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Algebraic Approach To Relativistic Landau Levels In The Symmetric Gauge, Ulrich D. Jentschura Jul 2023

Algebraic Approach To Relativistic Landau Levels In The Symmetric Gauge, Ulrich D. Jentschura

Physics Faculty Research & Creative Works

We use an algebraic approach to the calculation of Landau levels for a uniform magnetic field in the symmetric gauge with a vector potential A→=12(B→xr→), where B→ is assumed to be constant. The magnetron quantum number constitutes the degeneracy index. An overall complex phase of the wave function, given in terms of Laguerre polynomials, is a consequence of the algebraic structure. The relativistic generalization of the treatment leads to fully relativistic bispinor Landau levels in the symmetric gauge, in a representation which writes the relativistic states in terms of their nonrelativistic limit, and an algebraically accessible lower bispinor component. Negative-energy …

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Galaxy Clustering In The Mira-Titan Universe. I. Emulators For The Redshift Space Galaxy Correlation Function And Galaxy-Galaxy Lensing, Juliana Kwan, Shun Saito, Alexie Leauthaud, Katrin Heitmann, Salman Habib, Nicholas Frontiere, Hong Guo, Song Huang, Adrian Pope, Sergio Rodriguéz-Torres Jul 2023

Galaxy Clustering In The Mira-Titan Universe. I. Emulators For The Redshift Space Galaxy Correlation Function And Galaxy-Galaxy Lensing, Juliana Kwan, Shun Saito, Alexie Leauthaud, Katrin Heitmann, Salman Habib, Nicholas Frontiere, Hong Guo, Song Huang, Adrian Pope, Sergio Rodriguéz-Torres

Physics Faculty Research & Creative Works

We construct accurate emulators for the projected and redshift space galaxy correlation functions and excess surface density as measured by galaxy-galaxy lensing, based on halo occupation distribution modeling. Using the complete Mira-Titan suite of 111 N-body simulations, our emulators vary over eight cosmological parameters and include the effects of neutrino mass and dynamical dark energy. We demonstrate that our emulators are sufficiently accurate for the analysis of the Baryon Oscillation Spectroscopic Survey DR12 CMASS galaxy sample over the range 0.5 ≤ r ≤ 50 h −1 Mpc. Furthermore, we show that our emulators are capable of recovering unbiased cosmological constraints …

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New Methodology For Benchmarking Hydrodynamics In Bubble Columns With Intense Internals Using The Radioactive Particle Tracking (Rpt) Technique, Omar J. Farid, Alexandre França Velo, Binbin Qi, Muthanna H. Al-Dahhan Jul 2023

New Methodology For Benchmarking Hydrodynamics In Bubble Columns With Intense Internals Using The Radioactive Particle Tracking (Rpt) Technique, Omar J. Farid, Alexandre França Velo, Binbin Qi, Muthanna H. Al-Dahhan

Chemical and Biochemical Engineering Faculty Research & Creative Works

A new methodology for implementing radioactive particle tracking (RPT) in bubble columns with intense vertical rod internals was developed and implemented to investigate the effect of dense internals on hydrodynamics. The methodology utilizes a hybrid of Monte Carlo N-Particle (MCNP) simulation and an automated RPT calibration device to generate a large number of calibration points for accurate reconstruction of the instantaneous positions of radioactive particles using a similarity algorithm. Measurements were conducted in a 6-inch (15.24 cm) Plexiglas column using an air–water system at a superficial gas velocity of 40 cm/s. Vertical Plexiglas rods 0.5 in (1.27 cm) in diameter …

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Fusion Of Microgrid Control With Model-Free Reinforcement Learning: Review And Vision, Buxin She, Fangxing Li, Hantao Cui, Jingqiu Zhang, Rui Bo Jul 2023

Fusion Of Microgrid Control With Model-Free Reinforcement Learning: Review And Vision, Buxin She, Fangxing Li, Hantao Cui, Jingqiu Zhang, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

