Digital Commons Network^™

Novel Cyber Fault Prognosis And Resilience Control For Cyber-Physical Systems, Shanshan Bi, Tianchen Wang, Lei Wang, Maciej Zawodniok

Electrical and Computer Engineering Faculty Research & Creative Works

Cyber-physical systems (CPSs) consist of a network, computation, and physical process. Embedded networks, which deliver control and sensing signal, can potentially affect CPSs performance. However, the degradation of physical system performance caused by the embedded networks is frequently oversimplified with strong assumptions. The proposed scheme effectively relaxes those assumptions in the existing works that network delays are bounded in a specific range, or its distribution is time invariant. Most of the existing works on fault diagnosis and prognosis addressed the physical system fault detection and isolation and ignore cyber network faults. A novel cyber network fault prognosis scheme is proposed …

A Granger Causality Analysis Of Groundwater Patterns Over A Half-Century, Nitin K. Singh, David M. Borrok

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Groundwater depletion in many areas of the world has been broadly attributed to irrigation. However, more formal, data-driven, causal mechanisms of long-term groundwater patterns have not been assessed. Here, we conducted the first Granger causality analysis to identify the "causes" of groundwater patterns using the rice-producing parishes of Louisiana, USA, as an example. Trend analysis showed a decline of up to 6 m in groundwater level over 51 years. We found that no single cause explained groundwater patterns for all parishes. Causal linkages were noted between groundwater and area harvested, number of irrigation wells, summer precipitation totals, and drought. Bi-directional …

Cuinse₂ Nanotube Arrays For Efficient Solar Energy Conversion, Wipula Priya Liyanage, Manashi Nath

Chemistry Faculty Research & Creative Works

Highly uniform and vertically aligned p-type CuInSe₂ (CISe) nanotube arrays were fabricated through a unique protocol, incorporating confined electrodeposition on lithographically patterned nanoelectrodes. This protocol can be readily adapted to fabricate nanotube arrays of other photoabsorber and functional materials with precisely controllable design parameters. Ternary CISe nanotube arrays were electrodeposited congruently from a single electrolytic bath and the resulting nanotube arrays were studied through powder X-ray diffraction as well as elemental analysis which revealed compositional purity. Detailed photoelectrochemical (PEC) characterizations in a liquid junction cell were also carried out to investigate the photoconversion efficiency. It was observed that the …

Lattice Thermal Conductivity Of Quartz At High Pressure And Temperature From The Boltzmann Transport Equation, Xue Xiong, Eugene J. Ragasa, Aleksandr V. Chernatynskiy, Dawei Tang, Simon R. Phillpot

Physics Faculty Research & Creative Works

The thermal conductivities along the basal and hexagonal directions of α-quartz silica, the low-temperature form of crystalline SiO₂, are predicted from the solution of the Boltzmann transport equation combined with the van Beest, Kramer, and van Santen potential for the temperature up to 900 K and the pressure as high as 4 GPa. The thermal conductivities at atmospheric pressure, which show a negative and nonlinear dependence on temperature, are in reasonable agreement with the experimental data. The influence of pressure on thermal conductivity is positive and linear. The pressure (P) and temperature (T) dependences of the thermal conductivity …

Local Orbital Degeneracy Lifting As A Precursor To An Orbital-Selective Peierls Transition, E. S. Bozin, W. G. Yin, R. J. Koch, M. Abeykoon, Yew San Hor, H. Zheng, H. C. Lei, C. Petrovic, J. F. Mitchell, S. J. L. Billinge

Physics Faculty Research & Creative Works

Fundamental electronic principles underlying all transition metal compounds are the symmetry and filling of the d-electron orbitals and the influence of this filling on structural configurations and responses. Here we use a sensitive local structural technique, x-ray atomic pair distribution function analysis, to reveal the presence of fluctuating local-structural distortions at high temperature in one such compound, CuIr₂S₄. We show that this hitherto overlooked fluctuating symmetry-lowering is electronic in origin and will modify the energy-level spectrum and electronic and magnetic properties. The explanation is a local, fluctuating, orbital-degeneracy-lifted state. The natural extension of our result would …

Search For Gravitational Waves From Scorpius X-1 In The Second Advanced Ligo Observing Run With An Improved Hidden Markov Model, B. P. Abbott, R. Abbott, T. D. Abbott, Marco Cavaglia, For Full List Of Authors, See Publisher's Website.

