A versatile and simple method is presented for the rapid fabrication of close‐packed colloidal 2D crystals with large domain sizes by floating and redeposition of colloidal monolayers at the air/water interface. A detailed analysis of the particle surface transformation and packing during the individual steps of the monolayer fabrication process has been conducted. It was found that the quality of the monolayer depends on parameters like colloidal particle distribution on the initial substrate, subphase pH, and addition of surfactants. The floating monolayers could be transferred and stacked onto many substrate types, regardless of surface polarity, roughness, or curvature.

To serve as a dispersion relation, a cotangent bundle function must satisfy three simple algebraic properties. These conditions are derived from the inescapable physical requirements to have predictive matter field dynamics and an observer-independent notion of positive energy. Possible modifications of the standard relativistic dispersion relation are thereby severely restricted. For instance, the dispersion relations associated with popular deformations of Maxwell theory by Gambini-Pullin or Myers-Pospelov are not admissible

Cover: Highly ordered and robust monolayers of colloidal particles can be prepared by a simple and versatile floating method at the air-water interface. The floating monolayer can be efficiently transferred to corrugated and curved surfaces, allowing the stacking of complex layer architectures. Further details can be found in the article by M. Retsch, Z. Zhou, S. Rivera, M. Kappl, X. S. Zhao, U. Jonas, Q. Li on page 230.

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This paper summarizes the two testbeds, datasets, and results of the IEEE PES Working Group on Modern Heuristic Optimization (WGMHO) 2017 Competition on Smart Grid Operation Problems. The competition is organized with the aim of closing the gap between theory and real-world applications of evolutionary computation. Testbed 1 considers stochastic OPF (Optimal Power Flow) based Active-Reactive Power Dispatch (ARPD) under uncertainty and Testbed 2 large-scale optimal scheduling of distributed energy resources. Classical optimization methods are not able to deal with the proposed optimization problems within a reasonable time, often requiring more than one day to provide the optimal solution and a significant amount of memory to perform the computation. The proposed problems can be addressed using modern heuristic optimization approaches, enabling the achievement of good solutions in much lower execution times, adequate for the envisaged decision-making processes. Results from the competition show that metaheuristics can be successfully applied in search of efficient near-optimal solutions for the Stochastic Optimal Power Flow and large-scale energy resource management problems.