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Articles 50911 - 50940 of 294431

Full-Text Articles in Physical Sciences and Mathematics

Assessment Design For Digital Education: An Analytics-Based Authentic Assessment Approach, Lim Ming Soon Tristan, Gottipati Swapna, Michelle L. F. Cheong, Christopher Pang, Jun Wei Ng Dec 2022

Assessment Design For Digital Education: An Analytics-Based Authentic Assessment Approach, Lim Ming Soon Tristan, Gottipati Swapna, Michelle L. F. Cheong, Christopher Pang, Jun Wei Ng

Research Collection School Of Computing and Information Systems

This study looks to identify an assessment design construct that overcomes known issues in authentic assessment design practices in digital education. These include lack of "freedom-of-choice", lack of focus on multimodal nature of the digital process, and shortage of effective feedbacks. This study proposes an authentic assessment, that combines gamification (G) with heutagogy (H) and multimodality (M) of assessments, building upon learning analytics (A), known as GHMA. Proposed assessment design is a skills-oriented game-based assessment approach. Learners can determine their own goals and create individualized multimodal artefacts; receive cognitive challenge through cognitively complex tasks structured in gamified non-linear learning paths; …

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Learning Generalizable Models For Vehicle Routing Problems Via Knowledge Distillation, Jieyi Bi, Yining Ma, Jiahai Wang, Zhiguang Cao, Jinbiao Chen, Yuan Sun, Yeow Meng Chee Dec 2022

Learning Generalizable Models For Vehicle Routing Problems Via Knowledge Distillation, Jieyi Bi, Yining Ma, Jiahai Wang, Zhiguang Cao, Jinbiao Chen, Yuan Sun, Yeow Meng Chee

Research Collection School Of Computing and Information Systems

Recent neural methods for vehicle routing problems always train and test the deep models on the same instance distribution (i.e., uniform). To tackle the consequent cross-distribution generalization concerns, we bring the knowledge distillation to this field and propose an Adaptive Multi-Distribution Knowledge Distillation (AMDKD) scheme for learning more generalizable deep models. Particularly, our AMDKD leverages various knowledge from multiple teachers trained on exemplar distributions to yield a light-weight yet generalist student model. Meanwhile, we equip AMDKD with an adaptive strategy that allows the student to concentrate on difficult distributions, so as to absorb hard-to-master knowledge more effectively. Extensive experimental results …

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Learning Dynamic Multimodal Implicit And Explicit Networks For Multiple Financial Tasks, Meng Kiat Gary Ang, Ee-Peng Lim Dec 2022

Learning Dynamic Multimodal Implicit And Explicit Networks For Multiple Financial Tasks, Meng Kiat Gary Ang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Many financial f orecasting d eep l earning w orks focus on the single task of predicting stock returns for trading with unimodal numerical inputs. Investment and risk management however involves multiple financial t asks - f orecasts o f expected returns, risks and correlations of multiple stocks in portfolios, as well as important events affecting different stocks - to support decision making. Moreover, stock returns are influenced by large volumes of non-stationary time-series information from a variety of modalities and the propagation of such information across inter-company relationship networks. Such networks could be explicit - observed co-occurrences in online …

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Opportunities And Challenges In Code Search Tools, Chao Liu, Xin Xia, David Lo, Cuiying Gao, Xiaohu Yang, John Grundy Dec 2022

Opportunities And Challenges In Code Search Tools, Chao Liu, Xin Xia, David Lo, Cuiying Gao, Xiaohu Yang, John Grundy

Research Collection School Of Computing and Information Systems

Code search is a core software engineering task. Effective code search tools can help developers substantially improve their software development efficiency and effectiveness. In recent years, many code search studies have leveraged different techniques, such as deep learning and information retrieval approaches, to retrieve expected code from a large-scale codebase. However, there is a lack of a comprehensive comparative summary of existing code search approaches. To understand the research trends in existing code search studies, we systematically reviewed 81 relevant studies. We investigated the publication trends of code search studies, analyzed key components, such as codebase, query, and modeling technique …

