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The exploitation of high-efficiency and cheap bifunctional cathode electrocatalyst is of significant importance to rechargeable zinc−air batteries. In this paper, a bimetallic sulfide coupled with a CNT ((Co, Mg)S 2 @CNTs) hybrid catalyst is developed via a proposed vulcanization process. The (Co, Mg)S 2 @CNTs) with controllable Mg substitution has a tailored crystal structure (amorphous and crystalline), which catalyzes the oxygen reduction/evolution reaction (ORR/OER). The active sites of CoS 2 @CNTs are activated by doping Mg ions, which accelerates the kinetics of the oxygen adsorption for ORR and oxygen desorption for OER. Meanwhile, the hybrid catalyst exhibits a unique hierarchal morphology and a "catalytic buffer", which further accelerate the mass transfer of catalytic processes. In addition, the outer wall of CNTs as substrate effectively avoid the agglomeration of (Co, Mg)S 2 particles by reasonably providing adsorption sites. The inner and outer walls of CNTs form a highspeed conduction pathway, quickly transferring the electrons produced by oxygen catalytic reactions. As a result, the (Co, Mg)S 2 @ CNTs exhibit an ORR performance comparable with commercial catalyst Pt/C-RuO 2 and remarkable OER performance (E j=10 = 1.59 V). The high power density of 268 mW cm −2 and long-term charge/discharge stability of the zinc−air battery proves the feasibility of (Co, Mg)S 2 @CNTs application in high-power devices.

To solve the problem of front wheels being jammed due to the passive trajectory tracking of the conventional car-like robot in the leader-follower formation control, we propose a novel car-like robot with the integration of front-wheel driving and steering. We establish its kinematic model, then analyze its controllability via the method of chained form system, and design the trajectory-tracking controller via the backstepping method. Simulations and experimental results validate our algorithm. This novel car-like robot with the integration of front-wheel driving and steering system not only avoids the jamming in the formation motion, but also owes the advantages of compacter structure, lighter body, and lower energy consumption.

In this paper, a fuzzy scalar radial basis function neural network is proposed, in order to overcome the limitation of traditional aerodynamic reduced-order models having difficulty in adapting to input variables with different orders of magnitude. This network is a combination of fuzzy rules and standard radial basis function neural network, and all the basis functions are defined as scalar basis functions. The use of scalar basis function will increase the flexibility of the model, thus enhancing the generalization capability on complex dynamic behaviors. Particle swarm optimization algorithm is used to find the optimal width of the scalar basis function. The constructed reduced-order models are used to model the unsteady aerodynamics of an airfoil in transonic flow. Results indicate that the proposed reduced-order models can capture the dynamic characteristics of lift coefficients at different reduced frequencies and amplitudes very accurately. Compared with the conventional reduced-order model based on recursive radial basis function neural network, the fuzzy scalar radial basis function neural network shows better generalization capability for different test cases with multiple normalization methods.

In this paper, a multi-objective evolutionary algorithm based on adaptive discrete Differential Evolution is proposed for multi-objective optimization problems, especially in discrete domain. By introducing Differential Evolution to multi-objective optimization field, a novel adaptive discrete Differential Evolution strategy is presented firstly to enhance the ability of global exploration, so that the proposed multi-objective evolutionary algorithm can achieve the better approximate Pareto-optimal solutions. Furthermore, the proposed multi-objective evolutionary algorithm integrates the adaptive discrete Differential Evolution strategy with a fast Pareto ranking strategy and a truncating operation based on crowding density and Pareto rank to maintain the good diversity of evolutionary population. The simulations are conducted for a set of standard Multi-objective 0/1 knapsack problems which are the typical NP-hard problems. The performance of the proposed multi-objective evolutionary algorithm is compared with that of SPEA and NSGA-II which are state-of-the-art. Experimental results indicate that the proposed multi-objective evolutionary algorithm is more effective and efficient.