Ecological relevance of associative learning in fruit fly larvae

A few invertebrate models have been used for studying neurobiological and molecular aspects of associative learning. The ecological and evolutionary aspects of associative learning in these invertebrates are not yet well understood. To further this knowledge, I tested fruit fly larvae for their ability to learn to associate odors with preferred environmental conditions. The larvae learned to avoid odors associated with predation and to prefer odors associated with high-quality food, but failed to learn about odors associated with optimal temperature. It appears that the larvae possess a general ability to evaluate a suite of environmental parameters and associate preferred conditions with relevant stimuli. Received: 6 May 1998 / Accepted after revision: 11 October 1998     

Learning outcomes measured in zoo and aquarium conservation education

Communicating the topic of conservation to the public and encouraging proenvironmental behaviors can mitigate loss of biodiversity. Thus, the evaluation of educational efforts is important to ascertain the educational effects and provide high-quality conservation education. The learning outcomes of conservation education are diverse (e.g., attitudes, knowledge, and behavior). Considering the specific characteristics of these different outcomes and the factors that influence them is crucial to delivering successful conservation education. We reviewed 29 peer-reviewed articles published in English from January 2011 to April 2020 on empirical studies of learning outcomes of on-site conservation education in zoos and aquaria, institutions that seek to educate the public about conservation. We examined the range of learning outcomes, their definitions, and factors that influenced them. Cognitive outcomes were most frequently investigated (37%) in comparison with other outcomes (e.g., affective outcomes, 31%). The articles did not use explicit definitions for learning outcomes, and implicit or explorative definitions provided were inconsistent. Outcomes were influenced by various factors (e.g., prior experiences, staff interaction, animal behavior). Our results suggest the agenda of conservation education research should be broadened by examining all learning outcomes relevant to behavior change. Educational and behavior change theories should be used as a background for conservation education research to ensure clear and consistent definitions, derive appropriate instruments to measure learning outcomes, and relate learning outcomes to influencing factors. We recommend conservation education researchers and practitioners to treat conservation education holistically and acknowledge its learning outcomes’ full complexity.     

Prediction and cause investigation of ozone based on a double-stage attention mechanism recurrent neural network

● Used a double-stage attention mechanism model to predict ozone. ● The model can autonomously select the appropriate time series for forecasting. ● The model outperforms other machine learning models and WRF-CMAQ. ● We used the model to analyze the driving factors of VOCs that cause ozone pollution. Ozone is becoming a significant air pollutant in some regions, and VOCs are essential for ozone prediction as necessary ozone precursors. In this study, we proposed a recurrent neural network based on a double-stage attention mechanism model to predict ozone, selected an appropriate time series for prediction through the input attention and temporal attention mechanisms, and analyzed the cause of ozone generation according to the contribution of feature parameters. The experimental data show that our model had an RMSE of 7.71 μg/m³ and a mean absolute error of 5.97 μg/m³ for 1-h predictions. The DA-RNN model predicted ozone closer to observations than the other models. Based on the importance of the characteristics, we found that the ozone pollution in the Jinshan Industrial Zone mainly comes from the emissions of petrochemical enterprises, and the good generalization performance of the model is proved through testing multiple stations. Our experimental results demonstrate the validity and promising application of the DA-RNN model in predicting atmospheric pollutants and investigating their causes.     

考虑数据去噪分解的滑坡位移组合预测研究

为准确掌握滑坡位移变化规律,基于滑坡变形监测结果统计,对位移数据进行去噪分解处理,将滑坡位移数据分解为趋势项和误差项,并分别利用优化多核极限学习机和Arima模型构建预测模型,以实现滑坡位移的组合预测.结果表明:Morlet复小波较传统去噪模型分解效果更优,且通过优化处理,能更好地提高其分解能力;通过对多核极限学习机的...     

VOCs、细菌及颗粒物混合暴露致小鼠学习记忆障碍机制研究

室内空气污染的现状是多种污染源共存,挥发性有机物(volatile organic compounds, VOCs)、细菌及颗粒物等是典型室内空气污染物。神经行为学毒性是室内空气污染引起的敏感毒性作用之一。为了探讨VOCs、细菌及颗粒物混合暴露对小鼠学习记忆能力的影响及机制,选用雄性昆明小鼠70只,随机分为对照(G1)和2～7(G2～G7)号染毒组。采用水迷宫和抓力仪测定小鼠的学习记忆潜伏期和抓力,染毒结束后测定全脑中活性氧(ROS)、丙二醛(MDA),神经递质谷氨酸(Glu)、乙酰胆碱(Ach)含量以及胆碱能系统的乙酰胆碱转移酶(ChAT)和乙酰胆碱酯酶(TChE)活力,同时分析脑源性神经营养因子(BDNF)、胶质细胞神经营养因子(GDNF)及神经生长因子(NGF)的水平。结果显示,第6天,G5、G6及G7小鼠的抓力、逃避潜伏期及在原平台所在象限的探索时间较对照组存在显著差异,并且伴有ROS、MDA含量的显著升高,Glu含量的显著升高,Ach含量、ChAT及TChE活力的显著降低,以及神经营养因子的显著下调(P<0.05或P<0.01)。研究结果表明,VOCs、颗粒物及细菌混合暴露能够导致小鼠学习记忆障碍,混合暴露引起的氧化损伤诱导的神经兴奋或抑制性毒性作用,以及神经营养因子沿轴突逆向传递降低或中断两方面作用导致神经递质产生和释放异常,进而引起学习记忆能力降低。     

