Physical Sciences and Mathematics Commons^™

Label-Free Surface-Enhanced Raman Spectroscopy Coupled With Machine Learning Algorithms In Pathogenic Microbial Identification: Current Trends, Challenges, And Perspectives, Jia Wei Tang, Quan Yuan, Xin Ru Wen, Muhammad Usman, Alfred Chin Yen Tay, Liang Wang

Research outputs 2022 to 2026

Infectious diseases caused by microbial pathogens remain a primary contributor to global health burdens. Prompt control and effective prevention of these pathogens are critical for public health and medical diagnostics. Conventional microbial detection methods suffer from high complexity, low sensitivity, and poor selectivity. Therefore, developing rapid and reliable methods for microbial pathogen detection has become imperative. Surface-enhanced Raman Spectroscopy (SERS), as an innovative non-invasive diagnostic technique, holds significant promise in pathogenic microorganism detection due to its rapid, reliable, and cost-effective advantages. This review comprehensively outlines the fundamental theories of Raman Spectroscopy (RS) with a focus on label-free SERS strategy, reporting …

Strong Marine Heatwaves Trigger Flowering In Seagrass, Catalina A. García-Escudero, Victoria Litsi-Mizan, Pavlos T. Efthymiadis, Vasilis Gerakaris, Oscar Serrano, Eugenia T. Apostolaki

Research outputs 2022 to 2026

In recent decades, the global intensification of marine heatwaves has impacted several ecosystems and species, including the endemic Mediterranean seagrass Posidonia oceanica. However, the scarcity of research in Eastern Mediterranean meadows, where historical and present thermal conditions differ from those of the Western Mediterranean, hampers our ability to draw comprehensive conclusions regarding the species' response to elevated sea temperatures. Here, we studied flowering patterns of P. oceanica meadows (3–15 m depth) of the Greek seas and assessed their potential association with marine heatwaves, while also examining the effects on plant growth associated with the transition from vegetative to sexual reproduction. …

Testing Multiple Environmental Dna Substrates For Detection Of The Cryptic And Critically Endangered Burrowing Freshwater Crayfish Engaewa Pseudoreducta, Kathryn L. Dawkins, Paul Nevill, Brian Chambers, Shane Herbert, Quinton F. Burnham

Research outputs 2022 to 2026

Effective conservation of endangered species depends on knowledge of their distributions, but species detection can often be challenging. An example of this is provided by the Critically Endangered Margaret River burrowing crayfish (Engaewa pseudoreducta), which is highly cryptic. Due to the burrowing habit of this crayfish, detection of this species currently requires a great deal of effort, the results are often non-conclusive, and, as it involves manual excavation of their burrows, the habitat of this and other species is destroyed in the detection process. In response to these challenges, this study developed and optimized a species-specific probe-based qPCR assay targeting …

Cellulosic Rich Biomass Production With Optimized Process Parameters By Using Glycerol Pretreatment For Biofuels Applications, Muhammad Sulaiman, Hamayoun Mahmood, Haris M. Khan, Tanveer Iqbal, Nehar U. Khan, Muhammad M. Abbas, Mohammad Nur-E-Alam, Manzoore E. M. Soudagar

Research outputs 2022 to 2026

In this work, we conduct acidified aqueous glycerol pre-treatment (AAG) on rice husks (RH) and utilize the response surface methodology (RSM) to assess the impact of pre-treatment parameters. The primary objective of this research is to optimize the parameters to maximize the cellulose content within RH. The parameters under consideration encompassed temperature (ranging from 80 to 110 °C), retention time (spanning 15 to 45 min), and biomass loading (varying from 5 to 10 wt. %). To achieve this optimization, we perform the Box-Behnken Design (BBD) within the framework of RSM. Additionally, we scrutinize the interactive effects of these parameters on …

Empirical Insights Into Ai-Assisted Game Development: A Case Study On The Integration Of Generative Ai Tools In Creative Pipelines, Andrew Begemann, James Hutson

