Open Access. Powered by Scholars. Published by Universities.®

Digital Commons Network™

Open Access. Powered by Scholars. Published by Universities.®

San Jose State University

Discipline
Keyword
Publication Year
Publication
Publication Type
File Type

Articles 421 - 450 of 6909

Full-Text Articles in Entire DC Network

Mitigating The Risk Of Reentrancy Attack In Smart Contract Development, Eric Ngo Jan 2024

Mitigating The Risk Of Reentrancy Attack In Smart Contract Development, Eric Ngo

Master's Projects

Smart contracts, while revolutionizing the blockchain with their immutable nature, are prone to attacks such the reentrancy attack. This attack allows malicious adversaries to repeately enter a contract before previous executions are completed. SpartanScript, a custom dialect of Scheme, is a way for developers to write and develop contracts in an experimental blockchain environment like SpartanGold. Compared to cryptocurrencies that use a virtual machine to run on the blockchain, SpartanScript utilizes a simplified interpreter for rapid prototyping. However, SpartanScript does not have a way to detect and warn developers of reentrancy vulnerabilities. Hence, there is a need to implement reliable …

Go to article

Resume Content Generation Using Llama 2 With Adapters, Navaneeth Sai Nidadavolu Jan 2024

Resume Content Generation Using Llama 2 With Adapters, Navaneeth Sai Nidadavolu

Master's Projects

The primary objective of this project is to optimize the Llama language model to generate customized resumes containing domain-specific job descriptions and maintain the linguistic capabilities of the large language model. Building upon the prior research by Sumed Kale on Job Tailored Resume content generation using GPT-2, where he employed full fine-tuning of the model and demonstrated the capability of LLMs to generate resume content, it is evident that while effective, full fine-tuning has its limitations. Primarily, it is computationally expensive, which can pose constraints, especially for large models. Additionally, during the fine-tuning process, there is a risk of losing …

Go to article

Enhancing Restaurant Sales Prediction: The Dynamic Forecasting Engine, Rahul Sanjay Morishetti Jan 2024

Enhancing Restaurant Sales Prediction: The Dynamic Forecasting Engine, Rahul Sanjay Morishetti

Master's Projects

This project introduces a "dynamic forecasting engine," designed to transform the way restaurants predict sales. The engine dynamically handles seasonal ARIMA_HoltWinter hybrid model, XGBoost, and LSTM algorithms to dynamically select the best forecasting method based on data volume, variety, and customer taste preferences delving upon the spice level categorical sales. This guide differs from traditional crystal ball approaches because it has the ability to improve over time as new data comes in terms of spice levels. It emphasizes the importance of dataset size in the selection of machine learning algorithms through complexity for large datasets and simplicity for smaller ones …

Go to article

Instagram Data Analysis Using Machine Learning, Lakshmi Prasanna Gorrepati Jan 2024

Instagram Data Analysis Using Machine Learning, Lakshmi Prasanna Gorrepati

Master's Projects

With enormous amount of social media content, we can draw valuable insights. In this paper, we apply different Machine Learning and Deep Learning techniques on Instagram data to determine the techniques that work well to discover the engagement class of a social media post. Out of all the social media platforms, Instagram is growing rapidly not just in the number of users but also in terms of Advertisement and marketing surpassing YouTube’s advertisement revenue. The end goal of this paper is to propose a technique to predict the engagement class. We applied Random Forest (RF), Stacking Classifier, Extreme Gradient Boost …

Go to article

Parallel Powerplay: Optimizing Performance With Mapreduce And Kubernetes Fusion, Shradha Chaturvedi Jan 2024

Parallel Powerplay: Optimizing Performance With Mapreduce And Kubernetes Fusion, Shradha Chaturvedi

Master's Projects

The combination of MapReduce (MR) & Kubernetes (K8s) strengths is not explored, and this study leverages the synergy between the two frameworks to meet the growing demands of data-intensive applications. First, this report elaborates on the existing literature work to understand the pros and cons of using MR and K8s, in what use cases these frameworks come to use, and investigates the effectiveness of research studies that explore the combination. This study aims to research the efficacy of the fusion of MR and K8s, considering these factors - application use case, infrastructure design, resource allocation, load balancing, and hypertuning parameters …