Challenges and opportunities coexist in microgrids as a result of emerging large-scale distributed energy resources (DERs) and advanced control techniques. In this paper, a comprehensive review of microgrid control is presented with its fusion of model-free reinforcement learning (MFRL). A high-level research map of microgrid control is developed from six distinct perspectives, followed by bottom-level modularized control blocks illustrating the configurations of grid-following (GFL) and grid-forming (GFM) inverters. Then, mainstream MFRL algorithms are introduced with an explanation of how MFRL can be integrated into the existing control framework. Next, the application guideline of MFRL is summarized with a discussion of …

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Kinetic Particle Simulations Of Plasma Charging At Lunar Craters Under Severe Conditions, David Lund, Xiaoming He, Daoru Frank Han Jul 2023

Kinetic Particle Simulations Of Plasma Charging At Lunar Craters Under Severe Conditions, David Lund, Xiaoming He, Daoru Frank Han

Mathematics and Statistics Faculty Research & Creative Works

This paper presents fully kinetic particle simulations of plasma charging at lunar craters with the presence of lunar lander modules using the recently developed Parallel Immersed-Finite-Element Particle-in-Cell (PIFE-PIC) code. The computation model explicitly includes the lunar regolith layer on top of the lunar bedrock, taking into account the regolith layer thickness and permittivity as well as the lunar lander module in the simulation domain, resolving a nontrivial surface terrain or lunar lander configuration. Simulations were carried out to study the lunar surface and lunar lander module charging near craters at the lunar terminator region under mean and severe plasma environments. …

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Memory-Multi-Fractional Brownian Motion With Continuous Correlations, Wei Wang, Michał Balcerek, Krzysztof Burnecki, Aleksei V. Chechkin, Skirmantas Janušonis, Jakub Ślȩzak, Thomas Vojta, Agnieszka Wyłomańska, Ralf Metzler Jul 2023

Memory-Multi-Fractional Brownian Motion With Continuous Correlations, Wei Wang, Michał Balcerek, Krzysztof Burnecki, Aleksei V. Chechkin, Skirmantas Janušonis, Jakub Ślȩzak, Thomas Vojta, Agnieszka Wyłomańska, Ralf Metzler

Physics Faculty Research & Creative Works

We propose a generalization of the widely used fractional Brownian motion (FBM), memory-multi-FBM (MMFBM), to describe viscoelastic or persistent anomalous diffusion with time-dependent memory exponent α(t) in a changing environment. In MMFBM the built-in, long-range memory is continuously modulated by α(t). We derive the essential statistical properties of MMFBM such as its response function, mean-squared displacement (MSD), autocovariance function, and Gaussian distribution. In contrast to existing forms of FBM with time-varying memory exponents but a reset memory structure, the instantaneous dynamic of MMFBM is influenced by the process history, e.g., we show that after a steplike change of α(t) the …

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Final Report - Hyperspectral Imaging Analysis For Mechanical And Chemical Properties Of Concrete And Steel Surfaces, Genda Chen, Hongyan Ma, Pengfei Ma, Liang Fan, Abdullah Alhaj Jun 2023

Final Report - Hyperspectral Imaging Analysis For Mechanical And Chemical Properties Of Concrete And Steel Surfaces, Genda Chen, Hongyan Ma, Pengfei Ma, Liang Fan, Abdullah Alhaj

Project SN-5

This project aims to develop a hyperspectral imaging approach for characterizing the mechanical and chemical properties of concrete and steel surfaces. A dual-sensor camera is used to collect images for mortar classification, mortar compressive strength assessment, Cl- concentration determination, and steel rebar and plate corrosion evaluation. Each pixel in an image includes spectral features ranging from 400 nm to 2500 nm in wavelength. For mortar samples, reflectance intensities at a wavelength of 1920 nm to 1980 nm, associated with the O-H chemical bond, were extracted and averaged to classify the types of mortars with K-Nearest Neighbors (KNN) and Support Vector …

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Exploring Computing Science Programs' Admission Procedures With A Diversity And Inclusion Lens, Ouldooz Baghban Karimi, Giulia Toti, Mirela Gutica, Rebecca Robinson, Lisa Zhang, James H. Paterson, Peggy Lindner, Michael O'Dea Jun 2023

