This paper applies the Cheng projection to the support vector machine (SVM) in handling missing data. In the process of handling missing data, each sample with missing values is replaced by its Cheng projection in the original space. Additionally, two classification algorithms for handling linearly separable and nonlinearly separable datasets with missing data are presented. For linearly separable datasets with missing data, Cheng kernel function is introduced, and an SVM classification algorithm that improves the linear kernel function to the Cheng kernel function is proposed. For nonlinearly separable datasets, a generalized Gaussian Radial Basis Function kernel is introduced and an SVM classification algorithm for handling missing data is given. For both algorithms, two comparative experiments are conducted to demonstrate their effectiveness.

Both actuator faults and time delays degrade the performance of control systems. Although fault-tolerant mechanisms are commonly used in advanced control systems, no results are available in investigating the adaptive tracking problem of stochastic nonlinear time-delay systems in the presence of Markovian jump actuator faults. After establishing some mathematical fundamentals for stochastic differential delayed equations with multi-Markovian switching, this issue is tackled in this article, by proposing a novel adaptive backstepping fault-tolerant controller. Uncertainties caused by random actuator faults, unknown time-varying delays, the Wiener noise of unknown covariance as well as the unknown plant parameters are handled skillfully in a unified stochastic framework. By constructing a suitable Lyapunov-Krasovskii functional, it is proved that all closed-loop signals are bounded in probability, and the tracking error can converge into an arbitrarily small residual set in the sense of mean quartic value. In addition, the range of reference signals is greatly enlarged by comparison with the conventional backstepping controller. Two simulation examples are presented to illustrate the proposed theoretical findings.

In recent years, artificial cilia have attracted widespread research interest due to their enormous application prospects in the fields of medicine and environmental therapy. Deformation is a key issue to consider in the design and preparation of artificial cilia, however the corresponding mathematical analysis is still lacking. This paper introduces a multi-agent model for the magnetic artificial cilium, where each agent denoting a bead is influenced by the external magnetic field and neighboring agents. Then, the authors provide the existence and uniqueness of the solution to the proposed model, and give a stability condition for avoiding magnetic chain breakage and collisions between adjacent magnetic beads. To our best knowledge, it is the first mathematical result on the stability of magnetic bead chain. Finally, simulations are conducted to verify the proposed theoretical result.

一阶方法因其计算效率高、可扩展性强, 已被广泛应用于大规模优化问题的求解.早期研究主要集中于无约束优化问题或具有简单约束结构的情形.随着受约束机器学习等新兴应用的快速发展, 近年来研究重点逐渐转向函数约束优化问题中一阶方法的设计与理论分析.
本文系统综述了求解复杂约束优化问题的一阶算法研究进展, 涵盖多种不同的结构情形, 包括:确定性线性约束凸优化问题、确定性非线性约束凸优化问题、目标函数非凸但约束凸的确定性优化问题、目标函数与约束均为非凸的确定性优化问题, 以及具有凸或非凸结构的随机优化问题.针对不同方法, 本文总结并比较了其在产生 $\epsilon$-最优解或 $\epsilon$-KKT 点方面的复杂度结果, 为理解现有理论成果及未来研究方向提供系统参考.

This paper presents a primal-dual prediction-correction (PD-PC) method for solving linearly constrained time-varying convex optimization problems, which frequently arise in control, signal processing, and online learning applications. The proposed method establishes a novel integration of primal-dual gradient dynamics with a discrete-time prediction-correction structure, specifically designed for problems with time-dependent linear constraints. A tunable memory parameter is introduced in the prediction phase to perform linear extrapolation using past iterates, enabling a flexible trade-off between the amount of historical information stored and the computational cost of correction. In the correction phase, primal and dual variables are updated via gradient descent-ascent iterations, thus maintaining the computational efficiency of a first-order method without requiring Hessian or high-order derivative computations. Theoretical analysis shows that the method achieves $\mathcal{O}(h^2)$ asymptotic tracking accuracy for both primal and dual variables, matching the state-of-the-art performance among first-order methods even in unconstrained settings. Numerical experiments on problems with both time-invariant and time-varying constraints validate the theoretical findings and demonstrate the method's effectiveness.

