Literature Alive: Connecting to Story Through the Arts

How can I create \u27living\u27 experiences that will support my students to explore the \u27layers\u27 of meaning in a story? What type of learning activity supports students to build personal \u27connections\u27 with a story? We, along with language arts teachers, face these questions as we collaborate to develop literature studies. Our uncertainty seems to imply confusion as to whether we should emphasize story comprehension, reading skills, or a personal connection as the basis

Focal-Plane Imaging of Crossed Beams in Nonlinear Optics Experiments

An application of focal-plane imaging that can be used as a real time diagnostic of beam crossing in various optical techniques is reported. We discuss two specific versions and demonstrate the capability of maximizing system performance with an example in a combined dual-pump coherent anti-Stokes Raman scattering interferometric Rayleigh scattering experiment (CARS-IRS). We find that this imaging diagnostic significantly reduces beam alignment time and loss of CARS-IRS signals due to inadvertent misalignments

A comparative study of evolutionary approaches to the bi-objective dynamic Travelling Thief Problem

Dynamic evolutionary multi-objective optimization is a thriving research area. Recent contributions span the development of specialized algorithms and the construction of challenging benchmark problems. Here, we continue these research directions through the development and analysis of a new bi-objective problem, the dynamic Travelling Thief Problem (TTP), including three modes of dynamic change: city locations, item profit values, and item availability. The interconnected problem components embedded in the dynamic problem dictate that the effective tracking of good trade-off solutions that satisfy both objectives throughout dynamic events is non-trivial. Consequently, we examine the relative contribution to the non-dominated set from a variety of population seeding strategies, including exact solvers and greedy algorithms for the knapsack and tour components, and random techniques. We introduce this responsive seeding extension within an evolutionary algorithm framework. The efficacy of alternative seeding mechanisms is evaluated across a range of exemplary problem instances using ranking-based and quantitative statistical comparisons, which combines performance measurements taken throughout the optimization. Our detailed experiments show that the different dynamic TTP instances present varying difficulty to the seeding methods tested. We posit the dynamic TTP as a suitable benchmark capable of generating problem instances with different controllable characteristics aligning with many real-world problems

Using Drama In The Classroom

Drama is a potentially powerful tool for connecting students with learning and content. We know that learning is an active, constructive process of coming to know. And through our classroom involvement with students, we have found that drama can provide a process for learning by living through or experiencing an event. Drama by its very nature involves students in social contexts where they are required to think, talk, manipulate concrete materials, and share viewpoints in order to arrive at decisions (Siks, 1983). Thus, through drama, students explore both factual knowledge and content concepts while trying on social experiences. Heathcote (cited in Johnson and O\u27Neill, 1984) believes that drama confronts students with situations that may change them because of the issues and challenges they must face in the dramatic playing

Reproducibility and Baseline Reporting for Dynamic Multi-objective Benchmark Problems

Dynamic multi-objective optimization problems (DMOPs) are widely accepted to be more challenging than stationary problems due to the time-dependent nature of the objective functions and/or constraints. Evaluation of purpose-built algorithms for DMOPs is often performed on narrow selections of dynamic instances with differing change magnitude and frequency or a limited selection of problems. In this paper, we focus on the reproducibility of simulation experiments for parameters of DMOPs. Our framework is based on an extension of PlatEMO, allowing for the reproduction of results and performance measurements across a range of dynamic settings and problems. A baseline schema for dynamic algorithm evaluation is introduced, which provides a mechanism to interrogate performance and optimization behaviours of well-known evolutionary algorithms that were not designed specifically for DMOPs. Importantly, by determining the maximum capability of non-dynamic multi-objective evolutionary algorithms, we can establish the minimum capability required of purpose-built dynamic algorithms to be useful. The simplest modifications to manage dynamic changes introduce diversity. Allowing non-dynamic algorithms to incorporate mutated/random solutions after change events determines the improvement possible with minor algorithm modifications. Future expansion to include current dynamic algorithms will enable reproduction of their results and verification of their abilities and performance across DMOP benchmark space.Comment: Accepted for publication in "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) 2022

