Physical Sciences and Mathematics Commons^™

Predicting Forage Provision Of Grasslands Across Climate Zones By Hyperspectral Measurements, F. A. Männer, J. Muro, J. Ferner, S. Schmidtlein, A. Linstädter

IGC Proceedings (1997-2023)

The potential of grasslands’ fodder production is a crucial management measure, while its quantification is still laborious and costly. Remote sensing technologies, such as hyperspectral field measurements, enable fast and non-destructive estimation. However, such methods are still limited in transferability to other locations or climatic conditions. With this study, we aim to predict forage nutritive value, quantity, and energy yield from hyperspectral canopy reflections of grasslands across three climate zones. We took hyperspectral measurements with a field spectrometer from grassland canopies in temperate, tropical and semi-arid grasslands, and analyzed corresponding biomass samples for their quantity (BM), metabolizable energy content (ME) …

Molecular Understanding And Design Of Deep Eutectic Solvents And Proteins Using Computer Simulations And Machine Learning, Usman Lame Abbas

Theses and Dissertations--Chemical and Materials Engineering

Hydrophobic deep eutectic solvents (DESs) have emerged as excellent extractants. A major challenge is the lack of an efficient tool to discover DES candidates. Currently, the search relies heavily on the researchers’ intuition or a trial-and-error process, which leads to a low success rate or bypassing of promising candidates. DES performance depends on the heterogeneous hydrogen bond environment formed by multiple hydrogen bond donors and acceptors. Understanding this heterogeneous hydrogen bond environment can help develop principles for designing high performance DESs for extraction and other separation applications. This work investigates the structure and dynamics of hydrogen bonds in hydrophobic DESs …

Machine Learning As A Tool For Early Detection: A Focus On Late-Stage Colorectal Cancer Across Socioeconomic Spectrums, Hadiza Galadima, Rexford Anson-Dwamena, Ashley Johnson, Ghalib Bello, Georges Adunlin, James Blando

Community & Environmental Health Faculty Publications

Purpose: To assess the efficacy of various machine learning (ML) algorithms in predicting late-stage colorectal cancer (CRC) diagnoses against the backdrop of socio-economic and regional healthcare disparities. Methods: An innovative theoretical framework was developed to integrate individual- and census tract-level social determinants of health (SDOH) with sociodemographic factors. A comparative analysis of the ML models was conducted using key performance metrics such as AUC-ROC to evaluate their predictive accuracy. Spatio-temporal analysis was used to identify disparities in late-stage CRC diagnosis probabilities. Results: Gradient boosting emerged as the superior model, with the top predictors for late-stage CRC diagnosis being anatomic site, …

Deep Learning Image Analysis To Isolate And Characterize Different Stages Of S-Phase In Human Cells, Kevin A. Boyd, Rudranil Mitra, John Santerre, Christopher L. Sansam

SMU Data Science Review

Abstract. This research used deep learning for image analysis by isolating and characterizing distinct DNA replication patterns in human cells. By leveraging high-resolution microscopy images of multiple cells stained with 5-Ethynyl-2′-deoxyuridine (EdU), a replication marker, this analysis utilized Convolutional Neural Networks (CNNs) to perform image segmentation and to provide robust and reliable classification results. First multiple cells in a field of focus were identified using a pretrained CNN called Cellpose. After identifying the location of each cell in the image a python script was created to crop out each cell into individual .tif files. After careful annotation, a CNN was …

Evaluation Of Evapotranspiration Estimates Using An Existing Hybrid Machine Learning Model In A Natural And A Managed Dryland Site, Katya Esquivel Herrera

Open Access Theses & Dissertations

Evapotranspiration (ET) is a critical component of the hydrologic cycle, encompassing both evaporative water loss from surfaces and transpiration through plant stomata. The environmental factors influencing ET include water and energy availability, atmospheric capacity for water uptake, and various meteorological variables. ET serves as a unique climate variable linking water, energy, and carbon cycles. In agroecosystems, accurate ET quantification is vital for optimizing water use efficiency, irrigation management, and crop yield. Traditional methods for ET estimation involve direct measurements and indirect models, with both presenting limitations.

