基于模糊神经网络方法的信息系统安全风险评价研究

为提高信息系统安全风险评价的准确性,提出基于模糊神经网络的信息系统安全风险评价模型。该模型利用模糊理论隶属度反映各因子的相对状态,对模糊性具有很高的识别精度。将风险因子模糊隶属度矩阵输入BP神经网络中,增加了它对模糊性的识别能力。通过非线性分析处理的BP神经网络进行学习和测试,得到输出风险评价等级。实例分析结果证明算法的应用性。误差分析结果证明了模型的有效性。检验结果表明,模糊神经网络模型的识别精度高于单一的模糊评价模型和神经网络模型。     

由江西“11.24”火灾分析棚户区消防安全对策

以江西"11.24"棚户区危旧房火灾为案例,针对其火灾特点,总结火灾教训,并结合国内对棚户区消防问题已有的理论、实践成果,在深入调查、研究现状问题的基础上,分析其火灾成因、火灾危险性及火灾高发的原因。指出棚户区消防安全方面存在的问题,并就如何消除棚户区火灾隐患、加强棚户区消防安全提出了具体的措施。强调指出公安消防机构对棚户区消防治理工作力度的加大,政府各部门对棚户区消防安全工作的重视与管理,及消防社会宣传教育工作的开展,以有效降低棚户区火灾,尤其是重大火灾的发生。     

职业卫生信息化网络平台研究

当前我国职业卫生监管工作存在薄弱环节、监管手段相对落后,难以满足我国职业病防治工作形势的需求。将信息化技术应用于职业卫生监管,可有效提高职业卫生监管工作的科学性和有效性。阐述了职业卫生信息化网络平台建设的必要性,提出了职业卫生信息化建设的整体框架,分析并论述了具体建设内容以及建设过程中应注意的几点问题。     

工程项目现场安全标志有效性评价

为改善工程项目现场作业安全状况,需要对安全标志的有效性进行评价。通过划分施工人员的认知行为阶段,从人机工程学系统优化的角度构建安全标志有效性评价指标体系,确立指标打分规则,采用网络层次分析法(ANP),运用超级决策(SD)软件确定指标权重、综合评分,并加以示例说明。结果表明:安全标志的有效性受注意阶段的显著性、识别阶段的可理解性、判断阶段的安全倾向性和行为阶段的易遵守性交互影响,其中,安全倾向性权重值最高。     

人工神经网络用于铅的化学形态模拟计算

用前馈线性网络法求解水体系中Ｐｂ（２＋）与ＯＨ－之间的反应常数，不同训练算法对求解结果的精度、收敛速度及权值均有影响．结果表明，批处理算法的精度最好，权值不出现负值，但运算时间最长；在线算法的精度虽不如批处理算法，而比数据变换－在线算法好，权值有时会出现负值．运算时间较长；数据变换－在线算法的优点是运算时间短，但相对误差较大，权值出现负值的机会多。采用反馈网络模拟计算铅的各种化学形态的浓度．用物料核算的方法对反馈网络模型进行检验表明，此种模型用于平衡计算是可行的，详细分析了理论模拟和实验曲线的差异的原因，温度的影响最小，在４＜ｐＨ＜９时，ＣＯ有重要的影响．在国代检验时，ｎ值取整所引入的误差的影响亦不可忽视。从本文的结果可以看到，采用前馈网络和反馈网络相结合的方法考察水体中的化学形态是可行的．从而为解决这一类问题提供了一种可能的途径．     

人工神经网络在水环境质量评价中的应用

为了将人工神经网络应用于水环境质量评价,应用了人工神经网络Ｂ—Ｐ算法,构造了水环境质量评价模型,该模型应用于实例评价结果表明,人工神经网络用于环境质量评价具有客观性,通用性和实用性。     

Evaluation of ecological network analysis: Validation of output

Ecological network analysis (ENA) is a modeling approach increasingly being used to evaluate food webs and provide an ecosystem-based approach to resource management. Unfortunately, validation of ENA output is rarely performed. This study represents part of a larger effort to critically evaluate ENA. Here we validate ENA output using stable isotope analysis (SIA), and where validation is not met, determine the effects of modifying trophic networks to reflect validation.     

