Model Checking Control Communication of a FACTS Device

This paper concerns the design and verification of a realtime communication protocol for sensor data collection and processing between an embedded computer and a DSP. In such systems, a certain amount of data loss without recovery may be tolerated. The key issue is to define and verify the correctness in the presence of these lost data frames under real-time constraints. This paper describes a temporal verification that if the end processes do not detect that too many frames are lost, defined by comparison of error counters against given threshold values, then there will be a bounded delay between transmission of data frames and reception of control frames. This verification and others presented herein were performed with the model checkers SPIN and RT-SPIN

A Recursive Least Squares Training Algorithm for Multilayer Recurrent Neural Networks

Recurrent neural networks have the potential to perform significantly better than the commonly used feedforward neural networks due to their dynamical nature. However, they have received less attention because training algorithms/architectures have not been well developed. In this study, a recursive least squares algorithm to train recurrent neural networks with an arbitrary number of hidden layers is developed. The training algorithm is developed as an extension of the standard recursive estimation problem. Simulated results obtained for identification of the dynamics of a nonlinear dynamical system show promising results

Particle sizing in non-dilute dispersions using diffusing wave spectroscopy with multiple optical path lengths

Non-dilute dispersed phase systems, such as foams, emulsions, and suspensions, are an important class of final formulations and chemical process intermediates in a variety of industries. The utility of these systems hinges on their stability over the lifetime of use, and therefore an accurate assessment of chemical and physical dynamics, asformulated, is required. We describe a unified treatment of diffusing wave spectroscopy (DWS) data using a range of optical path length with a goniometric instrument. DWS correlation data from multiple angles and robust Monte Carlo simulations are used to determine accurate values of the photon transport mean free path length. The variance on each correlation function is used to determine the physical time range that the mean squared displacement can be analyzed. Using standard solid particle suspensions of polystyrene and SiO2, we determine the average particle size with accuracy comparable to dynamic light scattering

Identification of Cutting Force in End Milling Operations Using Recurrent Neural Networks

The problem of identifying the cutting force in end milling operations is considered in this study. Recurrent neural networks are used here and are trained using a recursive least squares training algorithm. Training results for data obtained from a SAJO 3-axis vertical milling machine for steady slot cuts are presented. The results show that a recurrent neural network can learn the functional relationship between the feed rate and steady-state average resultant cutting force very well. Furthermore, results for the Mackey-Glass time series prediction problem are presented to illustrate the faster learning capability of the neural network scheme presented her

Neural Modeling and Control of a Distillation Column

Control of a nine-stage three-component distillation column is considered. The control objective is achieved using a neural estimator and a neural controller. The neural estimator is trained to represent the chemical process accurately, and the neural controller is trained to give an input to the chemical process which will yield the desired output. Training of both the neural networks is accomplished using a recursive least squares training algorithm implemented on an Intel iPSC/2 multicomputer (hypercube). Simulated results are presented for a numerical example

Parallel Implementation of a Recursive Least Squares Neural Network Training Method on the Intel IPSC/2

An algorithm based on the Marquardt-Levenberg least-square optimization method has been shown by S. Kollias and D. Anastassiou (IEEE Trans. on Circuits Syst. vol.36, no.8, p.1092-101, Aug. 1989) to be a much more efficient training method than gradient descent, when applied to some small feedforward neural networks. Yet, for many applications, the increase in computational complexity of the method outweighs any gain in learning rate obtained over current training methods. However, the least-squares method can be more efficiently implemented on parallel architectures than standard methods. This is demonstrated by comparing computation times and learning rates for the least-squares method implemented on 1, 2, 4, 8, and 16 processors on an Intel iPSC/2 multicomputer. Two applications which demonstrate the faster real-time learning rate of the last-squares method over than of gradient descent are give

Investigations in Reduced-Order Aerodynamic Modeling of Aircraft Approaching Stall

No abstract availabl

Microenvironmental Influence on Pre-Clinical Activity of Polo-Like Kinase Inhibition in Multiple Myeloma: Implications for Clinical Translation

