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.

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.

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.

~1H and ^(13)C NMR Assignments for Amlodipine and Risperidone

Aim To investigate the NMR spectroscopy of amlodipine and risperidone.Methods 1D NMR and 2D NMR experimental techniques of gCOSY, gHSQC and gHMBC were wsed. Results Theassignments of the ~1H and ^(13) C NMR data for the two drugs were performed and confirmed by theevidence of J_(HF) and J_(CF). Conclusion The structures of amlodipine and risperidone wereconfirmed by careful analysis of regular 1D and 2D NMR spectroscopy.

A Discrete-Time Stochastic Traffic Assignment Model

A discrete time stochastic traffic assignment model is proposed. The model provides a discrete time description of the variations of flows on a road network during a day or a peak period. The congestion effect at links and link junctions are taken into account. The first in first out principle is enforced on all links at all periods of the day. A stochastic user equilibrium assignment is achieved when the tripmaker is unable to find better travel alternatives. A computational procedure is also presented.

Cross Point Assignment Algorithm Under Consideration of Very Long Nets

A cross point assignment algorithm is proposed under consideration of very long nets (LCPA).It is to consider not only the cost of connection between cross points and pins and the exclusive cost among cross points on the boundary of a global routing cell,but also the cost of displacement among cross points of the same net.The experiment results show that the quality and speed in the following detailed routing are improved obviously,especially for very long nets.

ACS-based resource assignment and task scheduling in grid

To solve the deadlock problem of tasks that the interdependence between tasks fails to consider during the course of resource assignment and task scheduling based on the heuristics algorithm, an improved ant colony system （ACS） based algorithm is proposed. First, how to map the resource assignment and task scheduling （RATS） problem into the optimization selection problem of task resource assignment graph （TRAG） and to add the semaphore mechanism in the optimal TRAG to solve deadlocks are explained. Secondly, how to utilize the grid pheromone system model to realize the algorithm based on ACS is explicated. This refers to the construction of TRAG by the random selection of appropriate resources for each task by the user agent and the optimization of TRAG through the positive feedback and distributed parallel computing mechanism of the ACS. Simulation results show that the proposed algorithm is effective and efficient in solving the deadlock problem.

Traffic-aware static channel assignment algorithm in wireless mesh networks

A channel assignment algorithm with awareness of link traffic is proposed in multi-radio multi-channel wireless mesh networks. First, the physical interference model based on the signal-to-interference-plus-noise ratio and successful transmission condition is described. The model is more suitable for a wireless communication environment than other existing models. Secondly, a pure integer quadratic programming （PIQP） model is used to solve the channel assignment problem and improve the capacity of wireless mesh networks. Consequently, a traffic- aware static channel assignment algorithm（TASC） is designed. The algorithm adopts some network parameters, including the network connectivity, the limitation of the number of radios and the successful transmission conditions in wireless communications. The TASC algorithm can diminish network interference and increase the efficiency of channel assignment while keeping the connectivity of the network. Finally, the feasibility and effectivity of the channel assignment solution are illustrated by the simulation results. Compared witb similar algorithms, the proposed algorithm can increase the capacity of WMNs.

Direct computing methods for turn flows in traffic assignment

Two methods based on a slight modification of the regular traffic assignmentalgorithms are proposed to directly compute turn flows instead of estimating them from link flows orobtaining them by expanding the networks. The first one is designed on the path-turn incidencerelationship, and it is similar to the computational procedure of link flows. It applies to thetraffic assignment algorithms that can provide detailed path structures. The second utilizes thelink-turn incidence relationship and the conservation of flow on links, a law deriving from thisrelationship. It is actually an improved version of Dial's logit assignment algorithm. The proposedapproaches can avoid the shortcomings both of the estimation methods, e. g. Furness's model andFrator's model, and of the network-expanding method in precision, stability and computation scale.Finally, they are validated by numerical examples.

