Airborne particulates are of particular significance for their human health impacts and their roles in both atmospheric radiative transfer and atmospheric chemistry. Observations of airborne particulates are typically made by environmental agencies using rather expensive instruments. Due to the expense of the instruments usually used by environment agencies, the number of sensors that can be deployed is limited. In this study we show that machine learning can be used to effectively calibrate lower cost optical particle counters. For this calibration it is critical that measurements of the atmospheric pressure, humidity, and temperature are also made.

The human body is an incredible and complex sensing system. Environmental factors trigger a wide range of automatic neurophysiological responses. Biometric sensors can capture these responses in real time, providing clues about the underlying biophysical mechanisms. In this prototype study, we demonstrate an experimental paradigm to holistically capture and evaluate the interactions between an environmental context and physiological markers of an individual operating that environment. A cyclist equipped with a biometric sensing suite is followed by an environmental survey vehicle during outdoor bike rides. The interactions between environment and physiology are then evaluated though the development of empirical machine learning models, which estimate particulate matter concentrations from biometric variables alone. Here, we show biometric variables can be used to accurately estimate particulate matter concentrations at ultra-fine spatial scales with high fidelity (r2 = 0.91) and that smaller particles are better estimated than larger ones. Inferring environmental conditions solely from biometric measurements allows us to disentangle key interactions between the environment and the body. This work sets the stage for future investigations of these interactions for a larger number of factors, e.g., black carbon, CO2, NO/NO2/NOx, and ozone. By tapping into our body’s ‘built-in’ sensing abilities, we can gain insights into how our environment influences our physical health and cognitive performance.

The human body is an incredible and complex sensing system. Environmental factors trigger a wide range of automatic neurophysiological responses. Biometric sensors can capture these responses in real time, providing clues to the underlying biophysical mechanisms. Here we show biometric variables can be used to accurately estimate ultra-local particulate matter concentrations in the ambient environment with high fidelity (r$^2$ = 0.91) and that smaller particles are better estimated than larger ones. Inferring environmental conditions solely from biometric measurements allows us to disentangle key interactions between the environment and the body. A deeper understanding of these interactions can have countless important applications in public health, preventative healthcare, city planning, human performance, and much more. By tapping into our body's `built-in' sensing abilities, we can gain insights to how our environment influences our physical health and cognitive performance.

A supermassive black hole moving through a field of stars will gravitationally scatter the stars, inducing a backreaction force on the black hole known as dynamical friction. In Newtonian gravity, the axisymmetry of the system about the black hole's velocity v implies that the dynamical friction must be anti-parallel to v. However, in general relativity the black hole's spin S need not be parallel to v, breaking the axisymmetry of the system and generating a new component of dynamical friction similar to the Lorentz force F = qv×B experienced by a particle with charge q moving in a magnetic field B. We call this new force gravitomagnetic dynamical friction and calculate its magnitude for a spinning black hole moving through a field of stars with Maxwellian velocity dispersion σ, assuming that both v and σ are much less than the speed of light c. We use post-Newtonian equations of motion accurate to O(v 3 /c 3 ) needed to capture the effect of spin-orbit coupling and also include direct stellar capture by the black hole's event horizon. Gravitomagnetic dynamical friction will cause a black hole with uniform speed to spiral about the direction of its spin, similar to a charged particle spiraling about a magnetic field line, and will exert a torque on a supermassive black hole orbiting a galactic center, causing the angular momentum of this orbit to slowly precess about the black-hole spin. As this effect is suppressed by a factor (σ/c) 2 in nonrelativistic systems, we expect it to be negligible in most astrophysical contexts but provide this calculation for its theoretical interest and potential application to relativistic systems.

This paper describes and demonstrates an autonomous robotic team that can rapidly learn the characteristics of environments that it has never seen before. The flexible paradigm is easily scalable to multi-robot, multi-sensor autonomous teams, and it is relevant to satellite calibration/validation and the creation of new remote sensing data products. A case study is described for the rapid characterisation of the aquatic environment, over a period of just a few minutes we acquired thousands of training data points. This training data allowed for our machine learning algorithms to rapidly learn by example and provide wide area maps of the composition of the environment. Along side these larger autonomous robots two smaller robots that can be deployed by a single individual were also deployed (a walking robot and a robotic hover-board), observing significant small scale spatial variability.