Physical Sciences and Mathematics Commons^™

Living With Alzheimer’S: A Guide To How To Lower The Risk Of Memory Loss & Care For Those With Alzheimer’S, Mona Muzammil, Washain Muzammil

School of Integrative Biological & Chemical Sciences (Formerly Dept. of Chemistry)

Despite the frequent characterization of Alzheimer's disease as a “loss of self,"" this enlightening book demonstrates unequivocally that a person's unique self persists throughout the course of the disease. The important message in Caring for People with Alzheimer’s is how much can be done in care settings to support a person's sense of identity and thereby enrich the lives of people experiencing the many losses associated with dementia. Drawing from a diverse body of research, this book brings together the recommendations of the best thinkers and practitioners in multiple disciplines to illustrate the meaning of self and the importance of …

Solitons, Breathers And Rogue Waves Of The Yajima–Oikawa-Newell Long Wave–Short Wave System, Marcos Caso-Huerta, Bao-Feng Feng, Sara Lombardo, Ken-Ichi Maruno

School of Mathematical & Statistical Sciences Faculty Publications

In this paper, we consider the recently-introduced Yajima–Oikawa–Newell (YON) system describing the nonlinear resonant interaction between a long wave and a short wave. It extends and generalises the Yajima–Oikawa (YO) and the Newell (N) systems, which can be obtained from the YON system for special choices of the two non-rescalable, arbitrary parameters that it features. Remarkably, for any choice of these latter constants, the YON system is integrable, in the sense of possessing a Lax pair. New families of solutions, including the bright and dark multi-solitons, as well as the breathers and the higher-order rogue waves are systematically derived by …

Exploring Transfer Learning For Deep Learning Polyp Detection In Colonoscopy Images Using Yolov8, Fabian Vazquez Jr., Jose Angel Nuñez, Xiaoyan Fu, Pengfei Gu, Bin Fu

Computer Science Faculty Publications

Deep learning methods have demonstrated strong performance in object detection tasks; however, their ability to learn domain-specific applications with limited training data remains a significant challenge. Transfer learning techniques address this issue by leveraging knowledge from pre-training on related datasets, enabling faster and more efficient learning for new tasks. Finding the right dataset for pre-training can play a critical role in determining the success of transfer learning and overall model performance. In this paper, we investigate the impact of pre-training a YOLOv8n model on seven distinct datasets, evaluating their effectiveness when transferred to the task of polyp detection. We compare …

Efficiency Of Labor In Winter Pruning Of Apple Trees Using Technological Innovations, Élio Fillipi Dos Santos, Wendel Paulo Silvestre, Lessandra Silva Rodrigues, Carine Cocco

School of Earth, Environmental, & Marine Sciences Faculty Publications

The increase of new technologies in fruit-growing crops has increased productivity in cultural practices and helped farmers expand their production areas. This work aimed to quantify labor performance in winter pruning of apple trees, using manual and electronic scissors associated with utilizing a pruning platform and ladders. The treatments consisted of two types of pruning shears and two support systems. A pruner carried out pruning for 16 days, with a journey of 10 h per day. The average yield in manual pruning with a ladder was 21.35 plants pruned per hour per man. Using a platform, the yield increased to …

Critical Appraisal Of Multidrug Therapy In The Ambulatory Management Of Patients With Covid-19 And Hypoxemia. Part Ii: Causal Inference Using The Bradford Hill Criteria, Eleftherios Gkioulekas, Peter A. Mccullough, Colleen Aldous

School of Mathematical & Statistical Sciences Faculty Publications

We continue the critical appraisal of three published case series of 119 COVID-19 patients with hypoxemia, treated in the United States, Zimbabwe, and Nigeria with similar ivermectin-based multidrug treatments, to assess the available evidence supporting a causal relationship between treatment and reduction in hospitalizations and mortality. A narrative review was conducted to assess the Bradford Hill criteria for a causal association. We used a previously proposed refinement of the Bradford Hill criteria that reorganized them into three categories of direct, mechanistic, and parallel evidence. The efficacy of the two most aggressive ivermectin-based multidrug protocols is supported by the Bradford Hill …

