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A hydraulic actuator designed for a thrust vector control system used as a shaker for a vehicle to determine the bending mode frequencies is described. The actuator is used as the prime mover and the frequency sensor for the flexible vehicle in a test tower. Advantages in using the actuator piston position with respect to a commanded position to obtain the bending mode frequencies are shown.
A single ejector equipped with only one vector control jet and a diffuser flap was installed close to the leading edge of the strake of a one-fifth scale, semi-span model of the aircraft, without wing, canard, or tail surface. Tests of the system at a nozzle pressure ratio of 1.24 indicated a thrust augmentation of 1.92 and a thrust in the flight direction of about 12% of the total thrust under static conditions. An ejector stall occured at a ratio of tunnel dynamic pressure to nozzle gage pressure of about 0.008. Ejector stall speed can be delayed by using a boundary layer control jet at the front inlet lip of the ejector.
Takeoff predictions for powered lift short takeoff (STO) aircraft have been added to NASA Ames Research Center's aircraft synthesis (ACSYNT) code. The new computer code predicts the aircraft engine and nozzle settings required to achieve the minimum takeoff roll. As a test case, it predicted takeoff around rolls and nozzle settings for the YAV-8B Harrier that were close to the actual values. Analysis of takeoff performance for an ejector-augmentor design and a vectoring-nozzle design indicated that ground roll can be decreased, for either configuration, by horizontally moving the rear thrust vector closer to the center of gravity, by increasing the vertical position of the ram drag-vector, or by moving the rear thrust vector farther below the center of gravity.
The performance of an experimental adaptive array is evaluated using signals from an existing geostationary satellite interference environment. To do this, an earth station antenna was built to receive signals from various geostationary satellites. In these experiments the received signals have a frequency of approximately 4 GHz (C-band) and have a bandwidth of over 35 MHz. These signals are downconverted to a 69 MHz intermediate frequency in the experimental system. Using the downconverted signals, the performance of the experimental system for various signal scenarios is evaluated. In this situation, due to the inherent thermal noise, qualitative instead of quantitative test results are presented. It is shown that the experimental system can null up to two interfering signals well below the noise level. However, to avoid the cancellation of the desired signal, the use a steering vector is needed. Various methods to obtain an estimate of the steering vector are proposed.
A simplified algorithm is derived for retrieving wind vectors from microwave scatterometer observations. The azimuthal dependence of the sea-surface radar cross section is modeled by a double-cosine function, rather than the traditional second-order cosine expansion. The algorithm is tested using the aircraft scatterometer observations obtained during the AAFE-RADSCAT Experiment in 1975 and 1976. There is little difference between the performance of the simplified algorithm and the more involved least-squares searching algorithm. The AAFE-RADSCAT aircraft observations are sampled so as to simulate the three-look satellite scatterometer NSCAT that will be launched on the Japanese Advanced Earth Observing Satellite. The results of the retrievals indicate that it is probably not possible to uniquely determine the wind direction using just NSCAT observations because of a 180 degree ambiguity. However, if forecast models can predict the wind direction to an accuracy of +/- 90 deg, thereby eliminating the ambiguity, then the results indicate that NSCAT can determine the wind direction to an accuracy of about +/- 20 deg. Better performance is obtained when the three observations are the same polarization (either v-pol or h-pol), as compared to using a mix of v-pol and h-pol observations.
The Environmental Geographic Information System (EGIS) at Stennis Space Center (SSC) covers four counties in Mississippi and four parishes in Louisiana. The EGIS includes 410 data layers including vector and raster data from various public and private sources. These data layers provide information on natural and cultural features. SSC initially used the EGIS to: 1) Monitor on and off-site impacts of propulsion testing; 2) Classify land cover at SSC to predict the impacts of future programs. This viewgraph presentation provides an overview of ongoing projects and future applications for the EGIS.
The Solar Dynamics Observatory (SDO) mission is the first Space Weather Research Network mission, part of NASA s Living With a Star program.1 This program seeks to understand the changing Sun and its effects on the Solar System, life, and society. To this end, the SDO spacecraft will carry three Sun-observing instruments to geosynchronous orbit: Helioseismic and Magnetic Imager (HMI), led by Stanford University; Atmospheric Imaging Assembly (AIA), led by Lockheed Martin Space and Astrophysics Laboratory; and Extreme Ultraviolet Variability Experiment (EVE), led by the University of Colorado. Links describing the instruments in detail may be found through the SDO web site.2 The basic mission goals are to observe the Sun for a very high percentage of the 5-year mission (10-year goal) with long stretches of uninterrupted observations and with constant, high-data-rate transmission to a dedicated ground station. These goals guided the design of the spacecraft bus that will carry and service the three-instrument payload. At the time of this publication, the SDO spacecraft bus is well into the integration and testing phase at the NASA Goddard Space Flight Center (GSFC). A three-axis stabilized attitude control system (ACS) is needed both to point at the Sun accurately and to keep the roll about the Sun vector correctly positioned. The ACS has four reaction wheel modes and 2 thruster actuated modes. More details about the ACS in general and the control modes in particular can be found in Refs. [3-6]. All four of SDO s wheel-actuated control modes involve Sun-pointing controllers, as might be expected from such a mission. Science mode, during which most science data is collected, uses specialized guide telescopes to point accurately at the Sun. Inertial mode has two sub-modes, one tracks a Sun-referenced target orientation, and another maintains an absolute (star-referenced) target orientation, that both employ a Kalman filter to process data from a digital Sun sensor and two star trackers. However, this paper is concerned only with the other two modes: Safe Hold (SH) and Sun Acquisition (SA).
