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.

Differentially Private Support Vector Machines with Knowledge Aggregation

With the widespread data collection and processing,privacy-preserving machine learning has become increasingly important in addressing privacy risks related to individuals.Support vector machine(SVM)is one of the most elementary learning models of machine learning.Privacy issues surrounding SVM classifier training have attracted increasing attention.In this paper,we investigate Differential Privacy-compliant Federated Machine Learning with Dimensionality Reduction,called FedDPDR-DPML,which greatly improves data utility while providing strong privacy guarantees.Considering in distributed learning scenarios,multiple participants usually hold unbalanced or small amounts of data.Therefore,FedDPDR-DPML enables multiple participants to collaboratively learn a global model based on weighted model averaging and knowledge aggregation and then the server distributes the global model to each participant to improve local data utility.Aiming at high-dimensional data,we adopt differential privacy in both the principal component analysis(PCA)-based dimensionality reduction phase and SVM classifiers training phase,which improves model accuracy while achieving strict differential privacy protection.Besides,we train Differential privacy(DP)-compliant SVM classifiers by adding noise to the objective function itself,thus leading to better data utility.Extensive experiments on three high-dimensional datasets demonstrate that FedDPDR-DPML can achieve high accuracy while ensuring strong privacy protection.

Utility and Application of a Versatile Analytical Method for MMF Calculation in AC Machines

A versatile analytical method(VAM) for calculating the harmonic components of the magnetomotive force(MMF) generated by diverse armature windings in AC machines has been proposed, and the versatility of this method has been established in early literature. However, its practical applications and significance in advancing the analysis of AC machines need further elaboration. This paper aims to complement VAM by augmenting its theory, offering additional insights into its conclusions, as well as demonstrating its utility in assessing armature windings and its application of calculating torque for permanent magnet synchronous machines(PMSM). This work contributes to advancing the analysis of AC machines and underscores the potential for improved design and performance optimization.

Electromagnetic Performance Analysis of Variable Flux Memory Machines with Series-magnetic-circuit and Different Rotor Topologies

In this paper,the electromagnetic performance of variable flux memory(VFM)machines with series-magnetic-circuit is investigated and compared for different rotor topologies.Based on a V-type VFM machine,five topologies with different interior permanent magnet(IPM)arrangements are evolved and optimized under same constrains.Based on two-dimensional(2-D)finite element(FE)method,their electromagnetic performance at magnetization and demagnetization states is evaluated.It reveals that the iron bridge and rotor lamination region between constant PM(CPM)and variable PM(VPM)play an important role in torque density and flux regulation(FR)capabilities.Besides,the global efficiency can be improved in VFM machines by adjusting magnetization state(MS)under different operating conditions.

Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source domain.However,in engineering scenarios,achieving such high-quality label annotation is difficult and expensive.The incorrect label annotation produces two negative effects:1)the complex decision boundary of diagnosis models lowers the generalization performance on the target domain,and2)the distribution of target domain samples becomes misaligned with the false-labeled samples.To overcome these negative effects,this article proposes a solution called the label recovery and trajectory designable network(LRTDN).LRTDN consists of three parts.First,a residual network with dual classifiers is to learn features from cross-domain samples.Second,an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source domain.With the training of relabeled samples,the complexity of diagnosis model is reduced via semi-supervised learning.Third,the adaptation trajectories are designed for sample distributions across domains.This ensures that the target domain samples are only adapted with the pure-labeled samples.The LRTDN is verified by two case studies,in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related machines.The results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.

Recent Advances in Video Coding for Machines Standard and Technologies

To improve the performance of video compression for machine vision analysis tasks,a video coding for machines(VCM)standard working group was established to promote standardization procedures.In this paper,recent advances in video coding for machine standards are presented and comprehensive introductions to the use cases,requirements,evaluation frameworks and corresponding metrics of the VCM standard are given.Then the existing methods are presented,introducing the existing proposals by category and the research progress of the latest VCM conference.Finally,we give conclusions.

Industrial sewing machines:Maximizing productivity in clothing manufacturing

In the fast-paced world of clothing manufacturing,productivity and efficiency are crucial for staying competitive.Industrial sewing machines play a vital role in this context,offering advanced features and capabilities that significantly enhance production output and quality.This article explores the various aspects of industrial sewing machines,their impact on productivity,and the emerging trends that are shaping the future of the clothing manufacturing industry.