Critical Appraisal Of Multidrug Therapy In The Ambulatory Management Of Patients With Covid-19 And Hypoxemia Part I. Evidence Supporting The Strength Of Association, Eleftherios Gkioulekas, Peter A. Mccullough, Colleen Aldous

School of Mathematical & Statistical Sciences Faculty Publications

This critical appraisal is focused on three published case series of 119 COVID-19 patients with hypoxemia who were successfully treated in the United States, Zimbabwe, and Nigeria with similar off-label ivermectin-based multidrug treatments that may include ivermectin, nebulized nanosilver, doxycycline, zinc, Vitamins C, and Vitamin D, resulting in rapid recovery of oxygen levels. We used a simplified self-controlled case series method to investigate the association between treatment and the existence of hospitalization rate reduction. External controls of hospitalized patients were compared against the subgroup of patients with baseline room air SpO2 ≤ 90% to investigate the association between treatment and …

A Sensitivity Curve Approach To Tuning A Pulsar Timing Array In The Detection Era, Jeremy G. Baier, ‪Jeffrey S. Hazboun, Joseph D. Romano

Physics & Astronomy Faculty Publications

As pulsar timing arrays (PTAs) transition into the detection era of the stochastic gravitational wave background (GWB), it is important for PTA collaborations to review and possibly revise their observing campaigns. The detection of a 'single source' would be a boon for gravitational astrophysics, as such a source would emit gravitational waves for millions of years in the PTA frequency band. Here we present generic methods for studying the effects of various observational strategies, taking advantage of detector sensitivity curves, i.e. noise-averaged, frequency-domain detection statistics. The statistical basis for these methods is presented along with myriad examples of how to …

Conditional Quantization For Uniform Distributions On Line Segments And Regular Polygons, Pigar Biteng, Mathieu Caguiat, Tsianna Dominguez, Mrinal Kanti Roychowdhury

School of Mathematical & Statistical Sciences Faculty Publications

Quantization for a Borel probability measure refers to the idea of estimating a given probability by a discrete probability with support containing a finite number of elements. If, in the quantization some of the elements in the support are preselected, then the quantization is called a conditional quantization. In this paper, we investigate the conditional quantization for the uniform distributions defined on the unit line segments and m-sided regular polygons, where 𝑚≥3, inscribed in a unit circle.

Dynamic Mean-Field Theory For Continuous Random Networks, Wilson A. Zuniga-Galindo

School of Mathematical & Statistical Sciences Faculty Publications

This article studies the dynamics of the mean-field approximation of continuous random networks. These networks are stochastic integrodifferential equations driven by Gaussian noise. The kernels in the integral operators are realizations of generalized Gaussian random variables. The equation controls the time evolution of a macroscopic state interpreted as neural activity, which depends on position and time. Such a network corresponds to a statistical field theory (SFT) given by a momenta-generating functional. Discrete versions of the mentioned networks appeared in spin glasses and as models of artificial neural networks. Each of these discrete networks corresponds to a lattice SFT, where the …

Bayesian Nonparametric Hypothesis Testing Methods On Multiple Comparisons, Qiuchen Hai, Zhuanzhuan Ma

School of Mathematical & Statistical Sciences Faculty Publications

In this paper, we introduce Bayesian testing procedures based on the Bayes factor to compare the means across multiple populations in classical nonparametric contexts. The proposed Bayesian methods are designed to maximize the probability of rejecting the null hypothesis when the Bayes factor exceeds a specified evidence threshold. It is shown that these procedures have straightforward closed-form expressions based on classical nonparametric test statistics and their corresponding critical values, allowing for easy computation. We also demonstrate that they effectively control Type I error and enable researchers to make consistent decisions aligned with both frequentist and Bayesian approaches, provided that the …

Quantization Dimensions For Inhomogeneous Bi-Lipschitz Iterated Function Systems, Amit Priyadarshi, Mrinal Kanti Roychowdhury, Manuj Verma

School of Mathematical & Statistical Sciences Faculty Publications

Let ν be a Borel probability measure on a d-dimensional Euclidean space R d , d ≥ 1 , with a compact support, and let ( p 0 , p 1 , p 2 , … , p N ) be a probability vector with p j > 0 for 0 ≤ j ≤ N . Let { S j : 1 ≤ j ≤ N } be a set of contractive mappings on R d . Then, a Borel probability measure μ on R d such that μ = ∑ N j = 1 p j μ ∘ S − …

