Digital Commons Network^™

Testing Effects Of Lorentz Invariance Violation In The Propagation Of Astroparticles With The Pierre Auger Observatory, P. Abreu, M. Aglietta, J. M. Albury, I. Allekotte, K. Almeida Cheminant

Faculty Publications

Lorentz invariance violation (LIV) is often described by dispersion relations of the form E-i(2) = m(i)(2) + p(i)(2) + delta E-i,n(2+ n) with delta different based on particle type i, with energy E, momentum p and rest mass m. Kinematics and energy thresholds of interactions are modified once the LIV terms become comparable to the squared masses of the particles involved. Thus, the strongest constraints on the LIV coefficients delta(i,n) tend to come from the highest energies. At sufficiently high energies, photons produced by cosmic ray interactions as they propagate through the Universe could be subluminal and unattenuated over cosmological …

Testing Xrf Identification Of Marine Washover Sediment Beds In A Coastal Lake In Southeastern Texas, Usa, Harry F. L. Williams, Chelsea E. Beaubouef, Kam-Biu Liu, Nicholas Culligan, Lance Riedlinger

Faculty Publications

This study tests the ability of a novel approach to identifying washover beds in coastal lakes. Combined X-Ray Fluorescence (XRF) and cluster analysis was used to identify hurricane washover beds in sediment cores from Clam Lake on the McFaddin National Wildlife Refuge in southeastern Texas. The lake is known to contain washover beds from recent hurricanes, but the washover sediment has similar microfossil, loss-on-ignition and textural characteristics to non-washover sediment and is not readily distinguishable. Sediment cores taken from marshes surrounding the lake do contain visually-recognizable sandy washover beds of Hurricanes Ike, Rita, Carla and Audrey. XRF analysis of these …

A Longitudinal Investigation Of The Semantic Receptive-Expressive Gap In Spanish-English Bilingual Children, Todd A. Gibson, Elizabeth D. Peña, Lisa M. Bedore, Kevin S. Mccarter

Faculty Publications

Although a semantic receptive-expressive gap appears to be a universal feature of early bilingualism, little is known about its development. We sought to determine if the magnitude of the discrepancy between receptive and expressive standard scores changed over time in bilingual children’s two languages. In this longitudinal study, standardized receptive and expressive semantics tests of 106 Spanish-English bilingual children with TD were taken at kindergarten and first grade in both English and Spanish. We used a multivariate analysis approach to identify interactions and main effects. Although both receptive and expressive standard scores improved across the year in both languages, the …

Monitoring Leaf Phenology In Moist Tropical Forests By Applying A Superpixel-Based Deep Learning Method To Time-Series Images Of Tree Canopies, Guangqin Song, Shengbiao Wu, Calvin K.F. Lee, Shawn P. Serbin, Brett T. Wolfe, Michael K. Ng, Kim S. Ely, Marc Bogonovich, Jing Wang, Ziyu Lin, Scott Saleska, Bruce W. Nelson, Alistair Rogers, Jin Wu

Faculty Publications

Tropical leaf phenology—particularly its variability at the tree-crown scale—dominates the seasonality of carbon and water fluxes. However, given enormous species diversity, accurate means of monitoring leaf phenology in tropical forests is still lacking. Time series of the Green Chromatic Coordinate (GCC) metric derived from tower-based red–greenblue (RGB) phenocams have been widely used to monitor leaf phenology in temperate forests, but its application in the tropics remains problematic. To improve monitoring of tropical phenology, we explored the use of a deep learning model (i.e. superpixel-based Residual Networks 50, SP-ResNet50) to automatically differentiate leaves from non-leaves in phenocam images and to derive …

Deriving Tree Growth Models From Stand Models Based On The Selfthinning Rule Of Chinese Fir Plantations, Xiongqing Zhang, Quang V. Cao, Yancheng Qu, Jianguo Zhang

Faculty Publications

Self-thinning due to density-dependent mortality usually occurs during the forest development. To improve predictions of such processes during forest successions under climate change, reliable stand-level models are needed. In this study, we developed an integrated system of tree-and stand-level models by deriving tree diameter and survival models from stand growth and survival models based on climate-sensitive self-thinning rule of Chinese fir plantations in subtropical China. The resulting integrated system, having a unified mathematical structure, should provide consistent estimates at both tree and stand levels. Predictions were reasonable at both stand and tree levels. Because stand-level values aggregated from the tree …

Development Of A Memories Vocabulary (Memvoc) For Food Products Using Coffee As Model, Adan Cabal-Prieto, Gabriela Teodoro-Bernabé, Cecilia Coria-Rincón, Lucia Sánchez-Arellano, Lorena Guadalupe Ramón-Canul, Jesús Rodríguez-Miranda, Witoon Prinyawiwatkul, José Manuel Juárez-Barrientos, José Andrés Herrera-Corredor, Emmanuel De Jesús Ramírez-Rivera

Faculty Publications

Multiple references in sensory science indicate that foods evoke memories during consumption, however, research regarding those memories is still limited. The objective of this study was to develop a vocabulary and implement it in a memories vocabulary related to food as a complement for the evaluation of sensory attributes and emotions, using coffee as a model. The memories vocabulary was generated with a series of successive studies that involved assigning positive and negative memories to different food groups and applying mathematical algorithms (WordCountAna and Factorial Approach) and contrasting memories with the scientific literature. Subsequently, the vocabulary was used to determine …

New Bounds For State Transition Matrices, Frederic Mazenc, Michael Malisoff

Faculty Publications

We address the problem of constructing matrix-valued interval observers for estimating state transition matrices for time-varying systems. We provide less conservative estimators than those in recent literature. We cover continuous-and discrete-time linear systems, under Metzler or nonnegativity conditions on the coefficient matrices. We show how to satisfy our Metzler conditions after simple changes of coordinates. We illustrate our method using a feedback stabilized underwater marine robotic dynamics with unknown control gains.

New Versions Of Halanay's Inequality With Multiple Gain Terms, Frederic Mazenc, Michael Malisoff

Faculty Publications

Halanay's inequality in its standard form is a widely used tool for the analysis of systems with delays and uncertainties, but the assumptions needed to use it are sometimes too restrictive to cover applications of interest. This letter provides new generalizations of Halanay's inequality where, instead of the usual supremum in the gain term in standard versions of Halanay's inequality, we use a weighted sum of suprema over different intervals. This allows us to derive sufficient conditions for asymptotic and input-to-state stability. We apply our results to linear systems with switched delays and other examples that illustrate how our results …

Almost Finite-Time Observers For A Family Of Nonlinear Continuous-Time Systems, Frederic Mazenc, Michael Malisoff

Faculty Publications

We provide a new class of observers for a class of nonlinear systems which are not required to be affine in the unmeasured states. The observers ensure exponential convergence of the observation errors to zero, under linear output measurements. The rate of exponential convergence converges to infinity, as the growth rate of the nonlinear state-dependent part of the dynamics converges to zero, so we call the observers almost finite-time. Under global Lipschitz conditions on the state-dependent part of the dynamics, our global result ensures convergence of the observers, for all initial states. For cases where the nonlinearity is of order …

New Finite-Time And Fast Converging Observers With A Single Delay, Frédéric Mazenc, Michael Malisoff

Faculty Publications

We provide new reduced order observer designs for a key class of nonlinear dynamics. When continuous output measurements are available, we prove that our observers converge in a fixed finite time in the absence of perturbations, and we prove a robustness result under uncertainties in the output measurements and in the dynamics, which bounds the observation error in terms of bounds on the uncertainties. The observers contain a dynamic extension with only one pointwise delay, and they use the observability Gramian to eliminate an invertibility condition that was present in earlier finite time observer designs. We also provide analogs for …

On A Capacitary Strong Type Inequality And Related Capacitary Estimates, Keng Hao Ooi, Nguyen Cong Phuc

Faculty Publications

We establish a Maz'ya type capacitary inequality which resolves a special case of a conjecture by David R. Adams. As a consequence, we obtain several equivalent norms for Choquet integrals associated to Bessel or Riesz capacities. This enables us to obtain bounds for the Hardy-Littlewood maximal function in a sublinear setting.

2-Modular Matrices, James Oxley, Zach Walsh

Faculty Publications

An integer matrix A is Δ -modular if the determinant of each rank(A) × rank(A) submatrix has absolute value at most Δ. The class of 1-modular, or unimodular, matrices is of fundamental significance in both integer programming theory and matroid theory. A 1957 result of Heller shows that the maximum number of nonzero, pairwise non-parallel columns of a rank-r unimodular matrix is (r+1/2). We prove that, for each sufficiently large integer r, the maximum number of nonzero, pairwise non-parallel columns of a rank-r 2-modular matrix is (r+2/2) - 2.

