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Articles 14221 - 14250 of 198032

Full-Text Articles in Engineering

Benchmarking Batch-Effect Correction Methods Towards The Construction Of A Triple-Negative Breast Cancer Cell Atlas, Peter Scheible, Amy H. Tang, Jing He, Jiangwen Sun Jan 2025

Benchmarking Batch-Effect Correction Methods Towards The Construction Of A Triple-Negative Breast Cancer Cell Atlas, Peter Scheible, Amy H. Tang, Jing He, Jiangwen Sun

Computer Science Faculty Publications

Triple-negative breast cancer (TNBC) requires detailed cellular mapping given its aggressive nature, immense tumor heterogeneity and genetic diversity. We integrated 156,794 cells from six scRNA-seq datasets—including tumors, metastases, and cell lines—to build a TNBC scRNA cell atlas, focusing on batch effect mitigation while maintaining biological and molecular details. Preprocessing f ilters noise, normalizes data, and leverages PCA for integration readiness. We utilized scANVI, a semi-supervised tool, to align datasets, preserving TNBC’s complex tumor heterogeneity via marker annotations [1]. UMAPs demonstrate biological clustering in integrated data, contrasted with datasetdriven unintegrated patterns. Assessments verifying effective batch correction. This method aligns with NASA’s …

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Uncertainty-Aware Deep Learning Framework For Forecasting Coastal Water Level In Virginia Beach, Md Mahmudul Hasan, Malachi Schram, Sridhar Katragadda, Diana Mcspadden, Alisa N. Udomvisawakul, Heather Richter, Frank Liu Jan 2025

Uncertainty-Aware Deep Learning Framework For Forecasting Coastal Water Level In Virginia Beach, Md Mahmudul Hasan, Malachi Schram, Sridhar Katragadda, Diana Mcspadden, Alisa N. Udomvisawakul, Heather Richter, Frank Liu

Computer Science Faculty Publications

Coastal areas like Virginia Beach, USA, are increasingly vulnerable to flooding. To mitigate the impact of flooding, it is crucial for the City of Virginia Beach to have reliable 72-hour-ahead (3 days) forecasts of water levels at key gauge locations. To support this effort, several sensors have been installed throughout the city to monitor water levels and other environmental parameters such as wind speed, precipitation, and atmospheric pressure. Leveraging sensor data from one of these locations, we developed an uncertainty-aware deep learning model to forecast water levels. We employed deep quantile regression (DQR) to quantify variability in the predictions and …

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Coldstartcpi: Induced-Fit Theory-Guided Dti Predictive Model With Improved Generalization Performance, Qichang Zhao, Haochen Zhao, Linyuan Gao, Kai Zheng, Yajie Li, Qiao Ling, Jing Tang, Yaohang Li, Jianxin Wang Jan 2025

Coldstartcpi: Induced-Fit Theory-Guided Dti Predictive Model With Improved Generalization Performance, Qichang Zhao, Haochen Zhao, Linyuan Gao, Kai Zheng, Yajie Li, Qiao Ling, Jing Tang, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Predicting compound-protein interactions (CPIs) plays a crucial role in drug discovery. Traditional methods, based on the key-lock theory and rigid docking, often fail with novel compounds and proteins due to their inability to account for molecular flexibility and the high sparsity of CPI data. Here, we introduce ColdstartCPI, a framework inspired by induced-fit theory, which leverages unsupervised pre-training features and a Transformer module to learn both compound and protein characteristics. ColdstartCPI treats proteins and compounds as flexible molecules during inference, aligning with biological insights. It outperforms state-of-the-art sequence-based models, particularly for unseen compounds and proteins, and shows strong generalization capability …

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Normalizing Images In Various Weather And Lighting Conditions Using Colorpix2pix Generative Adversarial Network, Sanjida Tasnim, Ashif Mahmud Mostafa, Azmain Morshed, Namreen Shaiyaz, Shakib Mahmud Dipto, Saad Aloteibi, Mohammad Ali Moni, Md. Golam Rabiul Alam, Md. Ashraful Alam Jan 2025

