A note on the paper “characterization of all optimal solutions and parametric maximal flows in networks” of G. Ruhe (optimization 16 (1985) 1, 51-61)

First it is shown that the implementation of the algorithm proposed in the considered paper may cause some problems if no further specification is made as to one of its steps. Secondly, such a specification is suggested.     

Effective medium representation and complex modes in 3D periodic metamaterials made of cubic resonators with large permittivity at mid-infrared frequencies

We review some of the techniques that lead to the effective medium representation of a three-dimensional (3D) periodic metamaterial. We consider a 3D lattice of lead telluride cubic resonators at mid-infrared (MIR) frequencies. Each cubic resonator is modeled with both an electric and a magnetic dipole, through a method called the dual dipole approximation. The electric and magnetic polarizabilities of a cubic resonator are computed via full-wave simulations by mapping the resonator's scattered field under electric/magnetic excitation only to the field radiated by an equivalent electric/magnetic dipole. We then analyze the allowed modes in the lattice, with transverse polarization and complex wavenumber, highlighting the attenuation that each mode experiences after one free space wavelength. We observe the presence of two modes with low attenuation constant, dominant in different frequency ranges, able to propagate inside the lattice: this allows the treatment of the metamaterial as a homogeneous material with effective parameters, evaluated by using various techniques. We then show that the metamaterial under analysis allows for the generation of artificial magnetism (i.e., relative effective permeability different than unity, including negative permeability with low losses) at MIR frequencies.     

Nanoelectronic content-addressable memory

A novel nanoelectronic single-electron content addressable memory is designed and simulated. The proposed memory has three important building blocks: a storage block, a comparison block and an addressing block. These building blocks were built based on single-electron circuits such as Reset-Set latches, exclusive-or gates and a WTA neural network. Each one of the building blocks was separately adjusted to provide room temperature operation before being connected together. Some analyses concerning stability of each block and of the whole memory circuit were made. The nanoelectronic memory was successfully validated by simulation.     

The Story of Collective Action: The Emergence of Ideological Leaders,Collective Action Network Leaders,and Cross-Sector Network Partners in Civil Society

Collective action and community ecology theories frame this study of longitudinal interorganizational networks in Croatia during the country's political transition. As time progresses toward political stability, grass-roots organizing activities shift through participation in new networks. Although engaged cross-sector communication was important in early stages of the transformation, homophilous partnering emerged as the system stabilized. System stability left room for organizations to exit the collective action network but with costs associated with centralized organizing. Over time, organizations embodied roles as ideological leaders, collective action network leaders, and within-sector network partners. We offer a unique contribution to community ecology and collective action theories with a communication-centered framework that emphasizes the nature of communication in interorganizational networks over a 4-year period.     

Predictive control of uncertain nonlinear parabolic PDE systems using a Galerkin/neural-network-based model

In this paper, a model predictive control (MPC) scheme for a class of parabolic partial differential equation (PDE) systems with unknown nonlinearities, arising in the context of transport-reaction processes, is proposed. A spatial operator of a parabolic PDE system is characterized by a spectrum that can be partitioned into a finite slow and an infinite fast complement. In this view, first, Galerkin method is used to derive a set of finite dimensional slow ordinary differential equation (ODE) system that captures the dominant dynamics of the initial PDE system. Then, a Multilayer Neural Network (MNN) is employed to parameterize the unknown nonlinearities in the resulting finite dimensional ODE model. Finally, a Galerkin/neural-network-based ODE model is used to predict future states in the MPC algorithm. The proposed controller is applied to stabilize an unstable steady-state of the temperature profile of a catalytic rod subject to input and state constraints.     

Model selection based on Lorenz and concentration curves,Gini indices and convex order

In order to determine an appropriate amount of premium, statistical goodness-of-fit criteria must be supplemented with actuarial ones when assessing performance of a given candidate pure premium. In this paper, concentration curves and Lorenz curves are shown to provide actuaries with effective tools to evaluate whether a premium is appropriate or to compare two competing alternatives. The idea is to compare the premium income for sub-portfolios gathering low risks (identified as low by means of the premiums under consideration) to the true one, or equivalently, to the actual losses. Numerical illustrations performed on hypothetical data and real ones demonstrate the usefulness of the proposed approach.     

局部纠缠交换对量子随机网络鲁棒性的影响

利用纠缠渗流的方法，在量子随机网络中任意两个节点间建立远距离纠缠态，同时还进行了局部纠缠交换的操作。通过此操作改变了网络中局部节点间的连接情况，也使整个网络的拓扑结构发生变化。重点探讨了局部操作对量子随机网络鲁棒性的影响，这种影响通过纠缠渗流的阈值变化情况来体现。通过理论分析和数值模拟，发现有操作时度大的节点数目明显增多，遭受随机攻击后的阈值变化也较小。这说明进行局部纠缠交换操作能够大幅度提高量子网络受到攻击后的连接鲁棒性。     

A highly dependable self-adaptive mixed-signal multi-core system-on-chip architecture

In this paper we propose a design of a dependable self-organizing and adaptive mixed-signal SoC. We introduce an Artificial Hormone System (AHS) as a general control mechanism, which addresses the goals of organic computing methodology. Regarding the coexistence of digital and analog components in SoCs, we developed two new AHS implementations, one pure analog approach and one mixed-signal approach. Besides the basics of the hormone based control mechanisms, especially for the analog domain, this paper adapts the AHS upon mixed-signal SoC and presents the evaluation of a completely simulated AHS-controlled SoC. This evaluation verifies the approaches including stability issues as well as upper timing bounds and shows the improvement achieved on the system reliability. We also state the advantages from the hormone system compared to other approaches, as well as the strong points of the different hormone systems to one another.     

半导体纳米复合材料的制备及其光电催化CO2还原

本实验通过模拟植物光合作用,设计制备了新颖的光电联合催化池3D-ZnO/Ni BiVO4/FTO,用电化学沉积法制备了泡沫镍负载的ZnO纳米棒光电阴极和BiVO4光电阳极,以0.1 mol·L^−1 KHCO3水溶液作为电解质,1 mmol·L^−1曙红Y为光敏剂,在−0.6 V硅太阳电池的电压下光电催化还原CO2得到了乙醇、乙酸和甲醇,总产率22.5μmol·L^−1·h^−1·cm^−2。实现了将太阳能贮存为化学能并减少了空气中的CO2,加深了学生对绿色化学和植物Calvin循环机理的理解。     

Mean field limits for nonlinear spatially extended Hawkes processes with exponential memory kernels

We consider spatially extended systems of interacting nonlinear Hawkes processes modeling large systems of neurons placed in ${\mathbb{R}}^d$ and study the associated mean field limits. As the total number of neurons tends to infinity, we prove that the evolution of a typical neuron, attached to a given spatial position, can be described by a nonlinear limit differential equation driven by a Poisson random measure. The limit process is described by a neural field equation. As a consequence, we provide a rigorous derivation of the neural field equation based on a thorough mean field analysis.