Student Scholarship

This study conducts an empirical exploration of generative Artificial Intelligence (AI) tools across the game development pipeline, from concept art creation to 3D model integration in a game engine. Employing AI generators like Leonardo AI, Scenario AI, Alpha 3D, and Luma AI, the research investigates their application in generating game assets. The process, documented in a diary-like format, ranges from producing concept art using fantasy game prompts to optimizing 3D models in Blender and applying them in Unreal Engine 5. The findings highlight the potential of AI to enhance the conceptualization phase and identify challenges in producing optimized, high-quality 3D …

Drone-Based Airborne Vlf-Em In Detecting Groundwater Contamination Due To Landfill Leachate, Chanho Park, Mine Dogan

Waldo Library Student Exhibits

Modern landfills follow a strict state and federal regulation on their location, design, operation and monitoring practices. One of the key focus in the regulations is in terms of leachate. While the exact composition of the leachates depends on various factors such as waste composition and degradation processes, in general they contain groups of dissolved organic matter, inorganic cations and heavy metals.

Very Low Frequency-Electromagnetic (VLF-EM) has been widely used for delineating leachate plume direction and boundaries. Previous studies using VLF-EM were carried out as on-ground measurements due to limitations in technologies. With the advancements in Unmanned Airborne Systems (UAS), …

An Integrated Space Test Lexicon: A Taxonomy For The Integrated Test And Evaluation Of Space Systems, Stephen Tullino, Andrew Keys, Robert A. Bettinger, Amy M. Cox, David R. Jacques

Faculty Publications

The proposed Integrated Space Test Lexicon is intended to amalgamate the numerous definitions of integrated (IT or IT&E), development test (DT or DT&E), and operational test (OT or OT&E) into unified, service-wide definitions, aligned with the Space Test Enterprise Vision. Refining such definitions will help distill the core characteristics of these fundamental test types to first identify space system activities composing what is traditionally known as DT and OT, then to provide a means of how these activities fit into the IT paradigm and support space system development. In forging a common understanding of how DT and OT support space …

Development Of A Rule-Based Monitoring System For Autonomous Heavy Equipment Safety, Amirpooya Shirazi

Department of Construction Engineering and Management: Dissertations, Theses, and Student Research

Roadway construction work zones are constantly exposed to interactions among construction equipment, workers, and vehicles. Furthermore, ensuring safety in these areas is considered a challenging task due to the complexity of the environment. As shown in the rising trend of fatal accidents in roadway work zones, current OSHA regulations in construction safety are insufficient in effectively detecting unsafe situations and mitigating the risks. Furthermore, best practices, such as internal traffic control planning (ITCP), exhibit critical limitations requiring continuous monitoring of active work zones as well as adjustments to the site coordination plans due to the dynamic nature of work zone …

Pressure Dependence Of The Rate Coefficient Of Ozone Using Master Equation Simulations Of Recombination Kinetics, Adil Yermek

Master's Theses (2009 -)

The ozone layer in Earth’s atmosphere is distinctive and crucial for the evolution of life. Investigating the process of ozone formation aids in comprehending the evolution of our planet’s atmosphere. The reaction that forms ozone is O + O2 → O3. One can describe ozone forming reaction approximately using Lindeman mechanism, which is very handy. However, its limitation arises from assuming a steady-state approximation, which neglects transient dynamics and complexities that could impact reaction kinetics under non-equilibrium conditions. For this reason, Master Equation simulations can be seen as an enhancement or extension of the Lindemann mechanism, providing a thorough understanding …

Spatio-Temporal Analysis Of The Roadside Transportation-Related Air Quality (Startraq 2022): Data-Driven Exposure Analysis By Transportation Modes, Jaymin Kwon, Yushin Ahn, Steve Chung

Mineta Transportation Institute

Particulate matter (PM) pollution poses significant health risks, influenced by various meteorological factors and seasonal variations. This study investigates the impact of temperature and other meteorological variables on PM10 and PM2.5 levels in Fresno County, known for high air pollution. Multiple linear regression (MLR) and generalized additive models (GAMs) assess the significance of these relationships. Analyzing data from Fresno County, we examine PM10 and PM2.5 levels across "hot" (June to August) and "cool" (September to May) seasons. Findings indicate PM10, both MLR and GAM models identify statistically significant variables, excluding temperature and wind direction in each season. However, during the …

