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Abstract This work presents a novel study for identifying alterations in the control states of a desuperheater system based on real closed-loop data from a coal-fired power plant operating under various loads using linear and nonlinear system identification techniques. Specifically, Transfer Functions (TFs) and Gaussian Processes within a Nonlinear AutoRegressive eXogenous model (GP-NARX) are utilized. The desuperheater system comprises two units, north and south, each modeled as a single-input single-output (SISO) system based on spray valve positions and outlet temperatures. To identify changes in the control states using TFs, deviations in the coefficients of three poles and two zeros transfer functions are analyzed. Significant shifts in the control states of the north desuperheater are observed when transitioning from nominal to half and low loads, with deviations of up to four orders of magnitude. Substantial changes in control states are also observed for the south desuperheater when moving from nominal to low load, with a deviation in the coefficients of up to five orders of magnitude, whereas the transition from nominal to half load shows a smaller deviation of up to three orders of magnitude. In the GP-NARX approach, model uncertainties are used to indicate the changes in the control states. The south desuperheater showed a significant uncertainty of up to 8°F from the nominal to the low load, evidencing a change in the control states. Regarding the north desuperheater, increased uncertainty, up to 6°F, is also observed but in shorter time intervals when compared to the south desuperheater. Ultimately, this work shows that both approaches can be used as a basis for system identification, employing real closed-loop power plant data. 

Molecular dynamics (MD) simulations provide a powerful means of exploring the dynamic behavior of biomolecular systems at the atomic level. However, analyzing the vast data sets generated by MD simulations poses significant challenges. This article discusses the energy landscape visualization method (ELViM), a multidimensional reduction technique inspired by the energy landscape theory. ELViM transcends one-dimensional representations, offering a comprehensive analysis of the effective conformational phase space without the need for predefined reaction coordinates. We apply the ELViM to study the folding landscape of the antimicrobial peptide Polybia-MP1, showcasing its versatility in capturing complex biomolecular dynamics. Using dissimilarity matrices and a force-scheme approach, the ELViM provides intuitive visualizations, revealing structural correlations and local conformational signatures. The method is demonstrated to be adaptable, robust, and applicable to various biomolecular systems. 

Topology optimization problems typically consider a single load case or a small, discrete number of load cases; however, practical structures are often subjected to infinitely many load cases that may vary in intensity, location and/or direction (e.g. moving/rotating loads or uncertain fixed loads). The variability of these loads significantly influences the stress distribution in a structure and should be considered during the design. We propose a locally stress-constrained topology optimization formulation that considers loads with continuously varying direction to ensure structural integrity under more realistic loading conditions. The problem is solved using an Augmented Lagrangian method, and the continuous range of load directions is incorporated through a series of analytic expressions that enables the computation of the worst-case maximum stress over all possible load directions. Variable load intensity is also handled by controlling the magnitude of load basis vectors used to derive the worst-case load. Several two- and three-dimensional examples demonstrate that topology-optimized designs are extremely sensitive to loads that vary in direction. The designs generated by this formulation are safer, more reliable, and more suitable for real applications, because they consider realistic loading conditions.