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51.
Texture analysis based on textons is extended by introducing a method for computing textons of arbitrary order. First-, second- and third-order textons are applied to classify screening mammograms as to indicate a low or high risk of breast cancer. First-order textons are found to provide better estimates of breast cancer risk than other orders on their own but the combination of first- and second-order textons outperforms first-order textons alone and other combinations of two orders. Combining all three orders of textons does not improve classification. This example indicates that including higher-order textons has the potential to improve classification performance. 相似文献
52.
53.
《矿业科学技术学报(英文版)》2017,27(5):831-838
The increasing use of diesel-powered equipment in confined spaces(underground mines) has the potential to over expose underground miners under the threat of diesel particulate matter(DPM). Miners in underground mines can be exposed to DPM concentrations far more than works in other industries. A great number of animal and epidemiological studies have shown that both short-term and long-term DPM exposure have adverse health effect. Based on reviews of related studies, especially some recent evidence, this paper investigated the long and short-term health effects based on animal studies and epidemiological studies. The exposure-response relationship studies were also explored and compared to the current DPM regulation or standards in some countries. This paper found that the DPM health effect studies specifically for miners are not sufficient to draw solid conclusions, and a recommendation limit of DPM concentration can be put in place for better protection of miners from DPM health risk. Current animal studies lack the use of species that have similar lung functions as human for understanding the cancer mode of action in human. And finally, the DPM health hazard will continue to be a challenging topic before the mode of action and reliable exposure-response relationship are established. 相似文献
54.
为了解决在肿瘤基因治疗中腺病毒载体介导的转染细胞效率低的问题,高效、低毒的转染试剂为其提供了一条新途径.采用正十二烷基-β-D-麦芽糖苷(n-Dodecyl-β-D-Maltoside,DDM)、DC-Chol、SDS联合腺病毒e GFP-Ad转染肝癌细胞Hep G2、食管癌细胞EC109及人正常肝细胞L-02与食管正常细胞Het-1A,48 h后观察绿色荧光蛋白表达情况并计阳性细胞率,评价联合转染试剂处理提高e GFP-Ad转染肿瘤细胞的效率.实验结果表明,DDM(3μg/m L)、DC-Chol(1 000μg/m L)、SDS(0.5μg/m L)联合e GFP-Ad转染肿瘤细胞Hep G2和EC109的效率分别可达85%和78%、80%和76%、45%和41%,与单独e GFP-Ad相比,转染效率分别提高16倍和14.6倍、15倍和14.2倍、8倍和7.2倍;且与正常细胞相比,DDM能显著提高对肿瘤细胞的转染效率,二者差异具有高度统计学意义.作为非脂质体类试剂,DDM能显著提高腺病毒对肿瘤细胞的转染效率,为进一步提高腺病毒介导的肿瘤基因治疗提供有效方法与技术参考. 相似文献
55.
采用2011年1月-2014年6月在云南省第一人民医院确诊并行手术治疗的58例胰腺癌患者,切除部分肿瘤组织作为实验标本,癌旁组织作为对照,利用免疫组化及Western blot检测晚期胰腺癌组织内神经生长因子(nerve growth factor,NGF)及神经生长因子前体(precursor of nerve growth factor,pro NGF)的表达,Gel-Pro analyzer 4图像分析系统分析Western条带密度.试验重复3次,取3次IOD值行统计学分析.免疫组化及Western blot结果显示肿瘤组织NGF的表达较癌旁组织明显增高,而pro NGF的表达较癌旁组织明显降低.半定量Western blot显示癌旁组织和肿瘤组织NGF表达的IOD值分别为113.33±8.54和285.00±15.13,pro NGF表达的IOD值分别为245.00±7.55和86.33±7.37,差异有统计学意义(P0.05).因此,晚期胰腺癌患者肿瘤组织内NGF/pro NGF的比例发生改变,组织内NGF蓄积,而pro NGF减少,这可能是导致胰腺癌肿瘤细胞凋亡减少,生长、浸润和迁徙功能增强的重要机制之一. 相似文献
56.
江颖 《南京工业职业技术学院学报》2014,(1):19-22
从话语干预、戏仿和时空处理等叙述角度,探讨了菲利普·罗斯1980年再版的小说《乳房》如何展现对犹太传统的思考和反叛、生活和艺术的矛盾、人的主体性和文学的本质等主题,彰显出作者叙事策略的运用对读者阅读产生的深远影响。 相似文献
57.
58.
Antonia Vlahou 《Proteomics. Clinical applications》2013,7(5-6):384-387
Clinical Proteomics has traveled a long way pinpointing potential biomarkers for a variety of diseases. However, the absence of clinical implementation of proteomics findings has led to a frank evaluation and reconsideration of applied practices in biomarker discovery, recruitment of technological tools for biomarker verification and generation of new guidelines for data reporting. Nevertheless, considering the need for vast clinical resources for biomarker validation, the frequent lack of clear definitions of contexts of use, in combination to the biomarker “high offer,” progress toward biomarker implementation will even more require the adoption of an extensive open-minded approach: disease-focused networks are needed to ensure rapid exchange of information, initiation of appropriate studies, parallel validation of multiple biomarkers and sharing of valuable clinical resources. This viewpoint article targets to reflect on these issues and advocates the added value of multidisciplinary networks in biomarker development using bladder cancer as a paradigm. 相似文献
59.
