A novel grid computing approach for probabilistic small signal analysis

Grid computing is an advanced technique for collaboratively solving complicated scientific problems using geographically and organisational dispersed computational, data storage and other recourses. Application of grid computing could provide significant benefits to all aspects of power system that involves using computers. Based on our previous research, this paper presents a novel grid computing approach for probabilistic small signal stability (PSSS) analysis in electric power systems with uncertainties. A prototype computing grid is successfully implemented in our research lab to carry out PSSS analysis on two benchmark systems. Comparing to traditional computing techniques, the gird computing has given better performances for PSSS analysis in terms of computing capacity, speed, accuracy and stability. In addition, a computing grid framework for power system analysis has been proposed based on the recent study.

Injury and frequency of use of playground equipment in public schools and parks in Brisbane, Australia

Objective: The purpose of this study was to determine the frequency of use of play equipment in public schools and parks in Brisbane, Australia, and to estimate an annual rate of injury per use of equipment, overall and for particular types of equipment. Methods: Injury data on all children injured from playground equipment and seeking medical attention at the emergency department of either of the two children's hospitals in the City of Brisbane were obtained for the years 1996 and 1997. Children were observed at play on five different pieces of play equipment in a random sample of 16 parks and 16 schools in the City of Brisbane. Children injured in the 16 parks and schools were counted, and rates of injury and use were calculated. Results: The ranked order for equipment use in the 16 schools was climbing equipment (3762 uses), horizontal ladders (2309 uses), and slides (856 uses). Each horizontal ladder was used 2.6 times more often than each piece of climbing equipment. Each horizontal ladder was used 7.8 times more than each piece of climbing equipment in the sample of public parks. Slides were used 4.6 times more than climbing equipment in parks and 1.2 times more in public schools. The annual injury rate for the 16 schools and 16 parks under observation was 0.59/100000 and 0.26/100000 uses of equipment, respectively. Conclusions: This study shows that annual number of injuries per standardized number of uses could be used to determine the relative risk of particular pieces of playground equipment. The low overall rate of injuries/100000 uses of equipment in this study suggests that the benefit of further reduction of injury in this community may be marginal and outweigh the economic costs in addition to reducing challenging play opportunities.

Ex ante evaluations of alternate data structures for end user queries: Theory and experimental test

The data structure of an information system can significantly impact the ability of end users to efficiently and effectively retrieve the information they need. This research develops a methodology for evaluating, ex ante, the relative desirability of alternative data structures for end user queries. This research theorizes that the data structure that yields the lowest weighted average complexity for a representative sample of information requests is the most desirable data structure for end user queries. The theory was tested in an experiment that compared queries from two different relational database schemas. As theorized, end users querying the data structure associated with the less complex queries performed better Complexity was measured using three different Halstead metrics. Each of the three metrics provided excellent predictions of end user performance. This research supplies strong evidence that organizations can use complexity metrics to evaluate, ex ante, the desirability of alternate data structures. Organizations can use these evaluations to enhance the efficient and effective retrieval of information by creating data structures that minimize end user query complexity.

The design and implementation of a repository for the management of spatial data integrity constraints

This paper reviews the key features of an environment to support domain users in spatial information system (SIS) development. It presents a full design and prototype implementation of a repository system for the storage and management of metadata, focusing on a subset of spatial data integrity constraint classes. The system is designed to support spatial system development and customization by users within the domain that the system will operate.

Cardiorespiratory monitoring equipment interferes with whole body impedance measurements

Bioelectrical impedance measurements are widely used for the study of body composition. Commonly measurements are made at 50 kHz to estimate total body water or at low frequencies (< 10 kHz) to estimate extracellular fluid volume. These measurements can be obtained as single measurements at discrete frequencies, or as fitted data interpolated from plots of measurements made at multiple frequencies. This study compared single frequency and multiple frequency (MF) measurements taken in the intensive care environment. MF bioimpedance (4-1000 kHz) was measured on an adult with and without cardiorespiratory monitoring, and on babies in the neonatal intensive care unit. Measurements obtained at individual frequencies were plotted against frequency and examined for the presence of outlying points. Fitted data for measurements obtained at 5 kHz and 50 kHz with and without cardiorespiratory monitoring were compared. Significant artefacts were detected in measurements at approximately 50 kHz and at integral divisions of this frequency as a result of interference from cardiorespiratory monitors. Single frequency measurements taken at these frequencies may be subject to errors that would be difficult to detect without the aid of information obtained from MF measurements.

