Effect of mesoporous carbon on oxygen reduction reaction activity as cathode catalyst support for proton exchange membrane fuel cell

In this paper, a new carbon support with a large number of mesoporous-structures is selected to prepare Pt/C catalysts. Transmission electron microscope (TEM) results show that the Pt/3# catalyst presents a sponge-like morphology, Pt particles are not only evenly distributed on the surface of carbon support, but also the smaller Pt particles are deposited in the mesoporous inside the support. The average diameter of Pt particles is only 2.8 nm. The membrane electrode assembly (MEA) based on Pt/3# catalyst also shows excellent performance. In conclusion, the 3# support is an idea carbon support for PEMFC, which helps to improve the oxygen reduction reaction (ORR) activity of the catalyst. Based on the “internal-Pt” structure of the support mesoporous, the efficient three-phase boundaries (TPBs) are construct to avoid the poisoning effect of ionomer on the nano-metal particles, reduce the activation impedance and oxygen mass transfer impedance, and improve the reaction efficiency.     

Influence of metal-support interactions on reaction pathways over Ni/CeZrOx–Al2O3 catalysts for ethanol steam reforming

This work investigates selective Ni locations over Ni/CeZrO_x–Al₂O₃ catalysts at different Ni loading contents and their influences on reaction pathways in ethanol steam reforming (ESR). Depending on the Ni loading contents, the added Ni selectively interacts with CeZrO_x–Al₂O₃, resulting in the stepwise locations of Ni over CeZrO_x–Al₂O₃. This behavior induces a remarkable difference in hydrogen production and coke formation in ESR. The selective interaction between Ni and CeZrO_x for 10-wt.% Ni generates more oxygen vacancies in the CeZrO_x lattice. The Ni sites near the oxygen vacancies enhance reforming via steam activation, resulting in the highest hydrogen production rate of 1863.0 μmol/g_cat·min. In contrast, for 15 and 20-wt.% Ni, excessive Ni is additionally deposited on Al₂O₃ after the saturation of Ni–CeZrO_x interactions. These Ni sites on Al₂O₃ accelerate coking from the ethylene produced on the acidic sites, resulting in a high coke amount of 19.1 mg_c/g_cat·h (20Ni/CZ-Al).     

基于多通道的ICU脑血管疾病死亡风险预测模型

死亡风险预测指根据病人临床体征监测数据来预测未来一段时间的死亡风险。对于ICU病患，通过死亡风险预测可以有针对性地对病人做出临床诊断，以及合理安排有限的医疗资源。基于临床使用的MEWS和Glasgow昏迷评分量表，针对ICU病人临床监测的17项生理参数，提出一种基于多通道的ICU脑血管疾病死亡风险预测模型。引入多通道概念应用于BiLSTM模型，用于突出每个生理参数对死亡风险预测的作用。采用Attention机制用于提高模型预测精度。实验数据来自MIMIC [Ⅲ]数据库，从中提取3?080位脑血管疾病患者的16?260条记录用于此次研究，除了六组超参数实验之外，将所提模型与LSTM、Multichannel-BiLSTM、逻辑回归（logistic regression）和支持向量机（support vector machine, SVM）四种模型进行了对比分析，准确率Accuracy、灵敏度Sensitive、特异性Specificity、AUC-ROC和AUC-PRC作为评价指标，实验结果表明，所提模型性能优于其他模型，AUC值达到94.3%。     

No-Reference Video Quality Assessment using novel hybrid features and two-stage hybrid regression for score level fusion

This paper presents a novel No-Reference Video Quality Assessment (NR-VQA) model that utilizes proposed 3D steerable wavelet transform-based Natural Video Statistics (NVS) features as well as human perceptual features. Additionally, we proposed a novel two-stage regression scheme that significantly improves the overall performance of quality estimation. In the first stage, transform-based NVS and human perceptual features are separately passed through the proposed hybrid regression scheme: Support Vector Regression (SVR) followed by Polynomial curve fitting. The two visual quality scores predicted from the first stage are then used as features for the similar second stage. This predicts the final quality scores of distorted videos by achieving score level fusion. Extensive experiments were conducted using five authentic and four synthetic distortion databases. Experimental results demonstrate that the proposed method outperforms other published state-of-the-art benchmark methods on synthetic distortion databases and is among the top performers on authentic distortion databases. The source code is available at https://github.com/anishVNIT/two-stage-vqa.     

