Physical Sciences and Mathematics Commons^™

Session 6: Model-Based Clustering Analysis On The Spatial-Temporal And Intensity Patterns Of Tornadoes, Yana Melnykov, Yingying Zhang, Rong Zheng

SDSU Data Science Symposium

Tornadoes are one of the nature’s most violent windstorms that can occur all over the world except Antarctica. Previous scientific efforts were spent on studying this nature hazard from facets such as: genesis, dynamics, detection, forecasting, warning, measuring, and assessing. While we want to model the tornado datasets by using modern sophisticated statistical and computational techniques. The goal of the paper is developing novel finite mixture models and performing clustering analysis on the spatial-temporal and intensity patterns of the tornadoes. To analyze the tornado dataset, we firstly try a Gaussian distribution with the mean vector and variance-covariance matrix represented as …

Session 6: The Size-Biased Lognormal Mixture With The Entropy Regularized Algorithm, Tatjana Miljkovic, Taehan Bae

SDSU Data Science Symposium

A size-biased left-truncated Lognormal (SB-ltLN) mixture is proposed as a robust alternative to the Erlang mixture for modeling left-truncated insurance losses with a heavy tail. The weak denseness property of the weighted Lognormal mixture is studied along with the tail behavior. Explicit analytical solutions are derived for moments and Tail Value at Risk based on the proposed model. An extension of the regularized expectation–maximization (REM) algorithm with Shannon's entropy weights (ewREM) is introduced for parameter estimation and variability assessment. The left-truncated internal fraud data set from the Operational Riskdata eXchange is used to illustrate applications of the proposed model. Finally, …

College Of Natural Sciences 2023 Year-End Publication, College Of Natural Sciences

College of Natural Sciences Newsletters and Reports

Page 1 Dean's Message
Page 3 Department Highlights
Page 4 One Day for State
Page 5 Noble Prize Winner Speaks on Campus
Page 6-7 Faculty Excellence
Page 8-9 Student Excellence
Page 10 Outreach Program
Page 10 Events and Traditions
Page 11 Connections Abroad
Page 12 Student Spotlight
Page 13 Alumni Spotlight
Page 14 First Ever Drone Day
Page 15 Grand Opening of POET Bioproducts Center
Page 16 Work Anniversaries

Can Phytoremediation-Induced Changes In The Microbiome Improve Saline/Sodic Soil And Plant Health?, Achal Neupane, Duncan Jukubowski, Douglas Fiedler, Liping Gu, Sharon A. Clay, David E. Clay, Shin-Yi Marzano

Agronomy, Horticulture and Plant Science Faculty Publications

Increasing soil salinity and/or sodicity is an expanding problem in the Northern Great Plains (NGP) of North America. This study investigated the impact of phytoremediation on the soil microbiome and if changes, in turn, had positive or negative effects on plant establishment. Amplicon sequencing and gas chromatograph/mass spectrometer analysis compared root metabolites and microbial composition of bulk vs. rhizosphere soils between two soil types (productive and saline/sodic). Beta-diversity analysis indicated that bacterial and fungal communities from both the bulk and rhizosphere soils from each soil type clustered separately, indicating dissimilar microbial composition. Plant species also influenced both root-associated bacterial and …

Impact Of Solar Radiation On Perchlorate Formation In The Atmosphere: Evidence From Ice Core Measurements, Bishnu Kunwar

Electronic Theses and Dissertations

Perchlorate, which derives from both anthropogenic and natural sources in the current environment, poses a substantial health hazard to humans as it competes with iodine uptake in the thyroid gland. Consequently, there has been considerable concern about minimizing human exposure to environmental perchlorate by restricting its release from man-made sources. However, the absence of a clear understanding regarding the respective contributions of man-made and natural sources has hindered widespread regulation efforts. A 300-year (1700–2007) Summit, Greenland ice core record from a previous study showed relatively stable perchlorate concentrations in Greenland snow prior to 1980, with some elevated perchlorate levels associated …

College Of Natural Sciences Newsletter, September - October 2023, College Of Natural Sciences

College of Natural Sciences Newsletters and Reports

Page 1 Dean's Message
Page 2 New Faculty an Staff for the Fall 2023 Semester
Page 3 Awards
Page 4 Student Ambassadors in CNS
Page 5 Meet our Jacks
Page 6-8 Events
Page 9-11 Media Coverage of CNS
Page 12-13 Spring 2023 Dean's List
Page 14 Open PRAIRIE Data

Belowground Growth Strategies Of Native And Invasive Rhizomatous Perennial Grasses In Response To Precipitation Variability, Clipping, And Competition, Surendra Bam, Jacqueline P. Ott, Jack Butler, Lan Xu

