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2020

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Articles 841 - 870 of 15207

Full-Text Articles in Physical Sciences and Mathematics

Shakespeare’S As You Like It And James Cameron’S Avatar: Reharmonizing Society With Nature, Erin Rebecca Turner Dec 2020

Shakespeare’S As You Like It And James Cameron’S Avatar: Reharmonizing Society With Nature, Erin Rebecca Turner

UNLV Theses, Dissertations, Professional Papers, and Capstones

Shakespeare’s As You Like It is a comedy whose main character, Rosalind, is forced out of her home among the upper caste of society through no fault of her own, but because of an issue with her father. She moves into the pastoral unknown disguised as a man to avoid the issues that come traveling as a woman, outside the protection of the home. Along the way, she finds pleasure with the power she holds as a man. She is heard when she speaks as a man and she is given access to knowledge she would not be given as …

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Clarity On Cronbach’S Alpha Use, Jack Barbera, Nicole Naibert, Regis Komperda, Thomas C. Pentecost Dec 2020

Clarity On Cronbach’S Alpha Use, Jack Barbera, Nicole Naibert, Regis Komperda, Thomas C. Pentecost

Chemistry Faculty Publications and Presentations

The Cronbach’s alpha (α) statistic is regularly reported in science education studies. However, recent reviews have noted that it is not well-understood. Therefore, this commentary provides additional clarity regarding the language used when describing and interpreting alpha and other estimates of reliability.

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Watch Out! Motion Is Blurring The Vision Of Your Deep Neural Networks, Qing Guo, Felix Juefei-Xu, Xiaofei Xie, Lei Ma, Jian Wang, Bing Yu, Wei Feng, Yang Liu Dec 2020

Watch Out! Motion Is Blurring The Vision Of Your Deep Neural Networks, Qing Guo, Felix Juefei-Xu, Xiaofei Xie, Lei Ma, Jian Wang, Bing Yu, Wei Feng, Yang Liu

Research Collection School Of Computing and Information Systems

The state-of-the-art deep neural networks (DNNs) are vulnerable to adversarial examples with additive random noise-like perturbations. While such examples are hardly found in the physical world, the image blurring effect caused by object motion, on the other hand, commonly occurs in practice, making the study of which greatly important especially for the widely adopted real-time image processing tasks (e.g., object detection, tracking). In this paper, we initiate the first step to comprehensively investigate the potential hazards of blur effect for DNN, caused by object motion. We propose a novel adversarial attack method that can generate visually natural motion-blurred adversarial examples, …

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Security Analysis Of Permission Re-Delegation Vulnerabilities In Android Apps, Biniam Fisseha Demissie, Mariano Ceccato, Lwin Khin Shar Dec 2020

Security Analysis Of Permission Re-Delegation Vulnerabilities In Android Apps, Biniam Fisseha Demissie, Mariano Ceccato, Lwin Khin Shar

Research Collection School Of Computing and Information Systems

The Android platform facilitates reuse of app functionalities by allowing an app to request an action from another app through inter-process communication mechanism. This feature is one of the reasons for the popularity of Android, but it also poses security risks to the end users because malicious, unprivileged apps could exploit this feature to make privileged apps perform privileged actions on behalf of them. In this paper, we investigate the hybrid use of program analysis, genetic algorithm based test generation, natural language processing, machine learning techniques for precise detection of permission re-delegation vulnerabilities in Android apps. Our approach first groups …

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Sharper Generalisation Bounds For Pairwise Learning, Yunwen Lei, Antoine Ledent, Marius Kloft Dec 2020

Sharper Generalisation Bounds For Pairwise Learning, Yunwen Lei, Antoine Ledent, Marius Kloft

Research Collection School Of Computing and Information Systems

Pairwise learning refers to learning tasks with loss functions depending on a pair of training examples, which includes ranking and metric learning as specific examples. Recently, there has been an increasing amount of attention on the generalization analysis of pairwise learning to understand its practical behavior. However, the existing stability analysis provides suboptimal high-probability generalization bounds. In this paper, we provide a refined stability analysis by developing generalization bounds which can be √nn-times faster than the existing results, where nn is the sample size. This implies excess risk bounds of the order O(n−1/2) (up to a logarithmic factor) for both …

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Unmaking California’S Central Valley, Sayd Randle Dec 2020

