Business Commons^™

Variation In The Growth Parameters And Biomass Of Rhizophora Mangle Seedlings With Distances From Playa Estrella, Bocas Del Toro, Panama, Thiny Tep

Independent Study Project (ISP) Collection

Mangrove is a salt-tolerant, intertidal, tropical tree or shrub and make up a rich community of various organism. On the Caribbean coast of Panama, in Bocas del Toro, mangrove forests cover 28 km² and are dominated by R. mangle, followed by L. racemosa and A. germinans. Simultaneously, Isla Colón, the most populated and developed among all islands in the Bocas del Toro Archipelago, is a tourist center. Unfortunately, tourism comes at the price of environmental degradation via alteration of natural habitats, solid and wastewater pollution. Therefore, this study aims to gain a preliminary understanding on how the …

Healthcare Disparities Among Incarcerated Populations: A Quality Improvement Project, Mariam Bradley, Morgan Callihan, Sandra Frederick, Riley Hall, Marty Helms, Aaliyah Manson, Katelyn Martha, Madison Mason, Suzi White

2023 Celebration of Student Scholarship - Poster Presentations

A population that has been consistently subject to unequal treatment when receiving medical attention is the population of prisoners or jail inmates. It is essential to explore the discrepancies prisoners face and the effects it has on their health. The objective of this research was to bring to light the disparities incarcerated patients experience, and how to eliminate these. In order to develop a better understanding of the prejudiced actions incarcerated patients face in the healthcare spectrum, numerous studies have been analyzed.

Are Gender And Social Disparities Associated With Stem Persistence In Kentucky Colleges?, Riley E. Hicks, Wilson Gonzalez-Espada

2023 Celebration of Student Scholarship - Poster Presentations

Children have the right to receive a quality education, but concerns about county disparities in school funding, facilities, and resources exist. This is known as the "zip code effect". Because high school STEM classes require lab space, materials, equipment, and specialized teachers, disparities in school funding can impact students who want STEM careers.

Visual Pollution Classification Using Convolutional Neural Networks, Jacob Vogelpohl, Heba Elgazzar

2023 Celebration of Student Scholarship - Poster Presentations

Visual pollution is an impairment on an individual's ability to enjoy their surroundings. It usually takes the form of a messy and chaotic environment that can cause overstimulation of the visual senses. This includes trash, advertisements, construction, electric cables, and similar objects. Convolutional Neural Networks (CNNs) are a form of artificial intelligence that use supervised learning to process and classify images. In this research, a CNN processed images of city streets and classified them as polluted or not polluted based on the visual characteristics that it learned from during its training period. The CNN achieved a training accuracy of 98% …

Predicting The Mechanism And Products Of Cs2 Capture By Nh3 - An Exemplar Benchmark Study, Shelbie A. Black, David A. Dixon, Zachary R. Lee

2023 Celebration of Student Scholarship - Poster Presentations

Carbon disulfide is a toxic gas emitted from industrial plants and a common greenhouse gas. Acute poisonings from carbon disulfide are rare, but recurring exposure to low doses can have long term health effects. Currently, there are two approaches being considered for the removal of acid gas pollutants: (1) sequester these gases from the atmosphere or (2) remove these gases directly upon combustion (post-combustion). Potential energy surfaces (PES) for a series of CS2 capture reactions by NH3 in the presence H2O were calculated at the MP2/a(D+d) evel in the gas phase. G3(MP2) and FPD calculations are in progress. Extensive thermodynamic …

A Simulation Of The Impacts Of Climate Change On Civil Aircraft Takeoff Performance, Thomas D. Pellegrin

Doctoral Dissertations and Master's Theses

Climate change affects the near-surface environmental conditions that prevail at airports worldwide. Among these, air density and headwind speed are major determinants of takeoff performance, and their sensitivity to global warming carries potential operational and economic implications for the commercial air transport industry. Previous archival and prospective research observed a weakening in headwind strength and predicted an increase in near-surface temperatures, respectively, resulting in an increase in takeoff distances and weight restrictions. The main purpose of the present study was to update and generalize the extant prospective research using a more representative sample of worldwide airports, a wider range of …

