Physical Sciences and Mathematics Commons^™

Meet Me In The Middle: A Scoping Review On Understanding Adolescent Needs In Climate Communication, Gwendolyn Monica Hoff Anderson

Master's Projects and Capstones

The greatest effects of climate change are likely to be felt by youth. Young people are disproportionately affected by climate change due to their critical developmental stage and lack of power, and they experience both higher severity and prevalence of mental health issues related to climate change. Strong emotions have long been recognized as potential catalysts for action, or they may lead to paralyzing feelings of being overwhelmed. Climate communication is a critical tool to spark climate concern and encourage action. Activism, in turn, may help youth manage their anxiety about climate change. This scoping review examines emerging evidence on …

The Loneliest Galaxies In The Universe: A Gama And Galaxy Zoo Study On Void Galaxy Morphology., Lori E. Porter

College of Arts & Sciences Senior Honors Theses

The large-scale structure (LSS) of the Universe is comprised of galaxy filaments, tendrils, and voids. The majority of the Universe’s volume is taken up by these voids, which exist as underdense, but not empty, regions. The galaxies found inside voids are void galaxies and expected to be some of the most isolated objects in the Universe. However, their standard morphology remains poorly studied. This study, using the Galaxy and Mass Assembly (GAMA) data and Galaxy Zoo survey, aims to remedy this. For completeness purposes, we use void galaxies identified by Alpaslan et al. (2014) with stellar masses (M*) of 10 …

Soil Health And Remediation For Urban Gardens In Utah, Melissa Chilinski

All Graduate Plan B and other Reports

Urban environments are at an increased risk of soil contamination and poor soil health due to anthropogenic causes. As healthy farmland is developed, new urban farmers and gardeners are often left growing food on unwanted land with unknown histories of previous use. Varied research and methods for obtaining healthy soils can cause new growers to make decisions that negatively impact soil health and fertility, or on the other hand, conflicting information can cause individuals to become unnecessarily fearful of common soil contaminants and their effect on human health. Common soil contaminants, like trace metals (often referred to as heavy metals) …

The Novel Chlorination Of Zirconium Metal And Its Application To A Recycling Protocol For Zircaloy Cladding From Spent Nuclear Fuel Rods, Breanna K. Vestal

Doctoral Dissertations

A novel protocol has been developed for the chemical removal of zirconium alloy (Zircaloy) cladding from spent nuclear fuel rods and subsequent isolation and purification of nuclear-grade zirconium chloride derived therefrom. This protocol is based on the chemistry developed from two new scientific findings.

First, two new oxidative chlorination reactions have been discovered for zirconium metal. In both solvents, zirconium can be quantitatively chlorinated at temperatures less than 150°C, with the operative equations seen below. In sulfur monochloride, the reaction is completed in 2 – 4 hours via surface etching, exhibiting 0^th order kinetic behavior. The elemental sulfur byproduct …

Subterraneans: A Regional Earth Dwelling For Comfort And Beauty, Isaak Benchoff

Architecture Undergraduate Honors Theses

Subterraneans is a study of an affordable, self-sufficient, sustainable, and dignified housing prototype for the region of Northwest Arkansas. It is a design process investigation based on the ideas of turning a local, sustainable material into affordable homes that can be built within a community of people sharing land and resources. The homes would utilize as many natural materials as possible to minimize the impact on the Earth and the cost to the owners.

Research looked at many different vernacular building precedents as well as the work done by contemporary design firms in the area of economical, low-impact, and passively …

Using Object Detection To Navigate A Game Playfield, Peter Kearnan Hyde-Smith

Master's Theses (2009 -)

Perhaps the crown jewel of AI is the self-navigating agent. To take many sources of data as input and use it to traverse complex and varied areas while mitigating risk and damage to the vehicle that is being controlled, visual object detection is a key part of the overall suite of this technology. While much efforts are being put towards real-world applications, for example self-driving cars, healthcare related issues and automated manufacturing, we apply object detection in a different way; the automation of movement across a video game play field. We take the TensorFlow Object Detection API and use it …

An Analysis Of Energy Production And Efficiency In Various Longbow Archery Models, Hannah Mcpherson

Honors Projects

An analysis of the energy production and efficiency of three lab-tested longbow models is undertaken. The first model, which is constructed to not allow flexing of limbs and which uses a frictionless cart and track in place of an arrow, demonstrated an efficiency of 50% +/- 40%. The second model, which is constructed similarly to the first with the exception of a wooden dowel now being used as an arrow-like object, demonstrated an efficiency of 13% +/- 3%. The last model, a 3D printed longbow with flexible limbs using the wooden dowel as an arrow-like object, demonstrated an efficiency of …

Logic And Rationality, Rohit J. Parikh

Publications and Research

Logic aims at truth, or more accurately, at deriving some truths from other truths. But why are we interested in truth in the first place? Surely one reason is that relying on truth makes it easier to make better choices.

