护环液压胀形法的新发展及外补液胀形法的生产应用

本文论述了现有液压胀形法生产护环所存在的不足,介绍了新一代液压胀形工艺即外补液胀形的原理、优点,以及在我国首次生产应用中所显示出的明显的技术经济效益。     

场效应晶体管弧焊逆变器可靠性研究

本文讨论了功率MOSFET晶体管弧焊逆变器的工作可靠性及其影响因素，并指出了弧焊逆变器设计中应注意的问题。     

长轴表面激光淬火变形与控制

应用激光淬火强化技术控制长轴的淬火变形，分析了激光淬火变形的原因和影响因素。研究结果表明，激光淬火不仅可使长轴振摆变形量控制在很小的范围内，而且可将已变形的长轴校直到很高的精度。     

Modified g-C3N4 derived from ionic liquid and urea for promoting visible-light photodegradation of organic pollutants

In this work, modified g-C₃N₄ was fabricated successfully by calcination of ionic liquid (IL) and urea. The addition of IL changed the polymerization mode of urea, induced the self-assembly of urea molecules, modified the morphological structure of the tightly packed g-C₃N₄, and extended the electron conjugation system. When using 1-butyl-3-methylimidazolium chloride ([Bmim]Cl) as a modifier, the heteroatom Cl could be inserted into the g-C₃N₄ to optimize the electronic structure. The results of characterizations indicate that the unique structure of modified g-C₃N₄ has an expanded electron delocalization range, introduces an interlayer charge transmission channel, promotes the charge transmission, reduces the band gap, enhances the absorption of visible light, and inhibits electron-hole recombination. Modified g-C₃N₄ showed excellent photocatalytic performance for the degradation of rhodamine B and tetracycline. Furthermore, the effect of different anions in 1-butyl-3-methylimidazolium salts ([Bmim]Cl, [Bmim]Br, [Bmim][BF₄], and [Bmim][PF₆]) on the structure and function of g-C₃N₄ are discussed.     

袋式过滤器在电厂锅炉补给水处理应用的研究

针对火电厂锅炉补给水原水改为中水后,超滤系统产水通量下降等不利于锅炉补给水制水设备运行的问题,在超滤系列前加装袋式过滤器,重点研究对超滤产水速率、运行成本的影响.结果表明,加装袋式过滤器可改善超滤系列入口水质,减小水中颗粒的质量浓度;提升超滤系列产水速率,且随运行时间增长提升效果越明显;可降低超滤系列12％清洗成本,单个超滤系列年节水100 t.     

Trace Identification and Visualization of Multiple Benzimidazole Pesticide Residues on Toona sinensis Leaves Using Terahertz Imaging Combined with Deep Learning

Molecular spectroscopy has been widely used to identify pesticides. The main limitation of this approach is the difficulty of identifying pesticides with similar molecular structures. When these pesticide residues are in trace and mixed states in plants, it poses great challenges for practical identification. This study proposed a state-of-the-art method for the rapid identification of trace (10 mg·L⁻¹) and multiple similar benzimidazole pesticide residues on the surface of Toona sinensis leaves, mainly including benzoyl (BNL), carbendazim (BCM), thiabendazole (TBZ), and their mixtures. The new method combines high-throughput terahertz (THz) imaging technology with a deep learning framework. To further improve the model reliability beyond the THz fingerprint peaks (BNL: 0.70, 1.07, 2.20 THz; BCM: 1.16, 1.35, 2.32 THz; TBZ: 0.92, 1.24, 1.66, 1.95, 2.58 THz), we extracted the absorption spectra in frequencies of 0.2–2.2 THz from images as the input to the deep convolution neural network (DCNN). Compared with fuzzy Sammon clustering and four back-propagation neural network (BPNN) models (TrainCGB, TrainCGF, TrainCGP, and TrainRP), DCNN achieved the highest prediction accuracies of 100%, 94.51%, 96.26%, 94.64%, 98.81%, 94.90%, 96.17%, and 96.99% for the control check group, BNL, BCM, TBZ, BNL + BCM, BNL + TBZ, BCM + TBZ, and BNL + BCM + TBZ, respectively. Taking advantage of THz imaging and DCNN, the image visualization of pesticide distribution and residue types on leaves was realized simultaneously. The results demonstrated that THz imaging and deep learning can be potentially adopted for rapid-sensing detection of trace multi-residues on leaf surfaces, which is of great significance for agriculture and food safety.     

Machine Learning Reduced Gene/Non-Coding RNA Features That Classify Schizophrenia Patients Accurately and Highlight Insightful Gene Clusters

RNA-seq has been a powerful method to detect the differentially expressed genes/long non-coding RNAs (lncRNAs) in schizophrenia (SCZ) patients; however, due to overfitting problems differentially expressed targets (DETs) cannot be used properly as biomarkers. This study used machine learning to reduce gene/non-coding RNA features. Dorsolateral prefrontal cortex (dlpfc) RNA-seq data from 254 individuals was obtained from the CommonMind consortium. The average predictive accuracy for SCZ patients was 67% based on coding genes, and 96% based on long non-coding RNAs (lncRNAs). Machine learning is a powerful algorithm to reduce functional biomarkers in SCZ patients. The lncRNAs capture the characteristics of SCZ tissue more accurately than mRNA as the former regulate every level of gene expression, not limited to mRNA levels.     

Effect and Evolution of Oxide Film in the HDH-Ti Powder Surface on Densification Behavior During Sintering

Metallurgical and Materials Transactions A - In this work, we used hydride-dehydride Ti (HDH-Ti) powders (with the oxygen levels of 0.17 and 0.51 wt pct, respectively) combined with microstructural...     

粉末注射成形耐热不锈钢的工艺研究

以元素混合粉为原料,研究了耐热不锈钢的注射成形工艺.重点讨论了烧结温度、时间及气氛对耐热不锈钢致密度、力学性能及微观组织的影响,获得的最佳工艺为烧结温度1375℃,时间60 min,真空气氛.在此条件下制备了致密度98.12%、抗拉强度510 MPa、铁素体组织的PIM耐热不锈钢.     

Tensile behavior change depending on the varying tungsten content of W–Ni–Fe alloys

Tungsten heavy alloys (WHAs) are metal–metal composites consisting of nearly pure spherical tungsten particles embedded in a Ni–Fe–W or Ni–Co–W or Ni–Cu–W ductile matrix. In this dual phase alloy, there are several complicated relations between the ductile matrix and hard tungsten particles. The aim of this research was to examine the effect of varying tungsten content on the microstructure and mechanical properties of tungsten heavy alloys. The microstructural parameters (grain size, connectivity, contiguity and solid volume fraction) were measured and were found to have a significant effect on the mechanical properties of tungsten-based heavy alloys. The result shows that the binding strength between the W and the matrix phase has a major influence on the ductility of tungsten-based alloys. The larger this binding force is, the better the ductility is.