Compound control allocation strategy of dual aero/jet vane control missile

To solve the control allocation problem of dual aero/jet vane control missile, dynamics e- quations in longitudinal plane are derived, and the structure of compound control loop is designed based on attitude autopilot. Four brief compound control allocation strategies are researched and an- alyzed. Furthermore, a new strategy called chain combination variable proportional coefficient strat- egy based on rudder effect is presented. By simulation of initial climb trajectory, the characteristics of all the strategies are researched, and the results illustrate that the new strategy can meet the re- quirement well.

A Pre-Allocation Strategy for Implement ADA95's Distrbuted Computing

In order to realize distributed computing of Ada95, this paper discusses Ada95's distributed system model and an implement model of Ada95's distributed computing-- workstation cluster model. Under this model, we presents a pre-allocation strategy for allocating the computation quantity of distributed units evenly among workstations and also reducing the communication expense between those distributed units.

A Dynamic Resource Allocation Strategy with Reinforcement Learning for Multimodal Multi-objective Optimization

Many isolation approaches, such as zoning search, have been proposed to preserve the diversity in the decision space of multimodal multi-objective optimization(MMO). However, these approaches allocate the same computing resources for subspaces with different difficulties and evolution states. In order to solve this issue, this paper proposes a dynamic resource allocation strategy(DRAS)with reinforcement learning for multimodal multi-objective optimization problems(MMOPs). In DRAS, relative contribution and improvement are utilized to define the aptitude of subspaces, which can capture the potentials of subspaces accurately. Moreover, the reinforcement learning method is used to dynamically allocate computing resources for each subspace. In addition, the proposed DRAS is applied to zoning searches. Experimental results demonstrate that DRAS can effectively assist zoning search in finding more and better distributed equivalent Pareto optimal solutions in the decision space.

HOW TASK ALLOCATION STRATEGY AFFECTS TEAM PERFOR- MANCE： A COMPUTATIONAL EXPERIMENT

This paper studies how to determine task allocation schemes according to the status and require-ments of various teams, to achieve optimal performance for a knowledge-intensive team （KIT）, whichis different from traditional task assignment. The way to allocate tasks to a team affects task processingand, in turn, influences the team itself after the task is processed. Considering the knowledge require-ment of tasks as a driving force and that knowledge exchange is pivotal, we build a KIT system modelbased on complex adaptive system theory and agent modeling technology, design task allocation strat-egies （TASs） and a team performance measurement scale utilizing computational experiment, and an-alyze how different TASs impact the different performance indicators of KITs. The experimental re-sults show the recommend TAS varies under different conditions, such as the knowledge levels ofmembers, team structures, and tasks to be assigned, particularly when the requirements to the team aredifferent. In conclusion, we put forward a new way of thinking and methodology for real task alloca-tion problems and provide support for allocation decision makers.

Differences in functional traits and reproductive allocations between native and invasive plants

Because co-occurring native and invasive plants are subjected to similar environmental selection pressures,the differences in functional traits and reproductive allocation strategies between native and invasive plants may be closely related to the success of the latter.Accordingly,this study examines differences in functional traits and reproductive allocation strategies between native and invasive plants in Eastern China.Plant height,branch number,reproductive branch number,the belowground-to-aboveground biomass ratio,and the reproductive allocation coefficient of invasive plants were all notably higher than those of native species.Additionally,the specific leaf area(SLA)values of invasive plants were remarkably lower than those of native species.Plasticity indexes of SLA,maximum branch angle,and branch number of invasive plants were each notably lower than those of native species.The reproductive allocation coefficient was positively correlated with reproductive branch number and the belowground-to-aboveground biomass ratio but exhibited negative correlations with SLA and aboveground biomass.Plant height,branch number,reproductive branch number,the belowground-to-aboveground biomass ratio,and the reproductive allocation coefficient of invasive plants may strongly influence the success of their invasions.