Unraveling Genetic Links Between Diabetes And Heart Failure-A Machine Learning Approach, Sunakhi Sahoo, Marzieh Ayati

Research Symposium

Background: Diabetic heart failure (DHF) is defined as a chronic and progressive disease which is associated with both diabetes and heart failure (HF). Even though there have been many developments in the knowledge of these diseases, there is still much to learn about the genetic crossovers between the two. In this study, we identified genes that are associated with diabetic heart failure and heart failure by using gene expression data from patients with DHF, HF, and a control group of patients who died of natural causes. We sought to identify genes that had altered expression levels which could possibly play …

Discounting Effect Size When Borrowing External Data In Clinical Studies, Zhuanzhuan Ma, Chul Ahn, Bin Wang, Xuefeng Li

Research Symposium

Background: When borrowing information from external data to augment a current trial, many available methods discount the sample size but retain the effect size from previous studies. Discounting the sample size is just one way to discount the prior information. It may not be appropriate if the underlying assumption of unbiased treatment effect does not hold, for example, when the treatment effect in the historical study is likely higher than the one expected in the current trial.

Methods: To tackle this potential issue, we study some methods to shrink the effect size from previous studies assuming that the prior effect …

Sparse Bayesian Variable Selection Using Global-Local Shrinkage Priors For The Analysis Of Cancer Datasets, Zhuanzhuan Ma

Research Symposium

Background: With a rapid development of data collection technology, high dimensional data, whose model dimension k may be growing or much larger than the sample size n, is becoming increasingly prevalent in different fields of study, such as ecology, genetics, among others. This data deluge is introducing new challenges to traditional statistical procedures and theories and is thus generating a renewed interest in the problems of variable selection and classification in high dimensional regression models. In large k, small n settings, variable selection is usually the first step for dimension reduction to uncover significant covariates, which contribute to …

Heart Disease Prediction Using Ensemble Tree Algorithms: A Supervised Learning Perspective, Enoch Sakyi-Yeboah, Edmund F. Agyemang, Vincent Agbenyeavu, Akua Osei- Nkwantabisa, Priscilla Kissi-Appiah, Lateef Moshood, Lawrence Agbota, Ezekiel N.N. Nortey

School of Mathematical & Statistical Sciences Faculty Publications

Heart disease stands as a leading cause of morbidity and mortality globally, presenting a significant public health challenge. Therefore, early prediction and detection are critical, leading to timely and appropriate interventions at early stages. Four ensemble tree-based algorithms were used in this study: adaptive boosting, extreme gradient boosting, random forest, and extremely randomized trees, investigating their ability to predict heart disease. Data related to heart disease clinical features was obtained from the open Kaggle Machine Learning Dataset repository. Adaptive Boosting stands out as the highest performer, achieving an average testing accuracy of 93.70%, precision of 93.71%, recall of 93.70%, and …

Extended High-Frequency Hearing And Suprathreshold Neural Synchrony In The Auditory Brainstem, Jithin Raj Balan, Sri Mishra, Hansapani Rodrigo

School of Mathematical & Statistical Sciences Faculty Publications

Elevated hearing thresholds in the extended high frequencies (EHFs) (>8 kHz) are often associated with poorer speech-in-noise recognition despite a clinically normal audiogram. However, whether EHF hearing loss is associated with disruptions in neural processing within the auditory brainstem remains uncertain. The objective of the present study was to investigate whether elevated EHF thresholds influence neural processing at lower frequencies in individuals with normal audiograms. Auditory brainstem responses (ABRs) were recorded at a suprathreshold level (80 dB normal hearing level) from 45 participants with clinically normal hearing. The recording protocol was optimized to obtain robust wave I of the …

Advanced Ligo Detector Performance In The Fourth Observing Run, E. Capote, W. Jia, N. Aritomi, M. Nakano, V. Xu, R. Abbott, I. Abouelfettouh, R. X. Adhikari, Francisco Llamas Villarreal, Volker Quetschke

