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.

Spectral properties of the tandem Jackson network, seen as a quasi-birth-and-death process

Quasi-birth-and-death (QBD) processes with infinite “phase spaces” can exhibit unusual and interesting behavior. One of the simplest examples of such a process is the two-node tandem Jackson network, with the “phase” giving the state of the first queue and the “level” giving the state of the second queue. In this paper, we undertake an extensive analysis of the properties of this QBD. In particular, we investigate the spectral properties of Neuts’s R-matrix and show that the decay rate of the stationary distribution of the “level” process is not always equal to the convergence norm of R. In fact, we show that we can obtain any decay rate from a certain range by controlling only the transition structure at level zero, which is independent of R. We also consider the sequence of tandem queues that is constructed by restricting the waiting room of the first queue to some finite capacity, and then allowing this capacity to increase to infinity. We show that the decay rates for the finite truncations converge to a value, which is not necessarily the decay rate in the infinite waiting room case. Finally, we show that the probability that the process hits level n before level 0 given that it starts in level 1 decays at a rate which is not necessarily the same as the decay rate for the stationary distribution.

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.

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.

The interorganizational virtual organization: Defining a Weberian ideal

The concept of the virtual organization (VO) has engendered great interest in the literature, yet there is still little common understanding of the concept, as evidenced by the multitude of labels applied to VOs. In this article, we focus on a “Weberian-ideal-type” definition of the interorganizational VO, posited in our earlier work (Kasper-Fuehrer and Ashkanasy 2001). We argue, however, that this definition left unanswered critical questions relating to the nature and effects of interorganizational VOs. We answer these questions here by explicating the terms in the definition and deriving ten corollaries, or “natural consequences” of our definition. The corollaries posit that interorganizational VOs are temporary in nature, are network organizations, are independent, and are based on swift trust. We suggest further that interorganizational VOs enable small to medium enterprises to exploit market opportunities, and enable VO member organizations to create a value-adding partnership. We also identify information and communication technology (ICT) as the essential enabler of VOs. Finally, we argue that interorganizational VOs act as a single organizational unit and that they therefore constitute a uniquely distinguishable organizational form. We conclude with suggestions for further research, including trust, organizational behavior, transaction economics, virtual HRM, and business strategy.

Development and evaluation of neural network freeway incident detection models using field data

This paper discusses a multi-layer feedforward (MLF) neural network incident detection model that was developed and evaluated using field data. In contrast to published neural network incident detection models which relied on simulated or limited field data for model development and testing, the model described in this paper was trained and tested on a real-world data set of 100 incidents. The model uses speed, flow and occupancy data measured at dual stations, averaged across all lanes and only from time interval t. The off-line performance of the model is reported under both incident and non-incident conditions. The incident detection performance of the model is reported based on a validation-test data set of 40 incidents that were independent of the 60 incidents used for training. The false alarm rates of the model are evaluated based on non-incident data that were collected from a freeway section which was video-taped for a period of 33 days. A comparative evaluation between the neural network model and the incident detection model in operation on Melbourne's freeways is also presented. The results of the comparative performance evaluation clearly demonstrate the substantial improvement in incident detection performance obtained by the neural network model. The paper also presents additional results that demonstrate how improvements in model performance can be achieved using variable decision thresholds. Finally, the model's fault-tolerance under conditions of corrupt or missing data is investigated and the impact of loop detector failure/malfunction on the performance of the trained model is evaluated and discussed. The results presented in this paper provide a comprehensive evaluation of the developed model and confirm that neural network models can provide fast and reliable incident detection on freeways. (C) 1997 Elsevier Science Ltd. All rights reserved.

Capillary network models for transport in packed beds: Considerations of pore aspect ratio

The conventional analysis for the estimation of the tortuosity factor for transport in porous media is modified here to account for the effect of pore aspect ratio. Structural models of the porous medium are also constructed for calculating the aspect ratio as a function of porosity. Comparison of the model predictions with the extensive data of Currie (1960) for the effective diffusivity of hydrogen in packed beds shows good agreement with a network model of randomly oriented intersecting pores for porosities upto about 50 percent, which is the region of practical interest. The predictions based on this network model are also found to be in better agreement with the data of Currie than earlier expressions developed for unconsolidated and grainy media.

Modeling the fluid-flow-induced stress and collapse in a dendritic network

An analytical approach to the stress development in the coherent dendritic network during solidification is proposed. Under the assumption that stresses are developed in the network as a result of the friction resisting shrinkage-induced interdendritic fluid flow, the model predicts the stresses in the solid. The calculations reflect the expected effects of postponed dendrite coherency, slower solidification conditions, and variations of eutectic volume fraction and shrinkage. Comparing the calculated stresses to the measured shear strength of equiaxed mushy zones shows that it is possible for the stresses to exceed the strength, thereby resulting in reorientation or collapse of the dendritic network.

An object-oriented neural network approach to short-term traffic forecasting

This paper discusses an object-oriented neural network model that was developed for predicting short-term traffic conditions on a section of the Pacific Highway between Brisbane and the Gold Coast in Queensland, Australia. The feasibility of this approach is demonstrated through a time-lag recurrent network (TLRN) which was developed for predicting speed data up to 15 minutes into the future. The results obtained indicate that the TLRN is capable of predicting speed up to 5 minutes into the future with a high degree of accuracy (90-94%). Similar models, which were developed for predicting freeway travel times on the same facility, were successful in predicting travel times up to 15 minutes into the future with a similar degree of accuracy (93-95%). These results represent substantial improvements on conventional model performance and clearly demonstrate the feasibility of using the object-oriented approach for short-term traffic prediction. (C) 2001 Elsevier Science B.V. All rights reserved.