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A Logistic Regression And Linear Programming Approach For Multi-Skill Staffing Optimization In Call Centers, Thuy Anh Ta, Tien Mai, Fabian Bastin, Pierre L'Ecuyer Dec 2022

A Logistic Regression And Linear Programming Approach For Multi-Skill Staffing Optimization In Call Centers, Thuy Anh Ta, Tien Mai, Fabian Bastin, Pierre L'Ecuyer

Research Collection School Of Computing and Information Systems

We study a staffing optimization problem in multi-skill call centers. The objective is to minimize the total cost of agents under some quality of service (QoS) constraints. The key challenge lies in the fact that the QoS functions have no closed-form and need to be approximated by simulation. In this paper we propose a new way to approximate the QoS functions by logistic functions and design a new algorithm that combines logistic regression, cut generations and logistic-based local search to efficiently find good staffing solutions. We report computational results using examples up to 65 call types and 89 agent groups …

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Scalable Distributional Robustness In A Class Of Non Convex Optimization With Guarantees, Avinandan Bose, Arunesh Sinha, Tien Mai Dec 2022

Scalable Distributional Robustness In A Class Of Non Convex Optimization With Guarantees, Avinandan Bose, Arunesh Sinha, Tien Mai

Research Collection School Of Computing and Information Systems

Distributionally robust optimization (DRO) has shown lot of promise in providing robustness in learning as well as sample based optimization problems. We endeavor to provide DRO solutions for a class of sum of fractionals, non-convex optimization which is used for decision making in prominent areas such as facility location and security games. In contrast to previous work, we find it more tractable to optimize the equivalent variance regularized form of DRO rather than the minimax form. We transform the variance regularized form to a mixed-integer second order cone program (MISOCP), which, while guaranteeing near global optimality, does not scale enough …

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R2f: A General Retrieval, Reading And Fusion Framework For Document-Level Natural Language Inference, Hao Wang, Yixin Cao, Yangguang Li, Zhen Huang, Kun Wang, Jing Shao Dec 2022

R2f: A General Retrieval, Reading And Fusion Framework For Document-Level Natural Language Inference, Hao Wang, Yixin Cao, Yangguang Li, Zhen Huang, Kun Wang, Jing Shao

Research Collection School Of Computing and Information Systems

Document-level natural language inference (DocNLI) is a new challenging task in natural language processing, aiming at judging the entailment relationship between a pair of hypothesis and premise documents. Current datasets and baselines largely follow sentence-level settings, but fail to address the issues raised by longer documents. In this paper, we establish a general solution, named Retrieval, Reading and Fusion (R2F) framework, and a new setting, by analyzing the main challenges of DocNLI: interpretability, long-range dependency, and cross-sentence inference. The basic idea of the framework is to simplify document-level task into a set of sentence-level tasks, and improve both performance and …

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Mitigating Popularity Bias In Recommendation With Unbalanced Interactions: A Gradient Perspective, Weijieying Ren, Lei Wang, Kunpeng Liu, Ruocheng Guo, Ee-Peng Lim, Yanjie Fu Dec 2022

Mitigating Popularity Bias In Recommendation With Unbalanced Interactions: A Gradient Perspective, Weijieying Ren, Lei Wang, Kunpeng Liu, Ruocheng Guo, Ee-Peng Lim, Yanjie Fu

Research Collection School Of Computing and Information Systems

Recommender systems learn from historical user-item interactions to identify preferred items for target users. These observed interactions are usually unbalanced following a long-tailed distribution. Such long-tailed data lead to popularity bias to recommend popular but not personalized items to users. We present a gradient perspective to understand two negative impacts of popularity bias in recommendation model optimization: (i) the gradient direction of popular item embeddings is closer to that of positive interactions, and (ii) the magnitude of positive gradient for popular items are much greater than that of unpopular items. To address these issues, we propose a simple yet efficient …

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Dronlomaly: Runtime Detection Of Anomalous Drone Behaviors Via Log Analysis And Deep Learning, Lwin Khin Shar, Wei Minn, Nguyen Binh Duong Ta, Jianli Fan, Lingxiao Jiang, Daniel Wai Kiat Lim Dec 2022