Thermal Hydraulic Analysis For Different Subchannels Of Generic Vver-1200, Mosaddak Ahamed Zahid, Md. Imam Mehedi, Shamsul Arefin Shibly, A. S. Mollah

International Journal of Nuclear Security

The demand for nuclear energy is steadily increasing all over the world. Most nuclear power is used for peaceful applications such as power generation, healthcare, agriculture, food security, industry, and research. One of the primary applications of nuclear energy is the generation of electricity through nuclear power plants based on nuclear reactors. Many developing countries around the world (such as Bangladesh) are moving toward nuclear power plants because they have huge advantages, including low-cost energy, reliable energy sources, zero carbon emissions, and high energy concentration. As a result, the demand for nuclear reactor protection and operational protection of nuclear power …

My Ai Companion: An Examination Of The Removal Of Erotic Role Play From Replika Through User Discussion On Reddit, Chelsee M. Allen

Department of Sociology: Dissertations, Theses, and Student Research

The development of artificial intelligence (AI) software has expanded rapidly in recent years, and thus has emerged the importance of exploring human relationships with AI chatbots. Replika, an app which uses AI to mimic human conversation, removed a function called Erotic Role Play (ERP) that allowed for sexual conversation with users’ customizable chatbots in February of 2023. This exploratory qualitative study examines the aftermath of ERP’s removal through an analysis of user interactions on Reddit. Five overarching themes emerged through the analysis of top posts to a Replika-specific subreddit, encompassing topics around mental health, stigma, coping, sex work and gendered …

Balloon Borne Gps-Enabled Radiosondes That Enable Simultaneous Multi-Point Atmospheric Sensing With A Single Ground Station, Peter A. Ribbens

Doctoral Dissertations and Master's Theses

Radiosondes are balloon borne atmospheric instruments that are a critical tool for understanding dynamics in the lower layers of the atmosphere. The low-cost radiosondes developed in the Space and Atmospheric Instrumentation Lab have been further developed to improve the system's use as a science-quality atmospheric instrument that is unique in its ability to simultaneously track multiple sondes with a single ground station. Sensors to measure temperature and pressure were added to improve measurements of the atmospheric state. A printed circuit board shield and 3D-printed shell were designed to make mass manufacturing possible. A thermistor-based temperature sensor was developed and tested …

Optimization Of A Plate Beam System For Energy Harvesting Using A Piezoelectric Material, Jose Manuel Almendros Espantaleon

Doctoral Dissertations and Master's Theses

With a continuously growing demand for power, driven by the need to reduce our environmental footprint, this research provides an examination of the potential of energy harvesting with smart materials technology and its practical applications. The energy harvesting system considered here works on generating energy through vibrations of a piezoelectric material beam which will undergo sustained vibrations due to flow of air over its surface. It is assumed that sustained limit cycle oscillations of this system will occur at the flutter velocity. This research creates an optimization framework to obtain the best values of parameters that will result in the …

Systematic Comparison Of Ultraviolet Vs. White Light For Lightboard Illumination, Craig Looney

Physics Faculty Publications

Since Peshkin’s invention of the original open-hardware lightboard, tempered low-iron glass has been the preferred lightboard writing surface: low-iron for maximal transparency, tempered for safety and durability. Unfortunately, the tempering process often leaves marks on the glass that become highly visible when illuminated with edge-mounted white LEDs. One obvious idea is to illuminate the glass with UV LEDs; the UV light should cause the fluorescent marker writing to visibly fluoresce, while UV light scattered by defects should be invisible. McCorkle and Whitener (2017, 2020) reported a qualitative performance enhancement for near-visible UV blacklight illumination, but no systematic investigations have been …

The Compass - Volume 93 Issue 1 - Complete Issue, Scott R. Beason

The Compass: Earth Science Journal of Sigma Gamma Epsilon

No abstract provided.

