Search

The construction of expressways in China has produced diverse habitats along slopes characterized by steep gradients, uneven water distribution, poor soil conditions, and no routine maintenance. Manually planting beneficial species is an essential method of effectively improving slope soils to prevent soil erosion. However, few studies have evaluated the reclamation effects and plant community composition and structure used to restore slopes along expressways. This study focused on the Zhengzhou-Xinxiang section of the Beijing-Hong Kong-Macao Expressway. A total of 10 representative plant communities were evaluated using the analytic hierarchy process (AHP)–fuzzy integrated evaluation method. The sites were divided into four layers, namely, plant communities, soil nutrients, soil physical properties, and other ecological factors, and 14 indicators were assessed. The evaluation results showed that four of these plant communities (PCs) were excellent, three PCs were good, one PC was normal, two PCs were poor. The four excellent PCs had high Shannon-Wiener index, pielou index, richness index or community productivity. It is worth noting that most excellent plant community structures were tree + shrub + herb. Based on these results, we recommend that fill slopes should be restored using a combination of trees, herbs, and shrubs; also, the vegetation should include native plants, such as B. papyrifera, U. pumila, A. fruticosa, and Cynodon dactylon (L.). This study could provide ideas for plant community composition and structure of new highway slopes in similar climate environment, and provide theoretical support for plant community composition and structure and soil improvement for the existing slope. [ABSTRACT FROM AUTHOR]

The rapid development of highways has caused a series of ecological problems, the restoration of which is an important part of highway construction. However, most related studies have focused only on the early stages of slope restoration. The present study investigated the Zhengzhou–Xinxiang section of the Beijing–Hong Kong–Macau Highway, which has been restored over more than 20 years, examining nine representative vegetation communities within this section and investigating their species diversity and soil physicochemical properties. Redundancy analysis and the grey correlation degree model were used to determine the relationship and coupling mechanism between vegetation diversity and soil physiochemical properties. The results showed some differences in the diversity of different vegetation communities and soil physicochemical properties; vegetation diversity was mainly influenced by organic material, total and available nitrogen, total and available phosphorus, slope, available potassium, and soil bulk density. Overall, environmental factors had a strong correlation with the Simpson dominance index and a weak correlation with the species richness index. The degree of coordination between vegetation community diversity and the soil coupling of the road slope remained on low and medium levels. Artificial vegetation restoration can regulate water and fertilizer resources and promote the restoration of highway slope vegetation. [ABSTRACT FROM AUTHOR]

Two Pt complexes with high quantum yields and photostability, and low cytotoxicity, were developed to track RNA G-quadruplexes (GQs) in live cells. Higher number and intensity, and longer lifetime of fluorescent foci in cancer cells than those in healthy cells suggest that the quantity and folding dynamics of RNA GQs could not only correlate to their biological functions, but be two novel biomarkers to characterize cancerous cells. [ABSTRACT FROM AUTHOR]

Differential Evolution (DE) was an easy-coding and efficient stochastic algorithm for global optimization, and the whole optimization process simulates biological evolution. Superior individuals of the population that were suitable for the environment were retained during the evolution, and consequently the tolerable solutions could be obtained in the end. Despite the excellent performance of DE algorithm, there were still some shortcomings. For example, the general performance of DE depended largely on mutation strategy and control parameters, how to design the appropriate control parameters and mutation strategy were difficult tasks. Here a novel DE variant was proposed to overcome these shortcomings. By incorporating the depth information of previous generations of populations, a better diversity of trial vector candidates could be secured during the evolution process. Moreover, the thought that successful parameters should be retained to guide the update of themselves during the evolution was also incorporated into the novel algorithm. The optimization performance of the new proposed DE variant was verified under CEC 2013 test suit containing 28 benchmarks, and the results showed its competitiveness with several state-of-the-art DE variants. [ABSTRACT FROM AUTHOR]

Differential Evolution (DE) algorithm generates a population of individuals by encoding with a floating point vector, and it is a simple and effective population-based stochastic optimization algorithm for global optimization of continuous space. Because of its excellent performance, DE variants can be applied in a wide range of applications in science and engineering. However, the performance of DE is sensitive to the choice of trial vector generation strategy and the associated control parameters. Therefore, it is necessary to choose appropriate mutation strategy and control parameters when tackling optimization applications. In this paper, an adaptive update mechanism is proposed to update control parameters F and Cr. The experimental results are verified on the CEC 2013 test suite which contains 28 benchmark functions for the evaluation of single objective real parameter optimization. The proposed algorithm is compared with jDE, iwPSO and ccPSO, and experiment results show its good performance. [ABSTRACT FROM AUTHOR]