Recent years have witnessed the integration of remote sensing and machine learning (ML) algorithms …

Convolutional Neural Network-Based Gene Prediction Using Buffalograss As A Model System, Michael Morikone

Complex Biosystems PhD Program: Dissertations

The task of gene prediction has been largely stagnant in algorithmic improvements compared to when algorithms were first developed for predicting genes thirty years ago. Rather than iteratively improving the underlying algorithms in gene prediction tools by utilizing better performing models, most current approaches update existing tools through incorporating increasing amounts of extrinsic data to improve gene prediction performance. The traditional method of predicting genes is done using Hidden Markov Models (HMMs). These HMMs are constrained by having strict assumptions made about the independence of genes that do not always hold true. To address this, a Convolutional Neural Network (CNN) …

Wearable Sensor-Based Walkability Assessment At Ferry Terminal Using Machine Learning: A Case Study Of Mokpo, Korea, Jungyeon Choi, Hwayoung Kim

Journal of Marine Science and Technology

Walkability assessments are becoming more popular, as walking offers numerous health, environmental, and economic benefits to communities. However, previous studies on ferry terminal walkability assessment have been inadequate. This study aimed to develop a wearable sensor system to automatically assess walkability at ferry terminals without conducting surveys. We applied seven machine learning (ML) classifiers to detect different walking environments, including flat ground (FG), downhill slope (DS), uphill slope (US), and uneven surface (UE). The ML models were evaluated across different combinations of classes: 2-class (FG vs. UE), 3-class (U) (FG vs. US vs. UE), 3-class (D) (FG vs. DS vs. …

Reconstructing 42 Years (1979–2020) Of Great Lakes Surface Temperature Through A Deep Learning Approach, Miraj Kayastha, Tao Liu, Daniel Titze, Timothy C. Havens, Chenfu Huang, Pengfei Xue

Michigan Tech Publications, Part 2

Accurate estimates for the lake surface temperature (LST) of the Great Lakes are critical to understanding the regional climate. Dedicated lake models of various complexity have been used to simulate LST but they suffer from noticeable biases and can be computationally expensive. Additionally, the available historical LST datasets are limited by either short temporal coverage (<30 >years) or lower spatial resolution (0.25° × 0.25°). Therefore, in this study, we employed a deep learning model based on Long Short-Term Memory (LSTM) neural networks to produce a daily LST dataset for the Great Lakes that spans an unparalleled 42 years (1979–2020) at …

Statistical And Biological Analyses Of Acoustic Signals In Estrildid Finches, Moises Rivera

Dissertations, Theses, and Capstone Projects

Acoustic communication is a process that involves auditory perception and signal processing. Discrimination and recognition further require cognitive processes and supporting mechanisms in order to successfully identify and appropriately respond to signal senders. Although acoustic communication is common across birds, classical research has largely disregarded the perceptual abilities of perinatal altricial taxa. Chapter 1 reviews the literature of perinatal acoustic stimulation in birds, highlighting the disproportionate focus on precocial birds (e.g., chickens, ducks, quails). The long-held belief that altricial birds were incapable of acoustic perception in ovo was only recently overturned, as researchers began to find behavioral and physiological evidence …

Continuum Modeling Of Active Nematics Via Data-Driven Equation Discovery, Connor Robertson

Dissertations

Data-driven modeling seeks to extract a parsimonious model for a physical system directly from measurement data. One of the most interpretable of these methods is Sparse Identification of Nonlinear Dynamics (SINDy), which selects a relatively sparse linear combination of model terms from a large set of (possibly nonlinear) candidates via optimization. This technique has shown promise for synthetic data generated by numerical simulations but the application of the techniques to real data is less developed. This dissertation applies SINDy to video data from a bio-inspired system of mictrotubule-motor protein assemblies, an example of nonequilibrium dynamics that has posed a significant …