The aim of this study is to evaluate the setup margins from the clinical target volume (CTV) to planning target volume (PTV) for cranial stereotactic radiosurgery (SRS) treatments guided by cone beam computed tomography (CBCT). We designed an end-to-end (E2E) test using a skull phantom with an embedded 6mm tungsten ball (target). A noncoplanar plan was computed (E2E plan) to irradiate the target. The CBCT-guided positioning of the skull phantom on the linac was performed. Megavoltage portal images were acquired after 15 independent deliveries of the E2E plan. The displacement 2-dimensional (2D) vector between the centers of the square field and the ball target on each portal image was used to quantify the isocenter accuracy. Geometrical margins on each patient's direction (left-right or LR, anterior-posterior or AP, superior-inferior or SI) were calculated. Dosimetric validation of the margins was performed in 5 real SRS cases: 3-dimesional (3D) isocenter deviations were mimicked, and changes in CTV dose coverage and organs-at-risk (OARs) dosage were analyzed. The CTV-PTV margins of 1.1 mm in LR direction, and 0.7 mm in AP and SI directions were derived from the E2E tests. The dosimetric analysis revealed that a 1-mm uniform margin was sufficient to ensure the CTV dose coverage, without compromising the OAR dose tolerances. The effect of isocenter uncertainty has been estimated to be 1 mm in our CBCT-guided SRS approach.
Here we study the implications of the premise that any new, relativistic, highly energetic neutral particle that interacts with quarks and gluons would create cascade-like events in the IceCube (IC) detector. Such events would be observationally indistinguishable from neutral current deep-inelastic (DIS) scattering events due to neutrinos. Consequently, one reason for deviations, breaks or excesses in the expected astrophysical power-law neutrino spectrum could be the flux of such a particle. Motivated by features in the recent 1347-day IceCube high energy starting event (HESE) data, we focus on particular boosted dark matter (χ) related realizations of this premise. Here, χ is assumed to be much lighter than, and the result of, the slow decay of a massive scalar (φ ) which constitutes a major fraction of the Universe's dark matter (DM) . We show that this hypothesis, coupled with a standard power-law astrophysical neutrino flux is capable of providing very good fits to the present data, along with a possible explanation of other features in the HESE sample. These features include a) the paucity of events beyond ~ 2 PeV b) a spectral feature resembling a dip or a spectral change in the 400 TeV–1 PeV region and c) an excess in the 50-100 TeV region. We consider two different boosted DM scenarios, and determine the allowed mass ranges and couplings for four different types of mediators (scalar, pseudoscalar, vector and axial-vector) which could connect the standard and dark sectors. We consider constraints from gamma-ray observations and collider searches. We find that the gamma-ray observations provide the most restrictive constraints, disfavouring the 1σ allowed parameter space from IC fits, while still being consistent with the 3σ allowed region. We also test our proposal and its implications against the (statistically independent) sample of six year through-going muon track data recently released by IceCube.
The Support Vector Machine (SVM) is a powerful algorithm, useful in classifying data into species. The SVMs implemented in this research were used as classifiers for the final stage in a Multistage Automatic Target Recognition (ATR) system. A single kernel SVM known as SVMlight, and a modified version known as a SVM with K-Means Clustering were used. These SVM algorithms were tested as classifiers under varying conditions. Image noise levels varied, and the orientation of the targets changed. The classifiers were then optimized to demonstrate their maximum potential as classifiers. Results demonstrate the reliability of SVM as a method for classification. From trial to trial, SVM produces consistent results.
