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In this research, the effect of carbon dioxide laser irradiation on various properties of raw and bleached cotton fabrics, including fabric weight, bending rigidity, wetting, and air permeability, as well as dyeing, was examined and compared. The experiments were carried out at three different laser powers ranging from 4.5 to 6 W to determine the effect of laser treatment on fabric properties. In particular, the influence of laser irradiation on the dyeing properties of treated fabrics with CI Reactive Blue 198 was studied. The colour change of laser‐treated fabrics was determined by calculation of the K/S values as a function of fabric reflectance. The morphological changes in laser‐treated fabrics were observed by scanning electron microscopy. The effects of laser treatment on the properties of raw and bleached cotton fabrics were varied. For instance, the wettability of raw cotton samples was reduced after laser irradiation, whereas the wettability of bleached cotton fabrics was greater. Possible reasons for the various dyeing behaviours observed with irradiation at different laser powers are discussed.  相似文献   
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Bulletin of Engineering Geology and the Environment - Terrestrial laser scanning (TLS) was performed in tectonically undisturbed flysch in the Elerji quarry (south-west Slovenia), which represents...  相似文献   
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Torrefied almond shells and wood chips were incorporated into polypropylene as fillers to produce torrefied biomass‐polymer composites. The composites were prepared by extrusion and injection molding. Response surface methodology was used to examine the effects of filler concentration, filler size, and lignin factor (relative lignin to cellulose concentration) on the material properties of the composites. The heat distortion temperatures, thermal properties, and tensile properties of the composites were characterized by thermomechanical analysis, differential scanning calorimetry, and tensile tests, respectively. The torrefied biomass composites had heat distortion temperatures of 8–24°C higher than that of neat polypropylene. This was due to the torrefied biomass restricting mobility of polypropylene chains, leading to higher temperatures for deformation. The incorporation of torrefied biomass generally resulted in an increase in glass transition temperature, but did not affect melting temperature. Also, the composites had lower tensile strength and elongation at break values than those of neat polypropylene, indicating weak adhesion between torrefied biomass and polypropylene. However, scanning electron microscopy results did indicate some adhesion between torrefied biomass and polypropylene. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 41582.  相似文献   
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Targeting of antigens to dendritic cells (DCs) to induce strong cellular immune response can be established by loading in a nano‐sized carrier and keeping the antigen associated with the particles until they are internalized by DCs. In the present study, a model antigen (ovalbumin, OVA) is immobilized in cationic dextran nanogels via disulfide bonds. These bonds are stable in the extracellular environment but are reduced in the cytosol of DCs due to the presence of glutathione. Reversible immobilization of OVA in the nanogels is demonstrated by the fact that hardly any release of the protein occurred at pH 7 in the absence of glutathione, whereas rapid release of OVA occurs once the nanogels are incubated in buffer with glutathione. Furthermore, these OVA conjugated nanogels show intracellular release of the antigen in DCs and boost the MHC class I antigen presentation, demonstrating the feasibility of this concept for the aimed intracellular antigen delivery.  相似文献   
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Recently, a number of modeling techniques have been developed for data mining and machine learning in relational and network domains where the instances are not independent and identically distributed (i.i.d.). These methods specifically exploit the statistical dependencies among instances in order to improve classification accuracy. However, there has been little focus on how these same dependencies affect our ability to draw accurate conclusions about the performance of the models. More specifically, the complex link structure and attribute dependencies in relational data violate the assumptions of many conventional statistical tests and make it difficult to use these tests to assess the models in an unbiased manner. In this work, we examine the task of within-network classification and the question of whether two algorithms will learn models that will result in significantly different levels of performance. We show that the commonly used form of evaluation (paired t-test on overlapping network samples) can result in an unacceptable level of Type I error. Furthermore, we show that Type I error increases as (1) the correlation among instances increases and (2) the size of the evaluation set increases (i.e., the proportion of labeled nodes in the network decreases). We propose a method for network cross-validation that combined with paired t-tests produces more acceptable levels of Type I error while still providing reasonable levels of statistical power (i.e., 1−Type II error).  相似文献   
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The design procedure is made for a mine shaft where permanent underground facilities are interconnected. The highly faulted grounds were identified using empirical and semi-empirical theories. Furthermore, the behavior types are presented. This paper presents excavation and support methods in such ground conditions and the calculations results show that the installation of the yielding elements have an effect on support elements and prevent shotcrete damage during the curing stage. Different numerical analyses carried out showed that, with the yielding elements installed, the total displacements increase but the final axial force reduces, and therefore, the characteristic compressive strength of shotcrete is not exceeded. The calculation results of ground loads and displacements on the designed support system are presented with a 3D numerical geo-mechanical model adopted for highly faulted ground surrounding deeper complex underground structures.  相似文献   
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Given their extremely small size and light weight, carbon nanotubes (CNTs) can be readily inhaled by human lungs resulting in increased rates of pulmonary disorders, particularly fibrosis. Although the fibrogenic potential of CNTs is well established, there is a lack of consensus regarding the contribution of physicochemical attributes of CNTs on the underlying fibrotic outcome. We designed an experimentally validated in vitro fibroblast culture model aimed at investigating the effect of fiber length on single-walled CNT (SWCNT)-induced pulmonary fibrosis. The fibrogenic response to short and long SWCNTs was assessed via oxidative stress generation, collagen expression and transforming growth factor-beta (TGF-β) production as potential fibrosis biomarkers. Long SWCNTs were significantly more potent than short SWCNTs in terms of reactive oxygen species (ROS) response, collagen production and TGF-β release. Furthermore, our finding on the length-dependent in vitro fibrogenic response was validated by the in vivo lung fibrosis outcome, thus supporting the predictive value of the in vitro model. Our results also demonstrated the key role of ROS in SWCNT-induced collagen expression and TGF-β activation, indicating the potential mechanisms of length-dependent SWCNT-induced fibrosis. Together, our study provides new evidence for the role of fiber length in SWCNT-induced lung fibrosis and offers a rapid cell-based assay for fibrogenicity testing of nanomaterials with the ability to predict pulmonary fibrogenic response in vivo.  相似文献   
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