Extrinsic pulmonary artery compression mimicking acute pulmonary embolism

published_or_final_versio

A domain decomposition non-intrusive reduced order model for turbulent flows

In this paper, a new Domain Decomposition Non-Intrusive Reduced Order Model (DDNIROM) is developed for turbulent flows. The method works by partitioning the computational domain into a number of subdomains in such a way that the summation of weights associated with the finite element nodes within each subdomain is approximately equal, and the communication between subdomains is minimised. With suitably chosen weights, it is expected that there will be approximately equal accuracy associated with each subdomain. This accuracy is maximised by allowing the partitioning to occur through areas of the domain that have relatively little flow activity, which, in this case, is characterised by the pointwise maximum Reynolds stresses.A Gaussian Process Regression (GPR) machine learning method is used to construct a set of local approximation functions (hypersurfaces) for each subdomain. Each local hypersurface represents not only the fluid dynamics over the subdomain it belongs to, but also the interactions of the flow dynamics with the surrounding subdomains. Thus, in this way, the surrounding subdomains may be viewed as providing boundary conditions for the current subdomain.We consider a specific example of turbulent air flow within an urban neighbourhood at a test site in London and demonstrate the effectiveness of the proposed DDNIROM

A real-time flow forecasting with deep convolutional generative adversarial network: Application to flooding event in Denmark

Real-time flood forecasting is crucial for supporting emergency responses to inundation-prone regions. Due to uncertainties in the future (e.g., meteorological conditions and model parameter inputs), it is challenging to make accurate forecasts of spatiotemporal floods. In this paper, a real-time predictive deep convolutional generative adversarial network (DCGAN) is developed for flooding forecasting. The proposed methodology consists of a two-stage process: (1) dynamic flow learning and (2) real-time forecasting. In dynamic flow learning, the deep convolutional neural networks are trained to capture the underlying flow patterns of spatiotemporal flow fields. In real-time forecasting, the DCGAN adopts a cascade predictive procedure. The last one-time step-ahead forecast from the DCGAN can act as a new input for the next time step-ahead forecast, which forms a long lead-time forecast in a recursive way. The model capability is assessed using a 100-year return period extreme flood event occurred in Greve, Denmark. The results indicate that the predictive fluid flows from the DCGAN and the high fidelity model are in a good agreement (the correlation coefficien

Nitrogen doped-ZnO/n-GaN heterojunctions

Nitrogen-doped ZnO nanorods were prepared by electrodeposition using two different Zn precursors (zinc nitrate and zinc acetate), while all other growth conditions (dopant precursor, concentration, growth temperature, and bias) were identical. We have shown that the precursor used affects the properties of the ZnO nanorods, and that the presence of rectifying properties in n-GaN/N:ZnO heterojunctions is strongly related to the use of nitrate precursor for ZnO growth. The difference in the properties of ZnO obtained from two precursors is attributed to the differences in native defect and impurity concentrations, which could affect the electronic properties of the samples. © 2011 American Institute of Physics.published_or_final_versio

Solution-based growth of ZnO nanorods for light-emitting devices: Hydrothermal vs. electrodeposition

ZnO nanorods have been grown by two inexpensive, solution-based, low-temperature methods: hydrothermal growth and electrodeposition. Heterojunction n-ZnO nanorods/p-GaN light-emitting diodes have been studied for different nanorod growth methods and different preparation of the seed layer. We demonstrate that both the nanorod properties and the device performance are strongly dependent on the growth method and seed layer. All the devices exhibit light emission under both forward and reverse bias, and the emission spectra can be tuned by ZnO nanorod deposition conditions. Electrodeposition of rods or a seed layer results in yellow emission, while conventional hydrothermal growth results in violet emission. © The Author(s) 2010. This article is published with open access at Springerlink.com.published_or_final_versionSpringer Open Choice, 01 Dec 201

Evaluation of Xpert® MTB/RIF and ustar easyNAT™ TB IAD for diagnosis of tuberculous lymphadenitis of children in Tanzania : a prospective descriptive study