Purpose: Intensity-modulated radiation therapy (IMRT) treatment planning typically combines multiple criteria into a single objective function by taking a weighted sum. The authors propose a statistical model that predicts objective function weights from patient anatomy for prostate IMRT treatment planning. This study provides a proof of concept for geometry-driven weight determination. Methods: A previously developed inverse optimization method (IOM) was used to generate optimal objective function weights for 24 patients using their historical treatment plans (i.e., dose distributions). These IOM weights were around 1% for each of the femoral heads, while bladder and rectum weights varied greatly between patients. A regression model was developed to predict a patient's rectum weight using the ratio of the overlap volume of the rectum and bladder with the planning target volume at a 1 cm expansion as the independent variable. The femoral head weights were fixed to 1% each and the bladder weight was calculated as one minus the rectum and femoral head weights. The model was validated using leave-one-out cross validation. Objective values and dose distributions generated through inverse planning using the predicted weights were compared to those generated using the original IOM weights, as well as an average of the IOM weights across all patients. Results: The IOM weight vectors were on average six times closer to the predicted weight vectors than to the average weight vector, usingl{sub 2} distance. Likewise, the bladder and rectum objective values achieved by the predicted weights were more similar to the objective values achieved by the IOM weights. The difference in objective value performance between the predicted and average weights was statistically significant according to a one-sided sign test. For all patients, the difference in rectum V54.3 Gy, rectum V70.0 Gy, bladder V54.3 Gy, and bladder V70.0 Gy values between the dose distributions generated by the predicted weights and IOM weights was less than 5 percentage points. Similarly, the difference in femoral head V54.3 Gy values between the two dose distributions was less than 5 percentage points for all but one patient. Conclusions: This study demonstrates a proof of concept that patient anatomy can be used to predict appropriate objective function weights for treatment planning. In the long term, such geometry-driven weights may serve as a starting point for iterative treatment plan design or may provide information about the most clinically relevant region of the Pareto surface to explore.
Not provided.
PurposeTo investigate predictive factors for liver necrosis after transcatheter arterial chemoembolization (TACE) of neuroendocrine liver metastases.MethodsA total of 164 patients receiving 374 TACE were reviewed retrospectively to analyze predictive factors of liver necrosis. We analyzed patient age and sex; metastasis number and location; percentage of liver involvement; baseline liver function test; and pretreatment imaging abnormalities such as bile duct dilatation (BDD), portal vein narrowing (PVN), and portal vein thrombosis (PVT). We analyzed TACE technique such as Lipiodol or drug-eluting beads (DEB) as the drug’s vector; dose of chemotherapy; diameter of DEB; and number, frequency, and selectivity of TACE.ResultsLiver necrosis developed after 23 (6.1 %) of 374 TACE. In multivariate analysis, DEB > 300 μm in size induced more liver necrosis compared to Lipiodol (odds ratio [OR] 35.20; p < 0.0001) or with DEB < 300 μm in size (OR 19.95; p < 0.010). Pretreatment BDD (OR 119.64; p < 0.0001) and PVT (OR 9.83; p = 0.030) were predictive of liver necrosis. BDD or PVT responsible for liver necrosis were present before TACE in 59 % (13 of 22) and were induced by a previous TACE in 41 % (9 of 22) of cases.ConclusionDEB > 300 μm in size, BDD, and PVT are responsible for increased rate of liver necrosis after TACE. Careful analysis of BDD or PVT on pretreatment images as well as images taken between two courses can help avoid TACE complications.
Traditional approaches to biomonitoring in aquatic systems, such as sample collection, sorting, and identification, require significant time and effort, thereby limiting the spatiotemporal resolution of sample collection. Additionally, collection and preservation of samples for subsequent taxonomic identification and enumeration leads to mortality of organisms. Recent advances in technologies that utilize optical imaging and machine learning have provided new opportunities to expedite biomonitoring and lead to significant cost savings. These technologies can be advantageous to scientists or managers that conduct routine biomonitoring to inform operations, as in the case of aquaculture facilities. The Small Aquatic Organism optical imaging system (SAO) is a high-throughput optical imaging and classification prototype system that relies on computer vision and machine learning (Support Vector Machines, or SVMs) to autonomously identify and enumerate aquatic organisms. The SAO provides a more sustainable method of collecting large volumes of data and has the benefit of being used in situ. In this study, we tested the performance of the SAO in providing comparable results to manual zooplankton community monitoring in ten ponds at an aquaculture facility. We performed a side-by-side study comparing the sampling methods of plankton tow nets, where major zooplankton taxonomic classes were manually identified and enumerated, to sampling with the SAO. Vouchered samples were used to develop a training library for the SAO, where classes consisted of water boatman and zooplankton groups: cladocerans, copepod adults, copepod nauplii, and rotifers. SAO imagery was manually classified and compared with predicted results for validation. Accuracy for the SVM classifier of the SAO was 37.4 %. Convolutional Neural Networks (CNN) and Random Forest classifiers were also applied to SAO imagery and image features for comparison. The best CNN model and our Random Forest model had accuracies of 80.4 % and 46.6 % respectively. Challenges faced included the small size of copepod nauplii and rotifers and the limited resolution of the imaging camera, although there are tradeoffs between imaging resolution and the sample processing rate. Furthermore, our comparison shows that advancement in both optical imaging and ML are needed in order for the SAO prototype to yield comparable results to manual community monitoring in an aquaculture facility.
