Digital Commons Network^™

Human In Vivo Medial Gastrocnemius Gear During Active And Passive Muscle Lengthening: Effect Of Inconsistent Methods And Nomenclature On Data Interpretation, Matheus Daros Pinto, Kazunori Nosaka, James M. Wakeling, Anthony J. Blazevich

Research outputs 2022 to 2026

‘Muscle gear’ is calculated as the ratio of fascicle-to-muscle length change, strain, or velocity. Inconsistencies in nomenclature and definitions of gear exist across disciplines partly due to differences in fascicle [curved (Lf) versus linear (Lf,straight)] and muscle [whole-muscle belly (Lb) versus belly segment (Lb,segment)] length calculation methods. We tested whether these differences affect gear magnitude during passive and active muscle lengthening of human medial gastrocnemius of young men (n=13, 26.3±5.0 years) using an isokinetic dynamometer. Lb, Lb,segment, Lf and Lf,straight were measured from motion analysis and ultrasound imaging data. Downshifts in belly gear but not belly segment gear occurred with …

The Wacdt, A Modern Vigilance Task For Network Defense, Oliver A. Guidetti, Craig Speelman, Peter Bouhlas

Research outputs 2022 to 2026

Vigilance decrement refers to a psychophysiological decline in the capacity to sustain attention to monotonous tasks after prolonged periods. A plethora of experimental tasks exist for researchers to study vigilance decrement in classic domains such as driving and air traffic control and baggage security; however, the only cyber vigilance tasks reported in the research literature exist in the possession of the United States Air Force (USAF). Moreover, existent cyber vigilance tasks have not kept up with advances in real-world cyber security and consequently no longer accurately reflect the cognitive load associated with modern network defense. The Western Australian Cyber Defense …

Identity-Based Edge Computing Anonymous Authentication Protocol, Naixin Kang, Zhenhu Ning, Shiqiang Zhang, Sadaqat Ur Rehman, Muhammad Waqas

Research outputs 2022 to 2026

With the development of sensor technology and wireless communication technology, edge computing has a wider range of applications. The privacy protection of edge computing is of great significance. In the edge computing system, in order to ensure the credibility of the source of terminal data, mobile edge computing (MEC) needs to verify the signature of the terminal node on the data. During the signature process, the computing power of edge devices such as wireless terminals can easily become the bottleneck of system performance. Therefore, it is very necessary to improve efficiency through computational offloading. Therefore, this paper proposes an identity-based …

Intrusion Detection Based On Bidirectional Long Short-Term Memory With Attention Mechanism, Yongjie Yang, Shanshan Tu, Raja Hashim Ali, Hisham Alasmary, Muhammad Waqas, Muhammad Nouman Amjad

Research outputs 2022 to 2026

With the recent developments in the Internet of Things (IoT), the amount of data collected has expanded tremendously, resulting in a higher demand for data storage, computational capacity, and real-time processing capabilities. Cloud computing has traditionally played an important role in establishing IoT. However, fog computing has recently emerged as a new field complementing cloud computing due to its enhanced mobility, location awareness, heterogeneity, scalability, low latency, and geographic distribution. However, IoT networks are vulnerable to unwanted assaults because of their open and shared nature. As a result, various fog computing-based security models that protect IoT networks have been developed. …

Developmental Consequences Of Hatching Asynchrony In Boat-Tailed Grackles (Quiscalus Major), Emily Radner

Scripps Senior Theses

Boat-tailed grackles (Quiscalus major) typically lay clutches of three eggs and begin incubation before the clutch is complete, resulting in hatching asynchrony. To study the developmental consequences of hatching asynchrony on boat-tailed grackles, data loggers measured nest temperatures and were compared to ambient temperatures to see when incubation began, and it appeared to begin after the second egg is laid. The third laid egg had a significantly shorter lay to pip time and lay to hatch time than the first laid egg, highlighting the need to investigate the consequences of a shortened incubation time on development. To do …

