336 resultados para DOUBLE-GYRE FLOW


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BACKGROUND: About 1-5% of cancer patients suffer from significant normal tissue reactions as a result of radiotherapy (RT). It is not possible at this time to predict how most patients' normal tissues will respond to RT. DNA repair dysfunction is implicated in sensitivity to RT particularly in genes that mediate the repair of DNA double-strand breaks (DSBs). Phosphorylation of histone H2AX (phosphorylated molecules are known as gammaH2AX) occurs rapidly in response to DNA DSBs, and, among its other roles, contributes to repair protein recruitment to these damaged sites. Mammalian cell lines have also been crucial in facilitating the successful cloning of many DNA DSB repair genes; yet, very few mutant cell lines exist for non-syndromic clinical radiosensitivity (RS). METHODS: Here, we survey DNA DSB induction and repair in whole cells from RS patients, as revealed by gammaH2AX foci assays, as potential predictive markers of clinical radiation response. RESULTS: With one exception, both DNA focus induction and repair in cell lines from RS patients were comparable with controls. Using gammaH2AX foci assays, we identified a RS cancer patient cell line with a novel ionising radiation-induced DNA DSB repair defect; these data were confirmed by an independent DNA DSB repair assay. CONCLUSION: gammaH2AX focus measurement has limited scope as a pre-RT predictive assay in lymphoblast cell lines from RT patients; however, the assay can successfully identify novel DNA DSB repair-defective patient cell lines, thus potentially facilitating the discovery of novel constitutional contributions to clinical RS.

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BACKGROUND: The evaluation of retinal image quality in cataract eyes has gained importance and the clinical modulation transfer functions (MTF) can obtained by aberrometer and double pass (DP) system. This study aimed to compare MTF derived from a ray tracing aberrometer and a DP system in early cataractous and normal eyes. METHODS: There were 128 subjects with 61 control eyes and 67 eyes with early cataract defined according to the Lens Opacities Classification System III. A laser ray-tracing wavefront aberrometer (iTrace) and a double pass (DP) system (OQAS) assessed ocular MTF for 6.0 mm pupil diameters following dilation. Areas under the MTF (AUMTF) and their correlations were analyzed. Stepwise multiple regression analysis assessed factors affecting the differences between iTrace- and OQAS-derived AUMTF for the early cataract group. RESULTS: For both early cataract and control groups, iTrace-derived MTFs were higher than OQAS-derived MTFs across a range of spatial frequencies (P < 0.01). No significant difference between the two groups occurred for iTrace-derived AUMTF, but the early cataract group had significantly smaller OQAS-derived AUMTF than did the control group (P < 0.01). AUMTF determined from both the techniques demonstrated significant correlations with nuclear opacities, higher-order aberrations (HOAs), visual acuity, and contrast sensitivity functions, while the OQAS-derived AUMTF also demonstrated significant correlations with age and cortical opacity grade. The factors significantly affecting the difference between iTrace and OQAS AUMTF were root-mean-squared HOAs (standardized beta coefficient = -0.63, P < 0.01) and age (standardized beta coefficient = 0.26, P < 0.01). CONCLUSIONS: MTFs determined from a iTrace and a DP system (OQAS) differ significantly in early cataractous and normal subjects. Correlations with visual performance were higher for the DP system. OQAS-derived MTF may be useful as an indicator of visual performance in early cataract eyes.

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Injection velocity has been recognized as a key variable in thermoplastic injection molding. Its closed-loop control is, however, difficult due to the complexity of the process dynamic characteristics. The basic requirements of the control system include tracking of a pre-determined injection velocity curve defined in a profile, load rejection and robustness. It is difficult for a conventional control scheme to meet all these requirements. Injection velocity dynamics are first analyzed in this paper. Then a novel double-controller scheme is adopted for the injection velocity control. This scheme allows an independent design of set-point tracking and load rejection and has good system robustness. The implementation of the double-controller scheme for injection velocity control is discussed. Special techniques such as profile transformation and shifting are also introduced to improve the velocity responses. The proposed velocity control has been experimentally demonstrated to be effective for a wide range of processing conditions.

