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91.
Molecular screening for pathogenic mutations in sudden cardiac death (SCD)-related genes is common practice for SCD cases. However, test results may lead to uncertainty because of the identification of variants of unknown significance (VUS) occurring in up to 70% of total identified variants due to a lack of experimental studies. Genetic variants affecting potential splice site variants are among the most difficult to interpret. The aim of this study was to examine rare intronic variants identified in the exonic flanking sequence to meet two main objectives: first, to validate that canonical intronic variants produce aberrant splicing; second, to determine whether rare intronic variants predicted as VUS may affect the splicing product. To achieve these objectives, 28 heart samples of cases of SCD carrying rare intronic variants were studied. Samples were analyzed using 85 SCD genes in custom panel sequencing. Our results showed that rare intronic variants affecting the most canonical splice sites displayed in 100% of cases that they would affect the splicing product, possibly causing aberrant isoforms. However, 25% of these cases (1/4) showed normal splicing, contradicting the in silico results. On the contrary, in silico results predicted an effect in 0% of cases, and experimental results showed >20% (3/14) unpredicted aberrant splicing. Thus, deep intron variants are likely predicted to not have an effect, which, based on our results, might be an underestimation of their effect and, therefore, of their pathogenicity classification and family members’ follow-up.  相似文献   
92.
    
In this paper the problem of performing external validation of the semantic coherence of topic models is considered. The Fowlkes-Mallows index, a known clustering validation metric, is generalized for the case of overlapping partitions and multi-labeled collections, thus making it suitable for validating topic modeling algorithms. In addition, we propose new probabilistic metrics inspired by the concepts of recall and precision. The proposed metrics also have clear probabilistic interpretations and can be applied to validate and compare other soft and overlapping clustering algorithms. The approach is exemplified by using the Reuters-21578 multi-labeled collection to validate LDA models, then using Monte Carlo simulations to show the convergence to the correct results. Additional statistical evidence is provided to better understand the relation of the metrics presented.  相似文献   
93.
94.
    
KI/ML-based Analysis and Interpretation of the IABSE-Bridge Collapse Database Statistical analyses of bridge collapse data show that concrete bridges collapse significantly less frequently than bridges made of steel or wood. Since the main causes of bridge collapses worldwide are floods and associated fluvial processes, such as scouring, debris flows, etc. and impacts, it is reasonable to assume that the high dead load of concrete bridges leads to an overall more robust behavior in these events. This paper will examine whether the IABSE collapse database confirms this hypothesis and whether indications of further causes can be identified. For this purpose, the IABSE collapse database is examined using artificial intelligence and machine learning (AI/ML) methods. However, the AI/ML analysis does not confirm the previous thesis. The reasons for the rejection of the thesis, such as the representativeness of the data, are also discussed. An extension of the database for events with large numbers of collapses is recommended.  相似文献   
95.
    
The lattice Boltzmann method (LBM) has been increasingly adopted for modelling multiphase fluid simulations in engineering problems. Although relatively easy to implement, the ubiquitous Shan–Chen pseudopotential model suffers from limitations such as thermodynamic consistency and the formation of spurious currents. In the literature, the Zhang–Chen, Kupershtokh et al., the β-scheme, and the Yang–He alternative models seek to mitigate these effects. Here, through analytical manipulations, we call attention to a unified model from which these multiphase interaction forces can be recovered. Isothermal phase-transition simulations of single-component in stationary and oscillating droplet conditions, as well as spinodal decomposition calculations, validate the model numerically and reinforce that the multiphase forces are essentially equivalent. Parameters are selected based on the vapour densities at low temperatures in the Maxwell coexistence curve, where there is a narrow range of optimal values. We find that expressing the model parameters as functions of the reduced temperature further enhances the thermodynamic consistency without losing stability or increasing spurious velocities.  相似文献   
96.
    
Estimating uncertainty in model predictions is a central task in quantitative biology. Biological models at the single-cell level are intrinsically stochastic and nonlinear, creating formidable challenges for their statistical estimation which inevitably has to rely on approximations that trade accuracy for tractability. Despite intensive interest, a sweet spot in this trade-off has not been found yet. We propose a flexible procedure for uncertainty quantification in a wide class of reaction networks describing stochastic gene expression including those with feedback. The method is based on creating a tractable coarse-graining of the model that is learned from simulations, a synthetic model, to approximate the likelihood function. We demonstrate that synthetic models can substantially outperform state-of-the-art approaches on a number of non-trivial systems and datasets, yielding an accurate and computationally viable solution to uncertainty quantification in stochastic models of gene expression.  相似文献   
97.
    
The accurate establishment and maintenance of DNA methylation patterns is vital for mammalian development and disruption to these processes causes human disease. Our understanding of DNA methylation mechanisms has been facilitated by mathematical modelling, particularly stochastic simulations. Megabase-scale variation in DNA methylation patterns is observed in development, cancer and ageing and the mechanisms generating these patterns are little understood. However, the computational cost of stochastic simulations prevents them from modelling such large genomic regions. Here, we test the utility of three different mean-field models to predict summary statistics associated with large-scale DNA methylation patterns. By comparison to stochastic simulations, we show that a cluster mean-field model accurately predicts the statistical properties of steady-state DNA methylation patterns, including the mean and variance of methylation levels calculated across a system of CpG sites, as well as the covariance and correlation of methylation levels between neighbouring sites. We also demonstrate that a cluster mean-field model can be used within an approximate Bayesian computation framework to accurately infer model parameters from data. As mean-field models can be solved numerically in a few seconds, our work demonstrates their utility for understanding the processes underpinning large-scale DNA methylation patterns.  相似文献   
98.
99.
    
Structural instabilities can be used to provide rapid responses activated by mechanical force or displacement thresholds. The emergence of responsive materials has opened the door for adapting such structural instabilities to actuators that will abruptly deform guided by external stimuli. However, fast configurational transitions between equilibrium states imply important viscoelastic roles at the material level that inevitably scale up to the structural level. A comprehensive understanding of viscoelastic effects is provided on bistable structural transitions combining a new experimental perspective and a thorough modeling analysis. The insights from these results are here translated to magneto-responsive bistable structures offering a route-map to design effective actuation conditions. The bistable transition functionally depends on the combination of magnetic field amplitude and application rate. The understanding of the viscoelastic and magneto-mechanical coupling provides efficient actuation via temporal magnetic pulses, removing the need of generating continued magnetic fields. Finally, these insights are combined to develop a responsive structure whose transient and steady bistable transitions can be modulated by the application rate of external magnetic stimuli.  相似文献   
100.
    
In this work, the potential of bio-inspired strategies for the synthesis of calcium sulfate (CaSO4·nH2O) materials for heritage conservation is explored. For this, a nonclassical multi-step crystallization mechanism to understand the effect of calcein– a fluorescent chelating agent with a high affinity for divalent cations— on the nucleation and growth of calcium sulfate phases is proposed. Moving from the nano- to the macro-scale, this strategy sets the basis for the design and production of fluorescent nano-bassanite (NB-C; CaSO4·0.5H2O), with application as a fully compatible consolidant for the conservation of historic plasterwork. Once applied to gypsum (CaSO4·2H2O) plaster specimens, cementation upon hydration of nano-bassanite results in a significant increase in mechanical strength, while intracrystalline occlusion of calcein in newly-formed gypsum cement improves its weathering resistance. Furthermore, under UV irradiation, the luminescence produced by calcein molecules occluded in gypsum crystals formed upon nano-bassanite hydration allows the easy identification of the newly deposited consolidant within the treated gypsum plaster without altering the substrate's appearance.  相似文献   
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