Applying ontologies to business and systems modelling techniques and perspectives: Lessons learned

For many years in the area of business systems analysis and design, practitioners and researchers alike have been searching for some comprehensive basis on which to evaluate, compare, and engineer techniques that are promoted for use in the modelling of systems' requirements. To date, while many frameworks, factors, and facets have been forthcoming, none appear to be based on a sound theory. In light of this dilemma, over the last 10 years, attention has been devoted by researchers to the use of ontology to provide some theoretical basis for the advancement of the business systems modelling discipline. This paper outlines how we have used a particular ontology for this purpose over the last five years. In particular we have learned that the understandability and the applicability of the selected ontology must be clear for IS professionals, the results of any ontological evaluation must be tempered by economic efficiency considerations of the stakeholders involved, and ontologies may have to be focused for the business purpose and type of user involved in the modelling situation.

Single-subject research designs and data analyses for assessing elite athletes' conditioning

Research in conditioning (all the processes of preparation for competition) has used group research designs, where multiple athletes are observed at one or more points in time. However, empirical reports of large inter-individual differences in response to conditioning regimens suggest that applied conditioning research would greatly benefit from single-subject research designs. Single-subject research designs allow us to find out the extent to which a specific conditioning regimen works for a specific athlete, as opposed to the average athlete, who is the focal point of group research designs. The aim of the following review is to outline the strategies and procedures of single-subject research as they pertain to.. the assessment of conditioning for individual athletes. The four main experimental designs in single-subject research are: the AB design, reversal (withdrawal) designs and their extensions, multiple baseline designs and alternating treatment designs. Visual and statistical analyses commonly used to analyse single-subject data, and advantages and limitations are discussed. Modelling of multivariate single-subject data using techniques such as dynamic factor analysis and structural equation modelling may identify individualised models of conditioning leading to better prediction of performance. Despite problems associated with data analyses in single-subject research (e.g. serial dependency), sports scientists should use single-subject research designs in applied conditioning research to understand how well an intervention (e.g. a training method) works and to predict performance for a particular athlete.

A metafrontier production function for estimation of technical efficiencies and technology gaps for firms operating under different technologies

This paper presents a metafrontier production function model for firms in different groups having different technologies. The metafrontier model enables the calculation of comparable technical efficiencies for firms operating under different technologies. The model also enables the technology gaps to be estimated for firms under different technologies relative to the potential technology available to the industry as a whole. The metafrontier model is applied in the analysis of panel data on garment firms in five different regions of Indonesia, assuming that the regional stochastic frontier production function models have technical inefficiency effects with the time-varying structure proposed by Battese and Coelli ( 1992).

Factors affecting the vigilance and flight behaviour of impalas

This study investigated the influences of various natural and anthropogenic factors on the vigilance and flight behaviour of impalas in the Save Valley Conservancy, Zimbabwe, using multivariate statistical techniques. The factor that most significantly affected the proportions of time that individuals spent being vigilant and their rates of vigilance was the position of a focal animal in the group; impalas on the periphery of a group were more vigilant than central impalas. Both measures of vigilance were also negatively related to group size. Males spent more total time being vigilant but females raised their heads more often. Impalas spent more time being vigilant in the late afternoon than in the early morning, when greater than ten metres from cover, and when predators had been nearby within the previous six hours. Impalas spent more time vigilant at the property where more impalas were hunted, possibly reflecting the differences in the intensity of hunting by humans on the two properties. Flight distances at the approach of humans were significantly greater at one property than the other, and were also greater for small groups. Further research into the effects of hunting by humans on animals' antipredator behaviours would provide valuable insights for wildlife managers.

Estimating variable returns to scale production frontiers with alternative stochastic assumptions

Two stochastic production frontier models are formulated within the generalized production function framework popularized by Zellner and Revankar (Rev. Econ. Stud. 36 (1969) 241) and Zellner and Ryu (J. Appl. Econometrics 13 (1998) 101). This framework is convenient for parsimonious modeling of a production function with returns to scale specified as a function of output. Two alternatives for introducing the stochastic inefficiency term and the stochastic error are considered. In the first the errors are added to an equation of the form h(log y, theta) = log f (x, beta) where y denotes output, x is a vector of inputs and (theta, beta) are parameters. In the second the equation h(log y,theta) = log f(x, beta) is solved for log y to yield a solution of the form log y = g[theta, log f(x, beta)] and the errors are added to this equation. The latter alternative is novel, but it is needed to preserve the usual definition of firm efficiency. The two alternative stochastic assumptions are considered in conjunction with two returns to scale functions, making a total of four models that are considered. A Bayesian framework for estimating all four models is described. The techniques are applied to USDA state-level data on agricultural output and four inputs. Posterior distributions for all parameters, for firm efficiencies and for the efficiency rankings of firms are obtained. The sensitivity of the results to the returns to scale specification and to the stochastic specification is examined. (c) 2004 Elsevier B.V. All rights reserved.

Improved algorithms for rare event simulation with heavy tails

The estimation of P(S-n > u) by simulation, where S, is the sum of independent. identically distributed random varibles Y-1,..., Y-n, is of importance in many applications. We propose two simulation estimators based upon the identity P(S-n > u) = nP(S, > u, M-n = Y-n), where M-n = max(Y-1,..., Y-n). One estimator uses importance sampling (for Y-n only), and the other uses conditional Monte Carlo conditioning upon Y1,..., Yn-1. Properties of the relative error of the estimators are derived and a numerical study given in terms of the M/G/1 queue in which n is replaced by an independent geometric random variable N. The conclusion is that the new estimators compare extremely favorably with previous ones. In particular, the conditional Monte Carlo estimator is the first heavy-tailed example of an estimator with bounded relative error. Further improvements are obtained in the random-N case, by incorporating control variates and stratification techniques into the new estimation procedures.

