Stabilizing temperature-capacitance dependence of (Sr,Pb, Bi)TiO3-Bi4Ti3O12 solutions for energy storage

(1-x)Sr_0.7Pb_0.15Bi_0.1TiO₃-xBi₄Ti₃O₁₂ ((1-x)SPBT-xBIT, x = 0-0.125) bulk ceramics were developed and calcined via the solid-state method, aimed at the application of pulsed power capacitors. The phase structures, temperature stability, hysteresis loop, and discharge properties were systematically investigated. Considering both the temperature stability and dielectric properties, 0.925SPBT-0.075BIT bulk ceramics with a capacitance variation satisfying the X7R specification were developed for pulsed power capacitors. The energy storage density was 0.252 J/cm³, and the ceramics showed high temperature stability at 80 kV/cm. The discharge current waveforms of the 0.925SPBT-0.075BIT ceramics were recorded. A high discharge power density of approximately 1.01 × 10⁸ W/kg with an 8 Ω load resistor and short discharge period of 84 ns were achieved at 50 kV/cm. The good temperature stability properties and high power density show that the 0.925SPBT-0.075BIT ceramics are well suited for pulsed power capacitors with a wide temperature range.     

Semantic word shifts in a scientific domain

Understanding semantic word shifts in scientific domains is essential for facilitating interdisciplinary communication. Using a data set of published papers in the field of information retrieval (IR), this paper studies the semantic shifts of words in IR based on mining per-word topic distribution over time. We propose that semantic word shifts not only occur over time, but also over topics. The shifts are examined from two perspectives, the topic-level and the context-level. According to the over-time word-topic distribution, stable words and unstable words are recognized. The diverging and converging trends in the unstable type reveal characteristics of the topic evolution process. The context-level shifts are further detected by similarities between word vectors. Our work associates semantic word shifts with the evolving of topics, which facilitates a better understanding of semantic word shifts from both topics and contexts.     

矿用无人卡车国内外研究现状及关键技术

分析了国内外露天矿山用卡车无人驾驶技术发展现状,详细介绍了露天矿山卡车无人驾驶关键技术;通过分析对比前装线控和后装线控两种技术路线的优缺点以及超声波雷达、毫米波雷达、激光雷达、4D光场智能感知系统、红外传感器、视觉传感器等环境感知技术的优缺点;提出多传感器高度融合将是露天矿山卡车实现全天候环境感知的发展方向。通过分析对比惯性导航系统、GNSS差分定位技术、车联网定位技术、电子地图定位技术、视觉传感器等定位导航技术的差异,指出多定位技术融合是露天矿山无人驾驶卡车定位方式的必然选择。结合露天煤矿的生产实际,提出将现有的无人驾驶各种技术算法与露天矿山标准作业流程有机融合是实现卡车无人驾驶的必由之路。最后指出无人驾驶卡车应用5G技术是露天矿山安全生产的关键和核心,是无人驾驶的必备技术。     

Nb和时效时间对热轧中锰TRIP钢中P偏聚的影响

热轧态中锰TRIP钢首先经650 ℃退火2 h,随后在550 ℃进行等温时效热处理,采用场发射扫描电镜(FE-SEM)研究该钢中P的偏聚和时效析出行为的变化情况。结果表明,中锰TRIP钢中P在晶界的偏聚是一种非平衡偏聚现象,临界时间约为50 h,与理论计算结果48 h较为吻合。在局部偏聚区域内,C与P存在共偏聚的关系,即P偏聚量高的地方,C含量也高。而合金元素Nb具有抑制P偏聚的效果,在20~70 h时效时间内,可以相对降低6.57%~19.5%的最大P偏聚量。根据电子背散射衍射(EBSD)菊池线分析,P偏聚量低于2.28at%时,P为固溶状态,高于2.28at.%时,P为析出状态。     

Headspace fingerprinting approach to identify the major pathway influencing volatile patterns of vinasse-cured duck processed by high pressure,as well as its impact on physicochemical and sensory attributes

As a decisive attribute, flavour could be influenced by HP treatments through multiple physical and chemical pathways within the high pressure (HP)-assisted meat curing process. This investigation aimed to identify the major pathway influencing volatile flavour patterns of two representative vinasse-cured duck (VCD) products with HP treatments (150–300 MPa/15 min), including wet and dry types, by employing headspace fingerprinting as an untargeted approach. Results suggested that HP treatments greatly lowered moisture contents and increased Warner-Bratzler shear force and thiobarbituric acid reactive substances of the cured samples. According to multivariate models, the volatile flavour patterns of the HP-processed VCD could be clearly separated from the unprocessed samples, but the VCD pressurised at different intensities represented similar volatile fingerprinting, which was validated by e-nose analysis. The discriminant analysis (OPLS-DA) model outlined vinasse-derived ethanol, acetic acid, 3-methyl-1-butanol, 2-methyl-1-butanol, phenethyl alcohol and 2-methyl-3-octanone as the major discriminant aromas across the unpressurised and pressurised samples.     

Image and Feature Space Based Domain Adaptation for Vehicle Detection

The application of deep learning in the field of object detection has experienced much progress. However, due to the domain shift problem, applying an off-the-shelf detector to another domain leads to a significant performance drop. A large number of ground truth labels are required when using another domain to train models, demanding a large amount of human and financial resources. In order to avoid excessive resource requirements and performance drop caused by domain shift, this paper proposes a new domain adaptive approach to cross-domain vehicle detection. Our approach improves the cross-domain vehicle detection model from image space and feature space. We employ objectives of the generative adversarial network and cycle consistency loss for image style transfer in image space. For feature space, we align feature distributions between the source domain and the target domain to improve the detection accuracy. Experiments are carried out using the method with two different datasets, proving that this technique effectively improves the accuracy of vehicle detection in the target domain.     

A Review on Friction Stir Processing of Titanium Alloy: Characterization,Method, Microstructure,Properties

Metallurgical and Materials Transactions B - In the past two decades, friction stir processing (FSP) technology has received considerable attention. FSP can be used to adjust and control the...