Hardening Interpretable Deep Learning Systems: Investigating Adversarial Threats And Defenses, Eldor Abdukhamidov, Mohammed Abuhamad, Simon S. Woo, Eric Chan-Tin, Tamer Abuhmed

Computer Science: Faculty Publications and Other Works

Deep learning methods have gained increasing attention in various applications due to their outstanding performance. For exploring how this high performance relates to the proper use of data artifacts and the accurate problem formulation of a given task, interpretation models have become a crucial component in developing deep learning-based systems. Interpretation models enable the understanding of the inner workings of deep learning models and offer a sense of security in detecting the misuse of artifacts in the input data. Similar to prediction models, interpretation models are also susceptible to adversarial inputs. This work introduces two attacks, AdvEdge and AdvEdge + …

True Contraction Decomposition And Almost Eth-Tight Bipartization For Unit-Disk Graphs, Sayan Bandyapadhyay, William Lochet, Daniel Lokshtanov, Saket Saurabh, Jie Xue

Computer Science Faculty Publications and Presentations

We prove a structural theorem for unit-disk graphs, which (roughly) states that given a set D of n unit disks inducing a unit-disk graph ...

Towards Automated Slide Augmentation To Discover Credible And Relevant Links, Dilan Dinushka Senarath Arachchige, Christopher M. Poskitt, Kwan Chin (Xu Guangjin) Koh, Heng Ngee Mok, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Learning from concise educational materials, such as lecture notes and presentation slides, often prompts students to seek additional resources. Newcomers to a subject may struggle to find the best keywords or lack confidence in the credibility of the supplementary materials they discover. To address these problems, we introduce Slide++, an automated tool that identifies keywords from lecture slides, and uses them to search for relevant links, videos, and Q&As. This interactive website integrates the original slides with recommended resources, and further allows instructors to 'pin' the most important ones. To evaluate the effectiveness of the tool, we trialled the system …

Jigsaw: Edge-Based Streaming Perception Over Spatially Overlapped Multi-Camera Deployments, Ila Gokarn, Yigong Hu, Tarek Abdelzaher, Archan Misra

Research Collection School Of Computing and Information Systems

We present JIGSAW, a novel system that performs edge-based streaming perception over multiple video streams, while additionally factoring in the redundancy offered by the spatial overlap often exhibited in urban, multi-camera deployments. To assure high streaming throughput, JIGSAW extracts and spatially multiplexes multiple regions-of-interest from different camera frames into a smaller canvas frame. Moreover, to ensure that perception stays abreast of evolving object kinematics, JIGSAW includes a utility-based weighted scheduler to preferentially prioritize and even skip object-specific tiles extracted from an incoming stream of camera frames. Using the CityflowV2 traffic surveillance dataset, we show that JIGSAW can simultaneously process 25 …

A Deep Learning Method To Predict Bacterial Adp-Ribosyltransferase Toxins, Dandan Zheng, Siyu Zhou, Lihong Chen, Guansong Pang, Jian Yang

Research Collection School Of Computing and Information Systems

Motivation: ADP-ribosylation is a critical modification involved in regulating diverse cellular processes, including chromatin structure regulation, RNA transcription, and cell death. Bacterial ADP-ribosyltransferase toxins (bARTTs) serve as potent virulence factors that orchestrate the manipulation of host cell functions to facilitate bacterial pathogenesis. Despite their pivotal role, the bioinformatic identification of novel bARTTs poses a formidable challenge due to limited verified data and the inherent sequence diversity among bARTT members. Results: We proposed a deep learning-based model, ARTNet, specifically engineered to predict bARTTs from bacterial genomes. Initially, we introduced an effective data augmentation method to address the issue of data scarcity …

Microkarta: Visualising Microservice Architectures, Oscar Manglaras, Alex Farkas, Peter Fule, Christoph Treude, Markus Wagner

