Digital Commons Network^™

Impact Of Pycnonuclear Fusion Uncertainties On The Cooling Of Accreting Neutron Star Crusts, R. Jain, E. F. Brown, H. Schatz, A. V. Afanasjev, M. Beard, L. R. Gasques, S. S. Gupta, G. W. Hitt, W. R. Hix, R. Lau, P. Möller, W. J. Ong, M. Wiescher, Y. Xu

Physics and Engineering Science

The observation of X-rays during quiescence from transiently accreting neutron stars provides unique clues about the nature of dense matter. This, however, requires extensive modeling of the crusts and matching the results to observations. The pycnonuclear fusion reaction rates implemented in these models are theoretically calculated by extending phenomenological expressions and have large uncertainties spanning many orders of magnitude. We present the first sensitivity studies of these pycnonuclear fusion reactions in realistic network calculations. We also couple the reaction network with the thermal evolution code dStar to further study their impact on the neutron star cooling curves in quiescence. Varying …

Emi (English-Medium Instruction) In Cambodian Higher Education, Benedict Lin, Kingsley Bolton, John Bacon-Shone, Bophan Khan

English Faculty Publications

This article is based on empirical research carried out at the Royal University of Phnom Penh (RUPP), Cambodia, between 2018 and 2019. The research involved both quantitative and qualitative approaches. In the case of the former, the researchers conducted a large-scale survey of students involving 956 respondents, of whom 79 were postgraduate students, while the overwhelming majority were studying at the undergraduate level. The qualitative data collected in this project comprised detailed interviews with undergraduates studying at RUPP. The results of both types of data collection indicated that, although many students faced difficulties in studying through the medium of English, …

Attenuated Typicality Effect In Category-Based Inferences: Implications For Autism Spectrum Disorder, Janani Rajagopalan

Dissertations, Theses, and Capstone Projects

Individuals diagnosed with Autism Spectrum Disorder (ASD) often encounter challenges in assimilating atypical exemplars into their categorical prototypes, a cognitive hurdle that can impact their interpretative and integrative capacities. Addressing this problem, the present research sought to develop a robust methodological approach to reduce the effect of this disparity. We examined the elements embedded within the category's internal structure, with the intention of experimentally attenuating the pronounced typicality effect, which is often exaggerated in ASD.

Across four experiments, the thesis investigated the cognitive and neural underpinnings that shape the access to the typicality gradient intrinsic to a category's structure. The …

Mineral Licks As A Potential Nidus For Parasite Transmission, William J. Severud, Todd M. Kautz, Jerrold L. Belant, Seth A. Moore

Natural Resource Management Faculty Publications

Discrete landscape features can concentrate animals in time and space, leading to non-random interspecific encounters. These encounters have implications for predator-prey interactions, habitat selection, intraspecific competition, and transmission of parasites and other pathogens. The lifecycle of the parasitic nematode Parelaphostrongylus tenuis requires an intermediate host of a terrestrial gastropod. Natural hosts of P. tenuis are whitetailed deer, and an aberrant host of conservation concern is moose, which are susceptible to high levels of mortality as a naive host to the parasite. Intermediate hosts become infected when P. tenuis larvae are shed in deer feces, then consumed or enter the gastropod …

Modeling The Moisture Content And Drying Rate Of Zucchini (Cucurbita Pepo L.) In A Solar Hybrid Dryer Using Ann And Anfis Methods, Halil Nusret Bulus, Aytac Moralar, Soner Celen

The Philippine Agricultural Scientist

Estimating product drying kinetics is critical to obtain the best drying process without compromising product quality and necessitates the development of numerical drying models. This research aims to compare the prediction models developed using artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS), two popular machine learning approaches in the recent years. Zucchini slices were chosen as samples and dried in a solar-assisted microwave belt dryer at 0.245 m/min belt speed and microwave powers of 0.7, 1, and 1.4 kW. On the data set obtained by computing the moisture content and drying rate values, prediction models were developed …

