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.

Analyzing track sprint cyclists' performances using position-specific maximal-torque and power-cadence relationships

Previous studies have demonstrated the importance of maximal Torque-Cadence (T-C) and Power-Cadence (P-C) relationships, for the performances of world class track sprint cyclists. If these relationships are affected by the function of the lower limb muscles, the ability of cyclists to generate torque and power at a given cadence may vary depending on their riding position. During sprint events (individual and team sprints and Keirin), cyclists alternate between standing and seated positions. The T-C and P-C relationships may change with the position adopted by the cyclists. PURPOSE: The aim of this study was to evaluate the necessity to define position specific maximal T-C and P-C relationships. METHODS: Eight junior elite track cyclists from the National Talent Identification squad undertook two inertial-load tests that consisted of four all-out sprints each. One test was undertaken at the velodrome in a standing position on a carbon fibre track bike, and the other test was completed in a seated position on an air-braked stationary ergometer. A calibrated SRM power meter interfaced to a custom instrumentation package was used for all mechanical measurements. Maximal T-C and P-C relationships were analysed to calculate maximal Torque (T0), maximal Power (Pmax) and optimal pedalling cadence (PCopt). RESULTS: All individual T-C and P-C relationships obtained for both body positions were fitted by linear regressions (r2=0.95 ± 0.02) and second order polynomials (r2=0.96 ± 0.01), respectively. T0 was higher (209 ± 2.2N.m vs. 177.0 ± 3.9N.m, p<0.05), PCopt was lower (112.5 ± 11.4rpm vs. 120.1 ± 6.7rpm, p<0.05), and Pmax was higher (1261 ± 235W vs. 1076 ± 183W, p<0.05) in standing position compared to seated position. CONCLUSION: Analysis of track sprint cyclists’ performances can be improved by the determination of position-specific maximal T-C and P-C relationships .

An efficient scheduling method for grid systems based on a hierarchical stochastic petri net

This paper addresses the problem of resource scheduling in a grid computing environment. One of the main goals of grid computing is to share system resources among geographically dispersed users, and schedule resource requests in an efficient manner. Grid computing resources are distributed, heterogeneous, dynamic, and autonomous, which makes resource scheduling a complex problem. This paper proposes a new approach to resource scheduling in grid computing environments, the hierarchical stochastic Petri net (HSPN). The HSPN optimizes grid resource sharing, by categorizing resource requests in three layers, where each layer has special functions for receiving subtasks from, and delivering data to, the layer above or below. We compare the HSPN performance with the Min-min and Max-min resource scheduling algorithms. Our results show that the HSPN performs better than Max-min, but slightly underperforms Min-min.

Associations between adverse social position and bone mineral density in women aged 50 years or older: data from the Manitoba bone density program

The authors examined the independent contribution of income to low bone mineral density in women aged 50 years and older. A significant dose–response association was observed between low income and low (bone mineral density) BMD, which was not explained by clinical risk factors or osteoporotic treatment in the year prior.

The association between social disadvantage and osteoporosis is attracting increased attention; however, little is known of the role played by income. We examined associations between income and bone mineral density (BMD) in 51,327 women aged ≥50 years from Manitoba, Canada.

A hierarchical cloud pricing system

Cloud computing is experiencing phenomenal growth and there are now many vendors offering their cloud services. In cloud computing, cloud providers cooperate together to offer their computing resource as a utility and software as a service to customers. The demands and the price of cloud service should be negotiated between providers and users based on the Service Level Agreement (SLA). In order to help cloud providers achieving an agreeable price for their services and maximizing the benefits of both cloud providers and clients, this paper proposes a cloud pricing system consisting of hierarchical system, M/M/c queuing model and pricing model. Simulation results verify the efficiency of our proposed system.

A hierarchical PCA-based anomaly detection model

A hierarchical intrusion detection model is proposed to detect both anomaly and misuse attacks. In order to further speed up the training and testing, PCA-based feature extraction algorithm is used to reduce the dimensionality of the data. A PCA-based algorithm is used to filter normal data out in the upper level. The experiment results show that PCA can reduce noise in the original data set and the PCA-based algorithm can reach the desirable performance.

The effect of obesity prevention interventions according to socioeconomic position: a systematic review

Obesity prevention is a major public health priority. It is important that all groups benefit from measures to prevent obesity, but we know little about the differential effectiveness of such interventions within particular population subgroups. This review aimed to identify interventions for obesity prevention that evaluated a change in adiposity according to socioeconomic position (SEP) and to determine the effectiveness of these interventions across different socioeconomic groups. A systematic search of published and grey literature was conducted. Studies that described an obesity prevention intervention and reported anthropometric outcomes according to a measure of SEP were included. Evidence was synthesized using narrative analysis. A total of 14 studies were analysed, representing a range of study designs and settings. All studies were from developed countries, with eight conducted among children. Three studies were shown to have no effect on anthropometric outcomes and were not further analysed. Interventions shown to be ineffective in lower SEP participants were primarily based on information provision directed at individual behaviour change. Studies that were shown to be effective in lower SEP participants primarily included community-based strategies or policies aimed at structural changes to the environment. Interventions targeting individual-level behaviour change may be less successful in lower SEP populations. It is essential that our efforts to prevent obesity do not leave behind the most disadvantaged members of society.

A high performance position sensorless surface permanent magnet synchronous motor drive based on flux angle

A position sensorless Surface Permanent Magnet Synchronous Motor (SPMSM) drive based on flux angle is presented in this paper. The motor equations are written in rotor fixed d-q reference frame. A PID controller is used to process the speed error to generate the reference torque current keeping the magnetizing current fixed. The estimated stator flux using Recurrent Neural Network (RNN) is used to find out the rotor position. The flux angle and the reference current phasor angle are used in vector rotator to generate the reference phase currents. Hysteresis current controller block controls the switching of the 3-phase inverter to apply voltage to the motor stator. Simulation studies on different operating conditions indicate the acceptability of the drive system. The drive system only requires a speed transducer and is free from position sensor requirement. The proposed control scheme is robust under load torque disturbances and motor parameter variations. It is also simple and low cost to implement in a practical environment.

Hybrid hierarchical patterns of gold nanoparticles and poly(ethylene glycol) microstructures

Hybrid surface micro-patterns composed of topographic structures of polyethylene glycol (PEG)-hydrogels and hierarchical lines of gold nanoparticles (Au NPs) were fabricated on silicon wafers. Micro-sized lines of Au NPs were first obtained on the surface of a silicon wafer via “micro-contact deprinting”, a method recently developed by our group. Topographic micro-patterns of PEG, of both low and high aspect ratio (AR up to 6), were then aligned on the pre-patterned surface via a procedure adapted from the soft lithographic method MIMIC (Micro-Molding in Capillaries), which is denoted as “adhesive embossing”. The result is a complex surface pattern consisting of alternating flat Au NP lines and thick PEG bars. Such patterns provide novel model surfaces for elucidating the interplay between (bio)chemical and physical cues on cell behavior.