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Extending The Horizon By Empowering Government Customer Service Officers With Acqar For Enhanced Citizen Service Delivery, Hui Shan Lee, Shankararaman, Venky, Eng Lieh Ouh Dec 2023

Extending The Horizon By Empowering Government Customer Service Officers With Acqar For Enhanced Citizen Service Delivery, Hui Shan Lee, Shankararaman, Venky, Eng Lieh Ouh

Research Collection School Of Computing and Information Systems

A previous study on the use of the Empath library in the prediction of Service Level Agreements (SLA) reveals the quality levels required for meaningful interaction between government customer service officers and citizens. On the other hand, past implementation of the Citizen Question-Answer system (CQAS), a type of Question-Answer model, suggests that such models if put in place can empower government customer service officers to reply faster and better with recommended answers. This study builds upon the research outcomes from both arenas of studies and introduces an innovative system design that allows the officers to incorporate the outputs from Empath …

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Class Participation: Using Technology To Enhance Efficiency And Fairness, Benjamin Gan, Eng Lieh Ouh Dec 2023

Class Participation: Using Technology To Enhance Efficiency And Fairness, Benjamin Gan, Eng Lieh Ouh

Research Collection School Of Computing and Information Systems

Class participation can be considered as contribution to discussion, attendance, presentations, unsolicited responses, questions, comments, etc. What counts may vary across individual teachers. The more students participate, the less memorization they do, and the more they engage in higher levels of thinking, including interpretation, analysis, and synthesis. However, only a handful of students in many classrooms participate regularly, a phenomenon dubbed as "consolidation of responsibility". This study provides a literature review of inclass participation, as well as pedagogies and technologies that enhance participation. Pedagogies such as active learning, group learning, project-based learning and flipped classroom. Technologies to automate attendance taking, …

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Sustainability Projects With A Community Partner: A Social Norm Nudging Effort, Benjamin Gan, Thomas Menkhoff, Eng Lieh Ouh Dec 2023

Sustainability Projects With A Community Partner: A Social Norm Nudging Effort, Benjamin Gan, Thomas Menkhoff, Eng Lieh Ouh

Research Collection School Of Computing and Information Systems

Singapore students from two inter-disciplinary courses worked with stakeholders of a local business association community partner on a series of sustainability topics to learn about climate change, its effects, and actions to mitigate them. They empathized with the association stakeholders, proposed a digital technology solution, tested their prototypes, and presented the final action plans. After the projects were completed, we found climate proficient (83%), motivated (83%), engaged (97%), and satisfied (70%) students; and two influencing predictors: interest/enjoyment and emotional engagement. The study results suggest that getting students interested and emotionally engaged in sustainability projects is an important first step towards …

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Mermaid: A Dataset And Framework For Multimodal Meme Semantic Understanding, Shaun Toh, Adriel Kuek, Wen Haw Chong, Roy Ka Wei Lee Dec 2023

Mermaid: A Dataset And Framework For Multimodal Meme Semantic Understanding, Shaun Toh, Adriel Kuek, Wen Haw Chong, Roy Ka Wei Lee

Research Collection School Of Computing and Information Systems

Memes are widely used to convey cultural and societal issues and have a significant impact on public opinion. However, little work has been done on understanding and explaining the semantics expressed in multimodal memes. To fill this research gap, we introduce MERMAID, a dataset consisting of 3,633 memes annotated with their entities and relations, and propose a novel MERF pipeline that extracts entities and their relationships in memes. Our framework combines state-of-the-art techniques from natural language processing and computer vision to extract text and image features and infer relationships between entities in memes. We evaluate the proposed framework on a …

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Vision Paper: Advancing Of Ai Explainability For The Use Of Chatgpt In Government Agencies: Proposal Of A 4-Step Framework, Hui Shan Lee, Shankararaman, Venky, Eng Lieh Ouh Dec 2023

Vision Paper: Advancing Of Ai Explainability For The Use Of Chatgpt In Government Agencies: Proposal Of A 4-Step Framework, Hui Shan Lee, Shankararaman, Venky, Eng Lieh Ouh