The cooperative output regulation problem for unknown linear multi-agent systems has been studied by both policy-iteration method and value-iteration method via distributed internal model approach. However, the original results were limited to single-input single-output linear multi-agent systems under the assumption that the communication digraph is acyclic. Recently, the authors have extended the existing result to multi-input multi-output linear multi-agent systems over a general static and connected digraph by a more efficient value-iteration method. Since the policy-iteration method is simpler and has a much faster convergence rate than the value-iteration method, in this paper, the authors further apply the policy-iteration method to the cooperative output regulation problem of unknown multi-input multi-output multi-agent systems over a general static and connected digraph. Compared with the existing policy-iteration method, the proposed policy-iteration approach not only drastically reduces the computational cost, but also significantly weakens the solvability conditions. Moreover, by introducing a virtual exosystem, the proposed policy-iteration approach eliminates the need for employing a distributed observer. As a result, the data collection can start at any time, and the computing cost for each agent is also reduced.

利用2009年至2023年华证ESG评级数据, 文章检验上市公司ESG评级对企业下一年度在股价、财务和经营三个方面的风险水平的影响. 研究发现, 较好的ESG评级对企业未来一年三项风险水平均有显著的抑制作用. 具体地, 对于股价崩盘风险, 高于基准水平的ESG评级作为一种强烈的市场信号对企业股价崩盘风险水平的降低更显著; 投资者关注程度高的个股, ESG降低企业股价崩盘风险作用更强; 此外, 在《环境保护法》实施后ESG和股价崩盘风险的负向关系更加显著. 而对于财务风险, ESG对于企业财务风险的降低具有边际递减效应, ESG评级从低到中的提高能够改善企业财务风险水平, 但随着ESG评级进一步的提高, 该作用变得不显著; 同时,企业自愿披露非财务信息行为强化了 ESG 对于财务风险的抑制作用. 对于经营风险, ESG评级降低经营风险作用同样具有边际递减效应; 同时股权性质对ESG评级降低经营风险有调节作用, 相比于私企, ESG评级对国有企业经营风险的抑制作用更强. 文章基于企业年限长短的分样本异质性检验结果显示, 对于成立年限长的企业, ESG评级对风险的抑制作用更强, 而对于成立年限短的企业则较弱.

文章基于东方财富股吧论坛投资者行为数据, 利用有效转移熵方法, 探讨股吧论坛在股票市场中的媒介作用更能表征投资者关注还是情绪. 研究发现: 1) 股吧论坛中的投资者关注向股票市场传递的信息流更有价值, 能够更精准地表征投资者行为; 2) 帖子净评论数作为投资者关注的重要指标, 与股票市场强相关, 显著影响个股波动率, 且这一影响可持续一个月; 3) 在不同情绪(积极、中立、消极)的帖子中, 投资者关注均能传递可预测个股波动率的信息流, 而投资者情绪向市场传递的信息流缺乏显著的信息增益价值. 文章进一步强调了股票论坛捕捉投资者关注的媒介作用, 特别是帖子净评论数的独特预测价值, 为投资者提供了分析在线股票论坛数据的新视角. 未来研究应聚焦于更全面的数据整合与深度挖掘, 以提升市场预测的准确性和投资决策的科学性.

文章关注汽车充电服务供给端, 构建以政府、运营商、第三方平台(下称平台)为主体的分散(即运营商和平台以自身利益最大化为目标)和集中(即运营商和平台以二者整体利益最大化为目标)两种充电服务运营决策微分博弈模型. 研究发现: 在收益分配比例固定的均衡状态下, 相较于分散决策模式, 集中决策模式可提高运营商与平台的努力程度、服务质量及社会效益; 当平台较为弱势而收益分配占比较低时, 采用集中决策模式可实现运营商和平台收益的帕累托改进; 当平台较为强势而收益分配占比较高时, 采用分散决策模式可使运营商和平台收益增加. 这表明随着平台由弱变强, 汽车充电服务供给的决策模式或将从集中转向分散, 此时政府的政策支持比例将提升, 社会效益和服务质量反而下降.

文章针对一阶线性不确定系统,研究了以饱和函数和以ReLU函数为激活函数的循环神经网络控制器的稳定性和鲁棒性.给出了闭环系统收敛到非零目标值的必要条件以及指数稳定的充分条件,定量刻画了状态初值、目标值以及对象未知参数鲁棒性范围与控制器参数之间的关系,分析结果表明,以ReLU函数为激活函数的RNN控制器的鲁棒性更强.