Natural Resource Management Faculty Publications

Invasive clonal species may exhibit different growth strategies than their native clonal competitors. In this study, we examined the spatial distribution of tiller outgrowth and the bud bank by comparing the investment in phalanx versus guerilla growth of a native and invasive perennial grass in North America. We also examined the efect of altered precipitation frequency, clipping, and competition on their clonal growth strategies. Investment in phalanx and guerilla growth was assessed by examining live propagule and tiller production from the plant crown versus its rhizomes. Although invasive Bromus inermis and native Pascopyrum smithii exhibited similar clonal growth strategies as …

Mineral Licks As A Potential Nidus For Parasite Transmission, William J. Severud, Todd M. Kautz, Jerrold L. Belant, Seth A. Moore

Natural Resource Management Faculty Publications

Discrete landscape features can concentrate animals in time and space, leading to non-random interspecific encounters. These encounters have implications for predator-prey interactions, habitat selection, intraspecific competition, and transmission of parasites and other pathogens. The lifecycle of the parasitic nematode Parelaphostrongylus tenuis requires an intermediate host of a terrestrial gastropod. Natural hosts of P. tenuis are whitetailed deer, and an aberrant host of conservation concern is moose, which are susceptible to high levels of mortality as a naive host to the parasite. Intermediate hosts become infected when P. tenuis larvae are shed in deer feces, then consumed or enter the gastropod …

Natural Resource Management Newsletter, June 2023, Department Of Natural Resource Management

NRM Newsletter

Page 1: Departmental Faculty and Staff, and Dean's Message
Page 2: NRM Newbies
Page 3: Emeriti Publications and Award, Donor Obituary, Recent Graduate News, and Ten Year Service Award
Page 4: Donor News
Page 5: Sampling of Faculty and Staff Activities & Recognition
Page 6: Sampling of Graduate Students Activities
Page 7: Sampling of Undergraduate Students Activities
Page 8: Club Activities
Page 9: NRM Events
Page 10: Opportunities to Support NRM

College Of Natural Sciences Newsletter, March - May 2023, College Of Natural Sciences

College of Natural Sciences Newsletters and Reports

Volume 4, Issue 2

Page 1 Dean's Message
Page 2-7 Awards and Recognition
Page 8 March 3rd Corothers Seminar
Page 9 54th Geography Convention
Page 10 Spring 2023 Day of Scholars
Page 11 2023 URSCAD Snaps
Page 12-14 Media Coverage of CNS
Page 15 Open PRAIRIE Data

College Of Natural Sciences Newsletter, February 2023, College Of Natural Sciences

College of Natural Sciences Newsletters and Reports

Volume 4, Issue 1

Page 1 Dean's Message
Page 2 Awards and Recognition
Page 3-4 Nobel Recipient Visits Campus
Page 4 Adopting the Pantry
Page 5 Growing a Recruitment Mindset
Page 6 February Outreach Events
Page 7 Media Coverage of CNS
Page 8 Open PRAIRIE Data
Page 9 54th Geography Convention, and Tom Loveland EROS Geography Scholarship
Page 10 Photos of Dr. Carolyn Bertozzi's Visit

College Of Natural Sciences Newsletter, November 2022 - January 2023, College Of Natural Sciences

College of Natural Sciences Newsletters and Reports

Volme 3, Issue 7

Page 1 Dean's Message
Page 2 Awards & Recognition
Page 3 Sioux Falls Middle School Visit
Page 4 Bio-Micro Day of Scholars
Page 5 GIS Day at USGS EROS
Page 6 Indigenous People's Festival, & Visiting Jack's Imagination Lab
Page 7 Media Coverage of CNS, & Research Highlights from Geography & Geospatial Sciences
Page 8 Media Coverage of CNS. cont.
Page 9 Open PRAIRIE Data
Page 10 Recent Publications from CNS
Page 11 Recent Publications from CNS. cont.
Page 12 CNS Holiday Snapshots
Page 13 & 14 Fall 2022 Dean's List
Page 12-14 Fall 2022 Outreach …

Session 12: Analysis Of State And Parameter Estimation Techniques Using Dynamic Perturbation Signals, Timothy M. Hansen

SDSU Data Science Symposium

The trend in electric power systems is the displacement of traditional synchronous generation (e.g., coal, natural gas) with renewable energy resources (e.g., wind, solar photovoltaic) and battery energy storage. These energy resources require power electronic converters (PECs) to interconnect to the grid and have different response characteristics and dynamic stability issues compared to conventional synchronous generators. As a result, there is a need for validated models to study and mitigate PEC-based stability issues, especially for converter dominated power systems (e.g., island power systems, remote microgrids).

