General and multiplicative non-parametric corporate performance models with interval ratio data

The increasing intensity of global competition has led organizations to utilize various types of performance measurement tools for improving the quality of their products and services. Data envelopment analysis (DEA) is a methodology for evaluating and measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. All the data in the conventional DEA with input and/or output ratios assumes the form of crisp numbers. However, the observed values of data in real-world problems are sometimes expressed as interval ratios. In this paper, we propose two new models: general and multiplicative non-parametric ratio models for DEA problems with interval data. The contributions of this paper are fourfold: (1) we consider input and output data expressed as interval ratios in DEA; (2) we address the gap in DEA literature for problems not suitable or difficult to model with crisp values; (3) we propose two new DEA models for evaluating the relative efficiencies of DMUs with interval ratios, and (4) we present a case study involving 20 banks with three interval ratios to demonstrate the applicability and efficacy of the proposed models where the traditional indicators are mostly financial ratios. © 2011 Elsevier Inc.

Semi-supervised learning of statistical models for natural language understanding

Natural language understanding is to specify a computational model that maps sentences to their semantic mean representation. In this paper, we propose a novel framework to train the statistical models without using expensive fully annotated data. In particular, the input of our framework is a set of sentences labeled with abstract semantic annotations. These annotations encode the underlying embedded semantic structural relations without explicit word/semantic tag alignment. The proposed framework can automatically induce derivation rules that map sentences to their semantic meaning representations. The learning framework is applied on two statistical models, the conditional random fields (CRFs) and the hidden Markov support vector machines (HM-SVMs). Our experimental results on the DARPA communicator data show that both CRFs and HM-SVMs outperform the baseline approach, previously proposed hidden vector state (HVS) model which is also trained on abstract semantic annotations. In addition, the proposed framework shows superior performance than two other baseline approaches, a hybrid framework combining HVS and HM-SVMs and discriminative training of HVS, with a relative error reduction rate of about 25% and 15% being achieved in F-measure.

Semi-analytical Q parameter estimate in linear and nonlinear transmission systems

We compare the Q parameter obtained from the semi-analytical model with scalar and vector models for two realistic transmission systems. First a linear system with a compensated dispersion map and second a soliton transmission system.

Modeling geometric rules in object based models:an XML / GML approach

Most object-based approaches to Geographical Information Systems (GIS) have concentrated on the representation of geometric properties of objects in terms of fixed geometry. In our road traffic marking application domain we have a requirement to represent the static locations of the road markings but also enforce the associated regulations, which are typically geometric in nature. For example a give way line of a pedestrian crossing in the UK must be within 1100-3000 mm of the edge of the crossing pattern. In previous studies of the application of spatial rules (often called 'business logic') in GIS emphasis has been placed on the representation of topological constraints and data integrity checks. There is very little GIS literature that describes models for geometric rules, although there are some examples in the Computer Aided Design (CAD) literature. This paper introduces some of the ideas from so called variational CAD models to the GIS application domain, and extends these using a Geography Markup Language (GML) based representation. In our application we have an additional requirement; the geometric rules are often changed and vary from country to country so should be represented in a flexible manner. In this paper we describe an elegant solution to the representation of geometric rules, such as requiring lines to be offset from other objects. The method uses a feature-property model embraced in GML 3.1 and extends the possible relationships in feature collections to permit the application of parameterized geometric constraints to sub features. We show the parametric rule model we have developed and discuss the advantage of using simple parametric expressions in the rule base. We discuss the possibilities and limitations of our approach and relate our data model to GML 3.1. © 2006 Springer-Verlag Berlin Heidelberg.

A semi-oriented radial measure for measuring the efficiency of decision making units with negative data, using DEA

Data Envelopment Analysis (DEA) is a nonparametric method for measuring the efficiency of a set of decision making units such as firms or public sector agencies, first introduced into the operational research and management science literature by Charnes, Cooper, and Rhodes (CCR) [Charnes, A., Cooper, W.W., Rhodes, E., 1978. Measuring the efficiency of decision making units. European Journal of Operational Research 2, 429–444]. The original DEA models were applicable only to technologies characterized by positive inputs/outputs. In subsequent literature there have been various approaches to enable DEA to deal with negative data. In this paper, we propose a semi-oriented radial measure, which permits the presence of variables which can take both negative and positive values. The model is applied to data on a notional effluent processing system to compare the results with those yielded by two alternative methods for dealing with negative data in DEA: The modified slacks-based model suggested by Sharp et al. [Sharp, J.A., Liu, W.B., Meng, W., 2006. A modified slacks-based measure model for data envelopment analysis with ‘natural’ negative outputs and inputs. Journal of Operational Research Society 57 (11) 1–6] and the range directional model developed by Portela et al. [Portela, M.C.A.S., Thanassoulis, E., Simpson, G., 2004. A directional distance approach to deal with negative data in DEA: An application to bank branches. Journal of Operational Research Society 55 (10) 1111–1121]. A further example explores the advantages of using the new model.

