Digital Commons Network^™

Mitigating Intensive Care Unit Noise: Design-Led Modeling Solutions, Calculated Acoustic Outcomes, And Cost Implications, Emil E. Jonescu, Benjamin Farrel, Chamil E. Ramanayaka, Christopher White, Giuseppe Costanzo, Lori Delaney, Rebecca Hahn, Janet Ferrier, Edward Litton

Research outputs 2022 to 2026

Objectives, Purpose, or Aim: The study aimed to decrease noise levels in the ICU, anticipated to have adverse effects on both patients and staff, by implementing enhancements in acoustic design. Background: Recognizing ICU noise as a significant disruptor of sleep and a potential hindrance to patient recovery, this study was conducted at a 40-bed ICU in Fiona Stanley Hospital in Perth, Australia. Methods: A comprehensive mixed-methods approach was employed, encompassing surveys, site analysis, and acoustic measurements. Survey data highlighted the importance of patient sleep quality, emphasizing the negative impact of noise on work performance, patient connection, and job satisfaction. Room …

Point Of View On Outcome Prediction Models In Post-Stroke Motor Recovery, Onno Van Der Groen, Manonita Ghosh, Richard Norman, Amy Kuceyeski, Ceren Tozlu, Teresa J. Kimberley, David J. Lin, Laurel J. Buxbaum, Gert Kwakkel, Steven C. Cramer, Dylan J. Edwards

Research outputs 2022 to 2026

Stroke is a leading cause of disability worldwide which can cause significant and persistent upper limb (UL) impairment. It is difficult to predict UL motor recovery after stroke and to forecast the expected outcomes of rehabilitation interventions during the acute and subacute phases when using clinical data alone. Accurate prediction of response to treatment could allow for more timely and targeted interventions, thereby improving recovery, resource allocation, and reducing the economic impact of post-stroke disability. Initial motor impairment is currently the strongest predictor of post-stroke motor recovery. Despite significant progress, current prediction models could be refined with additional predictors, and …

Powerlifting Participation And Engagement Across All Ages: A Retrospective, Longitudinal, Population Analysis With Comparison To Community Strength Norms, Daniel J. Van Den Hoek, Alistair Mallard, Joel M. Garrett, Patrick L. Beaumont, Robert J. Howells, Jemima G. Spathis, Joshua Pearson, Christopher Latella

Research outputs 2022 to 2026

Background: In Australia, one-third of people ≥ 15 years perform regular resistance training and 90% of those do not meet current health guidelines. All age groups should engage in regular resistance exercise, to maintain strength and function. Objectives: To identify trends in powerlifting competition participation in Australia by sex and age group from 1968 to 2022, and to compare the strength of powerlifting competitors to population age- and sex-based normative values. Method: The number of unique participants and total competition entries for each year were analysed using Australian powerlifting competition data. Subdomains of age and sex were investigated, and mean …

Blind Source Separation And Denoising Of Underwater Acoustic Signals, Ruba Zaheer, Iftekhar Ahmad, Quoc Viet Phung, Daryoush Habibi

Research outputs 2022 to 2026

Due to the addition of new underwater vessels and other natural noise contributors, the underwater environment is becoming congested and noisy. Undersea monitoring sonobuoys receive multiple mixed acoustic signals from different vessels that need to be separated and identified in the presence of underwater noise (UWN). It is extremely challenging to separate highly correlated acoustic signals from a noisy mixture without prior knowledge of mixing process and propagation channel. Also, in many cases, the separated signals from the noisy mixture doesn’t accurately describe the correct signal. This study proposes a novel multi-stage method to separate underwater acoustic source signals from …

Mathematical Connections In Preservice Secondary Mathematics Teachers’ Solution Strategies To Algebra Problems, Vesife Hatisaru, Kaye Stacey, Jon R. Star

