Dependence of the AES backscattering correction factor on the experimental configuration

The characteristic energy loss spectra of electrons reflected from freshly evaporated Al and Cu specimens have been measured for various primary electron energies in the range 9-265 ev. Contrary to expectation the ratio of the intensity of the 10 ev surface plasma loss to that of the 15 ev volume loss in Al, for electrons scattered through 90°, is found to decrease with decreasing primary energy in the range from about 240 ev to 80 ev. This appears to be due to the behaviour of the surface loss, which decreases rapidly in intensity with decreasing primary energy. Both the surface and volume plasma losses of Al cease to be excited when the primary energy is less than about 26-29 ev, the surface loss cutting off at a slightly lower primary energy than the volume loss. The modified surface loss of Al resulting from oxidation of the specimen surface is observed at 7.1 ev, and the half-width of the 10 ev surface loss increases from 3.8 ev to 5.3 ev with increasing surface oxidation, indicating that the surface plasma oscillations are damped by the contamination. The energy losses of Cu have been investigated by observing their intensity dependence on (i) the primary electron energy for a fixed scattering angle of 90° and (ii) the electron scattering angle for various fixed primary energies, scattering angles of 90° and 20° being employed. The results indicate that a loss observed at 3.5 ev is possibly due to excitation of surface plasma oscillations.

In this paper, a simplified aircraft system model is developed correctly emulating the nonlinear dynamics of complex aircraft models under closed-loop control. The simplified model is based on Dubins models used in optimization algorithms for trajectory control of multiple Unmanned Aeriel Vehicles (UAVs). Algorithms optimizing area coverage for reconnaissance based on UAV capabilities, fuel remaining, and time constraints require simple aircraft models providing ecient solutions in near real-time. However, the simplified models should accurately predict UAV dynamics for proper prediction. For this study, a realistic UAV simulation is developed including nonlinear aerodynamics and kinematics. A UAV autonomous navigation algorithm for waypoint-to-waypoint navigation is derived and integrated in the complex UAV simulation. A simplified system model based Dubins model is developed closely matching the full nonlinear UAV simulation. A Rhumb-line navigation algorithm including a velocity-hold and altitude-hold control system is used for closed-loop control. A navigation system utilizing trajectory planning with Dubins curves is also developed. Both control laws are implemented in a simplified Dubins model and tuned to match the full nonlinear simulation under autonomous control including provisions for head and tail wind conditions. Computer simulations are performed demonstrating waypointto-waypoint navigation with desired final heading trajectories for both the full nonlinear UAV model and the simplified Dubins simulation. Simulation time history analysis is used to verify the accuracy of the simplified model compared to the full nonlinear simulation model.

: Stress analyses of lumbar vertebrae were performed by a three dimensional finite element method for the purposes of (i) evaluating simplified models of the vertebrae which are suitable as injury post processors, and (ii) gaining a better understanding of injury mechanisms. The finite element analyses were linear and elastic. Axial and moment loads were applied over the end plates to simulate G sub z impact and on the facets to simulate load transmission between the articular facets and the vertebral bodies. The finite element model predicts that the maximum stresses under axial load are perpendicular to the axis of the vertebral body, which are called axial stresses; this is consistent with the predominance of compressive and wedge fractures. However, the maximum stresses predicted by the finite element model are only about a third of those predicted by the simplified injury model. This discrepancy is due to the fact that a substantial portion of the total load is transmitted through the vertebral centrum which is neglected in the simplified model. Furthermore, the finite element model exhibits a rather complex pattern of deformation even under axial load, which cannot be incorporated easily in a simplified model. The stresses predicted by the finite element model for bending also differ substantially from those given by the simplified model for the same reasons. Good agreement between the finite element model and simplified model is only achieved for torsion loads.

One method for simplified manipulator model construction and its application in quazioptimal trajectory synthesis

Highly detailed computer models are required for design and development of modern flight control systems, capable of emulating with high accuracy the behaviour of on-board equipment. At the same time, different simplified models are needed, specifically intended for operations such as the optimization of preliminary design and the development of diagnostic or prognostic strategies. These simplified models are required to combine sufficient levels of accuracy and reliability with reduced computational costs, to minimize the computational burden associated with prognostic and optimization algorithms. In this work, we focus on electro-hydraulic actuators, since they are critical subsystems in terms of safety and availability of the aircraft. Advanced monitoring and prognostic algorithms require new numerical models, combining an acceptable computational effort with a satisfying ability to simulate their performance and dynamics. To this purpose, this paper proposes a new simplified numerical model of the servovalve fluid-dynamic behaviour. This numerical algorithm, based on a very compact semi-empirical formulation, is intended to take into account in a simplified but sufficiently accurate way several typical effects related to the SV spool geometry and the operating conditions. To evaluate the approximations introduced by this model into a system-level simulation, it has been integrated into a dedicated numerical model simulating a simple electrohydraulic on-board actuator, and compared with a higher fidelity servovalve model.

