Digital Commons Network^™

A New Cache Replacement Policy In Named Data Network Based On Fib Table Information, Mehran Hosseinzadeh, Neda Moghim, Samira Taheri, Nasrin Gholami

VMASC Publications

Named Data Network (NDN) is proposed for the Internet as an information-centric architecture. Content storing in the router’s cache plays a significant role in NDN. When a router’s cache becomes full, a cache replacement policy determines which content should be discarded for the new content storage. This paper proposes a new cache replacement policy called Discard of Fast Retrievable Content (DFRC). In DFRC, the retrieval time of the content is evaluated using the FIB table information, and the content with less retrieval time receives more discard priority. An impact weight is also used to involve both the grade of retrieval …

Small-Strain Site Response Of Soft Soils In The Sacramento-San Joaquin Delta Region Of California Conditioned On Vₛ₃₀ And Mhvsr, Tristan E. Buckreis, Jonathan P. Stewart, Scott J. Brandenberg, Pengfei Wang

Civil & Environmental Engineering Faculty Publications

Sites located in the Sacramento-San Joaquin Delta region of California typically have peaty-organic soils near the ground surface, which are characteristically soft, with shear wave velocities as low as 30 m/s. These unusually soft geotechnical conditions, which are outside the range of applicability of existing ergodic site amplification models, can be anticipated to produce significant site effects during earthquake shaking. We evaluate site response for 36 seismic stations in the Delta region using non-ergodic methods with low-amplitude ground motion data. We model first-order site effects using a period-dependent relation conditioned on the 30 m time-averaged shear wave velocity (V …

A Survey On Few-Shot Class-Incremental Learning, Songsong Tian, Lusi Li, Weijun Li, Hang Ran, Xin Ning, Prayag Tiwari

Computer Science Faculty Publications

Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental …

A Chinese Power Text Classification Algorithm Based On Deep Active Learning, Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu

Computer Science Faculty Publications

The construction of knowledge graph is beneficial for grid production, electrical safety protection, fault diagnosis and traceability in an observable and controllable way. Highly-precision text classification algorithm is crucial to build a professional knowledge graph in power system. Unfortunately, there are a large number of poorly described and specialized texts in the power business system, and the amount of data containing valid labels in these texts is low. This will bring great challenges to improve the precision of text classification models. To offset the gap, we propose a classification algorithm for Chinese text in the power system based on deep …

Autonomous Strike Uavs For Counterterrorism Missions: Challenges And Preliminary Solutions, Meshari Aljohani, Ravi Mukkamala, Stephan Olariu

Computer Science Faculty Publications

UAVs are becoming a crucial tool in modern warfare, primarily due to their cost-effectiveness, risk reduction, and ability to perform a wider range of activities. The use of autonomous UAVs to conduct strike missions against highly valuable targets is the focus of this research. Due to developments in ledger technology, smart contracts, and machine learning, such activities formerly carried out by professionals or remotely flown UAVs are now feasible. Our study provides the first in-depth analysis of challenges and potential solutions for successful implementation of an autonomous UAV mission.

Plasma Protein Signatures Of Adult Asthma, Gordon J. Smilnak, Yura Lee, Abhijnan Chattopadhyay, Annah B. Wyss, Julie D. White, Sinjini Sikdar, Jianping Jin, Andrew J. Grant, Alison A. Motsinger-Reif, Jian-Liang Li, Mikyeong Lee, Bing Yu, Stephanie J. London

Mathematics & Statistics Faculty Publications

Background: Adult asthma is complex and incompletely understood. Plasma proteomics is an evolving technique that can both generate biomarkers and provide insights into disease mechanisms. We aimed to identify plasma proteomic signatures of adult asthma.