Exploring Computing Science Programs' Admission Procedures With A Diversity And Inclusion Lens, Ouldooz Baghban Karimi, Giulia Toti, Mirela Gutica, Rebecca Robinson, Lisa Zhang, James H. Paterson, Peggy Lindner, Michael O'Dea

Engineering Management and Systems Engineering Faculty Research & Creative Works

Computing science education has experienced low attendance and historic declines in registration from different minority groups. The past decade of enrollment surge in computer science undergraduate programs has increased the number of women and minorities in the field, but the improvements are inconsistent and less than expected. An increase in the use of computing science and in the demand of technology workforce is expected in the upcoming years. Thus, computing science is set to shape the future of technology for a diverse set of technology users. Therefore, it is important to analyze how undergraduate program admission procedures are affecting Equity, …

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Use Only What You Need: Judicious Parallelism For File Transfers In High Performance Networks, Md Arifuzzaman, Engin Arslan Jun 2023

Use Only What You Need: Judicious Parallelism For File Transfers In High Performance Networks, Md Arifuzzaman, Engin Arslan

Computer Science Faculty Research & Creative Works

Parallelism is key to efficiently utilizing high-speed research networks when transferring large volumes of data. However, the monolithic design of existing transfer applications requires the same level of parallelism to be used for reading, write, and network operations for file transfers. This, in turn, overburdens system resources since setting the parallelism level for the slowest component results in unnecessarily high parallelism for other components. Using more than necessary parallelism led to increased overhead on system resources and unfair resource allocation among competing transfers. In this paper, we introduce modular file transfer architecture, Marlin, to separate I/O and network operations for …

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Salinivibrio Sp. Eagsl As A Halophilic And Ectoine-Producing Bacteria For Broad Microbial Electrochemistry Applications, Isaac P.A. Guynn, Kevin Beaver, Erin M. Gaffney, Ana Bonizol Zani, Ashwini Dantanarayana, Shelley D. Minteer Jun 2023

Salinivibrio Sp. Eagsl As A Halophilic And Ectoine-Producing Bacteria For Broad Microbial Electrochemistry Applications, Isaac P.A. Guynn, Kevin Beaver, Erin M. Gaffney, Ana Bonizol Zani, Ashwini Dantanarayana, Shelley D. Minteer

Chemistry Faculty Research & Creative Works

Salini vibrio sp. EAGSL (S. EAGSL) is an extremophile that was isolated from the Great Salt Lake (UT, USA) in 2017, and this strain has since been the focus of promising research in the field of microbial electrochemistry. Namely, S. EAGSL is an organism with both halotolerance and electroactivity, giving this microbe the unique ability to bridge the gap between power output and halotolerance in microbial fuel cells. While studying the genome, a biosynthetic gene for ectoine was identified. Ectoine is an osmolyte that is deemed a value-added chemical due to its ability to stabilize proteins and other biomolecules in …

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Unlocking Road Stability: Machine Learning-Powered Prediction Of Bitumen Distortion Under Traffic Flow Influence, Sabahudin Vrtagic, Milan Dordevic, Fatih Dogan, Muhammed Codur, Mario Hoxha, Edis Softic Jun 2023

Unlocking Road Stability: Machine Learning-Powered Prediction Of Bitumen Distortion Under Traffic Flow Influence, Sabahudin Vrtagic, Milan Dordevic, Fatih Dogan, Muhammed Codur, Mario Hoxha, Edis Softic

Materials Science and Engineering Faculty Research & Creative Works

Road intersections are made of asphalt pavement, a popular road surface material used worldwide. The pavement may suffer deformities and deterioration, resulting in higher maintenance expenses and an elevated likelihood of road accidents, due to factors such as heavy vehicles and environmental variables like temperature and rainfall. To tackle these obstacles, researchers have devised several machine-learning algorithms and optimization techniques. These tools aim to forecast and scrutinize pavement deformation, with the goal of refining pavement design and maintenance approaches, as well as obtaining a more comprehensive comprehension of the factors that impact pavement effectiveness. This paper shows that heavy vehicles …

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Grm: Generative Relevance Modeling Using Relevance-Aware Sample Estimation For Document Retrieval, Iain Mackie, Ivan Sekulic, Shubham Chatterjee, Jeffrey Dalton, Fabio Crestani Jun 2023

