Estimating dyad association probability under imperfect and heterogeneous detection

In animal behaviour studies, association indices estimate the proportion of time two individuals (i.e. a dyad) spend in association. In terms of dyads, all association indices can be interpreted as estimators of the probability that a dyad is associated. However, traditional indices rely on the assumptions that the probability to detect a particular individual (p) is either approximately one and/or homogeneous between associated and not associated individuals. Based on marked individuals we develop a likelihood based model to estimate the probability a dyad is associated (ψ) accounting for p < 1 and possibly varying between associated and not associated individuals. The proposed likelihood based model allows for both individual and dyadic missing observations. In addition, the model can easily be extended to incorporate covariate information for modeling p and ψ. A simulation study showed that the likelihood based model approach yield reasonably unbiased estimates, even for low and heterogeneous individual detection probabilities, while, in contrast, traditional indices showed moderate to strong biases. The application of the proposed approach is illustrated using a real data set collected from a population of Commerson's dolphin (Cephalorhynchus commersonii) in Patagonia Argentina. Finally, we discuss possible extensions of the proposed model and its applicability in animal behaviour and ecological studies.     

Knowledge gain and behavioral change in citizen-science programs

Citizen-science programs are often touted as useful for advancing conservation literacy, scientific knowledge, and increasing scientific-reasoning skills among the public. Guidelines for collaboration among scientists and the public are lacking and the extent to which these citizen-science initiatives change behavior is relatively unstudied. Over two years, we studied 82 participants in a three-day program that included education about non-native invasive plants and collection of data on the occurrence of those plants. Volunteers were given background knowledge about invasive plant ecology and trained on a specific protocol for collecting invasive plant data. They then collected data and later gathered as a group to analyze data and discuss responsible environmental behavior with respect to invasive plants. We tested whether participants without experience in plant identification and with little knowledge of invasive plants increased their knowledge of invasive species ecology, participation increased knowledge of scientific methods, and participation affected behavior. Knowledge of invasive plants increased on average 24%, but participation was insufficient to increase understanding of how scientific research is conducted. Participants reported increased ability to recognize invasive plants and increased awareness of effects of invasive plants on the environment, but this translated into little change in behavior regarding invasive plants. Potential conflicts between scientific goals, educational goals, and the motivation of participants must be considered during program design.     

基于Hilbert谱信息熵的煤矸放落振动特征分析

针对煤矿安全生产中的综放工作面煤矸界面探测问题,提出利用煤矸下落冲击钢板的振动特征来探测煤矸界面的方法。煤矸振动信号表现出非平稳特征,采用经验模态分解方法将复杂矿井环境下的煤矸振动信号分解成固有模态分量。选择包含煤矸振动特征的前7个本征模函数(IMF)分量,通过Hilbert变换得到Hilbert谱。分析不同放煤状态下钢板振动信号的Hilbert谱发现,顶煤下落时的Hilbert谱分布较均匀,而煤矸混放时的Hilbert谱呈现不均匀分布。根据信息熵理论,提出了基于Hilbert谱信息熵的煤矸振动特征提取方法。试验结果表明,顶煤下落时的Hilbert谱信息熵要大于煤矸混放时的Hilbert谱信息熵,因此,煤矸振动的Hilbert谱信息熵特征能够准确地反映放煤状态。     

基于无人机的大气细颗粒物与臭氧污染探测与溯源研究进展

近年来,中国大气细颗粒物(PM_2.5)污染的治理已取得阶段性成效,但臭氧(O₃)污染快速上升,实现PM_2.5和O₃协同控制的基础与关键是针对大气污染物的精细化探测和污染溯源.随着无人机技术和传感器技术的迅速发展,基于无人机平台的大气污染探测可以有效获得近地层的PM_2.5和O₃结构特征,并结合计算机算法对大气污染事件进行精准溯源,具有高时效性、高灵活性和高时空分辨率的特征,有助于研究人员了解区域污染物的分布、变化以及来源,为大气复合污染的协同控制提供科学依据.通过回顾传统的大气污染探测方法,总结了污染探测领域常用的无人机飞行平台类型和探测仪器,归纳了基于无人机的PM_2.5和O₃污染探测应用与相关溯源算法,并展望了无人机大气探测的未来研究方向.     

含缺陷圆轴结构极限承载规律研究

目的 研究车辆传动轴在受损情况下极限承载能力的变化规律。方法 在圆轴上预制缺陷,并对含缺陷圆轴结构承受扭转载荷的能力进行试验和数值模拟研究。结果 当圆孔缺陷深径比保持不变时,圆轴极限扭矩下降比随圆孔缺陷孔径比的增加而减小;当圆孔缺陷孔径比保持不变时,圆轴极限扭矩下降比随圆孔缺陷深径比的增加而减小。当深径比大于0.4时,圆轴极限扭矩下降比与圆孔缺陷孔径比近似呈负线性相关。当缺陷孔径比大于0.4、深径比大于0.2时,圆轴最大扭角减小为15°~35°,各存在缺陷的圆轴的最大扭角差距不大。结论 圆轴扭转失效时,其最大扭角、极限扭矩与圆孔缺陷孔径比、深径比整体均呈负相关趋势。在孔径比和深径比逐渐增大的过程中,极限扭矩呈近似线性关系逐渐降低,而最大扭角先急剧减小,后逐渐趋于平稳。     

