mechanical & Aerospace Engineering Faculty Publications

This work develops a control-centric framework for a custom 4-DOF rigid-body manipulator by coupling a reduced-order Pontryagin’s Maximum Principle (PMP) controller with a physics-informed Gradient Descent stage. The reduced PMP model provides a closed-form optimal control law for the joint accelerations, while the Gradient Descent module determines the corresponding time horizons by minimizing a cost functional built directly from the full Rigid-Body Dynamics. Structural-mechanics reaction analysis is used only to initialize feasible joint velocities—most critically the azimuthal component—ensuring that the optimizer begins in a physically admissible region. The resulting kinematic trajectories and dynamically consistent time horizons are then supplied to …

Applying Transfer Learning For Street-Scale Nuisance Flood Forecasting In Coastal-Urban Cities, Binata Roy, Jonathan L. Goodall, Diana Mcspadden, Chetan Kumar, Steven Goldenberg, Yidi Wang, Malachi Schram

VMASC Publications

An important challenge with Machine Learning (ML) is its transferability; i.e., whether a ML model trained on one set of data can be applied to a second set of data without requiring full re-training of the model. Transfer Learning (TL) addresses this challenge by transferring knowledge learned in the source domain (the data it was trained on) to the target domain (a second set of data that is statistically different but related, which the model was not trained on). This study investigates the use of TL for street-scale nuisance flood forecasting by exploring whether a ML model trained for one …

Faithful Narratives From Complex Conceptual Models: Should Modelers Or Large Language Models Simplify Causal Maps, Tyler J. Gandee, Philippe J. Giabbanelli

VMASC Publications

(1) Background: Comprehensive conceptual models can result in complex artifacts, consisting of many concepts that interact through multiple mechanisms. This complexity can be acceptable and even expected when generating rich models, for instance to support ensuing analyses that find central concepts or decompose models into parts that can be managed by different actors. However, complexity can become a barrier when the conceptual model is used directly by individuals. A ‘transparent’ model can support learning among stakeholders (e.g., in group model building) and it can motivate the adoption of specific interventions (i.e., using a model as evidence base). Although advances in …

5g-Practical Byzantine Fault Tolerance: An Improved Pbft Consensus Algorithm For The 5g Network, Xin Liu, Xing Fan, Baoning Niu, Xianrong Zheng

Information Technology & Decision Sciences Faculty Publications

The consensus algorithm is the core technology of blockchain systems to maintain data consistency, and its performance directly affects the efficiency and security of the whole system. Practical Byzantine Fault Tolerance (PBFT) plays a crucial role in blockchain consensus algorithms by providing a robust mechanism to achieve fault-tolerant and deterministic consensus in distributed networks. With the development of 5G network technology, its features of high bandwidth, low latency, and high reliability provide a new approach for consensus algorithm optimization. To take advantage of the features of the 5G network, this paper proposes 5G-PBFT, which is an improved practical Byzantine fault-tolerant …

Atmospheric Chemistry Experiment (Ace) Winds, Matthew Wyatt, Peter F. Bernath, Chris Boone, Léo Lavy, Ryan Johnson

Chemistry & Biochemistry Faculty Publications

The Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) uses limb geometry to measure transmittance spectra of Earth's atmosphere by solar occultation. Line-of-sight wind speeds can be derived via Doppler shifts of molecular lines in infrared spectra. The wind look direction angles relative to geodetic North are derived from geometry. We validate the new ACE version 5.3 (v.5.3) line-of-sight winds with MIGHTI and meteor radar vector wind observations and find a ±15 m/s sunrise/sunset shift above 80 km. We also compare line-of-sight winds from ACE-FTS v.5.2 and v.5.3 with vector winds from the MERRA-2, HWM14, and WACCM-X models. A ±15 m/s …

Evaluating The Chemical Reactivity Of Wildfire-Derived Dissolved Organic Molecules: Glutathione Binding Through Kendrick Mass Defect Analysis, Hannah M. Hamontree, Patrick G. Hatcher