Research Collection School Of Computing and Information Systems

This paper explores ChatGPT’s potential in aiding government agencies, drawing from a case study based on a government agency in Singapore. While ChatGPT’s text generation abilities offer promise, it brings inherent challenges, including data opacity, potential misinformation, and occasional errors. These issues are especially critical in government decision-making.Public administration’s core values of transparency and accountability magnify these concerns. Ensuring AI alignment with these principles is imperative, given the potential repercussions on policy outcomes and citizen trust.AI explainability plays a central role in ChatGPT’s adoption within government agencies. To address these concerns, we propose strategies like prompt engineering, data governance, and …

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Do Contributing Files Provide Information About Oss Newcomers' Onboarding Barriers?, Felipe Fronchetti, David Shepherd, Igor Wiese, Christoph Treude, Marco Gerosa, Igor Steinmacher Dec 2023

Do Contributing Files Provide Information About Oss Newcomers' Onboarding Barriers?, Felipe Fronchetti, David Shepherd, Igor Wiese, Christoph Treude, Marco Gerosa, Igor Steinmacher

Research Collection School Of Computing and Information Systems

Effectively onboarding newcomers is essential for the success of open source projects. These projects often provide onboarding guidelines in their ‘CONTRIBUTING’ files (e.g., CONTRIBUTING.md on GitHub). These files explain, for example, how to find open tasks, implement solutions, and submit code for review. However, these files often do not follow a standard structure, can be too large, and miss barriers commonly found by newcomers. In this paper, we propose an automated approach to parse these CONTRIBUTING files and assess how they address onboarding barriers. We manually classified a sample of files according to a model of onboarding barriers from the …

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Evaluating Transfer Learning For Simplifying Github Readmes, Haoyu Gao, Christoph Treude, Mansooreh Zahedi Dec 2023

Evaluating Transfer Learning For Simplifying Github Readmes, Haoyu Gao, Christoph Treude, Mansooreh Zahedi

Research Collection School Of Computing and Information Systems

Software documentation captures detailed knowledge about a software product, e.g., code, technologies, and design. It plays an important role in the coordination of development teams and in conveying ideas to various stakeholders. However, software documentation can be hard to comprehend if it is written with jargon and complicated sentence structure. In this study, we explored the potential of text simplification techniques in the domain of software engineering to automatically simplify GitHub README files. We collected software-related pairs of GitHub README files consisting of 14,588 entries, aligned difficult sentences with their simplified counterparts, and trained a Transformer-based model to automatically simplify …

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Lessons From The Long Tail: Analysing Unsafe Dependency Updates Across Software Ecosystems, Supatsara Wattanakriengkrai, Raula Kula, Christoph Treude, Kenichi Matsumoto Dec 2023

Lessons From The Long Tail: Analysing Unsafe Dependency Updates Across Software Ecosystems, Supatsara Wattanakriengkrai, Raula Kula, Christoph Treude, Kenichi Matsumoto

Research Collection School Of Computing and Information Systems

A risk in adopting third-party dependencies into an application is their potential to serve as a doorway for malicious code to be injected (most often unknowingly). While many initiatives from both industry and research communities focus on the most critical dependencies (i.e., those most depended upon within the ecosystem), little is known about whether the rest of the ecosystem suffers the same fate. Our vision is to promote and establish safer practises throughout the ecosystem. To motivate our vision, in this paper, we present preliminary data based on three representative samples from a population of 88,416 pull requests (PRs) and …

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Offline Rl With Discrete Proxy Representations For Generalizability In Pomdps, Pengjie Gu, Xinyu Cai, Dong Xing, Xinrun Wang, Mengchen Zhao, Bo An Dec 2023

Offline Rl With Discrete Proxy Representations For Generalizability In Pomdps, Pengjie Gu, Xinyu Cai, Dong Xing, Xinrun Wang, Mengchen Zhao, Bo An

Research Collection School Of Computing and Information Systems

Offline Reinforcement Learning (RL) has demonstrated promising results in various applications by learning policies from previously collected datasets, reducing the need for online exploration and interactions. However, real-world scenarios usually involve partial observability, which brings crucial challenges of the deployment of offline RL methods: i) the policy trained on data with full observability is not robust against the masked observations during execution, and ii) the information of which parts of observations are masked is usually unknown during training. In order to address these challenges, we present Offline RL with DiscrEte pRoxy representations (ORDER), a probabilistic framework which leverages novel state …

