基于发展科学探究能力的高中化学教学实践 ——以"元素周期律"章节为例

基于学生在学习元素周期律时存在的普遍问题改进了教学实践,分析了元素周期律发展学生科学探究能力的价值,设计了四个教学环节——趣味导入,成果展示;分析规律,做出猜想;设计方案,收集证据;梳理模型,迁移应用,并提出了三点针对学生探究能力发展的教学建议.     

圆型限制性三体问题的动力学特征

禁行域在圆型限制性三体问题中对第三体的运动有极为重要的影响. 利用零速度曲线讨论天体禁行域与系统参数之间的关系. 禁行域与雅可比常数有关，且能影响天体的运动区域和轨道构型. 利用最大李雅普诺夫指数寻找该模型的混沌轨道，给出混沌轨道在不同系统参数下随天体初始位置的分布. 这些分布说明，当第三天体从两主天体连线的中点处释放时，容易产生混沌运动；当质量参量足够小时，体系无法产生混沌现象.     

一起发电机定子接地故障的分析与处理

文章介绍了海外某大型火力发电厂发生的一起发电机定子接地故障的分析和处理过程,对发电厂类似故障的处理和海外发电厂的运行和维护提供了参考。     

乘用车悬架静强度与疲劳载荷研究

为了快速获得平台悬架静强度与疲劳载荷,本文依据某A级平台5款量产车型试验场道路栽荷数据,以前麦弗逊悬架为研究对象,先对轮心载荷幅值进行统计分析,根据应力-强度干涉模型,得到该平台前悬架的强度载荷边界,及其与车辆满载轮荷的G载荷系数.再通过研究5款车型车轮轮心疲劳伪损伤与轮荷的关联关系,得到该平台悬架的疲劳栽荷.此方法可快速获得同平台悬架静强度与疲劳载荷,省去实车道路谱采集并提升了工作效率.     

Application of ferrous sulfate alleviates negative impact of cadmium in rice (<i>Oryza sativa</i> L.)

Soil contamination with toxic heavy metals [such as cadmium (Cd)] is becoming a serious global problem due to rapid development of social economy. Iron (Fe), being an important element, has been found effective in enhancing plant tolerance against biotic and abiotic stresses. The present study investigated the extent to which different levels of Ferrous sulphate (FeSO₄) modulated the Cd tolerance of rice (Oryza sativa L.), when maintained in artificially Cd spiked regimes. A pot experiment was conducted under controlled conditions for 146 days, by using natural soil, mixed with different levels of CdCl₂ [0 (no Cd), 0.5 and 1 mg/kg] together with the exogenous application of FeSO₄ at [0 (no Fe), 1.5 and 3 mg/kg] levels to monitor different growth, gaseous exchange characteristics, oxidative stress, antioxidative responses, minerals accumulation, organic acid exudation patterns of O. sativa. Our results depicted that addition of Cd to the soil significantly (P < 0.05) decreased plant growth and biomass, gaseous exchange parameters, mineral uptake by the plants, sugars (soluble, reducing, and non-reducing sugar) and altered the ultrastructure of chloroplasts, plastoglobuli, mitochondria, and many other cellular organelles in Cd-stressed O. sativa compared to those plants which were grown without the addition of Cd in the soil. However, Cd toxicity boosted the production of reactive oxygen species (ROS) by increasing the contents of malondialdehyde (MDA), which is the indication of oxidative stress in O. sativa and was also manifested by hydrogen peroxide (H₂O₂) contents and electrolyte leakage to the membrane bounded organelles. Although, activities of various antioxidative enzymes like superoxidase dismutase (SOD), peroxidase (POD), catalase (CAT) and ascorbate peroxidase (APX) and non-enzymatic antioxidants like phenolics, flavonoid, ascorbic acid, anthocyanin and proline contents increased up to a Cd level of 0.5 mg/kg in the soil but were significantly diminished at the highest Cd level of 1 mg/kg in the soil compared to those plants which were grown without the addition of Cd in the soil. The negative impacts of Cd injury were reduced by the application of FeSO₄ which increased plant growth and biomass, improved photosynthetic apparatus, antioxidant enzymes, minerals uptake together with diminished exudation of organic acids as well as oxidative stress indicators in roots and shoots of O. sativa by decreasing Cd retention in different plant parts. These results shed light on the effectiveness of FeSO₄ in improving the growth and upregulation of antioxidant enzyme activities of O. sativa in response to Cd stress. However, further studies at field levels are required to explore the mechanisms of FeSO₄-mediated reduction of the toxicity of not only Cd, but possibly also other heavy metals in plants.     

应用型人才培养模式下的材料科学基础实验课程改革

材料科学基础实验是我校高分子材料与工程专业必修的实验课程，是与材料科学基础课程理论知识相配套的应用实践课，本文结合我校应用型人才培养模式，针对目前开设的实验课程中的不足，通过对材料科学基础实验教学方法、实验类型以及考核方式等方面进行改革，提高学生学习兴趣、综合分析问题与实践创新能力，为培养社会发展需要的高质量应用型人才奠定基础。     

CDBN‑IKELM的轴承变工况故障诊断方法

针对现有方法在轴承变工况方面存在的诊断精度低、人工提取特征不充分等问题，提出了基于卷积深度置信网络（convolutional deep belief network，简称CDBN）与改进核极限学习机 （improved Kernel?based extreme learning machine，简称IKELM）的滚动轴承故障智能识别方法。首先，由卷积深度置信网络对原始信号内的故障特征进行深层自适应提取；其次，利用等距特征映射对提取的多维特征进行降维，去除冗余特征信息；然后，采用改进的核极限学习机对特征进行分类，使用粒子群（particle swarm optimization， 简称PSO）对模型重要参数进行优化，实现滚动轴承变工况下的故障识别；最后，将所提方法应用于不同工况下多种轴承故障的诊断。实验结果表明，该方法能够智能有效地识别变工况的轴承故障，诊断结果优于已有的智能故障诊断方法。