Informational Content Of Factor Structures In Simultaneous Discrete Response Models, Shakeeb Khan, Arnaud Maurel, Yichong Zhang

Research Collection School Of Economics

We study the informational content of factor structures in discrete triangular systems. Factor structures have been employed in a variety of settings in cross sectional and panel data models, and in this paper we attempt to formally quantify their informational content in a bivariate system often employed in the treatment effects literature. Our main findings are that under the factor structures often imposed in the literature, point identification of parameters of interest, such as both the treatment effect and the factor load, is attainable under weaker assumptions than usually required in these systems. For example, we show is that an …

Arousing Motives Or Eliciting Stories? On The Role Of Pictures In A Picture–Story Exercise, Philipp Schäpers, Stefan Krumm, Filip Lievens, Nikola Stenzel

Research Collection Lee Kong Chian School Of Business

Picture–story exercises (PSE) form a popular measurement approach that has been widely used for the assessment of implicit motives. However, current theorizing offers two diverging perspectives on the role of pictures in PSEs: either to elicit stories or to arouse motives. In the current study, we tested these perspectives in an experimental design. We administered a PSE either with or without pictures. Results from N = 281 participants revealed that the experimental manipulation had a medium to large effect for the affiliation and power motive domains, but no effect for the achievement motive domain. We conclude that the herein chosen …

Code Will Tell: Visual Identification Of Ponzi Schemes On Ethereum, Xiaolin Wen, Kim Siang Yeo, Yong Wang, Ling Cheng, Feida Zhu, Min Zhu

Research Collection School Of Computing and Information Systems

Ethereum has become a popular blockchain with smart contracts for investors nowadays. Due to the decentralization and anonymity of Ethereum, Ponzi schemes have been easily deployed and caused significant losses to investors. However, there are still no explainable and effective methods to help investors easily identify Ponzi schemes and validate whether a smart contract is actually a Ponzi scheme. To fill the research gap, we propose PonziLens, a novel visualization approach to help investors achieve early identification of Ponzi schemes by investigating the operation codes of smart contracts. Specifically, we conduct symbolic execution of opcode and extract the control flow …

Fraud Pattern Detection For Nft Markets, Andrew Leppla, Jorge Olmos, Jaideep Lamba

SMU Data Science Review

Non-Fungible Tokens (NFTs) enable ownership and transfer of digital assets using blockchain technology. As a relatively new financial asset class, NFTs lack robust oversight and regulations. These conditions create an environment that is susceptible to fraudulent activity and market manipulation schemes. This study examines the buyer-seller network transactional data from some of the most popular NFT marketplaces (e.g., AtomicHub, OpenSea) to identify and predict fraudulent activity. To accomplish this goal multiple features such as price, volume, and network metrics were extracted from NFT transactional data. These were fed into a Multiple-Scale Convolutional Neural Network that predicts suspected fraudulent activity based …

Self-Learning Algorithms For Intrusion Detection And Prevention Systems (Idps), Juan E. Nunez, Roger W. Tchegui Donfack, Rohit Rohit, Hayley Horn

SMU Data Science Review

Today, there is an increased risk to data privacy and information security due to cyberattacks that compromise data reliability and accessibility. New machine learning models are needed to detect and prevent these cyberattacks. One application of these models is cybersecurity threat detection and prevention systems that can create a baseline of a network's traffic patterns to detect anomalies without needing pre-labeled data; thus, enabling the identification of abnormal network events as threats. This research explored algorithms that can help automate anomaly detection on an enterprise network using Canadian Institute for Cybersecurity data. This study demonstrates that Neural Networks with Bayesian …

The Santa Clara, 2023-03-17, Santa Clara University

The Santa Clara

No abstract provided.

