Effect of laser power density on the electrochromic properties of WO3 films obtained by pulsed laser deposition

Pulsed laser deposition (PLD) was used to prepare tungsten trioxide (WO₃) films on ITO substrates with a varying laser power density of 4.0–5.5 W/cm². XPS indicated that when the laser power density decreased, the peak positions of the W 4f and O 1s orbits shifted slightly to low energy due to the difference in oxygen vacancies. As the laser power density decreased, W⁶⁺ gradually replaced the lattice position of O^2?, increasing oxygen vacancies in the lattice. The transmittance modulated values (ΔT) were over 44% at 830 nm, indicating strong absorption by the WO₃ thin films in the near-infrared ray. The switching time of the WO₃ thin films between bleached states and coloured states decreased as the laser power density increased due to the amorphous structure, morphology, and lower oxygen deficiency at a high power density. The high ΔT and very fast switching time of t_b (1.09 s) and t_c (6.01 s) demonstrated the excellent electrochromic (EC) properties of the WO₃ films prepared by PLD.     

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.     

Simultaneously achieving high energy-storage density and power density under low electric fields in NBT-based ceramics

Aiming at the problem that power density and energy density are difficult to obtain simultaneously under low field, a novel composition (1-x)Na_0·5Bi_0·5TiO₃-xBaZn_1/3Ta_2/3O₃((1-x)NBT-xBZT) was designed and fabricated via solid-state methods. With the addition of BZT, the crystal lattice, structural symmetry, grain size, and dense degree were all increased proved by XRD, Raman, and Archimedes drainage method et al. Because of the enhancement of relaxor behavior, the x=0.10 sample displayed a high permittivity ε_r of 2871±15% and a low dielectric loss tan δ ≤ 0.025 in the wide temperature range of 60–400 ^oC. This ceramic also showed maximum recoverable energy density W_d (2.07 J/cm³) with high efficiency η (71.5%) under a low field of 150 kV/cm. Moreover, pulse discharge testing proved that this ceramic possessed both a significant discharge energy density W_D (0.96 J/cm³) and a record high power density P_D (108.54 MW/cm³). This work provided a promising material for high power and energy applications.     

Micro-expression recognition from local facial regions

MiE is a facial involuntary reaction that reflects the real emotion and thoughts of a human being. It is very difficult for a normal human to detect a Micro-Expression (MiE), since it is a very fast and local face reaction with low intensity. As a consequence, it is a challenging task for researchers to build an automatic system for MiE recognition. Previous works for MiE recognition have attempted to use the whole face, yet a facial MiE appears in a small region of the face, which makes the extraction of relevant features a hard task. In this paper, we propose a novel deep learning approach that leverages the locality aspect of MiEs by learning spatio-temporal features from local facial regions using a composite architecture of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). The proposed solution succeeds to extract relevant local features for MiEs recognition. Experimental results on benchmark datasets demonstrate the highest recognition accuracy of our solution with respect to state-of-the-art methods.     

Dynamic energy and mass balance model for an industrial alkaline water electrolyzer plant process

This paper proposes a parameter adjustable dynamic mass and energy balance simulation model for an industrial alkaline water electrolyzer plant that enables cost and energy efficiency optimization by means of system dimensioning and control. Thus, the simulation model is based on mathematical models and white box coding, and it uses a practicable number of fixed parameters. Zero-dimensional energy and mass balances of each unit operation of a 3 MW, and 16 bar plant process were solved in MATLAB functions connected via a Simulink environment. Verification of the model was accomplished using an analogous industrial plant of the same power and pressure range having the same operational systems design. The electrochemical, mass flow and thermal behavior of the simulation and the industrial plant were compared to ascertain the accuracy of the model and to enable modification and detailed representation of real case scenarios so that the model is suitable for use in future plant optimization studies. The thermal model dynamically predicted the real case with 98.7 % accuracy. Shunt currents were the main contributor to relative low Faraday efficiency of 86 % at nominal load and steady-state operation and heat loss to ambient from stack was only 2.6 % of the total power loss.     

Enhancing sustainable bioelectricity generation using facile synthesis of nanostructures of bimetallic Co–Ni at the combined support of halloysite nanotubes and reduced graphene oxide as novel oxygen reduction reaction electrocatalyst in single-chambered microbial fuel cells

The present study aims to utilize the high surface area of the nanotube structure of halloysite (HNTs), an aluminosilicate clay, and conductivity of reduced graphene oxide (rGO) as support material for the deposition of nickel (Ni) and cobalt (Co) nanoparticles. With that aim, a novel bimetallic cathode electrocatalyst, Co–Ni @ HNTs-rGO (Catalyst H₃), is developed. This catalyst is characterized by X-ray diffraction (XRD), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), and Transmission Electron Microscopy (TEM). Catalyst H₃ demonstrates outstanding oxygen reduction reaction (ORR) activity, electrochemical stability, electrocatalytic performance, and lowest resistance in comparison to the other developed catalysts and conventional Pt/C. Catalyst H₃ is used in single-chambered MFCs (microbial fuel cells), where the anode is filled with molasses-laden wastewater. The attained maximum power density in MFC (catalyst H₃) is 455 ± 9 mW/m², which is higher than other catalysts. All the results indicate towards its potential use in MFC application.     

Image sequence-based risk behavior detection of power operation inspection personnel

A novel image sequence-based risk behavior detection method to achieve high-precision risk behavior detection for power maintenance personnel is proposed in this paper. In this method, the original image sequence data is first separated from the foreground and background. Then, the free anchor frame detection method is used in the foreground image to detect the personnel and correct their direction. Finally, human posture nodes are extracted from each frame of the image sequence, which are then used to identify the abnormal behavior of the human. Simulation experiment results demonstrate that the proposed algorithm has significant advantages in terms of the accuracy of human posture node detection and risk behavior identification.     

基于软件校准的短波发射机功率控制系统设计

短波发射机功率稳定一直是通信领域致力改善的重点问题,短波发射机功率不稳定会直接影响无线电通信质量,造成通信失真、表达不清晰等问题。针对上述问题,基于软件校准设计短波发射机功率控制系统。该系统借鉴MVC设计模式搭建系统数据库层、业务逻辑层、控制层以及界面显示层基础框架；将功率计与短波发射机相连,实时采集工作状态下的短波发射机功率数据,通过信号处理器实施处理后并存储,借鉴传输元件,将数据发送到控制器,通过控制器校准短波发射机功率与预期之间的偏差,以偏差量为输入,利用改进PID运算得出控制量,生成控制命令,通过输入输出信号接口板输出命令,控制驱动装置调节短波发射机运行参数,实现功率控制。结果表明：与 控制系统、自动调谐系统应用相比较,在所设计系统应用控制下,100s内短波发射机的功率变化曲线与预期曲线之间的拟合优度指数更大,更接近1,优于对比系统,说明相比于对比系统。本系统控制表现更好,更能维持短波发射机功率稳定,达到了研究目标。     

控制爆破法处理卡钻事故

介绍了在包头市某工程实施管道穿越黄河施工中,采用爆破法处理卡钻的经验。针对深水环境条件及钻杆内径小不宜采用集团装药的条件,确定采用"小直径爆破筒,钻杆内部装药"的爆破方案,阐述了爆破设计及施工注意事项。可供类似工程参考。