The rapid spread of the numerous outbreaks of the coronavirus disease 2019 (COVID-19) pandemic has fueled interest in mathematical models designed to understand and predict infectious disease spread, with the ultimate...
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The rapid spread of the numerous outbreaks of the coronavirus disease 2019 (COVID-19) pandemic has fueled interest in mathematical models designed to understand and predict infectious disease spread, with the ultimate goal of contributing to the decision making of public health authorities. Here, we propose a computational pipeline that dynamically parameterizes a modified SEIRD (susceptible-exposed-infected-recovered-deceased) model using standard daily series of COVID-19 cases and deaths, along with isolated estimates of population-level seroprevalence. We test our pipeline in five heavily impacted states of the US (New York, California, Florida, Illinois, and Texas) between March and August 2020, considering two scenarios with different calibration time horizons to assess the update in model performance as new epidemiologic data become available. Our results show a median normalized root mean squared error (NRMSE) of 2.38% and 4.28% in calibrating cumulative cases and deaths in the first scenario, and 2.41% and 2.30% when new data are assimilated in the second scenario, respectively. Then, 2-week (4-week) forecasts of the calibrated model resulted in median NRMSE of cumulative cases and deaths of 5.85% and 4.68% (8.60% and 17.94%) in the first scenario, and 1.86% and 1.93% (2.21% and 1.45%) in the second. Additionally, we show that our method provides significantly more accurate predictions of cases and deaths than a constant parameterization in the second scenario (p < 0.05). Thus, we posit that our methodology is a promising approach to analyze the dynamics of infectious disease outbreaks, and that our forecasts could contribute to designing effective pandemic-arresting public health policies.
AbstractThis paper describes a dynamic parameterization for a simple global climate model that is based on the planetary boundary layer equations of horizontal motion. This new parameterization resolves many of the pr...
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AbstractThis paper describes a dynamic parameterization for a simple global climate model that is based on the planetary boundary layer equations of horizontal motion. This new parameterization resolves many of the problems associated with other representations of the general circulation in simple climatic models. The model developed in this study accurately simulates the presently observed zonally‐averaged temperature and wind field patterns. Sensitivity tests demonstrate that a model with the new dynamic parameterization meets or exceeds the performance levels of other one‐dimensional energy balance global climate models. By solving many of the problems associated with the available alternative circulation parametcrizations, the new circulation representation based on the equations of motion appears to improve the quality of the popular simple climate mod
Particle Swarm Optimization (PSO) is a well known technique for solving various kinds of combinatorial optimization problems including scheduling, resource allocation and vehicle routing. However, basic PSO suffers fr...
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Particle Swarm Optimization (PSO) is a well known technique for solving various kinds of combinatorial optimization problems including scheduling, resource allocation and vehicle routing. However, basic PSO suffers from premature convergence problem. Many techniques have been proposed for alleviating this problem. One of the alternative approaches is hybridization. Genetic Algorithms (GAs) are one of the possible techniques used for hybridization. Most often, a mutation scheme is added to the PSO, but some applications of crossover have been added more recently. Some of these schemes use dynamic parameterization when applying the GA operators. In this work, dynamic parameterized mutation and crossover operators are combined with a PSO implementation individually and in combination to test the effectiveness of these additions. The results indicate that all the PSO hybrids with dynamic probability have shown satisfactory performance in finding the best distance of the Vehicle Routing Problem With Time Windows.
In this paper, we observe the implementation of flexible FPGA-based architectures by using the mechanism of parameterization, then we propose a classification of parameterization of FPGA-based projects by providing th...
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ISBN:
(纸本)9781479933037
In this paper, we observe the implementation of flexible FPGA-based architectures by using the mechanism of parameterization, then we propose a classification of parameterization of FPGA-based projects by providing them flexibility during the operating mode, also, a common approach to the FPGA-based scalable implementation using multiparameterization technique is offered. Finally, the method of FPGA-based scalable implementation using static parameterization of the number of parallel and sequential structures on different levels of decomposition of the project is suggested and recommendations of proposed methods that provide flexibility of a computer system according to some requirements specification are given.
Over the years, several classification algorithms have been proposed in the machine learning area to address challenges related to the continuous arrival of data over time, formally known as data stream. The implement...
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Over the years, several classification algorithms have been proposed in the machine learning area to address challenges related to the continuous arrival of data over time, formally known as data stream. The implementations of these approaches are of vital importance for the different applications where they are used, and they have also received modifications, specifically to address the problem of concept drift, a phenomenon present in classification problems with data streams. The K-nearest neighbors (k-NN) classification algorithm is one of the methods of the family of lazy approaches used to address this problem in online learning, but it still presents some challenges that can be improved, such as the efficient choice of the number of neighbors k used in the learning process. This article proposes paired k-NN learners with dynamically adjusted number of neighbors (PL-kNN), an innovative method which adjusts dynamically and incrementally the number of neighbors used by its pair of k-NN learners in the process of online learning regarding data streams with concept drifts. To validate it, experiments were carried out with both artificial and real-world datasets and the results were evaluated using the accuracy metric, run-time, memory usage, and the Friedman statistical test with the Nemenyi post hoc test. The experimental results show that PL-kNN improves and optimizes the accuracy performances of k-NN with fixed neighboring k values in most tested scenarios.
作者：
Schöll, C.Lehner, J.Lens, H.
Department Power Generation and Automatic Control Pfaffenwaldring 23 Stuttgart70569 Germany TransnetBW GmbH
Osloerstr. 15-17 Stuttgart70173 Germany
It is known that current-controlled voltage source converters do not contribute to the inertia of power systems, leading to a higher rate of change of frequency in case of power imbalances. Another fundamental effect ...
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The project aims to provide a scalable, reliable and cost effective cloud storage solution based on a Low Density Parity Check (LDPC) code-based framework. The novelties of the project lie in the following aspects. Fi...
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ISBN:
(纸本)9781479950638
The project aims to provide a scalable, reliable and cost effective cloud storage solution based on a Low Density Parity Check (LDPC) code-based framework. The novelties of the project lie in the following aspects. Firstly, the proposed framework utilizes a new technique called dynamic parameterization so that the existing resources can be used more efficiently. Secondly, a tailored error correction code with localized property is specifically designed to minimize the cost occurred during encoding and decoding for the distributed storage system. Thirdly, a neuroevolution approach is proposed, combining artificial neural network learning algorithm with evolutionary method, to develop predictive models for dynamic resource allocation and performance optimization.
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