Digital Commons Network^™

Calcium-Rich Dravite From The Arignac Gypsum Mine, France: Implications For Tourmaline Development In A Sulfate-Rich, Highly Magnesian Meta-Evaporite, Barbara Dutrow, Darrell Henry

Faculty Publications

Tourmaline occurs in a wide range of compositional environments, but its occurrence in meta-evaporites is less commonly investigated. Highly magnesian (XMg = 0.90–0.98), poikiloblastic tourmaline occurs in a sulfate-rich, anhydrite– gypsum-bearing meta-evaporite in the Arignac Gypsum Mine, France and preserves a petrologic record of this unusual geochemical environment. Originally a Triassic evaporite deposit, the sample is interpreted to have undergone high-tem-perature–low-pressure (HT–LP) metamorphism and subsequently experienced low-grade, highly deformed overprints. Poikiloblastic tourmaline preserves relicts of the HT–LP mineral assemblage, as inclusions of anhydrite, phlogopite, dolomite, tremolite, Cl-rich scapolite (71–85 % marialite component), rutile, zircon, and fluor-apatite. The low-grade deformational overprints …

Synthesis Of The Distribution Of Subsidence Of The Lower Ganges-Brahmaputra Delta, Bangladesh, Michael S. Steckler, Bar Oryan, Carol A. Wilson, Céline Grall, Scott L. Nooner, Dhiman R. Mondal, S. Humayun Akhter, Scott Dewolf, Steve L. Goodbred

Faculty Publications

Deltas, the low-lying land at river mouths, are sensitive to the delicate balance between sea level rise, land subsidence and sedimentation. Bangladesh and the Ganges-Brahmaputra Delta (GBD) have been highlighted as a region at risk from sea-level rise, but reliable estimates of land subsidence have been limited. While early studies suggested high rates of relative sea-level rise, recent papers estimate more modest rates. Our objective is to better quantify the magnitude, spatial variability, and depth variation of sediment compaction and land subsidence in the lower GBD to better evaluate the processes controlling them and the pattern of relative sea level …

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 …

Uncertainty-Autoencoder-Based Privacy And Utility Preserving Data Type Conscious Transformation, Bishwas Mandal, George Amariucai, Shuangqing Wei

Faculty Publications

We propose an adversarial learning framework that deals with the privacy-utility tradeoff problem under two types of conditions: data-type ignorant, and data-type aware. Under data-type aware conditions, the privacy mechanism provides a one-hot encoding of categorical features, representing exactly one class, while under data-type ignorant conditions the categorical variables are represented by a collection of scores, one for each class. We use a neural network architecture consisting of a generator and a discriminator, where the generator consists of an encoder-decoder pair, and the discriminator consists of an adversary and a utility provider. Unlike previous research considering this kind of architecture, …

Optimization Of Multi-Mode Classification For Process Monitoring, Z. T. Webb, M. Nnadili, E. E. Seghers, L. A. Briceno-Mena, J. A. Romagnoli

Faculty Publications

Process monitoring seeks to identify anomalous plant operating states so that operators can take the appropriate actions for recovery. Instrumental to process monitoring is the labeling of known operating states in historical data, so that departures from these states can be identified. This task can be challenging and time consuming as plant data is typically high dimensional and extensive. Moreover, automation of this procedure is not trivial since ground truth labels are often unavailable. In this contribution, this problem is approached as a multi-mode classification one, and an automatic framework for labeling using unsupervised Machine Learning (ML) methods is presented. …

Machine Learning-Based Surrogate Models And Transfer Learning For Derivative Free Optimization Of Htpem Fuel Cells, Luis A. Briceno-Mena, Christopher G. Arges, Jose A. Romagnoli

Faculty Publications

Widespread adoption of high-temperature electrochemical systems such as polymer electrolyte membrane fuel cells (HT-PEMFCs) requires models and computational tools for accurate optimization and guiding new materials for enhancing fuel cell performance and durability. In this contribution, knowledge-based modelling and data-driven modelling are combined using Few-Shot Learning and implementing an Automated Machine Learning framework for the generation of Machine Learning-based surrogate models.

