The main purpose of this paper is to give stability analysis and error estimates of the ultra-weak local discontinuous Galerkin (UWLDG) method coupled with a spectral deferred correction (SDC) temporal discretization method up to fourth order, for solving the fourth-order equation. The UWLDG method introduces fewer auxiliary variables than the local discontinuous Galerkin method and no internal penalty terms are required for stability, which is efficient for high order partial differential equations (PDEs). The SDC method we adopt in this paper is based on second-order time integration methods and the order of accuracy is increased by two for each additional iteration. With the energy techniques, we rigorously prove the fully discrete schemes are unconditionally stable. By the aid of special projections and initial conditions, the optimal error estimates of the fully discrete schemes are obtained. Furthermore, we generalize the analysis to PDEs with higher even-order derivatives. Numerical experiments are displayed to verify the theoretical results.

This report presents a series of implicit-explicit (IMEX) variable stepsize algorithms for natural convection equations. The presented method requires a minimally intrusive modification to an existing program, does not add to the computational complexity, and is conceptually simple. Here, IMEX means the nonlinear term is treated fully explicitly, while the remaining terms are treated implicitly. Due to the increasing demand for low memory solvers, the addition of time adaptive can improve the accuracy and efficiency of the algorithms. For the first-order algorithm, we prove the stability of the variable stepsize backward Euler scheme combined with Adams-Bashforth 2 (VSS BE-AB2) and analyze convergence. Then, the stability of Constant Timestep Filtered-BE-AB2 (BE-AB2+F) is proved. Moreover, we construct adaptive algorithms by extending the approach to variable stepsize. Finally, numerical tests confirm the convergence rates of our method and validate the theoretical results.

Resonant tunneling diodes (RTDs) exhibit a distinctive characteristic known as negative resistance. Accurately calculating the tunneling bias energy is indispensable for the design of quantum devices. This paper conducts a thorough investigation into the current-voltage (I-V) characteristics of RTDs utilizing various numerical methods. Through a series of numerical experiments, we verified that the transfer matrix method ensures robust convergence in I-V curves and proficiently determines the tunneling bias for energy potential functions with discontinuities. Our numerical analysis underscores the significant impact of variations in effective mass on I-V curves, emphasizing the need to consider this effect. Furthermore, we observe that increasing the doping concentration results in a reduction in tunneling bias and an enhancement in peak current. Leveraging the unique features of the I-V curve, we employ shallow neural networks to accurately fit the I-V curves, yielding satisfactory results with limited data.

For complex-valued or quaternionic neural networks, scholars and researchers usually decompose them into real-valued systems. The decomposed real-valued systems are equivalent to original systems. Then, the dynamical behaviors of real-valued systems obtained are investigated, including stability, synchronization, and chaos etc. In this paper, a class of quaternionic neural networks with time-varying delays is investigated. First, by designing a suitable PI controller, synchronization of the considered chaotic system is realized. By using a non-decomposition method and structuring a novel Lyapunov functional, sufficient conditions are derived to guarantee synchronization between the drive-response systems. It is worth mentioning that, unlike other methods, our approach does not require breaking down the quaternionic neural networks into four separate real-valued systems. Furthermore, we demonstrate the practical application of these chaotic quaternionic neural networks with time-varying delays in image encryption and decryption. Based on one sequence of chaotic signal from state trajectory of single quaternion-valued neuron and a new encryption algorithm, the application of chaotic system proposed, that is, image encryption, is researched. The process of image decryption is simply the reverse of the encryption process. Finally, numerical simulation examples are provided to validate the effectiveness of the designed PI controller and performance of image encryption and decryption.