Normalizing Images In Various Weather And Lighting Conditions Using Colorpix2pix Generative Adversarial Network, Sanjida Tasnim, Ashif Mahmud Mostafa, Azmain Morshed, Namreen Shaiyaz, Shakib Mahmud Dipto, Saad Aloteibi, Mohammad Ali Moni, Md. Golam Rabiul Alam, Md. Ashraful Alam

Computer Science Faculty Publications

Autonomous vehicles (AVs) are widely regarded as the future of transportation due to their tremendous benefits and user comfort. However, the AVs have been struggling with very crucial challenges, such as achieving reliable accuracy in object detection as well as faster computation required for quick decision-making. In recent years, perception systems in driverless cars have been significantly enhanced, mainly due to advances in deep-learning-based object detection systems. However, these perception systems are still heavily affected by environmental variables, such as changes in illumination, refractive interference, and adverse weather conditions, which may compromise their reliability and safety. This research proposes an …

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Insights In Adaptation: Examining Self-Reflection Strategies Of Job Seekers With Visual Impairments In India, Akshay Kolgar Nayak, Yash Prakash, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok Jan 2025

Insights In Adaptation: Examining Self-Reflection Strategies Of Job Seekers With Visual Impairments In India, Akshay Kolgar Nayak, Yash Prakash, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

Significant changes in the digital employment landscape, driven by rapid technological advancements and the COVID-19 pandemic, have introduced new opportunities for blind and visually impaired (BVI) individuals in developing countries like India. However, a significant portion of the BVI population in India remains unemployed despite extensive accessibility advancements and job search interventions. Therefore, we conducted semi-structured interviews with 20 BVI persons who were either pursuing or recently sought employment in the digital industry. Our findings reveal that despite gaining digital literacy and extensive training, BVI individuals struggle to meet industry requirements for fulfilling job openings. While they engage in self-reflection …

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S²Il: Structurally Stable Incremental Learning, S. Balasubramanian, P. Yedu Krishna, Talasu Sai Sriram, M. Sai Subramaniam, Manepalli Pranav Phanindra Sai, Ravi Mukkamala Jan 2025

S²Il: Structurally Stable Incremental Learning, S. Balasubramanian, P. Yedu Krishna, Talasu Sai Sriram, M. Sai Subramaniam, Manepalli Pranav Phanindra Sai, Ravi Mukkamala

Computer Science Faculty Publications

Feature Distillation (FD) strategies are proven to be effective in mitigating Catastrophic Forgetting (CF) seen in Class Incremental Learning (CIL). However, current FD approaches enforce strict alignment of feature magnitudes and directions across incremental steps, limiting the model’s ability to adapt to new knowledge. In this paper, we propose Structurally Stable Incremental Learning (S²IL), a FD method for CIL that mitigates forgetting by focusing on preserving the overall spatial patterns of features which promote flexible (plasticity) yet stable representations that preserve old knowledge (stability). We also demonstrate that our proposed method S²IL achieves strong incremental accuracy and outperforms other FD …

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An Optimized Generalized Multi-Color Point Implicit Solver For Intel Gpus Using Oneapi Esimd, Joseph Wassell, Mohammad Zubair, Aaron Walden, Gabriel Nastac, Eric Nielsen, Timothée Ewart Jan 2025

An Optimized Generalized Multi-Color Point Implicit Solver For Intel Gpus Using Oneapi Esimd, Joseph Wassell, Mohammad Zubair, Aaron Walden, Gabriel Nastac, Eric Nielsen, Timothée Ewart

Computer Science Faculty Publications

This paper presents an efficient implementation of a linear-solver kernel relevant to FUN3D, a suite of computational fluid dynamics software developed at NASA’s Langley Research Center. The linear solver is optimized for a range of block sizes commonly used in FUN3D. The implementation targets Aurora, the Argonne Leadership Computing Facility’s (ALCF) exascale machine featuring Intel Data Center Max 1550 GPUs. The linear solver’s performance is memory bandwidth-bound due to its low arithmetic intensity. The primary performance challenges stem from variable matrix row lengths and indirect memory access patterns inherent in unstructured-grid applications. Variable block sizes introduce additional complexity through differing …