This presentation will introduce methods related to dynamic state and parameter estimation via the design …

Session11: Skip-Gcn : A Framework For Hierarchical Graph Representation Learning, Jackson Cates, Justin Lewis, Randy Hoover, Kyle Caudle

SDSU Data Science Symposium

Recently there has been high demand for the representation learning of graphs. Graphs are a complex data structure that contains both topology and features. There are first several domains for graphs, such as infectious disease contact tracing and social media network communications interactions. The literature describes several methods developed that work to represent nodes in an embedding space, allowing for classical techniques to perform node classification and prediction. One such method is the graph convolutional neural network that aggregates the node neighbor’s features to create the embedding. Another method, Walklets, takes advantage of the topological information stored in a graph …

Two-Stage Approach For Forensic Handwriting Analysis, Ashlan J. Simpson, Danica M. Ommen

SDSU Data Science Symposium

Trained experts currently perform the handwriting analysis required in the criminal justice field, but this can create biases, delays, and expenses, leaving room for improvement. Prior research has sought to address this by analyzing handwriting through feature-based and score-based likelihood ratios for assessing evidence within a probabilistic framework. However, error rates are not well defined within this framework, making it difficult to evaluate the method and can lead to making a greater-than-expected number of errors when applying the approach. This research explores a method for assessing handwriting within the Two-Stage framework, which allows for quantifying error rates as recommended by …

2d Respiratory Sound Analysis To Detect Lung Abnormalities, Rafia Sharmin Alice, Kc Santosh

SDSU Data Science Symposium

Abstract. In this paper, we analyze deep visual features from 2D data representation(s) of the respiratory sound to detect evidence of lung abnormalities. The primary motivation behind this is that visual cues are more important in decision-making than raw data (lung sound). Early detection and prompt treatments are essential for any future possible respiratory disorders, and respiratory sound is proven to be one of the biomarkers. In contrast to state-of-the-art approaches, we aim at understanding/analyzing visual features using our Convolutional Neural Networks (CNN) tailored Deep Learning Models, where we consider all possible 2D data such as Spectrogram, Mel-frequency Cepstral Coefficients …

A Characterization Of Bias Introduced Into Forensic Source Identification When There Is A Subpopulation Structure In The Relevant Source Population., Dylan Borchert, Semhar Michael, Christopher Saunders

SDSU Data Science Symposium

In forensic source identification the forensic expert is responsible for providing a summary of the evidence that allows for a decision maker to make a logical and coherent decision concerning the source of some trace evidence of interest. The academic consensus is usually that this summary should take the form of a likelihood ratio (LR) that summarizes the likelihood of the trace evidence arising under two competing propositions. These competing propositions are usually referred to as the prosecution’s proposition, that the specified source is the actual source of the trace evidence, and the defense’s proposition, that another source in a …

Application Of Gaussian Mixture Models To Simulated Additive Manufacturing, Jason Hasse, Semhar Michael, Anamika Prasad

SDSU Data Science Symposium

Additive manufacturing (AM) is the process of building components through an iterative process of adding material in specific designs. AM has a wide range of process parameters that influence the quality of the component. This work applies Gaussian mixture models to detect clusters of similar stress values within and across components manufactured with varying process parameters. Further, a mixture of regression models is considered to simultaneously find groups and also fit regression within each group. The results are compared with a previous naive approach.

Finite Mixture Modeling For Hierarchically Structured Data With Application To Keystroke Dynamics, Andrew Simpson, Semhar Michael

SDSU Data Science Symposium

Keystroke dynamics has been used to both authenticate users of computer systems and detect unauthorized users who attempt to access the system. Monitoring keystroke dynamics adds another level to computer security as passwords are often compromised. Keystrokes can also be continuously monitored long after a password has been entered and the user is accessing the system for added security. Many of the current methods that have been proposed are supervised methods in that they assume that the true user of each keystroke is known apriori. This is not always true for example with businesses and government agencies which have internal …

Models For Predicting Maximum Potential Intensity Of Tropical Cyclones, Iftekhar Chowdhury, Gemechis Djira

SDSU Data Science Symposium

Tropical cyclones (TCs) are considered as extreme weather events, which has a low-pressure center, namely an eye, strong winds, and a spiral arrangement of thunderstorms that produces heavy rain, storm surges, and can cause severe destruction in coastal areas worldwide. Therefore, reliable forecasts of the maximum potential intensity (MPI) of TCs are critical to estimate the damages to properties, lives, and risk assessment. In this study, we explore and propose various regression models, to predict the potential intensity of TCs in the North Atlantic at 12, 24, 36, 48, 60, and 72- hour forecasting lead time. In addition, a popular …

Temporal Tensor Factorization For Multidimensional Forecasting, Jackson Cates, Karissa Scipke, Randy Hoover, Kyle Caudle

SDSU Data Science Symposium

In the era of big data, there is a need for forecasting high-dimensional time series that might be incomplete, sparse, and/or nonstationary. The current research aims to solve this problem for two-dimensional data through a combination of temporal matrix factorization (TMF) and low-rank tensor factorization. From this method, we propose an expansion of TMF to two-dimensional data: temporal tensor factorization (TTF). The current research aims to interpolate missing values via low-rank tensor factorization, which produces a latent space of the original multilinear time series. We then can perform forecasting in the latent space. We present experimental results of the proposed …

Session 8: Ensemble Of Score Likelihood Ratios For The Common Source Problem, Federico Veneri, Danica M. Ommen

SDSU Data Science Symposium

Machine learning-based Score Likelihood Ratios have been proposed as an alternative to traditional Likelihood Ratios and Bayes Factor to quantify the value of evidence when contrasting two opposing propositions.

