Synthesis, Crystal Structure and Photoluminescence of a 2D Two-fold Interpenetrating Cd(II) Coordination Polymer Based on a Flexible Reduced Schiff Base Ligand

By the reaction of cadmium chloride with a reduced Schiff base ligand （H2L= N-（4- carboxybenzyl）-glycine）, a novel Cd（Ⅱ） compound [Cd（HL）2]·4H2O （1） has been obtained. Single-crystal X-ray diffraction analysis reveals that it crystallizes in orthorhombic, space group Pbcn with a = 13.9788（8）, b = 24.4204（14）, c = 13.9580（8） A, V= 4764.8（5） A^3, Z = 8, Mr = 600.84, Dc = 1.675 g/cm^3,/z = 0.983 mm^-1, F（000） = 2448, S = 1.084, R = 0.0806 and wR = 0.1705 （I〉 2a（1））. It displays a 2D two-fold interpenetrating structure. The Cd（Ⅱ） ions are six-coordinated. The cross-linkage of Cd（Ⅱ） ions by the bridge of HL anions results in a [Cd（HL）2] layer. Every two [Cd（HL）2]n layers are penetrated with each other, resulting in a two-fold interpenetrating double-layer structure by π-π stacking interactions and hydrogen bonds. Such double-layers are further linked by hydrogen bonds into a supramolecular structure. Photoluminescent investigation shows that compound 1 displays strong emission in the purple region.

Operational effectiveness evaluation based on the reduced conjunctive belief rule base

To address the issue of rule premise combination explosion in the construction of the traditional complete conjunctive belief rule base(BRB),this paper introduces an orthogonal design method to reduce the conjunctive BRB.The reasoning method based on reduced conjunctive BRB is designed with the help of the conversion technology from conjunctive BRB to disjunctive BRB.Finally,the operational mission effectiveness evaluation is taken as an example to verify the proposed method.The results show that the method proposed in this paper is feasible and effective.

Derivation of energy-based base shear force coefficient considering hysteretic behavior and P-delta effects

A modified energy-balance equation accounting for P-delta effects and hysteretic behavior of reinforced concrete members is derived. Reduced hysteretic properties of structural components due to combined stiffness and strength degradation and pinching effects, and hysteretic damping are taken into account in a simple manner by utilizing plastic energy and seismic input energy modification factors. Having a pre-selected yield mechanism, energy balance of structure in inelastic range is considered. P-delta effects are included in derived equation by adding the external work of gravity loads to the work of equivalent inertia forces and equating the total external work to the modified plastic energy. Earthquake energy input to multi degree of freedom（MDOF） system is approximated by using the modal energy-decomposition. Energybased base shear coefficients are verified by means of both pushover analysis and nonlinear time history（NLTH） analysis of several RC frames having different number of stories. NLTH analyses of frames are performed by using the time histories of ten scaled ground motions compatible with elastic design acceleration spectrum and fulfilling duration/amplitude related requirements of Turkish Seismic Design Code. The observed correlation between energy-based base shear force coefficients and the average base shear force coefficients of NLTH analyses provides a reasonable confidence in estimation of nonlinear base shear force capacity of frames by using the derived equation.

Nonlinear Reduced DNN Models for State Estimation

We propose in this paper a data driven state estimation scheme for generating nonlinear reduced models for parametric families of PDEs, directly providingdata-to-state maps, represented in terms of Deep Neural Networks. A major constituentis a sensor-induced decomposition of a model-compliant Hilbert space warranting approximation in problem relevant metrics. It plays a similar role as in a ParametricBackground Data Weak framework for state estimators based on Reduced Basis concepts. Extensive numerical tests shed light on several optimization strategies that areto improve robustness and performance of such estimators.

High-performance predictor for critical unstable generators based on scalable parallelized neural networks

A high-performance predictor for critical unstable generators(CUGs) of power systems is presented in this paper. The predictor is driven by the Map Reduce based parallelized neural networks. Specifically, a group of back propagation neural networks(BPNNs), fed by massive response trajectories data, are efficiently organized and concurrently trained in Hadoop to identify dynamic behavior of individual generator. Rather than simply classifying global stability of power systems, the presented approach is able to distinguish unstable generators accurately with a few cycles of synchronized trajectories after fault clearing,enabling more in-depth emergency awareness based on wide-area implementation. In addition, the technique is of rich scalability due to Hadoop framework, which can be deployed in the control centers as a high-performance computing infrastructure for real-time instability alert.Numerical examples are studied using NPCC 48-machines test system and a realistic power system of China.

A recursive implementation of the ALIENOR optimization method

Purpose-A recursive scheme for the ALIENOR method is proposed as a remedy for the difficulties induced by the method.A progressive focusing on the most promising region,in combination with a variation of the density of the alpha-dense curve,is proposed.Design/methodology/approach-ALIENOR method is aimed at reducing the space dimensions of an optimization problem by spanning it by using a single alpha-dense curve:the curvilinear abscissa along the curve becomes the only design parameter for any design space.As a counterpart,the transformation of the objective function in the projected space is much more difficult to tackle.Findings-A fine tuning of the procedure has been performed in order to identity the correct balance between the different elements of the procedure.The proposed approach has been tested by using a set of algebraic functions with up to 1,024 design variables,demonstrating the ability of the method in solving large scale optimization problem.Also an industrial application is presented.Originality/value-In the knowledge of the author there is not a similar paper in the current literature.