Unmaking California’S Central Valley, Sayd Randle

Research Collection College of Integrative Studies

IN THIS YEAR of heat records and fire tornadoes, California faces another potential crisis: drought. In November 2020, more than 80 percent of the state’s land mass was classified as somewhere between “abnormally dry” and “extreme drought” by the United States Drought Monitor. The chances of the winter offering relief look slim, given what’s called a “La Niña climate pattern,” which is associated with arid conditions in much of California. The months ahead are, in general, far more likely to bring water worries than happy surprises.To longtime California residents, such fears are familiar. The state’s most recent drought began in …

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Graphmp: I/O-Efficient Big Graph Analytics On A Single Commodity Machine, Peng Sun, Yonggang Wen, Nguyen Binh Duong Ta, Xiaokui Xiao Dec 2020

Graphmp: I/O-Efficient Big Graph Analytics On A Single Commodity Machine, Peng Sun, Yonggang Wen, Nguyen Binh Duong Ta, Xiaokui Xiao

Research Collection School Of Computing and Information Systems

Recent studies showed that single-machine graph processing systems can be as highly competitive as cluster-based approaches on large-scale problems. While several out-of-core graph processing systems and computation models have been proposed, the high disk I/O overhead could significantly reduce performance in many practical cases. In this paper, we propose GraphMP to tackle big graph analytics on a single machine. GraphMP achieves low disk I/O overhead with three techniques. First, we design a vertex-centric sliding window (VSW) computation model to avoid reading and writing vertices on disk. Second, we propose a selective scheduling method to skip loading and processing unnecessary edge …

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A Deep Learning Framework Supporting Model Ownership Protection And Traitor Tracing, Guowen Xu, Hongwei Li, Yuan Zhang, Xiaodong Lin, Robert H. Deng, Xuemin (Sherman) Shen Dec 2020

A Deep Learning Framework Supporting Model Ownership Protection And Traitor Tracing, Guowen Xu, Hongwei Li, Yuan Zhang, Xiaodong Lin, Robert H. Deng, Xuemin (Sherman) Shen

Research Collection School Of Computing and Information Systems

Cloud-based deep learning (DL) solutions have been widely used in applications ranging from image recognition to speech recognition. Meanwhile, as commercial software and services, such solutions have raised the need for intellectual property rights protection of the underlying DL models. Watermarking is the mainstream of existing solutions to address this concern, by primarily embedding pre-defined secrets in a model's training process. However, existing efforts almost exclusively focus on detecting whether a target model is pirated, without considering traitor tracing. In this paper, we present SecureMark_DL, which enables a model owner to embed a unique fingerprint for every customer within parameters …

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Artificial Intelligence For Social Impact: Learning And Planning In The Data-To-Deployment Pipeline, Andrew Perrault, Fei Fang, Arunesh Sinha, Milind Tambe Dec 2020

Artificial Intelligence For Social Impact: Learning And Planning In The Data-To-Deployment Pipeline, Andrew Perrault, Fei Fang, Arunesh Sinha, Milind Tambe

Research Collection School Of Computing and Information Systems

With the maturing of artificial intelligence (AI) and multiagent systems research, we have a tremendous opportunity to direct these advances toward addressing complex societal problems. In pursuit of this goal of AI for social impact, we as AI researchers must go beyond improvements in computational methodology; it is important to step out in the field to demonstrate social impact. To this end, we focus on the problems of public safety and security, wildlife conservation, and public health in low-resource communities, and present research advances in multiagent systems to address one key cross-cutting challenge: how to effectively deploy our limited intervention …

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The Spatial And Temporal Impact Of Agricultural Crop Residual Burning On Local Land Surface Temperature In Three Provinces Across China From 2015 To 2017, Wenting Zhang, Mengmeng Yu, Qingqing He, Tianwei Wang, Lu Lin, Kai Cao, Wei Huang, Peihong Fu, Jiaxin Chen Dec 2020

The Spatial And Temporal Impact Of Agricultural Crop Residual Burning On Local Land Surface Temperature In Three Provinces Across China From 2015 To 2017, Wenting Zhang, Mengmeng Yu, Qingqing He, Tianwei Wang, Lu Lin, Kai Cao, Wei Huang, Peihong Fu, Jiaxin Chen

Research Collection School Of Computing and Information Systems

China has suffered from severe crop residue burning (CRB) for a long time. As a type of biomass burning, CRB leads to a huge alteration in climate due to the emission of greenhouse gases and particulates in the atmosphere and damages to surface characteristics on land. At present, a growing body of research focuses on the impact of biomass burning (BB) (e.g., forest fire, grass fire, and CRB) on climate change from the aspect of atmospheric process. Meanwhile, a small number of research studies have started to pay attention on the damage caused by BB (e.g. forest fire) on land …

