高分子材料导热性能影响因素研究进展

介绍了高分子材料导热性能影响因素研究进展，重点阐释了聚合物基体的结构特性（链结构、分子间相互作用、取向、结晶度等）、导热填料（种类、含量、形态、尺寸等）以及制备方法等对高分子材料导热性能的影响。     

On the design of secure primitives for real world applications

This editorial paper provides an overview of new advances on hardware security challenges and promising solutions for real world applications. It first presents the increasing hardware vulnerabilities with the emergence of diverse computing platforms. Then, it details the technical challenges, research opportunities, and potential solutions to cyber-attacks on -physical- devices.     

Magnetic porous nano-carbon catalysts supported silver nanoparticles derived from chitin and their application in catalytic reduction reactions

The exploitation of recycled carbonaceous catalysts from renewable biomass resources such as chitin is a crucial issue for the development of the sustainable society. In this article, the chitin-based N and O doped carbon microspheres (ChC) were fabricated by a simple dissolution, sol–gel transformation, and the carbonization methods. Subsequently, the novel magnetic Ag-Fe₃O₄@chitin-based carbon microspheres catalyst (MChC) was successfully constructed through the in situ redox reaction. The as-prepared MChC possessed rich micropores with high-surface area, and a narrow size distribution (50–120 μm). The Ag-Fe₃O₄ nanoparticles were immobilized through the interaction with C, N, and O atoms in the pores of MChC. The reduction of 4-nitrophenol was applied to evaluate the catalytic activity of MChC. 4-Nitrophenol (4-NP) could be fully reduced to 4-aminophenol (4-AP) in 5 min with the catalyst MChC-45. Moreover, MChC could be collected in solution with an external magnet in 8 s and remained relatively high-catalytic activity after 10 cycle times. This work provided novel ideas for the fabrication of doped carbon material from biomass and promoted its utilization in nanocatalytic applications.     

Efficient utilization of silanol group in rice husk to promote the synthesis of Ni-phyllosilicate for CO2 methanation

It is a challenge to prepare Ni-phyllosilicate catalysts deriving from the reaction of nickel salts and biomass-based silica with high Ni contents. In order to efficiently synthesize nickel phyllosilicate, the acid-treated rice husk without calcination (P-R) was directly used as the raw material. The as-synthesized Ni-phyllosilicate (N/P-R) exhibited high nickel content of 46.7 wt% owing to utilization of the abundant silanol group in the fresh rice husk. On the contrary, the Ni content of Ni-phyllosilicate sample (N/R–S) prepared using the calcinated rice husk-based silica was only 4.8 wt% because of the loss of silanol group during calcination process. As a result, N/P-R displayed much higher catalytic activity for CO₂ methanation than N/R–S. It was found that the silanol group was in favour of forming the important intermediate Si(OH)₄, and thereby facilitated the formation of nickel phyllosilicate. In addition, the N/P-R catalyst demonstrated superior stability and excellent anti-sintering ability in the long lifetime test. In short, the proposed method in this work could not only efficiently synthesize nickel phyllosilicate with high Ni content, but also reduce energy consumption and operation steps compared to the one prepared using biomass-based silica.     

Forecasting volatility and correlation between oil and gold prices using a novel multivariate GAS model

Forecasting the volatility and correlation among different kinds of assets has important applications in areas such as risk management, options pricing, and asset allocation. This paper mainly uses a novel multivariate Generalized Autoregressive Score (GAS) model to analyze and forecast volatilities and correlations between Brent, WTI and gold prices. The time-varying parameters of multivariate GAS model for a given distribution of crude oil and gold prices is observed which is supported by Doornik-Hansen test. The testing results of time-varying parameters based on LRT statistics reveal that the dependent structure between Brent and gold prices is more complex than those of WTI and gold. The estimation results show that the multivariate GAS method well captures the volatility persistence and nonlinear interaction effects between the crude oil and gold markets. In addition, we compare the forecasting performance of the GAS with the classical Dynamic Conditional Correlation Generalized Auto-Regressive Conditional Heteroskedasticity (DCC-GARCH) model, and find that the forecasting power of volatility and correlation in multivariate GAS model is better than the DCC-GARCH model.     

Hierarchical multi-reservoir optimization modeling for real-world complexity with application to the Three Gorges system

High dimensionality in real-world multi-reservoir systems greatly hinders the application and popularity of evolutionary algorithms, especially for systems with heterogeneous units. An efficient hierarchical optimization framework is presented for search space reduction, determining the best water distributions, not only between cascade reservoirs, but also among different types of hydropower units. The framework is applied to the Three Gorges Project (TGP) system and the results demonstrate that the difficulties of multi-reservoir optimization caused by high dimensionality can be effectively solved by the proposed hierarchical method. For the day studied, power output could be increased by 6.79 GWh using an optimal decision with the same amount of water actually used; while the same amount of power could be generated with 2.59 × 10⁷ m³ less water compared to the historical policy. The methodology proposed is general in that it can be used for other reservoir systems and other types of heterogeneous unit generators.     

