On the Relation between the Mysterious 21 Micrometer Emission Feature of Post-Asymptotic Giant Branch Stars and Their Mass Loss Rates

Over two decades ago, a prominent, mysterious emission band peaking at ~20.1 micrometer was serendipitously detected in four preplanetary nebulae (PPNe; also known as "protoplanetary nebulae"). So far, this spectral feature, designated as the "21 micrometer" feature, has been seen in 18 carbon-rich PPNe. The nature of the carriers of this feature remains unknown although many candidate materials have been proposed. The 21 micrometer sources also exhibit an equally mysterious, unidentified emission feature peaking at 30 micrometer. While the 21 micrometer feature is exclusively seen in PPNe, a short-lived evolutionary stage between the end of the asymptotic giant branch (AGB) and planetary nebula (PN) phases, the 30 micrometer feature is commonly observed in all stages of stellar evolution from the AGB through PPN to PNe phases. We derive the stellar mass loss rates (M_{loss}) of these 21 micrometer sources from their dust infrared (IR) emission, using the "2-DUST" radiative transfer code for axisymmetric dusty systems which allows one to distinguish the mass loss rates of the AGB phase (\dot{M_{AGB}}) from that of the superwind (\dot_{M_{SW}}) phase. We examine the correlation between \dot{M_{AGB}} or \dot_{M_{SW}} and the fluxes emitted from the 21 and 30 micrometer features. We find that both features tend to correlate with \dot{M_{AGB}}, suggesting that their carriers are probably formed in the AGB phase. The nondetection of the 21 micrometer feature in AGB stars suggests that, unlike the 30 micrometer feature, the excitation of the carriers of the 21 micrometer feature may require ultraviolet photons which are available in PPNe but not in AGB stars.Comment: 36 pages, 8 figures, 7 tables; accepted for publication in The Astrophysical Journa

Combining Physical Simulators and Object-Based Networks for Control

Physics engines play an important role in robot planning and control; however, many real-world control problems involve complex contact dynamics that cannot be characterized analytically. Most physics engines therefore employ . approximations that lead to a loss in precision. In this paper, we propose a hybrid dynamics model, simulator-augmented interaction networks (SAIN), combining a physics engine with an object-based neural network for dynamics modeling. Compared with existing models that are purely analytical or purely data-driven, our hybrid model captures the dynamics of interacting objects in a more accurate and data-efficient manner.Experiments both in simulation and on a real robot suggest that it also leads to better performance when used in complex control tasks. Finally, we show that our model generalizes to novel environments with varying object shapes and materials.Comment: ICRA 2019; Project page: http://sain.csail.mit.ed

New targets for resolution of airway remodeling in obstructive lung diseases.

Airway remodeling (AR) is a progressive pathological feature of the obstructive lung diseases, including asthma and chronic obstructive pulmonary disease (COPD). The pathology manifests itself in the form of significant, progressive, and (to date) seemingly irreversible changes to distinct respiratory structural compartments. Consequently, AR correlates with disease severity and the gradual decline in pulmonary function associated with asthma and COPD. Although current asthma/COPD drugs manage airway contraction and inflammation, none of these effectively prevent or reverse features of AR. In this review, we provide a brief overview of the features and putative mechanisms affecting AR. We further discuss recently proposed strategies with promise for deterring or treating AR

Expectation of forward-backward rapidity correlations in p+pp+p collisions at the LHC energies

