Good dielectric performance and broadband dielectric polarization in Ag,Nb co-doped TiO2

Due to the demand of miniaturization and integration for ceramic capacitors in electronic components market, TiO₂-based ceramics with colossal permittivity has become a research hotspot in recent years. In this work, we report that Ag⁺/Nb⁵⁺ co-doped (Ag_1/4Nb_3/4)_xTi_1−xO₂ (ANTOx) ceramics with colossal permittivity over a wide frequency and temperature range were successfully prepared by a traditional solid–state method. Notably, compositions of ANTO0.005 and ANTO0.01 respectively exhibit both low dielectric loss (0.040 and 0.050 at 1 kHz), high dielectric permittivity (9.2 × 10³ and 1.6 × 10⁴ at 1 kHz), and good thermal stability, which satisfy the requirements for the temperature range of application of X9R and X8R ceramic capacitors, respectively. The origin of the dielectric behavior was attributed to five dielectric relaxation phenomena, i.e., localized carriers' hopping, electron–pinned defect–dipoles, interfacial polarization, and oxygen vacancies ionization and diffusion, as suggested by dielectric temperature spectra and valence state analysis via XPS; wherein, electron-pinned defect–dipoles and internal barrier layer capacitance are believed to be the main causes for the giant dielectric permittivity in ANTOx ceramics.     

液相色谱-串联质谱技术在临床检验中的应用研究进展

液相色谱-串联质谱（LC-MS/MS）技术具有高灵敏度、高特异性、高分辨率和高效率的优点。近年来随着仪器灵敏度的提高，LC-MS/MS在常规临床检验中显示出极大的潜力，并在疾病早期预防和诊断中发挥着不可替代的作用。本文对LC-MS/MS在新生儿疾病筛查、维生素D检测、内分泌激素检测、肽类和蛋白质定量分析等临床检验方面的研究进展进行综述，并讨论了未来面临的挑战。     

Machine learning algorithm selection for windage alteration fault diagnosis of mine ventilation system

Machine learning algorithms have been widely used in mine fault diagnosis. The correct selection of the suitable algorithms is the key factor that affects the fault diagnosis. However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been fully evaluated. In this study, the windage alteration faults (WAFs) diagnosis models, which are based on K-nearest neighbor algorithm (KNN), multi-layer perceptron (MLP), support vector machine (SVM), and decision tree (DT), are constructed. Furthermore, the applicability of these four algorithms in the WAFs diagnosis is explored by a T-type ventilation network simulation experiment and the field empirical application research of Jinchuan No. 2 mine. The accuracy of the fault location diagnosis for the four models in both networks was 100%. In the simulation experiment, the mean absolute percentage error (MAPE) between the predicted values and the real values of the fault volume of the four models was 0.59%, 97.26%, 123.61%, and 8.78%, respectively. The MAPE for the field empirical application was 3.94%, 52.40%, 25.25%, and 7.15%, respectively. The results of the comprehensive evaluation of the fault location and fault volume diagnosis tests showed that the KNN model is the most suitable algorithm for the WAFs diagnosis, whereas the prediction performance of the DT model was the second-best. This study realizes the intelligent diagnosis of WAFs, and provides technical support for the realization of intelligent ventilation.     

Machine fault diagnosis with small sample based on variational information constrained generative adversarial network

In actual engineering scenarios, limited fault data leads to insufficient model training and over-fitting, which negatively affects the diagnostic performance of intelligent diagnostic models. To solve the problem, this paper proposes a variational information constrained generative adversarial network (VICGAN) for effective machine fault diagnosis. Firstly, by incorporating the encoder into the discriminator to map the deep features, an improved generative adversarial network with stronger data synthesis capability is established. Secondly, to promote the stable training of the model and guarantee better convergence, a variational information constraint technique is utilized, which constrains the input signals and deep features of the discriminator using the information bottleneck method. In addition, a representation matching module is added to impose restrictions on the generator, avoiding the mode collapse problem and boosting the sample diversity. Two rolling bearing datasets are utilized to verify the effectiveness and stability of the presented network, which demonstrates that the presented network has an admirable ability in processing fault diagnosis with few samples, and performs better than state-of-the-art approaches.     

SLM printing of cermet powders: Inhomogeneity from atomic scale to microstructure

It is very challenging for 3D printing based on the selective laser melting (SLM) technology to obtain cermet bulk materials with high density and homogeneous microstructures. In this work, the SLM process of the cermet powders was studied by both simulations and experiments using the WC-Co cemented carbides as an example. The results indicated that the evolution of the ceramic and metallic phases in the cermet particle during the heating, melting and solidification processes were all significantly inhomogeneous from atomic scale to mesoscale microstructures. As a consequence, the microstructural defects were caused intrinsically in the printed bulk material. The formation and growth of the bonding necks between the particles were mainly completed at the later stage of laser heating and the early stage of solidification. Both simulations and experiments demonstrated that thin amorphous layers formed at the ceramics/metal interfaces. This work disclosed the mechanisms for the evolution from the atomic scale to microstructure during the SLM printing of cermet powders, and discovered the origin of the defects in the printed cermet bulk materials.     