One could think of Game theory as a tool which bridges the gap between logic and rationality.

Decision theory - or single agent game theory tells us when to make the best choice in a game of us against nature. But nature has no desire to further or frustrate our efforts. Nature is mysterious but not malign. Things change when …

The Vehicle Routing Problem With Simultaneous Pickup And Delivery And Occasional Drivers, Vincent F. Yu, Grace Aloina, Panca Jodiawan, Aldy Gunawan, Tsung-C. Huang

Research Collection School Of Computing and Information Systems

This research addresses the Vehicle Routing Problem with Simultaneous Pickup and Delivery and Occasional Drivers (VRPSPDOD), which is inspired from the importance of addressing product returns and the emerging notion of involving available crowds to perform pickup and delivery activities in exchange for some compensation. At the depot, a set of regular vehicles is available to deliver and/or pick up customers’ goods. A set of occasional drivers, each defined by their origin, destination, and flexibility, is also able to help serve the customers. The objective of VRPSPDOD is to minimize the total traveling cost of operating regular vehicles and total …

Named Entity Recognition From Biomedical Text, Maged Guirguis

Theses and Dissertations

As vast amounts of unstructured data are becoming available digitally, computer-based methods to extract relevant and meaningful information are needed. Named entity recognition (NER) is the task of identifying text spans that mention named entities, and to classify them into predefined categories. Despite the existence of numerous and well-versed NER methods, the bio-medical domain remains under-studied. The objective of this research is to identify an efficient technique for NER tasks from biomedical data. This is achieved by investigating using deep learning technologies namely pre-trained BERT [1] model and its variances SciBERT [2] and BioBERT [3]. Preprocessing the data before passing …

Evidence Of Sea Level Rise At The Peruvian Coast (1942-2019), Bismarck Jigena-Antelo, Carol Estrada-Ludeña, Stephan Howden, Wilmer Rey, Jorge Paz-Acosta, Patricia Lopez-García, Eric Salazar-Rodriguez, Nieves Endrina, Juan J. Muñoz-Pérez

Faculty Publications

The present work aims to analyze the variability of the sea level of the Peruvian coast with time series over a long observation period (Seventy-eight years, from 1942 to 2019). Data came from the Talara, Callao and Matarani tide gauge stations located at the north, center and south of the coast. Variations of sea level as well as air and seawater surface temperature were analyzed. Among the different scenarios studied, a sea level rise of 6.79, 4.21 and 5.16 mm/year for Talara, Callao and Matarani, respectively was found during the 1979–1997 nodal cycle. However, these results decreased significantly during the …

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 …

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 …

Learning Comprehensive Global Features In Person Re-Identification: Ensuring Discriminativeness Of More Local Regions, Jiali Xia, Jianqiang Huang, Shibao Zheng, Qin Zhou, Bernt Schiele, Xian-Sheng Hua, Qianru Sun

Research Collection School Of Computing and Information Systems

Person re-identification (Re-ID) aims to retrieve person images from a large gallery given a query image of a person of interest. Global information and fine-grained local features are both essential for the representation. However, global embedding learned by naive classification model tends to be trapped in the most discriminative local region, leading to poor evaluation performance. To address the issue, we propose a novel baseline network that learns strong global feature termed as Comprehensive Global Embedding (CGE), ensuring more local regions of global feature maps to be discriminative. In this work, two key modules are proposed including Non-parameterized Local Classifier …

Safe Delivery Of Critical Services In Areas With Volatile Security Situation Via A Stackelberg Game Approach, Tien Mai, Arunesh Sinha