Exploring The Prospective Role Of Connected Vehicles In Monitoring And Response To Pandemics And Disasters, Mohammadhosein Pourgholamali, Aishwarya Sharma, Zhongnan Ye, Hessam Arefkhani, Eric J. Dietz, Samuel Labi

Center for Connected and Automated Transportation

In the era of connectivity and automation, the prospective vehicular applications of these technologies go beyond transportation operations and traffic management. Connected and automated vehicles (CAVs) are capable of centralized control, information sharing through connectivity, and integrated surveillance. Also, CAVs are readily amenable to innovative design concepts, including disease detection devices in the interior design that could inherently help reduce the propagation of infectious disease. The first chapter of this study attempts to shed light on the operation of CAVs during a pandemic to decrease the infection risk in a community. In this regard, a literature review on the fundamental …

Ride-Sharing With Advanced Air Mobility, Shuting Yang, Jiazhen Zhou, Dengfeng Sun, Daniel Delaurentis

Center for Connected and Automated Transportation

No abstract provided.

Advances And Perspectives Of Using Stable Isotope Probing (Sip)-Based Technologies In Contaminant Biodegradation, Jinha Kim, Myung Hwangbo, Chih-Hsuan Shih, Kung-Hui Chu

School of Earth, Environmental, & Marine Sciences Faculty Publications

Stable isotope probing (SIP) is a powerful tool to study microbial community structure and function in both nature and engineered environments. Coupling with advanced genomics and other techniques, SIP studies have generated substantial information to allow researchers to draw a clearer picture of what is occurring in complex microbial ecosystems. This review provides an overview of the advances of SIP-based technologies over time, summarizes the status of SIP applications to contaminant biodegradation, provides critical perspectives on ecological interactions within the community, and important factors (controllable and non-controllable) to be considered in SIP experimental designs and data interpretation. Current trend and …

Carbon-Aware Mine Planning With A Novel Multi-Objective Framework, Nurul Asyikeen Binte Azhar, Aldy Gunawan, Shih-Fen Cheng, Erwin Leonardi

Research Collection School Of Computing and Information Systems

The logistical complication of long-term mine planning involves deciding the sequential extraction of materials from the mine pit and their subsequent processing steps based on geological, geometrical, and resource constraints. The net present value (NPV) of profit over the mine's lifespan usually forms the sole objective for this problem, which is considered as the NP-hard precedence-constrained production scheduling problem (PCPSP) as well. However, increased pressure for more sustainable and carbon-aware industries also calls for environmental indicators to be considered. In this paper, we enhance the generic PCPSP formulation into a multi-objective optimization (MOO) problem whereby carbon cost forms an additional …

Experimental Comparison Of Features, Analyses, And Classifiers For Android Malware Detection, Lwin Khin Shar, Biniam Fisseha Demissie, Mariano Ceccato, Naing Tun Yan, David Lo, Lingxiao Jiang, Christoph Bienert

Research Collection School Of Computing and Information Systems

Android malware detection has been an active area of research. In the past decade, several machine learning-based approaches based on different types of features that may characterize Android malware behaviors have been proposed. The usually-analyzed features include API usages and sequences at various abstraction levels (e.g., class and package), extracted using static or dynamic analysis. Additionally, features that characterize permission uses, native API calls and reflection have also been analyzed. Initial works used conventional classifiers such as Random Forest to learn on those features. In recent years, deep learning-based classifiers such as Recurrent Neural Network have been explored. Considering various …

Rosas: Deep Semi-Supervised Anomaly Detection With Contamination-Resilient Continuous Supervision, Hongzuo Xu, Yijie Wang, Guansong Pang, Songlei Jian, Ning Liu, Yongjun Wang