Chemistry & Biochemistry Faculty Publications

The emerging risks to organisms of pyrogenic-derived dissolved organic matter (PyDOM) from forest fires are of concern due to its toxic and mutagenic potential (e.g., pro-oxidative responses in fauna through the depletion of glutathione, a nitrogen- and sulfur-containing tripeptide found in cells). This study simulates this phenomenon in a laboratory setting by identifying bonding between reduced l-glutathione and organic molecules in leachates from environmentally weathered biomass samples (charred and uncharred) using Kendrick Mass Defect (KMD) analysis from formula lists obtained from negative-mode electrospray ionization-Fourier transform-ion cyclotron resonance-mass spectrometry ((-)ESI-FT-ICR-MS). These formula lists reveal a 10-fold increase in nitrogen- and sulfur-containing …

The Importance Of Atomic Charges For Predicting Site-Selective Ir-, Ru-, And Rh-Catalyzed C-H Borylations, Shannon M. Stephens, Kyle M. Lambert

Chemistry & Biochemistry Faculty Publications

A supervised machine learning model has been developed that allows for the prediction of site selectivity in late-stage C-H borylations. Model development was accomplished using literature data for the site-selective (≥95%) C-H borylation of 189 unique arene, heteroarene, and aliphatic substrates that feature a total of 971 possible sp² or sp³ C-H borylation sites. The reported experimental data was supplemented with additional chemoinformatic descriptors, computed atomic charges at the C-H borylation sites, and data from parameterization of catalytically active tris-boryl complexes resulting from the combination of seven different Ir-, Ru-, and Rh-based precatalysts with eight different ligands. Of the over …

Challenges Associated With Pfas Detection Method In Africa, Abdullahi Tunde Aborode, Ridwan Olamilekan Adesola, Ibrahim Idris, Waheed Sakariyau Adio, Segun Olapade, Gladys Oluwafisayo, Israel Ayobami Onifade, Sodiq Fakorede, Taiwo Bakare-Abidola, Jelil Olaoye, Adedeji Daniel Ogunyemi, Oluwaseun Adeolu Ogundijo, Olamilekan Gabriel Banwo, Adetolase Azizat Bakre, Peter Oladoye, Grace Adegoye, Noimat Abeni Jinadu

Chemistry & Biochemistry Faculty Publications

Per- and polyfluoroalkyl substances (PFAS) are a group of man-made chemicals that are widely present in many industries. Monitoring and analyzing PFAS in Africa is challenging due to the limited availability of mass spectrometry (MS), which is an essential technique for detecting PFAS. This review assesses the scope and impact of the shortage of mass spectrometry instruments in Africa, emphasizing the resulting limitations in monitoring environmental and public health threats. The review analyzes the existing PFAS monitoring, the accessibility of MS instruments, and the technical capabilities within the continent. This study suggests that fewer African countries have sufficient MS instruments, …

Nucleotide-Derived Bacterial Alarmones Attenuate The Induction Of Type-I Interferon Responses In A Murine Macrophage Reporter Cell Line, Ryan P. Kilduff, Erin B. Purcell, Lisa M. Shollenberger

Chemistry & Biochemistry Faculty Publications

The stringent response is a well-studied phenomenon in many bacterial systems and regulates resource-consuming activities such as transcription, translation, and replication. The stringent response is a well-conserved signaling framework, as are the nucleotide-derived signaling mediators, collectively referred to as (p)ppGpp or as alarmones. There is a wealth of research evaluating nucleotide-derived alarmone signaling in bacterial models, however, their potential to modulate innate immune signaling has not yet been evaluated. Several common pathogen-synthesized molecules, such as lipopolysaccharide (LPS) and cyclic-di-AMP (c-di-AMP), act as pathogen-associated molecular patterns (PAMPs), which are common patterns that alert the innate immune system of bacterial infection. The …

Alternative Splicing In A Coral During Heat Stress Acclimation And Recovery, Nitin Baliga, Kathryn Stanklewicz, Jacob Valenzuela, Serdar Turkarsian, Wei-Ju Wu, Kelly Gomez-Campo, Nicolas Locatelli, Trinity Conn, Veronica Radice, Katherine Parker, Rachel Alderdice, Line Bay, Christian Voolstra, Daniel Barshis, Iliana Baums

Biological Sciences Faculty Publications

Climate change has caused drastic declines in corals. As sessile organisms, corals acclimate to environmental shifts through genome-wide changes in gene expression, epigenetic modifications, and alterations in microbiome composition. However, alternative splicing (AS), a conserved mechanism of stress response in many organisms, has been under-explored in corals. Using short-term acute thermal stress assays, we investigated patterns of AS in the scleractinian coral Acropora cervicornis during response to low (33°C), medium (35°C), and high (37°C) heat stress and subsequent overnight recovery. Our findings demonstrate reproducible dynamic shifts in AS of at least 40 percent of all genes during response to heat …

