基于改进SIR模型的知识密集型企业隐性知识转移研究

通过对比分析传染病传染机制与隐性知识的转移过程,引入SIR模型,根据隐性知识转移的特点对SIR模型进行改进,并在组织遗忘视角下,将员工知识遗忘细分为主动遗忘和被动遗忘,建立知识密集型企业隐性知识转移模型.而后,通过Matlab对该模型的演化函数进行模拟,分析知识接收方占比、知识转移能力、知识遗忘率及核心员工流失率对知识密集型企业隐性知识转移的影响.最后,基于模拟分析结果,为提升知识密集型企业隐性知识转移效益提出相应策略.     

不同补贴对象下闭环供应链信息共享价值研究

在一个制造商和一个零售商组成的二级闭环供应链中,为研究政府补贴下供应链需求信息共享对决策的影响及共享价值,针对两种补贴对象,构建并求解无信息共享和信息共享博弈模型,并进行仿真验证.研究表明:1)两种补贴对象下,制造商均能从信息共享中获益,零售商仅在绿色成本和回收成本较低时才会从信息共享中获益;绿色成本和回收成本稍高时,设计基于谈判势力的信息补偿机制能有效促进零售商共享信息.2)产品绿色度和回收率随预测需求量的提高而提高,批发价和零售价仅在回收成本较低时,才会随预测需求量的提高而提高.3)在仅补贴一方情况下,当补贴对象为低补贴一方时,两个主体所获的信息共享价值大;若对两者的补贴均较低,两个主体均希望补贴对象为零售商;若对两者的补贴均较高,零售商不愿共享需求信息.     

Enhanced 1T′-Phase Stabilization and Chemical Reactivity in a MoTe2 Monolayer through Contact with a 2D Ca2N Electride

Among the widely studied 2D transition metal dichalcogenides (TMDs), MoTe₂ has attracted special interest for phase-change applications due to its small 2H-1T′ energy difference, yet a large scale phase transition without structural disruption remains a significant challenge. Recently, an interesting long-range phase engineering of MoTe₂ has been realized experimentally by Ca₂N electride. However, the interface formed between them has not been well understood, and moreover, it remains elusive how the presence of Ca₂N would affect the basal plane reactivity of MoTe₂. To address this, we performed density functional theory (DFT) calculations to investigate the potential of tuning the phase stability and chemical reactivity of a MoTe₂ monolayer via interacting with Ca₂N to form a van der Walls heterostructure. We found that the contact nature at the 2H-MoTe₂/Ca₂N interface is Schottky-barrier-free, allowing for the spontaneous electron transfer from Ca₂N to 2H-MoTe₂ to make it strongly n-type doped. Moreover, Ca₂N doping significantly lowers the energy of 1T′-MoTe₂ and dynamically triggers the 2H-to-1T′ transformation. The Ca₂N-induced phase modulation can also be applied to tune the phase energetics of MoS₂ and MoSe₂. Furthermore, using H adsorption as the testing ground, we also find that the H binding on the basal plane of MoTe₂ is enhanced after forming heterostructure with Ca₂N, potentially providing basis for surface modification and other related catalytic applications.     

Insight into room-temperature catalytic oxidation of NO by CrO2(110): A DFT study

The first-principles DFT calculations together with microkinetic analysis reveal the complex catalytic mechanism of low-content NO oxidation on CrO₂(110) at room temperature. It quantitatively makes clear that CrO₂(110) can exhibit considerable activity with the Mars-van-Krevelen mechanism preferred, and the nitrate species serves as the key poisoning species.     

Universality of deep convolutional neural networks

Deep learning has been widely applied and brought breakthroughs in speech recognition, computer vision, and many other domains. Deep neural network architectures and computational issues have been well studied in machine learning. But there lacks a theoretical foundation for understanding the approximation or generalization ability of deep learning methods generated by the network architectures such as deep convolutional neural networks. Here we show that a deep convolutional neural network (CNN) is universal, meaning that it can be used to approximate any continuous function to an arbitrary accuracy when the depth of the neural network is large enough. This answers an open question in learning theory. Our quantitative estimate, given tightly in terms of the number of free parameters to be computed, verifies the efficiency of deep CNNs in dealing with large dimensional data. Our study also demonstrates the role of convolutions in deep CNNs.     

Information percolation and cutoff for the random‐cluster model

We consider the random‐cluster model (RCM) on with parameters p∈(0,1) and q ≥ 1. This is a generalization of the standard bond percolation (with edges open independently with probability p) which is biased by a factor q raised to the number of connected components. We study the well‐known Fortuin‐Kasteleyn (FK)‐dynamics on this model where the update at an edge depends on the global geometry of the system unlike the Glauber heat‐bath dynamics for spin systems, and prove that for all small enough p (depending on the dimension) and any q>1, the FK‐dynamics exhibits the cutoff phenomenon at with a window size , where λ_∞ is the large n limit of the spectral gap of the process. Our proof extends the information percolation framework of Lubetzky and Sly to the RCM and also relies on the arguments of Blanca and Sinclair who proved a sharp mixing time bound for the planar version. A key aspect of our proof is the analysis of the effect of a sequence of dependent (across time) Bernoulli percolations extracted from the graphical construction of the dynamics, on how information propagates.