Policy-constrained bio-inspired processes for autonomic route management

Autonomic networking systems must be designed to achieve an appropriate balance between the operation of decentralized algorithms and processes that seek to maintain optimal or near-optimal behavior in terms of global stability, improved performance and adaptability, robustness and security, with the requirement for top-down control of the system by humans to ensure business goals are met. Taking a communications networking survivability case study, we show how the operation of decentralized algorithms, inspired by the operation of biological systems, can be controlled and constrained through the deployment of management policies authored by network administrators. We present survivability-related routing algorithms (inspired by chemotaxis, reaction-diffusion and quorum sensing biological processes) which work together to effectively reconfigure network resources when transient link failures occur and demonstrate how these algorithms can be re-parameterized via policies to improve performance given prevailing network conditions. Simulation results show how the combined operation of these algorithms, as controlled by policies, allows the network to react well to survive link failure events.     

Mood and context-dependence: Positive mood increases and negative mood decreases the effects of context on perception.

Five studies show that mood affects context-dependence, such that negative mood promotes attention to a salient target, whereas positive mood enhances attention to both target and context. Judgments of temperature (Study 1), weight (Study 2), and size (Studies 3 and 4) were more strongly affected by the context in a positive than in a negative mood. Moreover, these effects extend to the social domain: When perceiving a target person's emotions, happy people were more influenced by the context than were sad people (Study 5). Thus, positive mood enhanced, and negative mood reduced, the magnitude of perceptual context effects. The results suggest that this pattern is not easily explained in terms of effort or depth of processing differences. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A Marine Propeller Aerodynamic Test Facility

Abstract:  Conventional propeller test facilities such as tow tanks, cavitation tunnels and the open sea have various limitations such as cost, accessibility, size and accuracy. An aerodynamic marine propeller test facility has been developed at the University of British Columbia to allow for performance evaluations of full-size marine propellers in the highly controlled environment of a wind tunnel. Thorough static and dynamic calibrations of the test rig confirmed that the sensors have a highly linear response with essentially zero crosstalk between the thrust and torque signals. Performance curves acquired from a commercial 24 × 24 propeller show that the repeatability error of the torque and thrust measurements is <1%. The uncertainties of the advance ratio, torque coefficient, thrust coefficient, and efficiency are 0.2%, 0.5%, 0.1%, and 1%, respectively. Performance curves measured over a range of the Reynolds numbers show that Reynolds number independence is achieved for Reynolds numbers above approximately 5 × 10⁵.     

Effect of a machining-induced defect on the tensile strength of a 3D composite material

This paper investigates the effect of finite specimen size upon the tensile failure of a tridirectional carbon-carbon composite along each reinforcement axis. Asymmetry in the position of load-bearing axial yarns across the cross-section is generated randomly by machining. This yields parasitic bending of the specimen, and thereby premature failure of the yarns subjected to the maximum bending stress. However, bending effects become negligible at final failure. Additionally, the composite failure strength _F is determined from the cross-sectional area of the actually load-bearing axial yarns, using both symmetrical and asymmetrical specimens. Results are in good agreement with previous work, and we show that the variability of _F is small.     

Adapting the CBA algorithm by means of intensity of implication

In recent years, extensive research has been carried out by using association rules to build more accurate classifiers. The idea behind these integrated approaches is to focus on a limited subset of association rules. This paper aims to contribute to this integrated framework by adapting the Classification Based on Associations (CBA) algorithm. CBA was adapted by coupling it with another measurement of the quality of association rules: i.e. intensity of implication. The new algorithm has been implemented and empirically tested on an authentic financial dataset for purposes of bankruptcy prediction. We validated our results with an association ruleset, with C4.5, with original CBA and with CART by statistically comparing its performance via the area under the ROC-curve. The adapted CBA algorithm presented in this paper proved to generate significantly better results than the other classifiers at the 5% level of significance.     

An abrupt change detection algorithm for buried landmines localization

Ground-penetrating radars (GPRs) are very promising sensors for landmine detection as they are capable of detecting landmines with low metal contents. GPRs deliver so-called Bscan data which are, roughly, vertical slice images of the ground. However, due to the high dielectric permittivity contrast at the air-ground interface, a strong response is recorded at early time by GPRs. This response is the main component of the so-called clutter noise and it blurs the responses of landmines buried at shallow depths. The landmine detection task is therefore quite difficult. This paper proposes a new method for automated detection and localization of buried objects from Bscan records. A support vector machine algorithm for online abrupt change detection is implemented and proves to be efficient in detecting buried landmines from Bscan data. The proposed procedure performance is evaluated using simulated and real data.     

Evolution of 3C-SiC islands nucleated from a liquid phase on Si face α-SiC substrates

The contact between α-SiC crystals and Si-Ge based melts provokes the nucleation of 3C-SiC islands on the crystal surface. Evolution of these islands as a function of various parameters was studied. On both 4H and 6H substrates, it was found that, after nucleation, 3C-SiC islands first enlarge and may form a complete 3C layer under certain conditions. The 3C deposit can then be dissolved by the liquid phase at high temperature or for prolonged contact at relatively moderate temperature. The graphite crucible is proposed to play a central role in these enlargement and dissolution mechanisms by providing extra carbon atoms on the seed surface (enlargement) or provoking thermal induced carbon transport toward the sidewall (dissolution). Several differences between the use of 4H and 6H substrates were also observed.     

An interactive computer-based interface to support the discovery of individuals’ mental representations and preferences in decisions problems: An application to travel behavior

Growing emphasis is currently given in decision modeling on process data to capture behavioral mechanisms that ground decision-making processes. Nevertheless, advanced applications to elicit such data are still lacking. The Causal Network Elicitation Technique interview and card-game, both face-to-face interviews, are examples of a behavioral process method to obtain individuals’ decision-making by eliciting temporary mental representations of particular problems. However, to portray and model these representations into formal modeling approaches, such as Bayesian decision networks, an extensive set of parameters has to be gathered for each individual. Thus, data collection procedures for large sample groups can be costly and time consuming. This paper reports on the methodological conversion and enhancement of the existing elicitation methods into a computer-based interface that allows to not only uncover individuals’ mental representations but also to automate the generation of preference parameter elicitation questions. Results of such studies can be used to understand individuals’ constructs and beliefs with respect to decision alternatives, predict individuals’ decision behavior at a disaggregate level, and to assess behavioral changes due to differences in contexts and constraints.