A new time-dependent analytic compact model for radiation-induced photocurrent in epitaxial structures.
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Purpose: Accurate patient-specific photon cross-section information is needed to support more accurate model-based dose calculation for low energy photon-emitting modalities in medicine such as brachytherapy and kilovoltage x-ray imaging procedures. A postprocessing dual-energy CT (pDECT) technique for noninvasivein vivo estimation of photon linear attenuation coefficients has been experimentally implemented on a commercial CT scanner and its accuracy assessed in idealized phantom geometries. Methods: Eight test materials of known composition and density were used to compare pDECT-estimated linear attenuation coefficients to NIST reference values over an energy range from 10 keV to 1 MeV. As statistical image reconstruction (SIR) has been shown to reconstruct images with less random and systematic error than conventional filtered backprojection (FBP), the pDECT technique was implemented with both an in-house polyenergetic SIR algorithm, alternating minimization (AM), as well as a conventional FBP reconstruction algorithm. Improvement from increased spectral separation was also investigated by filtering the high-energy beam with an additional 0.5 mm of tin. The law of propagated uncertainty was employed to assess the sensitivity of the pDECT process to errors in reconstructed images. Results: Mean pDECT-estimated linear attenuation coefficients for the eight test materials agreed within 1% of NIST reference values for energies from 1 MeV down to 30 keV, with mean errors rising to between 3% and 6% at 10 keV, indicating that the method is unbiased when measurement and calibration phantom geometries are matched. Reconstruction with FBP and AM algorithms conferred similar mean pDECT accuracy. However, single-voxel pDECT estimates reconstructed on a 1 × 1 × 3 mm{sup 3} grid are shown to be highly sensitive to reconstructed image uncertainty; in some cases pDECT attenuation coefficient estimates exhibited standard deviations on the order of 20% around the mean. Reconstruction with the statistical AM algorithm led to standard deviations roughly 40% to 60% less than FBP reconstruction. Additional tin filtration of the high energy beam exhibits similar pDECT estimation accuracy as the unfiltered beam, even when scanning with only 25% of the dose. Using the law of propagated uncertainty, low Z materials are found to be more sensitive to image reconstruction errors than high Z materials. Furthermore, it is estimated that reconstructed CT image uncertainty must be limited to less than 0.25% to achieve a target linear-attenuation coefficient estimation uncertainty of 3% at 28 keV. Conclusions: That pDECT supports mean linear attenuation coefficient measurement accuracies of 1% of reference values for energies greater than 30 keV is encouraging. However, the sensitivity of the pDECT measurements to noise and systematic errors in reconstructed CT images warrants further investigation in more complex phantom geometries. The investigated statistical reconstruction algorithm, AM, reduced random measurement uncertainty relative to FBP owing to improved noise performance. These early results also support efforts to increase DE spectral separation, which can further reduce the pDECT sensitivity to measurement uncertainty.
This code is a set of modules designed to detect certain classes of cyber attacks against equipment in power distribution grid substations, including attacks against transformer tap changers and capacitor bank switches, as well as "reconnaissance" attacks against equipment in power grid substations.
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Results are presented of a cold-flow and hot-fire experimental study of the mixing and atomization characteristics of injector elements incorporating noncircular orifices. Both liquid/liquid and gas/liquid element types are discussed. Unlike doublet and triplet elements (circular orifices only) were investigated for the liquid/liquid case while concentric tube elements were investigated for the gas/liquid case. It is concluded that noncircular shape can be employed to significant advantage in injector design for liquid rocket engines.
The performance capabilities and limitations of the instrument pointing system (IPS) are described. Suggestions of design modifications that result in overall improved IPS performance are included. Since the design and configuration of the IPS was modified a portion of the study was performed with the inside-out Gimbal configuration which was updated to the present coincident Gimbal system configuration. Due to the similarity of the two systems, the results obtained for the inside-out Gimbal also apply to the coincident Gimbal system.
The basic theoretical work required to understand sound transmission into an enclosed space (that is, one closed by the transmitting structure) is developed for random pressure fields and for harmonic (tonal) excitation. The analysis is used to predict the noise reducton of unpressurized unstiffened cylinder, and also the interior response of the cylinder given a tonal (plane wave) excitation. Predictions and measurements are compared and the transmission is analyzed. In addition, results for tonal (harmonic) mechanical excitation are considered.
A semianalytic method to predict the orbit of a spacecraft orbiting a planet and perturbed by oblateness and a gravity anomaly (mascon) is presented. The Hamiltonian is first expressed in closed form in terms of the nonsingular canonical set of Poincare elements where the mascon is represented by a point-mass. The planet global potential is limited to the second and third harmonics. The mascon potential in harmonics is limited to second degree and first order. The original Lie-Hori method is then applied to transform the canonical equations of motion from the osculating element space to the mean element space. Numerical integration of the new equations is used.