Research Collection School Of Computing and Information Systems

Conceptualising and debugging a microservice architecture can be a challenge for developers due to the complex topology of inter-service communication, which may only apparent when viewing the architecture as a whole. In this paper, we present MicroKarta, a dashboard containing three types of network diagram that visualise complex microservice architectures, and that are designed to address problems faced by developers of these architectures. Initial feedback from industry developers has been positive. This dashboard can be used by developers to explore and debug microservice architectures, and can be used to compare the effectiveness of different types of network visualisation for assisting …

Partial Solution Based Constraint Solving Cache In Symbolic Execution, Ziqi Shuai, Zhenbang Chen, Kelin Ma, Kunlin Liu, Yufeng Zhang, Jun Sun, Ji Wang

Research Collection School Of Computing and Information Systems

Constraint solving is one of the main challenges for symbolic execution. Caching is an effective mechanism to reduce the number of the solver invocations in symbolic execution and is adopted by many mainstream symbolic execution engines. However, caching can not perform well on all programs. How to improve caching’s effectiveness is challenging in general. In this work, we propose a partial solution-based caching method for improving caching’s effectiveness. Our key idea is to utilize the partial solutions inside the constraint solving to generate more cache entries. A partial solution may satisfy other constraints of symbolic execution. Hence, our partial solution-based …

Enhancing Adult Learner Success In Higher Education Through Decision Tree Models: A Machine Learning Approach, Emily Barnes, James Hutson, Karriem Perry

Faculty Scholarship

This article explores the use of machine learning, specifically Classification and Regression Trees (CART), to address the unique challenges faced by adult learners in higher education. These learners confront socio-cultural, economic, and institutional hurdles, such as stereotypes, financial constraints, and systemic inefficiencies. The study utilizes decision tree models to evaluate their effectiveness in predicting graduation outcomes, which helps in formulating tailored educational strategies. The research analyzed a comprehensive dataset spanning the academic years 2013–2014 to 2021–2022, evaluating the predictive accuracy of CART models using precision, recall, and F1 score. Findings indicate that attendance, age, and Pell Grant eligibility are key …

Final 2024 Residential Metals Abatement Program (Rmap) Park Soil Sampling Field Sampling Plan (Fsp) Submittal #13 [Copper Mountain Park], Mike Mcanulty

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Final 2024 Residential Metals Abatement Program (Rmap) Mandan Park Soil Remedial Action Work Plan (Rawp), Mike Mcanulty

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Revised Draft Final 2024 Residential Metals Abatement Program (Rmap) Quality Assurance Project Plan (Qapp) Annual Update (Non-Residential Parcels And Residential Daycares), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Revised Draft Final Quarterly Operations And Maintenance Report Butte Treatment Lagoon System – Fourth Quarter 2022, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Establishing Metrics To Encourage Broader Use Of Atomic Requirements – A Call For Exchange And Experimentation, William L. Honig

Computer Science: Faculty Publications and Other Works

There are seemingly many advantages to being able to identify, document, test, and trace single or “atomic” requirements during system development and maintenance. Ongoing work with Agile development has focused on “user stories” that can capture individual features for implementation. However, it is still difficult to evaluate the quality of such requirements and teaching their creation is difficult.

Based on a working definition of atomic requirement, this paper proposes a set of metrics for their evaluation. Ten metrics designed to measure atomic requirements are presented here: five used on individual requirements statements and five applied to a requirements document or …

Sequential Decision Learning For Social Good And Fairness, Dexun Li

Dissertations and Theses Collection (Open Access)

Sequential decision learning is one of the key research areas in artificial intelligence. Typically, a sequence of events is observed through a transformation that introduces uncertainty into the observations and based on these observations, the recognition process produces a hypothesis of the underlying events. This learning process is characterized by maximizing the sum of the reward signals. However, many real-life problems are inherently constrained by limited resources. Besides, when the learning algorithms are used to inform decisions involving human beings (e.g., Security and justice, health intervention, etc), they may inherit the potential, pre-existing bias in the dataset and exhibit similar …