Effect of Magnetotactic bacteria on the susceptibility in Chinese loess–paleosol sequences

A simulated experiment of Magnetotactic bacteria (MB) effect on susceptibility in the samples from the 5th to 6th loess layers (L₅–S₅–L₆) in Xifeng and Duangjiapo sections was performed. It is demonstrated that MB can increase the susceptibility of the samples and promote newly produced magnetic minerals from paleosol layers under the suitable condition. The tiny magnetic particles and arranged as a chain in the minerals less than 30 μm were found in the paleosol sample of Duangjiapo section.     

Geotechnical properties of the soft soil in Guangzhou College City

The paper describes some geotechnical properties of the soft soils in Guangzhou College City, China, which are difficult to both sample and test. Laboratory and in situ tests were carried out to assess the physical and mechanical indices of the soils. The data were statistically analyzed and linear regression undertaken such that equations could be developed by which the geotechnical properties of the soft soils can be predicted. The statistical validity of the degree of correlation confirmed that using these equations, the mechanical indices can be estimated from physical indices determined by routine testing.      

Chloride ingress data from field and laboratory exposure – Influence of salinity and temperature

Marine exposure conditions provide an aggressive environment for reinforced concrete structures, mainly due to the occurrence of chloride-induced reinforcement corrosion. There are several influencing factors, but despite a lot of research little is known about the influence from variations in the exposure conditions. Therefore an exposure programme has been made, where concrete specimens, made from one single concrete composition, have been exposed at 12 marine locations around the world, along with a parallel study under laboratory conditions. The specimens have been constantly submerged during the exposure to get well-defined exposure conditions. The purpose was to get a quantitative measure of how different exposure conditions influence the durability of reinforced concrete, measured in terms of chloride ingress into the concrete. The results indicate that the exposure conditions (salinity and temperature of seawater), as expected, influenced the chloride ingress. This is further analysed and discussed in the paper.     

Machine-learning paradigms for selecting ecologically significant input variables

Harmful algal blooms, which are considered a serious environmental problem nowadays, occur in coastal waters in many parts of the world. They cause acute ecological damage and ensuing economic losses, due to fish kills and shellfish poisoning as well as public health threats posed by toxic blooms. Recently, data-driven models including machine-learning (ML) techniques have been employed to mimic dynamics of algal blooms. One of the most important steps in the application of a ML technique is the selection of significant model input variables. In the present paper, we use two extensively used ML techniques, artificial neural networks (ANN) and genetic programming (GP) for selecting the significant input variables. The efficacy of these techniques is first demonstrated on a test problem with known dependence and then they are applied to a real-world case study of water quality data from Tolo Harbour, Hong Kong. These ML techniques overcome some of the limitations of the currently used techniques for input variable selection, a review of which is also presented. The interpretation of the weights of the trained ANN and the GP evolved equations demonstrate their ability to identify the ecologically significant variables precisely. The significant variables suggested by the ML techniques also indicate chlorophyll-a (Chl-a) itself to be the most significant input in predicting the algal blooms, suggesting an auto-regressive nature or persistence in the algal bloom dynamics, which may be related to the long flushing time in the semi-enclosed coastal waters. The study also confirms the previous understanding that the algal blooms in coastal waters of Hong Kong often occur with a life cycle of the order of 1–2 weeks.     

固体超强酸S_2O_8~(2-)/SnO_2-SiO_2催化合成环己酮1,2-丙二醇缩酮

采用沉淀-浸渍法制备了固体超强酸S2O28-/SnO2-SiO2,以它为催化剂催化环己酮和1,2-丙二醇合成了环己酮1,2-丙二醇缩酮;考察了带水剂种类及用量、酮醇摩尔比、催化剂用量、反应时间对产品收率的影响,并用正交实验对反应条件进行了优化。实验结果表明,适宜的反应条件为:n(环己酮)∶n(1,2-丙二醇)=1∶1.6、催化剂用量为反应物料总质量的2.0%、带水剂环己烷用量5.0mL、反应时间50min。在此条件下,环己酮1,2-丙二醇缩酮收率达到91.47%;催化剂的稳定性良好,在重复使用5次后环己酮1,2-丙二醇缩酮收率为82.20%,活性下降的主要原因为催化剂表面积碳和吸附了有机物;经傅里叶变换红外光谱和气相色谱质谱分析表明,产物为环己酮1,2-丙二醇缩酮,纯度为100%。     

A science and technology with rich vitality — desalination

Congratulations toDesalination for its prosperity and for the contributions it has made to benefit mankind. Review the past and look forward to the future for the development of desalination technologies in China.     

套管防磨保护措施

在深井,特别在高温高压深探井中,套管的磨损是一个比较突出的问题,且保护套管和保护钻杆往往是矛盾的。崖城4井采取控制平台漂移、钻直表层井段、采用合适的钻具来控制井斜和减小井斜变化率、选用合适的钻杆等作业保护措施非常成功地达到了既保护了套管,钻杆也没有出现过度磨损的目的。介绍了该井凭借有关资料制定的相应保护措施,通过与其他井的情况对比,分析总结了该井成功经验,可为以后钻探同类型的井提供一些启示。     

职业院校信息化建设的思考

针对职业院校信息化建设的目标、误区、内容和共性问题提出了自己思考与观点。     

A graph-based semi-supervised approach to classification learning in digital geographies

As the distinction between online and physical spaces rapidly degrades, social media have now become an integral component of how many people's everyday experiences are mediated. As such, increasing interest has emerged in exploring how the content shared through those online platforms comes to contribute to the collaborative creation of places in physical space at the urban scale. Exploring digital geographies of social media data using methods such as qualitative coding (i.e., content labelling) is a flexible but complex task, commonly limited to small samples due to its impracticality over large datasets. In this paper, we propose a new tool for studies in digital geographies, bridging qualitative and quantitative approaches, able to learn a set of arbitrary labels (qualitative codes) on a small, manually-created sample and apply the same labels on a larger set. We introduce a semi-supervised, deep neural network approach to classify geo-located social media posts based on their textual and image content, as well as geographical and temporal aspects. Our innovative approach is rooted in our understanding of social media posts as augmentations of the time-space configurations that places are, and it comprises a stacked multi-modal autoencoder neural network to create joint representations of text and images, and a spatio-temporal graph convolution neural network for semi-supervised classification. The results presented in this paper show that our approach performs the classification of social media content with higher accuracy than traditional machine learning models as well as two state-of-art deep learning frameworks.