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Visual analytics systems are becoming very popular. More domains now use interactive visualizations to analyze the ever-increasing amount and heterogeneity of data. More novel visualizations are being developed for more tasks and users. We need to ensure that these systems can be evaluated to determine that they are both useful and usable. A user-centered evaluation for visual analytics needs to be developed for these systems. While many of the typical human-computer interaction (HCI) evaluation methodologies can be applied as is, others will need modification. Additionally, new functionality in visual analytics systems needs new evaluation methodologies. There is a difference between usability evaluations and user-centered evaluations. Usability looks at the efficiency, effectiveness, and user satisfaction of users carrying out tasks with software applications. User-centered evaluation looks more specifically at the utility provided to the users by the software. This is reflected in the evaluations done and in the metrics used. In the visual analytics domain this is very challenging as users are most likely experts in a particular domain, the tasks they do are often not well defined, the software they use needs to support large amounts of different kinds of data, and often the tasks last for months. These difficulties are discussed more in the section on User-centered Evaluation. Our goal is to provide a discussion of user-centered evaluation practices for visual analytics, including existing practices that can be carried out and new methodologies and metrics that need to be developed and agreed upon by the visual analytics community. The material provided here should be of use for both researchers and practitioners in the field of visual analytics. Researchers and practitioners in HCI and interested in visual analytics will find this information useful as well as a discussion on changes that need to be made to current HCI practices to make them more suitable to visual analytics. A history of analysis and analysis techniques and problems is provided as well as an introduction to user-centered evaluation and various evaluation techniques for readers from different disciplines. The understanding of these techniques is imperative if we wish to support analysis in the visual analytics software we develop. Currently the evaluations that are conducted and published for visual analytics software are very informal and consist mainly of comments from users or potential users. Our goal is to help researchers in visual analytics to conduct more formal user-centered evaluations. While these are time-consuming and expensive to carryout, the outcomes of these studies will have a defining impact on the field of visual analytics and help point the direction for future features and visualizations to incorporate. While many researchers view work in user-centered evaluation as a less-than-exciting area to work, the opposite is true. First of all, the goal is user-centered evaluation is to help visual analytics software developers, researchers, and designers improve their solutions and discover creative ways to better accommodate their users. Working with the users is extremely rewarding as well. While we use the term “users” in almost all situations there are a wide variety of users that all need to be accommodated. Moreover, the domains that use visual analytics are varied and expanding. Just understanding the complexities of a number of these domains is exciting. Researchers are trying out different visualizations and interactions as well. And of course, the size and variety of data are expanding rapidly. User-centered evaluation in this context is rapidly changing. There are no standard processes and metrics and thus those of us working on user-centered evaluation must be creative in our work with both the users and with the researchers and developers.
Cancer is one of the most significant health care problems today. It is estimated that 1 in 2 men and women will be diagnosed with some form of cancer in their lifetime. Nearly two thirds of cancer patients will receive radiation therapy at some point in their care and the vast majority of these radiotherapy treatments will be performed with megavoltage photon therapy. Photon treatment techniques typically devote considerable attention to limiting the exposure of healthy tissues outside of the intended treatment volume. Nonetheless, small levels of unwanted stray radiation are incident upon the patient's whole body, which can increase the risk for numerous late effects, including second malignancies. When one considers that second cancers account for approximately 15% of all cancers and that it is projected that by the year 2020, there will be almost 20 million cancer survivors in the United States and four million in the UK, the need for improved accuracy in stray dose calculations appears likely to increase dramatically. Despite the obvious benefits this capability would afford, contemporary commercial treatment planning systems (TPSs) lack the ability to accurately predict stray dose outside the treatment field, e.g., beyond the 5% isodose surface. Thus, there is a large, variable, and systematic underestimation of stray radiation exposures by commonly used clinical TPSs for photon therapy. To overcome this problem, in a companion paper we proposed a simple, physics-based analytical model that calculates the total therapeutic and stray dose from photon treatment fields. The purpose of this work was to evaluate the accuracy of the basic physics in the analytical absorbed dose model for several simple treatment fields. We assessed the performance of the basic physics in an analytical model that calculates the total absorbed dose deposited by primary, leakage, and scattered photons both inside and outside of square treatment fields of 6 MV photon conventional radiotherapy. The assessment was accomplished by comparing model predictions of total dose with measured dose profiles in a water-phantom. The major finding of this study is that the physics-based model performed well, predicting dose in water within 11.5% dose difference or 5 mm distance to agreement with measured values in the therapeutic region and within 1.1 mGy/Gy or 0.5 mm distance to agreement with measured values in the out-of-field region at 90% of the locations considered. Contemporary external-beam photon TPSs do not include the capability to accurately predict the absorbed dose beyond a few centimeters outside of the treatment field. Our work suggests that it is feasible to improve dosimetric accuracy in the out-of-field region using a simple and computationally fast, physics-based analytical algorithm. This is demonstrated by the measured absorbed dose as well as the corresponding values calculated by two contemporary commercial TPSs (Pinnacle, Philips Health care, Andover, MA and Eclipse, Varian Medical Systems, Inc., Palo Alto, CA) and the analytical model considered in this work. It is apparent that both TPSs underestimated the absorbed dose by greater than 3 mGy/Gy at locations outside of the 0.5% relative dose level. In contrast, the analytical model evaluated in this work calculated absorbed dose in the same region within 0.4 mGy/Gy of measured values. The physics-based analytical dose model could prove useful in a number of different applications. The accurate knowledge of absorbed dose deposited outside of the treatment field is especially important for predicting radiation late effects, such as second cancer, fertility complications, and cardiac toxicity. It may also facilitate predictions of the effects of in utero exposure. We conclude that it is possible to accurately calculate the therapeutic and stray absorbed dose from a conventional radiotherapy treatment field of 6 MV photons using a physics-based analytical model. This was demonstrated by the agreement between measured and calculated doses in water within 11.5% relative dose difference or 5 mm distance to agreement in the therapeutic region, and within 1.1 mGy/Gy absolute dose difference or 0.5 mm distance to agreement in the out-of-field region at 90% of positions for all of the fields considered in this work. This is also illustrated by Figure 4 which plots dose values from analytical model predictions and measurements at five representative irradiation conditions and shows that the analytical model agreement is good in all cases. Furthermore, the model validated in this work evidently can predict dose values with better accuracy at out-of-field locations than contemporary TPSs. This and previous works, taken together, suggest that analytical physics-based models will offer advantages over TPSs or Monte Carlo models for whole-body dose calculations for several radiotherapy applications.