Establishment of a color tolerance for yarn-dyed fabrics from different color-depth yarns

Yarn-dyed fabric is often woven from warp and weft yarns in the same color depth to ensure a uniform color appearance. The difference in color depth between warp and weft tends to result in the uneven color of the yarn-dyed fabric. This article aims to establish a color tolerance for yarn-dyed fabric that can be woven with a qualified color appearance but from the warp and weft yarns in different color depths. A total of 27 yarn-dyed fabric samples in three color series (red, yellow, and blue) were evaluated by using the yarn-dyed fabric from warp and weft yarns in the same color depth of 2% (on weight of fabric, owf) as the standard. Visual assessment and instrumental measurement of color were carried out to establish the color tolerance ellipse that was defined as CMC (Color Measurement Committee) color differences (2:1) of no more than 1.00. It was found that the color strengths (K/S) and color differences (ΔE_CMC(2:1)) of these fabric samples for each color series had linear relationships with the color depths of warp and weft yarns. The color tolerance ellipses indicated that, even though the warp and weft yarns had an apparent color difference, they could be woven in fabrics with relatively uniform color appearance and meet the requirements for yarn-dyed fabric. This work provided valuable insight into the production of qualified yarn-dyed fabrics from unqualified dyed yarns.     

Thiol-Functionalized Covalent Organic Frameworks as Thermal History Indictor for Temperature and Time History Monitoring

Various products, including foods and pharmaceuticals, are sensitive to temperature fluctuations. Thus, temperature monitoring during production, transportation, and storage is critical. Facile indicators are required to monitor temperature conditions via color changes in real time. This study aimed to prepare and apply thiol-functionalized covalent organic frameworks (COFs) as a novel indicator for monitoring thermal history and temperature abuse. The COFs underwent obvious color changes from bright yellow to purple after exposure to different temperatures for varying durations. The reaction kinetics are analyzed under isothermal conditions, which reveal that the order of reaction rates is k_−20°C < k_4°C < k_20°C < k_35°C < k_55°C. The activation energy (E_a) of the COFs is calculated using the Arrhenius equation as 50.71 kJ moL⁻¹. The COFs are capable of sensitive color changes and offer a broad temperature tracking range, thereby demonstrating their application potential for the monitoring of temperature and time exposure history during production, transportation, and storage. This excellent performance thermal history indicator also shows promise for expanding the application field of COFs.     

Affective matches of fabric and lighting chromaticity

This study investigates the impact of lighting colors on subjective judgments of fabric: in particular, whether the influence of lighting varies depending on fabric types and color combinations. We conducted two visual assessments. In Study 1 (N = 44), eight illuminants and six types of fabric were presented as cloth stimuli. Derived from the literature review, four sets of adjectives (humble-luxurious, cool-warm, old-new, and not preferred-preferred) were used as metrics. In Study 2 (N = 41), five sets of fabric color combination swatches were assessed under lighting conditions that were identical to those of Study 1. Three bipolar scales (ordinary-characterful, classic-modern, and soft-rigid), were employed from factor analysis along with three unipolar scales (luxurious, preferred, harmonious with lighting). The results showed that hue characteristics of lighting and cloth types influenced participants' perceptions of the fabric. Overall, white lighting with 4000 K was the most preferred and luxurious lighting across various types of clothes, while a pinkish white with 4700 K (duv = −0.0127) was the best matched in every color combination. In addition, there were interaction effects between lighting colors, clothes types, and fabric color combinations with regard to each of the perceptual qualities. This study provides empirical evidence for optimally match lighting colors with fabric in the presentation of fabric goods.     

A Student's t-based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named Student's t-based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non-iterative clustering, to automatically decrease the image data volume. A Student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k-means, fuzzy c-means, mean-shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results.     

