[1] Pc1-2 ULF waves are strongly associated with the presence of various ions in the magnetosphere. We investigate the role of heavy ion resonances in nonuniform plasmas near the equatorial region. By adopting the invariant imbedding method, the coupled plasma wave equations are solved in an exact manner to calculate the resonant absorption at the ion-ion hybrid resonance. Our results show that irreversible mode conversion occurs at the resonance, which absorbs the fast wave energy. It is found that waves near the resonances appear with linear polarization, and their amplitude and frequency are sensitive to the properties of the heavy ion plasma composition. We examine how these resonances occur for various H + -He + populations in detail by performing an accurate calculation of the mode conversion efficiency. Because the multi-ion hybrid resonance locations in cold plasmas are determined by simple parameters, such as the fraction of the ion number density of each species and the magnetic field, we suggest that it is possible to monitor heavy ion composition by examining the peak frequencies of linearly polarized wave events in either electric field or magnetic field spectral data.

The Ultraviolet Coronagraph Spectrometer (UVCS) observed Doppler shifted material of a partial Halo Coronal Mass Ejection (CME) on December 13 2001. The observed ratio of [O V]/O V] is a reliable density diagnostic important for assessing the state of the plasma. Earlier UVCS observations of CMEs found evidence that the ejected plasma is heated long after the eruption. We have investigated the heating rates, which represent a significant fraction of the CME energy budget. The parameterized heating and radiative and adiabatic cooling have been used to evaluate the temperature evolution of the CME material with a time dependent ionization state model. The functional form of a flux rope model for interplanetary magnetic clouds was also used to parameterize the heating. We find that continuous heating is required to match the UVCS observations. To match the O VI-bright knots, a higher heating rate is required such that the heating energy is greater than the kinetic energy. The temperatures for the knots bright in Lyα and C III emission indicate that smaller heating rates are required for those regions. In the context of the flux rope model, about 75% of the magnetic energy must go into heat in order to match the O VI observations. We derive tighter constraints on the heating than earlier analyses, and we show that thermal conduction with the Spitzer conductivity is not sufficient to account for the heating at large heights.

[1] We have compared the prediction capability of two types of Sun-Earth connection models: (1) ensemble of physics-based shock propagation models (STOA, STOA-2, ISPM, and HAFv.2) and (2) empirical CME propagation (CME-ICME and CME-IP shock) models. For this purpose, we have selected 38 near-simultaneous pairs of coronal mass ejections (CMEs) and metric type II radio bursts. By applying the adopted models to these events, we have estimated the time difference between predicted and observed arrivals of interplanetary (IP) shocks and ICMEs at the Earth or L1. The mean absolute error of the shock arrival time (SAT) within an adopted window of ±24 hours is 9.8 hours for the ensemble of shock propagation models, 9.2 hours for the CME-IP shock model, and 11.6 hours for the CME-ICME model. It is also found that the success rate for all models is about 80% for the same window. The results imply that the adopted models are comparable in their prediction of the arrival times of IP shocks and interplanetary CMEs (ICMEs). The usefulness of these models is also discussed in terms of real-time forecasts, underlying physics, and identification of IP shocks and ICMEs at the Earth.

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We are living in the Internet of Things (IoT) era where all the (smart) objects around us are connected and communicated with each other to serve our life better without the need of explicit instruction. Soon we have to cope with trillions of heterogeneous data streams coming from IoT. Since data is not information, methods for discovering useful and correlative information from data and utilising them for the better life, in real-time mode, are the utmost requirements.In order to tackle this problem, we introduce an Event Information Management platform (EvIM) that can be used to develop applications run as cyber-physical-social systems. EvIM includes two components (1) EventWarehouse: is built for harvesting, storing, and analysing data coming from large scale heterogeneous sensors, and (2) EventShop: plays as a real-time complex spatio-temporal event processing system.Differ from conventional systems that use data-driven or pre-defined event-based approaches, the proposed platform can alleviate the burden of human intervention meanwhile increase the scalability, robustness, feasibility, and applicability by offering series of services that not only automatically discover correlations among sensors' data to extract useful information but also can help users design and monitor situations visually, efficiently and effectively, under on-fly mode.