Grm: Generative Relevance Modeling Using Relevance-Aware Sample Estimation For Document Retrieval, Iain Mackie, Ivan Sekulic, Shubham Chatterjee, Jeffrey Dalton, Fabio Crestani

Computer Science Faculty Research & Creative Works

Recent studies show that Generative Relevance Feedback (GRF), using text generated by Large Language Models (LLMs), can enhance the effectiveness of query expansion. However, LLMs can generate irrelevant information that harms retrieval effectiveness. To address this, we propose Generative Relevance Modeling (GRM) that uses Relevance-Aware Sample Estimation (RASE) for more accurate weighting of expansion terms. Specifically, we identify similar real documents for each generated document and use a neural re-ranker to estimate their relevance. Experiments on three standard document ranking benchmarks show that GRM improves MAP by 6-9% and R@1k by 2-4%, surpassing previous methods.

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Lagrangian Displacement Field Estimators In Cosmology, Atsuhisa Ota, Hee Jong Seo, Shun Saito, Florian Beutler Jun 2023

Lagrangian Displacement Field Estimators In Cosmology, Atsuhisa Ota, Hee Jong Seo, Shun Saito, Florian Beutler

Physics Faculty Research & Creative Works

The late-time nonlinear Lagrangian displacement field is highly correlated with the initial field, so reconstructing it could enable us to extract primordial cosmological information. Our previous work [A. Ota et al., Phys. Rev. D 104, 123508 (2021)PRVDAQ2470-001010.1103/PhysRevD.104.123508] carefully studied the displacement field reconstructed from the late-time density field using the iterative method proposed by Schmittfull et al. [Phys. Rev. D 96, 023505 (2017)PRVDAQ2470-001010.1103/PhysRevD.96.023505] and found that it does not fully converge to the true, underlying displacement field (e.g., ∼8% offset at k∼0.2 h Mpc-1 at z=0.6). We also constructed the Lagrangian perturbation theory model for the reconstructed field, but the …

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Anderson Localization Of Electromagnetic Waves In Three Dimensions, Alexey Yamilov, Sergey E. Skipetrov, Tyler W. Hughes, Momchil Minkov, Zongfu Yu, Hui Cao Jun 2023

Anderson Localization Of Electromagnetic Waves In Three Dimensions, Alexey Yamilov, Sergey E. Skipetrov, Tyler W. Hughes, Momchil Minkov, Zongfu Yu, Hui Cao

Physics Faculty Research & Creative Works

Anderson localization is a halt of diffusive wave propagation in disordered systems. Despite extensive studies over the past 40 years, Anderson localization of light in three dimensions has remained elusive, leading to the question of its very existence. Recent advances have enabled finite-difference time-domain calculations to be sped up by orders of magnitude, allowing us to conduct brute-force numerical simulations of light transport in fully disordered three-dimensional systems with unprecedented dimension and refractive index difference. We show numerically three-dimensional localization of vector electromagnetic waves in random aggregates of overlapping metallic spheres, in sharp contrast to the absence of localization for …

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Uniqueness For An Inverse Quantum-Dirac Problem With Given Weyl Function, Martin Bohner, Ayça Çetinkaya Jun 2023

Uniqueness For An Inverse Quantum-Dirac Problem With Given Weyl Function, Martin Bohner, Ayça Çetinkaya

Mathematics and Statistics Faculty Research & Creative Works

In this work, we consider a boundary value problem for a q-Dirac equation. We prove orthogonality of the eigenfunctions, realness of the eigenvalues, and we study asymptotic formulas of the eigenfunctions. We show that the eigenfunctions form a complete system, we obtain the expansion formula with respect to the eigenfunctions, and we derive Parseval's equality. We construct the Weyl solution and the Weyl function. We prove a uniqueness theorem for the solution of the inverse problem with respect to the Weyl function.

