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151.
《Journal of Cultural Economy》2013,6(4):286-298
ABSTRACTUsing a six-year case study of Glasgow’s Sustainable City business model, this paper examines interactions between financialised governance of cities and clean energy strategies. Research on the role of cities in developing clean energy has paid limited attention to the interaction with financialised governance of infrastructure, which makes the implementation of plans largely dependent on private investment. A conceptual approach combining economic sociology of actor networks and urban political economy is used to analyse the career of the business model designed to transform old infrastructures into new clean energy assets. The analysis focuses on interactions between city council, public bodies and electricity distribution network business. Climate policies are creating uncertainties for energy businesses over revenues from ageing networks, suggesting scope for alliance with local governments. Making new liquid assets for clean energy from old infrastructure is however shown to be a process marked by instability and reversals. In conclusion, it is argued that concepts from actor-network theory and urban political economy used together reveal the hidden contingencies of financialisation in particular socio-technical interactions, and their materiality in the context of climate change. 相似文献
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Brad Vogus 《Public Services Quarterly》2020,16(3):182-185
AbstractThe number of ebooks continues to grow in academic libraries. Ebooks have many advantages that make them attractive as a library resource. However, ebooks also have restrictions. These include use and sharing restrictions, license restrictions, and technological barriers. Some students also prefer print books for specific tasks. The following articles examine many aspects of ebooks in academic libraries, including student preference, overall ebook use, and the advantages and disadvantages of various ebook acquisition models. 相似文献
154.
Company business models are vulnerable to various contingencies in the business environment that may unexpectedly render their business logic ineffective. In particular, technological advancements, such as the Internet of things, big data, sharing economy and crowdsourcing, have enabled new forms of business models that can effectively and abruptly make traditional business models obsolete. By disrupting or even diminishing companies’ revenue streams, environmental contingencies may present a significant threat to business continuity (BC). Evaluating the resilience of business models against these contingencies should therefore be a core area of BC. However, existing BC approaches tend to focus on the continuity of the resources and processes through which a particular business model is accomplished in practice but omit the business model itself. We argue that in order for BC approaches to become holistic and strategic, business models need to become a part of the BC considerations, entailing an expansion of the scope of BC from value preservation to value creation. We propose an approach of Strategic Business Continuity Management, which consists of two parts: (1) sustaining the continuity of the company business model (value preservation) and (2) evaluating and modifying the business model (value creation). We illustrate conceptually the value creation part with an example drawn from the sharing economy. 相似文献
155.
Sandip Sinharay 《Educational Measurement》2021,40(1):79-91
Technical difficulties occasionally lead to missing item scores and hence to incomplete data on computerized tests. It is not straightforward to report scores to the examinees whose data are incomplete due to technical difficulties. Such reporting essentially involves imputation of missing scores. In this paper, a simulation study based on data from three educational tests is used to compare the performances of six approaches for imputation of missing scores. One of the approaches, based on data mining, is the first application of its kind to the problem of imputation of missing data. The approach based on data mining and a multiple imputation approach based on chained equations led to the most accurate imputation of missing scores, and hence to most accurate score reporting. A simple approach based on linear regression performed the next best overall. Several recommendations are made regarding the reporting of scores to examinees with incomplete data. 相似文献
156.
Senda Sammoud Alan Michael Nevill Yassine Negra Raja Bouguezzi Chaabene Helmi Younés Hachana 《Journal of sports sciences》2019,37(10):1162-1167
The purpose of this study was to estimate the optimal body size, limb-segment length, girth or breadth ratios for 100-m backstroke mean speed performance in young swimmers. Sixty-three young swimmers (boys [n = 30; age: 13.98 ± 0.58 years]; girls [n = 33; age: 13.02 ± 1.20 years]) participated in this study. To identify the optimal body size and body composition components associated with 100-m backstroke speed performance, we adopted a multiplicative allometric log-linear regression model, which was refined using backward elimination. The multiplicative allometric model exploring the association between 100-m backstroke mean speed performance and the different somatic measurements estimated that biological age, sitting height, leg length for the lower-limbs, and two girths (forearm and arm relaxed girth) are the key predictors. Stature and body mass did not contribute to the model, suggesting that the advantage of longer levers was limb-specific rather than a general whole-body advantage. In fact, it is only by adopting multiplicative allometric models that the abovementioned ratios could have been derived. These findings highlighted the importance of considering somatic characteristics of young backstroke swimmers and can help swimming coaches to classify their swimmers and enable them to suggest what might be the swimmers’ most appropriate stroke (talent identification). 相似文献
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Energy efficiency of public sector is an important issue in the context of smart cities due to the fact that buildings are the largest energy consumers, especially public buildings such as educational, health, government and other public institutions that have a large usage frequency. However, recent developments of machine learning within Big Data environment have not been exploited enough in this domain. This paper aims to answer the question of how to incorporate Big Data platform and machine learning into an intelligent system for managing energy efficiency of public sector as a substantial part of the smart city concept. Deep neural networks, Rpart regression tree and Random forest with variable reduction procedures were used to create prediction models of specific energy consumption of Croatian public sector buildings. The most accurate model was produced by Random forest method, and a comparison of important predictors extracted by all three methods has been conducted. The models could be implemented in the suggested intelligent system named MERIDA which integrates Big Data collection and predictive models of energy consumption for each energy source in public buildings, and enables their synergy into a managing platform for improving energy efficiency of the public sector within Big Data environment. The paper also discusses technological requirements for developing such a platform that could be used by public administration to plan reconstruction measures of public buildings, to reduce energy consumption and cost, as well as to connect such smart public buildings as part of smart cities. Such digital transformation of energy management can increase energy efficiency of public administration, its higher quality of service and healthier environment. 相似文献
159.
Document length is widely recognized as an important factor for adjusting retrieval systems. Many models tend to favor the
retrieval of either short or long documents and, thus, a length-based correction needs to be applied for avoiding any length
bias. In Language Modeling for Information Retrieval, smoothing methods are applied to move probability mass from document
terms to unseen words, which is often dependant upon document length. In this article, we perform an in-depth study of this
behavior, characterized by the document length retrieval trends, of three popular smoothing methods across a number of factors,
and its impact on the length of documents retrieved and retrieval performance. First, we theoretically analyze the Jelinek–Mercer,
Dirichlet prior and two-stage smoothing strategies and, then, conduct an empirical analysis. In our analysis we show how Dirichlet
prior smoothing caters for document length more appropriately than Jelinek–Mercer smoothing which leads to its superior retrieval
performance. In a follow up analysis, we posit that length-based priors can be used to offset any bias in the length retrieval
trends stemming from the retrieval formula derived by the smoothing technique. We show that the performance of Jelinek–Mercer
smoothing can be significantly improved by using such a prior, which provides a natural and simple alternative to decouple
the query and document modeling roles of smoothing. With the analysis of retrieval behavior conducted in this article, it
is possible to understand why the Dirichlet Prior smoothing performs better than the Jelinek–Mercer, and why the performance
of the Jelinek–Mercer method is improved by including a length-based prior.
相似文献
Leif AzzopardiEmail: |
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