Has DRG payment influenced the technical efficiency and productivity of diagnostic technologies in Portuguese public hospitals? An empirical analysis using parametric and non‐parametric methods

The use of Diagnosis Related Groups (DRG) as a mechanism for hospital financing is a currently debated topic in Portugal. The DRG system was scheduled to be initiated by the Health Ministry of Portugal on January 1, 1990 as an instrument for the allocation of public hospital budgets funded by the National Health Service (NHS), and as a method of payment for other third party payers (e.g., Public Employees (ADSE), private insurers, etc.). Based on experience from other countries such as the United States, it was expected that implementation of this system would result in more efficient hospital resource utilisation and a more equitable distribution of hospital budgets. However, in order to minimise the potentially adverse financial impact on hospitals, the Portuguese Health Ministry decided to gradually phase in the use of the DRG system for budget allocation by using blended hospitalspecific and national DRG casemix rates. Since implementation in 1990, the percentage of each hospitals budget based on hospital specific costs was to decrease, while the percentage based on DRG casemix was to increase. This was scheduled to continue until 1995 when the plan called for allocating yearly budgets on a 50% national and 50% hospitalspecific cost basis. While all other nonNHS third party payers are currently paying based on DRGs, the adoption of DRG casemix as a National Health Service budget setting tool has been slower than anticipated. There is now some argument in both the political and academic communities as to the appropriateness of DRGs as a budget setting criterion as well as to their impact on hospital efficiency in Portugal. This paper uses a twostage procedure to assess the impact of actual DRG payment on the productivity (through its components, i.e., technological change and technical efficiency change) of diagnostic technology in Portuguese hospitals during the years 1992–1994, using both parametric and nonparametric frontier models. We find evidence that the DRG payment system does appear to have had a positive impact on productivity and technical efficiency of some commonly employed diagnostic technologies in Portugal during this time span.

Assessing pupil and school performance by non-parametric and parametric techniques

Often observations are nested within other units. This is particularly the case in the educational sector where school performance in terms of value added is the result of school contribution as well as pupil academic ability and other features relating to the pupil. Traditionally, the literature uses parametric (i.e. it assumes a priori a particular function on the production process) Multi-Level Models to estimate the performance of nested entities. This paper discusses the use of the non-parametric (i.e. without a priori assumptions on the production process) Free Disposal Hull model as an alternative approach. While taking into account contextual characteristics as well as atypical observations, we show how to decompose non-parametrically the overall inefficiency of a pupil into a unit specific and a higher level (i.e. a school) component. By a sample of entry and exit attainments of 3017 girls in British ordinary single sex schools, we test the robustness of the non-parametric and parametric estimates. We find that the two methods agree in the relative measures of the scope for potential attainment improvement. Further, the two methods agree on the variation in pupil attainment and the proportion attributable to pupil and school level.

Computer interpretation of engineering drawings as solid models

Much of the geometrical data relating to engineering components and assemblies is stored in the form of orthographic views, either on paper or computer files. For various engineering applications, however, it is necessary to describe objects in formal geometric modelling terms. The work reported in this thesis is concerned with the development and implementation of concepts and algorithms for the automatic interpretation of orthographic views as solid models. The various rules and conventions associated with engineering drawings are reviewed and several geometric modelling representations are briefly examined. A review of existing techniques for the automatic, and semi-automatic, interpretation of engineering drawings as solid models is given. A new theoretical approach is then presented and discussed. The author shows how the implementation of such an approach for uniform thickness objects may be extended to more general objects by introducing the concept of `approximation models'. Means by which the quality of the transformations is monitored, are also described. Detailed descriptions of the interpretation algorithms and the software package that were developed for this project are given. The process is then illustrated by a number of practical examples. Finally, the thesis concludes that, using the techniques developed, a substantial percentage of drawings of engineering components could be converted into geometric models with a specific degree of accuracy. This degree is indicative of the suitability of the model for a particular application. Further work on important details is required before a commercially acceptable package is produced.

