Evaluating Behavioural Modelling Predictions in the Blue Shark (Prionace glauca) Enables Greater Insight on Habitat Use from Location only Argos Data

The relationship between habitat and behaviour provides important information for species management.For large,free roaming,marine animals satellite tags provide high resolution information on movement,but such datasets are restricted due to cost.Extracting additional biologically important information from these data would increase utilisation and value.Several modelling approaches have been developed to identify behavioural states in tracking data.The objective of this study was to evaluate a behavioural state prediction model for blue shark(Prionace glauca)ARGOS surface location-only data.The novel nature of the six SPLASH satellite tags used enabled behavioural events to be identified in blue shark dive data and accurately mapped spatio-temporally along respective surface location-only tracks.Behavioural states modelled along the six surface location-only tracks were then tested against observed behavioural events to evaluate the model’s accuracy.Results showed that the Behavioural Change Point Analysis(BCPA)model augmented with K means clustering analysis performed well for predicting foraging behaviour(correct 86%of the time).Prediction accuracy was lower for searching(52%)and travelling(63%)behaviour,likely related to the numerical dominance of foraging events in dive data.The model’s validation for predicting foraging behaviour justified its application to nine additional surface location-only(SPOT tag)tracks,substantially increasing the utilisation of expensive and rare data.Results enabled the critical behavioural state of foraging,to be mapped throughout the entire home range of blue sharks,allowing drivers of critical habitat to be investigated.This validation strengthens the use of such modelling to interpret historic and future datasets,for blue sharks but also other species,contributing to conservational management.

Customer Retention: Behaviour Perspective Model of Ghanaian Telecommunication Industry Using Multinomial Regression Analysis

To stay competitive, the mobile telecommunication companies spend millions of Ghana cedi each year on building long-term relationships with their customers. Marketing managers are constantly challenged with the problem of where to channel the limited resources in order to retain existing customers. This study approaches the customer retention problem in the mobile phone sector from a behavioural perspective, applying the Behavioural Perspective Model as the main analytical framework and further exploits some other factors that influence customer retention. The model includes a set of pre-behaviour and post-behaviour factors to study consumer choice, and explains its relevant drivers in a viable and comprehensive way, grounded in radical behaviourism. Data for the analysis were collected from tertiary students from Accra and Takoradi. Data collected were analysed using the multinomial regression technique. Analysis of the data revealed that the Behaviour setting factor is the only significant element in Behaviour Perspective Model. Further exploitation of behaviour situation revealed that the number of networks a customer uses, previous experience of a customer and customer’s intention are significant factors in determining customer retention in Ghana’s mobile telecommunication industry.

Catastrophe Theory Models for Stress-dependent Behaviour in a NiTi Shape Memory Alloy

The measurements by Huibin XU et al of the stress-dependence ot hysteresis in a NiTi shape memo ry alloy are modeled by catastrophe theory. The cusp catastrophe is used with the strain as the behaviour variable and the control parameters being functions of the stress and the temperature. A two constant model is found to be preferred to a four constant model.

Unusual Behaviour of Dislocation Velocity Exponent in Dislocation Multiplication Model

The chaotic behaviour of dislocation multiplication process was investigated. The change of Lyapunov exponent which is used to determine the stability of quasi-periodic and chaotic behavior as well as that of equilibrium points, and periodic solution was reported by using an iteration model of dislocation multiplication. An unusual behavior of Lyapunov exponent and Feigenbaum exponent which respond to the geometric convergence of orbit from bifurcation to chaos was shown by dislocation velocity exponent m and there is a distinction on the tendency of convergence for the dislocation multiplication model when it was compared with logistic map. It is reasonable for the difference to be analyzed from the materials viewpoint. (Edited author abstract) 9 Refs.

High-extensible scene graph framework based on component techniques

In this paper, a novel component-based scene graph is proposed, in which all objects in the scene are classified to different entities, and a scene can be represented as a hierarchical graph composed of the instances of entities. Each entity contains basic data and its operations which are encapsulated into the entity component. The entity possesses certain behaviours which are responses to rules and interaction defined by the high-level application. Such behaviours can be described by script or behaviours model. The component-based scene graph in the paper is more abstractive and high-level than traditional scene graphs. The contents of a scene could be extended flexibly by adding new entities and new entity components, and behaviour modification can be obtained by modifying the model components or behaviour scripts. Its robustness and efficiency are verified by many examples implemented in the Virtual Scenario developed by Peking University.

Emotion Modelling for Social Robotics Applications： A Review

Robots of today are eager to leave constrained industrial environments and embrace unexplored and unstructured areas, for extensive applications in the real world as service and social robots. Hence, in addition to these new physical frontiers, they must face human ones, too. This implies the need to consider a human-robot interaction from the beginning oft_he design; the possibility for a robot to recognize users＇ emotions and, in a certain way, to properly react and ＂behave＂. This could play a fundamental role in their integration in society. However, this capability is still far from being achieved. Over the past decade, several attempts to implement automata for different applications, outside of the industry, have been pursued. But very few applications have tried to consider the emotional state of users in the behavioural model of the robot, since it raises questions such as： how should human emotions be modelled for a correct representation of their state of mind？ Which sensing modalities and which classification methods could be the most feasible to obtain this desired knowl- edge？ Furthermore, which applications are the most suitable for the robot to have such sensitivity？ In this context, this paper aims to provide a general overview of recent attempts to enable robots to recognize human emotions and interact properly.