混合式教学模式在中学地理课程中的探索——以人教版“行政区划”为例

混合式教学模式是既能发挥教师在课堂中的启发、引导与监控作用又能提高学生参与度、体现个性化学习的一种教学模式。目前中学地理课堂仍强调死记硬背,学生学习的主观能动性不强。为了实现地理课堂的高效性,笔者应用新型智慧教学工具——“雨课堂”,通过中学地理教学实例说明实现混合式教学的基本路径,包括课前准备、课堂应用以及课后统计。     

广州城市流浪乞讨者的空间管治与日常生活实践

以广州流浪乞讨者为研究对象,通过深度访谈等质性研究方法,解读针对流浪乞讨者的空间管治政策与手段,分析流浪乞讨者的日常生活实践与生境协商。研究发现：在地方城市政策对流动性与公共秩序限制的双重压力下,在流浪乞讨者的日常生活实践中,不仅有针对管治行动者而刻意为之的特殊营建,亦有基于程式化生活流程对自我身份认同的重塑与真情实感的展演。地方政府基于空间目标的救助与真实存在的日常生活实践存在着一种张力。研究关注了城市边缘群体日常生活对地理意义的主动塑造过程,对全面理解并规范管理中国城市流浪乞讨现象具有借鉴意义。     

Bibliometric Analysis of Global Research Progress on Coastal Flooding 1995–2016

Global research progress on coastal flooding was studied using a bibliometric evaluation of publications listed in the Web of Science extended scientific citation index. There was substantial growth in coastal flooding research output, with increasing publications, a higher collaboration index, and more references during the 1995–2016 period. The USA has taken a dominant position in coastal flooding research, with the US Geological Survey leading the publications ranking. Research collaborations at institutional scales have become more important than those at global scales. International collaborative publications consistently drew more citations than those from a single country. Furthermore, coastal flooding research included combinations of multi-disciplinary categories, including ‘Geology' and ‘Environmental Sciences Ecology'. The most important coastal flooding research sites were wetlands and estuaries. While numerical modeling and 3 S(Remote sensing, RS; Geography information systems, GIS; Global positioning systems, GPS) technology were the most commonly used methods for studying coastal flooding, Lidar gained in popularity. The vulnerability and adaptation of coastal environments, their resilience after flooding, and ecosystem services function showed increases in interest.     

风和径流量对长江口缺氧影响的数值模拟

受自然和人类活动的影响，海洋缺氧现象日益严重，威胁着海洋生态环境，海洋缺氧问题已经引起了人们的广泛关注。本文应用区域海洋模式并耦合生态模式，对东海的生态系统进行了数值模拟和分析研究。与观测数据比较显示，该模型能较好地模拟长江口外生态变量的分布趋势。另外本文通过设置不同敏感性实验，探讨风和径流量对长江口底层缺氧现象的影响，结果分析表明，风和径流量对长江口外缺氧区的形成有显著的影响。径流量变化虽然对长江口外缺氧区的季节变化影响并不显著，但是对缺氧区域面积却存在显著的影响。径流量增加，水体层化增强，表层叶绿素浓度增加，最终导致缺氧区域范围扩展；径流量减小，水体层化减弱，表层叶绿素浓度减小，缺氧区域范围缩小。风向和风速的改变不仅影响长江口外缺氧区的季节变化，还影响缺氧区域面积。     

Implementation of a coupled plastic damage distinct lattice spring model for dynamic crack propagation in geomaterials

This paper studies dynamic crack propagation by employing the distinct lattice spring model (DLSM) and 3‐dimensional (3D) printing technique. A damage‐plasticity model was developed and implemented in a 2D DLSM. Applicability of the damage‐plasticity DLSM was verified against analytical elastic solutions and experimental results for crack propagation. As a physical analogy, dynamic fracturing tests were conducted on 3D printed specimens using the split Hopkinson pressure bar. The dynamic stress intensity factors were recorded, and crack paths were captured by a high‐speed camera. A parametric study was conducted to find the influences of the parameters on cracking behaviors, including initial and peak fracture toughness, crack speed, and crack patterns. Finally, selection of parameters for the damage‐plasticity model was determined through the comparison of numerical predictions and the experimentally observed cracking features.     

