Collective Molecular Machines: Multidimensionality and Reconfigurability

Molecular machines are key to cellular activity where they are involved in converting chemical and light energy into efficient mechanical work.During the last 60 years,designing molecular structures capable of generating unidirectional mechanical motion at the nanoscale has been the topic of intense research.Effective progress has been made,attributed to advances in various fields such as supramolecular chemistry,biology and nanotechnology,and informatics.However,individual molecular machines are only capable of producing nanometer work and generally have only a single functionality.In order to address these problems,collective behaviors realized by integrating several or more of these individual mechanical units in space and time have become a new paradigm.In this review,we comprehensively discuss recent developments in the collective behaviors of molecular machines.In particular,collective behavior is divided into two paradigms.One is the appropriate integration of molecular machines to efficiently amplify molecular motions and deformations to construct novel functional materials.The other is the construction of swarming modes at the supramolecular level to perform nanoscale or microscale operations.We discuss design strategies for both modes and focus on the modulation of features and properties.Subsequently,in order to address existing challenges,the idea of transferring experience gained in the field of micro/nano robotics is presented,offering prospects for future developments in the collective behavior of molecular machines.

Using microscopic video data measures for driver behavior analysis during adverse winter weather:opportunities and challenges

This paper presents a driver behavior analysis using microscopic video data measures including vehicle speed, lane-changing ratio, and time to collision. An analytical framework was developed to evaluate the effect of adverse winter weather conditions on highway driving behavior based on automated （computer） and manual methods. The research was conducted through two case studies. The first case study was conducted to evaluate the feasibility of applying an au- tomated approach to extracting driver behavior data based on 15 video recordings obtained in the winter 2013 at three dif- ferent locations on the Don Valley Parkway in Toronto, Canada. A comparison was made between the automated approach and manual approach, and issues in collecting data using the automated approach under winter conditions were identified. The second case study was based on high quality data collected in the winter 2014, at a location on Highway 25 in Montreal, Canada. The results demonstrate the effectiveness of the automated analytical framework in analyzing driver behavior, as well as evaluating the impact of adverse winter weather conditions on driver behavior. This approach could be applied to evaluate winter maintenance strategies and crash risk on highways during adverse winter weather conditions.

Simulation of Crowd Motion Based on Boids Flocking Behavior and Social Force Model

In the process of crowd movement,pedestrians are often affected by their neighbors.In order to describe the consistency of adjacent individuals and collectivity of a group,this paper learns from the rules of the flocking behavior,such as segregation,alignment and cohesion,and proposes a method for crowd motion simulation based on the Boids model and social force model.Firstly,the perception area of individuals is divided into zone of segregation,alignment and cohesion.Secondly,the interactive force among individuals is calculated based upon the zone information,velocity vector and the group information.The interactive force among individuals is the synthesis of three forces:the repulsion force to avoid collisions,the alignment force to keep consistent with the velocity direction,and the attractive force to get close to the members of group.In segregation and alignment areas,the repulsion force and alignment force among pedestrians are limited by visual field factors.Finally,the interactive force among individuals,the driving force of destination and the repulsion force of obstacles work together to drive the behavior of crowd motion.The simulation results show that the proposed method can not only effectively simulate the interactive behavior between adjacent individuals but also the collective behavior of group.

NFT数字藏品交易行为法律属性认定及规制路径

NFT数字藏品是经过区块链技术处理的,以数字化形态存在的文件,具有唯一性、无法篡改性与可交易性。其法律属性及相关交易行为性质仍存争议,“发行说”忽视了技术对法律属性的影响,区块链技术消弭了作品的非排他性与非竞争性,故交易链上的NFT数字藏品无需由《著作权法》保护;“物权说”忽略了“物”应有的独立性。NFT数字藏品应为债权凭证。基于此,将与NFT数字藏品有关的行为,以交易链条与行为对象特性为界分,分为链上行为与链下行为,链上行为受民法调整,链下行为由著作权法规制。这既能够保证法律的统一性与可期待性,又能抑制著作权不当扩张,推动NFT数字藏品市场的健康发展。

