Plane geometry and convexity of polynomial stability regions

The set of controllers stabilizing a linear system is generally non-convex in the parameter space. In the case of two-parameter controller design (e.g. PI control or static output feedback with one input and two outputs), we observe however that quite often for benchmark problem instances, the set of stabilizing controllers seems to be convex. In this note we use elementary techniques from real algebraic geometry (resultants and Bezoutian matrices) to explain this phenomenon. As a byproduct, we derive a convex linear matrix inequality (LMI) formulation of two-parameter fixed-order controller design problem, when possible

Synthesis of flow‐compatible Ru-Me/Al2O3 catalysts and their application in hydrogenation of 1-iodo-4-nitrobenzene

The development of an active, selective, and long-term stable heterogeneous catalyst for the reductive hydrogenation of substituted nitrorarenes in continuous operation mode is still challenging. In this work, Ru based nanoparticles catalysts promoted with different transition metals (Zn, Co, Cu, Sn, or Fe) were supported on alumina spheres using spray wet impregnation method. The freshly prepared catalysts were characterized using complementary methods including scanning transmission electron microscopy (STEM) and temperature programmed reduction (TPR). The hydrogenation of 1-iodo-4-nitrobenzene served as model reaction to assess the catalytic performance of the prepared catalysts. The addition of the promotor affected the reducibility of Ru nanoparticles as well as the performance of the catalyst in the hydrogenation reaction. The highest yield of 4-iodoaniline (89 %) was obtained in a continuous flow process using Ru-Sn/Al2O3. The performance of this catalyst was also followed in a long-term experiment. With increasing operation time, a catalyst deactivation occurred which could only briefly compensate by an increase of the reaction temperature

Inferring dynamic topology for decoding spatiotemporal structures in complex heterogeneous networks

Extracting complex interactions (i.e., dynamic topologies) has been an essential, but difficult, step toward understanding large, complex, and diverse systems including biological, financial, and electrical networks. However, reliable and efficient methods for the recovery or estimation of network topology remain a challenge due to the tremendous scale of emerging systems (e.g., brain and social networks) and the inherent nonlinearity within and between individual units. We develop a unified, data-driven approach to efficiently infer connections of networks (ICON). We apply ICON to determine topology of networks of oscillators with different periodicities, degree nodes, coupling functions, and time scales, arising in silico, and in electrochemistry, neuronal networks, and groups of mice. This method enables the formulation of these large-scale, nonlinear estimation problems as a linear inverse problem that can be solved using parallel computing. Working with data from networks, ICON is robust and versatile enough to reliably reveal full and partial resonance among fast chemical oscillators, coherent circadian rhythms among hundreds of cells, and functional connectivity mediating social synchronization of circadian rhythmicity among mice over weeks