公路噪声源强预测模型研究进展

随着公路建设和城镇化的不断发展，车辆行驶过程中产生的环境噪声对沿线居民、学校的影响也日益明显。针对目前我国的标准规范中未明确给出公路噪声源强预测模型的问题，对国外公路环境噪声源强模型进行系统的分析和总结，并对我国公路噪声源强预测模型的研究提出建议。     

企业需求预测准确性存在的问题及其应对策略(一)

鉴于需求预测在企业经营活动中具有重要地位,且会受到受各种因素影响,本文在对企业实际需求预测的方法、过程、系统、管理等问题进行梳理和分析的基础上,指出了通过优化需求预测方法、完善需求预测系统、改进需求预测管理,可有效控制需求预测和未来市场情况的偏差,从而持续提高需求预测的准确性,促进企业生产、销售的良性运行。     

Stability,elastic and electronic properties of the Rh–Zr compounds from first-principles calculations

A systematic investigation on structural, elastic and electronic properties of Rh–Zr intermetallic compounds is conducted using first-principles electronic structure total energy calculations. The equilibrium lattice parameters, enthalpies of formation (E_for), cohesive energies (E_coh) and elastic constants are presented. Of the eleven considered candidate structures, Rh₄Zr₃ is most stable with the lowest E_for. The two orthogonal-type, relative to the CsCl-type, are the competing ground-state structures of RhZr. The result is in agreement with the experimental reports in the literature. The analysis of E_for and mechanical stability excludes the presence of Rh₂Zr and RhZr₄ at low temperature mentioned by .Curtarolo et al. [Calphad 29, 163 (2005)]. It is found that the bulk modulus B increases monotonously with Rh concentration, whereas all other quantities (shear modulus G, Young's modulus E, Poisson's ratio σ and ductility measured by B/G) show nonmonotonic variation. RhZr₂ exhibits the smallest shear/Young's modulus, the largest Poisson's ratio and ductility. Our results also indicate that all the Rh–Zr compounds considered are ductile. Furthermore, the detailed electronic structure analysis is implemented to understand the essence of stability.     

Molecular Cloning,Structural Analysis and Tissue Expression of Protein Phosphatase 3 Catalytic Subunit Alpha Isoform (PPP3CA) Gene in Tianfu Goat Muscle

Calcineurin, a Ca²⁺/calmodulin-dependent protein phosphatase, plays a critical role in controlling skeletal muscle fiber type. However, little information is available concerning the expression of calcineurin in goat. Therefore, protein phosphatase 3 catalytic subunit alpha isoform (PPP3CA) gene, also called calcineurin Aα, was cloned and its expression characterized in Tianfu goat muscle. Real time quantitative polymerase chain reaction (RT-qPCR) analyses revealed that Tianfu goat PPP3CA was detected in cardiac muscle, biceps femoris muscle, abdominal muscle, longissimus dors muscle, and soleus muscle. High expression levels were found in biceps femoris muscle, longissimus muscle and abdominal muscle (p < 0.01), and low expression levels were seen in cardiac muscle and soleus muscle (p > 0.05). In addition, the spatial-temporal mRNA expression levels showed different variation trends in different muscles with the age of the goats. Western blotting further revealed that PPP3CA protein was expressed in the above-mentioned tissues, with the highest level in biceps femoris muscle, and the lowest level in soleus muscle. In this study, we isolated the full-length coding sequence of Tianfu goat PPP3CA gene, analyzed its structure, and investigated its expression in different muscle tissues from different age stages. These results provide a foundation for understanding the function of the PPP3CA gene in goats.     

Churn prediction using comprehensible support vector machine: An analytical CRM application

Support vector machine (SVM) is currently state-of-the-art for classification tasks due to its ability to model nonlinearities. However, the main drawback of SVM is that it generates “black box” model, i.e. it does not reveal the knowledge learnt during training in human comprehensible form. The process of converting such opaque models into a transparent model is often regarded as rule extraction. In this paper we proposed a hybrid approach for extracting rules from SVM for customer relationship management (CRM) purposes. The proposed hybrid approach consists of three phases. (i) During first phase; SVM-RFE (SVM-recursive feature elimination) is employed to reduce the feature set. (ii) Dataset with reduced features is then used in the second phase to obtain SVM model and support vectors are extracted. (iii) Rules are then generated using Naive Bayes Tree (NBTree) in the final phase. The dataset analyzed in this research study is about Churn prediction in bank credit card customer (Business Intelligence Cup 2004) and it is highly unbalanced with 93.24% loyal and 6.76% churned customers. Further we employed various standard balancing approaches to balance the data and extracted rules. It is observed from the empirical results that the proposed hybrid outperformed all other techniques tested. As the reduced feature dataset is used, it is also observed that the proposed approach extracts smaller length rules, thereby improving the comprehensibility of the system. The generated rules act as an early warning expert system to the bank management.     