Computational Investigation Of The Ionization Potential Of Lead Sulfide Quantum Dots, Jessica Beyer

Scripps Senior Theses

The purpose of this work was to determine the impact of quantum dot size on ionization potential and to determine how the presence of carbonyl-based ligands affect the ionization potential of lead sulfide quantum dot systems. Ionization potential (IP) is defined as the energy required to remove an electron from an atom, molecule, or material. IP helps scientists determine how reactive the material of interest is, which is crucial information when manufacturing nanomaterials. Accurate quantum chemical calculations of ionization potential are challenging due to the computational cost associated with the numerical solution of the Dyson equation. In this work, the …

An Autistic Spine: The Importance Of Disability Representation In Theatre, Ella S. De Castro

Scripps Senior Theses

The goal of this thesis is to discuss the ways in which the current theatre industry is alienating disabled actors and creatives. The stigmatization around disability in any field is contributing to the added misunderstanding and misrepresentation of disability on stage and in film. This is done through a dramaturgical exploration of the history and present of disability representation in theatre; followed by the summary and analysis of a live theatrical recital with scenes from plays with psychological themes. To add to the message, the recital was also performed and run entirely by people with cognitive or physical disabilities.

Interpersonal Emotions As Emergent Phenomena: Social Neuroscience Beyond Western Cultural Constructions, Kaitlyn Penchina

Scripps Senior Theses

Because science as it exists today is a cultural construction of the West, studies of neuroscience have often been limited by Western perspectives. In particular, the Western proclivity towards individualism has led to a field of neuroscience which has historically focused on studying single individuals, as opposed to social or collective neuroscience. For the most part, it has just been assumed that collective phenomena such as interpersonal emotions must be able to be reduced in terms of individual phenomena such as individual emotions. However, closer review reveals that interpersonal emotions have emergent properties that individual emotions alone do not account …

(E)S(S): An Exploration Of Sound And Self, Natalie Antonia Rezek

Scripps Senior Theses

This text consists of free verse poetry interspersed amongst a narrative-oriented collection of Spenserian stanzas, Petrarchan sonnets, and Shakespearean sonnets. It aims to explore the importance of independence and personal identity through a sonic landscape and personified portrayal of the letter S.

Wine, Culture, And Economics: The Barolo Model For Small Historical Wine Regions, Ravenna June Lee

Scripps Senior Theses

This thesis investigates the resurgence of the Barolo wine industry as a paradigm for small historical wine regions seeking to revitalize and assert their independence in the face of corporate domination. Employing a multidisciplinary approach, the study blends economic analysis with cultural preservation, focusing on the unique circumstances of the Barolo region.

The Italian section of the thesis delves into the preservation of wine culture, emphasizing the role of tradition in maintaining the passion for wine among future generations amidst the threats of globalization. This section contributes to the broader discourse on regional identity, economic sustainability, and the intricate interplay …

Perspective On Predictive Modeling: Current Status, New High-Order Methodology And Outlook For Energy Systems, Dan Gabriel Cacuci

Faculty Publications

This work presents a perspective on deterministic predictive modeling methodologies, which aim at extracting best-estimate values for model responses and parameters along with reduced predicted uncertainties for these best-estimate values. The two oldest such methodologies are the data-adjustment method, which stems from the nuclear energy field, and the data-assimilation method, which is implemented in the geophysical sciences. Both of these methodologies attempt to minimize, in the least-square sense, a user-defined functional that represents the discrepancies between computed and measured model responses. These two methodologies were briefly reviewed and shown to be inconsistent even to first-order in the sensitivities of the …

Optical Tweezers: Exerting Force With Light, Gabriella Seifert

Scripps Senior Theses

Photons carry momentum. When a tightly-focused beam of photons hit a particle, they transfer some of their momentum to the particle, exerting a force. Optical tweezers take advantage of this phenomenon to trap (or “tweeze”) a spherical bead just after the focus of a diverging laser beam, creating a potential well that pulls in beads. In this thesis, I predict the force exerted on trapped beads and measure the actual force using an optical tweezers setup that I built. To predict the force, I follow the path of all possible rays from a diverging beam incident on a spherical bead …