Novel approach to tailoring molecular weight distribution and structure with a difunctional RAFT agent

This work has demonstrated that for the first time a single RAFT agent (i. e., difunctional) can be used in conjunction with a radical initiator to obtain a desired M-n and PDI with controlled rates of polymerization. Simulations were used not only to verify the model but also to provide us with a predictive tool to generate other MWDs. It was also shown that all the MWDs prepared in this work could be translated to higher molecular weights through chain extension experiments with little or no compromise in the control of end group functionality. The ratio of monofunctional to difunctional SdC(CH2Ph)S- end groups, XPX and XP (where X) S=C(CH2Ph) S-), can be controlled by simply changing the concentration of initiator, AIBN. Importantly, the amount of dead polymer is extremely low and fulfils the criterion as suggested by Szwarc (Nature 1956) that to meet living requirements nonfunctional polymeric species formed by side reactions in the process should be undetectable by analytical techniques. In addition, this novel methodology will allow the synthesis of AB, ABA, and statistical multiblock copolymers with predetermined ratios to be produced in a one-pot reaction.

Customer information system data pre-processing with feature selection techniques for non-technical losses prediction in an electricity market

Non-technical losses (NTL) identification and prediction are important tasks for many utilities. Data from customer information system (CIS) can be used for NTL analysis. However, in order to accurately and efficiently perform NTL analysis, the original data from CIS need to be pre-processed before any detailed NTL analysis can be carried out. In this paper, we propose a feature selection based method for CIS data pre-processing in order to extract the most relevant information for further analysis such as clustering and classifications. By removing irrelevant and redundant features, feature selection is an essential step in data mining process in finding optimal subset of features to improve the quality of result by giving faster time processing, higher accuracy and simpler results with fewer features. Detailed feature selection analysis is presented in the paper. Both time-domain and load shape data are compared based on the accuracy, consistency and statistical dependencies between features.

Verification and validation of autonomous systems

NASA is working on complex future missions that require cooperation between multiple satellites or rovers. To implement these systems, developers are proposing and using intelligent and autonomous systems. These autonomous missions are new to NASA, and the software development community is just learning to develop such systems. With these new systems, new verification and validation techniques must be used. Current techniques have been developed based on large monolithic systems. These techniques have worked well and reliably, but do not translate to the new autonomous systems that are highly parallel and nondeterministic.

Physical and MAC layer techniques for next generation satellite communications

In this thesis, we consider four different scenarios of interest in modern satellite communications. For each scenario, we will propose the use of advanced solutions aimed at increasing the spectral efficiency of the communication links. First, we will investigate the optimization of the current standard for digital video broadcasting. We will increase the symbol rate of the signal and determine the optimal signal bandwidth. We will apply the time packing technique and propose a specifically design constellation. We will then compare some receiver architectures with different performance and complexity. The second scenario still addresses broadcast transmissions, but in a network composed of two satellites. We will compare three alternative transceiver strategies, namely, signals completely overlapped in frequency, frequency division multiplexing, and the Alamouti space-time block code, and, for each technique, we will derive theoretical results on the achievable rates. We will also evaluate the performance of said techniques in three different channel models. The third scenario deals with the application of multiuser detection in multibeam satellite systems. We will analyze a case in which the users are near the edge of the coverage area and, hence, they experience a high level of interference from adjacent cells. Also in this case, three different approaches will be compared. A classical approach in which each beam carries information for a user, a cooperative solution based on time division multiplexing, and the Alamouti scheme. The information theoretical analysis will be followed by the study of practical coded schemes. We will show that the theoretical bounds can be approached by a properly designed code or bit mapping. Finally, we will consider an Earth observation scenario, in which data is generated on the satellite and then transmitted to the ground. We will study two channel models, taking into account one or two transmit antennas, and apply techniques such as time and frequency packing, signal predistortion, multiuser detection and the Alamouti scheme.

Statistical physics of irregular low-density parity-check codes

Low-density parity-check codes with irregular constructions have recently been shown to outperform the most advanced error-correcting codes to date. In this paper we apply methods of statistical physics to study the typical properties of simple irregular codes. We use the replica method to find a phase transition which coincides with Shannon's coding bound when appropriate parameters are chosen. The decoding by belief propagation is also studied using statistical physics arguments; the theoretical solutions obtained are in good agreement with simulation results. We compare the performance of irregular codes with that of regular codes and discuss the factors that contribute to the improvement in performance.

Statistical mechanics of low-density parity-check codes

We review recent theoretical progress on the statistical mechanics of error correcting codes, focusing on low-density parity-check (LDPC) codes in general, and on Gallager and MacKay-Neal codes in particular. By exploiting the relation between LDPC codes and Ising spin systems with multispin interactions, one can carry out a statistical mechanics based analysis that determines the practical and theoretical limitations of various code constructions, corresponding to dynamical and thermodynamical transitions, respectively, as well as the behaviour of error-exponents averaged over the corresponding code ensemble as a function of channel noise. We also contrast the results obtained using methods of statistical mechanics with those derived in the information theory literature, and show how these methods can be generalized to include other channel types and related communication problems.

Average and reliability error exponents in low-density parity-check codes

We present a theoretical method for a direct evaluation of the average and reliability error exponents in low-density parity-check error-correcting codes using methods of statistical physics. Results for the binary symmetric channel are presented for codes of both finite and infinite connectivity.