Ionic Liquids: New Perspectives for Inorganic Synthesis?

Ionic liquids are credited with a number of unusual properties. These include a low vapor pressure, a wide liquid‐phase range, weakly coordinating properties, and a high thermal/chemical stability. These properties are certainly of great interest for inorganic synthesis and the creation of novel inorganic compounds. On the other hand, the synthesis repertoire for preparing inorganic compounds has always been broad, ranging from syntheses in solutions and melts to solid‐state reactions, and from crystal growth in the gas phase to high‐pressure syntheses. What new aspects can ionic liquids then add to the synthesis of inorganic compounds? This Minireview uses some early examples to show that the use of ionic liquids indeed provides access to unusual inorganic compounds.     

Modeling of a magnetorheological damper by recursive lazy learning

Nowadays dampers based on magnetorheological (MR) fluids are receiving significant attention specially for control of structural vibration and automotive suspensions systems. In most cases, it is necessary to develop an appropriate control strategy which is practically implementable when a suitable model for MR dampers is available. It is not a trivial task to model the dynamic of MR dampers because of their inherent non-linear and hysteretic dynamics. In this paper, a recursive lazy learning method based on neural networks is considered to model the MR damper behavior. The proposed method is validated by comparison with experimental obtained responses. Results show the estimated model correlates very well with the data obtained experimentally. The method proposed learns quickly that it is only necessarily a learning cycle, it can learn on-line and it is easy to select the network structure and calculate the model parameters.     

Scale effects on strength of geomaterials, case study: Coal

Scale effects on the strength of coal are studied using a discrete element model. The key point of the model is its capability to discriminate between the “strictly sample size” effect and the “Discrete Fracture Network (DFN) density” effect on the mechanical response. Simulations of true triaxial compression tests are carried out to identify their respective roles. The possible bias due to the discretization size distribution of the discrete element model is investigated in detail by considering low-resolution configurations. The model is shown to be capable of quantitatively reproducing the dependency of the maximum strength on the size of the sample. This relationship mainly relies on the DFN density. For all given sizes, as long as the DFN density remains constant with a uniform distribution or if discontinuities are absent in the considered medium, the maximum strength of the material remains constant.     

Artificial neural network prediction to the hot compressive deformation behavior of Al–Cu–Mg–Ag heat-resistant aluminum alloy

The behavior of the flow stress of Al-Cu-Mg-Ag heat-resistant aluminum alloys during hot compression deformation was studied by thermal simulation test. The temperature and the strain rate during hot compression were 340-500 °C, 0.001 s⁻¹ to 10 s⁻¹, respectively. Constitutive equations and an artificial neural network (ANN) model were developed for the analysis and simulation of the flow behavior of the Al-Cu-Mg-Ag alloys. The inputs of the model are temperature, strain rate and strain. The output of the model is the flow stress. Comparison between constitutive equations and ANN results shows that ANN model has a better prediction power than the constitutive equations.     

Robust airfoil optimization based on improved particle swarm optimization method

A robust airfoil optimization platform is constructed based on the modified particle swarm optimization method (i.e., the second-order oscillating particle swarm method), which consists of an efficient optimization algorithm, a precise aerodynamic analysis program, a high accuracy surrogate model, and a classical airfoil parametric method. There are two improvements for the modified particle swarm method compared with the standard particle swarm method. First, the particle velocity is represented by the combination of the particle position and the variation of position, which makes the particle swarm algorithm a second-order precision method with respect to the particle position. Second, for the sake of adding diversity to the swarm and enlarging the parameter searching domain to improve the global convergence performance of the algorithm, an oscillating term is introduced to the update formula of the particle velocity. At last, taking two airfoils as examples, the aerodynamic shapes are optimized on this optimization platform. It is shown from the optimization results that the aerodynamic characteristic of the airfoils is greatly improved in a broad design range.     

一种应用于CMOS图像传感器的电容阵列PGA研究

研究了一种应用于1 024×1 024CMOS图像传感器中的温度计码电容阵列型可编程增益放大器（PGA）。该PGA采用开关电容阵列结构,通过改变输入与反馈电容值,实现了0～18dB的增益;同时,该PGA具有结构简单、电容阵列面积小、带宽大和反馈系数大等特点,其增益带宽50MHz、总谐波失真（THD）小于50dB。该PGA的信号处理能力完全满足12MHz速率、69dB动态范围CMOS图像传感器内部集成ADC对模拟输入信号的要求。     

Co-authorship model with link strength

We generalize a network formation model for co-authorship introducing the possibility of the connections having different link strengths. Different link strengths represent the fact that authors may put different efforts into different collaborations. To evaluate the model, we consider the notions of efficiency and pairwise stability, which are based on a utility function that measures the benefits for an author to belonging to a certain network structure. We divide the analysis in two cases, considering that link strengths are unbounded or bounded. In the first case, we show that if there are more than two authors in the network, then there is no pairwise stable network. In the second case, we show that the pairwise stable networks consist of completely connected disjoint components where essentially all link strengths are maximal. Regarding efficiency, in both cases, if the number of authors is even, then the unique efficient network structure consists of pairs of connected authors.     

A class of mixture of experts models for general insurance: Theoretical developments

In the Property and Casualty (P&C) ratemaking process, it is critical to understand the effect of policyholders’ risk profile to the number and amount of claims, the dependence among various business lines and the claim distributions. To include all the above features, it is essential to develop a regression model which is flexible and theoretically justified. Motivated by the issues above, we propose a class of logit-weighted reduced mixture of experts (LRMoE) models for multivariate claim frequencies or severities distributions. LRMoE is interpretable, as it has two components: Gating functions, which classify policyholders into various latent sub-classes; and Expert functions, which govern the distributional properties of the claims. Also, upon the development of denseness theory in regression setting, we can heuristically interpret the LRMoE as a “fully flexible” model to capture any distributional, dependence and regression structures subject to a denseness condition. Further, the mathematical tractability of the LRMoE is guaranteed since it satisfies various marginalization and moment properties. Finally, we discuss some special choices of expert functions that make the corresponding LRMoE “fully flexible”. In the subsequent paper (Fung et al., 2019b), we will focus on the estimation and application aspects of the LRMoE.     

Dissipativity and synchronization control of fractional‐order memristive neural networks with reaction‐diffusion terms

This paper deals with the dissipativity and synchronization control of fractional‐order memristive neural networks (FOMNNs) with reaction‐diffusion terms. By means of fractional Halanay inequality, Wirtinger inequality, and Lyapunov functional, some sufficient conditions are provided to ensure global dissipativity and exponential synchronization of FOMNNs with reaction‐diffusion terms, respectively. The underlying model and the obtained results are more general since the reaction‐diffusion terms are first introduced into FOMNNs. The given conditions are easy to be checked, and the correctness of the obtained results is confirmed by a living example.     

不确定条件下的乡村中小学布局优化

学校的合理规划布局是实现教育资源优化配置、提高办学效益和推动教育均衡发展的重要途径。已有许多学者研究了学校的布局问题,但基本上都忽略了交通网络条件以及不确定因素对学校布局的影响。本研究将在前人研究基础上,重点考虑交通网络对乡村中小学选址的影响,并假设旅行时间具有不确定性,从而以最小化学生旅行成本、学校建设、道路修建和道路升级成本为目标,构建不确定条件下的设施区位设计模型。在算法求解方面提出混合模拟退火算法,用于确定新建学校的最佳位置,以及新道路的修建和原有道路的升级情况。最后,将提出的模型和算法应用到实际案例中。