Physics & Astronomy Faculty Publications

On May 24, 2023, the Advanced Laser Interferometer Gravitational-Wave Observatory (LIGO), joined by the Advanced Virgo and KAGRA detectors, began the fourth observing run for a two-year-long dedicated search for gravitational waves. The LIGO Hanford and Livingston detectors have achieved an unprecedented sensitivity to gravitational waves, with an angle-averaged median range to binary neutron star mergers of 152 and 160 Mpc, and duty cycles of 65.0% and 71.2%, respectively, with a coincident duty cycle of 52.6%. The maximum range achieved by the LIGO Hanford detector is 165 Mpc and the LIGO Livingston detector 177 Mpc, both achieved during the second …

Characterization Of Btex Species At Texas Commission On Environmental Quality (Tceq) Continuous Ambient Monitoring Station (Cams) Sites In Houston, Texas, Usa During 2018, Amit U. Raysoni, Sai Deepak Pinakana, August Luna, Esmeralda Mendez, Gabriel Ibarra-Mejia

School of Earth, Environmental, & Marine Sciences Faculty Publications

Volatile Organic Compounds (VOCs) in the atmosphere negatively impact human and environmental health. Various sources of VOCs include gasoline evaporation, solvent usage, traffic, etc. A dataset 1-year (2018) consisting of BTEX (benzene, toluene, ethylbenzene, and m, p, and o-xylenes) concentrations in Houston, Texas, was analyzed to understand the spatial trends and sources of BTEX in the region. This study assesses 24-hour data concentrations from the Continuous Ambient Monitoring Station (CAMS) operated by the TCEQ Texas Commission on Environmental Quality (TCEQ) in Houston. Spatial variations of the BTEX species across the various TCEQ CAMS sites were determined using multiple statistical analyses, …

Intelligent Soccer Event Detection And Highlights Generation With Broadcast Cues Integration, Anirudh Narayanan, Sergei Chuprov, Leon Reznik, Raman Zatsarenko, Dmitrii Korobeinikov

Computer Science Faculty Publications

In this paper, we present an innovative approach to automate key event detection and highlights generation from soccer match videostreams that allows to improve accuracy and reliability, as well as to reduce data consumption and training time. Our method segments the videostream into distinct frames based on camera angles and activities, and integrates intelligent video analytics with additional visual information provided by broadcasters. As our major novelty in comparison to other intelligent soccer video analysis approaches, we deploy a Multi-Class Image Classifier to segment the video into wide-angle overviews, close-ups, and in-game replays, which allows us to improve the event …

Detection, Characterization, And Antibiotic Resistance Profiling Of Multidrug-Resistant Bacteria Isolated From Circulating Currency In The Northeastern Region Of Bangladesh, Moshiul Alam Mishu, Ashik Imran, Akash Saha, Shahida Ferdousee, Md. Rubiath Islam, Mohammad Abdullah-Al-Shoeb, Shahidul Islam, Muhammad Abul Kalam Azad

School of Earth, Environmental, & Marine Sciences Faculty Publications

Objectives

The study aims to investigate the prevalence of multidrug resistant bacteria on paper and coin currency obtained from various occupational groups in Bangladesh and to identify the bacterial species present. The research further seeks to evaluate the antibiotic resistance patterns of the identified bacterial isolates.

Methods

84 paper currency notes and 56 coins were collected from seven different sources. Bacterial contamination was assessed using standard bacteriological and biochemical tests to identify and characterize the bacteria. Antibiotic susceptibility of the isolated strains was evaluated using an antibiogram study.

Results

A total of 368 bacterial isolates were detected across the sampled …

The Effect Of Vera C. Rubin Observatory Cadence Selections On Kilonova Detectability, Cristina Andrade, Raiyah Alserkal, Luis Salazar Manzano, Emma Martin, Igor Andreoni, Michael W. Coughlin, Nidhal Guessoum, Liliana Rivera Sandoval

Physics & Astronomy Faculty Publications

The discovery of the optical/infra-red counterpart (AT2017gfo) to the binary neutron star gravitational-wave detection (GW170817), which was followed by a short gamma-ray burst (GRB 170817), marked a groundbreaking moment in multi-messenger astronomy. To date, it remains the only confirmed joint detection of its kind. However, many experiments are actively searching for similar fast-fading electromagnetic counterparts, known as kilonovae. Fortunately, the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) provides excellent prospects for identifying kilonova candidates either from, or independent of, gravitational-wave and GRB triggers. Cadence choices for LSST surveys are especially important for maximising the likelihood of …