Dronlomaly: Runtime Detection Of Anomalous Drone Behaviors Via Log Analysis And Deep Learning, Lwin Khin Shar, Wei Minn, Nguyen Binh Duong Ta, Jianli Fan, Lingxiao Jiang, Daniel Wai Kiat Lim

Research Collection School Of Computing and Information Systems

Drones are increasingly popular and getting used in a variety of missions such as area surveillance, pipeline inspection, cinematography, etc. While the drone is conducting a mission, anomalies such as sensor fault, actuator fault, configuration errors, bugs in controller program, remote cyber- attack, etc., may affect the drone’s physical stability and cause serious safety violations such as crashing into the public. During a flight mission, drones typically log flight status and state units such as GPS coordinates, actuator outputs, accelerator readings, gyroscopic readings, etc. These log data may reflect the above-mentioned anomalies. In this paper, we propose a novel, deep …

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Differentiated Security Architecture For Secure And Efficient Infotainment Data Communication In Iov Networks, Jiani Fan, Lwin Khin Shar, Jiale Guo, Wenzhuo Yang, Dusit Niyato, Kwok-Yan Lam Dec 2022

Differentiated Security Architecture For Secure And Efficient Infotainment Data Communication In Iov Networks, Jiani Fan, Lwin Khin Shar, Jiale Guo, Wenzhuo Yang, Dusit Niyato, Kwok-Yan Lam

Research Collection School Of Computing and Information Systems

This paper aims to provide differentiated security protection for infotainment data commu- nication in Internet-of-Vehicle (IoV) networks. The IoV is a network of vehicles that uses various sensors, software, built-in hardware, and communication technologies to enable information exchange between pedestrians, cars, and urban infrastructure. Negligence on the security of infotainment data commu- nication in IoV networks can unintentionally open an easy access point for social engineering attacks. The attacker can spread false information about traffic conditions, mislead drivers in their directions, and interfere with traffic management. Such attacks can also cause distractions to the driver, which has a potential implication …

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Interventional Training For Out-Of-Distribution Natural Language Understanding, Sicheng Yu, Jing Jiang, Hao Zhang, Yulei Niu, Qianru Sun, Lidong Bing Dec 2022

Interventional Training For Out-Of-Distribution Natural Language Understanding, Sicheng Yu, Jing Jiang, Hao Zhang, Yulei Niu, Qianru Sun, Lidong Bing

Research Collection School Of Computing and Information Systems

Out-of-distribution (OOD) settings are used to measure a model’s performance when the distribution of the test data is different from that of the training data. NLU models are known to suffer in OOD settings (Utama et al., 2020b). We study this issue from the perspective of causality, which sees confounding bias as the reason for models to learn spurious correlations. While a common solution is to perform intervention, existing methods handle only known and single confounder, but in many NLU tasks the confounders can be both unknown and multifactorial. In this paper, we propose a novel interventional training method called …

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Cold Calls To Enhance Class Participation And Student Engagement, Manoj Thulasidas, Aldy Gunawan Dec 2022

Cold Calls To Enhance Class Participation And Student Engagement, Manoj Thulasidas, Aldy Gunawan

Research Collection School Of Computing and Information Systems

The question whether cold calls increase student engagement in the classroom has not been conclusively answered in the literature. This study describes the automated system to implement unbiased, randomized cold calling by posing a question, allowing all students to think first and then calling on a particular student to respond. Since we already have a measure of the level of student engagement as the self-reported classparticipation entries from the students, its correlation to cold calling is also further studied. The results show that there is a statistically significant increase in the class participation reported, and therefore in student engagement, in …

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Vr Computing Lab: An Immersive Classroom For Computing Learning, Shawn Pang, Kyong Jin Shim, Yi Meng Lau, Swapna Gottipati Dec 2022

Vr Computing Lab: An Immersive Classroom For Computing Learning, Shawn Pang, Kyong Jin Shim, Yi Meng Lau, Swapna Gottipati