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Blockchain-Based Public Auditing And Secure Deduplication With Fair Arbitration, Haoran Yuan, Xiaofeng Chen, Jianfeng Wang, Jiaming Yuan, Hongyang Yan, Willy Susilo Dec 2020

Blockchain-Based Public Auditing And Secure Deduplication With Fair Arbitration, Haoran Yuan, Xiaofeng Chen, Jianfeng Wang, Jiaming Yuan, Hongyang Yan, Willy Susilo

Research Collection School Of Computing and Information Systems

Data auditing enables data owners to verify the integrity of their sensitive data stored at an untrusted cloud without retrieving them. This feature has been widely adopted by commercial cloud storage. However, the existing approaches still have some drawbacks. On the one hand, the existing schemes have a defect of fair arbitration, i.e., existing auditing schemes lack an effective method to punish the malicious cloud service provider (CSP) and compensate users whose data integrity is destroyed. On the other hand, a CSP may store redundant and repetitive data. These redundant data inevitably increase management overhead and computational cost during the …

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Driving Cybersecurity Policy Insights From Information On The Internet, Qiu-Hong Wang, Steven Mark Miller, Robert H. Deng Dec 2020

Driving Cybersecurity Policy Insights From Information On The Internet, Qiu-Hong Wang, Steven Mark Miller, Robert H. Deng

Research Collection School Of Computing and Information Systems

Cybersecurity policy analytics quantitatively evaluates the effectiveness of cybersecurity protection measures consisting of both technical and managerial countermeasures and is inherently interdisciplinary work, drawing on the concepts and methods from economics, business, social science, and law.

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Differential Privacy Protection Over Deep Learning: An Investigation Of Its Impacted Factors, Ying Lin, Ling-Yan Bao, Ze-Minghui Li, Shu-Sheng Si, Chao-Hsien Chu Dec 2020

Differential Privacy Protection Over Deep Learning: An Investigation Of Its Impacted Factors, Ying Lin, Ling-Yan Bao, Ze-Minghui Li, Shu-Sheng Si, Chao-Hsien Chu

Research Collection School Of Computing and Information Systems

Deep learning (DL) has been widely applied to achieve promising results in many fields, but it still exists various privacy concerns and issues. Applying differential privacy (DP) to DL models is an effective way to ensure privacy-preserving training and classification. In this paper, we revisit the DP stochastic gradient descent (DP-SGD) method, which has been used by several algorithms and systems and achieved good privacy protection. However, several factors, such as the sequence of adding noise, the models used etc., may impact its performance with various degrees. We empirically show that adding noise first and clipping second will not only …

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Interventional Few-Shot Learning, Zhongqi Yue, Zhang Hanwang, Qianru Sun, Xian-Sheng Hua Dec 2020

Interventional Few-Shot Learning, Zhongqi Yue, Zhang Hanwang, Qianru Sun, Xian-Sheng Hua

Research Collection School Of Computing and Information Systems

We uncover an ever-overlooked deficiency in the prevailing Few-Shot Learning (FSL) methods: the pre-trained knowledge is indeed a confounder that limits the performance. This finding is rooted from our causal assumption: a Structural Causal Model (SCM) for the causalities among the pre-trained knowledge, sample features, and labels. Thanks to it, we propose a novel FSL paradigm: Interventional Few-Shot Learning (IFSL). Specifically, we develop three effective IFSL algorithmic implementations based on the backdoor adjustment, which is essentially a causal intervention towards the SCM of many-shot learning: the upper-bound of FSL in a causal view. It is worth noting that the contribution …

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Debunking Rumors On Twitter With Tree Transformer, Jing Ma, Wei Gao Dec 2020

Debunking Rumors On Twitter With Tree Transformer, Jing Ma, Wei Gao

Research Collection School Of Computing and Information Systems

Rumors are manufactured with no respect for accuracy, but can circulate quickly and widely by "word-of-post" through social media conversations. Conversation tree encodes important information indicative of the credibility of rumor. Existing conversation-based techniques for rumor detection either just strictly follow tree edges or treat all the posts fully-connected during feature learning. In this paper, we propose a novel detection model based on tree transformer to better utilize user interactions in the dialogue where post-level self-attention plays the key role for aggregating the intra-/inter-subtree stances. Experimental results on the TWITTER and PHEME datasets show that the proposed approach consistently improves …

