非常规气藏储层改造体积评价方法研究

为精确描述非常规气藏压裂后的复杂流动特征及定量评价储层改造体积（SRV），利用自主研发的缝网重构算法“破裂树生长法”建立压后缝网模型，并以此为基础提出了使用拟稳态流动时特定的压力等值线来确定SRV范围的定量评价方法，最后以长宁201井区为例进行了矿场实例分析。该方法根据微地震监测点的位置，重构出微裂缝网的连通关系。在复杂微裂缝网的基础上建立离散裂缝地质模型并进行生产数值模拟，根据数值模拟结果的压力分布精确划定SRV的范围。利用该方法计算出长宁201井区的SRV体积为0.052 1 km3，以该缝网模型的数值模拟产量预测结果符合实际生产规律，方法实用性较好。     

How Far Are We from Prescribing Fasting as Anticancer Medicine?

(1) Background: the present review provides a comprehensive and up-to date overview of the potential exploitation of fasting as an anticancer strategy. The rationale for this concept is that fasting elicits a differential stress response in the setting of unfavorable conditions, empowering the survival of normal cells, while killing cancer cells. (2) Methods: the present narrative review presents the basic aspects of the hormonal, molecular, and cellular response to fasting, focusing on the interrelationship of fasting with oxidative stress. It also presents nonclinical and clinical evidence concerning the implementation of fasting as adjuvant to chemotherapy, highlighting current challenges and future perspectives. (3) Results: there is ample nonclinical evidence indicating that fasting can mitigate the toxicity of chemotherapy and/or increase the efficacy of chemotherapy. The relevant clinical research is encouraging, albeit still in its infancy. The path forward for implementing fasting in oncology is a personalized approach, entailing counteraction of current challenges, including: (i) patient selection; (ii) fasting patterns; (iii) timeline of fasting and refeeding; (iv) validation of biomarkers for assessment of fasting; and (v) establishment of protocols for patients’ monitoring. (4) Conclusion: prescribing fasting as anticancer medicine may not be far away if large randomized clinical trials consolidate its safety and efficacy.     

改进Elman神经网络在短期热负荷预测中的应用

热电厂的短期热负荷预测在城市集中供暖中起着至关重要的作用,直接影响热电厂的经济效益和热能利用率。电厂的短期热负荷一般采用神经网络预测模型进行预测,而BP神经网络应用最为广泛。Elman神经网络算法在BP神经网络基础上加入了承接层,作为一步延时算子,实现记忆能力,使系统具备适应时变能力,增强系统全局稳定性。但Elman神经网络算法模型的构造依然需要大量样本的支撑,而且输入层的变量多,导致预测时间依然很长,收敛速度慢。该文在Elman神经网络预测前,进行了相关系数预处理和对样本中异常值的平均化预处理,通过数据归一化运算,使Elman神经网络输入层变量大幅减少。仿真实验表明,改进的Elman神经网络算法使预测模型快速寻优,减少预测时间的同时明显提高预测精度。     

Genetic algorithm to simultaneously optimise stope sequencing and equipment dispatching in underground short-term mine planning under time uncertainty

ABSTRACT

The main issue in short-term planning optimisation for underground mining is organising the mining process with limited resources in the form of equipment and materials to satisfy production targets and stable feed grade requirements. In this paper, an integrated optimisation model is proposed based on an individual generation algorithm and an improved Genetic Algorithm to simultaneously optimise stope extraction sequencing and timing, extracted ore grade and equipment dispatching. The model objectives are to shorten the time gap between the stope mining processes and the overall working time. When the uncertainty of equipment working time is taken into account in a short-term scheduling model, the Monte Carlo simulation is applied to evaluate the risk of not meeting the production target. A modification strategy is defined to evaluate equipment failure. Consequently, any available equipment is automatically reassigned to the mining site to replace the broken-down equipment. A case study is used to validate the model in the Sanshandao gold mine of China to formulate an optimal monthly schedule. Compared with the conventional approach, the new model could reduce the variance of ore tonnage and feed grade and improve the equipment allocation efficiency. Discussions are presented to address the uncertainty.     