注册制是中国资本市场发展的关键改革,推行询价制改革,旨在将定价权更多交由市场主体,提升新股定价效率.因此,围绕注册制下的IPO定价效率评价和询价制改革效果的系统性研究备受关注.文章基于双边随机前沿模型测度2016–2023年A股2365家上市公司IPO定价效率,并验证注册制下询价制改革对IPO定价的系统性影响.研究发现,2016–2023年A股市场首发定价中抑价效应强于抬价效应,整体定价低于合理水平7.06%,且在不同板块、年份、所有制形式和破发与否的条件下有其明显特征区别.询价制改革整体推高了股票首发定价,主要由股票市场内科创板依靠板块特性独自拉动,而其他板块则呈现下降趋势.在注册制推广阶段,科创板和创业板存在明显竞争,主板改革效果并不明显.同时,梳理出询价机构数量和报价有效性两种路径来解释其影响.最终,文章依据研究结论为监管部门询价制改革方向提出针对性建议.

稀疏性是高维建模过程中一个重要的假设,即在处理高维回归分析中,往往只有少部分变量对响应变量具有显著影响,这便是稀疏性的体现.文章在变系数模型的基础上,结合变量选择,针对纵向数据提出具有混合效应的变稀疏系数分位回归(VSCMEQ)模型,其中待估系数函数使用B样条估计,通过对随机效应和固定效应施加惩罚来研究相关重要因素(变化效应、常数效应)的影响.最后通过应用原发性胆汁性肝硬化(PBC)数据进行建模分析,得出在不同分位数下对患者疾病进展(生物指标)的重要因素影响.

文章考虑由两个供应商和一个零售商构成的供应链,其中一个供应商为供销一体型,另一个为纯供应商.在该系统中,零售商从纯供应商进行补货并在其供应中断时从供销一体型供应商进行紧急补货.文章主要讨论在纯供应商发生供应中断且供应中断结束时间随机情形下零售商的紧急补货决策和该情形下供销一体型供应商的零售价格决策问题.对此,基于供应链各成员效益最大化,建立了一个优化模型,并通过模型分析给出了模型的最优解,从而给出了供应链各成员的最优决策.数值实验讨论了主要参数对零售商紧急补货量和供应商零售价格及双方效益的影响.

趋势滤波是一种广泛应用于从时间序列数据中提取长期趋势和去除短期噪声的方法.为了准确捕捉潜在趋势的全局变化规律和局部波动,文章基于刻画稀疏性的本原函数提出带复合$\ell_0$约束的广义趋势滤波(L0CTF)模型并分析其最优性理论.复合$\ell_0$函数的组合属性和不可分性导致求解L0CTF模型是一项极具挑战的任务.为此,基于复合$\ell_0$约束的特点,文章将L0CTF模型转化为含第一类特殊有序集的混合整数规划问题并分析其与L0CTF模型在全局最优解意义下的等价性.最后,在模拟数据集和真实数据集上的实验结果表明,所提方法在提取潜在趋势方面优于主流的趋势滤波方法.

在工程实际中,通常难以获得单个部件的精确失效时间,相反地,仅仅知道部件失效的次数和系统运行的总时长,即聚合寿命数据.基于此种数据对寿命分布进行推断是十分具有挑战性的.文章提出了基于矩的点估计方法,并采用偏差校正Bootstrap方法来构建感兴趣量的置信区间.极大似然方法需要写出聚合寿命数据的似然函数,这仅对少数几个具有卷积运算封闭性质的分布才适用.与之相比,所提方法规避了似然函数,因此能适用于更多的分布类型.最后,文章通过模拟研究和实证分析对所提方法进行了验证和说明.

本文旨在探讨时谐波动方程反源问题的一些研究进展,并建立一般情形下反源问题的稳定性理论.我们针对确定和随机波动方程的散射模型,总结已有理论和数值结果并阐明研究思路.