The ultraviolet structure of s-channel mediator dark matter simplified models at hadron colliders is considered. In terms of commonly studied s-channel mediator simplified models it is argued that at arbitrarily high energies the perturbative description of dark matter production in high energy scattering can break down. This is analogous to the well documented breakdown of an EFT description of dark matter collider production. With this in mind, to diagnose whether or not the use of simplified models at the LHC is valid, perturbative unitarity of the scattering amplitude in the processes relevant to LHC dark matter searches is studied. The results are as one would expect: at the LHC and future proton colliders the simplified model descriptions of dark matter production are in general valid. As a result of the general discussion, a simple class of ‘Fermiophobic Scalar’ simplified models is proposed, in which a scalar mediator couples to electroweak vector bosons. The Fermiophobic simplified model is well motivated and exhibits interesting collider and direct detection phenomenology.

BackgroundTraumatic brain injury remains a global health problem. Understanding the relative importance of outcome predictors helps optimize our treatment strategies by informing assessment protocols, clinical decisions and trial designs. In this study we establish importance ranking for outcome predictors based on receiver operating indices to identify key predictors of outcome and create simple predictive models. We then explore the associations between key outcome predictors using Bayesian networks to gain further insight into predictor importance.MethodsWe analyzed the corticosteroid randomization after significant head injury (CRASH) trial database of 10008 patients and included patients for whom demographics, injury characteristics, computer tomography (CT) findings and Glasgow Outcome Scale (GCS) were recorded (total of 13 predictors, which would be available to clinicians within a few hours following the injury in 6945 patients). Predictions of clinical outcome (death or severe disability at 6 months) were performed using logistic regression models with 5-fold cross validation. Predictive performance was measured using standardized partial area (pAUC) under the receiver operating curve (ROC) and we used Delong test for comparisons. Variable importance ranking was based on pAUC targeted at specificity (pAUCSP) and sensitivity (pAUCSE) intervals of 90–100%. Probabilistic associations were depicted using Bayesian networks.ResultsComplete AUC analysis showed very good predictive power (AUC = 0.8237, 95% CI: 0.8138–0.8336) for the complete model. Specificity focused importance ranking highlighted age, pupillary, motor responses, obliteration of basal cisterns/3rd ventricle and midline shift. Interestingly when targeting model sensitivity, the highest-ranking variables were age, severe extracranial injury, verbal response, hematoma on CT and motor response. Simplified models, which included only these key predictors, had similar performance (pAUCSP = 0.6523, 95% CI: 0.6402–0.6641 and pAUCSE = 0.6332, 95% CI: 0.62–0.6477) compared to the complete models (pAUCSP = 0.6664, 95% CI: 0.6543–0.679, pAUCSE = 0.6436, 95% CI: 0.6289–0.6585, de Long p value 0.1165 and 0.3448 respectively). Bayesian networks showed the predictors that did not feature in the simplified models were associated with those that did.ConclusionWe demonstrate that importance based variable selection allows simplified predictive models to be created while maintaining prediction accuracy. Variable selection targeting specificity confirmed key components of clinical assessment in TBI whereas sensitivity based ranking suggested extracranial injury as one of the important predictors. These results help refine our approach to head injury assessment, decision-making and outcome prediction targeted at model sensitivity and specificity. Bayesian networks proved to be a comprehensive tool for depicting probabilistic associations for key predictors giving insight into why the simplified model has maintained accuracy.

Neuroprosthetic devices can be an innovative solution to support subjects suffering from a limb loss. The possibility of restoring tactile sensations by using sensory feedback represents a research area whose outcomes could significantly improve the quality of life of prosthesis users. One of the best techniques to restore close-to-natural and selective tactile sensations is the electrical current stimulation of nerves using neural electrodes. Their interaction with nerves is an important aspect to be studied. The aim of this work is to deepen, in the framework of computational modeling, the possibility of approximating a realistic 3D human median nerve model, based on anatomical imaging, to a simplified model using the hybrid FEM-Neuron approach. Often, a high computational simulation time is also related to the complexity of the model geometry; therefore using a geometrical simplified model can be an important aspect to be analyzed. The simplified model is built approximating inner fascicles shape to simple geometrical shapes, i.e. ellipses. The electrical current stimulation is studied in Comsol environment by using a ds-FILE electrode model implanted in the human median nerve. The results obtained from computational simulations using both anatomical and simplified models, allow concluding that the percentage activation ranges at different distances from the active site obtained by the two models are comparable.