Wastewater Pandemic Preparedness: Toward An End-To-End Pathogen Monitoring Program, Justin R Clark, Austen Terwilliger, Vasanthi Avadhanula, Michael Tisza, Juwan Cormier, Sara Javornik-Cregeen, Matthew Clayton Ross, Kristi Louise Hoffman, Catherine Troisi, Blake Hanson, Joseph Petrosino, John Balliew, Pedro A Piedra, Janelle Rios, Jennifer Deegan, Cici Bauer, Fuqing Wu, Kristina D Mena, Eric Boerwinkle, Anthony W Maresso

Faculty, Staff and Student Publications

Molecular analysis of public wastewater has great potential as a harbinger for community health and health threats. Long-used to monitor the presence of enteric viruses, in particular polio, recent successes of wastewater as a reliable lead indicator for trends in SARS-CoV-2 levels and hospital admissions has generated optimism and emerging evidence that similar science can be applied to other pathogens of pandemic potential (PPPs), especially respiratory viruses and their variants of concern (VOC). However, there are substantial challenges associated with implementation of this ideal, namely that multiple and distinct fields of inquiry must be bridged and coordinated. These include engineering, …

Reinforcement Learning-Guided Quadratically Constrained Quadratic Programming For Enhanced Convergence And Optimality, Chaoying Pei, Zhi Xu, Sixiong You, Jeffrey Sun, Ran Dai

Mechanical and Aerospace Engineering Faculty Research & Creative Works

In the context of Quadratically Constrained Quadratic Programming (QCQP) with dynamic parameters, the effectiveness of various optimization approaches is heavily influenced by the quality of the initial guess. To address this challenge, this paper proposes a novel approach that leverages reinforcement learning (RL) to generate high-performing initial guesses for iterative algorithms, with the dynamic parameters serving as inputs. Our approach aims to accelerate convergence and improve the objective value, thereby enabling efficient and effective solutions to the QCQP problem under variability. In this study, we evaluate the proposed approach by applying it to an iterative algorithm, specifically the Iterative Rank …

Mixed-Input Learning For Multi-Point Landing Guidance With Hazard Avoidance Part I: Offline Mission Planning Based On Multi-Stage Optimization, Chaoying Pei, Sixiong You, Ran Dai, Jeremy R. Rea

Mechanical and Aerospace Engineering Faculty Research & Creative Works

This paper proposes a multi-stage optimization framework based on iterative second-order cone programming (SOCP) to solve the three-dimensional (3D) multi-point landing guidance (MLG) problem with hazard avoidance. The approach is used to generate the offline optimal trajectories for database construction in Part II of this paper, it aims to select a safe landing point while finding an optimal path to the selected landing point with minimum fuel consumption. First, by introducing binary variables associated with quadratic constraints, the MLG problem with hazard avoidance is equivalently reformulated as a quadratically constrained quadratic programming (QCQP) problem. Next, to solve the reformulated QCQP …

Feature-Based Learning For Optimal Abort Guidance, Vinay Kenny, Sixiong You, Godfrey Hendrix, Chaoying Pei, Roha Gul, Ran Dai, Jeremy R. Rea

Mechanical and Aerospace Engineering Faculty Research & Creative Works

The abort mission refers to the mission where the landing vehicle needs to terminate the landing mission when an anomaly happens and be safely guided to the desired orbit. This paper focuses on solving the time-optimal abort guidance (TOAG) problem in real-time via the feature-based learning method. First, according to the optimal control theory, the features are identified to represent the optimal solutions of TOAG using a few parameters. After that, a sufficiently large dataset of time-optimal abort trajectories is generated offline by solving the TOAG problems with different initial conditions. Then the features are extracted for all generated cases. …

