History-aware, real-time risk detection in business processes (Extended Version)

This paper proposes a novel approach for identifying risks in executable business processes and detecting them at run time. The approach considers risks in all phases of the business process management lifecycle, and is realized via a distributed, sensor-based architecture. At design-time, sensors are defined to specify risk conditions which when fulfilled, are a likely indicator of faults to occur. Both historical and current execution data can be used to compose such conditions. At run-time, each sensor independently notifies a sensor manager when a risk is detected. In turn, the sensor manager interacts with the monitoring component of a process automation suite to prompt the results to the user who may take remedial actions. The proposed architecture has been implemented in the YAWL system and its performance has been evaluated in practice.

Facilitating learning in a real time strategy computer game

The aim of this project was to implement a just-in-time hints help system into a real time strategy (RTS) computer game that would deliver information to the user at the time that it would be of the most benefit. The goal of this help system is to improve the user’s learning in terms of their rate of learning, retention and avoidance of stagnation. The first stage of this project was implementing a computer game to incorporate four different types of skill that the user must acquire, namely motor, perceptual, declarative knowledge and strategic. Subsequently, the just-in-time hints help system was incorporated into the game to assess the user’s knowledge and deliver hints accordingly. The final stage of the project was to test the effectiveness of this help system by conducting two phases of testing. The goal of this testing was to demonstrate an increase in the user’s assessment of the helpfulness of the system from phase one to phase two. The results of this testing showed that there was no significant difference in the user’s responses in the two phases. However, when the results were analysed with respect to several categories of hints that were identified, it became apparent that patterns in the data were beginning to emerge. The conclusions of the project were that further testing with a larger sample size would be required to provide more reliable results and that factors such as the user’s skill level and different types of goals should be taken into account.

Land Sales Act 1984: The time limit for an application for exemption

The decision of Wilson J in Wan and Ors v NPD Property Development Pty Ltd [2004] QSC 232 also concerned the operation of the Land Sales Act 1984 (Qld) (‘the Act’). As previously noted, s 8(1) of the Act provides that a proposed allotment of freehold land might be sold only in certain circumstances. An agreement made in contravention of s 8(1) is void. Section 19 allows a purchaser (and others) to apply for an exemption from any of the provisions of Pt 2. By s 19(6), notwithstanding s 8, a person may agree to sell a proposed allotment if the instrument that binds a person to purchase the proposed allotment is conditional upon the grant of an exemption. By s 19(7) an application for exemption must be made ‘within 30 days after the event that marks the entry of a purchaser upon the purchase of the proposed allotment.’

Probability distributed time delays: integrating spatial effects into temporal models

Background In order to provide insights into the complex biochemical processes inside a cell, modelling approaches must find a balance between achieving an adequate representation of the physical phenomena and keeping the associated computational cost within reasonable limits. This issue is particularly stressed when spatial inhomogeneities have a significant effect on system's behaviour. In such cases, a spatially-resolved stochastic method can better portray the biological reality, but the corresponding computer simulations can in turn be prohibitively expensive. Results We present a method that incorporates spatial information by means of tailored, probability distributed time-delays. These distributions can be directly obtained by single in silico or a suitable set of in vitro experiments and are subsequently fed into a delay stochastic simulation algorithm (DSSA), achieving a good compromise between computational costs and a much more accurate representation of spatial processes such as molecular diffusion and translocation between cell compartments. Additionally, we present a novel alternative approach based on delay differential equations (DDE) that can be used in scenarios of high molecular concentrations and low noise propagation. Conclusions Our proposed methodologies accurately capture and incorporate certain spatial processes into temporal stochastic and deterministic simulations, increasing their accuracy at low computational costs. This is of particular importance given that time spans of cellular processes are generally larger (possibly by several orders of magnitude) than those achievable by current spatially-resolved stochastic simulators. Hence, our methodology allows users to explore cellular scenarios under the effects of diffusion and stochasticity in time spans that were, until now, simply unfeasible. Our methodologies are supported by theoretical considerations on the different modelling regimes, i.e. spatial vs. delay-temporal, as indicated by the corresponding Master Equations and presented elsewhere.

Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference

This paper presents a fault diagnosis method based on adaptive neuro-fuzzy inference system (ANFIS) in combination with decision trees. Classification and regression tree (CART) which is one of the decision tree methods is used as a feature selection procedure to select pertinent features from data set. The crisp rules obtained from the decision tree are then converted to fuzzy if-then rules that are employed to identify the structure of ANFIS classifier. The hybrid of back-propagation and least squares algorithm are utilized to tune the parameters of the membership functions. In order to evaluate the proposed algorithm, the data sets obtained from vibration signals and current signals of the induction motors are used. The results indicate that the CART–ANFIS model has potential for fault diagnosis of induction motors.