IMPROVED MODEL OF MULTIPATH TRAFFIC ASSIGNENT AND NODE ASSIGNENT ALGORITHM

In the paper, multipeth traffic assignment model is improved, a quick aI8orithm,Nede Assignment Algorithm, of this medel is presented, and some problems such as dispolof network, arransement of assigned nodes and determination of parameters are discussed

Channel assignment and power allocation for throughput improvement with PPO in B5G heterogeneous edge networks

In Beyond the Fifth Generation(B5G)heterogeneous edge networks,numerous users are multiplexed on a channel or served on the same frequency resource block,in which case the transmitter applies coding and the receiver uses interference cancellation.Unfortunately,uncoordinated radio resource allocation can reduce system throughput and lead to user inequity,for this reason,in this paper,channel allocation and power allocation problems are formulated to maximize the system sum rate and minimum user achievable rate.Since the construction model is non-convex and the response variables are high-dimensional,a distributed Deep Reinforcement Learning(DRL)framework called distributed Proximal Policy Optimization(PPO)is proposed to allocate or assign resources.Specifically,several simulated agents are trained in a heterogeneous environment to find robust behaviors that perform well in channel assignment and power allocation.Moreover,agents in the collection stage slow down,which hinders the learning of other agents.Therefore,a preemption strategy is further proposed in this paper to optimize the distributed PPO,form DP-PPO and successfully mitigate the straggler problem.The experimental results show that our mechanism named DP-PPO improves the performance over other DRL methods.

Development of SNP parentage assignment techniques in the yellowfin seabream Acanthopagrus latus

Acanthopagrus latus is an essential aquaculture species on the south coast of China.However,there is a lack of systematic breeding of A.latus,which considerably limits the sustainable development of A.latus.As a result,genetic improvements are urgently needed to breed new strains of A.latus with rapid growth and strong resistance to disease.During selective breeding,it is necessary to estimate the genetic parameters of the target trait,which in turn depends on an accurate disentangled pedigree for the selective population.Therefore,it is necessary to establish the parentage assignment technique for A.latus.In this study,95 individuals selected from their parents and their 14 families were used as experimental material.SNPs were developed by genome resequencing,and highly polymorphic SNPs were screened on the basis of optimized filtering parameters.A total of 14392738 SNPs were discovered and 205 SNPs were selected for parentage assignment using the CERVUS software.In the model where the gender of the parents is known,the assignment success rate is 98.61%for the male parent,97.22%for the female parent,and 95.83%for the parent pair.In the model where the gender of the parents is unknown,the assignment success rate is 100%for a single parent and 90.28%for the parent pair.The results of this study were expected to serve as a reference for the breeding of new varieties of A.latus.

A self-organization formation configuration based assignment probability and collision detection

The formation control of multiple unmanned aerial vehicles(multi-UAVs)has always been a research hotspot.Based on the straight line trajectory,a multi-UAVs target point assignment algorithm based on the assignment probability is proposed to achieve the shortest overall formation path of multi-UAVs with low complexity and reduce the energy consumption.In order to avoid the collision between UAVs in the formation process,the concept of safety ball is introduced,and the collision detection based on continuous motion of two time slots and the lane occupation detection after motion is proposed to avoid collision between UAVs.Based on the idea of game theory,a method of UAV motion form setting based on the maximization of interests is proposed,including the maximization of self-interest and the maximization of formation interest is proposed,so that multi-UAVs can complete the formation task quickly and reasonably with the linear trajectory assigned in advance.Finally,through simulation verification,the multi-UAVs target assignment algorithm based on the assignment probability proposed in this paper can effectively reduce the total path length,and the UAV motion selection method based on the maximization interests can effectively complete the task formation.