Go to article

Birdsong Classification Using Deep Learning And Mixit, Sasanka Kosuru Jan 2024

Birdsong Classification Using Deep Learning And Mixit, Sasanka Kosuru

Master's Projects

The identification of bird species using deep learning techniques presents a novel approach in bioacoustics, by significantly advancing our understanding and enhancing our capabilities in bird species recognition from audio recordings. The value of audio over visual data for monitoring ecological patterns in birds can be highlighted with the deployment of automated recording devices in remote wildlife sensing, offering a more cost-effective, non-invasive, and practical solution. However, the methods of processing and classifying the audio remain challenging due to the complexity of bird audio, characterized by diverse vocalizations and imminent environmental noise, which poses difficult challenges to perform effective classification. …

Go to article

Optimization Of Inter-Satellite Routing Using Lstm-Based Path Prediction Model, Yash Bhamare Jan 2024

Optimization Of Inter-Satellite Routing Using Lstm-Based Path Prediction Model, Yash Bhamare

Master's Projects

Satellite networks are one of the most important components that fulfill the world’s need for connectivity. To ensure that communication is efficient and reliable, robust routing algorithms are a must. Because, although it is true that certain routing characteristics may not be permanently and continuously flawless, a routing technique must effectively adapt to modifications in such network characteristics. The new routing method uses a Long Short-Term Memory (LSTM) model to manage dynamic metrics for Low Earth Orbit satellite networks. This LSTM model is aimed at predicting the optimal routing direction on the premise that a satellite is soon to be, …

Go to article

Emulating Human Personality With Large Language Models Through Contextual Prompts And Fine-Tuning, Mrunal Zambre Jan 2024

Emulating Human Personality With Large Language Models Through Contextual Prompts And Fine-Tuning, Mrunal Zambre

Master's Projects

The quest for AI systems that can mirror the intricate aspects of human emotion and personality is crucial for enhancing their performance. This project delves into the capabilities of Large Language Models (LLMs) to mimic the Big Five personality traits in human-written essays by utilizing contextual prompts and fine-tuning methods. Diverging from traditional research in this domain, this project explores smaller, open-source LLMs, including LLaMA 2 7B chat, LLaMA 2 13B chat, and Vicuna v.15 13B, to assess their potential in personality prediction tasks, thereby making high-level personality emulation more accessible and practical for application integration. Through meticulous prompt engineering, …

Go to article

Sudoku As A Proof Of Useful Work Protocol On The Blockchain, Abishek Padaki Jan 2024

Sudoku As A Proof Of Useful Work Protocol On The Blockchain, Abishek Padaki

Master's Projects

The Proof of Work (PoW) consensus used by many blockchain networks like Bitcoin has been criticized for its excessive energy consumption and lack of tangible utility beyond maintaining the network. This report proposes a Proof of Useful Work (uPoW) protocol as an alternative consensus mechanism that utilizes computational resources to solve intrinsically valuable problems. Specifically, it explores the implementation of uPoW on the SpartanGold blockchain test network, where miners must solve Sudoku puzzles to validate new blocks. The report examines the shortcomings of traditional Proof-of-Work protocols, such as their environmental impact and inefficient use of computing power. It then delves …

Go to article

Explaining The Maliciousness Of Urls Using Shap And Lime, Ayush Nair Jan 2024

Explaining The Maliciousness Of Urls Using Shap And Lime, Ayush Nair

Master's Projects

No system has ever reached the levels of proliferation that the Internet now enjoys. It stands as the most widely spread distributed system across the globe; yet this evolution has given rise to an ever-growing wave of malintent that challenges every user and entity on the vast expanse of cyberspace. Malicious URLs loom large as vulnerabilities leaving users naked as they traverse online landscapes, but cybersecurity experts craft models with esoteric algorithms in a bid to stem this tide and shield users from cybercrime. However, peering into the decision-making corridors of these models holds key importance, it’s through understanding such …

Go to article

Domain Switch On Sentiment Analysis Using Gradient Reversal Layer, Hemish Veeraboina Jan 2024

Domain Switch On Sentiment Analysis Using Gradient Reversal Layer, Hemish Veeraboina

Master's Projects

Switching domains in sentiment analysis presents the challenge of transferring learned knowledge from one context to another without the need to label data. Traditional methods often struggle when dealing with differences in data distribution a problem known as the domain shift issue. To tackle this using Gradient Reversal Layers (GRL) has emerged as a solution for adapting to different domains in an unsupervised learning setting. This study introduces an enhancement to the standard GRL approach by incorporating a sigmoid function that gradually adjusts how intensely domain adaptation occurs during training. This upgraded GRL technique ensures controlled learning outcomes making it …