Research Collection School Of Computing and Information Systems

Semi-supervised anomaly detection methods leverage a few anomaly examples to yield drastically improved performance compared to unsupervised models. However, they still suffer from two limitations: 1) unlabeled anomalies (i.e., anomaly contamination) may mislead the learning process when all the unlabeled data are employed as inliers for model training; 2) only discrete supervision information (such as binary or ordinal data labels) is exploited, which leads to suboptimal learning of anomaly scores that essentially take on a continuous distribution. Therefore, this paper proposes a novel semi-supervised anomaly detection method, which devises contamination-resilient continuous supervisory signals. Specifically, we propose a mass interpolation method …

Generative Model-Based Testing On Decision-Making Policies, Zhuo Li, Xiongfei Wu, Derui Zhu, Mingfei Cheng, Siyuan Chen, Fuyuan Zhang, Xiaofei Xie, Lei Ma, Jianjun Zhao

Research Collection School Of Computing and Information Systems

The reliability of decision-making policies is urgently important today as they have established the fundamentals of many critical applications, such as autonomous driving and robotics. To ensure reliability, there have been a number of research efforts on testing decision-making policies that solve Markov decision processes (MDPs). However, due to the deep neural network (DNN)-based inherit and infinite state space, developing scalable and effective testing frameworks for decision-making policies still remains open and challenging.In this paper, we present an effective testing framework for decision-making policies. The framework adopts a generative diffusion model-based test case generator that can easily adapt to different …

Edge Distraction-Aware Salient Object Detection, Sucheng Ren, Wenxi Liu, Jianbo Jiao, Guoqiang Han, Shengfeng He

Research Collection School Of Computing and Information Systems

Integrating low-level edge features has been proven to be effective in preserving clear boundaries of salient objects. However, the locality of edge features makes it difficult to capture globally salient edges, leading to distraction in the final predictions. To address this problem, we propose to produce distraction-free edge features by incorporating cross-scale holistic interdependencies between high-level features. In particular, we first formulate our edge features extraction process as a boundary-filling problem. In this way, we enforce edge features to focus on closed boundaries instead of those disconnected background edges. Second, we propose to explore cross-scale holistic contextual connections between every …

Fine-Grained In-Context Permission Classification For Android Apps Using Control-Flow Graph Embedding, Vikas Kumar Malviya, Naing Tun Yan, Chee Wei Leow, Ailys Xynyn Tee, Lwin Khin Shar, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Android is the most popular operating system for mobile devices nowadays. Permissions are a very important part of Android security architecture. Apps frequently need the users’ permission, but many of them only ask for it once—when the user uses the app for the first time—and then they keep and abuse the given permissions. Longing to enhance Android permission security and users’ private data protection is the driving factor behind our approach to explore fine-grained contextsensitive permission usage analysis and thereby identify misuses in Android apps. In this work, we propose an approach for classifying the fine-grained permission uses for each …

Graph-Level Anomaly Detection Via Hierarchical Memory Networks, Chaoxi Niu, Guansong Pang, Ling Chen

Research Collection School Of Computing and Information Systems

Graph-level anomaly detection aims to identify abnormal graphs that exhibit deviant structures and node attributes compared to the majority in a graph set. One primary challenge is to learn normal patterns manifested in both fine-grained and holistic views of graphs for identifying graphs that are abnormal in part or in whole. To tackle this challenge, we propose a novel approach called Hierarchical Memory Networks (HimNet), which learns hierarchical memory modules---node and graph memory modules---via a graph autoencoder network architecture. The node-level memory module is trained to model fine-grained, internal graph interactions among nodes for detecting locally abnormal graphs, while the …

Are We Ready To Embrace Generative Ai For Software Q&A?, Bowen Xu, Thanh-Dat Nguyen, Thanh Le-Cong, Thong Hoang, Jiakun Liu, Kisub Kim, Chen Gong, Changan Niu, Chenyu Wang, David Lo, David Lo