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Digital Social Listening On Conversations About Sexual Harassment, Xuesi Sim, Ern Rae Chang, Yu Xiang Ong, Jie Ying Yeo, Christine Bai Shuang Yan, Eugene Wen Jia Choy, Kyong Jin Shim Dec 2020

Digital Social Listening On Conversations About Sexual Harassment, Xuesi Sim, Ern Rae Chang, Yu Xiang Ong, Jie Ying Yeo, Christine Bai Shuang Yan, Eugene Wen Jia Choy, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

In light of the #MeToo movement and publicized sexual harassment incidents in Singapore in recent years, we built an analytics pipeline for performing digital social listening on conversations about sexual harassment for AWARE (Association of Women for Action and Research). Our social network analysis results identified key influencers that AWARE can engage for sexual harassment awareness campaigns. Further, our analysis results suggest new hashtags that AWARE can use to run social media campaigns and achieve greater reach.

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Co-Embedding Attributed Networks With External Knowledge, Pei-Chi Lo, Ee Peng Lim Dec 2020

Co-Embedding Attributed Networks With External Knowledge, Pei-Chi Lo, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Attributed network embedding aims to learn representations of nodes and their attributes in a low-dimensional space that preserves their semantics. The existing embedding models, however, consider node connectivity and node attributes only while ignoring external knowledge that can enhance node representations for downstream applications. In this paper, we propose a set of new VAE-based embedding models called External Knowledge-Aware Co-Embedding Attributed Network (ECAN) Embeddings to incorporate associations among attributes from relevant external knowledge. Such external knowledge can be extracted from text corpus and knowledge graphs. We use multi-VAE structures to model the attribute associations. To cope with joint encoding of …

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Towards Theoretically Understanding Why Sgd Generalizes Better Than Adam In Deep Learning, Pan Zhou, Jiashi Feng, Chao Ma, Caiming Xiong, Steven C. H. Hoi, Weinan E Dec 2020

Towards Theoretically Understanding Why Sgd Generalizes Better Than Adam In Deep Learning, Pan Zhou, Jiashi Feng, Chao Ma, Caiming Xiong, Steven C. H. Hoi, Weinan E

Research Collection School Of Computing and Information Systems

It is not clear yet why ADAM-alike adaptive gradient algorithms suffer from worse generalization performance than SGD despite their faster training speed. This work aims to provide understandings on this generalization gap by analyzing their local convergence behaviors. Specifically, we observe the heavy tails of gradient noise in these algorithms. This motivates us to analyze these algorithms through their Lévy-driven stochastic differential equations (SDEs) because of the similar convergence behaviors of an algorithm and its SDE. Then we establish the escaping time of these SDEs from a local basin. The result shows that (1) the escaping time of both SGD …

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Algorithms And Hardness Results For Computing Cores Of Markov Chains, Ali Ahmadi, Krishnendu Chatterjee, Amir Kafshdar Goharshady, Tobias Meggendorfer, Roodabeh Safavi, Dorde Zikelic Dec 2020

Algorithms And Hardness Results For Computing Cores Of Markov Chains, Ali Ahmadi, Krishnendu Chatterjee, Amir Kafshdar Goharshady, Tobias Meggendorfer, Roodabeh Safavi, Dorde Zikelic

Research Collection School Of Computing and Information Systems

Given a Markov chain M = (V,v0,δ), with state space V and a starting state v0, and a probability threshold ϵ, an ϵ-core is a subset C of states that is left with probability at most ϵ. More formally, C ⊆V is an ϵ-core, iff P reach(V\C) ≤ ϵ. Cores have been applied in a wide variety of verification problems over Markov chains, Markov decision processes, and probabilistic programs, as a means of discarding uninteresting and low-probability parts of a probabilistic system and instead being able to focus on the states that are likely to be encountered in a real-world …

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Agricultural Irrigation Induced Evaporation In A Temperate Study Area: A Stable Isotope Approach, Lincoln Grevengoed Dec 2020

Agricultural Irrigation Induced Evaporation In A Temperate Study Area: A Stable Isotope Approach, Lincoln Grevengoed

Masters Theses

In regions where groundwater is used for irrigation, significant water losses take place due to evaporation. Previous studies demonstrated the utility of stable oxygen and hydrogen isotopes in estimating evaporative water loss experienced during return flow back to an aquifer. Unlike arid regions where the other studies took place, this study examined the region around Kalamazoo, Michigan, the United States, which experiences a more temperate climate. Irrigation in the Kalamazoo area primarily uses center-pivot systems supplied by wells, unlike flood irrigation in previous study areas. Water samples were taken periodically from wells close to center-pivot irrigation systems. Water losses due …

