Mineral Processing by Short Circuits in Protoplanetary Disks

Meteoritic chondrules were formed in the early solar system by brief heating of silicate dust to melting temperatures. Some highly refractory grains (Type B calcium-aluminum-rich inclusions, CAIs) also show signs of transient heating. A similar process may occur in other protoplanetary disks, as evidenced by observations of spectra characteristic of crystalline silicates. One possible environment for this process is the turbulent magnetohydrodynamic flow thought to drive accretion in these disks. Such flows generally form thin current sheets, which are sites of magnetic reconnection, and dissipate the magnetic fields amplified by a disk dynamo. We suggest that it is possible to heat precursor grains for chondrules and other high-temperature minerals in current sheets that have been concentrated by our recently described short-circuit instability. We extend our work on this process by including the effects of radiative cooling, taking into account the temperature dependence of the opacity; and by examining current sheet geometry in three-dimensional, global models of magnetorotational instability. We find that temperatures above 1600 K can be reached for favorable parameters that match the ideal global models. This mechanism could provide an efficient means of tapping the gravitational potential energy of the protoplanetary disk to heat grains strongly enough to form high-temperature minerals. The volume-filling nature of turbulent magnetic reconnection is compatible with constraints from chondrule-matrix complementarity, chondrule-chondrule complementarity, the occurrence of igneous rims, and compound chondrules. The same short-circuit mechanism may perform other high-temperature mineral processing in protoplanetary disks such as the production of crystalline silicates and CAIs.Comment: 6 pages, 3 figures, ApJL published versio

Decentralized Data Fusion and Active Sensing with Mobile Sensors for Modeling and Predicting Spatiotemporal Traffic Phenomena

The problem of modeling and predicting spatiotemporal traffic phenomena over an urban road network is important to many traffic applications such as detecting and forecasting congestion hotspots. This paper presents a decentralized data fusion and active sensing (D2FAS) algorithm for mobile sensors to actively explore the road network to gather and assimilate the most informative data for predicting the traffic phenomenon. We analyze the time and communication complexity of D2FAS and demonstrate that it can scale well with a large number of observations and sensors. We provide a theoretical guarantee on its predictive performance to be equivalent to that of a sophisticated centralized sparse approximation for the Gaussian process (GP) model: The computation of such a sparse approximate GP model can thus be parallelized and distributed among the mobile sensors (in a Google-like MapReduce paradigm), thereby achieving efficient and scalable prediction. We also theoretically guarantee its active sensing performance that improves under various practical environmental conditions. Empirical evaluation on real-world urban road network data shows that our D2FAS algorithm is significantly more time-efficient and scalable than state-of-the-art centralized algorithms while achieving comparable predictive performance.Comment: 28th Conference on Uncertainty in Artificial Intelligence (UAI 2012), Extended version with proofs, 13 page

Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations

Gaussian processes (GP) are Bayesian non- parametric models that are widely used for probabilistic regression. Unfortunately, it cannot scale well with large data nor perform real-time predictions due to its cubic time cost in the data size. This paper presents two parallel GP regression methods that exploit low-rank covariance matrix approximations for distributing the computational load among parallel machines to achieve time efficiency and scalability. We theoretically guarantee the predictive performance of our proposed parallel GPs to be equivalent to that of some centralized approximate GP regression methods: The computation of their centralized counterparts can be distributed among parallel machines, hence achieving greater time efficiency and scalability. We analytically compare the properties of our parallel GPs such as time, space, and communication complexity. Empirical evaluation on two real-world datasets in a cluster of 20 computing nodes shows that our parallel GPs are significantly more time-efficient and scalable than their centralized counterparts and exact/full GP while achieving predictive performances comparable to full GP

Phurbas: An Adaptive, Lagrangian, Meshless, Magnetohydrodynamics Code. II. Implementation and Tests

We present an algorithm for simulating the equations of ideal magnetohydrodynamics and other systems of differential equations on an unstructured set of points represented by sample particles. The particles move with the fluid, so the time step is not limited by the Eulerian Courant-Friedrichs-Lewy condition. Full spatial adaptivity is required to ensure the particles fill the computational volume, and gives the algorithm substantial flexibility and power. A target resolution is specified for each point in space, with particles being added and deleted as needed to meet this target. We have parallelized the code by adapting the framework provided by GADGET-2. A set of standard test problems, including 1e-6 amplitude linear MHD waves, magnetized shock tubes, and Kelvin-Helmholtz instabilities is presented. Finally we demonstrate good agreement with analytic predictions of linear growth rates for magnetorotational instability in a cylindrical geometry. This paper documents the Phurbas algorithm as implemented in Phurbas version 1.1.Comment: 14 pages, 14 figures, ApJS accepted, revised in accordance with changes to paper I (arXiv:1110.0835

Short Circuits in Thermally Ionized Plasmas: A Mechanism for Intermittent Heating of Protoplanetary Disks

Many astrophysical systems of interest, including protoplanetary accretion disks, are made of turbu- lent magnetized gas with near solar metallicity. Thermal ionization of alkali metals in such gas exceeds non-thermal ionization when temperatures climb above roughly 1000 K. As a result, the conductiv- ity, proportional to the ionization fraction, gains a strong, positive dependence on temperature. In this paper, we demonstrate that this relation between the temperature and the conductivity triggers an exponential instability that acts similarly to an electrical short, where the increased conductivity concentrates the current and locally increases the Ohmic heating. This contrasts with the resistiv- ity increase expected in an ideal magnetic reconnection region. The instability acts to focus narrow current sheets into even narrower sheets with far higher currents and temparatures. We lay out the basic principles of this behavior in this paper using protoplanetary disks as our example host system, motivated by observations of chondritic meteorites and their ancestors, dust grains in protoplanetary disks, that reveal the existence of strong, frequent heating events that this instability could explain.Comment: 9 pages, 6 figures, 1 table Accepted, Ap

Factors associated with deep surgical site infection in spinal surgery

Introduction: Surgical site infection (SSI) rate in spinal surgery ranges from 1% to 9%, depending on the type of procedure and institution. SSI gives rise to increased morbidity, poorer outcomes and increased healthcare costs. Various risk factors have been reported in the literature but there is no such related report from Malaysia. This pilot study aimed to determine the incidence and risk factors of deep surgical site infections which require surgical debridement in patients who had undergone spinal surgeries. Materials and Methods: Patients who had undergone spinal surgery at Hospital Tengku Ampuan Afzan, Kuantan from the 1st January 2016 to the 31st December 2017 were included in this study. Associations between SSI and risk factors were analysed with IBM SPSS version 21. Age, body mass index, number of vertebral level involvement, hemoglobin reduction and white blood cell count were analysed by the student t-test while gender, smoking status, spinal cord involvement, fracture dislocation at thoraco-lumbar junction and history of pre-operative blood product transfusion were analysed by Fisher’s exact test. Results: Four (17%) out of 24 patients developed deep SSI which required surgical debridement. Fracture dislocation at the thoraco-lumbar junction (p=0.008) and history of pre-operative blood product transfusion (p=0.003) were associated with deep SSI. Conclusions: This study highlighted different risk factors associated with deep SSI in spinal surgeries. A larger study is needed to further confirm these findings