Regional Characteristics and Causes of Haze Events in Northeast China

Northeast China experiences severe atmospheric pollution, with an increasing occurrence of heavy haze episodes. However, the underlying forces driving haze formation during different seasons are poorly understood. In this study, we explored the spatio-temporal characteristics and causes of haze events in Northeast China by combining a range of data sources(i.e., ground monitoring, satellite-based products, and meteorological products). It was found that the ‘Shenyang-Changchun-Harbin(SCH)'city belt was the most polluted area in the region on an annual scale. The spatial distribution of air quality index(AQI) values had a clear seasonality, with the worst pollution occurring in winter, an approximately oval-shaped polluted area around western Jilin Province in spring, and the best air quality occurring in summer and most of the autumn. The three periods that typically experienced intense haze events were Period I from mid-October to mid-November(i.e., late autumn and early winter), Period II from late-December to February(i.e., the coldest time in winter), and Period III from April to mid-May(i.e., spring). During Period I, strong PM_(2.5) emissions from seasonal crop residue burning and coal burning for winter heating were the dominant reasons for the occurrence of extreme haze events(AQI 300). Period II had frequent heavy haze events(200 AQI 300) in the coldest months of January and February, which were due to high PM_(2.5) emissions from coal burning and vehicle fuel consumption, a lower atmospheric boundary layer, and stagnant atmospheric conditions. Haze events in Period III, with high PM_(10) concentrations, were primarily caused by the regional transportation of windblown dust from degraded grassland in central Inner Mongolia and bare soil in western Jilin Province. Local agricultural tilling could also release PM_(10) and enhance the levels of windblown dust from tilled soil. Better control of coal burning, fuel consumption, and crop residue burning in winter and autumn is urgently needed to address the haze problem in Northeast China.     

Responses of soil moisture to vegetation restoration type and slope length on the loess hillslope

Soil moisture, a critical variable in the hydrologic cycle, is highly influenced by vegetation restoration type. However, the relationship between spatial variation of soil moisture, vegetation restoration type and slope length is controversial. Therefore, soil moisture across soil layers (0-400 cm depth) was measured before and after the rainy season in severe drought (2015) and normal hydrological year (2016) in three vegetation restoration areas (artificial forestland, natural forestland and grassland), on the hillslopes of the Caijiachuan Catchment in the Loess area, China. The results showed that artificial forestland had the lowest soil moisture and most severe water deficit in 100-200 cm soil layers. Water depletion was higher in artificial and natural forestlands than in natural grassland. Moreover, soil moisture in the shallow soil layers (0-100 cm) under the three vegetation restoration types did not significantly vary with slope length, but a significant increase with slope length was observed in deep soil layers (below 100 cm). In 2015, a severe drought hydrological year, higher water depletion was observed at lower slope positions under three vegetation restoration types due to higher transpiration and evapotranspiration and unlikely recharge from upslope runoff. However, in 2016, a normal hydrological year, there was lower water depletion, even infiltration recharge at lower slope positions, indicating receiving a large amount of water from upslope. Vegetation restoration type, precipitation, slope length and soil depth during a rainy season, in descending order of influence, had significant effects on soil moisture. Generally, natural grassland is more beneficial for vegetation restoration than natural and artificial forestlands, and the results can provide useful information for understanding hydrological processes and improving vegetation restoration practices on the Loess Plateau     

Effects of stocking density on the growth and immunity of Atlantic salmon salmo salar reared in recirculating aquaculture system (RAS)

Atlantic salmon reared in recirculating aquaculture system (RAS) may lead to inappropriately high stocking density, because fish live in a limited space. Finding the suitable stocking density of Atlantic salmon reared in RAS is very important for RAS industry. In this paper, the influence of stocking density on growth and some stress related physiological factors were investigated to evaluate the effects of stocking density. The fish were reared for 220 days at five densities (A: 24 kg/m³; B: 21 kg/m³; C: 15 kg/m³; D: 9 kg/ m³ and E: 6 kg/m³ ). The results show that 30 kg/m³ might be the maximum density which RAS can afford in China. The stocking densities under 30 kg/m³ have no effect on mortality of Atlantic salmon reared in RAS. However, the specific growth rate (SGR), final weight and weight gain in the high density group were significantly lower than the lower density groups and middle density groups. Moreover, feed conversion rate (FCR) had a negative correlation with density. Plasma hormone T₃ and GH showed significant decrease with the increase of the stocking density of the experiment. Furthermore, thyroid hormone (T₃), GH (growth hormone) activities were decreased with stocking density increase. However, plasma cortisol, GOT (glutamic oxalacetic transaminase) and GPT (glutamic pyruvic transaminase) activities were increase with stocking density increase. And the stocking density has no effects on plasma lysozyme of Atlantic salmon reared in RAS. These investigations would also help devise efficient ways to rear adult Atlantic salmon in China and may, in a way, help spread salmon mariculture in China.

     

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.