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Kape: Knn-Based Performance Testing For Deep Code Search, Yuejun Guo, Qiang Hu, Xiaofei Xie, Cordy Maxime, Mike Papadakis, Yves Le Traon Dec 2023

Kape: Knn-Based Performance Testing For Deep Code Search, Yuejun Guo, Qiang Hu, Xiaofei Xie, Cordy Maxime, Mike Papadakis, Yves Le Traon

Research Collection School Of Computing and Information Systems

Code search is a common yet important activity of software developers. An efficient code search model can largely facilitate the development process and improve the programming quality. Given the superb performance of learning the contextual representations, deep learning models, especially pre-trained language models, have been widely explored for the code search task. However, studies mainly focus on proposing new architectures for ever-better performance on designed test sets but ignore the performance on unseen test data where only natural language queries are available. The same problem in other domains, e.g., CV and NLP, is usually solved by test input selection that …

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Prompting And Evaluating Large Language Models For Proactive Dialogues: Clarification, Target-Guided, And Non-Collaboration, Yang Deng, Lizi Liao, Liang Chen, Hongru Wang, Wenqiang Lei, Tat-Seng Chua Dec 2023

Prompting And Evaluating Large Language Models For Proactive Dialogues: Clarification, Target-Guided, And Non-Collaboration, Yang Deng, Lizi Liao, Liang Chen, Hongru Wang, Wenqiang Lei, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Conversational systems based on Large Language Models (LLMs), such as ChatGPT, show exceptional proficiency in context understanding and response generation. However, they still possess limitations, such as failing to ask clarifying questions to ambiguous queries or refuse users' unreasonable requests, both of which are considered as key aspects of a conversational agent's proactivity. This raises the question of whether LLM-based conversational systems are equipped to handle proactive dialogue problems. In this work, we conduct a comprehensive analysis of LLM-based conversational systems, specifically focusing on three key aspects of proactive dialogues: clarification, target-guided, and non-collaborative dialogues. To trigger the proactivity of …

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Attack Prompt Generation For Red Teaming And Defending Large Language Models, Boyi Deng, Wenjie Wang, Fuli Feng, Yang Deng, Qifan Wang, Xiangnan He Dec 2023

Attack Prompt Generation For Red Teaming And Defending Large Language Models, Boyi Deng, Wenjie Wang, Fuli Feng, Yang Deng, Qifan Wang, Xiangnan He

Research Collection School Of Computing and Information Systems

Large language models (LLMs) are susceptible to red teaming attacks, which can induce LLMs to generate harmful content. Previous research constructs attack prompts via manual or automatic methods, which have their own limitations on construction cost and quality. To address these issues, we propose an integrated approach that combines manual and automatic methods to economically generate high-quality attack prompts. Specifically, considering the impressive capabilities of newly emerged LLMs, we propose an attack framework to instruct LLMs to mimic human-generated prompts through in-context learning. Furthermore, we propose a defense framework that fine-tunes victim LLMs through iterative interactions with the attack framework …

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Depwignn: A Depth-Wise Graph Neural Network For Multi-Hop Spatial Reasoning In Text, Shuaiyi Li, Yang Deng, Wai Lam Dec 2023

Depwignn: A Depth-Wise Graph Neural Network For Multi-Hop Spatial Reasoning In Text, Shuaiyi Li, Yang Deng, Wai Lam

Research Collection School Of Computing and Information Systems

Spatial reasoning in text plays a crucial role in various real-world applications. Existing approaches for spatial reasoning typically infer spatial relations from pure text, which overlook the gap between natural language and symbolic structures. Graph neural networks (GNNs) have showcased exceptional proficiency in inducing and aggregating symbolic structures. However, classical GNNs face challenges in handling multi-hop spatial reasoning due to the over-smoothing issue, i.e., the performance decreases substantially as the number of graph layers increases. To cope with these challenges, we propose a novel Depth-Wise Graph Neural Network (DepWiGNN). Specifically, we design a novel node memory scheme and aggregate the …