Under the common source problem, the opposing proposition relates to the inferential problem of assessing whether two items come from the same source. Machine learning techniques can be used to construct a (dis)similarity score for complex data when developing a traditional model is infeasible, and density estimation is used to estimate the likelihood of the scores under both propositions.

In practice, the metric and its distribution are developed using pairwise comparisons …

Session 2: The Effect Of Boom Leveling On Spray Dispersion, Travis A. Burgers, Miguel Bustamante, Juan F. Vivanco

SDSU Data Science Symposium

Self-propelled sprayers are commonly used in agriculture to disperse agrichemicals. These sprayers commonly have two boom wings with dozens of nozzles that disperse the chemicals. Automatic boom height systems reduce the variability of agricultural sprayer boom height, which is important to reduce uneven spray dispersion if the boom is not at the target height.

A computational model was created to simulate the spray dispersion under the following conditions: a) one stationary nozzle based on the measured spray pattern from one nozzle, b) one stationary model due to an angled boom, c) superposition of multiple stationary nozzles due an angled boom, …

Trophically Integrated Ecometric Models As Tools For Demonstrating Spatial And Temporal Functional Changes In Mammal Communities, Rachel A. Short, Jenny L. Mcguire, P. David Polly, A. Michelle Lawing

Natural Resource Management Faculty Publications

We are in a modern biodiversity crisis that will restructure community compositions and ecological functions globally. Large mammals, important contributors to ecosystem function, have been affected directly by purposeful extermination and indirectly by climate and land-use changes, yet functional turnover is rarely assessed on a global scale using metrics based on functional traits. Using ecometrics, the study of functional trait distributions and functional turnover, we examine the relationship between vegetation cover and locomotor traits for artiodactyl and carnivoran communities. We show that the ability to detect a functional relationship is strengthened when locomotor traits of both primary consumers (artiodactyls, n …

Development Of Spintronic Materials By Stoichiometric Engineering Of Cofeval, Gavin Baker, Matthew Wieberdink, Jax Wysong

The Journal of Undergraduate Research

We have carried out an experimental investigation of the Heusler Alloy CoFeVAl and its two variants Co_1.5Fe_0.5VAl and CoFeVAl_0.5Si_0.5 for their potential application in the field of spintronics. Heusler alloys are investigated for their many remarkable properties, including half-metallicity and spin-gapless semi-conductivity. Spintronic technology utilizes the intrinsic spin of an electron for information storage and manipulation in solid state devices. We synthesized these alloys using arc-melting and annealing. All three alloys were found to have cubic crystal structures with varying disorders. The parent alloy CoFeVAl shows a magnetic transition at 65 K. However, …

Structural And Magnetic Properties Of Heusler Alloys: Fecrmn1-Xvxal (X = 0, 0.5, 0.75), Jax Wysong, Gavin Baker

The Journal of Undergraduate Research

Heusler alloys are important to investigate due to their multiple interesting properties including half-metallicity and spin-gapless semi conductivity. Materials exhibiting these properties are desired for spin-transport-based devices. These devices provide the storing and delivering of information through the utilization of the spin property of electrons. The magnetic and electronic band properties of these alloys can be modified by tuning the elemental composition. This work investigates structural and magnetic properties of the three Heusler alloys FeCrMnAl, FeCrMn_0.5V_0.5Al, and FeCrMn_0.25V_0.75Al. It was found that all three alloys crystallize in cubic crystal structure with an …

Assessing The Economic Feasibility Of Capturing And Utilizing Carbon Dioxide From Ethanol Production In South Dakota, Makiah Stukel

Electronic Theses and Dissertations

Since the Industrial Revolution, anthropogenic greenhouse gas (GHG) emissions have spiked dramatically, prompting discussions on climate change. Mitigating climate change requires significant reductions in global carbon dioxide (CO2) emissions as CO2 is the most abundant anthropogenic GHG. A process that assists in offsetting the exponential growth in CO2 emissions is carbon capture and storage (CCS). Integrating carbon capture technology into the ethanol industry can provide an economically feasible way to achieve net reductions in CO2 emissions. The proposed work investigates the economic viability of applying CCS technologies to the 16 ethanol facilities in South Dakota (SD) and quantifies the potential …