On Explicit Solutions For Coupled Reaction-Diffusion And Burgers-Type Equations With Variable Coefficients Through A Riccati System, Jose M. Escorcia, Erwin Suazo

School of Mathematical & Statistical Sciences Faculty Publications

This work is concerned with the study of explicit solutions for generalized coupled reaction-diffusion and Burgers-type systems with variable coefficients. Including nonlinear models with variable coefficients such as the diffusive Lotka-Volterra model, the Gray-Scott model, the Burgers equations. The equations' integrability (via the explicit formulation of the solutions) is accomplished by using similarity transformations and requiring that the coefficients fulfill a Riccati system. We present traveling wave-type solutions as well as solutions with more complex dynamics and relevant features such as bending. A Mathematica file has been prepared as supplementary material, verifying the Riccati systems used in the construction of …

Ground-Based Reconnaissance Observations Of 21 Exoplanet Atmospheres With The Exoplanet Transmission Spectroscopy Imager, Ryan J. Oelkers, Luke M. Schmidt, Erika Cook, Mary Anne Limbach, Darren L. Depoy, J. L. Marshall, Jimmy Ardoin, Mitchell Barry, Evan Batteas, Alexandra Boone

Physics & Astronomy Faculty Publications

One of the most prolific methods of studying exoplanet atmospheres is transmission spectroscopy, which measures the difference between the depth of an exoplanet's transit signal at various wavelengths and attempts to correlate the depth changes to potential features in the exoplanet's atmosphere. Here we present reconnaissance observations of 21 exoplanet atmospheres measured with the Exoplanet Transmission Spectroscopy Imager (ETSI), a recently deployed spectrophotometer on the McDonald Observatory Otto Struve 2.1 m telescope. ETSI measurements are mostly free of systematics through the use of a novel observing technique called common-path multiband imaging (CMI), which has been shown to achieve photometric color …

Bpen: Brain Posterior Evidential Network For Trustworthy Brain Imaging Analysis, Kai Ye, Haoteng Tang, Siyuan Dai, Igor Fortel, Paul M. Thompson, R. Scott Mackin, Alex Leow, Heng Huang, Liang Zhan

Computer Science Faculty Publications

The application of deep learning techniques to analyze brain functional magnetic resonance imaging (fMRI) data has led to significant advancements in identifying prospective biomarkers associated with various clinical phenotypes and neurological conditions. Despite these achievements, the aspect of prediction uncertainty has been relatively underexplored in brain fMRI data analysis. Accurate uncertainty estimation is essential for trustworthy learning, given the challenges associated with brain fMRI data acquisition and the potential diagnostic implications for patients. To address this gap, we introduce a novel posterior evidential network, named the Brain Posterior Evidential Network (BPEN), designed to capture both aleatoric and epistemic uncertainty in …

The Nanograv 15 Yr Dataset: Posterior Predictive Checks For Gravitational-Wave Detection With Pulsar Timing Arrays, Gabriella Agazie, Akash Anumarlapudi, Anne M. Archibald, Zaven Arzoumanian, Jeremy G. Baier, Paul T. Baker, Bence Becsy, Joey S. Key, Jing Luo, Joseph D. Romano

Physics & Astronomy Faculty Publications

Pulsar timing array experiments have reported evidence for a stochastic background of nanohertz gravitational waves consistent with the signal expected from a population of supermassive black hole binaries. Their analyses assume power-law spectra for intrinsic pulsar noise and for the background, as well as a Hellings-Downs cross-correlation pattern among the gravitational-wave-induced residuals across pulsars. These assumptions may not be realized in actuality. We test them in the NANOGrav 15 yr dataset using Bayesian posterior predictive checks. After fitting our fiducial model to real data, we generate a population of simulated dataset replications. We use the replications to assess whether the …

China’S Readiness For Transitioning To A Low-Carbon Economy: Mitigant And Catalyst Factors For A Geopolitical Conflict, Yan Chen, Jiayi Lyu, Jaime Ortiz, Wenbin Song

