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%。     

Experimental study on the propagation characteristics of hydrogen/methane/air premixed flames in a narrow channel

This work is focused on the explosion characteristics of premixed gas containing different volume fractions of hydrogen in a narrow channel (1000 mm × 50 mm × 10 mm) under the circumstance of stoichiometric ratio. The ignition positions were set in the closed end and the middle of the pipeline respectively. The results showed that when the gas was ignited at the pipeline closed end, the propagating flame was tulip structure for different premixed gas. When the hydrogen volume fraction was less than 40%, the flame propagation speed increased significantly with the rise of hydrogen volume fraction, and the overpressure peak also appeared obviously in advance. However, when the volume fraction of hydrogen was more than 40%, the increase of flame propagation speed and the overpressure peak occurrence time varied slightly. Furthermore, when the ignition position was placed in the middle of the pipeline, the flame propagation speed propagating to the opening end was much faster than that propagating to the closing end, and there was no tulip shape when the flame propagates to the opening end. The flame propagating to the closed end appeared tulip shape under the influence of airflow, and high-frequency flame oscillation occurred during the propagation. This work shows that the hydrogen volume fraction and ignition position significantly affected the flame structure, flame front speed, and explosion overpressure.     

Synergistic Effects of Polybenzimidazole and Aramide on Enhancing Flame-Retardancy and Solubility

Aromatic and functional polymers with processibility derived from biobased starting materials are prerequisite considering sustainable society. Poly(2,5-benzimidazole)s are rigid-rod polymers to show ultrahigh thermal stability such as flame retardance, while usually suffer from poor solubility. Here, poly(benzimidazole-co-amide)s are synthesized from two biobased monomers, 3,4-diaminobenzoic acid and a semirigid comonomer, 4-aminohydrocinnamic acid. The copolymers with an amide composition of 80 mol% and higher are soluble in widely used polar solvents to fabricate the films keeping high flame retardance, which is comparable with popular high-performance polymers such as aromatic polyimides, polyetheretherketone, polyphenylene sulfide, etc.     

低分子量聚丙烯酸钾的合成及其应用

采用水溶液聚合法制备了低分子量聚丙烯酸钾（PAAK），并作为新型消焰剂加入单基发射药中。通过火焰原子吸收光谱法测试了PAAK中钾的含量；用乌氏黏度计测定了特性黏度；采用DSC法研究不同pH值的PAAK与硝化棉（NC）的相容性；利用充氮氧弹法对添加PAAK、硝酸钾KNO₃、硫酸钾K₂SO₄的单基发射药的燃烧残渣进行了对比研究。结果表明，合成的PAAK中，钾的质量分数为15.21%，相对分子量在3 000左右，有利于和NC均匀混合，且在中性或微碱性（pH＝7.0~7.5）的情况与NC相容性良好。与传统的KNO₃、K₂SO₄消焰剂相比，PAAK能够和NC均匀混合，制备均质透明的单基发射药；PAAK发射药的燃烧残渣最少，占发射药质量的0.18%。     

用于厚屏蔽小探测器的蒙特卡罗模拟减方差方法研究

反应堆屏蔽计算中经常出现厚屏蔽、小探测器问题，常规蒙特卡罗方法难以有效解决。基于自动重要抽样（AIS）方法，本文提出了小探测器自动重要抽样(SDAIS)方法，并针对小探测器问题，优化了AIS方法的虚粒子赌分裂算法。该方法在自主开发的蒙特卡罗屏蔽程序MCShield上进行了实现。使用NUREG/CR-6115 PWR基准题验证该方法的正确性和计算效率。结果表明，SDAIS方法可有效地解决厚屏蔽小探测器问题，相比AIS方法及传统的重要性方法，计算效率提升1~2个量级。     