Go to article

Rocketship Education: An Exploratory Public Policy Case Study, Vladimir Gresham Ivanovic Jan 2024

Rocketship Education: An Exploratory Public Policy Case Study, Vladimir Gresham Ivanovic

Dissertations

This dissertation is an exploratory case study of the finances of the Rocketship charter school chain, especially those related to real estate. Rocketship is a not-for-profit charter management organization, one of the first in Santa Clara County, California. This study seeks to determine if the financial transactions related to Rocketship charter schools yield profits for investors, despite Rocketship itself being a non-profit entity, and if they do, how and where do they do so. In order to characterize fairly and completely the profits of Rocketship Education itself and Rocketship-related entities, this study uses publicly available documents to track money flowing …

Go to article

In Their Words: Voices Of High School Graduates Who Were Labeled English Learners, Yolanda K. Jimenez Jan 2024

In Their Words: Voices Of High School Graduates Who Were Labeled English Learners, Yolanda K. Jimenez

Dissertations

This research captures the experiences of high school graduates who were labeled English Learners while they were enrolled in a public high school in the greater Sacramento area of California. The study examined what these bilingual high school graduates report as beneficial and detrimental experiences faced while completing the course requirements for high school graduation. This study builds on research that includes secondary classroom teachers and their perceptions of English Learners, classroom teaching and learning, and experiences of emergent bilingual students. The study expands the body of research and informs praxis pertaining to supporting emergent bilingual students as they attempt …

Go to article

A Framework For Scientific Data Indexing, Searching And Sharing, Apoorva Mohite Jan 2024

A Framework For Scientific Data Indexing, Searching And Sharing, Apoorva Mohite

Master's Projects

Scientific data continues to grow. Wildfire simulation experiments performed by the WIRC team at SJSU have generated over 138 TB of data so far and it is expected to keep growing. It becomes difficult for researchers to search through that data to find the data of their interest. This data is stored on an HPC cluster that external users do not have access to. The WIRC team also conducts experiments and publishes their research, but the size of data makes it difficult to share these datasets. This project introduces a novel solution to indexing scientific data, searching through the data …

Go to article

Ml-Based User Identification Through Mouse Dynamics, Rakshit Gupta Jan 2024

Ml-Based User Identification Through Mouse Dynamics, Rakshit Gupta

Master's Projects

User authentication and identification plays a crucial role in ensuring the security and integrity of digital systems. Traditional authentication methods, such as passwords and biometrics, have inherent limitations that can compromise system security. This research proposes a novel approach to user authentication by leveraging machine learning techniques and behavioral biometrics, specifically mouse dynamics. The primary objective is to develop a sophisticated framework that can accurately identify individuals based on their unique mouse behavior patterns. The study explores and compares multiple deep learning architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Transformer models, to generate embeddings from …

Go to article

Optimizing Field-Linked Simulations Of Dry Season Uptake And Monsoon Infiltration Within An Aspen-Mixed Conifer Forest, Raymond J. Hess Jan 2024

Optimizing Field-Linked Simulations Of Dry Season Uptake And Monsoon Infiltration Within An Aspen-Mixed Conifer Forest, Raymond J. Hess

Master's Theses

Climate models forecast that headwater catchments in the western U.S. will undergo a reduction in snowpack, early season snowmelt, and increases in evapotranspiration. The resulting extended dry season will stress vegetation in mountainous watersheds throughout the Upper Colorado River Basin. We investigate infiltration patterns and root water uptake in response to dry season disturbances within the East River watershed in Colorado. To do this, we collected soil cores, measured matric potential and sap flow, and monitored tree xylem and soil for stable isotopes of water (2H, 18O) in two soil profiles to 90 cm depth (with three Engelmann spruce and …

Go to article

Episodic Memory Assessment: Effects Of Sex And Age On Performance And Response Time During A Continuous Recognition Task, James O. Clifford, Sulekha Anand, Franck Tarpin-Bernard, Michael F. Bergeron, Curtis B. Ashford, Peter J. Bayley, John Wesson Ashford Jan 2024

Episodic Memory Assessment: Effects Of Sex And Age On Performance And Response Time During A Continuous Recognition Task, James O. Clifford, Sulekha Anand, Franck Tarpin-Bernard, Michael F. Bergeron, Curtis B. Ashford, Peter J. Bayley, John Wesson Ashford

