高师生实验探究洋葱中挥发性化学物质

利用实验室一般仪器,对洋葱中挥发性化学成分如含硫化合物、醇类、醛类、羧酸进行了定性检测,旨在让高师生体验探究学习,掌握探究方法,发展实验设计和探究能力。     

指向学习体验的物质及其转化复习的教学实践——以“钠及其化合物”为例

学习体验是学生对学习内容、过程、方法、意义的自我感受和评估,对学生学习志趣、学习素养提升有积极正向作用。以钠及其化合物复习为例,将各类别物质间转化关系的构建、物质制备原理和条件的优选作为学习体验对象,依据学习体验的内在发展历程设计学习任务及活动,引导学生亲身体验学科认知方式、学科思维方法、学科应用价值,评价体验结果,激励学生自主学习发生。     

基于社会性科学议题的化学微项目学习*——以“物质的性质与转化”单元复习为例

在化学课堂中开展社会性科学议题的教学能够发展学生的社会参与意识,结合微项目学习模式,从课堂教学角度出发,构建选定项目、启动项目、实施项目、交流成果等4个教学流程,确立实施教学的核心问题,设计了鲁科版高一化学“物质的性质与转化单元复习课”,进行了基于社会性科学议题的化学微项目学习的实践教学,结合实践的基本情况提出4点反思。     

高中学业水平考试试题难度模型建构*

利用主成分分析方法厘清试题难度影响因素之间的关系,进而从问题表征、问题解决和结果输出等3个维度上建立了影响高中学业水平考试试题难度的主要因素。通过编写高中学业水平考试测试题进行实证研究,建立影响因素常见呈现类型的难易赋值规则。选用机器学习的线性回归方法建构试题难度分析模型,并使用高考化学江苏卷部分试题的实测难度数据进行校验,模型难度预测值和高考难度实测值具有较好的拟合度,表明模型具有较好的应用价值和推广意义。     

Machine Learning Approaches for Metalloproteins

Metalloproteins are a family of proteins characterized by metal ion binding, whereby the presence of these ions confers key catalytic and ligand-binding properties. Due to their ubiquity among biological systems, researchers have made immense efforts to predict the structural and functional roles of metalloproteins. Ultimately, having a comprehensive understanding of metalloproteins will lead to tangible applications, such as designing potent inhibitors in drug discovery. Recently, there has been an acceleration in the number of studies applying machine learning to predict metalloprotein properties, primarily driven by the advent of more sophisticated machine learning algorithms. This review covers how machine learning tools have consolidated and expanded our comprehension of various aspects of metalloproteins (structure, function, stability, ligand-binding interactions, and inhibitors). Future avenues of exploration are also discussed.     

合作性学习模式在电类课程教学中的应用

本文将合作性学习模式应用在电类课程的教学中,说明了合作性学习的概念及基本要素.并详细给出了电类课程合作性学习的步骤与方法,包括调查授课对象基本情况、组建有效的学习团队、自主学习、组内交流、组间交流、教师精讲、竞赛练习及总结评价.实践证明,合作性学习模式在三本学生的电类课程教学中较有成效,学生的自学能力、交流合作能力等得到了提高,学习习惯也得到了培养.     

Ensemble Approach for Detection of Depression Using EEG Features

Depression is a public health issue that severely affects one’s well being and can cause negative social and economic effects to society. To raise awareness of these problems, this research aims at determining whether the long-lasting effects of depression can be determined from electroencephalographic (EEG) signals. The article contains an accuracy comparison for SVM, LDA, NB, kNN, and D3 binary classifiers, which were trained using linear (relative band power, alpha power variability, spectral asymmetry index) and nonlinear (Higuchi fractal dimension, Lempel–Ziv complexity, detrended fluctuation analysis) EEG features. The age- and gender-matched dataset consisted of 10 healthy subjects and 10 subjects diagnosed with depression at some point in their lifetime. Most of the proposed feature selection and classifier combinations achieved accuracy in the range of 80% to 95%, and all the models were evaluated using a 10-fold cross-validation. The results showed that the motioned EEG features used in classifying ongoing depression also work for classifying the long-lasting effects of depression.     

TLP-CCC: Temporal Link Prediction Based on Collective Community and Centrality Feature Fusion

In the domain of network science, the future link between nodes is a significant problem in social network analysis. Recently, temporal network link prediction has attracted many researchers due to its valuable real-world applications. However, the methods based on network structure similarity are generally limited to static networks, and the methods based on deep neural networks often have high computational costs. This paper fully mines the network structure information and time-domain attenuation information, and proposes a novel temporal link prediction method. Firstly, the network collective influence (CI) method is used to calculate the weights of nodes and edges. Then, the graph is divided into several community subgraphs by removing the weak link. Moreover, the biased random walk method is proposed, and the embedded representation vector is obtained by the modified Skip-gram model. Finally, this paper proposes a novel temporal link prediction method named TLP-CCC, which integrates collective influence, the community walk features, and the centrality features. Experimental results on nine real dynamic network data sets show that the proposed method performs better for area under curve (AUC) evaluation compared with the classical link prediction methods.     

Modelling decision making and learning by low latitude hunter gatherers

Recent anthropological studies have demonstrated that low latitude ‘encounter’ foragers exploit their environments in energetically very efficient manners and closely track the environment as it changes. The paper begins to investigate how they manage to do this by proposing a simple decision making and learning rule developed from an evolutionary ecological basis. Having described the mathematical model the paper refers to simulation studies exploring this model which suggest that some of the seemingly complex aspects of hunter gatherer behaviour may result from the use of simple decision making and learning processes.     

Rapprochement of artificial intelligence and dynamics

It is proposed that continuous time is in effect discretized in the brain by dynamic pattern recognition mechanisms in neurons. Time discretization is required to support formal computations in continuous time systems consisting of a large number of components. The ability to perform formal computations is necessary if the system is to execute high level algorithms of the type used in present day artificial intelligence. The weakness of such algorithms is that they work efficiently only when the forms of patterns and objects presented to them are highly constrained. The dynamic mechanisms which discretize the brain's time line also serve to code patterns into constrained forms suitable for high level processing.