Solutions to the nonlinear Schrodinger equation with Dirac mass initial data

We study the Nonlinear Schrodinger Equation Dirac mass initial data. We use scattering and inverse scattering theory to pose a Riemann Hilbert problem with a regularized reflection coefficient. We study the asymptotic behaviour of this RHP as the regularizing parameter tends to zero. We also establish asymptotic descriptions of solutions for sequences of initial data that converge to a Dirac mass, using a connection to previously known long time asymptotics

Experimental Evaluation of Wireless Simulation Assumptions

All analytical and simulation research on ad hoc wireless networks must necessarily model radio propagation using simplifying assumptions. We provide a comprehensive review of six assumptions that are still part of many ad hoc network simulation studies, despite increasing awareness of the need to represent more realistic features, including hills, obstacles, link asymmetries, and unpredictable fading. We use an extensive set of measurements from a large outdoor routing experiment to demonstrate the weakness of these assumptions, and show how these assumptions cause simulation results to differ significantly from experimental results. We close with a series of recommendations for researchers, whether they develop protocols, analytic models, or simulators for ad hoc wireless networks

Dynamic Modelling under Uncertainty: The Case of Trypanosoma brucei Energy Metabolism

Kinetic models of metabolism require detailed knowledge of kinetic parameters. However, due to measurement errors or lack of data this knowledge is often uncertain. The model of glycolysis in the parasitic protozoan Trypanosoma brucei is a particularly well analysed example of a quantitative metabolic model, but so far it has been studied with a fixed set of parameters only. Here we evaluate the effect of parameter uncertainty. In order to define probability distributions for each parameter, information about the experimental sources and confidence intervals for all parameters were collected. We created a wiki-based website dedicated to the detailed documentation of this information: the SilicoTryp wiki (http://silicotryp.ibls.gla.ac.uk/wiki/Glycolysis). Using information collected in the wiki, we then assigned probability distributions to all parameters of the model. This allowed us to sample sets of alternative models, accurately representing our degree of uncertainty. Some properties of the model, such as the repartition of the glycolytic flux between the glycerol and pyruvate producing branches, are robust to these uncertainties. However, our analysis also allowed us to identify fragilities of the model leading to the accumulation of 3-phosphoglycerate and/or pyruvate. The analysis of the control coefficients revealed the importance of taking into account the uncertainties about the parameters, as the ranking of the reactions can be greatly affected. This work will now form the basis for a comprehensive Bayesian analysis and extension of the model considering alternative topologies

Transcriptional Changes in Schistosoma mansoni during Early Schistosomula Development and in the Presence of Erythrocytes

Schistosome blood flukes cause more mortality and morbidity than any other human worm infection, but current control methods primarily rely on a single drug. There is a desperate need for new approaches to control this parasite, including vaccines. People become infected when the free-swimming larva, the cercaria, enters through the skin and becomes the schistosomulum. Schistosomula are susceptible to immune responses during their first few days in the host before they become adult parasites. We characterised the genes that these newly transformed parasites switch on when they enter the host to identify molecules that are critical for survival in the human host. Some of these highly up-regulated genes can be targeted for future development of new vaccines and drugs

Localization of type 1 diabetes susceptibility to the MHC class I genes HLA-B and HLA-A

The major histocompatibility complex (MHC) on chromosome 6 is associated with susceptibility to more common diseases than any other region of the human genome, including almost all disorders classified as autoimmune. In type 1 diabetes the major genetic susceptibility determinants have been mapped to the MHC class II genes HLA-DQB1 and HLA-DRB1 (refs 1-3), but these genes cannot completely explain the association between type 1 diabetes and the MHC region. Owing to the region's extreme gene density, the multiplicity of disease-associated alleles, strong associations between alleles, limited genotyping capability, and inadequate statistical approaches and sample sizes, which, and how many, loci within the MHC determine susceptibility remains unclear. Here, in several large type 1 diabetes data sets, we analyse a combined total of 1,729 polymorphisms, and apply statistical methods - recursive partitioning and regression - to pinpoint disease susceptibility to the MHC class I genes HLA-B and HLA-A (risk ratios >1.5; Pcombined = 2.01 × 10-19 and 2.35 × 10-13, respectively) in addition to the established associations of the MHC class II genes. Other loci with smaller and/or rarer effects might also be involved, but to find these, future searches must take into account both the HLA class II and class I genes and use even larger samples. Taken together with previous studies, we conclude that MHC-class-I-mediated events, principally involving HLA-B*39, contribute to the aetiology of type 1 diabetes. ©2007 Nature Publishing Group

The Association Between Familial Risk and Brain Abnormalities Is Disease Specific: An ENIGMA-Relatives Study of Schizophrenia and Bipolar Disorder

