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.     

H∞ control for uncertain one‐sided Lipschitz nonlinear systems in finite frequency domain

The current article discusses the H_∞ disturbance attenuation control design problem for one‐sided Lipschitz systems in finite frequency domain. Models containing norm‐bounded parameter uncertainties, disturbances, and input nonlinearities are considered. By contrast to existing full frequency methods, the H_∞ controller is computed depending on the frequency ranges of disturbances. The finite frequency disturbance attenuation index is initially defined. Thanks to Finsler's lemma, sufficient and less conservative analysis conditions are also derived for the closed‐loop system. Then, synthesis conditions in the low, middle, and high frequency ranges as well as the whole frequency range, are formulated in terms of linear matrix inequalities. At last, to prove the effectiveness and the superiority of the proposed approach, a physical example is used and a comparative study is done.     

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.     

计及时间相关性的微能源网鲁棒规划方法

微能源网以能源的梯级利用为原则,可实现风、光等多种新能源的高比例消纳,满足区域内电、热、冷等多种能源需求。微能源网中存在新能源出力与冷、热、电负荷的多重不确定性。为增强系统规划结果的鲁棒性,常采用不确定集表述新能源出力与多种用能需求的不确定性,实现针对微能源网的鲁棒规划设计,往往使系统规划结果过于保守,降低系统经济性。为克服以上问题,规避实际运行中不可能发生的场景,降低系统规划结果的保守性,文章提出一种考虑时间相关性的微能源网鲁棒规划模型。该模型在计及多重源荷不确定性的基础上,进一步考虑新能源出力与冷、热、电负荷的时间相关性。通过算例对传统不确定集与所提出的改进不确定集进行对比分析,验证了所提模型及方法的优越性和有效性。     

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。