Faculty Research, Scholarly, and Creative Activity

Introduction: Continuous recognition tasks (CRTs) assess episodic memory (EM), the central functional disturbance in Alzheimer’s disease and several related disorders. The online MemTrax computerized CRT provides a platform for screening and assessment that is engaging and can be repeated frequently. MemTrax presents complex visual stimuli, which require complex involvement of the lateral and medial temporal lobes and can be completed in less than 2 min. Results include number of correct recognitions (HITs), recognition failures (MISSes = 1-HITs), correct rejections (CRs), false alarms (FAs = 1-CRs), total correct (TC = HITs + CRs), and response times (RTs) for each HIT and …

Go to article

In And Ex Spheres Of A Tetrahedron, Hidefumi Katsuura Jan 2024

In And Ex Spheres Of A Tetrahedron, Hidefumi Katsuura

Faculty Research, Scholarly, and Creative Activity

We prove that (1) a tetrahedron is isosceles if and only if the vertices of its twin tetrahedron are the excenters of the tetrahedron, (2) if a tetrahedron is orthocentric, and if the orthocenter is either the incenter, the centroid, or the circumcenter, then the tetrahedron is regular, (3) a tetrahedron is regular if and only if the four ex-spheres are tangent to the in-sphere, and (4) we prove an inequality relating the in-radius, circumradius, and the distances between the in-center and the vertices of a tetrahedron.

Go to article

Vehicular Traffic Flow Prediction Via Decentralized Federated Meta-Learning, Andrew Selvia, Ankur Singh, Wencen Wu Jan 2024

Vehicular Traffic Flow Prediction Via Decentralized Federated Meta-Learning, Andrew Selvia, Ankur Singh, Wencen Wu

Faculty Research, Scholarly, and Creative Activity

Traffic flow prediction (TFP) has become a crucial problem for billions of people who rely on traffic predictions to optimize their travels every day. In this paper, we present a decentralized federated meta-learning (DFML) framework for traffic flow prediction. Within this framework, traffic detectors spread across a road system can form federations with nearby peers in order to predict traffic collaboratively. A node that assembles a federation assumes the role of receiver while the remainder are assigned as broadcasters. The broadcasters learn model parameters from local data and then broadcast those models to receivers, thereby enriching the available information from …

Go to article

Research On Teachers Of Color And Indigenous Teachers: Developing And Fortifying Policies That Diversify The Educator Workforce, Conra Gist, Travis J. Bristol, Saili Kulkarni Jan 2024

Research On Teachers Of Color And Indigenous Teachers: Developing And Fortifying Policies That Diversify The Educator Workforce, Conra Gist, Travis J. Bristol, Saili Kulkarni

Faculty Research, Scholarly, and Creative Activity

This special issue expands upon the Handbook of Research on Teachers of Color and Indigenous Teachers (Gist & Bristol, 2022a) and the practitioner-focused Phi Delta Kappan special issue, “Learning from the Voices of Black, Indigenous and People of Color Educators: Charting New Directions for Research, Policy, and Practice” (Gist & Bristol, 2021). The co-created articles in this special issue highlight the combined expertise of policy makers and education researchers. While the Handbook featured empirical research, and the Phi Delta Kappan special issue featured research briefs and teacher testimonies, the policy-driven articles in this special issue explore how research can be …

Go to article

Student Understanding Of Divergence And Curl In Upper-Division Electromagnetism, J. Toby Stuckey, Gina M. Quan Jan 2024

Student Understanding Of Divergence And Curl In Upper-Division Electromagnetism, J. Toby Stuckey, Gina M. Quan

Faculty Research, Scholarly, and Creative Activity

The purpose of this paper is to understand and characterize student thinking about the physics concepts of divergence and curl in the context of upper-division electromagnetism for physics majors. We interviewed five students at a west coast university who had taken at least one semester of upper-division electricity and magnetism. The interview prompted students to describe divergence and curl in multiple representations (visually, mathematically, qualitatively). Our analysis identified that students can notice but struggle to resolve discrepancies between the mathematical calculations and the graphical representations of vector fields. We illustrate examples of when students appear to have a disconnect between …

Go to article

The Applicability Of Llms In Generating Textual Samples For Analysis Of Imbalanced Datasets, Saroj Gopali, Faranak Abri, Akbar Siami Namin, Keith S. Jones Jan 2024