大数据驱动的税收征管与非上市公司高质量发展——基于创新行为的研究视角

作为我国经济发展的重要力量,非上市公司创新行为是社会技术进步不可或缺的推动力。利用独特的全国税收调查数据,以金税三期工程在我国各省市的逐步试点作为政策冲击,探讨大数据驱动的税收征管是否以及如何影响非上市公司的创新行为。实证研究发现,在金税三期工程试点实施后,非上市公司加大了创新投入。机制检验发现,这种影响主要是通过大数据税收征管的公司治理效应和信息质量效应获得实现。异质性检验发现,大数据税收征管对非上市公司创新投入的影响与金税三期工程实施的客观条件和主观能动性,以及企业融资约束程度密切相关。进一步分析发现,在金税三期工程试点实施后,非上市公司的创新产出有了明显提升。

平行行为的反垄断规制

长期以来,传统寡头市场中大企业协调一致的平行行为的反垄断规制一直困难重重,也由此产生了一系列相互交错的制度工具。这些制度工具包含多种相互关联的基本概念,也反映了反垄断规制对于平行行为的复杂的理论立场。随着算法时代的来临,反垄断法将面对数量更多、更为稳固的平行行为。必须进一步将缺乏合意证据的“协议”、滥用共同支配地位行为、算法合谋等视为一个同源的整体——平行行为,并明确反垄断法相应的制度工具及其发展变化,才能更好地实现对平行行为的规制。

基于感知价值的数字藏品用户购买行为研究

元宇宙概念的兴起促使数字藏品市场迅速发展。但用户对数字藏品关注虽多,然而实际购买行为较少。基于感知价值理论,研究了数字藏品用户购买行为的影响因素。结果显示,功能价值(功能质量、价格效用)、情感价值(感知愉悦)、社会价值(社会自我形象)、技术信任对用户购买意向具有显著的正向影响,社会影响对购买意向的影响不显著。研究结果显示数字藏品平台需要关注用户的感知价值,从而促进其购买行为,确保数字藏品市场的持续快速发展。

数字化转型视野下教师网络集体研修行为投入的效能识别

数字化转型对教师队伍的集群化发展提出了新要求,教师网络集体研修因此得到重视。对研修行为投入的效能识别,有助于把握不同教师群体在行为投入和效能上的关联特征规律,从而进行差异化干预,以优化研修成效。为此,文章参考国内外相关研究成果,设计了“明确总体方案→行为投入编码→基于序列分析的模型构建→基于二次序列分析的模式讨论→提出干预建议”的研究流程。之后,文章以参与“在线教研共同体”公益研修活动的36名中小学教师为研究对象,按照前述研究流程,首先对教师网络集体研修行为投入进行编码;接着进行聚类分析和相关分析,构建教师网络集体研修行为投入的效能层次模型;随后进行二次序列分析,得到五种类型教师的差异化研修行为投入序列转化模式,分别定义为风险投入型、游离投入型、顺应投入型、自主投入型和综合投入型,其中综合投入型教师的集体效能最高;最后面向非综合投入型教师提出干预建议,以推动深投入、强效能的网络集体研修生态形成。文章的研究对于指导教师优化研修行为投入、提升网络集体研修成效有一定的价值,并可为数字化转型视野下的教师网络集体研修实践提供参考。

SOFT CONTROL ON COLLECTIVE BEHAVIOR OF A GROUP OF AUTONOMOUS AGENTS BY A SHILL AGENT

This paper asks a new question： how can we control the collective behavior of self-organized multi-agent systems？ We try to answer the question by proposing a new notion called ＇Soft Control＇ which keeps the local rule of the existing agents in the system. We show the feasibility of soft control by a case study. Consider the simple but typical distributed multi-agent model proposed by Vicsek et al. for flocking of birds： each agent moves with the same speed but with different headings which are updated using a local rule based on the average of its own heading and the headings of its neighbors. Most studies of this model are about the self-organized collective behavior, such as synchronization of headings. We want to intervene in the collective behavior （headings） of the group by soft control. A specified method is to add a special agent, called a ＇Shill＇, which can be controlled by us but is treated as an ordinary agent by other agents. We construct a control law for the shill so that it can synchronize the whole group to an objective heading. This control law is proved to be effective analytically and numerieally. Note that soft control is different from the approach of distributed control. It is a natural way to intervene in the distributed systems. It may bring out many interesting issues and challenges on the control of complex systems.