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Unifying Text, Tables, And Images For Multimodal Question Answering, Haohao Luo, Ying Shen, Yang Deng Dec 2023

Unifying Text, Tables, And Images For Multimodal Question Answering, Haohao Luo, Ying Shen, Yang Deng

Research Collection School Of Computing and Information Systems

Multimodal question answering (MMQA), which aims to derive the answer from multiple knowledge modalities (e.g., text, tables, and images), has received increasing attention due to its board applications. Current approaches to MMQA often rely on single-modal or bi-modal QA models, which limits their ability to effectively integrate information across all modalities and leverage the power of pre-trained language models. To address these limitations, we propose a novel framework called UniMMQA, which unifies three different input modalities into a text-to-text format by employing position-enhanced table linearization and diversified image captioning techniques. Additionally, we enhance cross-modal reasoning by incorporating a multimodal rationale …

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Large Language Models As Source Planner For Personalized Knowledge-Grounded Dialogues, Hongru Wang, Minda Hu, Yang Deng, Rui Wang, Fei Mi, Weichao Wang, Yasheng Wang, Wai-Chung Kwan, Irwin King, Kam-Fai Wong Dec 2023

Large Language Models As Source Planner For Personalized Knowledge-Grounded Dialogues, Hongru Wang, Minda Hu, Yang Deng, Rui Wang, Fei Mi, Weichao Wang, Yasheng Wang, Wai-Chung Kwan, Irwin King, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Open-domain dialogue system usually requires different sources of knowledge to generate more informative and evidential responses. However, existing knowledge-grounded dialogue systems either focus on a single knowledge source or overlook the dependency between multiple sources of knowledge, which may result in generating inconsistent or even paradoxical responses. To incorporate multiple knowledge sources and dependencies between them, we propose SAFARI, a novel framework that leverages the exceptional capabilities of large language models (LLMs) in planning, understanding, and incorporating under both supervised and unsupervised settings. Specifically, SAFARI decouples the knowledge grounding into multiple sources and response generation, which allows easy extension to …

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Controlling Type Confounding In Ad Hoc Teamwork With Instance-Wise Teammate Feedback Rectification, Dong Xing, Pengjie Gu, Qian Zheng, Xinrun Wang, Shanqi Liu, Longtao Zheng, Bo An, Gang Pan Dec 2023

Controlling Type Confounding In Ad Hoc Teamwork With Instance-Wise Teammate Feedback Rectification, Dong Xing, Pengjie Gu, Qian Zheng, Xinrun Wang, Shanqi Liu, Longtao Zheng, Bo An, Gang Pan

Research Collection School Of Computing and Information Systems

Ad hoc teamwork requires an agent to cooperate with unknown teammates without prior coordination. Many works propose to abstract teammate instances into high-level representation of types and then pre-train the best response for each type. However, most of them do not consider the distribution of teammate instances within a type. This could expose the agent to the hidden risk of type confounding. In the worst case, the best response for an abstract teammate type could be the worst response for all specific instances of that type. This work addresses the issue from the lens of causal inference. We first theoretically …

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Ethical Considerations For Artificial Intelligence In Educational Assessments, Lim Ming Soon Tristan, Gottipati Swapna, Michelle L. F. Cheong Dec 2023

Ethical Considerations For Artificial Intelligence In Educational Assessments, Lim Ming Soon Tristan, Gottipati Swapna, Michelle L. F. Cheong

Research Collection School Of Computing and Information Systems

In the vital context of education, the application of artificial intelligence (AI) to assessments necessitates a nuanced examination of the boundaries between ethically permissible and impermissible practices. In this chapter, the authors applied a systematic literature mapping methodology to scour extant research, so as to holistically structure the landscape into explicit topical research clusters. Through topic modelling and network analyses, research mapped key ethical principles to different assessment phases in a triadic ontological framework. The chapter looks to provide researchers and practitioners the insights into the ethical challenges that exist across an end-to-end assessment pipeline.