为了对冲突事件进行结构化表示,识别事件的逻辑关系,发现事件演变规律和影响事件发展的关键节点,文章基于在线媒体新闻数据构建标准语料库,使用融合注意力机制和依存句法分析的图神经网络解决冲突事件抽取存在的事件要素长距离依赖问题,使用基于RoBERTa的fine-tuning方法解决隐性事件关系识别问题,提出冲突事件本体模型、冲突事件知识图谱的自动化构建框架和关键路径识别算法.结果表明,冲突事件识别算法SGAEE和基于RoBERTa微调的事件关系识别算法均优于对比算法,覆盖重要节点的最少路径识别算法MPCCN可以识别出影响冲突事件发展的关键事件和关键路径.文章提出的冲突事件知识图谱构建方法可以实现对冲突事件进行多层次和多视角的分析,帮助专家掌握冲突事件发展脉络,提高决策的全面性和科学性.

在火工品可靠性设计和评估中,高效准确地估计感度分布的参数及分位数至关重要.文章采用贝叶斯方法,建立感度数据的半参数广义线性模型,利用Hamiltonian Monte Carlo算法进行后验推断,并借助偏差信息准则和对数伪边际似然,以数据驱动的方式筛选“最优"模型.模拟比较验表明所提方法在小样本情况下可以获得感度分布参数的准确估计.最后,实际数据分析验证了新方法的有效性,并为感度数据的分析提供了一种替代和补充的建模工具.

Duadic码是一类重要的循环码,构造最小距离具有平方根下界的duadic码是一个有趣的研究问题.文章提出了两种构造最小距离具有平方根下界的odd-like duadic码的构造方法,得到两类最小距离具有平方根下界的odd-like duadic码.

The authors extend the marginal coordinate test for predictor contribution (Cook, 2004) to the case with multivariate responses. Instead of explicitly specifying the link functions between the responses and the predictors, an asymptotic test is proposed under the normality assumption of the predictors as well as an asymmetry assumption about the unknown regression mean function. When these assumptions are violated, the asymptotic test with elliptical trimming and clustering is still valid with desirable numerical performances.

This paper examines whether the parametric regression model is correctly specified for both source and target data and whether the regression pattern in the source domain aligns with that of the target domain. This evaluation is a critical prerequisite for applying model-based transfer learning methods under covariate shift assumptions. Traditional regression model checks and two-sample regression tests are insufficient to address this issue. To overcome these limitations, the authors propose a novel adaptive-to-regression test statistic that is asymptotically distribution-free. Under the null hypothesis, the test follows a chi-square weak limit, preserving the significance level and enabling critical value determination without resampling techniques. Additionally, the authors systematically analyze the test’s power performance, highlighting its sensitivity to different sub-local alternatives that deviate from the null hypothesis. Numerical studies, including simulations, assess finite-sample performance, and a real-world data example is provided for illustration.

液压作动器广泛应用于工业控制系统中, 其精确的位移控制对系统性能至关重要. 传统的物理建模方法难以准确捕捉液压作动器的非线性和时序特性, 限制了其在复杂环境下的应用. 文章提出了一种基于长短期记忆(LSTM)神经网络的液压作动器位移建模方法. 通过采集电压输入和位移输出的时序数据, 使用LSTM网络对液压作动器的动态行为进行建模. LSTM网络能够有效捕捉数据中的长期依赖关系, 适应液压系统的非线性时序特性. 在模型训练过程中, 采用均方误差作为优化目标, 并通过实验验证了LSTM网络的有效性. 实验结果表明, 相较于传统方法LSTM网络在测试集上取得了更低的预测误差, 具有更强的建模能力和较高的精度.

自创区作为引领创新发展的重要引擎, 以体制机制改革和政策先行先试为出发点, 辐射带动周边区域创新协调发展. 高铁网络的逐步完善为“双循环”打开新格局, 扩大了自创区创新溢出作用的范围. 文章基于2008-2019年中国高铁城市对数据, 采用多期双重差分法, 系统研究开通连接到自创区城市的高铁后对普通城市创新水平的提升效果和作用机制. 研究发现, 开通连接到自创区城市的高铁后能显著提高普通城市创新水平, 自创区城市的创新溢出作用对东部地区城市、创新环境较好的城市和大规模城市的效果更明显. 机制检验表明, 自创区城市的创新溢出效应通过利用创新禀赋、政府引导创新和示范带动效应三个方面实现. 文章为高铁网络化背景下自创区城市发挥示范带动作用, 进一步优化创新资源空间配置和加快发展新质生产力, 实现经济高质量发展提供了经验证据和政策启示.