Urban wild meat consumption and trade in central Amazonia

The switch from hunting wild meat for home consumption to supplying more lucrative city markets in Amazonia can adversely affect some game species. Despite this, information on the amounts of wild meat eaten in Amazonian cities is still limited. We estimated wild meat consumption rates in 5 cities in the State of Amazonas in Brazil through 1046 door-to-door household interviews conducted from 2004 to 2012. With these data, we modeled the relationship between wild meat use and a selection of socioeconomic indices. We then scaled up our model to determine the amounts of wild meat likely to be consumed annually in the 62 urban centers in central Amazonia. A total of 80.3% of all interviewees reported consuming wild meat during an average of 29.3 (CI 11.6) days per year. Most wild meat was reported as bought in local markets (80.1%) or hunted by a family member (14.9%). Twenty-one taxa were cited as consumed, mostly mammals (71.6%), followed by reptiles (23.2%) and then birds (5.2%). The declared frequency of wild meat consumption was positively correlated with the proportion of rural population as well as with the per capita gross domestic product of the municipality (administrative divisions) where the cities were seated. We estimated that as much as 10,691 t of wild meat might be consumed annually in the 62 urban centers within central Amazonia, the equivalent of 6.49 kg per person per year. In monetary terms, this amounts to US$21.72 per person per year or US$35.1 million overall, the latter figure is comparable to fish and timber production in the region. Given this magnitude of wild meat trade in central Amazonia, it is fundamental to integrate this activity into the formal economy and actively develop policies that allow the trade of more resilient taxa and restrict trade in species sensitive to hunting.     

Key considerations and challenges in the application of social-network research for environmental decision making

Attempts to better understand the social context in which conservation and environmental decisions are made has led to increased interest in human social networks. To improve the use of social-network analysis in conservation, we reviewed recent studies in the literature in which such methods were applied. In our review, we looked for problems in research design and analysis that limit the utility of network analysis. Nineteen of 55 articles published from January 2016 to June 2019 exhibited at least 1 of the following problems: application of analytical methods inadequate or sensitive to incomplete network data; application of statistical approaches that ignore dependency in the network; or lack of connection between the theoretical base, research question, and choice of analytical techniques. By drawing attention to these specific areas of concern and highlighting research frontiers and challenges, including causality, network dynamics, and new approaches, we responded to calls for increasing the rigorous application of social science in conservation.     

基于BP神经网络算法的密云水库水质参数反演研究

密云水库是北京市重要的地表饮用水源地,在保障首都水源安全方面起着重要作用,而密云水库水质参数的区域监测已成为当前亟待解决的问题。为了实现密云水库水质参数大范围、实时获取,该文基于遥感和GIS技术,采用BP神经网络算法,结合地面监测数据和Landsat 8遥感影像,分别建立了反演总磷、总氮、氨氮和COD(化学需氧量)4个水质参数的BP神经网络模型,并反演了密云水库2013-2018年非结冰期主要水质参数,分析了密云水库主要水质参数的年际变化特征、季节变化特征和空间分异特征。结果表明,(1)水质参数的Landsat 8敏感波段分别为:总氮为1、4波段,氨氮为1-7波段,总磷为1、3-7波段,COD为2-5波段。(2)密云水库主要水质参数在2013-2018年总体呈下降趋势,氨氮和COD为Ⅰ类水质,总磷为Ⅱ类水质,总氮为Ⅲ类水质。(3)4个水质参数指标春季最高、秋季次之、夏季最低,总氮、总磷、氨氮和COD的春季值分别是夏季值的1.08、1.36、1.6、1.45倍。(4)密云水库不同水质参数的空间差异性较大,总体来看,水库北部和东部的4个水质参数含量相对较高,这种分布与北部和东部村庄密集以及密云水库两大入库河流有关。综上所述,基于BP神经网络算法的密云水库水质反演研究是可行的,且得到了较为可信的研究结果,该研究可为密云水库水质管理与政策制定提供重要的科学依据。