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In order to obtain deformation behavior and volumetric characteristic of fancy weft knitted fabric, loop models are built on improved particle systems in this article. The problem of the non-uniform rational B-splines (NURBS) curves, which cannot pass through all control points, is solved by using an interpolation algorithm which can generate new auxiliary points. To simulate the twist of folded yarns, the NURBS curves are regarded as the geometric center, which is rotated with cylinders whose three relative Euler angles are calculated by the spatial coordinates of adjacent points. By analyzing the relationship between the deformation of the loop and the displacement of the particles, the deformation behavior of fancy weft knitted stitches is simulated. Velocity-Verlet, a numerical integration, is introduced to simulate fancy weft knitted stitches, and stable results are obtained. The results show that these models and algorithm accurately display the deformation behavior of fancy weft knitted stitches, as demonstrated by qualitative comparisons to measure the deformations of actual samples, and the simulator can scale up to animations with complex dynamic motion.

Nitrogen is the primary nutrient limiting ecosystem productivity over most of the US. Although soil nitrogen content is important, knowledge about its spatial extent at the continental scale is limited. The objective of this study was to estimate net nitrogen mineralization for the conterminous US (CONUS) using an empirical modeling approach by scaling up site level measurements. Net nitrogen mineralization and total soil nitrogen data across the CONUS were obtained from three different ecosystems: low elevation forests, high elevation forests, and grasslands. Equations to predict net nitrogen mineralization were developed through stepwise linear regression using total Kjeldahl nitrogen, air temperature, precipitation, and nitrogen deposition as predictor variables for four categories: low elevation high temperature forests (coefficient of determination, R 2 = 0.83), low elevation low temperature forests (R 2 = 0.74), high elevation forests (R 2 = 0.80), and grasslands (R 2 = 0.88). A map of net nitrogen mineralization was developed in GIS using these equations and national-scale databases for the CONUS. The result shows that net nitrogen mineralization varies widely across the US. Grasslands were predicted to have the lowest net nitrogen mineralization, while low elevation forests in the east had the highest. Mean values were 14.3 kg•ha −1 •yr −1 for grasslands, 22.6 kg•ha −1 •yr −1 for high elevation forests, 58 kg•ha −1 •yr −1 for low elevation low temperature forests, and 82.9 kg•ha −1 •yr −1 for low elevation high temperature forests. This continental scale estimation of net nitrogen mineralization provides a means of comparing net nitrogen mineralization across regions, and the databases developed from this study are useful for accounting for nitrogen limitations in large scale ecosystem modeling.

I wish to acknowledge the time and expertise that was contributed to this body of research by members of academia, industry, and research institutes in the field of textile design engineering. Survey and case study participants provided intelligent, thoughtful observations and opinions about this design process, and were generous in sharing their experiences with engineered designing. I also want to acknowledge the support of the College of Textiles, and TATM department for funding my graduate studies, and providing access to state of the art facilities in which to conduct my research. I am sincerely grateful to Dr. Lori Rothenberg for provided guidance in survey analysis, and to Dr. Helmut Hergith who assisted with translation from German to English for one of my case study interviews. I would like to extend my appreciation to TC 2 , for their assistance with engineered design and digital printing, and to Cotton Incorporated who allowed me to interview their clients. I am grateful for the assistance that Hari Kenkare, my friend at Lectra, provided with access to software programs. Thank you also to my friend Ji-Hyun Bae, for her assistance with woven and printed engineered design.

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The separation and identification of colourants from aqueous matrices could potentially benefit the coloration industry. In this work, we report a new method that combines hydrophilic interaction liquid chromatography (HILIC) and high‐resolution mass spectrometry (HRMS) for reactive dye separation and identification without employing ion‐pairing agents. The conditions outlined allowed the successful separation of a mixture of four commercial reactive dyes in an aqueous solution, which consisted of CI Reactive Black 5, CI Reactive Orange 35, CI Reactive Blue 49 and CI Reactive Red 31. To further demonstrate the feasibility of this new method, we conducted deeper research into the analysis of CI Reactive Red 31 and its hydrolysis products. Based on the high efficiency of HILIC for polar compounds, and its combination with HRMS, we were able to identify several isomers of CI Reactive Red 31 and its derivatives, which were further characterised by tandem mass spectrometry. This method could potentially benefit chemical evaluations in dye applications, including synthetic processes, because it provides reliable results and simplified operation conditions compared with common traditional high‐performance liquid chromatography methods.

Pretreatment and yarn type alter the characteristics of fabric surfaces and impact the quality of inkjet digital printing including colorfastness, color shade, colorfulness, and color strength. Specifically, printed pigments are subject to crocking due to physical attachment on a fiber surface without diffusing into the fibers, which increases color transfer to another fabric. This remains a challenge in obtaining high colorfastness as well as full color shade, colorfulness, and color strength. Thus, this study investigated and quantitatively analyzed the effects of pretreatment and yarn type on shade, colorfulness, color strength, and crockfastness. Fabric wettability was also observed to examine the impact on printing quality.

PurposeThe study primarily aims to examine an emerging fashion technology, direct-to-garment (DTG) printing, using data mining-driven social network analysis (SNA). Simultaneously, the study also demonstrates application of a group novel computational technique to capture, analyze and visually depict data for strategic insight into the fashion industry.Design/methodology/approachA total of 5,060 tweets related to DTG were captured using Crimson Hexagon. Python and Gephi were applied to convert, calculate and visualize the yearly networks for 2016–2019. Based on graph theory, degree centrality and betweenness centrality indices guide interpretation of the outcome networks.FindingsThe findings reveal insights into DTG printing technology networks through identification of interrelated indicators (i.e. nodes, edges and communities) over time. Deeper interpretation of the dominant indicators and the unique changes within each of the DTG communities were investigated and discussed.Practical implicationsThree SNA models suggest directions including the dominant apparel categories for DTG application, competing alternatives for apparel decorating approaches to DTG and growing market niches for DTG. Interpretation of the yearly networks suggests evolution of this domain over the investigation period.Originality/valueThe social media based, data mining-driven SNA method provides a novel path and a powerful technique for scholars and practitioners to investigate information among complex, abstract or novel topics such as DTG. Context specific findings provide initial insight into the evolving competitive structures driving DTG in the fashion market.