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The purpose of this paper is to provide an efficient tool for simulating electrospinning process in virtual 3D space and optimizing experimental parameters. The fiber orientation from virtual or real electrospinning process can be easily measured using the image analysis technique. Using the semi-implicit Euler integration, the time integration can be more fast and stable, which enabled optimization of the electrospinning process. Also boundary conditions can be easily adopted during conjugate gradient matrix solving step.

To simulate the electrospinning process, the authors have adopted a particle-based modeling technique using the molecular dynamics theory, which is known to be suitable for modeling materials with nonlinear and nonhomogeneous behavior such as fibers or fabrics. Gravitational, tensional, and electrostatical forces and their Jacobians were carefully defined and chosen to maintain the stability of the governing equation. Preconditioned conjugate gradient method was used to solve the matrix iteratively with boundary conditions. The 2-D metaball fitting technique, which was applied in the previous research (Sul et al., 2009) on experimental nanofiber scanning electron microscopy images, was utilized with virtual nanofiber images. A staircase function and a new shading language were proposed to automatically calculate the orientation and radius distribution of the graphically simulated electrospun fiber structures. The automatic measurement procedure was verified via graphically designed virtual replica images. Also the orientation tendency acquired from the simulation was compared with that of experimental data.

Simulation result of fiber orientation showed linear relationship with the collecting drum speed. Use of particle-based method generated a simple system to simulate electrospinning process.

The semi-implicit Euler integration was applied to the electrospinning process and the final linear system was numerically stable to solve.

Fibers have been produced from a blend of poly(acrylonitrile) and poly(styrene hydantoin). The fibers were extruded from a single solvent, dimethyl acetamide. The fiber properties from the blended polymers were somewhat poorer than those from the unmodified acrylic fiber, but were still acceptable. The fibers from the polymer blend could be chlorinated to produce halamines and thus rendered antimicrobial. Antimicrobial test results showed efficacy against S. aureus. The ability to regenerate the halamines (and the antimicrobial functionality) lasted through 50 home laundry washings. The chlorine absorption was shown to be proportional to the specific surface area (m²/g) of the fibers, as might be expected for hydrophobic materials which can react with aqueous reagents only on the fiber surface. Although the fibers produced here were rather larger than those typically used in clothing, the data suggest that a typical diameter fiber of the composition produced here would have a specific surface area to allow chlorine absorption approaching 0.5 %. This level of retained chlorine would certainly produce a highly satisfactory antimicrobial effect - substantially better than the experimental data shown herein.

As an extended work of the previous paper (Sul, 2010), this paper provides a guideline information for an anonymous garment pattern in sewing process. The purpose of this paper is to first, provide garment pattern database. By simply taking pictures of garment patterns, the shape database is constructed. Once the shape database is prepared, data retrieval can be done by image indexing, i.e., simply inserting garment pattern boundary shape again to the database. Using shock graph methodology, the pattern sets used for database preparation can be exactly retrieved. Second, to find the nearest shape of a given input pattern shape in the database. If the input garment pattern shape does not exist in the database, the shape matching algorithm provides the next similar pattern data. The user, who is assumed to be non-expert in garment sewing process, can easily predict the position and combination information of various patterns.

Image processing is used to construct the garment pattern shape database. The boundary shapes are extracted from the photographs of garment patterns and their shape recognition information, especially shock graph, is also recorded for later pattern data retrieval.

Using the image processing technique, garment patterns can be converted to electronic format easily. Also the prepared pattern database can be used for finding the nearest shape of an additional given input garment pattern. Patterns with irregular shapes were retrieved easily, while those with a simple shape, such as rectangle, showed a little erroneous result.

Shape recognition has been adopted in various industrial areas, except for garment sewing process. Using the provided methodology, garment pattern shapes can be easily saved and retrieved only by taking pictures of them.

This study aims to empirically analyse the influence mechanism of perceived interactivity in real estate APP which affects consumers' psychological well-being. With the growing application of human–machine interaction in real estate APP, it is crucial to utilize human–machine interaction to stimulate perceived interactivity between humans and machines to positively impact consumers' psychological well-being and sustainable development of real estate APP. However, it is unclear whether perceived interactivity improves consumers' psychological well-being.

This study proposes and examines a theoretical model grounded in the perceived interactivity theory, considers the relationship between perceived interactivity and consumers' psychological well-being and explores the mediating effect of perceived value and the moderating role of privacy concerns. It takes real estate APP as the research object, analyses the data of 568 consumer samples collected through questionnaires and then employs structural equation modelling to explore and examine the proposed theoretical model of this study.

The findings are that perceived interactivity (i.e. human–human interaction and human–information interaction) positively influences perceived value, which in turn affects psychological well-being, and that perceived value partially mediates the effect of perceived interaction on psychological well-being. More important findings are that privacy concerns not only negatively moderate human–information interaction on perceived value, but also negatively moderate the indirect effects of human–information interaction on users' psychological well-being through perceived value.

This study expands the context on perceived interaction and psychological well-being in the field of real estate APP, validating the mediating role and boundary conditions of perceived interactivity created by human–machine interaction on consumers' psychological well-being, and suggesting positive implications for practitioners exploring human–machine interaction technologies to improve the perceived interaction between humans and machines and thus enhance consumer psychological well-being and span sustainable development of real estate APP.