Effect of mixture sowing on biomass allocation in the artificially-planted pastures,Southeastern Tibetan

Artificial planting is an important measure to promote the restoration of degraded grassland and protect the ecological environment. The objectives of the current study were to investigate the allocation pattern between aboveground biomass(AGB) and belowground biomass(BGB) in different seeding types of artificially-planted pastures. We explored the variation in biomass and the relationship between above-and belowground biomass in four artificiallyplanted pastures with one species from Elymus nutans Griseb(EN, perennial), Elymus sibiricus Linn(ES, perennial), Medicago sativa Linn(MS, perennial), and Avena sativa Linn(AS, annual) and in six artificially-planted communities with mixtures of two species by seeding ratio 1:1 from the abovementioned grasses(EN + AS, MS + AS, EN + ES, MS + EN, MS + ES, AS + ES) in 2015 and 2016. The results showed that E. nutans is the most productive species with the highest biomass production among the single crops. MS + ES was the most productive group in 2015, while the group with the highest biomass production changed to AS + ES in 2016. AGB was positively correlated to BGB in the surface soil layer in the first year, but positively related to BGB in the subsoil layer in the second year. In the early stageof artificial grassland succession, plants allocated more biomass to aboveground parts, with a root to shoot(R/S) ratio of 1.98. The slope of the log-log relationship between AGB and BGB was 1.07 in 2016, which is consistent with the isometric theory. Different sowing patterns strongly affected the accumulation and allocation of biomass in artificiallyplanted grassland, E. sibiricus was the suitable plant in the alpine regions, which will be conducive to understanding vegetation restoration and plant interactions in the future.     

聊天机器人技术浅析

对话系统已经成为人工智能时代重要的人机交互接口.聊天机器人技术在对话系统技术发展中扮演着至关重要的角色,代表了对话系统技术的前沿发展.从聊天机器人技术的兴起入手,深入分析聊天机器人技术的发展趋势,介绍国内外学术界和产业界的聊天机器人技术研究进展,重点介绍聊天机器人的3项关键技术,即基于检索的多轮对话技术、基于生成的对话...     

基于案例推理的东海区鲐鱼中心渔场预报

根据1998～2005年的东海区上海、宁波、江苏和舟山四大渔业公司的鲐鱼(Pneumatophorus japonicus)生产统计数据以及同期的卫星资料反演的海表温度、叶绿素a浓度数据,利用案例推理方法,设定了上级结果的相似距对下级检索的影响权重因子,进一步研究渔场渔情的分析预报.通过试验性预报实例的预报结果与实际情况比较表明,预测精度达到75%,可以较好地反映出渔场的分布,为渔业资源的开发利用服务.     

Classifiers vs. input variables—The drivers in image classification for land cover mapping

The study investigates the performance of image classifiers for landscape-scale land cover mapping and the relevance of ancillary data for the classification success in order to assess and to quantify the importance of these components in image classification. Specifically tested are the performance of maximum likelihood classification (MLC), artificial neural networks (ANN) and discriminant analysis (DA) based on Landsat7 ETM+ spectral data in combination with topographic measures and NDVI. ANN produced high accuracies of more than 75% also with limited input information, while MLC and DA produced comparable results only by incorporating ancillary data into the classification process. The superiority of ANN classification was less pronounced on the level of the single land cover classes. The use of ancillary data generally increased classification accuracy and showed a similar potential for increasing classification accuracy than the selection of the classifier. Therefore, a stronger focus on the development of appropriate and optimised sets of input variables is suggested. Also the definition and selection of land cover classes has shown to be crucial and not to be simply adaptable from existing land cover class schemes. A stronger research focus towards discriminating land cover classes by their typical spectral, topographic or seasonal properties is therefore suggested to advance image classification.     

尖紫蛤的人工育苗

1997年 7月开始进行尖紫蛤人工育苗的研究 ,1999年 11月培育出壳长 4 mm左右的幼苗近30万只 ,首次获得尖紫蛤人工育苗的成功     

开展竞争情报研究工作的思考

文章介绍了竞争情报的一些基本概念,对在地勘单位中开展竞争情报工作的必要性和可行性进行论述,提出一些建议及地勘单位的情报工作在转轨过程中如何发展的思路     

Sequential Gaussian simulation for geosystems modeling: A machine learning approach