Effect Of The Demethanizer Improved Control Strategy On The Separation Train For The Ngl Separation Process, Marta Mandis, Jorge A. Chebeir, José A. Romagnoli, Roberto Baratti, Stefania Tronci

Faculty Publications

In recent years the attention on natural gas production and utilization is growing due to different fundamental aspects. First, the availability of natural gas has increased thanks to technological improvements in the extraction techniques that have made possible the production from unconventional reservoirs. Second, the interest in clean energy is growing, aiming to reduce CO2 emission and thus global warming. Natural gas is a cleaner fossil fuel compared with other traditional energy sources such as oil and coal. Another reason that drives the attention on this fossil fuel is the increasing economic interest of recovering the heavier hydrocarbon fractions contained …

Effect Of Ldh On The Dissolution And Adsorption Behaviors Of Sulfate In Portland Cement Early Hydration Process, Zedong Qiu, Limin Deng, Shuang Lu, Guoqiang Li, Zhen Tang

Faculty Publications

In recent years, it has been widely recognized that the incorporation of Mg-Al-LDH into cement-based materials can improve the salt corrosion resistance of cement-based materials. The reason for the improvement comes from the anion adsorption capacity of Mg-Al-LDH. It was confirmed that the addition of Mg-Al-LDH would accelerate the setting and hardening of cement paste. With the increase in the Mg-Al-LDH content, the initial setting time of cement slurry with different gypsum contents will decrease by 10-50% and the viscosity of the cement slurry will increase by 100-200%. Depending on different gypsum contents, the degree of cement hydration varied. This …

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 …

Decay Of Oil Residues In The Soil Is Enhanced By The Presence Of Spartina Alterniflora, With No Additional Effect From Microbiome Manipulation, Stephen K. Formel, Allyson M. Martin, John H. Pardue, Vijaikrishnah Elango, Kristina Johnson, Claudia K. Gunsch, Emilie Lefèvre, Paige M. Varner, Yeon Ji Kim, Brittany M. Bernik, Sunshine A. Van Bael

Faculty Publications

Recent work has suggested that the phytoremediation potential of S. alterniflora may be linked to a selection by the plant for oil-degrading microbial communities in the soil, in combination with enhanced delivery of oxygen and plant enzymes to the soil. In salt marshes, where the soil is saline and hypoxic, this relationship may be enhanced as plants in extreme environments have been found to be especially dependent on their microbiome for resilience to stress and to respond to toxins in the soil. Optimizing methods for restoration of oiled salt marshes would be especially meaningful in the Gulf of Mexico, where …

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 …

Multiple-Inputs Convolutional Neural Network For Covid-19 Classification And Critical Region Screening From Chest X-Ray Radiographs: Model Development And Performance Evaluation, Zhongqiang Li, Zheng Li, Luke Yao, Qing Chen, Jian Zhang, Xin Li, Ji Ming Feng, Yanping Li, Jian Xu

Faculty Publications

Background: The COVID-19 pandemic is becoming one of the largest, unprecedented health crises, and chest X-ray radiography (CXR) plays a vital role in diagnosing COVID-19. However, extracting and finding useful image features from CXRs demand a heavy workload for radiologists. Objective: The aim of this study was to design a novel multiple-inputs (MI) convolutional neural network (CNN) for the classification of COVID-19 and extraction of critical regions from CXRs. We also investigated the effect of the number of inputs on the performance of our new MI-CNN model. Methods: A total of 6205 CXR images (including 3021 COVID-19 CXRs and 3184 …

From Merging Frameworks To Merging Stars: Experiences Using Hpx, Kokkos And Simd Types, Gregor Dais, Srinivas Yadav Singanaboina, Patrick Diehl, Hartmut Kaiser, Dirk Pfluger

Faculty Publications

Octo-Tiger, a large-scale 3D AMR code for the merger of stars, uses a combination of HPX, Kokkos and explicit SIMD types, aiming to achieve performance-portability for a broad range of heterogeneous hardware. However, on A64FX CPUs, we encountered several missing pieces, hindering performance by causing problems with the SIMD vectorization. Therefore, we add std:experimental:simd as an option to use in Octo-Tiger's Kokkos kernels alongside Kokkos SIMD, and further add a new SVE (Scalable Vector Extensions) SIMD backend. Additionally, we amend missing SIMD implementations in the Kokkos kernels within Octo-Tiger's hydro solver. We test our changes by running Octo-Tiger on three …

Preface, Kisung Lee, Liang Jie Zhang

Faculty Publications

No abstract provided.