Methods: Protein abundance in plasma was measured in individuals from the Agricultural Lung Health Study (ALHS) (761 asthma, 1095 non-case) and the Atherosclerosis Risk in Communities study (470 asthma, 10,669 non-case) using the SOMAScan 5K array. Associations with asthma were estimated using covariate adjusted logistic regression and meta-analyzed using inverse-variance weighting. Additionally, in ALHS, we examined phenotypes based on both asthma and seroatopy (asthma with …

Synergistic Effects Of Nanosecond Pulsed Plasma And Electric Field On Inactivation Of Pancreatic Cancer Cells In Vitro, Edwin A. Oshin, Zobia Minhas, Ruben M. L. Colunga Biancatelli, John D. Catravas, Richard Heller, Siqi Guo, Chunqi Jiang

Bioelectrics Publications

Nanosecond pulsed atmospheric pressure plasma jets (ns-APPJs) produce reactive plasma species, including charged particles and reactive oxygen and nitrogen species (RONS), which can induce oxidative stress in biological cells. Nanosecond pulsed electric field (nsPEF) has also been found to cause permeabilization of cell membranes and induce apoptosis or cell death. Combining the treatment of ns-APPJ and nsPEF may enhance the effectiveness of cancer cell inactivation with only moderate doses of both treatments. Employing ns-APPJ powered by 9 kV, 200 ns pulses at 2 kHz and 60-nsPEF of 50 kV/cm at 1 Hz, the synergistic effects on pancreatic cancer cells (Pan02) …

Nanosecond Pulsed Electric Fields Increase Antibiotic Susceptibility In Methicillin-Resistant Staphylococcus Aureus, Alexandra E. Chittams-Miles, Areej Malik, Erin B. Purcell, Claudia Muratori

Bioelectrics Publications

Staphylococcus aureus is the leading cause of skin and soft-tissue infections (SSTIs). SSTIs caused by bacteria resistant to antimicrobials, such as methicillin-resistant S. aureus (MRSA), are increasing in incidence and have led to higher rates of hospitalization. In this study, we measured MRSA inactivation by nanosecond pulsed electric fields (nsPEF), a promising new cell ablation technology. Our results show that treatment with 120 pulses of 600 ns duration (28 kV/cm, 1 Hz), caused modest inactivation, indicating cellular damage. We anticipated that the perturbation created by nsPEF could increase antibiotic efficacy if nsPEF were applied as a co-treatment. To test this …

Energy Efficiency In Additive Manufacturing: Condensed Review, Ismail Fidan, Vivekanand Naikwadi, Suhas Alkunte, Roshan Mishra, Khalid Tantawi

Engineering Technology Faculty Publications

Today, it is significant that the use of additive manufacturing (AM) has growing in almost every aspect of the daily life. A high number of sectors are adapting and implementing this revolutionary production technology in their domain to increase production volumes, reduce the cost of production, fabricate light weight and complex parts in a short period of time, and respond to the manufacturing needs of customers. It is clear that the AM technologies consume energy to complete the production tasks of each part. Therefore, it is imperative to know the impact of energy efficiency in order to economically and properly …

Decompositions Of Nonlinear Input-Output Systems To Zero The Output, W. Steven Gray, Kurusch Ebrahimi-Fard, Alexander Schmeding

Electrical & Computer Engineering Faculty Publications

Consider an input–output system where the output is the tracking error given some desired reference signal. It is natural to consider under what conditions the problem has an exact solution, that is, the tracking error is exactly the zero function. If the system has a well defined relative degree and the zero function is in the range of the input–output map, then it is well known that the system is locally left invertible, and thus, the problem has a unique exact solution. A system will fail to have relative degree when more than one exact solution exists. The general goal …

Urban Flood Extent Segmentation And Evaluation From Real-World Surveillance Camera Images Using Deep Convolutional Neural Network, Yidi Wang, Yawen Shen, Behrouz Salahshour, Mecit Cetin, Khan Iftekharuddin, Navid Tahvildari, Guoping Huang, Devin K. Harris, Kwame Ampofo, Jonathan L. Goodall

Civil & Environmental Engineering Faculty Publications

This study explores the use of Deep Convolutional Neural Network (DCNN) for semantic segmentation of flood images. Imagery datasets of urban flooding were used to train two DCNN-based models, and camera images were used to test the application of the models with real-world data. Validation results show that both models extracted flood extent with a mean F1-score over 0.9. The factors that affected the performance included still water surface with specular reflection, wet road surface, and low illumination. In testing, reduced visibility during a storm and raindrops on surveillance cameras were major problems that affected the segmentation of flood extent. …

