Implementation of graphene multilayer electrodes in quantum dot light-emitting devices

Graphene is a highly attractive candidate for implementation as electrodes in next-generation large-area optoelectronic devices thanks to its high electrical conductivity and high optical transparency. In this study, we show all-solution-processed quantum dot-based light-emitting devices (QD-LEDs) using graphene mono- and multilayers as transparent electrodes. Here, the effect of the number of graphene layers (up to three) on the QD-LEDs performance was studied. While the implementation of a second graphene layer was found to reduce the turn-on voltage from 2.6 to 1.8 V, a third graphene layer was observed to increase the turn-on voltage again, which is attributed to an increased roughness of the graphene layer stack. © 2015, Springer-Verlag Berlin Heidelberg

A Dynamic Island-Based Genetic Algorithms Framework

This work presents a dynamic island model framework for helping the resolution of combinatorial optimization problems with evolutionary algorithms. In this framework, the possible migrations among islands are represented by a complete graph. The migrations probabilities associated to each edge are dynamically updated with respect to the last migrations impact. This new framework is tested on the well-known 0/1 Knapsack problem and MAX-SAT problem. Good results are obtained and several properties of this framework are studied

Alkalizing Reactions Streamline Cellular Metabolism in Acidogenic Microorganisms

An understanding of the integrated relationships among the principal cellular functions that govern the bioenergetic reactions of an organism is necessary to determine how cells remain viable and optimise their fitness in the environment. Urease is a complex enzyme that catalyzes the hydrolysis of urea to ammonia and carbonic acid. While the induction of urease activity by several microorganisms has been predominantly considered a stress-response that is initiated to generate a nitrogen source in response to a low environmental pH, here we demonstrate a new role of urease in the optimisation of cellular bioenergetics. We show that urea hydrolysis increases the catabolic efficiency of Streptococcus thermophilus, a lactic acid bacterium that is widely used in the industrial manufacture of dairy products. By modulating the intracellular pH and thereby increasing the activity of β-galactosidase, glycolytic enzymes and lactate dehydrogenase, urease increases the overall change in enthalpy generated by the bioenergetic reactions. A cooperative altruistic behaviour of urease-positive microorganisms on the urease-negative microorganisms within the same environment was also observed. The physiological role of a single enzymatic activity demonstrates a novel and unexpected view of the non-transcriptional regulatory mechanisms that govern the bioenergetics of a bacterial cell, highlighting a new role for cytosol-alkalizing biochemical pathways in acidogenic microorganisms

Optimisation of Perioperative Cardiovascular Management to Improve Surgical Outcome II (OPTIMISE II) trial: study protocol for a multicentre international trial of cardiac output-guided fluid therapy with low-dose inotrope infusion compared with usual care in patients undergoing major elective gastrointestinal surgery.

INTRODUCTION: Postoperative morbidity and mortality in older patients with comorbidities undergoing gastrointestinal surgery are a major burden on healthcare systems. Infections after surgery are common in such patients, prolonging hospitalisation and reducing postoperative short-term and long-term survival. Optimal management of perioperative intravenous fluids and inotropic drugs may reduce infection rates and improve outcomes from surgery. Previous small trials of cardiac-output-guided haemodynamic therapy algorithms suggested a modest reduction in postoperative morbidity. A large definitive trial is needed to confirm or refute this and inform widespread clinical practice. METHODS: The Optimisation of Perioperative Cardiovascular Management to Improve Surgical Outcome II (OPTIMISE II) trial is a multicentre, international, parallel group, open, randomised controlled trial. 2502 high-risk patients undergoing major elective gastrointestinal surgery will be randomly allocated in a 1:1 ratio using minimisation to minimally invasive cardiac output monitoring to guide protocolised administration of intravenous fluid combined with low-dose inotrope infusion, or usual care. The trial intervention will be carried out during and for 4 hours after surgery. The primary outcome is postoperative infection of Clavien-Dindo grade II or higher within 30 days of randomisation. Participants and those delivering the intervention will not be blinded to treatment allocation; however, outcome assessors will be blinded when feasible. Participant recruitment started in January 2017 and is scheduled to last 3 years, within 50 hospitals worldwide. ETHICS/DISSEMINATION: The OPTIMISE II trial has been approved by the UK National Research Ethics Service and has been approved by responsible ethics committees in all participating countries. The findings will be disseminated through publication in a widely accessible peer-reviewed scientific journal. TRIAL REGISTRATION NUMBER: ISRCTN39653756.The OPTIMISE II trial is supported by Edwards Lifesciences (Irvine, CA) and the UK National Institute for Health Research through RMP’s NIHR Professorship

Thermische Spannungen in Waermedaemm-Verbundsystemen Hohlstellen, Verspruenge, Rand- und Eckbereiche

TIB: RN 5973 (1961) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman

Learn-and-Optimize: a Parameter Tuning Framework for Evolutionary AI Planning

Abstract. Learn-and-Optimize (LaO) is a generic surrogate based method for parameter tuning combining learning and optimization. In this paper LaO is used to tune Divide-and-Evolve (DaE), an Evolutionary Algorithm for AI Planning. The LaO framework makes it possible to learn the relation between some features describing a given instance and the optimal parameters for this instance, thus it enables to extrapolate this relation to unknown instances in the same domain. Moreover, the learned knowledge is used as a surrogate-model to accelerate the search for the optimal parameters. The proposed implementation of LaO uses an Artificial Neural Network for learning the mapping between features and optimal parameters, and the Covariance Matrix Adaptation Evolution Strategy for optimization. Results demonstrate that LaO is capable of improving the quality of the DaE results even with only a few iterations. The main limitation of the DaE case-study is the limited amount of meaningful features that are available to describe the instances. However, the learned model reaches almost the same performance on the test instances, which means that it is capable of generalization.