Analyzing Tweeting Patterns And Public Engagement On Twitter During The Recognition Period Of The Covid-19 Pandemic: A Study Of Two U.S. States, Misbah Ul Hoque, Kisung Lee, Jessica L. Beyer, Sara R. Curran, Katie S. Gonser, Nina S.N. Lam, Volodymyr V. Mihunov, Kejin Wang

Faculty Publications

The abundance of available information on social media can provide invaluable insights into people's responses to health information and public health guidance concerning COVID-19. This study examines tweeting patterns and public engagement on Twitter, as forms of social media, related to public health messaging in two U.S. states (Washington and Louisiana) during the early stage of the pandemic. We analyze more than 7M tweets and 571K COVID-19-related tweets posted by users in the two states over the first 25 days of the pandemic in the U.S. (Feb. 23, 2020, to Mar. 18, 2020). We also qualitatively code and examine 460 …

Accuracy Of A Sacral Drilling With A Custom 3d Printed Drilling Guide Or Free-Hand Technique In Canine Experimental Sacroiliac Luxation, Daniel Mccarthy

LSU Master's Theses

ABSTRACT

The objective of this study was to improve the accuracy of drilling during the repair of sacroiliac luxation with a 3D-printed patient-specific drill guide (3D-GDT) compared to free-hand drilling technique (FHDT). A blinded, randomized, prospective study was performed including sixteen canine cadavers (20-25 kg) euthanized for reasons not related to the study. Dorsal, bilateral, sacroiliac luxations (SILs) were created experimentally. A pelvic CT was performed pre- and post-drilling. The FHDT was drilled followed by 3D-GDT for each sacrum. CT and 3D measurements of craniocaudal and dorsoventral angles were compared between FHDT and 3D-GDT, as well as deviations of entry …

Injection Data Analysis Using Material Balance Time For Co2 Storage Capacity Estimation In Deep Closed Saline Aquifers, Mohamed Abdelaal, Mehdi Zeidouni

Faculty Publications

Estimating the ultimate storage capacity of deep saline aquifers is important to address the formation potential to store the envisioned large volumes of CO2. Injection data (i.e. injection rate, bottomhole pressure, and cumulative injected volume of CO2) are routinely recorded during storage operations. These data contain valuable information on the subsurface (e.g. the reservoir pore volume and the formation storage capacity) that can be extracted. In this paper, we present a two-step graphical technique to infer the pore volume and the ultimate storage capacity of closed saline aquifers by analyzing the available injection data. First, the pore volume is inferred …

Magnetic Weyl Semimetallic Phase In Thin Films Of Eu2ir2 O7, Xiaoran Liu, Shiang Fang, Yixing Fu, Wenbo Ge, Mikhail Kareev, Jong Woo Kim, Yongseong Choi, Evguenia Karapetrova, Qinghua Zhang, Lin Gu, Eun Sang Choi, Fangdi Wen, Justin H. Wilson, Gilberto Fabbris, Philip J. Ryan, John W. Freeland, Daniel Haskel, Weida Wu, J. H. Pixley, Jak Chakhalian

Faculty Publications

The interplay between electronic interactions and strong spin-orbit coupling is expected to create a plethora of fascinating correlated topological states of quantum matter. Of particular interest are magnetic Weyl semimetals originally proposed in the pyrochlore iridates, which are only expected to reveal their topological nature in thin film form. To date, however, direct experimental demonstrations of these exotic phases remain elusive, due to the lack of usable single crystals and the insufficient quality of available films. Here, we report on the discovery of signatures for the long-sought magnetic Weyl semimetallic phase in (111)-oriented Eu2Ir2O7 high-quality epitaxial thin films. We observed …