当今时代,数据资源日益呈现出多源化的特点,即数据由不同的主体或来源方所持有.对多源数据进行融合开发是激活数据要素潜能、释放数据资源价值的前提和基础.然而,大部分多源数据仍然以“数据孤岛”的形式存在,数据间的互联互通仍然十分困难.与此同时,数据安全问题备受关注,如何在保护数据隐私的前提下实现多源数据的安全开发变得尤为重要.为了解决上述问题,文章提出了一种基于隐私保护的多源数据分析范式.该方法依托纵向联邦学习框架,允许各数据持有方在不暴露各自原始数据的前提下协作完成数据分析任务.与此同时,为了进一步防止攻击者对数据的恶意攻击, 文章在纵向联邦学习框架中引入差分隐私,通过在传输信息时加入噪声以保护个体隐私.最后,文章以工商管理中的企业违规风险预测为例展示了上述方法的实际应用效果,通过结合工商部门、市场监督管理部门、法院等多个部门的数据,提升了企业违规风险预测的准确性.

In this paper, disturbance attenuation is considered for linear systems with partially modeled disturbance. The disturbance signal is composed of known signals and uncertain parameters that leads to some difficulties for solving the disturbance rejection problem. To overcome this issue, the original system is reformulated as a linear parameter-varying (LPV) system by absorbing the unknown parameters in disturbance. Then an adaptive state-disturbance-feedback controller relying on a dictionary of state-feedback gains and disturbance-feedback gains is designed to estimate the uncertain parameters in the LPV system. Moreover, the presence of multiple variables in the sufficient condition given to reject the external disturbance of the LPV system also brings challenges. To tackle this problem, the quadratic separation technology is applied into the sufficient condition, and the original unsolvable condition can be successfully transferred into a solvable one. Furthermore, by adding the known part of the disturbance signal into the feedback loop, more information of the whole system can be utilized. Meanwhile, the asymptotical stability of the closed-loop system can be achieved and the $H_\infty$ performance index of the closed-loop system is verified to be smaller. Numerical simulations are given to illustrate the merits of the proposed approach.

The authors consider a two-period joint inventory and pricing decision problem for a retailer facing strategic customers with behavioral preferences such as reference dependence, loss aversion and risk preferences. The authors develop and analyze a model that accounts for customers' these behavioral preferences as well as value depreciation on the product, and makes predictions on the retailer's optimal decisions. Moreover, the authors demonstrate how the presence of these behavioral preferences and primary parameters will leverage the retailer's optimal decisions. It is revealed that strategic customers' loss aversion behavior could benefit the retailer from pushing up the regular price, the stocking quantity and hence the expected profit. However, customer's value depreciation on the product will drive down these aspects. To alleviate the negative effect of the strategic customers' behavioral preferences, the authors suggest the retailer applying inventory commitment strategy and price guarantee policy, which could increase the retailer's profit beyond the rational expectation equilibrium level in some situations.

How to evaluate the system reliability through the test data of components is one of the key challenges in the field of reliability. In this study, the authors focus on calculating the Bayesian lower credible limit. Although the approximation methods are widely used in reliability evaluation, how to apply them to the Bayesian context remains to be solved. Some previous studies have attempted to address this issue. However, their approaches might result in instability, and they have imposed significant constraints on component and system structures. A high-order saddlepoint approximation method for high accuracy is proposed, as well as a feasible procedure for determining the saddlepoint method's asymptotic variable. The proposed framework allows us to analyze the components following various posterior distributions without limiting the system structure. Numerical experiments on various systems are presented to demonstrate the effectiveness and accuracy of the proposed method. In comparison, it consistently outperforms other commonly used approximation approaches.

灾前规划应急设施和灾后抢修调度是提高城市道路交通系统韧性的关键策略.文章综合考虑受损道路位置、类型和修复工期多维不确定性以及抢修任务异质性,以应急设施覆盖率与系统综合韧性指标之和最大为目标,构建城市道路交通系统应急设施选址与抢修调度两阶段随机规划模型.根据所构建模型的结构特征,设计基于浓度集的样本均值近似算法.实验结果表明:文章提出的韧性最优恢复策略优于传统的随机恢复、边介数优先恢复、流量优先恢复和长度优先恢复策略,且基于浓度集的样本均值近似算法可以快速求解该问题.研究成果可为制定面向韧性提升的城市道路交通系统应急设施选址和抢修调度方案提供决策依据和算法支撑.

