On minimal realisations of dynamical structure functions

Motivated by the fact that transfer functions do not contain structural information about networks, dynamical structure functions were introduced to capture causal relationships between measured nodes in networks. From the dynamical structure functions, a) we show that the actual number of hidden states can be larger than the number of hidden states estimated from the corresponding transfer function; b) we can obtain partial information about the true state-space equation, which cannot in general be obtained from the transfer function. Based on these properties, this paper proposes algorithms to find minimal realisations for a given dynamical structure function. This helps to estimate the minimal number of hidden states, to better understand the complexity of the network, and to identify potential targets for new measurements

Model Reduction of Multi-Dimensional and Uncertain Systems

We present model reduction methods with guaranteed error bounds for systems represented by a Linear Fractional Transformation (LFT) on a repeated scalar uncertainty structure. These reduction methods can be interpreted either as doing state order reduction for multi-dimensionalsystems, or as uncertainty simplification in the case of uncertain systems, and are based on finding solutions to a pair of Linear Matrix Inequalities (LMIs). A related necessary and sufficient condition for the exact reducibility of stable uncertain systems is also presented

A J-Spectral Factorization Approach to ℋ∞ Control

Necessary and sufficient conditions for the existence of suboptimal solutions to the standard model matching problem associated with ℋ∞ control, are derived using J-spectral factorization theory. The existence of solutions to the model matching problem is shown to be equivalent to the existence of solutions to two coupled J-spectral factorization problems, with the second factor providing a parametrization of all solutions to the model matching problem. The existence of the J-spectral factors is then shown to be equivalent to the existence of nonnegative definite, stabilizing solutions to two indefinite algebraic Riccati equations, allowing a state-space formula for a linear fractional representation of all controllers to be given. A virtue of the approach is that a very general class of problems may be tackled within a conceptually simple framework, and no additional auxiliary Riccati equations are required

Systems of johnsongrass control in soybeans

The objective of this study was to determine an effective combination of herbicides for controlling johnsongrass while growing soybeans. Experiments were conducted at four locations in Tennessee in 1972. Observations were made on the control of seedling and rhizomatous john songrass, soybean injury, and the broadleaf weed control. Soybean seed yield from each plot was measured. A split-block experimental design was used. Main blocks consisted of two preplant foliar treatments for the control of rhizome johnson grass, viz., dalapon (2,2-dichloropropionic acid) and glyphosate tN-Cphosphonomethyl)glycine]. Subblocks consisted of four herbicides to control seedling johnsongrass, i.e., alachlor [2-chloro-2\u27,6\u27-diethyl- N-(methoxymethyl)acetanilide], nitralin [4-Cniethylsulfonyl)-2,6-dinitro- N,N-dipropylanilide], trifluralin Ca,a,a-trifluoro-2,6-dinitro-N,Ndipropyl- p-toluidine), and vernolate (S-propyL dipropylthiocarbamate), plus a weed-free check and a weedy check included in each main treatment of each replication. Johnsongrass rhizome control was good to excellent at all locations with both dalapon and glyphosate treatments. Significant differences between these two herbicides existed only at the Middle Tennessee Experi ment Station. No significant differences among herbicides used for seedling johnsongrass control were found at any location. All treatments gave good to excellent control of most broadleaf species present. All herbicide treatments caused some soybean injury with vernolate causing the most severe injury. No significant differences existed between soybean seed yields at Ames Plantation. At Mascot and Pulaski, yields from glyphosate treated plots were significantly higher than were those from dalapon treated plots. Dalapon treated plots yielded significantly more soybeans at the Middle Tennessee Experiment Station, probably due to the fact that the glyphosate treated plots were turned after only four days

A Human Capital Approach to Reduce Health Disparities

Objective: To introduce a human capital approach to reduce health disparities in South Carolina by increasing the number and quality of trained minority professionals in public health practice and research. Methods: The conceptual basis and elements of Project EXPORT in South Carolina are described. Project EXPORT is a community based participatory research (CBPR) translational project designed to build human capital in public health practice and research. This project involves Claflin University (CU), a Historically Black College University (HBCU) and the African American community of Orangeburg, South Carolina to reduce health disparities, utilizing resources from the University of South Carolina (USC), a level 1 research institution to build expertise at a minority serving institution. The elements of Project EXPORT were created to advance the science base of disparities reduction, increase trained minority researchers, and engage the African American community at all stages of research. Conclusion: Building upon past collaborations between HBCU’s in South Carolina and USC, this project holds promise for a public health human capital approach to reduce health disparities

State-space solutions to standard H_2 and H_∞ control problems

Simple state-space formulas are presented for a controller solving a standard H_∞-problem. The controller has the same state-dimension as the plant, its computation involves only two Riccati equations, and it has a separation structure reminiscent of classical LQG (i.e., H_2) theory. This paper is also intended to be of tutorial value, so a standard H_2-solution is developed in parallel

Mixed H_2 and H∞ control

Mixed H_2 and H∞ norm analysis and synthesis problems are considered in this paper. It is shown that the mixed norm analysis combined with structured uncertainty can be used to give bounds on robust H_2 and H∞ performance. It is also shown that the mixed norm controller shares a separation property similar to those of pure H_2 or H∞ controllers. The obvious advantage for a mixed norm is that it gives a natural trade-off between H_2 performance and H∞ performance, and provides a potential framework for extending the μ-synthesis framework to address robust H_2 performance. A simple example is used to motivate the possible advantages such a framework might have over a pure H∞ theory

Mixed H_2 and H∞ performance objectives. II. Optimal control

This paper considers the analysis and synthesis of control systems subject to two types of disturbance signals: white signals and signals with bounded power. The resulting control problem involves minimizing a mixed H_2 and H∞ norm of the system. It is shown that the controller shares a separation property similar to those of pure H_2 or H∞ controllers. Necessary conditions and sufficient conditions are obtained for the existence of a solution to the mixed problem. Explicit state-space formulas are also given for the optimal controller

Integration of the Unfolded Protein and Oxidative Stress Responses through SKN-1/Nrf

The Unfolded Protein Response (UPR) maintains homeostasis in the endoplasmic reticulum (ER) and defends against ER stress, an underlying factor in various human diseases. During the UPR, numerous genes are activated that sustain and protect the ER. These responses are known to involve the canonical UPR transcription factors XBP1, ATF4, and ATF6. Here, we show in C. elegans that the conserved stress defense factor SKN-1/Nrf plays a central and essential role in the transcriptional UPR. While SKN-1/Nrf has a well-established function in protection against oxidative and xenobiotic stress, we find that it also mobilizes an overlapping but distinct response to ER stress. SKN-1/Nrf is regulated by the UPR, directly controls UPR signaling and transcription factor genes, binds to common downstream targets with XBP-1 and ATF-6, and is present at the ER. SKN-1/Nrf is also essential for resistance to ER stress, including reductive stress. Remarkably, SKN-1/Nrf-mediated responses to oxidative stress depend upon signaling from the ER. We conclude that SKN-1/Nrf plays a critical role in the UPR, but orchestrates a distinct oxidative stress response that is licensed by ER signaling. Regulatory integration through SKN-1/Nrf may coordinate ER and cytoplasmic homeostasis