Physics Faculty Research & Creative Works

We present results from a semicoherent search for continuous gravitational waves from the low-mass x-ray binary Scorpius X-1, using a hidden Markov model (HMM) to track spin wandering. This search improves on previous HMM-based searches of LIGO data by using an improved frequency domain matched filter, the J-statistic, and by analyzing data from Advanced LIGO's second observing run. In the frequency range searched, from 60 to 650 Hz, we find no evidence of gravitational radiation. At 194.6 Hz, the most sensitive search frequency, we report an upper limit on gravitational wave strain (at 95% confidence) of h₀^95% = …

The Conundrum Of Relaxation Volumes In First-Principles Calculations Of Charged Defects In Uo₂, Anuj Goyal, Kiran Mathew, Richard G. Hennig, Aleksandr V. Chernatynskiy

Physics Faculty Research & Creative Works

The defect relaxation volumes obtained from density-functional theory (DFT) calculations of charged vacancies and interstitials are much larger than their neutral counterparts, seemingly unphysically large. We focus on UO2 as our primary material of interest, but also consider Si and GaAs to reveal the generality of our results. In this work, we investigate the possible reasons for this and revisit the methods that address the calculation of charged defects in periodic DFT. We probe the dependence of the proposed energy corrections to charged defect formation energies on relaxation volumes and find that corrections such as potential alignment remain ambiguous with …

The Bridge Newsletter Winter 2019, Missouri University Of Science And Technology

The Bridge Newsletter

-Dehghani joins S&T
-Life after death of solar panels
-Civil engineering senior studies in Hong Kong
-Planning for future climate changes in the Artic

Detection And Mitigation Of Attacks In Nonlinear Stochastic System Using Modified Detector, Chandreyee Bhowmick, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

A novel attack detection method is presented for a nonlinear system with known dynamics using the measured output in the presence of additive process and measurement noise. False data injection (FDI) and replay attacks are considered using a modified fault detector. The difference between the measured and the estimated output from an adaptive observer, often known as the innovation signal, is generated and shown to have a Gaussian distribution with non-zero mean. This innovation signal in conjunction with the modified detector is utilized to detect attacks under a stable controller using the estimated state vector. Unlike FDI attack, where the …

Missouri S&T Magazine Fall/Winter 2019, Missouri S&T Marketing And Communications Department, Miner Alumni Association

Missouri S&T Magazine

No abstract provided.

Predicting The Daily Return Direction Of The Stock Market Using Hybrid Machine Learning Algorithms, X. Zhong, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields, including stock market investment. However, few studies have focused on forecasting daily stock market returns, especially when using powerful machine learning techniques, such as deep neural networks (DNNs), to perform the analyses. DNNs employ various deep learning algorithms based on the combination of network structure, activation function, and model parameters, with their performance depending on the format of the data representation. This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 …

Hedge Fund Replication Using Strategy Specific Factors, Sujit Subhash, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

Hedge funds have traditionally served wealthy individuals and institutional investors with the promise of delivering protection of capital and uncorrelated positive returns irrespective of market direction, allowing them to better manage portfolio risk. However, the financial crisis of 2008 has heightened investor sensitivity to the high fees, illiquidity, lack of transparency, and lockup periods typically associated with hedge funds. Hedge fund replication products, or clones, seek to answer these challenges by providing daily liquidity, transparency, and immediate exposure to a desired hedge fund strategy. Nonetheless, although lowering cost and adding simplicity by using a common set of factors, traditional replication …

Facile One-Pot Synthesis Of Nico₂Se₄-Rgo On Ni Foam For High Performance Hybrid Supercapacitors, Bahareh Golrokh Amin, Jahangir Masud, Manashi Nath

