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.

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.

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.

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.

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.

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.

Frequency-Domain Full-Waveform Inversion Based on Tunnel Space Seismic Data

Tunnel seismic detection methods are effective for obtaining the geological structure around the tunnel face,which is critical for safe construction and disaster mitigation in tunnel engineering.However,there is often a lack of accuracy in the acquired geological information and physical properties ahead of the tunnel face in the current tunnel seismic detection methods.Thus,we apply a frequency-domain acoustic full-waveform inversion(FWI)method to obtain high-resolution results for the tunnel structure.We discuss the influence of the frequency group selection strategy and the tunnel observation system settings regarding the inversion results and determine the structural imaging and physical property parameter inversion of abnormal geological bodies ahead of the tunnel face.Based on the conventional strategies of frequency-domain acoustic FWI,we propose a frequency group selection strategy that combines a low-frequency selection covering the vertical wavenumber and a high-frequency selection of antialiasing.This strategy can effectively obtain the spatial structure and physical parameters of the geology ahead of the tunnel face and improve the inversion resolution.In addition,by linearly increasing the side length of the tunnel observation system,we share the influence of the length of the two sides of the observation systems of different tunnels on the inversion results.We found out that the inversion results are the best when the side length is approximately five times the width of the tunnel face,and the influence of increasing the side observation length beyond this range on the inversion results can be ignored.Finally,based on this approach,we invert for the complex multi-stratum model,and an accurate structure and physical property parameters of the complex stratum ahead of the tunnel face are obtained,which verifies the feasibility of the proposed method.

A Selective Moving Window Partial Least Squares Method and Its Application in Process Modeling

A selective moving window partial least squares(SMW-PLS) soft sensor was proposed in this paper and applied to a hydro-isomerization process for on-line estimation of para-xylene(PX) content. Aiming at the high frequency of model updating in previous recursive PLS methods, a selective updating strategy was developed. The model adaptation is activated once the prediction error is larger than a preset threshold, or the model is kept unchanged.As a result, the frequency of model updating is reduced greatly, while the change of prediction accuracy is minor.The performance of the proposed model is better as compared with that of other PLS-based model. The compromise between prediction accuracy and real-time performance can be obtained by regulating the threshold. The guidelines to determine the model parameters are illustrated. In summary, the proposed SMW-PLS method can deal with the slow time-varying processes effectively.

Parameter value selection strategy for complete coverage path planning based on the Lüsystem to perform specific types of missions

We propose a novel parameter value selection strategy for the Lüsystem to construct a chaotic robot to accomplish the complete coverage path planning(CCPP)task.The algorithm can meet the requirements of high randomness and coverage rate to perform specific types of missions.First,we roughly determine the value range of the parameter of the Lüsystem to meet the requirement of being a dissipative system.Second,we calculate the Lyapunov exponents to narrow the value range further.Next,we draw the phase planes of the system to approximately judge the topological distribution characteristics of its trajectories.Furthermore,we calculate the Pearson correlation coefficient of the variable for those good ones to judge its random characteristics.Finally,we construct a chaotic robot using variables with the determined parameter values and simulate and test the coverage rate to study the relationship between the coverage rate and the random characteristics of the variables.The above selection strategy gradually narrows the value range of the system parameter according to the randomness requirement of the coverage trajectory.Using the proposed strategy,proper variables can be chosen with a larger Lyapunov exponent to construct a chaotic robot with a higher coverage rate.Another chaotic system,the Lorenz system,is used to verify the feasibility and effectiveness of the designed strategy.The proposed strategy for enhancing the coverage rate of the mobile robot can improve the efficiency of accomplishing CCPP tasks under specific types of missions.