Maternal Mindful Eating As A Target For Improving Metabolic Outcomes In Pregnant Women With Obesity, Karen L. Lindsay, Jasper Most, Kerrie Buehler, Maryam Kebbe, Abby D. Altazan, Leanne M. Redman

Clinical Research Faculty Publications

Background: Maternal diet and eating behaviors have the potential to influence the metabolic milieu in pregnancies complicated by obesity, with implications for the developmental programming of offspring obesity. Emerging evidence suggests that mindfulness during eating may influence metabolic health in non-pregnant populations, but its effects in the context of pregnancy is less well understood. This study explored the individual and combined effects of mindful eating and diet quality on metabolic outcomes among pregnant women with obesity. Methods: In 46 pregnant women (body mas index >30 kg/m2) enrolled in the MomEE observational study, mindful eating (Mindful Eating Questionnaire, MEQ) and energy-adjusted …

A Combined Molecular Dynamics/Monte Carlo Simulation Of Cu Thin Film Growth On Tin Substrates: Illustration Of Growth Mechanisms And Comparison With Experiments, Reza Namakian, Brian R. Novak, Xiaoman Zhang, Wen Jin Meng, Dorel Moldovan

Faculty Publications

Using a sequential molecular dynamics (MD)/time-stamped force-bias Monte Carlo (tfMC) algorithm to simulate the deposition of Cu species onto a TiN(001) substrate at 600 K, it is shown for the first time that at the very early stage of growth, BCC-Cu grows pseudomorphically on the TiN(001) substrate as a very thin continuous film with the BCC-Cu[001]//TiN[001] growth direction. By increasing the thickness of the Cu thin film, however, the film transforms through the Nishiyama-Wasserman mechanism from BCC into predominantly FCC-Cu with abundant nanotwins, which is the same type of structure obtained in the experiment conducted here via a dc magnetron …

Vapor Isotopic Evidence For The Worsening Of Winter Air Quality By Anthropogenic Combustion-Derived Water, Meng Xing, Weiguo Liu, Xia Li, Weijian Zhou, Qiyuan Wang, Jie Tian, Xiaofei Li, Xuexi Tie, Guohui Li, Junji Cao, Huiming Bao, Zhisheng An

Faculty Publications

© 2020 National Academy of Sciences. All rights reserved. Anthropogenic combustion-derived water (CDW) may accumulate in an airshed due to stagnant air, which may further enhance the formation of secondary aerosols and worsen air quality. Here we collected three-winter-season, hourly resolution, water-vapor stable H and O isotope compositions together with atmospheric physical and chemical data from the city of Xi’an, located in the Guanzhong Basin (GZB) in northwestern China, to elucidate the role of CDW in particulate pollution. Based on our experimentally determined water vapor isotope composition of the CDW for individual and weighted fuels in the basin, we found …

Vapor Isotopic Evidence For The Worsening Of Winter Air Quality By Anthropogenic Combustion-Derived Water, Meng Xing, Weiguo Liu, Xia Li, Weijian Zhou, Qiyuan Wang, Jie Tian, Xiaofei Li, Xuexi Tie, Guohui Li, Junji Cao, Huiming Bao, Zhisheng An

Faculty Publications

© 2020 National Academy of Sciences. All rights reserved. Anthropogenic combustion-derived water (CDW) may accumulate in an airshed due to stagnant air, which may further enhance the formation of secondary aerosols and worsen air quality. Here we collected three-winter-season, hourly resolution, water-vapor stable H and O isotope compositions together with atmospheric physical and chemical data from the city of Xi’an, located in the Guanzhong Basin (GZB) in northwestern China, to elucidate the role of CDW in particulate pollution. Based on our experimentally determined water vapor isotope composition of the CDW for individual and weighted fuels in the basin, we found …

A Da+2:46ta-Intensive Approach To Allocating Owner Vs. Nfip Portion Of Average Annual Flood Losses, Adilur Rahim, Carol Freidland, Robert Rohli, Nazla Bushra, Rubayet Bin Mostafiz

Faculty Publications

No abstract provided.