Objectives: To provide novel quantification and advanced measurements of surface temperatures (Ts) in playgrounds, employing multiple scales of data, and provide insight into hot-hazard mitigation techniques and designs for improved environmental and public health.

Methods: We conduct an analysis of Ts in two Metro-Phoenix playgrounds at three scales: neighborhood (1 km resolution), microscale (6.8 m resolution), and touch-scale (1 cm resolution). Data were derived from two sources: airborne remote sensing (neighborhood and microscale) and in situ (playground site) infrared Ts (touch-scale). Metrics of surface-to-air temperature deltas (Ts–a) and scale offsets (errors) are introduced.

Results: Select in situ Ts in direct sunlight are shown to approach or surpass values likely to result in burns to children at touch-scales much finer than Ts resolved by airborne remote sensing. Scale offsets based on neighbourhood and microscale ground observations are 3.8 ◦C and 7.3 ◦C less than the Ts–a at the 1 cm touch-scale, respectively, and 6.6 ◦C and 10.1 ◦C lower than touch-scale playground equipment Ts, respectively. Hence, the coarser scales underestimate high Ts within playgrounds. Both natural (tree) and artificial (shade sail) shade types are associated with significant reductions in Ts.

Conclusions: A scale mismatch exists based on differing methods of urban Ts measurement. The sub-meter touch-scale is the spatial scale at which data must be collected and policies of urban landscape design and health must be executed in order to mitigate high Ts in high-contact environments such as playgrounds. Shade implementation is the most promising mitigation technique to reduce child burns, increase park usability, and mitigate urban heating.

Human activity recognition is the task of identifying a person’s movement from sensors in a wearable device, such as a smartphone, smartwatch, or a medical-grade device. A great method for this task is machine learning, which is the study of algorithms that learn and improve on their own with the help of massive amounts of useful data. These classification models can accurately classify activities with the time-series data from accelerometers and gyroscopes. A significant way to improve the accuracy of these machine learning models is preprocessing the data, essentially augmenting data to make the identification of each activity, or class, easier for the model. <br/>On this topic, this paper explains the design of SigNorm, a new web application which lets users conveniently transform time-series data and view the effects of those transformations in a code-free, browser-based user interface. The second and final section explains my take on a human activity recognition problem, which involves comparing a preprocessed dataset to an un-augmented one, and comparing the differences in accuracy using a one-dimensional convolutional neural network to make classifications.

Serial femtosecond crystallography requires reliable and efficient delivery of fresh crystals across the beam of an X-ray free-electron laser over the course of an experiment. We introduce a double-flow focusing nozzle to meet this challenge, with significantly reduced sample consumption, while improving jet stability over previous generations of nozzles. We demonstrate its use to determine the first room-temperature structure of RNA polymerase II at high resolution, revealing new structural details. Moreover, the double flow-focusing nozzles were successfully tested with three other protein samples and the first room temperature structure of an extradiol ring-cleaving dioxygenase was solved by utilizing the improved operation and characteristics of these devices.

X-ray free-electron lasers provide novel opportunities to conduct single particle analysis on nanoscale particles. Coherent diffractive imaging experiments were performed at the Linac Coherent Light Source (LCLS), SLAC National Laboratory, exposing single inorganic core-shell nanoparticles to femtosecond hard-X-ray pulses. Each facetted nanoparticle consisted of a crystalline gold core and a differently shaped palladium shell. Scattered intensities were observed up to about 7 nm resolution. Analysis of the scattering patterns revealed the size distribution of the samples, which is consistent with that obtained from direct real-space imaging by electron microscopy. Scattering patterns resulting from single particles were selected and compiled into a dataset which can be valuable for algorithm developments in single particle scattering research.

Single particle diffractive imaging data from Rice Dwarf Virus (RDV) were recorded using the Coherent X-ray Imaging (CXI) instrument at the Linac Coherent Light Source (LCLS). RDV was chosen as it is a well-characterized model system, useful for proof-of-principle experiments, system optimization and algorithm development. RDV, an icosahedral virus of about 70 nm in diameter, was aerosolized and injected into the approximately 0.1 μm diameter focused hard X-ray beam at the CXI instrument of LCLS. Diffraction patterns from RDV with signal to 5.9 Ångström were recorded. The diffraction data are available through the Coherent X-ray Imaging Data Bank (CXIDB) as a resource for algorithm development, the contents of which are described here.