Large-scale unit commitment under uncertainty: an updated literature survey

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject

Quantitative analysis of astrogliosis in drug-dependent humans

Drug addiction is a chronic, relapsing disease caused by neurochemical and molecular changes in the brain. In this human autopsy study qualitative and quantitative changes of glial fibrillary acidic protein (GFAP)-positive astrocytes in the hippocampus of 26 lethally intoxicated drug addicts and 35 matched controls are described. The morphological characterization of these cells reflected alterations representative for astrogliosis. But, neither quantification of GFAP-positive cells nor the Western blot analysis indicated statistical significant differences between drug fatalities versus controls. However, by semi-quantitative scoring a significant shift towards higher numbers of activated astrocytes in the drug group was detected. To assess morphological changes quantitatively, graph-based representations of astrocyte morphology were obtained from single cell images captured by confocal laser scanning microscopy. Their underlying structures were used to quantify changes in astroglial fibers in an automated fashion. This morphometric analysis yielded significant differences between the investigated groups for four different measures of fiber characteristics (Euclidean distance, graph distance, number of graph elements, fiber skeleton distance), indicating that, e.g., astrocytes in drug addicts on average exhibit significant elongation of fiber structures as well as two-fold increase in GFAP-positive fibers as compared with those in controls. In conclusion, the present data show characteristic differences in morphology of hippocampal astrocytes in drug addicts versus controls and further supports the involvement of astrocytes in human pathophysiology of drug addiction. The automated quantification of astrocyte morphologies provides a novel, testable way to assess the fiber structures in a quantitative manner as opposed to standard, qualitative descriptions