Research outputs 2022 to 2026

This study investigated the mathematical connections found in the solutions provided by 22 preservice secondary mathematics teachers to a set of algebra problems. Interest in, and research on, mathematical connections has gained prominence of the past decade. Here, we use the Extended Theory of Mathematical Connections or ETMC to explore the types of connections that this framework does and does not capture in the preservice teachers’ solutions. The ETMC surfaced four types of mathematical connections across four problems: ‘different representations’, ‘procedural’, ‘part-whole’ and ‘meaning’. The other types of connections defined in ETMC such as ‘reversibility’ or ‘feature’ were not found …

Towards Blockchain-Based Secure Bgp Routing, Challenges And Future Research Directions, Qiong Yang, Li Ma, Shanshan Tu, Sami Ullah, Muhammad Waqas, Hisham Alasmary

Research outputs 2022 to 2026

Border Gateway Protocol (BGP) is a standard inter-domain routing protocol for the Internet that conveys network layer reachability information and establishes routes to different destinations. The BGP protocol exhibits security design defects, such as an unconditional trust mechanism and the default acceptance of BGP route announcements from peers by BGP neighboring nodes, easily triggering prefix hijacking, path forgery, route leakage, and other BGP security threats. Meanwhile, the traditional BGP security mechanism, relying on a public key infrastructure, faces issues like a single point of failure and a single point of trust. The decentralization, anti-tampering, and traceability advantages of blockchain offer …

Achieving Covert Communication With A Probabilistic Jamming Strategy, Xun Chen, Fujun Gao, Min Qiu, Jia Zhang, Feng Shu, Shihao Yan

Research outputs 2022 to 2026

In this work, we consider a covert communication scenario, where a transmitter Alice communicates to a receiver Bob with the aid of a probabilistic and uninformed jammer against an adversary warden's detection. The transmission status and power of the jammer are random and follow some priori probabilities. We first analyze the warden's detection performance as a function of the jammer's transmission probability, transmit power distribution, and Alice's transmit power. We then maximize the covert throughput from Alice to Bob subject to a covertness constraint, by designing the covert communication strategies from three different perspectives: Alice's perspective, the jammer's perspective, and …

Assessing The Impact Of Geochemical Mechanism And Interpolation Factor Selection On The Precision Of Low-Salinity Waterflooding Modelling: A Comparative Study, Malek Jalilian, Sedigheh Mahdavi, Peyman Pourafshary, Zheinjiang You, Amir H. Mohammadi

Research outputs 2022 to 2026

The efficacy of low salinity waterflooding (LSWF) as an enhanced oil recovery (EOR) method in carbonate formations has been well established; however, its geochemical modelling in such reservoirs remains insufficiently explored. This study aims to bridge this gap by conducting a comparative analysis of three well-recognised geochemical mechanisms including fines migration, rock dissolution/precipitation, and multi-ion exchange (MIE). Our research encompasses two parts: experimental and modelling. First, we conducted four coreflooding experiments to study the effect of LSWF on oil recovery in core scale as well as on rock wettability by measuring the contact angle of the crude oil-brine-rock system. In …

Identifying And Promoting Qualitative Methods For Impact Assessment, Heidi Walker, Jenny Pope, Angus Morrison-Saunders, Alan Bond, Alan P. Diduck, A. John Sinclair, Brendan Middel, Francois Retief

Research outputs 2022 to 2026

Qualitative methods for impact assessment (IA) represent a broad spectrum of approaches that are important for realising effective IA practice. The purpose of this paper is to identify and promote qualitative methods that are available for use in contemporary and future (next-generation) IA processes. From an extensive literature review, an international survey (145 responses), expert interviews (48 interviewees), and a workshop attended by 27 IA practitioners, 17 qualitative method categories were identified. These were further subdivided into three classes: conventional qualitative methods, highly participatory methods, and mixed methods. Each method is described, and an indication given of how each can …

Secrecy Rate Maximization For Active Reconfigurable Intelligent Surface Assisted Mimo Systems, Bin Gao, Jingru Zhao, Shihao Yan, Shaozhang Xiao