Forward-backward correlation strength ($b$) as a function of pesudorapidity intervals for experimental data from $p+\bar{p}$ non-singly diffractive collisions are compared to PYTHIA and PHOJET model calculations. The correlations are discussed as a function of rapidity window ($\Delta \eta$) symmetric about the central rapidity as well as rapidity window separated by a gap ($\eta_{gap}$) between forward and backward regions. While the correlations are observed to be independent of $\Delta \eta$, it is found to decrease with increase in $\eta_{gap}$. This reflects the role of short range correlations and justifies the use of $\eta_{gap}$ to obtain the accurate information about the physics of interest, the long range correlations. The experimental $b$ value shows a linear dependence on $\ln \sqrt{s}$ with the maximum value of unity being reached at $\sqrt{s}$ = 16 TeV, beyond the top LHC energy. However calculations from the PYTHIA and PHOJET models indicate a deviation from linear dependence on $\ln \sqrt{s}$ and saturation in the $b$ values being reached beyond $\sqrt{s}$ = 1.8 TeV. Such a saturation in correlation values could have interesting physical interpretations related to clan structures in particle production. Strong forward-backward correlations are associated with cluster production in the collisions. The average number of charged particles to which the clusters fragments, called the cluster size, are found to also increase linearly with $\ln \sqrt{s}$ for both data and the models studied. The rate of increase in cluster size vs. $\ln \sqrt{s}$ from models studied are larger compared to those from the data and higher for PHOJET compared to PYTHIA. Our study indicates that the forward-backward measurements will provide a clear distinguishing observable for the models studied at LHC energies.Comment: 15 pages, 14 Figures, accepted for publication in International Journal of Modern Physics

A vector-based method for bank-material tracking in coupled models of meandering and landscape evolution

Sinuous channels commonly migrate laterally and interact with banks of different strengths—an interplay that links geomorphology and life and shapes diverse landscapes from the seafloor to planetary surfaces. To investigate feedbacks between meandering rivers and landscapes over geomorphic timescales, numerical models typically represent bank properties using grids; however, this approach produces results inherently dependent on grid resolution. Herein we assess existing techniques for tracking landscape and bank-strength evolution in numerical models of meandering channels and show that grid-based models implicitly include unintended thresholds for bank migration that can control simulated landscape evolution. Building on stratigraphic modeling techniques, we develop a vector-based method for land surface- and subsurface-material tracking that overcomes the resolution-dependence inherent in grid-based techniques by allowing high-fidelity representation of bank-material properties for curvilinear banks and low channel lateral migration rates. We illustrate four specific applications of the new technique: (1) the effect of resistant mud-rich deposits in abandoned meander cutoff loops on meander belt evolution; (2) the stratigraphic architecture of aggrading, alluvial meandering channels that interact with cohesive-bank and floodplain material; (3) the evolution of an incising, meandering river with mixed bedrock and alluvial banks within a confined bedrock valley; and (4) the effect of a bank-height dependent lateral-erosion rate for a meandering river in an aggrading floodplain. In all cases the vector-based approach overcomes numerical artifacts with the grid-based model. Because of its geometric flexibility, the vector-based material tracking approach provides new opportunities for exploring the coevolution of meandering rivers and surrounding landscapes over geologic timescales

Numerical simulations of bedrock valley evolution by meandering rivers with variable bank material

Bedrock river valleys are fundamental components of many landscapes, and their morphologies—from slot canyons with incised meanders to wide valleys with strath terraces—may record environmental history. Several formation mechanisms for particular valley types have been proposed that involve changes in climatic and tectonic forcing, but the uniqueness of valley evolution pathways and the long-term stability of valley morphology under constant forcing are unknown and are not predicted in existing numerical models for vertically incising rivers. Because rivers often migrate more rapidly through alluvium than through bedrock, we explore the hypothesis that the distribution of bank materials strongly influences river meandering kinematics and can explain the diversity of bedrock river valley morphology. Simulations using a numerical model of river meandering with vector-based bank-material tracking indicate that channel lateral erosion rate in sediment and bedrock, vertical erosion rate, and initial alluvial-belt width explain first-order differences in bedrock valley type; that bedrock-bound channels can evolve under steady forcing from alluvial states; and that weak bedrock and low vertical incision rates favor wide, shallow valleys, while resistant bedrock and high vertical incision rates favor narrow, deep valleys. During vertical incision, sustained planation of the valley floor is favored when bedrock boundaries restrict channel migration to a zone of thin sediment fill. The inherent unsteadiness of river meandering in space and time is enhanced by evolving spatial contrasts in bank strength between sediment and bedrock and can account for several valley features—including strath terraces and underfit valleys—commonly ascribed to external drivers

College Enquiry Chatbot Using A.L.I.C.E

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