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Delivery Of Medical Supplies To Remote Locations Via Unmanned Aerial Vehicles: Approaches, Challenges, And Solutions, Meshari Aljohani, Ravi Mukkamala, Stephanie Olariu Jan 2025

Delivery Of Medical Supplies To Remote Locations Via Unmanned Aerial Vehicles: Approaches, Challenges, And Solutions, Meshari Aljohani, Ravi Mukkamala, Stephanie Olariu

Computer Science Faculty Publications

Unmanned Aerial Vehicles (UAVs) are becoming more important in improving healthcare logistics, in particular due to their cost effectiveness, minimized risk, and versatile operational capabilities. This study explores the deployment of autonomous UAVs to deliver medical supplies to remote areas. Advances in ledger technology, smart contracts, and machine learning have transformed tasks previously managed by human teams or manually controlled UAVs into fully autonomous missions. We present a comprehensive analysis of the challenges and initial solutions vital for the effective use of autonomous UAVs in the delivery of medical supplies. In addition, we propose a machine-learning model to optimize UAV …

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Investigation Of New Superconducting Materials For The Next Generation High-Performance Rf Superconducting Cavities For Particle Accelerators, Alex Gurevich, Jean Delayen, Chang-Beom Eom, Gianluigi Ciovati Jan 2025

Investigation Of New Superconducting Materials For The Next Generation High-Performance Rf Superconducting Cavities For Particle Accelerators, Alex Gurevich, Jean Delayen, Chang-Beom Eom, Gianluigi Ciovati

Physics Faculty Publications

In this DOE-funded project DE-SC0010081-020 Old Dominion University (ODU) in collaboration with University of Wisconsin (UW) and Jefferson Laboratory have investigated both experimentally and theoretically electromagnetic response and losses in multilayered superconducting structures made of new SRF materials which can push the field and Q performance limits of accelerating cavities.

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Modeling Strain And Quantum Confinement In Gaas/GaXIn1-XP Superlattices For Spin-Polarized Electron Sources, A. Kachwala, G. Blume, S. Marsillac, J. Grames, M. Grau Jan 2025

Modeling Strain And Quantum Confinement In Gaas/GaXIn1-XP Superlattices For Spin-Polarized Electron Sources, A. Kachwala, G. Blume, S. Marsillac, J. Grames, M. Grau

Physics Faculty Publications

In this study, we systematically design and simulate a series of GaAs-based superlattice configurations aimed at enhancing heavy-hole–light-hole band splitting while simultaneously optimizing band alignment to reduce the conduction band barrier, thereby facilitating efficient electron transport. These combined effects are crucial for achieving high electron spin polarization and high quantum efficiency, the two key performance metrics of next-generation spin-polarized electron sources. We investigated three types of superlattice architectures: (1) compressively strained GaAs wells on GaInP barriers, yielding a maximum band splitting of 140 meV, (2) lattice-matched GaAs/GaInP structures, resulting in the maximum band splitting of 75 meV, and (3) tensile …

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A Biphasic Microstructural Model Of Skeletal Muscle To Study Structure-Function Mechanisms Under Multiaxial Tensile And Compressive Conditions, Benjamin Wheatley, Minhaj U. Bhuiyan, Sabrina S. Lorza, Kevin M. Moerman, Pierre-Yves Rohan Jan 2025

A Biphasic Microstructural Model Of Skeletal Muscle To Study Structure-Function Mechanisms Under Multiaxial Tensile And Compressive Conditions, Benjamin Wheatley, Minhaj U. Bhuiyan, Sabrina S. Lorza, Kevin M. Moerman, Pierre-Yves Rohan

Faculty Journal Articles

No abstract provided.