This paper deals with the numerical solutions of two-dimensional (2D) semi-linear reaction-diffusion equations (SLRDEs) with piecewise continuous argument (PCA) in reaction term. A high-order compact difference method called I-type basic scheme is developed for solving the equations and it is proved under the suitable conditions that this method has the computational accuracy $\mathcal{O}\left(\tau^2+h_x^4+h_y^4\right)$, where $\tau, h_x$ and $h_y$ are the calculation stepsizes of the method in $t$-, $x$ - and $y$-direction, respectively. With the above method and Newton linearized technique, a II-type basic scheme is also suggested. Based on the both basic schemes, the corresponding I- and II-type alternating direction implicit (ADI) schemes are derived. Finally, with a series of numerical experiments, the computational accuracy and efficiency of the four numerical schemes are further illustrated.

The $k$-th ($k=3,4,5$) order backward differential formula ($\mathrm{BDF} k$) is applied to develop the high order energy stable schemes for the molecular beam epitaxial model with slope selection. The numerical schemes are established by combining the convex splitting technique with the $k$-th order accurate Douglas-Dupont stabilization term in the form of $S \tau^{k-1} \Delta_h\left(\phi^n-\phi^{n-1}\right)$. With the help of the new constructed discrete gradient structure of the $k$-th order explicit extrapolation formula, the stabilized $\mathrm{BDF} k$ scheme is proved to preserve energy dissipation law at the discrete levels and unconditionally stable in the energy norm. By using the discrete orthogonal convolution kernels and the associated convolution embedding inequalities, the $L^2$ norm error estimate is established under a weak constraint of time-step size. Numerical simulations are presented to demonstrate the accuracy and efficiency of the proposed numerical schemes.

This study proposes a class of augmented subspace schemes for the weak Galerkin (WG) finite element method used to solve eigenvalue problems. The augmented subspace is built with the conforming linear finite element space defined on the coarse mesh and the eigen-function approximations in the WG finite element space defined on the fine mesh. Based on this augmented subspace, solving the eigenvalue problem in the fine WG finite element space can be reduced to the solution of the linear boundary value problem in the same WG finite element space and a low dimensional eigenvalue problem in the augmented subspace. The proposed augmented subspace techniques have the second order convergence rate with respect to the coarse mesh size, as demonstrated by the accompanying error estimates. Finally, a few numerical examples are provided to validate the proposed numerical techniques.

In this paper, we develop the stabilization-free virtual element method for the Helmholtz transmission eigenvalue problem on anisotropic media. The eigenvalue problem is a variable-coefficient, non-elliptic, non-selfadjoint and nonlinear model. Separating the cases of the index of refraction n ≠ 1 and n ≡ 1, the stabilization-free virtual element schemes are proposed, respectively. Furthermore, we prove the spectral approximation property and error estimates in a unified theoretical framework. Finally, a series of numerical examples are provided to verify the theoretical results, show the benefits of the stabilization-free virtual element method applied to eigenvalue problems, and implement the extensions to high-order and high-dimensional cases.

We analyze the long-time behavior of numerical schemes for a class of monotone stochastic partial differential equations (SPDEs) driven by multiplicative noise. By deriving several time-independent a priori estimates for the numerical solutions, combined with the ergodic theory of Markov processes, we establish the exponential ergodicity of these schemes with a unique invariant measure, respectively. Applying these results to the stochastic Allen-Cahn equation indicates that these schemes always have at least one invariant measure, respectively, and converge strongly to the exact solution with sharp time-independent rates. We also show that these numerical invariant measures are exponentially ergodic and thus give an affirmative answer to a question proposed in [J. Cui et al., Stochastic Process. Appl., 134 (2021)], provided that the interface thickness is not too small.

In this paper, we propose and analyze two second-order accurate finite difference schemes for the one-dimensional heat equation with concentrated capacity on a computational domain $\Omega=[a, b]$. We first transform the target equation into the standard heat equation on the domain excluding the singular point equipped with an inner interface matching (IIM) condition on the singular point $x=\xi \in(a, b)$, then adopt Taylor's expansion to approximate the IIM condition at the singular point and apply second-order finite difference method to approximate the standard heat equation at the nonsingular points. This discrete procedure allows us to choose different grid sizes to partition the two sub-domains $[a, \xi]$ and $[\xi, b]$, which ensures that $x=\xi$ is a grid point, and hence the proposed schemes can be generalized to the heat equation with more than one concentrated capacities. We prove that the two proposed schemes are uniquely solvable. And through in-depth analysis of the local truncation errors, we rigorously prove that the two schemes are second-order accurate both in temporal and spatial directions in the maximum norm without any constraint on the grid ratio. Numerical experiments are carried out to verify our theoretical conclusions.

