Assessing the competitiveness of loss allocation methods in a deregulated electricity market

This paper critically assesses several loss allocation methods based on the type of competition each method promotes. This understanding assists in determining which method will promote more efficient network operations when implemented in deregulated electricity industries. The methods addressed in this paper include the pro rata [1], proportional sharing [2], loss formula [3], incremental [4], and a new method proposed by the authors of this paper, which is loop-based [5]. These methods are tested on a modified Nordic 32-bus network, where different case studies of different operating points are investigated. The varying results obtained for each allocation method at different operating points make it possible to distinguish methods that promote unhealthy competition from those that encourage better system operation.

Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network

Motivation: Prediction methods for identifying binding peptides could minimize the number of peptides required to be synthesized and assayed, and thereby facilitate the identification of potential T-cell epitopes. We developed a bioinformatic method for the prediction of peptide binding to MHC class II molecules. Results: Experimental binding data and expert knowledge of anchor positions and binding motifs were combined with an evolutionary algorithm (EA) and an artificial neural network (ANN): binding data extraction --> peptide alignment --> ANN training and classification. This method, termed PERUN, was implemented for the prediction of peptides that bind to HLA-DR4(B1*0401). The respective positive predictive values of PERUN predictions of high-, moderate-, low- and zero-affinity binder-a were assessed as 0.8, 0.7, 0.5 and 0.8 by cross-validation, and 1.0, 0.8, 0.3 and 0.7 by experimental binding. This illustrates the synergy between experimentation and computer modeling, and its application to the identification of potential immunotheraaeutic peptides.

Methods for prediction of peptide binding to MHC molecules: A comparative study

Background: A variety of methods for prediction of peptide binding to major histocompatibility complex (MHC) have been proposed. These methods are based on binding motifs, binding matrices, hidden Markov models (HMM), or artificial neural networks (ANN). There has been little prior work on the comparative analysis of these methods. Materials and Methods: We performed a comparison of the performance of six methods applied to the prediction of two human MHC class I molecules, including binding matrices and motifs, ANNs, and HMMs. Results: The selection of the optimal prediction method depends on the amount of available data (the number of peptides of known binding affinity to the MHC molecule of interest), the biases in the data set and the intended purpose of the prediction (screening of a single protein versus mass screening). When little or no peptide data are available, binding motifs are the most useful alternative to random guessing or use of a complete overlapping set of peptides for selection of candidate binders. As the number of known peptide binders increases, binding matrices and HMM become more useful predictors. ANN and HMM are the predictive methods of choice for MHC alleles with more than 100 known binding peptides. Conclusion: The ability of bioinformatic methods to reliably predict MHC binding peptides, and thereby potential T-cell epitopes, has major implications for clinical immunology, particularly in the area of vaccine design.

Computational methods for prediction of T-cell epitopes - a framework for modelling, testing, and applications

Computational models complement laboratory experimentation for efficient identification of MHC-binding peptides and T-cell epitopes. Methods for prediction of MHC-binding peptides include binding motifs, quantitative matrices, artificial neural networks, hidden Markov models, and molecular modelling. Models derived by these methods have been successfully used for prediction of T-cell epitopes in cancer, autoimmunity, infectious disease, and allergy. For maximum benefit, the use of computer models must be treated as experiments analogous to standard laboratory procedures and performed according to strict standards. This requires careful selection of data for model building, and adequate testing and validation. A range of web-based databases and MHC-binding prediction programs are available. Although some available prediction programs for particular MHC alleles have reasonable accuracy, there is no guarantee that all models produce good quality predictions. In this article, we present and discuss a framework for modelling, testing, and applications of computational methods used in predictions of T-cell epitopes. (C) 2004 Elsevier Inc. All rights reserved.

A review of telehealth

Telehealth is an exciting new technique for improving access to specialist care, especially in underserviced areas. As with any new health care intervention, proper evaluations must be carried out to ensure that limited health care resources are employed appropriately. Evidence suggests that the most successful telehealth initiatives are those that have focussed on organisational and deployment issues, rather than on the technology itself. In the right circumstances, telehealth offers a cost-effective alternative to the traditional methods of health care delivery Copyright (C) 2001 S. Karger AG, Basel.

Efficient simulation of a Tandem Jackson Network

The two-node tandem Jackson network serves as a convenient reference model for the analysis and testing of different methodologies and techniques in rare event simulation. In this paper we consider a new approach to efficiently estimate the probability that the content of the second buffer exceeds some high level L before it becomes empty, starting from a given state. The approach is based on a Markov additive process representation of the buffer processes, leading to an exponential change of measure to be used in an importance sampling procedure. Unlike changes of measures proposed and studied in recent literature, the one derived here is a function of the content of the first buffer. We prove that when the first buffer is finite, this method yields asymptotically efficient simulation for any set of arrival and service rates. In fact, the relative error is bounded independent of the level L; a new result which is not established for any other known method. When the first buffer is infinite, we propose a natural extension of the exponential change of measure for the finite buffer case. In this case, the relative error is shown to be bounded (independent of L) only when the second server is the bottleneck; a result which is known to hold for some other methods derived through large deviations analysis. When the first server is the bottleneck, experimental results using our method seem to suggest that the relative error is bounded linearly in L.

