The 'Paris-end' of town? Urban typology through machine learning

The confluence of recent advances in availability of geospatial information, computing power, and artificial intelligence offers new opportunities to understand how and where our cities differ or are alike. Departing from a traditional `top-down' analysis of urban design features, this project analyses millions of images of urban form (consisting of street view, satellite imagery, and street maps) to find shared characteristics. A (novel) neural network-based framework is trained with imagery from the largest 1692 cities in the world and the resulting models are used to compare within-city locations from Melbourne and Sydney to determine the closest connections between these areas and their international comparators. This work demonstrates a new, consistent, and objective method to begin to understand the relationship between cities and their health, transport, and environmental consequences of their design. The results show specific advantages and disadvantages using each type of imagery. Neural networks trained with map imagery will be highly influenced by the mix of roads, public transport, and green and blue space as well as the structure of these elements. The colours of natural and built features stand out as dominant characteristics in satellite imagery. The use of street view imagery will emphasise the features of a human scaled visual geography of streetscapes. Finally, and perhaps most importantly, this research also answers the age-old question, ``Is there really a `Paris-end' to your city?''

Identifying safe intersection design through unsupervised feature extraction from satellite imagery

The World Health Organization has listed the design of safer intersections as a key intervention to reduce global road trauma. This article presents the first study to systematically analyze the design of all intersections in a large country, based on aerial imagery and deep learning. Approximately 900,000 satellite images were downloaded for all intersections in Australia and customized computer vision techniques emphasized the road infrastructure. A deep autoencoder extracted high-level features, including the intersection's type, size, shape, lane markings, and complexity, which were used to cluster similar designs. An Australian telematics data set linked infrastructure design to driving behaviors captured during 66 million kilometers of driving. This showed more frequent hard acceleration events (per vehicle) at four- than three-way intersections, relatively low hard deceleration frequencies at T-intersections, and consistently low average speeds on roundabouts. Overall, domain-specific feature extraction enabled the identification of infrastructure improvements that could result in safer driving behaviors, potentially reducing road trauma.Comment: 16 pages, 10 figures. Computer-Aided Civil and Infrastructure Engineering (2020

Flora and vegetation of greenstone formations of the Yilgarn Craton: south-west Ravensthorpe Greenstone Belt

ABSTRACT A quadrat-based survey of the flora of the south-west region of the Ravensthorpe Greenstone Belt identified 321 taxa, including six taxa of conservation significance and three weed species. All of the conservation-listed taxa were known to the area. Range extensions were recorded for three taxa and additional collections of two Austrostipa species currently under taxonomic description were made. Six community types were derived from statistical classification of the 50 quadrats. These community types were similar to those described from other parts of the Ravensthorpe Greenstone Belt. As with the other greenstone belts of the Yilgarn Craton, soil chemical parameters and site physical characteristics were influential in delineating community types. Only a small portion of the study area is in the conservation estate; mining and exploration pressures remain the primary threat to this species-rich area

A picogram and nanometer scale photonic crystal opto-mechanical cavity

We describe the design, fabrication, and measurement of a cavity opto-mechanical system consisting of two nanobeams of silicon nitride in the near-field of each other, forming a so-called "zipper" cavity. A photonic crystal patterning is applied to the nanobeams to localize optical and mechanical energy to the same cubic-micron-scale volume. The picrogram-scale mass of the structure, along with the strong per-photon optical gradient force, results in a giant optical spring effect. In addition, a novel damping regime is explored in which the small heat capacity of the zipper cavity results in blue-detuned opto-mechanical damping.Comment: 15 pages, 4 figure

Characterization of radiation pressure and thermal effects in a nanoscale optomechanical cavity

Optical forces in guided-wave nanostructures have recently been proposed as an effective means of mechanically actuating and tuning optical components. In this work, we study the properties of a photonic crystal optomechanical cavity consisting of a pair of patterned silicon nitride nanobeams. Internal stresses in the stoichiometric silicon nitride thin-film are used to produce inter-beam slot-gaps ranging from 560 to 40nm. A general pump-probe measurement scheme is described which determines, self-consistently, the contributions of thermo-mechanical, thermo-optic, and radiation pressure effects. For devices with 40nm slot-gap, the optical gradient force is measured to be 134fN per cavity photon for the strongly coupled symmetric cavity supermode, producing a static cavity tuning greater than five times that of either the parasitic thermo-mechanical or thermo-optic effects.Comment: 6 pages, 4 figure

Optical and mechanical design of a "zipper" photonic crystal optomechanical cavity

Design of a doubly-clamped beam structure capable of localizing mechanical and optical energy at the nanoscale is presented. The optical design is based upon photonic crystal concepts in which patterning of a nanoscale-cross-section beam can result in strong optical localization to an effective optical mode volume of 0.2 cubic wavelengths ((\lambda_{c})^3). By placing two identical nanobeams within the near field of each other, strong optomechanical coupling can be realized for differential motion between the beams. Current designs for thin film silicon nitride beams at a wavelength of 1.5 microns indicate that such structures can simultaneously realize an optical Q-factor of 7x10^6, motional mass m~40 picograms, mechanical mode frequency ~170 MHz, and an optomechanical coupling factor (g_{OM}=d\omega_{c}/dx = \omega_{c}/L_{OM}) with effective length L_{OM} ~ \lambda = 1.5 microns.Comment: 16 pages, 10 figure

Peripheral vasoconstriction influences thenar oxygen saturation as measured by near-infrared spectroscopy

Purpose: Near-infrared spectroscopy has been used as a noninvasive monitoring tool for tissue oxygen saturation (StO2) in acutely ill patients. This study aimed to investigate whether local vasoconstriction induced by body surface cooling significantly influences thenar StO2 as measured by InSpectra model 650. Methods: Eight healthy individuals (age 26 ± 6 years) participated in the study. Using a cooling blanket, we aimed to cool the entire body surface to induce vasoconstriction in the skin without any changes in central temperature. Thenar StO2 was noninvasively measured during a 3-min vascular occlusion test using InSpectra model 650 with a 15-mm probe. Measurements were analyzed for resting StO2 values, rate of StO2 desaturation (RdecStO2, %/min), and rate of StO2 recovery (RincStO2, %/s) before, during, and after skin cooling. Measurements also included heart rate (HR), mean arterial pressure (MAP), cardiac output (CO), stroke volume (SV), capillary refill time (CRT), forearm-to-fingertip skintemperature gradient (Tskin-diff), perfusion index (PI), and tissue hemoglobin index (THI). Results: In all subjects MAP, CO, SV, and core temperature did not change during the procedure. Skin cooling resulted in a significant decrease in StO2 from 82% (80-87) to 72% (70-77) (P\0.05) and in RincStO2 from 3.0%/s (2.8-3.3) to 1.7%/s (1.1-2.0) (P\0.05). Similar changes in CRT, Tskin-diff, and PI were also observed: from 2.5 s (2.0-3.0) to 8.5 s (7.2-11.0) (P\0.05), from 1.0 (-1.6-1.8) to 3.1 (P\0.05), and from 10.0% (9.1-11.7) to 2.5