The strong convergence of an explicit full-discrete scheme is investigated for the stochastic Burgers-Huxley equation driven by additive space-time white noise, which possesses both Burgers-type and cubic nonlinearities. To discretize the continuous problem in space, we utilize a spectral Galerkin method. Subsequently, we introduce a nonlinear-tamed exponential integrator scheme, resulting in a fully discrete scheme. Within the framework of semigroup theory, this study provides precise estimations of the Sobolev regularity, L^∞ regularity in space, and Hölder continuity in time for the mild solution, as well as for its semi-discrete and full-discrete approximations. Building upon these results, we establish moment boundedness for the numerical solution and obtain strong convergence rates in both spatial and temporal dimensions. A numerical example is presented to validate the theoretical findings.

In this paper, we propose a systematic approach for accelerating finite element-type methods by machine learning for the numerical solution of partial differential equations (PDEs). The main idea is to use a neural network to learn the solution map of the PDEs and to do so in an element-wise fashion. This map takes input of the element geometry and the PDE’s parameters on that element, and gives output of two operators: (1) the in2out operator for inter-element communication, and (2) the in2sol operator (Green’s function) for element-wise solution recovery. A significant advantage of this approach is that, once trained, this network can be used for the numerical solution of the PDE for any domain geometry and any parameter distribution without retraining. Also, the training is significantly simpler since it is done on the element level instead on the entire domain. We call this approach element learning. This method is closely related to hybridizable discontinuous Galerkin (HDG) methods in the sense that the local solvers of HDG are replaced by machine learning approaches. Numerical tests are presented for an example PDE, the radiative transfer or radiation transport equation, in a variety of scenarios with idealized or realistic cloud fields, with smooth or sharp gradient in the cloud boundary transition. Under a fixed accuracy level of 10^-3 in the relative L² error, and polynomial degree p = 6 in each element, we observe an approximately 5 to 10 times speed-up by element learning compared to a classical finite element-type method.

装备保障需求预测能力是装备保障能力的重要内容, 面向任务的保障需求预测直接决定任务成败. 针对任务中保障需求预测的成组和分层特性, 运用多层网络系统进化思想, 建立基于成组和分层的装备保障需求预测模型. 通过分析装备 “任务-系统” 结构关系, 考虑多阶段任务、多层次需求和多层次任务成功率特点, 建立装备保障需求层次模型和任务成功率模型, 设计单装和编组两个层次的保障需求预测算法. 案例分析表明, 任务成功率要求较高时, 需增加重点资源组需求量, 任务成功率要求较低时, 可按需调整参与任务的系统要素.

实现系统数据的不确定性挖掘与校准是提升数据分析质量和管理决策精准度的重要前提. 本文基于云模型理论提出了一种面向决策分析问题的云校准方法, 通过云数字特征提取、云发生器构建和隶属度转换实现原始指标到隶属度的规范化校准. 与现有数据归一化、标准化、函数转换校准等方法进行案例数据的校准实验对比, 发现新提出的校准方法在综合建模性能评价上优于其他几种方法. 另外采用大中小型样本、高中低维特征的 11 个数据集构建不同方法校准结果的随机森林、逻辑回归和神经网络等机器学习模型, 进一步验证了新方法的优越性与稳健性, 研究成果为有效提升数据分析质量和决策建模提供了一定的理论指导和方法论支持.