新《公司法》若干重要问题解读(笔谈)

新《公司法》修订所涉及的内容非常丰富,修订内容包括公司登记制度、股东出资责任、公司治理制度、公司资本制度、控股股东义务、董监高信义义务等方面。赵旭东教授指出,本次《公司法》修订在公司登记与信息公示制度、股东出资责任和法律后果、公司资本制度、公司治理制度、股东权益保护和控股股东义务这五个关键领域实现了重大突破和创新。周林彬教授集中解析了“国家出资公司组织机构的特别规定”一章,认为新《公司法》的亮点之一是通过新设这一章积极回应了我国国有企业治理的“本土实践”与“中国问题”,给出新时期国有企业治理的“中国方案”,推进了中国特色的国有企业治理制度的设计,重塑了国有企业的规范体系。刘凯湘教授对本次《公司法》修订中与公司资本制度相关的若干条文展开解析,包括限期认缴制、新增有限公司股东出资义务加速到期制度、股东违反出资义务的责任承担规则、董事会的催缴出资义务、股东失权制度、董监高对股东抽逃出资的连带责任、类别股的规定、授权资本制等。赵万一教授深入探讨了这次《公司法》修改的主要特点,认为新《公司法》既坚持了其商法属性,同时又充分考虑了《民法典》的既有制度内容,适当兼顾了与《民法典》之间的制度衔接关系,从而既为科学解决民法与商法的关系提供了理念和制度支持,也为中国式法律制度体系的创建提供了完美的立法实践样本。周友苏教授深入解析了新《公司法》在完善股东出资制度方面增加的股东出资缴纳、股东出资“加速到期”、股东失权、股东出资责任这四条新规定,认为新《公司法》在完善股东出资制度方面增加的这些新规定,积极回应了我国自2013年以来公司数量大幅增长所带来的股东盲目认缴、天价认缴、期限过长的现象突出,损害债权人利益的纠纷案件呈明显上升态势等问题,极大地强化了保护债权人的力度。李建伟教授深入解析了《公司法》修订中与法定代表人制度相关的若干条文,认为新《公司法》关于法定代表人的规定较之以往更加体系化,并有望基本解决困扰公司实践许多年的诸多难题,但其中亦存在不完全规范的问题。

中国式员工持股计划与公司违规:监督者抑或合谋者

以我国员工持股计划重启为背景,以2011—2019年A股上市公司为样本,验证员工持股计划对公司违规的影响。结果显示,员工持股计划降低了公司违规概率与频率。机制检验发现,员工持股计划一方面通过促进中小股东投票与委派董事涉入公司内部治理,另一方面通过信息传递提升独立董事履职效率并增加分析师跟踪。进一步地,区分违规类型后发现,员工持股计划的抑制作用主要存在于信息披露违规与一般违规;区分员工持股计划方案后发现,员工持股规模高、锁定期长、高购买折价、无杠杆、无控股股东担保或兜底、无业绩条件的员工持股计划对公司违规的抑制更显著。经济后果分析发现,员工持股计划对公司违规的监督能够抑制股价崩盘风险。研究结论从公司违规角度验证了员工持股计划的监督者角色,为我国员工持股计划实施效果提供了新的经验证据。

我国农业上市公司资本结构对财务绩效影响研究

农业在我国国民经济中发挥着至关重要的作用,农业上市公司良好的绩效水平对产业结构调整具有一定导向作用,而资本结构既影响企业的财务绩效,也影响农业企业价值的提升。文章以资本结构为切入点,研究我国农业上市公司资本结构与财务绩效之间的关系,选取农业上市公司2017—2022年数据作为研究样本,建立评价我国农业上市公司财务绩效的多元线性回归模型,以揭示其资本结构与财务绩效的关系。研究结果表明,我国农业上市公司的资产负债率与财务绩效之间呈显著负相关关系,流动负债率与财务绩效之间呈线性负相关关系但不显著,第一大股东持股比例与财务绩效之间呈显著正相关关系。根据研究结果,提出优化农业上市公司资本结构,提高企业财务绩效的相关建议。

商业银行设立理财子公司能够实现风险隔离吗?

本文以中国商业银行设立理财子公司为准自然实验,基于2018—2021年84家银行的季度财务数据,采用渐进双重差分模型(DID)考察商业银行设立理财子公司的风险隔离效应。研究发现:设立理财子公司显著降低了母公司商业银行的风险承担,能够实现理财业务与其他业务的风险隔离,且设立时间越长,风险隔离效应越显著。异质性检验和机制分析表明,这种风险隔离效应在城商行与农商行中最弱;“去嵌套、降杠杆”和“去池化、缓错配”是实现风险隔离的主要机制。对母公司商业银行和理财子公司并表的进一步分析发现,设立理财子公司不能降低银行控股集团的整体风险承担,仅能实现风险隔离。这些研究发现对于完善商业银行内部控制和外部监管具有重要启示。

明星分析师关注、企业投资行为与公司价值

企业投资行为对于中国经济高质量发展与“双循环”国家战略实施发挥重要作用。通过手工搜集2016—2021年明星分析师研究报告数据,研究明星分析师对公司投资行为的影响。研究发现,明星分析师通过缓解融资约束、增强股票流动性、解读研发行为,促进企业投资行为,而且明星分析师在管理层持股比例较低、大股东持股比例低、成长性水平较低、公司透明度较低的样本中,结果更加显著,体现其较好的专业胜任能力。进一步分析发现,明星分析师能够解读不同盈利分组的投资行为,不存在明显的选择性偏好;与没有明星分析师跟踪的样本相比,明星分析师跟踪对投资行为具有更强的影响;明星分析师通过正向解读投资行为途径,对公司价值挖掘,发挥积极作用。研究结论对发挥明星分析师信息中介作用,优化投资结构与提升投资绩效,提供经验参考。

系统重要性金融科技公司的法律规制

随着金融科技的加速应用,金融科技的系统性风险已成为监管重点。金融科技巨头以关键技术服务商、金融基础设施和互联网金融控股集团等样态深度参与金融活动,在规模性、复杂性、可替代性和关联性等方面开始具备系统重要性特征,成为金融科技时代的“大而不能倒”。有必要参考系统重要性金融机构的监管经验,并结合金融科技公司系统性风险的生成机理,对系统重要性金融科技公司予以特殊规制。在有效识别系统重要性金融科技公司的基础上,整合金融监管、数据治理、网络安全和市场竞争等四大规制场域,建立以协调性为目标的监管体制、以可靠性为目标的审慎监管体系、以可承受性为目标的风险处置机制,构建系统重要性金融科技公司的监管框架和规制方案,有效防范其运行失灵给金融稳定带来的冲击。

地区空气污染的“压力”与企业绿色转型的“动力”——基于城市PM2.5和公司并购的实证发现

地区空气污染会影响域内各类市场主体行为,包括微观企业决策。碳达峰、碳中和的提出要求各地加强空气污染治理,推动企业绿色转型,而绿色并购是企业绿色转型的重要方式。利用2013—2018年我国A股上市的污染企业发起的并购为样本,并手动搜集整理确定绿色并购样本,实证研究地区空气污染对域内企业绿色转型的影响。研究发现,地区空气污染加剧会提升域内污染企业绿色并购的意愿。在此基础上,通过并购前后企业信息披露质量和社会责任承担变化,研究发现地区空气污染加剧下的企业绿色转型并非源于自主绿色转型发展的内生“动力”,更多是外部“压力”下的“工具主义”行为。从影响路径来看,地区空气污染引致的压力会通过行业竞争和融资约束这两个途径影响污染企业绿色转型的“动力”。最后,异质性检验发现在市场化程度高和非国有企业样本,地区空气污染加剧对企业绿色并购的促进作用更为显著。

