The thermoluminescence behavior of CaSO4-Dy (TLD-900) for doses up to 30 Mrad

The thermoluminescence behavior of CaSO₄-Dy(TLD-900) was studied at a dose region between 100 rad and 30 Mrad. The shape of the glow curves at high doses differs substantially from those of low doses, with the appearance of a new high temperature peak at 420°C. The response of the dosimetric peak, 210°C, was found to exhibit supralinearity in the region between 10³ rad and 5 × 10⁵ rad. The total TL at the temperature region between 50 and 600°C was found to increase for doses up to 10⁶ rad at which point it becomes saturated. The peak at 420°C was extensively investigated and its thermoluminescent parameters E and S were found using different methods. The results are discussed and several explanations are offered.     

Influence of soil structures on corrosion performance of floating-DC transit systems

The production of stray currents by DC-transit systems leads to the corrosion of nearby buried metallic structures, such as pipelines and cable sheaths. The paper details the corrosion performance of a DC transit system with a floating return rail, for a number of different soil-resistivity structures: uniform, horizontal and vertical-layer models. This builds on previous work carried out in homogenous soils. It is shown that a variation in soil type along the route of a transit system can lead to high local leakage-current densities on buried metallic structures, increasing their vulnerability to corrosion damage.     

New explicit filters and smoothers for diffusions with nonlinear drift and measurements

The optimal least-squares filtering of a diffusion x(t) from its noisy measurements {y(τ); 0 τ t} is given by the conditional mean E[x(t)|y(τ); 0 τ t]. When x(t) satisfies the stochastic diffusion equation dx(t) = f(x(t)) dt + dw(t) and y(t) = ∫₀^tx(s) ds + b(t), where f(·) is a global solution of the Riccati equation /xf(x) + f(x)² = f(x)² = αx² + βx + γ, for some , and w(·), b(·) are independent Brownian motions, Benes gave an explicit formula for computing the conditional mean. This paper extends Benes results to measurements y(t) = ∫₀^tx(s) ds + ∫₀^t dx(s) + b(t) (and its multidimensional version) without imposing additional conditions on f(·). Analogous results are also derived for the optimal least-squares smoothed estimate E[x(s)|y(τ); 0 τ t], s < t. The methodology relies on Girsanov's measure transformations, gauge transformations, function space integrations, Lie algebras, and the Duncan-Mortensen-Zakai equation.     

Group Modeling: A Unified Velocity‐Based Approach

Crowd simulators are commonly used to populate movie or game scenes in the entertainment industry. Even though it is crucial to consider the presence of groups for the believability of a virtual crowd, most crowd simulations only take into account individual characters or a limited set of group behaviors. We introduce a unified solution that allows for simulations of crowds that have diverse group properties such as social groups, marches, tourists and guides, etc. We extend the Velocity Obstacle approach for agent‐based crowd simulations by introducing Velocity Connection; the set of velocities that keep agents moving together while avoiding collisions and achieving goals. We demonstrate our approach to be robust, controllable, and able to cover a large set of group behaviors.     

A Security-Oriented Manual Quality Sampling Methodology for Water Systems

The management and security of water resources will be a key challenge in the years ahead. This work investigates the water quality manual sampling scheduling problem in drinking water distribution networks. This work contributes to research by presenting a problem formulation based on the underlying substance propagation dynamics, coupled with the impact dynamics describing the ??damage?? caused by a contamination in a drinking water distribution network. The proposed solution methodology optimizes a risk-objective, in order to compute a manual sampling scheme comprised of sampling nodes and times, using evolutionary computation techniques. To illustrate the methodology, we present simulation results on a real water distribution network.     

