Slash fire hazard analysis on the Siskiyou National Forest

Potential increase in fire hazard as a result of timber harvesting is a concern of forest managers throughout the United States. Treating fuels can help reduce unacceptable fire hazards. To evaluate alternative fuel treatments, managers need to know their effects on fire hazard. A decision analysis approach to estimating fire hazard in terms of expected burned area was applied to a watershed in the Siskiyou National Forest (Oregon). Three treatment alternatives (do nothing and two levels of yarding unmerchantable material) were evaluated, and the effects of the treatments were projected over a 90-yr period. Initially, the effects of applying a treatment are small. After 50 years of treatment, the most intense alternative can be expected to show almost a 50% reduction in burned area compared to no treatment. The procedure also estimates burned are by fire size and fire intensity classes. Managers may find this useful for estimating expected fire effects associated with a particular fuel treatment regime.     

Use of hydrodynamic models for the management of the Danube Delta wetlands: The case study of Sontea-Fortuna ecosystem

Due to its high biodiversity the Danube Delta, in Romania, is recognized as a UNESCO World Heritage Natural Site and it is listed as a RAMSAR wetland. The Danube River variable discharges have a great impact on the habitats and the overall ecological status of the delta. One of its most vulnerable parts, from both hydrodynamic and morphological point of view is the Sontea-Fortuna wetland located in the upstream of the Danube Delta. Sontea-Fortuna wetland is susceptible to both floods and droughts. On a long term, decision makers in the area need to know which measures to implement and how these will impact/improve the environment.This article presents how a 3D hydrodynamic model can be used as support for making sound decisions regarding the management of deltaic ecosystems. In particular, the methodology is applied on the Sontea-Fortuna wetland. The case study is part of a wider research in the area, which was developed within the EnviroGRIDS EU FP7 research project. EnviroGRIDS aimed at building capacity for scientists, decision-makers and the general public in the Black Sea Catchment, through collection and sharing of environmental data and models at the basin scale.     

Green defaults: Information presentation and pro-environmental behaviour

There is inconsistency in many people's choice of electricity. When asked, they say they prefer a ‘green’ (i.e., environmentally friendly) source for this energy. Yet, although green electricity is available in many markets, people do not generally buy it. Why not? Motivated by behavioural decision research, we argue that the format of information presentation drastically affects the choice of electricity. Specifically, we hypothesise that people use the kind of electricity that is offered to them as the default. We present two natural studies and two experiments in the laboratory that support this hypothesis. In the two real-world situations, there was a green default, and most people used it. In the first laboratory experiment, more participants chose the green utility when it was the default than when ‘grey’ electricity was the default. In the second laboratory experiment, participants asked for more money to give up green electricity than they were willing to pay for it. We argue that changing defaults can be used to promote pro-environmental behaviour. Potential policy-making applications of this work are discussed.     

基于FTA-SVM的车辆发动机故障识别方法

发动机结构日益复杂,其故障具有多样性和频发性的特点,收集大量故障样本存在很多实施障碍。为了提高车辆发动机的故障识别的效率和准确性,提出了一种新的结合故障树(FTA)和支持向量机(SVM)各自特点,从故障模式分析到故障类型识别的FTA-SVM故障识别方法。首先利用故障树在复杂系统故障模式分析中的优势,找出系统的故障模式,建立故障树模型,通过对故障树模型中各故障事件的分析,采集与故障事件状态相关的数据,建立数据与故障树底事件的映射模型,最后利用支持向量机在小样本数据处理中的优势,进行故障类型的识别。以发动机的失火故障为例建立了发动机失火故障树模型及故障数据与故障模式映射模型,验证了FTA-SVM方法的有效性和适用性。     

Implementing SDG 15: Can large‐scale public programs help deliver biodiversity conservation,restoration and management,while assisting human development?

Among the Sustainable Development Goals (SDGs), the proposed SDG 15 promotes activities that, inter alia, “Protect, restore and promote sustainable use of terrestrial ecosystems”. An important potential contribution in achieving SDG 15 is through public programmes designed to jointly promote human development through poverty alleviation and improvement of human livelihoods and biodiversity conservation/management/restoration. An analysis of twenty public programmes with such joint objectives yielded twelve lessons learned. In addition to financial commitments, government and intergovernmental agency input for such public programmes includes ensuring political will and appropriate legal frameworks. Local communities and civil society provide input through traditional and indigenous ecological knowledge and stewardship. Appropriate shared inputs in development and the implementation of such public programmes, with communication between local community, broader civil society, the scientific community and governments will result in: better use and management of biodiversity; alleviation of poverty; security of livelihoods and better governance systems. The Ecosystem Approach of the Convention on Biological Diversity provides an ideal framework when planning and implementing new programmes. Application of the lessons learned to new public programmes will ensure that the answer to the question posed in the title is an emphatic “Yes”, and assist with the achievement of SDG 15.     