Multiobjective PSO based adaption of neural network topology for pixel classification in satellite imagery

The proposed work involves the multiobjective PSO based adaption of optimal neural network topology for the classification of multispectral satellite images. It is per pixel supervised classification using spectral bands (original feature space). This paper also presents a thorough experimental analysis to investigate the behavior of neural network classifier for given problem. Based on 1050 number of experiments, we conclude that following two critical issues needs to be addressed: (1) selection of most discriminative spectral bands and (2) determination of optimal number of nodes in hidden layer. We propose new methodology based on multiobjective particle swarm optimization (MOPSO) technique to determine discriminative spectral bands and the number of hidden layer node simultaneously. The accuracy with neural network structure thus obtained is compared with that of traditional classifiers like MLC and Euclidean classifier. The performance of proposed classifier is evaluated quantitatively using Xie-Beni and β indexes. The result shows the superiority of the proposed method to the conventional one.     

A bi-layer optimization approach for a hybrid flow shop scheduling problem involving controllable processing times in the steelmaking industry

A steelmaking-continuous casting (SCC) scheduling problem is an example of complex hybrid flow shop scheduling problem (HFSSP) with a strong industrial background. This paper investigates the SCC scheduling problem that involves controllable processing times (CPT) with multiple objectives concerning the total waiting time, earliness/tardiness and adjusting cost. The SCC scheduling problem with CPT is seldom discussed in the existing literature. This study is motivated by the practical situation of a large integrated steel company in which the just-in-time (JIT) and cost-cutting production strategy have become a significant concern. To address this complex HFSSP, the scheduling problem is decomposed into two subproblems: a parallel machine scheduling problem (PMSP) in the last stage and an HFSSP in the upstream stages. First, a hybrid differential evolution (HDE) algorithm combined with a variable neighborhood decomposition search (VNDS) is proposed for the former subproblem. Second, an iterative backward list scheduling (IBLS) algorithm is presented to solve the latter subproblem. The effectiveness of this bi-layer optimization approach is verified by computational experiments on well-designed and real-world scheduling instances. This study provides a new perspective on modeling and solving practical SCC scheduling problems.     

Framework to manage multiple goals in community-based energy sharing network in smart grid

Smart grid has opened up a new role of “prosumer” in an energy value network, transforming many conventional energy consumers into prosumers, who not only generate green energy but also share the surplus with utilities and other consumers. The concept of a goal-oriented prosumer community group (PCG) has emerged recently as an effective way to fulfill sustainable energy exchange. Such community-based energy sharing networks comprise multiple irreconcilable objectives such as demand constraints, cost constraints, and income maximization. In many cases, one goal may be achievable only at the expense of other goals. This necessitates the development of an effective framework to manage the multiple goals and reduce the gap with their achievement levels. Therefore, in this research paper, an effective framework is developed to negotiate among the multiple goals and thus to define optimal mutual goals for each PCG in a more sustainable manner using multiple-criteria goal programming techniques. Simulation results are presented to illustrate how the methods work in practical situations, where each of the objective measure is given a target value and the unwanted deviations from this set are minimized in an achievement function.     

The relationship between centrality and land use patterns: Empirical evidence from five Chinese metropolises

The relationship between location and land use patterns is one of the classic theoretical issues in urban studies. Classic models based on the monocentricity hypothesis have limitations in the interpretation of modern urban structure. China has experienced institutional transformation in recent decades, and the interaction of national government policy, market forces and the natural environment has influenced urban planning in Chinese metropolises, resulting in urban structures with special characteristics. This paper examines the distribution of location and land use intensity, and tested the Alonso model by the relationship between them in five Chinese metropolises using Point of Interest data, space syntax methodology, the grid weighted statistical method and the Geographically Weighted Regression (GWR) model. Universal patterns about the scaling relation between intensity of land use types and the centrality of location are revealed. The elasticity of land use types to location, from high to low sensitivity, is commercial, residential then industrial land in most of the five metropolises studied. The sensitivity sequence of land use studied suggests that the hypothetical model based on the classical Alonso model can explain the spatial structure of modern metropolises in China to some extent, especially for the commercial land. But the order of sensitivity of residential land and industrial land to location does not conform to the model. The spatial heterogeneity in land use intensity and centrality were explored and the factors embedded were discussed. It can be found that the relation between centrality and land use intensity conforms to power law. In most of the metropolises studied, when the scaling relation between land use intensity and centrality is super linear, the sequence of the frequency value from high to low are commercial, residential and industrial land; when the scaling relation is sublinear, the sequence of the frequency value is industrial, residential and commercial land.