Assessment of molecular markers and marker-assisted selection for drought tolerance in barley(Hordeum vulgare L.)

This review updates the present status of the field of molecular markers and marker-assisted selection(MAS),using the example of drought tolerance in barley.The accuracy of selected quantitative trait loci(QTLs),candidate genes and suggested markers was assessed in the barley genome cv.Morex.Six common strategies are described for molecular marker development,candidate gene identification and verification,and their possible applications in MAS to improve the grain yield and yield components in barley under drought stress.These strategies are based on the following five principles:(1)Molecular markers are designated as genomic‘tags’,and their‘prediction’is strongly dependent on their distance from a candidate gene on genetic or physical maps;(2)plants react differently under favourable and stressful conditions or depending on their stage of development;(3)each candidate gene must be verified by confirming its expression in the relevant conditions,e.g.,drought;(4)the molecular marker identified must be validated for MAS for tolerance to drought stress and improved grain yield;and(5)the small number of molecular markers realized for MAS in breeding,from among the many studies targeting candidate genes,can be explained by the complex nature of drought stress,and multiple stress-responsive genes in each barley genotype that are expressed differentially depending on many other factors.

Flexible nest site selection of the endangered Oriental Storks(Ciconia boyciana):Trade-off from adaptive strategies

Nest site selection is a vital component of bird reproduction success,and an adaptive behavior conducted to decrease nest predation risk with avoiding external disturbances.Understanding patterns of nest site selection can provide insights into how species adapt to changes in their habitat and has important conservation implications.In this study,we used microhabitat variables and multi-scale data with a field survey of nest occurrence to determine nest site selection patterns and adaptive strategies of the breeding Oriental Storks(Ciconia boyciana)in different nest areas.Results demonstrate that the nest site microhabitat characteristics of the breeding Oriental Storks significantly differed among the three nesting areas,and nest height was higher in the middle and lower Yangtze River floodplain than in the Northeast China and Bohai Bay nest areas.The food resources and intensity of human disturbance had the greatest effects on the nest site selection of the breeding Oriental Storks.The intensity of human disturbance was positively correlated with the nest height of the breeding Oriental Storks in Bohai Bay and the middle and lower Yangtze River floodplain;however,nest height decreased with the abundance of food resources in the Northeast China nest area.Our findings indicate that the nest site selection patterns of Oriental Storks showed flexible adaptive strategies.In safer environments,nests were lower and closer to food resources,which allows parent storks to invest more in the nestlings.However,in areas where human activity was intense,nests were higher to ensure the safety of their offspring.Some measures that could be taken to improve the breeding habitat of Oriental Storks include increasing the percentage of wetland areas in nesting areas to enhance food resources availability and setting artificial nests at suitable heights in potential nesting grounds to encourage nesting.Finally,the establishment of soft barriers around the nesting areas could increase the safety of nests.

Multi-Strategy Boosted Spider Monkey Optimization Algorithm for Feature Selection

To solve the problem of slow convergence and easy to get into the local optimum of the spider monkey optimization algorithm,this paper presents a new algorithm based on multi-strategy(ISMO).First,the initial population is generated by a refracted opposition-based learning strategy to enhance diversity and ergodicity.Second,this paper introduces a non-linear adaptive dynamic weight factor to improve convergence efficiency.Then,using the crisscross strategy,using the horizontal crossover to enhance the global search and vertical crossover to keep the diversity of the population to avoid being trapped in the local optimum.At last,we adopt a Gauss-Cauchy mutation strategy to improve the stability of the algorithm by mutation of the optimal individuals.Therefore,the application of ISMO is validated by ten benchmark functions and feature selection.It is proved that the proposed method can resolve the problem of feature selection.

Building up a general selection strategy and catalytic performance prediction expressions of heteronuclear double-atom catalysts for N_(2)reduction

The severe environmental problems and the demand for energy urgently require electrocatalysis to replace Haber-Bosch for the nitrogen reduction reaction(NRR).The descriptors and important properties of single-atom and homonuclear double-atom catalysts have been preliminarily explored,but the relationship between the inherent properties and catalytic activity of heteronuclear double-atom catalysts with better performance remains unclear.Therefore,it is very significant to explore the prediction expressions of catalytic activity of heteronuclear double-atom catalysts based on their inherent properties and find the rule for selecting catalytic centers.Herein,by summarizing the free energy for the key steps of NRR on 55 catalysts calculated through the first-principle,the expressions of predicting the free energy and the corresponding descriptors are deduced by the machine learning,and the strategy for selecting the appropriate catalytic center is proposed.The selection strategy for the central atom of heteronuclear double-atom catalysts is that the atomic number of central B atom should be between group VB and VIIIB,and the electron difference between central A atom and B atom should be large enough,and the selectivity of NRR or hydrogen evolution reaction(HER)could be calculated through the prediction formula.Moreover,five catalysts are screened to have low limiting potential and excellent selectivity,and are further analyzed by electron transfer.This work explores the relationship between the inherent properties of heteronuclear double-atom catalysts and the catalytic activity,and puts forward the rules for selecting the heteronuclear double-atom catalytic center,which has guiding significance for the experiment.