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Deeparc: Modularizing Neural Networks For The Model Maintenance, Xiaoning Ren, Yun Lin, Yinxing Xue, Ruofan Liu, Jun Sun, Zhiyong Feng, Jinsong Dong Dec 2023

Deeparc: Modularizing Neural Networks For The Model Maintenance, Xiaoning Ren, Yun Lin, Yinxing Xue, Ruofan Liu, Jun Sun, Zhiyong Feng, Jinsong Dong

Research Collection School Of Computing and Information Systems

Neural networks are an emerging data-driven programming paradigm widely used in many areas. Unlike traditional software systems consisting of decomposable modules, a neural network is usually delivered as a monolithic package, raising challenges for some maintenance tasks such as model restructure and re-adaption. In this work, we propose DeepArc, a novel modularization method for neural networks, to reduce the cost of model maintenance tasks. Specifically, DeepArc decomposes a neural network into several consecutive modules, each of which encapsulates consecutive layers with similar semantics. The network modularization facilitates practical tasks such as refactoring the model to preserve existing features (e.g., model …

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Software Composition Analysis For Vulnerability Detection: An Empirical Study On Java Projects, Lida Zhao, Sen Chen, Zhengzi Xu, Lyuye Zhang, Jiahui Wu, Jun Sun, Yang Liu Dec 2023

Software Composition Analysis For Vulnerability Detection: An Empirical Study On Java Projects, Lida Zhao, Sen Chen, Zhengzi Xu, Lyuye Zhang, Jiahui Wu, Jun Sun, Yang Liu

Research Collection School Of Computing and Information Systems

Software composition analysis (SCA) tools are proposed to detect potential vulnerabilities introduced by open-source software (OSS) imported as third-party libraries (TPL). With the increasing complexity of software functionality, SCA tools may encounter various scenarios during the dependency resolution process, such as diverse formats of artifacts, diverse dependency imports, and diverse dependency specifications. However, there still lacks a comprehensive evaluation of SCA tools for Java that takes into account the above scenarios. This could lead to a confined interpretation of comparisons, improper use of tools, and hinder further improvements of the tools. To fill this gap, we proposed an Evaluation Model …

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An "Off-The-Shelf" Cd2 Universal Car-T Therapy For T-Cell Malignancies, Jingyu Xiang, Jessica M Devenport, Alun J Carter, Karl W Staser, Miriam Y Kim, Julie O' Neal, Julie K Ritchey, Michael P Rettig, Feng Gao, Garrett Rettig, Rolf Turk, Byung Ha Lee, Matthew L Cooper, John F Dipersio Dec 2023

An "Off-The-Shelf" Cd2 Universal Car-T Therapy For T-Cell Malignancies, Jingyu Xiang, Jessica M Devenport, Alun J Carter, Karl W Staser, Miriam Y Kim, Julie O' Neal, Julie K Ritchey, Michael P Rettig, Feng Gao, Garrett Rettig, Rolf Turk, Byung Ha Lee, Matthew L Cooper, John F Dipersio

2020-Current year OA Pubs

T-cell malignancies are associated with frequent relapse and high morbidity, which is partly due to the lack of effective or targeted treatment options. To broaden the use of CAR-T cells in pan T-cell malignancies, we developed an allogeneic "universal" CD2-targeting CAR-T cell (UCART2), in which the CD2 antigen is deleted to prevent fratricide, and the T-cell receptor is removed to prevent GvHD. UCART2 demonstrated efficacy against T-ALL and CTCL and prolonged the survival of tumor-engrafted NSG mice in vivo. To evaluate the impact of CD2 on CAR-T function, we generated CD19 CAR-T cells (UCART19) with or without CD2 deletion, single-cell …

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Single-Cell Analysis Of Chromatin Accessibility In The Adult Mouse Brain, Songpeng Zu, Yang Eric Li, Et Al. Dec 2023

Single-Cell Analysis Of Chromatin Accessibility In The Adult Mouse Brain, Songpeng Zu, Yang Eric Li, Et Al.

