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ABSTRACT The concept of digital game-based learning (DGBL) evolves rapidly together with technological enhancements of virtual reality (VR) and smart phones. However, the mental workload (MWL) that VR-training applications demand and motivational qualities originating from user experience (UX) should be identified in order to create effective and enjoyable training/learning challenges that fit with individual users’ capabilities. This study examined the effects of reality-based interaction (RBI) and VR on measures of student motivation and MWL, in a mental arithmetic game for secondary school pupils. In a randomised controlled trial with sixty school children, a mental arithmetic game was tested with three different interaction and two different presentation methods – VR RBI, VR head-mounted-display tapping and tablet flick-gesture. Results found a significant effect of RBI on MWL but no differences in enjoyment of training were found between VR-experience and tablet training-experience. In fact, adding the gaming-context to the mental arithmetic task created an enjoyable, motivating experience regardless of presentation or interaction-style. 相似文献
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针对朱仙庄矿 84 15工作面切眼特殊的地质条件 ,介绍了高强锚杆 (索 )支护技术成功应用于特厚极软煤层的经验 ,并对巷道支护成本进行了分析 相似文献
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反应器和钢烟囱整体吊装的难点和对策 总被引:1,自引:0,他引:1
介绍了在金陵石化炼油厂200万t/a柴油加氢装置建设中,将质量为237t的反应器和高60m的钢烟囱成功进行整体吊装过程中的关键技术.通过此项工程实践得出以下结论象钢烟囱这样细长重物的整体吊装,由两台吊车抬立起后,再由主吊车独立吊装就位的施工方法简单易行,安全可靠,应用范围广;结构复杂细长杆的吊装强度和挠度计算比较复杂,在计算过程中可以忽略部分次要件的质量,分别以两吊点为支点计算上下吊点所受的力,然后再按比例放余量,使两吊点受力之和等于细长杆的重力,计算挠度时,可以适当地把问题夸大化,从而建立近似的力学模型;高压厚壁重型容器的吊装,宜直接设置专门吊盖与大法兰用螺栓把紧起吊;施工现场地基坚实是保证吊车站位和吊装施工的前提条件. 相似文献
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Ready-to-drinks (RTDs) are composed of an alcoholic component and a soft-drink base and are primarily consumed by a youth market. The authors explored whether liking and experience with an RTD soft-drink base predicts liking for the RTD. Participants (N=350) from ages 12 to 30 years sampled 3 RTDs and their respective soft-drink and alcoholic components. For milk- and fruit-based RTDs, liking for and familiarity with their soft-drink base was the best predictor of liking for and familiarity with the RTD itself. For the Coke-based RTD, familiarity with and liking for bourbon best predicted familiarity with and liking for the RTD. All of these effects were consistent across blind and nonblind testing. The authors' results suggest that where there is perceptual similarity between the RTD and its soft-drink base, these beverages may provide an easy transition into alcohol consumption for novice drinkers. (PsycINFO Database Record (c) 2010 APA, all rights reserved) 相似文献
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岑雪苇 《浙江工业大学学报》2007,(1)
禅宗解脱的实质是生命的自由。通过明心见性、转迷入悟的途径,心灵摆脱烦恼业障,进入无我境界,从而体验到一种想象的自由。这一过程在很多方面类似审美活动,在体验上具有审美自由的特征。但禅宗的解脱,以及解脱所达到的审美的人生境界之可能,其关键在于生命主体的自我消解。无我的或曰没有主体的自由,并不是一种真正的自由。因而,禅宗关于人的解放的理论和实践,其价值仅仅在宗教方面和美学方面。 相似文献
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Algorithms for automatic playlist generation solve the problem of tedious and time consuming manual selection of musical playlists. These algorithms generate playlists according to the user’s music preferences of the moment. The user describes his preferences either by manually inputting a couple of example songs, or by defining constraints for the choice of music. The approaches to automatic playlist generation up to now were based on examining the metadata attached to the music pieces. Some of them took also the listening history into account. But anyway, a heavy accent has been put on the metadata, while the listening history, if it was used at all, had a minor role. Missings and errors in metadata frequently appear, especially when the music is acquired from the Internet. When the metadata is missing or wrong, the approaches proposed so far cannot work. Besides, entering constraints for the playlist generation can be a difficult activity. In our approach we ignored the metadata and focused on examining the listening habits. We developed two simple algorithms that track the listening habits and form a listener model—a profile of listening habits. The listener model is then used for automatic playlist generation. We developed a simple media player which tracks the listening habits and generates playlists according to the listener model. We tried the solution with a group of users. The experiment was not a successful one, but it threw some new light on the relationship between the listening habits and playlist generation. 相似文献
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