Chemistry Faculty Research & Creative Works

A facile, innovative synthesis for the fabrication of NiCo₂Se₄-rGO on a Ni foam nanocomposite via a simple hydrothermal reaction is proposed. The as-prepared NiCo₂Se₄-rGO@Ni foam electrode was tested through pxrd, TEM, SEM, and EDS to characterize the morphology and the purity of the material. The bimetallic electrode exhibited outstanding electrochemical performance with a high specific capacitance of 2038.55 F g^-1 at 1 A g^-1. NiCo₂Se₄-rGO@Ni foam exhibits an extensive cycling stability after 1000 cycles by retaining 90% of its initial capacity. A superior energy density …

Sample Transfer Optimization With Adaptive Deep Neural Network, Hemanta Sapkota, Md Arifuzzaman, Engin Arslan

Computer Science Faculty Research & Creative Works

Application-layer transfer configurations play a crucial role in achieving desirable performance in high-speed networks. However, finding the optimal configuration for a given transfer task is a difficult problem as it depends on various factors including dataset characteristics, network settings, and background traffic. The state-of-the-art transfer tuning solutions rely on real-time sample transfers to evaluate various configurations and estimate the optimal one. However, existing approaches to run sample transfers incur high delay and measurement errors, thus significantly limit the efficiency of the transfer tuning algorithms. In this paper, we introduce adaptive feed forward deep neural network (DNN) to minimize the error …

Introduction Of A Hybrid Monitor For Cyber-Physical Systems, J. Ceasar Aguma, Bruce M. Mcmillin, Amelia Regan

Computer Science Faculty Research & Creative Works

Computing systems and mobile technologies have changed dramatically since the introduction of firewall technology in 1988. The internet has grown from a simple network of networks to a cyber and physical entity that encompasses the entire planet. Cyber-physical systems(CPS) now control most of the day to day operations of human civilization from autonomous cars to nuclear energy plants. While phenomenal, this growth has created new security threats. These are threats that cannot be blocked by a firewall for they are not only cyber but cyber-physical. In light of these cyber-physical threats, this paper proposes a security measure that promises to …

Analytical Treatment Of The Interaction Quench Dynamics Of Two Bosons In A Two-Dimensional Harmonic Trap, G. Bougas, Simeon I. Mistakidis, P. Schmelcher

Physics Faculty Research & Creative Works

We Investigate The Quantum Dynamics Of Two Bosons, Trapped In A Two-Dimensional Harmonic Trap, Upon Quenching Arbitrarily Their Interaction Strength And Thereby Covering The Entire Energy Spectrum. Utilizing The Exact Analytical Solution Of The Stationary System, We Derive A Closed Analytical Form Of The Expansion Coefficients Of The Time-Evolved Two-Body Wave Function, Whose Dynamics Is Determined By An Expansion Over The Postquench Eigenstates. The Emergent Dynamical Response Of The System Is Analyzed In Detail By Inspecting Several Observables Such As The Fidelity, The Reduced One-Body Densities, The Radial Probability Density Of The Relative Wave Function In Both Real And Momentum …

Social And Geographical Disparities In Twitter Use During Hurricane Harvey, Lei Zou, Nina S.N. Lam, Shayan Shams, Heng Cai, Michelle A. Meyer, Seungwon Yang, Kisung Lee, Seung Jong Park, Margaret A. Reams

Computer Science Faculty Research & Creative Works

Social media such as Twitter is increasingly being used as an effective platform to observe human behaviors in disastrous events. However, uneven social media use among different groups of population in different regions could lead to biased consequences and affect disaster resilience. This paper studies the Twitter use during 2017 Hurricane Harvey in 76 counties in Texas and Louisiana. We seek to answer a fundamental question: did social-geographical disparities of Twitter use exist during the three phases of emergency management (preparedness, response, recovery)? We employed a Twitter data mining framework to process the data and calculate two indexes: Ratio and …

An Optimal Hybrid Learning Approach For Attack Detection In Linear Networked Control Systems, Haifeng Niu, Avimanyu Sahoo, Chandreyee Bhowmick, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

