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When humans navigate through complex environments, they coordinate gaze and steering to sample the visual information needed to guide movement. Gaze and steering behavior have been extensively studied in the context of automobile driving along a winding road, leading to accounts of movement along well-defined paths over flat, obstacle-free surfaces. However, humans are also capable of visually guiding self-motion in environments that are cluttered with obstacles and lack an explicit path. An extreme example of such behavior occurs during first-person view drone racing, in which pilots maneuver at high speeds through a dense forest. In this study, we explored the gaze and steering behavior of skilled drone pilots. Subjects guided a simulated quadcopter along a racecourse embedded within a custom-designed forest-like virtual environment. The environment was viewed through a head-mounted display equipped with an eye tracker to record gaze behavior. In two experiments, subjects performed the task in multiple conditions that varied in terms of the presence of obstacles (trees), waypoints (hoops to fly through), and a path to follow. Subjects often looked in the general direction of things that they wanted to steer toward, but gaze fell on nearby objects and surfaces more often than on the actual path or hoops. Nevertheless, subjects were able to perform the task successfully, steering at high speeds while remaining on the path, passing through hoops, and avoiding collisions. In conditions that contained hoops, subjects adapted how they approached the most immediate hoop in anticipation of the position of the subsequent hoop. Taken together, these findings challenge existing models of steering that assume that steering is tightly coupled to where actors look. We consider the study’s broader implications as well as limitations, including the focus on a small sample of highly skilled subjects and inherent noise in measurement of gaze direction. 

DOI: 10.1007/978-3-030-92559-8_5 The sixth Intergovernmental Panel on Climate Change report (IPCC) recently released predicts a deep reduction in emissions to meet global goals of 1.5 °C reduction in temperature. It states that concentrations of CO₂ have continuously increased in the atmosphere reaching averages of 410 ppm in 2019. Therefore, it becomes imperative to reduce CO₂ in any way possible. Silicon, which is an important material for renewable energy, electronics, and metallurgy, is primarily produced by the carbothermic reduction of quartz. This metallurgical grade silicon is then refined by the Siemens Process to solar grade silicon using hydrogen chloride. The by-product of trichlorosilane from this process is highly volatile and unstable. This work aims to achieve the above process of reduction in a single step using electrochemistry. This would eliminate multiple steps and save energy and cost and reduce emissions if a suitable inert anode is used in production. Understanding electrochemical cell characteristics therefore is needed to prove and scale this technology. Macroscopic models help engineers to design, develop, and improve the efficiency of electrochemical cells. They solve conservation equations of mass, momentum, and energy and help determine electrode current distribution, fluid flow, heat distribution, and stability of the cell. They also help in correlating experimental work and understanding measurements in cells from a lab scale to a plant scale. However, they do not predict the microstructure and plating of material on the cathode. This can be calculated using phase field models. These phase field models predict interface stability and deposition morphology in the cell. In this work, we present these models in addition to proof-of-concept experiments.