Flower pollination algorithm: a novel approach for multiobjective optimization

Multiobjective design optimization problems require multiobjective optimization techniques to solve, and it is often very challenging to obtain high-quality Pareto fronts accurately. In this article, the recently developed flower pollination algorithm (FPA) is extended to solve multiobjective optimization problems. The proposed method is used to solve a set of multiobjective test functions and two bi-objective design benchmarks, and a comparison of the proposed algorithm with other algorithms has been made, which shows that the FPA is efficient with a good convergence rate. Finally, the importance for further parametric studies and theoretical analysis is highlighted and discussed

FEDSM-ICNMM2010-30110 FULLY COUPLED FLUID-STRUCTURE INTERACTION SIMULATIONS OF VOCAL FOLD VIBRATION

ABSTRACT The human vocal folds are modeled and simulated using a fully coupled fluid-structure interaction method. This numerical approach is efficient in simulating fluid and deformable structure interactions. The two domains are fully coupled using an interpolation scheme without expensive mesh updating or re-meshing. The method has been validated through rigorous convergence and accuracy tests. The response of the fluid affects the elastic structure deformation and vice versa. The goal of this study is to utilize this numerical tool to examine the entire fluid-structure system and predict the motion and vocal folds by providing constant inlet and outlet pressure. The input parameters and material properties, i.e. elastic and density of the vocal folds used in the model are physiological. In our numerical results, the glottal jet can be clearly identified; the corresponding pressure field distribution and velocity field are presented

Multi-label feature selection via constraint mapping space regularization

Multi-label feature selection, an essential means of data dimension reduction in multi-label learning, has become one of the research hotspots in the field of machine learning. Because the linear assumption of sample space and label space is not suitable in most cases, many scholars use pseudo-label space. However, the use of pseudo-label space will increase the number of model variables and may lead to the loss of sample or label information. A multi-label feature selection scheme based on constraint mapping space regularization is proposed to solve this problem. The model first maps the sample space to the label space through the use of linear mapping. Second, given that the sample cannot be perfectly mapped to the label space, the mapping space should be closest to the label space and still retain the space of the basic manifold structure of the sample space, so combining the Hilbert-Schmidt independence criterion with the sample manifold, basic properties of constraint mapping space. Finally, the proposed algorithm is compared with MRDM, SSFS, and other algorithms on multiple classical multi-label data sets; the results show that the proposed algorithm is effective on multiple indicators

An Enhanced Adaptive Differential Evolution Algorithm With Multi-Mutation Schemes and Weighted Control Parameter Setting

Differential evolution (DE) algorithm is one of the most effective and efficient heuristic approaches for solving complex black box problems. But it still easily suffers from premature convergence and stagnation. To alleviate these defects, this paper presents a novel DE variant, named enhanced adaptive differential evolution algorithm with multi-mutation schemes and weighted control parameter setting (MWADE), to further strengthen its search capability. In MWADE, a multi-schemes mutation strategy is first proposed to properly exploit or explore the promising information of each individual. Herein, the whole population is dynamically grouped into three subpopulations according to their fitness values and search performance, and three different mutant operators with various search characteristics are respectively adopted for each subpopulation. Meanwhile, in order to ensure the exploration of algorithm at the later evolutionary stage, a weight-controlled parameter setting is proposed to suitably assign scale factors for different differential vectors. Moreover, a random opposition mechanism with greedy selection is introduced to avoid trapping in local optima or stagnation, and an adaptive population size reduction scheme is devised to further promote the search effectiveness of algorithm. Finally, to illustrate the performance of MWADE, thirteen typical algorithms are adopted and compared with MWADE on 30 functions from IEEE CEC 2017 test suite with different dimensions, and the effectiveness of its proposed components are also investigated. Numerical results indicate that the proposed algorithm has a better search performance

