意大利Corefon涂料在中国的应用

分析意大利corefon涂料的特性,研究在不同铸件应用中的典型案例。结果表明,corefon涂料可以提升汽车发动机缸体缸盖内腔清洁度、解决工程液压阀体内腔油道粘砂烧结问题、提高增压器零部件涡轮涡壳气道光洁度。铁型覆膜砂外型喷涂corefon涂料后,浇铸的铸件表面光滑,无粘砂、无烧结,不需要人工打磨和特别抛丸处理。充分体现了corefon涂料在性能上的优越性,可以解决铸造厂铸件内腔粘砂、烧结、表面粗糙的问题。     

A novel PEM fuel cell remaining useful life prediction method based on singular spectrum analysis and deep Gaussian processes

Accurate remaining useful life (RUL) prediction of proton exchange membrane fuel cells (PEMFCs) can assess the reliability of fuel cells to determine the occurrence of failures and mitigate their operational risk. However, is it quite challenging to design a high-precision prediction method because the implicit degradation details of PEMFCs are difficult to learn well from the measurement data with high-frequency noise. Recognizing this, a novel RUL prediction method based on singular spectrum analysis (SSA) and deep Gaussian process (DGP) is proposed in this paper. The SSA-based method is firstly employed to preprocess the measurement data, which can strengthen the effective information of PEMFC degradation data at the same time remove the noise and spikes that interfere with degradation prediction. As a deep structural model, DGP has strong feature learning ability which can represent the nonlinear details of degradation data and give more accurate prediction results. At the same time, it serves as a probabilistic model that can provide the confidence interval to enhance reliability of RUL prediction. The effectiveness of the proposed method is evaluated by experimental data of the PEMFCs under steady-state conditions, and the results show that the SSA-DGP method has higher accuracy and reliability than conventional methods.     

Performance degradation prediction of proton exchange membrane fuel cell using a hybrid prognostic approach

The proton exchange membrane fuel cell has been widely used for industrial systems; however, its performance gradually degrades during use. Therefore, the study on the performance degradation prediction of fuel cells is helpful to extend its lifespan. In this paper, a novel hybrid approach using a combination of model-based adaptive Kalman filter and data-driven NARX neural network is proposed to predict the degradation of fuel cells. The overall degradation trend (i.e., irreversible degradation process) is captured by an empirical aging model and adaptive Kalman filter. Meanwhile, the detail degradation information (i.e., reversible degradation process) is depicted by the NARX neural network. Moreover, the correlation analysis of the reversible voltage time series is carried out to obtain the number of delays of the NARX neural network based on the autocorrelation function and the partial autocorrelation function. Then, the total degradation prediction is the sum of the overall degradation prediction and the detail degradation prediction. Finally, the prognostic capability of the proposed method is verified by two aging datasets, and the results show the effectiveness and superiority of the proposed method which can provide accurate degradation forecasting and remaining useful life.     

润滑油基础油的生产工艺及发展趋势

本文主要分析"老三套"生产工艺、异构脱蜡和加氢生产工艺,并探讨我国润滑油基础油生产工艺的发展方向。     

电视民生新闻现状及发展趋势探析

自2002年起,我国开始出现电视民生新闻,并得到观众的广泛喜爱,但在整个发展过程当中电视新闻存在一些问题有待解决,探讨电视民生新闻的现状及发展趋势,对其中存在的问题提出解决方案。     

吴庄铁矿盘区间柱回采优化研究

基于吴庄铁矿盘间柱宽度不确定性、两侧均为充填体且高度大的现状，为安全高效回采盘区间柱，制定了盘区间柱两侧采场充填体取芯设计方案，并对充填体进行制样，进行了实验室试块抗压强度试验。试验结果表明：一步骤采场（矿柱）充填体强度整体符合设计强度要求，二步采场充填体强度整体偏低，盘区间柱宽度较设计变化较大，宽度极其不规整，整体呈上窄下宽的规律。鉴于盘区间柱宽度不规则、二步骤采场充填体强度低于预期、采场高度大和盘区间柱回采时不能大面积暴露的特点，采用留设一定厚度间柱和采用较小采场长度的措施进行回采方案制定，制定了小采场分段空场嗣后充填法回采方案。该方案的制定和实施，对于其他类似地下矿山三步骤间柱回采工程具有借鉴意义。     

