核心素养视域下的小学科学教学设计 ——对对J省省10篇篇获获奖奖教教学学设设计计的的文文本本分分析析

发展学生核心素养的重要载体是课堂教学,而教学设计决定了课堂教学效果的优劣,本文以"2020年吉林省基于核心素养的小学学科教学设计评比"活动中10篇获奖小学科学教学设计为文本分析对象,在学生发展核心素养视域下,从多方面分析在小学科学教学设计的目标预设、活动探究及归纳反思等环节中如何培养学生的核心素养.     

Ecological civilization: China's effort to build a shared future for all life on Earth

After hundreds of years of technological revolution and rapid development,hu-manity has reached a moment of crisis in its relationship with nature[1].To find be...     

Ultra-small hollow ternary alloy nanoparticles for efficient hydrogen evolution reaction

Hollow nanoparticles with large specific surface area and high atom utilization are promising catalysts for the hydrogen evolution reaction (HER). We describe herein the design and synthesis of a series of ultra-small hollow ternary alloy nanostructures using a simple one-pot strategy. The same technique was demonstrated for hollow PtNiCu nanoparticles, hollow PtCoCu nanoparticles and hollow CuNiCo nanoparticles. During synthesis, the displacement reaction and oxidative etching played important roles in the formation of hollow structures. Moreover, our hollow PtNiCu and PtCoCu nanoparticles were single crystalline, with an average diameter of 5 nm. Impressively, ultra-small hollow PtNiCu nanoparticles, containing only 10% Pt, exhibited greater electrocatalytic HER activity and stability than a commercial Pt/C catalyst. The overpotential of hollow PtNiCu nanoparticles at 10 mA cm⁻² was 28 mV versus reversible hydrogen electrode (RHE). The mass activity was 4.54 A mg_Pt⁻¹ at −70 mV versus RHE, which is 5.62-fold greater than that of a commercial Pt/C system (0.81 A mg_Pt⁻¹). Through analyses of bonding and antibonding orbital filling, density functional theory calculations demonstrated that the bonding strength of different metals to the hydrogen intermediate (H^*) was in the order of Pt > Co > Ni > Cu. The excellent HER performance of our hollow PtNiCu nanoparticles derives from moderately synergistic interactions between the three metals and H^*. This work demonstrates a new strategy for the design of low-cost and high-activity HER catalysts.     

金属离子水解常数的计算

在改进失屏参数计算方法的基础上,根据失屏参数与许多金属元素化学性质相关联,研究得到只用一个参数计算各类金属离子水解常数的方法。     

人工神经网络机械故障诊断方法及其应用

本文阐述了利用人工神经网络模型诊断减速机轴承故障的方法,建立了诊断用的BP网络模型,并对网络模型进行了试验验证。     

大型钢结构箱型柱施工技术

采用多头式自动气割机下料,埋弧自动焊机施焊,以及一系列的施工工艺进行箱型钢柱的施工,完全达到了控制其焊接变形的目的,并通过在实际工程中的应用,取得了很好的效果,其成功经验对今后类似施工具有很大的参考价值。     

The Performance of Multilevel Models When Outcome Data Are Incomplete

When data for multiple outcomes are collected in a multilevel design, researchers can select a univariate or multivariate analysis to examine group-mean differences. When correlated outcomes are incomplete, a multivariate multilevel model (MVMM) may provide greater power than univariate multilevel models (MLMs). For a two-group multilevel design with two correlated outcomes, a simulation study was conducted to compare the performance of MVMM to MLMs. The results showed that MVMM and MLM performed similarly when data were complete or missing completely at random. However, when outcome data were missing at random, MVMM continued to provide unbiased estimates, whereas MLM produced grossly biased estimates and severely inflated Type I error rates. As such, this study provides further support for using MVMM rather than univariate analyses, particularly when outcome data are incomplete.     

论《可食用的女人》中的女性自我意识

<可食用的女人>是一部关于选择的小说.小说试图说明在上世纪下半叶瞬息万变、超载运行的世界上,人们如何保持自己的理智和人格.女主人公玛丽安具有寻找自我、发现自我的意识,从她的身上,可以看到西方社会中女性的生存状况和与命运抗争的精神.     

On-chip assay of the effect of topographical microenvironment on cell growth and cell-cell interactions during wound healing

Wound healing is an essential physiological process for tissue homeostasis, involving multiple types of cells, extracellular matrices, and growth factor/chemokine interactions. Many in vitro studies have investigated the interactions between cues mentioned above; however, most of them only focused on a single factor. In the present study, we design a wound healing device to recapitulate in vivo complex microenvironments and heterogeneous cell situations to investigate how three types of physiologically related cells interact with their microenvironments around and with each other during a wound healing process. Briefly, a microfluidic device with a micropillar substrate, where diameter and interspacing can be tuned to mimic the topographical features of the 3D extracellular matrix, was designed to perform positional cell loading on the micropillar substrate, co-culture of three types of physiologically related cells, keratinocytes, dermal fibroblasts, and human umbilical vein endothelial cells, as well as an investigation of their interactions during wound healing. The result showed that cell attachment, morphology, cytoskeleton distribution, and nucleus shape were strongly affected by the micropillars, and these cells showed collaborative response to heal the wound. Taken together, these findings highlight the dynamic relationship between cells and their microenvironments. Also, this reproducible device may facilitate the in vitro investigation of numerous physiological and pathological processes such as cancer metastasis, angiogenesis, and tissue engineering.     

Incremental cue phrase learning and bootstrapping method for causality extraction using cue phrase and word pair probabilities

This work aims to extract possible causal relations that exist between noun phrases. Some causal relations are manifested by lexical patterns like causal verbs and their sub-categorization. We use lexical patterns as a filter to find causality candidates and we transfer the causality extraction problem to the binary classification. To solve the problem, we introduce probabilities for word pair and concept pair that could be part of causal noun phrase pairs. We also use the cue phrase probability that could be a causality pattern. These probabilities are learned from the raw corpus in an unsupervised manner. With this probabilistic model, we increase both precision and recall. Our causality extraction shows an F-score of 77.37%, which is an improvement of 21.14 percentage points over the baseline model. The long distance causal relation is extracted with the binary tree-styled cue phrase. We propose an incremental cue phrase learning method based on the cue phrase confidence score that was measured after each causal classifier learning step. A better recall of 15.37 percentage points is acquired after the cue phrase learning.