Research Collection School Of Computing and Information Systems

In recent years, virtual reality (VR) is gaining popularity amongst educators and learners. If a picture is worth a thousand words, a VR session is worth a trillion words. VR technology completely immerses users with an experience that transports them into a simulated world. Universities across the United States, United Kingdom, and other countries have already started using VR for higher education in areas such as medicine, business, architecture, vocational training, social work, virtual field trips, virtual campuses, helping students with special needs, and many more. In this paper, we propose a novel VR platform learning framework which maps elements …

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Gamified Online Industry Learning Platform For Teaching Of Foundational Computing Skills, Yi Meng Lau, Rafael Jose Barros Barrios, Gottipati Swapna, Kyong Jin Shim Dec 2022

Gamified Online Industry Learning Platform For Teaching Of Foundational Computing Skills, Yi Meng Lau, Rafael Jose Barros Barrios, Gottipati Swapna, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

Online industry learning platforms are widely used by organizations for employee training and upskilling. Courses or lessons offered by these platforms can be generic or specific to an enterprise application. The increased demand of new hires to learn these platforms or who are already certified in some of these courses has led universities to look at the opportunities for integrating online industry learning platforms into their curricula. Universities hope to use these platforms to aid students in their learning of concepts and theories. At the same time, these platforms can equip students with industryrecognized certifications or digital badges. This paper …

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Innovative And Effective Spreadsheet Tool For Learning Sentiment Analysis And Prediction, Michelle L. F. Cheong, Jean Yun-Chen Chen Dec 2022

Innovative And Effective Spreadsheet Tool For Learning Sentiment Analysis And Prediction, Michelle L. F. Cheong, Jean Yun-Chen Chen

Research Collection School Of Computing and Information Systems

In this paper, we describe a spreadsheet tool which was developed and implemented to teach sentiment analysis and prediction to university students who are less technically inclined. We adopted the “Use-Modify-Create” cycle to ensure that the students learn the computing concepts and practices in a progressive and active learning manner, and we evaluated the effectiveness of the tool using the Learning Object Evaluation Scale for Students (LOES-S). The evaluation shows that our tool performed well in terms of quality, learn and engagement constructs, and our paired T-test shows strong evidence that, on average, the course module with the tool does …

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S-Prompts Learning With Pre-Trained Transformers: An Occam's Razor For Domain Incremental Learning, Yabin Wang, Zhiwu Huang, Xiaopeng. Hong Dec 2022

S-Prompts Learning With Pre-Trained Transformers: An Occam's Razor For Domain Incremental Learning, Yabin Wang, Zhiwu Huang, Xiaopeng. Hong

Research Collection School Of Computing and Information Systems

State-of-the-art deep neural networks are still struggling to address the catastrophic forgetting problem in continual learning. In this paper, we propose one simple paradigm (named as S-Prompting) and two concrete approaches to highly reduce the forgetting degree in one of the most typical continual learning scenarios, i.e., domain increment learning (DIL). The key idea of the paradigm is to learn prompts independently across domains with pre-trained transformers, avoiding the use of exemplars that commonly appear in conventional methods. This results in a win-win game where the prompting can achieve the best for each domain. The independent prompting across domains only …

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Dataset For Electronic And Optical Properties Of Y2o2s And Er Dopped Y2o2s Calculated Using Density Functional Theory And Simulated X-Ray Near Edge Spectra, Nicholas Dimakis, Eric Baldemar Rodriguez Jr., Kofi Nketia Ackaah-Gyasi, Madhab Pokhrel Dec 2022

Dataset For Electronic And Optical Properties Of Y2o2s And Er Dopped Y2o2s Calculated Using Density Functional Theory And Simulated X-Ray Near Edge Spectra, Nicholas Dimakis, Eric Baldemar Rodriguez Jr., Kofi Nketia Ackaah-Gyasi, Madhab Pokhrel

Physics & Astronomy Faculty Publications

The computational data presented in this paper refer to the research article “Optical properties and simulated x-ray near edge spectra for Y2O2S and Er doped Y2O2S”. We present the data used to calculate the structural, electronic, and optical properties of the Y2O2S and its Er+3 doped counterparts at various concentrations using density functional theory (DFT) and simulated X-ray near edge (XANES) spectra. We report electronic information from DFT and DFT+U generated from the Vienna Ab initio Simulation Package (VASP) using PAW pseudopotentials. We also report VASP calculated optical properties for the host Y2O2S using the independent particle approximation (IPA), the …

