864 resultados para Field of vision.
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The Hamiltonian formulation of the teleparallel equivalent of general relativity is considered. Definitions of energy, momentum and angular momentum of the gravitational field arise from the integral form of the constraint equations of the theory. In particular, the gravitational energy-momentum is given by the integral of scalar densities over a three-dimensional spacelike hypersurface. The definition for the gravitational energy is investigated in the context of the Kerr black hole. In the evaluation of the energy contained within the external event horizon of the Kerr black hole, we obtain a value strikingly close to the irreducible mass of the latter. The gravitational angular momentum is evaluated for the gravitational field of a thin, slowly rotating mass shell. © 2002 The American Physical Society.
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The pressure field of a high-power klystron amplifier in the cathode and anode region was investigated. The investigation was performed using a 1.3 GHz, 100 A and 240 kV high-power klystron with five reentrant coaxial cavities, assembled in cylindrical drift tube 1.2 m long. The diffusion equation in mathematical model was also solved by using a 3-D finite element method code, in order to obtain pressure profile in region of interest. The results show that density profile of molecules between cathode-anode region was determined, where cathode pressure is approximately 10% higher than anode pressure.
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Includes bibliography
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Optical transition radiation (OTR) plays an important role in beam diagnostics for high energy particle accelerators. Its linear intensity with beam current is a great advantage as compared to fluorescent screens, which are subject to saturation. Moreover, the measurement of the angular distribution of the emitted radiation enables the determination of many beam parameters in a single observation point. However, few works deals with the application of OTR to monitor low energy beams. In this work we describe the design of an OTR based beam monitor used to measure the transverse beam charge distribution of the 1.9-MeV electron beam of the linac injector of the IFUSP microtron using a standard vision machine camera. The average beam current in pulsed operation mode is of the order of tens of nano-Amps. Low energy and low beam current make OTR observation difficult. To improve sensitivity, the beam incidence angle on the target was chosen to maximize the photon flux in the camera field-of-view. Measurements that assess OTR observation (linearity with beam current, polarization, and spectrum shape) are presented, as well as a typical 1.9-MeV electron beam charge distribution obtained from OTR. Some aspects of emittance measurement using this device are also discussed. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4748519]
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We have studied the possibility of affecting the entanglement measure of 2-qubit system consisting of two photons with different fi xed frequencies but with two arbitrary linear polarizations, moving in the same direction, by the help of an applied external magnetic field. The interaction between the magnetic fi eld and the photons in our model is achieved through intermediate electrons that interact with both the photons and the magnetic fi eld. The possibility of exact theoretical analysis of this scheme is based on known exact solutions that describe the interaction of an electron subjected to an external magnetic fi eld (or a medium of electrons not interacting with each other) with a quantized field of two photons. We adapt these exact solutions to the case under consideration. Using explicit wave functions for the resulting electromagnetic fi eld, we calculate the entanglement measure of the photon beam as a function of the applied magnetic field and parameters of the electron medium.
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Food technologies today mean reducing agricultural food waste, improvement of food security, enhancement of food sensory properties, enlargement of food market and food economies. Food technologists must be high-skilled technicians with good scientific knowledge of food hygiene, food chemistry, industrial technologies and food engineering, sensory evaluation experience and analytical chemistry. Their role is to apply the modern vision of science in the field of human nutrition, rising up knowledge in food science. The present PhD project starts with the aim of studying and improving frozen fruits quality. Freezing process in very powerful in preserve initial raw material characteristics, but pre-treatment before the freezing process are necessary to improve quality, in particular to improve texture and enzymatic activity of frozen foods. Osmotic Dehydration (OD) and Vacuum Impregnation (VI), are useful techniques to modify fruits and vegetables composition and prepare them to freezing process. These techniques permit to introduce cryo-protective agent into the food matrices, without significant changes of the original structure, but cause a slight leaching of important intrinsic compounds. Phenolic and polyphenolic compounds for example in apples and nectarines treated with hypertonic solutions are slightly decreased, but the effect of concentration due to water removal driven out from the osmotic gradient, cause a final content of phenolic compounds similar to that of the raw material. In many experiment, a very important change in fruit composition regard the aroma profile. This occur in strawberries osmo-dehydrated under vacuum condition or under atmospheric pressure condition. The increment of some volatiles, probably due to fermentative metabolism induced by the osmotic stress of hypertonic treatment, induce a sensory profile modification of frozen fruits, that in some way result in a better acceptability of consumer, that prefer treated frozen fruits to untreated frozen fruits. Among different processes used, a very interesting result was obtained with the application of a osmotic pre-treatment driven out at refrigerated temperature for long time. The final quality of frozen strawberries was very high and a peculiar increment of phenolic profile was detected. This interesting phenomenon was probably due to induction of phenolic biological synthesis (for example as reaction to osmotic stress), or to hydrolysis of polymeric phenolic compounds. Aside this investigation in the cryo-stabilization and dehydrofreezing of fruits, deeper investigation in VI techniques were carried out, as studies of changes in vacuum impregnated prickly pear texture, and in use of VI and ultrasound (US) in aroma enrichment of fruit pieces. Moreover, to develop sensory evaluation tools and analytical chemistry determination (of volatiles and phenolic compounds), some researches were bring off and published in these fields. Specifically dealing with off-flavour development during storage of boiled potato, and capillary zonal electrophoresis (CZE) and high performance liquid chromatography (HPLC) determination of phenolic compounds.
