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Jiwon Choi Jun Seop Yun Hyeeun Song Yong-Keol Shin Young-Hoon Kang Palinda Ruvan Munashingha Jeongyeon Yoon Nam Hee Kim Hyun Sil Kim Jong In Yook Dongseob Tark Yun-Sook Lim Soon B. Hwang 《Molecules (Basel, Switzerland)》2021,26(12)
African swine fever virus (ASFV) causes a highly contagious and severe hemorrhagic viral disease with high mortality in domestic pigs of all ages. Although the virus is harmless to humans, the ongoing ASFV epidemic could have severe economic consequences for global food security. Recent studies have found a few antiviral agents that can inhibit ASFV infections. However, currently, there are no vaccines or antiviral drugs. Hence, there is an urgent need to identify new drugs to treat ASFV. Based on the structural information data on the targets of ASFV, we used molecular docking and machine learning models to identify novel antiviral agents. We confirmed that compounds with high affinity present in the region of interest belonged to subsets in the chemical space using principal component analysis and k-means clustering in molecular docking studies of FDA-approved drugs. These methods predicted pentagastrin as a potential antiviral drug against ASFVs. Finally, it was also observed that the compound had an inhibitory effect on AsfvPolX activity. Results from the present study suggest that molecular docking and machine learning models can play an important role in identifying potential antiviral drugs against ASFVs. 相似文献
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Eduardo Tejera Yunierkis Prez-Castillo Andrea Chamorro Alejandro Cabrera-Andrade Maria Eugenia Sanchez 《Molecules (Basel, Switzerland)》2021,26(4)
Preeclampsia is a hypertensive disorder that occurs during pregnancy. It is a complex disease with unknown pathogenesis and the leading cause of fetal and maternal mortality during pregnancy. Using all drugs currently under clinical trial for preeclampsia, we extracted all their possible targets from the DrugBank and ChEMBL databases and labeled them as “targets”. The proteins labeled as “off-targets” were extracted in the same way but while taking all antihypertensive drugs which are inhibitors of ACE and/or angiotensin receptor antagonist as query molecules. Classification models were obtained for each of the 55 total proteins (45 targets and 10 off-targets) using the TPOT pipeline optimization tool. The average accuracy of the models in predicting the external dataset for targets and off-targets was 0.830 and 0.850, respectively. The combinations of models maximizing their virtual screening performance were explored by combining the desirability function and genetic algorithms. The virtual screening performance metrics for the best model were: the Boltzmann-Enhanced Discrimination of ROC (BEDROC)α=160.9 = 0.258, the Enrichment Factor (EF)1% = 31.55 and the Area Under the Accumulation Curve (AUAC) = 0.831. The most relevant targets for preeclampsia were: AR, VDR, SLC6A2, NOS3 and CHRM4, while ABCG2, ERBB2, CES1 and REN led to the most relevant off-targets. A virtual screening of the DrugBank database identified estradiol, estriol, vitamins E and D, lynestrenol, mifrepristone, simvastatin, ambroxol, and some antibiotics and antiparasitics as drugs with potential application in the treatment of preeclampsia. 相似文献
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随着智能电网和绿色能源技术的快速发展,双向充放电机也越来越受到关注;文章主要研究双向充放电机充电过程中电压的稳定性和放电过程中并网电流波形的畸变率;提出了一种基于RTW的matlab/simulink控制方法,在simulink环境下搭建仿真模型和控制模型,然后将达到预期效果的仿真模型所对应的控制模型编译为代码并烧写到DSP中;此种方法在能够保证在恒流源和恒压源两种充电模式中电流和电压的稳定性,在放电过程中并网谐波电流总畸变率小于5%,功率因数基本为1;仿真结果和基于DSP2812的硬件平台实验都证明了这种方法的有效性。 相似文献
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鉴于传统神经网络和支持向量机机理复杂、计算量大的缺陷,很难实时跟踪磷酸铁锂电池组复杂快速的内部反应,影响电池荷电状态的估算精度,提出应用一种简单、有效的极限学习机对一额定容量为100Ah、额定电压为72V的纯电动汽车磷酸铁锂电池组建模,并分别与BP神经网络、RBF神经网络、支持向量机进行对比。随后,以学习时间和泛化性能为优化目标,应用粒子群方法寻找最佳隐层节点个数。结果表明,基于极限学习机的磷酸铁锂电池组模型的学习时间、泛化性能优于BP神经网络、RBF神经网络、支持向量机;隐层节点优化后,模型的学习时间和泛化性能达到最优。 相似文献
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为了实现漆包机生产的远程监控,设计了一款基于Web的实时漆包机远程监控平台。该平台使用模块化,可扩展的设计方案,采用了Spring MVC的后台Web应用程序框架,基于Modbus协议的数据交互,Ajax方式的网络数据异步提交以及无刷新的图表显示技术。传统的监控平台只能通过以太网在企业内部访问,而该平台可接入互联网,可实现随时随地通过电脑及或者移动终端对生产设备各部分的温度点、电机风机转速、耗电量统计的实时监视,对温度的上下限及电机转速进行远程控制,接收设备状态异常的实时报警,数据存储分析。实验测试表明,该平台的实时性和稳定性均能达到车间生产的要求。 相似文献
100.
We study electron energies in a double concentric quantum ring with anisotropy in the rims heights in the presence of the external magnetic field applied along the symmetry axis. To this end, we consider a model in which the thickness grows linearly from the axis up to the inner rim with a slope different from one between the inner and the outer rims. The anisotropy in the rims heights originated by the presence in the structure of various valleys we simulate by periodic dependence of the slope on the radial direction. We show that the wave functions of the electron confined in such structure can be found analytically if the slopes in all radial directions are the same, and by using a simple exact diagonalization procedure otherwise. The behavior of the electron energies as functions of the magnetic field, rings radii and rims heights, as well as the number of the valleys and their depths is consistently described with our formalism. The entanglement of the states with different radial and orbital quantum numbers, the period and the amplitude of the Aharonov–Bohm oscillations are very sensible to any variations of the rims heights. 相似文献