939 resultados para Build-Up Back To Back LSB, Cold-Formed Steel Structures, Lateral Distortional Buckling
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Part 1 sold Oct. 27-29, 1947; pt. 2, Oct. 25-26, 1948.
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Thirteen experiments investigated the dynamics of stream segregation. Experiments 1-6b used a similar method, where a same-frequency induction sequence (usually 10 repetitions of an identical pure tone) promoted segregation in a subsequent, briefer test sequence (of alternating low- and high-frequency tones). Experiments 1-2 measured streaming using a direct report of perception and a temporal-discrimination task, respectively. Creating a single deviant by altering the final inducer (e.g. in level or replacement with silence) reduced segregation, often substantially. As the prior inducers remained unaltered, it is proposed that the single change actively reset build-up. The extent of resetting varied gradually with the size of a frequency change, once noticeable (experiments 3a-3b). By manipulating the serial position of a change, experiments 4a-4b demonstrated that resetting only occurred when the final inducer was replaced with silence, as build-up is very rapid during a same-frequency induction sequence. Therefore, the observed resetting cannot be explained by fewer inducers being presented. Experiment 5 showed that resetting caused by a single deviant did not increase when prior inducers were made unpredictable in frequency (four-semitone range). Experiments 6a-6b demonstrated that actual and perceived continuity have a similar effect on subsequent streaming judgements promoting either integration or segregation, depending on listening context. Experiment 7 found that same-frequency inducers were considerably more effective at promoting segregation than an alternating-frequency inducer, and that a trend for deviant-tone resetting was only apparent for the same-frequency case. Using temporal-order judgments, experiments 8-9 demonstrated the stream segregation of pure-tone-like percepts, evoked by sudden changes in amplitude or interaural time difference for individual components of a complex tone, Active resetting was observed when a deviant was inserted into a sequence of these percepts (Experiment 10). Overall, these experiments offer new insight into the segregation-promotIng effect of induction sequences, and the factors which can reset this effect.
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We study the radiation build-up in laminar and turbulent generation regimes in quasi-CW Raman fiber laser. We found the resulted spectral shape and generation type is defined by the total spectral broadening/narrowing balance over laser cavity round-trip, which is substantially different in different regimes starting from first round-trips of the radiation build-up. In turbulent regime, the steady-state is reached only after a few round-trips, while in the laminar regime the laser approaches the equilibrium spectrum shape asymptotically.
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Tin oxide is considered to be one of the most promising semiconductor oxide materials for use as a gas sensor. However, a simple route for the controllable build-up of nanostructured, sufficiently pure and hierarchical SnO2 structures for gas sensor applications is still a challenge. In the current work, an aqueous SnO2 nanoparticulate precursor sol, which is free of organic contaminants and sorbed ions and is fully stable over time, was prepared in a highly reproducible manner from an alkoxide Sn(OR)4 just by mixing it with a large excess of pure neutral water. The precursor is formed as a separate liquid phase. The structure and purity of the precursor is revealed using XRD, SAXS, EXAFS, HRTEM imaging, FTIR, and XRF analysis. An unconventional approach for the estimation of the particle size based on the quantification of the Sn-Sn contacts in the structure was developed using EXAFS spectroscopy and verified using HRTEM. To construct sensors with a hierarchical 3D structure, we employed an unusual emulsification technique not involving any additives or surfactants, using simply the extraction of the liquid phase, water, with the help of dry butanol under ambient conditions. The originally generated crystalline but yet highly reactive nanoparticles form relatively uniform spheres through self-assembly and solidify instantly. The spheres floating in butanol were left to deposit on the surface of quartz plates bearing sputtered gold electrodes, producing ready-for-use gas sensors in the form of ca. 50 μm thick sphere-based-films. The films were dried for 24 h and calcined at 300°C in air before use. The gas sensitivity of the structures was tested in the temperature range of 150-400°C. The materials showed a very quickly emerging and reversible (20-30 times) increase in electrical conductivity as a response to exposure to air containing 100 ppm of H2 or CO and short (10 s) recovery times when the gas flow was stopped.
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In recent years, the boundaries between e-commerce and social networking have become increasingly blurred. Many e-commerce websites support the mechanism of social login where users can sign on the websites using their social network identities such as their Facebook or Twitter accounts. Users can also post their newly purchased products on microblogs with links to the e-commerce product web pages. In this paper, we propose a novel solution for cross-site cold-start product recommendation, which aims to recommend products from e-commerce websites to users at social networking sites in 'cold-start' situations, a problem which has rarely been explored before. A major challenge is how to leverage knowledge extracted from social networking sites for cross-site cold-start product recommendation. We propose to use the linked users across social networking sites and e-commerce websites (users who have social networking accounts and have made purchases on e-commerce websites) as a bridge to map users' social networking features to another feature representation for product recommendation. In specific, we propose learning both users' and products' feature representations (called user embeddings and product embeddings, respectively) from data collected from e-commerce websites using recurrent neural networks and then apply a modified gradient boosting trees method to transform users' social networking features into user embeddings. We then develop a feature-based matrix factorization approach which can leverage the learnt user embeddings for cold-start product recommendation. Experimental results on a large dataset constructed from the largest Chinese microblogging service Sina Weibo and the largest Chinese B2C e-commerce website JingDong have shown the effectiveness of our proposed framework.