The Applicability Of Llms In Generating Textual Samples For Analysis Of Imbalanced Datasets, Saroj Gopali, Faranak Abri, Akbar Siami Namin, Keith S. Jones

Faculty Research, Scholarly, and Creative Activity

In machine learning class imbalance is a pressing issue, where the model is biased towards the majority classes and underperforms in the minority classes. In textual data, the natural language processing (NLP) model bias significantly reduces overall accuracy, along with poor performance in minority classes. This paper investigates and compares the performance of transformer-based models, such as Multi-head Attention with the data levels and algorithmic levels approaches and BERT (Bidirectional Encoder Representations from Transformers) with LLM-based data augmentation. The research utilized the approaches, such as Random Over Sampler, Synthetic Minority Over-sampling Technique (SMOTE), SMOTEENN, data augmentation at word level, class …

Go to article

Topological Models Of Abstract Commensurators, Edgar A. Bering, Daniel Studenmund Jan 2024

Topological Models Of Abstract Commensurators, Edgar A. Bering, Daniel Studenmund

Faculty Research, Scholarly, and Creative Activity

The full solenoid over a topological space X is the inverse limit of all finite covers. When X is a compact Hausdorff space admitting a locally path-connected universal cover, we relate the pointed homotopy equivalences of the full solenoid to the abstract commensurator of the fundamental group π1(X). The relationship is an isomorphism when X is an aspherical CW complex. If X is additionally a geodesic metric space and π1(X) is residually finite, we show that this topological model is compatible with the realization of the abstract commensurator as a subgroup of the quasi-isometry group of π1(X). This is a …

Go to article

Enhancing Diagnostic Accuracy: A Hybrid Approach To Mitigate Speckle And Rician Noise In Medical Images, Naveenkumar Jayakumar, Saurabh Saoji, Sagar V. Joshi, Hemlata Mane, Roshni Narkhede, Shrikant Jadhav Jan 2024

Enhancing Diagnostic Accuracy: A Hybrid Approach To Mitigate Speckle And Rician Noise In Medical Images, Naveenkumar Jayakumar, Saurabh Saoji, Sagar V. Joshi, Hemlata Mane, Roshni Narkhede, Shrikant Jadhav

Faculty Research, Scholarly, and Creative Activity

This research explores the detrimental effects of Rician and speckle noise in medical imaging, particularly within MRI and radiographic modalities. Such noise can significantly obscure critical details, potentially leading to misdiagnoses and inaccurate measurements. Rician noise candidly dangles on the signal strength. Removing this noise affects the origial information in image that makes this noise removal perplexing. On other hand speckle noise is visualised like granular clog in ultrasound imageries. It creates ambiguous details and edges in the images. To address this challenge, the study introduces a novel hybrid method implemented in Java. The method employs a frequency-based decomposition approach …

Go to article

Tensor Z -Transform, Shih Yu Chang, Hsiao Chun Wu Jan 2024

Tensor Z -Transform, Shih Yu Chang, Hsiao Chun Wu

Faculty Research, Scholarly, and Creative Activity

The multi-input multioutput (MIMO) systems involving multirelational signals generated from distributed sources have been emerging as the most generalized model in practice. The existing work for characterizing such a MIMO system is to build a corresponding transform tensor, each of whose entries turns out to be the individual z-transform of a discrete-time impulse response sequence. However, when a MIMO system has a global feedback mechanism, which also involves multirelational signals, the aforementioned individual z-transforms of the overall transfer tensor are quite difficult to formulate. Therefore, a new mathematical framework to govern both feedforward and feedback MIMO systems is in crucial …

Go to article

Fpdclustering: A Comprehensive R Package For Probabilistic Distance Clustering Based Methods, Cristina Tortora, Francesco Palumbo Jan 2024

Fpdclustering: A Comprehensive R Package For Probabilistic Distance Clustering Based Methods, Cristina Tortora, Francesco Palumbo

Faculty Research, Scholarly, and Creative Activity

Data clustering has a long history and refers to a vast range of models and methods that exploit the ever-more-performing numerical optimization algorithms and are designed to find homogeneous groups of observations in data. In this framework, the probability distance clustering (PDC) family methods offer a numerically effective alternative to model-based clustering methods and a more flexible opportunity in the framework of geometric data clustering. Given nJ-dimensional data vectors arranged in a data matrix and the number K of clusters, PDC maximizes the joint density function that is defined as the sum of the products between the distance and the …