Selective Nutrient Incorporation May Underestimate Heterotrophy Of A Mixotrophic Reef-Building Coral, Connor R. Love, Marleen Stuhr, Michael D. Fox, Veronica Z. Radice, Maoz Fine, Kelton W. Mcmahon, David L. Valentine

Biological Sciences Faculty Publications

Mixotrophic reef-building corals acquire nutrition via photosynthate translocation from endosymbiotic microalgae and by heterotrophic prey/particle capture. Heterotrophy promotes resistance to, and recovery from, environmental stress, but quantifying coral heterotrophy remains difficult due to complex resource exchanges within the coral holobiont. We interrogated the response of multiple biomarkers to coral heterotrophy using fatty acid profiling and stable isotope analysis of Stylophora pistillata grown along a controlled feeding gradient from pure autotrophy to pure heterotrophy. We found that fatty acids and nitrogen were effectively incorporated into both coral host and symbiont tissues, while carbon, which is the primary target for conventional heterotrophy …

Multipliers On Weighted Sequence Spaces, Gilbert Acheampong, Raymond Cheng

Mathematics & Statistics Faculty Publications

The space ℓp,α of complex sequences a = (a0, a1,a2,...) for which

[[formula omitted]]

is studied. Each such sequence can be identified with the analytic function with power series

[[formula omitted]]

In this setting, the point evaluation and the difference quotient mappings are shown to be bounded; the cases are identified in which ℓp,α is boundedly contained in ℓr,β. Conditions on the parameters are derived for the analytic functions of ℓp,α to have radial limits almost everywhere on the boundary, and for ℓp,α to be an algebra. Smoothness properties of the boundary function are investigated. Basic properties of multipliers on …

Comprehensive Benchmarking Of Several Machine Learning And Bayesian Models For Early-Stage Diabetes Risk Prediction: A Large-Scale Comparative Study, Md. Iqbal Hossain, Najila Alam Porno

Mathematics & Statistics Faculty Publications

Diabetes remains a critical global health challenge, with early detection is crucial for effective management. This study presents a comprehensive benchmarking analysis of 14 diverse machine learning and Bayesian models for early-stage diabetes risk prediction using clinical data [2] from Sylhet, Bangladesh. This research evaluated traditional methods (Logistic Regression, Decision Trees), ensemble techniques (Random Forest, XGBoost, LightGBM), Bayesian approaches (BART, Bayesian Logistic Regression), and advanced neural architectures (Deep Belief Networks) using both 70-30 train-test splits and 10-fold cross-validation. The results demonstrate that ensemble methods consistently outperformed other approaches, with Random Forest(RF) achieving the highest cross-validated AUC (0.9951) and accuracy (0.9699). …

Suntan (And Other Solar Tigonometric Functions), John Adam

Mathematics & Statistics Faculty Publications

Question 1: If I₀ is the solar irradiance (power per unit area, W/m²) reaching my head, express the intensity on the side of my face (I_s) in terms of θ. Assume for now that the irradiance is independent of path length through the atmosphere and that my face is normal to the direction θ = 90°.

Using the 1962 U.S. Standard Atmosphere,² Hottel (1976)³ expressed the solar irradiance using the formula

I = I₀(a₀ + a₁e^{−k sec θ}), where A is the elevation in kilometers and

a₀ = 0.4237 − 0.00821(6 − A)²; a₁ = 0.5055 …

Golden Spirals Everywhere?, John Adam

Mathematics & Statistics Faculty Publications

The article explores different types of spirals, including Archimedean, hyperbolic, and logarithmic spirals, with a focus on the golden ratio and golden spirals. It discusses the misconception that golden rectangles and spirals can be found in various natural and man-made objects, emphasizing the importance of understanding the properties of logarithmic spirals. The text provides mathematical equations for logarithmic spirals and poses questions for readers to explore the concept further. The author, John Adam, invites readers to engage in Fermi Questions and submit ideas for consideration.