Optimization of quinacridone magenta,Cu-phthalocyanine cyan,and arylide yellow ink films formulated for maximum color gamut

Investigation of ink formulation options with the purpose to obtain color-gamut-optimal set of Cyan Magenta and Yellow CMY inks is reported. Implementation of the thickness dependent Kubelka-Munk model on multiple ink layers having different and well-defined thicknesses, provides characteristic absorption and scattering (K, S ) spectra of the ink ingredients. These data enable accurate computation of the reflectance spectrum and thus the L*a*b* color coordinates for any given ink thickness or substrate. Pigment materials investigated are quinacridone as magenta, copper-phthalocyanine as cyan, and arylide yellow. Scaling the peak of the absorption band to the number of molecules per unit area for the specific pigments studied in this article provides the molar extinction coefficients, 1.21 × 10⁴ , 4.7 × 10⁴ , and 3.3 × 10⁴ cm²/millimole respectively, regardless of the different ink formulations used, in accord with Avogadro's principle. Having a set of three pairs of K, S spectra is used to compute the color gamut of any CMY color combination in the L*a*b* space as a function of ink layer thickness and formulation. Using an iterative algorithm, a color-gamut-optimal set of CMY inks is obtained.     

基于蛋白质组学技术研究秦川牛肉宰后贮藏过程中肌红蛋白含量及其衍生物转化

本实验采用4D-非标记蛋白质组学技术研究秦川牛肉贮藏过程中（0～8 d）肌红蛋白含量及其衍生物的转化情况，阐释冷却秦川牛肉中肌红蛋白含量及其衍生物转化的分子机制。结果表明：贮藏过程中，肌红蛋白表达量在宰后0～4 d上升、4～8 d下降，利用非标记蛋白质组学技术鉴定出与肌红蛋白及其衍生物相关的差异蛋白14 种，具体包括代谢酶、氧化还原酶、过氧化物酶、伴侣蛋白4 类，这4 类蛋白的表达共同调控贮藏过程中肌红蛋白含量的变化及其3 种衍生物之间的转化，具体表现为贮藏过程中肌红蛋白表达量整体呈下降趋势，氧合肌红蛋白相对含量持续下降，脱氧肌红蛋白、高铁肌红蛋白相对含量逐渐增加，导致肉色发生褐变。本研究结果有利于理解秦川牛肉贮藏过程肉类变色的复杂生化变化机制。     

新型着色材料——速溶色浆粉的测试与应用

介绍了通过初级颜料的润湿、研磨与分散、稳定处理技术,使助剂和树脂均匀地包裹在颜料表面,并通过特殊工艺制得水性和油性色浆粉,并赋予了很好的速溶性和分散性。     

Three‐dimensional color prediction modeling of single‐ and double‐layered woven fabrics

In this research, the three‐dimensional structural and colorimetric modeling of three‐dimensional woven fabrics was conducted for accurate color predictions. One‐hundred forty single‐ and double‐layered woven samples in a wide range of colors were produced. With the consideration of their three‐dimensional structural parameters, three‐dimensional color prediction models, K/S‐, R‐, and L*a*b*‐based models, were developed through the optimization of previous two‐dimensional models which have been reported to be the three most accurate models for single‐layered woven structures. The accuracy of the new three‐dimensional models was evaluated by calculating the color differences ΔL*, ΔC*, Δh°, and ΔE_CMC(2:1) between the measured and the predicted colors of the samples, and then the error values were compared to those of the two‐dimensional models. As a result, there has been an overall improvement in color predictions of all models with a decrease in ΔE_CMC(2:1) from 10.30 to 5.25 units on average after the three‐dimensional modeling.     

A scalable architecture for geometric correction of multi-projector display systems

Multi-projector displays allow the realization of large and immersive projection environments by allowing the tiling of projections from multiple projectors. Such tiled displays require real time geometrical warping of the content that is being projected from each projector. This geometrical warping is a computationally intensive operation and is typically applied using high-end graphics processing units (GPUs) that are able to process a defined number of projector channels. Furthermore, this limits the applicability of such multi-projector display systems only to the content that is being generated using desktop based systems. In this paper we propose a platform independent FPGA based scalable hardware architecture for geometric correction of projected content that allows addition of each projector channel at a fractional increase in logic area. The proposed scheme provides real time correction of HD quality video streams and thus enables the use of this technology for embedded and standalone devices.