Small Cocircuits In Minimally Vertically 4-Connected Matroids, James Oxley, Zach Walsh

Faculty Publications

Halin proved that every minimally k-connected graph has a vertex of degree k. More generally, does every minimally vertically k-connected matroid have a k-element cocircuit? Results of Murty and Wong give an affirmative answer when k \leq 3. We show that every minimally vertically 4-connected matroid with at least six elements has a 4-element cocircuit, or a 5-element cocircuit that contains a triangle, with the exception of a specific nonbinary 9-element matroid. Consequently, every minimally vertically 4-connected binary matroid with at least six elements has a 4-element cocircuit.

Strengthened Chain Theorems For Different Versions Of 4-Connectivity, Guoli Ding, Chengfu Qin

Faculty Publications

The chain theorem of Tutte states that every 3-connected graph can be constructed from a wheel Wn by repeatedly adding edges and splitting vertices. It is not difficult to prove the following strengthening of this theorem: every non-wheel 3-connected graph can be constructed from W4 by repeatedly adding edges and splitting vertices. In this paper, we similarly strengthen several chain theorems for various versions of 4-connectivity.

On The Asymptotic Convergence And Acceleration Of Gradient Methods, Yakui Huang, Yu Hong Dai, Xin Wei Liu, Hongchao Zhang

Faculty Publications

We consider the asymptotic behavior of a family of gradient methods, which include the steepest descent and minimal gradient methods as special instances. It is proved that each method in the family will asymptotically zigzag between two directions. Asymptotic convergence results of the objective value, gradient norm, and stepsize are presented as well. To accelerate the family of gradient methods, we further exploit spectral properties of stepsizes to break the zigzagging pattern. In particular, a new stepsize is derived by imposing finite termination on minimizing two-dimensional strictly convex quadratic function. It is shown that, for the general quadratic function, the …

Forecasting Equity Index Volatility By Measuring The Linkage Among Component Stocks∗, Yue Qiu, Tian Xie, Jun Yu, Qiankun Zhou

Faculty Publications

The linkage among the realized volatilities of component stocks is important when modeling and forecasting the relevant index volatility. In this article, the linkage is measured via an extended Common Correlated Effects (CCEs) approach under a panel heterogeneous autoregression model where unobserved common factors in errors are assumed. Consistency of the CCE estimator is obtained. The common factors are extracted using the principal component analysis. Empirical studies show that realized volatility models exploiting the linkage effects lead to significantly better out-of-sample forecast performance, for example, an up to 32% increase in the pseudo R2. We also conduct various forecasting exercises …

Unequal Investment: A Regulatory Case Study, Emily R. Winston

Faculty Publications

Growing economic inequality in the United States has reduced social mobility, placing financial security farther out of reach for a growing number of Americans. During the COVID-19 pandemic, U.S. stock prices have grown simultaneously with unemployment and food insecurity, highlighting the fact that prosperity is unequally distributed in the U.S. economy.

Many Americans do not benefit when the stock market soars because they do not have the means to invest. However, even ordinary American families who do have wealth to invest in the capital markets will face enormous obstacles in narrowing the wealth divide through investment. This is because ordinary …

Mid-Breton Sediment Diversion (Mbrsd) Assessment – Final Report, Jerry D. Wiggert, Brandy N. Armstrong, M. Kemal Cambazoglu, Sandeep Kalathupurath Kuttan

Faculty Publications

The purpose of this project is to provide managers at the Mississippi Department of Marine Resources (MDMR) with the scientific information needed to accurately address public concerns regarding the potential effects of the Louisiana Coastal Master Plan / Coastal Protection and Restoration Authority (CPRA) Mid-Breton Sediment Diversion (MBrSD) on the jurisdictional waters and resources of Mississippi. The stated design purpose of the MBrSD is to reconnect and re-establish the deltaic sediment deposition process between the Mississippi River and the Breton Sound Basin through a diversion that will deliver up to 75,000 cfs of sediment-laden freshwater. The report presented herein provides …