基于机器视觉的轮胎表面缺陷检测系统的研究与应用

在传统的轮胎表面缺陷依靠人工检测,存在劳动强度高、受人的主观影响大以及效率低下的问题。针对这一现象,研究了一种基于机器视觉的轮胎表面缺陷3D检测系统。该系统依靠机器视觉系统获取检测轮胎的表面图像,然后创建3D模型、判定缺陷类型,最终实现实时自动预警,为轮胎生产商提供一种自动化检测方案。系统集成了先进的技术、软件和工具,配套的信息管控系统可以对轮胎型号和生产数据进行采集、存储、分析,以便在生产过程中实现更高效、更可靠的质量控制,具有较高的实际应用推广价值。     

印刷电路板缺陷检测的实际应用

电路板在我们的日常生活中非常常见,这就使得印刷电路板的缺陷检测显得尤为重要。AOI作为新兴的检测PCB板缺陷的系统,在生产实际中正在被大家熟知并且应用。相较于传统的检测方式,AOI系统比较灵活,无论是在检测时间还是系统运算上,或者是对相关技术人员的要求相较于传统方式都比较有优势,本文就AOI系统在实际中的应用展开讨论,分析并且介绍了在实际应用中的具体细则。     

Decreased CD10-positive granulocytes for the differential diagnosis of myelodysplastic syndrome

Myelodysplastic syndromes (MDS) are highly heterogeneous myeloid neoplasms, and a large number of patients are difficult to diagnose and classify by blood and bone marrow examination. As a surface marker of granulocyte, studies have shown CD10 can be used to define the degree of granulocyte maturation in MDS patients. However, whether it can be used for differential diagnosis of MDS and other hematological diseases remains inconclusive. To explore the value of CD10 for differential diagnosis of MDS, 60 newly diagnosed MDS, 20 aplastic anemia (AA) patients, and 35 iron-deficient anemia (IDA) patients were selected for this study. Bone marrow (BM) specimens were processed for surface marker analysis and labeled with pre-conjugated monoclonal antibodies. Stained cells were detected by flow cytometry. Our results indicated that CD10-positive granulocytes were significantly decreased in BM of MDS patients than AA and IDA patients, and the level of CD10-positive mature granulocytes was not associated with the clinical stages of malignancy. Receiver operating characteristic (ROC) areas under the curve (AUC) of CD10-positive granulocytes was 0.86 and 0.85, respectively, in MDS patients than the IDA group and AA group with good specificity and sensitivity. Further, CD10-positive granulocytes were increased after effective treatment. In conclusion, we found the decrease in CD10-positive granulocytes has a differential diagnostic value of MDS.     

基于压缩机-支架系统的异常噪声研究

针对某乘用车发动机转速在1 573 r/min，压缩机开启时车内噪声异常的问题，对样车进行试验分析与诊断，对压缩机-支架系统进行仿真分析，提出改进方案并验证改进效果。利用LMS声振信号采集系统采集振动噪声数据，采用频谱分析、阶次追踪等方法，并结合压缩机-支架系统模态仿真结果，确定车内异常噪声是压缩机轴频21阶与压缩机-支架系统3阶模态频率接近发生共振造成的。通过优化支架结构来提高压缩机-支架系统3阶模态频率以此来避免共振，并换装橡胶驱动盘缓和压缩机输入扭矩波动。将改进结构进行整车试验，结果表明：匀速工况空调开启时问题转速下，车内噪声降低了2.5 dB(A)；匀加速工况空调开启时发动机转速1 500~1 650 r/min区间，车内噪声无峰值，其余转速空调开启时改进前/后车内噪声基本不变，噪声波动趋势平缓。     

基于启发式BP算法的软件缺陷预测模型

针对软件缺陷预测时缺陷数据集中存在的类别分布不平衡问题，结合上采样算法SMOTE与Edited Nearest Neighbor (ENN) 数据清洗策略，提出了一种基于启发式BP神经网络算法的软件缺陷预测模型。模型中采用上采样算法SMOTE增加少数类样本以改善项目中的数据不平衡状况，并针对采样后数据噪声问题进行ENN数据清洗，结合基于启发式学习的模拟退火算法改进四层BP神经网络后建立分类预测模型，在AEEEM数据库上使用交叉验证对提出的方案进行性能评估，结果表明所提出的算法能够有效提高模型在预测类不平衡数据时的分类准确度。