Go to article

A Comparative Multivariate Analysis Of Var And Deep Learning-Based Models For Forecasting Volatile Time Series Data, Saroj Gopali, Sima Siami-Namini, Faranak Abri, Akbar Siami Namin Jan 2024

A Comparative Multivariate Analysis Of Var And Deep Learning-Based Models For Forecasting Volatile Time Series Data, Saroj Gopali, Sima Siami-Namini, Faranak Abri, Akbar Siami Namin

Faculty Research, Scholarly, and Creative Activity

The existing literature on forecasting time series data is primarily based on univariate analysis and techniques such as Univariate Autoregressive (UAR), Univariate Moving Average (UMA), Simple Exponential Smoothing (SES), deep learning models, and, most notably, univariate Long Short-Term Memory (LSTM) built based on univariate variable where the next lag of time series is leveraged for forecasting the next cycle of data. This paper takes this line of research to the next level by focusing on forecasting time series data based on “multivariate” modeling and analysis. To have a better insight of the performance of various deep learning-based models when multivariate …

Go to article

Anti-Trafficking And Humanitarian Operations: Transferring Learnings For A Better World, Kezban Yagci Sokat, Maria Besiou Jan 2024

Anti-Trafficking And Humanitarian Operations: Transferring Learnings For A Better World, Kezban Yagci Sokat, Maria Besiou

Faculty Research, Scholarly, and Creative Activity

Purpose: The purpose of this study is twofold: first, to draw insights from the rich literature on humanitarian operations efforts to combat human trafficking; second, to inspire humanitarian operations researchers to work more on human anti-trafficking. Design/methodology/approach: This is a conceptual paper inspired by recent relevant reports, the academic literature and the authors’ years of involvement in both humanitarian operations and anti-trafficking. Findings: Humanitarian supply chains and human trafficking supply chains very often operate in the same environments and hence face similar challenges. The paper highlights the overlaps between the two domains and demonstrates how two decades of learnings from …

Go to article

Assessment Of Digital Collaboration Skills, Esperanza Huerta, Ana Lidia Franzoni Velázquez, Scott Jensen Jan 2024

Assessment Of Digital Collaboration Skills, Esperanza Huerta, Ana Lidia Franzoni Velázquez, Scott Jensen

Faculty Research, Scholarly, and Creative Activity

This exploratory study proposes a methodology for assessing digital collaboration skills based on the students' actual behavior. Students' comments using a digital collaboration tool during a one-month long project were manually coded. This methodology is not context specific and can be used across different domains. This assessment contrasts with self-reported measures in which students rate themselves as already possessing collaboration skills. Finally, the study explores the use of generative AI to automatically code student's comments to alleviate the labor-intensive process that coding requires and to enable scalability in coding data.

Go to article

Correction To: A Fresh Take: Seasonal Changes In Terrestrial Freshwater Inputs Impact Salt Marsh Hydrology And Vegetation Dynamics (Estuaries And Coasts, (2024), 10.1007/S12237-024-01392-1), Maya S. Montalvo, Emilio Grande, Anna E. Braswell, Ate Visser, Bhavna Arora, Erin C. Seybold, Corianne Tatariw, John C. Haskins, Charlie A. Endris, Fuller Gerbl, Mong Han Huang, Darya Morozov, Margaret A. Zimmer Jan 2024

Correction To: A Fresh Take: Seasonal Changes In Terrestrial Freshwater Inputs Impact Salt Marsh Hydrology And Vegetation Dynamics (Estuaries And Coasts, (2024), 10.1007/S12237-024-01392-1), Maya S. Montalvo, Emilio Grande, Anna E. Braswell, Ate Visser, Bhavna Arora, Erin C. Seybold, Corianne Tatariw, John C. Haskins, Charlie A. Endris, Fuller Gerbl, Mong Han Huang, Darya Morozov, Margaret A. Zimmer

Faculty Research, Scholarly, and Creative Activity

In the original online version of the article the following information was missing from the Funding section: Department of Energy, Small Business Innovation Research Award Number DE-SC0021480. In addition, the following was missing from the Acknowledgements: The authors would like to thank Elkhorn Slough Foundation for providing and facilitating the opportunity to conduct this work at the Cowell Ranch site. Also, the study site noted in the Background section was incorrect. The study was conducted at the Elkhorn Slough on the property of the Elkhorn Slough Foundation. The original article was corrected.

Go to article

First Prev Next Last