A buffering layer to support derived types and proprietary networks for Java HPC

MPJ Express is our implementation of MPI-like bindings for Java. In this paper we discuss our intermediate buffering layer that makes use of the so-called direct byte buffers introduced in the Java New I/O package. The purpose of this layer is to support the implementation of derived datatypes. MPJ Express is the first Java messaging library that implements this feature using pure Java. In addition, this buffering layer allows efficient implementation of communication devices based on proprietary networks such as Myrinet. In this paper we evaluate the performance of our buffering layer and demonstrate the usefulness of direct byte buffers. Also, we evaluate the performance of MPJ Express against other messaging systems using Myrinet and show that our buffering layer has made it possible to avoid the overheads suffered by other Java systems such as mpiJava that relies on the Java Native Interface.

A buffering layer to support derived types and proprietary networks for Java HPC

MPJ Express is our implementation of MPI-like bindings for Java. In this paper we discuss our intermediate buffering layer that makes use of the so-called direct byte buffers introduced in the Java New I/O package. The purpose of this layer is to support the implementation of derived datatypes. MPJ Express is the first Java messaging library that implements this feature using pure Java. In addition, this buffering layer allows efficient implementation of communication devices based on proprietary networks such as Myrinet. In this paper we evaluate the performance of our buffering layer and demonstrate the usefulness of direct byte buffers. Also, we evaluate the performance of MPJ Express against other messaging systems using Myrinet and show that our buffering layer has made it possible to avoid the overheads suffered by other Java systems such as mpiJava that relies on the Java Native Interface.

Characterization and classification of tumor lesions using computerized fractal-based texture analysis and support vector machines in digital mammograms

Objective: This paper presents a detailed study of fractal-based methods for texture characterization of mammographic mass lesions and architectural distortion. The purpose of this study is to explore the use of fractal and lacunarity analysis for the characterization and classification of both tumor lesions and normal breast parenchyma in mammography. Materials and methods: We conducted comparative evaluations of five popular fractal dimension estimation methods for the characterization of the texture of mass lesions and architectural distortion. We applied the concept of lacunarity to the description of the spatial distribution of the pixel intensities in mammographic images. These methods were tested with a set of 57 breast masses and 60 normal breast parenchyma (dataset1), and with another set of 19 architectural distortions and 41 normal breast parenchyma (dataset2). Support vector machines (SVM) were used as a pattern classification method for tumor classification. Results: Experimental results showed that the fractal dimension of region of interest (ROIs) depicting mass lesions and architectural distortion was statistically significantly lower than that of normal breast parenchyma for all five methods. Receiver operating characteristic (ROC) analysis showed that fractional Brownian motion (FBM) method generated the highest area under ROC curve (A z = 0.839 for dataset1, 0.828 for dataset2, respectively) among five methods for both datasets. Lacunarity analysis showed that the ROIs depicting mass lesions and architectural distortion had higher lacunarities than those of ROIs depicting normal breast parenchyma. The combination of FBM fractal dimension and lacunarity yielded the highest A z value (0.903 and 0.875, respectively) than those based on single feature alone for both given datasets. The application of the SVM improved the performance of the fractal-based features in differentiating tumor lesions from normal breast parenchyma by generating higher A z value. Conclusion: FBM texture model is the most appropriate model for characterizing mammographic images due to self-affinity assumption of the method being a better approximation. Lacunarity is an effective counterpart measure of the fractal dimension in texture feature extraction in mammographic images. The classification results obtained in this work suggest that the SVM is an effective method with great potential for classification in mammographic image analysis.

Intelligent agents and distributed models for cooperative task support

Purpose – To describe some research done, as part of an EPSRC funded project, to assist engineers working together on collaborative tasks. Design/methodology/approach – Distributed finite state modelling and agent techniques are used successfully in a new hybrid self-organising decision making system applied to collaborative work support. For the particular application, analysis of the tasks involved has been performed and these tasks are modelled. The system then employs a novel generic agent model, where task and domain knowledge are isolated from the support system, which provides relevant information to the engineers. Findings – The method is applied in the despatch of transmission commands within the control room of The National Grid Company Plc (NGC) – tasks are completed significantly faster when the system is utilised. Research limitations/implications – The paper describes a generic approach and it would be interesting to investigate how well it works in other applications. Practical implications – Although only one application has been studied, the methodology could equally be applied to a general class of cooperative work environments. Originality/value – One key part of the work is the novel generic agent model that enables the task and domain knowledge, which are application specific, to be isolated from the support system, and hence allows the method to be applied in other domains.

