混合液体火灾爆炸危险性——闪点预测与实验研究

支持向量机应用核函数技术,已经成为当前国际上一个研究的热点,由于支持向量机具有良好的理论基础和泛化性能,可将其引入到混合液体闪点预测的研究之中,以期建立准确、高效的预测模型。本文建立了一个基于支持向量机的理论模型,用于预测二元互溶混合液体的闪点。根据所研究混合液体的物理性质,选择了纯物质的粘度、表面张力、配比、燃烧下限等物理参数来表征闪点,以这些参数作为输入参数,二元混合液体的闪点作为输出值,应用支持向量机方法对两者之间的内在定量关系进行模拟。结果表明,闪点预测值与实验值符合良好。本方法的提出为工程上提出了一种预测二元互溶液体闪点的有效方法,可应用于评估混合溶液的火灾爆炸危害性及本质较安全设计。     

不同土地利用类型下土壤特性及重金属评价

在哈尔滨市周家镇选择典型的五种土地利用类型(耕地、居民点及工矿用地、交通用地、林地、水域),24个样品,研究乡级小城镇不同土地利用类型下土壤特性及重金属状况.结果显示:(1)耕地的土壤理化性状最低,有机质与全氮有显著的正相关;(2)乡级小城镇不同土地利用类型下土壤中Zn、Pb含量存在显著差异,交通用地与耕地含量较高,同时各土地利用类型的内梅罗综合污染指数均达到中污染;(3)人类活动对土壤质量影响最大的是交通活动,其次是耕地施肥、使用农药及翻耕等,在次是居民生活.     

染料废水处理技术现状与发展

文章介绍了染料废水的物理、化学和生物处理技术,并分析了其去除原理和工艺优缺点,目的在于为染料废水处理工作提供理论基础和现实指导,并提出未来染料废水处理技术的发展走向———高效、经济、适应性强、清洁性的废水处理技术。     

黄河源区斑块化退化高寒草甸土壤细菌群落多样性变化

为研究海拔变化和退化过程中高寒草甸土壤细菌群落多样性的变化规律,利用MiSeq高通量测序技术,分析不同海拔活动斑块、非活动斑块、恢复斑块和高寒草甸土壤细菌群落多样性,利用冗余分析对细菌多样性和环境因子进行分析.结果发现,3种类型斑块中主要的土壤细菌门均是变形菌门（Proteobacteria）、放线菌门（Actinobacteriota）和酸杆菌门（Acidobacteriota）.细菌优势属为RB 41 、鞘脂单胞菌属（Sphingomonas）和慢生根瘤菌属（Bradyrhizobium）.RB 41 和慢生根瘤菌属相对丰度随海拔升高而下降,随斑块演替而增加,但3种类型退化斑块相对丰度均显著低于高寒草甸（P<0.05）.退化斑块土壤碳固定功能的细菌丰度,大于健康高寒草甸.不同斑块的细菌Chao1指数和物种数显著高于高寒草甸（P<0.05）.冗余分析发现,生物结皮盖度和全氮是海拔4013 m处细菌优势门的主要影响因子;生物量、全氮和pH对高海拔4224 m细菌优势门影响较大.生物量和全钾显著影响海拔4013 m的细菌属分布,海拔4224 m地区莎草科盖度和速效氮为细菌优势属的主要影响因子.生物结皮和pH对细菌多样性影响较大.不同海拔地区细菌的影响因子发生着较大变化,在研究细菌多样性变化的过程中,不仅要关注高寒草地退化的影响,还应考虑海拔高度的作用.     

Modelling spills of water-reactive chemicals

This paper describes part of a programme of work undertaken at the Health and Safety Laboratory (HSL) to investigate the behaviour of selected water-reactive chemicals. Following an accidental release, such substances react exothermically with any water present, generating acidic vapours. The STAWaRS (Source Term Assessment of Water Reactive Substances) software was developed for the Health and Safety Executive (HSE) by ESR Technology to model this complex process. The aims of the study described here were to provide experimental validation of the heats of hydrolysis used within STAWaRS, and to perform sensitivity studies on selected STAWaRS input parameters.The heat of hydrolysis of acetyl chloride was measured and showed good correlation with the value used within STAWaRS. Some of the variables that influence the severity of acetyl chloride spills are examined, with reference to predictions made by the STAWaRS model. The heats of hydrolysis of titanium tetrachloride previously measured at HSL are also discussed, and the effect of adopting these experimentally derived values for modelling spills is shown for a hypothetical land use planning case. This study demonstrates the importance of using experimentally validated values for STAWaRS input parameters.     

我国高校体育产业化发展的现状分析和对策研究

作为体育产业重要组成部分的高校体育产业正面临着巨大的商机,蕴藏着巨大潜力.本文运用文献资料法,对我国高校体育产业化的现状进行分析,结合实际对我国高校体育产业化的发展提出相应的对策:更新观念、提高认识,积极创建体育俱乐部,充分利用自身的资源优势,开展多项经营,加速对体育经营管理人才的培养,严格遵循市场经济规律等.     

物理化学与环境科学

环境化学从化学的角度来研究污染中的化学规律。物理化学的理论和方法被运用到环保中 ,为生态系统中能量流动提供理论依据 ,探索污染的形成机理 ,研究O3层的破坏机理 ,为环境检测提供了新技术     

城市大气中颗粒物的研究现状及健康效应

综述了大气尘(TSP、PM10、PM2.5)的研究现状,重点围绕TSP、PM10、PM2.5的物理特征、化学组成(有机组成、重金属元素的总含量及其各化学形态分布)及其源解析进行描述,也提及了大气颗粒物与健康的关系.     

电石渣理化性质的分析与表征

电石渣是电石水解获取乙炔气后产生的一种工业废渣。文章采用SEM、XRD和DSC/TG等现代测试手段,对其理化性质进行分析和表征,结果表明:电石渣是由一些十分细微颗粒组成,其化学成分主要是CaO,其次是Al2O3、SiO2等。电石渣中微量元素未超过排放标准、放射性符合建筑主体材料技术要求。电石渣是以Ca(OH)2为主晶相,并含有少量CaCO3。     

Modelling and Prediction of Diesel Engine Performance using Relevance Vector Machine

Diesel engines are being increasingly adopted by many car manufacturers today, yet no exact mathematical diesel engine model exists due to its highly nonlinear nature. In the current literature, black-box identification has been widely used for diesel engine modelling and many artificial neural network (ANN) based models have been developed. However, ANN has many drawbacks such as multiple local minima, user burden on selection of optimal network structure, large training data size, and over-fitting risk. To overcome these drawbacks, this article proposes to apply an emerging machine learning technique, relevance vector machine (RVM), to model and predict the diesel engine performance. The property of global optimal solution of RVM allows the model to be trained using only a few experimental data sets. In this study, the inputs of the model are engine speed, load, and cooling water temperature, while the output parameters are the brake-specific fuel consumption and the amount of exhaust emissions like nitrogen oxides and carbon dioxide. Experimental results show that the model accuracy is satisfactory even the training data is scarce. Moreover, the model accuracy is compared with that using typical ANN. Evaluation results also show that RVM is superior to typical ANN approach.