A dynamic model of skeletal muscle based on collective behavior of myosin motors-Biomechanics of skeletal muscle based on working mechanism of myosin motors(I)

A dynalnic model of skeletal muscle is developed to describe its activation kinetics and contraction dynamics based on the collective working mechanism of myosin II motors with a statistical mechanics method. According to the structure of sar- comeres arranged in series and in parallel, the mechanical properties of skeletal muscle are studied. This model reveals the re- lations between action potential and muscle characteristics. It is shown that calcium concentration in sarcoplasmic （SP） in- creases linearly with the increasing stimulation frequency and gradually reaches saturation. Active force and contraction veloc- ity follow the trend of calcium concentration and reach a peak value at the maximum stimulation frequency. Contraction ve- locity is inversely proportional to the load while the contraction power increases to maximum and then reduces to zero with the increasing load. These properties are consistent with published physiological experimental results of skeletal muscle.

Four development stages of collective intelligence

The new generation of artificial intelligence(AI)research initiated by Chinese scholars conforms to the needs of a new information environment changes,and strives to advance traditional artificial intelligence(AI 1.0)to a new stage of AI 2.0.As one of the important components of AI,collective intelligence(CI 1.0),i.e.,swarm intelligence,is developing to the stage of CI 2.0(crowd intelligence).Through in-depth analysis and informative argumentation,it is found that an incompatibility exists between CI 1.0 and CI 2.0.Therefore,CI 1.5 is introduced to build a bridge between the above two stages,which is based on biocollaborative behavioral mimicry.CI 1.5 is the transition from CI 1.0 to CI 2.0,which contributes to the compatibility of the two stages.Then,a new interpretation of the meta-synthesis of wisdom proposed by Qian Xuesen is given.The meta-synthesis of wisdom,as an improvement of crowd intelligence,is an advanced stage of bionic intelligence,i.e.,CI 3.0.It is pointed out that the dual-wheel drive of large language models and big data with deep uncertainty is an evolutionary path from CI 2.0 to CI 3.0,and some elaboration is made.As a result,we propose four development stages(CI 1.0,CI 1.5,CI 2.0,and CI 3.0),which form a complete framework for the development of CI.These different stages are progressively improved and have good compatibility.Due to the dominant role of cooperation in the development stages of CI,three types of cooperation in CI are discussed:indirect regulatory cooperation in lower organisms,direct communicative cooperation in higher organisms,and shared intention based collaboration in humans.Labor division is the main form of achieving cooperation and,for this reason,this paper investigates the relationship between the complexity of behavior and types of labor division.Finally,based on the overall understanding of the four development stages of CI,the future development direction and research issues of CI are explored.

造锍捕金过程中金在熔锍和熔渣之间的分配行为

针对难处理金矿日益增多,造锍捕金法难以实现高效率捕金的问题,本研究通过高温平衡实验,研究温度、熔渣中m(Fe)/m(SiO_(2))和CaO含量等因素对熔锍捕金率的影响,探究不同反应时间金在熔锍和熔渣之间的分配行为。结果表明,当熔炼温度由1250℃升至1300℃时,熔锍的品位及捕金率均得到明显改善;熔锍品位(以Cu含量表示,下同)及捕金率均随m(Fe)/m(SiO_(2))和CaO含量的增加而降低。当m(Fe)/m(SiO_(2))=0.86、CaO含量为4.00%时,熔锍的捕金率高达96.54%,并且高温熔炼反应0.5 h后,金即向熔锍中迁移,直至达到平衡态。