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The electric dipole response of neutron-rich nickel isotopes has been investigated using the LAND setup at GSI in Darmstadt (Germany). Relativistic secondary beams of 56−57Ni and 67−72Ni at approximately 500 AMeV have been generated using projectile fragmentation of stable ions on a 4 g/cm2 Be target and subsequent separation in the magnetic dipole fields of the FRagment Separator (FRS). After reaching the LAND setup in Cave C, the radioactive ions were excited electromagnetically in the electric field of a Pb target. The decay products have been measured in inverse kinematics using various detectors. Neutron-rich 67−69Ni isotopes decay by the emission of neutrons, which are detected in the LAND detector. The present analysis concentrates on the (gamma,n) and (gamma,2n) channels in these nuclei, since the proton and three-neutron thresholds are unlikely to be reached considering the virtual photon spectrum for nickel ions at 500 AMeV. A measurement of the stable 58Ni isotope is used as a benchmark to check the accuracy of the present results with previously published data. The measured (gamma,n) and (gamma,np) channels are compared with an inclusive photoneutron measurement by Fultz and coworkers, which are consistent within the respective errors. The measured excitation energy distributions of 67−69Ni contain a large portion of the Giant Dipole Resonance (GDR) strength predicted by the Thomas-Reiche-Kuhn energy-weighted sum rule, as well as a significant amount of low-lying E1 strength, that cannot be attributed to the GDR alone. The GDR distribution parameters are calculated using well-established semi-empirical systematic models, providing the peak energies and widths. The GDR strength is extracted from the chi-square minimization of the model GDR to the measured data of the (gamma,2n) channel, thereby excluding any influence of eventual low-lying strength. The subtraction of the obtained GDR distribution from the total measured E1 strength provides the low-lying E1 strength distribution, which is attributed to the Pygmy Dipole Resonance (PDR). The extraction of the peak energy, width and strength is performed using a Gaussian function. The minimization of trial Gaussian distributions to the data does not converge towards a sharp minimum. Therefore, the results are presented by a chi-square distribution as a function of all three Gaussian parameters. Various predictions of PDR distributions exist, as well as a recent measurement of the 68Ni pygmy dipole-resonance obtained by virtual photon scattering, to which the present pygmy dipole-resonance distribution is also compared.
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Recent advances in the fast growing area of therapeutic/diagnostic proteins and antibodies - novel and highly specific drugs - as well as the progress in the field of functional proteomics regarding the correlation between the aggregation of damaged proteins and (immuno) senescence or aging-related pathologies, underline the need for adequate analytical methods for the detection, separation, characterization and quantification of protein aggregates, regardless of the their origin or formation mechanism. Hollow fiber flow field-flow fractionation (HF5), the miniaturized version of FlowFFF and integral part of the Eclipse DUALTEC FFF separation system, was the focus of this research; this flow-based separation technique proved to be uniquely suited for the hydrodynamic size-based separation of proteins and protein aggregates in a very broad size and molecular weight (MW) range, often present at trace levels. HF5 has shown to be (a) highly selective in terms of protein diffusion coefficients, (b) versatile in terms of bio-compatible carrier solution choice, (c) able to preserve the biophysical properties/molecular conformation of the proteins/protein aggregates and (d) able to discriminate between different types of protein aggregates. Thanks to the miniaturization advantages and the online coupling with highly sensitive detection techniques (UV/Vis, intrinsic fluorescence and multi-angle light scattering), HF5 had very low detection/quantification limits for protein aggregates. Compared to size-exclusion chromatography (SEC), HF5 demonstrated superior selectivity and potential as orthogonal analytical method in the extended characterization assays, often required by therapeutic protein formulations. In addition, the developed HF5 methods have proven to be rapid, highly selective, sensitive and repeatable. HF5 was ideally suitable as first dimension of separation of aging-related protein aggregates from whole cell lysates (proteome pre-fractionation method) and, by HF5-(UV)-MALS online coupling, important biophysical information on the fractionated proteins and protein aggregates was gathered: size (rms radius and hydrodynamic radius), absolute MW and conformation.
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This thesis will focus on the residual function and visual and attentional deficits in human patients, which accompany damage to the visual cortex or its thalamic afferents, and plastic changes, which follow it. In particular, I will focus on homonymous visual field defects, which comprise a broad set of central disorders of vision. I will present experimental evidence that when the primary visual pathway is completely damaged, the only signal that can be implicitly processed via subcortical visual networks is fear. I will also present data showing that in a patient with relative deafferentation of visual cortex, changes in the spatial tuning and response gain of the contralesional and ipsilesional cortex are observed, which are accompanied by changes in functional connectivity with regions belonging to the dorsal attentional network and the default mode network. I will also discuss how cortical plasticity might be harnessed to improve recovery through novel treatments. Moreover, I will show how treatment interventions aimed at recruiting spared subcortical pathway supporting multisensory orienting can drive network level change.
