Flow-aligned, single-shot fiber diffraction using a femtosecond X-ray free-electron laser

A major goal for X-ray free-electron laser (XFEL) based science is to elucidate structures of biological molecules without the need for crystals. Filament systems may provide some of the first single macromolecular structures elucidated by XFEL radiation, since they contain one-dimensional translational symmetry and thereby occupy the diffraction intensity region between the extremes of crystals and single molecules. Here, we demonstrate flow alignment of as few as 100 filaments (Escherichia coli pili, F-actin, and amyloid fibrils), which when intersected by femtosecond X-ray pulses result in diffraction patterns similar to those obtained from classical fiber diffraction studies. We also determine that F-actin can be flow-aligned to a disorientation of approximately 5 degrees. Using this XFEL-based technique, we determine that gelsolin amyloids are comprised of stacked β-strands running perpendicular to the filament axis, and that a range of order from fibrillar to crystalline is discernable for individual α-synuclein amyloids

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

On joint probabilistic constraints with Gaussian coefficient matrix

The paper deals with joint probabilistic constraints defined by a Gaussiancoefficient matrix. It is shown how to explicitly reduce the computation ofvalues and gradients of the underlying probability function to that of Gaussiandistribution functions. This allows to employ existing efficient algorithms forcalculating this latter class of function in order to solve probabilistically constrainedoptimization problems of the indicated type. Results are illustratedby an example from energy production

On probabilistic constraints induced by rectangular sets and multivariate normal distributions

In this paper, we consider optimization problems under probabilistic constraints which aredefined by two-sided inequalities for the underlying normally distributed random vector. Asa main step for an algorithmic solution of such problems, we derive a derivative formula for(normal) probabilities of rectangles as functions of their lower or upper bounds. This formulaallows to reduce the calculus of such derivatives to the calculus of (normal) probabilitiesof rectangles themselves thus generalizing a similar well-known statement for multivariatenormal distribution functions. As an application, we consider a problem from water reservoirmanagement. One of the outcomes of the problem solution is that the (still frequentlyencountered) use of simple individual probabilistic can completely fail. In contrast, the (more difficult) use of joint probabilistic constraints which heavily depends on the derivative formula mentioned before yields very reasonable and robust solutions over the whole time horizon considered

operres.pdf

a b s t r a c t The paper deals with joint probabilistic constraints defined by a Gaussian coefficient matrix. It is shown how to explicitly reduce the computation of values and gradients of the underlying probability function to that of Gaussian distribution functions. This allows us to employ existing efficient algorithms for calculating this latter class of functions in order to solve probabilistically constrained optimization problems of the indicated type. Results are illustrated by an example from energy production

Traitement des signaux de contrôle par courants de foucault : Coopération numérique/symbolique

La coopération entre la programmation procédurale, 'le "numérique", et la programmation déclarative, le "symbolique", permet de conférer à des systèmes de traitement de l'information une autonomie remarquable vis-à-vis de décisions complexes traditionnellement prises par des humains. Nous illustrons notre propos dans le contexte du contrôle par courants de Foucault des tubes de générateurs de vapeur avec les résultats de la maquette logicielle Extracsion®

INES: 3D Eddy Current Imaging for a Nondestructive Evaluation System Applied to Steam Generator Tubes

Nuclear power plants supply about 80% of the total production of electricity in France. Non-Destructive Evaluation (NDE) is of prime importance in verifying the soundness of components such as the steam generator (SG), casted elbows, core, etc. In order to facilitate diagnostic, now mainly performed through signal processing and human interpretations, we attempt to image damaged internal structures of these components through data inversion. This NDE inverse approach, similar to medical imaging, was successfully applied to radiographic NDE of casted elbows. From a few number of projections (less than 10) and after rough localization of defects, our software SIROCCO3D is able to reconstruct 3D images of flaws using ART and markovian inverse algorithms [1].</p

Quantum Dots for Anion Detection in Capillary Electrophoresis

Nuclear power plants supply about 80% of the total production of electricity in France. Non-Destructive Evaluation (NDE) is of prime importance in verifying the soundness of components such as the steam generator (SG), casted elbows, core, etc. In order to facilitate diagnostic, now mainly performed through signal processing and human interpretations, we attempt to image damaged internal structures of these components through data inversion. This NDE inverse approach, similar to medical imaging, was successfully applied to radiographic NDE of casted elbows. From a few number of projections (less than 10) and after rough localization of defects, our software SIROCCO3D is able to reconstruct 3D images of flaws using ART and markovian inverse algorithms [1]

Eddy-current imaging : approaches, formulations and problems

SIGLEAvailable at INIST (FR), Document Supply Service, under shelf-number : 26165 A, issue : a.1995 n.169 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc