Accurately forecasting gasoline volatility is significant for risk management, economic analysis, and option pricing formulas for future contracts. This study proposes a novel interval-valued hierarchical decomposition and ensemble (IHDE) approach to investigate gasoline price volatility. Our interval-based IHDE method can decompose the complex price process into different components to capture the distinct features of each component, which is helpful for forecasting and analyzing complex price processes. By using interval-valued data, the dynamics of gasoline prices in terms of levels and variations can be fully utilized in this study. Fully utilizing the informational gain of interval-valued data improves forecasting performance. In forecasting weekly gasoline volatility, we document that the proposed IHDE approach outperforms the GARCH, EGARCH, CARR, and ACI models, indicating the importance of capturing features of different frequency components and utilizing the informational gain of interval-valued data for gasoline volatility forecasts.

The purpose of the study is to develop an approach to optimising the investment decision-making process using the dynamic programming method and its further integration into the enterprise information system using new technologies. The following methods were used in the study: System-structural analysis, dynamic programming method, graphical, and tabular methods. As a result of the conducted study, the role of information in the investment process was determined. An approach to the formation of the structure of an enterprise information system designed to optimise the investment decision-making process was proposed. The components of the structural elements of this system are considered — information collection, processing, and interpretation. A roadmap for information support for the development and implementation of an investment project is proposed, the blocks of which correspond to a certain direction of information processing, depending on the stage of implementation of the investment project — preparation, implementation, and final stage. The existing solutions are considered, and ready-made software products designed to optimise the investment process are characterised, primarily from the standpoint of risk assessment. An approach to optimising the investment decision-making process based on the dynamic programming method is proposed. An example of using this method to select one of the proposed alternatives according to the profit maximisation criterion is given. The results of the analysis can be used by the management board of enterprises to optimise the investment decision-making process.

This study investigates the role of carbon emissions trading (CET) in advancing regional high-quality and green development (QGD) from quality and efficiency perspectives. Firstly, theoretical analysis via an evolutionary game model formulates hypotheses. Secondly, a multi-period DID model, based on panel data from 30 Chinese provinces (2007-2020), evaluates the impact of CET pilot policies on provincial QGD. Robustness and heterogeneity analyses assess variations by region, industrial structure, and economic development. Additionally, the moderating effects of government regulation and market conditions on the CET-QGD relationship are examined. Key findings include: 1) CET significantly improves both the quality and efficiency of QGD, with a stronger impact on efficiency. 2) Geographical heterogeneity reveals CET impacts peak in eastern provinces, moderate in central regions, and diminish in the west, amplified by industrialization and economic development. 3) Indirect governmental regulations, such as environmental spending and resource taxes, strengthen CET's effectiveness, whereas direct interventions like pollution control investments may hinder green transformation, adversely impacting QGD. 4) Increased carbon trading volumes improve QGD quality, while elevated prices enhance quality but reduce efficiency by favoring short-term emissions cuts over long-term green transitions. Therefore, this study recommends addressing regional disparities, balancing regulation with market mechanisms, and ensuring a competitive, transparent national CET market.

Developing rural revitalization industries is crucial for consolidating and expanding the achievements of poverty alleviation and establishing a solid material foundation for comprehensive rural revitalization. The "New Community Factory" in the Qinba Mountain area of Shaanxi Province, as a typical model for industrial revitalization, contributes to the high-quality development of rural areas. This article is based on the ocean model theory and conducts a comprehensive assessment and prevention of industrial risks in the "New Community Factory" model. The results indicate that the industrial risks faced by the "New Community Factory" throughout its development process fall into the category of "medium risk". Among them, the policy risk and environmental risk were highest during the period from 2014 to 2016, the economic risk was highest during the period from 2017 to 2019, and the scale risk and development risk were highest during the period from 2020 to 2022. To address prominent risks such as environmental risk and economic risk, it is urgent for the government to implement special financial policies, strengthen talent cultivation and guidance, support independent brand innovation, and improve the internal and external environment to promote the gathering and development of rural revitalization industries. This article not only enriches and expands the research scope of the ocean model but also has theoretical and practical significance for improving the risk assessment system of rural revitalization industries.

As an important path to promote the transmission of international knowledge and technology, the impact of two-way FDI coordinated development on one country's technological innovation efficiency deserves in-depth exploration. This study uses the fixed effect panel stochastic frontier analysis model and the coupled coordination model to measure the technological innovation efficiency and the two-way FDI coordinated development level of China's 31 provincial administrative regions from 2011 to 2021, and uses the fixed effect panel least squares regression model, the fixed effect panel quantile regression model, the Moran's index model, the fixed effect spatial Durbin panel model and the fixed effect panel β convergence model to test the impact of two-way FDI coordinated development on technological innovation efficiency. The results show that two-way FDI coordinated development can directly and significantly promote the improvement of technological innovation efficiency in the province, and with the improvement of technological innovation efficiency, the promoting effect of two-way FDI coordinated development has also become stronger. In addition, two-way FDI coordinated development can also indirectly generate significant positive spatial spillover effects on the technological innovation efficiency of neighboring provinces, narrow the technological innovation efficiency gap between regions, and thereby promote the efficient and balanced development of regional technological innovation activities. The research conclusions reveal the positive impact of two-way FDI coordinated development on the efficient and balanced development of regional technological innovation activities, which has enlightening significance for building a strong country in science and technology.

In this paper, an evaluation index system for the coordinated development of regional economy was constructed by considering the aspects of regional development coordination, economic development coordination, economic and social coordination, and resource and environment coordination, which is used to perform comprehensive evaluation of the coordinated development of regional economy. On this basis, the impact of the upgrading of industrial structure, scientific and technological innovation, and government intervention on the coordinated development of regional economy were also investigated. According to the findings of our study, the coordinated development of regional economy in the eastern region was obviously better than that in the central region, while the western region had the lowest coordinated development of regional economy; the upgrading of regional industrial structure promoted the coordinated development of regional economy, while the promoting effect of the scientific and technological innovation on the coordinated development of regional economy were partly achieved by its promotion of the upgrading of industrial structure; the effect of government intervention couldn't be well reflected in the coordination index of regional economy.

This paper investigates the influence of various knowledge roles on knowledge diffusion empirically. Exponential random graph models (ERGM) are constructed, which provides a novel perspective for examining the factors that influence knowledge diffusion. Our empirical findings reveal that the endogenous structural effects of the network have a significant impact on the formation of diffusion relationships in citation networks and that there is a correlation between the number of the three knowledge roles - contributors, seekers and brokers - and the likelihood of citation relationship formation in citation networks.

Quantum computation and artificial intelligence for military applications bring new capabilities, leading toquantum warfare. In this paper, we present a Holistic Warfare Model, introduced as a high-dimensional entangled warfare category including asymmetric conflicts. Its underlying metaphysics is entangled states fusion: This is the macroscopic entanglement concept inspired by high-dimensional quantum computation with the entangled wave-functions. From this entangled view, wars and battles are seen essentially as a holistic phenomenon: The distinct operation behaviors within different battlefields tend to the worldwide war. The mathematical Holistic Warfare framework developed in this paper expresses this fundamental view of arbitrary many interaction conflicts, each of them defined by its own battle-manifold and evolution simultaneously on the planet; we call this entangled category mathcal{WAR}. At last, we introduce the research directions in terms of the future wars, such multilateral conflict, cognitive warfare, biological warfare and unmanned operation so on.

科技金融有别于传统金融部门, 能够有效支持科技活动, 对增强我国自主创新能力和实现经济高质量发展具有重要意义. 本文使用熵值法, 从资源、经费、融资、产出四个维度综合评估科技金融发展水平, 基于2007-2021年31个省份的面板数据, 构建空间计量模型实证检验了科技金融对技术创新的影响. 研究发现, 科技金融不仅对本地的技术创新具有显著的促进作用, 且具有明显的空间溢出效应, 若不考虑空间溢出效应, 会低估科技金融对技术创新的影响. 进一步研究表明, 在东部地区, 科技金融对技术创新的直接效应和空间溢出效应更显著, 科技金融通过缓解企业融资约束和优化产业结构提高区域技术创新水平. 本文的研究为评估科技金融的影响效果提供了数据支撑, 也为技术创新提升路径的探索提供了政策参考.

工业机器人应用推动了制造业的智能化进程, 深刻改变了企业用工结构, 为经济高质量发展提供了重要契机. 本文基于2012至2022年中国制造业企业机器人数据, 实证检验了工业机器人应用对企业人力资本结构的影响. 研究发现: 1)工业机器人应用能够促进企业人力资本结构升级, 且在一系列稳健性检验后, 该结论依然成立. 2)渠道检验表明, 工业机器人应用通过发挥替代效应和创造效应, 进而优化企业人力资本结构. 3)异质性检验表明, 在劳动密集型企业、技术更迭速度更快与处于要素市场发育程度更高地区的企业中, 工业机器人应用对企业人力资本结构升级的促进作用更为显著. 在国家出台 "延迟退休"政策以缓解人口老龄化趋势加速与劳动力成本持续上升的背景下, 本文研究为实现企业提质增效与经济高质量发展提供了政策启示和经验证据.

人工智能创新是形成新质生产力的引擎, 能够创造更多社会财富的同时, 也会影响就业机会, 冲击居民消费. 能否通过人工智能创新实现供给侧优化产品供给体系和需求侧提升居民消费能力来破解居民消费不足的现实困境, 是推动经济高质量发展的关键. 本文在人工智能创新任务模型基础上, 构建人工智能创新与居民消费的理论模型, 从理论上厘清人工智能创新影响居民消费的作用渠道, 并对理论模型进行数值模拟. 进一步利用2012-2020年各省投入产出数据和国家知识产权局专利数据对理论模型进行实证检验. 研究结果表明: 1)人工智能创新促进了居民消费, 促进效应存在于大部分分类消费中, 并且对享受型消费的促进效应最大, 是实现居民消费提质扩容的牵引力; 异质性分析发现, 人工智能创新对东部及城镇与高技能要素密集行业的居民消费促进效应更大. 2)供给侧路径分析发现, 人工智能创新通过提高生产效率和实现产品创新来提升居民消费水平, 提升效应最大的作用渠道是产品创新; 分城乡来看, 人工智能创新均对城镇居民消费提升效果更大. 3)需求侧路径分析发现, 人工智能创新带来的"岗位创造效应"拉伸技能溢价程度小于"鲶鱼效应"抑制技能溢价程度, 提高居民消费能力, 促进居民消费增长.

以数字要素赋能绿色创新, 是企业培育竞争优势和实现绿色升级的关键路径. 随之而来的问题是, 目前数字化转型能否有效赋能绿色创新的全面迭代优化? 既有研究仅讨论了数字化转型对绿色创新的积极作用, 却忽视了数字化转型中绿色创新内部不同领域创新活动的地位和结构变化, 未能明晰当前数字化与绿色化的阶段性匹配问题及内在机理. 基于此, 本文依据"国际专利分类绿色清单"对2007-2022年A股上市企业绿色专利信息检索分类, 按照应用领域归纳得到经营管理与设计类、生产节能类与末端治理类绿色创新, 基于资源编排理论和技术一致性理论分析, 实证考察了数字化转型对绿色创新的非对称作用. 研究发现: 数字化转型显著推动了经营管理与设计类绿色创新, 对生产节能类与末端治理类绿色创新的赋能作用不足; 相较于数字业务场景应用, 数字底层技术的布局和发展对绿色创新的非对称作用更为显著; 从研发资源配置的角度来看, 数字化转型推动经营管理与设计类绿色创新既是新增研发资源的结果, 也以挤占其他领域绿色创新的研发资源为代价. 同时, 绿色创新的三元共治因素(市场、政府和社会)在数字化与绿色化协同演进中具有重要的作用. 本文研究结论反映了以数字要素培育绿色竞争优势的现状, 即数字化转型主要通过信息技术领域的绿色升级实现经营流程中资源利用效率的提高, 具有显著的"局部赋能"作用. 这一发现不仅为政府制定更具针对性的"两化协同"政策措施提供了现实参考, 也为企业创新资源的优化配置与创新战略的制定提供了实践依据.

绿色创新是推动企业可持续发展的关键因素, 其与企业绩效的关系受到广泛关注. 然而, 现有研究缺乏针对不同类型绿色创新对不同类型企业绩效影响机制的深入分析与比较. 为此, 基于2008-2022年中国上市公司数据, 本文比较分析了绿色管理创新和绿色技术创新对企业短期绩效和长期绩效的影响机制. 研究发现, 在样本区间内, 绿色创新可以提高企业绩效, 但绿色管理创新仅对企业短期绩效存在显著正向影响, 而绿色技术创新仅对企业长期绩效存在显著正向影响. 利益相关者参与在绿色创新与企业绩效关系中发挥了显著正向调节作用, 区域市场化程度仅在绿色技术创新与企业长期绩效关系中发挥了显著正向调节作用, 行业竞争程度在绿色创新与企业绩效关系中的调节作用不显著.

数字孪生通过仿真建模映射物理实体, 基于虚实交互, 利用数据融合、行为模拟、优化决策和可视化技术, 显著提升系统可靠性并降低维护成本. 在智能运维实践中, 普遍存在数据质量不佳、异常样本稀缺、退化过程不明等痛点, 数字孪生技术为这些问题的解决提供了新范式. 本文针对装备的运维需求, 系统梳理了数字孪生中的仿真技术和建模技术, 总结了在异常检测、剩余使用寿命预测、故障诊断和运维决策等关键领域的研究进展, 进而围绕数字孪生驱动的智能运维, 对相关研究成果与技术路线进行归纳分析. 基于前期理论研究与应用实践, 本文提出数字孪生驱动的装备全寿命周期智能运维等级——以孪检实(DM1)、以孪预实(DM2)、以孪诊实(DM3)和以孪优实(DM4). 进一步, 通过舰船运维的真实案例, 展示了数字孪生驱动的装备全寿命周期智能运维在具体业务场景中的应用. 最后, 基于研究现状和工程实践, 提出若干未来发展方向, 为数字孪生驱动的装备全寿命周期智能运维提供借鉴与参考.

全球产业链供应链加速重构背景下, 美国试图实行友岸化近岸化战略以减少其产业链供应链对我国的依赖. 东南亚和墨西哥等经济体成为我国产业转移的主要去向, 这势必会对我国产业部门在全球价值链上的影响范围、获利能力和抗风险能力带来不利影响. 本文采用多区域投入产出(MRIO)数据库的中间产品贸易数据构建了全球生产网络模型, 并分别提取出从不同视角反映全球价值链骨干的现实网络(零模型)和人造网络(反事实模型), 然后在此基础上分析了美国对华贸易政策对全球生产网络重构和我国产业链外迁风险的潜在影响. 研究表明: 美国依托"阿尔塔西亚(Altasia)''实行的友岸化战略和依托"美墨加三国协议(USMCA)''实行的近岸化战略会导致全球范围内产业链供应链的部分脱钩, 且友岸化战略加剧了经济逆全球化趋势和我国产业链外迁风险. 最后, 本文提出在美国针对中国供应链实施"去风险化(De-Risking)''政策背景下, 提升我国产业链供应链韧性和安全水平的政策建议.

企业跨国经营是我国构建双循环新发展格局不可忽视的重要环节, 但企业跨国经营是否会加剧内部股东机会主义行为, 既有研究尚未给出解答. 为此, 本文以2007-2021年中国A股上市公司为样本, 基于本文提出的"减持三角模型", 实证探讨了企业跨国经营对企业内部股东股票减持行为与减持动机的影响. 研究发现, 跨国经营会显著加剧企业内部股东股票减持行为. 而且, 跨国经营企业内部股东减持具有明显的机会主义动机与套利减持倾向, 意味着跨国经营企业存在较高的违规减持风险. 该结论经过多时点双重差分模型、Bartik工具变量法等一系列检验后依然成立. 机制分析发现, 跨国经营加剧了企业面临的市场风险、信息不对称问题与股价泡沫现象, 进而分别构成内部股东减持压力、减持机会与减持借口. 异质性检验发现, 跨国经营主要加剧了董事的减持行为, 并没有加剧监事与高管的减持行为. 经济后果检验发现, 跨国经营企业内部股东减持会加剧公司股价崩盘风险. 不过, 持续稳定的出口规模, 强有力的政策监管, 良好的机构投资者治理环境、内部控制环境与审计师治理环境能够有效遏制跨国经营企业内部股东减持行为. 文章丰富了企业跨国经营经济后果及内部股东减持动因方面的研究, 研究结论对于引导资本市场利益相关者关注跨国经营企业潜在的违规减持风险具有一定的现实意义.

服务化成为了当下制造企业对抗市场冲击时的重要选项, 关于该战略实施的落脚点的研究已遍及企业经营的各个领域. 但从目前来看, 尚缺乏从供应链视角进行的观察. 本文将对供应商的贸易信贷的影响作为服务化战略实施的对象进行探索, 通过信号理论的视角分析企业服务化所释放的信号对于企业获得贸易信贷的可能影响, 并采用上市制造企业的数据进行论证. 实证分析结果表明, 服务化与贸易信贷呈"U"型关系. 在企业自身特征中, 财务宽松程度对这种"U"型关系有负面调节作用, 而服务相关性则对上述关系没有产生显著的影响. 在分组检验中, 相比于非国有企业以及装备制造业的企业, 国有以及轻工业企业服务化与贸易信贷之间的"U"型关系会相对缓和. 本文基于信号理论实证验证了服务化对制造业企业贸易信贷的影响, 研究结果可以为运营管理领域的学者和从业者提供一些重要的理论和管理启示.

社交媒体的崛起改变了资本市场的信息格局和参与者行为. 在此背景下, 本文利用新浪微博数据, 探究了上市公司社交媒体账号使用对分析师报告市场反应的影响. 借助回归分析以及工具变量法等, 研究发现, 分析师报告发布前一周内, 若上市公司新浪微博存在更新, 市场对分析师报告的反应会显著降低, 即上市公司微博可以部分替代分析师报告向市场传递信息. 此外, 微博发布的次数、字数、评论量和转发量越多, 上市公司的机构持股比例越低、分析师跟踪人数越少, 上市公司微博使用对分析师报告所引起的市场反应的削弱作用越明显. 本文的研究发现丰富了对资本市场中信息中介交互以及社交媒体在信息传播中作用发挥的理解.

本文创新性地从实时互动性强、难以事前完全准备的业绩说明会入手, 研究了我国上市公司由管理层自主把控、战略决策的异常情感语调与债券信用利差的关系. 研究发现, 业绩说明会管理层异常情感语调与债券信用利差显著负相关. 机制研究发现, 业绩说明会的管理层异常情感语调与公司未来业绩显著正相关, 与未来破产风险显著负相关, 即异常情感语调可作为预测公司未来业绩和破产风险的可靠信号. 这说明异常情感语调符合信息增量观, 具有财务定量信息以外的信息增量, 使得债券信用利差的定价更精准化. 此外, 在公司信息透明度更低、机构投资者持股比例更少、民营企业和债券票面利率更高的子样本中, 异常情感语调与债券信用利差的负相关关系更加显著. 最后, 异常情感语调与债券信用评级和发行规模显著正相关, 与发行利差显著负相关. 上述结果表明, 上市公司业绩说明会的异常情感语调会影响企业在债券市场的直接融资成本, 使更高质量的企业获得更低的融资成本, 从而对债券市场产生了多方面的跨市场积极溢出效应. 这些研究发现对提升债券市场定价效率、化解债券市场风险和促进资本市场高质量信息披露等具有重要启示.

碳中和背景下, 林业碳汇项目投资成本高、回报周期长, 碳排放企业面临资金约束等困难. 针对这一问题, 本文基于现实林业碳汇融资机制, 构建了银行碳汇预期收益权质押、产业投资基金与BOT三种融资模式模型, 并从碳排放企业利润与碳汇产出水平视角, 对比分析了不同融资模式下碳排放企业的均衡结果. 研究表明, 当碳排放总量较高时, 无论自有资金量如何变化, 企业基于利润最大化将首选产业投资基金融资模式; 当自有资金较高但碳排放总量较低时, 企业将优先选择银行质押融资模式; 当自有资金与碳排放总量均较低时, BOT融资模式将成为企业的最佳融资决策. 基于可持续发展视角, 政府应推动企业采用产业投资基金融资模式以实现社会经济与环境的双赢.

根据实践中三种不同形式的生产者责任延伸(EPR)制度, 面向由原始设备生产商(OEM)和独立再制造商(IR)组成的制造-再制造竞争系统, 通过构建动态博弈模型, 设计三种与碳税规制相结合且具有EPR特征的混合规制, 探讨EPR制度内涵的引入对于碳税规制政策效能的提升作用. 研究表明: 对于发挥混合规制政策效能而言, 减排技术溢出效应的存在至关重要; 相对于碳税规制, 三种混合规制的实施均可提高消费者剩余和环境绩效, 并具有更强的减排及再制造激励效果, 但适用范围存在较大差异; 基于"征补-奖惩"的混合规制可较好地平衡减排激励效果、企业利润、环境绩效, 并在一定程度上带来更高的社会福利. 研究结论对于EPR与碳税的组合政策设计具有一定参考价值.

数据规模的增大与数据更新频率的加快为效率评价带来挑战. Free Disposal Hull (FDH) 是非凸技术下一种经典效率评价方法. 相比于凸性技术下的数据包络分析, FDH效率求解的过程更加复杂, 且在数据快速更新的情形下难以保证评价结果的时效性. 针对上述问题, 首先在现有枚举算法的基础上提出了一种基于决策单元之间支配关系与参考关系的快速枚举算法(fast enumeration algorithm, FEA), 用于计算大规模样本FDH效率值. 其次, 结合支配关系和参考关系的传递性提出了动态快速枚举算法(dynamic fast enumeration algorithm, DFEA), 用于更新FDH效率评价结果. 最后, 通过数值模拟与好大夫平台的医生评价应用验证算法的有效性. 实验结果表明, 与枚举算法相比, FEA完成大规模样本FDH效率评价的用时明显减少, 且DFEA能够对大规模样本FDH效率评价结果进行实时更新.

恒定应力加速退化试验(ADT)是可靠性评估的有效手段, 通过分析高应力水平下的退化数据, 外推产品在正常应力下的可靠性水平. 处理退化数据的常用方法包括一步法和两步法, 一步法通过最大化基于退化数据的对数似然函数进行模型参数估计和后续推断; 而两步法首先估计每个样本的伪寿命, 进而转化为加速寿命试验(ALT)分析. 随着计算能力的提升, 一步法的计算变得更加容易, 已有研究对一步法和两步法在非加速情境下进行了比较. 然而, 在考虑ADT情境下, 有关这两种方法比较的文献相对匮乏. 本研究旨在针对恒定应力ADT系统地比较这两种方法, 为选择适用于ADT的可靠性评估方法提供更为准确和有效的指导. 在本文中, 我们提出了伪寿命对真实寿命高估或低估的判别标准, 并通过数值模拟在线性退化情况下比较了一步法和两步法对平均失效时间(MTTF)的评估效果. 此外, 我们应用于一组经典数据集, 其结果与模拟结论相符合. 总体而言, 模拟结果表明, 对于不同样本量和观测次数, 相较于基于不同分布的伪寿命的两步法, 一步法在产品MTTF评估方面具有更高的准确性, 且在小样本情形下尤为明显.

在复杂多变的金融市场环境中, 充分利用高频交易数据中的当前不确定信息有助于改善资产波动率的建模与预测表现. 本研究将该信息纳入已实现乘法误差模型, 构建已实现实时乘法误差模型(realized real-time MEM模型)对波动率与已实现波动率测度进行联合建模. 与现有模型不同的是, 该模型将当前已实现测度观测值经尺度变换后的随机扰动项看作是高频信息的"实时"日内因子变量, 从而将资产收益的当期条件波动刻画为由历史已实现测度观测值与"实时"日内因子所共同驱动的混合函数. 在此模型框架下, 探讨已实现测度的条件分布定理及相关性质、模型的弱平稳性和强平稳性条件、参数的拟极大似然估计方法和样本外向前多步波动率预测定理. 另外, 对此模型进一步拓展以纳入该信息的杠杆效应和波动反馈效应特征. 以四组国际指数为研究对象, 实证结果表明: 1) 高频数据的当前不确定信息使得已实现测度的条件分布具有时变峰度特征, 以此提升模型对资产收益波动的样本内建模能力. 2) 相比于基准模型, realized real-time MEM族模型在波动率、已实现测度的条件分布及波动率的在险值(VolaR)等方面均具有更高的样本外预测精度.

近年来, 全球资本市场剧烈震荡, 黄金期货的避险与保值功能愈加凸显, 其价格预测研究受到投资者的高度关注. 本文结合MEMD多频率尺度同步分解功能与WGAN-GP高效提取多元时序复杂模式优势, 提出基于LASSO影响因素体系的沪金价格多频率尺度集成预测模型MEMD-WGAN-GP. 首先, 从宏观政策、黄金市场、股票市场及原油市场中选取30个指标变量, 再通过LASSO筛选构建沪金价格影响因素体系; 其次, 对解释变量集和沪金价格序列进行MEMD同步分解, 获得各频率尺度下若干组IMF分量, 并分别建立每一频率尺度下IMF分量的WGAN-GP预测模型; 然后, 从预测有效性角度对IMF组合方式进行优化, 并加和集成优化后各IMF分量预测值得到沪金价格整体预测值; 最后, 全面评估了本文模型在沪金价格不同市场行情下的预测性能. 研究表明: MEMD-WGAN-GP具有最强趋势预测能力、最小回归误差和最低预测滞后性.

快速发展的社交媒体不仅为商家提供产品营销平台, 也是产品销售渠道. 在传统电商渠道基础上引入社交电商渠道有助于拓宽消费者市场, 但会加剧渠道竞争, 为此, 对制造商来说需要充分考虑用户特性, 进行有效的渠道策略选择. 本文基于三种不同的供应链结构: 不引入社交渠道、引入自营社交渠道和引入第三方社交渠道, 构建模型研究社交渠道对制造商的影响. 研究发现: 制造商引入自营社交渠道时, 随着渠道间价格竞争程度的加剧, 若社交渠道潜在需求量较低, 其会降低批发价; 反之, 则提高批发价. 同时, 当社交渠道潜在需求量较大或当社交渠道潜在需求量较小, 渠道间价格竞争程度较弱, 粉丝比例较低且粉丝和普通用户间差异较大时, 制造商总是引入社交渠道. 还发现制造商引入社交渠道时, 随着粉丝和普通用户间差异的扩大, 若社交渠道潜在需求量较低, 其兼任社交零售商的可能性增多; 反之, 则与第三方社交零售商合作的可能性增多.

城市特定区域往往同时存在路内、路外和共享三种停车设施, 它们的服务特征具有显著差异. 区域内交通系统在其演化过程及对其状态指标的观测过程中通常均存在不确定. 本文以最小化区域内交通参与者总出行成本(包括区域通行成本、寻泊成本、管理成本和步行成本)为目标, 构建基于滚动优化与数据融合估计的双驱动模型, 对区域多类型停车服务的动态供给策略进行设计, 并通过蒙特卡罗数值模拟验证了策略的有效性. 研究发现: 首先, 所构建的动态优化问题可等价转化为带有不等式约束的二次规划问题, 且该规划问题的解若存在则一定为唯一全局最优解; 其次, 在考虑过程和观测两类不确定性的情况下, 系统观测结果与系统目标轨迹之间会存在较大误差; 最后, 引入卡尔曼滤波器能够有效减少后验状态估计与系统目标轨迹之间的误差, 进而提高区域内通行效率并降低总出行成本.

在港口腹地集疏运中, 不确定的在港周转时间给集卡调度带来重重挑战. 本文提出了一种基于在港周转时间预测的外集卡调度优化方法, 通过分析港口的历史闸口数据, 提取相关特征, 并应用随机森林的机器学习方法, 训练集卡周转时间的预测模型, 在外集卡调度优化模型中, 通过对随机森林预测模型的反复调用, 将周转时间反馈到优化模型中, 从而制定更可靠的集卡调度方案. 基于我国华南某集装箱码头的闸口数据训练预测模型, 结果显示其在拟合港口集卡周转时间预测上具有较高的精度, 拟合优度均超过0.9; 在优化模型数值实验中, 误差中位数均分布在1%, 表明基于机器学习的在港周转时间预测与调度优化模型的结合, 能减少在港周转时间不确定对调度计划的扰动, 提升集卡公司集疏运调度提供的可靠性和有效性.

This paper concerns the decentralized event-based $H_{\infty}$ filter design problem for networked dynamic system (NDS). A more practical situation is studied, in which the communication between subsystems is affected by uncertainties and only local sampled measurement output is available for each filter in the developed filter scheme. Firstly, an event-triggered mechanism is introduced for each subsystem to process the sampled output in order to reduce the communication load. Secondly, on the basis of the well-posedness, the augmented filtering error system composed of the original NDS and the filter is modeled as a time-delay system of high dimension. After that, by employing the Lyapunov functional approach and space construction method, novel computationally attractive sufficient conditions are derived to check the well-posedness, asymptotic stability and $H_{\infty}$ performance of the augmented filtering error system. Thirdly, a co-design method of the filter and event-trigger matrices is obtained by using Finsler lemma and slack matrix approach. Finally, a numerical example is provided to demonstrate the effectiveness of the derived design approach.