Model-Free Organization Of Patient Reported Outcomes Data: Geometrical Rep-Resentation Of The Modified Compartmen-Talization Method, Manasi Sheth, N. Rao Chaganty

Mathematics & Statistics Faculty Publications

There is a recent advancement in the field of mathematics and statistics to understand the geometry or connectedness of the data due to the massive amounts of data being generated. The data provided for analyses are usually very large and need to be organized and minimized in order to make it more useful and meaningful. In biostatistics or medical field, it is important for patients to have access to high-quality, safe and effective and/ or efficacious medical products. It is quite necessary to ascertain that the patients and their care-partners stay at the center of the regulatory decision-making process. In …

A Comparative Analysis Of Preprocessing Filters For Deep Learning-Based Equipment Power Efficiency Classification And Prediction Models, Sang-Ha Sung, Chang-Sung Seo, Michael Pokojovy, Sangjin Kim

Mathematics & Statistics Faculty Publications

The quality of input data is critical to the performance of time-series classification models, particularly in the domain for industrial sensor data where noise and anomalies are frequent. This study investigates how various filtering-based preprocessing techniques impact the accuracy and robustness of a Transformer model that predicts power efficiency states (Normal, Caution, Warning) from minute-level IIoT sensor data. We evaluated five techniques: a baseline, Simple Moving Average, Median filter, Hampel filter, and Kalman filter. For each technique, we conducted systematic experiments across time windows (360 and 720 min) that reflect real-world industrial inspection cycles, along with five prediction offsets (up …

A Decision-Space Model Explains Context-Specific Decision Making, Dirk W. Beck, Cory N. Heaton, Luis D. Davila, Lara I. Rakocevic, Sabrina M. Drammis, Danil Tyulmankov, Atanu Giri, Shreeya Umashankar Beck, Qingyang Zhang, Michael Pokojovy, Kenichiro Negishi, Alexis A. Salcido, Neftali F. Reyes, Andrea Y. Macias, Serina A. Batson, Paulina Vara, Raquel J. Ibáñez Alcalá, Safa B. Hossain, Graham L. Waller, Laura E. O'Dell, Travis M. Moschak, Ki A. Goosens, Alexander Friedman

Mathematics & Statistics Faculty Publications

Optimal decision-making requires consideration of internal and external contexts. Biased decision-making is a transdiagnostic symptom of neuropsychiatric disorders. We created a computational model demonstrating how the striosome compartment of the striatum constructs a context-dependent mathematical space for decision-making computations, and how the matrix compartment uses this space to define action value. The model explains multiple experimental results and unifies other theories like reward prediction error, roles of the direct versus indirect pathways, and roles of the striosome versus matrix, under one framework. We also found, through new analyses, that striosome and matrix neurons increase their synchrony during difficult tasks, caused …

A Bridge Too Low: Solutions For Fermi Questions, May 2025, John Adam

Mathematics & Statistics Faculty Publications

The article discusses a low bridge in Keswick, England, near the River Greta, with an arch shaped like a semiellipse. It presents Fermi questions related to the bridge's dimensions, such as the maximum distance a person of a certain height can walk under it without hitting their head. The solutions involve mathematical calculations and approximations, including the use of formulas provided by the Indian mathematician Srinivasa Ramanujan.

Moderate Heating Renders 7.8-Million-Year-Old Sedimentary Organic Matter Bioavailable, Shuchai Gan, Verena B. Heuer, Frauke Schmidt, Lars Wormer, Faming Wang, Rishi R. Adhikari, Patrick Hatcher, Ann Pearson, Kai-Uwe Hinrichs

Chemistry & Biochemistry Faculty Publications

Marine sediments are a large reservoir of recalcitrant organic matter and host microbes at subsurface depths exceeding 2.4 kilometers and temperatures up to 120°C, yet the mechanisms supplying bioavailable substrates remain unclear. Here, we investigated 7.8-million-year-old sediment from IODP Site C0012 off the Nankai Trough, Japan, through incubations at 20°, 35°, 55°, and 85°C to simulate burial temperatures. Using 3D fluorescence spectroscopy and ultrahigh-resolution mass spectrometry, we tracked changes in dissolved organic matter (DOM). At 35°C, humic-like DOM was released alongside metal ions, exhibiting low bioavailability. At 55°C, abiotic decomposition of humic compounds generated smaller, more bioavailable DOM, promoting fermentation. …