Abmscore: A Heuristic Algorithm For Forming Strategic Coalitions In Agent-Based Simulation, Andrew J. Collins, Gayane Grigoryan

Engineering Management & Systems Engineering Faculty Publications

Integrating human behavior into agent-based models has been challenging due to its diversity. An example is strategic coalition formation, which occurs when an individual decides to collaborate with others because it strategically benefits them, thereby increasing the expected utility of the situation. An algorithm called ABMSCORE was developed to help model strategic coalition formation in agent-based models. The ABMSCORE algorithm employs hedonic games from cooperative game theory and has been applied to various situations, including refugee egress and smallholder farming cooperatives. This paper discusses ABMSCORE, including its mechanism, requirements, limitations, and application. To demonstrate the potential of ABMSCORE, a new …

From Carnival Games To Plastic Filters: Preparing Elementary Preservice Teachers To Teach Engineering, Jennifer Kidd, Pilar Pazos, Kristie Gutierrez, Danielle Rhemer, Stacie Ringleb, Krishna Kaipa, Isaac Kumi, Orlando Ayala, Francisco Cima

Teaching & Learning Faculty Publications

Preservice teachers (PSTs) in an educational foundations course were tasked with leading elementary students in an engineering design challenge. In order to explore different approaches for helping the PSTs develop competence in engineering education, two implementation methods were tested. In Spring 2022, PSTs collaborated with undergraduate engineering students to develop carnival-themed design challenge lessons. In Fall 2022, PSTs worked with their PST classmates to teach a professionally prepared engineering lesson focused on designing plastic filters. PSTs’ knowledge of engineering and engineering pedagogy were compared across the two semesters using an exploratory approach. Both groups showed increases in engineering knowledge and …

De-Risking Pretreatment Of Microalgae To Produce Fuels And Chemical Co-Products, Jacob S. Kruger, Skylar Schutter, Eric P. Knoshaug, Bonnie Panczak, Hannah Alt, Alicia Sowell, Stafanie Van Wychen, Matthew Fowler, Kyoko Hirayama, Anuj Thakkar, Sandeep Kumar

Civil & Environmental Engineering Faculty Publications

Conversion of microalgae to renewable fuels and chemical co-products by pretreating and fractionation holds promise as an algal biorefinery concept, but a better understanding of the pretreatment performance as a function of algae strain and composition is necessary to de-risk algae conversion operations. Similarly, there are few examples of algae pretreatment at scales larger than the bench scale. This work aims to de-risk algal biorefinery operations by evaluating the pretreatment performance across nine different microalgae samples and five different pretreatment methods at small (5 mL) scale and further de-risk the operation by scaling pretreatment for one species to the 80 …

Using Feature Selection Enhancement To Evaluate Attack Detection In The Internet Of Things Environment, Khawlah Harahsheh, Rami Al-Naimat, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

The rapid evolution of technology has given rise to a connected world where billions of devices interact seamlessly, forming what is known as the Internet of Things (IoT). While the IoT offers incredible convenience and efficiency, it presents a significant challenge to cybersecurity and is characterized by various power, capacity, and computational process limitations. Machine learning techniques, particularly those encompassing supervised classification techniques, offer a systematic approach to training models using labeled datasets. These techniques enable intrusion detection systems (IDSs) to discern patterns indicative of potential attacks amidst the vast amounts of IoT data. Our investigation delves into various aspects …

Contribution Of High Turbidity To Tidal Dynamics In A Curved Channel In Zhoushan Islands, China, Li Li, Fangzhou Shen, Zhiguo He, Gangfeng Ma, Jiachen Wang, Kailong Huangfu