This paper focuses on the McKean-Vlasov system's stochastic optimal control problem with Markov regime-switching. To this end, the authors establish a new Itô's formula using the linear derivative on the Wasserstein space. This formula enables us to derive the Hamilton-Jacobi-Bellman equation and verification theorems for McKean-Vlasov optimal controls with regime-switching using dynamic programming. As concrete applications, the authors first study the McKean-Vlasov stochastic linear quadratic optimal control problem of the Markov regime-switching system, where all the coefficients can depend on the jump that switches among a finite number of states. Then, the authors represent the optimal control by four highly coupled Riccati equations. Besides, the authors revisit a continuous-time Markowitz mean-variance portfolio selection model (incomplete market) for a market consisting of one bank account and multiple stocks, in which the bank interest rate, the appreciation and volatility rates of the stocks are Markov-modulated. The mean-variance efficient portfolios can be derived explicitly in closed forms based on solutions of four Riccati equations.

This paper investigates the output tracking control problem of heterogeneous linear multi-agent systems with a novel dynamic event-triggered control strategy. In contrast to existing observer methods, the learning algorithm is first developed and applied to the observer such that the each observer corresponding to each follower can provide an optimal estimation of the leader's state by optimizing a specified cost function. Then, a controller consisting of the observer's state and the agent's state is designed and learned by a data-based off-policy learning algorithm to achieve the optimal output tracking control. Under the learned gain matrix, to reduce the communication burden for each agent, a model-free dynamic event-triggered control strategy for each agent is developed to realize the optimal event-triggered output tracking control without depending on any prior knowledge. Rigorous analysis shows that the proposed algorithms not only ensure the model-free output tracking control while saving the limited bandwidth, but also exclude Zeno behavior. Finally, a numerical example is provided to verify the theoretical analysis.

文章以线性水波方程来描述二维有界长方形区域内理想水体的波动,并使用Craig-Sulem变换将水波方程转化成以速度势函数和水波波高为状态变量的线性发展方程.文章假设水波的波高是系统的测量输出,并在此基础上分析水深和速度势函数的可辨识性,设计同步辨识算法从波高中估计水深和势函数.文章设计了基于伴随法的数值辨识算法,该算法能够同时有效地估计水深和势函数. 首先,该算法通过引入拉格朗日乘子将传统的二次型目标泛函改进为带有系统模型约束的目标泛函.其次, 利用拉格朗日泛函微分公式导出了水波方程的伴随方程,并通过求解该方程得到了目标泛函的梯度. 最后,采用梯度下降法来迭代估计水深和速度势函数,并通过数值仿真验证了所提算法的有效性和准确性.

为了提升供应链效率, 地理位置不同的工厂协同完成生产任务,已成为标准生产模式. 构建符合实际情况的调度模型,并设计简单有效的优化算法, 是实现高效协同的关键.针对分布式柔性作业车间调度问题的研究应运而生,成为建模和优化这一类问题的有力工具. 然而,现有研究很少考虑序相关机器准备时间的约束, 而是将该时间假设为常数.这种近似模型容易导致低质量的调度方案, 从而影响系统效率.文章建立了一个混合整数规划模型,用于描述考虑序相关机器准备时间的分布式柔性作业车间调度问题.为了解决该问题, 提出了一种基于$Q$学习的迭代贪婪算法, 利用$Q$学习机制动态选择合适的扰动程度,有效克服了迭代贪婪算法因不合理的扰动而导致搜索性能下降的问题.通过在带有机器适用限制的分布式柔性作业车间调度基准测试算例中引入机器准备时间与操作序列的关联性, 构造了207个测试算例.所提算法与3种扰动程度固定的迭代贪婪算法、模拟退火算法、分散搜索算法、基于回溯搜索的超启发式算法和基于随机置换下降的超启发式算法进行了比较.实验结果表明, 所提出的基于Q学习的迭代贪婪算法具有更高的搜索质量和更快的收敛速度.

加盟制快递业高速发展的同时,也存在由于利益不协调、服务质量低等引发的合作不稳定问题,这难以满足消费者日益增长的高质量高服务的需求. 为此,文章从风险成本的角度出发,使用演化博弈的方法动态分析了快递公司监管策略、末端加盟商生产行为以及政府部门监管奖惩策略的演化稳定,揭示了不同决策参数对演化稳定的影响, 证明了演化稳定的条件.数值分析结果表明, 当风险成本和利润分享比例位于不同阈值区间时,快递公司-末端加盟商博弈系统呈现出四种不同的演化稳定结果; 此外,政府部门在制定奖惩政策时, 要确保对各方的奖惩之和大于其投机收益,才能保证快递服务企业的规范运营和合作稳定性.文章的研究结果对建立适宜的违约惩罚制度、风险识别和预警机制、提升政府监管职能的同时营造市场良好运营环境具有重要意义.