Reinforcement Learning Based Quantization Strategy Optimal Assignment Algorithm for Mixed Precision

The quantization algorithm compresses the original network by reducing the numerical bit width of the model,which improves the computation speed. Because different layers have different redundancy and sensitivity to databit width. Reducing the data bit width will result in a loss of accuracy. Therefore, it is difficult to determinethe optimal bit width for different parts of the network with guaranteed accuracy. Mixed precision quantizationcan effectively reduce the amount of computation while keeping the model accuracy basically unchanged. In thispaper, a hardware-aware mixed precision quantization strategy optimal assignment algorithm adapted to low bitwidth is proposed, and reinforcement learning is used to automatically predict the mixed precision that meets theconstraints of hardware resources. In the state-space design, the standard deviation of weights is used to measurethe distribution difference of data, the execution speed feedback of simulated neural network accelerator inferenceis used as the environment to limit the action space of the agent, and the accuracy of the quantization model afterretraining is used as the reward function to guide the agent to carry out deep reinforcement learning training. Theexperimental results show that the proposed method obtains a suitable model layer-by-layer quantization strategyunder the condition that the computational resources are satisfied, and themodel accuracy is effectively improved.The proposed method has strong intelligence and certain universality and has strong application potential in thefield of mixed precision quantization and embedded neural network model deployment.

Structural Characteristics and Influencing Factors of Carbon Emission Spatial Association Network:A Case Study of Yangtze River Delta City Cluster,China

City cluster is an effective platform for encouraging regionally coordinated development.Coordinated reduction of carbon emissions within city cluster via the spatial association network between cities can help coordinate the regional carbon emission management,realize sustainable development,and assist China in achieving the carbon peaking and carbon neutrality goals.This paper applies the improved gravity model and social network analysis(SNA)to the study of spatial correlation of carbon emissions in city clusters and analyzes the structural characteristics of the spatial correlation network of carbon emissions in the Yangtze River Delta(YRD)city cluster in China and its influencing factors.The results demonstrate that:1)the spatial association of carbon emissions in the YRD city cluster exhibits a typical and complex multi-threaded network structure.The network association number and density show an upward trend,indicating closer spatial association between cities,but their values remain generally low.Meanwhile,the network hierarchy and network efficiency show a downward trend but remain high.2)The spatial association network of carbon emissions in the YRD city cluster shows an obvious‘core-edge’distribution pattern.The network is centered around Shanghai,Suzhou and Wuxi,all of which play the role of‘bridges’,while cities such as Zhoushan,Ma'anshan,Tongling and other cities characterized by the remote location,single transportation mode or lower economic level are positioned at the edge of the network.3)Geographic proximity,varying levels of economic development,different industrial structures,degrees of urbanization,levels of technological innovation,energy intensities and environmental regulation are important influencing factors on the spatial association of within the YRD city cluster.Finally,policy implications are provided from four aspects:government macro-control and market mechanism guidance,structural characteristics of the‘core-edge’network,reconfiguration and optimization of the spatial layout of the YRD city cluster,and the application of advanced technologies.

不变性原则与Python亮点

针对目前Python教学中“以C语言知识解释Python语法”的倾向及其所导致的一些基本概念错误,回顾“软件危机”发生以来人们在“去变量赋值”副作用和函数式编程两个方面的努力,从不变性原则的高度阐述这些举措对于提高程序质量的意义,在此基础上提出Python程序设计语言的亮点并强调对于回归Python本质的重要性,目的是为进一步学习和应用打下坚实基础。

Glorious Missions Assigned by the Human History

Frontiers of Engineering Management,after two years’preparation,came,in spring2014,to offer its readers,with the mission and the editor inclination to promote the advancement of engineering management,so as to propel the advancement of engineering science and technology.It is a general consensus of the scientific communities that engineering science is

Validity and reliability of the Thai version of the Utrecht Gender Dysphoria Scale-Gender Spectrum(UGDS-GS)in Thai youths and young adults with gender dysphoria

