Inflammatory Biomarkers as Differential Predictors of Antidepressant Response

Although antidepressants are generally effective in the treatment of major depressive disorder (MDD), it can still take weeks before patients feel the full antidepressant effects. Despite the efficacy of standard treatments, approximately two-thirds of patients with MDD fail to respond to pharmacotherapy. Therefore, the identification of blood biomarkers that can predict the treatment response to antidepressants would be highly useful in order to improve this situation. This article discusses inflammatory molecules as predictive biomarkers for antidepressant responses to several classes of antidepressants, including the N-methyl-d-aspartate (NMDA) receptor antagonist ketamine.     

Distributed Control Strategy with Smith's Predictor in a Pilot-Scale Diabatic Distillation Unit

Intrinsic characteristics of distillation such as dead time and high nonlinearities do not allow the complete elimination of transient times when any external disturbance or set-point change occurs. Thus, aiming at the use of easy-tuning systems, a distributed-action control in trays of a diabatic distillation unit with Smith's predictor was implemented in the Simulink environment to further reduce transient times and out-of-specification product. The distributed-action strategy with Smith's predictor led to a reduction of 33.3 min (33 %) in the transient time of the top temperature control loop and 66 % in out-of-specification product, when compared with the conventional strategy, and thus is shown to be an efficient approach to increasing the productivity of distillation plants.     

Generalized predictor based active disturbance rejection control for non-minimum phase systems

In this paper, a generalized predictor based control scheme is proposed to improve system performance of set-point tracking and disturbance rejection for non-minimum phase (NMP) systems. By using a generalized predictor to estimate the system output without time delay, a model-based extended state observer (MESO) is designed to simultaneously estimate the system state and disturbance. Accordingly, an active disturbance rejection control design is developed which consists of a state feedback control and a feedforward control for the disturbance rejection. The MESO and feedback controllers are analytically derived by specifying the desired characteristic roots of MESO and closed-loop system poles, respectively. To improve the output tracking performance, a pre-filter is designed based on a desired closed-loop transfer function for the set-point tracking. A sufficient condition guaranteeing robust stability of the closed-loop system against time-varying uncertainties is established in terms of linear matrix inequalities (LMIs). Three illustrative examples from the literature are used to demonstrate the effectiveness and merit of the proposed control scheme.     

Delay-dependent guaranteed-cost control based on combination of Smith predictor and equivalent-input-disturbance approach

This paper presents a new system configuration and a design method to improve control performance for a system with an input time delay and disturbances. The equivalent-input-disturbance approach is extended to handle a time-delay system. It is combined with the Smith predictor to reject disturbances. A delay-dependent stability condition is devised in terms of a matrix inequality by using the free-weighting matrix approach. The gain of the observer is designed by applying the cone complementary linearization method to the matrix inequality. A numerical example demonstrates the validity of the method.     

Geographically distributed real-time digital simulations using linear prediction

Real-time (RT) simulator is a powerful tool for analyzing operational and control algorithms in electric power systems engineering. For understanding the dynamic and transient behavior of a power systems, significant RT computation capabilities are essential. A single unit of RT simulator has limited simulation capabilities. The most common way of augmenting simulation capability is using a bank of locally connected RT simulators. However, creating a large-sized bank of RT simulators involves significant financial investments and hence may not be feasible at all research facilities. Power and energy systems research facilities that use RT simulators are at diverse physical locations. In addition to RT simulators, research facilities around the world house an array of facilities with unique power, energy, and control systems for innovative research. To leverage these unique research facilities, geographically distributed RT simulation based on Wide Area Network (WAN) is required. Typical RT simulators perform simulations with time-steps in the order of milliseconds to microseconds, whereas data latency for communication on WAN may be as high as a few hundred milliseconds. Such communication latency between RT simulators may lead to inaccuracies and instabilities in geographically distributed RT simulations. In this paper, the effect of communication latency on geographically distributed RT simulation is discussed and analyzed. In order to reduce the effect of the communication latency, a Real-Time Predictor (RTP), based on linear curve fitting is developed and integrated into the distributed RT simulation environment. Two geographically distributed digital RT simulators are used to perform dynamic simulations of an electric power system with a fixed communication latency and the predictor. Empirical results demonstrate the effects of communication latency on the simulation and the performance of the RTP to improve the accuracy of simulations.     

Data‐driven bounded‐error fault detection

In this paper, a new data‐driven fault‐detection method is proposed. This method is based on a new nonparametric system identification approach, which constitutes the principal contribution to this work. The fault‐detection method is a parametric model‐free approach that can be applied to nonlinear systems that work at various operating points. Not only can the fault‐detection process be applied to the steady state of each operating point, but it can also be applied to the transient state resulting from a change in the operating point. In order to detect faults, the proposed method uses an interval predictor based on bounded‐error techniques. The utilization of techniques based on bounded error enables system uncertainties to be included in an explicit way. This in turn leads to the possibility of obtaining interval predictions of the behaviour of the system, which include information on the reliability of the prediction itself. In order to show the effectiveness of the fault‐detection method, two examples are presented: in the form of a simulated process (counter‐flow shell‐and‐tube heat‐exchanger system) and an example of a real application (two‐tanks system). A comparison with two fault‐detection methods has also been included. Copyright © 2013 John Wiley & Sons, Ltd.     

A second-order Mehrotra-type predictor-corrector algorithm with a new wide neighbourhood for semi-definite programming

In this paper, we present a new second-order Mehrotra-type predictor–corrector algorithm for semi-definite programming (SDP). The proposed algorithm is based on a new wide neighbourhood. We are particularly concerned with an important inequality. Based on the inequality, the convergence is shown for a specific class of search directions. In particular, the complexity bound is O(√nlog?^?1) for the Nesterov–Todd search direction and O(n log?^?1) for the Helmberg-Kojima-Monteiro search direction. The derived complexity bounds coincide with the currently best known theoretical complexity bounds obtained so far for SDP. We provide some preliminary numerical results as well.     

Applied Nonparametric Statistical Methods

In the analysis of spatial data, it is common to predict a spatial exceedance and its associated exceedance region. This is scientifically important, because unusual events tend to strongly affect the environment. We use classes of loss functions based on image metrics (e.g., Baddeley's loss function) to predict the spatial-exceedance region. We then propose a joint loss to predict a spatial quantile and its exceedance region. The optimal predictor is obtained by minimizing the posterior expected loss given the process parameters, which we achieve by simulated annealing. Various predictors are compared through simulation. This methodology is applied to a spatial data set of temperature change over the Americas.