Parametric study of the behaviour of reinforced concrete columns in fire

The research is concerned with the application of the computer simulation technique to study the performance of reinforced concrete columns in a fire environment. The effect of three different concrete constitutive models incorporated in the computer simulation on the structural response of reinforced concrete columns exposed to fire is investigated. The material models differed mainly in respect to the formulation of the mechanical properties of concrete. The results from the simulation have clearly illustrated that a more realistic response of a reinforced concrete column exposed to fire is given by a constitutive model with transient creep or appropriate strain effect The assessment of the relative effect of the three concrete material models is considered from the analysis by adopting the approach of a parametric study, carried out using the results from a series of analyses on columns heated on three sides which produce substantial thermal gradients. Three different loading conditions were used on the column; axial loading and eccentric loading both to induce moments in the same sense and opposite sense to those induced by the thermal gradient. An axially loaded column heated on four sides was also considered. The computer modelling technique adopted separated the thermal and structural responses into two distinct computer programs. A finite element heat transfer analysis was used to determine the thermal response of the reinforced concrete columns when exposed to the ISO 834 furnace environment. The temperature distribution histories obtained were then used in conjunction with a structural response program. The effect of the occurrence of spalling on the structural behaviour of reinforced concrete column is also investigated. There is general recognition of the potential problems of spalling but no real investigation into what effect spalling has on the fire resistance of reinforced concrete members. In an attempt to address the situation, a method has been developed to model concrete columns exposed to fire which incorporates the effect of spalling. A total of 224 computer simulations were undertaken by varying the amounts of concrete lost during a specified period of exposure to fire. An array of six percentages of spalling were chosen for one range of simulation while a two stage progressive spalling regime was used for a second range. The quantification of the reduction in fire resistance of the columns against the amount of spalling, heating and loading patterns, and the time at which the concrete spalls appears to indicate that it is the amount of spalling which is the most significant variable in the reduction of fire resistance.

Modelling data and voice traffic over IP networks using continuous-time Markov models

Common approaches to IP-traffic modelling have featured the use of stochastic models, based on the Markov property, which can be classified into black box and white box models based on the approach used for modelling traffic. White box models, are simple to understand, transparent and have a physical meaning attributed to each of the associated parameters. To exploit this key advantage, this thesis explores the use of simple classic continuous-time Markov models based on a white box approach, to model, not only the network traffic statistics but also the source behaviour with respect to the network and application. The thesis is divided into two parts: The first part focuses on the use of simple Markov and Semi-Markov traffic models, starting from the simplest two-state model moving upwards to n-state models with Poisson and non-Poisson statistics. The thesis then introduces the convenient to use, mathematically derived, Gaussian Markov models which are used to model the measured network IP traffic statistics. As one of the most significant contributions, the thesis establishes the significance of the second-order density statistics as it reveals that, in contrast to first-order density, they carry much more unique information on traffic sources and behaviour. The thesis then exploits the use of Gaussian Markov models to model these unique features and finally shows how the use of simple classic Markov models coupled with use of second-order density statistics provides an excellent tool for capturing maximum traffic detail, which in itself is the essence of good traffic modelling. The second part of the thesis, studies the ON-OFF characteristics of VoIP traffic with reference to accurate measurements of the ON and OFF periods, made from a large multi-lingual database of over 100 hours worth of VoIP call recordings. The impact of the language, prosodic structure and speech rate of the speaker on the statistics of the ON-OFF periods is analysed and relevant conclusions are presented. Finally, an ON-OFF VoIP source model with log-normal transitions is contributed as an ideal candidate to model VoIP traffic and the results of this model are compared with those of previously published work.

Evaluating productive efficiency:comparative study of commercial banks in Gulf countries

