Global positioning system data analysis: velocity ranges and a new definition of sprinting for field sport athletes

Global positioning system (GPS) technology has improved the speed, accuracy, and ease of time-motion analyses of field sport athletes. The large volume of numerical data generated by GPS technology is usually summarized by reporting the distance traveled and time spent in various locomotor categories (e.g., walking, jogging, and running). There are a variety of definitions used in the literature to represent these categories, which makes it nearly impossible to compare findings among studies.

The purpose of this work was to propose standard definitions (velocity ranges) that were determined by an objective analysis of time-motion data. In addition, we discuss the limitations of the existing definition of a sprint and present a new definition of sprinting for field sport athletes.

Twenty-five GPS data files collected from 5 different sports (men’s and women’s field hockey, men’s and women’s soccer, and Australian Rules Football) were analyzed to identify the average velocity distribution. A curve fitting process was then used to determine the optimal placement of 4 Gaussian curves representing the typical locomotor categories. 

Performance study of a wind-grid system in a semi- arid region and major integration issues

The phenomenal growth in economy experienced in developed countries throughout the 20th century has largely been driven by the availability of conventional energy sources for electricity generation. However, increased concern about fossil fuels and adverse effect of carbon dioxide emission in to atmosphere changed the conventional power system to a viable one by integrating renewable energy sources into the existing system. Among the Renewable Energy (RE) sources, wind energy is one of the fastest growing technologies in reducing the Green House Gas (GHG) emissions in to the atmosphere due to its continuous availability throughout a period. Hence, this paper discusses the performance of a wind-grid connected system in a semi-arid region by conducting a case study. Wilson promontory, one of the best locations for wind generation in Victoria is considered as a case study. Hybrid Optimization Model for Electric Renewable (HOMER) is used as a simulating tool for this analysis. This study also presents the influences of storage system in the proposed Hybrid Power System (HPS) allowing energy to be stored during higher generations or lower load demands. In addition this paper also discusses the major integration issues to facilitate the large scale wind energy into the grid for reliable power generation and distribution.

Global positioning system: a new opportunity in physical activity measurement

Accurate measurement of physical activity is a pre-requisite to monitor population physical activity levels and design effective interventions. Global Positioning System (GPS) technology offers potential to improve the measurement of physical activity. This paper 1) reviews the extant literature on the application of GPS to monitor human movement, with a particular emphasis on free-living physical activity, 2) discusses issues associated with GPS use, and 3) provides recommendations for future research. Overall findings show that GPS is a useful tool to augment our understanding of physical activity by providing the context (location) of the activity and used together with Geographical Information Systems can provide some insight into how people interact with the environment. However, no studies have shown that GPS alone is a reliable and valid measure of physical activity.

Analytical communication networks model for enterprise grid computing

This paper addresses the problem of performance analysis based on the communication modeling of large-scale heterogeneous distributed systems, with an emphasis on enterprise Grid computing systems. The study of communication layers is important, as the overall performance of a distributed system often critically hinges on the effectiveness of this part. We propose an analytical model that is based on probabilistic analysis and queuing networks. The proposed model considers the processor as well as network heterogeneity of the enterprise Grid system. The model is validated through comprehensive simulations, which demonstrate that the proposed model exhibits a good degree of accuracy for various system sizes, and under different working conditions.

On the discrete time dynamics of a self-stabilizing MCA learning algorithm

The stability of minor component analysis (MCA) learning algorithms is an important problem in many signal processing applications. In this paper, we propose an effective MCA learning algorithm that can offer better stability. The dynamics of the proposed algorithm are analyzed via a corresponding deterministic discrete time (DDT) system. It is proven that if the learning rate satisfies some mild conditions, almost all trajectories of the DDT system starting from points in an invariant set are bounded, and will converge to the minor component of the autocorrelation matrix of the input data. Simulation results will be furnished to illustrate the theoretical results achieved.