Recent progress in additive manufacturing of ceramic dental restorations

Ceramics are highly regarded in dental restorations owing to their favorable mechanical properties, chemical resistance, biocompatibility, and aesthetic features. Ceramic additive manufacturing (AM) technology has emerged as a promising solution that offers advantages over traditional techniques such as injection molding, die pressing, tape casting, and milling. Ceramic AM is, however, still under development, with new technologies and devices continuously emerging. This paper provides a comprehensive review of the latest research and applications of ceramic AM in dental restoration, focusing on the progress made within the past five years. Three perspectives are discussed: ceramic AM technologies, commonly used printable ceramic materials, and different types of dental restorations. Among these, vat photopolymerization is the most widely researched and promising AM technology for large-scale applications. ZrO2 remains the primary material used in AM research, whereas crowns and bridges are the most frequently studied and are the closest to industrialized dental restorations. Currently, ceramic AM satisfies the clinical requirements of accuracy, mechanical performance, and biocompatibility. However, compared with traditional methods, it lacks significant advantages in terms of cost and manufacturing efficiency, limiting its large-scale application. Further improvements are necessary in all stages, including raw materials, equipment, post-processing, and standardization

The cellular immune mechanism after transfer of chemically extracted acellular nerve xenografts.

Severe peripheral nerve defect by injuries causing functional loss require nerve grafting. Autograft has limitations for clinical use because it results in the creation of a new nerve injury and the generation of donor site morbidity. Based on these limitations, nerve allografts and xenografts provide a readily accessible alternative strategy. The aim of the present study was to observe the immune mechanism underlying the rejection of chemically extracted acellular nerve xenografts, and further evaluate immunogenicity of chemically treated acellular nerve grafts for clinical applications. A total of 160 BALB/c mice were randomly divided into a negative contrast group (NC, 40 mice), a fresh autograft group (AG, 40 mice), a fresh xenogeneic nerve group (FXN, 40 mice) and a chemically extracted acellular xenogeneic nerve group (CEXN, 40 mice). Various types of nerve grafts were implanted into the thigh muscle of BALB/C mice in the corresponding groups. At 3, 7, 14 and 28 days post-operation, the mice (10 mice from each group) were sacrificed and their spleens were extracted. The spleens were ground into paste. The erythrocytes and other cells were lysed using distilled water and the T lymphocytes were collected. Fluorescein isothiocyanate (FITC) -labeled monoclonal antibodies (CD3, CD4, CD8, CD25, IL-2, IFN-γ and TNF-α) were then added to the solution. The Fluorescence Activated Cell Sorting (FACS) was used to determine the positivity rate of the cells combined with the monoclonal antibodies above. No significant statistical differences were observed between the CEXN, NC and AG groups, so that no obvious immune rejections were observed among the chemically extracted acellular nerve xenografts

Comparison of T lymphocytes and activated T lymphocytes 3 days after surgery in all the experimental groups (single-factor ANOVA).

<p>NC, negative control group; AG, fresh autograft group; FXN, fresh xenogeneic nerve group; CEXN, chemically extracted acellular xenogeneic nerve group. ANOVA showed no significant differences between each group. Values represent mean (SD).</p

Comparison of T lymphocytes and activated T lymphocytes 7 days after surgery in all the experimental groups (single-factor ANOVA).

<p>ANOVA showed significant differences within groups (P<0.01). For pairwise comparison between each group, the Bonferroni method was used, Values represent mean (SD). <b>a:</b> FXN group compared with NC group (P<0.01), AG group (P<0.01), and CEXN group (P<0.01). <b>b:</b> FXN group compared with NC group (P<0.01), FXN group (P<0.01), and CEXN group (P<0.01). <b>c:</b> FXN group compared with AG group (P<0.05).</p

Counts of CD25+ T lymphocytes 14 days after surgery; (A) CEXN group (B) FXN group.

<p>Counts of CD25+ T lymphocytes 14 days after surgery; (A) CEXN group (B) FXN group.</p