基于贝叶斯网络的建筑火灾动态风险评估方法研究

为提高建筑火灾风险评估的准确性,建立1种智能化的动态风险评估方法。针对具体建筑的风险评估,以物联网技术为基础,构建智能消防监测系统,在建筑日常使用过程中通过动态风险评估,实现火灾风险要素的实时监测、数据传输,充分发挥大数据、云计算的支撑作用,将贝叶斯网络方法引入火灾风险定量评估过程,构建火灾动态风险评估模型;结合具体的应用实例,分析不确定因素对风险评估结果的影响。研究结果表明:基于贝叶斯网络的动态风险评估方法能较准确地反映建筑火灾风险的可能性,达到实时监测、动态评估的效果。     

作业场所个体防护装备信息数据元标准化研究

针对目前我国PPE(个体防护装备)在作业场所的管理存在诸多问题,需要进一步加强对PPE的监管。为了构建PPE在作业场所中安全有效的信息管理平台,因此首要的基础性工作就是对PPE信息进行标准化,本文提出了PPE信息的线面分类,业务流程分析等方法,研究了数据元的标准化过程,结果确立了用户信息、人事信息、PPE信息、选型规则信息作为PPE信息管理平台层级数据元目录,确定了用户编号、地区代码、PPE类别代码、工种等86个数据元,基本涵盖了建立在作业场所中PPE信息平台所需要的数据信息,能为后续建立PPE智能选型数据库平台系统提供理论基础和技术支持。     

城市用地扩张、规模经济与经济增长质量

城市用地扩张不仅影响经济增长数量,还通过产业结构升级、城市化、生态环境变化和规模经济等路径影响区域经济增长质量。城市用地扩张,促进市场范围扩大,有利于区域内与区域间开展分工合作,提升城市的集聚经济效益与规模经济水平;但若城市用地扩张使区域突破最优规模经济状态后,因管理与服务成本、交通成本和环境成本等快速增加,城市规模效率会下降,对经济增长质量产生负影响。实证研究结果表明:在全国、中部和西部地区3个空间维度,城市用地扩张与规模效率间存在倒U型关系,而在东部地区此关系并不成立;在4个空间维度,城市用地扩张通过规模经济路径对全要素生产率的影响都为负,表明城市用地扩张已使各区域突破最优规模经济状态,土地要素投入存在冗余现象,与资本、劳动等要素配置比例非最优。研究结果还表明,人力资本、城市化、产业结构升级、基础设施等变量对经济增长质量有正影响。     

PM_(2.5)和PM_(2.5~10)资料同化及在南京青奥会期间的应用试验

建立了面向PM_(2.5)和PM10观测资料的三维变分同化系统,并在南京地区青奥会期间进行了同化和预报试验.同化系统的控制变量为PM_(2.5)和PM_(2.5~10)(PM10中扣除PM_(2.5)后剩余部分),利用南京地区2014年8月的WRF-Chem模拟结果,估计了PM_(2.5)和PM_(2.5~10)的背景误差协方差,发现在水平和垂直方向上PM_(2.5)的相关系数随距离的衰减均小于PM_(2.5~10),这可能与PM_(2.5)粒径小、生命史长,在大气中传播地更远有关.利用南京及周边区域的134个监测站PM_(2.5)和PM_(10)逐时观测资料,对青奥会期间(2014年8月16~28日)进行滚动同化和预报试验,并利用模式最内层观测资料进行检验分析,结果表明同化对初始场有显著改进,PM_(2.5)和PM10的相关系数均提高53%以上,均方根误差降低55%以上,平均偏差则降低了90%左右;同化试验对其后的预报场也有明显改进,正效应可以持续到20h以后,模式对PM10的预报效果好于PM_(2.5).     

A data-driven allocation tool for in-kind resources distributed by a state health department

Objective: The objective of this study was to leverage a state health department's operational data to allocate in-kind resources (children's car seats) to counties, with the proposition that need-based allocation could ultimately improve public health outcomes.

Methods: This study used a retrospective analysis of administrative data on car seats distributed to counties statewide by the Georgia Department of Public Health and development of a need-based allocation tool (presented as interactive supplemental digital content, adaptable to other types of in-kind public health resources) that relies on current county-level injury and sociodemographic data.

Results: Car seat allocation using public health data and a need-based formula resulted in substantially different recommended allocations to individual counties compared to historic distribution.