Prediction of Flooding in Packed Liquid-Liquid and High-Pressure Extraction Columns Using a Gaussian Process

Reliable prediction of flooding conditions is needed for sizing and operating packed extraction columns. Due to the complex interplay of physicochemical properties, operational parameters and the packing-specific properties, it is challenging to develop accurate semi-empirical or rigorous models with a high validity range. State of the art models may therefore fail to predict flooding accurately. To overcome this problem, a data-driven model based on Gaussian processes is developed to predict flooding for packed liquid-liquid and high-pressure extraction columns. The optimized Gaussian process for the liquid-liquid extraction column results in an average absolute relative error (AARE) of 15.23 %, whereas the algorithm for the high-pressure extraction column results in an AARE of 13.68 %. Both algorithms can predict flooding curves for different packing geometries and chemical systems precisely.     

Highly stable nanocarbon supported Pt catalyst for fuel cell via a molten salt graphitization strategy

Proton exchange membrane fuel cells (PEMFCs) durability has been severely hindered by carbon support poor stability in the cathodic Pt-based catalyst. Herein, a high-surface-area nitrogen-doped graphitic nanocarbon (N-G-CA) with mesopores is developed as Pt support to address PEMFCs durability challenge. Resorcinol-formaldehyde aerogel pyrolyzed carbon aerogel is selected as N-G-CA raw material. Nitrogen atoms are introduced into carbon aerogel via NH₃ heat treatment. Then, nitrogen-doped carbon aerogel is transferred into N-G-CA via heating together with transition-metal salts (one of FeCl₃, FeCl₂, CoCl₂, or MnCl₂, etc.) at 1200 °C. As ORR catalyst, Pt/N-G-CA half-wave potential only lost 10 mV, after 30, 000 cycles accelerated aging test in the rotating-desk-electrode. Only 12 mV voltage loss at 1.5 A/cm2 is observed, after 5, 000 cycles for membrane electrode. Pt/N-G-CA exhibits superior durability and activity than commercial Pt/C. High durability of Pt/N-G-CA is due to N-G-CA high graphitization extent, as well as the interactions between doping nitrogen and Pt. N-G-CA is promising as stable support for durable Pt-based catalysts in PEMFCs, thanks to enhanced carbon corrosion resistance, uniformly dispersed Pt, and strong support-metals interaction.     

基于FLAC3D的破碎围岩巷道支护效果分析

为了解破碎围岩分别采用锚杆支护、锚喷支护以及锚喷+锚索耦合三种支护方式下的支护效果,进而为破碎围岩巷道选择合理的支护方式提供参考。通过借助FLAC^3D软件建立数值模型,分析不同支护条件下的破碎围岩巷道位移量、应力分布以及塑性区的时空演化特征。结果表明,采用锚喷+锚索耦合支护时,可以较好的控制巷道围岩的位移量、减小应力集中效应、缩小塑性区的影响范围。     

Flexible Distribution-Based Regression Models for Count Data: Application to Medical Diagnosis

Abstract

Data mining techniques have been successfully utilized in different applications of significant fields, including medical research. With the wealth of data available within the health-care systems, there is a lack of practical analysis tools to discover hidden relationships and trends in data. The complexity of medical data that is unfavorable for most models is a considerable challenge in prediction. The ability of a model to perform accurately and efficiently in disease diagnosis is extremely significant. Thus, the model must be selected to fit the data better, such that the learning from previous data is most efficient, and the diagnosis of the disease is highly accurate. This work is motivated by the limited number of regression analysis tools for multivariate counts in the literature. We propose two regression models for count data based on flexible distributions, namely, the multinomial Beta-Liouville and multinomial scaled Dirichlet, and evaluated the proposed models in the problem of disease diagnosis. The performance is evaluated based on the accuracy of the prediction which depends on the nature and complexity of the dataset. Our results show the efficiency of the two proposed regression models where the prediction performance of both models is competitive to other previously used regression models for count data and to the best results in the literature.     

Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques

The aim of the research is evaluating the classification performances of eight different machine-learning methods on the antepartum cardiotocography (CTG) data. The classification is necessary to predict newborn health, especially for the critical cases. Cardiotocography is used for assisting the obstetricians’ to obtain detailed information during the pregnancy as a technique of measuring fetal well-being, essentially in pregnant women having potential complications. The obstetricians describe CTG shortly as a continuous electronic record of the baby's heart rate took from the mother's abdomen. The acquired information is necessary to visualize unhealthiness of the embryo and gives an opportunity for early intervention prior to happening a permanent impairment to the embryo. The aim of the machine learning methods is by using attributes of data obtained from the uterine contraction (UC) and fetal heart rate (FHR) signals to classify as pathological or normal. The dataset contains 1831 instances with 21 attributes, examined by applying the methods. In the paper, the highest accuracy displayed as 99.2%.