运用机器学习方法预测空气中臭氧浓度

臭氧(O_3)浓度变化与天然源、移动源和点源的排放量存在某些隐含的关联。根据臭氧浓度变化的特性,基于污染源在线排放数据、气象监测数据以及空气质量监测数据构造特征,运用机器学习方法进行逐小时臭氧浓度预测。该方法不仅充分利用了臭氧浓度变化时序数据,而且考虑了气象条件变化对污染物浓度变化的影响,最重要的是将点源排放氮氧化物这一臭氧生成的重要前体物纳入模型考虑。在金砖国家领导人厦门会晤前后(2017年8月31日至9月9日),运用该方法对厦门市溪东、洪文、鼓浪屿和湖里中学四个大气自动监测站的臭氧小时浓度平均值进行滚动预报,比较准确地模拟出臭氧浓度的日周期性变化,同时对峰值和低谷能够进行较为有效的捕捉和刻画。按照《环境空气质量标准》(GB3095—2012)臭氧日最大八小时浓度平均值进行评价,四个站点均取得了90%的预报等级准确率。     

机器学习在微塑料识别与环境风险评估中的应用研究进展

微塑料是一种新型污染物,可以在环境中长期存在并造成生态风险.目前,微塑料污染已成为全球性的重大环境问题.借助新的技术途径,提高微塑料识别的简便性和可靠性,并系统分析各类环境介质中微塑料的污染特征,明确微塑料的环境效应,对科学准确评价微塑料污染的环境风险具有重要意义.机器学习技术通过学习和解析大量数据建立结果评估或预测模型,目前已广泛应用于微塑料领域的相关研究.机器学习的应用可以提高视觉和光谱识别微塑料的自动化程度和识别效率,为微塑料污染溯源提供方法支撑并有助于揭示微塑料的复杂环境效应机制.通过综述机器学习技术在微塑料识别与环境风险评估中的应用研究进展,概括了机器学习在上述方向的应用特点和局限性,为机器学习在相关方向的发展和应用提出建议与展望.     

公共危机管理中的组织学习研究

目前国外对危机管理的研究已经趋于成熟,已经进一步提出了在危机中学习的概念并取得一些进展。而在经历了几次大的危机之后,国内学者开始关注公共危机管理。随着危机种类越来越多,发生频率越来越高,政府的公共部门必须要具备组织学习的能力。但是国内对于组织学习研究在公共管理领域的延伸还十分有限,我国仅少量学者探讨了学习型政府,对其它类型公共部门的组织学习研究则更为罕见。显然,这种研究的缺失不利于公共组织构建组织学习能力和提高服务水平。未来的国内学习型组织研究需要在公共组领域有所突破,特别是危机情境下的公共组织学习。因此本文以分析文献的方式,借鉴国内外学者对公共危机管理,以及组织学习的研究,希望能为公共部门组织学习的深层探索提供思路。     

系统思考在企业安全管理中的应用探讨

结合系统思考的内涵和企业安全管理的特点,提出了一个企业安全管理的系统思考模型。模型集中体现了系统思考的核心理念,用整体动态思考的方法审视系统要素及其之间的关系。在系统的动态反馈和系统学习中．让管理者对安全系统的结构及其行为的动态变化进行整体上的把握．以便进行有用的模拟和分析,达到有效的事故预防和控制。     

Organisational learning and self-adaptation in dynamic disaster environments

This paper examines the problems associated with inter-organisational learning and adaptation in the dynamic environments that characterise disasters. The research uses both qualitative and quantitative methods to investigate whether organisational learning took place during and in the time in between five disaster response operations in Turkey. The availability of information and its exchange and distribution within and among organisational actors determine whether self-adaptation happens in the course of a disaster response operation. Organisational flexibility supported by an appropriate information infrastructure creates conditions conducive to essential interaction and permits the flow of information. The study found that no significant organisational learning occurred within Turkish disaster management following the earthquakes in Erzincan (1992), Dinar (1995) and Ceyhan (1998). By contrast, the 'symmetry-breaking' Marmara earthquake of 1999 initiated a 'double loop' learning process that led to change in the organisational, technical and cultural aspects of Turkish disaster management, as revealed by the Duzce earthquake response operations.