Polo-like kinases (PLKs) play an important role in cell cycle progression, checkpoint control and mitosis. The high mitotic index and chromosomal instability of advanced cancers suggest that PLK inhibitors may be an attractive therapeutic option for presently incurable advanced neoplasias with systemic involvement, such as multiple myeloma (MM). We studied the PLK 1, 2, 3 inhibitor BI 2536 and observed potent (IC50<40 nM) and rapid (commitment to cell death <24 hrs) in vitro activity against MM cells in isolation, as well as in vivo activity against a traditional subcutaneous xenograft mouse model. Tumor cells in MM patients, however, don't exist in isolation, but reside in and interact with the bone microenvironment. Therefore conventional in vitro and in vivo preclinical assays don't take into account how interactions between MM cells and the bone microenvironment can potentially confer drug resistance. To probe this question, we performed tumor cell compartment-specific bioluminescence imaging assays to compare the preclinical anti-MM activity of BI 2536 in vitro in the presence vs. absence of stromal cells or osteoclasts. We observed that the presence of these bone marrow non-malignant cells led to decreased anti-MM activity of BI 2536. We further validated these results in an orthotopic in vivo mouse model of diffuse MM bone lesions where tumor cells interact with non-malignant cells of the bone microenvironment. We again observed that BI 2536 had decreased activity in this in vivo model of tumor-bone microenvironment interactions highlighting that, despite BI 2536's promising activity in conventional assays, its lack of activity in microenvironmental models raises concerns for its clinical development for MM. More broadly, preclinical drug testing in the absence of relevant tumor microenvironment interactions may overestimate potential clinical activity, thus explaining at least in part the gap between preclinical vs. clinical efficacy in MM and other cancers

Serum kynurenic acid is reduced in affective psychosis

A subgroup of individuals with mood and psychotic disorders shows evidence of inflammation that leads to activation of the kynurenine pathway and the increased production of neuroactive kynurenine metabolites. Depression is hypothesized to be causally associated with an imbalance in the kynurenine pathway, with an increased metabolism down the 3-hydroxykynurenine (3HK) branch of the pathway leading to increased levels of the neurotoxic metabolite, quinolinic acid (QA), which is a putative Nmethyl- D-aspartate (NMDA) receptor agonist. In contrast, schizophrenia and psychosis are hypothesized to arise from increased metabolism of the NMDA receptor antagonist, kynurenic acid (KynA), leading to hypofunction of GABAergic interneurons, the disinhibition of pyramidal neurons and striatal hyperdopaminergia. Here we present results that challenge the model of excess KynA production in affective psychosis. After rigorous control of potential confounders and multiple testing we find significant reductions in serum KynA and/or KynA/QA in acutely ill inpatients with major depressive disorder (N = 35), bipolar disorder (N = 53) and schizoaffective disorder (N = 40) versus healthy controls (N = 92). No significant difference was found between acutely ill inpatients with schizophrenia (n = 21) and healthy controls. Further, a post hoc comparison of patients divided into the categories of non-psychotic affective disorder, affective psychosis and psychotic disorder (non-affective) showed that the greatest decrease in KynA was in the affective psychosis group relative to the other diagnostic groups. Our results are consistent with reports of elevations in proinflammatory cytokines in psychosis, and preclinical work showing that inflammation upregulates the enzyme, kynurenine mono-oxygenase (KMO), which converts kynurenine into 3-hydroxykynurenine and quinolinic acid

Emergence of Anti-Cancer Drug Resistance: Exploring the Importance of the Microenvironmental Niche via a Spatial Model

Practically, all chemotherapeutic agents lead to drug resistance. Clinically, it is a challenge to determine whether resistance arises prior to, or as a result of, cancer therapy. Further, a number of different intracellular and microenvironmental factors have been correlated with the emergence of drug resistance. With the goal of better understanding drug resistance and its connection with the tumor microenvironment, we have developed a hybrid discrete-continuous mathematical model. In this model, cancer cells described through a particle-spring approach respond to dynamically changing oxygen and DNA damaging drug concentrations described through partial differential equations. We thoroughly explored the behavior of our self-calibrated model under the following common conditions: a fixed layout of the vasculature, an identical initial configuration of cancer cells, the same mechanism of drug action, and one mechanism of cellular response to the drug. We considered one set of simulations in which drug resistance existed prior to the start of treatment, and another set in which drug resistance is acquired in response to treatment. This allows us to compare how both kinds of resistance influence the spatial and temporal dynamics of the developing tumor, and its clonal diversity. We show that both pre-existing and acquired resistance can give rise to three biologically distinct parameter regimes: successful tumor eradication, reduced effectiveness of drug during the course of treatment (resistance), and complete treatment failure