Intercalation-pseudocapacitance hybrid anode for high rate and energy lithium-ion capacitors

Existing rechargeable batteries not only fail to meet the demand for high power applications but also cause heavy metal pollution.Li-ion capacitors(LICs),which can achieve higher charging speeds and energy densities than supercapacitors,have attracted extensive attention.Nevertheless,sluggish Li-ion diffusion of the battery-type anode results in limited rate performance of LICs.Herein,highperformance LICs using both battery and capacitor type Mn₂V₂O₇-graphene(MVO-G)anodes and hempstem-derivated activated carbon(HSAC)cathodes with a large surface area are first reported.In addition to high pseudocapacitance,the MVO-G possesses the advantage of fast Li⁺storage performance making it a suitable choice for advanced LIC anodes.Graphene is employed to enhance overall conductivity and cycling stability leading to enhanced energy storage.The MVO-G//HSAC LICs exhibit a high energy density of 148.1 Wh kg^-1 at a power density of 150 W kg^-1 and 25 Wh kg^-1 even at15 k W kg^-1.More importantly,the MVO-G//HSAC LICs also show excellent cycling stability of 90%after15,000 cycles,which is expected for high performance energy storage systems.     

Thermoanalytical studies of rubber oxidation catalyzed by metallic ions

The effects of several metallic ions on the oxidation of natural rubber were studied by differential scanning calorimetry (DSC). The activation energy of oxidation was evaluated from DSC curves. Cobalt, manganese, iron and copper ions decreased the activation energy of oxidation. Nickel, zinc and tin ions did not affect the activation energy. The effective ions are those which are able to undergo one-electron transfer redox reaction with hydroperoxide. Based on the activation energy values, the catalytic activities of metallic ions are: Co > Mn > Cu ? Fe > Ni ? Zn ? Sn.     

Balancing exploration and exploitation with adaptive variation for evolutionary multi-objective optimization

Although recent studies have shown that evolutionary algorithms are effective tools for solving multi-objective optimization problems, their performances are often bottlenecked by the suitability of the evolutionary operators with respect to the optimization problem at hand and their corresponding parametric settings. To adapt the search dynamic of evolutionary operation in multi-objective optimization, this paper proposes an adaptive variation operator that exploits the chromosomal structure of binary representation and synergizes the function of crossover and mutation. The overall search ability is deterministically tuned online to maintain a balance between extensive exploration and local fine-tuning at different stages of the evolutionary search. Also, the coordination between the two variation operators is achieved by means of an adaptive control that ensures an efficient exchange of information between the different chromosomal sub-structures throughout the evolutionary search. Extensive comparative studies with several representative variation operators are performed on different benchmark problems and significant algorithmic performance improvements in terms of proximity, uniformity and diversity are obtained with the incorporation of the proposed adaptive variation operator into the evolutionary multi-objective optimization process.     

On centripetal flows of entities in scale‐free networks with nodes of finite capability

We examine the transmission of entities from the peripheries of scale‐free networks toward their centers when the nodes of the network have finite processing capabilities. We look at varying network utilization, U and find that clogging of the network sets in after a threshold value has been exceeded, and that the congestion sets in at the downstream nodes (those nearer to the collector) having large numbers of upstream neighbors. Investigation of the question of the degree of correlation of several characteristics of scale‐free networks (such as the average path length to the collector <l_(min)> and the average clustering coefficient ) with the dynamics of centripetal flow in them reveals a negative answer: any correlation is indirect and will manifest in the number of producer nodes (which dictate the effective heaviness of the flow) and the interconnectedness of the feeder nodes, those nodes which are immediate neighbors of the collector node. An examination of reinforcement strategies shows dramatic improvements in both the finishing rate, and the average total transmission time, when the more centrally‐placed nodes are reinforced first, showing that the entities spend a large amount of their lifetime waiting in line at those nodes (which constitute the bottlenecks in the network) compared to the nodes in the periphery. Our results reinforce the importance of a network's hubs and their immediate environs, and suggest strategies for prioritizing elements of a network for optimization. © 2014 Wiley Periodicals, Inc. Complexity 21: 283–295, 2015     

Construction of Biorthogonal Multiwavelets Using the Lifting Scheme

The lifting scheme has been found to be a flexible method for constructing scalar wavelets with desirable properties. Here it is extended to the construction of multiwavelets. It is shown that any set of compactly supported biorthogonal multiwavelets can be obtained from the Lazy matrix filters with a finite number of lifting steps. As an illustration of the general theory, compactly supported biorthogonal multiwavelets with optimum time–frequency resolution are constructed. In addition, experimental results of applying these multiwavelets to image compression are presented.     

Terminality,normality, and transversality conditions

The maximum principle distinguishes between two phases of the optimal control problem. Some of the stated conditions are to be satisfied at points other than endpoints and some conditions are to be satisfied specifically at the endpoints. This paper utilizes the first set of conditions from the maximum principle to reexamine the second set. In the process, a new necessary condition to be satisfied at the endpoints is obtained. This condition is for certain cases easier to apply than the transversality conditions, yields additional information which may be of computational advantage, and lends itself quite naturally to an exposition of abnormal solutions. The relationship of the new condition to the transversality conditions and a discussion on normality are included. Several examples are given to illustrate the results.This research was supported in part by NASA under Grant NGR-03-002-224 and NSF Science Faculty Fellowship. The authors are indebted to Professors G. Leitmann, G. Basile, and E. Cliff for their helpful comments and suggestions.     

Stochastic Optimization Problems with CVaR Risk Measure and Their Sample Average Approximation

We provide a refined convergence analysis for the SAA (sample average approximation) method applied to stochastic optimization problems with either single or mixed CVaR (conditional value-at-risk) measures. Under certain regularity conditions, it is shown that any accumulation point of the weak GKKT (generalized Karush-Kuhn-Tucker) points produced by the SAA method is almost surely a weak stationary point of the original CVaR or mixed CVaR optimization problems. In addition, it is shown that, as the sample size increases, the difference of the optimal values between the SAA problems and the original problem tends to zero with probability approaching one exponentially fast.     

A structured solution framework for fuzzy minimum spanning tree problem and its variants under different criteria

This paper develops a unified and structured solution framework for the minimum spanning tree (MST) problem and its variants (e.g., constrained MST problem and inverse MST problem) on networks with fuzzy link weights. It is applicable to any additive decision criterion under fuzziness (e.g., expected value, value at risk, and conditional value at risk), for generalized cases that the link weights may be represented by arbitrary types of fuzzy variables. It also applies to the entropy criterion while the link weights are continuous fuzzy variables. Following the optimality conditions of the fuzzy MST under different decision criteria proved first in this paper, it is shown that the MST problem and its variants on a fuzzy network can be converted into equivalent deterministic counterparts on their corresponding crisp networks. Consequently, these problems can be effectively solved via their deterministic counterparts without fuzzy simulation, and meanwhile, the performance of the trees under a specified criterion is precisely measured. The accuracy and efficiency are both significantly improved compared with other fuzzy simulation-based approaches. Numerical examples illustrate the superiority of the proposed solution framework. Furthermore, some new theoretical conclusions on the MST problem under fuzziness are also presented.