Organizational Safety Culture in Pilot Training Schools: Case of North Texas in the USA and South Korea

The sudden increased need for qualified pilots can cause potential risks for aviation training schools in South Korea because new pilot training programs need to be created, or existing organizations need to be expanded quickly. This study investigates safety culture at pilot training schools, builds a conceptual framework, and identifies the relationship between the sub-safety culture category and safety culture level in commercial pilot training schools. The authors survey the safety culture and management for the organizational aspect of these pilot training schools to clarify essential concepts and generate a conceptual safety management model. The authors examine the differences in safety culture between pilot training schools in the USA and South Korea and the effects these differences have on the organizations. Results show that the safety culture between pilot training schools in north Texas in the USA and South Korea is different. A pilot training school has to have a well-defined safety culture and management procedures in place and an awareness of the diverse cultural backgrounds of its student pilots to avoid potential cultural clashes and needless accidents/incidents

Medium-frequency-link power conversion for high power density renewable energy systems

Recent advances in solid-state semiconductors and magnetic materials have provided the impetus for medium frequency-link based medium voltage power conversion systems, which would be a possible solution to reducing the weight and volume of renewable power generation systems. To verify this new concept, in this paper, a laboratory prototype of 1.73 kVA medium-frequency-link power conversion system is developed for a scaled down 1 kV grid applications. The design and implementation of the prototyping, test platform, and the experimental results are analyzed and discussed. It is expected that the proposed new technology would have great potential for future renewable and smart grid applications. © 2013 IEEE

High-frequency magnetic-link medium-voltage converter for superconducting generator-based high-power density wind generation systems

© 2015 IEEE. Recent advances in solid-state semiconductors and magnetic materials have provided the impetus for high-frequency magnetic-link-based modular medium-voltage power conversion systems, which would be a possible solution to reduce further the weight and volume of superconducting generator-based wind generation systems. To verify this new concept, in this paper, a laboratory prototype of 5 kVA high-frequency magnetic-link modular power conversion system is developed for a scaled down 1.2 kV grid application. The design and implementation of the prototyping, test platform, and the experimental results are analyzed and discussed. It is expected that the proposed new technology will have great potential for superconducting generator-based wind farm applications

Higher Spin Black Holes from CFT

Higher spin gravity in three dimensions has explicit black holes solutions, carrying higher spin charge. We compute the free energy of a charged black hole from the holographic dual, a 2d CFT with extended conformal symmetry, and find exact agreement with the bulk thermodynamics. In the CFT, higher spin corrections to the free energy can be calculated at high temperature from correlation functions of W-algebra currents.Comment: 24 pages; v2 reference adde

The impact of chromosomal translocation locus and fusion oncogene coding sequence in synovial sarcomagenesis.

Synovial sarcomas are aggressive soft-tissue malignancies that express chromosomal translocation-generated fusion genes, SS18-SSX1 or SS18-SSX2 in most cases. Here, we report a mouse sarcoma model expressing SS18-SSX1, complementing our prior model expressing SS18-SSX2. Exome sequencing identified no recurrent secondary mutations in tumors of either genotype. Most of the few mutations identified in single tumors were present in genes that were minimally or not expressed in any of the tumors. Chromosome 6, either entirely or around the fusion gene expression locus, demonstrated a copy number gain in a majority of tumors of both genotypes. Thus, by fusion oncogene coding sequence alone, SS18-SSX1 and SS18-SSX2 can each drive comparable synovial sarcomagenesis, independent from other genetic drivers. SS18-SSX1 and SS18-SSX2 tumor transcriptomes demonstrated very few consistent differences overall. In direct tumorigenesis comparisons, SS18-SSX2 was slightly more sarcomagenic than SS18-SSX1, but equivalent in its generation of biphasic histologic features. Meta-analysis of human synovial sarcoma patient series identified two tumor-gentoype-phenotype correlations that were not modeled by the mice, namely a scarcity of male hosts and biphasic histologic features among SS18-SSX2 tumors. Re-analysis of human SS18-SSX1 and SS18-SSX2 tumor transcriptomes demonstrated very few consistent differences, but highlighted increased native SSX2 expression in SS18-SSX1 tumors. This suggests that the translocated locus may drive genotype-phenotype differences more than the coding sequence of the fusion gene created. Two possible roles for native SSX2 in synovial sarcomagenesis are explored. Thus, even specific partial failures of mouse genetic modeling can be instructive to human tumor biology

Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems

Simulating complex physical systems often involves solving partial differential equations (PDEs) with some closures due to the presence of multi-scale physics that cannot be fully resolved. Although the advancement of high performance computing has made resolving small-scale physics possible, such simulations are still very expensive. Therefore, reliable and accurate closure models for the unresolved physics remains an important requirement for many computational physics problems, e.g., turbulence simulation. Recently, several researchers have adopted generative adversarial networks (GANs), a novel paradigm of training machine learning models, to generate solutions of PDEs-governed complex systems without having to numerically solve these PDEs. However, GANs are known to be difficult in training and likely to converge to local minima, where the generated samples do not capture the true statistics of the training data. In this work, we present a statistical constrained generative adversarial network by enforcing constraints of covariance from the training data, which results in an improved machine-learning-based emulator to capture the statistics of the training data generated by solving fully resolved PDEs. We show that such a statistical regularization leads to better performance compared to standard GANs, measured by (1) the constrained model's ability to more faithfully emulate certain physical properties of the system and (2) the significantly reduced (by up to 80%) training time to reach the solution. We exemplify this approach on the Rayleigh-Bénard convection, a turbulent flow system that is an idealized model of the Earth's atmosphere. With the growth of high-fidelity simulation databases of physical systems, this work suggests great potential for being an alternative to the explicit modeling of closures or parameterizations for unresolved physics, which are known to be a major source of uncertainty in simulating multi-scale physical systems, e.g., turbulence or Earth's climate

Current-density functional theory of time-dependent linear response in quantal fluids: recent progress

Vignale and Kohn have recently formulated a local density approximation to the time-dependent linear response of an inhomogeneous electron system in terms of a vector potential for exchange and correlation. The vector potential depends on the induced current density through spectral kernels to be evaluated on the homogeneous electron-gas. After a brief review of their theory, the case of inhomogeneous Bose superfluids is considered, with main focus on dynamic Kohn-Sham equations for the condensate in the linear response regime and on quantal generalized hydrodynamic equations in the weak inhomogeneity limit. We also present the results of calculations of the exchange-correlation spectra in both electron and superfluid boson systems.Comment: 12 pages, 2 figures, Postscript fil

A mobile assisted coverage hole patching scheme based on particle swarm optimization for WSNs

Wireless sensor networks (WSNs) have drawn much research attention in recent years due to the superior performance in multiple applications, such as military and industrial monitoring, smart home, disaster restoration etc. In such applications, massive sensor nodes are randomly deployed and they remain static after the deployment, to fully cover the target sensing area. This will usually cause coverage redundancy or coverage hole problem. In order to effectively deploy sensors to cover whole area, we present a novel node deployment algorithm based on mobile sensors. First, sensor nodes are randomly deployed in target area, and they remain static or switch to the sleep mode after deployment. Second, we partition the network into grids and calculate the coverage rate of each grid. We select grids with lower coverage rate as candidate grids. Finally, we awake mobile sensors from sleep mode to fix coverage hole, particle swarm optimization (PSO) algorithm is used to calculate moving position of mobile sensors. Simulation results show that our algorithm can effectively improve the coverage rate of WSNs