Chatgpt As Metamorphosis Designer For The Future Of Artificial Intelligence (Ai): A Conceptual Investigation, Amarjit Kumar Singh (Library Assistant), Dr. Pankaj Mathur (Deputy Librarian)

Library Philosophy and Practice (e-journal)

Abstract

Purpose: The purpose of this research paper is to explore ChatGPT’s potential as an innovative designer tool for the future development of artificial intelligence. Specifically, this conceptual investigation aims to analyze ChatGPT’s capabilities as a tool for designing and developing near about human intelligent systems for futuristic used and developed in the field of Artificial Intelligence (AI). Also with the helps of this paper, researchers are analyzed the strengths and weaknesses of ChatGPT as a tool, and identify possible areas for improvement in its development and implementation. This investigation focused on the various features and functions of ChatGPT that …

Private Pete Fights Illiteracy At Fort Ontario: The Men In Charge, Christian Wright, Adrian Mandzy

2023 Celebration of Student Scholarship - Poster Presentations

From June of 1943 to February of 1944, the 1210th Special Training Unit at Fort Ontario in Oswego, New York taught pre-basic military training to thousands of illiterate, slow learning, and non-English-speaking soldiers in the United States Army. The training center at Fort Ontario conducted programs in disciplinary barracks, specialized military, technical, academic and vocational education.

Classification Of Road Objects Using Convolutional Neural Networks, Mann Patel, Heba Elgazzar

2023 Celebration of Student Scholarship - Poster Presentations

Driving is the primary means of transportation for many people around the world. Whether the use is to assist human drivers or create autonomous driving, the use of machine learning can create safer road conditions. Drivers must consider other objects on the road, most commonly other vehicles and pedestrians. These three components, road signs, pedestrians, and vehicles, make up a large majority of objects that a driver will encounter when on the road. This research applies machine learning algorithms, specifically Convolutional Neural Networks (CNN), to classify these road objects. The goal is to create a classification model that can reliably …

The Role Of Year And Animal Origin On Key Determinants Of Ewe Longevity, Audrey Burton, Annika Weaver, Rebekah Mills, Flint Herrelson, Patricia Harrelson

2023 Celebration of Student Scholarship - Poster Presentations

The MSU sheep flock is an Innovation Flock in the Sheep GEMS project through the University of Nebraska-Lincoln. The Sheep GEMS project is a national, multi-breed project that is focused on evaluating different sheep breeds and their longevity in different climates. As a participant, we collect/send raw data that is compiled. Our preliminary data from the 2022 (Year 1) and 2023 (Year 2) lambing season has been included. We collected measurements from Katahdin ewes (n = 38; 1-4.5 years old). We measured fecal egg counts (FEC), FAMACHA scores, body condition scores (BCS), teat and udder scores. Using the MIXED procedures …

Addressing Plastic Pollution Through Green Consumption: Predicting Intentions To Use Menstrual Cups In The Philippines, Alvin Patrick M. Valentin, Ma. Regina Hechanova-Alampay

Quantitative Methods and Information Technology Faculty Publications

Plastic pollution is a global environmental crisis that poses a huge threat to the health of people and marine ecosystems worldwide. A significant source of plastic pollution is menstrual hygiene management, and an approach that can help address this crisis is the usage of washable and reusable menstrual cups. Using an extended theory of planned behavior model that includes self-identity and perceived quality, the study predicted intentions to use menstrual cups in the Philippines. Structural equation modeling results showed that perceived quality predicted attitudes towards menstrual cup usage. Moreover, attitudes, perceived behavioral control, and self-identity predicted intentions to use menstrual …

A Study Of The Impact Of Data Intelligence On Software Delivery Performance, Yongdong Dong

Dissertations and Theses Collection (Open Access)

