^(1)H and ^(13)C NMR spectral assignments for low-concentration bile acids in biological samples

Bile acids are the main body of enterohepatic circulation in vivo.They have essential functions such as emulsifying fat,bacteriostasis and regulating multiple metabolic pathways as signal molecules.However,the assignments of NMR signals for some lowconcentration bile acids are still needed.This study combined 1D nuclear magnetic resonance(NMR)and 2D NMR techniques including 1He1H correlation spectroscopy(COSY),1He1H total correlation spectroscopy(TOCSY),1H J-resolved spectroscopy(J-Res),1He13C heteronuclear single quantum coherence spectroscopy(HSQC),and 1He13C heteronuclear multiple bond correlation spectroscopy(HMBC)to assign the 1H and 13C signals of six bile acids in aqueous solution at physiological pH(~7.4)and nine bile acids in methanol.These data are of importance to the NMR-based studies on lipid digestion,absorption,and metabolism.

Configuration and Spin Assignments of Yrast Band for Doubly Odd Nucleus ^(122)Cs

Energy spectrum of yrast band for ^(122)Cs is studied by using particle-rotor model(PRM)of odd-odd nuclei,in which special attention is paid to the model basis accounting for the effect of the instabilityγvibration perturbation around axial symmetry.In order to check the assignments of this band,two calculation schemes are put into practice.The first one is for previousπh11/2 vg7/2 configuration with bandhead spin I_(0)=(6^(-))which was obtained from cranked shell model(CSM)calculation,and the other one is forπh_(11/2) vh_(11/2) configuration with I_(0)=(9^(+))supported by the systematic analyses of experimental data.A qualitative comparison between the present PRM calculation and that of CSM has also been made.The results indicate that,rather thanπh_(11/2) vg_(7/2) I_(0)=(6^(-)),πh_(11/2) vh_(11/2) with I_(0)=(9^(+))is a more reasonable assignment to the yrast band in ^(122)Cs.

Graphs with <i>k</i>-Role Assignments

For a given graph G, a k-role assignment of G is a surjective function ?such that , where N(x) and N(y) are the neighborhoods of x and y, respectively. Furthermore, as we limit the number of different roles in the neighborhood of an individual, we call r a restricted size k-role assignment. When the hausdorff distance between the sets of roles assigned to their neighbors is at most 1, we call r a k-threshold close role assignment. In this paper we study the graphs that have k-role assignments, restricted size k-role assignments and k-threshold close role assignments, respectively. By the end we discuss the maximal and minimal graphs which have k-role assignments.

Strategies for Designing Online Course Assignments: Based on “XuetangX” Course Samples

Assignments are an important tool to evaluate learners’learning effectiveness in online courses.Clarifying assignment design strategies is of great significance for promoting the quality construction of online education courses.This paper uses Bloom’s taxonomy framework revised by Anderson and Krathwohl(2001)as a reference to label the knowledge types and cognitive dimensions in the assignment context of eight courses.Combined with a literature review,a discipline–objective–schedule(DOS)three-dimensional analysis framework based on the achievement of curriculum objectives,the design of chapter schedule,and the heterogeneity of disciplines is constructed to conduct an in-depth analysis of online course assignment design strategies.The research findings show that the online course assignment design strategy has an obvious curriculum objective orientation,follows the gradual learning rule,and presents typical disciplinary differences.The study finds that the current assignment design of online courses has three issues:first,a mismatch between assignment design and curriculum objectives;second,a lack of diversity in assignment formats;and third,insufficient comprehensiveness of some subject assignments.Based on the above discussions,corresponding suggestions are provided.

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。

Test Generation with Unspecified Variable Assignments

ATPG for very large scale integrated circuit designs is an important problem in industry. With the advent of SOC designs, testing and verification of the core-based designs become a challenging problem. This paper presents an algebraic test generation algorithm with unspecified variable assignments. Given a stuck at fault of the circuit with unspecified signals, the proposed algorithm uses a new encoding scheme for unspecified variable assignments, and solves the Boolean satisfiability formula representing the Boolean difference to obtain a test pattern. Experimental results demonstrate the efficiency and feasibility of the proposed algorithm.

Absolute Configurational Assignments of Amino Acid Esters by a CD-Sensitive Malonamide-Linked Zinc Bisporphyrinate Host

A novel malonamide-linked zinc bisporphyrinate[Zn_(2)-1]has been designed and synthesized.UV-vis and NMR spectroscopic studies suggest the molecule aggregates in solution.Such zinc bisporphyrinate is very CD-sensitive when it is mixed with amino acid ethyl esters.The amplitude value of the induced circular dichroism(ICD)is up to ca.1500 L•mol^(−1)•cm^(−1).Further studies by ^(1)H NMR and UV-vis spectroscopies reveal amino acid esters function as monodentate ligands,and[Zn_(2)-1]interacts with amino acid ethyl esters through coordination and hydrogen bonding interactions.

A New Approach to Pointer Analysis for Assignments

Pointer analysis is a technique to identify at compile-time the potential values of the pointer expressions in a program, which promises significant benefits for optimizing and parallelizing compilers. In this paper, a new approach to pointer analysis for assignments is presented. In this approach, assignments are classified into three categories: pointer assignments, structure (union) assignments and normal assignments which don't affect the point-to information. Pointer analyses for these three kinds of assignments respectively make up the integrated algorithm. When analyzing a pointer assignment, a new method called expression expansion is used to calculate both the left targets and the right targets. The integration of recursive data structure analysis into pointer analysis is a significant originality of this paper, which uniforms the pointer analysis for heap variables and the pointer analysis for stack variables. This algorithm is implemented in Agassiz, an analyzing tool for C programs developed by institute of Parallel Processing, Fudan University. Its accuracy and effectiveness are illustrated by experimental data.

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.

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.

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.

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.

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.