Lazy loading algorithm for traffic assignment of road networks under fixed charge condition

The measures of path charge are important considerations in traffic assignment of road networks. Factors, such as travel time, fixed charge and traffic congestion which affect road users＇ choices of trip paths, are analyzed. Travelers usually decide their trip paths based on their personal habits, preferences and the information at hand. By considering both deterministic and stochastic factors which affect the value of time （VOT） during the process of path choosing, a variational inequality model is proposed to describe the problem of traffic assignment. A lazy loading algorithm for traffic assignment is designed to solve the proposed model, and the calculation steps are given. Numerical experiment results show that compared with the all-or-nothing assignment, the proposed model and the algorithm can provide more optimal traffic assignments for road networks. The results of this study can be used to optimize traffic planning and management.

Survey of the research on dynamic weapon-target assignment problem

The basic concepts and models of weapon-target assignment （WTA） are introduced and the mathematical nature of the WTA models is also analyzed. A systematic survey of research on WTA problem is provided. The present research on WTA is focused on models and algorithms. In the research on models of WTA, the static WTA models are mainly studied and the dynamic WTA models are not fully studied in deed. In the research on algorithms of WTA, the intelligent algorithms are often used to solve the WTA problem. The small scale of static WTA problems has been solved very well, however, the large scale of dynamic WTA problems has not been solved effectively so far. Finally, the characteristics of dynamic WTA are analyzed and directions for the future research on dynamic WTA are discussed.

Strategies for Effective Comments on Students’ Written Assignments

Abstract:Comments on students’assignments are important for English course.The loopholes in teachers’working schemes-comments of their students’written assignments are the clues around which the paper spreads.In the process of teaching,some English teachers pay so much attention to the contents taught in class but ignore the comments on students’written assignments.Consequently,it is not applicable for students to correct their mistakes in time and they probably make the same mistakes repeatedly.

Weapon target assignment problem satisfying expected damage probabilities based on ant colony algorithm

A weapon target assignment （WTA） model satisfying expected damage probabilities with an ant colony algorithm is proposed. In order to save armament resource and attack the targets effectively, the strategy of the weapon assignment is that the target with greater threat degree has higher priority to be intercepted. The effect of this WTA model is not maximizing the damage probability but satisfying the whole assignment result. Ant colony algorithm has been successfully used in many fields, especially in combination optimization. The ant colony algorithm for this WTA problem is described by analyzing path selection, pheromone update, and tabu table update. The effectiveness of the model and the algorithm is demonstrated with an example.

Assignment and expression patterns of porcine muscle-specific isoform of phosphoglycerate mutase gene

It has been reported that the muscle-specific isoform （type M, PGAM2） of phosphoglycerate mutase （PGAM） is a housekeeping enzyme; it catalyzes the conversion of 3-phosphoglycerate into 2-phosphoglycerate in the glycolysis process to release energy. It is encoded by the Pgam2 gene. In this study, the cDNA of the porcine Pgam2 was cloned. This gene contains an open reading frame of 765 bp encoding a protein of 253 residues, and the predicted protein sequences share high similarity with other mammalians, 96% identity with humans, and 94% identity with mouse and rats. Pgam2 was mapped to SSC18q13-q21 by the RH panel. In this region, there are several QTLs, such as fat ratio, lean percentage, and diameter of muslce fiber, which affect meat production and quality. The reverse transcriptase-polymerase chain reaction revealed that the porcine Pgam2 gene was mainly expressed in the muscle tissue （skeletal muscle and cardiac muscle）, and was expressed highly at skeletal muscle development stages （embryonic periods： 33, 65, and 90 days post-conception （dpo）; postnatal pigs： 4 days and adult）. This indicates that the Pgam2 gene plays an important role in muscle growth and development. In addition, it was demonstrated that PGAM2 locates both in cytoplasm and nuclei, and takes part in the glycometabolism process of cytoplasm and nuclei.