Lifelong Deep Learning-Based Control Of Robot Manipulators, Irfan Ganie, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

This study proposes a lifelong deep learning control scheme for robotic manipulators with bounded disturbances. This scheme involves the use of an online tunable deep neural network (DNN) to approximate the unknown nonlinear dynamics of the robot. The control scheme is developed by using a singular value decomposition-based direct tracking error-driven approach, which is utilized to derive the weight update laws for the DNN. To avoid catastrophic forgetting in multi-task scenarios and to ensure lifelong learning (LL), a novel online LL scheme based on elastic weight consolidation is included in the DNN weight-tuning laws. Our results demonstrate that the resulting …

Rafid: A Lightweight Approach To Radio Frequency Interference Detection In Time Domain Using Lstm And Statistical Analysis, Luke A. Smith, Vishesh Kumar Tanwar, Maciej Jan Zawodniok, Sanjay Kumar Madria

Electrical and Computer Engineering Faculty Research & Creative Works

Recently, the utilization of Radio Frequency (RF) devices has increased exponentially over numerous vertical platforms. This rise has led to an abundance of Radio Frequency Interference (RFI) continues to plague RF systems today. The continued crowding of the RF spectrum makes RFI efficient and lightweight mitigation critical. Detecting and localizing the interfering signals is the foremost step for mitigating RFI concerns. Addressing these challenges, we propose a novel and lightweight approach, namely RaFID, to detect and locate the RFI by incorporating deep neural networks (DNNs) and statistical analysis via batch-wise mean aggregation and standard deviation (SD) calculations. RaFID investigates the …

Modeling Thermal Radiation In Combustion Environments: Progress And Challenges, Sandip Mazumder, Somesh P. Roy

Mechanical Engineering Faculty Research and Publications

Modeling thermal radiation in combustion environments can be extremely challenging for two main reasons. First, the radiative transfer equation (RTE), which is the cornerstone of modeling radiation in such environments, is a five-dimensional integro-differential equation. Second, the absorption and scattering coefficients of molecular gases and particulates prevalent in combustion environments oscillate strongly with the wavenumber (or wavelength), i.e., the medium is strongly nongray, requiring the solution of the RTE for a large number of wavenumbers. This article reviews the progress that has been made in this area to date with an emphasis on the work performed over the past three …

Continual Learning-Based Optimal Output Tracking Of Nonlinear Discrete-Time Systems With Constraints: Application To Safe Cargo Transfer, Behzad Farzanegan, S. (Sarangapani) Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This Paper Addresses a Novel Lifelong Learning (LL)-Based Optimal Output Tracking Control of Uncertain Non-Linear Affine Discrete-Time Systems (DT) with State Constraints. First, to Deal with Optimal Tracking and Reduce the Steady State Error, a Novel Augmented System, Including Tracking Error and its Integral Value and Desired Trajectory, is Proposed. to Guarantee Safety, an Asymmetric Barrier Function (BF) is Incorporated into the Utility Function to Keep the Tracking Error in a Safe Region. Then, an Adaptive Neural Network (NN) Observer is Employed to Estimate the State Vector and the Control Input Matrix of the Uncertain Nonlinear System. Next, an NN-Based …

Analysis On Extraction Of Potential Radiated Emission Limit Line For Data Center Equipment From 10 Ghz To 40 Ghz, Zhekun Peng, Wei Zhang, Jong Hwa Kwon, Donghyun Kim

Electrical and Computer Engineering Faculty Research & Creative Works

The Radiated Emission (RE) Potential Limit Line for Router System is Analyzed from 10 GHz to 40 GHz based on CISPR TR 16-4-4 Standard. Statistical Data is Collected for the Limit Line Extraction from Measurement, Numerical Analysis, 3D Full-Wave Simulation and Literature Studies. All the Factors Considered in Limit Line Calculation Are Analyzed for Data Center Equipment Operation. the Result of Extracted Potential Limit Line Shows the Rising Limit Line between 10 GHz and 40 GHz Frequency Band for Potential Future Radiated Emission Limit Line above 40 GHz Targeted for High-Speed Equipment Radiated Emission. This Paper is Not a Standard …