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Engaging Empirical Dynamic Modeling To Detect Intrusions In Cyber-Physical Systems, David R. Crow, Scott R. Graham, Brett J. Borghetti, Patrick J. Sweeney Dec 2020

Engaging Empirical Dynamic Modeling To Detect Intrusions In Cyber-Physical Systems, David R. Crow, Scott R. Graham, Brett J. Borghetti, Patrick J. Sweeney

Faculty Publications

Modern cyber-physical systems require effective intrusion detection systems to ensure adequate critical infrastructure protection. Developing an intrusion detection capability requires an understanding of the behavior of a cyber-physical system and causality of its components. Such an understanding enables the characterization of normal behavior and the identification and reporting of anomalous behavior. This chapter explores a relatively new time series analysis technique, empirical dynamic modeling, that can contribute to system understanding. Specifically, it examines if the technique can adequately describe causality in cyber-physical systems and provides insights into it serving as a foundation for intrusion detection.

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Promoting The Sustainability Of The Gulf Of Maine Recreational Groundfish Fishery Through Discard Mortality Estimation, Mitigation, And Outreach, Connor W. Capizzano Dec 2020

Promoting The Sustainability Of The Gulf Of Maine Recreational Groundfish Fishery Through Discard Mortality Estimation, Mitigation, And Outreach, Connor W. Capizzano

Graduate Doctoral Dissertations

Recreational fishing (i.e., angling), a popular leisure activity that provides socio-economic benefits to human societies around the world, can represent a significant source of fishing mortality and impact fish populations and marine ecosystems. Although fish are often released by recreational anglers to reduce fishing mortality rates, the efficacy of discarding fish is often criticized given that fish can die from the factors experienced during the capture, handling, and release process (i.e., discard mortality). Despite this recognition, the rate at which fish suffer discard mortality in specific commercial and recreational fisheries is often unknown and difficult to obtain due to logistical …

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Novel Tools And Techniques To Investigate And Reduce The Impacts Of Capture-And-Handling, Ryan J. Knotek Dec 2020

Novel Tools And Techniques To Investigate And Reduce The Impacts Of Capture-And-Handling, Ryan J. Knotek

Graduate Doctoral Dissertations

Fish have been an important food resource for humans throughout history, but with growing populations increasing the demand for this resource, many fish stocks have become overfished. In response, management has traditionally addressed overfishing by establishing regulations that reduce the directed fishing mortality associated with harvesting. However, these practices often do not take into account the mortality associated with fish that are incidentally captured and discarded (as bycatch). This discard mortality (DM) can represent a potentially large source of removals for species that are particularly susceptible to the stressors of capture-and-handling and/or discarded at high rates. It is therefore vital …

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Predicting Personality Type From Writing Style, Tanay Gottigundala Dec 2020

Predicting Personality Type From Writing Style, Tanay Gottigundala

Master's Theses

The study of personality types gained traction in the early 20th century, when Carl Jung's theory of psychological types attempted to categorize individual differences into the first modern personality typology. Iterating on Jung's theories, the Myers-Briggs Type Indicator (MBTI) tried to categorize each individual into one of sixteen types, with the theory that an individual's personality type manifests in virtually all aspects of their life. This study explores the relationship between an individual's MBTI type and various aspects of their writing style. Using a MBTI-labeled dataset of user posts on a personality forum, three ensemble classifiers were created to predict …

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Selective Electrocatalytic Reduction Of Co2 To Co With Iron Nheterocyclic Carbene Complexes And Near-Infrared Absorbance Of Ruthenium(Ii) Photosensitizers Containing A Merocyanine Π-Acceptor, Peter Andrew Catsoulis Dec 2020

Selective Electrocatalytic Reduction Of Co2 To Co With Iron Nheterocyclic Carbene Complexes And Near-Infrared Absorbance Of Ruthenium(Ii) Photosensitizers Containing A Merocyanine Π-Acceptor, Peter Andrew Catsoulis

Graduate Masters Theses

The catalytic proton-coupled reduction of carbon dioxide into C-1 or “common engine” liquid fuels is currently a highly desirable and green approach to removing anthropogenic CO2 from the atmosphere. The most common approach to this is through electrocatalytic homogeneous reductions utilizing inorganic complexes as catalysts. Current research has moved towards the use of first-row transition metals in catalysts due to their high natural abundances and cheap cost. Iron porphyrin complexes have been vastly studied due to their high product selectivity and turnover frequencies. However, these complexes exhibit short lived activity because of competing reactions in their catalytic cycles, rendering them …