Sequential Gaussian Simulation(SGSIM)as a stochastic method has been developed to avoid the smoothing effect produced in deterministic methods by generating various stochastic realizations.One of the main issues of this technique is,however,an intensive computation related to the inverse operation in solving the Kriging system,which significantly limits its application when several realizations need to be produced for uncertainty quantification.In this paper,a physics-informed machine learning(PIML)model is proposed to improve the computational efficiency of the SGSIM.To this end,only a small amount of data produced by SGSIM are used as the training dataset based on which the model can discover the spatial correlations between available data and unsampled points.To achieve this,the governing equations of the SGSIM algorithm are incorporated into our proposed network.The quality of realizations produced by the PIML model is compared for both 2D and 3D cases,visually and quantitatively.Furthermore,computational performance is evaluated on different grid sizes.Our results demonstrate that the proposed PIML model can reduce the computational time of SGSIM by several orders of magnitude while similar results can be produced in a matter of seconds.     

Hybrid meta-heuristic machine learning methods applied to landslide susceptibility mapping in the Sahel-Algiers

Landslides are considered as one among many phenomena jeopardizing human beings as well as their constructions. To prevent this disastrous problem, researchers have used several approaches for landslide susceptibility modeling, for the purpose of preparing accurate maps marking landslide prone areas. Among the most frequently used approaches for landslide susceptibility mapping is the Artificial Neural Network (ANN) method. However, the effectiveness of ANN methods could be enhanced by using hybrid metaheuristic algorithms, which are scarcely applied in landslide mapping. In the current study, nine hybrid metaheuristic algorithms, genetic algorithm (GA)-ANN, evolutionary strategy (ES)-ANN, ant colony optimization (ACO)-ANN, particle swarm optimization (PSO)-ANN, biogeography based optimization (BBO)-ANN, gravitational search algorithm (GHA)-ANN, particle swarm optimization and gravitational search algorithm (PSOGSA)-ANN, grey wolves optimization (GWO)-ANN, and probability based incremental learning (PBIL)-ANN have been used to spatially predict landslide susceptibility in Algiers’ Sahel, Algeria. The modeling phase was done using a database of 78 landslides collected utilizing Google Earth images, field surveys, and six conditioning factors (lithology, elevation, slope, land cover, distance to stream, and distance to road). Initially, a gamma test was used to decrease the input variable numbers. Furthermore, the optimal inputs have been modeled by the mean of hybrid metaheuristic ANN techniques and their performance was assessed through seven statistical indicators. The comparative study proves the effectiveness of the co-evolutionary PSOGSA-ANN model, which yielded higher performance in predicting landslide susceptibility compared to the other models. Sensitivity analysis using the step-by-step technique was done afterward, which revealed that the distance to the stream is the most influential factor on landslide susceptibility, followed by the slope factor which ranked second. Lithology and the distance to road have demonstrated a moderate effect on landslide susceptibility. Based on these findings, an accurate map has been designed to help land-use managers and decision-makers to mitigate landslide hazards.     

A comparative analysis of seismic response of shallow buried underground structure under incident P,SV and Rayleigh waves

In this study, A time-domain seismic response analysis method and a calculation model of the underground structure that can realize the input of seismic P, SV and Rayleigh waves are established, based on the viscoelastic artificial boundary elements and the boundary substructure method for seismic wave input. After verifying the calculation accuracy, a comparative study on seismic response of a shallow-buried, double-deck, double-span subway station structure under incident P, SV and Rayleigh waves is conducted. The research results show that there are certain differences in the cross-sectional internal force distribution characteristics of underground structures under different types of seismic waves. The research results show that there are certain differences in the internal force distribution characteristics of underground structures under different types of seismic waves. At the bottom of the side wall, the top and bottom of the center pillar of the underground structure, the section bending moments of the underground structure under the incidences of SV wave and Rayleigh wave are relatively close, and are significantly larger than the calculation result under the incidence of P wave. At the center of the side wall and the top floor of the structure, the peak value of the cross-sectional internal force under the incident Rayleigh wave is larger than the calculation result under SV wave. In addition, the floor of the underground structure under Rayleigh waves vibrates in both the horizontal and vertical directions, and the magnification effect in the vertical direction is more significant. Considering that the current seismic research of underground structures mainly considers the effect of body waves such as the shear waves, sufficient attention should be paid to the incidence of Rayleigh waves in the future seismic design of shallow underground structures.     

多形态崩塌智能监测系统在北京突发地质灾害监测预警工程中的应用

采用基于微纳技术(MEMS技术)的高性能三维运动姿态测量系统,主要包括三轴陀螺仪.三轴加速度计,三轴电子罗盘等运动传感器,实现多形态崩塌智能监测。