Background Many people who are gender variant have undiagnosed gender dysphoria,resulting in delayed receipt of gender-affirming support and prolonged distress in living with their gender-non-conforming sex.The Utrecht Gender Dysphoria Scale-Gender Spectrum(UGDS-GS)is a newly developed tool that measures dissatisfaction with gender identity and expression.However,there is no translated version of this tool in Thai.Moreover,the sensitivity,specificity and cut-off point of the UGDS-GS to detect gender dysphoria in people who are transgender remain unknown.Aims This study translated the UGDS-GS into Thai and then examined the validity and reliability of the Thai UGDS-GS.Methods 185 participants with and without gender dysphoria were selected from the Gender Variation Clinic in Ramathibodi Hospital and from social media platforms.The UGDS-GS was translated into Thai according to the World Health Organization(WHO)guidelines on translation.The medical records of patients with gender dysphoria and semi-structured interviews were used to confirm the diagnosis of gender dysphoria.Subsequently,the validity and reliability of the instrument were analysed.Results The mean age of participants was 30.43(7.98)years among the 51 assigned males(27.6%)and 134 assigned females(72.4%)at birth.The Thai UGDS-GS average score was 77.82(9.71)for those with gender dysphoria(n=95)and 46.03(10.71)for those without gender dysphoria(n=90).Cronbach’s alpha coefficient was 0.962,showing excellent internal consistency.In addition,exploratory factor analysis showed compatibility with the original version’s metrics.The value of the area under the curve was 0.976(95%confidence interval:0.954 to 0.998),indicating outstanding concordance.At the cut-off point of‘60’,sensitivity and specificity were good(96.84%and 91.11%,respectively).Conclusions The Thai UGDS-GS is an excellent,psychometrically reliable and valid tool for screening gender dysphoria in clinical and community settings in Thailand.The cut-off point of‘60’scores suggests a positive indicator or a high chance of gender dysphoria.

The mean age of gender dysphoria diagnosis is decreasing

INTRODUCTION Gender dysphoria(GD),or gender identity disorder,is defined as persistent distress stemming from the incongruence between one's assigned sex at birth and gender identity.'GD has traditionally been introduced as a rare condition predominant in assigned males at birth(AMABs).2 However,recent studies have shown an upward trend in assigned females at birth(AFABs)with a dramatic reversal of the AMAB:AFAB ratio-.5-The actual AMAB:AFAB ratio varies by age group and study population.4-Questions have been raised concerning the increasing number of youth who seek professional care for GD,especially adolescent AFABs.

On the Validity of the Generic Nomen Liuixalus Li, Che, Bain, Zhao, and Zhang, 2008 (Anura: Rhacophoridae)

Dear Editor,The genus Liuixalus Li,Che,Bain,Zhao,and Zhang,2008 is a group of small-bodied Asian rhacophorid tree frogs and currently includes six species distributed in southern China and northern Vietnam(AmphibiaChina,2022;Pham et al.,2018;Qin et al.,2015).The type species of Liuixalus,L.romeri(Smith,1953),was originally assigned to the genus Chiromantis by Smith(1953)based on morphology.However,using molecular phylogenetic analysis,Li et al.(2008)found that C.romeri formed its own clade with an unidentified tree frog species,which was a sister group of the remaining taxa of the subfamily Rhacophorinae.

Nirogacestat,a γ-Secretase Inhibitor for Desmoid Tumors

Background:Desmoid tumors are rare,locally aggressive,highly recurrent soft-tissue tumors without approved treatments.Methods:We conducted a phase 3,international,double-blind,randomized,placebo-controlled trial of nirogacestat in adults with progressing desmoid tumors according to the Response Evaluation Criteria in Solid Tumors,version 1.1.Patients were assigned in a 1:1 ratio to receive the oralγ-secretase inhibitor nirogacestat(150 mg)or placebo twice daily.The primary end point was progression-free survival.

Task assignment in ground-to-air confrontation based on multiagent deep reinforcement learning

The scale of ground-to-air confrontation task assignments is large and needs to deal with many concurrent task assignments and random events.Aiming at the problems where existing task assignment methods are applied to ground-to-air confrontation,there is low efficiency in dealing with complex tasks,and there are interactive conflicts in multiagent systems.This study proposes a multiagent architecture based on a one-general agent with multiple narrow agents(OGMN)to reduce task assignment conflicts.Considering the slow speed of traditional dynamic task assignment algorithms,this paper proposes the proximal policy optimization for task assignment of general and narrow agents(PPOTAGNA)algorithm.The algorithm based on the idea of the optimal assignment strategy algorithm and combined with the training framework of deep reinforcement learning(DRL)adds a multihead attention mechanism and a stage reward mechanism to the bilateral band clipping PPO algorithm to solve the problem of low training efficiency.Finally,simulation experiments are carried out in the digital battlefield.The multiagent architecture based on OGMN combined with the PPO-TAGNA algorithm can obtain higher rewards faster and has a higher win ratio.By analyzing agent behavior,the efficiency,superiority and rationality of resource utilization of this method are verified.