Research on Network Maintenance Strategy Selection Based on Analytic Hierarchy Process and Technique for Order Preference by Similarity to Ideal Solution Algorithm

Network maintenance strategy selection is a multi-objective decision making topic. It mostly depends on the uncertainty and fuzziness of decision makers and conditions. In this paper, based on analytic hierarchy process(AHP) and technique for order preference by similarity to ideal solution(TOPSIS), TOPSIS partial order method is proposed to choose the optimal maintenance strategy. This method uses AHP to determine the weights of evaluation indexes. The optimal maintenance strategy choice is given as an example to demonstrate the effectiveness of the method.

Mobile node aware opportunistic routing in dynamic wireless sensor network

Opportunistic routing （OR） could adapt to dynamic wireless sensor networks （WSNs） because of its inherent broadcast nature. Most of the existing OR protocols focus on the variations of propagation environment which are caused by channel fading. However, a few works deal with the dynamic scenario with mobile nodes. In this paper, a mobile node （MN） aware OR （MN-OR） is proposed and applied to a WSN in the high-speed railway scenario where the destination node is deployed inside a high speed moving train, and the MN-OR not only considers the mobility of node but also utilizes the candidate waiting time induced by the timer-based coordination scheme. Specifically, to reduce the number of duplicate transmissions and mitigate the delay of information transmission, a new selection strategy of the candidate forwarders is presented. In addition, two priority assignment methods of the candidate forwarders are proposed for the general relay nodes （GRNs） and the rail-side nodes （RSNs） according to their different routing requirements. Extensive simulation results demonstrate that the proposed MN-OR protocol can achieve better network performances compared with some existing routing schemes such as the well-known Ad-hoc on-demand distance vector routing （AODV） and the extremely opportunistic routing （ExOR） protocols.

Novel PIO Algorithm with Multiple Selection Strategies for Many-Objective Optimization Problems

With the increase of problem dimensions,most solutions of existing many-objective optimization algorithms are non-dominant.Therefore,the selection of individuals and the retention of elite individuals are important.Existing algorithms cannot provide sufficient solution precision and guarantee the diversity and convergence of solution sets when solving practical many-objective industrial problems.Thus,this work proposes an improved many-objective pigeon-inspired optimization(ImMAPIO)algorithm with multiple selection strategies to solve many-objective optimization problems.Multiple selection strategies integrating hypervolume,knee point,and vector angles are utilized to increase selection pressure to the true Pareto Front.Thus,the accuracy,convergence,and diversity of solutions are improved.ImMAPIO is applied to the DTLZ and WFG test functions with four to fifteen objectives and compared against NSGA-III,GrEA,MOEA/D,RVEA,and many-objective Pigeon-inspired optimization algorithm.Experimental results indicate the superiority of ImMAPIO on these test functions.

Automatic Approach to Ontology Evolution Based on Change Impact Comparisons

Ontology evolution is the timely adaptation of ontologies to changing requirements, which is becoming more and more important as ontologies become widely used in different fields. This paper shows how to address the problem of evolving ontologies with less manual case-based reasoning using an automatic selection mechanism. An automatic ontology evolution strategy selection framework is presented that automates the evolution. A minimal change impact algorithm is also developed for the framework. The method is shown to be effective in a case study.

Multitarget tracking control algorithm under local information selection interaction mechanism

This study focuses on the problem of multitarget tracking.To address the existing problems of current tracking algorithms,as manifested by the time consumption of subgroup separation and the uneven group size of unmanned aerial vehicles(UAVs)for target tracking,a multitarget tracking control algorithm under local information selection interaction is proposed.First,on the basis of location,number,and perceived target information of neighboring UAVs,a temporary leader selection strategy is designed to realize the local follow-up movement of UAVs when the UAVs cannot fully perceive the target.Second,in combination with the basic rules of cluster movement and target information perception factors,distributed control equations are designed to achieve a rapid gathering of UAVs and consistent tracking of multiple targets.Lastly,the simulation experiments are conducted in two-and three-dimensional spaces.Under a certain number of UAVs,clustering speed of the proposed algorithm is less than 3 s,and the equal probability of the UAV subgroup size after group separation is over 78%.