基于空间拟合和神经网络的机场噪声预测集成模型

将集成学习方法引入到机场噪声预测中,提出一种基于空间拟合和神经网络的机场噪声预测集成模型.该模型采用空间拟合算法和BP神经网络算法构建基学习器,然后通过所提出的基于观察学习的异构集成算法将基学习器集成起来,获得集成的机场噪声预测结果.该模型通过集成多个异构机场噪声预测基学习器,能够有效提升预测准确率.实验结果表明,本文所提出的基于观察学习的异构集成算法,较之其他异构集成算法,在解决机场噪声预测问题上准确性更高、容错性更强.     

基于GA-ELM的冲击地压危险性预测研究

为提高冲击地压预测的效率和准确率,在分析冲击地压影响因素的基础上,提出了一种将遗传算法(GA)与极限学习机(ELM)相结合的冲击地压预测的新方法。为了避免ELM受输入权值矩阵和隐含层偏差随机性的影响,算法采用GA对ELM的输入权值矩阵和隐含层偏差进行优化,建立GA-ELM冲击地压预测模型。利用某矿冲击地压统计数据对该模型进行了实例分析,将ELM、SVM和BP算法预测结果与该模型进行了对比分析。结果表明:GA-ELM模型具有较高的预测精度,可以相对准确、有效地对冲击地压发生的可能性进行预测。     

The role of social learning for soil conservation: the case of Amba Zuria land management,Ethiopia

Social learning plays key roles in sustainable natural resource management; however, studies on its role show mixed results. Even though most current studies highlight positive outcomes, there are also negative effects of social learning with respect to natural resource management. This paper explores the influence of social learning outcomes on the adoption of soil conservation practices in Amba Zuria, Ethiopia. Data were collected through semi-structured interviews, group discussions and in workshops. The interviews were transcribed and analysed with the qualitative data analysis software ATLAS.ti. In addition, a review of pertinent documents and literature complements the analysis. We found that social learning results in positive outcomes in interactions among the actors, broader understanding on soil conservation, trust and mutual understanding, and the creation of opportunities to apply both indigenous and scientific knowledge. These outcomes, in turn, foster the adoption of stone terraces to conserve soils. No such positive results were found for farmers who were not involved in the learning process and who did not adopt stone terraces. The non-adoption can partly be explained by their exclusion from sharing the positive outcomes of social learning. The study also shows limitations related to the short duration of the learning platforms, insufficient representation of women and irregular patterns of learning schedules. Hence, Ethiopian soil conservation strategies should put emphasis on social learning in order to strengthen the collective processes of soil conservation measures.     

The vulnerable do‐gooders: security strategies of German aid agencies

Humanitarian and development agencies have confronted growing insecurity in some of the regions in which they work over the course of the past decade. Consequently, aid agencies are changing their approach to security issues. Compared to aid agencies from other countries, especially the United Kingdom and the United States, German aid agencies have been relatively slow to adopt security measures. In addition, the security measures they have selected differ from one agency to another, even in comparable security contexts. The literature on organisational learning helps to explain these differences. The findings show that external and internal organisational factors influence an aid agency's choice of a particular security measure. The different responses of German aid agencies to the changing security environment demonstrate that security measures are not only influenced by an organisation's identity but also can be a way for them to demonstrate their identity and thereby distinguish themselves from other actors that deliver aid.     

基于GA-ELM浆体管道输送临界流速预测模型研究

针对浆体管道输送临界流速预测难度大、精确度低等技术难题,提出了基于极限学习机(ELM)的临界流速预测模型,用训练集对模型进行训练,以验证集预测值的均方误差作为适应度函数,利用遗传算法(GA)对ELM模型参数进行优化,应用优化得到的ELM模型对预测集进行预测。以某矿山为例,模型参数优化结果如下:隐含层节点数L为400,输入权值ai、偏置向量bi最优组合下预测结果适应度为0.0201。采用优化的ELM模型对预测集进行预测,预测结果的最大相对误差x=3.96%,平均相对误差y=1.58%,对比BP神经网络(x=12.95%)和SVM模型(x=3.19%),表明ELM模型更加精确、高效。     