International Business and Entrepreneurship Faculty Publications

The transition from a carbon-intensive economy to a carbon-neutral one has become a critical global objective to address climate change. This study examines the relationship between Low-Carbon Economic Development (LCED) and Geopolitical Risk (GPR) in China by focusing on the challenges and opportunities they present. We use a qualitative analysis to identify that rising GPR, exacerbated by market instability, resource allocation conflicts, and trade disputes, significantly hinders LCED progress. However, these geopolitical tensions also act as a catalyst for accelerating the development of renewable energy, reducing reliance on traditional energy sources, and fostering low-carbon technologies. Furthermore, LCED can ameliorate GPR …

Addressing Class Imbalance Problem In Health Data Classification: Practical Application From An Oversampling Viewpoint, Edmund F. Agyemang, Joseph A. Mensah, Eric Nyarko, Dennis Arku, Benedict Mbeah-Baiden, Enock Opoku, Ezekiel Nii Noye Nortey

School of Mathematical & Statistical Sciences Faculty Publications

While analyzing health data is important for improving health outcomes, class imbalance in datasets poses major challenges to machine learning classification models. This work, therefore, considers the class imbalance problem in stroke prediction using models such as K-nearest neighbors, support vector machine, logistic regression, random forest, and decision tree. This work balances the stroke dataset, thereby enhancing model performance, through various oversampling strategies: random oversampling (RO), ADASYN, SMOTE, and SMOTE–Tomek. Compared to the results of the imbalanced dataset, all applied oversampling techniques enhanced the correct classification of stroke events by the ML model. Among these, RO–SVM with RBF kernel was …

Hilbert’S 10th Problem Via Mordell Curves, Somnath Jha, Debanjana Kundu, Dipramit Majumdar

School of Mathematical & Statistical Sciences Faculty Publications

We show that for 5 / 6 -th of all primes p, Hilbert’s 10th problem is unsolvable for the ring of integers of Q ( ζ 3 , 3 √ p ) . We also show that there is an infinite set S of square-free integers such that Hilbert’s 10th problem is unsolvable over the ring of integers of Q ( ζ 3 , √ D , 3 √ p ) for every D ∈ S and for every prime p ≡ 2 , 5 ( mod 9 ) . We use the CM elliptic curves y 2 = x …

Sin-Seg: A Joint Spatial-Spectral Information Fusion Model For Medical Image Segmentation, Siyuan Dai, Kai Ye, Charlie Zhan, Haoteng Tang, Liang Zhan

Computer Science Faculty Publications

In recent years, the application of deep convolutional neural networks (DCNNs) to medical image segmentation has shown significant promise in computer-aided detection and diagnosis (CAD). Leveraging features from different spaces (i.e. Euclidean, non-Euclidean, and spectrum spaces) and multi-modalities of data have the potential to improve the information available to the CAD system, enhancing both effectiveness and efficiency. However, directly acquiring data from different spaces across multi-modalities is often prohibitively expensive and time-consuming. Consequently, most current medical image segmentation techniques are confined to the spatial domain, which is limited to utilizing scanned images from MRI, CT, PET, etc. Here, we …

Stabilization Of Argan Oil Nanoemulsions Using Chitosan Extracted From Pink Shrimp Shells, Yousra Mdarhri, Ikram Bouziane, Ilyace Korodowou, Narjisse Mokhtari, Lama Rissouli, Fakhita Touhami, Mohamed Chabbi, Ahmed Touhami

Physics & Astronomy Faculty Publications

This study investigates the use of chitosan, extracted from pink shrimp shells, as a stabilizer for argan oil nanoemulsions. Chitosan was obtained through demineralization, deproteination, bleaching, deacetylation, and purification, then characterized through potentiometric titration, viscometry, X-ray diffraction (XRD), scanning electron microscopy (SEM), and purity assessments. The resulting chitosan was used to stabilize nanoemulsions formulated with extra-virgin argan oil and a nonionic surfactant using the phase inversion composition (PIC) method. The stability of the nanoemulsion was assessed through light scattering, zeta potential, viscosity, and pH measurements. The extracted chitosan exhibited high purity, a deacetylation degree of 88.34%, a molecular weight of …