Can regional policies shape migration flows?

Our empirical analysis focuses on the effect of regional policies on migration attraction factors in Europe. We employ a regression discontinuity design to assess the causal relationship between the reception of large amounts of public funds and migration flows in the EU-15 regions. In highly-subsidised regions, we find a large increase in the share of foreign citizens from less-developed countries when compared to low-subsidised regions with similar pre-treatment characteristics. The analysis shows that such an increase is due to the positive impact of the European regional policy on job market opportunities as well as the improvement of public goods supply.     

No-Reference Video Quality Assessment using novel hybrid features and two-stage hybrid regression for score level fusion

This paper presents a novel No-Reference Video Quality Assessment (NR-VQA) model that utilizes proposed 3D steerable wavelet transform-based Natural Video Statistics (NVS) features as well as human perceptual features. Additionally, we proposed a novel two-stage regression scheme that significantly improves the overall performance of quality estimation. In the first stage, transform-based NVS and human perceptual features are separately passed through the proposed hybrid regression scheme: Support Vector Regression (SVR) followed by Polynomial curve fitting. The two visual quality scores predicted from the first stage are then used as features for the similar second stage. This predicts the final quality scores of distorted videos by achieving score level fusion. Extensive experiments were conducted using five authentic and four synthetic distortion databases. Experimental results demonstrate that the proposed method outperforms other published state-of-the-art benchmark methods on synthetic distortion databases and is among the top performers on authentic distortion databases. The source code is available at https://github.com/anishVNIT/two-stage-vqa.     

Prediction models for multiphase-flow-induced corrosion of API X80 steel in CO2/H2S environment

In the offshore oil and gas industry, mainly focusing on the use of rigid or flexible pipes of subsea infrastructure applied to risers or flowlines, one of the greatest difficulties is the interpretation of the combined effects of the various correlated phenomena (hydrodynamic effects of intermittent flow, the effects of corrosivity of the environment in addition to variations in pressure, temperature, and dynamic loading). On the basis of this scenario, defining the degree of severity of each of the correlated system variables becomes of fundamental importance for establishing reliable criteria for selecting materials for subsea application. The established flow pattern directly affects the corrosion rate (or the pipe material mass loss), but the balance of other variables including possible changes in the physical and transported fluid chemical properties may increase the damage up to an order of magnitude, which is a piece of information normally not foreseen in design criteria. Therefore, to improve the understanding of the corrosion study influenced by multiphase flow, a testing loop was designed and assembled at the Corrosion and Protection Laboratory of the Institute for Technological Research, in which API X80 steel coupons were positioned in locations with a 0° and 45° inclinations. Tests were conducted by varying the partial pressure of the gaseous phase containing blends of CO₂ and H₂S with N₂ balance, mixed with the liquid phase containing light oil and heavy oil in water with salinity (NaCl)-simulating oil well conditions with 80% water cut. The main objective of this study is to establish models that can predict the corrosion intensity in conditions close to those obtained experimentally. To achieve results, the multiple regression and Box–Cox transformation methods were applied. These models will make possible damage prediction and optimization of matrix parameters for the multiphase-loop test.     

Generalized linear model based monitoring methods for high-yield processes

Emerge in technology brought well-organized manufacturing systems to produce high-quality items. Therefore, monitoring and control of products have become a challenging task for quality inspectors. From these highly efficient processes, produced items are mostly zero-defect and modeled based on zero-inflated distributions. The zero-inflated Poisson (ZIP) and zero-inflated Negative Binomial (ZINB) distributions are the most common distributions, used to model the high-yield and rare health-related processes. Therefore, data-based control charts under ZIP and ZINB distributions (i.e., Y-ZIP and Y-ZINB) are proposed for the monitoring of high-quality processes. Usually, with the defect counts, few covariates are also measured in the process, and the generalized linear model based on the ZIP and ZINB distributions are used to estimate their parameters. In this study, we have designed monitoring structures (i.e., PR-ZIP and PR-ZINB) based on the ZIP and ZINB regression models which will provide the monitoring of defect counts by accounting the single covariate. Further, proposed model-based charts are compared with the existing data-based charts. The simulation study is designed to access the performance of monitoring methods in terms of run length properties and a case study on the number of flight delays between Atlanta and Orlando during 2012–2014 is also provided to highlight the importance of the stated research.     

Development of a statistical model to classify driving stress levels using galvanic skin responses

Many studies have demonstrated the strong relationships between physiological responses and driving stress, but they have done little to build a model that could be used to identify a driver's stress accurately in real time. The objective of this study is to develop a model that accurately classifies driving stress by monitoring physiological responses—specifically galvanic skin response (GSR). GSR data were collected from nine drivers with licenses obtained in the US in real road driving situations with two stress conditions—rest period (low stress) and highway or city driving (high stress). The validation drive was performed by one driver with licenses obtained in South Korea in real long‐term road driving situations with two stress conditions—rural area (low stress) and highway or highway under construction (high stress). Those two conditions were used to build a binary logistic regression model to classify low stress or high stress based on a driver's measured hand GSR. The overall classification accuracy of the developed model was found to be 85.3%, and the accuracy of cross validation, with a testing dataset, was found to be 83.2%. A simple logit model was developed to identify drivers' stress by incorporating their GSR data. The developed model can be embedded in a wearable device equipped with GSR sensors for drivers to detect their stress level in real time.     

