Minimum temperature forecasting by stochastic techniques : An evidence of the heat island effect

This paper deals mainly with the forecasting of minimum temperatures (Tm) from an stochastic viewpoint. Some appropriate theoretical considerations lead to a choice of those variables significant connected to Tm. Modelling has been carried out for two nearby observatories, one in the centre of Madrid, the other one in the border (Barajas airport). The obtained models allow to show that the significant variables are the same for both locations, but performance in the peripheral area is of a rather inferior quality. It is shown, taking into account the characteristics of both places, that the difference between them can be allotted to the heat-island effect in the centre of Madrid.

Cross Section and Fission Yields Induced Uncertainty in the Vver-440 Burnup Calculation

The properties of nuclear fuel depend on the actual isotopic composition which develops during a reactor operation. In practice, the prediction accuracy of burnup calculations serves as the basis for the future precise estimation of a core lifetime and other safety-based core characteristics. The present study quantifies nuclear data induced uncertainties of nuclide concentrations and multiplication factors in VVER-440 fuel depletion analysis. The well-known SCALE system and the TRITON sequence are used with the NEWT deterministic solver in the SAMPLER module that implements stochastic techniques to assess the uncertainty in computed results. The propagation of uncertainties in neutron cross section and fission yields is studied through the depletion calculation of 2D heterogeneous VVER-440 fuel assembly with an average enrichment of 4.87 wt % of 235U and six gadolinium rods with 3.35 % of Gd2O3. In the paper, fixed nominal depletion conditions are based on the real operational data of the Slovak NPP Bohunice unit 4 during cycle 30. In total 250 cases with uncertain parameters are computed and the results are evaluated by an auxiliary tool.

A Review of Tracking and Trajectory Prediction Methods for Autonomous Driving

This paper provides a literature review of some of the most important concepts, techniques, and methodologies used within autonomous car systems. Specifically, we focus on two aspects extensively explored in the related literature: tracking, i.e., identifying pedestrians, cars or obstacles from images, observations or sensor data, and prediction, i.e., anticipating the future trajectories and motion of other vehicles in order to facilitate navigating through various traffic conditions. Approaches based on deep neural networks and others, especially stochastic techniques, are reported.

Analysis of Queueing Networks in Equilibrium

Equilibria of queueing networks are a means for performance analysis of real communication networks introduced as Markov chains. In this paper, the authors developed, evaluated, and compared computational procedures to obtain numerical solutions for queueing networks in equilibrium with the use of direct, iterative, and aggregative techniques in steady-state analysis of Markov chains. Advanced computational procedures are developed with the use of Gaussian elimination, power iteration, Courtois' decomposition, and Takahashi's iteration techniques. Numerical examples are provided together with comparative analysis of obtained results. The authors consider these procedures are also applicable to other domains where systems are described with comparable queuing models and stochastic techniques are sufficiently relevant. Several suitable domains of applicability are proposed.