基于PCA-ELM模型的露采爆破振动对民房破坏的预测分析

针对露天采矿爆破振动对民房破坏的预测问题,采用主成分分析(PCA)和极限学习机(ELM)方法,选取爆破振幅、主频率、主频率持续时间、灰缝强度、砖墙面积率、房屋高度、屋盖形式、圈梁立柱、施工质量、场地条件10个主要影响因素。引入相关性分析在主成分分析过程中,对相关性高的指标进行降维,把得到的3个综合因子和爆破振幅、主频率、主频率持续时间、砖墙面积率作为输入变量,构建露天煤矿PCA-ELM预测模型。选取露天矿实际爆破过程中测量的100组数据作为模型训练样本,用另外20组数据作为测试样本进行预测。结果表明:对民房破坏影响因素中灰缝强度、房屋高度、屋盖形式、圈梁立柱、施工质量、场地条件之间具有较高的关联度。该模型处理高维数据时较传统的ELM算法具有预测精度高、稳定性好等特点,可准确预测爆破振动对民房的破坏程度,误判率为1/20。     

基于EMD-MFOA-ELM的瓦斯涌出量时变序列预测研究

为准确分析工作面绝对瓦斯涌出量的非平稳特征,实现瓦斯涌出量的准确预测,基于经验模态分解(EMD)、修正的果蝇优化算法(MFOA)和极限学习机(ELM)基本原理,构建瓦斯涌出量的EMD-MFOA-ELM多尺度时变预测模型。通过EMD将瓦斯涌出量时变序列进行深层次分解,获得多尺度本征模态函数(IMF);采用MFOA-ELM对各IMF时变序列建立动态预测模型,等权叠加各预测值,得到模型最终预测结果。以晋煤某矿瓦斯涌出量监测时序样本为例进行研究分析,结果表明:EMD能充分挖掘出监测数据隐含信息,有效降低数据复杂度;该模型预测相对误差为0.024 3%~0.651 0%,平均值仅为0.252 6%,预测精度和泛化能力高于未经EMD分解模型,能很好地适用于非平稳时变序列预测。     

毒死蜱低剂量重复染毒致学习记忆功能障碍及CREB信号途径的抑制(Repeated Exposures to Low-Level Chlorpyrifos Result in Learning Memory Impairment and Inhibition of the CREB Signal System)

毒死蜱(CPF)是我国推荐使用的低毒有机磷农药之一,目前在全世界广泛应用.选择不引起动物出现全身系统毒性的CPF剂量(1、5、10mg·kg-1)对大鼠连续染毒4周,评价CPF对动物的学习记忆功能和CREB信号通路的影响.结果显示:CPF连续染毒后动物出现记忆能力的损伤,随着染毒剂量的增加,海马、皮质和纹状体CREB及ERKI/Ⅱ磷酸化蛋白表达逐渐减少,这表明ERK/CREB信号通路可能是参与毒死蜱低剂量重复暴露致学习记忆功能改变的重要途径.     

Working Life Across Cultures: “Work Life 2000: Quality in Work” and Occupational Health Education in Developing Countries

The article reflects on the changing world of work, and the challenges presented to both occupational health and occupational health education. We draw on the 63 preparatory workshops and the international conference in the “Work Life 2000: Quality in Work” program, an initiative of the Swedish Presidency of the European Union. The International Commission on Occupational Health is introduced, with particular concentration on a current practical project initiated by the Department of Health in South Africa, intended to lead to a set of projects, networking for occupational health education in developing countries. The practical initiatives cast light on a new set of issues that arise when occupational health and safety crosses cultural barriers, and previously separate comparative cases are linked.     

The interactive effects of conscientiousness,openness to experience,and political skill on job performance in complex jobs: The importance of context

Caveats concerning the ability of personality to predict job performance have been raised because of seemingly modest criterion‐related validity. The goal of the present research was to test whether narrowing the context via the type of job (i.e., jobs with complex task demands) and adding a social skill‐related moderator (i.e., political skill) would improve performance prediction. Further, along with political skill, the broad factor of personality demonstrated in prior research to have the strongest criterion validity (i.e., conscientiousness) was joined with a narrow construct closely related to openness to experience (i.e., learning approach) in a three‐way interactive prediction of supervisor‐rated task performance. With the employee–supervisor dyads among professionals, but not with the control group of non‐professional employees, task performance was predicted by the three‐way interaction, such that those high on all three received the highest performance ratings. Implications, strengths and limitations, and directions for future research are discussed. Copyright © 2012 John Wiley & Sons, Ltd.