Observations on Twinning in Zone-refined Tungsten

Observations on twinning in zone-refined tungste

High gas velocity burner tests on silicon carbide and silicon nitride at 1200 C

Specimens of silicon carbide and silicon nitride were exposed to a Mach one gas velocity burner simulating a turbine engine environment. Cyclic tests up to 100 hour duration were conducted at specimen temperatures of 1200 C. A specimen geometry was used that develops thermal stresses during thermal cycling in a manner similar to blades and vanes of a gas turbine engine. Materials were compared on a basis of weight change, dimensional reductions, metallography, fluorescent penetrant inspection, X-ray diffraction analyses, failure mode, and general appearance. One hot pressed SiC, one reaction sintered SiC, and three hot pressed Si3N4 specimens survived the program goal of 100 one-hour cycle exposures. Of the materials that failed to meet the program goal, thermal fatigue was identified as the exclusive failure mode

Powder metallurgy approaches to high temperature components for gas turbine engines

Research is reported for the tensile strength, ductility, and heat performance characterisitics of powder metallurgy (p/m) superalloys. Oxide dispersion strengthened alloys were also evaluated for their strength during thermal processing. The mechanical attributes evident in both p/m supperalloys and dispersion strengthened alloys are discussed in terms of research into their possible combination

High temperature mechanical properties of polycrystalline hafnium carbide and hafnium carbide containing 13-volume-percent hafnium diboride

High temperature mechanical properties of polycrystalline hafnium carbide containing 13- volume-percent hafnium diborid

Anisotropic rare-earth spin ensemble strongly coupled to a superconducting resonator

Interfacing photonic and solid-state qubits within a hybrid quantum architecture offers a promising route towards large scale distributed quantum computing. Ideal candidates for coherent qubit interconversion are optically active spins magnetically coupled to a superconducting resonator. We report on a cavity QED experiment with magnetically anisotropic Er3+:Y2SiO5 crystals and demonstrate strong coupling of rare-earth spins to a lumped element resonator. In addition, the electron spin resonance and relaxation dynamics of the erbium spins are detected via direct microwave absorption, without aid of a cavity

Integrating e-learning into postgraduate radiotherapy and oncology education: a case study

Training health professionals within University environments has traditionally focused on face-to-face methods. Practitioners working within the UK National Health Service (NHS) have found it difficult to gain leave from work to attend for study due to the demands of the NHS and staff shortages. In response, we developed a distance e learning course to match our current traditional taught programme.The first e learning module (a research methods module) was comparable in content to the traditional taught module, and developed to incorporate a formative assessment design throughout; embodying the Constructivist approach and the principle of assessment for learning. We evaluated student experiences with the e learning module using a pre and post module questionnaire, with the taught cohort used as a comparison. We tested the importance of support, levels of information technology (IT) skills, preferences for e learning and intrinsic motivation. The results identified that highly satisfied e learning students were more likely to report a preference for e learning initially, show higher levels of intrinsic motivation and report good support from tutors and fellow students. This article discusses the development and evaluation of the pilot module and some of the lessons learned from providing e learning to health workers

Using Evolutionary Strategies for the Real-Time Learning of Controllers for Autonomous Agents in Xpilot-AI

Real-time learning is the process of an artificial intelligence agent learning behavior(s) at the same pace as it operates in the real world. Video games tend to be an excellent locale for testing real-time learning agents, as the action happens at real speeds with a good visual feedback mechanism, coupled with the possibility of comparing human performance to that of the agent\u27s. In addition, players want to be competing against a consistently challenging opponent. This paper is a discussion of a controller for an agent in the space combat game Xpilot and the evolution of said controller using two different methods. The controller is a multilayer neural network, which controls all facets of the agent\u27s behavior that are not created in the initial set-up. The neural network is evolved using 1-to-1 evolutionary strategies in one method and genetic algorithms in the other method. Using three independent trials per methodology, it was shown that evolutionary strategies learned faster, while genetic algorithms learned more consistently, leading to the idea that genetic algorithms may be superior when there is ample time before use, but evolutionary strategies are better when pressed for learning time as in real-time learning