Suchergebnisse

Using stock market and economic data from 1900 to 2008 from 27 separate presidential administrations in the United States (U.S.), including 15 Republican and 12 Democratic, this paper examines the relationships between the market return after each Election Day and economic performance during the presidential term. Using the theoretical framework of political economy, the authors examine how Wall Street's reaction to a presidential election acts as a predictive measure of future economic performance. The analysis shows that the after-election market movement has progressively been more accurate in predicting the future Gross Domestic Product (GDP) growth but not the future unemployment rates. Given that the results show a higher correlation over time, the model appears to provide a good starting point for judging the economic potential of future presidential administrations. ; published_or_final_version

This Article briefly summarizes research needs of the California Department of Transportation (Caltrans) for earthquake planning, response, andrecovery. It is basedon documentation stemming from the 1989 Loma Prieta andthe 1994 Northridge earthquakes, and a series of meetings with key Caltrans divisions including Structural Maintenance, District Traffic Operations, and Maintenance. This article serves to clarify research needs and contributes to the strategic planning process of the research program, thereby helping to foster partnerships between Caltrans and the research community.

Gene expression programming (GEP) is a data driven evolutionary technique that is well suited to correlation mining of system components. With the rapid development of industry 4.0, the number of components in a complex industrial system has increased significantly with a high complexity of correlations. As a result, a major challenge in employing GEP to solve system engineering problems lies in computation efficiency of the evolution process. To address this challenge, this paper presents EGEP, an Event Tracker enhanced Gene Expression Programming which filters irrelevant system components to ensure the evolution process to converge quickly. Furthermore, we introduce three theorems to mathematically validate the effectiveness of EGEP based on Gene expression programming schema theory. Experiment results also confirm that EGEP outperforms Gene expression programming with a shorter computation time in evolution. ; European Union's Horizon 2020 research and innovation program; 10.13039/501100012166-National Basic Research Program of China (973 Program); 10.13039/501100003399-Science and Technology Commission of Shanghai Municipality;

ABSTRACT Novel 3-D passive particle tracking experiments were performed in the northwest Atlantic to elucidate connectivity among areas closed to protect vulnerable marine ecosystems. We examined (1) the degree of vertical movement of particles released at different depths and locations; (2) the location of potential source populations for the deep-sea taxa protected by the closures; and (3) the degree of functional connectivity. A long-term oceanographic dataset (EN4) was queried to characterize the temperature and salinity regimes in each of the closed areas as a basis for interpreting recently published climate change projections. Using the Parcels Lagrangian particle tracking framework and the BNAM hydrodynamic model, we found enhanced connectivity over previously developed 2-D models and unexpected, current-driven, strong (to a maximum of about 1340 m) downward displacement at depth (450, 1000 and 2250 m), with weaker upward displacement except for the release depth of 2250 m which showed upward movement of 955 m with a drift duration of 3 months. The current velocities create down-stream interdependence among closed areas and allow redundancy to develop in some of the areas of the network, with some of the larger areas also showing retention. Source populations for sponges in the upstream closure are likely in adjacent waters of the Canadian continental shelf. Collectively this information can be used to inform management decisions related to the size and placement of these closed areas, and vertical velocity surfaces have potential for use in species distribution modeling of benthic species and habitats. ; ACKNOWLEDGEMENTS This study was funded by the Fisheries & Oceans Canada's International Governance Strategy Science Program through project "Science Advice in Support of the 2020 NAFO Review of Closed Areas to Protect Vulnerable Marine Ecosystems in the NRA" led by EK (2019–2021) and is a contribution towards the European Union's Horizon 2020 research and innovation program under Grant Agreement No. ...

Advanced energy algorithms running at big-data scale will be necessary to identify, realize, and verify energy savings to meet government and utility goals of building energy efficiency. Any algorithm must be well characterized and validated before it is trusted to run at these scales. Smart meter data from real buildings will ultimately be required for the development, testing, and validation of these energy algorithms and processes. However, for initial development and testing, smart meter data are difficult to work with due to privacy restrictions, noise from unknown sources, data accessibility, and other concerns which can complicate algorithm development and validation. This study describes a new methodology to generate synthetic smart meter data of electricity use in buildings using detailed building energy modeling, which aims to capture the variability and stochastics of real energy use in buildings. The methodology can create datasets tailored to represent specific scenarios with known truth and controllable amounts of synthetic noise. Knowledge of ground truth also allows the development and validation of enhanced processes which leverage building metadata, such as building type or size (floor area), in addition to smart meter data. The methodology described in this paper includes the key influencing factors of real-world building energy use including weather data, occupant-driven loads, building operation and maintenance practices, and special events. Data formats to support workflows leveraging both synthetic meter data and associated metadata are proposed and discussed. Finally, example use cases of the synthetic meter data are described to illustrate potential applications.

Understanding and improving public flood risk perception is conducive to the implementation of effective flood risk management and disaster reduction policies. In the flood-prone city of Jingdezhen, flood disaster is one of the most destructive natural hazards to impact the society and economy. However, few studies have been attempted to focus on public flood risk perception in the small and medium-size city in China, like Jingdezhen. Therefore, the purpose of this study was to investigate the public flood risk perception in four districts of Jingdezhen and examine the related influencing factors. A questionnaire survey of 719 randomly sampled respondents was conducted in 16 subdistricts of Jingdezhen. Analysis of variance was conducted to identify the correlations between the impact factors and public flood risk perception. Then, the flood risk perception differences between different groups under the same impact factor were compared. The results indicated that the socio-demographic characteristics of the respondents (except occupation), flood experience, flood knowledge education, flood protection responsibility, and trust in government were strongly correlated with flood risk perception. The findings will help decision makers to develop effective flood risk communication strategies and flood risk reduction policies. (PDF) Analysis of the Public Flood Risk Perception in a Flood-Prone City: The Case of Jingdezhen City in China. Available from: https://www.researchgate.net/publication/328750736_Analysis_of_the_Public_Flood_Risk_Perception_in_a_Flood-Prone_City_The_Case_of_Jingdezhen_City_in_China [accessed Nov 12 2018].