Generating Questions Based on Semi-Automated and End-to-End Neural Network

With the emergence of large-scale knowledge base, how to use triple information to generate natural questions is a key technology in question answering systems. The traditional way of generating questions require a lot of manual intervention and produce lots of noise. To solve these problems, we propose a joint model based on semi-automated model and End-to-End neural network to automatically generate questions. The semi-automated model can generate question templates and real questions combining the knowledge base and center graph. The End-to-End neural network directly sends the knowledge base and real questions to BiLSTM network. Meanwhile, the attention mechanism is utilized in the decoding layer, which makes the triples and generated questions more relevant. Finally, the experimental results on SimpleQuestions demonstrate the effectiveness of the proposed approach.     

Anaerobic co-digestion of food waste and FOG with sewage sludge – realising its potential in Ireland

The severe environmental pollution in many countries is caused by indiscriminate discharge of large quantities of food waste (FW), fat oil and grease (FOG) and sewage sludge (SS) to the environment. There are many possible treatment routes, but anaerobic digestion (AD) is now well accepted for treating several kinds of organic wastes. But AD of FW alone presents some operational challenges because of substrates and variability. Anaerobic co-digestion of two or more substrates is better than single substrate digestion. This can use a plant’s unused capacity, in line with the trend to renewable energy. Co-digestion technology, although well established in many European countries, is still in its infancy in Ireland. There are problems with different regulatory arrangements. They should be resolved. The paper reviews anaerobic co-digestion technology is reviewed, with special focus on possible application in Ireland.     

急性炎性疾病患者乳酸林格氏液液体动力学

目的:探索炎性疾病患者的乳酸林格氏液（Ringer's lactate,RL）液体动力学特征以及炎性生物标记物是否可以作为协变量影响RL分布和排泄。方法:本研究为前瞻性队列研究。选择40例美国麻醉医师分级（ASA）I-II级，腹腔镜下择期胆囊切除术（胆囊炎组，n=20）或者腹腔镜下急诊阑尾切除术（阑尾炎组，n=20）。所有患者麻醉诱导前开始输注RL，按15 mL/kg，35 min内输毕。采用酶联免疫（enzyme-linked immunosorbent assay,ELISA）方法测定血浆炎症（TNF-α,IL-10和CRP）或者内皮损伤生物标记物（syndecan-1,SDC-1）；利用血红蛋白（Hb）稀释-时间曲线和尿量，使用Phoenix软件，采用非线性混合效应模型分析计算RL液体动力学参数和协变量的影响。结果:与胆囊炎组相比，阑尾炎组RL从组织间隙到血浆的转运速率常数（k21）显著降低（14×10-3min-1 versus 35×10-3min-1;P=0.012）。阑尾炎组C反应蛋白（CRP）升高［中位数38.1(1.8-143.6) μg/mL versus 1.3(0.1-159.0) μg/mL;P<0.001］；与清醒状态相比，麻醉期间（输液开始后30～45 min），液体从中央室中到外周室的转运速率常数（k12）显著增加（57×10-3min-1 versus 32×10-3min-1；P<0.01）。清除速率常数（k10）降低90%（0.6×10-3min-1 versus 5.3×10-3min-1;P<0.001）。无论在清醒状态还是麻醉状态下低血压均能降低液体清除；炎症或者内膜损伤的生物标记物不能作为显著影响RL液体动力学参数的协变量。结论:阑尾炎或者胆囊炎患者术前输入液体后“炎症反应的生物标记物”不是RL的液体动力学的协变量，但是两组患者中，全身麻醉期间输入液体的清除率下降。     

SDN中基于KMOBPSO的高可靠性控制器部署算法

针对SDN中控制器系统的单节点故障问题,兼顾系统成本和系统时延,应用N+1冗余备份模型来提高SDN控制器部署的可靠性,并将其抽象为多目标优化问题.同时,提出了一种融合K-means聚类算法和遗传算子的多目标二进制粒子群算法——KMOBPSO算法,以求解SDN控制器高可靠性部署问题的解.仿真结果表明,所提算法具有求解精度高、分布均匀、沿Pareto前沿面覆盖广的特点,能够显著提高SDN中控制器部署的可靠性.     

我国钻井装备现状及其相关配套技术

结合目前国外钻机现状以及国际石油钻井市场对钻机的配套要求,分析指出我国目前陆地钻机存在的不足与差距,对我国钻机及其配套技术的发展提出建议。     

Characteristic of polysaccharides from Flammulina velutipes in vitro digestion under salivary,simulated gastric and small intestinal conditions and fermentation by human gut microbiota

In this study, in vitro digestion and fermentation of Flammulina velutipes -derived polysaccharides (FVP) were investigated. It was found that FVP mainly consisted of 48.45% glucose, 15.40% mannose, 14.60% xylose, 11.80% fucose and 9.90% galactose. The -human saliva, simulated gastric and small intestinal juices conditions did not break down the FVP. Based on in vitro fermentation tests, FVP modulated the composition of gut microbiota by elevating the amounts of Bifidobacteriaceae and Bacteroidaceae and reducing the numbers of genera Lachnospiraceae and Enterococcaceae. Meanwhile, FVP affected the synthesis of short-chain fatty acids derived from gut microbiota.