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The Galilee and Eromanga basins are sub-basins of the Great Artesian Basin (GAB). In this study, a multivariate statistical approach (hierarchical cluster analysis, principal component analysis and factor analysis) is carried out to identify hydrochemical patterns and assess the processes that control hydrochemical evolution within key aquifers of the GAB in these basins. The results of the hydrochemical assessment are integrated into a 3D geological model (previously developed) to support the analysis of spatial patterns of hydrochemistry, and to identify the hydrochemical and hydrological processes that control hydrochemical variability. In this area of the GAB, the hydrochemical evolution of groundwater is dominated by evapotranspiration near the recharge area resulting in a dominance of the Na–Cl water types. This is shown conceptually using two selected cross-sections which represent discrete groundwater flow paths from the recharge areas to the deeper parts of the basins. With increasing distance from the recharge area, a shift towards a dominance of carbonate (e.g. Na–HCO3 water type) has been observed. The assessment of hydrochemical changes along groundwater flow paths highlights how aquifers are separated in some areas, and how mixing between groundwater from different aquifers occurs elsewhere controlled by geological structures, including between GAB aquifers and coal bearing strata of the Galilee Basin. The results of this study suggest that distinct hydrochemical differences can be observed within the previously defined Early Cretaceous–Jurassic aquifer sequence of the GAB. A revision of the two previously recognised hydrochemical sequences is being proposed, resulting in three hydrochemical sequences based on systematic differences in hydrochemistry, salinity and dominant hydrochemical processes. The integrated approach presented in this study which combines different complementary multivariate statistical techniques with a detailed assessment of the geological framework of these sedimentary basins, can be adopted in other complex multi-aquifer systems to assess hydrochemical evolution and its geological controls.

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Successful prediction of groundwater flow and solute transport through highly heterogeneous aquifers has remained elusive due to the limitations of methods to characterize hydraulic conductivity (K) and generate realistic stochastic fields from such data. As a result, many studies have suggested that the classical advective-dispersive equation (ADE) cannot reproduce such transport behavior. Here we demonstrate that when high-resolution K data are used with a fractal stochastic method that produces K fields with adequate connectivity, the classical ADE can accurately predict solute transport at the macrodispersion experiment site in Mississippi. This development provides great promise to accurately predict contaminant plume migration, design more effective remediation schemes, and reduce environmental risks. Key Points Non-Gaussian transport behavior at the MADE site is unraveledADE can reproduce tracer transport in heterogeneous aquifers with no calibrationNew fractal method generates heterogeneous K fields with adequate connectivity

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Numerical study has been performed in this study to investigate the turbulent convection heat transfer on a rectangular plate mounted over a flat surface. Thermal and fluid dynamic performances of extended surfaces having various types of lateral perforations with square, circular, triangular and hexagonal cross sections are investigated. RANS (Reynolds averaged Navier–Stokes) based modified k–ω turbulence model is used to calculate the fluid flow and heat transfer parameters. Numerical results are compared with the results of previously published experimental data and obtained results are in reasonable agreement. Flow and heat transfer parameters are presented for Reynolds numbers from 2000 to 5000 based on the fin thickness.

