粉体含能材料生产与储运中的静电安全及防护

分析了静电产生的原因,阐述了粉体含能材料生产中的静电起电现象、静电的危害、静电安全性评估标准以及建立在此标准基础上的静电放电危险的评价办法,提出了粉体含能材料在生产、运输中所需要采取的静电防护措施。     

Electricity Production

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

Industrial sugar beets to biofuel: Field to fuel production system and cost estimates

Specialized varieties of sugar beets (Beta vulgaris L.) may be an eligible feedstock for advanced biofuel designation under the USA Energy Independence and Security Act of 2007. These non-food industrial beets could double ethanol production per hectare compared to alternative feedstocks. A mixed-integer mathematical programming model was constructed to determine the breakeven price of ethanol produced from industrial beets, and to determine the optimal size and biorefinery location. The model, based on limited field data, evaluates Southern Plains beet production in a 3-year crop rotation, and beet harvest, transportation, and processing. The optimal strategy depends critically on several assumptions including a just-in-time harvest and delivery system that remains to be tested in field trials. Based on a wet beet to ethanol conversion rate of 110 dm³ Mg⁻¹ and capital cost of 128 M$ for a 152 dam³ y⁻¹ biorefinery, the estimated breakeven ethanol price was 507 $ m⁻³. The average breakeven production cost of corn (Zea mays L.) grain ethanol ranged from 430 to 552 $ m⁻³ based on average net corn feedstock cost of 254 and 396 $ m⁻³ in 2014 and 2013, respectively. The estimated net beet ethanol delivered cost of 207 $ m⁻³ was lower than the average net corn feedstock cost of 254–396$ m⁻³ in 2013 and 2014. If for a mature industry, the cost to process beets was equal to the cost to process corn, the beet breakeven ethanol price would be $387 m^-3 (587 $ m⁻³ gasoline equivalent).     

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.     

Electricity Production

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

Electricity Production

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

基于粒子群优化和支持向量机的花粉浓度预测模型

为了提高花粉浓度预报的准确率，解决现有花粉浓度预报准确率不高的问题，提出了一种基于粒子群优化（PSO）算法和支持向量机（SVM）的花粉浓度预报模型。首先，综合考虑气温、气温日较差、相对湿度、降水量、风力、日照时数等多种气象要素，选择与花粉浓度相关性较强的气象要素构成特征向量；其次，利用特征向量与花粉浓度数据建立SVM预测模型，并使用PSO算法找出最优参数；然后利用最优参数优化花粉浓度预测模型；最后，使用优化后的模型对花粉未来24 h浓度进行预测，并与未优化的SVM、多元线性回归法（MLR）、反向神经网络（BPNN）作对比。此外使用优化后的模型对某市南郊观象台和密云两个站点进行逐日花粉浓度预测。实验结果表明，相比其他预报方法，所提方法能有效提高花粉浓度未来24 h预测精度，并具有较高的泛化能力。     

Backtesting portfolio value-at-risk with estimated portfolio weights

This article theoretically and empirically analyzes backtesting portfolio value-at-risk (VaR) with estimation risk in an intrinsically multi-variate framework. It particularly takes into account the estimation of portfolio weights in forecasting portfolio VaR and its impact on backtesting. It shows that the estimation risk from estimating portfolio weights and that from estimating the multi-variate dynamic model make the existing methods in a univariate framework inapplicable. It proposes a general theory to quantify estimation risk applicable to the present problem and suggests practitioners a simple but effective way to implement valid inference to overcome the effect of estimation risk in backtesting portfolio VaR. In particular, we apply our theory to the efficient mean-variance-skewness portfolio for a multi-variate generalized autoregressive conditional heteroscedasticity model with multi-variate general hyperbolic distributed innovations. Some Monte Carlo simulations and an empirical application demonstrate the merits of our method.     

Mixed coordination mechanisms for scheduling games on hierarchical machines

In this paper, we study scheduling games under mixed coordination mechanisms on hierarchical machines. The two scheduling policies involved are ‐ and ‐, where ‐ (resp., ‐) policy sequences jobs in nondecreasing order of their hierarchies, and jobs of the same hierarchy in nonincreasing (resp., nondecreasing) order of their processing times. We first show the existence of a Nash equilibrium. Then we present the price of anarchy and the price of stability for the games with social costs of minimizing the makespan and maximizing the minimum machine load. All the bounds given in this paper are tight.     

基于承包商预期收益不变的不平衡报价模型

在招标实践中,通常采用最低价中标原则,使得投标人不得不压低其投标价格。基于承包商预期收益不变的不平衡报价模型,是在“工程量清单报价”的条件下,考虑到施工中工程量的变化及施工顺序的影响,体现了资金的时间价值,在保持承包商预期收益不变的前提下,通过调整各分项工程的单价,来降低工程报价,以增加中标机会而建立的。所建立的模型简单,易于操作,并配以具体的工程实例,通过计算机求得其最优解,可供承包商投标报价时参考。