Nestmate recognition in ants is possible without tactile interaction

Ants of the genus Camponotus are able to discriminate recognition cues of colony members (nestmates) from recognition cues of workers of a different colony (non-nestmates) from a distance of 1 cm. Free moving, individual Camponotus floridanus workers encountered differently treated dummies on a T-bar and their behavior was recorded. Aggressive behavior was scored as mandibular threat towards dummies. Dummies were treated with hexane extracts of postpharyngeal glands (PPGs) from nestmates or non-nestmates which contain long-chain hydrocarbons in ratios comparable to what is found on the cuticle. The cuticular hydrocarbon profile bears cues which are essential for nestmate recognition. Although workers were prevented from antennating the dummies, they showed significantly less aggressive behavior towards dummies treated with nestmate PPG extracts than towards dummies treated with non-nestmate PPG extracts. In an additional experiment, we show that cis-9-tricosene, an alkene naturally not found in C. floridanus' cuticular profile, is behaviorally active and can interfere with nestmate recognition when presented together with a nestmate PPG extract. Our study demonstrates for the first time that the complex multi-component recognition cues can be perceived and discriminated by ants at close range. We conclude that contact chemosensilla are not crucial for nestmate recognition since tactile interaction is not necessary.     

综合防尘技术在开元公司的应用

主要介绍了采煤机高压引射降尘系统、液压支架自动喷雾、粉尘浓度在线检测超限自动喷雾等综合防尘技术在开元公司矿井中的应用。通过对降尘系统效果监测和分析,降尘效率达到84．1％,降尘效果明显,改善了作业场所的劳动卫生条件。     

室内甲醛检测方法及防治措施研究进展

介绍室内空气污染物甲醛的来源以及对人体健康造成的危害。探讨了现有室内甲醛污染物的检测方法,并对检测方法中存在的问题和应用条件的限制因素进行了讨论。为减少室内甲醛对人体健康造成的危害,提出了八种降解室内甲醛污染物的防治方法,并重点介绍了光催化氧化降解室内空气中甲醛的作用机理。提出了对检测方法和防治措施中存在问题的改进。改进检测方法和改良治理措施以及对装饰材料的选择是减少室内空气甲醛对人体健康造成危害的有效途径。     

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.     

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.