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Disruption is a serious and common problem for the airline industry. High utilisation of aircraft and airport resources mean that disruptive events can have large knock-on effects for the rest of the schedule. The airline must rearrange their schedule to reduce the impact. The focus in this paper is on the Aircraft Recovery Problem. The complexity and uncertainty involved in the industry makes this a difficult problem to solve. Many deterministic modelling approaches have been proposed, but these struggle to handle the inherent variability in the problem. This paper proposes a multi-fidelity modelling framework, enabling uncertain elements of the environment to be included within the decision making process. We combine a deterministic integer program to find initial solutions and a novel simulation optimisation procedure to improve these solutions. This allows the solutions to be evaluated whilst accounting for the uncertainty of the problem. The empirical evaluation suggests that the combination consistently finds good rescheduling options. 

Potassium ion (K + ) plays a critical role as an essential electrolyte in all biological systems. Genetically-encoded fluorescent K + biosensors are promising tools to further improve our understanding of K + -dependent processes under normal and pathological conditions. Here, we report the crystal structure of a previously reported genetically-encoded fluorescent K + biosensor, GINKO1, in the K + -bound state. Using structure-guided optimization and directed evolution, we have engineered an improved K + biosensor, designated GINKO2, with higher sensitivity and specificity. We have demonstrated the utility of GINKO2 for in vivo detection and imaging of K + dynamics in multiple model organisms, including bacteria, plants, and mice. 

Computational thinking (CT) is ubiquitous in modern science, yet rarely integrated at the elementary school level. Moreover, access to computer science education at the PK-12 level is inequitably distributed. We believe that access to CT must be available earlier and implemented with the support of an equitable pedagogical framework. Our poster will describe our Accessible Computational Thinking (ACT) research project exploring professional development with elementary teachers on integrating computational thinking with Culturally Responsive Teaching practices. 

Computational thinking (CT) is ubiquitous in modern science, yet rarely integrated at the elementary school level. Moreover, access to computer science education at the PK-12 level is inequitably distributed. We believe that access to CT must be available earlier and implemented with the support of an equitable pedagogical framework. Our poster will describe our Accessible Computational Thinking (ACT) research project exploring professional development with elementary teachers on integrating computational thinking with Culturally Responsive Teaching practices. 

The nonstationary Poisson process (NSPP) is a workhorse tool for modeling and simulating arrival processes with time-dependent rates. In many applications only a single sequence of arrival times are observed. While one sample path is sufficient for estimating the arrival rate or integrated rate function of the process—as we illustrate in this paper—we show that testing for Poissonness, in the general case, is futile. In other words, when only a single sequence of arrival data are observed then one can fit an NSPP to it, but the choice of “NSPP” can only be justified by an understanding of the underlying process physics, or a leap of faith, not by testing the data. This result suggests the need for sensitivity analysis when such a model is used to generate arrivals in a simulation. 

Cheap parallel computing has greatly extended the reach of ranking & selection (R&S) for simulation optimization. In this paper we present an evaluation of bi-PASS, a R&S procedure created specifically for parallel implementation and very large numbers of system designs. We compare bi-PASS to the state-ofthe- art Good Selection Procedure and an easy-to-implement subset selection procedure. This is one of the few papers to consider both computational and statistical comparison of parallel R&S procedures. 

Global change drivers of land-use/cover change (LUCC) like population dynamics, economic development, and climate change are increasingly important to local sustainability studies, and can only be properly analyzed at fine-scales that capture local biophysical and socio-economic conditions. When sufficiently widespread, local feedback to stresses originating from global drivers can have regional, national, and even global impacts. A multiscale, global-to-local-to-global (GLG) framework is thus needed for comprehensive analyses of LUCC and leakage. The number of GLG-LUCC studies has grown substantially over the past years, but no reviews of this literature and their contributions have been completed so far. In fact, the largest body of literature pertains to global-to-local impacts exclusively, whereas research on local feedback to regional, national, and global spheres remain scarce, and are almost solely undertaken within large modeling institutes. As such, those are rarely readily accessible for modification and extension by outside contributors. This review of the recent GLG-LUCC studies calls for more open-source modeling and availability of data, arguing that the latter is the real constraint to more widespread analyses of GLG-LUCC impacts. Progress in this field will require contributions from hundreds of researchers around the world and from a wide variety of disciplines.