Automated Delineation Of Visual Area Boundaries And Eccentricities By A Cnn Using Functional, Anatomical, And Diffusion-Weighted Mri Data, Noah C. Benson, Bogeng Song, Toshikazu Miyata, Hiromasa Takemura, Jonathan Winawer

MODVIS Workshop

Delineating visual field maps and iso-eccentricities from fMRI data is an important but time-consuming task for many neuroimaging studies on the human visual cortex because the traditional methods of doing so using retinotopic mapping experiments require substantial expertise as well as scanner, computer, and human time. Automated methods based on gray-matter anatomy or a combination of anatomy and functional mapping can reduce these requirements but are less accurate than experts. Convolutional Neural Networks (CNNs) are powerful tools for automated medical image segmentation. We hypothesize that CNNs can define visual area boundaries with high accuracy. We trained U-Net CNNs with ResNet18 …

Quantum Chemistry–Machine Learning Approach For Predicting Properties Of Lewis Acid–Lewis Base Adducts, Hieu Huynh, Thomas J. Kelly, Linh Vu, Tung Hoang, Phuc An Nguyen, Tu C. Le, Emily Jarvis, Hung Phan

Chemistry and Biochemistry Faculty Works

Synthetic design allowing predictive control of charge transfer and other optoelectronic properties of Lewis acid adducts remains elusive. This challenge must be addressed through complementary methods combining experimental with computational insights from first principles. Ab initio calculations for optoelectronic properties can be computationally expensive and less straightforward than those sufficient for simple ground-state properties, especially for adducts of large conjugated molecules and Lewis acids. In this contribution, we show that machine learning (ML) can accurately predict density functional theory (DFT)-calculated charge transfer and even properties associated with excited states of adducts from readily obtained molecular descriptors. Seven ML models, built …

Wearable Sensor Gait Analysis For Fall Detection Using Deep Learning Methods, Haben Girmay Yhdego

Electrical & Computer Engineering Theses & Dissertations

World Health Organization (WHO) data show that around 684,000 people die from falls yearly, making it the second-highest mortality rate after traffic accidents [1]. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. In light of the recent widespread adoption of wearable sensors, it has become increasingly critical that fall detection models are developed that can effectively process large and sequential sensor signal data. Several researchers have recently developed fall detection algorithms based on wearable sensor data. However, real-time fall detection remains challenging because of the wide …

An Advanced Deep Learning Models-Based Plant Disease Detection: A Review Of Recent Research, Muhammad Shoaib, Babar Shah, Shaker Ei-Sappagh, Akhtar Ali, Asad Ullah, Fayadh Alenezi, Tsanko Gechev, Tariq Hussain, Farman Ali

All Works

Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on …

Discriminating Stay-Green Grasses Using Hyperspectral Imaging And Chemometrics, J. Taylor, B. Moore, J. J. Rowland, H. Thomas, H. J. Ougham

IGC Proceedings (1997-2023)

Screening of plant collections for traits can be expensive, in terms of the number of plants to be screened, the duration of the plant lifecycle and the required observations. This study describes the application of a non-invasive method, hyperspectral imaging, combined with multivariate analysis, to distinguish between homozygous wild-type (YY) Lolium multiflorum and Lolium multiflorum F2 back cross plants heterozygous for y, a recessive Festuca pratensis stay-green gene (Thomas et al., 1997).