The global obesity pandemic : shaped by global drivers and local environments

The simultaneous increases in obesity in almost all countries seem to be driven mainly by changes in the global food system, which is producing more processed, affordable, and effectively marketed food than ever before. This passive overconsumption of energy leading to obesity is a predictable outcome of market economies predicated on consumption-based growth. The global food system drivers interact with local environmental factors to create a wide variation in obesity prevalence between populations. Within populations, the interactions between environmental and individual factors, including genetic makeup, explain variability in body size between individuals. However, even with this individual variation, the epidemic has predictable patterns in subpopulations. In low-income countries, obesity mostly affects middle-aged adults (especially women) from wealthy, urban environments; whereas in high-income countries it affects both sexes and all ages, but is disproportionately greater in disadvantaged groups. Unlike other major causes of preventable death and disability, such as tobacco use, injuries, and infectious diseases, there are no exemplar populations in which the obesity epidemic has been reversed by public health measures. This absence increases the urgency for evidence-creating policy action, with a priority on reduction of the supply-side drivers.

An efficient meta-heuristic algorithm for grid computing

A grid computing system consists of a group of programs and resources that are spread across machines in the grid. A grid system has a dynamic environment and decentralized distributed resources, so it is important to provide efficient scheduling for applications. Task scheduling is an NP-hard problem and deterministic algorithms are inadequate and heuristic algorithms such as particle swarm optimization (PSO) are needed to solve the problem. PSO is a simple parallel algorithm that can be applied in different ways to resolve optimization problems. PSO searches the problem space globally and needs to be combined with other methods to search locally as well. In this paper, we propose a hybrid-scheduling algorithm to solve the independent task- scheduling problem in grid computing. We have combined PSO with the gravitational emulation local search (GELS) algorithm to form a new method, PSO–GELS. Our experimental results demonstrate the effectiveness of PSO–GELS compared to other algorithms.

Use of geographical information system and remote sensing techniques for planning culture-based fisheries in non-perennial reservoirs of Sri Lanka

 The presence of a wide areal extent of small-sized village reservoirs offers a considerable potential for the development of culture-based fisheries (CBFs) in Sri Lanka. To this end, this study uses geographical information systems (GISs) and remote sensing (RS) techniques to determine the morphometric and biological characteristics most useful for classifying non-perennial reservoirs for CBF development and for assessing the influence of catchment land-use patterns on potential CBF yields. The reservoir shorelines at full water supply level were mapped with a Global Positioning System to determine shoreline length and reservoir areal extent. The ratio of shoreline length to reservoir extent, which was reported to be a powerful predictor variable of CBF yields, could be reliably quantified using RS techniques. The areal extent of reservoirs, quantified with RS techniques (RS extent), was used to estimate the ratio of forest cover plus scrubland cover to RS extent and was found to be significantly related to the CBF yield (R2 = 0.400; P < 0.05). The results of this study indicated that morphometric characteristics and catchment land-use patterns, which might be viewed as indices of biological productivity, can be quantified using RS and GIS techniques. © 2014 Wiley Publishing Asia Pty Ltd.

Estimates of Heterozostera tasmanica, Zostera muelleri and Ruppia megacarpa distribution and biomass in the Hopkins Estuary, western Victoria, by GIS