Civil & Environmental Engineering Faculty Publications

The curved tidal channel, Luotou Deep-water Navigational Channel, is the main channel of the Ningbo Zhoushan Port, which is ranked first in the world. Tidal dynamics in the channel are spatially and temporally asymmetric. In this study, the three-dimensional tidal dynamics in the channel were analyzed using field data and simulated using FVCOM. The results show that the tides in the channel flood/ebb along the northern/southern bank near the bottom/surface layer and these asymmetries are due to the imbalanced Coriolis force, centrifugal force, sea-level gradient, and density gradient. Residual current velocity peaks (0.7 m/s) in the middle of the channel …

Identification And Characterization Of Two Novel Kcnh2 Mutations Contributing To Long Qt Syndrome, Anthony Owusu-Mensah, Jacqueline Treat, Joyce Bernardi, Ryan Pfeiffer, Robert Goodrow, Bright Tsevi, Victoria Lam, Michel Audette, Jonathan M. Cordeiro, Makarand Deo

Electrical & Computer Engineering Faculty Publications

We identified two different inherited mutations in KCNH2 gene, or human ether-a-go-go related gene (hERG), which are linked to Long QT Syndrome. The first mutation was in a 1-day-old infant, whereas the second was in a 14-year-old girl. The two KCNH2 mutations were transiently transfected into either human embryonic kidney (HEK) cells or human induced pluripotent stem-cell derived cardiomyocytes. We performed associated multiscale computer simulations to elucidate the arrhythmogenic potentials of the KCNH2 mutations. Genetic screening of the first and second index patients revealed a heterozygous missense mutation in KCNH2, resulting in an amino acid change (P632L) in the …

Sub-Band Backdoor Attack In Remote Sensing Imagery, Kazi Aminul Islam, Hongyi Wu, Chunsheng Xin, Rui Ning, Liuwan Zhu, Jiang Li

Electrical & Computer Engineering Faculty Publications

Remote sensing datasets usually have a wide range of spatial and spectral resolutions. They provide unique advantages in surveillance systems, and many government organizations use remote sensing multispectral imagery to monitor security-critical infrastructures or targets. Artificial Intelligence (AI) has advanced rapidly in recent years and has been widely applied to remote image analysis, achieving state-of-the-art (SOTA) performance. However, AI models are vulnerable and can be easily deceived or poisoned. A malicious user may poison an AI model by creating a stealthy backdoor. A backdoored AI model performs well on clean data but behaves abnormally when a planted trigger appears in …

Domain Adaptive Federated Learning For Multi-Institution Molecular Mutation Prediction And Bias Identification, W. Farzana, M. A. Witherow, I. Longoria, M. S. Sadique, A. Temtam, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Deep learning models have shown potential in medical image analysis tasks. However, training a generalized deep learning model requires huge amounts of patient data that is usually gathered from multiple institutions which may raise privacy concerns. Federated learning (FL) provides an alternative to sharing data across institutions. Nonetheless, FL is susceptible to a few challenges including inversion attacks on model weights, heterogenous data distributions, and bias. This study addresses heterogeneity and bias issues for multi-institution patient data by proposing domain adaptive FL modeling using several radiomics (volume, fractal, texture) features for O6-methylguanine-DNA methyltransferase (MGMT) classification across multiple institutions. The proposed …

Underlying Substrate Effect On Electrochemical Activity For Hydrogen Evolution Reaction With Low-Platinum-Loaded Catalysts, Baleeswaraiah Muchharla, Peter V. Sushko, Kishor K. Sadasivuni, Wei Cao, Akash Tomar, Hani Elsayed-Ali, Adetayo Adedeji, Abdennaceur Karoui, Joshua M. Spurgeon, Bijandra Kumar

Electrical & Computer Engineering Faculty Publications

Platinum is known as the best catalyst for the hydrogen evolution reaction (HER) but the scarcity and high cost of Pt limit its widespread applicability. Herein, the role of the underlying substrate on the HER activity of dispersed Pt atoms is uncovered. A direct current magnetron sputtering technique is utilized to deposit transition metal (TM) thin films of W, Ti, and Ta as underlying substrates for extremely low loading of Pt (

Recent Progress In Microrna Detection Using Integrated Electric Fields And Optical Detection Methods, Logeeshan Velmanickam, Dharmakeerthi Nawarathna

