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Hydrological data provide valuable information for the decision-making process in water resources management, where long and complete time series are always desired. However, it is common to deal with missing data when working on streamflow time series. Rainfall-streamflow modeling is an alternative to overcome such a difficulty. In this paper, self-organizing maps (SOM) were developed to simulate monthly inflows to a reservoir based on satellite-estimated gridded precipitation time series. Three different calibration datasets from Três Marias Reservoir, composed of inflows (targets) and 91 TRMM-estimated rainfall data (inputs), from 1998 to 2019, were used. The results showed that the inflow data homogeneity pattern influenced the rainfall-streamflow modeling. The models generally showed superior performance during the calibration phase, whereas the outcomes varied depending on the data homogeneity pattern and the chosen SOM structure in the testing phase. Regardless of the input data homogeneity, the SOM networks showed excellent results for the rainfall-runoff modeling, presenting Nash–Sutcliffe coefficients greater than 0.90.
Graphical Abstract 相似文献The European space-economy represents a complex system with a great internal heterogeneity, intensive socioeconomic interactions and differential growth trajectories among countries and regions. The present study aims to investigate the connectivity between spatial competitiveness and resilience in Europe and seeks to design an operational framework for concerted strategies of competitive and resilient regions. To assess the linkage between resilience and competitiveness, we have developed a new measure, viz. the Resilience and Competitiveness Index (RACI) as a function of two constituent sub-indices: Resilience and Competitiveness. This approach is tested on the basis of detailed data on European regions. The empirical results from 268 EU NUTS2 regions offer a solid anchor point for the proposed operational framework for concerted development strategies of competitive and resilient regions. Our research distinguishes and proposes several systematic types of concerted regional strategies according to the performance of a region measured by Resilience and Competiveness sub-indices. A key result of the study is the design of an operational constellation for strategic regional policy evaluation, with a major added value for policy- and decision-making purposes. The use of official data from Eurostat and of standard indicators in our research assures continuity and consistency with the official Regional Competitiveness Index (RCI) classification and measurement, so that policy makers are able to compare the performance of their regions over time and to develop proper concerted strategies accordingly. The clear evidence of a connectivity between regional competitiveness and resilience may help to develop a governance approach that balances competitiveness (mainly represented by productive assets) with resilience (mainly represented by sustainability and ecological awareness) and thus to deal with the complexity in socioeconomic systems.
相似文献Probabilistic topic modeling algorithms like Latent Dirichlet Allocation (LDA) have become powerful tools for the analysis of large collections of documents (such as papers, projects, or funding applications) in science, technology an innovation (STI) policy design and monitoring. However, selecting an appropriate and stable topic model for a specific application (by adjusting the hyperparameters of the algorithm) is not a trivial problem. Common validation metrics like coherence or perplexity, which are focused on the quality of topics, are not a good fit in applications where the quality of the document similarity relations inferred from the topic model is especially relevant. Relying on graph analysis techniques, the aim of our work is to state a new methodology for the selection of hyperparameters which is specifically oriented to optimize the similarity metrics emanating from the topic model. In order to do this, we propose two graph metrics: the first measures the variability of the similarity graphs that result from different runs of the algorithm for a fixed value of the hyperparameters, while the second metric measures the alignment between the graph derived from the LDA model and another obtained using metadata available for the corresponding corpus. Through experiments on various corpora related to STI, it is shown that the proposed metrics provide relevant indicators to select the number of topics and build persistent topic models that are consistent with the metadata. Their use, which can be extended to other topic models beyond LDA, could facilitate the systematic adoption of this kind of techniques in STI policy analysis and design.
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