A curve shortening flow rule for closed embedded plane curves with a prescribed rate of change in enclosed area

Motivated by a problem from fluid mechanics, we consider a generalization of the standard curve shortening flow problem for a closed embedded plane curve such that the area enclosed by the curve is forced to decrease at a prescribed rate. Using formal asymptotic and numerical techniques, we derive possible extinction shapes as the curve contracts to a point, dependent on the rate of decreasing area; we find there is a wider class of extinction shapes than for standard curve shortening, for which initially simple closed curves are always asymptotically circular. We also provide numerical evidence that self-intersection is possible for non-convex initial conditions, distinguishing between pinch-off and coalescence of the curve interior.

Using a glow graph to represent data flow and dependency in event logs

The idea of extracting knowledge in process mining is a descendant of data mining. Both mining disciplines emphasise data flow and relations among elements in the data. Unfortunately, challenges have been encountered when working with the data flow and relations. One of the challenges is that the representation of the data flow between a pair of elements or tasks is insufficiently simplified and formulated, as it considers only a one-to-one data flow relation. In this paper, we discuss how the effectiveness of knowledge representation can be extended in both disciplines. To this end, we introduce a new representation of the data flow and dependency formulation using a flow graph. The flow graph solves the issue of the insufficiency of presenting other relation types, such as many-to-one and one-to-many relations. As an experiment, a new evaluation framework is applied to the Teleclaim process in order to show how this method can provide us with more precise results when compared with other representations.

Operationalising and measuring flow in video games

This paper explores the obstacles associated with designing video game levels for the purpose of objectively measuring flow. We sought to create three video game levels capable of inducing a flow state, an overload state (low-flow), and a boredom state (low-flow). A pilot study, in which participants self-reported levels of flow after playing all three game levels, was undertaken. Unexpected results point to the challenges of operationalising flow in video game research, obstacles in experimental design for invoking flow and low-flow, concerns about flow as a construct for measuring video game enjoyment, the applicability of self-report flow scales, and the experience of flow in video game play despite substantial challenge-skill differences.

Flow during individual and co-operative gameplay

This study aims to further research in the field of video games by examining flow during individual and co-operative gameplay. Using a puzzle game called Droppit, we examined differences in flow based on two modes of play: single player vs. co-operative gameplay. Co-operative gameplay was found to induce greater flow in participants than single player gameplay. Additionally, co-operative gameplay participants had increased feelings of Challenge-Skill Balance, Unambiguous Feedback, Transformation of Time and Autotelic Experience. Our findings suggest that co-operative gameplay, involving puzzle-based problems, may result in increased flow during video game play.

Young driver perceptions of police traffic enforcement and self reported driving offences

Purpose Road policing is a key method used to improve driver compliance with road laws. However, we have a very limited understanding of the perceptions of young drivers regarding police enforcement of road laws. This paper addresses this gap. Design/Methodology/Approach Within this study 238 young drivers from Queensland, Australia, aged 17-24 years (M = 18, SD = 1.54), with a provisional (intermediate) driver’s licence completed an online survey regarding their perceptions of police enforcement and their driver thrill seeking tendencies. This study considered whether these factors influenced self-reported transient (e.g., travelling speed) and fixed (e.g., blood alcohol concentration) road violations by the young drivers. Findings The results indicate that being detected by police for a traffic offence, and the frequency with which they display P-plates on their vehicle to indicate their licence status, are associated with both self-reported transient and fixed rule violations. Licence type, police avoidance behaviours and driver thrill seeking affected transient rule violations only, while perceptions of police enforcement affected fixed rule violations only. Practical implications This study suggests that police enforcement of young driver violations of traffic laws may not be as effective as expected and that we need to improve the way in which police enforce road laws for young novice drivers. Originality/value: This paper identifies that perceptions of police enforcement by young drivers does not influence all types of road offences.

Understanding data flow and security requirements in wireless Body Area Networks for healthcare

The Body Area Network (BAN) is an emerging technology that focuses on monitoring physiological data in, on and around the human body. BAN technology permits wearable and implanted sensors to collect vital data about the human body and transmit it to other nodes via low-energy communication. In this paper, we investigate interactions in terms of data flows between parties involved in BANs under four different scenarios targeting outdoor and indoor medical environments: hospital, home, emergency and open areas. Based on these scenarios, we identify data flow requirements between BAN elements such as sensors and control units (CUs) and parties involved in BANs such as the patient, doctors, nurses and relatives. Identified requirements are used to generate BAN data flow models. Petri Nets (PNs) are used as the formal modelling language. We check the validity of the models and compare them with the existing related work. Finally, using the models, we identify communication and security requirements based on the most common active and passive attack scenarios.

Predicting crashes using traffic offences. A meta-analysis that examines potential bias between self-report and archival data

Background Traffic offences have been considered an important predictor of crash involvement, and have often been used as a proxy safety variable for crashes. However the association between crashes and offences has never been meta-analysed and the population effect size never established. Research is yet to determine the extent to which this relationship may be spuriously inflated through systematic measurement error, with obvious implications for researchers endeavouring to accurately identify salient factors predictive of crashes. Methodology and Principal Findings Studies yielding a correlation between crashes and traffic offences were collated and a meta-analysis of 144 effects drawn from 99 road safety studies conducted. Potential impact of factors such as age, time period, crash and offence rates, crash severity and data type, sourced from either self-report surveys or archival records, were considered and discussed. After weighting for sample size, an average correlation of r = .18 was observed over the mean time period of 3.2 years. Evidence emerged suggesting the strength of this correlation is decreasing over time. Stronger correlations between crashes and offences were generally found in studies involving younger drivers. Consistent with common method variance effects, a within country analysis found stronger effect sizes in self-reported data even controlling for crash mean. Significance The effectiveness of traffic offences as a proxy for crashes may be limited. Inclusion of elements such as independently validated crash and offence histories or accurate measures of exposure to the road would facilitate a better understanding of the factors that influence crash involvement.