多载位自动小车一次能同时搬运多个货箱, 这既增加了自动小车的作业能力和灵活性, 又给自动小车的作业优化带来了更大的挑战. 为此, 针对多载位机器人存取系统的储位分配与路径规划协同优化问题, 建立了以最小化补货总距离和货箱加权距离之和为目标的非线性混合 0-1 整数规划模型, 并对其进行线性化; 根据该问题包含储位分配、车辆分配和路线排序三部分决策的特征, 设计了 7 个局部搜索算子、自适应选择算子策略、全局路径规划修复策略和自适应大邻域搜索算法; 基于 400 个算例的仿真对比与统计检验结果表明, 自适应大邻域搜索算法的求解效果整体优于精确算法、遗传算法、模拟退火算法和变邻域搜索算法; 与上述算法相比, 问题的规模越大, 本文算法的优势越明显; 与两阶段优化相比, 协同优化在求解该问题时具有更好的效果; 进一步分析发现, 选择合适的多载位自动小车 (比如, 最大载位数为 6$\sim$7) 和补货策略 (比如, 缺货比例为 0.25$\sim $0.30), 有助于企业综合权衡其成本与效率.

在航空发动机中, 外部管路的合理布局直接影响管路系统的性能表现. 当前关于航空发动机管路多目标布局优化的研究由于存在具有冲突特性的多个目标, 从而导致最终布局效果的欠佳. 本文以管路长度最短、管路弯头数最少和管路能量值最小为优化目标, 建立了航空发动机管路多目标布局优化 (MOPLP-AE) 的数学模型. 本文提出了一种创新的基于三方博弈的改进遗传算法 (TGIGA), 首次将博弈论应用于 MOPLP-AE 的多目标优化问题, 将各优化目标视作博弈参与者, 通过纳什均衡点实现全局稳定解, 有效解决了目标冲突和 Pareto 最优解评估难题. TGIGA 融合了帕累托分类、优化的杂交与变异策略, 并集成 2-opt 局部搜索, 显著提升了求解效率和解的质量. 通过航空发动机的仿真实例验证了纳什均衡解的有效性及方法的可行性. 本研究有效提高了航空发动机管路布局的客观性、准确性和布局效率, 为减少材料使用和制造复杂性、降低泄漏和故障风险、加速设计周期并降低生产成本提供了更为全面的数学模型以及一套新的方法与求解途径.

多主体参与项目复杂团队合作模式是国家和企业提高竞争力与创新能力的重要途径之一, 但却存在较高的管理难度和复杂性. 鉴于激励机制在促进合作中的重要作用, 以及互惠偏好对人类行为的重要影响, 本文考虑了任务关联性和努力冲突等项目内外部因素对各参与主体努力水平的动态影响, 构建了基于互惠偏好和代理人努力水平不确定的多任务随机微分博弈模型, 探讨了项目复杂团队的激励机制选择, 以及互惠偏好在其中的影响作用. 研究结果表明: 1) 代理人的互惠偏好会影响其最优努力选择, 并会影响激励机制的适用条件; 2) 互惠偏好并不总是有利于项目产出; 3) 提高利润分享系数并不一定总能带来正向的激励作用, 这与项目属性、代理人的互惠偏好、激励机制类型等因素密切相关. 本文的研究可以为项目复杂团队激励机制设计与管理提供理论支持与策略建议.

数字技术推动下, 越来越多平台企业利用人们的社交需求, 通过直播带货、好友转发、视频 “种草” 等方式进行产品推广. 以社交互动为关键的社交化电商兴起. 为探讨社交行为对平台供应链的影响, 本文构建一个由制造型平台、入驻平台的制造商、实体零售商组成的二级制造型平台供应链. 考虑现实中销售渠道的多样性, 制造型平台允许消费者线上购买线下取货 (BOPS). 制造型平台供应链内价格竞争与 BOPS 合作共存. 本文利用非合作-合作两型博弈方法构建模型并求解, 得到制造型平台供应链成员的最优策略与利润. 分析消费者麻烦成本、对入驻平台的制造商的偏好、社交行为对均衡结果的影响. 研究表明, 社交化电商下, 制造型平台与实体零售商通过 BOPS 利润共享可以达成 BOPS 合作并创造联盟最大利益, 实现制造型平台与实体零售商的双赢. 在价格竞争与 BOPS 合作共存下, 加大社交行为投资、提高社交行为影响力对制造型平台与入驻制造商并不一定总是有利的, 但能够有效增大消费者剩余.