Research outputs 2022 to 2026

Reconfigurable intelligent surface (RIS) is a promising technology for future 6G communications and has been used to enhance secrecy performance. However, the performance improvement is restricted by the 'double fading' effect of the reflection channel link. To address this issue, we introduce an active RIS design, where the reflecting elements of RIS not only adjust the phase shift but also amplify the reflected signal through the amplifier integrated into its elements. To obtain a satisfactory solution to the non-convex problem resulting from this design, the penalty dual decomposition based alternating gradient projection (PDDAPG) method is proposed. We compare the proposed …

Students' Preferences For Setting And/Or Mixed-Ability Grouping In Secondary School Physical Education In England, Shaun D. Wilkinson, Dawn Penney

Research outputs 2022 to 2026

There is an extensive international literature on different forms of ability grouping in schools, much of which describes their impact on students' academic achievement, motivation, self-concept and/or attitudes towards learning. Comparatively little research has focused on students' perspectives of these practices, while the research that has been conducted has primarily focused on mathematics, English and science. There is a conspicuous and arguably significant absence of research that explores students' perspectives on different forms of ability grouping in other areas of the school curriculum, including physical education (PE). In contrast to the relative privacy of classroom-based subjects, students' bodies, physical competencies …

Investigating Factors Contributing To Differences In Air Pollutants Between Metropolitan Areas In The Us, Ellen Hu

Scripps Senior Theses

Densely populated regions around the United States exhibit varying concentrations of air pollutants that can impact the health of urban populations and surrounding ecosystems. Understanding why these differences exist can be important in addressing health issues as the human population continues to increase and communities across the globe move to accommodate these trends. To investigate the impact of natural processes and anthropogenic sources on pollutant concentrations, EPA daily fine particulate matter (PM_2.5), nitrogen dioxide (NO₂), and ozone (O₃) pollutant concentrations from seven metropolitan regions across the US for 2018-2022 were used. A Spearman Rank-Order …

Decreasing Cross-Coupling In The Tianlai Radio Antenna Array, Nadia Marie Schwartz Bolef

Scripps Senior Theses

The 21-cm line is the electromagnetic radiation spectral line created by the change in energy state of neutral hydrogen atoms. This signal can be used for intensity mapping, a method of mapping the large-scale structure of the universe at different redshifts. However, challenges are being faced in the designing and building of the radio antenna arrays used to detect the 21-cm line signal. Currently, cross-coupling between antenna feeds is introducing beam errors and correlated noise that could overpower the signal. One possible solution we are working on is to use a phased array of antenna feeds instead of dishes and …

Improving Accessibility And Understanding: Studying The Impact Of Human Activities And Dam Removals On Anadromous Fish Species In The Pacific Northwest, Adia F. Bennett

Scripps Senior Theses

Human activities in North American watersheds have significantly impacted anadromous fish, causing some species of salmon and steelhead to become threatened and endangered. These fish hold ecological, cultural, and economic importance, yet factors such as habitat destruction, overfishing, pollution, climate change, and hydroelectric dams continue to degrade populations. Dams are particularly harmful to anadromous fish, sparking a recent movement advocating for dam removals. This study uses NOAA’s 2022 Biological Viability Assessment to examine the response of anadromous fish to recent dam removals in the Pacific Northwest. Four populations were selected to analyze due to their interaction with sites that had …

Bayesian Inference In Reinforcement Learning Neural Networks During A Markov Decision Processes?, Katherine Graham

Scripps Senior Theses

The predictive mind theory proposes that brains work in a way that makes predictions about future stimuli to process information efficiently and accurately. Bayesian brain theory suggests that the brain utilizes Bayesian probability models to make predictions, while the free-energy minimization hypothesis proposes that these predictions are made to minimize energy or uncertainty, ensuring accurate perceptions. Vertechi et al. (2020) explored animal participants’ utilization of stimulus-bound strategy versus inference-based strategy to solve a Markov decision process with a 2-state environment, one of which is always active. These sites have a certain probability of switching to a different site and the …

A Petrographic And Geochemical Study Of The Rangataua Lava Flows, Mount Ruapehu, Aotearoa New Zealand, Lucy Smith

Scripps Senior Theses

Mount Ruapehu is a large stratovolcano on the North Island of Aotearoa New Zealand. The Rangataua lava flow is situated on the southern flanks of Ruapehu, and extends for 15-16 km. The flow is of andesite composition and comprised of three units: proximal, medial and distal. Mount Ruapehu is both scientifically and culturally significant.

