557 research outputs found
Corrosion mechanism and evaluation of anodized magnesium alloys
The corrosion of anodized Mg alloys is investigated by means of immersion, salt spray, polarization curve, AC electrochemical impedance spectroscopy (EIS), SEM and optical microscopy analyses. Based on the blocking, retarding and passivating effects of an anodized coating on corrosion of Mg alloys, a corrosion model is proposed to illustrate the corrosion reaction at the coating/substrate interface in coating through-pores. It is found that EIS can sensitively respond to the occurrence of corrosion in anodized Mg alloys and reflect the protection performance of anodized coatings, which may be used as an in situ method of monitoring corrosion for anodized Mg alloys
Influence of Al and Y on the ignition and flammability of Mg alloys
The influence of alloying on the ignition and flammability was studied. One end of a cylindrical specimen was exposed to a free diffusion flame. Ignition required at least partial melting. Burning extinguished once the flame was withdrawn. Specimen tips of pure Mg, AZ61, and AZ91 ignited upon prolonged flame exposure. There was smouldering and delayed ignition for Mg-1Y. There was no ignition for Mg-5Y specimen tips, attributed to a protective surface oxide containing Y. The results indicate that (i) vigorous burning requires a continued supply of Mg vapour, and (ii) a critical alloy concentration is required to change ignition behaviour. (C) 2011 Elsevier Ltd. All rights reserved
Effects of Collisional Decoherence on Multipartite Entanglement - How would entanglement not be relatively common?
We consider the collision model of Ziman {\em et al.} and study the
robustness of -qubit Greenberger-Horne-Zeilinger (GHZ), W, and linear
cluster states. Our results show that -qubit entanglement of GHZ states
would be extremely fragile under collisional decoherence, and that of W states
could be more robust than of linear cluster states. We indicate that the
collision model of Ziman {\em et al.} could provide a physical mechanism to
some known results in this area of investigations. More importantly, we show
that it could give a clue as to how -partite distillable entanglement would
be relatively rare in our macroscopic classical world.Comment: 10 page
Low apparent valence of Mg during corrosion
Our recent data on Mg corrosion has been reanalysed because of the recent criticism that our previous data analysis was inadequate. Re-analysis leads to similar conclusions as previously. The apparent valence of Mg during corrosion was in each case less than 2.0, and in many cases less than 1.0. Moreover, these values were probably over-estimates. The low values were consistent with the evolving hydrogen gas acting as an insulator, so that the corrosion of parts of the specimen could occur isolated from the electrochemical measurement system
Integration of Blockchain and Auction Models: A Survey, Some Applications, and Challenges
In recent years, blockchain has gained widespread attention as an emerging
technology for decentralization, transparency, and immutability in advancing
online activities over public networks. As an essential market process,
auctions have been well studied and applied in many business fields due to
their efficiency and contributions to fair trade. Complementary features
between blockchain and auction models trigger a great potential for research
and innovation. On the one hand, the decentralized nature of blockchain can
provide a trustworthy, secure, and cost-effective mechanism to manage the
auction process; on the other hand, auction models can be utilized to design
incentive and consensus protocols in blockchain architectures. These
opportunities have attracted enormous research and innovation activities in
both academia and industry; however, there is a lack of an in-depth review of
existing solutions and achievements. In this paper, we conduct a comprehensive
state-of-the-art survey of these two research topics. We review the existing
solutions for integrating blockchain and auction models, with some
application-oriented taxonomies generated. Additionally, we highlight some open
research challenges and future directions towards integrated blockchain-auction
models
Dynamic aspiration based on Win-Stay-Lose-Learn rule in Spatial Prisoner's Dilemma Gam
Prisoner's dilemma game is the most commonly used model of spatial
evolutionary game which is considered as a paradigm to portray competition
among selfish individuals. In recent years, Win-Stay-Lose-Learn, a strategy
updating rule base on aspiration, has been proved to be an effective model to
promote cooperation in spatial prisoner's dilemma game, which leads aspiration
to receive lots of attention. But in many research the assumption that
individual's aspiration is fixed is inconsistent with recent results from
psychology. In this paper, according to Expected Value Theory and Achievement
Motivation Theory, we propose a dynamic aspiration model based on
Win-Stay-Lose-Learn rule in which individual's aspiration is inspired by its
payoff. It is found that dynamic aspiration has a significant impact on the
evolution process, and different initial aspirations lead to different results,
which are called Stable Coexistence under Low Aspiration, Dependent Coexistence
under Moderate aspiration and Defection Explosion under High Aspiration
respectively. Furthermore, a deep analysis is performed on the local structures
which cause cooperator's existence or defector's expansion, and the evolution
process for different parameters including strategy and aspiration. As a
result, the intrinsic structures leading to defectors' expansion and
cooperators' survival are achieved for different evolution process, which
provides a penetrating understanding of the evolution. Compared to fixed
aspiration model, dynamic aspiration introduces a more satisfactory explanation
on population evolution laws and can promote deeper comprehension for the
principle of prisoner's dilemma.Comment: 17 pages, 13 figure
SceneDM: Scene-level Multi-agent Trajectory Generation with Consistent Diffusion Models
Realistic scene-level multi-agent motion simulations are crucial for
developing and evaluating self-driving algorithms. However, most existing works
focus on generating trajectories for a certain single agent type, and typically
ignore the consistency of generated trajectories. In this paper, we propose a
novel framework based on diffusion models, called SceneDM, to generate joint
and consistent future motions of all the agents, including vehicles, bicycles,
pedestrians, etc., in a scene. To enhance the consistency of the generated
trajectories, we resort to a new Transformer-based network to effectively
handle agent-agent interactions in the inverse process of motion diffusion. In
consideration of the smoothness of agent trajectories, we further design a
simple yet effective consistent diffusion approach, to improve the model in
exploiting short-term temporal dependencies. Furthermore, a scene-level scoring
function is attached to evaluate the safety and road-adherence of the generated
agent's motions and help filter out unrealistic simulations. Finally, SceneDM
achieves state-of-the-art results on the Waymo Sim Agents Benchmark. Project
webpage is available at https://alperen-hub.github.io/SceneDM
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