Statistical characterisation of the growth and spatial scales of the substorm onset arc

We present the first multi-event study of the spatial and temporal structuring of the aurora to provide statistical evidence of the near-Earth plasma instability which causes the substorm onset arc. Using data from ground-based auroral imagers, we study repeatable signatures of along-arc auroral beads, which are thought to represent the ionospheric projection of magnetospheric instability in the near-Earth plasma sheet. We show that the growth and spatial scales of these wave-like fluctuations are similar across multiple events, indicating that each sudden auroral brightening has a common explanation. We find statistically that growth rates for auroral beads peak at low wavenumber with the most unstable spatial scales mapping to an azimuthal wavelength λ≈1700 − 2500 km in the equatorial magnetosphere at around 9-12 RE. We compare growth rates and spatial scales with a range of theoretical predictions of magnetotail instabilities, including the cross-field current instability and the shear-flow ballooning instability. We conclude that, although the cross-field current instability can generate similar magnitude of growth rates, the range of unstable wavenumbers indicates that the shear-flow ballooning instability is the most likely explanation for our observations.

Supporting athlete selection and strategic planning in track cycling omnium: A statistical and machine learning approach

This article describes the implementation of machine learning techniques that assist cycling experts in the crucial decision-making processes for athlete selection and strategic planning in the track cycling omnium. The omnium is a multi-event competition that was included in the Olympic Games for the first time in 2012. Presently, selectors and cycling coaches make decisions based on experience and opinion. They rarely have access to knowledge that helps predict athletic performances. The omnium presents a unique and complex decision-making challenge as it is not clear what type of athlete is best suited to the omnium (e.g., sprint or endurance specialist) and tactical decisions made by the coach and athlete during the event will have significant effects on the overall performance of the athlete. In the present work, a variety of machine learning techniques were used to analyze omnium competition data from the World Championships since 2007. The analysis indicates that sprint events have slightly more influence in determining the medalists, than endurance-based events. Using a probabilistic analysis, we created a model of performance prediction that provides an unprecedented level of supporting information that assists coaches with strategic and tactical decisions during the omnium.

Influence of field position and phase of play on the physical demands of match-play in professional rugby league forwards

This article describes the utilisation of an unsupervised machine learning technique and statistical approaches (e.g., the Kolmogorov-Smirnov test) that assist cycling experts in the crucial decision-making processes for athlete selection, training, and strategic planning in the track cycling Omnium. The Omnium is a multi-event competition that will be included in the summer Olympic Games for the first time in 2012. Presently, selectors and cycling coaches make decisions based on experience and intuition. They rarely have access to objective data. We analysed both the old five-event (first raced internationally in 2007) and new six-event (first raced internationally in 2011) Omniums and found that the addition of the elimination race component to the Omnium has, contrary to expectations, not favoured track endurance riders. We analysed the Omnium data and also determined the inter-relationships between different individual events as well as between those events and the final standings of riders. In further analysis, we found that there is no maximum ranking (poorest performance) in each individual event that riders can afford whilst still winning a medal. We also found the required times for riders to finish the timed components that are necessary for medal winning. The results of this study consider the scoring system of the Omnium and inform decision-making toward successful participation in future major Omnium competitions.

Modelling and analysing track cycling Omnium performances using statistical and machine learning techniques

This article describes the utilisation of an unsupervised machine learning technique and statistical approaches (e.g., the Kolmogorov-Smirnov test) that assist cycling experts in the crucial decision-making processes for athlete selection, training, and strategic planning in the track cycling Omnium. The Omnium is a multi-event competition that will be included in the summer Olympic Games for the first time in 2012. Presently, selectors and cycling coaches make decisions based on experience and intuition. They rarely have access to objective data. We analysed both the old five-event (first raced internationally in 2007) and new six-event (first raced internationally in 2011) Omniums and found that the addition of the elimination race component to the Omnium has, contrary to expectations, not favoured track endurance riders. We analysed the Omnium data and also determined the inter-relationships between different individual events as well as between those events and the final standings of riders. In further analysis, we found that there is no maximum ranking (poorest performance) in each individual event that riders can afford whilst still winning a medal. We also found the required times for riders to finish the timed components that are necessary for medal winning. The results of this study consider the scoring system of the Omnium and inform decision-making toward successful participation in future major Omnium competitions.

