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The detected variety in chemistry and circumstellar shell morphology of the limited sample of Galactic post-AGB stars is so large that there is no consensus yet on how the different objects are linked by evolutionary channels. The evaluation is complicated by the fact that their distances and hence luminosities remain largely unknown. Via cross-correlation of the Spitzer SAGE catalogue with optical catalogues we selected a sample of LMC post-AGB candidates based on their [8]-[24] colour index and estimated luminosity. We determined the fundamental properties of the central stars of 105 of these objects using low-resolution, optical spectra that we obtained at Siding Spring Observatory and SAAO, and constructed a catalogue of 70 high probability and 1337 candidate post-AGB stars that is available at the CDS. The sample forms an ideal testbed for stellar evolution theory predictions of the final phase of low- and intermediate-mass stars, because the distance and hence luminosity and also the current and initial mass of these objects is well constrained. About half of the objects in our sample of post-AGB candidates show a spectral energy distribution (SED) that is indicative of a disc rather than an expanding and cooling AGB remnant. Like in the Galaxy, the disc sources are likely associated with binary evolution. Important side products of this research are catalogues of candidate young stellar objects, candidate supergiants with circumstellar dust, and discarded objects for which a spectrum was obtained. These too are available at the CDS.

Load forecasting has long been recognized as an important building block for all utility operational planning efforts. Over the recent years, it has become ever more challenging to make accurate forecasts due to the proliferation of distributed energy resources, despite the abundance of existing load forecasting methods. In this paper, we identify one drawback suffered by most load forecasting methods: neglect to thoroughly address the impact of input errors on load forecasts. As a potential solution, we propose to incorporate input modeling and uncertainty quantification to improve load forecasting performance via a two-stage approach. The proposed two-stage approach has the following merits. (1) It provides input modeling and quantifies the impact of input errors, rather than neglecting or mitigating the impact, a prevalent practice of existing methods. (2) It propagates the impact of input errors into the ultimate point and interval predictions for the target customer's load to improve predictive performance. (3) A variance-based global sensitivity analysis method is further proposed for input-space dimensionality reduction in both stages to enhance the computational efficiency. Numerical experiments show that the proposed two-stage approach outperforms competing load forecasting methods in terms of both point predictive accuracy and coverage ability of the predictive intervals. 9 pages, 4 figures, journal

Distributed generation resources have become significantly more prevalent in the electric power system over the past few years. This warrants reconsideration on how the coordination of generation resources is achieved. In this paper, we particularly focus on secondary frequency control and how to enhance it by exploiting peer-to-peer communication among the resources. We design a control framework based on a consensus-plus-global-innovation approach, which guarantees bringing the frequency back to its nominal value. The control signals of the distributed resources are updated in response to a global innovation corresponding to the ACE signal, and additional information exchanged via communication among neighboring resources. We show that such a distributed control scheme can be very well approximated by a PI controller and can stabilize the system. Moreover, since our control scheme takes advantage of both the ACE signal and peer-to-peer communication, simulation results demonstrate that our control scheme can stabilize the system significantly faster than the AGC framework. Also, an important feature of our scheme is that it performs $c��$-close to the centralized optimal economic dispatch, where $c$ is a positive constant depending only on the cost parameters and the communication topology and $��$ denotes the maximum rate of change of overall system. Submitted for publication

Abstract We single out the role of fully coherent induced gluon radiation on light hadron production in pA collisions. The effect has the same general features as for quarkonium production, however with a richer color structure as the induced radiation depends on the global color charge of the partonic subprocess final state. Baseline predictions for light hadron nuclear suppression in pPb collisions at the LHC are provided, taking into account only the effect of fully coherent energy loss, which proves to be of the same order of magnitude as gluon shadowing or saturation. This underlines the need to include fully coherent energy loss in phenomenological studies of hadron production in pA collisions.

For fast timescales or long prediction horizons, the AC optimal power flow (OPF) problem becomes a computational challenge for large-scale, realistic AC networks. To overcome this challenge, this paper presents a novel network reduction methodology that leverages an efficient mixed-integer linear programming (MILP) formulation of a Kron-based reduction that is optimal in the sense that it balances the degree of the reduction with resulting modeling errors in the reduced network. The method takes as inputs the full AC network and a pre-computed library of AC load flow data and uses the graph Laplacian to constraint nodal reductions to only be feasible for neighbors of non-reduced nodes. This results in a highly effective MILP formulation which is embedded within an iterative scheme to successively improve the Kron-based network reduction until convergence. The resulting optimal network reduction is, thus, grounded in the physics of the full network. The accuracy of the network reduction methodology is then explored for a 100+ node medium-voltage radial distribution feeder example across a wide range of operating conditions. It is finally shown that a network reduction of 25-85% can be achieved within seconds and with worst-case voltage magnitude deviation errors within any super node cluster of less than 0.01pu. These results illustrate that the proposed optimization-based approach to Kron reduction of networks is viable for larger networks and suitable for use within various power system applications.

Traditional state estimation (SE) methods that are based on nonlinear minimization of the sum of localized measurement error functionals are known to suffer from non-convergence and large residual errors. In this paper we propose an equivalent circuit formulation (ECF)-based SE approach that inherently considers the complete network topology and associated physical constraints. We analyze the mathematical differences between the two approaches and show that our approach produces a linear state-estimator that is mathematically a quadratic programming (QP) problem with closed-form solution. Furthermore, this formulation imposes additional topology-based constraints that provably shrink the feasible region and promote convergence to a more physically meaningful solution. From a probabilistic viewpoint, we show that our method applies prior knowledge into the estimate, thus converging to a more physics-based estimate than the traditional observation-driven maximum likelihood estimator (MLE). Importantly, incorporation of the entire system topology and underlying physics, while being linear, makes ECF-based SE advantageous for large-scale systems.

From a reanalysis of the published literature, the low-shear viscosity of suspensions of hard spheres is shown to have a dynamic crossover in its concentration dependence, from a stretched exponential at lower concentrations to a power law at elevated concentrations. The crossover is sharp, with no transition region in which neither form applies, and occurs at a volume fraction (ca. 0.41) and relative viscosity (ca. 11) well below the sphere volume fraction and relative viscosity (0.494, 49, respectively) of the lower phase boundary of the hard sphere melting transition. For soft spheres -- taking many-arm star polymers as a model -- with increasing sphere hardness $��(��)$ shows a crossover from random-coil polymer behavior toward the behavior shown by true hard spheres. 8 pages, 10 figures Full Paper corresponding to the Note J Colloid Interface Science 248, 528=529 (2002)