（S）-噻吗洛尔半水合物的合成及表征

为了开发β受体阻断剂新药（S）-噻吗洛尔半水合物，采用3-吗啉-4-氯-1，2，5-噻二唑为起始原料，经水解反应得到中间体1（3-吗啉-4-羟基-1，2，5-噻二唑）。中间体1与R-环氧氯丙烷发生醚化反应，经后处理及重结晶得到中间体2 {（R）-4-［4-（环氧乙烷-2-基甲氧基）-1，2，5-噻二唑-3-基］吗啉}。中间体2经胺化反应、马来酸成盐及重结晶得到（S）-马来酸噻吗洛尔。（S）-马来酸噻吗洛尔经游离、纯水转晶得到符合药典标准的（S）-噻吗洛尔半水合物，总收率14.05%且e.e.值为99.66%。最终成品经IR、1H-NMR、13C-NMR、MS、TGA、DSC表征，并优化各步反应条件。结果表明:以三乙胺为醚化反应缚酸剂75 ℃反应最佳；以乙醇为胺化反应溶剂46 ℃反应16 h最佳；S-噻吗洛尔的转晶拆分以水作溶剂，比传统不对称合成工艺安全稳定，操作简单，适合工业化生产。     

改进HSR-FCN的服装图像识别分类算法研究

目前网络上的服装图像数量增长迅猛，对于大量服装图像实现智能分类的需求日益增加。将基于区域的全卷积网络（Region-Based Fully Convolutional Networks，R-FCN）引入到服装图像识别中，针对服装图像分类中网络训练时间长、形变服装图像识别率低的问题，提出一种新颖的改进框架HSR-FCN。新框架将R-FCN中的区域建议网络和HyperNet网络相融合，改变图片特征学习方式，使得HSR-FCN可以在更短的训练时间内达到更高的准确率。在模型中引入了空间转换网络，对输入服装图像和特征图进行了空间变换及对齐，加强了对多角度服装和形变服装的特征学习。实验结果表明，改进后的HSR-FCN模型有效地加强了对形变服装图像的学习，且在训练时间更短的情况下，比原来的网络模型R-FCN平均准确率提高了大约3个百分点，达到96.69%。     

Flame characteristics of premixed H2-air mixtures explosion venting in a spherical container through a duct

The explosion venting duct can effectively reduce the hazard degree of a gas explosion and conduct the venting energy to the safe area. To investigate the flame quantitative propagation law of explosion venting with a duct, the effects of hydrogen fraction and explosion venting duct length on jet flame propagation characteristics of premixed H₂-air mixtures were analyzed through experiment and simulation. The experiment results under initial conditions of room temperature and 1 atm show that when hydrogen fraction was high enough, part of the unburned hydrogen was mixed with air again to reach an ignitable concentration, resulting in the secondary combustion was easier produced and the duration of the secondary flame increased. With the increase of venting duct length, the flame front distance and propagation velocity increased. Meanwhile, the spatial distribution of pressure field and temperature field, and the propagation process and mechanism of the flame venting with a duct were analyzed using FLUENT software. The variation of the pressure wave and the pressure reflection oscillation law in the explosion venting duct was captured. Therefore, in the industrial explosion venting design with a duct, the hazard caused by the coupling of venting pressure and venting flame under different fractions should be considered comprehensively.     

TiO2/MXene-Assisted LDI-MS for Urine Metabolic Profiling in Urinary Disease

Cancer remains an intractable medical problem. Rapid diagnosis and identification of cancer are critical to differentiate it from nonmalignant diseases. High-throughput biofluid metabolic analysis has potential for cancer diagnosis. Nevertheless, the present metabolite analysis method does not meet the demand for high-throughput screening of diseases. Herein, a high-throughput, cost-effective, and noninvasive urine metabolic profiling method based on TiO₂/MXene-assisted laser desorption/ionization mass spectrometry (LDI-MS) is presented for the efficient screening of bladder cancer (BC) and nonmalignant urinary disease. Combined with machine learning, TiO₂/MXene-assisted LDI-MS enables high diagnostic accuracy (96.8%) for the classification of patient groups (including 47 BC and 46 ureteral calculus (UC) patients) from healthy controls (113 cases). In addition, BC patients can also be identified from noncancerous UC individuals with an accuracy of 88.3% in the independent test cohort. Furthermore, metabolite variations between BC and UC individuals are investigated based on relative quantification, and related pathways are also discussed. These results suggest that this method, based on urine metabolic patterns, provides a potential tool for rapidly distinguishing urinary diseases and it may pave the way for precision medicine.