Dayside transients, such as hot flow anomalies, foreshock bubbles, magnetosheath jets, flux transfer events, and surface waves, are frequently observed upstream from the bow shock, in the magnetosheath, and at the magnetopause. They play a significant role in the solar wind-magnetosphere-ionosphere coupling. Foreshock transient phenomena, associated with variations in the solar wind dynamic pressure, deform the magnetopause, and in turn generates field-aligned currents (FACs) connected to the auroral ionosphere. Solar wind dynamic pressure variations and transient phenomena at the dayside magnetopause drive magnetospheric ultra low frequency (ULF) waves, which can play an important role in the dynamics of Earth’s radiation belts. These transient phenomena and their geoeffects have been investigated using coordinated in-situ spacecraft observations, spacecraft-borne imagers, ground-based observations, and numerical simulations. Cluster, THEMIS, Geotail, and MMS multi-mission observations allow us to track the motion and time evolution of transient phenomena at different spatial and temporal scales in detail, whereas ground-based experiments can observe the ionospheric projections of transient magnetopause phenomena such as waves on the magnetopause driven by hot flow anomalies or flux transfer events produced by bursty reconnection across their full longitudinal and latitudinal extent. Magnetohydrodynamics (MHD), hybrid, and particle-in-cell (PIC) simulations are powerful tools to simulate the dayside transient phenomena. This paper provides a comprehensive review of the present understanding of dayside transient phenomena at Earth and other planets, their geoeffects, and outstanding questions.
Web browsing is an activity that billions of mobile users perform on a daily basis. Battery life is a primary concern to many mobile users who often find their phone has died at most inconvenient times. The heterogeneous multi-core architecture is a solution for energy-efficient processing. However, the current mobile web browsers rely on the operating system to exploit the underlying hardware, which has no knowledge of individual web contents and often leads to poor energy efficiency. This paper describes an automatic approach to render mobile web workloads for performance and energy efficiency. It achieves this by developing a machine learning based approach to predict which processor to use to run the web rendering engine and at what frequencies the processors should operate. Our predictor learns offline from a set of training web workloads. The built predictor is then integrated into the browser to predict the optimal processor configuration at runtime, taking into account the web workload characteristics and the optimisation goal: whether it is load time, energy consumption or a trade-off between them. We evaluate our approach on a representative ARM big.LITTLE mobile architecture using the hottest 500 webpages. Our approach achieves 80% of the performance delivered by an ideal predictor. We obtain, on average, 45%, 63.5% and 81% improvement respectively for load time, energy consumption and the energy delay product, when compared to the Linux heterogeneous multi-processing scheduler.
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