Imaging the initial condition of heavy-ion collisions and nuclear structure across the nuclide chart

High-energy nuclear collisions encompass three key stages:the structure of the colliding nuclei,informed by low-energy nuclear physics,the initial condition,leading to the formation of quark-gluon plasma(QGP),and the hydrodynamic expansion and hadronization of the QGP,leading to fnal-state hadron distributions that are observed experimentally.Recent advances in both experimental and theoretical methods have ushered in a precision era of heavy-ion collisions,enabling an increasingly accurate understanding of these stages.However,most approaches involve simultaneously determining both QGP properties and initial conditions from a single collision system,creating complexity due to the coupled contributions of these stages to the fnal-state observables.To avoid this,we propose leveraging established knowledge of low-energy nuclear structures and hydrodynamic observables to independently constrain the QGP's initial condition.By conducting comparative studies of collisions involving isobar-like nuclei—species with similar mass numbers but diferent ground-state geometries—we can disentangle the initial condition's impacts from the QGP properties.This approach not only refnes our understanding of the initial stages of the collisions but also turns high-energy nuclear experiments into a precision tool for imaging nuclear structures,ofering insights that complement traditional low-energy approaches.Opportunities for carrying out such comparative experiments at the Large Hadron Collider and other facilities could signifcantly advance both highenergy and low-energy nuclear physics.Additionally,this approach has implications for the future electron-ion collider.While the possibilities are extensive,we focus on selected proposals that could beneft both the high-energy and low-energy nuclear physics communities.Originally prepared as input for the long-range plan of U.S.nuclear physics,this white paper refects the status as of September 2022,with a brief update on developments since then.

Phase Transition in a Dense Swarm of Self-Propelled Bots

Swarms of self-organizing bots are becoming important elements in various technical systems,which include the control of bacterial cyborgs in biomedical applications,technologies for creating new metamaterials with internal structure,self-assembly processes of complex supramolecular structures in disordered media,etc.In this work,we theoretically study the effect of sudden fluidization of a dense group of bots,each of which is a source of heat and follows a simple algorithm to move in the direction of the gradient of the global temperature field.We show that,under certain conditions,an aggregate of self-propelled bots can fluidize,which leads to a second-order phase transition.The bots’program,which forces them to search for the temperature field maximum,acts as an effective buoyancy force.As a consequence,one can observe a sudden macroscopic circulation of bots from the edge of the group to its center and back again,which resembles classical Rayleigh-Benard thermal convection.In the continuum approximation,we have developed a mathematical model of the phenomenon,which reduces to the equation of a self-gravitating porous disk saturated with an incompressible fluid that generates heat.We derive governing equations in the Darcy-Boussinesq approximation and formulate a nonlinear boundary value problem.An exact solution to the linearized problem for infinitesimal perturbations of the base state is obtained,and the critical values of the control parameter for the onset of the bot circulation are calculated.Then we apply weakly nonlinear analysis using the method of multiple time scales.We found that as the number of bots increases,the swarm exhibits increasingly complex patterns of circulation.

面向图书馆数字资源的用户信息行为采集系统设计与实现

随着信息化技术的普及,数字资源已成为图书馆馆藏资源建设的核心部分。选择合适的数字资源供应商和向读者提供定制化、精准的阅读服务显得尤为关键。而获取用户在使用数字资源时的信息行为数据,是实现这一目标的基石。为解决现有研究在采集用户行为数据时消耗巨大存储空间和算力以及不够及时的局限,文章提出基于Vertx的反向代理机制的用户信息行为采集系统,旨在精准化的进行用户信息行为采集,解决算力、存储和实时性的局限。