Research Collection School Of Computing and Information Systems

Stack Overflow, the world's largest software Q&A (SQA) website, is facing a significant traffic drop due to the emergence of generative AI techniques. ChatGPT is banned by Stack Overflow after only 6 days from its release. The main reason provided by the official Stack Overflow is that the answers generated by ChatGPT are of low quality. To verify this, we conduct a comparative evaluation of human-written and ChatGPT-generated answers. Our methodology employs both automatic comparison and a manual study. Our results suggest that human-written and ChatGPT-generated answers are semantically similar, however, human-written answers outperform ChatGPT-generated ones consistently across multiple aspects, …

Real: A Representative Error-Driven Approach For Active Learning, Cheng Chen, Yong Wang, Lizi Liao, Yueguo Chen, Xiaoyong Du

Research Collection School Of Computing and Information Systems

Given a limited labeling budget, active learning (al) aims to sample the most informative instances from an unlabeled pool to acquire labels for subsequent model training. To achieve this, al typically measures the informativeness of unlabeled instances based on uncertainty and diversity. However, it does not consider erroneous instances with their neighborhood error density, which have great potential to improve the model performance. To address this limitation, we propose Real, a novel approach to select data instances with Representative Errors for Active Learning. It identifies minority predictions as pseudo errors within a cluster and allocates an adaptive sampling budget for …

Development Of An Enhanced Hybrid Bi-Catalytic Electrode Containing Bimetallic Composite Catalyst And Immobilized Enzyme For Complete Glucose Electrooxidation, Jefferson Honorio Franco, João Victor Bonaldo, Rodrigo Garcia Da Silva, Shelley D. Minteer, Adalgisa R. De Andrade

Chemistry Faculty Research & Creative Works

We describe an enzymatic fuel cell (EFC) that can electrooxidize glucose completely. the EFC contains the hybrid Ni@Pt-CNT/OxOx bioanode, composed of a bimetallic composite catalyst (Ni@Pt-CNT) and the enzyme oxalate oxidase (OxOx), which can cleave carbon-carbon bonds. Ni@Pt-CNT/OxOx displayed 3-fold higher catalytic activity in the presence than in the absence of glucose (1.3 and 0.4 mA cm−2, respectively), indicating that Ni@Pt-CNT and OxOx acted synergistically. Electrochemical impedance spectroscopy showed that Ni@Pt-CNT/OxOx had higher charge transfer resistance and double layer capacitance than Ni@Pt-CNT. Long-term bulk electrolysis (18 h) revealed that the EFC operating with Ni@Pt-CNT/OxOx presented better current density and stability …

A New Approach To Seasonal Energy Consumption Forecasting Using Temporal Convolutional Networks, Abdul Khalique Shaikh, Amril Nazir, Nadia Khalique, Abdul Salam Shah, Naresh Adhikari

All Works

There has been a significant increase in the attention paid to resource management in smart grids, and several energy forecasting models have been published in the literature. It is well known that energy forecasting plays a crucial role in several applications in smart grids, including demand-side management, optimum dispatch, and load shedding. A significant challenge in smart grid models is managing forecasts efficiently while ensuring the slightest feasible prediction error. A type of artificial neural networks such as recurrent neural networks, are frequently used to forecast time series data. However, due to certain limitations like vanishing gradients and lack of …

Alternative Evidence Rules For Arbitration, Henry Zhuhao Wang

Nevada Law Journal

Today, arbitration is broadly used for resolving civil disputes domestically and internationally. It is a subject so popular in practice that calling it an “alternative dispute resolution” may in fact be a misnomer. Regarding the taking of evidence in arbitration, the cliché is that no rules apply, and that arbitrators have the ultimate authority. In practice, too many arbitrators simply let everything in, no matter how prejudicial, cumulative, incredible, suspect, or otherwise useless, for fear that ruling out anything may lead to vacatur.