Geochemical analyses of 13 whole rock samples, Electron Microprobe analyses of three polished thin sections, and geothermobarometry calculations were conducted to obtain data on pre-eruptive intensive parameters. Petrographic observations were used to investigate disequilibrium textures and shed light on magma chamber processes.

Results of this study …

Geographically Dependent Thermodynamic And Geochemical Evolution In The Post-Batholith Upper Mantle Beneath The Sierra Nevada: Analysis Of The Blue Knob Xenoliths, Eleanor Ruos

Scripps Senior Theses

Xenoliths entrained in recent (Miocene) volcanics from the Sierra Nevada provide insights into a continuously evolving upper mantle during post-batholith generation periods (early Cenozoic–present). Hypothesized mantle refertilization followed by subsequent delamination of the lithospheric root beneath the Sierra provides a contextual framework for analyzing how the upper mantle responds to such events. This study contributes new geochemical data from the ultramafic xenoliths of Blue Knob, including bulk-rock major and trace element data, thin-section petrography, mineral chemistry and thermobarometry, and oxygen isotopes (delta¹⁸O). Blue knob (Eastern Sierra) data are compared to previous works on xenoliths from Chinese Peak and …

The Sketchy Science Of Memory: Exploring The Benefits Of Integrating Drawing Into Learning, Eve Smith

Scripps Senior Theses

The drawing effect, wherein drawing enhances information recall compared to writing, has been established for words and short phrases. Previous research has emphasized the efficacy of the drawing effect, particularly for long-term memory, and previous research has shown the enjoyable nature of arts integration into education. This study investigates the applicability of the drawing effect to lecture-based information and assesses the impact of arts integration, like drawing, on learning. Participants in the experimental condition learned and utilized a visual note-taking technique known as sketchnoting, while those in the control group took standard written notes. Participants' recall was evaluated immediately and …

Optimization Of A Low-Cost Corona Dielectric-Barrier Discharge Plasma Wastewater Treatment System Through Central Composite Design/Response Surface Methodology With Mechanistic And Efficiency Analysis, Yiting Xiao, Yang Tian, Yuanhang Zhan, Jun Zhu

Biological and Agricultural Engineering Faculty Publications and Presentations

Water pollution, which is intensified by the release of hard-to-degrade pollutants, poses severe threats to ecosystems, human health, and economic development. The existing advanced oxidation processes often involve high operational costs and can potentially result in secondary pollution, highlighting the necessity for innovative and more sustainable solutions. To address these challenges, our study introduces a cost-effective and eco-friendly corona dielectric-barrier discharge for wastewater treatment. Using the central composite design/response surface methodology, a high decolorization rate of 98% of methylene blue (MB) was achieved within 10 min by optimizing parameters such as pH and voltage. Furthermore, the mechanisms underlying the generation …

Prediction Of Chlorophyll-A As An Index Of Harmful Algal Blooms Using Machine Learning Models, Ibrahim Busari, Debrabata Sahoo, R. Daren Harmel, Brian E. Haggard

Biological and Agricultural Engineering Faculty Publications and Presentations

The complex dynamics of freshwater harmful algal blooms (HABs) necessitate proactive monitoring approaches to mitigate their impacts. The rapid breakthrough in computing prowess and statistical advances is triggering the development of data-driven techniques such as machine learning (ML) models, which have been shown in different fields to be instrumental in finding patterns for explaining relationships in observed data. This study assesses the ability of ML models for HABs monitoring in a lake using chlorophyll-a concentration as the index. The selected models for this study were regression tree, random forest (RF), multilayer perceptron (MLP), support vector regression (SVR), long short-term memory …