基础研究对中国上市公司技术创新的作用研究

企业在创新链与产业链融合中具有重要作用。从企业视角研究基础研究对技术创新的作用,有助于理解企业参与基础研究的重要性,掌握创新链与产业链融合规律,制定有效的科技创新政策。本文从科学关联度、专利引文时滞、科学循环周期、学科多样性等角度构建分析指标,并以上市公司为例展开实证分析。研究发现:①上市公司技术创新对基础研究成果的吸收较少;②领域内龙头企业技术创新能力强,基础研究的支撑作用显著;③科学关联度越高的产业领域,基础研究成果向技术创新转化的速度越慢;④基础研究向技术创新传递知识的速度逐渐加快;⑤高新技术产业领域的学科多样性高于其他传统产业领域。

论公司法规制国家出资公司的口径与路径

新修订的公司法增加了第七章“国家出资公司组织机构的特别规定”,实现公司法对国家出资公司的规制体例编排“从节到章”、条款数量“从少到多”以及适用范围“从国有独资到国家出资”的重要进步,相当程度上克服了原公司法中存在的规制理念陈旧、口径局限、路径紊乱等缺陷。但新公司法仍存在规制口径模糊、组织规范制度供给不足等痼疾,需要进一步校准国家出资公司的规制口径,将国有独资公司的全资子公司纳入其中,保证规制逻辑的周延性;继续改进规制路径与立法技术,通过引致技术简化关联部门法的重复性规定,增加具有制度特色的治理规则供给,包括完善党组织条款,细化外部董事条款,鼓励并引导国有控股公司章程设置赋权性的差异化治理条款,以提升公司法的适应性品格。

新《公司法》背景下的审计委员会研究:综述与展望

审计委员会是公司治理结构中的关键部门,其履职效能广受关注。新修订的《公司法》实现了审计委员会的职能入法,弥补了审计委员会履职依据在顶层制度方面的缺失,为审计委员会研究赋予了新的研究背景。本文以企业审计委员会相关研究文献为分析对象,通过构建审计委员会特征与治理功能的二维框架来整合已有研究,结果发现:已有研究主要集中于检验审计委员会个人与群体特征能否实现预期治理功能,对预期外治理功能的探索不足;关系网络与兼任、独立董事视角与透明度研究等新兴研究视角普遍落脚于信息披露,落脚于内部控制与外部审计的研究不足。此外,本文还围绕审计委员会成员面临的关系约束与履职困境、多个会计专家的聘用动机与审计委员会中独立董事的咨询职能进行研究展望。

Slope stability prediction based on a long short-term memory neural network:comparisons with convolutional neural networks,support vector machines and random forest models

The numerical simulation and slope stability prediction are the focus of slope disaster research.Recently,machine learning models are commonly used in the slope stability prediction.However,these machine learning models have some problems,such as poor nonlinear performance,local optimum and incomplete factors feature extraction.These issues can affect the accuracy of slope stability prediction.Therefore,a deep learning algorithm called Long short-term memory(LSTM)has been innovatively proposed to predict slope stability.Taking the Ganzhou City in China as the study area,the landslide inventory and their characteristics of geotechnical parameters,slope height and slope angle are analyzed.Based on these characteristics,typical soil slopes are constructed using the Geo-Studio software.Five control factors affecting slope stability,including slope height,slope angle,internal friction angle,cohesion and volumetric weight,are selected to form different slope and construct model input variables.Then,the limit equilibrium method is used to calculate the stability coefficients of these typical soil slopes under different control factors.Each slope stability coefficient and its corresponding control factors is a slope sample.As a result,a total of 2160 training samples and 450 testing samples are constructed.These sample sets are imported into LSTM for modelling and compared with the support vector machine(SVM),random forest(RF)and convo-lutional neural network(CNN).The results show that the LSTM overcomes the problem that the commonly used machine learning models have difficulty extracting global features.Furthermore,LSTM has a better prediction performance for slope stability compared to SVM,RF and CNN models.

反向刺破一人公司面纱的现行法规则

法答网问题:一人有限责任公司的股东不能证明公司财产独立于股东自己的财产,股东应当对公司债务承担连带责任。在此种情形下,一人公司应否对公司股东的债务承担连带责任问题?一、简单的理论介绍一人公司不能证明公司财产独立于股东自己的财产的情形,如果是公司的债权人向公司主张承担债务责任,并追加股东承担连带责任,称之为“正向刺破公司面纱”;如果属于股东的债权人向股东主张承担债务责任,追加公司承担连带责任,称之为“反向刺破公司面纱”。