Electrical & Computer Engineering Faculty Publications

Low-cost, highly-sensitivity, and minimally invasive tests for the detection and monitoring of life-threatening diseases and disorders can reduce the worldwide disease burden. Despite a number of interdisciplinary research efforts, there are still challenges remaining to be addressed, so clinically significant amounts of relevant biomarkers in body fluids can be detected with low assay cost, high sensitivity, and speed at point-of-care settings. Although the conventional proteomic technologies have shown promise, their ability to detect all levels of disease progression from early to advanced stages is limited to a limited number of diseases. One potential avenue for early diagnosis is microRNA (miRNA). …

Effect Of Resin Bleed Out On Compaction Behavior Of The Fiber Tow Gap Region During Automated Fiber Placement Manufacturing, Von Clyde Jamora, Virginia Rauch, Sergii G. Kravchenko, Oleksandr G. Kravchenko

Mechanical & Aerospace Engineering Faculty Publications

Automated fiber placement is a state-of-the-art manufacturing method which allows for precise control over layup design. However, AFP results in irregular morphology due to fiber tow deposition induced features such as tow gaps and overlaps. Factors such as the squeeze flow and resin bleed out, combined with large non-linear deformation, lead to morphological variability. To understand these complex interacting phenomena, a coupled multiphysics finite element framework was developed to simulate the compaction behavior around fiber tow gap regions, which consists of coupled chemo-rheological and flow-compaction analysis. The compaction analysis incorporated a visco-hyperelastic constitutive model with anisotropic tensorial prepreg viscosity, which …

Development Of A Two-Finger Haptic Robotic Hand With Novel Stiffness Detection And Impedance Control, Vahid Mohammadi, Ramin Shahbad, Mojtaba Hosseini, Mohammad Hossein Gholampour, Saeed Shiry Ghidary, Farshid Najafi, Ahad Behboodi

Mechanical & Aerospace Engineering Faculty Publications

Haptic hands and grippers, designed to enable skillful object manipulation, are pivotal for high-precision interaction with environments. These technologies are particularly vital in fields such as minimally invasive surgery, where they enhance surgical accuracy and tactile feedback: in the development of advanced prosthetic limbs, offering users improved functionality and a more natural sense of touch, and within industrial automation and manufacturing, they contribute to more efficient, safe, and flexible production processes. This paper presents the development of a two-finger robotic hand that employs simple yet precise strategies to manipulate objects without damaging or dropping them. Our innovative approach fused force-sensitive …

Parametric Optimization Of Friction Stir Welding Of Aa6061-T6 Samples Using The Copper Donor Stir-Assisted Material Method, Aiman H. Al-Allaq, Joseph Maniscalco, Srinivasa Naik Bhukya, Zhenhua Wu, Abdelmageed Elmustafa

Mechanical & Aerospace Engineering Faculty Publications

This study presents an optimization of the process parameters for the effect of copper (Cu) donor material percentage on the friction stir welding (FSW) of AA6061-T6 alloy. Extensive factorial experiments were conducted to determine the significance of the rotational speed (ω), the transverse speed (v), the interface coefficient of friction (μ), and the Cu donor material percentage in the plunge, left, right, and downstream zones. Design Expert 13 software was used to identify the number of simulation experiments to be conducted using the Abaqus simulation software. From Design Expert 13, which is a thorough multi-objective optimization analysis software, we were …

Ethical Decision-Making In Older Drivers During Critical Driving Situations: An Online Experiment, Amandeep Singh, Sarah Yahoodik, Yovela Murzello, Samuel Petkac, Yusuke Yamani, Siby Samuel

Psychology Faculty Publications

The present study examined the impact of aging on ethical decision-making in simulated critical driving scenarios. 204 participants from North America, grouped into two age groups (18–30 years and 65 years and above), were asked to decide whether their simulated automated vehicle should stay in or change from the current lane in scenarios mimicking the Trolley Problem. Each participant viewed a video clip rendered by the driving simulator at Old Dominion University and pressed the space-bar if they decided to intervene in the control of the simulated automated vehicle in an online experiment. Bayesian hierarchical models were used to analyze …