UAVs cooperative task assignment and trajectory optimization with safety and time constraints

This paper proposes new methods and strategies for Multi-UAVs cooperative attacks with safety and time constraints in a complex environment.Delaunay triangle is designed to construct a map of the complex flight environment for aerial vehicles.Delaunay-Map,Safe Flight Corridor(SFC),and Relative Safe Flight Corridor(RSFC)are applied to ensure each UAV flight trajectory's safety.By using such techniques,it is possible to avoid the collision with obstacles and collision between UAVs.Bezier-curve is further developed to ensure that multi-UAVs can simultaneously reach the target at the specified time,and the trajectory is within the flight corridor.The trajectory tracking controller is also designed based on model predictive control to track the planned trajectory accurately.The simulation and experiment results are presented to verifying developed strategies of Multi-UAV cooperative attacks.

Shuffled frog leaping algorithm with non-dominated sorting for dynamic weapon-target assignment

The dynamic weapon target assignment(DWTA)problem is of great significance in modern air combat.However,DWTA is a highly complex constrained multi-objective combinatorial optimization problem.An improved elitist non-dominated sorting genetic algorithm-II(NSGA-II)called the non-dominated shuffled frog leaping algorithm(NSFLA)is proposed to maximize damage to enemy targets and minimize the self-threat in air combat constraints.In NSFLA,the shuffled frog leaping algorithm(SFLA)is introduced to NSGA-II to replace the inside evolutionary scheme of the genetic algorithm(GA),displaying low optimization speed and heterogeneous space search defects.Two improvements have also been raised to promote the internal optimization performance of SFLA.Firstly,the local evolution scheme,a novel crossover mechanism,ensures that each individual participates in updating instead of only the worst ones,which can expand the diversity of the population.Secondly,a discrete adaptive mutation algorithm based on the function change rate is applied to balance the global and local search.Finally,the scheme is verified in various air combat scenarios.The results show that the proposed NSFLA has apparent advantages in solution quality and efficiency,especially in many aircraft and the dynamic air combat environment.

返还原物请求权与口头辩论终结后的受让人——对积极说的反驳

返还原物判决确定后,其既判力是否应当向口头辩论终结后的标的物受让人扩张?围绕这一问题,日本形成了积极说和消极说。文章首先介绍了两个学说的基本观点,表明自己支持消极说,然后将积极说总结为纯粹失权效说、义务继受说和实体适格继受说,借用四个设例,并区分原告胜诉判决和败诉判决,依次展开批判。最后,讨论了既判力的本质以及既判力与执行力的关系。文章讨论的问题在我国本就有很大争议,伴随着中国民事执行法修法工作的展开,既判力是否应当扩张?执行力扩张与既判力扩张关系如何?扩张的依据又何在?这些问题成为重要的修法议题。而文章详细介绍了日本的相关学说及各学说的正当化依据,是了解日本理论现状的第一手资料,同时文章开创了用既判力冲突的可能性和返还原物请求权的相对性为消极说证成的视角,颇具启发性。

Joint target assignment and power allocation in the netted C-MIMO radar when tracking multi-targets in the presence of self-defense blanket jamming

The netted radar system(NRS)has been proved to possess unique advantages in anti-jamming and improving target tracking performance.Effective resource management can greatly ensure the combat capability of the NRS.In this paper,based on the netted collocated multiple input multiple output(CMIMO)radar,an effective joint target assignment and power allocation(JTAPA)strategy for tracking multi-targets under self-defense blanket jamming is proposed.An architecture based on the distributed fusion is used in the radar network to estimate target state parameters.By deriving the predicted conditional Cramer-Rao lower bound(PC-CRLB)based on the obtained state estimation information,the objective function is formulated.To maximize the worst case tracking accuracy,the proposed JTAPA strategy implements an online target assignment and power allocation of all active nodes,subject to some resource constraints.Since the formulated JTAPA is non-convex,we propose an efficient two-step solution strategy.In terms of the simulation results,the proposed algorithm can effectively improve tracking performance in the worst case.