基于VMD和改进TCN的短期光伏发电功率预测

光伏发电功率存在波动性,且光伏出力易受各种气象特征影响,传统TCN网络容易过度强化空间特性而弱化个体特性。针对上述问题,文中提出一种基于VMD和改进TCN的短期光伏发电功率预测模型。通过VMD将原始光伏发电功率时间序列分解为若干不同频率的模态分量,将各个模态分量以及相对应的气象数据输入至改进TCN网络进行建模学习。利用中心频率法确定VMD的最优分解模态分解个数。在传统TCN预测模型的基础上,使用DropBlock正则化取代Dropout正则化以达到抑制卷积层中信息协同的效果,并引入注意力机制自主挖掘并突出关键气象输入特征的影响,量化各气象因素对光伏发电的影响,从而提高预测精度。以江苏省某光伏电站真实数据为例进行仿真实验,结果表明所提预测方法的RMSE为0.62 MW,MAPE为2.03%。     

How do normalization schemes affect net spillovers? A replication of the Diebold and Yilmaz (2012) study

This paper replicates the Diebold and Yilmaz (2012) study on the connectedness of the commodity market and three other financial markets: the stock market, the bond market, and the FX market, based on the Generalized Forecast Error Variance Decomposition, GEFVD. We show that the net spillover indices (of directional connectedness), used to assess the net contribution of one market to overall risk in the system, are sensitive to the normalization scheme applied to the GEFVD. We show that, considering data generating processes characterized by different degrees of persistence and covariance, a scalar-based normalization of the Generalized Forecast Error Variance Decomposition is preferable to the row normalization suggested by Diebold and Yilmaz since it yields net spillovers free of sign and ranking errors.     

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.     

Nonlinear Factor-Augmented Predictive Regression Models with Functional Coefficients

This article introduces a new class of functional-coefficient predictive regression models, where the regressors consist of auto-regressors and latent factor regressors, and the coefficients vary with certain index variable. The unobservable factor regressors are estimated through imposing an approximate factor model on high dimensional exogenous variables and subsequently implementing the classical principal component analysis. With the estimated factor regressors, a local linear smoothing method is used to estimate the coefficient functions (with appropriate rotation) and obtain a one-step ahead nonlinear forecast of the response variable, and then a wild bootstrap procedure is introduced to construct the prediction interval. Under regularity conditions, the asymptotic properties of the proposed methods are derived, showing that the local linear estimator and the nonlinear forecast using the estimated factor regressors are asymptotically equivalent to those using the true latent factor regressors. The developed model and methodology are further generalized to the factor-augmented vector predictive regression with functional coefficients. Finally, some extensive simulation studies and an empirical application to forecast the UK inflation are given to examine the finite-sample performance of the proposed model and methodology.     

基于SARIMA-GRNN-SVM的短期商业电力负荷组合预测方法

针对短期商业电力负荷预测准确性与周期难以满足现有电力现货市场的问题,提出了一种基于SARIMAGRNN-SVM(seasonal autoregressive integrated moving average-generalized regression neural network-support vector machine)的商业电力负荷组合预测模型。首先,对商业电力负荷变化的周期规律与随机因素的复杂影响进行了分析;然后,结合以上分析,选用SARIMA和GRNN为单一预测模型对商业电力负荷进行预测,并利用SVM进行组合,实现日前商业电力负荷预测;最后,通过某商业综合体的电力负荷数据进行验证。所提组合预测模型较单一预测模型拥有更优的预测精度与鲁棒性,可以为短期商业电力负荷预测提供借鉴。     

复频电导技术在隧洞超前探水中的应用

围岩富水带是隧道超前预报最关注的问题，目前，多使用电磁方法来预报围岩的含水性，但受到场地条件限制，面临掌子面条件、金属机具干扰、三维波场定位等诸多困难，特别是掌子面前方100 m范围内围岩的含水性预报，在国内外都是一个新的高难课题。在研究了岩体电导率与电容率复频特性的基础上，结合巴基斯坦NJ-TBM引水隧洞工程，开发了复频电导探水(CFC)技术。该技术基于电磁波反射与相干原理，选用100 kHz～10 MHz频率范围，采用电偶极子发射与阵列接收方式和偏移成像技术，突破了隧道内场地条件的限制。依据1/4相干波长确定含水体的位置，相干能量确定含水量的大小。在巴基斯坦NJ-TBM隧洞超前探水的应用，证明了该方法的可行性与有效性。 