基于行为检测的叉车安全监控系统的设计与实现

随着工程应用场景中叉车数量的激增,船舶修造企业叉车伤人安全事故多发频发。视频监控和人工智能技术的日趋成熟为减少叉车安全事故的发生提供了一条新的思路。文章设计并实现了一套基于行为检测的叉车安全监控系统,该系统通过智能可视化的方式对叉车的各种不安全行为进行检测,并对驾驶人员预警提示,从而大大降低船舶修造企业叉车安全事故发生率,同时也为其它工程机械提供了一种可行的参考借鉴方案。

Using multilayer network analysis to explore the temporal dynamics of collective behavior

Social organisms often show collective behaviors such as group foraging or movement.Collective behaviors can emerge from interactions between group members and may depend on the behavior of key individuals.When social interactions change over time,collective behaviors may change because these behaviors emerge from interactions among individuals.Despite the importance of,and growing interest in,the temporal dynamics of social interactions,it is not clear how to quantify changes in interactions over time or measure their stability.Furthermore,the temporal scale at which we should observe changes in social networks to detect biologically meaningful changes is not always apparent.Here we use multilayer network analysis to quantify temporal dynamics of social networks of the social spider Stegodyphus dumicola and determine how these dynamics relate to individual and group behaviors.We found that social interactions changed over time at a constant rate.Variation in both network structure and the identity of a keystone individual was not related to the mean or variance of the collective prey attack speed.Individuals that maintained a large and stable number of connections,despite changes in network structure,were the boldest individuals in the group.Therefore,social interactions and boldness are linked across time,but group collective behavior is not influenced by the stability of the social network.Our work demonstrates that dynamic social networks can be modeled in a multilayer framework.This approach may reveal biologically important temporal changes to social structure in other systems.

Chase,Pounce,and Escape Optimization Algorithm

While many metaheuristic optimization algorithms strive to address optimization challenges,they often grapple with the delicate balance between exploration and exploitation,leading to issues such as premature convergence,sensitivity to parameter settings,and difficulty in maintaining population diversity.In response to these challenges,this study introduces the Chase,Pounce,and Escape(CPE)algorithm,drawing inspiration from predator-prey dynamics.Unlike traditional optimization approaches,the CPE algorithm divides the population into two groups,each independently exploring the search space to efficiently navigate complex problem domains and avoid local optima.By incorporating a unique search mechanism that integrates both the average of the best solution and the current solution,the CPE algorithm demonstrates superior convergence properties.Additionally,the inclusion of a pouncing process facilitates rapid movement towards optimal solutions.Through comprehensive evaluations across various optimization scenarios,including standard test functions,Congress on Evolutionary Computation(CEC)-2017 benchmarks,and real-world engineering challenges,the effectiveness of the CPE algorithm is demonstrated.Results consistently highlight the algorithm’s performance,surpassing that of other well-known optimization techniques,and achieving remarkable outcomes in terms of mean,best,and standard deviation values across different problem domains,underscoring its robustness and versatility.

基于Python的社交网站用户行为数据采集方法

传统数据采集方法存在适用范围较窄、重复性工作量较大等问题,导致社交网站用户行为数据采集效率较差,提出基于Python的社交网站用户行为数据采集方法。采用情境标记法确定社交网站用户行为数据采集时机,基于Python语言搭建一个以MJU采样算法为URL地址管理中心的Scrapy爬虫框架,执行Scrapy爬虫框架完成社交网站用户行为数据的采集流程。实验结果表明,本文方法在采集社交网站的用户行为数据时,采集速率为830个/h,验证了该方法具有快速性。

信贷经济下催收行业法律规制困境剖析

信贷经济规模化运作下,催收行业发展迅速,已形成一套相对成熟的业务操作模式。催收既能帮助债权人回收逾期款项,又能修复债务人信用,应加以合理运用。受法律规范滞后性影响,暴力、胁迫等违规催收事件频发。对此,可围绕催收机构设置组织类型、资本最低限额等准入条件,并将催收范畴界定为法律主体基于合法交易产生,可以货币作价估量的债务。此外,还应严格规范催收行为,落实相应责任,充分发挥行业自律与行政监管效能。