Network structures: Working differently and changing expectations

There is a growing need for innovative methods of dealing with complex, social problems. New types of collaborative efforts have emerged as a result of the inability of more traditional bureaucratic hierarchical arrangements such as departmental program, to resolve these problems. Network structures are one such arrangement that Is at the forefront of this movement. Although collaboration through network structures establishes an innovative response to dealing with social issues, there remains an expectation that outcomes and processes are based on traditional ways of working. It is necessary for practitioners and policy makers alike to begin to understand the realities of what can be expected from network structures in order to maximize the benefits of these unique mechanisms.

PROST: A programmable structured peer-to-peer overlay network

We present the idea of a programmable structured P2P architecture. Our proposed system allows the key-based routing infrastructure, which is common to all structured P2P overlays, to be shared by multiple applications. Furthermore, our architecture allows the dynamic and on-demand deployment of new applications and services on top of the shared routing layer.

Tracking the states of a nonlinear system in the weight-space of a feed-forward neural network

Nonlinear, non-stationary signals are commonly found in a variety of disciplines such as biology, medicine, geology and financial modeling. The complexity (e.g. nonlinearity and non-stationarity) of such signals and their low signal to noise ratios often make it a challenging task to use them in critical applications. In this paper we propose a new neural network based technique to address those problems. We show that a feed forward, multi-layered neural network can conveniently capture the states of a nonlinear system in its connection weight-space, after a process of supervised training. The performance of the proposed method is investigated via computer simulations.

Globalisation and connectedness: A network approach to international business

There have been recent calls for the field of International Business to retool its routines by becoming genuinely interdisciplinary. This paper takes such an approach by using recent advances in the fields of evolutionary economics and applying them to IB. Evolutionary economists are now viewing the economy as an actual network. Consequently, one the key analytical tools in this approach is network analysis. Some of the basic methods in network analysis are reviewed. The paper then looks at how using these tools might be of use in IB studies. In particular, it outlines fruitful research paths in the areas of globalisation and regionalisation, and the measurement of performance in multi-national firms and alliances. In each case, propositions are put forward which can be analytically tested with the use of network analysis. The paper concludes with a brief outline of a research agenda which utilises this approach in International Business studies.

Context-enhanced authentication for infrastructureless network environments

Infrastructureless networks are becoming more popular with the increased prevalence of wireless networking technology. A significant challenge faced by these infrastructureless networks is that of providing security. In this paper we examine the issue of authentication, a fundamental component of most security approaches, and show how it can be performed despite an absence of trusted infrastructure and limited or no existing trust relationship between network nodes. Our approach enables nodes to authenticate using a combination of contextual information, harvested from the environment, and traditional authentication factors (such as public key cryptography). Underlying our solution is a generic threshold signature scheme that enables distributed generation of digital certificates.

On sensor network segmentation for urban water distribution monitoring

Identifying water wastage in forms of leaks in a water distribution network of any city becomes essential as droughts are presenting serious threats to few major cities. In this paper, we propose a deployment of sensor network for monitoring water flow in any water distribution network. We cover the issues related with designing such a dedicated sensor network by considering types of sensors required, sensors' functionality, data collection, and providing computation serving as leak detection mechanism. The main focus of this paper is on appropriate network segmentation that provides the base for hierarchical approach to pipes' failure detection. We show a method for sensors allocation to the network in order to facilitate effective pipes monitoring. In general, the identified computational problem belongs to hard problems. The paper shows a heuristic method to build effective hierarchy of the network segmentation.

Vertical handovers as adaption methods in pervasive systems

Pervasive systems need to be context aware and need to adapt to context changes, including network disconnections and changes in network Quality of Service (QoS). Vertical handover (handover between heterogeneous networks) is one of possible adaptation methods. It allows users to roam freely between heterogeneous networks while maintaining continuity of their applications. This paper proposes a vertical handover approach suitable for multimedia applications in pervasive systems. It describes the adaptability decision making process which uses vertical handovers to support users mobility and provision of QoS suitable for users’ applications. The process evaluates context information regarding user devices, User location, network environment, and user perceived QoS of applications.

A learning approach for performance evaluation of local network

In this paper, a novel approach is developed to evaluate the overall performance of a local area network as well as to monitor some possible intrusion detections. The data is obtained via system utility 'ping' and huge data is analyzed via statistical methods. Finally, an overall performance index is defined and simulation experiments in three months proved the effectiveness of the proposed performance index. A software package is developed based on these ideas.