Ambient Electromagnetic Radiation As A Predictor Of Honey Bee (Apis Mellifera) Traffic In Linear And Non-Linear Regression: Numerical Stability, Physical Time And Energy Efficiency, Vladimir Kulyukin, Daniel Coster, Anastasiia Tkachenko, Daniel Hornberger, Aleksey V. Kulyukin

Computer Science Faculty and Staff Publications

Since bee traffic is a contributing factor to hive health and electromagnetic radiation has a growing presence in the urban milieu, we investigate ambient electromagnetic radiation as a predictor of bee traffic in the hive’s vicinity in an urban environment. To that end, we built two multi-sensor stations and deployed them for four and a half months at a private apiary in Logan, Utah, U.S.A. to record ambient weather and electromagnetic radiation. We placed two non-invasive video loggers on two hives at the apiary to extract omnidirectional bee motion counts from videos. The time-aligned datasets were used to evaluate 200 …

Invasive Buckthorn Mapping: A Uav-Based Approach Utilizing Machine Learning, Gis, And Remote Sensing Techniques In The Upper Peninsula Of Michigan, Vikranth Madeppa

Dissertations, Master's Theses and Master's Reports

An Invasive species is a species that is alien or non-native to the ecosystem which causes harm to economic, environmental, or human health (E.O. 13112 of Feb 3, 1999). Invasive species have posed a serious threat to ecosystems across the globe. These invasive species have impacts on the biodiversity and productivity of invaded forests. Remotely sensed data is a valuable resource for understanding and addressing issues related to invasive species. This study presents a novel approach for mapping the distribution of two invasive plant species, Common and Glossy Buckthorn, using unmanned aerial vehicles (UAVs), machine learning algorithms, geographic information systems …

Eeg-Based Spanish Language Proficiency Classification: An Eeg Power Spectrum And Cross-Spectrum Analysis, Blaise Xavier O'Mara, Skyler Baumer

Honors Theses and Capstones

Second language proficiency may be predicted with electrophysiological techniques. In a machine learning application, this electrophysiological data may be used for language instructors and language students to assess their language learning. This study identifies how electroencephalogram (EEG) power spectrum and cross spectrum data of the brain cortex relates to Spanish second language (L2) proficiency of 20 Spanish language students of varying proficiency levels at the University of New Hampshire. The two metrics for assessing cortical power and processing were event-related desynchronization (ERD)—a measure of relative change in power—of the alpha (8-12 Hz) brain frequency band, and alpha and beta (13-30Hz) …

An Approach To Developing Benchmark Datasets For Protein Secondary Structure Segmentation From Cryo-Em Density Maps, Thu Nguyen, Yongcheng Mu, Jiangwen Sun, Jing He

Computer Science Faculty Publications

More and more deep learning approaches have been proposed to segment secondary structures from cryo-electron density maps at medium resolution range (5--10Å). Although the deep learning approaches show great potential, only a few small experimental data sets have been used to test the approaches. There is limited understanding about potential factors, in data, that affect the performance of segmentation. We propose an approach to generate data sets with desired specifications in three potential factors - the protein sequence identity, structural contents, and data quality. The approach was implemented and has generated a test set and various training sets to study …

Detection And Diagnosis Of Bacterial Pathogens In Blood And Urine Using Laser-Induced Breakdown Spectroscopy, Emma J.M. Blanchette

Electronic Theses and Dissertations

The aim of this thesis is to expand on and improve the existing techniques used for detecting and identifying bacterial pathogens in clinical specimens with laser-induced breakdown spectroscopy (LIBS). Specifically, the existing experimental procedures, including bacterial sample preparation and data acquisition, as well as the data analysis with chemometric algorithms were investigated. Substantial reductions in LIBS background signal were achieved by implementing rigorous cleaning steps and the introduction of the use of ultrapure water. Following this, a database of LIBS spectra was acquired from specimens of E. coli, S. aureus, E. cloacae, M. smegmatis, and P. …

Applying Data Science And Machine Learning To Understand Health Care Transition For Adolescents And Emerging Adults With Special Health Care Needs, Lisamarie Turk

Nursing ETDs

A problem of classification places adolescents and emerging adults with special health care needs among the most at risk for poor or life-threatening health outcomes. This preliminary proof-of-concept study was conducted to determine if phenotypes of health care transition (HCT) for this vulnerable population could be established. Such phenotypes could support development of future studies that require data classifications as input. Mining of electronic health record data and cluster analysis were implemented to identify phenotypes. Subsequently, a machine learning concept model was developed for predicting acute care and medical condition severity. Three clusters were identified and described (Cluster 1, n …