Traffic resource allocation for complex networks

In this paper, an optimal resource allocation strategy is proposed to enhance traffic dynamics in complex networks. The network resources are the total node packet-delivering capacity and the total link bandwidth. An analytical method is developed to estimate the overall network capacity by using the concept of efficient betweenness （ratio of algorithmic betweenness and local processing capacity）. Three network structures （scale-free, small-world, and random networks） and two typical routing protocols （shortest path protocol and efficient routing protocol） are adopted to demonstrate the performance of the proposed strategy. Our results show that the network capacity is reversely proportional to the average path length for a particular routing protocol and the shortest path protocol can achieve the largest network capacity when the proposed resource allocation strategy is adopted.

Contact pattern,current immune barrier,and pathogen virulence determines the optimal strategy of further vaccination

Background:The current outbreak of novel coronavirus disease 2019 has caused a seriousdisease burden worldwide.Vaccines are an important factor to sustain the epidemic.Although with a relatively high-vaccination worldwide,the decay of vaccine efficacy andthe arising of new variants lead us to the challenge of maintaining a sufficient immunebarrier to protect the population.Method:A case-contact tracking data in Hunan,China,is used to estimate the contactpattern of cases for scenarios including school,workspace,etc,rather than ordinary susceptible population.Based on the estimated vaccine coverage and efficacy,a multi-groupvaccinated-exposed-presymptomatic-symptomatic-asymptomatic-removed model(VEFIAR)with 8 age groups,with each partitioned into 4 vaccination status groups isdeveloped.The optimal dose-wise vaccinating strategy is optimized based on the currentlyestimated immunity barrier of coverage and efficacy,using the greedy algorithm thatminimizes the cumulative cases,population size of hospitalization and fatality respectivelyin a certain future interval.Parameters of Delta and Omicron variants are used respectivelyin the optimization.Results:The estimated contact matrices of cases showed a concentration on middle ages,and has compatible magnitudes compared to estimations from contact surveys in otherstudies.The VEFIAR model is numerically stable.The optimal controled vaccination strategy requires immediate vaccination on the un-vaccinated high-contact population of age30e39 to reduce the cumulative cases,and is stable with different basic reproductionnumbers(R_(0)).As for minimizing hospitalization and fatality,the optimized strategy requires vaccination on the un-vaccinated of both aged 30e39 of high contact frequencyand the vulnerable older.Conclusion:The objective of reducing transmission requires vaccination in age groups ofthe highest contact frequency,with more priority for un-vaccinated than un-fully or fullyvaccinated.The objective of reducing total hospitalization and fatality requires not only toreduce transmission but also to protect the vulnerable older.The priority changes byvaccination progress.For any region,if the local contact pattern is available,then with thevaccination coverage,efficacy,and disease characteristics of relative risks in heterogeneouspopulations,the optimal dose-wise vaccinating process will be obtained and gives hintsfor decision-making.

Growth and production characters of elymus cylindricus in different shortening heading time

Ramet modules in a certain population differ in terms of functions,which accounts for different contributions of the same ramets.Shortening heading time brings about different contributions of such modules.Ramets heading one after another were treated as a continuum in respective cohorts of Elymus cylindricus aged two.The reproductive ramets that head earlier were marked with tags every four days during the whole heading stage from the beginning to the end,after which all the labeled ramets at the maturity period were gathered.The results showed that,the height and biomass of ramets,the length and biomass of inflorescences,percentage of inflorescence length to ramet height,percentage of inflorescence biomass to ramet biomass,the number and biomass of seeds,seed-setting rate,and percentage of seed biomass to ramet biomass declined with the increasing intensity of heading time shortening as displayed with linear or quadratic function.Ramet characteristics weakened remarkably when shortened heading time added up to 17 days.The biomass distribution in relation to inflorescence and seed maintain a stable rate at the early heading stage and dwindled quickly at the near-end stage,but the biomass of ramets remain constant throughout the entire heading stage.The ramets with earlier heading time make greater contribution to the survival of population than the shortened heading time in this species of bunchgrass.

Improvement of Incentive Mechanism on BitTorrent-like Peer-to-Peer File Sharing Systems

BitTorrent is a very popular Peer-to-Peer file sharing system, which adopts a set of incentive mechanisms to encourage contribution and prevent free-riding. However, we find that BitTorrent’s incentive mechanism can prevent free-riding effectively in a system with a relatively low number of seeds, but may fail in producing a disincentive for free-riding in a system with a high number of seeds. The reason is that BitTorrent does not provide effective mechanisms for seeds to guard against free-riding. Therefore, we propose a seed bandwidth allocation strategy for the BitTorrent system to reduce the effect of seeds on free-riding. Our target is that a downloader which provides more service to the system will be granted a higher benefit than downloaders which provide lower service when some downloaders ask for downloading file from a seed. Finally, simulation results are given, which validate the effectiveness of the proposed strategy.