On asymptotic stability of continuous-time risk-sensitive filters with respect to initial conditions

In this paper, we consider the problem of risk-sensitive filtering for continuous-time stochastic linear Gaussian time-invariant systems. In particular, we address the problem of forgetting of initial conditions. Our results show that suboptimal risk-sensitive filters initialized with arbitrary Gaussian initial conditions asymptotically approach the optimal risk-sensitive filter for a linear Gaussian system with Gaussian but unknown initial conditions in the mean square sense at an exponential rate, provided the arbitrary initial covariance matrix results in a stabilizing solution of the (H_∞-like) Riccati equation associated with the risk-sensitive problem. More importantly, in the case of non-Gaussian initial conditions, a suboptimal risk-sensitive filter asymptotically approaches the optimal risk-sensitive filter in the mean square sense under a boundedness condition satisfied by the fourth order absolute moment of the initial non-Gaussian density and a slow growth condition satisfied by a certain Radon–Nikodym derivative.     

The PAG Crowd: A Graph Based Approach for Efficient Data‐Driven Crowd Simulation

We present a data‐driven method for the real‐time synthesis of believable steering behaviours for virtual crowds. The proposed method interlinks the input examples into a structure we call the perception‐action graph (PAG) which can be used at run‐time to efficiently synthesize believable virtual crowds. A virtual character's state is encoded using a temporal representation, the Temporal Perception Pattern (TPP). The graph nodes store groups of similar TPPs whereas edges connecting the nodes store actions (trajectories) that were partially responsible for the transformation between the TPPs. The proposed method is being tested on various scenarios using different input data and compared against a nearest neighbours approach which is commonly employed in other data‐driven crowd simulation systems. The results show up to an order of magnitude speed‐up with similar or better simulation quality.     

Lie algebraic methods in optimal control of stochastic systems with exponential-of-integral cost

The purpose of this paper is to formulate and study the optimal control of partially observed stochastic systems with exponential-of-integral-sample cost, known as risk-sensitive problems, using Lie algebraic tools. This leads to the introduction of the sufficient statistic algebra, , through which one can determine á priori the maximum order of the controller. When , the construction of the control laws is addressed through extensions of the Wei–Norman method, as in nonlinear filtering problems. Aside from specific known finite-dimensional examples which are studied in order to delineate the application of the Lie algebraic tools, new classes of finite-dimensional controllers are identified as well. In addition, relations with minimax dynamic games are explored to best assess the importance and generality of the finite-dimensional control systems.     

Robust Artificial Neural Networks for Pricing of European Options

The option pricing ability of Robust Artificial Neural Networks optimized with the Huber function is compared against those optimized with Least Squares. Comparison is in respect to pricing European call options on the S&P 500 using daily data for the period April 1998 to August 2001. The analysis is augmented with the use of several historical and implied volatility measures. Implied volatilities are the overall average, and the average per maturity. Beyond the standard neural networks, hybrid networks that directly incorporate information from the parametric model are included in the analysis. It is shown that the artificial neural network models with the use of the Huber function outperform the ones optimized with least squares. JEL Classification: G13, G14     

Insight into the BiFeO3 flash sintering process by in-situ energy dispersive X-ray diffraction (ED-XRD)

The sintering mechanism of BiFeO₃ has been investigated in-situ by energy dispersive X-ray diffraction (ED-XRD) using a high-energy white collimated X-ray beam from the Advanced Photon Source (Argonne National Laboratories). Such radiation is very penetrating thereby allowing measurements of the sample even when placed inside the flash sintering set up. Additionally, the fast ED-XRD measurements permit monitoring the flash sintering process by providing information about phase composition and sample temperature in real time. Moreover, profile scans, obtained by moving the stage vertically while recording the ED-XRD spectra, permit investigating the homogeneity of the flash for the entire length of the sample. All experiments have been complemented by ex-situ studies. It has been concluded that flash sintering of BiFeO₃ is a homogeneous process without any directionality effects. Furthermore, flash sintering takes place at quite low temperatures (below the Tc $\approx$ 830?°C), which may be related to the high quality of the samples, as pure, highly insulating ceramics without evidence of secondary phases with a homogenous nanostructured grain size distribution are obtained by this technique. Moreover, it is also evidenced that the rapid heating of the sample does not seem to justify, at least by itself, the densification process. Therefore, it appears that the electric current should play a role in the enhanced mobility during the sintering process.