Tuning microwave absorption properties of Ti_(3)C_(2)T_(x) MXene-based materials:Component optimization and structure modulation

The current electromagnetic environment brings a growing demand for efficient microwave absorption(MA)materials.Ti_(3)C_(2)T_(x) MXene,one of the 2D transition-metal carbides,is considered to be a promising MA material owing to its superior dielectric properties and structural processability.In order to further improve the MA performance and environmental adaptability of Ti_(3)C_(2)T_(x) MXene,Ti_(3)C_(2)T_(x) MXene-based MA materials enhanced by composition and structure design have been extensively studied and the regu-lation ideas for its MA properties can be outlined as component optimization and structure manipulation strategies based on the microwave absorption mechanism.Herein,we briefly introduced the microwave absorption mechanism and focused on the design strategies of Ti_(3)C_(2)T_(x) MXene-based MA materials based on recent advances.In addition,the prospects of Ti_(3)C_(2)T_(x) MXene-based MA materials were also discussed.

Application of Particle Swarm Optimization to Fault Condition Recognition Based on Kernel Principal Component Analysis

Panicle swarm optimization （PSO） is an optimization algorithm based on the swarm intelligent principle. In this paper the modified PSO is applied to a kernel principal component analysis （ KPCA ） for an optimal kernel function parameter. We first comprehensively considered within-class scatter and between-class scatter of the sample features. Then, the fitness function of an optimized kernel function parameter is constructed, and the particle swarm optimization algorithm with adaptive acceleration （CPSO） is applied to optimizing it. It is used for gearbox condi- tion recognition, and the result is compared with the recognized results based on principal component analysis （PCA）. The results show that KPCA optimized by CPSO can effectively recognize fault conditions of the gearbox by reducing bind set-up of the kernel function parameter, and its results of fault recognition outperform those of PCA. We draw the conclusion that KPCA based on CPSO has an advantage in nonlinear feature extraction of mechanical failure, and is helpful for fault condition recognition of complicated machines.

Developing high-efficiency base editors by combining optimized synergistic core components with new types of nuclear localization signal peptide

The clustered regularly interspaced short palindromic repeats(CRISPR)–CRISPR-associated protein(Cas) system has been widely used for genome editing. In this system, the cytosine base editor(CBE) and adenine base editor(ABE) allow generating precise and irreversible base mutations in a programmable manner and have been used in many different types of cells and organisms. However, their applications are limited by low editing efficiency at certain genomic target sites or at specific target cytosine(C) or adenine(A) residues. Using a strategy of combining optimized synergistic core components, we developed a new multiplex super-assembled ABE(sABE) in rice that showed higher base-editing efficiency than previously developed ABEs. We also designed a new type of nuclear localization signal(NLS) comprising a FLAG epitope tag with four copies of a codon-optimized NLS(F4NLS^(r2)) to generate another ABE named F4NLS-sABE. This new NLS increased editing efficiency or edited additional A at several target sites. A new multiplex super-assembled CBE(sCBE) and F4NLS^(r2) involved F4NLS-sCBE were also created using the same strategy. F4NLS-sCBE was proven to be much more efficient than sCBE in rice. These optimized base editors will serve as powerful genome-editing tools for basic research or molecular breeding in rice and will provide a reference for the development of superior editing tools for other plants or animals.

Aerodynamic multi-objective integrated optimization based on principal component analysis

Based on improved multi-objective particle swarm optimization（MOPSO） algorithm with principal component analysis（PCA） methodology, an efficient high-dimension multiobjective optimization method is proposed, which, as the purpose of this paper, aims to improve the convergence of Pareto front in multi-objective optimization design. The mathematical efficiency,the physical reasonableness and the reliability in dealing with redundant objectives of PCA are verified by typical DTLZ5 test function and multi-objective correlation analysis of supercritical airfoil,and the proposed method is integrated into aircraft multi-disciplinary design（AMDEsign） platform, which contains aerodynamics, stealth and structure weight analysis and optimization module.Then the proposed method is used for the multi-point integrated aerodynamic optimization of a wide-body passenger aircraft, in which the redundant objectives identified by PCA are transformed to optimization constraints, and several design methods are compared. The design results illustrate that the strategy used in this paper is sufficient and multi-point design requirements of the passenger aircraft are reached. The visualization level of non-dominant Pareto set is improved by effectively reducing the dimension without losing the primary feature of the problem.

NEW OPTIMAL LARGE ANGLE MANEUVER STRATEGY FOR SINGLE FLEXIBLE LINK

A component synthesis vibration suppression (CSVS) method for flexible structures is put forward. It can eliminate any unwanted orders of flexible vibration modes while achieves desired rigid motion. This method has robustness to uncertainty of frequency, which makes it practical in engineering. Several time optimal and time-fuel optimal control strategies are designed for a kind of single flexible link. Simulation results validate the feasibility of our method.

Structural Optimization of Fiber-Reinforced Material Based on Moving Morphable Components (MMCs)

Fiber-reinforced composite materials have excellent specific stiffness,specific strength,and other properties,and have been increasingly widely used in the field of advanced structures.However,the design space dimensions of fiber-reinforced composite materials will expand explosively,bringing challenges to the efficient analysis and optimal design of structures.In this paper,the authors propose an explicit topology optimization method based on the moving morphable components for designing the fiber-reinforced material.We constrain the intersection area between components to guarantee the independence of each component and avoid the situation that one component is cut by other components.Adding the fiber orientation angle as a design variable,the method can optimize the structural layout and the fiber orientation angle concurrently under the given number of fiber layers and layer thickness.We use two classical examples to verify the feasibility and accuracy of the proposed method.The optimized results are in good agreement with the designs obtained by the 99-line code.The authors also popularize the proposed method to engineering structure.The results manifest that the proposed method has great value in engineering application.

Optimal Feature Extraction Using Greedy Approach for Random Image Components and Subspace Approach in Face Recognition

An innovative and uniform framework based on a combination of Gabor wavelets with principal component analysis （PCA） and multiple discriminant analysis （MDA） is presented in this paper. In this framework, features are extracted from the optimal random image components using greedy approach. These feature vectors are then projected to subspaces for dimensionality reduction which is used for solving linear problems. The design of Gabor filters, PCA and MDA are crucial processes used for facial feature extraction. The FERET, ORL and YALE face databases are used to generate the results. Experiments show that optimal random image component selection （ORICS） plus MDA outperforms ORICS and subspace projection approach such as ORICS plus PCA. Our method achieves 96.25%, 99.44% and 100% recognition accuracy on the FERET, ORL and YALE databases for 30% training respectively. This is a considerably improved performance compared with other standard methodologies described in the literature.