Self-construction of pea-like Cu/Cu2S Mott-Schottky electrocatalyst for the oxygen evolution reaction

The speed of the oxygen evolution reaction seriously affects the hydrogen production efficiency of water electrolysis. Hence it is crucial to develop efficient and durable OER electrocatalysts. Construction of heterojunction catalysts is also one of the strategies to develop efficient catalysts. In this paper, a pea-like Cu/Cu₂S–C3 Mott?Schottky electrocatalyst was self-constructed by vapor deposition, while CF (copper foam) was used as substrate material and copper source, and thiourea was served as sulfur source. The built-in electric field is formed at the metal-semiconductor interface, which endows it with promising electrocatalytic performance. As the working electrode, the overpotentials of Cu/Cu₂S–C3 required to reach the current density of 10 and 50 mA cm^?2 were about 170 and 335 mV. The impact of the Mott-Schottky structure on the catalyst was also reflected in stability. The i-t tests of the sample Cu/Cu₂S–C3 were carried out under 10 and 60 mA cm^?2 and performed well.     

Learning shape and texture progression for young child face aging

Face aging (FA) for young faces refers to rendering the aging faces at target age for an individual, generally under 20s, which is an important topic of facial age analysis. Unlike traditional FA for adults, it is challenging to age children with one deep learning-based FA network, since there are deformations of facial shapes and variations of textural details. To alleviate the deficiency, a unified FA framework for young faces is proposed, which consists of two decoupled networks to apply aging image translation. It explicitly models transformations of geometry and appearance using two components: GD-GAN, which simulates the Geometric Deformation using Generative Adversarial Network; TV-GAN, which simulates the Textural Variations guided by the age-related saliency map. Extensive experiments demonstrate that our method has advantages over the state-of-the-art methods in terms of synthesizing visually plausible images for young faces, as well as preserving the personalized features.     

Structural health monitoring system based on multi-agent coordination and fusion for large structure

In practical applications of structural health monitoring technology, a large number of distributed sensors are usually adopted to monitor the big dimension structures and different kinds of damage. The monitored structures are usually divided into different sub-structures and monitored by different sensor sets. Under this situation, how to manage the distributed sensor set and fuse different methods to obtain a fast and accurate evaluation result is an important problem to be addressed deeply. In the paper, a multi-agent fusion and coordination system is presented to deal with the damage identification for the strain distribution and joint failure in the large structure. Firstly, the monitoring system is adopted to distributedly monitor two kinds of damages, and it self-judges whether the static load happens in the monitored sub-region, and focuses on the static load on the sub-region boundary to obtain the sensor network information with blackboard model. Then, the improved contract net protocol is used to dynamically distribute the damage evaluation module for monitoring two kinds of damage uninterruptedly. Lastly, a reliable assessment for the whole structure is given by combing various heterogeneous classifiers strengths with voting-based fusion. The proposed multi-agent system is illustrated through a large aerospace aluminum plate structure experiment. The result shows that the method can significantly improve the monitoring performance for the large-scale structure.     

A modified particle swarm optimization with multiple subpopulations for multimodal function optimization problems

In this paper, a modified particle swarm optimization (PSO) algorithm is developed for solving multimodal function optimization problems. The difference between the proposed method and the general PSO is to split up the original single population into several subpopulations according to the order of particles. The best particle within each subpopulation is recorded and then applied into the velocity updating formula to replace the original global best particle in the whole population. To update all particles in each subpopulation, the modified velocity formula is utilized. Based on the idea of multiple subpopulations, for the multimodal function optimization the several optima including the global and local solutions may probably be found by these best particles separately. To show the efficiency of the proposed method, two kinds of function optimizations are provided, including a single modal function optimization and a complex multimodal function optimization. Simulation results will demonstrate the convergence behavior of particles by the number of iterations, and the global and local system solutions are solved by these best particles of subpopulations.     