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Coresets For Vertical Federated Learning: Regularized Linear Regression And K-Means Clustering, Lingxiao Huang, Zhize Li, Jialin Sun, Haoyu Zhao Dec 2022

Coresets For Vertical Federated Learning: Regularized Linear Regression And K-Means Clustering, Lingxiao Huang, Zhize Li, Jialin Sun, Haoyu Zhao

Research Collection School Of Computing and Information Systems

Vertical federated learning (VFL), where data features are stored in multiple parties distributively, is an important area in machine learning. However, the communication complexity for VFL is typically very high. In this paper, we propose a unified framework by constructing coresets in a distributed fashion for communication-efficient VFL. We study two important learning tasks in the VFL setting: regularized linear regression and $k$-means clustering, and apply our coreset framework to both problems. We theoretically show that using coresets can drastically alleviate the communication complexity, while nearly maintain the solution quality. Numerical experiments are conducted to corroborate our theoretical findings.

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Beer: Fast O(1/T) Rate For Decentralized Nonconvex Optimization With Communication Compression, Haoyu Zhao, Boyue Li, Zhize Li, Peter Richtarik, Yuejie Chi Dec 2022

Beer: Fast O(1/T) Rate For Decentralized Nonconvex Optimization With Communication Compression, Haoyu Zhao, Boyue Li, Zhize Li, Peter Richtarik, Yuejie Chi

Research Collection School Of Computing and Information Systems

Communication efficiency has been widely recognized as the bottleneck for large-scale decentralized machine learning applications in multi-agent or federated environments. To tackle the communication bottleneck, there have been many efforts to design communication-compressed algorithms for decentralized nonconvex optimization, where the clients are only allowed to communicate a small amount of quantized information (aka bits) with their neighbors over a predefined graph topology. Despite significant efforts, the state-of-the-art algorithm in the nonconvex setting still suffers from a slower rate of convergence $O((G/T)^{2/3})$ compared with their uncompressed counterpart, where $G$ measures the data heterogeneity across different clients, and $T$ is the number …

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Soteriafl: A Unified Framework For Private Federated Learning With Communication Compression, Zhize Li, Haoyu Zhao, Boyue Li, Yuejie Chi Dec 2022

Soteriafl: A Unified Framework For Private Federated Learning With Communication Compression, Zhize Li, Haoyu Zhao, Boyue Li, Yuejie Chi

Research Collection School Of Computing and Information Systems

To enable large-scale machine learning in bandwidth-hungry environments such as wireless networks, significant progress has been made recently in designing communication-efficient federated learning algorithms with the aid of communication compression. On the other end, privacy-preserving, especially at the client level, is another important desideratum that has not been addressed simultaneously in the presence of advanced communication compression techniques yet. In this paper, we propose a unified framework that enhances the communication efficiency of private federated learning with communication compression. Exploiting both general compression operators and local differential privacy, we first examine a simple algorithm that applies compression directly to differentially-private …

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Conreader: Exploring Implicit Relations In Contracts For Contract Clause Extraction, Weiwen Xu, Yang Deng, Wenqiang Lei, Wenlong Zhao, Tat-Seng Chua, Wai Lam Dec 2022

Conreader: Exploring Implicit Relations In Contracts For Contract Clause Extraction, Weiwen Xu, Yang Deng, Wenqiang Lei, Wenlong Zhao, Tat-Seng Chua, Wai Lam

Research Collection School Of Computing and Information Systems

We study automatic Contract Clause Extraction (CCE) by modeling implicit relations in legal contracts. Existing CCE methods mostly treat contracts as plain text, creating a substantial barrier to understanding contracts of high complexity. In this work, we first comprehensively analyze the complexity issues of contracts and distill out three implicit relations commonly found in contracts, namely, 1) Long-range Context Relation that captures the correlations of distant clauses; 2) Term-Definition Relation that captures the relation between important terms with their corresponding definitions; and 3) Similar Clause Relation that captures the similarities between clauses of the same type. Then we propose a …