This Article probes into the importance and feasibility of having evidence rules in arbitration. It argues that …

Superior Performance Competencies Achieved Through Destination Imagination Experiences, James Ward, Yaprak Dalat Ward, Johnny Wells, Kimberly Bejot

Applied Business Faculty Publications

Today’s workforce not only requires specific content-area knowledge and skills achieved through formal education but also, and most critically, superior performance competencies including distinguishable behaviors. While formal education prioritizes easily detectable and measurable content-specific knowledge and skills, superior performance competencies such as cognitive, affective, and motivational skills remain secondary unless students engage in extracurricular activities in real-world settings. This qualitative study investigated the phenomenon of achieving an array of superior performance competencies through learning by doing, at one of the 2023 Destination Imagination events. This study marked the first fieldwork in which the principal researcher directly observed the event in …

Computation Of High-Order Sensitivities Of Model Responses To Model Parameters—Ii: Introducing The Second-Order Adjoint Sensitivity Analysis Methodology For Computing Response Sensitivities To Functions/Features Of Parameters, Dan Gabriel Cacuci

Faculty Publications

This work introduces a new methodology, which generalizes the extant second-order adjoint sensitivity analysis methodology for computing sensitivities of model responses to primary model parameters. This new methodology enables the computation, with unparalleled efficiency, of second-order sensitivities of responses to functions of uncertain model parameters, including uncertain boundaries and internal interfaces, for linear and/or nonlinear models. Such functions of primary model parameters customarily describe characteristic “features” of the system under consideration, including correlations modeling material properties, flow regimes, etc. The number of such “feature” functions is considerably smaller than the total number of primary model parameters. By enabling the computations …

Robust Weakly Supervised Learning For Real-World Anomaly Detection, Hitesh Sapkota

Theses

Anomaly detection has attracted increasing attentions from diverse domains, including medicine, public safety, and military operations. Despite its wide applicability, anomaly detection is inherently challenging as abnormal activities are usually rare and unbounded in nature. This makes it difficult and expensive to identify all potential anomalous events and label them during the training phase so that a detection model can provide robust prediction on unseen event data. Unsupervised and semi-supervised learning models have been explored, which achieve a decent detection performance with no or much less annotations. However, these models can be highly sensitive to outliers (\ie normal samples that …

Analysis And Modeling Of Early Estradiol-Induced Greb1 Single Allele Gene Transcription At The Population Level, S Mahmood Ghasemi, Pankaj K Singh, Hannah L Johnson, Ayse Koksoy, Michael A Mancini, Fabio Stossi, Robert Azencott

Faculty, Staff and Students Publications

Single molecule fluorescence in situ hybridization (smFISH) can be used to visualize transcriptional activation at the single allele level. We and others have applied this approach to better understand the mechanisms of activation by steroid nuclear receptors. However, there is limited understanding of the interconnection between the activation of target gene alleles inside the same nucleus and within large cell populations.

Using the GREB1 gene as an early estrogen receptor (ER) response target, we applied smFISH to track E2-activated GREB1 allelic transcription over early time points to evaluate potential dependencies between alleles within the same nucleus. We compared two types …

Meta Echo: Using All-Payers Claims Data Analysis As Part Of A Multi-Layered Approach To Plan An Evaluate Echo Programs, Marcy Doyle, Erica Plante

Faculty Publications

No abstract provided.

Constrained Quantization For A Uniform Distribution With Respect To A Family Of Constraints, Megha Pandey, Mrinal Kanti Roychowdhury

School of Mathematical & Statistical Sciences Faculty Publications

In this paper, with respect to a family of constraints for a uniform probability distribution we determine the optimal sets of n-points and the nth constrained quantization errors for all positive integers n. We also calculate the constrained quantization dimension and the constrained quantization coefficient. The work in this paper shows that the constrained quantization dimension of an absolutely continuous probability measure depends on the family of constraints and is not always equal to the Euclidean dimension of the underlying space where the support of the probability measure is defined.

