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...     

基于机器学习的典型社会安全事件发生规律研究及对雄安新区的启示

针对雄安新区建设和发展过程中对社会安全事件的防控需求,以盗窃作为典型社会安全事件,提出基于机器学习模型的社会安全事件分析预测方法,并以A市2012—2016年的实际盗窃犯罪数据为基础,提取发案时间、发案地点、实施手段和损失金额作为分类特征,通过比较多种机器学习算法,研究盗窃前科人员的预测方法,并根据预测结果挖掘盗窃前科人员的作案规律。研究结果表明:随机森林算法表现最优,查准率、查全率和F1均达到了0.85以上;对于盗窃这一典型社会安全事件,其前科人员倾向于选择下午时段和人流量大的地区实施,盗窃金额明显高于初犯和惯犯。最后,基于前述研究,提出构建数据驱动的社会安全事件预测预警和综合研判系统,并针对该系统的前期建设和后期使用,给出“制定统一的数据格式”、“实现数据实时接入”的建议。相关研究成果可为雄安新区社会安全事件预测预警以及治安防控工作的开展提供参考和借鉴。     

基于微信平台微学习的安全宣教新模式研究

为促进安全宣传教育,打破传统安全宣教的局限性与不足,提出了基于微信平台微学习的安全宣教新模式。对安全宣教的内涵进行了分析,从现有宣教模式的不足出发,结合微信平台在移动教育上的优势,论证了基于微信平台开展安全宣教的可行性;分析了构建基于微信平台微学习的安全宣教新模式的核心理论:负荷理论、活动理论和安全教育理论;遵循PDCA循环构建了基于微信平台微学习的安全宣教新模式,包括前端分析、安全宣教实施过程、安全宣教绩效评估和安全宣教模式改进4个阶段;提出了应用安全宣教新模式的优化建议。研究结果表明:基于微信平台微学习的安全宣教新模式可以弥补现有安全宣教方法的不足,并能丰富安全宣传教育的理论基础。     

How does it feel and how does it look? The role of employee motivation in organizational learning type

Employees' work outcomes vary as a function of their focus on exploring new possibilities versus exploiting current opportunities. But what determines how employees divide their attention between these contrasting work behaviors? Drawing on studies on work motivation and employees' impression management concerns, we examine how intrinsic work motivation and self‐enhancement motivation relate to the exploration–exploitation balance and how environmental dynamism moderates these relationships. Based on the analyses of a sample of 638 employees in 34 organizations in Finland, we find that intrinsic work motivation is positively associated with employees' focus on exploration relative to exploitation. By contrast, self‐enhancement motivation negatively associates with exploration relative to exploitation, but this relationship is nonlinear, such that as self‐enhancement motivation increases, its positive association with exploitation diminishes. The findings also show that the hypothesized nonlinear relationship between self‐enhancement motivation and exploration is particularly pronounced in stable business environments. Our findings contribute to organizational learning research and provide a new theoretical perspective on pursuing exploration and exploitation in organizations.     

复杂事件事故树“人-机-环”简易展开模型及应用

为了寻找编制复杂事件事故树时中间事件的展开方式,采用理论分析的方法,剖析事故原因,发现事故的发生均是事故致因理论中"人-机-环"三因素作用导致的。基于此,根据系统分割准则和事故致因理论,研究了一种事故树基本展开型式,提出事故树编制时的"二分法"原则,建立了通用的事故树简易展开模型。利用模型将事故的"人-机-环"三因素分析方法应用于高处坠落这一事件的事故树分析中,确定了高处坠落事故的主要影响因素,编制出包含7个中间事件和11个基本事件的高处坠落事故树。研究表明,模型可有效实现复杂事件事故树的编制及影响因素分析,研究结论将传统的"因素分析"从定性向半定量方向推进了一步。     

复杂背景环境下基于SSD_MobileNet深度学习模型的火焰图像识别研究

针对现有的火灾火焰图像识别方法在光照和红花等类似火焰干扰的复杂环境下存在错检和漏检的问题, 提出一种基于SSD_MobileNet的复杂环境火焰区域标记方法。首先,将深度卷积神经网络SSD300的基础卷积网络VGG16替换为MobileNet网络,应用深度可分离卷积,降低网络参数,进而构建一种火焰图像检测的SSD_MobileNet模型;然后,迁移第一次训练模型所有的卷积层参数,初始化新的待训练模型;最后,加入新的数据样本用于削弱光照、红花等干扰对象的影响。通过与SSD300、以及深度学习的目标检测算法Faster R-CNN和YOLOv3-tiny对比,实验结果表明,构建的火焰检测和火焰区域标记SSD_MobileNet模型的综合性能优于Faster R-CNN和YOLOv3-tiny模型,更适用于实时火焰检测领域。     

A deep model method for recognizing activities of workers on offshore drilling platform by multistage convolutional pose machine

The growing diversity of image scenes brings a great challenge to human activity recognition in practice. Traditional activity recognition methods cannot satisfy the demand of precise action recognition in complex scenes. In this work, we build a training set of worker's activities on offshore drilling platform by collecting data from offshore drilling monitor, and then an improved multi-level convolutional pose machine (MCPM) method is proposed and trained to recognize activities of workers on the platforms. In human object detection, a multi-rule region proposal marker algorithm is developed to separate the seawater area, and the ducts of similar personnel is pre-discriminated by support vector machine. We use the characteristics of the human body key-points not affected by complex background noise to assist the detection of the human target. As results, it shown that our method performs better than Faster-RCNN, MobileNet-SSD and SSD algorithms in detecting human target on the offshore drilling platform, and achieves well accuracy in recognizing many key activities. To our best acknowledge, it is the first attempt of using deep model to recognize worker's activities on offshore drilling platform.