Dynamic Weapon Target Assignment Based on Intuitionistic Fuzzy Entropy of Discrete Particle Swarm

Aiming at the problems of convergence-slow and convergence-free of Discrete Particle Swarm Optimization Algorithm(DPSO) in solving large scale or complicated discrete problem, this article proposes Intuitionistic Fuzzy Entropy of Discrete Particle Swarm Optimization(IFDPSO) and makes it applied to Dynamic Weapon Target Assignment(WTA). First, the strategy of choosing intuitionistic fuzzy parameters of particle swarm is defined, making intuitionistic fuzzy entropy as a basic parameter for measure and velocity mutation. Second, through analyzing the defects of DPSO, an adjusting parameter for balancing two cognition, velocity mutation mechanism and position mutation strategy are designed, and then two sets of improved and derivative algorithms for IFDPSO are put forward, which ensures the IFDPSO possibly search as much as possible sub-optimal positions and its neighborhood and the algorithm ability of searching global optimal value in solving large scale 0-1 knapsack problem is intensified. Third, focusing on the problem of WTA, some parameters including dynamic parameter for shifting firepower and constraints are designed to solve the problems of weapon target assignment. In addition, WTA Optimization Model with time and resource constraints is finally set up, which also intensifies the algorithm ability of searching global and local best value in the solution of WTA problem. Finally, the superiority of IFDPSO is proved by several simulation experiments. Particularly, IFDPSO, IFDPSO1~IFDPSO3 are respectively effective in solving large scale, medium scale or strict constraint problems such as 0-1 knapsack problem and WTA problem.

Eigenstructure assignment in a class of second-order dynamic systems

Eigenstructure assignment using the proportional-plus-derivative feedback controller in a class of secondorder dynamic system is investigated. Simple, general, complete parametric expressions for both the closed-loop eigenvector matrix and the feedback gains are established based on two simple Smith form reductions. The approach utilizes directly the original system data and involves manipulations only on n-dimensional matrices. Furthermore, it reveals all the degrees of freedom which can be further utilized to achieve additional system specifications. An example shows the effect of the proposed approach.

Special secret sharing scheme with the function of assignment

A concept of secret sharing scheme with the function of assignment is proposed. It provides great capabilities for many practical applications. In this scheme, the dealer can randomly assign one or more than one participant to get the secret at any time, but these participants can get nothing about the secret before that moment. At the same time, the other participants cannot get anything about the secret by stealing the secret value when it is transferred. However, if the dealer is lost, a certain number or more partidtmnts of them can reoonstruct the secret by ccoperating. In order to clear this concept, an illustrating scheme with geometry method and a practical scheme with algebra method is given.

Cross Point Assignment Algorithm with Crosstalk Constraint

An algorithm to resolve the coupling effect problem is proposed during the cross point assignment (CPA) stage.In the algorithm,the priority queue concept and the rip-up and reroute strategy are combined to control crosstalk noise caused by interconnect coupling capacitance.First,the nets are arranged into different priority queues according to their weighted sum of their length and criticality.Then,the CPA problem for one queue of nets is translated into a linear assignment problem.After the assignment of one queue of nets,a post-CPA checking routine is performed to check and rip up the net pairs which violate the crosstalk noise constraint and then push them into the next queue to be reassigned.The algorithm is tested by a set of bench mark examples,and the experimental results are promising...

Colony location algorithm for assignment problems

A novel algorithm called Colony Location Algorithm (CLA) is proposed. It mimics the phenomena in biotic community that colonies of species could be located in the places most suitable to their growth. The factors working on the species location such as the nutrient of soil, resource competition between species, growth and decline process, and effect on environment were considered in CLA via the nutrient function, growth and decline rates, environment evaluation and fertilization strategy. CLA was applied to solve the classical assignment problems. The computation results show that CLA can achieve the optimal solution with higher possibility and shorter running time.