We develop a model of early Gamma-Ray Burst (GRB) afterglows with dominant X-ray contribution from the reverse shock (RS) propagating in highly relativistic (Lorentz factor γ w ∼10 6 ) magnetized wind of a long-lasting central engine. The model reproduces, in a fairly natural way, the overall trends and yet allows for variations in the temporal and spectral evolution of early optical and X-ray afterglows. The high energy and the optical synchrotron emission from the RS particles occurs in the fast cooling regime; the resulting synchrotron power L s is a large fraction of the wind luminosity,w (L w and σ w are wind power and magnetization). Thus, plateaus -parts of afterglow light curves that show slowly decreasing spectral power-are a natural consequence of the RS emission. Contribution from the forward shock (FS) is negligible in the X-rays, but in the optical both FS and RS contribute similarly: FS optical emission is in the slow cooling regime, producing smooth components, while RS optical emission is in the fast cooling regime, and thus can both produce optical plateaus and account for fast optical variability correlated with the X-rays, e.g., due to changes in the wind properties. We discuss how the RS emission in the X-rays and combined FS and RS emission in the optical can explain many puzzling properties of early GRB afterglows.
La gestión de cadenas de suministro implica la formulación de modelos y metodologías de mejora y optimización de operaciones y procesos internos, con el fin de aumentar la eficiencia y la capacidad de respuesta y a su vez reducir costos. En la gestión de cadenas de suministro, las actividades llevadas a cabo en almacenes y centros de distribución son fundamentales para garantizar un nivel de servicio óptimo y para obtener ahorros significativos en los costos logísticos totales. El slotting y el picking son dos actividades fundamentales en la operación y administración eficiente de las instalaciones de almacenamiento. Por ello, en este artículo se hace una revisión bibliográfica exhaustiva sobre los modelos y metodologías usados en la optimización de dichas actividades entre los años 2000 y 2018, identificando aplicaciones indirectas en otro tipo de industrias y oportunidades y tendencias de investigación, considerando los factores que influyen en los flujos de materiales y componentes estocásticos en la planeación de inventarios: acomodo dinámico, análisis de rutas, metodologías de ubicación de clústeres y división de almacén por tipo de productos.
In this study, nonlinear dynamics techniques toward detecting cardiac murmurs from phonocardiograms (PCG) are used. With this purpose, a methodology for tuning parameters (reconstruction delay −τ and embedding dimension −m) involved in the reconstruction of a meaningful state space from scalar time series is presented, using genetic algorithms (GA), as well as constructing a metaalgorithm combined with support vector regression to adjust the GA parameters in order to decrease the computational cost. The forecasting capacity is used as cost function of the GA. The PCG records belong to the National University of Colombia, 360 beats were chosen by specialist, 180 normal and 180 with cardiac murmur evidence. The obtained results show that by using the tuned GA an efficient procedure for the consistent determination of τ and m is achieved. Murmur detection by using nonlinear features was obtained with classification accuracy of 96% using a k nearest neighbor classifier in cross-validation with 10 folds. IntroductionDue to the complexity involved in cardiac dynamics [1], nonlinear dynamics techniques for detecting cardiac murmurs from phonocardiographic signals (PCG) have been proposed [2]. A topological equivalent corresponding to the true state space of a system can be reconstructed with the method of delays [3]. The embedding dimension m and the reconstruction delay τ are mandatory information for the state space reconstruction, or embedding, of a series [4,5]. Estimating these values allows for the construction of state vectors, also called regressors, which contain all the information about system dynamics [6]. However, in time series analysis there exist several difficulties. Usually, the attractor dimension is not known and therefore there is no idea about the minimal embedding dimension m. In another way, the reconstruction delay τ is not subject to the embedding theorems; in principle, arbitrary τ yields an embedding, and according to this, the main difficulty is related to the observability of interesting structures [7]. . The goal is to select the regressor components, as uncorrelated or independent as possible. Genetic Algorithms (GA) have been widely used for tuning parameters taking into account an adequate cost function, for example, in [12], an approach based on evolutionary algorithms was developed to find parameters for optimal embedding and, the application of chaotic time series forecasting as cost function to build a nonlinear model from the dataset is proposed in [13]. However, when the task implies building the state space from a lot of different systems, computational complexity considerably increases.In this study, the use of nonlinear dynamics techniques for characterization of FCG signals in the detection of cardiac murmurs is proposed. The problem lies in the adjustment of τ and m of the nonlinear model, so we propose the use of GA to accomplish that task. The evaluation function is based on measuring the forecasting capabilities obtained with each pair of values for m and τ . A pr...
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