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The effects of common vulcanization （CV) system, semi-effective vulcanization （SEV) system, effective vulcanization （EV) system, and dicumene peroxide （DCP) vulcanization system on the mechanical properties and electromagnetic shielding effectiveness of Eucommia ulmoides gum （EUG) composites were studied. The results showed that the difference in vulcanization rates of CV system, SEV system and EV system was not significant, and the DCP vulcanization system had the lowest vulcanization rate and crosslinking degree, and exhibited the highest crystallinity degree. The elongation at break of EUG composites of the sulfur vulcanization systems were all higher than that of EUG composite of the peroxide vulcanization system, but the tensile strength was lower, and when DCP was used as the vulcanization agent, the tensile strength of EUG composite could increase by up to 10.6% compared with that of EUG composites of the sulfur vulcanization systems. EUG composites of sulfur vulcanization system exhibited good conductivity and the higher crosslinking density, which was conducive to the formation of conductive network, and the electromagnetic shielding effectiveness of EUG compo-sites of the EV system was 22% higher than that of EUG composites of the unvulcanized system.

Few studies of cryogenic treatment were focused on nonferrous alloys, such as magnesium alloy. In this work, the effect of cryogenic treatment (77 K) before extrusion on microstructure and mechanical properties of ZK60 alloy was investigated. The results showed that many fine G.P. zones were formed during the cryogenic treatment and then grew to short fine β1′{\beta }_{1}^{^{\prime} } precipitates when heating before extrusion. These precipitates pinned dynamic recrystallized grain boundaries in the subsequent extrusion, resulting in fine gains and dispersed spherical precipitates. By the cryogenic treatment before extrusion, the extruded ZK60 alloy showed good tensile strength and elongation balance. Especially, elongation was improved by 29%.

With the advancement of micro- and nanomanufacturing technologies, electronic components and chips are increasingly being miniaturized. To automatically identify their packaging materials for ensuring the reliability of ICs, a hybrid deep learning framework termed as CNN–transformer interaction (CTI) model is designed on IC packaging images in this paper, in which several cascaded CTI blocks are designed to bidirectionally capture local and global features from the IC packaging image. Each CTI block involves a CNN branch with two designed convolutional neural networks (CNNs) for CNN local features and a transformer branch with two transformers for transformer global features and transformer local-window features. A bidirectional interaction mechanism is designed to interactively transfer the features in channel and spatial dimensions between the CNNs and transformers. Experimental results indicate that the hybrid framework can recognize three types of IC packaging materials with a good performance of 96.16% F1-score and 97.92% accuracy, which is superior to some existing deep learning methods. [ABSTRACT FROM AUTHOR]

Chemical composition analysis, chromatography and spectroscopy are dominate quality evaluation methods for agarwood, which are cumbersome and time-consuming. To facilitate its quality evaluation, a global-to-local optimization method is proposed to automatically inspect the appearance of the burned agarwood stick. First, a dissimilarity coefficient is defined by the attributes of the connected domains to coarsely localize the carbon line region. Then, the threshold for the coarsely localized carbon line region is adaptively determined based on grayscale characteristics of image patches partitioned from the coarsely localized carbon line region. Next, the threshold is used to extract the contour of the carbon line region and to establish the fine localization model for locally and precisely localizing the carbon line region. Finally, an ash shrinkage compensation coefficient is defined to calculate the ash shrinkage rate (ASR). The ASR combined with carbon line height is utilized to characterize the appearance of burned agarwood. Experimental results indicate that the proposed inspection method can well detect the carbon line regions and ashes of burned agarwood sticks, with a mean ASR error of 0.74%, which is superior to some existing inspection methods. [ABSTRACT FROM AUTHOR]