Analytical solution to the zero-inertia problem for surge flow phenomena in nonprismatic channels

Surge flow phenomena. e.g.. as a consequence of a dam failure or a flash flood, represent free boundary problems. ne extending computational domain together with the discontinuities involved renders their numerical solution a cumbersome procedure. This contribution proposes an analytical solution to the problem, It is based on the slightly modified zero-inertia (ZI) differential equations for nonprismatic channels and uses exclusively physical parameters. Employing the concept of a momentum-representative cross section of the moving water body together with a specific relationship for describing the cross sectional geometry leads, after considerable mathematical calculus. to the analytical solution. The hydrodynamic analytical model is free of numerical troubles, easy to run, computationally efficient. and fully satisfies the law of volume conservation. In a first test series, the hydrodynamic analytical ZI model compares very favorably with a full hydrodynamic numerical model in respect to published results of surge flow simulations in different types of prismatic channels. In order to extend these considerations to natural rivers, the accuracy of the analytical model in describing an irregular cross section is investigated and tested successfully. A sensitivity and error analysis reveals the important impact of the hydraulic radius on the velocity of the surge, and this underlines the importance of an adequate description of the topography, The new approach is finally applied to simulate a surge propagating down the irregularly shaped Isar Valley in the Bavarian Alps after a hypothetical dam failure. The straightforward and fully stable computation of the flood hydrograph along the Isar Valley clearly reflects the impact of the strongly varying topographic characteristics on the How phenomenon. Apart from treating surge flow phenomena as a whole, the analytical solution also offers a rigorous alternative to both (a) the approximate Whitham solution, for generating initial values, and (b) the rough volume balance techniques used to model the wave tip in numerical surge flow computations.

How to do safe sternal reentry and the risk factors of redo cardiac surgery: A 21-year review with zero major cardiac injury

Objectives: Resternotomy is a common part of cardiac surgical practice. Associated with resternotomy are the risks of cardiac injury and catastrophic hemorrhage and the subsequent elevated morbidity and mortality in the operating room or during the postoperative period. The technique of direct vision resternotomy is safe and has fewer, if any, serious cardiac injuries. The technique, the reduced need for groin cannulation and the overall low operative mortality and morbidity are the focus of this restrospective analysis. Methods: The records of 495 patients undergoing 546 resternotomies over a 21-year period to January 2000 were reviewed. All consecutive reoperations by the one surgeon comprised patients over the age of 20 at first resternotomy: M:F 343:203, mean age 57 years (range 20 to 85, median age 60). The mean NYHA grade was 2.3 [with 67 patients (1), 273 (11),159 (111), 43 (IV), and 4 (V classification)] with elective reoperation in 94.6%. Cardiac injury was graded into five groups and the incidence and reasons for groin cannulation estimated. The morbidity and mortality as a result of the reoperation and resternotomy were assessed. Results: The hospital/30 day mortality was 2.9% (95% Cl: 1.6%-4.4%) (16 deaths) over the 21 years. First (481), second (53), and third (12) resternotomies produced 307 uncomplicated technical reopenings, 203 slower but uncomplicated procedures, 9 minor superficial cardiac lacerations, and no moderate or severe cardiac injuries. Direct vision resternotomy is crystalized into the principle that only adhesions that are visualized from below are divided and only sternal bone that is freed of adhesions is sewn. Groin exposure was never performed prophylactically for resternotomy. Fourteen patients (2.6%) had such cannulation for aortic dissection/aneurysm (9 patients), excessive sternal adherence of cardiac structures (3 patients), presurgery cardiac arrest (1 patient), and high aortic cannulation desired and not possible (1 patient). The average postop blood loss was 594 mL (95% CI:558-631) in the first 12 hours. The need to return to the operating room for control of excessive bleeding was 2% (11 patients). Blood transfusion was given in 65% of the resternotomy procedures over the 21 years (mean 854 mL 95% Cl 765-945 mL) and 41% over the last 5 years. Conclusions: The technique of direct vision resternotomy has been associated with zero moderate or major cardiac injury/catastrophic hemorrhage at reoperation. Few patients have required groin cannulation. In the postoperative period, there was acceptable blood loss, transfusion rates, reduced morbidity, and moderate low mortality for this potentially high risk group.

Inter-subband redundancy prediction using neural network for video coding

High performance video codec is mandatory for multimedia applications such as video-on-demand and video conferencing. Recent research has proposed numerous video coding techniques to meet the requirement in bandwidth, delay, loss and Quality-of-Service (QoS). In this paper, we present our investigations on inter-subband self-similarity within the wavelet-decomposed video frames using neural networks, and study the performance of applying the spatial network model to all video frames over time. The goal of our proposed method is to restore the highest perceptual quality for video transmitted over a highly congested network. Our contributions in this paper are: (1) A new coding model with neural network based, inter-subband redundancy (ISR) prediction for video coding using wavelet (2) The performance of 1D and 2D ISR prediction, including multiple levels of wavelet decompositions. Our result shows a short-term quality enhancement may be obtained using both 1D and 2D ISR prediction.