Hybrid Quantum-Classical Unit Commitment, Reza Mahroo, Amin Kargarian

Faculty Publications

This paper proposes a hybrid quantum-classical algorithm to solve a fundamental power system problem called unit commitment (UC). The UC problem is decomposed into a quadratic subproblem, a quadratic unconstrained binary optimization (QUBO) subproblem, and an unconstrained quadratic subproblem. A classical optimization solver solves the first and third subproblems, while the QUBO subproblem is solved by a quantum algorithm called quantum approximate optimization algorithm (QAOA). The three subproblems are then coordinated iteratively using a three-block alternating direction method of multipliers algorithm. Using Qiskit on the IBM Q system as the simulation environment, simulation results demonstrate the validity of the proposed …

Flood-Aware Optimal Power Flow For Proactive Day-Ahead Transmission Substation Hardening, Mohadese Movahednia, Amin Kargarian

Faculty Publications

Power system components, particularly electrical substations, may be severely damaged due to flooding, resulting in prolonged power outages and resilience degradation. This problem is more severe in low-elevated regions such as Louisiana. Protective operational actions such as placing tiger dams around substations before flooding can reduce substation vulnerability, damage costs, and energy not supplied cost, thus enhancing power grid resilience. This paper proposes a stochastic mixed-integer programming model for protecting transmission substations one day before flood events using tiger dams. A flood-aware optimal power flow problem is formulated for transmission system operators with respect to the protected/unprotected status of substations. …

A Deep Learning Approach To Optimal Sampling Problems, Xinxin Wang, Xiangyu Meng, Fangfei Li

Faculty Publications

Time-triggered and event-triggered sampling methods have been widely adopted in control systems. Optimal sampling problems of the two mechanisms have also received great attentions. However, for high-dimensional systems, analytical methods have some limitations. In this study, we propose a model-free method, called soft greedy policy for neural network fitting, to calculate the optimal sampling period of the time-triggered impulse control and the optimal threshold of the event-triggered impulse control. A neural network is used to approximate the objective function and then is trained. This approach is more widely applicable than the analytical method. At the same time, compared with different …

Event-Triggered Control For Discrete-Time Systems Using A Positive Systems Approach, Frederic Mazenc, Michael Malisoff, Corina Barbalata, Zhong Ping Jiang

Faculty Publications

We provide an output feedback event-triggered controller for discrete-time linear systems. We make novel use of positive systems, interval observers, an event-triggered state estimator, and triggering times that are computed from estimator values. This provides a discrete-time analog of our recent positive systems approach for continuous-time systems. A key novel ingredient in our discrete-time event triggers is their use of vectors of absolute values, instead of the usual Euclidean norm. We illustrate the benefits of our method using a model for event-triggered BlueROV2 underwater vehicles.

Cable Decoupling And Cable-Based Stiffening Of Continuum Robots, Parsa Molaei, Nekita A. Pitts, Genevieve Palardy, Ji Su, Matthew K. Mahlin, James H. Neilan, Hunter B. Gilbert

Faculty Publications

Cable-driven continuum robots, which are robots with a continuously flexible backbone and no identifiable joints that are actuated by cables, have shown great potential for many applications in unstructured, uncertain environments. However, the standard design for a cable-driven continuum robot segment, which bends a continuous backbone along a circular arc, has many compliant modes of deformation which are uncontrolled, and which may result in buckling or other undesirable behaviors if not ameliorated. In this paper, we detail an approach for using additional cables to selectively stiffen planar cable-driven robots without substantial coupling to the actuating cables. A mechanics-based model based …

Classification Of Surface Pavement Cracks As Top-Down, Bottom-Up, And Cement-Treated Reflective Cracking Based On Deep Learning Methods, Nirmal Dhakal, Mostafa A. Elseifi, Zia U.A. Zihan, Zhongjie Zhang, Christophe N. Fillastre, Jagannath Upadhyay

Faculty Publications

The treatment and repair strategies for reflective and fatigue cracking that initiate at the pavement surface (i.e., top-down cracking) and at the bottom of the asphalt concrete layer (i.e., bottom-up cracking) are noticeably different. However, pavement management engineers are facing difficulties in identifying these cracks in the field because they usually appear in visually identical patterns. The objective of this study was to develop artificial neural network (ANN) and convolutional neural network (CNN) applications to differentiate and classify top-down, bottom-up, and cement-treated reflective cracking in in-service flexible pavements using deep-learning models. The developed CNN model achieved an accuracy of 93.8% …