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Vallée-Poussin Theorem For Equations With Caputo Fractional Derivative, Martin Bohner, Alexander Domoshnitsky, Seshadev Padhi, Satyam Narayan Srivastava Jun 2023

Vallée-Poussin Theorem For Equations With Caputo Fractional Derivative, Martin Bohner, Alexander Domoshnitsky, Seshadev Padhi, Satyam Narayan Srivastava

Mathematics and Statistics Faculty Research & Creative Works

In this paper, the functional differential equation (CDaα+x)(t) + mΣi=0 (Tix(i))(t) = f(t); t 2 [a; b]; with Caputo fractional derivative CDaα+ is studied. The operators Ti act from the space of continuous to the space of essentially bounded functions. They can be operators with deviations (delayed and advanced), integral operators and their various linear combinations and superpositions. Such equations could appear in various applications and in the study of systems of, for example, two fractional differential equations, when one of the components can be …

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Modeling And A Domain Decomposition Method With Finite Element Discretization For Coupled Dual-Porosity Flow And Navier–Stokes Flow, Jiangyong Hou, Dan Hu, Xuejian Li, Xiaoming He Jun 2023

Modeling And A Domain Decomposition Method With Finite Element Discretization For Coupled Dual-Porosity Flow And Navier–Stokes Flow, Jiangyong Hou, Dan Hu, Xuejian Li, Xiaoming He

Mathematics and Statistics Faculty Research & Creative Works

In This Paper, We First Propose and Analyze a Steady State Dual-Porosity-Navier–Stokes Model, Which Describes Both Dual-Porosity Flow and Free Flow (Governed by Navier–Stokes Equation) Coupled through Four Interface Conditions, Including the Beavers–Joseph Interface Condition. Then We Propose a Domain Decomposition Method for Efficiently Solving Such a Large Complex System. Robin Boundary Conditions Are Used to Decouple the Dual-Porosity Equations from the Navier–Stokes Equations in the Coupled System. based on the Two Decoupled Sub-Problems, a Parallel Robin-Robin Domain Decomposition Method is Constructed and Then Discretized by Finite Elements. We Analyze the Convergence of the Domain Decomposition Method with the Finite …

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Asymptotic Stability Of Solitary Waves For The 1d Nls With An Attractive Delta Potential, Satoshi Masaki, Jason Murphy, Jun Ichi Segata Jun 2023

Asymptotic Stability Of Solitary Waves For The 1d Nls With An Attractive Delta Potential, Satoshi Masaki, Jason Murphy, Jun Ichi Segata

Mathematics and Statistics Faculty Research & Creative Works

We Consider the One-Dimensional Nonlinear Schrödinger Equation with an Attractive Delta Potential and Mass-Supercritical Nonlinearity. This Equation Admits a One-Parameter Family of Solitary Wave Solutions in Both the Focusing and Defocusing Cases. We Establish Asymptotic Stability for All Solitary Waves Satisfying a Suitable Spectral Condition, Namely, that the Linearized Operator Around the Solitary Wave Has a Two-Dimensional Generalized Kernel and No Other Eigenvalues or Resonances. in Particular, We Extend Our Previous Result [35] Beyond the Regime of Small Solitary Waves and Extend the Results of [19, 29] from Orbital to Asymptotic Stability for a Suitable Family of Solitary Waves.

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Experimental And Machine Learning Studies On Chitosan-Polyacrylamide Copolymers For Selective Separation Of Metal Sulfides In The Froth Flotation Process, Keitumetse Monyake, Taihao Han, Danish Ali, Lana Z. Alagha, Aditya Kumar Jun 2023

Experimental And Machine Learning Studies On Chitosan-Polyacrylamide Copolymers For Selective Separation Of Metal Sulfides In The Froth Flotation Process, Keitumetse Monyake, Taihao Han, Danish Ali, Lana Z. Alagha, Aditya Kumar

Mining Engineering Faculty Research & Creative Works

The froth flotation process is extensively used for the selective separation of valuable base metal sulfides from uneconomic associated minerals. However, in this complex multiphase process, various parameters need to be optimized to ensure separation selectivity and peak performance. In this study, two machine learning (ML) models, artificial neural network (ANN) and random forests (RF), were used to predict the efficiency of in-house synthesized chitosan-polyacrylamide copolymers (C-PAMs) in the depression of iron sulfide minerals (i.e., pyrite) while valuable base metal sulfides (i.e., galena and chalcopyrite) were floated using nine flotation variables as inputs to the models. The prediction performance of …