基于切花产业的新兴实践, 本文建立了一个具有两期货架期的易逝品再制造库存系统. 该系统的显著特征在于考虑了旧产品的感知质量. 关注到产品质量恶化和需求信息更新, 本文将销售季节分为两个周期: 第一周期的特征是需求不确定性, 第二周期的特征是新旧产品的竞争. 采用逆向动态规划的方法, 获得企业在不同情形下的最优订购和定价决策. 研究表明, 再制造成本是影响企业再制造策略选择的关键因素, 而剩余库存和感知质量则是影响企业生产和再制造决策的核心要素. 若再制造成本较低, 企业应当实施完全再制造策略; 否则, 应当实施部分再制造策略. 在部分再制造策略中, 当剩余库存水平较高或感知质量较低时, 企业不进行再制造. 最后, 数值实验表明再制造策略有助于降低安全库存的储备, 显著提升收益; 减少生产量, 缓解切花对环境的负面影响, 具有较高的经济价值和生态价值.

现有观点大都认为, 生产规模不经济性通常会削弱农户对绿色技术采纳的意愿. 然而, 在农户具有绿色技术采纳成本信息优势的农业供应链中, 信息不对称的存在可能会改变这一结论. 本研究在信息不对称的框架下, 采用信息经济学中的机制设计方法, 构建了农户与零售商之间的委托-代理模型, 探讨了零售商的最优信息甄别合同设计和农户的绿色技术采纳策略. 研究发现: 当农户的生产不规模经济性水平较高时, 采纳绿色技术能够提高生产数量, 进而对农户更有利; 农户拥有的成本信息优势使其能够获得额外的信息租金, 而信息不对称则会导致零售商和供应链系统利润下降; 此外, 当农户与零售商之间的议价能力满足一定条件时, 双方可以达成信息共享谈判并实现 “双赢”. 最后, 通过案例分析验证了理论结果. 相关研究结论为农业供应链管理中农户绿色技术采纳以及零售商的合同设计决策提供了理论依据.

“双碳” 目标下, 企业为实现 “节能降碳” 往往需要进行低碳投资, 在带来资金压力的同时还会引起产出的不确定. 为研究低碳政策下具有产出不确定性的制造商面临资金约束时的融资与减排决策, 本文构建一条由单一制造商和单一供应商组成的两级供应链, 运用 Stackelberg 博弈模型考察制造商分别采用债权融资、股权融资与投贷联动融资时的供应链均衡状态, 比较了三种融资模式的碳减排量、市场需求、产品价格、供应链成员利润, 并分析了产出不确定性对上述供应链参数的影响. 研究表明: 股权融资在三种融资模式中具有相对优势, 主要体现在碳减排量、低碳产品市场需求、供应商利润等参数皆为最佳, 但不能确保制造商利润水平最高. 为引导制造商理性选择融资模式, 以具有相对优势的股权融资为例, 验证了供应链成员合作决策存在的优越性, 运用改进 Nash 讨价还价博弈模型协调供应链, 并解得供应链完美协调的最优利润分配比例系数.

