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International audienceThis paper proposes a clustered model reduction method for semistable positive linear systems evolving over directed networks. In this method, we construct a set of clusters, i.e., disjoint sets of state variables, based on a notion of cluster reducibility, defined as the uncontrollability of local states. By aggregating the reducible clusters with aggregation coefficients associated with the Frobenius eigenvector, we obtain an approximate model that preserves not only a network structure among clusters, but also several fundamental properties, such as semistability, positivity, and steady state characteristics. Furthermore, it is found that the cluster reducibility can be characterized for semistable systems based on a projected controllability Gramian that leads to an a priori H2-error bound of the state discrepancy caused by aggregation. The efficiency of the proposed method is demonstrated through an illustrative example of enzyme-catalyzed reaction systems described by a chemical master equation. This captures the time evolution of chemical reaction systems in terms of a set of ordinary differential equations

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In this paper, we address a class of distributed optimization problems in the presence of inter-agent communication delays based on passivity. We first focus on unconstrained distributed optimization and provide a passivity-based perspective for distributed optimization algorithms. This perspective allows us to handle communication delays while using scattering transformation. Moreover, we extend the results to constrained distributed optimization, where it is shown that the problem is solved by just adding one more feedback loop of a passive system to the solution of the unconstrained ones. We also show that delays can be incorporated in the same way as the unconstrained problems. Finally, the algorithm is applied to a visual human localization problem using a pedestrian detection algorithm.

The viral transactivator proteins Rex and Rev are necessary for the expression of structural proteins of human T-cell leukemia virus type I and human immunodeficiency virus type 1, respectively. Although the interaction of Rex/Rev with a cellular cofactor(s) has been thought to be required for Rex/Rev action, there is no suitable system to search for the cofactor(s) in mammalian cells. We found that a Rex mutant, TAgRex, which contains a simian virus 40 nuclear localization signal in place of the N-terminal 19 amino acids of Rex, could dominantly inhibit wild-type Rex/Rev functions. The inhibition did not require either Rev response element/Rex response element binding or the oligomerization ability of the mutant, but it did require a region around amino acid 90 of the Rex protein, suggesting that TAgRex sequestered the cellular cofactor. Complementation with the eukaryotic translation initiation factor 5A (eIF-5A) in this system could restore the impaired Rex function. These results indicate that eIF-5A is the cofactor indispensable for Rex function. Additionally, by using a two-hybrid system, the homo-oligomer formation of Rex was found to be mediated by the region around amino acid 90 in addition to Tyr-64 and Trp-65 of Rex protein. Thus, eIF-5A may play a part in the formation of the Rex homo-oligomer.

In this paper, we propose a retrofit control method for stable network systems. The proposed approach is a control method that, rather than an entire system model, requires a model of the subsystem of interest for controller design. To design the retrofit controller, we use a novel approach based on hierarchical state-space expansion that generates a higher-dimensional cascade realization of a given network system. The upstream dynamics of the cascade realization corresponds to an isolated model of the subsystem of interest, which is stabilized by a local controller. The downstream dynamics can be seen as a dynamical model representing the propagation of interference signals among subsystems, the stability of which is equivalent to that of the original system. This cascade structure enables a systematic analysis of both the stability and control performance of the resultant closed-loop system. The resultant retrofit controller is formed as a cascade interconnection of the local controller and an output rectifier that rectifies an output signal of the subsystem of interest so as to conform to an output signal of the isolated subsystem model while acquiring complementary signals neglected in the local controller design, such as interconnection signals from neighboring subsystems. Finally, the efficiency of the retrofit control method is demonstrated through numerical examples of power systems control and vehicle platoon control.

This article presents a suite of new control designs for next-generation electric smart grids. The future grid will consist of thousands of non-conventional renewable generation sources such as wind, solar, and energy storage. These new components are collectively referred to as distributed energy resources (DER). The article presents a comprehensive list of dynamic models for DERs, and shows their coupling with the conventional generators and loads. It then presents several innovative control designs that can be used for facilitating large-scale DER integration. Ideas from decentralized retrofit control and distributed sparsity-promoting optimal control are used for developing these designs, followed by illustrations on an IEEE power system test model.

The nucleotide sequence of a 2.1-kilobase-pair fragment containing the Streptomyces choA gene, which codes a secreted cholesterol oxidase, was determined. A single open reading frame encodes a mature cholesterol oxidase of 504 amino acids, with a calculated Mr of 54,913. The leader peptides extend over 42 amino acids and have the characteristics of a signal sequence, including basic amino acids near the amino terminus and a hydrophobic core near the signal cleavage site. Analyses of the total amino acid composition and amino acid sequencing of the first 21 amino acids from the N terminus of the purified extracellular enzyme agree with the values deduced from nucleotide sequencing data.