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In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of "intelligence". The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them. CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems. HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain. In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.
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The optical properties of a match-like plasmonic nanostructure are numerically investigated using full-wave finite-difference time-domain analysis in conjunction with dispersive material models. This work is mainly motivated by the developed technique enabling reproducible fabrication of nanomatch structures as well as the growing applications that utilize the localized field enhancement in plasmonic nanostructures. Our research revealed that due to the pronounced field enhancement and larger resonance tunabilities, some nanomatch topologies show potentials for various applications in the field of, e.g., sensing as well as a novel scheme for highly reproducible tips in scanning near field optical microscopy, among others. Despite the additional degrees of freedom that are offered by the composite nature of the proposed nanomatch topology, the paper also reflects on a fundamental complication intrinsic to the material interfaces especially in the nanoscale: stoichiometric mixing. We conclude that the specificity in material modeling will become a significant issue in future research on functionalized composite nanostructures.
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Choroidal metastasis represents the most common type of intraocular malignancy and preferably involves the posterior uveal tract. Breast and lung cancer - known or so far undiagnosed - are most frequently identified as the underlying tumor disease. The majority of patients diagnosed with uveal metastasis have additional metastatic manifestations elsewhere, so re-staging before treatment is recommended. The importance of a multidisciplinary approach is obvious. Early diagnosis and timely initiation of treatment are mandatory in case of vision-threatening situations. External beam radiation remains the therapy of choice. Overall survival of patients with uveal metastasis is limited, averaging six to twelve months. The other eye is frequently enough affected as well, justifying regular ophthalmologic follow-up during the further course of the disease.
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Sensitivity to spatial and temporal patterns is a fundamental aspect of vision. Herein, we investigated this sensitivity in adult zebrafish for a wide range of spatial (0.014 to 0.511 cycles/degree [c/d]) and temporal frequencies (0.025 to 6 cycles/s) to better understand their visual system. Measurements were performed at photopic (1.8 log cd m(-2)) and scotopic (-4.5 log cd m(-2)) light levels to assess the optokinetic response (OKR). The resulting spatiotemporal contrast sensitivity (CS) functions revealed that the OKR of zebrafish is tuned to spatial frequency and speed but not to temporal frequencies. Thereby, optimal test parameters for CS measurements were identified. At photopic light levels, a spatial frequency of 0.116 ± 0.01 c/d (mean ± SD) and a grating speed of 8.42 ± 2.15 degrees/second (d/s) was ideal; at scotopic light levels, these values were 0.110 ± 0.02 c/d and 5.45 ± 1.31 d/s, respectively. This study allows to better characterize zebrafish mutants with altered vision and to distinguish between defects of rod and cone photoreceptors as measurements were performed under different light conditions.
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Two genetically different types of authigenic carbonate mounds are studied: (1) from an active hydrothermal field related to serpentinite protrusions in a zone of intersection of a transform fracture zone with the Mid-Atlantic Ridge, (2) from an active field of methane seepings in the Dnieper canyon of the Black sea. General geochemical conditions, under which authigenic carbonate formation occurs within these two fields, were found. They include: presence of reduced H2S, H2, and CH4 gases at absence of free oxygen; high alkalinity of waters participating in carbonate formation; similarity of textural and structural features of authigenic aragonite, which represents the initial carbonate mineral of the mounds; paragenesis of aragonite with sulfide minerals; close relation of carbonate mounds with communities of sulfate-reducing and methane-oxidizing microorganisms. A new mechanism of formation of hydrothermal authigenic carbonates is suggested. It implies their microbial sulfate reduction over hydrogen from fluid in the subsurface mixing zone of hydrothermal solution and adjacent seawater.
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The analysis of time-dependent data is an important problem in many application domains, and interactive visualization of time-series data can help in understanding patterns in large time series data. Many effective approaches already exist for visual analysis of univariate time series supporting tasks such as assessment of data quality, detection of outliers, or identification of periodically or frequently occurring patterns. However, much fewer approaches exist which support multivariate time series. The existence of multiple values per time stamp makes the analysis task per se harder, and existing visualization techniques often do not scale well. We introduce an approach for visual analysis of large multivariate time-dependent data, based on the idea of projecting multivariate measurements to a 2D display, visualizing the time dimension by trajectories. We use visual data aggregation metaphors based on grouping of similar data elements to scale with multivariate time series. Aggregation procedures can either be based on statistical properties of the data or on data clustering routines. Appropriately defined user controls allow to navigate and explore the data and interactively steer the parameters of the data aggregation to enhance data analysis. We present an implementation of our approach and apply it on a comprehensive data set from the field of earth bservation, demonstrating the applicability and usefulness of our approach.