全基因组关联研究通常采用两阶段分析策略,两个阶段的结果相互验证,以减少混杂因素的干扰,从而有效地降低假关联比例.现有文献中关于两阶段分析主要针对单一疾病,尚未见对多个疾病的情况.针对多个疾病,文章提出了两种两阶段分析方法:独立分析与联合分析.针对两种方法的不同阶段,分别构建表型与单核苷酸多态性位点关联检验的二次型统计量.在零假设成立的条件下,构建的统计量与混合卡方随机变量加权和具有相同的渐近分布,并利用近似分布给出了检验的$p$-值.数值结果表明,两种方法在不同样本量和显著性水平下均表现出较高的统计功效,当次等位基因频率MAF (minor allele frequency)较小时,联合分析优于独立分析;而当MAF较大时,独立分析优于联合分析,同时随着MAF的增加,两种分析的功效都在增加.基于在小鼠基因型-表型关联研究中的实证结果显示,两种分析方法都能有效识别出48个具有显著关联的单核苷酸突变位点.

Barzilai-Borwein (BB)型算法是一种被广泛应用于求解无约束优化问题的有效方法,其具有存储量小、迭代简单等优点,而积极集识别技术具有准确识别最优解附近的零分量的强大能力,该技术可以将每个迭代点区分成零分量和非零分量两部分.文章提出了一种求解大规模$\ell_1$正则化问题的子空间Barzilai-Borwein (BB)方法,通过积极集识别技术,结合非单调线搜索技术和合适的BB步长,在适当的条件下,文章证明了所提出算法的收敛性.通过数值实验与现有的算法进行比较,证明了所提出算法运行的CPU时间更短、迭代次数更少,数值性能上更优.

国际政治和经济环境日趋复杂多变, 产业链供应链韧性和安全面临极大挑战. 作为供应链管理中的重要环节, 采购因受多种不确定因素 (如供应中断、运输延误、价格波动等) 的影响而面临巨大挑战, 直接影响企业成本和供应链网络的韧性. 本文针对风险冲击下韧性供应商选择与订单分配问题进行综述, 给出了问题的基本描述和一般研究框架, 重点对四类不同风险的描述及建模、风险应对策略、三种主流的建模方法、通常考虑的因素、问题的求解算法等方面进行了分析和总结, 最后从不同角度对未来研究方向进行了展望.

粮食是安天下、稳民心、惠民生的战略性资源.确保粮食供应链安全稳定是大国经济必须具备的重要特征.在追求经济高效与资源节约的当下,文章以成本与耗损量最小为主要优化目标,构建粮食“供应商-加工商-经销商”协调优化模型,通过引入第二代非支配遗传算法求解,并结合AHP-CRITIC组合赋权法进行精准施策.案例分析表明:NSGA-II算法可以获得数量较多、质量较好的Pareto解,据此找到能最大限度降低粮食三级供应链成本与损耗量的优质解集,以便提升粮食供应链韧性和安全水平,实现三级供应链双重优化,为国家实现“稳粮保供”提供科学决策依据.

推广应用港航区块链电子放货平台, 已成为当前提升港口通关效率和航运物流透明度的关键抓手. 然而, 区块链电子放货平台投资及政府补贴规律尚不清晰. 为此, 本文构建由一个港口、两个航运公司和一系列托运人组成的航运供应链系统. 首先, 对传统集装箱放货情形、港口投资区块链电子放货平台情形、航运公司投资区块链电子放货平台情形和第三方 (非港口或航运企业) 投资区块链电子放货平台的四种博弈模型进行分析. 研究发现, 与传统集装箱放货模型相比, 无论港口投资、航运公司投资还是第三方投资区块链电子放货平台均提高了港口通关效率和航运物流透明度, 当投资效率在一定阈值范围内时, 港航系统成员效益随投资效率的增加而下降, 港口投资下社会福利最高. 其次, 讨论政府透明度补贴和区块链技术创新研发补贴对港航系统成员效益影响. 结果表明, 合理范围的政府补贴有利于提高港航系统成员效益, 港口和航运公司经济效益分别在区块链技术创新研发补贴和透明度补贴模型下改善效果更好. 本文研究结论为港航区块链电子放货平台的推广应用提供有益管理启示.