Sensor Tasking For Low Earth Orbit Objects: Leveraging Space Sensor Data For Ground-Based Optical Observations, Smriti Nandan Paul, Hang Woon Lee

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Because of recent advancements in space technologies, easier and more economical access to space, and an increase in commercial interests, the near-Earth space environment has witnessed an exploding number of objects being put into orbit. In particular, the low Earth orbit (LEO) region is at an increased risk of orbital collisions from large satellite constellation projects. Thus, monitoring LEO objects for space situational awareness and space traffic management has become increasingly imperative. In this paper, we use the concept of limited-CDF (cumulative distribution function) surface and mutual information for designing sensor tasking algorithms focusing on regular observation of known catalog …

Empowering Causal Machine Learning For Large-Scale Manufacturing Pipelines With Knowledge Graphs, Yuxin Zi, Cory Henson, Amit P. Sheth

Faculty Publications

Understanding causal relations within manufacturing pipelines is crucial for key manufacturing tasks such as anomaly detection and root cause analysis. However, existing causal machine learning (causal ML) approaches struggle to scale effectively to the vast number of variables present in manufacturing settings. We advocate for incorporating domain knowledge within the manufacturing pipelines, represented as knowledge graphs (KGs), for designing causal ML methods for large-scale manufacturing problems. Knowledge graphs can encode rich contextual information about the interactions and dependencies between different components and stages of the manufacturing pipeline, providing a structured framework to guide the discovery of causal relationships. By incorporating …

A Hybrid Model-Based Data-Driven Framework For The Electromagnetic Near-Field Scanning, Yanming Zhang, Lijun Jiang

Electrical and Computer Engineering Faculty Research & Creative Works

This article presents a novel hybrid approach for electromagnetic near-field scanning, combining model-based, i.e., Gaussian processes regression, and data-driven, i.e., dynamic mode decomposition, techniques. We first leverage the Latin hypercube sampling technique to achieve spatially sparse measurements. Subsequently, dynamic mode decomposition is applied to analyze the resulting spatiotemporal data with sparse spatial sampling, enabling the extraction of both frequency information and sparse dynamic modes. Finally, the Gaussian processes regression, also known as the Kriging method, is adopted for the full-state reconstruction. The proposed hybrid approach is benchmarked by an example of the crossed dipole antennas. The obtained results demonstrate that …

Lifelong Learning-Based Optimal Trajectory Tracking Control Of Constrained Nonlinear Affine Systems Using Deep Neural Networks, Irfan Ganie, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This article presents a novel lifelong integral reinforcement learning (LIRL)-based optimal trajectory tracking scheme using the multilayer (MNN) or deep neural network (Deep NN) for the uncertain nonlinear continuous-time (CT) affine systems subject to state constraints. A critic MNN, which approximates the value function, and a second NN identifier are together used to generate the optimal control policies. The weights of the critic MNN are tuned online using a novel singular value decomposition (SVD)-based method, which can be extended to MNN with the N-hidden layers. Moreover, an online lifelong learning (LL) scheme is incorporated with the critic MNN to mitigate …

Impact Of Ai On The Hri Dynamic In Search And Rescue Operations Using Uav Swarms, Jordan Morrow, Maciej Jan Zawodniok, Anhar Sami Mohammed

Electrical and Computer Engineering Faculty Research & Creative Works

Artificial intelligence (AI) offers significant benefits in search and rescue applications by enhancing the efficiency and effectiveness of the search. However, an over-reliance on AI can hinder the operation due to biases embedded in the underlying algorithms. This partiality, if left un-monitored, can pose a risk to the safety of those in need of disaster relief. Typically manifests into inaccuracies in the decision-making processes and if not carefully monitored can cause larger issues. This paper extends the knowledge presented in a previous work, which presents the design and modeling of search and rescue operations using unmanned aerial vehicle (UAV) swarms. …