A novel learning-based attack detection and estimation scheme is proposed for linear networked control systems NCS, wherein the attacks on the communication network in the feedback loop are expected to increase network induced delays and packet losses, thus changing the physical system dynamics. First, the network traffic flow is modeled as a linear system with uncertain state matrix and an optimal Q-learning based control scheme over finite-horizon is utilized to stabilize the flow. Next, an adaptive observer is proposed to generate the detection residual, which is subsequently used to determine the onset of an attack when it exceeds a predefined …

A Deep Learning Approach For Tweet Classification And Rescue Scheduling For Effective Disaster Management, Md. Yasin Kabir, Sanjay Kumar Madria

Computer Science Faculty Research & Creative Works

Every activity in disaster management demands accurate and up-todate information to allow a quick, easy, and cost-efective response to reduce the possible loss of lives and properties. It is a challenging and complex task to acquire information from diferent regions of a disaster-afected area in a timely fashion. The extensive spread and reach of social media and networks such as Twitter allow people to share information in real-time. However, gathering of valuable information requires a series of operations such as (1) processing each tweet for the text classiication, (2) possible location determination of people needing help based on tweets, and …

Use Cases Of Lossy Compression For Floating-Point Data In Scientific Data Sets, Franck Cappello, Sheng Di, Sihuan Li, Xin Liang, Ali Murat Gok, Dingwen Tao, For Full List Of Authors, See Publisher's Website.

Computer Science Faculty Research & Creative Works

Architectural and technological trends of systems used for scientific computing call for a significant reduction of scientific data sets that are composed mainly of floating-point data. This article surveys and presents experimental results of currently identified use cases of generic lossy compression to address the different limitations of scientific computing systems. The article shows from a collection of experiments run on parallel systems of a leadership facility that lossy data compression not only can reduce the footprint of scientific data sets on storage but also can reduce I/O and checkpoint/restart times, accelerate computation, and even allow significantly larger problems to …

Blended Root Finding Algorithm Outperforms Bisection And Regula Falsi Algorithms, Chaman Sabharwal

Computer Science Faculty Research & Creative Works

Finding the roots of an equation is a fundamental problem in various fields, including numerical computing, social and physical sciences. Numerical techniques are used when an analytic solution is not available. There is not a single algorithm that works best for every function. We designed and implemented a new algorithm that is a dynamic blend of the bisection and regula falsi algorithms. The implementation results validate that the new algorithm outperforms both bisection and regula falsi algorithms. It is also observed that the new algorithm outperforms the secant algorithm and the Newton-Raphson algorithm because the new algorithm requires fewer computational …

Collective Representation Learning On Spatiotemporal Heterogeneous Information Networks, Dakshak Keerthi Chandra, Pengyang Wang, Jennifer Leopold, Yanjie Fu

Computer Science Faculty Research & Creative Works

Representation learning is a technique that is used to capture the underlying latent features of complex data. Representation learning on networks has been widely implemented for learning network structure and embedding it in a low dimensional vector space. In recent years, network embedding using representation learning has attracted increasing attention, and many deep architectures have been widely proposed. However, existing network embedding techniques ignore the multi-class spatial and temporal relationships that crucially reflect the complex nature among vertices and links in spatiotemporal heterogeneous information networks(SHINs).

To address this problem, in this paper, we present two types of collective representation learning …

Enhanced Piezoresponse And Nonlinear Optical Properties Of Fluorinated Self-Assembled Peptide Nanotubes, Soma Khanra, Sandra V. Vassiliades, Wendel A. Alves, Kaidi Yang, Rainer Glaser, Kartik Ghosh, Payal Bhattacharya, Ping Yu, Suchismita Guha

Chemistry Faculty Research & Creative Works

Self-assembled L,L-diphenylalanine (FF) nanostructures offer an attractive platform for photonics and nonlinear optics. The nonlinear optical (NLO) coefficients of FF nanotubes depend on the diameter of the tube [S. Khanra et al. Phys. Chem. Chem. Phys. 19(4), 3084-3093 (2017)]. To further enhance the NLO properties of FF, we search for structural modifications. Here, we report on the synthesis of fluorinated FF dipeptides by replacing one ortho-hydrogen atom in each of the phenyl groups of FF by a fluorine atom. Density-functional theoretical calculations yield insights into minimum energy conformers of fluorinated FF (Fl-FF). Fl-FF self-assembles akin to FF into micron-length tubes. …

