基于分形插值的改进趋势面拟合法在地面沉降中的应用

针对区域地面沉降监测点数量有限、分布不均的情况,一般采用空间插值的手段建立表面拟合模型来解决。基于分形插值,采用随机选择迭代函数的思想对传统的趋势面拟合法作出改进,改进后拟合优度系数提高0.03,达到0.995,且改进前后的拟合结果符合显著性检验的要求。实验结果表明:改进前后趋势面拟合法拟合结果满足地面沉降监测的精度要求,改进后方法的拟合优度更高,对现实地面沉降量变化的描绘更加真实,可为沉降灾害评价工作提供更准确的依据。     

CAN-BUS系统应用一例

CAN-BUS系统自问世以来,已将近30年,当代各品牌车辆纷纷采用,但大部分技术人员对CAN总线系统既熟悉又陌生。经常能听到,但具体操作又显得有些力不从心。本文通过对奇瑞A3品牌轿车CAN总线的巧妙应用,引导大家进一步理解和应用CAN总线。     

综放高瓦斯矿井煤层瓦斯涌出量综合预测

为了成功预测竹林山煤矿综放高瓦斯矿井大采高工作面煤层瓦斯涌出量,以主采3号煤层为主要研究对象,针对3号煤层以往开采情况,通过布设测点测量其煤层瓦斯含量和了解相邻矿井瓦斯含量,采用分源预测法、回归法及统计法等预测方法得到了3号煤层瓦斯含量的分布规律,并绘制了3号煤层的瓦斯含量等值线图。对矿井不同生产时期的瓦斯含量进行预测,得到了生产前期、中期及后期采区的最大绝对瓦斯涌出量和最大相对瓦斯涌出量,说明了竹林山煤矿各个时期均属于高瓦斯矿井。     

A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price

Creating an intelligent system that can accurately predict stock price in a robust way has always been a subject of great interest for many investors and financial analysts. Predicting future trends of financial markets is more remarkable these days especially after the recent global financial crisis. So traders who access to a powerful engine for extracting helpful information throw raw data can meet the success. In this paper we propose a new intelligent model in a multi-agent framework called bat-neural network multi-agent system (BNNMAS) to predict stock price. The model performs in a four layer multi-agent framework to predict eight years of DAX stock price in quarterly periods. The capability of BNNMAS is evaluated by applying both on fundamental and technical DAX stock price data and comparing the outcomes with the results of other methods such as genetic algorithm neural network (GANN) and some standard models like generalized regression neural network (GRNN), etc. The model tested for predicting DAX stock price a period of time that global financial crisis was faced to economics. The results show that BNNMAS significantly performs accurate and reliable, so it can be considered as a suitable tool for predicting stock price specially in a long term periods.     

Prediction of methane adsorption content in continental coal-bearing shale reservoir using SLD model

Shale gas, as an important unconventional resource, has drawn global attention. It is mainly composed of adsorption gas and free gas. Adsorption gas content could play an important guiding role on both the selection of favorable perspective area and the exploration and exploitation of shale gas resources. In order to accurately measure adsorption gas content, a new approach was established to predict the adsorption isotherm of methane on shale. Based on the simplified local-density (SLD) method, both the adsorption isotherms of illite, illite/smectite mixed-layer, cholorite and type III kerogen and the total shale rock could be well fitted. The fitting results show good coincidences with the true experimental test data, which proves the method is reasonable and dependable and the prediction results are effective and credible. In addition, the good simulation results show that the SLD parameters can reflect the pore structure characteristics and corresponding adsorption characteristics of the shale samples, which can be used for the quantitative characterization of shale pore system.     

A gradient boosting decision tree approach for insider trading identification: An empirical model evaluation of China stock market

Insider trading is a kind of criminal behavior in stock market by using nonpublic information. In recent years, it has become the major illegal activity in China’s stock market. In this study, a combination approach of GBDT (Gradient Boosting Decision Tree) and DE (Differential Evolution) is proposed to identify insider trading activities by using data of relevant indicators. First, insider trading samples occurred from year 2007 to 2017 and corresponding non-insider trading samples are collected. Next, the proposed method is trained by the GBDT, and initial parameters of the GBDT are optimized by the DE. Finally, out-of-samples are classified by the trained GBDT–DE model and its performances are evaluated. The experiment results show that our proposed method performed the best for insider trading identification under time window length of ninety days, indicating the relevant indicators under 90-days time window length are relatively more useful. Additionally, under all three time window lengths, relative importance result shows that several indicators are consistently crucial for insider trading identification. Furthermore, the proposed approach significantly outperforms other benchmark methods, demonstrating that it could be applied as an intelligent system to improve identification accuracy and efficiency for insider trading regulation in China stock market.     

澳大利亚“基于能力本位的评估”模式及对我国高职教育 质量评估的启 示

澳大利亚职业教育在全球享有较高的声誉,这与其坚持严格的"基于能力本位的评估"(competency based assessment, CBA)密不可分。介绍了澳大利亚CBA的内涵特征,并详细描述"准备评估、开发评估工具、开展评估、对评估结果进行评估"的CBA运作过程,关注评估工具的开发和后续常规系统性检查及完善,以期对我国的高等职业教育质量评估起到启示和借鉴作用。     

Natural Gas Production

Current data on natural gas production, as well as a breakdown of production by country. Updated on a monthly basis.     

Occurrence and distribution of dibenzofurans and benzo[b]naphthofurans in the crude oils from the Northern and offshore Niger Delta basin,Nigeria

The occurrence and distributions of dibenzofurans (DBFs) and benzo[b]naphthofurans were investigated in crude oils from Niger Delta, Nigeria, by gas chromatography-mass spectrometry-mass spectrometry. The distribution of DBFs was characterized by the predominance of C₂-dibenzofurans. 4-Methyldibenzofuran was the most abundant among the methyldibenzofurans isomers while dimethyldibenzofuran-2 (DMDBF-2), ethyldibenzofuran-1, DMDBF-3, and DMDBF-6 occurred in higher amounts when compared with other DMDBFs. Among the benzonaphthofurans, the abundance of benzo[b]naphtho[2,1-d]furan was higher than other isomers. The DBFs distributions in the oils were not affected by source facies and depositional environments. However, the DBFs concentrations increased with increasing maturity in oils from ADL and MJO oilfields.     

Regulation of sensory nerve conduction velocity of human bodies responding to annual temperature variations in natural environments

The extensive research interests in environmental temperature can be linked to human productivity / performance as well as comfort and health; while the mechanisms of physiological indices responding to temperature variations remain incompletely understood. This study adopted a physiological sensory nerve conduction velocity (SCV) as a temperature‐sensitive biomarker to explore the thermoregulatory mechanisms of human responding to annual temperatures. The measurements of subjects’ SCV (over 600 samples) were conducted in a naturally ventilated environment over all four seasons. The results showed a positive correlation between SCV and annual temperatures and a Boltzmann model was adopted to depict the S‐shaped trend of SCV with operative temperatures from 5°C to 40°C. The SCV increased linearly with operative temperatures from 14.28°C to 20.5°C and responded sensitively for 10.19°C‐24.59°C, while tended to be stable beyond that. The subjects’ thermal sensations were linearly related to SCV, elaborating the relation between human physiological regulations and subjective thermal perception variations. The findings reveal the body SCV regulatory characteristics in different operative temperature intervals, thereby giving a deeper insight into human autonomic thermoregulation and benefiting for built environment designs, meantime minimizing the temperature‐invoked risks to human health and well‐being.