Background: Schizophrenia and bipolar disorder share genetic liability, and some structural brain abnormalities are common to both conditions. First-degree relatives of patients with schizophrenia (FDRs-SZ) show similar brain abnormalities to patients, albeit with smaller effect sizes. Imaging findings in first-degree relatives of patients with bipolar disorder (FDRs-BD) have been inconsistent in the past, but recent studies report regionally greater volumes compared with control subjects. Methods: We performed a meta-analysis of global and subcortical brain measures of 6008 individuals (1228 FDRs-SZ, 852 FDRs-BD, 2246 control subjects, 1016 patients with schizophrenia, 666 patients with bipolar disorder) from 34 schizophrenia and/or bipolar disorder family cohorts with standardized methods. Analyses were repeated with a correction for intracranial volume (ICV) and for the presence of any psychopathology in the relatives and control subjects. Results: FDRs-BD had significantly larger ICV (d = +0.16, q <.05 corrected), whereas FDRs-SZ showed smaller thalamic volumes than control subjects (d = −0.12, q <.05 corrected). ICV explained the enlargements in the brain measures in FDRs-BD. In FDRs-SZ, after correction for ICV, total brain, cortical gray matter, cerebral white matter, cerebellar gray and white matter, and thalamus volumes were significantly smaller; the cortex was thinner (d < −0.09, q <.05 corrected); and third ventricle was larger (d = +0.15, q <.05 corrected). The findings were not explained by psychopathology in the relatives or control subjects. Conclusions: Despite shared genetic liability, FDRs-SZ and FDRs-BD show a differential pattern of structural brain abnormalities, specifically a divergent effect in ICV. This may imply that the neurodevelopmental trajectories leading to brain anomalies in schizophrenia or bipolar disorder are distinct

A Compact Modeling Approach to Enhance Collaborative Design of Thermal-Fluid Systems

This paper presents an approach for reducing detailed numerical models of electronic equipment into compact thermal-fluid models. These compact models have been created using network analogies representing mass, momentum and energy transport to reduce computational demand, preserve manufacturer intellectual property, and enable software independent exchange of information between supplier and integrator. A strategic approach is demonstrated for a steady state case from reduction to model integration within a global environment. The compact model is robust to boundary condition variation by developing a boundary condition response matrix for the network layout. A practical example of electronic equipment cooled naturally in air is presented. Solution times were reduced from ∼100 to ∼10−3 CPU hours when using the compact model. Nodal information was predicted with O(10%) accuracy compared to detailed solutions. For parametric design studies, the reduced model can provide 1800 solutions in the same time required to run a single detailed numerical simulation. The information generated by the reduction process also enhances collaborative design by providing the equipment integrator with ordered initial conditions for the equipment in the optimization of the global design. Sensitivity of the compact model to spatial variations on the boundary node faces has also been assessed. Overall, the compact modeling approach developed extends the use of compact models beyond preliminary design and into detailed phases of the product design lifecycle.</jats:p

Development of Compact Thermal–Fluid Models at the Electronic Equipment Level

The introduction of compact thermal models (CTM) into computational fluid dynamics (CFD) codes has significantly reduced computational requirements when representing complex, multilayered, and orthotropic heat generating electronic components in the design of electronic equipment. This study develops a novel procedure for generating compact thermal–fluid models (CTFM) of electronic equipment that are independent over a boundary condition set. This boundary condition set is estimated based on the information received at the preliminary design stages of a product. In this procedure, CFD has been used to generate a detailed model of the electronic equipment. Compact models have been constructed using a network approach, where thermal and pressure-flow characteristics of the system are represented by simplified thermal and fluid paths. Data from CFD solutions are reduced for the compact model and coupled with an optimization of an objective function to minimize discrepancies between detailed and compact solutions. In turn, an accurate prediction tool is created that is a fraction of the computational demand of detailed simulations. A method to successively integrate multiple scales of electronics into an accurate compact model that can predict junction temperatures within 10% of a detailed solution has been demonstrated. It was determined that CTFM nodal temperatures could predict the corresponding area averaged temperatures from the detailed CFD model with acceptable accuracy over the intended boundary condition range. The approach presented has the potential to reduce CFD requirements for multiscale electronic systems and also has the ability to integrate experimental data in the latter product design stages.</jats:p

Development and validation of a compact thermal model for an aircraft compartment

The development of accurate structural/thermal numerical models of complex systems, such as aircraft fuselage barrels, is often limited and determined by the smallest scales that need to be modelled. The development of reduced order models of the smallest scales and consequently their integration with higher level models can be a way to minimise the bottle neck present, while still having efficient, robust and accurate numerical models. In this paper a methodology on how to develop compact thermal fluid models (CTFMs) for compartments where mixed convection regimes are present is demonstrated. Detailed numerical simulations (CFD) have been developed for an aircraft crown compartment and validated against experimental data obtained from a 1:1 scale compartment rig. The crown compartment is defined as the confined area between the upper fuselage and the passenger cabin in a single aisle commercial aircraft. CFD results were utilised to extract average quantities (temperature and heat fluxes) and characteristic parameters (heat transfer coefficients) to generate CTFMs.The CTFMs have then been compared with the results obtained from the detailed models showing average errors for temperature predictions lower than 5%. This error can be deemed acceptable when compared to the nominal experimental error associated with the thermocouple measurements. The CTFMs methodology developed allows to generate accurate reduced order models where accuracy is restricted to the region of Boundary Conditions applied. This limitation arises from the sensitivity of the internal flow structures to the applied boundary condition set. CTFMs thus generated can be then integrated in complex numerical modelling of whole fuselage sections.Further steps in the development of an exhaustive methodology would be the implementation of a logic ruled based approach to extract directly from the CFD simulations numbers and positions of the nodes for the CTFM. (C) 2013 Elsevier Ltd. All rights reserved.ACCEPTEDpeer-reviewe