Photonic Monitoring Of Atmospheric Fauna, Adrien P. Genoud

Dissertations

Insects play a quintessential role in the Earth’s ecosystems and their recent decline in abundance and diversity is alarming. Monitoring their population is paramount to understand the causes of their decline, as well as to guide and evaluate the efficiency of conservation policies. Monitoring populations of flying insects is generally done using physical traps, but this method requires long and expensive laboratory analysis where each insect must be identified by qualified personnel. Lack of reliable data on insect populations is now considered a significant issue in the field of entomology, often referred to as a “data crisis” in the field. …

Improved Computational Prediction Of Function And Structural Representation Of Self-Cleaving Ribozymes With Enhanced Parameter Selection And Library Design, James D. Beck

Boise State University Theses and Dissertations

Biomolecules could be engineered to solve many societal challenges, including disease diagnosis and treatment, environmental sustainability, and food security. However, our limited understanding of how mutational variants alter molecular structures and functional performance has constrained the potential of important technological advances, such as high-throughput sequencing and gene editing. Ribonuleic Acid (RNA) sequences are thought to play a central role within many of these challenges. Their continual discovery throughout all domains of life is evidence of their significant biological importance (Weinreb et al., 2016). The self-cleaving ribozyme is a class of noncoding Ribonuleic Acid (ncRNA) that has been useful for …

Determining The Effects Of Elevated Carbon Dioxide On Soil Acidification, Cation Depletion, And Soil Inorganic Carbon And Mapping Soil Carbons Using Artificial Intelligence, Jannatul Ferdush

Theses and Dissertations

Soil carbon is the largest sink and source of the global carbon cycle and is disturbed by several natural, anthropogenic, and environmental factors. The global increase of atmospheric CO₂ affects soil carbon cycling through varied biogeochemical processes. The first chapter is a compilation of current information on potential factors triggering soil acidification and weathering mechanisms under elevated CO₂ and their consequences on soil inorganic carbon (SIC) pool and quality. Soil water content and precipitation were critical factors influencing elevated CO₂ effects on the SIC pool. The second chapter examines a detailed column experiment in which six soils …

Classification Models For 2,4-D Formulations In Damaged Enlist Crops Through The Application Of Ftir Spectroscopy And Machine Learning Algorithms, Benjamin Blackburn

Theses and Dissertations

With new 2,4-Dichlorophenoxyacetic acid (2,4-D) tolerant crops, increases in off-target movement events are expected. New formulations may mitigate these events, but standard lab techniques are ineffective in identifying these 2,4-D formulations. Using Fourier-transform infrared spectroscopy and machine learning algorithms, research was conducted to classify 2,4-D formulations in treated herbicide-tolerant soybeans and cotton and observe the influence of leaf treatment status and collection timing on classification accuracy. Pooled Classification models using k-nearest neighbor classified 2,4-D formulations with over 65% accuracy in cotton and soybean. Tissue collected 14 DAT and 21 DAT for cotton and soybean respectively produced higher accuracies than the …

Nabat Ml: Utilizing Deep Learning To Enable Crowdsourced Development Of Automated, Scalable Solutions For Documenting North American Bat Populations, Ali Khalighifar, Benjamin S. Gotthold, Erin Adams, Jenny Barnett, Laura O. Beard, Eric R. Britzke, Paul A. Burger, Kimberly Chase, Zackary Cordes, Paul M. Cryan, Emily Emily, Christopher T. Fill, Scott E. Gibson, G. Scott Haulton, Kathryn M. Irvine, Lara S. Katz, William L. Kendall, Christen A. Long, Oisin Mac Aodha, Tessa Mcburney, Sara Mccarthy, Matthew W. Mckown, Joy O'Keefe, Lucy D. Patterson, Kristopher A. Pitcher, Matthew Rustand, Jordi L. Segers, Kyle Seppanen, Jeremy L. Siemers, Christian Stratton, Bethany R. Straw, Theodore J. Weller, Brian E. Reichert

Nebraska Cooperative Fish and Wildlife Research Unit: Staff Publications

1. Bats play crucial ecological roles and provide valuable ecosystem services, yet many populations face serious threats from various ecological disturbances. The North American Bat Monitoring Program (NABat) aims to use its technology infrastructure to assess status and trends of bat populations, while developing innovative and community-driven conservation solutions.

