Particle swarm optimization (PSO) is a population‐based stochastic optimization technique in a smooth search space. However, in a category of trajectory optimization problem with arbitrary final time and multiple control variables, the smoothness of variables cannot be satisfied since the linear interpolation is widely used. In the paper, a novel Legendre cooperative PSO (LCPSO) is proposed by introducing Legendre orthogonal polynomials instead of the linear interpolation. An additional control variable is introduced to transcribe the original optimal problem with arbitrary final time to the fixed one. Then, a practical fast one‐dimensional interval search algorithm is designed to optimize the additional control variable. Furthermore, to improve the convergence and prevent explosion of the LCPSO, a theorem on how to determine the boundaries of the coefficient of polynomials is given and proven. Finally, in the numeral simulations, compared with the ordinary PSO and other typical intelligent optimization algorithms GA and DE, the proposed LCPSO has traits of lower dimension, faster speed of convergence, and higher accuracy, while providing smoother control variables.
The theory of intuitionistic fuzzy set expands the expression ability of classics fuzzy set, which can put forward new method for the problem of emitter threat evaluating. Firstly, the intuitionistic fuzzy subjection function of threat evaluating index are given. Then the compatibility between the set pair analysis theory and intuitionistic fuzzy set theory are analyzed in this paper. Then the single attribute and synthesis attributes relation degree formula of emitter are calculated by the set pair analysis theory. The emitter can be ranked by the ranking formula. The validity of this method is demonstrated in an example.
Hepatitis E virus (HEV) is an important zoonotic pathogen infecting a wide range of host species. It has a positive-sense, single-stranded RNA genome encoding three open reading frames (ORFs). Synonymous codon usages of viruses essentially determine their survival and adaptation to susceptible hosts. To better understand the interplay between the ever-expanding host range and synonymous codon usages of HEV, we quantified the dispersion of synonymous codon usages of HEV genomes isolated from different hosts via Vs calculation and information entropy. HEV ORFs show species-specific synonymous codon usage patterns. Ruminant-derived HEV ORFs own the most synonymous codons with stable usage patterns (Vs value <0.1) which leads to the stable overall codon usage patterns (R value being close to zero). Swine-derived HEV ORFs own more concentrated synonymous codons than those from wild boar. Compared with HEV strains isolated from other hosts, the human-derived HEV exhibits a distinct pattern at the overall codon usage (R < 0). Generally, ORF1 contains more synonymous codons with stable usage patterns (Vs < 0.1) than those of ORFs 2 and 3. Moreover, ORF3 contains more synonymous codons with varied patterns (Vs > 1.0) than ORFs 1 and 2. The host factor serving as one of the evolutionary dynamics probably influences synonymous codon usage patterns of the HEV genome. Taken together, synonymous codons with stable usage patterns in ORF1 might help to sustain the infection, while that with varied usage patterns in ORF3 may facilitate cross-species infection and expand the host range.
Histone methylation assumes a crucial role in the intricate process of enamel development. Our study has illuminated the substantial prevalence of H3K4me3 distribution, spanning from the cap stage to the late bell stage of dental germs. In order to delve into the role of H3K4me3 modification in amelogenesis and unravel the underlying mechanisms, we performed a conditional knockout of Ash2l, a core subunit essential for the establishment of H3K4me3 within the dental epithelium of mice. The absence of Ash2l resulted in reduced H3K4me3 modification, subsequently leading to abnormal morphology of dental germ at the late bell stage. Notably, knockout of Ash2l resulted in a loss of polarity in ameloblasts and odontoblasts. The proliferation and apoptosis of the inner enamel epithelium cells underwent dysregulation. Moreover, there was a notable reduction in the expression of matrix-related genes, Amelx and Dspp, accompanied with impaired enamel and dentin formation. Cut&Tag-seq (cleavage under targets and tagmentation sequencing) analysis substantiated a reduction of H3K4me3 modification on Shh, Trp63, Sp6, and others in the dental epithelium of Ash2l knockout mice. Validation through real-time polymerase chain reaction, immunohistochemistry, and immunofluorescence consistently affirmed the observed downregulation of Shh and Sp6 in the dental epithelium following Ash2l knockout. Intriguingly, the expression of Trp63 isomers, DNp63 and TAp63, was perturbed in Ash2l defect dental epithelium. Furthermore, the downstream target of TAp63, P21, exhibited aberrant expression within the cervical loop of mandibular first molars and incisors. Collectively, our findings suggest that ASH2L orchestrates the regulation of crucial amelogenesis-associated genes, such as Shh, Trp63, and others, by modulating H3K4me3 modification. Loss of ASH2L and H3K4me3 can lead to aberrant differentiation, proliferation, and apoptosis of the dental epithelium by affecting the expression of Shh, Trp63, and others genes, thereby contributing to the defects of amelogenesis.
The optimal control problem for switched linear systems with internally forced switching has more constraints than with externally forced switching. Heavy computations and slow convergence in solving this problem is a major obstacle. In this paper we describe a new approach for solving this problem, which is called Migrant Particle Swarm Optimization (Migrant PSO). Imitating the behavior of a flock of migrant birds, the Migrant PSO applies naturally to both continuous and discrete spaces, in which definitive optimization algorithm and stochastic search method are combined. The efficacy of the proposed algorithm is illustrated via a numerical example.
Heavy computations and slow convergence in solving the optimal control problem for switched nonlinear systems is a major obstacle for traditional methods. In this paper we present a new approach for solving this problem, which is called Migrant Particle Swarm Optimization (Migrant PSO) based on Interval Analysis. Imitating the behavior of a flock of migrant birds, the Interval Analysis Migrant PSO applies naturally to both continuous and discrete spaces, in which definitive optimization algorithm and stochastic search method are combined. A new method based on interval analysis is applied to define the migration velocity function. The efficacy of the proposed algorithm is illustrated via a numerical example.
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