A shirt containing multistage phase change material and active cooling components was associated with increased exercise capacity in a hot,humid environment

Recent advances in clothing design include the incorporation of phase change materials (PCM) and other active cooling components (ACC) to provide better body heat dissipation. The purpose of this study was to determine the effect of wearing a shirt containing multistage PCM/ACC on exercise capacity at low (5.0), moderate–high (7.5) and extreme (9.0) levels of the physiological strain index (PSI). Fourteen individuals tested two shirts (control vs. cooling) during 45-min of interval running in a hot, humid (35 ± 1 °C; 55 ± 6% RH) environment. The cooling shirt resulted in an 8% improvement in exercise capacity at a PSI of 7.5 (p < 0.05). The observed increase in exercise capacity would likely translate to a significant improvement in exercise performance. More research is needed to determine a best practice approach for the use of cooling clothing as a counter to exercise-induced heat exposure.

Practitioner Summary: In this report, we demonstrate that when forced to exercise in a hot, humid environment, an individual’s exercise capacity may increase by as much as 8% when wearing a shirt composed of multistage phase change material and active cooling components.     

Evolving lane merge traffic behaviour simulations via a macroscopic objective function and a machine learning system trained through bootstrapped human judgement

Multi-agent based traffic simulation models have become increasingly important in simulating, studying and analysing traffic behaviours due to their ability to model more sophisticated behaviours of traffic by codifying simple rules into agents. However, such models require selection of appropriate rules and tuning of parameters for the selected rules. This process demands extensive resources if to be done manually. Further, high complexity models (in terms of number of rules and parameters) require a large computational cost to run, imposing scalability problems. In this work, four simple rules are introduced by reformulating existing concepts in the literature in order to simulate the self-organising behaviour of traffic where two lanes form into one and when two types of vehicles (cars and trucks) are present. The optimal rule and parameter combinations are explored via an evolutionary framework to overcome the resource demanding nature of the process. Two forms of objective functions - 1) a macroscopic objective function which focuses on macroscopic properties of traffic 2) a machine learning system trained based on human judgement concerning microscopic interaction of traffic - are studied in order to evolve low complexity and high fidelity traffic simulations. The differences in the rule sets evolved by the two objective functions are discussed highlighting the importance of selecting an appropriate objective function based on the simulation requirements and available resources. Finally, the change of the rule distribution as a function of generation in the evolutionary process is investigated in order to understand the complexity change in the simulations as a function of rule count as simulations are evolving towards high fidelity. This provides an abstract understanding of the relationship between complexity and fidelity in multi-agent based simulations concerning the particular problem of simulation of lane merge traffic.     

Field observation of tornadic supercells by multiple autonomous fixed‐wing unmanned aircraft

This paper presents the results of the design and field deployment of multiple autonomous fixed‐wing unmanned aircraft into supercell thunderstorms. As part of a field campaign in Spring 2019, up to three fixed‐wing unmanned aircraft were deployed simultaneously into different regions of supercell thunderstorms, To learn more about the atmospheric conditions that lead to the formation of tornadoes. Successful field deployment is attributed to (a) a nomadic concept of operations that allows the unmanned aircraft system team and science team to work seamlessly together while satisfying all aviation regulations and (b) the ruggedized RAAVEN unmanned aircraft system with modular features that favor rapid, ease‐of‐use over the brute strength of previous designs. The concept of operations and the unmanned aircraft system are described along with results from a 4 day window where four storms were sampled: two of these storms were tornadic (formed tornadoes before, during, or after being sampled) and two were not. These results validate the feasibility of nomadic operation of multiple unmanned aircraft simultaneously in severe weather conditions. Further, the successful field deployments demonstrate the importance of the modular unmanned aircraft design.     

Introduction of Feedback Linearization to Robust LQR and LQI Control – Analysis of Results from an Unmanned Bicycle Robot with Reaction Wheel

In this paper, the Jacobian‐linearization‐ and feedback‐linearization‐based techniques of obtaining linearized model approaches are combined with a family of robust LQR control laws to identify the pairing which results in superior control performance of the bicycle robot, despite uncertainty and constraints, what is the main contribution of the paper. The control performance is analyzed using various indices, related, e.g. to energy consumption of the considered laws, with the experiments conducted on a real bicycle robot. As a result, the easily‐implementable controller is obtained, which requires only to perform a set of off‐line computations with a single additional parameter δ in comparison with a standard linear‐quadratic controller, to obtain a state‐feedback vector, which, when implemented to the control system, ensures proper regulation of the output signal of the plant, despite uncertainty or possible actuator failures, obtaining energy‐efficient control law.     

Potential-based reward shaping for finite horizon online POMDP planning

In this paper, we address the problem of suboptimal behavior during online partially observable Markov decision process (POMDP) planning caused by time constraints on planning. Taking inspiration from the related field of reinforcement learning (RL), our solution is to shape the agent’s reward function in order to lead the agent to large future rewards without having to spend as much time explicitly estimating cumulative future rewards, enabling the agent to save time to improve the breadth planning and build higher quality plans. Specifically, we extend potential-based reward shaping (PBRS) from RL to online POMDP planning. In our extension, information about belief states is added to the function optimized by the agent during planning. This information provides hints of where the agent might find high future rewards beyond its planning horizon, and thus achieve greater cumulative rewards. We develop novel potential functions measuring information useful to agent metareasoning in POMDPs (reflecting on agent knowledge and/or histories of experience with the environment), theoretically prove several important properties and benefits of using PBRS for online POMDP planning, and empirically demonstrate these results in a range of classic benchmark POMDP planning problems.     

Subdividing Long-Running,Variable-Length Analyses Into Short,Fixed-Length BOINC Workunits

We describe a scheme for subdividing long-running, variable-length analyses into short, fixed-length boinc workunits using phylogenetic analyses as an example. Fixed-length workunits decrease variance in analysis runtime, improve overall system throughput, and make boinc a more useful resource for analyses that require a relatively fast turnaround time, such as the phylogenetic analyses submitted by users of the garli web service at molecularevolution.org. Additionally, we explain why these changes will benefit volunteers who contribute their processing power to boinc projects, such as the Lattice boinc Project (http://boinc.umiacs.umd.edu). Our results, which demonstrate the advantages of relatively short workunits, should be of general interest to anyone who develops and deploys an application on the boinc platform.