With the rise of big data and artificial intelligence, data intelligence has gradually become the focus of academia and industry. Data intelligence has two obvious characteristics: big data drive and application scene drive. More and more enterprises extract valuable patterns contained in data with prediction and decision analysis methods and technologies such as large-scale data mining, machine learning and deep learning and use them to improve the management and decision in complex practice, so as to promote changes of new business modes, organizational structures and even business strategies, and improve the operational efficiency of organizations. However, there are few studies …

Creating The Capacity For Digital Government, Cheow Hoe Chan, Steven M. Miller

Asian Management Insights

This article explains how a well-thought-out data policy, supported by a tech stack and cloud infrastructure, an agile way of working, and coordinated whole-of-government leadership, are fundamental to successful government digital transformation efforts, as exemplified by the Singapore government’s digital journey. As part of explaining how to create the capacity for digital government, the main sections of this article cover:

• The origins of GovTech
• How thinking big, starting small and acting fast is a practical strategy for organisational learning
• The importance of horizontal platforms and other enablers of a horizontal approach
• Data architecture and policy
• “Shifting left” with internal technology …

Towards A Design Space For Storytelling On The Fashion Technology Runway, Sydney Pratte, Anthony Tang, Shannon Hoover, Maria Elena Hoover, Matt Laprarie, Catherine Larose, Lora Oehlberg

Research Collection School Of Computing and Information Systems

Fashion is driven by a narrative, i.e. a story or idea that the designer wants to convey to the audience. Fashion-tech now adds another dimension to this narrative through dynamically changing aspects of the garments. Many factors of presentation in a runway show affect how fashion-tech garments communicate a story to the audience. In this pictorial, we review a set of twenty-eight storytelling fashion-tech garments. We identify, catalogue, and categorize the factors designers used to convey stories to the audience from the runway. The design space consists of three levels: (1) the artifact-level, (2) the viewer-level, and (3) the context-level. …

Air Force Cadet To Career Field Matching Problem, Ian P. Macdonald

Theses and Dissertations

This research examines the Cadet to Air Force Specialty Code (AFSC) Matching Problem (CAMP). Currently, the matching problem occurs annually at the Air Force Personnel Center (AFPC) using an integer program and value focused thinking approach. This paper presents a novel method to match cadets with AFSCs using a generalized structure of the Hospitals Residents problem with special emphasis on lower quotas. This paper also examines the United States Army Matching problem and compares it to the techniques and constraints applied to solve the CAMP. The research culminates in the presentation of three algorithms created to solve the CAMP and …

Machine Learning Techniques For Stock Price Prediction And Graphic Signal Recognition, Junde Chen, Yuxin Wen, Y. A. Nanehkaran, M. D. Suzauddola, Weirong Chen, Defu Zhang

Engineering Faculty Articles and Research

Stock market analysis is extremely important for investors because knowing the future trend and grasping the changing characteristics of stock prices will decrease the risk of investing capital for profit. Thereupon, the prediction of stock prices and identifying the graphic signals of candlestick charts, which are two crucial tasks in stock price analysis, attract much attention from investors owing to the returns and risks that coexist in financial markets. To introduce a reliable approach for addressing these challenges, this paper proposes the modeling strategies based on machine learning (ML) techniques. A vector autoregression (VAR)-based rolling prediction model is proposed for …

A Review On Derivative Hedging Using Reinforcement Learning, Peng Liu

Research Collection Lee Kong Chian School Of Business

Hedging is a common trading activity to manage the risk of engaging in transactions that involve derivatives such as options. Perfect and timely hedging, however, is an impossible task in the real market that characterizes discrete-time transactions with costs. Recent years have witnessed reinforcement learning (RL) in formulating optimal hedging strategies. Specifically, different RL algorithms have been applied to learn the optimal offsetting position based on market conditions, offering an automatic risk management solution that proposes optimal hedging strategies while catering to both market dynamics and restrictions. In this article, the author provides a comprehensive review of the use of …

Automatic Identification Of Crash-Inducing Smart Contracts, Chao Ni, Cong Tian, Kaiwen Yang, David Lo, Jiachi Chen, Xiaohu Yang

