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31.
We consider a quark model based on QCD scale anomaly in which the quarks move in the field of an effective glueball field.
Hadrons correspond to solitonic bags of higher energy density in a nonperturbative sea of condensed gluons. We calculate the
static properties of nucleon in this model and find that the nucleon mass is far too large (2.4–4 GeV) and the proton charge
radius (0.37–0.54 fm) is low. The proton gyromagnetic ratio and gA/gv are in reasonable agreement with the experimental numbers. 相似文献
32.
33.
David Morris 《Applied biochemistry and biotechnology》2004,113(1-3):5-12
This article addresses two questions: Has the effectiveness of the US governments federal research and development (R&;D) spending suffered from the post-1980 strategic change from freely shared and publicly owned to privately owned scientific advances? What criteria would a federal R&;D program use to design a strategy that most effectively enhances the well-being of farmers and rural communities? Several studies found that the pre-1980 US Department of Agriculture research strategy was very effective. No comparable studies have analyzed the comparative effectiveness of the post-1980 strategy of restricting access to the results of public research. Recent experience and several analytical studies suggest that to significantly enhance the health of rural economies from an expanded use of plant matter as an industrial material, federal policy should channel scientific and engineering research into small- and medium-sized production and processing technologies and should encourage farmer-owned, value-added enterprises. 相似文献
34.
相对论重离子碰撞实验中混合事件方法的研究 总被引:1,自引:0,他引:1
把RQMD(Relativistic Quantum Molecular Dynamics)产生器产生的数据输入作为原始事例取样,讨论了在比较复杂的背景情况下,一种新的混合事件方法,用以证实在相对论重离子碰撞实验中高能量激发共振态的存在.并以共振态重子Δ++为例演示了这一方法的应用. 相似文献
35.
We consider the problem of minimizing an SC1 function subject to inequality constraints. We propose a local algorithm whose distinguishing features are that: (a) a fast convergence rate is achieved under reasonable assumptions that do not include strict complementarity at the solution; (b) the solution of only linear systems is required at each iteration; (c) all the points generated are feasible. After analyzing a basic Newton algorithm, we propose some variants aimed at reducing the computational costs and, in particular, we consider a quasi-Newton version of the algorithm. 相似文献
36.
本对于全局优化问题提出一个改进的进化规划算法,该算法以概率p接收基于电磁理论求出合力方向作为随机搜索方向,以概率1-p接收按正态分布产生的随机搜索方向。改进算法不仅克服了传统进化规划算法随机搜索的盲目性,而且保留了传统进化规划算法全局搜索性。本算法应用于几个典型例题,数值结果表明本算法是可行的,有效的。 相似文献
37.
从决策有限理性角度,引入行为金融理论于机构投资风险优化系统,对多心理账户条件下机构投资的风险优化设计进行了研究。以Friedman和Savage之谜为释例,对机构投资风险优化中的诸多非标准金融异像进行了解释。以Shefrin和Statman行为证券组合理论为核心,建立了多心理账户条件下机构投资的风险优化模型,为机构投资的风险优化实践提供了一种量化分析工具。 相似文献
38.
本文利用我国上市公司1997至2002年的有关财务数据,建立了我国上市公司资本成本面板数据模型,运用该模型分析了我国上市公司资本成本与财务杠杆、企业规模的关系.结果表明,财务杠杆的提升、企业规模的扩大将使资本成本下降.该结论对我国上市公司优化资本结构、提高企业价值具有一定的参考价值. 相似文献
39.
40.
The Sample Average Approximation Method Applied to Stochastic Routing Problems: A Computational Study 总被引:1,自引:0,他引:1
Bram Verweij Shabbir Ahmed Anton J. Kleywegt George Nemhauser Alexander Shapiro 《Computational Optimization and Applications》2003,24(2-3):289-333
The sample average approximation (SAA) method is an approach for solving stochastic optimization problems by using Monte Carlo simulation. In this technique the expected objective function of the stochastic problem is approximated by a sample average estimate derived from a random sample. The resulting sample average approximating problem is then solved by deterministic optimization techniques. The process is repeated with different samples to obtain candidate solutions along with statistical estimates of their optimality gaps.We present a detailed computational study of the application of the SAA method to solve three classes of stochastic routing problems. These stochastic problems involve an extremely large number of scenarios and first-stage integer variables. For each of the three problem classes, we use decomposition and branch-and-cut to solve the approximating problem within the SAA scheme. Our computational results indicate that the proposed method is successful in solving problems with up to 21694 scenarios to within an estimated 1.0% of optimality. Furthermore, a surprising observation is that the number of optimality cuts required to solve the approximating problem to optimality does not significantly increase with the size of the sample. Therefore, the observed computation times needed to find optimal solutions to the approximating problems grow only linearly with the sample size. As a result, we are able to find provably near-optimal solutions to these difficult stochastic programs using only a moderate amount of computation time. 相似文献