针对带有非匹配扰动和未建模动态的桥式吊车系统抗摆控制问题,提出一种基于未知系统动态估计器的滑模控制方法.通过引入一阶低通滤波器,设计未知系统动态估计器,补偿包括非匹配扰动在内的系统未知动态,提高系统抗干扰能力.构造基于两相幂次趋近律的滑模控制器,能较准确获得滑模变量的收敛时间,保证滑模变量具有较快的收敛速度.实验结果表明,所提控制方法能有效确保桥式吊车具有良好的抗摆性能和定位精度.

在试验设计领域,寻求有效的偏差下界是均匀设计理论研究中的一个重要方向.文章系统地研究了最新的均匀性准则——绝对偏差的下界问题,推导了对称平衡多水平设计的绝对偏差的一般性下界,并讨论了绝对偏差在两水平和三水平情形下的一些新的紧下界.这些关于绝对偏差下界的理论结果,可以作为评价设计均匀性的指标,也可作为构造均匀设计的基准.同时,文章提供了基于绝对偏差的均匀设计的构造方法.

文章运用OP法和事件研究法, 探讨区块链技术应用对企业全要素生产率和股票价格影响, 分析企业区块链技术应用是否切实促进了企业全要素生产率的提高, 或只是为公司股价制造了更多的“泡沫”? 文章主要的结论有:区块链技术应用主要通过降低融资约束促进全要素生产率的提高, 且对于大型企业和国有企业全要素生产率的提升更大; 此外, 企业公告区块链技术应用后, 公司股票价格水平显著升高, 即公司能够从基于区块链技术的公告中获取更高的股票溢价.

在实际研究中, 数据可能会来自不同分布, 用单一分布模型对数据拟合时会存在一定的局限性, 为克服这一问题, 运用混合模型可以更好地适应复杂的数据类型.指数分布与瑞利分布在可靠性分析中都是重要的寿命分布, 在删失数据背景下相关的混合模型研究较少, 文章提出双参数指数分布与双参数瑞利分布的混合模型, 并运用EM算法在右删失数据下对混合模型进行参数估计.同时进行大量的数值模拟研究, 验证了在有限样本下EM算法在所提混合模型估计的有效性.最后将模型运用到实际数据中, 并进行拟合优度检验, 验证了所提模型是合适的, 进一步说明该模型可以更好地适应复杂的数据特征, 具有一定的现实意义.

为插值指定的Frenet标架以及曲率和挠率, 提出空间五次F³插值曲线的构造及形状优化方法.空间曲线的F³连续是一种特殊的k阶Frenet标架连续, 可确保满足G²连续和挠率插值.首先, 构造一条插值F³数据的五次Bézier曲线, 其控制顶点包含两个表示曲线在端点处切矢长度的参数.然后, 通过最小化曲线的二次能量函数确定参数的最优值.最后, 定义目标函数为曲率和挠率加权平方和的积分, 提出另一种更优的形状优化方法.相比之前的G²插值方法, 新方法虽使用更严格的端点连续约束, 但总能生成曲率和挠率分布更满意的曲线形状.

针对端口哈密顿微分代数系统,通过$\varepsilon$-嵌入将其转化为含参数$\varepsilon$的端口哈密顿常微分系统.然后基于此参数化系统,提出了两种保结构的模型降阶方法.一是参数矩匹配法:构造参数系统相应频率参数$s$和嵌入参数$\varepsilon$的参数矩,进而通过参数矩匹配获得参数系统降阶模型.得到的参数降阶系统匹配原系统的若干参数矩.最后,取嵌入参数$\varepsilon=0$,得到原始端口哈密顿微分代数系统的保结构降阶模型;二是低秩平衡截断法:利用Laguerre函数构造参数系统的可控和可观Gram矩阵的低秩分解因子,通过投影变换得到近似平衡系统,最后截断较小Hankel奇异值对应的状态变量构造降阶模型.此过程具有一定的自适应性,可以构造满足给定精度的降阶模型,并且计算复杂度较低.上述两种算法均通过施密特正交化构造投影矩阵以保持原系统的微分代数结构.最后,通过数值算例验证了算法的有效性.