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A Framework For Fair And Trustworthy Multimodal Video Anomaly Detection: Fusion, Dataset Quality Auditing, Explainability, And Fairness, Omeshamisu Judith Anigala Jan 2025

A Framework For Fair And Trustworthy Multimodal Video Anomaly Detection: Fusion, Dataset Quality Auditing, Explainability, And Fairness, Omeshamisu Judith Anigala

Electronic Theses and Dissertations

No abstract provided.

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Largely Enhanced Out-Of-Plane Electromechanical Coupling Effects In Two-Dimensional Molybdenum-Disulfide/Boron-Nitride Heterostructures, Qiong Liu, Vijay Kumar Choyal, James E. Morris, Timon Rabczuk, Xiaoning Jiang, Xiaoying Zhuang Jan 2025

Largely Enhanced Out-Of-Plane Electromechanical Coupling Effects In Two-Dimensional Molybdenum-Disulfide/Boron-Nitride Heterostructures, Qiong Liu, Vijay Kumar Choyal, James E. Morris, Timon Rabczuk, Xiaoning Jiang, Xiaoying Zhuang

Electrical and Computer Engineering Faculty Publications and Presentations

The electromechanical coupling effects in two-dimensional (2D) transition metal dichalcogenides (TMDs) have attracted great interest. However, for 2D TMDs, piezoelectricity is confined to the basal plane, and the flexoelectricity-derived out-of-plane electromechanical response is usually faint, limiting the applications of this material family using the out-of-plane electromechanical effects. Here, this work reports a facile strategy to greatly enhance the out-of-plane electromechanical response of hexagonal molybdenum disulfide (2H-MoS2) nanoflakes by stacking monolayer hexagonal boron nitride (h-BN) on 2H-MoS2 nanoflakes to form MoS2/BN heterostructures. The deff/33 coefficient of MoS2/BN can reach a value comparable …

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Ecobuoys For Scalable Oceanography, Anuscheh Nawaz, Michael Steele, Ruth Branch, David Burnett, Kuotian Liao, Mallory Parker, Eleftheria Roumeli Jan 2025

Ecobuoys For Scalable Oceanography, Anuscheh Nawaz, Michael Steele, Ruth Branch, David Burnett, Kuotian Liao, Mallory Parker, Eleftheria Roumeli

Electrical and Computer Engineering Faculty Publications and Presentations

An approach to scalable surface-drifting buoys is needed to enable the high spatial and temporal resolution of oceanographic data that the science and meteorological communities are asking for. With the number of active buoys predicted to increase by a factor of 100 or more, the impact on the environment becomes even more important. Here, we present a pathway to a scalable and sustainable generation of buoys. We identify the main criteria to be used when developing such buoys to be low cost, with reliable data and neutral or even positive environmental impact. For each buoy subsystem—hull, electronics, energy generation and …

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An Offline Approach For Frequency Event Detection In Power Systems: Tkeo And Statistical Analysis, Hussain A. Alghamdi, Midrar Adham, Umar Farooq, Robert Bass Jan 2025

An Offline Approach For Frequency Event Detection In Power Systems: Tkeo And Statistical Analysis, Hussain A. Alghamdi, Midrar Adham, Umar Farooq, Robert Bass

Electrical and Computer Engineering Faculty Publications and Presentations

The increasing integration of renewable energy sources in power grids and the transition from conventional thermal-based generation to inverter-based resources for power generation have reduced power system inertia and increased the rate of change of frequency, which constitutes a challenge for frequency stability in modern power systems. These changes necessitate robust frequency event detection algorithms that can rapidly and accurately identify events, providing essential support to maintain stability. This paper proposes a frequency event detection algorithm with six tunable parameters for offline post-processing, leveraging the Teager-Kaiser Energy Operator method and statistical analysis. The algorithm is tested on a dataset comprised …

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Work In Progress: Using Internships As Means For Indirect Assessment Of Abet Criteria 3 “1-7” Student Outcomes, Robert Bass Jan 2025

Work In Progress: Using Internships As Means For Indirect Assessment Of Abet Criteria 3 “1-7” Student Outcomes, Robert Bass