Research Collection School Of Computing and Information Systems

Smart contract, a special software code running on and resided in the blockchain, enlarges the general application of blockchain and exchanges assets without dependence of external parties. With blockchain's characteristic of immutability, they cannot be modified once deployed. Thus, the contract and the records are persisted on the blockchain forever, including failed transactions that are caused by runtime errors and result in the waste of computation, storage, and fees. In this paper, we refer to smart contracts which will cause runtime errors as crash-inducing smart contracts. However, automatic identification of crash-inducing smart contracts is limited investigated in the literature. The …

Prudex-Compass: Towards Systematic Evaluation Of Reinforcement Learning In Financial Markets, Shuo Sun, Molei Qin, Xinrun Wang, Bo An

Research Collection School Of Computing and Information Systems

The financial markets, which involve more than $90 trillion market capitals, attract the attention of innumerable investors around the world. Recently, reinforcement learning in financial markets (FinRL) has emerged as a promising direction to train agents for making profitable investment decisions. However, the evaluation of most FinRL methods only focuses on profit-related measures and ignores many critical axes, which are far from satisfactory for financial practitioners to deploy these methods into real-world financial markets. Therefore, we introduce PRUDEX-Compass, which has 6 axes, i.e., Profitability, Risk-control, Universality, Diversity, rEliability, and eXplainability, with a total of 17 measures for a systematic evaluation. …

Garbage In ≠ Garbage Out: Exploring Gan Resilience To Image Training Set Degradations, Nicholas M. Crino

Theses and Dissertations

Generative Adversarial Networks (GANs) have received increasing attention in recent years due to their ability to capture complex, high-dimensional data distributions without the need for extensive labeling. Since their conception in 2014, a wide array of GAN variants have been proposed featuring alternative architectures, optimizers, and loss functions with the goal of improving performance and training stability. While this research has yielded GAN variants robust to training set shrinkage and corruption, our research focuses on quantifying the resilience of a GAN architecture to specific modes of image degradation. We conduct systematic experimentation to determine empirically the effects of 10 fundamental …

The Santa Clara, 2023-02-17, Santa Clara University

The Santa Clara

No abstract provided.

Bridging The Cultural Divide: A Single Case Study Exploring Connections Between Multi-Cultural Education, Identity, Self-Esteem And Leadership, Amy Britton

Journal of Multicultural Affairs

This qualitative single case study explores connections between multicultural education, identity development, self-esteem, and leadership. The study focuses on the lived experiences of a lifelong learner, educator, and leader in higher education with the pseudonym, Rachel. The interview with Rachel traced how she experiences diversity within her academic experiences as a learner and her professional experiences as an educator and leader.

The Emerging Scholars Issue: Insights On Teaching And Leading Through Reshaping Policy And Practice, Lakia M. Scott, Taylor D. Bunn

Journal of Multicultural Affairs

The Emerging Scholars program began at the 2019 Texas-NAME conference with five graduate students, four of which were enrolled in a doctoral program. Students participated in preconference workshops on establishing a research agenda, understanding academia and higher education institutions, and creating a network as an education researcher. Since its inception, the program has continued introducing students to collaborations and publication opportunities through Texas-NAME. This special issue provides doctoral students (some of whom have since graduated) with an opportunity to be single-authors in their scholar. Organized in three distinct sections, readers will be exposed to research and policy briefs and critical …

The Merchant And The Mathematician: Commerce And Accounting, Graziano Gentili, Luisa Simonutti, Daniele C. Struppa

Journal of Humanistic Mathematics

In this article we describe the invention of double-entry bookkeeping (or partita doppiaas it was called in Italian), as a fertile intersection between mathematics and early commerce. We focus our attention on this seemingly simple technique that requires only minimal mathematical expertise, but whose discovery is clearly the result of a mathematical way of thinking, in order to make a conceptual point about the role of mathematics as the humus from which disciplines as different as operations research, computer science, and data science have evolved.