当今各类社会事件频繁发生,对人们的日常生活和社会发展产生深远影响.对未来事件的预测可以帮助分析人员理解社会动态,做出快速、准确的决策.文章提出了一种时序图学习模型,将事件的目标实体预测问题形式化为时序事件图中的推理任务.该方法首先根据历史事件信息构建了时序事件图,为了挖掘不同类型事件间的相互影响,设计了基于节点和边的双重注意力机制进行信息聚合.之后,模型通过门控循环单元编码时间信息,将嵌入向量输入全连接层实现对事件目标实体的预测.此外,鉴于许多社会事件在历史时间轴上重复出现的特点,模型进一步引入了拷贝机制,对预测函数进行了修正.在多个社会事件数据集上的实验表明,该方法优于其他的对比基线模型.

当前,中国推出了大规模设备更新改造政策,规模养殖粪污收集处理设施的更新改造涵盖在内,这一政策将如何影响规模养殖粪污治理及资源化利用,现有研究还未曾涉及.为此,文章构建了中小规模养殖企业“更新设备”策略与第三方公司“分类收费”策略的演化博弈流率基本入树模型,并在中小规模养殖企业的决策中引入前景理论与心理账户理论,充分考虑中小规模养殖企业的风险偏好意识,将期望效用函数与价值感知函数结合使用,基于“谁投资,谁获得设备更新补贴”的原则,论证了由哪个主体实施更新改造更有利于政策的推行,并对不同策略进行仿真分析,总结了规模养殖粪污收集处理设施更新改造的系统基模.研究发现:第一,将大规模设备更新补贴给予第三方公司,由第三方公司为中小规模养殖企业的粪污收集处理设施进行更新改造,更有利于政策的推进;第二,第三方公司应积极实施“分类收费”策略;第三,基于大规模设备更新改造的养殖粪污收集处理系统构成了成长上限基模,其杠杆解在于解除平衡回路的制约,即通过政策补贴鼓励第三方公司投资中小规模养殖企业的粪污收集处理设施,并采用分类收费策略获得更高质量的粪污原料,促进养殖企业投入粪污治理,进一步增加养殖企业获得补贴的比例.

本文采用文本分析方法, 从政府工作报告中提取经济治理专业化等隐含信息, 为地方经济增长目标偏差的来源提供了新的解释. 研究结果表明, 政府经济治理的专业化能够带来超预期的经济增长, 具体体现为实际经济增速超过政府工作报告中公布的预期目标, 这与其可以有效配置资源要素有关, 同时也受到 “晋升锦标赛” 等因素的激励. 随着地方政府绩效考核体系发生转变, 政府经济治理专业化水平对于经济增长目标偏差的影响程度下降, 但在不同地区或不同行政等级的城市中, 经济治理专业化在促进经济增速提升方面总体上是有效的. 进一步地, 如果地方政府经济治理中对于未来展望过多、执行力不强, 与本市或本省之前政策的创新度越低、遵从度越高, 则会降低经济治理专业化对经济增长目标偏差的影响, 不利于超预期经济增长目标的实现. 本文的研究对于更好地发挥政府在资源配置以及经济增长中的作用具有借鉴意义.

Driven by different promotion pressures, different decisions made by government officials may change the development path of cities and directly affect the ability to cope with crises, thus playing an all-encompassing and sustained role in urban economic resilience (UER). Considering that the COVID-19 pandemic that occurred at the end of 2019 is a large external shock, which may cause a large disturbance to economic resilience, this article tests the impact of official promotion pressure (OPP) on UER using data from 265 cities in China from 2004 to 2019. This paper also explores the role of the “National Civilized City” (NCC) selection mechanism in the process. The findings indicate a positive correlation and spatial spillover effect between OPP and UER. Moreover, the impact of both civilization status and civilization intensity on OPP is negative, which means that obtaining the title weakens OPP, and the positive effect on UER is weakened. And this effect becomes increasingly obvious with the increase in the duration of the title of NCC. Furthermore, the heterogeneity analysis yields rich findings, which provide new perspectives for the policy recommendations in this paper.