基于社会网络分析的江西各地市公路行业碳排放的关联与分异特征研究

针对各地公路碳排放的空间差异与关联特征，制定合理的低碳策略，实现各地协同碳减排，是行业可持续发展的热点问题。以江西省为研究对象，采用空间自相关、社会网络分析和引力模型等分析11地市公路行业碳排放的空间差异与关联，识别社会网络的结构特征及其演变趋势，明确不同地市在网络中的地位和作用，结果显示：（1）公路货运是公路行业碳排放的最主要来源；（2）江西省公路行业碳排放整体上呈现空间随机分布，不存在全局的空间聚集性，且局部空间聚集特征不断变化；（3）公路行业碳排放网络逐渐从南昌、新余双中心结构演变为南昌为主的单中心结构，网络的协同作用整体呈减弱趋势，核心地市对外围地市的影响力和带动作用不足。     

发挥环保监督网络作用 搞好“一控双达标”

介绍了新余发电有限责任公司建立环保监督网络和开展环保技术监督，做好“一控双达标”工作的具体做法。     

基于BP神经网络的苯储罐泄漏事故风险评价模型研究

为了在事故发生之前对苯储罐进行风险评价，提出1种基于BP神经网络的泄漏事故风险评价方法，利用该方法构建了苯储罐的风险评价模型，并对模型进行了训练及验证。研究结果表明:BP神经网络成功完成了建模任务，且模型训练结果较好，可利用基于BP神经网络所构建的苯泄漏事故风险评价模型对苯储罐发生泄漏事故的风险进行评价。     

A methodological framework for improving air quality monitoring network layout. Applications to environment management

This work aims to provide a methodology framework which allows to improve the performance and efficiency of an air quality monitoring network (AQMN). It requires to be constituted by a minimum and reliable number of measurement sites. Nevertheless, the AQMN efficiency should be assessed over time, as a consequence of the possible emergence of new emission sources of air pollutants, which could lead to variations on their spatial distribution within the target area. PM₁₀ particles data monitored by the Community of Madrid's (Spain) AQMN between 2008 and 2017 were used to develop a methodology to optimize the AQMN performance. The annual spatial distribution of average PM₁₀ levels over the studied period monitored by all current stations vs those more representative was provided by a geographic information system (GIS), and the percentage of similarity between both postulates was quantified using simple linear regression (> 95%). As one innovative tool of this study, the practical application of the proposed methodology was validated using PM₁₀ particles data measured by AQMN during 2007 and 2018, reaching a similitude degree higher than 95%. The influence of temporal variation on the proposed methodological framework was around 20%.The proposed methodology sets criteria for identifying non-redundant stations within AQMN, it is also able to appropriately assess the representativeness of fixed monitoring sites within an AQMN and it complements the guidelines set by European legislation on air pollutants monitoring at fixed stations, which could help to tackle efforts to improve the air quality management.     

Stochastic Transfer Function Model and Neural Networks to Estimate Soil Moisture1

Abstract: A practical methodology is proposed to estimate the three‐dimensional variability of soil moisture based on a stochastic transfer function model, which is an approximation of the Richard’s equation. Satellite, radar and in situ observations are the major sources of information to develop a model that represents the dynamic water content in the soil. The soil‐moisture observations were collected from 17 stations located in Puerto Rico (PR), and a sequential quadratic programming algorithm was used to estimate the parameters of the transfer function (TF) at each station. Soil texture information, terrain elevation, vegetation index, surface temperature, and accumulated rainfall for every grid cell were input into a self‐organized artificial neural network to identify similarities on terrain spatial variability and to determine the TF that best resembles the properties of a particular grid point. Soil moisture observed at 20 cm depth, soil texture, and cumulative rainfall were also used to train a feedforward artificial neural network to estimate soil moisture at 5, 10, 50, and 100 cm depth. A validation procedure was implemented to measure the horizontal and vertical estimation accuracy of soil moisture. Validation results from spatial and temporal variation of volumetric water content (vwc) showed that the proposed algorithm estimated soil moisture with a root mean squared error (RMSE) of 2.31% vwc, and the vertical profile shows a RMSE of 2.50% vwc. The algorithm estimates soil moisture in an hourly basis at 1 km spatial resolution, and up to 1 m depth, and was successfully applied under PR climate conditions.     

基于贝叶斯网络的航空维修人因可靠性分析

根据维修人为因素分析和分类扩展系统的框架选取影响因素,在航空维修领域应用贝叶斯网络进行人因可靠性分析,建立飞机维修效能模型,直观地表示影响因素与维修效能之间的关系。同时以目视检测为例,结合专家意见确定随机影响因素,通过专家访谈、事故报告、调查问卷、操作记录等渠道获取数据,得出条件概率表,进而建立目视检测表现模型,展示贝叶斯网络的建模流程。案例研究结果表明,组织文化、视觉信息、设备、疲劳、检测距离等因素对目视检测表现的影响非常显著,欲改善目视检测表现,必须对多影响因素进行综合管理。     

热浮力及室外风压耦合驱动下高层建筑疏散走廊烟气运动网络模拟分析

火灾发生后,火灾烟气主要通过疏散走廊向建筑的其他部位蔓延.有效控制疏散走廊中的烟气,可以阻止其进一步蔓延到楼梯间.了解火灾烟气在疏散走廊中的运动规律,是控制其蔓延扩散的前提.热浮力和室外风压是烟气在走廊中运动的主要驱动力,研究二者耦合作用对走廊中烟气运动的影响,对进一步弄清火灾烟气的流动规律具有较大的意义.采用网络模拟软件CONTAM 3.0模拟疏散走廊中火灾烟气在上述两种驱动力作用下的运动情况.结果表明,随室外风速增大,疏散走廊中火灾烟气的运动速度增大,远大于单纯热浮力作用时烟气的运动速度;当热浮力和室外风压耦合驱动时,室外风压对烟气运动的影响起主要作用.     

外浮顶罐充氮管网设计及有效性试验验证

为了预防外浮顶罐密封圈雷击火灾,提出在密封圈内充入氮气的保护方法.将安全含氧量作为充氮的惰化目标,对外浮顶罐充氮管网进行设计,并通过试验来验证充氮管网的有效性.试验得到了充氮流量、进出气孔数量及管径与有效充氮时间的关系.若以142 m3/h的流量对容量为10×104 m3的外浮顶罐密封圈内充氮,则46.5 min内可以达到充氮惰化目标.雷电预警时间为60 ～ 90 min的条件下,充氮时间小于预警时间,表明所设计的管网是有效的.     

Adaptive co-management and network learning in the Room for the River programme

Adaptive co-management and learning are paramount for integrated flood risk management. Relevant literature focuses on adaptation at the level of physical and societal systems. The level of projects and programmes is largely overlooked, but they comprise interventions that adapt our physical systems and they provide opportunities for learning to contribute to transitions of societal systems. This paper aims to increase understanding on how learning takes place and can be stimulated within a programme. The mixed-method case study of Room for the River, a €2.3 billion programme for flood risk management, shows that a programme can be organised using various governance arrangements to stimulate learning and be a means for adaptive co-management to deliver upon environmental objectives.     

基于人工神经网络建模的PTC功率智能检测技术

PTC是家用空调器中主要器件之一,具有耗电量大、非线性的特征。目前PTC常用的功率检测方法是通过"专用电量计量芯片结合电压、电流采样电路"来实现,存在成本高和可靠性低问题。提出一种基于人工神经网络的PTC功率纯软件计算方法,无需增加任何硬件成本。通过实验验证,精度达±3%,满足产品化需求,成熟可靠。