A note on using the Hodrick–Prescott filter in electricity markets

Recently, Nowotarski et al. (2013) have found that wavelet-based models for the long-term seasonal component (LTSC) are not only better in extracting the LTSC from a series of spot electricity prices but also significantly more accurate in terms of forecasting these prices up to a year ahead than the commonly used monthly dummies and sine-based models. However, a clear disadvantage of the wavelet-based approach is the increased complexity of the technique, as compared to the other two classes of LTSC models, and the resulting need for dedicated numerical software, which may not be readily available to practitioners in their work environments. To facilitate this problem, we propose here a much simpler, yet equally powerful method for identifying the LTSC in electricity spot price series. It makes use of the Hodrick–Prescott (HP) filter, a widely-recognized tool in macroeconomics.     

Numerical investigation of a multichannel reactor for syngas production by methanol steam reforming at various operating conditions

A novel multichannel reactor with a bifurcation inlet manifold, a rectangular outlet manifold, and sixteen parallel minichannels with commercial CuO/ZnO/Al₂O₃ catalyst for methanol steam reforming was numerically investigated in this paper. A three-dimensional numerical model was established to study the heat and mass transfer characteristics as well as the chemical reaction rates. The numerical model adopted the triple rate kinetic model of methanol steam reforming which can accurately calculate the consumption and generation of each species in the reactor. The effects of steam to carbon molar ratio, weight hourly space velocity, operating temperature and catalyst layer thickness on the methanol steam reforming performance were evaluated and discussed. The distributions of temperature, velocity, species concentration, and reaction rates in the reactor were obtained and analyzed to explain the mechanisms of different effects. It is suggested that the operating temperature of 548 K, steam to carbon ratio of 1.3, and weight hourly space velocity of 0.67 h⁻¹ are recommended operating conditions for methanol steam reforming by the novel multichannel reactor with catalyst fully packed in the parallel minichannels.     

An interpretative structural modeling based network reconfiguration strategy for power systems

The network reconfiguration is an important stage of restoring a power system after a complete blackout or a local outage. Reasonable planning of the network reconfiguration procedure is essential for rapidly restoring the power system concerned. An approach for evaluating the importance of a line is first proposed based on the line contraction concept. Then, the interpretative structural modeling (ISM) is employed to analyze the relationship among the factors having impacts on the network reconfiguration. The security and speediness of restoring generating units are considered with priority, and a method is next proposed to select the generating unit to be restored by maximizing the restoration benefit with both the generation capacity of the restored generating unit and the importance of the line in the restoration path considered. Both the start-up sequence of generating units and the related restoration paths are optimized together in the proposed method, and in this way the shortcomings of separately solving these two issues in the existing methods are avoided. Finally, the New England 10-unit 39-bus power system and the Guangdong power system in South China are employed to demonstrate the basic features of the proposed method.     

Optimised phase compositions and improved microwave dielectric properties based on calcium tin silicates

The Ca_(1+2y)Sn_(1-x)Si_(1+y)O_(5-2x+4y) low-permittivity microwave dielectric ceramics were prepared through solid-state reaction at 1350–1450 °C for 5 h. The relations between microwave dielectric properties and phase compositions for non-stoichiometric Ca_(1+2y)Sn_(1-x)Si_(1+y)O_(5-2x+4y) ceramics have been investigated. A single CaSnSiO₅ phase with abnormally positive temperature coefficient of resonant frequency (τ_f = + 62.5 ppm/°C) was synthesised at 1450 °C. This composition was an effective τ_f compensator of CaSiO₃ and Ca₃SnSi₂O₉ phases with typically negative τ_f value. The CaSiO₃ second phase was related to the Sn deficiency in the CaSn_(1-x)SiO_(5-2x) (0 < x < 1.0) composition, whereas the Ca₃SnSi₂O₉ second phase was obtained by controlling the Ca:Sn:Si ratios on the basis of the Ca_(1+2y)SnSi_(1+y)O_(5+4y) (0 < y < 1.0) composition. A promising low-permittivity millimetre-wave ceramic with most excellent microwave dielectric properties (ε_r = 10.2, Q×f = 81,000 GHz and τ_f = −4.8 ppm/°C) was produced from the Ca_(1+2y)SnSi_(1+y)O_(5+4y) (y = 0.4) ceramic.