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Pacific: Towards Proactive Conversational Question Answering Over Tabular And Textual Data In Finance, Yang Deng, Wenqiang Lei, Wenxuan Zhang, Wai Lam, Tat-Seng Chua Dec 2022

Pacific: Towards Proactive Conversational Question Answering Over Tabular And Textual Data In Finance, Yang Deng, Wenqiang Lei, Wenxuan Zhang, Wai Lam, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

To facilitate conversational question answering (CQA) over hybrid contexts in finance, we present a new dataset, named PACIFIC. Compared with existing CQA datasets, PACIFIC exhibits three key features: (i) proactivity, (ii) numerical reasoning, and (iii) hybrid context of tables and text. A new task is defined accordingly to study Proactive Conversational Question Answering (PCQA), which combines clarification question generation and CQA. In addition, we propose a novel method, namely UniPCQA, to adapt a hybrid format of input and output content in PCQA into the Seq2Seq problem, including the reformulation of the numerical reasoning process as code generation. UniPCQA performs multi-task …

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Distilled Siamese Networks For Visual Tracking, Jianbing Shen, Yuanpei Liu, Xingping Dong, Xiankai Lu, Fahad Shahbaz Khan, Steven Hoi Dec 2022

Distilled Siamese Networks For Visual Tracking, Jianbing Shen, Yuanpei Liu, Xingping Dong, Xiankai Lu, Fahad Shahbaz Khan, Steven Hoi

Research Collection School Of Computing and Information Systems

In recent years, Siamese network based trackers have significantly advanced the state-of-the-art in real-time tracking. Despite their success, Siamese trackers tend to suffer from high memory costs, which restrict their applicability to mobile devices with tight memory budgets. To address this issue, we propose a distilled Siamese tracking framework to learn small, fast and accurate trackers (students), which capture critical knowledge from large Siamese trackers (teachers) by a teacher-students knowledge distillation model. This model is intuitively inspired by the one teacher versus multiple students learning method typically employed in schools. In particular, our model contains a single teacher-student distillation module …

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On The Merge Of K-Nn Graph, Wan-Lei Zhao, Hui Wang, Peng-Cheng Lin, Chong-Wah Ngo Dec 2022

On The Merge Of K-Nn Graph, Wan-Lei Zhao, Hui Wang, Peng-Cheng Lin, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

k-nearest neighbor graph is a fundamental data structure in many disciplines such as information retrieval, data-mining, pattern recognition, and machine learning, etc. In the literature, considerable research has been focusing on how to efficiently build an approximate k-nearest neighbor graph (k-NN graph) for a fixed dataset. Unfortunately, a closely related issue of how to merge two existing k-NN graphs has been overlooked. In this paper, we address the issue of k-NN graph merging in two different scenarios. In the first scenario, a symmetric merge algorithm is proposed to combine two approximate k-NN graphs. The algorithm facilitates large-scale processing by the …

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Cyber Resilience Analytics For Cyber-Physical Systems, Md Ariful Haque Dec 2022

Cyber Resilience Analytics For Cyber-Physical Systems, Md Ariful Haque

Electrical & Computer Engineering Theses & Dissertations

Cyber-physical systems (CPSs) are complex systems that evolve from the integrations of components dealing with physical processes and real-time computations, along with networking. CPSs often incorporate approaches merging from different scientific fields such as embedded systems, control systems, operational technology, information technology systems (ITS), and cybernetics. Today critical infrastructures (CIs) (e.g., energy systems, electric grids, etc.) and other CPSs (e.g., manufacturing industries, autonomous transportation systems, etc.) are experiencing challenges in dealing with cyberattacks. Major cybersecurity concerns are rising around CPSs because of their ever-growing use of information technology based automation. Often the security concerns are limited to probability-based possible attack …

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Dual Energy Electron Storage Ring Cooler Design For Relativistic Ion Beams, Bhawin Dhital Dec 2022

Dual Energy Electron Storage Ring Cooler Design For Relativistic Ion Beams, Bhawin Dhital