Agnieszka Latosinska Maria Frantzi Antonia Vlahou Harald Mischak 《Proteomics. Clinical applications》2013,7(11-12):779-793
A major requirement in the application of proteins as clinical biomarkers is that they provide a highly sensitive and specific result in disease assessment. Since single biomarkers are generally of limited accuracy, a group or panel of well-characterized biomarkers appears appropriate, providing a more robust and sensitive MS-based analytical platform. CE coupled to MS has been successfully used in biomarker discovery and application, as it enables the selective detection of peptides and small proteins, combining the high separation capacity of CE with the advanced sensitivity of MS. CE-MS allows the characterization of highly complex samples (such as urine, plasma, and other biofluids) in a consistent and reproducible way. It has a range of applications, many focusing especially in studies on urinary peptide biomarkers in kidney and cardiovascular diseases. Another major field of interest has been malignancy of the genitourinary system. In the first part of this review, we cover technical aspects and performance characteristics of CE-MS, with special focus on the requirements for biomarker discovery and clinical application. In the second part, we review the potential and development of CE-MS in the management of genitourinary cancers, especially bladder cancer. CE-MS has been employed in several studies aimed at discovering biomarkers for bladder cancer that may be useful in diagnosis, monitoring for recurrence, and prediction of the risk for the muscle-invasive stage. In the last part of the review, we discuss current challenges and provide an outlook for ongoing and possible future developments. 相似文献
60.
In this paper we present a novel methodology based on non-parametric deformable prototype templates for reconstructing the
outline of a shape from a degraded image. Our method is versatile and fast and has the potential to provide an automatic procedure
for classifying pathologies. We test our approach on synthetic and real data from a variety of medical and biological applications.
In these studies it is important to reconstruct accurately the shape of the object under investigation from very noisy data.
Here we assume that we have some prior knowledge about the object outline represented by a prototype shape. Our procedure
deforms this shape by means of non-affine transformations and the contour is reconstructed by minimizing a newly developed
objective function that depends on the transformation parameters. We introduce an iterative template deformation procedure
in which the scale of the deformation decreases as the algorithm proceeds. We compare our results with those from a Gaussian
Mixture Model segmentation and two state-of-the-art Level Set methods. This comparison shows that the proposed procedure performs
consistently well on both real and simulated data. As a by-product we develop a new filter that recovers the connectivity
of a shape.
Francesco de Pasquale received his Ph.D. in Applied Statistics from the University of Plymouth, United Kingdom in 2004 discussing a thesis on Bayesian and Template based methods for image analysis. Since his degree in Physics obtained at the University of Rome ‘La Sapienza’in 1999 his work has been focused on developing models and methods for Magnetic Resonance Imaging, in particular image registration, classification and segmentation in a Bayesian framework. After being appointed a 2-year contract as a Lecturer at the University of Plymouth from 2003 to 2004 he is now a post-Doc researcher at the ITAB, Institute for Advanced Biomedical Technologies, University of Chieti, Italy and he works on the analysis of fMRI and MEG data. Julian Stander was born in Plymouth, UK in 1964. He received a BA in Mathematics with first class honours from University of Oxford in 1987, a Diploma in Mathematical Statistics with distinction from University of Cambridge in 1988, and a PhD from University of Bath in 1992. He has been a lecturer at the School of Mathematics and Statistics, University of Plymouth, since 1993, and was promoted to Reader in 2006. His fields of interest are: applications of statistics including image analysis, spatial modelling and disclosure limitation. He has published over 20 refereed journal articles. 相似文献
Francesco de PasqualeEmail: |
Francesco de Pasquale received his Ph.D. in Applied Statistics from the University of Plymouth, United Kingdom in 2004 discussing a thesis on Bayesian and Template based methods for image analysis. Since his degree in Physics obtained at the University of Rome ‘La Sapienza’in 1999 his work has been focused on developing models and methods for Magnetic Resonance Imaging, in particular image registration, classification and segmentation in a Bayesian framework. After being appointed a 2-year contract as a Lecturer at the University of Plymouth from 2003 to 2004 he is now a post-Doc researcher at the ITAB, Institute for Advanced Biomedical Technologies, University of Chieti, Italy and he works on the analysis of fMRI and MEG data. Julian Stander was born in Plymouth, UK in 1964. He received a BA in Mathematics with first class honours from University of Oxford in 1987, a Diploma in Mathematical Statistics with distinction from University of Cambridge in 1988, and a PhD from University of Bath in 1992. He has been a lecturer at the School of Mathematics and Statistics, University of Plymouth, since 1993, and was promoted to Reader in 2006. His fields of interest are: applications of statistics including image analysis, spatial modelling and disclosure limitation. He has published over 20 refereed journal articles. 相似文献