A biodiversity evaluation framework for restoration of aquatic macrophyte communities in shallow lakes driven by hydrological process management

Macrophyte community diversity and composition respond to ecosystem conservation and local environmental factors. In this study, we developed a multidimensional diversity framework for macrophyte communities, including the taxonomic and functional alpha and beta diversity. We used the framework to explore the relationships among water level regimes and these diversity parameters in a case study of China's Baiyangdian Lake. Analysis of indicators of hydrologic alteration divided the water level from 1959 to 2019 into four regimes (dry, <6.42 m; low, 6.42–7.23 m; medium, 7.23–8.19 m; high, >8.19 m). Alpha and beta diversity were significantly higher in the medium regime than in the low and high regimes. Redundancy analysis indicated that the maximum water depth significantly affected taxonomic alpha diversity, and total nitrogen (TN) and chemical oxygen demand (COD) concentration significantly affected functional alpha diversity, respectively. Mantel tests showed that TN, Secchi depth (SD), and water depth in the high water level regime significantly increased the total beta diversity and turnover components. TN was the main factor that increased total taxonomic beta diversity. Water level regime mainly influenced interspecific relationships by changing the TN and COD concentration. The water level should be maintained between the medium and high water level regimes to promote restoration of the macrophyte community and improve ecosystem stability. The biodiversity evaluation framework would provide a deeper insight into the hydrological process management for restoration of aquatic macrophyte communities in shallow lakes.     

Developing constitutive models from EPR‐based self‐learning finite element analysis

A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted.     

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.     

金州地区震旦系基本层序与沉积环境

金州地区广泛发育震旦纪地层,具分布连续、出露完整、沉积相标志明显的特点。本文运用现代沉积学和地层学理论,对区内震旦系岩石地层进行了基本层序研究与沉积环境分析。     

Prediction of Stellar Atmospheric Parameters using Instance-Based Machine Learning and Genetic Algorithms

In this article we present a method for the automated prediction of stellar atmospheric parameters from spectral indices. This method uses a genetic algorithm (GA) for the selection of relevant spectral indices and prototypical stars and predicts their properties, using the k-nearest neighbors method (KNN). We have applied the method to predict the effective temperature, surface gravity, metallicity, luminosity class and spectral class of stars from spectral indices. Our experimental results show that the feature selection performed by the genetic algorithm reduces the running time of KNN up to 92%, and the predictive accuracy error up to 35%. This revised version was published online in July 2006 with corrections to the Cover Date.     

试论城市地貌环境与城市道路系统的关系*——以四川省几个城市为例

本文分析了城市地貌环境对城市道路系统的影响,认为地貌过程不仅影响了城市道路的建设和管理,而且是城市道路系统布局和发展的重要控制因素。以四川省几个城市为例,分析了山地城市、丘陵城市和平原城市道路系统的不同特点。     

Giant submarine landslide grooves in the Neoproterozoic/Lower Cambrian Phe Formation, northwest Himalaya: Mechanisms of formation and palaeogeographic implications

Giant groove casts have been found in the upper Proterozoic to Lower Cambrian Phe Formation (Haimanta Group), a siliciclastic sandstone/shale succession in the Tethyan Zone of the Higher Himalaya tectonic unit. The grooves are among the largest linear erosion structures related to submarine mass-movements observed in the geologic record. They are up to 4 m wide, about 0.2 m deep and can be traced for more than 35 m without changing their character. The grooves are straight, subparallel to cross-cutting striations with shallow semi-circular cross-sections and well-defined superimposed minor ridges and grooves. Groove casts exist on the soles of several sandstone beds within a 73 m thick logged section, commonly associated with flute casts. Their characteristics were compared with several other types of ancient and modern submarine linear erosion structures. A sand-rich, non-channelized basin floor depositional environment is inferred from the lithofacies, the combination of sedimentary structures, the lack of coarse-grained pebbly facies, the lateral continuity of beds, and the lack of channel structures. The grooves probably formed by laminar debris flows/concentrated density flows dragging blocks of already lithified sediment across the basin floor. When the bedding is structurally rotated back to horizontal, the groove casts show consistent North–South oriented palaeocurrent trends, with South-directed palaeocurrent directions indicated by flute casts. These palaeocurrent orientations contrast with previous palaeogeographic reconstructions of this area, which propose sediment delivery from the South. We therefore suggest a new “double provenance” model for the spatial relationship of late Proterozoic to Early Cambrian strata of the Himalaya, in which Lesser and Tethyan Himalayan age-equivalent sediment was deposited in a connected basin, where the former received detritus from the South, and the latter from a hitherto unknown source in the North. One possible candidate for this northern source is the South China Block and an associated Neoproterozoic volcanic arc.     

柴达木盆地北缘侏罗系不同沉积环境烃源岩生物标志物特征及其应用

柴达木盆地北缘地区侏罗系(中下侏罗统)的优质烃源岩主要发育于湖相与三角洲相环境。研究了不同时代、不同沉积环境中烃源岩的生物标志物组成特征,结果发现,有3类化合物的分布与组成差异显著,包括三环萜烷(C19、C20、C21)的分布型式、重排藿烷的丰度以及规则甾烷的相对组成。其中,不同时代的差异主要体现在规则甾烷组成上;而不同沉积环境的差异主要体现为三环萜烷(C19、C20、C21)的分布型式及重排藿烷的丰度。分析认为,这些差异与烃源岩的沉积环境及其生源组成有密切关系。据此,初步将这些参数应用于两方面研究,一是为划分地层沉积环境提供“生物标志物相标志”,二是研究油源对比,取得良好效果。因此,本文研究结果具有重要实用价值与参考意义。     

青岛地区的环境水文地质问题及其防治对策

随着工农业的发展,围绕水资源的开发利用而产生的环境水文地质问题,日益向广度和深度发展。青岛地区近年来产生的环境水文地质问题主要有:地下水超采造成的区域性水位下降、海水入侵及地下水污染。另外,部分地区存在原生环境水文地质问题之一——地方性氟病。本文试就这些问题出现的原因、发展程度、防治措施及进一步的研究方向,作了一些有益的探讨。     

海沧邻近海域表层沉积物中重矿物研究

对厦门海沧邻近海域表层沉积物中的重矿物(0．063～0．125mm粒级)进行了分析和研究，结果表明该粒级的重矿物共计49种，其优势和特征矿物为磁铁矿、赤铁矿、褐铁矿、绿帘石、铁铁矿、角闪石、锆石、红柱石等；重矿物的平均质量分数为9．56％；根据重矿物的分布和含量，并结合主要标志矿物的颗粒百分含量和分布特征，将该海域划分为5个矿物组合区，根据各区的矿物组合类型分析了该海域的物质来源及影响重矿物分布和组合的因素，进而探讨了九龙江河口湾和厦门西港海湾的沉积环境。