Three‐dimensional color prediction modeling of single‐ and double‐layered woven fabrics

In this research, the three‐dimensional structural and colorimetric modeling of three‐dimensional woven fabrics was conducted for accurate color predictions. One‐hundred forty single‐ and double‐layered woven samples in a wide range of colors were produced. With the consideration of their three‐dimensional structural parameters, three‐dimensional color prediction models, K/S‐, R‐, and L*a*b*‐based models, were developed through the optimization of previous two‐dimensional models which have been reported to be the three most accurate models for single‐layered woven structures. The accuracy of the new three‐dimensional models was evaluated by calculating the color differences ΔL*, ΔC*, Δh°, and ΔE_CMC(2:1) between the measured and the predicted colors of the samples, and then the error values were compared to those of the two‐dimensional models. As a result, there has been an overall improvement in color predictions of all models with a decrease in ΔE_CMC(2:1) from 10.30 to 5.25 units on average after the three‐dimensional modeling.     

樟芝多糖通过降低NLRP3 Caspase1炎性小体表达改善帕金森小鼠神经行为学的机制研究

目的:研究樟芝多糖通过降低NLRP3-Caspase1炎性小体表达改善6-OHDA构建的帕金森小鼠模型的行为学机制。方法:利用6-OHDA脑内注射构建帕金森小鼠模型，通过酪氨酸羟化酶（TH）免疫组化染色和行为学判定小鼠模型的构建成功。利用樟芝多糖进行干预，分别在干预前、干预后的第1、3、7天4个时间点进行神经行为学实验，分别采用转棒实验、爬杆实验检测小鼠自主行为能力以及协调能力，4个时间点取小鼠尾静脉外周血采用ELISA法检测外周血中Caspase1和IL-1β的表达，樟芝多糖干预第7天时待进行完行为学实验后小鼠断颈处死，取小鼠脑组织-纹状体，Western blot法检测纹状体中Caspase1、proCaspase1、NLRP3的表达，高效液相色谱检测纹状体中单胺类神经递质的表达，RT-QPCR检测Caspase1、NLRP3、IL-1β、IL-4、IL-6的mRNA表达。NISSl染色检测小鼠脑组织神经细胞凋亡情况。 结果:6-OHDA脑内注射可以造成小鼠帕金森样病变，且TH蛋白表达显著下调，樟芝多糖干预后小鼠的行为学得到显著改善（P<0.05），纹状体中Caspase1、proCaspase1、NLRP3的表达显著下调，与模型小鼠相比具有统计学差异（P<0.05），且相关炎症因子Caspase1、NLRP3、IL-1β、IL-4、IL-6的mRNA表达下调（P<0.05），纹状体中单胺类神经递质表达上升（P<0.05）。结论:樟芝多糖可以通过下调NLRP3-Caspase1炎性小体表达来改善6-OHDA构建的帕金森小鼠模型行为改善，这可能是樟芝多糖治疗帕金森的机制之一。     

A Novel Method of Heart Failure Prediction Based on DPCNNXGBOOST Model

The occurrence of perioperative heart failure will affect the quality of medical services and threaten the safety of patients. Existing methods depend on the judgment of doctors, the results are affected by many factors such as doctors’ knowledge and experience. The accuracy is difficult to guarantee and has a serious lag. In this paper, a mixture prediction model is proposed for perioperative adverse events of heart failure, which combined with the advantages of the Deep Pyramid Convolutional Neural Networks (DPCNN) and Extreme Gradient Boosting (XGBOOST). The DPCNN was used to automatically extract features from patient’s diagnostic texts, and the text features were integrated with the preoperative examination and intraoperative monitoring values of patients, then the XGBOOST algorithm was used to construct the prediction model of heart failure. An experimental comparison was conducted on the model based on the data of patients with heart failure in southwest hospital from 2014 to 2018. The results showed that the DPCNN-XGBOOST model improved the predictive sensitivity of the model by 3% and 31% compared with the text-based DPCNN Model and the numeric-based XGBOOST Model.