Extractive distillation: Advances in conceptual design, solvent selection,and separation strategies

Extractive distillation(ED) is one of the most promising approaches for the separation of the azeotropic or closeboiling mixtures in the chemical industry. The purpose of this paper is to provide a broad overview of the recent development of key aspects in the ED process involving conceptual design, solvent selection, and separation strategies. To obtain the minimum entrainer feed flow rate and reflux ratio for the ED process, the conceptual design of azeotropic mixture separation based on a topological analysis via thermodynamic feasibility insights involving residue curve maps, univolatility lines, and unidistribution curves is presented. The method is applicable to arbitrary multicomponent mixtures and allows direct screening of design alternatives. The determination of a suitable solvent is one of the key steps to ensure an effective and economical ED process. Candidate entrainers can be obtained from heuristics or literature studies while computer aided molecular design(CAMD) has superiority in efficiency and reliability. To achieve optimized extractive distillation systems, a brief review of evaluation method for both entrainer design and selection through CAMD is presented. Extractive distillation can be operated either in continuous extractive distillation(CED) or batch extractive distillation(BED), and both modes have been well-studied depending on the advantages in flexibility and low capital costs. To improve the energy efficiency, several configurations and technological alternatives can be used for both CED and BED depending on strategies and main azeotropic feeds. The challenge and chance of the further ED development involving screening the best potential solvents and exploring the energy-intensive separation strategies are discussed aiming at promoting the industrial application of this environmentally friendly separation technique.

Exit selection strategy in pedestrian evacuation simulation with multi-exits

A mixed strategy of the exit selection in a pedestrian evacuation simulation with multi-exits is constructed by fusing the distance-based and time-based strategies through a cognitive coefficient, in order to reduce the evacuation imbalance caused by the asymmetry of exits or pedestrian layout, to find a critical density to distinguish whether the strategy of exit selection takes effect or not, and to analyze the exit selection results with different cognitive coefficients. The strategy of exit selection is embedded in the computation of the shortest estimated distance in a dynamic parameter model, in which the concept of a jam area layer and the procedure of step-by-step expending are introduced. Simulation results indicate the characteristics of evacuation time gradually varying against cognitive coefficient and the effectiveness of reducing evacuation imbalance caused by the asymmetry of pedestrian or exit layout. It is found that there is a critical density to distinguish whether a pedestrian jam occurs in the evacuation and whether an exit selection strategy is in effect. It is also shown that the strategy of exit selection has no effect on the evacuation process in the no-effect phase with a low density, and that evacuation time and exit selection are dependent on the cognitive coefficient and pedestrian initial density in the in-effect phase with a high density.

Control strategy of central pattern generator gait movement under condition of attention selection

As a typical rhythmic movement, human being＇s rhythmic gait movement can be generated by a central pattern generator （CPG） located in a spinal cord by self- oscillation. Some kinds of gait movements are caused by gait frequency and amplitude variances. As an important property of human being＇s motion vision, the attention selection mechanism plays a vital part in the regulation of gait movement. In this paper, the CPG model is amended under the condition of attention selection on the theoretical basis of Matsuoka neural oscillators. Regulation of attention selection signal for the CPG model parameters and structure is studied, which consequentially causes the frequency and amplitude changes of gait movement output. Further, the control strategy of the CPG model gait movement under the condition of attention selection is discussed, showing that the attention selection model can regulate the output model of CPG gait movement in three different ways. The realization of regulation on the gait movement frequency and amplitude shows a variety of regulation on the CPG gait movement made by attention selection and enriches the controllability of CPG gait movement, which demonstrates potential influence in engineering applications.

Application of Rollout Strategy to Test Points Selection for Integer-Coded Fault Wise Table

Test points selection for integer-coded fault wise table is a discrete optimization problem. The global minimum set of test points can only be guaranteed by an exhaustive search which is eompurationally expensive. In this paper, this problem is formulated as a heuristic depth-first graph search problem at first. The graph node expanding method and rules are given. Then, rollout strategies are applied, which can be combined with the heuristic graph search algorithms, in a computationally more efficient manner than the optimal strategies, to obtain solutions superior to those using the greedy heuristic algorithms. The proposed rollout-based test points selection algorithm is illustrated and tested using an analog circuit and a set of simulated integer-coded fault wise tables. Computa- tional results are shown, which suggest that the rollout strategy policies are significantly better than other strategies.