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Decomposition Of The Wall-Heat Flux Of Compressible Boundary Layers, Dongdong Xu, Pierre Ricco, Lian Duan Jun 2023

Decomposition Of The Wall-Heat Flux Of Compressible Boundary Layers, Dongdong Xu, Pierre Ricco, Lian Duan

Mechanical and Aerospace Engineering Faculty Research & Creative Works

We use the method developed by Elnahhas and Johnson ["On the enhancement of boundary layer skin friction by turbulence: An angular momentum approach," J. Fluid Mech. 940, A36 (2022)] and Xu et al. ["Decomposition of the skin-friction coefficient of compressible boundary layers," Phys. Fluids 35, 035107 (2023)] for the decomposition of the skin-friction coefficient to integrate the mean temperature equation for high-Reynolds-number compressible boundary layers and arrive at an identity for the decomposition of the wall-heat flux. The physical interpretation of the identity and the limitations of this approach are discussed. We perform an integration on the mean temperature equation …

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A Hybrid Admm For Six-Degree-Of-Freedom Entry Trajectory Optimization Based On Dual Quaternions, Chaoying Pei, Changhuang Wan, Ran Dai, Jeremy R. Rea Jun 2023

A Hybrid Admm For Six-Degree-Of-Freedom Entry Trajectory Optimization Based On Dual Quaternions, Chaoying Pei, Changhuang Wan, Ran Dai, Jeremy R. Rea

Mechanical and Aerospace Engineering Faculty Research & Creative Works

This article investigates the six-degree-of-freedom (6-DoF) entry trajectory optimization problem in a Human-Mars entry, powered descent, and landing mission. During the entry phase, aerodynamic forces are employed to decelerate the vehicle. Instead of being treated as a point mass, both translational and rotational motions of the entry vehicle are considered. Specifically, the 6-DoF rigid body motion of the entry vehicle is modeled using the unit dual quaternion representations to avoid highly nonlinear terms in the flight dynamics expression originally based on the flight-path coordinates. Then, the entry trajectory optimization problem is to minimize the terminal speed subject to dynamical, operational, …

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Time-Domain Shielding Effectiveness Analysis Based On Dgtd Method Accelerated By Local Time-Stepping And Parallel Techniques, Zheng Lang Jia, Huan Huan Zhang, Da Zhi Ding, Lei Zhao, Qiang Ren, Li (Lijun) Jun Jiang Jun 2023

Time-Domain Shielding Effectiveness Analysis Based On Dgtd Method Accelerated By Local Time-Stepping And Parallel Techniques, Zheng Lang Jia, Huan Huan Zhang, Da Zhi Ding, Lei Zhao, Qiang Ren, Li (Lijun) Jun Jiang

Electrical and Computer Engineering Faculty Research & Creative Works

The discontinuous Galerkin time-domain (DGTD) method accelerated by local time-stepping (LTS) and parallel techniques is employed to analyze the time-domain shielding effectiveness (TDSE) of metallic enclosures. The detailed formulations and implementation procedures of the DGTD method and LTS and parallel techniques are introduced. Numerical results validate the correctness of the proposed method. Moreover, the influence of various design parameters of the metallic enclosure, including aperture dimensions, wall thickness, spacing of aperture arrays, etc., on the TDSE are analyzed by the proposed method. Some rules are given for the design of the enclosure with rectangular apertures from the perspective of TDSE. …

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Solitary Waves In A Quantum Droplet-Bearing System, Garyfallia Katsimiga, Simeon I. Mistakidis, G. N. Koutsokostas, D. J. Frantzeskakis, R. Carretero-González, P. G. Kevrekidis Jun 2023

Solitary Waves In A Quantum Droplet-Bearing System, Garyfallia Katsimiga, Simeon I. Mistakidis, G. N. Koutsokostas, D. J. Frantzeskakis, R. Carretero-González, P. G. Kevrekidis

