Plant Process Emulator

The purpose of this project is to provide the VCU Engineering Students with a training system to simulate the use of Industrial Automation systems. Students need a wide variety of training systems to adequately train and improve their knowledge of all the fundamentals of PLC systems. There are multiple companies that sell a very expensive training setup that can teach students about Proportional-Integral-Derivative (PID) control systems and mechanical systems but those systems cost too much (~$20,000+) for a small university or trade school to fund. The training system that was built provides the student with real world control and monitoring of physical plant attributes like fluid level control and temperature control. A Programmable Logic Controller (PLC) is used to instantiate the PID’s for both level control and temperature control. A level transmitter and a thermocouple act as the process variables and the solenoid valves and a heater act as the manipulating variables to adjust the level and temperature respectively. All components of the system work harmoniously together to simulate a physical plant process. The demonstrations run through this trainer show how the hardware and software work together to allow the operator control of the system. The goal is to allow students a chance to be exposed to different uses of PLC’s and PID’s.https://scholarscompass.vcu.edu/capstone/1192/thumbnail.jp

Borrelia recurrentis employs a novel multifunctional surface protein with anti-complement, anti-opsonic and invasive potential to escape innate immunity

Borrelia recurrentis, the etiologic agent of louse-borne relapsing fever in humans, has evolved strategies, including antigenic variation, to evade immune defence, thereby causing severe diseases with high mortality rates. Here we identify for the first time a multifunctional surface lipoprotein of B. recurrentis, termed HcpA, and demonstrate that it binds human complement regulators, Factor H, CFHR-1, and simultaneously, the host protease plasminogen. Cell surface bound factor H was found to retain its activity and to confer resistance to complement attack. Moreover, ectopic expression of HcpA in a B. burgdorferi B313 strain, deficient in Factor H binding proteins, protected the transformed spirochetes from complement-mediated killing. Furthermore, HcpA-bound plasminogen/plasmin endows B. recurrentis with the potential to resist opsonization and to degrade extracellular matrix components. Together, the present study underscores the high virulence potential of B. recurrentis. The elucidation of the molecular basis underlying the versatile strategies of B. recurrentis to escape innate immunity and to persist in human tissues, including the brain, may help to understand the pathological processes underlying louse-borne relapsing fever

Two-Channel Passive Detection Exploiting Cyclostationarity

This paper addresses a two-channel passive detection problem exploiting cyclostationarity. Given a reference channel (RC) and a surveillance channel (SC), the goal is to detect a target echo present at the surveillance array transmitted by an illuminator of opportunity equipped with multiple antennas. Since common transmission signals are cyclostationary, we exploit this information at the detector. Specifically, we derive an asymptotic generalized likelihood ratio test (GLRT) to detect the presence of a cyclostationary signal at the SC given observations from RC and SC. This detector tests for different covariance structures. Simulation results show good performance of the proposed detector compared to competing techniques that do not exploit cyclostationarity

Joint Detection of Almost-Cyclostationary Signals and Estimation of Their Cycle Period

We propose a technique that jointly detects the presence of almost-cyclostationary (ACS ) signals in wide-sense stationary noise and provides an estimate of their cycle period. Since the cycle period of an ACS process is not an integer, the approach is based on a combination of a resampling stage and a multiple hypothesis test, which deal separately with the fractional part and the integer part of the cycle period. The approach requires resampling the signal at many different rates, which is computationally expensive. For this reason, we propose a filter hank structure that allows us to efficiently resample a signal at many different rates by identifying common interpolation stages among the set of resampling rates.The work of D. Ram ́ırez was supported in part by the Ministerio de Econom ́ıaof Spain under projects: OTOSIS (TEC2013-41718-R) and the COMONSENSNetwork (TEC2015-69648-REDC), in part by the Ministerio de Econom ́ıa ofSpain jointly with the European Commission (ERDF) under projects ADVEN-TURE (TEC2015-69868-C2-1-R) and CAIMAN (TEC2017-86921-C2-2-R), inpart by the Comunidad de Madrid under project CASI-CAM-CM (S2013/ICE-2845), and in part by the German Research Foundation (DFG) under projectRA 2662/2-1. The work of S. Horstmann and P. J. Schreier were supported bythe German Research Foundation (DFG) under Grant SCHR 1384/6-

Dopamine release, diffusion and uptake : A computational model for synaptic and volume transmission