Online Continual Safe Reinforcement Learning-Based Optimal Control Of Mobile Robot Formations, Irfan Ganie, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In this work, a leader-follower tracking and formation control strategy for mobile robots (MRs) with uncertain dynamics is proposed. This strategy utilizes a continual lifelong safe reinforcement learning (CLSRL) framework based on multilayer neural networks (MNNs). The proposed design employs actor-critic MNNs, incorporating a barrier function. This function is derived from the Bellman optimality principle. It addresses the state constraints throughout the control design process. A novel online continual lifelong learning (CLL) method is introduced for MR formation. This method leverages the Bellman residual error for weight significance in MNNs. It addresses catastrophic forgetting and interlayer dependence through layer-specific regularizers. …

Reinforcement Learning-Based Constrained Optimal Control Of Strict-Feedback Nonlinear Systems: Application To Autonomous Underwater Vehicles, Behzad Farzanegan, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper addresses a constrained neural network (NN)-based optimal tracking scheme for a class of uncertain nonlinear discrete-time systems in strict-feedback form by using a control barrier function (CBF). First, a modified barrier-type cost function is introduced for each subsystem, guiding the actual system trajectory toward the safe set or desired trajectory while avoiding unwanted sets. To address the tracking problem, an augmented system is employed to convert the time-varying optimal tracking to a time-invariant optimal regulation. Then, an actor-critic framework is employed with the backstepping technique to obtain both virtual and actual optimal control policies for each subsystem to …

Learning From The Past: Using Peer Data To Improve Course Recommendations In Personalized Education, Colton Walker, Sahra Sedigh Sarvestani, Ali R. Hurson

Electrical and Computer Engineering Faculty Research & Creative Works

This research introduces a recommendation system designed to enhance student success by intelligently personalizing the semester schedules and graduation path based on the student's performance, interests, and background; and inspired by the academic journeys of similar students who have successfully graduated in the past. The proposed recommender system leverages a combination of Markov decision processes, Q-Learning, and collaborative filtering techniques to identify graduation paths with a higher likelihood of success for the student. The proposed model is versatile and generic and can be adapted to various disciplines if sufficient past historical data is available. The proposed model has been prototyped …

Survey Of Hidden Markov Models (Hmms) For Sign Language Recognition (Slr), Iwan Sandjaja, Ahmad Alsharoa, Donald Wunsch, Jian Liu

Electrical and Computer Engineering Faculty Research & Creative Works

This paper surveyed several significant papers on specific topics applying the Hidden Markov Models (HMMs) for Sign Language Recognition (SLR), divided into five main episodes: Classical HMMs, Extended HMMs, HMMs and Machine Learning, HMMs and Sensor Fusion, and HMMs and Big Data. This stringent survey would contribute significantly to advanced research on unification brain models such as neural networks, adaptive resonance theory, and confabulation theory. First, the HMM was introduced as one of the popular methods of performing SLR, and each episode of its development was expounded. In each episode, a main paper and several supporting papers were summarized. Next, …

Tightening Qc Relaxations Of Ac Optimal Power Flow Through Improved Linear Convex Envelopes, Mohammad Rasoul Narimani, Daniel K. Molzahn, Katherine R. Davis, Mariesa L. Crow

Electrical and Computer Engineering Faculty Research & Creative Works

AC optimal power flow (AC OPF) is a fundamental problem in power system operations. Accurately modeling the network physics via the AC power flow equations makes AC OPF a challenging nonconvex problem. To search for global optima, recent research has developed various convex relaxations that bound the optimal objective values of AC OPF problems. The QC relaxation convexifies the AC OPF problem by enclosing the non-convex terms within convex envelopes. The QC relaxation's accuracy strongly depends on the tightness of these envelopes. This paper proposes two improvements for tightening QC relaxations of OPF problems. We first consider a particular nonlinear …