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This paper presents a work-in-progress on a learning system that will provide robotics students with a personalized learning environment. This addresses both the scarcity of skilled robotics instructors, particularly in community colleges and the expensive demand for training equipment. The study of robotics at the college level represents a wide range of interests, experiences, and aims. This project works to provide students the flexibility to adapt their learning to their own goals and prior experience. We are developing a system to enable robotics instruction through a web-based interface that is compatible with less expensive hardware. Therefore, the free distribution of teaching materials will empower educators. This project has the potential to increase the number of robotics courses offered at both two- and four-year schools and universities. The course materials are being designed with small units and a hierarchical dependency tree in mind; students will be able to customize their course of study based on the robotics skills they have already mastered. We present an evaluation of a five module mini-course in robotics. Students indicated that they had a positive experience with the online content. They also scored the experience highly on relatedness, mastery, and autonomy perspectives, demonstrating strong motivation potential for this approach. 

Olfactory navigation is observed across species and plays a crucial role in locating resources for survival. In the laboratory, understanding the behavioral strategies and neural circuits underlying odor-taxis requires a detailed understanding of the animal’s sensory environment. For small model organisms likeCaenorhabditis elegansand larvalDrosophila melanogaster, controlling and measuring the odor environment experienced by the animal can be challenging, especially for airborne odors, which are subject to subtle effects from airflow, temperature variation, and from the odor’s adhesion, adsorption, or reemission. Here, we present a method to control and measure airborne odor concentration in an arena compatible with an agar substrate. Our method allows continuous controlling and monitoring of the odor profile while imaging animal behavior. We construct stationary chemical landscapes in an odor flow chamber through spatially patterned odorized air. The odor concentration is measured with a spatially distributed array of digital gas sensors. Careful placement of the sensors allows the odor concentration across the arena to be continuously inferred in space and monitored through time. We use this approach to measure the odor concentration that each animal experiences as it undergoes chemotaxis behavior and report chemotaxis strategies forC. elegansandD. melanogasterlarvae populations as they navigate spatial odor landscapes.

 

Abstract Paper-based electrochemical sensors provide the opportunity for low-cost, portable and environmentally friendly single-use chemical analysis and there are various reports of surface-functionalized paper electrodes. Here we report a composite paper electrode that is fabricated through designed papermaking using cellulose, carbon fibers (CF), and graphene oxide (GO). The composite paper has well-controlled structure, stable, and repeatable properties, and offers the electrocatalytic activities for sensitive and selective chemical detection. We demonstrate that this CF/GO/cellulose composite paper can be reduced electrochemically using relatively mild conditions and this GO reduction confers electrocatalytic properties to the composite paper. Finally, we demonstrate that this composite paper offers sensing performance (sensitivity and selectivity) comparable to, or better than, paper-based sensors prepared by small-batch surface-modification (e.g., printing) methods. We envision this coupling of industrialized papermaking technologies with interfacial engineering and electrochemical reduction can provide a platform for single-use and portable chemical detection for a wide range of applications. 

This study collected summer meltwater runoff samples from several glacier watersheds of the northeast Tibetan Plateau during June-July 2017, and measured the concentrations of 17 trace elements (Li, Be, Sc, V, Cr, Co, Ni, Cu, Zn, Ga, Rb, Mo, Cd, In, Sb, Cs, Ba) in meltwater suspended particulate matter (SPM), in order to reveal the elemental concentration, spatial distribution, and water quality in remote glacier watershed under regional anthropogenic activities. Results showed that, the concentration of heavy metal elements was relatively high in Yuzhufeng Glacier basin, ranging from 0.57 μg/L (In) to 1,551.6 μg/L (Ba), whereas in Qiyi Glacier basin it was the lowest, ranging from 0.02 to 85.05 μg/L; and relatively medium in other glacier watersheds, with total elemental concentration varying from 1,503.9 to 1726.2 μg/L. Moreover, enrichment factors (EFs) of SPM heavy metals showed significantly higher value in the downstream than that of upper glacier region of the watershed. Most heavy metals with low EFs mainly originated from crust dust, while others with higher EFs (e.g., Cd, Sb) probably originated from anthropogenic sources. Spatially, the EFs of heavy metals were higher in Yuzhufeng and Laohugou Glacier basins; while in other regions the EFs were relatively low, which may be caused by regional land-surface and atmospheric environmental differences surrounding the various glacier watersheds. Compared with other remote locations in global range, heavy metals level (e.g., Cu, Ni, and Zn) in this region is relatively higher. Meanwhile, we find that, though the water quality of the glacier basin in northeast Tibetan Plateau was relatively clean and pollution-free, it is still obviously affected by regional anthropogenic activities. Mining activities, transportation and natural weathering and erosion processes in the study areas have important effects on the content of heavy metal pollutants of river-water SPM in the glacier watershed. Moreover, backward air-mass trajectories demonstrated the potential atmospheric pollutants transport from the surrounding cities and suburbs, to deposit in the snowpack and glaciers, and then melted out and released into meltwater runoff. This study provides a new perspective on more complete view of heavy metals distribution in glacier watershed, and new understanding for the cryosphere water environment evaluation in the Tibetan Plateau region. 

Sampling based planning is an important step for long-range navigation for an autonomous vehicle. This work proposes a GPU-accelerated sampling based path planning algorithm which can be used as a global planner in autonomous navigation tasks. A modified version of the generation portion for the Probabilistic Road Map (PRM) algorithm is presented which reorders some steps of the algorithm in order to allow for parallelization and thus can benefit highly from utilization of a GPU. The GPU and CPU algorithms were compared using a simulated navigation environment with graph generation tasks of several different sizes. It was found that the GPU-accelerated version of the PRM algorithm had significant speedup over the CPU version (up to 78×). This results provides promising motivation towards implementation of a real-time autonomous navigation system in the future.