A new kinetic model for Ni(II) ion flotation based on gene expression programming

ABSTRACT

The Ni(II) ion flotation kinetics with sodium dodecylsulfate as collector was studied with the aim of developing new models to predict the flotation rate constant (k) of Ni(II) ions and water removal during the process of Ni(II) ion flotation. In this regard, the most influential parameters on Ni(II) ion flotation efficiency including the ratio of collector concentration on the Ni(II) ion concentration, impeller speed, activity coefficient, and pH were used to develop predictive models. The results show that the proposed gene expression programming models can be used to predict the flotation rate constant of Ni(II) ions and water removal.     

WRF-LTNGDA: A lightning data assimilation technique implemented in the WRF model for improving precipitation forecasts

This study introduces WRF-LTNGDA, a lightning data assimilation technique implemented in the Weather Research and Forecasting (WRF) model. This technique employs lightning for improving the representation of convection by means of controlling the triggering of the model's convection parameterization scheme. The development and implementation of WRF-LTNGDA was carried out in a framework that could easily allow for its exploitation in real-time forecasting activities. The assimilation algorithm was evaluated over eight precipitation events that took place in Greece in the years 2010–2013. Results clearly show that lightning forcing has a positive impact on model performance. The conducted analysis revealed that the employment of WRF-LTNGDA induces statistically significant improvements in precipitation verification scores, especially for high rainfall accumulations. Separate examination of one of the eight case studies highlighted the overall better agreement between the modelled and observed spatial distribution of precipitation when lightning data assimilation was applied, than in the control simulation.     

Machine learning ensembles for wind power prediction

For a sustainable integration of wind power into the electricity grid, a precise prediction method is required. In this work, we investigate the use of machine learning ensembles for wind power prediction. We first analyze homogeneous ensemble regressors that make use of a single base algorithm and compare decision trees to k-nearest neighbors and support vector regression. As next step, we construct heterogeneous ensembles that make use of multiple base algorithms and benefit from a gain of diversity among the weak predictors. In the experimental evaluation, we show that a combination of decision trees and support vector regression outperforms state-of-the-art predictors (improvements of up to 37% compared to support vector regression) as well as homogeneous ensembles while requiring a shorter runtime (speed-ups from 1.60× to 8.78×). Furthermore, we show the heterogeneous ensemble prediction can be improved when using high-dimensional patterns by increasing the number of past steps considered and hereby the spatio-temporal information available by the measurements of the nearby turbines. The experiments are based on a large wind time series data set from simulations and real measurements.     

Prediction of Mechanical Properties of Hot Rolled Strip by Using Semi-Parametric Single-Index Model

 A semi-parametric single-index model based approach was proposed for prediction of mechanical properties of hot rolled strip. Based on industrial production data, a semi-parametric single-index model was developed by choosing the appropriate kernel function and window width to predict the yield strength, tensile strength and elongation. When data samples are limited, compared with regression method and neural network method, the prediction results show that the semi-parametric single-index model based method is more adaptive and the prediction performance is superior to those by both regression and neural network methods.     

Solving online dynamic time-linkage problems under unreliable prediction

Dynamic time-linkage optimization problems (DTPs) are a special class of dynamic optimization problems (DOPs) with the feature of time-linkage. Time-linkage means that the decisions taken now could influence the problem states in future. Although DTPs are common in practice, attention from the field of evolutionary optimization is little. To date, the prediction method is the major approach to solve DTPs in the field of evolutionary optimization. However, in existing studies, the method of how to deal with the situation where the prediction is unreliable has not been studied yet for the complete Black-Box Optimization (BBO) case. In this paper, the prediction approach EA + predictor, proposed by Bosman, is improved to handle such situation. A stochastic-ranking selection scheme based on the prediction accuracy is designed to improve EA + predictor under unreliable prediction, where the prediction accuracy is based on the rank of the individuals but not the fitness. Experimental results show that, compared with the original prediction approach, the performance of the improved algorithm is competitive.