Event-Triggered Prediction-Based Delay Compensation Approach, Frederic Mazenc, Michael Malisoff, Corina Barbalata

Faculty Publications

We provide a new event-triggered delay compensation approach for linear systems with arbitrarily long constant input delays. Our prediction map is expressible as a solution of a discrete time system. Our method ensures input-to-state stability. We also provide an analog under measurement delays, where the prediction map is expressible as a solution of a continuous-discrete system. Significant novel features are our combined use of matrices of absolute values and our prediction based event triggers, instead of Euclidean norms, and the fact that the predictor dynamics always has the same dimension as that of the original system. Our marine robotic example …

Estimation For Model Parameters And Maximum Power Points Of Photovoltaic Modules Using Stochastic Fractal Search Algorithms, Duy C. Huynh, Matthew W. Dunnigan, Corina Barbalata

Faculty Publications

The performance of a photovoltaic (PV) power generation system could be improved through the optimal control and operation of a PV module which is one of the fundamental components of this system. Thus, an appropriate PV module model along with precise knowledge of its parameters is necessary. This paper proposes a novel technique to estimate the source current, the saturation current of diodes, the shunt resistance, the series resistance, the ideality coefficient of diodes and the maximum power points (MPPs) of PV modules at the same time. This estimation problem can be described by the minimization of the root mean …

Event-Triggered Control For Continuous-Time Linear Systems With A Delay In The Input, Frederic Mazenc, Michael Malisoff, Corina Barbalata

Faculty Publications

We provide an event-triggered control technique for a family of linear time-varying continuous-time systems with a constant known pointwise delay in the input. We adopt a subpredictor based prediction technique, and we provide sufficient conditions that ensure that Zeno behavior does not occur. At each time, only delayed measurements are needed to implement the control. Also, the delay can be an arbitrarily large constant. We prove an input-to-state stability property for the closed-loop system, using the theory of cooperative systems. We apply our method to a gyroscopic control problem for a curve tracking dynamics arising in marine robotics.

Thermodynamic Characterization Of Grease Oxidation–Thermal Stability Via Pressure Differential Scanning Calorimetry, Jude A. Osara, Piet M. Lugt, Michael D. Bryant, Michael M. Khonsari

Faculty Publications

This study investigates and characterizes, via thermodynamic laws, a widely employed standard for measuring oxidation-thermal stability of lubricating greases—ASTM 5483—based on first-order chemical kinetics, using pressure differential scanning calorimetry (PDSC). Steps and active mechanisms in the controlled test are analyzed and modeled using energy and entropy transformations. Applying the degradation-entropy generation (DEG) theorem, oxidation induction time is related to accumulated oxidation entropy and entropy transfer by mass flow to obtain characteristic degradation coefficients. These DEG coefficients are calibrated using available measured data from the literature and subsequently used to predict induction times at various temperatures. DEG elements—trajectories, planes, and domain—presented …

Microcore: Using Online Playable Cases To Increase Student Engagement In Online Writing Environments, Jon Balzotti, Kevin Haws, Amy A. Rogers, Jason K. Mcdonald, Matt Baker

Faculty Publications

This case study explores a type of educational simulation, an alternative reality game we call a playable case study (PCS), and how its use influenced student engagement in an online writing classroom. The goal of the simulation was to help students create professional communication artifacts and experience real-world professional communication situations. This article reports the effectiveness of the playable case study as a tool specifically for online writing instruction (OWI). The context of our research was a PCS called Microcore. Acting as interns for a company, students are asked to investigate a serious problem that occurs and present a solution …

Using Open-Source Software For Business, Urban, And Other Applications Of Deep Neural Networks, Machine Learning, And Data Analytics Tools, Richard S. Segall, Vidhya Sankarasubbu

Faculty Publications

This article provides an overview with examples of what Neural Networks (NN), Machine Learning (ML), and Artificial Intelligence (AI) and Data Analytics are, and with their applications in business, urban and biomedical situations. The characteristics of 29 types of neural networks are provided including their distinctive graphical illustrations. A survey of current open-source software (OSS) for neural networks, neural network software available for free trial download for limited time use, open-source software (OSS) for Machine Learning (ML), and open-source software (OSS) for Data Analytics tools are provided. Characteristics of Artificial Intelligence (AI) technologies for Machine Learning available as open-source are …