The elimination race in track cycling : patterns and predictors of performance

The track cycling Omnium is a multi-event competition that has recently been expanded to include the Elimination Race (ER), which presents a unique set of physical and tactical demands. The purpose of this research was to characterise the performance attributes of successful and unsuccessful cyclists in the ER, that are also predictive of performance. Video recordings of four international level ERs were analysed. The performance attributes measured related to the cyclists’ velocity and two dimensional position in the peloton. The average velocity of the peloton up to lap 30 (of 50) was relatively high and consistent (52.2±1.5 km/h). After lap 30, there was a significant (p<0.001) change in velocity (49.9±2.4 km/h), characterised by more fluctuations in lap-to-lap velocity. Successful ER cyclists adopted a tactic of remaining in the middle of the peloton, in the lower lanes of the velodrome, thus avoiding the risk of elimination at the rear and the extra effort required to remain on the front of the peloton. Unsuccessful cyclists tended to reside in the rear and upper (higher) portions of the peloton, risking elimination more often and having to ride faster than those in the lower lanes of the velodrome. The physiological demands of the Elimination Race that are determined by velocity, vary throughout the Elimination Race and the pattern of movement within the peloton is different for successful and unsuccessful cyclists. The findings of the present study may confirm some aspects of race tactics that are currently thought to be optimal, but they also reveal novel information that is useful to coaches and cyclists who compete in the Elimination Race.

An efficient and robust system for multi-person event detection in real world indoor surveillance scenes

Due to the popularity of security cameras in public places, it is of interest to design an intelligent system that can efficiently detect events automatically. This paper proposes a novel algorithm for multi-person event detection. To ensure greater than real-time performance, features are extracted directly from compressed MPEG video. A novel histogram-based feature descriptor that captures the angles between extracted particle trajectories is proposed, which allows us to capture motion patterns of multi-person events in the video. To alleviate the need for fine-grained annotation, we propose the use of Labelled Latent Dirichlet Allocation, a “weakly supervised” method that allows the use of coarse temporal annotations which are much simpler to obtain. This novel system is able to run at approximately ten times real-time, while preserving state-of-theart detection performance for multi-person events on a 100-hour real-world surveillance dataset (TRECVid SED).

The second chance project: A multi-level examination of secondary prevention practices for Saudi people following a recent cardiac event

The study examined the health-related behaviours of Saudi people following a recent cardiac event and identified the factors that influence these behaviours using McLeroy et al.'s (1988) Ecological Model of Health Behaviours as a guiding framework. The study was one of the first in Saudi Arabia to examine the health-related behaviours of Saudi people following a recent cardiac event. The study findings emphasise the importance of a program that integrates secondary prevention practices, educational approaches and targeted supportive services in cardiac care in Saudi Arabia.

Multi-ionization of helium and lithium using the independent electron and independent event models with intrinsic CDW

Cross sections for the multi-ionization of He and Li are presented for impact energies in the range of 50 to 1000 keV/amu. These are calculated using the eikonal initial state approximation to represent the input and exit channels of the active electrons. The ionization process is simulated in a variety of ways, most notably an attempt to account for the effects of electron correlation via the inclusion of a continuum density of states (CDS) term. Inadequacies, of the CDW formulation at small impact parameters, and of the models themselves, are discussed and conclusions are drawn on what repercussions this has for the cross sections calculated.