Physics Faculty Research & Creative Works

We Unravel The Existence And Stability Properties Of Dark Soliton Solutions As They Extend From The Regime Of Trapped Quantum Droplets Towards The Thomas-Fermi Limit In Homonuclear Symmetric Bose Mixtures. Leveraging A Phase-Plane Analysis, We Identify The Regimes Of Existence Of Different Types Of Quantum Droplets And Subsequently Examine The Possibility Of Black And Gray Solitons And Kink-Type Structures In This System. Moreover, We Employ The Landau Dynamics Approach To Extract An Analytical Estimate Of The Oscillation Frequency Of A Single Dark Soliton In The Relevant Extended Gross-Pitaevskii Model. Within This Framework, We Also Find That The Single Soliton Immersed …

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Fate And Distribution Of Orally-Ingested Ceo2-Nanoparticles Based On A Mouse Model: Implication For Human Health, Xingmao Ma, Xiaoxuan Wang, Lei Xu, Honglan Shi, Hu Yang, Kerstin K. Landrock, Virender K. Sharma, Robert S. Chapkin Jun 2023

Fate And Distribution Of Orally-Ingested Ceo2-Nanoparticles Based On A Mouse Model: Implication For Human Health, Xingmao Ma, Xiaoxuan Wang, Lei Xu, Honglan Shi, Hu Yang, Kerstin K. Landrock, Virender K. Sharma, Robert S. Chapkin

Chemistry Faculty Research & Creative Works

The use of nanoparticles in agrichemical formula and food products as additives has increased their chances of accumulation in humans via oral intake. Due to their potential toxicity, it is critical to understand their fate and distribution following oral intake. Cerium oxide nanoparticle (CeO2NP) is commonly used in agriculture and is highly stable in the environment. as such, it has been used as a model chemical to investigate nanoparticle's distribution and clearance. based on their estimated human exposure levels, 0.15–0.75 mg/kg body weight/day of CeO2NPs with different sizes and surface charges (30–50 nm with negative charge and <25 >nm with positive …

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When Brain-Inspired Ai Meets Agi, Lin Zhao, Lu Zhang, Zihao Wu, Yuzhong Chen, Haixing Dai, Xiaowei Yu, Zhengliang Liu, Tuo Zhang, Xintao Hu, Xi Jiang, Xiang Li, Dajiang Zhu, Dinggang Shen, Tianming Liu Jun 2023

When Brain-Inspired Ai Meets Agi, Lin Zhao, Lu Zhang, Zihao Wu, Yuzhong Chen, Haixing Dai, Xiaowei Yu, Zhengliang Liu, Tuo Zhang, Xintao Hu, Xi Jiang, Xiang Li, Dajiang Zhu, Dinggang Shen, Tianming Liu

Computer Science Faculty Research & Creative Works

Artificial General Intelligence (AGI) has been a long-standing goal of humanity, with the aim of creating machines capable of performing any intellectual task that humans can do. To achieve this, AGI researchers draw inspiration from the human brain and seek to replicate its principles in intelligent machines. Brain-inspired artificial intelligence is a field that has emerged from this endeavor, combining insights from neuroscience, psychology, and computer science to develop more efficient and powerful AI systems. In this article, we provide a comprehensive overview of brain-inspired AI from the perspective of AGI. We begin with the current progress in brain-inspired AI …

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Chargex: Exploring State Switching Attack On Electric Vehicle Charging Systems, Ce Zhou, Qiben Yan, Zhiyuan Yu, Eshan Dixit, Ning Zhang, Huacheng Zeng, Alireza Safdari Ghanhdari May 2023

Chargex: Exploring State Switching Attack On Electric Vehicle Charging Systems, Ce Zhou, Qiben Yan, Zhiyuan Yu, Eshan Dixit, Ning Zhang, Huacheng Zeng, Alireza Safdari Ghanhdari

Computer Science Faculty Research & Creative Works

Electric Vehicle (EV) has become one of the promising solutions to the ever-evolving environmental and energy crisis. The key to the wide adoption of EVs is a pervasive charging infrastructure, composed of both private/home chargers and public/commercial charging stations. The security of EV charging, however, has not been thoroughly investigated. This paper investigates the communication mechanisms between the chargers and EVs and exposes the lack of protection on the authenticity in the SAE J1772 charging control protocol. To showcase our discoveries, we propose a new class of attacks, ChargeX, which aims to manipulate the charging states or charging rates of …