The role of situation awareness in accidents of large-scale technological systems

In the last two decades, several serious accidents at large-scale technological systems that have had grave consequences, such as that at Bhopal, have primarily been attributed to human error. However, further investigations have revealed that humans are not the primary cause of these accidents, but have inherited the problems and difficulties of working with complex systems created by engineers. The operators have to comprehend malfunctions in real time, respond quickly, and make rapid decisions to return operational units to normal conditions, but under these circumstances, the mental workload of operators rises sharply, and a mental workload that is too high increases the rate of error. Therefore, cognivitive human features such as situation awareness (SA)—one of the most important prerequisite for decision-making—should be considered and analyzed appropriately. This paper applys the SA Error Taxonomy methodology to analyze the role of SA in three different accidents: (1) A runaway chemical reaction at Institute, West Virginia killing two employees, injuring eight people, and requiring the evacuation of more than 40,000 residents adjacent to the facility, (2) The ignition of a vapor cloud at Bellwood, Illinois that killed one person, injured two employees, and caused significant business interruption, and (3) An explosion at Ontario, California injuring four workers and caused extensive damage to the facility. In addition, the paper presents certain requirements for cognitive operator support system development and operator training under abnormal situations to promote operators’ SA in the process industry.     

岩溶塌陷倾向性等级的KPCA-SVM预测模型

为了快速、有效地预测岩溶塌陷倾向性等级,在统计分析大量观测实例的基础上,选取岩性系数、岩体结构系数、地下水系数、覆盖层系数、地形地貌系数和环境条件系数作为特征指标。利用核主成分分析(KPCA)方法在高维空间提取岩溶塌陷影响因子的主成分,将获取的主成分作为支持向量机(SVM)的特征向量,建立基于KPCA的岩溶塌陷倾向性等级的SVM预测模型。将12组观测数据作为学习样本对模型进行训练。采用回代估计法进行回检,误判率为0。利用训练好的模型对2组待判样本进行预测。结果表明:经KPCA后指标个数减少,相关性降低,SVM运算的复杂度降低。用该模型所得预测结果的准确率为100%。     

战场环境下装备保障信息系统安全风险与防护

目的研究战场环境下装备保障信息系统的安全问题。方法从装备保障及装备保障信息系统的概念、分类和复杂战场环境下装备保障信息系统所面临的安全威胁入手,依照五分法把装备保障信息系统安全风险划分为5个等级,推导出风险评估模型,提出实施装备保障信息系统安全防护的技战术策略。结果战场环境下,为确保整个作战进程中装备保障活动安全、有效、稳定地进行,装备保障信息系统防护必须遵循统一协调、综合利用的积极防护原则,采用有效的风险控制措施加以应对。结论通过积极探索和发展装备保障信息安全与防护理论,找准战场环境信息防护中存在的薄弱环节,制定了装备保障信息安全防护策略,加强装备保障信息系统的安全管理工作,最终提高我军装备保障信息系统的防护能力。     

有机化学品不同温度下(过冷)液体蒸气压预测模型的建立与评价

(过冷)液体蒸气压(PL)是评价化学品在环境中分配、迁移和归趋行为的重要参数。PL具有较强的温度依附性。发展一种能够精确预测不同环境温度下化学品PL的方法,有助于填补化学品生态风险评估的大量数据缺失。本研究收集整理了661种有机化合物在不同温度下(200~830 K)共计10 478个log PL值。在此基础上,采用偏最小二乘(PLS)回归和支持向量机(SVM)方法,构建了PL的线性和非线性预测模型。结果表明:2种模型均具有良好的拟合度、稳健性及预测能力,SVM模型的预测性能略高于PLS模型(PLS:R2adj.tra=0.912,RMSEtra=0.477,Q2ext=0.910;SVM:R2adj.tra=0.997,RMSEtra=0.092,Q2ext=0.967)。机理分析表明,温度是影响PL的主要因素,温度越高,蒸气压越大;其次,X1sol也影响PL大小,X1sol用来描述分子间的色散作用,分子间色散力越小,蒸气压越大;此外,化合物的氢键个数、极性和分子构型等因素也影响PL大小。采用Wiliams plot方法表征了PLS模型应用域。所建立的模型可用来预测烷烃、烯烃、醇、酮、羧酸、苯、酚、联苯、卤代芳香烃、含N化合物及含S化合物在不同温度下的PL数据。     

Prioritisation of citizen‐centric information for disaster response

In the aftermath of a disaster event, and in the absence of trained professionals, many responsibilities are assumed by uninjured citizens who are willing and able to help, such as care of the injured or search and rescue. These citizens are constrained by communications and logistics problems but are less equipped to deal with them as most often they are cut off from any coordinated assistance. The method proposed in this study would increase the survivability of those injured or trapped by a disaster event by providing a facility to allow citizens to coordinate and share information among themselves. This is facilitated by the proposed deployment and the autonomous management of an ad hoc infrastructure that liaises directly with survivors without central control. Furthermore, as energy concerns present critical constraints to these networks, this research proposes a system of categorising information elements within the network to ensure efficient information exchange.