We present a new generation of substellar atmosphere and evolution models, appropriate for application to studies of L, T, and Y-type brown dwarfs and self-luminous extrasolar planets. The atmosphere models describe the expected temperature-pressure profiles and emergent spectra of atmospheres in radiative-convective equilibrium with effective temperatures and gravities within the ranges 200 ≤ T eff ≤ 2400 K and 2.5 ≤ log g ≤ 5.5. These ranges encompass masses from about 0.5 to 85 Jupiter masses for a set of metallicities ([M/H] = −0.5 to +0.5), C/O ratios (from 0.5 to 1.5 times that of solar), and ages. The evolution tables describe the cooling of these substellar objects through time. These models expand the diversity of model atmospheres currently available, notably to cooler effective temperatures and greater ranges in C/O. Notable improvements from past such models include updated opacities and atmospheric chemistry. Here we describe our modeling approach and present our initial tranche of models for cloudless, chemical equilibrium atmospheres. We compare the modeled spectra, photometry, and evolution to various datasets.

No abstract

No abstract

No abstract

In recent years, more serious games are used in different learning areas to improve the process of knowledge acquisition and learning outcomes. This study discusses a game mode based on a jigsaw puzzle with scaffolding-aid for cultural heritage learning. A case study using an historical pattern from China examines the effectiveness of improving learning performance and motivation. By using this game in an experimental setting in comparison with traditional video learning, this paper observes and evaluates the learning outcomes of 42 freshmen with no prior knowledge of bronze mirrors` patterns and subjects. Their learning outcomes, experience and motivation change were collected via a series of tests and motivation scale. The result of the experiment demonstrates that compared with traditional video learning, the game process based on a digital puzzle can help learners better identify and retain pattern structure and improve learning motivation. This study provides experience in designing serious games for cultural heritage learning activities.

The Cassini spacecraft revealed that Saturn's magnetic field displayed oscillations at a period originally thought to match the planetary rotation rate but later found not to. One of many proposed theories predicts that a polar twin‐cell neutral weather system drives this variation, producing observable differences in flows within Saturn's ionosphere. Here, using spectral observations of auroral H3+ ${\mathrm{H}}_{3}^{+}$ emission lines taken by the Keck Observatory's Near Infrared Echelle Spectrograph (Keck‐NIRSPEC) in 2017, we derive ion line‐of‐sight velocity maps after grouping spectra into rotational quadrants matching phases of the planetary magnetic field. We measure 0.5 km s−1 wind systems in the ionosphere consistent with predicted neutral twin‐vortex flow patterns. These findings demonstrate that neutral winds in Saturn's polar regions cause the rotational period, as determined via the magnetic field, to exhibit differences and time variabilities relative to the planet's true period of rotation in a process never before seen within planetary atmospheres.

In this report, we describe a Theano-based AlexNet (Krizhevsky et al., 2012) implementation and its naive data parallelism on multiple GPUs. Our performance on 2 GPUs is comparable with the state-of-art Caffe library (Jia et al., 2014) run on 1 GPU. To the best of our knowledge, this is the first open-source Python-based AlexNet implementation to-date.

ABSTRACT:This study investigates the usability of low-attitude unmanned aerial vehicle (UAV) acquiring high resolution images for the geometry reconstruction of opencast mine. Image modelling techniques like Structure from Motion (SfM) and Patch-based Multiview Stereo (PMVS) algorithms are used to generate dense 3D point cloud from UAV collections. Then, precision of 3D point cloud will be first evaluated based on Real-time Kinematic (RTK) ground control points (GCPs) at point level. The experimental result shows that the mean square error of the UAV point cloud is 0.11m. Digital surface model (DSM) of the study area is generated from UAV point cloud, and compared with that from the Terrestrial Laser Scanner (TLS) data for further comparison at the surface level. Discrepancy map of 3D distances based on DSMs shows that most deviation is less than ±0.4m and the relative error of the volume is 1.55%.