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Background: Conventional biodiesel production relies on trans-esterification of lipids extracted from vegetable crops. However, the use of valuable vegetable food stocks as raw material for biodiesel production makes it an unfeasibly expensive process. Used cooking oil is a finite resource and requires extra downstream processing, which affects the amount of biodiesel that can be produced and the economics of the process. Lipids extracted from microalgae are considered an alternative raw material for biodiesel production. This is primarily due to the fast growth rate of these species in a simple aquaculture environment. However, the dilute nature of microalgae culture puts a huge economic burden on the dewatering process especially on an industrial scale. This current study explores the performance and economic viability of chemical flocculation and tangential flow filtration (TFF) for the dewatering of Tetraselmis suecicamicroalgae culture. Results: Results show that TFF concentrates the microalgae feedstock up to 148 times by consuming 2.06 kWh m-3 of energy while flocculation consumes 14.81 kWhm-3 to concentrate the microalgae up to 357 times. Economic evaluation demonstrates that even though TFF has higher initial capital investment than polymer flocculation, the payback period for TFF at the upper extreme ofmicroalgae revenue is ∼1.5 years while that of flocculation is ∼3 years. Conclusion: These results illustrate that improved dewatering levels can be achieved more economically by employing TFF. The performances of these two techniques are also compared with other dewatering techniques.

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The preparation of macroporous methacrylate monolithic material with controlled pore structures can be carried out in an unstirred mould through careful and precise control of the polymerisation kinetics and parameters. Contemporary synthesis conditions of methacrylate monolithic polymers are based on existing polymerisation schemes without an in-depth understanding of the dynamics of pore structure and formation. This leads to poor performance in polymer usage thereby affecting final product recovery and purity, retention time, productivity and process economics. The unique porosity of methacrylate monolithic polymer which propels its usage in many industrial applications can be controlled easily during its preparation. Control of the kinetics of the overall process through changes in reaction time, temperature and overall composition such as cross-linker and initiator contents allow the fine tuning of the macroporous structure and provide an understanding of the mechanism of pore formation within the unstirred mould. The significant effect of temperature of the reaction kinetics serves as an effectual means to control and optimise the pore structure and allows the preparation of polymers with different pore size distributions from the same composition of the polymerisation mixture. Increasing the concentration of the cross-linking monomer affects the composition of the final monoliths and also decreases the average pore size as a result of pre-mature formation of highly cross-linked globules with a reduced propensity to coalesce. The choice and concentration of porogen solvent is also imperative. Different porogens and porogen mixtures present different pore structure output. Example, larger pores are obtained in a poor solvent due to early phase separation.

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This paper presents an extension to the Rapidly-exploring Random Tree (RRT) algorithm applied to autonomous, drifting underwater vehicles. The proposed algorithm is able to plan paths that guarantee convergence in the presence of time-varying ocean dynamics. The method utilizes 4-Dimensional, ocean model prediction data as an evolving basis for expanding the tree from the start location to the goal. The performance of the proposed method is validated through Monte-Carlo simulations. Results illustrate the importance of the temporal variance in path execution, and demonstrate the convergence guarantee of the proposed methods.

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We propose the use of optical flow information as a method for detecting and describing changes in the environment, from the perspective of a mobile camera. We analyze the characteristics of the optical flow signal and demonstrate how robust flow vectors can be generated and used for the detection of depth discontinuities and appearance changes at key locations. To successfully achieve this task, a full discussion on camera positioning, distortion compensation, noise filtering, and parameter estimation is presented. We then extract statistical attributes from the flow signal to describe the location of the scene changes. We also employ clustering and dominant shape of vectors to increase the descriptiveness. Once a database of nodes (where a node is a detected scene change) and their corresponding flow features is created, matching can be performed whenever nodes are encountered, such that topological localization can be achieved. We retrieve the most likely node according to the Mahalanobis and Chi-square distances between the current frame and the database. The results illustrate the applicability of the technique for detecting and describing scene changes in diverse lighting conditions, considering indoor and outdoor environments and different robot platforms.

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In this paper we propose a novel approach to multi-action recognition that performs joint segmentation and classification. This approach models each action using a Gaussian mixture using robust low-dimensional action features. Segmentation is achieved by performing classification on overlapping temporal windows, which are then merged to produce the final result. This approach is considerably less complicated than previous methods which use dynamic programming or computationally expensive hidden Markov models (HMMs). Initial experiments on a stitched version of the KTH dataset show that the proposed approach achieves an accuracy of 78.3%, outperforming a recent HMM-based approach which obtained 71.2%.