A PMU data recovering method based on preferred selection strategy

Nowadays, the technology of renewable sources grid-connection and DC transmission has a rapid development. And phasor measurement units(PMUs) become more notable in power grids, due to the necessary of real time monitoring and close-loop control applications. However, the PMUs data quality issue affects applications based on PMUs a lot. This paper proposes a simple yet effective method for recovering PMU data. To simply the issue, two different scenarios of PMUs data loss are first defined. Then a key combination of preferred selection strategies is introduced. And the missing data is recovered by the function of spline interpolation. This method has been tested by artificial data and field data obtained from on-site PMUs. The results demonstrate that the proposed method recovers the missing PMU data quickly and accurately. And it is much better than other methods when missing data are massive and continuous. This paper also presents the interesting direction for future work.

Adaptive Agent Model with Hybrid Routing Selection Strategy for Improving the Road-Network Congestion Problem

This paper proposes an adaptive agent model with a hybrid routing selection strategy for studying the road-network congestion problem. We focus on improving those severely congested links. Firstly,a multi-agent system is built,where each agent stands for a vehicle,and it makes its routing selection by considering the shortest path and the minimum congested degree of the target link simultaneously. The agent-based model captures the nonlinear feedback between vehicle routing behaviors and road-network congestion status.Secondly,a hybrid routing selection strategy is provided,which guides the vehicle routes adapting to the realtime road-network congestion status. On this basis, we execute simulation experiments and compare the simulation results of network congestion distribution,by Floyd agent with shortest path strategy and our proposed adaptive agent with hybrid strategy. The simulation results show that our proposed model has reduced the congestion degree of those seriously congested links of road-network. Finally,we execute our model on a real road map. The results finds that those seriously congested roads have some common features such as located at the road junction or near the unique road connecting two areas. And,the results also show an effectiveness of our model on reduction of those seriously congested links in this actual road network. Such a bottom-up congestion control approach with a hybrid congestion optimization perspective will have its significance for actual traffic congestion control.

Influence of Strategy Selection of Dairy Enterprises on Performance

Dairy enterprises have to cope with intense market pressure and survival pressure.In such complex and competitive market environment,it is of great realistic significance for studying the influence of strategy selection of dairy enterprises on their performance.Through selecting 115 dairy enterprises,empirical analysis was made,and multiple linear regression model and structural equation model were established with the aid of SPSS and Amos software.Comparative analysis was carried out for performance of different strategy selection.Results indicate that dairy enterprises selecting vertical strategies obtain the best performance,followed by horizontal strategy,and the worst is multiple strategy,and the strategy selection exerts positive and direct influence on performance of dairy enterprises.

Mode Selection Strategy of Dual Distribution Channel Based on Revenue Analysis

Within the scope of dual distribution channel(DDC)modes—ME&T-C and M-T&E-C,a game model designed for channel members was proposed.Based on this game model,the game equilibrium under both centralized and decentralized decisionmaking situations was analyzed,the channel members' and overall revenues of two modes under the same decision-making situation are compared,and the influence of demand shift coefficient to the overall and members' revenue was also studied through example analysis.Based on the comparison and analysis of the revenue yielded from the two DDC modes,it's discovered that within a certain hypothetical range,the M-T&E-C mode seems to be a better option for the manufacturer than the ME&T-C mode.Therefore,this discovery can be served as a theoretical reference for manufacturers when choosing the optimal DDC mode in real life.

Feature Selection with a Local Search Strategy Based on the Forest Optimization Algorithm

Feature selection has been widely used in data mining and machine learning.Its objective is to select a minimal subset of features according to some reasonable criteria so as to solve the original task more quickly.In this article,a feature selection algorithm with local search strategy based on the forest optimization algorithm,namely FSLSFOA,is proposed.The novel local search strategy in local seeding process guarantees the quality of the feature subset in the forest.Next,the fitness function is improved,which not only considers the classification accuracy,but also considers the size of the feature subset.To avoid falling into local optimum,a novel global seeding method is attempted,which selects trees on the bottom of candidate set and gives the algorithm more diversities.Finally,FSLSFOA is compared with four feature selection methods to verify its effectiveness.Most of the results are superior to these comparative methods.

