Grain size dependence of the activation parameters for plastic deformation: Influence of crystal structure,slip system,and rate-controlling dislocation mechanism

A critical review of available results on the dependence of grain size on the activation parameters for deformation, specifically, the activation volume, V*, and the thermal component of flow stress, σ*, has been carried out with a view to verifying the Armstrong prediction that identifies the Hall-Petch (H-P) intercept with the easy slip system and the H-P slope with the most difficult system in polycrystals. The influence of slip system choice is demonstrated using results on Cd and Zr. The Armstrong prediction is valid for basal slip hcp metals, such as Cd and Zn, with V* and σ* determined by the difficult pyramidal slip. For the prism slip metals such as Zr and Ti, V* and σ* are controlled by interstitial solutes and are independent of grain size. The results on Zr are used to highlight the influence of dynamic strain aging on the H-P parameters. In bcc metals, in which the Peierls-Nabarro barrier is the rate-controlling obstacle, V* and σ* are again independent of grain size. For fcc metals, correlation of the H-P slope with the cross-slip stress, predicted by the Armstrong model, has been demonstrated for a few cases. The variation of V* with grain size in Ni as reported by Narutani and Takamura (Acta Metall. Mater., 1991, vol. 227, pp. 2037–49) is newly interpreted in terms of the Armstrong model that associates the H-P intercept in fcc metals with dislocation intersections and the H-P slope with cross-slip, and provides realistic results for the activation volumes for the two processes. This article is based on a presentation given in the symposium “Dynamic Deformation: Constitutive Modeling, Grain Size, and Other Effects: In Honor of Prof. Ronald W. Armstrong,” March 2–6, 2003, at the 2003 TMS/ASM Annual Meeting, San Diego, California, under the auspices of the TMS/ASM Joint Mechanical Behavior of Materials Committee.     

Kinetics and mechanism of carbothermic reduction of magnesia

The reaction between MgO and graphite powders under flowing argon atmosphere was studied using a dynamic thermogravimetric method. In the temperature range 293 to 1973 K, the effects of compacting pressure, magnesia/carbon ratio, heating rate, Ar carrier-gas flow rate, and CO-partial pressure were investigated. An experimentally determined reaction mechanism was proposed and discussed. The reduction process could be divided into two stages. The first stage includes the direct reaction between MgO and graphite particles and partial gas-solid reaction at relatively low temperature (below 1750 K). The overall reaction rate depends on the solid phase-boundary reaction between magnesia and carbon particles. The second stage is the gas-solid reaction between CO and MgO, which determines the overall reaction rate. The apparent activation energies of the two stages were estimated to be 208.29 and 374.13 kJ/mol, respectively.     

Passivity Analysis of Dynamic Neural Networks with Different Time-scales

Dynamic neural networks with different time-scales include the aspects of fast and slow phenomenons. Some applications require that the equilibrium points of the designed networks are stable. In this paper, the passivity-based approach is used to derive stability conditions for dynamic neural networks with different time-scales. Several stability properties, such as passivity, asymptotic stability, input-to-state stability and bounded input bounded output stability, are guaranteed in certain senses. A numerical example is also given to demonstrate the effectiveness of the theoretical results.     

Improving Neural Networks for Mechanism Kinematic Chain Isomorphism Identification

Detection of isomorphism among kinematic chains is essential in mechanical design, but difficult and computationally expensive. It has been shown that both traditional methods and previously presented neural networks still have a lot to be desired in aspects such as simplifying procedure of identification and adapting automatic computation. Therefore, a new algorithm based on a competitive Hopfield network is developed for automatic computation in the kinematic chain isomorphism problem. The neural approach provides directly interpretable solutions and does not demand tuning of parameters. We have tested the algorithm by solving problems reported in the recent mechanical literature. Simulation results show the effectiveness of the network that rapidly identifies isomorphic kinematic chains.     

Semi-supervised model adaptation for statistical machine translation

Statistical machine translation systems are usually trained on large amounts of bilingual text (used to learn a translation model), and also large amounts of monolingual text in the target language (used to train a language model). In this article we explore the use of semi-supervised model adaptation methods for the effective use of monolingual data from the source language in order to improve translation quality. We propose several algorithms with this aim, and present the strengths and weaknesses of each one. We present detailed experimental evaluations on the French–English EuroParl data set and on data from the NIST Chinese–English large-data track. We show a significant improvement in translation quality on both tasks.     

Fast and accurate pricing of discretely monitored barrier options by numerical path integration

Barrier options are financial derivative contracts that are activated or deactivated according to the crossing of specified barriers by an underlying asset price. Exact models for pricing barrier options assume continuous monitoring of the underlying dynamics, usually a stock price. Barrier options in traded markets, however, nearly always assume less frequent observation, e.g. daily or weekly. These situations require approximate solutions to the pricing problem. We present a new approach to pricing such discretely monitored barrier options that may be applied in many realistic situations. In particular, we study daily monitored up-and-out call options of the European type with a single underlying stock. The approach is based on numerical approximation of the transition probability density associated with the stochastic differential equation describing the stock price dynamics, and provides accurate results in less than one second whenever a contract expires in a year or less. The flexibility of the method permits more complex underlying dynamics than the Black and Scholes paradigm, and its relative simplicity renders it quite easy to implement.     

Brokers and bricoleurs: entrepreneurship in Wales' online music scene

 The power of some new entrants to the music industry derives from their position as brokers in computer-mediated environments. Brokers act instrumentally to exploit their position within a network which, in turn, depends on their ability to build and sustain links (and, in computer-mediated environments, hyperlinks). Bricolage in computer-mediated entrepreneurship refers to the intuitive manipulation of resources in order to achieve (perhaps tacit) goals. Without careful stewardship of the new intellectual wealth thus created, bricolage may profit neither the individual nor the wider community. Accepted 14 March 2002     

Quality of manual data collection in Java software: an empirical investigation

Data collection, both automatic and manual, lies at the heart of all empirical studies. The quality of data collected from software informs decisions on maintenance, testing and wider issues such as the need for system re-engineering. While of the two types stated, automatic data collection is preferable, there are numerous occasions when manual data collection is unavoidable. Yet, very little evidence exists to assess the error-proneness of the latter. Herein, we investigate the extent to which manual data collection for Java software compared with its automatic counterpart for the same data. We investigate three hypotheses relating to the difference between automated and manual data collection. Five Java systems were used to support our investigation. Results showed that, as expected, manual data collection was error-prone, but nowhere near the extent we had initially envisaged. Key indicators of mistakes in manual data collection were found to be poor developer coding style, poor adherence to sound OO coding principles, and the existence of relatively large classes in some systems. Some interesting results were found relating to the collection of public class features and the types of error made during manual data collection. The study thus offers an insight into some of the typical problems associated with collecting data manually; more significantly, it highlights the problems that poorly written systems have on the quality of visually extracted data.     

Two topographic maps for data visualisation

We review a new form of self-organizing map which is based on a nonlinear projection of latent points into data space, identical to that performed in the Generative Topographic Mapping (GTM) [Bishop et al. (1997) Neurl Comput 10(1): 215–234]. But whereas the GTM is an extension of a mixture of experts, our new model is an extension of a product of experts [Hinton (2000) Technical report GCNU TR 2000-004, Gatsby Computational Neuroscience Unit, University College, London]. We show visualisation results on some real data sets and compare with the GTM. We then introduce a second mapping based on harmonic averages and show that it too creates a topographic mapping of the data. We compare these mappings on real and artificial data sets. Responsible editor: Soumen Chakrabarti.