Research Collection School Of Computing and Information Systems

Vaccine delivery in under-resourced locations with security risks is not just challenging but also life threatening. The COVID pandemic and the need to vaccinate added even more urgency to this issue. Motivated by this problem, we propose a general framework to set-up limited temporary (vaccination) centers that balance physical security and desired (vaccine) service coverage with limited resources. We set-up the problem as a Stackelberg game between the centers operator (defender) and an adversary, where the set of centers is not fixed a priori but is part of the decision output. This results in a mixed combinatorial and continuous optimization …

A Fair Incentive Scheme For Community Health Workers, Avinandan Bose, Tracey Li, Arunesh Sinha, Tien Mai

Research Collection School Of Computing and Information Systems

Community health workers (CHWs) play a crucial role in the last mile delivery of essential health services to under-served populations in low-income countries. Many non-governmental organizations (NGOs) provide training and support to enable CHWs to deliver health services to their communities, with no charge to the recipients of the services. This includes monetary compensation for the work that CHWs perform, which is broken down into a series of well-defined tasks. In this work, we partner with a NGO D-Tree International to design a fair monetary compensation scheme for tasks performed by CHWs in the semi-autonomous region of Zanzibar in Tanzania, …

Online Hyperparameter Optimization For Class-Incremental Learning, Yaoyao Liu, Yingying Li, Bernt Schiele, Qianru Sun

Research Collection School Of Computing and Information Systems

Class-incremental learning (CIL) aims to train a classification model while the number of classes increases phase-by-phase. An inherent challenge of CIL is the stability-plasticity tradeoff, i.e., CIL models should keep stable to retain old knowledge and keep plastic to absorb new knowledge. However, none of the existing CIL models can achieve the optimal tradeoff in different data-receiving settings—where typically the training-from-half (TFH) setting needs more stability, but the training-from-scratch (TFS) needs more plasticity. To this end, we design an online learning method that can adaptively optimize the tradeoff without knowing the setting as a priori. Specifically, we first introduce the …

Towards Carbon Neutrality: Prediction Of Wave Energy Based On Improved Gru In Maritime Transportation, Zhihan Lv, Nana Wang, Ranran Lou, Yajun Tian, Mohsen Guizani

Machine Learning Faculty Publications

Efficient use of renewable energy is one of the critical measures to achieve carbon neutrality. Countries have introduced policies to put carbon neutrality on the agenda to achieve relatively zero emissions of greenhouse gases and to cope with the crisis brought about by global warming. This work analyzes the wave energy with high energy density and wide distribution based on understanding of various renewable energy sources. This study provides a wave energy prediction model for energy harvesting. At the same time, the Gated Recurrent Unit network (GRU), Bayesian optimization algorithm, and attention mechanism are introduced to improve the model's performance. …

Enhanced Convolutional Neural Network For Non-Small Cell Lung Cancer Classification, Yahya Tashtoush, Rasha Obeidat, Abdallah Al-Shorman, Omar Darwish, Mohammad A. Al-Ramahi, Dirar Darweesh

Computer Information Systems Faculty Publications

Lung cancer is a common type of cancer that causes death if not detected
early enough. Doctors use computed tomography (CT) images to diagnose
lung cancer. The accuracy of the diagnosis relies highly on the doctor's
expertise. Recently, clinical decision support systems based on deep learning
valuable recommendations to doctors in their diagnoses. In this paper, we
present several deep learning models to detect non-small cell lung cancer in
CT images and differentiate its main subtypes namely adenocarcinoma,
large cell carcinoma, and squamous cell carcinoma. We adopted standard
convolutional neural networks (CNN), visual geometry group-16 (VGG16),
and VGG19. Besides, we …

Optimized Peptide Nanomaterials As Delivery Vehicles For Hydrophobic Metal-Based Anticancer Agents, Yaron Marciano

Dissertations, Theses, and Capstone Projects

Enzyme-responsive materials have been well explored, particularly as therapeutic and diagnostic agents. In this thesis we demonstrate that anionic self-assembling peptides can be utilized as delivery vehicles for metal-based hydrophobic payloads. The tunability of the system is highlighted as well as the increase in cytotoxicity and selectivity in vitro. The rapid degradation of peptides in cell media may lead to the formation of new peptide-drug bioconjugates with increased activity and selectivity. The physiological stability of these peptide delivery vehicles has been optimized by capping the N-terminus with an acetyl group. This simple backbone modification was shown to not prevent self-assembly, …