Challenges And Solutions For Automotive Ota Testing, Jiyu Wu, Yihong Qi, Penghui Shen, Wei Yu, Lie Liu, James L. Drewniak

Electrical and Computer Engineering Faculty Research & Creative Works

OTA (Over-The-Air) Testing is Essential for Developing Assisted and Autonomous Driving Systems in Vehicles, as It Plays a Crucial Role in the Localization, Perception, and Intelligent Driving Capabilities of ICVs (Intelligent Connected Vehicles). Automotive Antennas, Typically Much Smaller in Size Than the Vehicle itself and Can Be Located in Various Positions, Require Spherical Near-Field Measurement for OTA Testing. While There Are Established Standards for OTA Testing Methods and Uncertainties for Mobile Devices, Base Stations, and Satellite Components, There Are Still Many Challenges in the OTA Testing of Automotive Systems. These Challenges, specifically in SISO (Single Input Single Output) and MIMO …

Design And Construction Of An Arbitrary Pulse Compressive Amplifier, Cody Goins, Aaron Harmon, Victor Khilkevich, Daryl G. Beetner

Electrical and Computer Engineering Faculty Research & Creative Works

Compressive Pulse Amplifiers Are a Class of Amplifiers that Convert Long Low Amplitude Signals into Very Broadband Pulses of High Amplitude, Yielding a Very High Instantaneous Peak Power Output Pulse. However, in the Realm of Electronic Immunity and Susceptibility Testing, Very Broadband Short Pulses Are Not Always Desired. This Work Presents a Design for a Compressive Amplifier that is Aimed at Creating Arbitrary Pulsed Signals of Varying Bandwidths. Limitations of the Achievable Gain and Methods Used Are Discussed.

Towards Robust Consensus For Intelligent Decision-Making In Iot Blockchain Networks, Charles Rawlins, S. (Sarangapani) Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

Distributed consensus is the core aspect of blockchain protocol security design. Recent protocols like IOTA have improved concurrency and scalability over Proof-of-work (PoW) with Bitcoin but have core design decisions that are inefficient for limited devices and do not take advantage of previous network experience to reduce calculations. This work proposes the first blockchain consensus protocol based on active machine-learning decisions, called Proof-of-history (PoH). PoH is setup as a distributed reinforcement-learning task for monitoring classification and training of blockchain transactions with an inner deep classifier. Early theoretical analysis and simulations show that PoH is robust to uncoordinated byzantine attacks through …

Improved Intelligent Ledger Construction For Realistic Iot Blockchain Networks, Charles Rawlins, S. (Sarangapani) Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

Scalability is essential for next generation blockchain technology to integrate with large mobile networks like Internet of Things (IoT). The IOTA distributed ledger protocol has combined transaction generation and verification to address this, but at the expense of increased reliance on connectivity to resolve conflicts with a novel ledger data structure. Intelligent Ledger Construction (ILC) was proposed as an auditable lightweight reinforcement-learning scheme to address this constraint with proposal of local conflict resolution with machine-learning classification. This effort presents an improved reliability reward model to enhance training for ILC and further reduce adversarial gaming and resource usage. Testing this revision …

Predicting Dissolution Kinetics Of Tricalcium Silicate Using Deep Learning And Analytical Models, Taihao Han, Sai Akshay Ponduru, Arianit Reka, Jie Huang, Gaurav Sant, Aditya Kumar