Conclusions: Results indicate that making an in-kind public health resource like car seats universally available results in a less equitable distribution of that resource compared to deliberate allocation according to public health need. Public health agencies can use local data to allocate in-kind resources consistent with health objectives; that is, in a manner offering the greatest potential health impact. Future analysis can determine whether the change to a more equitable allocation of resources is also more efficient, resulting in measurably improved public health outcomes.     

威布尔分布环境折合因子计算及其应用研究

目的对不同环境条件试验下的数据进行融合。方法针对应用最为普遍的威布尔分布类型的环境折合因子进行研究,给出威布尔分布环境折合因子的定义,根据极大似然估计法对威布尔分布环境折合因子进行计算。结果得出了相关环境折合因子的计算结果,在此基础上给出了应用环境折合因子进行数据融合的方法和步骤。结论应用威布尔分布环境折合因子可以合理的对不同环境下试验数据进行融合,达到扩大试验样本的目的。     

基于DEA-Malmquist指数的山东省生态效率评价研究

以2004牟-2012年面板数据为样本,运用DAE和Malmquist指数模型对山东省17地市生态效率及其变化特征进行测评.结果显示:山东省生态效率呈现波动变化,各地市之间差异明显,有6市达到了效率最优,烟尘、工业固废、工业粉尘和SO2是影响非前沿面城市生态效率提升的重要因素;各地市资源效率与环境效率存在差异,环境效率发展较好的城市数量多于资源效率;技术进步对生态效率的提高起着明显的促进作用;驱动技术创新、优化产业布局,发展循环经济是山东省提升生态效率的有效途径.     

Data classification and performance evaluation for the most commonly-used univariate alarm systems

Alarm systems are critically important for safe and efficient operations of industrial plants, but many industrial alarm systems are suffering from too many nuisance alarms. This paper proposes a method to classify normal and abnormal data segments and evaluate performance indices for the most commonly used univariate alarm systems. The proposed method consists of three steps. First, piece-wise linear representations are exploited in separating historical data samples of an analog process variable configured with alarms into data segments with same qualitative trends. Second, data segments are classified into normal, abnormal and unclassified conditions via a mean hypothesis test; a required assumption is that data segments in normal and abnormal conditions have different mean values being distinguishable from alarm thresholds. Third, based on the normal and abnormal data, performance indices of univariate alarm systems are calculated, including two newly formulated ones as the false alarm duration ratio and the missed alarm duration ratio. The effectiveness of the proposed method is illustrated by numerical and industrial examples.     

Rank aggregation of local expert knowledge for conservation planning of the critically endangered saola

There has been much recent interest in using local knowledge and expert opinion for conservation planning, particularly for hard‐to‐detect species. Although it is possible to ask for direct estimation of quantities such as population size, relative abundance is easier to estimate. However, an expert's knowledge is often geographically restricted relative to the area of interest. Combining (or aggregating) experts’ assessments of relative abundance is difficult when each expert only knows a part of the area of interest. We used Google's PageRank algorithm to aggregate ranked abundance scores elicited from local experts through a rapid rural‐appraisal method. We applied this technique to conservation planning for the saola (Pseudoryx nghetinhensis), a poorly known bovid. Near a priority landscape for the species, composed of 3 contiguous protected areas, we asked groups of local people to indicate relative abundances of saola and other species by placing beans on community maps. For each village, we used this information to rank areas within the knowledge area of that village for saola abundance. We used simulations to compare alternative methods to aggregate the rankings from the different villages. The best‐performing method was then used to produce a single map of relative abundance across the entire landscape, an area larger than that known to any one village. This map has informed prioritization of surveys and conservation action in the continued absence of direct information about the saola.     

Prediction of response of zooplankton biomass to climatic and oceanic changes

This paper examines the long-term variation in zooplankton biomass in response to climatic and oceanic changes, using a neural network as a nonlinear multivariate analysis method. Zooplankton data collected from 1951 to 1990 off the shore of northeastern Japan were analyzed. We considered patterns of the Kuroshio and the Oyashio, sea surface temperature, and meteorological parameters as environmental factors that affect zooplankton biomass. Back propagation neural networks were trained to generate mapping functions between environmental variables and zooplankton biomass. The performance of the network models was tested by varying the numbers of input and hidden units. Changes in zooplankton biomass could be predicted from environmental conditions. The neural network yielded predictions with smaller errors than those of predictions determined by linear multiple regression. The sensitivity analysis of networks was used to extract predictive knowledge. The air pressure, sea surface temperature, and some indices of atmospheric circulation were the primary factors for predictions. The patterns of the Kuroshio and the Oyashio demonstrated different effects among sea areas.