The impact of sensory learning modalities on children’s sensitivity to sensory cues in the perception of their school environment

Learning style models recognised that individuals differ in the sense learning modality of stimuli from which they best absorb, retain and process new information. The effect of sensory learning modalities on individual children’s sensitivity to sensory cues has so far been neglected. This paper reports on a study that sought to establish whether individual differences in children’s sensitivity to sensory cues in their perception of their school environmental features is influenced by their sensory learning modalities. Participants included Key Stage 1 and 2 pupils (N = 151) from four primary schools in South Gloucestershire, UK. The study used a child-friendly Visual, Auditory and Kinaesthetic questionnaire to establish children’s learning style modality. All children, independent of their learning style category, were exposed to three types of sensory cues, consisting of photo-safari, speech frequency and Global Positioning System. The analysis revealed that children’s sensitivity to sensory cues in their perception of school environmental features varied significantly between the three sensory learning modalities: visual, auditory and kinaesthetic. The implications of these findings on research, policy and practice are discussed.     

Benefits of Collaborative Learning for Environmental Management: Applying the Integrated Systems for Knowledge Management Approach to Support Animal Pest Control

Resource management issues continually change over time in response to coevolving social, economic, and ecological systems. Under these conditions adaptive management, or “learning by doing,” offers an opportunity for more proactive and collaborative approaches to resolving environmental problems. In turn, this will require the implementation of learning-based extension approaches alongside more traditional linear technology transfer approaches within the area of environmental extension. In this paper the Integrated Systems for Knowledge Management (ISKM) approach is presented to illustrate how such learning-based approaches can be used to help communities develop, apply, and refine technical information within a larger context of shared understanding. To outline how this works in practice, we use a case study involving pest management. Particular attention is paid to the issues that emerge as a result of multiple stakeholder involvement within environmental problem situations. Finally, the potential role of the Internet in supporting and disseminating the experience gained through ongoing adaptive management processes is examined.     

Study of high-frequency electromagnetic field effect on some somatic and neuro-behavioral characteristics in healthy and neurodefective mice

The effect of long-term exposure to high frequency electromagnetic field (HF EMF) on some somatic and neural characteristics was studied in neurodefective Lurcher mutant (+/Lc) and normal wild type mice (+/+). Both newborn and young adult (3 months) animals derived from two strains (C3H, B6CBA) were exposed to HF EMF (870 MHz) from 1st to 21st day or from 91st to 120th day respectively. In animals of both groups and controls we observed the development of body weight. Moreover, in the HF EMF exposed adult B6CBA animals we studied spatial learning ability, motor functions and the CNS excitability. To investigate specific energy absorption rate (SAR) in experimental animals we have done the basic 3D calculations of the electromagnetic energy distribution in the simplified model of the mouse. The HF EMF exposed animals exhibited mild differences of body weight between them and unexposed controls. The long-term exposure to HF EMF did not significantly influence the ability to learn in the Morris water maze. However, significant lower swimming speed was found in the irradiated +/Lc as well as lower motor activity of +/+ in the open field when compared to controls. No significant differences were found between HF EMF irradiated animals and controls in examination of the CNS excitability and motor functions.     

Evolution of disaster relief law under multiple transformations: Progressive learning or walking in a circle?

The aim of this paper is to examine the association between double-loop disaster learning and transformations of political systems. The particular question is how disasters increase the rapidity and complexity, with which the transforming regimes face the unsuitability and inadequacy of the past forms and processes of disaster governance. The centennial evolution of the disaster relief law at a country scale (Czech Republic, resp. Czechoslovakia, 1918-present) is used as an example. The thorough search in digital repository of Czech and Slovak Parliamentary Library was performed to obtain legislative norms and protocols from parliamentary debates, whereas major disasters that induced new laws (1927 flash flood, 1947 drought, 1997 and 2002 floods) were identified from existing databases and from detailed reconstructions of historical disastrous events. The described cases enabled to add new empirical evidence contesting (i) the process scheme of disaster politics during critical junctures and institutional transformations, and (ii) the progressive nature of the double-loop learning approach related to disasters. It is shown that the double-loop learning from disasters and its benefits should be considered as time-dependent and related to permissive and productive conditions that facilitated the adoption of the new disaster relief law (i.e. the learning process). Finally, the implications for the research agenda (historical disaster research) and for disaster policy (evaluation of disaster relief effectiveness) are summarized.     