2. Here, we present NABat ML, an automated machine-learning algorithm that improves the scalability and scientific transparency of NABat acoustic monitoring. This model combines signal processing techniques and convolutional neural networks (CNNs) to detect and classify recorded bat echolocation calls. We developed our CNN model with internet-based computing resources (‘cloud environment’), and …

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Where To Invest Project Efforts For Greater Benefit: A Framework Formanagement Performance Mapping With Examples For Potato Seed Health, C. E. Buddenhagen, Y. Xing, J. L. Andrade-Piedra, G. A. Forbes, P. Kromann, I. Navarrete, S. Thomas-Sharma, Robin A. Choudhury, K. F. Andersen Onofre, E. Schulte-Geldermann

School of Earth, Environmental, and Marine Sciences Faculty Publications and Presentations

Policymakers and donors often need to identify the locations where technologies are most likely to have important effects, to increase the benefits from agricultural development or extension efforts. Higher-quality information may help to target the high-benefit locations, but often actions are needed with limited information. The value of information (VOI) in this context is formalized by evaluating the results of decision making guided by a set of specific information compared with the results of acting without considering that information. We present a framework for management performance mapping that includes evaluating the VOI for decision making about geographic priorities in regional …

Using Fine-Scale Aquatic Habitat Data To Construct Dreissenid Sdms In The Laurentian Great Lakes, Grace C. Henderson

USF Tampa Graduate Theses and Dissertations

The invasion of the Laurentian Great Lakes by aquatic invasive species (AIS) has been the subject of investigation for decades, due to their dramatic alterations to the ecosystem and high economic costs. Two AIS with the largest impacts are dreissenid zebra and quagga mussels, and though these species have been studied extensively, questions remain about what factors control their distributions, and whether lake warming will alter these distributions. Species distribution models (SDMs) offer a powerful tool to examine the relationship between species presences and environmental variables, which are typically bioclimactic data. The creation of the Aquatic Habitat (AqHab) dataset containing …

Finding The Best Predictors For Foot Traffic In Us Seafood Restaurants, Isabel Paige Beaulieu

Honors Theses and Capstones

COVID-19 caused state and nation-wide lockdowns, which altered human foot traffic, especially in restaurants. The seafood sector in particular suffered greatly as there was an increase in illegal fishing, it is made up of perishable goods, it is seasonal in some places, and imports and exports were slowed. Foot traffic data is useful for business owners to have to know how much to order, how many employees to schedule, etc. One issue is that the data is very expensive, hard to get, and not available until months after it is recorded. Our goal is to not only find covariates that …

Fine Scale Mapping Of Laurentian Mixed Forest Natural Habitat Communities Using Multispectral Naip And Uav Datasets Combined With Machine Learning Methods, Parth P. Bhatt

Dissertations, Master's Theses and Master's Reports

Natural habitat communities are an important element of any forest ecosystem. Mapping and monitoring Laurentian Mixed Forest natural communities using high spatial resolution imagery is vital for management and conservation purposes. This study developed integrated spatial, spectral and Machine Learning (ML) approaches for mapping complex vegetation communities. The study utilized ultra-high and high spatial resolution National Agriculture Imagery Program (NAIP) and Unmanned Aerial Vehicle (UAV) datasets, and Digital Elevation Model (DEM). Complex natural vegetation community habitats in the Laurentian Mixed Forest of the Upper Midwest. A detailed workflow is presented to effectively process UAV imageries in a dense forest environment …