Physics Theses & Dissertations

Collider experiments demand small beam emittances in order to achieve high luminosity. For light particles such as electrons, there exists a natural synchrotron radiation damping resulting in low emittance beams at equilibrium. In the case of heavy particle beams such as proton or ion beams, there is no significant synchrotron radiation damping effect and some cooling mechanism is needed to get to low emittance beams. A dual energy storage ring cooler is a novel concept proposed to cool hadron beams at higher energies. The design consists of two rings: a low energy ring and a high energy ring connected by …

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Case Study Of A District-University Partnership: Developing Culturally Responsive Educators In A Rural Setting, Trenice Shauntel Durio Dec 2022

Case Study Of A District-University Partnership: Developing Culturally Responsive Educators In A Rural Setting, Trenice Shauntel Durio

Educational Leadership & Workforce Development Theses & Dissertations

Using a theoretical framework of critical race theory and conceptual frameworks of cultural education, this study explores the intersection of district-university partnerships and culturally responsive education. The purpose of this mixed methods case study was to examine the formulation and outcomes of a district-university partnership established to offer a college level course focused on anti-discrimination, equity and inclusion, and social justice in schools. Using survey data, I explored the relationship between participation in the course and the participants’ self-reflection in the areas of empathic concern and perspective taking, preparation for culturally responsive teaching and equitable practices, and comfortability with discussions …

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Numerical Simulation Of Electroosmotic Flow Of Viscoelastic Fluid In Microchannel, Jianyu Ji Dec 2022

Numerical Simulation Of Electroosmotic Flow Of Viscoelastic Fluid In Microchannel, Jianyu Ji

Mechanical & Aerospace Engineering Theses & Dissertations

Electroosmotic flow (EOF) has been widely used in various biochemical microfluidic applications, many of which often involve the use of viscoelastic non-Newtonian fluids. Due to the existence of the elastic effect, the viscoelastic EOF develops into chaotic flow under extremely low Reynolds numbers, which is known as elastic turbulence. The mechanism of elastic turbulence in electroosmotic flow remains unclear. Numerical simulation plays an important role in understanding the mechanisms of elastic turbulence. This dissertation is aimed to study the EOF of viscoelastic fluids in constriction microchannels under various direct current (DC) and alternating current (AC) electric fields. First, the EOF …

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Raman Scattering Measurements And Analyses Of Gan Thin Films Grown On Zno Substrates By Metalorganic Chemical Vapor Deposition, Zane Mcdaniel, Zhe Chuan Feng, Kevin Stokes Nov 2022

Raman Scattering Measurements And Analyses Of Gan Thin Films Grown On Zno Substrates By Metalorganic Chemical Vapor Deposition, Zane Mcdaniel, Zhe Chuan Feng, Kevin Stokes

Symposium of Student Scholars

Metalorganic chemical vapor deposition (MOCVD) is a popularly used method of growing thin films of GaN on ZnO (GZ) substrates, which pair well due to their structural and characteristic similarities. In this research, optical characterization of the surface quality of GZ sample films is measured by analyzing Raman scattering (RS) using a Renishaw inVia spectrometer fitted with a 532nm laser. Samples were grown in an improved double injection block rotating disc reactor. Multiple samples' spectra show broad peaks that correspond with the E2 (high) and A1 (LO) branches of GaN, and nicely fitted curves are observed for the characteristic E2 …

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Secure Decentralized Blockchain Based Web Application For Medical Records, Sri Harshini Popuri, Liang Zhao Nov 2022

Secure Decentralized Blockchain Based Web Application For Medical Records, Sri Harshini Popuri, Liang Zhao

Symposium of Student Scholars

The online storage and sharing of electronic health records has undergone a paradigm shift in recent years. The introduction of a centralized cloud computing concept to streamline records transfer between patients and healthcare providers has been an easy task. As a result, the availability of electronically stored health records with minimal operational costs is made possible, but the primary concern is related to the privacy and security of records. How can we securely exchange medical documents online while maintaining strong security standards? This research suggests a framework that fuses online federated learning with blockchain technology. In particular, we develop a …

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