Knowledge of the spatial arrangement of the seagrass distribution and biomass within the Hopkins Estuary is an essential step towards gaining an understanding of the functioning of the estuarine ecosystem. This study marks the first attempt to map seagrass distribution and model seagrass biomass and epiphyte biomass along depth gradients by the use of global positioning system (GPS) and geographical information system (GIS) technologies in the estuary. For mapping seagrass in small estuaries, ground-surveying the entire system is feasible. Three species of seagrasses, Heterozostera tasmanica (Martens ex Aschers), Zostera muelleri (Irmisch ex Aschers) and Ruppia megacarpa (Mason), were identified in the Hopkins Estuary. All beds investigated contained a mixed species relationship. Three harvest techniques were trialed in a pilot study, with the 25 × 25-cm quadrat statistically most appropriate. Biomass of seagrasses and epiphytes was found to vary significantly with depth, but not between sites. The average estimate of biomass for total seagrasses and their epiphytes in the estuary in January 2000 was 222.7 g m^–2 (dry weight). Of the total biomass, 50.6% or 112.7 g m^–2 (dry weight) was contributed by seagrasses and 49.4% of the biomass (110.0 g m^–2) were epiphytes. Of the 50.6% of the total biomass represented by seagrasses, 39.3% (87.5 g m^–2) were leaves and 11.3% (25.2 g m^–2) were rhizomes. The total area of seagrasses present in the Hopkins Estuary was estimated to be 0.4 ± 0.005 km², with the total area of the estuary estimated to be 1.6 ± 0.02 km² (25% cover). The total standing crop of seagrasses and epiphytes in the Hopkins Estuary in January 2000 was estimated to be 102.3 ± 57 t in dry weight, 56% (56.9 ± 17 t, dry weight) seagrasses and 44% (45.4 ± 19 t, dry weight) epiphytes. Of the seagrass biomass, 39% (39.7 ± 13 t, dry weight) was contributed by leaves and 17% (17.3 ± 7 t, dry weight) by rhizomes.

An education and training framework for disaster risk management in banks

Banks are the most significant financial institutions operating within nation-state and the global financial system. These institutions are exposed to a wide range of operational risks. Disaster risk management is a critical component of the wider operational risk management. The Bank for International Settlements, in conjunction with nation-state prudential regulators, is introducing measures that will require banks to identify, measure and manage operational risks within the context of new capital adequacy requirements. An essential part of any risk management process is education and training. This paper presents a structured education and training framework that will support the achievement of banks’ disaster risk management objectives. The education and training framework comprises three specific programs: (1) an induction/awareness program targeted to all personnel, (2) a contingency planning program – a specialist program for disaster risk management personnel, and (3) an executive program designed for senior management, directors and strategic decision makers.

Mobile location based services

Mobility has become a key factor around the world, as the use of ubiquitous devices, including laptops, personal digital assistants (PDAs), and mobile phones, are increasingly becoming part of daily life (Steinfield, 2004). Adding mobility to computing power, and with advanced personalization of technologies, new business applications are emerging in the area of mobile communications (Jagoe, 2003). The fastest growing segment among these applications is location-based services. This article offers a brief overview of services and their supporting technologies, and provides an outlook for their future.

Using community-based monitoring with GIS to create habitat maps for a marine protected area in Australia

In recent years there has been an increase in community-based monitoring programmes developed and implemented worldwide. This paper describes how the data collected from such a programme could be integrated into a Geographic Information System (GIS) to create temperate subtidal marine habitat maps. A differential Global Positioning System was utilized to accurately record the location of the trained community-based SCUBA diver data. These georeferenced data sets were then used to classify benthic habitats using an aerial photograph and digitizing techniques. This study demonstrated that trained community-based volunteers can collect data that can be utilized within a GIS to create reliable and cost-effective maps of shallow temperate subtidal rocky reef systems.

A neural networks learning algorithm for minor component analysis and its convergence analysis

The eigenvector associated with the smallest eigenvalue of the autocorrelation matrix of input signals is called minor component. Minor component analysis (MCA) is a statistical approach for extracting minor component from input signals and has been applied in many fields of signal processing and data analysis. In this letter, we propose a neural networks learning algorithm for estimating adaptively minor component from input signals. Dynamics of the proposed algorithm are analyzed via a deterministic discrete time (DDT) method. Some sufficient conditions are obtained to guarantee convergence of the proposed algorithm.

A stable MCA learning algorithm

Minor component analysis (MCA) is an important statistical tool for signal processing and data analysis. Neural networks can be used to extract online minor component from input data. Compared with traditional algebraic  approaches, a neural network method has a lower computational complexity. Stability of neural networks learning algorithms is crucial to practical applications. In this paper, we propose a stable MCA neural networks learning algorithm, which has a more satisfactory numerical stability than some existing MCA algorithms. Dynamical behaviors of the proposed algorithm are analyzed via deterministic discrete time (DDT) method and the conditions are obtained to guarantee convergence. Simulations are carried out to illustrate the theoretical results achieved.