A field experiment characterizing variable detection rates during plant surveys

Surveys aimed at finding threatened and invasive species can be challenging due to individual rarity and low and variable individual detection rates. Detection rate in plant surveys typically varies due to differences among observers, among the individual plants being surveyed (targets), and across background environments. Interactions among these 3 components may occur but are rarely estimated due to limited replication and control during data collection. We conducted an experiment to investigate sources of variation in detection of 2 Pilosella species that are invasive and sparsely distributed in the Alpine National Park, Australia. These species are superficially similar in appearance to other yellow-flowered plants occurring in this landscape. We controlled the presence and color of flowers on target Pilosella plants and controlled their placement in plots, which were selected for their variation in cover of non-target yellow flowers and dominant vegetation type. Observers mimicked Pilosella surveys in the plots and reported 1 categorical and 4 quantitative indicators of their survey experience level. We applied survival analysis to detection data to model the influence of both controlled and uncontrolled variables on detection rate. Orange- and yellow-flowering Pilosella in grass- and heath-dominated vegetation were detected at a higher rate than nonflowering Pilosella. However, this detection gain diminished as the cover of other co-occurring yellow-flowering species increased. Recent experience with Pilosella surveys improved detection rate. Detection experiments are a direct and accessible means of understanding detection processes and interpreting survey data for threatened and invasive species. Our detection findings have been used for survey planning and can inform progress toward eradication. Interaction of target and background characteristics determined detection rate, which enhanced predictions in the Pilosella eradication program and demonstrated the difficulty of transferring detection findings into untested environments.     

A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning

• A spectral machine learning approach is proposed for predicting mixed antibiotic. • Pretreatment is far simpler than traditional detection methods. • Performance of the model is compared in different influencing factors. • Spectral machine learning is promising in the detection of complex substances. Antibiotics are widely used in medicine and animal husbandry. However, due to the resistance of antibiotics to degradation, large amounts of antibiotics enter the environment, posing a potential risk to the ecosystem and public health. Therefore, the detection of antibiotics in the environment is necessary. Nevertheless, conventional detection methods usually involve complex pretreatment techniques and expensive instrumentation, which impose considerable time and economic costs. In this paper, we proposed a method for the fast detection of mixed antibiotics based on simplified pretreatment using spectral machine learning. With the help of a modified spectrometer, a large number of characteristic images were generated to map antibiotic information. The relationship between characteristic images and antibiotic concentrations was established by machine learning model. The coefficient of determination and root mean squared error were used to evaluate the prediction performance of the machine learning model. The results show that a well-trained machine learning model can accurately predict multiple antibiotic concentrations simultaneously with almost no pretreatment. The results from this study have some referential value for promoting the development of environmental detection technologies and digital environmental management strategies.     

电法填埋场渗漏检测供电电极接地电阻研究

采用电法进行垃圾填埋场渗漏检测时,供电电极的接地电阻对回路电流起决定性作用.分别推导了半球状供电电极和圆柱状供电电极的接地电阻表达式,并对影响接地电阻大小的主要因素进行了分析和试验验证.结果表明:①电极的接地电阻主要由电极附近的土壤电阻决定;②随着电极埋深的增加,电极的接地电阻减少,但当埋深超过40 cm时,随着电极埋深的增加,电极接地电阻的减小趋势变缓;③随着电极直径的增加,电极的接地电阻减少.实际应用中,电极的埋深和直径应尽可能大一些,但电极直径较大时,电极质量也会显著增加,给操作带来不便.因此,可将电极直径定为10～15 cm.此外,由于土壤一般为颗粒状,这使得土壤和电极不能完全接触,为降低接地电阻,可对电极附近的土壤进行浇水.      

农药残留速测技术研究进展

本文综述了国内外农药快速检测技术的研究状况。在速测技术中 ,色 -质联用分析法已发展得比较成熟 ,所得结果比较可靠 ,但该法的测定时间仍较长 ;免疫分析法具有简单、快速、价廉、特异性高、需样品量少等优点 ;生物传感器法更方便、快捷。     

中国西部本底大气中CO的连续测量

在中国西北部的瓦里关全球基准站(36°17'N,100°54'E,3810m asl)建立了气相色谱-汞置换法大气CO连续测量系统.组装调试和运行标定表明,色谱方法的基线稳定,分离度高,检测方法可靠,系统具有高自动化的操作性能和严格的质量控制,其设计完全符合世界气象组织(WMO)全球大气本底测量的要求.根据采样现场大气CO浓度的变化,采用经国际比对的CO标准气系列,用峰高外标法设定可调整的标准工作曲线计算环境CO浓度,系统对CO的检测限低至1×10^-9(V/V),检测精度小于0.1%,准确度在±0.05%以内,可提供我国本底地区近地层大气CO连续而精确的现场测量结果.