Electrical and Computer Engineering Faculty Publications and Presentations

The Electrical & Computer Engineering (ECE) Department at Portland State University (PSU) has developed a Power Engineering Internship (PEI) program that provides engineering career development pathways within the electric utility industry. The PEI is supported by several U.S. Department of Energy grants that aim to develop quality career opportunities and develop a future electric utility workforce that represents the nation’s diverse populations. The PSU ECE Department intends to use surveys of internship participants as assessment tools for its ABET accreditation process, in particular, the ABET Criteria 3 Student Outcomes (SOs). SOs relate to the knowledge, skills, and behaviors that students …

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Environmentally Friendly Chelation For Enhanced Algal Biomass Deashing, Agyare Asante, George Daramola, Ryan W. Davis, Sandeep Kumar Jan 2025

Environmentally Friendly Chelation For Enhanced Algal Biomass Deashing, Agyare Asante, George Daramola, Ryan W. Davis, Sandeep Kumar

Civil & Environmental Engineering Faculty Publications

High ash content in algal biomass limits its suitability for biofuel production by reducing combustion efficiency and increasing fouling. This study presents a green deashing strategy using nitrilotriacetic acid (NTA) and deionized (DI) water to purify Scenedesmus algae, which was selected for its high ash removal potential. The optimized sequential treatment (DI, NTA chelation, and DI+NTA treatment at 90–130 °C) achieved up to 83.07% ash removal, reducing ash content from 15.2% to 3.8%. Elevated temperatures enhanced the removal of calcium, magnesium, and potassium, while heavy metals like lead and copper were reduced below detection limits. CHN analysis confirmed minimal …

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Sustainable Management Of Erosive Shores: An Interdisciplinary Approach Integrating Engineering And Social Sciences At A Tide-Dominant Beach Area, Jun Ik Sohn, Hyun Dong Kim, Kiran Adhithya Ramakrishnan Jan 2025

Sustainable Management Of Erosive Shores: An Interdisciplinary Approach Integrating Engineering And Social Sciences At A Tide-Dominant Beach Area, Jun Ik Sohn, Hyun Dong Kim, Kiran Adhithya Ramakrishnan

Civil & Environmental Engineering Faculty Publications

This study investigates the causes and consequences of shoreline erosion at Kkotji Beach, a prominent tourist destination on the west coast of South Korea, where the degradation of the coastal environment has increasingly threatened the local tourism industry and economy, by employing a mixed-methods approach that combines field observations with MIKE 21 hydrodynamic simulations and by integrating perspectives from coastal engineering and the social sciences to develop practical, site-specific strategies for mitigating erosion, enhancing public awareness, and promoting sustainable coastal planning and development that support long-term environmental resilience and economic stability. The results show that dominant ebb currents drive southward …

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Physics-Informed Deep Learning With Kalman Filter Mixture For Traffic State Prediction, Niharika Deshpande, Hyoshin (John) Park Jan 2025

Physics-Informed Deep Learning With Kalman Filter Mixture For Traffic State Prediction, Niharika Deshpande, Hyoshin (John) Park

Engineering Management & Systems Engineering Faculty Publications

Accurate traffic forecasting is crucial for understanding and managing congestion for efficient transportation planning. However, conventional approaches often neglect epistemic uncertainty, which arises from incomplete knowledge across different spatiotemporal scales. This study addresses this challenge by introducing a novel methodology to establish dynamic spatiotemporal correlations that captures the unobserved heterogeneity in travel time through distinct peaks in probability density functions, guided by physics-based principles. We propose an innovative approach to modifying both prediction and correction steps of the Kalman Filter (KF) algorithm by leveraging established spatiotemporal correlations. Central to our approach is the development of a novel deep learning model …

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Proactive Distributed Emergency Response With Heterogeneous Tasks Allocation, Justice Darko, Hyoshin Park Jan 2025

Proactive Distributed Emergency Response With Heterogeneous Tasks Allocation, Justice Darko, Hyoshin Park

