北京城区河流沉水植物分布特征及环境因子关系研究

通过北京城区11条河流30个点位沉水植物及水环境因子调查,对沉水物物种分布、生态位特征及与水环境因子相互关系进行了研究,结果表明:北京城区河流均有沉水植物分布,春季优势种为菹草、穗花狐尾藻和篦齿眼子菜,夏季为金鱼藻、狐尾藻、篦齿眼子菜、黑藻、苦草及菹草.水深为影响城区河流沉水植物种类数量的重要因子,菹草为春季沉水植物管理关键物种.研究城区河流沉水植物分布特征及与水环境因子的关系,对北京市河流水生态环境的管理和持续改善具有重要的参考和指导意义.     

白浮泉形成及断流原因分析

本研究在分析已有资料的基础上,通过资料分析和水化学测试,查明白浮泉主要接受东北部兴寿地区同一含水层顺层流动补给和北部十三陵地区地表水沿大宫门古河道补给,在化庄地区水流受相对隔水层阻挡,沿山前基岩破碎带排泄形成的.随着区域大气降水的减少和十三陵水库采取的防渗措施,以及地下水开采量增加,白浮泉接受的补给量大幅减少,导致其断流.本研究通过资料搜集、水化学分析等手段查明了白浮泉形成和断流原因,为白浮泉复涌提供了基础资料支撑.     

济南市1972─2016年降水特性

运用线性滑动平均、Mann-Kendall非参数趋势检验、Sen′s坡度、小波分析及多元线性回归方法,选取济南市24个降水站点45年(1972—2016年)逐日降水资料,分析了济南市在年和季节尺度上的降水特征以及降水量的决定性影响因素。结果表明:年降水量以8.23mm/(10a)的速率增加,且呈现出丰枯交替的变化周期,最大主周期为10a;降水量主要集中在夏季,占全年降水量的64.5%,冬季降水占比最小,占全年降水量的3.4%,春、夏和冬季降水具有不显著增加趋势,秋季的降水量具有不显著下降趋势;降水量的年际、春夏季变化主要受降水强度变化的影响;秋冬季变化则主要受降水频率变化的影响。     

Modeling resonator frequency fluctuations induced by adsorbing and desorbing surface molecules

Resonator frequency fluctuations due to adsorption and desorption of molecules on plate electrodes are studied using the principle of mass-loading effects of adsorbed molecules. The study is based on a 525 MHz, AT-cut quartz resonator enclosed in a small crystal holder. Equations relating the surface adsorption rates of the crystal holder to pressure were derived and found to be quadratic polynomial functions of the adsorption rates. Calculations based on these equations show that a contaminant gas with a higher desorption energy creates larger changes in pressure when the temperature is varied. The function describing the frequency fluctuations due to any one contaminant site is a continuous-time Markov chain. Kolmogorov equations and an autocorrelation function for the Markov chain are derived. The autocorrelation and spectral density function of resonator frequency fluctuations are derived. The spectral density of frequency fluctuations at 1 Hz is studied as a function of pressure, temperature, and desorption energy of molecules. The noise levels for a contaminant gas with one type of molecules are found to be lower for lower desorption energies, and higher at lower pressures.     

Simple Question Answering over Knowledge Graph Enhanced by Question Pattern Classification

Knowledge and Information Systems - Question answering over knowledge graph (KGQA), which automatically answers natural language questions by querying the facts in knowledge graph (KG), has drawn...     

A two-stage adaptive multi-fidelity surrogate model-assisted multi-objective genetic algorithm for computationally expensive problems

Surrogate model-assisted multi-objective genetic algorithms (MOGA) show great potential in solving engineering design problems since they can save computational cost by reducing the calls of expensive simulations. In this paper, a two-stage adaptive multi-fidelity surrogate (MFS) model-assisted MOGA (AMFS-MOGA) is developed to further relieve their computational burden. In the warm-up stage, a preliminary Pareto frontier is obtained relying only on the data from the low-fidelity (LF) model. In the second stage, an initial MFS model is constructed based on the data from both LF and high-fidelity (HF) models at the samples, which are selected from the preliminary Pareto set according to the crowding distance in the objective space. Then the fitness values of individuals are evaluated using the MFS model, which is adaptively updated according to two developed strategies, an individual-based updating strategy and a generation-based updating strategy. The former considers the prediction uncertainty from the MFS model, while the latter takes the discrete degree of the population into consideration. The effectiveness and merits of the proposed AMFS-MOGA approach are illustrated using three benchmark tests and the design optimization of a stiffened cylindrical shell. The comparisons between the proposed AMFS-MOGA approach and some existing approaches considering the quality of the obtained Pareto frontiers and computational efficiency are made. The results show that the proposed AMFS-MOGA method can obtain Pareto frontiers comparable to that obtained by the MOGA with HF model, while significantly reducing the number of evaluations of the expensive HF model.

     

Stock Price Forecasting: An Echo State Network Approach

Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage of capturing nonlinear data. Mean absolute error (MAE) was used to present the accuracy results. The MAEs of the data forecast by ESN were 0.024, 0.024, and 0.025, which were, respectively, 0.065, 0.007, and 0.009 less than those of LSTM. In terms of convergence, ESN has a reservoir state-space structure, which makes it perform faster than other models. Root-mean-square error (RMSE) was used to present the convergence time. In our experiment, the RMSEs of ESN were 0.22, 0.27, and 0.26, which were, respectively, 0.08, 0.01, and 0.12 less than those of LSTM. In terms of network structure, ESN consists only of input, reservoir, and output spaces, making it a much simpler model than the others. The proposed ESN was found to be an effective model that, compared to others, converges faster, forecasts more accurately, and builds time-series analyses more easily.     

Land‐use dynamic discovery based on heterogeneous mobility sources

Nowadays, cities are the most relevant type of human settlement and their population has been endlessly growing for decades. At the same time, we are witnessing an explosion of digital data that capture many different aspects and details of city life. This allows detecting human mobility patterns in urban areas with more detail than ever before. In this context, based on the fusion of mobility data from different and heterogeneous sources, such as public transport, transport‐network connectivity and Online Social Networks, this study puts forward a novel approach to uncover the actual land use of a city. Unlike previous solutions, our work avoids a time‐invariant approach and it considers the temporal factor based on the assumption that urban areas are not used by citizens all the time in the same manner. We have tested our solution in two different cities showing high accuracy rates.     

Dual local learning regularized nonnegative matrix factorization and its semi-supervised extension for clustering

Neural Computing and Applications - Nonnegative matrix factorization (NMF) has received considerable attention in data representation due to its strong interpretability. However, traditional NMF...     

Effects of assembly errors on axial positioning accuracy for rotating machinery with magnetoresistance-based magnetic encoders

Microsystem Technologies - In positioning systems, magnetic scales consisting of magnetoresistive sensors are widely used in the generation of position signals for machine tools. To obtain a...