Major Sport Event Operational Planning Issues and Strategies: A Multi-Case Delphi Study

This study examined the operational planning, implementation and execution issues of major sport events, as well as the mitigation and management strategies used to address these issues, with the aim of determining best practices in sport event operational planning. The three Research Questions were: 1) What can previous major sport events provide to guide the operational management of future events? 2) What are the operational issues that arise in the planning and execution of a major sport event, how are they mitigated and what are the strategies used to deal with these issues? 3) What are the best practices for sport event operational planning and how can these practices aid future events? Data collection involved a modified Delphi technique that consisted of one round of in-depth interviews followed by two rounds of questionnaires. Both data collection and analysis were guided by an adaptation of the work of Parent, Rouillard & Leopkey (2011) with a focus on previously established issue and strategy categories. The results provided a list of Top 26 Prominent Issues and Top 17 Prominent Strategies with additional issue-strategy links that can be used to aid event managers producing future major sport events. The following issue categories emerged as having had the highest impact on previous major sport events that participants had managed: timing, funding and knowledge management. In addition, participants used strategies from the following categories most frequently: other, formalized agreements and communication.

Multi-instrument ground-based observations of a travelling convection vortices event

A coordinated ground-based observational campaign using the IMAGE magnetometer network, EISCAT radars and optical instruments on Svalbard has made possible detailed studies of a travelling convection vortices (TCV) event on 6 January 1992. Combining the data from these facilities allows us to draw a very detailed picture of the features and dynamics of this TCV event. On the way from the noon to the drawn meridian, the vortices went through a remarkable development. The propagation velocity in the ionosphere increased from 2.5 to 7.4 km s−1, and the orientation of the major axes of the vortices rotated from being almost parallel to the magnetic meridian near noon to essentially perpendicular at dawn. By combining electric fields obtained by EISCAT and ionospheric currents deduced from magnetic field recordings, conductivities associated with the vortices could be estimated. Contrary to expectations we found higher conductivities below the downward field aligned current (FAC) filament than below the upward directed. Unexpected results also emerged from the optical observations. For most of the time there were no discrete aurora at 557.7 nm associated with the TCVs. Only once did a discrete form appear at the foot of the upward FAC. This aurora subsequently expanded eastward and westward leaving its centre at the same longitude while the TCV continued to travel westward. Also we try to identify the source regions of TCVs in the magnetosphere and discuss possible generation mechanisms.

Mining sequential causal patterns with user-specified skeletons in multi-sequence of event data

The approaches proposed in the past for discovering sequential patterns mainly focused on single sequential data. In the real world, however, some sequential patterns hide their essences among multi-sequential event data. It has been noted that knowledge discovery with either user-specified constraints, or templates, or skeletons is receiving wide attention because it is more efficient and avoids the tedious selection of useful patterns from the mass-produced results. In this paper, a novel pattern in multi-sequential event data that are correlated and its mining approach are presented. We call this pattern sequential causal pattern. A group of skeletons of sequential causal patterns, which may be specified by the user or generated by the program, are verified or mined by embedding them into the mining engine. Experiments show that this method, when applied to discovering the occurring regularities of a crop pest in a region, is successful in mining sequential causal patterns with user-specified skeletons in multi-sequential event data.

Calibration of audio-video sensors for multi-modal event indexing

This paper addresses the coordinated use of video and audio cues to capture and index surveillance events with multimodal labels. The focus of this paper is the development of a joint-sensor calibration technique that uses audio-visual observations to improve the calibration process. One significant feature of this approach is the ability to continuously check and update the calibration status of the sensor suite, making it resilient to independent drift in the individual sensors. We present scenarios in which this system is used to enhance surveillance.

Charles W. Merideth at a Multi-Cultural Week Event

Charles W. Merideth is pictured at the "Asia at a Glance"exhibit. The exhibit was a part of the May 1991 Multi-Cultural Week at City Tech. Charles W. Merideth was the sixth president of the City Tech. He was formerly installed on October 19, 1990. Before coming to City Tech, Merideth had a long career in science and higher education. Under Merideth, the College expanded the number of baccalaureate programs offered by the College.

Supply chain turbulence: the impact of a mid-range event in multi-tier, multi-dimension supply chains

Nota: A autora agradece à Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) pela concessão de bolsa de estudos para o desenvolvimento deste projeto de pesquisa.