Electrical and Computer Engineering Faculty Research & Creative Works

The dissolution kinetics of Portland cement is a critical factor in controlling the hydration reaction and improving the performance of concrete. Tricalcium silicate (C₃S), the primary phase in Portland cement, is known to have complex dissolution mechanisms that involve multiple reactions and changes to particle surfaces. As a result, current analytical models are unable to accurately predict the dissolution kinetics of C₃S in various solvents when it is undersaturated with respect to the solvent. This paper employs the deep forest (DF) model to predict the dissolution rate of C₃S in the undersaturated solvent. The …

Predicting Compressive Strength And Hydration Products Of Calcium Aluminate Cement Using Data-Driven Approach, Sai Akshay Ponduru, Taihao Han, Jie Huang, Aditya Kumar

Electrical and Computer Engineering Faculty Research & Creative Works

Calcium aluminate cement (CAC) has been explored as a sustainable alternative to Portland cement, the most widely used type of cement. However, the hydration reaction and mechanical properties of CAC can be influenced by various factors such as water content, Li₂CO₃ content, and age. Due to the complex interactions between the precursors in CAC, traditional analytical models have struggled to predict CAC binders' compressive strength and porosity accurately. To overcome this limitation, this study utilizes machine learning (ML) to predict the properties of CAC. The study begins by using thermodynamic simulations to determine the phase assemblages of …

Flipped Constitutional Supremacy: Inferior Local Law Blocking Federal Policy, Steven Ferrey

Utah Law Review

All cities and towns in the U.S. utilize electric power. Electric power needs to be generated. Now, energized by larger issues of rapid climate change, the U.S. and all nations must transition to lower-carbon-emission sources of power generation, of which wind power currently is the most prominent and used technology. Any community hostile to wind power can pass a highly restrictive amendment to its zoning ordinance that makes the community unattractive or costprohibitive to wind or other power generation projects. There is no requirement under state law for states to allow tens of thousands of cities and towns carte blanche …

Nitrate-Stimulated Release Of Naturally Occurring Sedimentary Uranium, Jeffrey Westrop, Pooja Yadav, Pj Nolan, Kate M. Campbell, Rajesh Singh, Sharon E. Bone, Alicia H. Chan, Anthony Kohtz, Donald Pan, Olivia Healy, John R. Bargar, Daniel D. Snow, Karrie A. Weber

Department of Earth and Atmospheric Sciences: Faculty Publications

Groundwater uranium (U) concentrations have been measured above the U.S. EPA maximum contaminant level (30 μg/L) in many U.S. aquifers, including in areas not associated with anthropogenic contamination by milling or mining. In addition to carbonate, nitrate has been correlated to uranium groundwater concentrations in two major U.S. aquifers. However, to date, direct evidence that nitrate mobilizes naturally occurring U from aquifer sediments has not been presented. Here, we demonstrate that the influx of high-nitrate porewater through High Plains alluvial aquifer silt sediments bearing naturally occurring U(IV) can stimulate a nitrate-reducing microbial community capable of catalyzing the oxidation and mobilization …

Holocene Variations In Lake Titicaca Water Level And Their Implications For Sociopolitical Developments In The Central Andes, Stéphane Guédron, Christophe Delaere, Sherilyn C. Fritz, Julie Tolu, Pierre Sabatier, Anne-Lise Devel, Carlos Heredia, Claire Vérin, Eduardo Q. Alves, Paul A. Baker

Department of Earth and Atmospheric Sciences: Faculty Publications

Holocene climate in the high tropical Andes was characterized by both gradual and abrupt changes, which disrupted the hydrological cycle and impacted landscapes and societies. High-resolution paleoenvironmental records are essential to contextualize archaeological data and to evaluate the sociopolitical response of ancient societies to environmental variability. Middle-to-Late Holocene water levels in Lake Titicaca were reevaluated through a transfer function model based on measurements of organic carbon stable isotopes, combined with high-resolution profiles of other geochemical variables and paleoshoreline indicators. Our reconstruction indicates that following a prolonged low stand during the Middle Holocene (4000 to 2400 BCE), lake level rose rapidly …