2020-Current year OA Pubs

Recent advances in single-cell technologies have led to the discovery of thousands of brain cell types; however, our understanding of the gene regulatory programs in these cell types is far from complete

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Computational Quantum Chemistry Studies Of The Stabilities Of Radical Adducts Formed During The Oxidation Of Melatonin Derivatives, James Horne Dec 2023

Computational Quantum Chemistry Studies Of The Stabilities Of Radical Adducts Formed During The Oxidation Of Melatonin Derivatives, James Horne

Electronic Theses and Dissertations

Melatonin is a natural antioxidant that has been investigated for properties as a potential spin trap to identify short-lived free radicals. Computational quantum chemistry studies have been performed for the oxidation of melatonin to N1-acetyl-N2-formyl-5-methoxykynuramine. This research focused on modification of melatonin into derivatives and analyzing the change in total molecular energy from melatonin to its oxidation product, as well as the corresponding derivatives. Each of the molecular geometries were optimized at the DFT/B3LYP/6-31G(d), DFT/B3LYP/cc-pVXZ (X = D, T), HF/6-31G(d), HF/cc-PVXZ (X = D, T), MP2/6-31G(d), and MP2/cc-PVXZ (X = D, T) levels of theory. …

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Synthesis Of Bacterial Glycerophospholipids For Biomembrane Model Studies: A Means To Advanced Biofuels, Felix Adulley Dec 2023

Synthesis Of Bacterial Glycerophospholipids For Biomembrane Model Studies: A Means To Advanced Biofuels, Felix Adulley

Electronic Theses and Dissertations

To reduce reliance on fossil fuels, sustainable biofuels are being pursued, especially advanced biofuels like 1-butanol that have higher energy content and greater compatibility with existing infrastructure than ethanol. A persistent challenge is the yield-limiting toxicity of biofuels and process solvents, such as tetrahydrofuran, to the microbes that ferment biomass into biofuel. The cell membrane is a focal point of toxicity, and understanding how it interacts with fuels and solvents is key to improving yield. Phospholipid bilayers are the core of biomembranes, and model biomembranes of defined composition provide the ideal platform for biophysical studies. To this end, glycerophospholipids characteristic …

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The Impact Of Aces On College Students And Their Major Choice, Britten Harrison Dec 2023

The Impact Of Aces On College Students And Their Major Choice, Britten Harrison

Electronic Theses and Dissertations

Research examining Adverse Childhood Experiences (ACEs) has mostly focused on the long-term effects of these experiences on adult mental and physical health. Less attention has been focused on the impact of ACEs on college student satisfaction, dropout rates, and major choice. The overarching questions for the current study sought to further research by (1) assessing if there is a relationship between the ACE scores of college students and their mental and physical health, (2) if ACEs play a role in student satisfaction or desire to drop out, and (3) determining if there is a connection between students with high ACE …

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Proteomics And Machine Learning For Pulmonary Embolism Risk With Protein Markers, Yaa Amankwah Awuah Dec 2023

Proteomics And Machine Learning For Pulmonary Embolism Risk With Protein Markers, Yaa Amankwah Awuah

Electronic Theses and Dissertations

This thesis investigates protein markers linked to pulmonary embolism risk using proteomics and statistical methods, employing unsupervised and supervised machine learning techniques. The research analyzes existing datasets, identifies significant features, and observes gender differences through MANOVA. Principal Component Analysis reduces variables from 378 to 59, and Random Forest achieves 70% accuracy. These findings contribute to our understanding of pulmonary embolism and may lead to diagnostic biomarkers. MANOVA reveals significant gender differences, and applying proteomics holds promise for clinical practice and research.

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Women Leaders In The Oil And Gas Industry: How They Lead With Authenticity And Deal With Underrepresentation And Gender Bias In The Macro, Meso, And Micro Levels Of Society, Michelle Ann Ballard Dec 2023

Women Leaders In The Oil And Gas Industry: How They Lead With Authenticity And Deal With Underrepresentation And Gender Bias In The Macro, Meso, And Micro Levels Of Society, Michelle Ann Ballard