Analysis Of Fire-Induced Circulations During The Fireflux2 Experiment, Jeremy T. Benik, Angel Farguell, Jeffrey D. Mirocha, Craig B. Clements, Adam K. Kochanski

Faculty Research, Scholarly, and Creative Activity

Despite recent advances in both coupled fire modeling and measurement techniques to sample the fire environment, the fire–atmosphere coupling mechanisms that lead to fast propagating wildfires remain poorly understood. This knowledge gap adversely affects fire management when wildland fires propagate unexpectedly rapidly and shift direction due to the fire impacts on local wind conditions. In this work, we utilized observational data from the FireFlux2 prescribed burn and numerical simulations performed with a coupled fire–atmosphere model WRF-SFIRE to assess the small-scale impacts of fire on local micrometeorology under moderate wind conditions (10–12 m/s). The FireFlux2 prescribed burn provided a comprehensive observational …

A Pipeline For The Rapid Collection Of Color Data From Photographs, Yvonne Luong, Ariel Gasca-Herrera, Tracy M. Misiewicz, Benjamin E. Carter

Faculty Research, Scholarly, and Creative Activity

Premise: There are relatively few studies of flower color at landscape scales that can address the relative importance of competing mechanisms (e.g., biotic: pollinators; abiotic: ultraviolet radiation, drought stress) at landscape scales. Methods: We developed an R shiny pipeline to sample color from images that were automatically downloaded using query results from a search using iNaturalist or the Global Biodiversity Information Facility (GBIF). The pipeline was used to sample ca. 4800 North American wallflower (Erysimum, Brassicaceae) images from iNaturalist. We tested whether flower color was distributed non-randomly across the landscape and whether spatial patterns were correlated with climate. We also …

Bacteroides Fragilis In The Gut Microbiomes Of Alzheimer’S Disease Activates Microglia And Triggers Pathogenesis In Neuronal C/Ebpβ Transgenic Mice, Yiyuan Xia, Yifan Xiao, Zi-Hao Wang, Ashfaqul M. Alam, John P. Haran, Beth A. Mccormick, Xiji Shu, Xiaochuan Wang, Keqiang Ye

Markey Cancer Center Faculty Publications

Gut dysbiosis contributes to Alzheimer’s disease (AD) pathogenesis, and Bacteroides strains are selectively elevated in AD gut microbiota. However, it remains unknown which Bacteroides species and how their metabolites trigger AD pathologies. Here we show that Bacteroides fragilis and their metabolites 12-hydroxy-heptadecatrienoic acid (12-HHTrE) and Prostaglandin E2 (PGE2) activate microglia and induce AD pathogenesis in neuronal C/EBPβ transgenic mice. Recolonization of antibiotics cocktail-pretreated Thy1-C/EBPβ transgenic mice with AD patient fecal samples elicits AD pathologies, associated with C/EBPβ/Asparaginyl endopeptidase (AEP) pathway upregulation, microglia activation, and cognitive disorders compared to mice receiving healthy donors’ fecal microbiota transplantation (FMT). Microbial 16S rRNA sequencing …

Syne1 Mutation Is Associated With Increased Tumor Mutation Burden And Immune Cell Infiltration In Ovarian Cancer, Laura M. Harbin, Nan Lin, Frederick R. Ueland, Jill M. Kolesar

Markey Cancer Center Faculty Publications

SYNE1, a nuclear envelope protein critical for cellular structure and signaling, is downregulated in numerous malignancies. SYNE1 alterations are found in 10% of gynecologic malignancies and 5% of epithelial ovarian cancers. Previous studies demonstrated an association between SYNE1 mutation, increased tumor mutation burden (TMB), and immunotherapy response. This study evaluates the SYNE1 mutation frequency, association with TMB, and downstream effects of SYNE1 mutation in ovarian cancer. Genetic information, including whole-exome sequencing, RNA analysis, and somatic tumor testing, was obtained for consenting ovarian cancer patients at an academic medical center. Mutation frequencies were compared between the institutional cohort and The Cancer …