Engineering Management & Systems Engineering Faculty Publications

Traditionally, traffic incident management (TIM) programs coordinate the deployment of emergency resources to immediate incident requests without accommodating the interdependencies on incident evolutions in the environment. However, ignoring these inherent interdependencies while making current deployment decisions is shortsighted, and the resulting naive deployment strategy can significantly worsen the overall incident delay impact on the network. The interdependencies on incident evolution in the environment, including those between incident occurrences and those between resource availability in near-future requests and the anticipated duration of the immediate incident request, should be considered through a look-ahead model when making current-stage deployment decisions. This study develops …

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Quantifying Multidimensional Effects Of Physicochemical Parameters On Pfas Adsorption Using A Hybrid Response Surface Methodology-Machine Learning Approach, Harsh V. Patel, Jazmin Green, John Park, Stephanie Luster-Teasley Pass, Renzun Zhao Jan 2025

Quantifying Multidimensional Effects Of Physicochemical Parameters On Pfas Adsorption Using A Hybrid Response Surface Methodology-Machine Learning Approach, Harsh V. Patel, Jazmin Green, John Park, Stephanie Luster-Teasley Pass, Renzun Zhao

Engineering Management & Systems Engineering Faculty Publications

Per- and polyfluoroalkyl substances (PFAS) contamination has posed a significant environmental and public health challenge due to their ubiquitous nature. Adsorption has emerged as a promising remediation technique, yet optimizing adsorption efficiency remains complex due to the diverse physicochemical properties of PFAS and the wide range of adsorbent materials. Traditional modeling approaches, such as response surface methodology (RSM), struggled to capture nonlinear interactions, while standalone machine learning (ML) models required extensive datasets. This study addressed these limitations by developing hybrid RSM-ML models to improve the prediction and optimization of PFAS adsorption. A comprehensive dataset was constructed using experimental adsorption data, …

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Zero Trust Architecture As A Risk Countermeasure In Small-Medium Enterprises And Advanced Technology Systems, Ahmed M. Abdelmagid, Rafael Diaz Jan 2025

Zero Trust Architecture As A Risk Countermeasure In Small-Medium Enterprises And Advanced Technology Systems, Ahmed M. Abdelmagid, Rafael Diaz

Engineering Management & Systems Engineering Faculty Publications

The growing sophistication of cyberattacks exposes small- and medium-sized businesses (SMBs) to a widening range of security risks. As these threats evolve in complexity, the need for advanced security measures becomes increasingly pressing. This necessitates a proactive approach to defending against potential cyber intrusions. Emerging technologies, such as blockchain, artificial intelligence, and Zero Trust security framework, offer crucial tools for strengthening the digital infrastructure of SMBs. The Zero Trust architecture (ZTA) holds significant promise as a critical strategy for protecting SMBs. While existing literature explores the implementation of ZTA in various business settings, discussions specifically addressing the financial, human resource, …

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Integrating Data Management Plans Into The Unified Architecture Framework Standards Views, Cansu Yalim, Holly A. H. Handley Jan 2025

Integrating Data Management Plans Into The Unified Architecture Framework Standards Views, Cansu Yalim, Holly A. H. Handley

Engineering Management & Systems Engineering Faculty Publications

System Architecting translates an operational concept into a model of the system to be realized. There is a need for a Data Management Plan (DMP) to be included in the overall system engineering process with the advent of Digital Engineering. Data longevity, accessibility, and integrity can all be improved throughout the system's lifecycle by a well-defined DMP. System engineers use an architecture framework to arrange the system data into several sets of viewpoints. Incorporating a DMP at this point specifies the procedures for gathering, storing, retrieving, and maintaining data to ensure that all interested parties have access to current, correct …

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The Association Between The Multidimensional Evaluation Of Attitudes Toward Security Recommendations And The Intention Of Following Those Recommendations, Miguel A. Toro-Jarrin, Pilar Pazos, Miguel A. Padilla Jan 2025

The Association Between The Multidimensional Evaluation Of Attitudes Toward Security Recommendations And The Intention Of Following Those Recommendations, Miguel A. Toro-Jarrin, Pilar Pazos, Miguel A. Padilla

