A systematic survey of the integration of animal behavior into conservation

The role of behavioral ecology in improving wildlife conservation and management has been the subject of much recent debate. We sought to answer 2 foundational questions about the current use of behavioral knowledge in conservation: To what extent is behavioral knowledge used in wildlife conservation and management, and how does the use of animal behavior differ among conservation fields in both frequency and types of use? We searched the literature for intersections between key fields of animal behavior and conservation and created a systematic heat map (i.e., graphical representation of data where values are represented as colors) to visualize relative efforts. Some behaviors, such as dispersal and foraging, were commonly considered (mean [SE] of 1147.38 [353.11] and 439.44 [108.85] papers per cell, respectively). In contrast, other behaviors, such as learning, social, and antipredatory behaviors were rarely considered (mean [SE] of 33.88 [7.62], 44.81 [10.65], and 22.69 [6.37] papers per cell, respectively). In many cases, awareness of the importance of behavior did not translate into applicable management tools. Our results challenge previous suggestions that there is little association between the fields of behavioral ecology and conservation and reveals tremendous variation in the use of different behaviors in conservation. We recommend that researchers focus on examining underutilized intersections of behavior and conservation themes for which preliminary work shows a potential for improving conservation and management, translating behavioral theory into applicable and testable predictions, and creating systematic reviews to summarize the behavioral evidence within the behavior‐conservation intersections for which many studies exist.     

The role of agent-based models in wildlife ecology and management

Conservation planning of critical habitats for wildlife species at risk is a priority topic that requires the knowledge of how animals select and use their habitat, and how they respond to future developmental changes in their environment. This paper explores the role of a habitat-modeling methodological approach, agent-based modeling, which we advocate as a promising approach for ecological research. Agent-based models (ABMs) are capable of simultaneously distinguishing animal densities from habitat quality, can explicitly represent the environment and its dynamism, can accommodate spatial patterns of inter- and intra-species mechanisms, and can explore feedbacks and adaptations inherent in these systems. ABMs comprise autonomous, individual entities; each with dynamic, adaptive behaviors and heterogeneous characteristics that interact with each other and with their environment. These interactions result in emergent outcomes that can be used to quantitatively examine critical habitats from the individual- to population-level. ABMs can also explore how wildlife will respond to potential changes in environmental conditions, since they can readily incorporate adaptive animal-movement ecology in a changing landscape. This paper describes the necessary elements of an ABM developed specifically for understanding wildlife habitat selection, reviews the current empirical literature on ABMs in wildlife ecology and management, and evaluates the current and future roles these ABMs can play, specifically with regards to scenario planning of designated critical habitats.     

松散煤体表面温度与热源温度对应关系的热红外实验研究

煤层自燃火灾的自燃火源位置的精确探测是防灭火的关键,也是一项世界性难题。近年来,应用红外技术探测煤层隐蔽火源位置已成为一个热点,通过红外技术定性和定量研究松散煤体升温过程中红外辐射特征变化及影响因素,掌握松散煤体内部热量传递规律,对于利用红外成像技术探测井下煤层自燃隐蔽火源具有重要的参考价值。本文采用在松散煤体中加线状热源,保持热源位置不变,改变热源温度,当温度场达到稳定时用红外热像仪采集表面照片的方法,研究松散煤体表面温度与热源温度的对应关系。结果表明热源上方为高温区域,近似椭圆状,随着热源温度的升高,高温区域不断扩大,表面温度t与热源温度T、像素坐标χ满足关系式。     

Lenses for learning: visual techniques in natural resource management

In this study, we explored the use of selected visual techniques (e.g. video, photography, diagramming) in facilitating learning among Indigenous communities living in remote protected areas at sites in Vietnam and Australia. The techniques were employed during interviews and workshops aimed at accessing and enhancing local peoples' perspectives on their landscape and on specific natural resource management issues. The effectiveness of the different techniques for enabling learning varied markedly with the context, highlighting the need for facilitator skill and flexibility in application of techniques. Visual techniques helped to engage participants; encourage unrestrained and lateral thinking; provide opportunities for self-expression and reflection; and to expose participants to perspectives of other community members. Valuable insights emerged on broad aspects of learning and these were incorporated into a simple model that highlights three types of conceptualisation found to be important in these processes.     