Fine needle aspiration biopsy has become a standard approach for diagnosis of peripheral tuberculous lymphadenitis. The aim of this study was to compare the performance of Xpert MTB/RIF and Ustar EasyNAT TB IAD nucleic acid amplification assays, against acid-fast bacilli microscopy, cytology and mycobacterial culture for the diagnosis of TB lymphadenitis in children from a TB-endemic setting in Tanzania.; Children of 8 weeks to 16 years of age, suspected of having TB lymphadenitis, were recruited at a district hospital in Tanzania. Fine needle aspirates of lymph nodes were analysed using acid-fast bacilli microscopy, liquid TB culture, cytology, Xpert MTB/RIF and EasyNAT. Latent class analysis and comparison against a composite reference standard comprising "culture and/or cytology" was done, to assess the performance of Xpert MTB/RIF and EasyNAT for the diagnosis of TB lymphadenitis.; Seventy-nine children were recruited; 4 were excluded from analysis. Against a composite reference standard of culture and/or cytology, Xpert MTB/RIF and EasyNAT had a sensitivity and specificity of 58 % and 93 %; and 19 % and 100 % respectively. Relative to latent class definitions, cytology had a sensitivity of 100 % and specificity of 94.7 %.; Combining clinical assessment, cytology and Xpert MTB/RIF may allow for a rapid and accurate diagnosis of childhood TB lymphadenitis. Larger diagnostic evaluation studies are recommended to validate these findings and on Xpert MTB/RIF to assess its use as a solitary initial test for TB lymphadenitis in children

Non-intrusive reduced order modelling of the Navier–Stokes equations

This article presents two new non-intrusive reduced order models based upon proper orthogonal decomposition (POD) for solving the Navier–Stokes equations. The novelty of these methods resides in how the reduced order models are formed, that is, how the coefficients of the POD expansions are calculated. Rather than taking a standard approach of projecting the underlying equations onto the reduced space through a Galerkin projection, here two different techniques are employed. The first method applies a second order Taylor series to calculate the POD coefficients at each time step from the POD coefficients at earlier time steps. The second method uses a Smolyak sparse grid collocation method to calculate the POD coefficients, where again the coefficients at earlier time steps are used as the inputs. The advantage of both approaches are that they are non-intrusive and so do not require modifications to a system code; they are therefore very easy to implement. They also provide accurate solutions for modelling flow problems, and this has been demonstrated by the simulation of flows past a cylinder and within a gyre. It is demonstrated that accuracy relative to the high fidelity model is maintained whilst CPU times are reduced by several orders of magnitude in comparison to high fidelity models

Microbial fuel cells: a green and alternative source for bioenergy production

Microbial fuel cell (MFC) represents one of the green technologies for the production of bioenergy. MFCs using microalgae produce bioenergy by converting solar energy into electrical energy as a function of metabolic and anabolic pathways of the cells. In the MFCs with bacteria, bioenergy is generated as a result of the organic substrate oxidation. MFCs have received high attention from researchers in the last years due to the simplicity of the process, the absence in toxic by-products, and low requirements for the algae growth. Many studies have been conducted on MFC and investigated the factors affecting the MFC performance. In the current chapter, the performance of MFC in producing bioenergy as well as the factors which influence the efficacy of MFCs is discussed. It appears that the main factors affecting MFC’s performance include bacterial and algae species, pH, temperature, salinity, substrate, mechanism of electron transfer in an anodic chamber, electrodes materials, surface area, and electron acceptor in a cathodic chamber. These factors are becoming more influential and might lead to overproduction of bioenergy when they are optimized using response surface methodology (RSM)

Molecular Cloning of a New Immunomodulatory Protein from Anoectochilus formosanus which Induces B Cell IgM Secretion through a T-Independent Mechanism

An immunomodulatory protein (IPAF) was purified and cloned from Anoectochilus formosanus, an Orchidaceae herbal plant in Asia. The major targeting immune cells of IPAF and its modulating effects toward B lymphocytes were investigated. Rapid amplification of cDNA ends (RACE) was conducted to clone the IPAF gene, and the obtained sequence was BLAST compared on the NCBI database. MACS-purified mouse T and B lymphocytes were stimulated with IPAF and the cell proliferation, activation, and Igs production were examined. IPAF comprised a 25 amino acids signal peptide and a 138 amino acids protein which was homologous to the lectins from Orchidaceae plant. IPAF selectively induced the cell proliferation in mouse splenic B lymphocytes but not T lymphocytes. The IPAF-induced B cells exhibited increased CD69 and MHC class II expression, and a dose- and time-dependent enhancement in IgM production. These results suggested potential benefits of IPAF to strengthen the humoral immunity