Benchmark value determination of energy efficiency indexes for coal-fired power units based on data mining methods

The operational optimisation of coal-fired power units is important for saving energy and reducing losses in the electric power industry. One of the key issues is how to determine the benchmark values of the energy efficiency indexes of the units. Therefore, a new framework for determining these benchmark values is proposed, based on data mining methods. First, the energy efficiency key performance indicators (KPIs) associated with the net coal consumption rate (NCCR) were selected based on the domain knowledge. Second, the decision-making samples with minimal NCCR were acquired with the fuzzy C-means (FCM) clustering algorithm, and the corresponding clustering centres were employed as the benchmark values. Finally, based on the support vector regression (SVR) algorithm, the target values of the NCCR were obtained with the KPIs as input, and the energy saving potential was evaluated by comparing the target values with the historical values of the NCCR. An actual on-duty 1000 MW unit was taken as study unit, and the results show that the energy saving potential is remarkable when the operators adjust the KPIs based on the calculated benchmark values.     

基于主成分分析和BP神经网络的赣江流域中长期径流预报

针对赣江流域开展水量调度对中长期径流预报的迫切需求,在分析赣江流域径流特性的基础上,以降雨、径流等常规因子和130项大气环流指数等相关因子为预报因子,分别构建基于相关系数法、逐步回归方法、主成分分析法三种因子筛选方法的BP神经网络中长期径流预报模型。研究结果表明,主成分分析方法筛选的预报因子可较好描述未来径流的变化趋势,所构建的基于主成分分析的BP神经网络中长期径流预报模型在率定期和检验期的合格率均满足规范对作业预报模型的精度要求,可作为赣江流域中长期径流预报的支撑模型。研究成果为赣江流域开展水资源优化配置和水量调度提供了依据。     

提升超深层超高压气藏动态储量评价可靠性的新方法——物质平衡实用化分析方法

超深层大气田一般都具有高压超高压、基质致密、裂缝发育等特点,其动态储量评价结果具有较强的不确定性。为了准确评价该类型气藏的动态储量,首先基于高压超高压气藏物质平衡方程,深入分析了岩石有效压缩系数与岩石累积有效压缩系数的相关关系,优选出适合于高压超高压气藏动态储量评价的物质平衡分析方法 ;然后,基于非线性回归法确定了动态储量评价的起算条件,针对未达到起算条件的情形建立了半对数典型曲线拟合法,并采用该方法计算了3个超高压气田(藏)的动态储量,进而验证其可靠性。研究结果表明:(1)高压超高压气藏物质平衡方程中的气藏累积有效压缩系数是影响该类气藏动态储量评价结果的关键参数,该参数是原始地层压力和当前平均地层压力的函数,而其数值难以通过岩心实验测得;(2)针对高压超高压气藏,推荐采用不需要压缩系数的非线性回归法进行动态储量评价;(3)采用非线性回归法计算动态储量的起算点(无量纲视地层压力与累计产气量关系曲线偏离直线的起点)无法通过理论计算得到,基于图解法的统计结果得到不同无量纲线性系数(ωD)情形下起算点对应的无量纲视地层压力衰竭程度介于0.06～0.38,基于实例气藏数据统计得到的起算点也在此范围内;(4)未达到起算条件时可采用半对数典型曲线拟合法估算动态储量,动态储量与视地质储量的比值(G/G_(app))是ωD的函数,ωD越大,(G/G_(app))越小;(5)处于试采阶段的高压超高压气藏,应尽可能延长试采时间,以提高动态储量评价的可靠性;对处于开发中后期的高压超高压气藏,则应以动态储量为基础制订气藏综合治理措施,进而不断改善气藏的开发效果。     

遥感技术在水电站地址勘察中的应用

基于降雨径流模型确定水电站地址勘察已成为水资源评价中的一个重要课题。随着卫星技术的发展，遥感、地理信息系统能够快速地确定水电站地址勘察。选取位于云南省中部滇中盆地地区澜沧江流域作为研究区，利用遥感和地理信息系统技术，从数字高程模型角度出发，确定了流域尺度、流域边界和最小面积、平均高程、流域平均坡度等水文信息。运用分辨率为2.5 m的IRS-P5立体卫星数据导出数字高程模型。该数字高程模型用于生成盆地的流向和流量累积图。通过对这些图像的分析得出了综合排水网；利用地形资料，包括流域面积、平均流域高程和降雨资料的有限点观测值，推导出整个流域的回归模型，使用平均面雨量计算其雨量分布图，结合Kriging统计方法生成子流域验证这个回归模型，并给出了水电站地址勘察建设地点。