When thin overlayers on a bulk material have to be analysed by EPMA (electron probe microanalysis) or AES (Auger electron spectroscopy), the signal obtained is enhanced by three factors: (i) back‐scattering, (ii) characteristic x‐rays induced in the bulk by the primary beam and (iii) continuous x‐rays (bremsstrahlung) created also in the bulk. The Monte Carlo techniques are applied in order to evaluate the above three enhancement factors when the primary energy is varied in the range 5–50 keV. It is shown that at high primary energies the second and third factors are no longer negligible and become significant in some cases. The approximation to ignore the third factor in EPMA and both the second and third factors in AES is not always valid and introduces an appreciable error. Results are shown for the case of thin films (Al, Au, Cu) on a bulk material (Au, Cu, Si, Ni, Pt).

Traffic load is identified as one of the greatest sources of uncertainty in the assessment of bridges. In recent years, simulation techniques, using measured traffic data, have been used to predict the characteristic traffic load effects on bridges. However, the techniques are complex, sensitive to the assumptions adopted and require specialist statistical expertise. This work presents a simplified site-specific traffic load model that generates comparable load effects to the corresponding results from a full simulation. While the simplified model is still sensitive to the underlying assumptions, these can be carefully reviewed prior to the method being approved. Further, the simplified method can be employed by practising engineers for bridge assessment.

In this study, we have investigated how to link ”large-scale simulations with the simplified models” to ”mesoscale simulations with the detailed models.” For the simplified model, we have applied so-called the generalized Ohta-Kawasaki (gOK) model. Our simulation flow was implemented by two steps: 1) parallel computations of block copolymer annealing with the simplified model, 2) detailed analysis of the defects with the SCFT. The local volumetric densities of block copolymers calculated by the simplified models were used as an input for the SCFT. Then the SCFT simulations were performed under the constraints in which the density field was driven to be the one obtained from the simplified model. Using the resultant partition functions, we were able to obtain spatial distributions of the free chain ends and the connection points of the blocks. Note that the chain conformation of block copolymer is an important, but missing component of the simplified models; this multi-scale approach is expected to be useful for further understanding the origin and stability of DSA defects.

Simplified pyrolytic model of RP-3 fuel at high conversion rates and the effects of primary reaction on the regenerative cooling process

Reinforcement learning (RL) for controlling building heating, ventilation, and air conditioning (HVAC) systems has shown promise in delivering energy-efficient, energy-flexible and resilient buildings. However, training RL agents in real-time in buildings is generally considered time and cost prohibitive. RL agents can be trained offline in simulated environments, reducing the time to performance once online, but complex models for simulation can be difficult to generate. What level of complexity is required in an offline training model to produce acceptable online results is unknown. This work seeks to determine how simplified models can be used to train RL agents for building HVAC control. The work uses the EnergyPlus simulation environment paired with HVAC systems modeled in Modelica to act as the real-world analogue environment. Simplified reduced-order models of the building were developed using the lumped capacitance method. The results show that an RL agent trained using Deep Q Learning on a simplified building model can be transferable to a more complex building simulation environment with comparable performance. In virtually all cases, the RL agents were able to deliver improved performance over the baseline proportional controller. Online learning gave mixed results in terms of enhancing the policy transfer process. The implementation in this paper was more suitable for improving the performance of poor policy transfer rather than fine-tuning already well-performing policies.

Predicting the failure of spot welds remains one of the most important modeling challenges in automotive engineering. In essence, the structure surrounding a spot weld corresponds to a graded material with severe variations in the plasticity and fracture properties as a function of the distance to the center of the weld nugget. While it is common practice to identify hardness variations around a spot weld, a newly developed micro-tensile testing technique is used to characterize spatial variations in both strain hardening and fracture strains. Furthermore, a new experimental device is presented to subject spot welds to combined tension, shear and bending loads. Following a careful experimental characterization of resistance spot welds connecting two 1.5 mm thick DP600 sheets, a detailed finite element model is built which accounts for the identified property gradients. In addition, a simplified surrogate model for use in conjunction with coarse shell element meshes is also calibrated. Simulations are performed of all structural experiments to assess the range of validity of the detailed and simplified FE models. It is found that the detailed model captures the experimentally observed failure modes including changes in the location of fracture initiation as a function of the loading, while the simplified model is able to predict the spot weld strength with reasonable accuracy.

Simplified structural behaviours of post-tensioned inter-module connection for modular buildings