Engineering Management & Systems Engineering Faculty Publications

This study focused on the attitudes toward security recommendations as determinants of compliance with these recommendations in the workplace. It aimed to determine whether attitudes toward an overall concept (security recommendations in organizations) are associated with Information Security (IS) actions. We also examined the relationship between various dimensions of attitudes toward security recommendations and IS actions that follow these recommendations. We conducted a national survey of American workers. Six hundred and eighty-two responses were retained. We also collected demographic data to explore their moderating role in the relation between predictors and IS action-intention. The survey included four workplace scenarios and …

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Sampling Balanced High-Quality Data To Train An Automatic Mesh Generator, Jie Pan, Jingwei Huang, Gengdong Cheng, Yong Zeng Jan 2025

Sampling Balanced High-Quality Data To Train An Automatic Mesh Generator, Jie Pan, Jingwei Huang, Gengdong Cheng, Yong Zeng

Engineering Management & Systems Engineering Faculty Publications

In real-world scenarios, high-quality data are often scarce and imbalanced, yet it is essential for the optimal performance of data-driven algorithmic models. Data synthesis methods are commonly used to address this issue; however, they typically rely heavily on the original dataset, which limits their ability to significantly improve performance. This article presents a quality function-based method for directly generating high-quality data and applies it to a mesh generation algorithm to demonstrate its efficiency and effectiveness. The proposed approach samples input-output pairs of the algorithm based on their feature spaces, selects high-quality samples using a defined quality function that evaluates the …

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Predictive Maintenance In Naval Vessel Propulsion Systems For Enhanced Marine Operations Using A Bigmm-Hmm Framework With Divergence-Based Clustering, Farshid Javadnejad, Hyoshin John Park, Samuel Kovacic, Andres Sousa-Poza Jan 2025

Predictive Maintenance In Naval Vessel Propulsion Systems For Enhanced Marine Operations Using A Bigmm-Hmm Framework With Divergence-Based Clustering, Farshid Javadnejad, Hyoshin John Park, Samuel Kovacic, Andres Sousa-Poza

Engineering Management & Systems Engineering Faculty Publications

This study introduces a BiGMM-HMM Integration Framework designed to improve predictive maintenance strategies for naval vessel propulsion systems, addressing the need for efficient and reliable operation in marine engineering applications. The framework effectively manages multimodal sensor data by leveraging a unique combination of Gaussian Mixture Models (GMMs) and Hidden Markov Models (HMMs) in a bidirectional architecture. It analyses the dynamic interactions between sensors and subsystems. Two preprocessing methods are evaluated: Method 1 focuses on subsystem interactions, employing divergence-based root cause analysis to identify key sensor variables by clustering of sensors and subsystems. In contrast, Method 2 processes the entire dataset …

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A Governance-Centric Framework For Strengthening Healthcare Cybersecurity: A Systems Perspective, Sujatha Alla, Sai Gireesh Komaragiri, Teresa Duvall, Satluk Karahan, Nagesh Bheesetty, Vijay Kumar Chattu Jan 2025

A Governance-Centric Framework For Strengthening Healthcare Cybersecurity: A Systems Perspective, Sujatha Alla, Sai Gireesh Komaragiri, Teresa Duvall, Satluk Karahan, Nagesh Bheesetty, Vijay Kumar Chattu

Engineering Management & Systems Engineering Faculty Publications

Healthcare systems face unprecedented security and privacy challenges due to increasing digitization and interconnectedness. This paper provides a comprehensive analysis of these challenges by examining various cyberattacks, defensive mechanisms, and governance frameworks within modern healthcare infrastructure. The research systematically categorizes prevalent security threats, such as ransomware, insider threats, and data breaches, identifying vulnerabilities specific to healthcare systems. Furthermore, the study evaluates current defensive strategies, including encryption techniques, access control systems, and intrusion detection tools, assessing their effectiveness against complex cyber threats. A key focus is placed on governance structures and their role in cybersecurity resilience. The research explores how regulatory …