基于机器学习和非参数估计的PM2.5风险评估

为开展区域风险评估,融合手机信令、气象和地理信息等多源数据,引入随机森林机器学习、非参数估计分位数图示法和非监督学习K-mean等方法,构建了区域PM_2.5风险评估及特征识别评价框架,在南京市区以0.3km分辨率网格为基础单元开展了案例研究.结果表明,该技术既可有效模拟PM_2.5浓度时空分布,十折交叉验证R²达到0.76,证明了准确度较高,并基于此识别出4种主要污染特征;也可有效捕捉短期人口流动导致的风险,在污染浓度不变的情况下短期人口流动会导致风险增加0.30~0.97倍.综合PM_2.5浓度和人口分布,识别出4种主要暴露风险模式,其中,研究区域6.5%的面积为高风险地区,23.0%的面积为低风险地区.“十四五”期间应加快现代科学技术在环境保护领域的应用,实施网格化和差异化的风险控制政策,维护人群健康.     

大气环境分类的SVM联合决策方法

目的 实现城市大气环境的精准快速归类预测。方法 基于支持向量机（SVM）构建多分类问题的联合决策算法,将大量城市环境因素数据的主成分聚类结果作为输入,通过机器学习训练,组建大气环境的SVM联合决策模型。结果 该模型根据大气环境因素将数据集91个城市划分为9类,其中河内与海防环境相似度最高,巴东与格尔木差异最大。9个SVM二分类器组建的联合决策模型通过逐点预测在主成分数据空间形成了分区预测云图。结论 SVM联合决策模型可实现城市环境的快速分类辨识,分类预测结果精度高于95%。     

基于机器学习算法的精细化流场模拟研究

基于决策树和随机森林两种机器学习算法,以长沙市国控站气象观测数据和WRF模式模拟得到的风场数据构建数据集,对WRF预报长沙市城区风场数据进行优化.同时,利用树模型特征选择法,筛选对近地面风场数据有重要影响的气象要素,将其作为两种机器学习算法的输入,并利用网格搜索法进行调参建模.最后,将训练结果与地面观测数据进行对比评估...     

草甘膦铵盐暴露对乌龟胚胎发育、孵出幼体运动及学习表现的影响

草甘膦暴露可能对某些水生动物具有致死及亚致死毒性,但未见有涉及龟鳖类动物的行为毒理学的研究报道。用不同浓度草甘膦铵盐处理孵化中的乌龟卵来检测草甘膦暴露对其胚胎发育、孵出幼体大小、翻身能力以及空间学习能力的影响。结果表明,2～2 000 mg·L~(-1)浓度范围内的草甘膦铵盐暴露并不会影响乌龟卵孵化成功率、胚胎发育速率以及孵出个体的体质量和翻身时间,但对孵出幼体的空间学习能力产生一定影响。较高浓度的草甘膦铵盐暴露会导致孵出幼体的觅食时间延长、觅食过程中的运动距离增大。因此,胚胎期草甘膦铵盐暴露可能对乌龟孵出幼体的后续生长和存活有一定的负面效应。     

湖南空气质量预报中的数据预处理和特征工程

为提高空气质量预报的准确率,建立了融合气象和环境观测资料、结合机器学习和数值天气预报,且预测时效较长、预测精度较高的机器学习模型库。以湖南6个城市(长沙、株洲、湘潭、益阳、常德、岳阳)的空气质量预报为例,将数据预处理、特征工程方法运用到模型之中,得出以下几点结论:①数据预处理工作包括样本收集、数据清洗、缺失值处理、异常值剔除等,对提高模型预测稳定性帮助很大。②点、线、面的特征组合有助于完整地描述污染物的生消过程。引入传输指数后,株洲市模型对传输型污染过程的预测性能得到明显提高,对轻度、中度、重度污染的分类准确度分别提升了23.6%、16.6%、30.0%。引入静稳指数后,长沙市模型PM_2.5浓度测试的相关系数由0.938提升至0.959,均方根误差由10.33下降至8.46,且模型对中度以上污染天气的极值预报结果更接近实况;益阳市模型在高浓度样本预测中存在的系统性偏低现象得到改善,对轻度以上污染天气的预报结果得到较大矫正。③随机森林的特征重要性排序功能可以大幅度减少特征的数量,使得模型的可解释性和稳定性增强。     

SVD在串联故障电弧检测及选相中的应用*

煤矿井下发生串联故障电弧易引发火灾等安全事故,为了预防电气火灾、指导线路维修,利用三相电动机及变频器负载开展不同线路、不同电流条件下的串联故障电弧实验,研究三相串联故障电弧的检测及选相方法。首先,对单相电流进行一阶差分处理后,建立改进的吸引子轨迹矩阵作为故障特征矩阵;其次,对故障特征矩阵进行奇异值分解,采用特征矩阵的奇异值构建串联故障电弧检测及选相的特征向量;最后,利用极限学习机建立故障电弧检测及选相模型,并测试检测及选相准确率。研究结果表明:提出的SVD方法可以利用单相电流实现三相电动机及变频器负载回路中的串联故障电弧检测及选相。