Electronic Theses and Dissertations

This study was conducted between March 2023 and April 2023 and measured if women leaders in the oil and gas industry could lead authentically while dealing with gender biases. Women executives encounter macro-level challenges such as recruitment, retention, maintaining a career in oil and gas, feeling a sense of belonging, and gender prejudices. Women have faced organizational obstacles and challenges such as modifying their personalities, being underrepresented in their organization, unequal compensation, discriminatory stereotypes, and scarcity of resources. However, women in the industry also confront nonorganizational deterrents such as work-life conflict, harassment, and being regarded as poor advocates for their …

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Evaluation Of Cardiovascular Risk Compliance For Primary Prevention: A Quality Improvement Project, Rachelle S. Campbell Dec 2023

Evaluation Of Cardiovascular Risk Compliance For Primary Prevention: A Quality Improvement Project, Rachelle S. Campbell

Electronic Theses and Dissertations

The American College of Cardiology and American Heart Association clinical practice guidelines recommend a cardiovascular risk assessment every 4–6 years in ages 40–75 to prevent cardiovascular disease. This DNP project aimed to improve awareness, compliance with cardiac risk assessments, and stratification of high and low-risk patients. This quality improvement project utilized the Johns Hopkins nursing evidence-based model to create the inquiry question and foundational plan for implementing the clinical practice guideline, calculating the cardiac risk score, and creating a clinical workflow process. A retrospective chart audit was guided by the Plan-Do-Study-Act (PDSA) method to collect pre- and postintervention data on …

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Educational Robotics And Computational Thinking In Elementary School Students, Sonia D. Jordan Dec 2023

Educational Robotics And Computational Thinking In Elementary School Students, Sonia D. Jordan

Electronic Theses and Dissertations

This study examined the role of educational robotics in fostering computational thinking in elementary settings, both in classrooms and extracurricular programs. Among growing concerns over K–12 students’ computational thinking deficits, the research evaluated the impact of Lego EV3 and VEX IQ platforms. Data was sourced from lesson plans, student work surveys, and teacher interviews and then subjected to thematic analysis using a qualitative approach. The participants were Texas educators engaged in robotics instruction, even though specific robotics statistics are absent in the Texas Education Agency. Instructional strategies varied from hands-on experiences to translating mathematical concepts into robotic actions. A key …

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The Relationship Between Job Challenge And Leadership Aspiration From The Perspective Of The Entry Level Nurse Leader, Rose M. Johnson Dec 2023

The Relationship Between Job Challenge And Leadership Aspiration From The Perspective Of The Entry Level Nurse Leader, Rose M. Johnson

Electronic Theses and Dissertations

While the nurse leader plays an important and integral role in the success of healthcare organizations, the nurse leader pipeline has experienced a troubling and downward trajectory for several years. The lack of leadership aspiration among direct care nurses has been noted as one of several reasons for the downward trajectory. In addition, researchers identified the lack of understanding of perspectives of the various nurse leader roles and the lack of understanding of the effectiveness of experiential learning in the nurse leader role as potential contributions to the problem. The purpose of this quantitative, correlational research was to examine the …

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Substantial Blue Carbon Sequestration In The World’S Largest Seagrass Meadow, Chuancheng Fu, Sofia Frappi, Michelle N. Havlik, Wells Howe, S. David Harris, Elisa Laiolo, Austin J. Gallagher, Pere Masqué, Carlos M. Duarte Dec 2023

Substantial Blue Carbon Sequestration In The World’S Largest Seagrass Meadow, Chuancheng Fu, Sofia Frappi, Michelle N. Havlik, Wells Howe, S. David Harris, Elisa Laiolo, Austin J. Gallagher, Pere Masqué, Carlos M. Duarte

Research outputs 2022 to 2026

Seagrass meadows are important sinks for organic carbon and provide co-benefits. However, data on the organic carbon stock in seagrass sediments are scarce for many regions, particularly The Bahamas, which accounts for up to 40.7% of the documented global seagrass area, limiting formulation of blue carbon strategies. Here, we sampled 10 seagrass meadows across an extensive island chain in The Bahamas. We estimate that Bahamas seagrass meadows store 0.42–0.59 Pg organic carbon in the top-meter sediments with an accumulation rate of 2.1–2.9 Tg annually, representing a substantial global blue carbon hotspot. Autochthonous organic carbon in sediments decreased from ~1980 onwards, …

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