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Reduced Set-Shifting Processing Speed In Male Rats Following Low Dose (10 Cgy) Proton Exposure, Hui Ho Vanessa Chang, Gyutae Kim, Kyu-Sung Kim, Richard A. Britten Jan 2025

Reduced Set-Shifting Processing Speed In Male Rats Following Low Dose (10 Cgy) Proton Exposure, Hui Ho Vanessa Chang, Gyutae Kim, Kyu-Sung Kim, Richard A. Britten

Center for Integrative Neuroscience and Inflammatory Diseases (CINID) Faculty Publications

Space radiation (SR) exposure poses significant biomedical risks, including effects on the central nervous system (CNS). These risks are particularly relevant to cognitive function during long-duration space missions. One critical cognitive skill is decision-making, which requires attentional set-shifting (ATSET)—the ability to quickly assess problems, evaluate options, and select the best actions. Previous studies have shown that exposure to However, the impact of low LET (< 1 keV/μm) protons, which significantly contribute to the total radiation flux astronauts encounter within spacecraft, on ATSET performance is unknown. To address this gap, we evaluated the effects of cranial irradiation with 10 cGy of 100 MeV/n protons (LET = 0.732 keV/μm) on ATSET performance in male Sprague-Dawley rats. We also investigated whether concurrent exposure to variable gravity (hypergravity step-up, step down, purported to have the same effect as exposure to microgravity (another major spaceflight stressor) exacerbated SR-induced cognitive deficits. Our findings indicate that proton exposure alone significantly impaired ATSET performance, as evidenced by decreased processing speed while performing compound discrimination reversal and extra-dimensional shifting. Notably, no additive or synergistic effects were observed when hypergravity was combined with proton exposure. The impact that low-dose proton exposure has on CNS functionality, particularly in reducing processing speed during complex tasks, warrant further investigation. If similar cognitive deficits were to occur in astronauts exposed to galactic cosmic rays, mission success and safety could be significantly compromised.

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Contextual Augmentation In Artificial Intelligence, Emmanuel Joshua Balogun Jan 2025

Contextual Augmentation In Artificial Intelligence, Emmanuel Joshua Balogun

College of Graduate Studies: Theses & Dissertations

Contextual understanding is a significant challenge of Large Language Models (LLMs), which are typically trained on general-purpose datasets. Due to this, LLMs fail to capture nuanced or domain-specific information and may struggle to interpret user queries accurately. Consequently, prompt engineering can become complex in automating, and LLMs are prone to “hallucinating”—generating random or irrelevant texts—when they lack sufficient context. This undermines their ability to provide focused, accurate responses. Accordingly, this thesis seeks to enhance the contextual understanding capabilities of Artificial Intelligence systems to facilitate more precise and relevant answer generation. Study A looks into a new approach to combating misinformation …

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Sogar: Self-Supervised Spatiotemporal Attention-Based Social Group Activity Recognition, Naga Venkata Sai Raviteja Chappa, Pha Nguyen, Alexander H. Nelson, Han-Seok Seo, Xin Li, Page Daniels Dobbs, Khoa Luu Jan 2025

Sogar: Self-Supervised Spatiotemporal Attention-Based Social Group Activity Recognition, Naga Venkata Sai Raviteja Chappa, Pha Nguyen, Alexander H. Nelson, Han-Seok Seo, Xin Li, Page Daniels Dobbs, Khoa Luu

Electrical Engineering and Computer Science Faculty Publications and Presentations

Social group activity recognition is crucial for various applications including surveillance, human-robot interaction, and behavioral analysis. Current approaches often require extensive manual annotations and rely heavily on pre-trained detectors, limiting their practical applications. Additionally, existing methods struggle to effectively model long-term spatiotemporal relationships in group activities. This paper introduces a novel approach to Social Group Activity Recognition (SoGAR) using Self-supervised Transformers network that can effectively utilize unlabeled video data. To extract spatio-temporal information, we create local and global views with varying frame rates. Our self-supervised objective ensures that features extracted from contrasting views of the same video are consistent across …

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