7 resultados para Optical bias control
Resumo:
Control of the collective response of plasma particles to intense laser light is intrinsic to relativistic optics, the development of compact laser-driven particle and radiation sources, as well as investigations of some laboratory astrophysics phenomena. We recently demonstrated that a relativistic plasma aperture produced in an ultra-thin foil at the focus of intense laser radiation can induce diffraction, enabling polarization-based control of the collective motion of plasma electrons. Here we show that under these conditions the electron dynamics are mapped into the beam of protons accelerated via strong charge-separation-induced electrostatic fields. It is demonstrated experimentally and numerically via 3D particle-in-cell simulations that the degree of ellipticity of the laser polarization strongly influences the spatial-intensity distribution of the beam of multi-MeV protons. The influence on both sheath-accelerated and radiation pressure-accelerated protons is investigated. This approach opens up a potential new route to control laser-driven ion sources.
Resumo:
Induced conformational change provides a powerful mechanism to modulate the structure and function of molecules. Here we describe the synthesis of chiral, surface-functionalized oligomeric pyridine/imidazolidin-2-one foldamers, and interrogate their acid-mediated transition between linear and helical topologies.
Resumo:
Microneedles (MNs) are emerging devices that can be used for the delivery of drugs at specific locations1. Their performance is primarily judged by different features and the penetration through tissue is one of the most important aspects to evaluate. For detailed studies of MN performance different kind of in-vitro, exvivo and in-vivo tests should be performed. The main limitation of some of these tests is that biological tissue is too heterogeneous, unstable and difficult to obtain. In addition the use of biological materials sometimes present legal issues. There are many studies dealing with artificial membranes for drug diffusion2, but studies of artificial membranes for Microneedle mechanical characterization are scarce3. In order to overcome these limitations we have developed tests using synthetic polymeric membranes instead of biological tissue. The selected artificial membrane is homogeneous, stable, and readily available. This material is mainly composed of a roughly equal blend of a hydrocarbon wax and a polyolefin and it is commercially available under the brand name Parafilm®. The insertion of different kind of MN arrays prepared from crosslinked polymers were performed using this membrane and correlated with the insertion of the MN arrays in ex-vivo neonatal porcine skin. The insertion depth of the MNs was evaluated using Optical coherence tomography (OCT). The implementation of MN transdermal patches in the market can be improved by make this product user-friendly and easy to use. Therefore, manual insertion is preferred to other kind of procedures. Consequently, the insertion studies were performed in neonatal porcine skin and the artificial membrane using a manual insertion force applied by human volunteers. The insertion studies using manual forces correlated very well with the same studies performed with a Texture Analyzer equipment. These synthetic membranes seem to mimic closely the mechanical properties of the skin for the insertion of MNs using different methods of insertion. In conclusion, this artificial membrane substrate offers a valid alternative to biological tissue for the testing of MN insertion and can be a good candidate for developing a reliable quality control MN insertion test.
Resumo:
BACKGROUND:
Evidence regarding the association of the built environment with physical activity is influencing policy recommendations that advocate changing the built environment to increase population-level physical activity. However, to date there has been no rigorous appraisal of the quality of the evidence on the effects of changing the built environment. The aim of this review was to conduct a thorough quantitative appraisal of the risk of bias present in those natural experiments with the strongest experimental designs for assessing the causal effects of the built environment on physical activity.
METHODS:
Eligible studies had to evaluate the effects of changing the built environment on physical activity, include at least one measurement before and one measurement of physical activity after changes in the environment, and have at least one intervention site and non-intervention comparison site. Given the large number of systematic reviews in this area, studies were identified from three exemplar systematic reviews; these were published in the past five years and were selected to provide a range of different built environment interventions. The risk of bias in these studies was analysed using the Cochrane Risk of Bias Assessment Tool: for Non-Randomized Studies of Interventions (ACROBAT-NRSI).
RESULTS:
Twelve eligible natural experiments were identified. Risk of bias assessments were conducted for each physical activity outcome from all studies, resulting in a total of fifteen outcomes being analysed. Intervention sites included parks, urban greenways/trails, bicycle lanes, paths, vacant lots, and a senior citizen's centre. All outcomes had an overall critical (n = 12) or serious (n = 3) risk of bias. Domains with the highest risk of bias were confounding (due to inadequate control sites and poor control of confounding variables), measurement of outcomes, and selection of the reported result.
CONCLUSIONS:
The present review focused on the strongest natural experiments conducted to date. Given this, the failure of existing studies to adequately control for potential sources of bias highlights the need for more rigorous research to underpin policy recommendations for changing the built environment to increase physical activity. Suggestions are proposed for how future natural experiments in this area can be improved.
Resumo:
The measurement of fast changing temperature fluctuations is a challenging problem due to the inherent limited bandwidth of temperature sensors. This results in a measured signal that is a lagged and attenuated version of the input. Compensation can be performed provided an accurate, parameterised sensor model is available. However, to account for the influence of the measurement environment and changing conditions such as gas velocity, the model must be estimated in-situ. The cross-relation method of blind deconvolution is one approach for in-situ characterisation of sensors. However, a drawback with the method is that it becomes positively biased and unstable at high noise levels. In this paper, the cross-relation method is cast in the discrete-time domain and a bias compensation approach is developed. It is shown that the proposed compensation scheme is robust and yields unbiased estimates with lower estimation variance than the uncompensated version. All results are verified using Monte-Carlo simulations.
Resumo:
To maintain the pace of development set by Moore's law, production processes in semiconductor manufacturing are becoming more and more complex. The development of efficient and interpretable anomaly detection systems is fundamental to keeping production costs low. As the dimension of process monitoring data can become extremely high anomaly detection systems are impacted by the curse of dimensionality, hence dimensionality reduction plays an important role. Classical dimensionality reduction approaches, such as Principal Component Analysis, generally involve transformations that seek to maximize the explained variance. In datasets with several clusters of correlated variables the contributions of isolated variables to explained variance may be insignificant, with the result that they may not be included in the reduced data representation. It is then not possible to detect an anomaly if it is only reflected in such isolated variables. In this paper we present a new dimensionality reduction technique that takes account of such isolated variables and demonstrate how it can be used to build an interpretable and robust anomaly detection system for Optical Emission Spectroscopy data.
Resumo:
Permanent magnet synchronous motors (PMSMs) provide a competitive technology for EV traction drives owing to their high power density and high efficiency. In this paper, three types of interior PMSMs with different PM arrangements are modeled by the finite element method (FEM). For a given amount of permanent magnet materials, the V-shape interior PMSM is found better than the U-shape and the conventional rotor topologies for EV traction drives. Then the V-shape interior PMSM is further analyzed with the effects of stator slot opening and the permanent magnet pole chamfering on cogging torque and output torque performance. A vector-controlled flux-weakening method is developed and simulated in Matlab to expand the motor speed range for EV drive system. The results show good dynamic and steady-state performance with a capability of expanding speed up to four times of the rated. A prototype of the V-shape interior PMSM is also manufactured and tested to validate the numerical models built by the FEM.