Financial institutes are an integral part of any modern economy. In the 1970s and 1980s, Gulf Cooperation Council (GCC) countries made significant progress in financial deepening and in building a modern financial infrastructure. This study aims to evaluate the performance (efficiency) of financial institutes (banking sector) in GCC countries. Since, the selected variables include negative data for some banks and positive for others, and the available evaluation methods are not helpful in this case, so we developed a Semi Oriented Radial Model to perform this evaluation. Furthermore, since the SORM evaluation result provides a limited information for any decision maker (bankers, investors, etc...), we proposed a second stage analysis using classification and regression (C&R) method to get further results combining SORM results with other environmental data (Financial, economical and political) to set rules for the efficient banks, hence, the results will be useful for bankers in order to improve their bank performance and to the investors, maximize their returns. Mainly there are two approaches to evaluate the performance of Decision Making Units (DMUs), under each of them there are different methods with different assumptions. Parametric approach is based on the econometric regression theory and nonparametric approach is based on a mathematical linear programming theory. Under the nonparametric approaches, there are two methods: Data Envelopment Analysis (DEA) and Free Disposal Hull (FDH). While there are three methods under the parametric approach: Stochastic Frontier Analysis (SFA); Thick Frontier Analysis (TFA) and Distribution-Free Analysis (DFA). The result shows that DEA and SFA are the most applicable methods in banking sector, but DEA is seem to be most popular between researchers. However DEA as SFA still facing many challenges, one of these challenges is how to deal with negative data, since it requires the assumption that all the input and output values are non-negative, while in many applications negative outputs could appear e.g. losses in contrast with profit. Although there are few developed Models under DEA to deal with negative data but we believe that each of them has it is own limitations, therefore we developed a Semi-Oriented-Radial-Model (SORM) that could handle the negativity issue in DEA. The application result using SORM shows that the overall performance of GCC banking is relatively high (85.6%). Although, the efficiency score is fluctuated over the study period (1998-2007) due to the second Gulf War and to the international financial crisis, but still higher than the efficiency score of their counterpart in other countries. Banks operating in Saudi Arabia seem to be the highest efficient banks followed by UAE, Omani and Bahraini banks, while banks operating in Qatar and Kuwait seem to be the lowest efficient banks; this is because these two countries are the most affected country in the second Gulf War. Also, the result shows that there is no statistical relationship between the operating style (Islamic or Conventional) and bank efficiency. Even though there is no statistical differences due to the operational style, but Islamic bank seem to be more efficient than the Conventional bank, since on average their efficiency score is 86.33% compare to 85.38% for Conventional banks. Furthermore, the Islamic banks seem to be more affected by the political crisis (second Gulf War), whereas Conventional banks seem to be more affected by the financial crisis.

Using architecture models to support the generation and operation of component-based adaptive systems

Modelling architectural information is particularly important because of the acknowledged crucial role of software architecture in raising the level of abstraction during development. In the MDE area, the level of abstraction of models has frequently been related to low-level design concepts. However, model-driven techniques can be further exploited to model software artefacts that take into account the architecture of the system and its changes according to variations of the environment. In this paper, we propose model-driven techniques and dynamic variability as concepts useful for modelling the dynamic fluctuation of the environment and its impact on the architecture. Using the mappings from the models to implementation, generative techniques allow the (semi) automatic generation of artefacts making the process more efficient and promoting software reuse. The automatic generation of configurations and reconfigurations from models provides the basis for safer execution. The architectural perspective offered by the models shift focus away from implementation details to the whole view of the system and its runtime change promoting high-level analysis. © 2009 Springer Berlin Heidelberg.

On the boundedness of the SORM DEA models with negative data

Emrouznejad et al. (2010) proposed a Semi-Oriented Radial Measure (SORM) model for assessing the efficiency of Decision Making Units (DMUs) by Data Envelopment Analysis (DEA) with negative data. This paper provides a necessary and sufficient condition for boundedness of the input and output oriented SORM models.

A modified semi-oriented radial measure for target setting with negative data

Over the last few years Data Envelopment Analysis (DEA) has been gaining increasing popularity as a tool for measuring efficiency and productivity of Decision Making Units (DMUs). Conventional DEA models assume non-negative inputs and outputs. However, in many real applications, some inputs and/or outputs can take negative values. Recently, Emrouznejad et al. [6] introduced a Semi-Oriented Radial Measure (SORM) for modelling DEA with negative data. This paper points out some issues in target setting with SORM models and introduces a modified SORM approach. An empirical study in bank sector demonstrates the applicability of the proposed model. © 2014 Elsevier Ltd. All rights reserved.

Density forecasts of crude-oil prices using option-implied and ARCH-type models

The predictive accuracy of competing crude-oil price forecast densities is investigated for the 1994–2006 period. Moving beyond standard ARCH type models that rely exclusively on past returns, we examine the benefits of utilizing the forward-looking information that is embedded in the prices of derivative contracts. Risk-neutral densities, obtained from panels of crude-oil option prices, are adjusted to reflect real-world risks using either a parametric or a non-parametric calibration approach. The relative performance of the models is evaluated for the entire support of the density, as well as for regions and intervals that are of special interest for the economic agent. We find that non-parametric adjustments of risk-neutral density forecasts perform significantly better than their parametric counterparts. Goodness-of-fit tests and out-of-sample likelihood comparisons favor forecast densities obtained by option prices and non-parametric calibration methods over those constructed using historical returns and simulated ARCH processes. © 2010 Wiley Periodicals, Inc. Jrl Fut Mark 31:727–754, 2011