Dynamic multi-objective optimization using evolutionary algorithms

Dynamic Multi-objective Optimization Problems (DMOPs) offer an opportunity to examine and solve challenging real world scenarios where trade-off solutions between conflicting objectives change over time. Definition of benchmark problems allows modelling of industry scenarios across transport, power and communications networks, manufacturing and logistics. Recently, significant progress has been made in the variety and complexity of DMOP benchmarks and the incorporation of realistic dynamic characteristics. However, significant gaps still exist in standardised methodology for DMOPs, specific problem domain examples and in the understanding of the impacts and explanations of dynamic characteristics. This thesis provides major contributions on these three topics within evolutionary dynamic multi-objective optimization. Firstly, experimental protocols for DMOPs are varied. This limits the applicability and relevance of results produced and conclusions made in the field. A major source of the inconsistency lies in the parameters used to define specific problem instances being examined. The uninformed selection of these has historically held back understanding of their impacts and standardisation in experimental approach to these parameters in the multi-objective problem domain. Using the frequency and severity (or magnitude) of change events, a more informed approach to DMOP experimentation is conceptualized, implemented and evaluated. Establishment of a baseline performance expectation across a comprehensive range of dynamic instances for well-studied DMOP benchmarks is analyzed. To maximize relevance, these profiles are composed from the performance of evolutionary algorithms commonly used for baseline comparisons and those with simple dynamic responses. Comparison and contrast with the coverage of parameter combinations in the sampled literature highlights the importance of these contributions. Secondly, the provision of useful and realistic DMOPs in the combinatorial domain is limited in previous literature. A novel dynamic benchmark problem is presented by the extension of the Travelling Thief Problem (TTP) to include a variety of realistic and contextually justified dynamic changes. Investigation of problem information exploitation and it's potential application as a dynamic response is a key output of these results and context is provided through comparison to results obtained by adapting existing TTP heuristics. Observation driven iterative development prompted the investigation of multi-population island model strategies, together with improvements in the approaches to accurately describe and compare the performance of algorithm models for DMOPs, a contribution which is applicable beyond the dynamic TTP. Thirdly, the purpose of DMOPs is to reconstruct realistic scenarios, or features from them, to allow for experimentation and development of better optimization algorithms. However, numerous important characteristics from real systems still require implementation and will drive research and development of algorithms and mechanisms to handle these industrially relevant problem classes. The novel challenges associated with these implementations are significant and diverse, even for a simple development such as consideration of DMOPs with multiple time dependencies. Real world systems with dynamics are likely to contain multiple temporally changing aspects, particularly in energy and transport domains. Problems with more than one dynamic problem component allow for asynchronous changes and a differing severity between components that leads to an explosion in the size of the possible dynamic instance space. Both continuous and combinatorial problem domains require structured investigation into the best practices for experimental design, algorithm application and performance measurement, comparison and visualization. Highlighting the challenges, the key requirements for effective progress and recommendations on experimentation are explored here

Predictive Value of the Functional Movement Screen as it Relates to Anterior Cruciate Ligament Injury

Introduction: Anterior cruciate ligament injuries occur over 200,000 times annually in the United States alone (Brophy, et al. 2009). This injury strains the healthcare system and affects the players, teams, parents, and the organization they are a part of. There have been, however, clinically researched risk factors that predispose athletes to ACL injury (Gignac, et al. 2015; Laible, et al. 2014). As a result, there is a clinical need for an effective screening tool to identify those athletes at risk for ACL injury. The Functional Movement Screen has been shown to be an effective screening tool for detecting athletes who are at a greater risk for generalized injury, but its predictive value has never been tested for specific injury rates (Kiesel, et al. 2007; Chorba, et al. 2010; Kiesel, et al. 2015; Letafatkar, et al. 2014). Methods: We performed a prospective study on 20 freshman participants who were athletes on a NCAA Division II varsity soccer, basketball, or volleyball team. Results: The results of the study to this point include one men’s soccer athlete with a torn ACL and an FMS score of 19, leading us to believe that no correlation exists between FMS score and incidence of ACL injury at this time. The purpose of this study was to determine if FMS can be an effective tool for predicting risk of ACL injury in athletes