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Generative And Pseudo-Relevant Feedback For Sparse, Dense And Learned Sparse Retrieval, Iain Mackie, Shubham Chatterjee, Jeffrey Dalton May 2023

Generative And Pseudo-Relevant Feedback For Sparse, Dense And Learned Sparse Retrieval, Iain Mackie, Shubham Chatterjee, Jeffrey Dalton

Computer Science Faculty Research & Creative Works

Pseudo-relevance feedback (PRF) is a classical approach to address lexical mismatch by enriching the query using first-pass retrieval. Moreover, recent work on generative-relevance feedback (GRF) shows that query expansion models using text generated from large language models can improve sparse retrieval without depending on first-pass retrieval effectiveness. This work extends GRF to dense and learned sparse retrieval paradigms with experiments over six standard document ranking benchmarks. We find that GRF improves over comparable PRF techniques by around 10% on both precision and recall-oriented measures. Nonetheless, query analysis shows that GRF and PRF have contrasting benefits, with GRF providing external context …

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Exploring Electrolyte Effects On Metal-Alkyl Bond Stability: Impact And Implications For Electrosynthesis, Dylan G. Boucher, Zachary A. Nguyen, Shelley D. Minteer May 2023

Exploring Electrolyte Effects On Metal-Alkyl Bond Stability: Impact And Implications For Electrosynthesis, Dylan G. Boucher, Zachary A. Nguyen, Shelley D. Minteer

Chemistry Faculty Research & Creative Works

Transition metal catalysis hinges on the formation of metal-carbon bonds during catalytic cycles. the stability and reactivity of these bonds are what determine product chemo-, regio-, and enantioselectivity. the advent of electro synthetic methodologies has placed the current understanding of these metal-alkyl bonds into a new environment of charged species and electrochemically induced reactivity. in this paper, we explore the often-neglected impact of supporting electrolyte on homogeneous electrocatalytic mechanisms using the catalytic reduction of benzyl chlorides via Co and Fe tetraphenyl porphyrins as a model reaction. the mechanism of this reaction is confirmed to proceed through the formation of the …

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Fedar+: A Federated Learning Approach To Appliance Recognition With Mislabeled Data In Residential Environments, Ashish Gupta, Hari Prabhat Gupta, Sajal K. Das May 2023

Fedar+: A Federated Learning Approach To Appliance Recognition With Mislabeled Data In Residential Environments, Ashish Gupta, Hari Prabhat Gupta, Sajal K. Das

Computer Science Faculty Research & Creative Works

With the enhancement of people's living standards and the rapid evolution of cyber-physical systems, residential environments are becoming smart and well-connected, causing a significant raise in overall energy consumption. As household appliances are major energy consumers, their accurate recognition becomes crucial to avoid unattended usage and minimize peak-time load on the smart grids, thereby conserving energy and making smart environments more sustainable. Traditionally, an appliance recognition model is trained at a central server (service provider) by collecting electricity consumption data via smart plugs from the clients (consumers), causing a privacy breach. Besides that, the data are susceptible to noisy labels …

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Fedar+: A Federated Learning Approach To Appliance Recognition With Mislabeled Data In Residential Environments, Ashish Gupta, Hari Prabhat Gupta, Sajal K. Das May 2023

Fedar+: A Federated Learning Approach To Appliance Recognition With Mislabeled Data In Residential Environments, Ashish Gupta, Hari Prabhat Gupta, Sajal K. Das

Computer Science Faculty Research & Creative Works

With the enhancement of people's living standards and the rapid evolution of cyber-physical systems, residential environments are becoming smart and well-connected, causing a significant raise in overall energy consumption. as household appliances are major energy consumers, their accurate recognition becomes crucial to avoid unattended usage and minimize peak-time load on the smart grids, thereby conserving energy and making smart environments more sustainable. Traditionally, an appliance recognition model is trained at a central server (service provider) by collecting electricity consumption data via smart plugs from the clients (consumers), causing a privacy breach. Besides that, the data are susceptible to noisy labels …

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