浅析矿产资源开发中的利益分配博弈

由于矿产资源的特殊性,利益分配不均现象在矿产资源开发中尤为显著,资源富饶的地区经济发展还较为落后,居民生活水平提高不大,矛盾冲突多,社会安定堪忧.本文根据对云南部分矿种和矿区的实际调研,利用相关的博弈理论和方法,构建了矿产资源开发企业和矿区居民之间的不完全信息动态博弈模型,通过对该模型精炼贝叶斯均衡的求解,表明矿产资源开发中企业倾向于向矿区居民分配很低的利益且这种分配策略属于“混同均衡”,进而推导出了各均衡点存在的必要条件,并以实际调研案例验证了其存在的真实性.在此基础上从监管、补偿机制及分配制度三个方面提出了确保在矿产资源开发中进行利益合理分配的对策建议,为维护矿区社会稳定、实现经济社会的可持续发展提供参考.     

中兴矿1205沿空留巷无煤柱开采技术的应用

中兴矿自建矿以来一直采用“两进一回”通风巷道布置形式,由于矿井地质条件复杂多变,巷道维护困难,导致矿井采掘衔接不上,影响矿井安全生产。通过采用新技术沿空留巷Y型通风无煤柱开采方法,提高矿井回收率,缓解矿井采掘衔接紧张局面,同时有效降低了采掘成本,增加经济效益,实现矿井的安全高效可持续发展。     

企业的技术进步与经济效益定量分析

本文运用矿山生产函数模型，采用增长速度方程法建立了评价矿山企业技术进步与经济效益的评价指标，为测度矿山企业的技术进步与经济效益及制订企业的发展规划提供了一种科学依据。     

Computer aids for production systems management in iron ore mining

Iron ore is railed to port, crushed to lump and fines, stockpiled and shipped. Product quality requires consistent composition, in iron and other minerals. Decision support systems described here aid daily ore selection, to maintain target composition. The method evolved from a batch system, building each stockpile to target, to a flow process, targeting an exponentially smoothed continuous stockpile. This has decoupled mine, rail and port operations, lessening costly rehandling. A quadratic programming solution guides ore selection. Some ill-defined and cost related constraints require human judgment. The emphasis is on decision-support, not decision-making, in a fruitful example of human/computer interaction.     

基于数据挖掘技术的人民币反洗钱系统设计

国际反洗钱实践表明,数据挖掘技术在大额和可疑支付交易数据分析中具有广阔的应用前景.探讨数据挖掘技术在大额和可疑交易报告制度中应用的必要性与可行性,全面把握数据挖掘技术的各种主要算法及其在大额和可疑交易数据分析中的应用前景,针对目前我国反洗钱工作的实际,设计一套人民币大额和可疑支付交易数据挖掘系统(CRMB-LSADMS).     

Text-based crude oil price forecasting: A deep learning approach

This study proposes a new, novel crude oil price forecasting method based on online media text mining, with the aim of capturing the more immediate market antecedents of price fluctuations. Specifically, this is an early attempt to apply deep learning techniques to crude oil forecasting, and to extract hidden patterns within online news media using a convolutional neural network (CNN). While the news-text sentiment features and the features extracted by the CNN model reveal significant relationships with the price change, they need to be grouped according to their topics in the price forecasting in order to obtain a greater forecasting accuracy. This study further proposes a feature grouping method based on the Latent Dirichlet Allocation (LDA) topic model for distinguishing effects from various online news topics. Optimized input variable combination is constructed using lag order selection and feature selection methods. Our empirical results suggest that the proposed topic-sentiment synthesis forecasting models perform better than the older benchmark models. In addition, text features and financial features are shown to be complementary in producing more accurate crude oil price forecasts.     

基于逻辑回归的企业大额可疑外汇资金交易识别模型

本文根据逻辑回归原理与数据挖掘技术,建立了企业大额可疑外汇资金交易识别模型,用于分析银行企业客户洗钱的概率及洗钱事件发生的可能性。本文的目的是通过对具有洗钱嫌疑的银行企业客户进行识别和预测,为银行反洗钱技术提供参考。     

资源性城市可持续发展的问题及其对策--从大庆城乡经济发展说起

矿业资源城市是伴随着化石资源的开发而兴起的城市，如何实现经济的持续稳定协调发展是矿业资源城市发展的重大课题。本文从典型的矿业资源城市大庆市为例，运用定性与定量分析方法对大庆市经济可持续发展存在的问题进行了分析研究，在此基础上提出了大庆市场经济可持续发展的相应对策。     

数据挖掘——CRM中的动力

客户关系管理(CRM)在中国已经取得了长足的进步,越来越多的企业将客户作为一切工作的出发点,比以往更加重视客户忠诚度和客户利润贡献率的提升。但是,在CRM发展的过程中依然存在着很多问题需要解决,最为显著的莫过于面对庞大的客户和销售数据,我们该如何去利用?正是为了满足这种要求,从大量数据中提取出隐藏在其中有用信息的数据挖掘技术得到了长足的发展。数据挖掘的出现和应用必将使得CRM取得成功。文章从CRM和数据挖掘的涵义谈起,论证了数据挖掘技术在CRM中应用的可行性和必要性,着重阐述了如何在CRM中应用数据挖掘技术的问题。     

Measuring risk in the mining sector with ARCH models with important observations on sample size

This paper uses the GARCH technique to estimate time-varying individual firm risk measures for the mining sector. In general, the mining industry is riskier than the market with estimated betas greater than one. The results also show that the level of risk in the industry is quite volatile and can be divided into three distinct time periods according to the magnitude and variability of the firm's betas. Additionally, the number of observations in a sample has a strong relationship to the detection and estimation of an ARCH effect in the data, which cannot be explained by firm financial size.