Recently, the emergence of an important alien plant pathogen in Europe was evident when the Olive Quick Decline Syndrome (OQDS), a previously unknown disease causing rapid scorching and death of the trees, invested with particular virulence a substantial portion of the vast olive wood of Southern Italy (Salento, part of the Apulia region). Early evidence indicated a connection between the OQDS and the gram-negative bacterium Xylella fastidiosa. This bacterium can target several important crops, so that researchers from all over the world have investigated its association with host plants and vectors, the molecular biology of the infection mechanism, and the molecular reaction of the infected plants. Potentially resistant or tolerant cultivars and molecular targets which might be useful to control the infection have been identified. In vitro tests of compounds active against Xylella have also been performed. In this contribution, the literature and the available data will be reviewed to provide an up-to-date picture of the currently available knowledge on the role of Xylella in OQDS, and in diseases of other plants, with focus on the emerging threats to European farming.
In this paper, we develop new techniques for solving the large, coupled linear systems that arise from fully implicit Runge–Kutta methods. This method makes use of the iterative preconditioned GMRES algorithm for solving the linear systems, which has seen success for fluid flow problems and discontinuous Galerkin discretizations. By transforming the resulting linear system of equations, one can obtain a method which is much less computationally expensive than the untransformed formulation, and which compares competitively with other time-integration schemes, such as diagonally implicit Runge–Kutta (DIRK) methods. We develop and test several ILU-based preconditioners effective for these large systems. We additionally employ a parallel-in-time strategy to compute the Runge–Kutta stages simultaneously. Numerical experiments are performed on the Navier–Stokes equations using Euler vortex and 2D and 3D NACA airfoil test cases in serial and in parallel settings. In conclusion, the fully implicit Radau IIA Runge–Kutta methods compare favorably with equal-order DIRK methods in terms of accuracy, number of GMRES iterations, number of matrix–vector multiplications, and wall-clock time, for a wide range of time steps.
Bayesian approaches have been successfully integrated into training deep neural networks. One popular family is stochastic gradient Markov chain Monte Carlo methods (SG-MCMC), which have gained increasing interest due to their ability to handle large datasets and the potential to avoid overfitting. Although standard SG-MCMC methods have shown great performance in a variety of problems, they may be inefficient when the random variables in the target posterior densities have scale differences or are highly correlated. Here, we present an adaptive Hessian approximated stochastic gradient MCMC method to incorporate local geometric information while sampling from the posterior. The idea is to apply stochastic approximation (SA) to sequentially update a preconditioning matrix at each iteration. The preconditioner possesses second-order information and can guide the random walk of a sampler efficiently. Instead of computing and saving the full Hessian of the log posterior, we use limited memory of the samples and their stochastic gradients to approximate the inverse Hessian-vector multiplication in the updating formula. Moreover, by smoothly optimizing the preconditioning matrix via SA, our proposed algorithm can asymptotically converge to the target distribution with a controllable bias under mild conditions. To reduce the training and testing computational burden, we adopt a magnitude-based weight pruning method to enforce the sparsity of the network. Our method is user-friendly and demonstrates better learning results compared to standard SG-MCMC updating rules. The approximation of inverse Hessian alleviates storage and computational complexities for large dimensional models. Numerical experiments are performed on several problems, including sampling from 2D correlated distribution, synthetic regression problems, and learning the numerical solutions of heterogeneous elliptic PDE. The numerical results demonstrate great improvement in both the convergence rate and accuracy.
Manley et al. report the observation of an unexpected, weak, phonon mode located in energy between that of the transverse acoustic (TA) and soft transverse optic (TO) modes in the relaxor ferroelectric Pb[(Mg 1/3 Nb 2/3 ) 1-x Ti x ]O 3 (PMN-xPT) with Ti content x = 0.30. Referred to as the local mode (LM), the energy of this extra mode varies little with wave vector in the cubic (paraelectric) phase, and this was interpreted as evidence of phonon localization. Any excitation that is localized in space must be extended in reciprocal space Q. To confirm this property, we performed a standard test on a PMN-xPT single crystal of nominally identical composition. This was done by comparing the rates at which the Bragg, TA phonon, and LM neutron scattering cross-sections decrease as the cubic crystallographic [100] axis is tilted out of the horizontal scattering plane by rotating about the orthogonal [010] axis. We find that the LM cross section varies with tilt in a manner identical to that of the Bragg peak. A local mode with short-range spatial correlations that decay exponentially will exhibit a broad Lorentzian peak as a function of tilt. Instead, we observe a narrow Gaussian peak. We conclude that the LM is not a real mode.