Strict greedy design paradigm applied to the stochastic multi-armed bandit problem

The process of making decisions is something humans do inherently and routinely,to the extent that it appears commonplace. However,in order to achieve good overall performance,decisions must take into account both the outcomes of past decisions and opportunities of future ones. Reinforcement learning,which is fundamental to sequential decision-making,consists of the following components: 1 A set of decisions epochs; 2 A set of environment states; 3 A set of available actions to transition states; 4 State-action dependent immediate rewards for each action.At each decision,the environment state provides the decision maker with a set of available actions from which to choose. As a result of selecting a particular action in the state,the environment generates an immediate reward for the decision maker and shifts to a different state and decision. The ultimate goal for the decision maker is to maximize the total reward after a sequence of time steps.This paper will focus on an archetypal example of reinforcement learning,the stochastic multi-armed bandit problem. After introducing the dilemma,I will briefly cover the most common methods used to solve it,namely the UCB and εn- greedy algorithms. I will also introduce my own greedy implementation,the strict-greedy algorithm,which more tightly follows the greedy pattern in algorithm design,and show that it runs comparably to the two accepted algorithms.

Allocation strategies of carbon,nitrogen and phosphorus following a gradient of wildfire severities

Wildfire is crucial in the regulation of nutrient allocation during the succession of boreal forests.However,the allocation strategies of carbon(C),nitrogen(N)and phosphorus(P)between leaves and fine roots in response to wildfire severities remain poorly studied.We aimed to explore the allocation strategies of C,N and P between leaves and fine roots among different fire severities.We selected four wildfire severities(unburned,low,moderate and high severity)after 10 years recovery in the Great Xing’an Mountains,northeast China,and compared C,N and P concentrations in leaves and fine roots of all species among fire severities using stoichiometry theory and allometric growth equations.Compared with unburned treatment,C concentrations in leaves and fine roots increased at low severity,and leaf N concentration was the greatest at high severity,but the lowest fine root N concentration occurred at high severity.Plant nutrient utilization tended to be P-limited at high fire severity according to the mean value of N:P ratio>16.More importantly,C,N and P allocation strategies between fine roots and leaves changed from allometry to isometry with increasing fire severities,which showed more elements allocated to leaves than to fine roots with increasing fire severities.These changes in patterns suggest that the allocation strategies of elements between leaves and fine roots are of imbalance with the wildfire severity.This study deepens our understanding of nutrient dynamics between plant and soil in ecosystem succession.

Generalized Energy Storage Allocation Strategies for Load Aggregator in Hierarchical Electricity Markets

The uncertainty of user-side resource response will affect the response quality and economic benefit of load aggregator(LA).Therefore,this paper regards the flexible user-side resources as a virtual energy storage(VES),and uses the traditional narrow sense energy storage(NSES)to alleviate the uncertainty of VES.In order to further enhance the competitive advantage of LA in electricity market transactions,the operation mechanism of LA in day-ahead and real-time market is analyzed,respectively.Besides,truncated normal distribution is used to simulate the response accuracy of VES,and the response model of NSES is constructed at the same time.Then,the hierarchical market access index(HMAI)is introduced to quantify the risk of LA being eliminated in the market competition.Finally,combined with the priority response strategy of VES and HMAI,the capacity allocation model of NSES is established.As the capacity model is nonlinear,Monte Carlo simulation and adaptive particle swarm optimization algorithm are used to solve it.In order to verify the effectiveness of the model,the data from PJM market in the United States is used for testing.Simulation results show that the model established can provide the effective NSES capacity allocation strategy for LA to compensate the uncertainty of VES response,and the economic benefit of LA can be increased by 52.2%at its maximum.Through the reasonable NSES capacity allocation,LA is encouraged to improve its own resource level,thus forming a virtuous circle of market competition.