文章综合考虑期权定价模型的假设条件及碳期权标的资产价格的变化特征,基于2021年1月4日至2021年9月27日EUA DEC22碳期货期权市场数据,利用遗传算法对定价模型参数进行估计.根据稳定的参数估计值对B-S模型、分形布朗运动模型及Heston模型的期权定价效果进行对比分析,选取最适合碳期权市场的定价模型,并为碳市场定价机制的完善及平稳运行提供相关建议.结果显示:Heston模型在碳期权市场的定价表现最优,分形布朗运动模型次之,B-S模型相对较差.因此基于Heston模型对碳期权进行定价,能够提高碳期权定价精度,有助于完善碳市场定价机制,规避碳市场交易风险,保障碳市场的平稳运行,进而促进“双碳”战略目标的实现.

灾害极端情境下传统科层制应急管理模式存在救援资源错配严重、信息不对称、动态调整能力匮乏等诸多问题, 智慧应急互助信息平台依托交互式社会化应急救援模式为解决这些问题提供了新的思路. 本文基于 “物理-社会-信息” 三元空间资源视角, 构建了智慧应急互助信息平台下求助者和施救者救援资源动态配置的微分博弈模型, 分析了应急互助信息平台中信息共享、信息不足或失真随机干扰、应急救援时间、应急资源配置效率因子等重要参数对求助者和施救者应急互助资源配置均衡决策和效用稳定状态的影响, 探究了求助者信息化能力的异质性对参与主体行为决策和系统效用的作用机制. 研究表明, 1) 平台存在信息不足或失真等随机干扰因素时, 求助者和施救者应急互助资源配置量降低, 效用也会受到抑制和削减. 2) 随着参与主体在应急互助信息平台上共享信息程度提升, 求助者和施救者会投入更多资源, 能够有效增强灾害救援的韧性安全水平. 3) 只有信息共享程度较大时, 求助者和施救者资源配置量和效用才随着资源配置效率因子的增加呈递增的动态变化关系, 且与求助者异质性无关. 4) 识别求助者信息化能力异质性并非总是明智之举. 当求助者信息化能力处于较低水平、低于某阈值时, 识别其异质性能够通过精准定位需求和个性化响应, 提升参与主体的应急资源配置量和系统效用; 随着求助者信息化能力逐渐增加, 将求助者同质化则更有助于实现精准资源匹配和统一的资源调配. 研究通过应急互助信息平台下参与主体应急互助资源最大程度配置和动态优化, 为提高灾害应对韧性提供理论支撑.

产品属性权重表示消费者对产品属性的重视程度,是消费者进行产品选择和商家进行产品优化的重要依据.但目前基于在线评论确定属性权重的方法存在主观性较大和结果易偏差的弊端.因此,文章提出了一种新的产品属性权重确定方法.首先基于改进LDA主题模型挖掘出产品属性,然后基于属性情感分析对评论中属性满意度进行标注.之后先对满意度标注样本进行$N$折交叉处理,然后计算所有不同样本下各属性的信息增益值,以此对各属性重要性值分布进行参数估计.然后,计算两两分布的距离因子,最后运用AHP方法确定属性权重.文章以肉类生鲜产品为对象进行实证分析,结果表明,该方法能够为各属性准确赋权来表达消费者对属性的重视程度.

本文研究拍卖中竞标者身份维度的信息不对称问题. 我们构建了包含两个竞标者的第一价格密封共同价值拍卖模型, 其中一个竞标者有可能是比对手拥有更多信息的专家. 我们描述了该博弈的贝叶斯纳什均衡. 进一步地, 我们在该模型的基础上增加了一个信号传递机制, 用来描述竞标者可能进行的有关身份信息的交流. 我们发现, 在诚实披露的假设下, 外行竞标者可以通过披露自身身份来获得更高回报, 最终形成分离的完美贝叶斯均衡. 反之在廉价交流的假设下, 专家竞标者总是希望让他人误认为自己是外行, 而外行竞标者也有可能希望让他人误认为自己是专家, 导致身份的信息不对称无法消除.