The Proportional-Integral-Derivative (PID) control has enjoyed significant success and widespread adoption in aviation, automotive, robotics, and various other domains. However, to align with the current trend of networked control systems and optimize communication resource utilization, the authors introduce an extended PID (EPID) control framework that leverages an event-triggered mechanism. This controller is designed for single-input single-output (SISO) high-order affine nonlinear systems, overcoming the limitation of traditional PID control, which typically guarantees stability only for second-order systems. Leveraging the open unbounded parameter manifold and event-triggered conditions of the controller parameters, the authors prove through the Lyapunov method that our proposed controller achieves uniformly ultimately bounded stability and guarantees the absence of the Zeno phenomenon in the event-triggered EPID (ET-EPID) system. The efficacy of the ET-EPID control system approach is exhibited through simulation of a third-order system as well as practical experiment conducted on a second-order direct current motor.

The allocation of heterogeneous battlefield resources is crucial in Command and Control (C2). Balancing multiple competing objectives under complex constraints so as to provide decision-makers with diverse feasible candidate decision schemes remains an urgent challenge. Based on these requirements, a constrained multi-objective multi-stage weapon-target assignment (CMOMWTA) model is established in this paper. To solve this problem, three constraint-feature-guided multi-objective evolutionary algorithms (CFG-MOEAs) are proposed under three typical multi-objective evolutionary frameworks (i.e., NSGA-II, NSGA-III, and MOEA/D) to obtain various high-quality candidate decision schemes. Firstly, a constraint-feature-guided reproduction strategy incorporating crossover, mutation, and repair is developed to handle complex constraints. It extracts common row and column features from different linear constraints to generate the feasible offspring population. Then, a variable-length integer encoding method is adopted to concisely denote the decision schemes. Moreover, a hybrid initialization method incorporating both heuristic methods and random sampling is designed to better guide the population. Systemic experiments are conducted on three CFG-MOEAs to verify their effectiveness. The superior algorithm CFG-NSGA-II among three CFG-MOEAs is compared with two state-of-the-art CMOMWTA algorithms, and extensive experimental results demonstrate the effectiveness and superiority of CFG-NSGA-II.

In this paper, a cross-sensor generative self-supervised learning network is proposed for fault detection of multi-sensor. By modeling the sensor signals in multiple dimensions to achieve correlation information mining between channels to deal with the pretext task, the shared features between multi-sensor data can be captured, and the gap between channel data features will be reduced. Meanwhile, in order to model fault features in the downstream task, the salience module is developed to optimize cross-sensor data features based on a small amount of labeled data to make warning feature information prominent for improving the separator accuracy. Finally, experimental results on the public datasets FEMTO-ST dataset and the private datasets SMT shock absorber dataset (SMT-SA dataset) show that the proposed method performs favorably against other STATE-of-the-art methods.

新型城镇化建设是扩大内需的重要抓手, 是畅通国内大循环的关键举措. 本文基于新型城镇化综合试点, 采用中国流动人口动态监测调查数据评估了试点开展对农业转移人口消费水平的影响. 研究表明: 相比非试点地区, 试点地区农业转移人口的消费水平明显提升, 一系列稳健性检验都对该结论给予了支持. 机制分析表明, 试点开展通过增加收入、增强公共服务可及性和身份认同感, 提升了农业转移人口的消费水平. 试点在方言多样性程度低的地区具有更强的效应, 并且更能提高受雇和省内流动农业转移人口的消费水平. 此外, 试点开展使农业转移人口日常消费和住房消费均得到提升, 并且在县和县级市具有更强的效应. 本文结论为建立扩大内需的长效机制提供了理论阐释和经验证据.

数字平台如何赋能企业实现颠覆式创新是数字经济背景下管理者关注的焦点问题. 本文基于平台生态系统理论和动态能力理论, 以209家自建或桥接数字平台的中国高新技术企业数据为研究样本, 深入探究数字平台能力对颠覆式创新的影响及作用机理. 研究发现, 数字平台能力能够显著促进颠覆式创新. 进一步研究发现, 组织结构敏捷性和舍却式学习在数字平台能力和颠覆式创新之间起部分中介作用, 一方面数字平台赋能塑造结构敏捷性, 打破僵化的组织惯例, 识别并服务新兴细分市场, 另一方面数字平台赋能组织进行舍却式学习, 打破原有的知识路径依赖, 获取颠覆式创新所需的互补性知识. 组织结构敏捷性是舍却式学习的前因变量, 组织结构敏捷性和舍却式学习在数字平台能力与颠覆式创新之间起到链式中介作用. 本文丰富了平台情境下高新技术企业颠覆式创新的形成机制, 从微观视角回答了高新技术企业进行颠覆式创新需要培育哪些能力的现实问题, 为数字化转型背景下企业利用数字平台实现颠覆式创新提供了理论参考.