Optimization demands exist everywhere in the real world especially in science studies and engineering practices, and it is important that the method to deal with intricacy optimization problems should itself be relative simple. Particle Swarm Optimization (PSO) and Differential Evolution (DE) both are simple evolutionary algorithms (EAs) which are proposed for single-objective optimization and both of them have been proved to be efficient methods for optimizing applications, however, there are still some weakness existing within them. A innovative evolutionary method named QUasi-Affine TRansformation Evolutionary (QUATRE) algorithm which is derived from, also tackles some weaknesses of PSO and DE algorithm, and obtains better performance on commonly used test suites. The key characteristic of QUATRE is that an automatically generated matrix named evolution matrix M is implemented in evolutionary process, which is taken as an alternative of employing the crossover rate CR. Here in this paper, we present a novel QUATRE variant, named IS-QUATRE, which can explore the search area in a better way comparison with the previous method, and relatively good optimization ability can be obtained by our proposed IS-QUATRE algorithm under CEC2013 test suit. And the conducted experimental results validate that our proposed IS-QUATRE is competitive with some other famous PSO and DE variants. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new DE variant incorporating a new mutation strategy and a bionic bi-population structure to enhance the overall performance of the canonical DE algorithm. As it is known to all that the guidance of a handful top superior elites is usually better than the guidance of only one elite in many cases, therefore, a handful top superior individuals are employed in the newly proposed mutation strategy for individual evolution. Moreover, hybridization of two populations of a certain species can improve the adaptation to the environment, and business and information exchanges between tribes can help their economical and technological development, so a bionic bi-population structure is also employed in the new DE variant for information exchanges. Both of the two aspects make the new algorithm achieve an overall better performance, and experiment results show that a handful top superior individuals with bionic bi-population structure has better performances on optimization accuracy, convergence speed and success rate under these tested benchmark functions especially those other algorithms may be trapped into local optima. [ABSTRACT FROM AUTHOR]

Abstract Differential Evolution (DE) variants have been proven to be excellent algorithms in tackling real-parameter single objective numerical optimization because they have secured the front ranks of these competitions for many years. Nevertheless, there are still some weaknesses, e.g. (1) improper control parameter adaptation schemes; and (2) defect in a given mutation strategy., existing in some state-of-the-art DE variants, which may result in slow convergence and worse optimization performance. Therefore, in this paper, a novel Parameter adaptive DE (PaDE) is proposed to tackle the above mentioned weaknesses and the PaDE algorithm has three advantages: (1) A grouping strategy with novel adaptation scheme for C r is proposed to tackle the improper adaptation schemes of C r in some state-of-the-art DE variants; (2) A novel parabolic population size reduction scheme is proposed to tackle the weakness in linear population size reduction scheme; (3) An enhanced time stamp based mutation strategy is proposed to tackle the weakness in a former mutation strategy. The novel PaDE algorithm is verified under 58 benchmarks from two Congress on Evolutionary Computation (CEC) Competition test suites on real-parameter single objective numerical optimization, and experiment results show that the proposed PaDE algorithm is competitive with the other state-of-the-art DE variants. [ABSTRACT FROM AUTHOR]

Understanding the molecular flexibility and dynamics is central to the analysis of biomolecular functions. In this work, complex multiscale virtual particle model based elastic network models (CMVP-ENMs) have been proposed for the normal mode analysis of biomolecular complexes or biomolecular assemblies. The term complex used in our CMVP-ENMs refers to the multi-material or multi-constituent. Different “materials” or constituents contribute differently to the general flexibility and dynamics of complex biomolecular structures. In our CMVP-ENMs, the key idea is to incorporate relative density or weight information of different components into the spring parameter of elastic network models. Two different models, including the CMVP based Gaussian network model (CMVP-GNM) and the CMVP based anisotropic network model (CMVP-ANM), have been proposed. With the consideration of complex component information, our CMVP-GNM, compared with the traditional GNM, can deliver a better accuracy in the B-factor prediction of protein–nucleic acid complexes. Moreover, our CMVP-ANM can be used to remove the “tip effect” by systematically suppressing the extremely-large vectors, in the highly flexible regions, of the normal modes generated by the ANM. In this way, our CMVP-ANM can be used to handle biomolecular structures with large hanging loops or extruding ends, which usually cause an irrationally-large-vector problem in ANM predictions. Finally, we explore the potential applications of our method by the cryo-EM data analysis. We find that by tuning the relative density ratio, we can systematically enhance or suppress the modes in different components, so that it can reveal the dynamics of the special regions that we are interested in. [ABSTRACT FROM AUTHOR]

Optimization demands are ubiquitous in science and engineering. The key point is that the approach to tackle a complex optimization problem should not itself be difficult. Differential Evolution (DE) is such a simple method, and it is arguably a very powerful stochastic real-parameter algorithm for single-objective optimization. However, the performance of DE is highly dependent on control parameters and mutation strategies. Both tuning the control parameters and selecting the proper mutation strategy are still tedious but important tasks for users. In this paper, we proposed an enhanced structure for DE algorithm with less control parameters to be tuned. The crossover rate control parameter Cr is replaced by an automatically generated evolution matrix and the control parameter F can be renewed in an adaptive manner during the whole evolution. Moreover, an enhanced mutation strategy with time stamp mechanism is advanced as well in this paper. CEC2013 test suite for real-parameter single objective optimization is employed in the verification of the proposed algorithm. Experiment results show that our proposed algorithm is competitive with several well-known DE variants. [ABSTRACT FROM AUTHOR]

Differential Evolution (DE) is a simple but powerful population-based stochastic optimization algorithm. Owing to its simplicity, easy implementation and excellent performance, DE has been wildly applied in scientific and engineering areas. However, there are still some inconveniences and weaknesses in DE algorithm, such as the inconveniences in the choice of proper control parameters and the defects existing in a given mutation strategy. In this paper, a new DE variant, called Parameters with Adaptive Learning Mechanism Differential Evolution (PALM-DE), is proposed to tackle the inconvenience in control parameter selection as well as to enhance a former mutation strategy. The new variant is verified on 44 commonly used real-parameter single objective benchmark functions selected from CEC2013 and CEC2014 competitions. Several recently proposed well-known DE variants are also contrasted in the paper, and the experiment results show that the proposed PALM-DE algorithm is competitive in comparison with these DE variants. An attempt to enhance the performance of PALM-DE by employing linear population size reduction is also presented, and the performance is still competitive. [ABSTRACT FROM AUTHOR]