The magnetic properties of sediment and sedimentary environment in the Huanghe Delta area after the last glacial maximum

On the basis of measuring the magnetic parameters of sediment in Core YDZ1, combined with a grain size analysis and Carbon-14 dating, the magnetic properties of sediment and sedimentary environment in the Huanghe（Yellow River） Delta area after the last glacial maximum have been studied. The results show that the ferrimagnetic minerals of a pseudo single domain and multi domain particles dominate the magnetic properties of sediment in Core YDZ1. The imperfect anti ferrimagnetic minerals have more contribution on sediment in a depth of 24.0–22.1 m, and more stable-single domain and pseudo single domain particles exist. The susceptibility of anhysteretic remanent magnetization and the ratio of the susceptibility of anhysteretic remanent magnetization to saturation isothermal remanent magnetization show a decrease trend below depth of 24 m, a marked increase trend in a depth of 24.0–13.5 m, and a rapid decrease at depth of 13.5 m, then a fluctuation trend upward. The above two magnetic parameters and the ratio of the susceptibility of anhysteretic remanent magnetization to the mass susceptibility can be regarded as the proxy indicators for the content of clay（〈4 μm）and the fine-grained size（〈32 μm）. The sedimentary environment after the last glacial maximum in the Huanghe Delta area has experienced the fluvial facies, the tidal flat facies, the neritic facies, the pro delta facies, the delta front facies and the floodplain facies. Thickness of the Holocene transgression layer is 10.5 m and the depth of substrate is about 24 m according to the YDZ1 core. The sedimentary dynamic has a variation trend with strongweak-strong, which has been proved by the Flemming triangular schema.

Assessment of Water Provenances in Koxkar Glacier Region at the South Slope of Mt. Tianshan,China

Various water samples were collected for electrical conductivity (EC) and δ^18O analysis,and the proportion and contribution of atmospheric precipitation,glacier ice and shallow groundwater to discharge in the Koxkar glacier basin at the south slope of the Tianshan Mountains were studied.The results show that glacial ice-water recharge was dominant,accounting for 72.11% of the annual runoff.It also had a significant positive correlation with temperature during the warm season (from May to September).However,glacier ice ablation replenishment still existed when the temperature in the cold season was below the critical temperature of 0 ℃.This could be that the heat generated by the friction between the ice body and the ice bed during the subglacial ice sliding process led ice to melt,what's more,the stored water in the geometric passages inside and below the glacier could slowly release.Groundwater recharge accounted for 16.38% of the total runoff.The supplement was small and its variation range was relatively small in the cold season.But in the warm season,the amount of groundwater recharge increased and changed drastically.It might be that the seasonal frozen soil in the basin was widely developed and was affected by temperature changes.Atmospheric precipitation replenishment only accounted for 11.51%.The daily precipitation recharge river water had a significant response to regional precipitation,but there was hysteresis in time,and there was still precipitation recharge runoff even in the absence of precipitation.

An investigation of the visual features of urban street vitality using a convolutional neural network

As a well-known urban landscape concept to describe urban space quality,urban street vitality is a subjective human perception of the urban environment but difficult to evaluate directly from the physical space.The study utilized a modern machine learning computer vision algorithm in the urban build environment to simulate the process,which starts with the visual perception of the urban street landscape and ends with the human reaction to street vitality.By analyzing the optimized trained model,we tried to identify urban street vitality’s visual features and evaluate their importance.A region around the Mochou Lake in Nanjing,China,was set as our study area.Seven investigators surveyed the area,recorded their evaluation score on each site’s vitality level with a corresponding picture taken on site.A total of 370 pictures and recorded score pairs from 231 valid survey sites were used to train a convolutional neural network.After optimization,a deep neural network model with 43 layers,including 11 convolutional ones,was created.Heat maps were then used to identify the features which lead to high vitality score outputs.The spatial distributions of different types of feature entities were also analyzed to help identify the spatial effects.The study found that visual features,including human,construction site,shop front,and roadside/walking pavement,are vital ones that correspond to the vitality of the urban street.The consistency of these critical features with traditional urban vitality features indicates the model had learned useful knowledge from the training process.Applying the trained model in urban planning practices can help to improve the city environment for better attraction of residents’activities and communications.