Computational modeling of dopamine transmission is challenged by complex underlying mechanisms. Here we present a new computational model that (I) simultaneously regards release, diffusion and uptake of dopamine, (II) considers multiple terminal release events and (III) comprises both synaptic and volume transmission by incorporating the geometry of the synaptic cleft. We were able to validate our model in that it simulates concentration values comparable to physiological values observed in empirical studies. Further, although synaptic dopamine diffuses into extra-synaptic space, our model reflects a very localized signal occurring on the synaptic level, i.e. synaptic dopamine release is negligibly recognized by neighboring synapses. Moreover, increasing evidence suggests that cognitive performance can be predicted by signal variability of neuroimaging data (e.g. BOLD). Signal variability in target areas of dopaminergic neurons (striatum, cortex) may arise from dopamine concentration variability. On that account we compared spatio-temporal variability in a simulation mimicking normal dopamine transmission in striatum to scenarios of enhanced dopamine release and dopamine uptake inhibition. We found different variability characteristics between the three settings, which may in part account for differences in empirical observations. From a clinical perspective, differences in striatal dopaminergic signaling contribute to differential learning and reward processing, with relevant implications for addictive- and compulsive-like behavior. Specifically, dopaminergic tone is assumed to impact on phasic dopamine and hence on the integration of reward-related signals. However, in humans DA tone is classically assessed using PET, which is an indirect measure of endogenous DA availability and suffers from temporal and spatial resolution issues. We discuss how this can lead to discrepancies with observations from other methods such as microdialysis and show how computational modeling can help to refine our understanding of DA transmission. Author summary The dopaminergic system of the brain is very complex and affects various cognitive domains like memory, learning and motor control. Alterations have been observed e.g. in Parkinson's or Huntington's Disease, ADHD, addiction and compulsive disorders, such as pathological gambling and also in obesity. We present a new computational model that allows to simulate the process of dopamine transmission from dopaminergic neurons originated in source brain regions like the VTA to target areas such as the striatum on a synaptic and on a larger, volume-spanning level. The model can further be used for simulations of dopamine related diseases or pharmacological interventions. In general, computational modeling helps to extend our understanding, gained from empirical research, to situations were in vivo measurements are not feasible.Peer reviewe

Державне регулювання системи факторів оцінки та мінімізації ризиків легалізації коштів, одержаних злочинним шляхом в процесі фінансового моніторингу комерційних банків України

Основною ціллю данної статті є формулювання важливості впливу чітко визначених факторів, які впливають на процеси фінансового моніторингу в комерційних банків України. Виходячи з поставлених цілей, завданнями даної статті є розроблення моделі оцінки ризиків банківської установи щодо протидії легалізації коштів одержаних злочинним шляхом в системі внутрішньобанківського фінансового моніторингу, та використання в подальшому запропонованих стратегій управління наслідками даних ризиків

Bayesian Model Selection of Lithium-Ion Battery Models via Bayesian Quadrature

A wide variety of battery models are available, and it is not always obvious which model `best' describes a dataset. This paper presents a Bayesian model selection approach using Bayesian quadrature. The model evidence is adopted as the selection metric, choosing the simplest model that describes the data, in the spirit of Occam's razor. However, estimating this requires integral computations over parameter space, which is usually prohibitively expensive. Bayesian quadrature offers sample-efficient integration via model-based inference that minimises the number of battery model evaluations. The posterior distribution of model parameters can also be inferred as a byproduct without further computation. Here, the simplest lithium-ion battery models, equivalent circuit models, were used to analyse the sensitivity of the selection criterion to given different datasets and model configurations. We show that popular model selection criteria, such as root-mean-square error and Bayesian information criterion, can fail to select a parsimonious model in the case of a multimodal posterior. The model evidence can spot the optimal model in such cases, simultaneously providing the variance of the evidence inference itself as an indication of confidence. We also show that Bayesian quadrature can compute the evidence faster than popular Monte Carlo based solvers.Comment: 11 pages, 2 figures, accepted at IFAC202

Measurement of Jet Shapes in Photoproduction at HERA

The shape of jets produced in quasi-real photon-proton collisions at centre-of-mass energies in the range $134-277$ GeV has been measured using the hadronic energy flow. The measurement was done with the ZEUS detector at HERA. Jets are identified using a cone algorithm in the $\eta - \phi$ plane with a cone radius of one unit. Measured jet shapes both in inclusive jet and dijet production with transverse energies $E^{jet}_T>14$ GeV are presented. The jet shape broadens as the jet pseudorapidity ($\eta^{jet}$) increases and narrows as $E^{jet}_T$ increases. In dijet photoproduction, the jet shapes have been measured separately for samples dominated by resolved and by direct processes. Leading-logarithm parton-shower Monte Carlo calculations of resolved and direct processes describe well the measured jet shapes except for the inclusive production of jets with high $\eta^{jet}$ and low $E^{jet}_T$. The observed broadening of the jet shape as $\eta^{jet}$ increases is consistent with the predicted increase in the fraction of final state gluon jets.Comment: 29 pages including 9 figure