Strategy Selection for Moving Target Defense in Incomplete Information Game

As a core component of the network,web applications have become one of the preferred targets for attackers because the static configuration of web applications simplifies the exploitation of vulnerabilities by attackers.Although the moving target defense(MTD)has been proposed to increase the attack difficulty for the attackers,there is no solo approach can cope with different attacks;in addition,it is impossible to implement all these approaches simultaneously due to the resource limitation.Thus,the selection of an optimal defense strategy based on MTD has become the focus of research.In general,the confrontation of two players in the security domain is viewed as a stochastic game,and the reward matrices are known to both players.However,in a real security confrontation,this scenario represents an incomplete information game.Each player can only observe the actions performed by the opponent,and the observed actions are not completely accurate.To accurately describe the attacker’s reward function to reach the Nash equilibrium,this work simulated and updated the strategy selection distribution of the attacker by observing and investigating the strategy selection history of the attacker.Next,the possible rewards of the attacker in each confrontation via the observation matrix were corrected.On this basis,the Nash-Q learning algorithm with reward quantification was proposed to select the optimal strategy.Moreover,the performances of the Minimax-Q learning algorithm and Naive-Q learning algorithm were compared and analyzed in the MTD environment.Finally,the experimental results showed that the strategy selection algorithm can enable defenders to select a more reasonable defensive strategy and achieve the maximum possible reward.

Two-Phase Bidirectional Dual-Relay Selection Strategy for Wireless Relay Networks

In this article,we introduce a new bi-directional dual-relay selection strategy with its bit error rate(BER)performance analysis.During the first step of the proposed strategy,two relays out of a set of N relay-nodes are selected in a way to optimize the system performance in terms of BER,based on the suggested algorithm which checks if the selected relays using the maxmin criterion are the best ones.In the second step,the chosen relay-nodes perform an orthogonal space-time coding scheme using the two-phase relaying protocol to establish a bi-directional communication between the communicating terminals,leading to a significant improvement in the achievable coding and diversity gain.To further improve the overall system performance,the selected relay-nodes apply also a digital network coding scheme.Furthermore,this paper discusses the analytical approximation of the BER performance of the proposed strategy,where we prove that the analytical results match almost perfectly the simulated ones.Finally,our simulation results show that the proposed strategy outperforms the current state-of-the-art ones.

Stock-Selection Strategies Optimization of Fund Management Companies by Analytic Hierarchy Process

Securities investment fund is one of the most important institutional investors in the securities market. The quality of the management of the securities investment fund by the fund management company not only directly affects its survival and development,but also plays a decisive role in the stable operation of securities market and even the macroeconomy. As China is in rapid development of funds,scientific and effective investment strategies will be the only road for the long-term development of wellestablished fund management companies. We attempt to combine the value investment strategies with quantitative investment strategies to build a system of optimized investment strategies with the use of analytic hierarchy process so as to improve business performance of fund management companies and to foster the concept of value investment. Moreover,it possesses certain significance in enriching investment strategies for securities investment fund and promoting the healthy growth of the Chinese securities investment fund.

Stimulation Study of Gene Pyramiding inAnimals by Marker-Assisted Selection

This gene pyramiding strategy is based on the idea of efficiently pyramiding genes of interest by crosses and selection to obtain a population with favorable alleles from different breeds or lines, which is called an ideal population. We investigate impacts of some factors on the pyramiding efficiencies by simulation. These factors include selection strategies （the breeding value selection, the molecular scores selection and the index selection）, proportion selected （2, 10 and 20%）, recombination rates between adjacent target genes （0.1, 0.3 and 0.5） and different mating types （the random mating and the positive assortative mating avoiding sib mating）. The results show that： （1） The more recombination rate and the lower proportion male selected, the better pyramiding efficiency; （2） the ideal population is obtained via various selection strategies, while different selection strategies are suitable for different breeding objectives. From the perspective of pyramiding target genes merely, the molecular scores selection is the best one, for the purpose of pyramiding target genes and recovering genetic background of the target trait, the index selection is the best one, while from the saving cost point of view, the breeding value selection is the best one; （3） the positive assortative mating is more efficient for gene pyramiding compared with the random mating in the terms of the number of generations of intercross for getting the ideal population.

Ensemble feature selection integrating elitist roles and quantum game model

To accelerate the selection process of feature subsets in the rough set theory （RST）, an ensemble elitist roles based quantum game （EERQG） algorithm is proposed for feature selec- tion. Firstly, the multilevel elitist roles based dynamics equilibrium strategy is established, and both immigration and emigration of elitists are able to be self-adaptive to balance between exploration and exploitation for feature selection. Secondly, the utility matrix of trust margins is introduced to the model of multilevel elitist roles to enhance various elitist roles＇ performance of searching the optimal feature subsets, and the win-win utility solutions for feature selec- tion can be attained. Meanwhile, a novel ensemble quantum game strategy is designed as an intriguing exhibiting structure to perfect the dynamics equilibrium of multilevel elitist roles. Finally, the en- semble manner of multilevel elitist roles is employed to achieve the global minimal feature subset, which will greatly improve the fea- sibility and effectiveness. Experiment results show the proposed EERQG algorithm has superiority compared to the existing feature selection algorithms.