Assessing the accuracy of a computerized decision support system for digoxin dosing in primary care: an observational study

Background: This study was carried out as part of a European Union funded project (PharmDIS-e+), to develop and evaluate software aimed at assisting physicians with drug dosing. A drug that causes particular problems with drug dosing in primary care is digoxin because of its narrow therapeutic range and low therapeutic index. Objectives: To determine (i) accuracy of the PharmDIS-e+ software for predicting serum digoxin levels in patients who are taking this drug regularly; (ii) whether there are statistically significant differences between predicted digoxin levels and those measured by a laboratory and (iii) whether there are differences between doses prescribed by general practitioners and those suggested by the program. Methods: We needed 45 patients to have 95% Power to reject the null hypothesis that the mean serum digoxin concentration was within 10% of the mean predicted digoxin concentration. Patients were recruited from two general practices and had been taking digoxin for at least 4 months. Exclusion criteria were dementia, low adherence to digoxin and use of other medications known to interact to a clinically important extent with digoxin. Results: Forty-five patients were recruited. There was a correlation of 0·65 between measured and predicted digoxin concentrations (P < 0·001). The mean difference was 0·12 μg/L (SD 0·26; 95% CI 0·04, 0·19, P = 0·005). Forty-seven per cent of the patients were prescribed the same dose as recommended by the software, 44% were prescribed a higher dose and 9% a lower dose than recommended. Conclusion: PharmDIS-e+ software was able to predict serum digoxin levels with acceptable accuracy in most patients.

European Union: Shadow WTO agricultural domestic support notifications

The notification of the level of domestic support to the World Trade Organization (WTO) is intended to reflect compliance with obligations entered into at the time of the Uruguay Round. WTO members have often been slow to provide notification of domestic support levels. This makes the process of notification less useful as an indicator of the degree to which changes in policy have or have not benefited the trade system as a whole and exporting countries in particular. The notification of domestic support in the E.U. illustrates the value of a measure that reflects current policies and can therefore act as a basis for negotiation of further disciplines where these are necessary. The E.U. has made major changes in its Common Agricultural Policy (CAP) over the period since 1992 when the MacSharry reforms were implemented. Payments originally notified in the blue box (related to supply control) have over time been changed until in their present form they are unrelated to current production or price levels, and hence can satisfy the criteria for the green box. The E.U. has therefore much more latitude in trade talks to agree to reductions in the allowable trade-distorting support. This paper reproduced the E.U. notifications relating to 2003/04 and extends these with official statistics to the year 2006/07. It then projects forward the components of domestic support until the year 2013/14, based on forecasts of future production and estimates of policy parameters. The impact of a successful Doha Round is simulated, showing that the constraints envisaged in the WTO draft modalities document of May 19, 2008, would be binding by the year 2013, at about the time the next budget cycle in the E.U. starts. Without the Doha Round constraints, further reform might still happen for domestic reasons, but the framework provided by the WTO for domestic policy spending would be less relevant. In that case, much could hinge on the legitimacy of the Single Farm Payment system under the current rules governing the green box.

Entropic risk analysis by a high level decision support system (e-FDSS) for construction SMEs

The method of entropy has been useful in evaluating inconsistency on human judgments. This paper illustrates an entropy-based decision support system called e-FDSS to the solution of multicriterion risk and decision analysis in projects of construction small and medium enterprises (SMEs). It is optimized and solved by fuzzy logic, entropy, and genetic algorithms. A case study demonstrated the use of entropy in e-FDSS on analyzing multiple risk criteria in the predevelopment stage of SME projects. Survey data studying the degree of impact of selected project risk criteria on different projects were input into the system in order to evaluate the preidentified project risks in an impartial environment. Without taking into account the amount of uncertainty embedded in the evaluation process; the results showed that all decision vectors are indeed full of bias and the deviations of decisions are finally quantified providing a more objective decision and risk assessment profile to the stakeholders of projects in order to search and screen the most profitable projects.