SiamDLA: Dynamic Label Assignment for Siamese Visual Tracking

Label assignment refers to determining positive/negative labels foreach sample to supervise the training process. Existing Siamese-based trackersprimarily use fixed label assignment strategies according to human priorknowledge;thus, they can be sensitive to predefined hyperparameters and failto fit the spatial and scale variations of samples. In this study, we first developa novel dynamic label assignment (DLA) module to handle the diverse datadistributions and adaptively distinguish the foreground from the backgroundbased on the statistical characteristics of the target in visual object tracking.The core of DLA module is a two-step selection mechanism. The first stepselects candidate samples according to the Euclidean distance between trainingsamples and ground truth, and the second step selects positive/negativesamples based on the mean and standard deviation of candidate samples.The proposed approach is general-purpose and can be easily integrated intoanchor-based and anchor-free trackers for optimal sample-label matching.According to extensive experimental findings, Siamese-based trackers withDLA modules can refine target locations and outperformbaseline trackers onOTB100, VOT2019, UAV123 and LaSOT. Particularly, DLA-SiamRPN++improves SiamRPN++ by 1% AUC and DLA-SiamCAR improves Siam-CAR by 2.5% AUC on OTB100. Furthermore, hyper-parameters analysisexperiments show that DLA module hardly increases spatio-temporal complexity,the proposed approach maintains the same speed as the originaltracker without additional overhead.

An Intelligent Algorithm for Solving Weapon-Target Assignment Problem:DDPG-DNPE Algorithm

Aiming at the problems of traditional dynamic weapon-target assignment algorithms in command decisionmaking,such as large computational amount,slow solution speed,and low calculation accuracy,combined with deep reinforcement learning theory,an improved Deep Deterministic Policy Gradient algorithm with dual noise and prioritized experience replay is proposed,which uses a double noise mechanism to expand the search range of the action,and introduces a priority experience playback mechanism to effectively achieve data utilization.Finally,the algorithm is simulated and validated on the ground-to-air countermeasures digital battlefield.The results of the experiment show that,under the framework of the deep neural network for intelligent weapon-target assignment proposed in this paper,compared to the traditional RELU algorithm,the agent trained with reinforcement learning algorithms,such asDeepDeterministic Policy Gradient algorithm,Asynchronous Advantage Actor-Critic algorithm,Deep Q Network algorithm performs better.It shows that the use of deep reinforcement learning algorithms to solve the weapon-target assignment problem in the field of air defense operations is scientific.In contrast to other reinforcement learning algorithms,the agent trained by the improved Deep Deterministic Policy Gradient algorithm has a higher win rate and reward in confrontation,and the use of weapon resources is more efficient.It shows that the model and algorithm have certain superiority and rationality.The results of this paper provide new ideas for solving the problemof weapon-target assignment in air defense combat command decisions.

Bee Colony Optimization Algorithm for Routing and Wavelength Assignment Based on Directional Guidance in Satellite Optical Networks

With the development of satellite communication,in order to solve the problems of shortage of on-board resources and refinement of delay requirements to improve the communication performance of satellite optical networks,this paper proposes a bee colony optimization algorithm for routing and wavelength assignment based on directional guidance(DBCO-RWA)in satellite optical networks.In D-BCORWA,directional guidance based on relative position and link load is defined,and then the link cost function in the path search stage is established based on the directional guidance factor.Finally,feasible solutions are expanded in the global optimization stage.The wavelength utilization,communication success probability,blocking rate,communication hops and convergence characteristic are simulated.The results show that the performance of the proposed algorithm is improved compared with existing algorithms.