Extraction And Visualization Of Orientation Data From Virtual Geologic Surfaces With Matlab®, Avery J. Welker, John Patrick Hogan, Andreas Eckert

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

High-resolution visualization of surfaces of geologic interest, at a multitude of scales, using 3D point cloud technologies provides an opportunity to analyze spatial relationships of surfaces using orientation data. We present a MATLAB® script that produces planar geologic attitude data (e.g., strike, dip, and dip-direction data) from 3D datasets (e.g., point clouds, 3D scanning). The method utilizes Cartesian coordinates of triangular planar surfaces and converts them into matrices of conventional geologic attitude data. Spatial relationships among data points can be investigated, using polar tangent diagrams, stereographic analysis, or geologic curvature analysis. We utilize this script to create "synthetic" graphical plots …

Angular Memory Effect Of Transmission Eigenchannels, Hasan Yllmaz, Chia Wei Hsu, Arthur Goetschy, Stefan Bittner, Stefan Rotter, Alexey Yamilov, Hui Cao

Physics Faculty Research & Creative Works

The optical memory effect has emerged as a powerful tool for imaging through multiple-scattering media; however, the finite angular range of the memory effect limits the field of view. Here, we demonstrate experimentally that selective coupling of incident light into a high-transmission channel increases the angular memory-effect range. This enhancement is attributed to the robustness of the high-transmission channels against perturbations such as sample tilt or wave front tilt. Our work shows that the high-transmission channels provide an enhanced field of view for memory-effect-based imaging through diffusive media.

Tests Of General Relativity With The Binary Black Hole Signals From The Ligo-Virgo Catalog Gwtc-1, B. P. Abbott, R. Abbott, T. D. Abbott, Marco Cavaglia, For Full List Of Authors, See Publisher's Website.

Physics Faculty Research & Creative Works

The detection of gravitational waves by Advanced LIGO and Advanced Virgo provides an opportunity to test general relativity in a regime that is inaccessible to traditional astronomical observations and laboratory tests. We present four tests of the consistency of the data with binary black hole gravitational waveforms predicted by general relativity. One test subtracts the best-fit waveform from the data and checks the consistency of the residual with detector noise. The second test checks the consistency of the low- and high-frequency parts of the observed signals. The third test checks that phenomenological deviations introduced in the waveform model (including in …

A Multi-Step Approach To Modeling The 24-Hour Daily Profiles Of Electricity Load Using Daily Splines, Abdelmonaem Jornaz, V. A. Samaranayake

Mathematics and Statistics Faculty Research & Creative Works

Forecasting of real-time electricity load has been an important research topic over many years. Electricity load is driven by many factors, including economic conditions and weather. Furthermore, the demand for electricity varies with time, with different hours of the day and different days of the week having an effect on the load. This paper proposes a hybrid load-forecasting method that combines classical time series formulations with cubic splines to model electricity load. It is shown that this approach produces a model capable of making short-term forecasts with reasonable accuracy. In contrast to forecasting models that utilize a multitude of regressor …

The Missouri Miner, October 30, 2019

The Missouri Miner Newspaper

No abstract provided.

Risk Awareness Enhancement Systems For Hazmat Transportation: Prototyping And Technology Evaluation, Jian Xue, Katherine Linville, Yu Li, Pranav Nitin Godse, Suzanna Long, Ruwen Qin

Engineering Management and Systems Engineering Faculty Research & Creative Works

Workers of hazardous material (hazmat) transportation have a higher chance than other workers to be exposed to various risks in their workplace. Assisting them to safely operate in their workplace in a near real-time manner is in particular need. This paper presents a study of designing, prototyping and developing feedback systems to help increase the risk awareness of workers in the loading and uploading phases of hazmat transportation. The first system was prototyped on an Arduino board, serving as the reference for system development. Then, the second system, named a Bluetooth Low Energy (BLE) beacon based system, was designed as …

Faculty Senate Meeting Minutes September 26, 2019, Missouri University Of Science And Technology Faculty Senate

Minutes & Agendas

No abstract provided.