Approximate processing of massive continuous quantile queries over high-speed data streams

Quantile computation has many applications including data mining and financial data analysis. It has been shown that an is an element of-approximate summary can be maintained so that, given a quantile query d (phi, is an element of), the data item at rank [phi N] may be approximately obtained within the rank error precision is an element of N over all N data items in a data stream or in a sliding window. However, scalable online processing of massive continuous quantile queries with different phi and is an element of poses a new challenge because the summary is continuously updated with new arrivals of data items. In this paper, first we aim to dramatically reduce the number of distinct query results by grouping a set of different queries into a cluster so that they can be processed virtually as a single query while the precision requirements from users can be retained. Second, we aim to minimize the total query processing costs. Efficient algorithms are developed to minimize the total number of times for reprocessing clusters and to produce the minimum number of clusters, respectively. The techniques are extended to maintain near-optimal clustering when queries are registered and removed in an arbitrary fashion against whole data streams or sliding windows. In addition to theoretical analysis, our performance study indicates that the proposed techniques are indeed scalable with respect to the number of input queries as well as the number of items and the item arrival rate in a data stream.

Optimum conditions for adsorptive storage

The storage of gases in porous adsorbents, such as activated carbon and carbon nanotubes, is examined here thermodynamically from a systems viewpoint, considering the entire adsorption-desorption cycle. The results provide concrete objective criteria to guide the search for the Holy Grail adsorbent, for which the adsorptive delivery is maximized. It is shown that, for ambient temperature storage of hydrogen and delivery between 30 and 1.5 bar pressure, for the optimum adsorbent the adsorption enthalpy change is 15.1 kJ/mol. For carbons, for which the average enthalpy change is typically 5.8 kJ/mol, an optimum operating temperature of about 115 K is predicted. For methane, an optimum enthalpy change of 18.8 kJ/mol is found, with the optimum temperature for carbons being 254 K. It is also demonstrated that for maximum delivery of the gas the optimum adsorbent must be homogeneous, and that introduction of heterogeneity, such as by ball milling, irradiation, and other means, can only provide small increases in physisorption-related delivery for hydrogen. For methane, heterogeneity is always detrimental, at any value of average adsorption enthalpy change. These results are confirmed with the help of experimental data from the literature, as well as extensive Monte Carlo simulations conducted here using slit pore models of activated carbons as well as atomistic models of carbon nanotubes. The simulations also demonstrate that carbon nanotubes offer little or no advantage over activated carbons in terms of enhanced delivery, when used as storage media for either hydrogen or methane.

A scaleless data model for direct and progressive spatial query processing

A progressive spatial query retrieves spatial data based on previous queries (e.g., to fetch data in a more restricted area with higher resolution). A direct query, on the other side, is defined as an isolated window query. A multi-resolution spatial database system should support both progressive queries and traditional direct queries. It is conceptually challenging to support both types of query at the same time, as direct queries favour location-based data clustering, whereas progressive queries require fragmented data clustered by resolutions. Two new scaleless data structures are proposed in this paper. Experimental results using both synthetic and real world datasets demonstrate that the query processing time based on the new multiresolution approaches is comparable and often better than multi-representation data structures for both types of queries.

Space efficient quantile summary for constrained sliding windows on a data stream

In many online applications, we need to maintain quantile statistics for a sliding window on a data stream. The sliding windows in natural form are defined as the most recent N data items. In this paper, we study the problem of estimating quantiles over other types of sliding windows. We present a uniform framework to process quantile queries for time constrained and filter based sliding windows. Our algorithm makes one pass on the data stream and maintains an E-approximate summary. It uses O((1)/(epsilon2) log(2) epsilonN) space where N is the number of data items in the window. We extend this framework to further process generalized constrained sliding window queries and proved that our technique is applicable for flexible window settings. Our performance study indicates that the space required in practice is much less than the given theoretical bound and the algorithm supports high speed data streams.