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Augmentation rhinoplasty is one of the most common plastic surgery procedures performed in Asia. Most Asian patients desire not only a natural-looking nose but also a nose with natural feel. Achieving such rhinoplasty outcomes with grafts has been a challenge for surgeons due to rigidity of grafting material. We propose a novel technique to address this limitation. A total of 200 healthy adult patients aged from 18 to 25 years were randomly chosen and classified into 5 groups: A, B, C, D, and control. Each group included 40 patients. The patients assigned to conventional grafting underwent rhinoplasty with L-shaped silicone prosthesis (group A) or expanded polytetrafluoroethylene (e-PTFE; group B), using traditional carving methods. The patients assigned to dynamic rhinoplasty underwent silicone (group C) or e-PTFE grafts (group D) using the modified double “V” method, which involves removing bilateral wedges from the graft to decrease rigidity. Patients in control group do not undergo the surgery. A 3-dimensional raster surface scanner was used to capture the images of the patients accurately and nasal mobility was measured. Subjective evaluations were carried out by a series of questionnaires asked to the patients. The angle α of nasal mobility was significantly lower in conventional grafting (23.09 [5.34] mm for silicone and 17.88 [4.96] mm for e-PTFE) versus the “V” carving (30.53 [3.76] mm for silicone and 23.77 [4.53] mm for e-PTFE; P < .05). The double “V” carving method is a simple, effective, and practical method for improving dynamic nasal outcomes in patient undergoing augmentation rhinoplasty.

Different animation characters are loved by people at different ages, in different countries and from different background, which implies the difficulties of designing characters that might be loved by many. Grouping different designers together by computer networks or the Internet is surely a good solution to design a character with expected popularity. This paper presents a character modeling method based on an improved non-dominated sorting genetic algorithm II (CMIN). CMIN borrows ideas from biological evolution, especially from multi-objective genetic algorithm (MOGA) and is formalized as a procedure for character modeling. CMIN adopts binary tree data structure to express transformation rules which are used to diversify character models and uses crossover and mutation operators of genetic algorithm to generate new rules. CMIN also adopts cooperative multi-objective evaluation on generated characters. The objectives are designed to embody both qualitative and quantitative aspects of character personalities, which are assigned by different cooperative designers and calculated automatically by computers respectively. The incorporation of qualitative and quantitative evaluation is formally realized by introducing a MOGA framwork. A multi-objective evaluation-based cooperative character modeling system (MOCMS) was developed to verify the proposed CMIN. Representative case studies demonstrate that the proposed method can evolve character models according to the designers' intentions and preferences and generate creative character models far beyond man's own imagination.

Cell therapy has shown its power to promote diabetic chronic wound healing. However, problems of scar formation and loss of appendages have not yet been solved. Our study aims to explore the potential of using embryonic skin cells (ESkCs) to repair diabetic wounds. Circular wound was created on the back of the diabetic mice, and ESkCs stained with CM-DIL were transplanted into the wound. Wound area was recorded at the day 4, 7, 11, and 14 after transplantation. The tissue samples were obtained at week 1, 2, and 3, and the tissue sections were stained by transforming growth factor β1 (TGF-β1), TGF-β3, vascular endothelial growth factor (VEGF), and CD31. The new skin formed on the wound of the diabetic mice with ESkC treatment at week 1 but not on the wounds of the non-treatment group. The histological scores of diabetic group with ESkC treatment were significantly better than the non-treatment group ( P

Component-based product design usually appears as a multi-objective optimization problem (MOP). Traditional methods solving MOPs are robust and have proven their effectiveness in handling many classes of optimization problems. However, such techniques can encounter difficulties such as getting trapped in local minima, increasing computational complexity, and not being applicable to certain classes of objective functions. Multi-Objective Evolutionary Algorithms (MOEAs) can overcome these disadvantages and have shown great potentials to solve MOPs. In this paper, an h-MOEA is proposed by employing effective strategies from evolutionary computation, which is suitable for solving the MOP in design optimization and can generate more diverse solutions in an accepted time span. Then, the effectiveness and correctness of h-MOEA is verified using several popular benchmark functions. Also, a prototype is developed and used in component-based product design optimization. Finally, the optimization results of a product design case are shown in detail.