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Eelgrass (Zostera Marina) Populatin Delcine In Morro Bay, Ca: A Meta-Analysis Of Herbicide Application In San Luis Obispo County And Morro Bay Watershed, Tyler Sinnott Dec 2020

Eelgrass (Zostera Marina) Populatin Delcine In Morro Bay, Ca: A Meta-Analysis Of Herbicide Application In San Luis Obispo County And Morro Bay Watershed, Tyler Sinnott

Master of Science in Environmental Sciences and Management Projects

The endemic eelgrass (Zostera marina) community of Morro Bay Estuary, located on the central coast of California, has experienced an estimated decline of 95% in occupied area (reduction of 344 acres to 20 acres) from 2008 to 2017 for reasons that are not yet definitively clear. One possible driver of degradation, that has yet to be investigated is the role of herbicides from agricultural fields in the watershed that feeds into the estuary. Thus, the primary research goal of this project was to better understand temporal and spatial trends of herbicide use within the context of San Luis Obispo (SLO) …

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The History Of Nintendo: The Company, Consoles And Games, Laurie Takeda Dec 2020

The History Of Nintendo: The Company, Consoles And Games, Laurie Takeda

ART 108: Introduction to Games Studies

From the start, Nintendo has evolved overall as a company; from a playing card manufacturer to developing a wide variety of video game consoles and video games that are played worldwide, they continue to research and expand upon what the company can offer.

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Open Data, Collaborative Working Platforms, And Interdisciplinary Collaboration: Building An Early Career Scientist Community Of Practice To Leverage Ocean Observatories Initiative Data To Address Critical Questions In Marine Science, Robert M. Levine, Kristen E. Fogaren, Johna E. Rudzin, Christopher J. Russoniello, Dax C. Soule, Justine M. Whitaker Dec 2020

Open Data, Collaborative Working Platforms, And Interdisciplinary Collaboration: Building An Early Career Scientist Community Of Practice To Leverage Ocean Observatories Initiative Data To Address Critical Questions In Marine Science, Robert M. Levine, Kristen E. Fogaren, Johna E. Rudzin, Christopher J. Russoniello, Dax C. Soule, Justine M. Whitaker

Publications and Research

Ocean observing systems are well-recognized as platforms for long-term monitoring of near-shore and remote locations in the global ocean. High-quality observatory data is freely available and accessible to all members of the global oceanographic community—a democratization of data that is particularly useful for early career scientists (ECS), enabling ECS to conduct research independent of traditional funding models or access to laboratory and field equipment. The concurrent collection of distinct data types with relevance for oceanographic disciplines including physics, chemistry, biology, and geology yields a unique incubator for cutting-edge, timely, interdisciplinary research. These data are both an opportunity and an incentive …

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Spectroscopic Diagnostics For Supersonic Air Microwave Discharges, James E. Caplinger Dec 2020

Spectroscopic Diagnostics For Supersonic Air Microwave Discharges, James E. Caplinger

Theses and Dissertations

Optical Emission Spectroscopy (OES) is an increasingly relevant technique in plasma diagnostics due to its inherent non-invasive nature and simple application relative to other popular techniques. In this work, common OES techniques are combined with novel methods, developed here, in an effort to provide comprehensive OES techniques for stationary and supersonic air microwave discharges. To this end, a detailed collisional-radiative model for strong atomic oxygen lines has been developed and used to identify the importance of often overlooked mechanisms including cascade emission and metastable excitation. Using these results, a combined argon actinometry technique was developed which makes use of the …

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Design, Synthesis And Application Of Janus Gold Nanoprisms For Directed Self-Assembly., Md. Emtias Chowdhury Dec 2020

Design, Synthesis And Application Of Janus Gold Nanoprisms For Directed Self-Assembly., Md. Emtias Chowdhury

Electronic Theses and Dissertations

Colloidal Janus particles that possess more than one type of surface chemistry or functionalities have drawn significant interest due to their enormous potential in bottom-up synthetic strategies for complex superstructures. Moreover, the property of molecular recognition, tunability, and predictability of the DNA-mediated interactions enable a high degree of control over particle assembly to generate highly ordered nanostructures with emergent applications. In this dissertation, we present our works on the synthesis of Janus particles from anisotropic gold nanoprisms, and DNA- mediated assembly of nanoprisms and polymer beads in four major areas: 1) Facet selective asymmetric functionalization of gold nanoprisms for Janus …

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