Meep在烧结机机头除尘的应用

对烧结机粉尘进行了综合分析,结合Meep技术的特点,阐述了其对烧结机机头粉尘的适应性,是目前烧结机机头除尘排放达标、节能降耗的有效除尘技术.     

信息融合技术在火灾探测中的应用研究

为减少火灾探测中的误报警,基于信息融合技术对火灾传感器输出的信息进行处理。充分利用火灾探测系统的在线和离线数据,采用改进的主元分析法(PCA)、粗糙集(RS)理论、支持向量机(SVM)等3种方法的融合与互补,通过对系统的输入数据进行简化,消除原有信息的各分量之间的相关性,降低特征信息维数;实施最优最小约简,特征提取优化;构造自适应核函数,确定最优分类超平面,进行样本训练,获得火灾探测结果。从数据级、特征级、决策级3个层次上实现火灾信息融合。结果表明:该方法减少了融合过程中的信息损失,降低了计算的复杂性,有效地提高了火灾探测系统的可靠性和准确度。     

基于RS-SVM模型的煤与瓦斯突出多因素风险评价

为挖掘瓦斯突出风险与煤矿开采中各影响因素间的关系,应用支持向量机(SVM)理论从模式判别角度分析瓦斯突出风险与各地质因素组成的特征向量间的判别关系,基于粗糙集(RS)理论对待分析数据进行知识约简,提取核心判别指标,建立基于粗糙集-支持向量机(RS-SVM)的瓦斯突出风险判别模型。研究结果表明,RS知识约简方法可以很好地对原始数据中的冗余指标进行约简,通过对约简后指标数据进行SVM回归分析,可对煤与瓦斯突出模式进行很好的判别,所建立的瓦斯突出风险判别模型较一般SVM模型具有更高的预测精度,同时指标约简过程降低SVM运算中的复杂度,提高运算效率。     

基于ARIMA与SVM的飞行事故组合预测方法

飞行事故预测的目的在于预防事故。为提高事故预防的针对性和有效性,必须加强预测,以增强预防飞行事故的主动性。在ARIMA和SVM基础上,提出一种飞行事故组合预测方法。首先建立ARIMA模型,用以描述历史数据中的线性关系;然后,对ARIMA模型的残差构建SVM模型,用以模拟数据中的非线性规律,两者预测值之和就是最后的预测结果。美国空军1954—1993年飞行事故损坏飞机万时率的实证分析结果表明:利用该方法所建立的模型,能够对飞行事故作出较为准确的预测,模型精度总体优于单一的ARIMA或SVM模型。     

基于多通道信息融合的疲劳驾驶行为分析研究

为了克服单一通道信息在驾驶疲劳行为判定中的局限性,提出了综合运用多通道信息融合共同判定驾驶疲劳行为的方法。该方法在充分考虑各信息源相关性和互补性的基础上,优化采用驾驶人疲劳特征ECD、车道偏离程度SAAE、方向盘转动程度SWA等疲劳判别指标,运用MVAR进行多维特征向量提取,以有向无环支持向量机为融合算法,建立了基于多分类支持向量机的驾驶疲劳行为判定模型。结果表明,运用DAG-SVM进行多通道信息决策提高了疲劳驾驶行为检测的准确性和可靠性。     

典型染整企业定型机废气排放特征及潜在环境危害浅析

为探究纺织染整定型过程中产生挥发性有机物(VOCs)的排放特征及潜在环境危害.通过选取浙江省5家典型印染企业为监测对象,分析定型机废气中12种VOCs(如苯系物、甲醛、甲醇等)的实际排放情况,以此为基础初步估算全省定型机废气VOCs的排放量;并通过臭氧产生潜力分析初步评估其对环境产生危害.结果表明,定型机废气中VOCs浓度虽低,但含有1类致癌物苯和甲醛,且最高检出浓度可分别达到1.53 mg·m-3和15.4 mg·m-3,可能产生较为严重的环境和人体健康危害;浙江全省每年由定型机排放的VOCs量介于200.9~2 239.37 t,并且以甲醛为主,约占总量的50%;排放的VOCs可产生臭氧量介于860.4~16 715.5 t之间,年均值为7 729.6 t,主要源于废气中二甲苯、甲醛和甲苯这3种物质,合计可占总量的90%以上.另外,通过简单的估算,行业集聚区绍兴县定型机排放的VOCs产生的O3能够对环境空气质量产生较大的影响,最小贡献率为3.1%.