Quantification Of Antiviral Drug Tenofovir (Tfv) By Surface-Enhanced Raman Spectroscopy (Sers) Using Cumulative Distribution Functions (Cdfs), Marguerite R. Butler, Jana Hrncirova, Meredith Clark, Sucharita Dutta, John B. Cooper

Chemistry & Biochemistry Faculty Publications

Surface-enhanced Raman spectroscopy (SERS) is an ultrasensitive spectroscopic technique that generates signal-enhanced fingerprint vibrational spectra of small molecules. However, without rigorous control of SERS substrate active sites, geometry, surface area, or surface functionality, SERS is notoriously irreproducible, complicating the consistent quantitative analysis of small molecules. While evaporatively prepared samples yield significant SERS enhancement resulting in lower detection limits, the distribution of these enhancements along the SERS surface is inherently stochastic. Acquiring spatially resolved SERS spectra of these dried surfaces, we have shown that this enhancement is governed by a power law as a function of analyte concentration. Consequently, by definition, …

Ground Tire Rubber As A Sustainable Additive: Transforming Desert Sand Behavior, Nabil Ismael, Dalya Ismael, Asmaa Al-Ahmad

Engineering Technology Faculty Publications

Managing waste tires presents a significant challenge globally, particularly in regions experiencing high temperatures and shortage of landfill sites. This issue is affecting countries like Kuwait, where the abundance of waste tires is a major source of environmental and safety risks, particularly during the intensely hot summer months. This extreme heat has sparked numerous fires, leading to substantial air pollution due to thick black smoke. Given the limited disposal options, recycling waste tires and finding practical applications for ground tire rubber (GTR) is essential. To address the challenge, a comprehensive laboratory testing program was conducted, using locally produced rubber aggregates …

The Transformative Integration Of Artificial Intelligence With Cmmc And Nist 800-171 For Advanced Risk Management And Compliance, Mia Lunati

Cybersecurity Undergraduate Research Showcase

This paper explores the transformative potential of integrating Artificial Intelligence (AI) with established cybersecurity frameworks such as the Cybersecurity Maturity Model Certification (CMMC) and the National Institute of Standards and Technology (NIST) Special Publication 800-171. The thesis argues that the relationship between AI and these frameworks has the capacity to transform risk management in cybersecurity, where it could serve as a critical element in threat mitigation. In addition to addressing AI’s capabilities, this paper acknowledges the risks and limitations of these systems, highlighting the need for extensive research and monitoring when relying on AI. One must understand boundaries when integrating …

Potential Security Vulnerabilities In Raspberry Pi Devices With Mitigation Strategies, Briana Tolleson

Cybersecurity Undergraduate Research Showcase

For this research project I used a Raspberry Pi device and conducted online research to investigate potential security vulnerabilities along with mitigation strategies. I configured the Raspberry Pi by using the proper peripherals such as an HDMI cord, a microUSB adapter that provided 5V and at least 700mA of current, a TV monitor, PiSwitch, SD Card, keyboard, and mouse. I installed the Rasbian operating system (OS). The process to install the Rasbian took about 10 minutes to boot starting at 21:08 on 10/27/2023 and ending at 21:18. 1,513 megabytes (MB) was written to the SD card running at (2.5 MB/sec). …

Integrating Ai Into Uavs, Huong Quach

Cybersecurity Undergraduate Research Showcase

This research project explores the application of Deep Learning (DL) techniques, specifically Convolutional Neural Networks (CNNs), to develop a smoke detection algorithm for deployment on mobile platforms, such as drones and self-driving vehicles. The project focuses on enhancing the decision-making capabilities of these platforms in emergency response situations. The methodology involves three phases: algorithm development, algorithm implementation, and testing and optimization. The developed CNN model, based on ResNet50 architecture, is trained on a dataset of fire, smoke, and neutral images obtained from the web. The algorithm is implemented on the Jetson Nano platform to provide responsive support for first responders. …