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41.
《Current biology : CB》2022,32(11):2505-2516.e8
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42.
Development and operation of a trickling biofilter system for continuous treatment of gas-phase trichloroethylene 总被引:3,自引:0,他引:3
A parallel trickling biofilter (TBF) system that consists of two TBFs units in parallel, one for biodegradation of trichloroethylene (TCE) and the other for reactivation of an inactivated biofilm, was developed and operated for continuous treatment of gas-phase TCE by Burkholderia cepacia G4. For inlet loadings below 8.6 mg TCE l–1 d–1, complete removal of TCE was achieved. The maximal TCE elimination capacity was 17 mg l–1 d–1. 相似文献
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Martin Dominik Vollmer Karin Stadler-Fritzsche Michael Schlömann 《Archives of microbiology》1993,159(2):182-188
2,4-Dichlorophenoxyacetate (2,4-D) in Alcaligenes eutrophus JMP134 (pJP4) is degraded via 2-chloromaleylacetate as an intermediate. The latter compound was found to be reduced by NADH in a maleylacetate reductase catalyzed reaction. Maleylacetate and chloride were formed as products of 2-chloromaleylacetate reduction, the former being funnelled into the 3-oxoadipate pathway by a second reductive step. There was no indication for an involvement of a pJP4-encoded enzyme in either the reduction or the dechlorination reaction.Abbreviations 2,4-D
2,4-dichlorophenoxyacetate 相似文献
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Diego I. Gallardo;Márcia Brandão;Jeremias Leão;Marcelo Bourguignon;Vinicius Calsavara; 《Biometrical journal. Biometrische Zeitschrift》2024,66(6):e202300257
We introduce a new modelling for long-term survival models, assuming that the number of competing causes follows a mixture of Poisson and the Birnbaum-Saunders distribution. In this context, we present some statistical properties of our model and demonstrate that the promotion time model emerges as a limiting case. We delve into detailed discussions of specific models within this class. Notably, we examine the expected number of competing causes, which depends on covariates. This allows for direct modeling of the cure rate as a function of covariates. We present an Expectation-Maximization (EM) algorithm for parameter estimation, to discuss the estimation via maximum likelihood (ML) and provide insights into parameter inference for this model. Additionally, we outline sufficient conditions for ensuring the consistency and asymptotic normal distribution of ML estimators. To evaluate the performance of our estimation method, we conduct a Monte Carlo simulation to provide asymptotic properties and a power study of LR test by contrasting our methodology against the promotion time model. To demonstrate the practical applicability of our model, we apply it to a real medical dataset from a population-based study of incidence of breast cancer in São Paulo, Brazil. Our results illustrate that the proposed model can outperform traditional approaches in terms of model fitting, highlighting its potential utility in real-world scenarios. 相似文献
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Abstract A third-order algorithm for stochastic dynamics (SD) simulations is proposed, identical to the powerful molecular dynamics leap-frog algorithm in the limit of infinitely small friction coefficient γ. It belongs to the class of SD algorithms, in which the integration time step Δt is not limited by the condition Δt ≤ γ?1, but only by the properties of the systematic force. It is shown how constraints, such as bond length or bond angle constraints, can be incorporated in the computational scheme. It is argued that the third-order Verlet-type SD algorithm proposed earlier may be simplified without loosing its third-order accuracy. The leap-frog SD algorithm is proven to be equivalent to the verlet-type SD algorithm. Both these SD algorithms are slightly more economical on computer storage than the Beeman-type SD algorithm. 相似文献
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《仿生工程学报(英文版)》2024,21(2)
Parkinson's disease is a neurodegenerative disorder that inflicts irreversible damage on humans.Some experimental data regarding Parkinson's patients are redundant and irrelevant,posing significant challenges for disease detection.Therefore,there is a need to devise an effective method for the selective extraction of disease-specific information,ensuring both accu-racy and the utilization of fewer features.In this paper,a Binary Hybrid Artificial Hummingbird and Flower Pollination Algorithm(FPA),called BFAHA,is proposed to solve the problem of Parkinson's disease diagnosis based on speech signals.First,combining FPA with Artificial Hummingbird Algorithm(AHA)can take advantage of the strong global exploration ability possessed by FPA to improve the disadvantages of AHA,such as premature convergence and easy falling into local optimum.Second,the Hemming distance is used to determine the difference between the other individuals in the population and the optimal individual after each iteration,if the difference is too significant,the cross-mutation strategy in the genetic algorithm(GA)is used to induce the population individuals to keep approaching the optimal individual in the random search process to speed up finding the optimal solution.Finally,an S-shaped function converts the improved algorithm into a binary version to suit the characteristics of the feature selection(FS)tasks.In this paper,10 high-dimensional datasets from UCI and the ASU are used to test the performance of BFAHA and apply it to Parkinson's disease diagnosis.Compared with other state-of-the-art algorithms,BFAHA shows excellent competitiveness in both the test datasets and the classification problem,indicating that the algorithm proposed in this study has apparent advantages in the field of feature selection. 相似文献
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Therapy for patients with a recurrent disease focuses on delaying disease recurrence and prolonging survival. A common analysis approach for such data is to estimate the distribution of disease-free survival, that is, the time to the first disease recurrence or death, whichever happens first. However, treating death similarly as disease recurrence may give misleading results. Also considering only the first recurrence and ignoring subsequent ones can result in loss of statistical power. We use a joint frailty model to simultaneously analyze disease recurrences and survival. Separate parameters for disease recurrence and survival are used in the joint model to distinguish treatment effects on these two types of events. The correlation between disease recurrences and survival is taken into account by a shared frailty. The effect of disease recurrence on survival can also be estimated by this model. The EM algorithm is used to fit the model, with Markov chain Monte Carlo simulations in the E-steps. The method is evaluated by simulation studies and illustrated through a study of patients with heart failure. Sensitivity analysis for the parametric assumption of the frailty distribution is assessed by simulations. 相似文献