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.

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.

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.

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.

Differential tumor surveillance by natural killer (NK) and NKT cells

Natural tumor surveillance capabilities of the host were investigated in six different mouse tumor models where endogenous interleukin (IL)-12. does or does not dictate the efficiency of the innate immune response. Gene-targeted and lymphocyte subset-depleted mice were used to establish the relative importance of natural killer (NK) and NK1.1(+) T (NKT) cells in protection from tumor initiation and metastasis. In the models examined, CD3(-) NK cells were responsible for tumor rejection and protection from metastasis in models where control of major histocompatibility complex class I-deficient tumors was independent of IL-12, A protective role for NKT cells was only observed when tumor rejection required endogenous IL-12 activity. In particular, T cell receptor J alpha 281 gene-targeted mice confirmed a critical function for NKT cells in protection from spontaneous tumors initiated by the chemical carcinogen, methylcholanthrene. This is the first description of an antitumor function for NKT cells in the absence of exogenously administered potent stimulators such as IL-12 or alpha-galactosylceramide.

Perforin-mediated cytotoxicity is critical for surveillance of spontaneous lymphoma

Immune surveillance by cytotoxic lymphocytes against cancer has been postulated for decades, but direct evidence for the role of cytotoxic lymphocytes in protecting against spontaneous malignancy has been lacking. As the rejection of many experimental cancers by cytotoxic T lymphocytes and natural killer cells is dependent on the pore-forming protein perforin (pfp), we examined pfp-deficient mice for increased cancer susceptibility. Here we show that pfp-deficient mice have a high incidence of malignancy in distinct lymphoid cell lineages (T, B, NKT), indicating a specific requirement for pfp in protection against lymphomagenesis. The susceptibility to lymphoma was accentuated by simultaneous lack of expression of the p53 gene, mutations in which also commonly predispose to human malignancies, including lymphoma. In contrast, the incidence and age of onset of sarcoma was unaffected in p53-deficient mice. Pfp-deficient mice were at least 1,000-fold more susceptible to these lymphomas when transplanted, compared with immunocompetent mice in which tumor rejection was controlled by CD8(+) T lymphocytes. This study is the first that implicates direct cytotoxicity by lymphocytes in regulating lymphomagenesis.

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.

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.

Innate immune surveillance of spontaneous B cell lymphomas by natural killer cells and gamma delta T cells

Few studies have demonstrated that innate lymphocytes play a major role in preventing spontaneous tumor formation. We evaluated the development of spontaneous tumors in mice lacking beta-2 microglobulin (beta2m; and thus MHC class I, CD1d, and CD16) and/or perform, since these tumor cells would be expected to activate innate effector cells. Approximately half the cohort of perform gene-targeted mice succumbed to spontaneous disseminated B cell lymphomas and in mice that also lacked beta2m, the lymphomas developed earlier (by more than 100 d) and with greater incidence (84%). B cell lymphomas from perforin/beta2m gene-targeted mice effectively primed cell-mediated cytotoxicity and perform, but not IFN-gamma, IL-12, or IL-18, was absolutely essential for tumor rejection. Activated NK1.1(+) and gammadeltaTCR(+) T cells were abundant at the tumor site, and transplanted tumors were strongly rejected by either, or both, of these cell types. Blockade of a number of different known costimulatory pathways failed to prevent tumor rejection. These results reflect a critical role for NK cells and gammadeltaTCP(+) T cells in innate immune surveillance of B cell lymphomas, mediated by as yet undetermined pathway(s) of tumor recognition.

Targeted! Population segmentation, electronic surveillance and governing the unemployed in Australia

Targeting is increasingly used to manage people. It operates by segmenting populations and providing different levels of opportunities and services to these groups. Each group is subject to different levels of surveillance and scrutiny. This article examines the deployment of targeting in Australian social security. Three case studies of targeting are presented in Australia's management of benefit overpayment and fraud, the distribution of employment services and the application of workfare. In conceptualizing surveillance as governance, the analysis examines the rationalities, technologies and practices that make targeting thinkable, practicable and achievable. In the case studies, targeting is variously conceptualized and justified by calculative risk discourses, moral discourses of obligation and notions of welfare dependency Advanced information technologies are also seen as particularly important in giving rise to the capacity to think about and act on population segments.

Mathematically gifted male adolescents activate a unique brain network during mental rotation

Mental rotation involves the creation and manipulation of internal images, with the later being particularly useful cognitive capacities when applied to high-level mathematical thinking and reasoning. Many neuroimaging studies have demonstrated mental rotation to be mediated primarily by the parietal lobes, particularly on the right side. Here, we use fMRI to show for the first time that when performing 3-dimensional mental rotations, mathematically gifted male adolescents engage a qualitatively different brain network than those of average math ability, one that involves bilateral activation of the parietal lobes and frontal cortex, along with heightened activation of the anterior cingulate. Reliance on the processing characteristics of this uniquely bilateral system and the interplay of these anterior/posterior regions may be contributors to their mathematical precocity.