Comparative empirical analysis of temporal relationships between construction investment and economic growth in the United States

The majority of policymakers believe that investments in construction infrastructure would boost the economy of the United States (U.S.). They also assume that construction investment in infrastructure has similar impact on the economies of different U.S. states. In contrast, there have been studies showing the negative impact of construction activities on the economy. However, there has not been any research attempt to empirically test the temporal relationships between construction investment and economic growth in the U.S. states, to determine the longitudinal impact of construction investment on the economy of each state. The objective of this study is to investigate whether Construction Value Added (CVA) is the leading (or lagging) indicator of real Gross Domestic Product (real GDP) for every individual state of the U.S. using empirical time series tests. The results of Granger causality tests showed that CVA is a leading indicator of state real GDP in 18 states and the District of Columbia; real GDP is a leading indicator of CVA in 10 states and the District of Columbia. There is a bidirectional relationship between CVA and real GDP in 5 states and the District of Columbia. In 8 states and the District of Columbia, not only do CVA and real GDP have leading/lagging relationships, but they are also cointegrated. These results highlight the important role of the construction industry in these states. The results also show that leading (or lagging) lengths vary for different states. The results of the comparative empirical analysis reject the hypothesis that CVA is a leading indicator of real GDP in the states with the highest shares of construction in the real GDP. The findings of this research contribute to the state of knowledge by quantifying the temporal relationships between construction investment and economic growth in the U.S. states. It is expected that the results help policymakers better understand the impact of construction investment on the economic growth in various U.S. states

Fast Minutia-based Palmprint Matching Using CNN and Generalized Hough Transform

Due to the large number of minutiae in a palmprint, the match-ing process between two palm images is time consuming. Oneway to address this issue is aligning all palmprint images to a ref-erence image. In this paper, using convolutional neural network(CNN) and generalized Hough transform (GHT), we propose a newmethod to find the corresponding rotation and displacement be-tween any palmprint and the reference palm image. Furthermore,the proposed method is capable of distinguishing between left andright palmprint automatically which helps to speed up the match-ing process. The proposed registration method followed by minutia-cylinder code (MCC) matching algorithm has been evaluated on theTHUPALMLAB database, and the results show the superiority of ouralgorithm over most of the state-of-the-art

Torque Ripple Minimization for a Permanent Magnet Synchronous Motor Using a Modified Quasi-Z-Source Inverter

This paper presents a torque ripple minimization method for a permanent magnet synchronous motor (PMSM) drive system that utilizes a modified quasi-Z-source (qZS) inverter. The proposed modified qZS network is designed by adding an extra switching device to the conventional qZS topology and provides a wider range of capabilities for inverter input voltage control, e.g., both step-up and step-down operations. It also allows for modification of the traditional switching sequence selection scheme when using the space vector modulation (SVM) for switching. The provided flexibilities are leveraged to develop a control system that minimizes the torque ripples during PMSM operation while satisfying conventional control objectives such as shaft speed control. The control system is comprised of an input voltage optimization subsystem with the goal of torque ripple minimization, which provides the reference for a cascaded modulated model predictive control subsystem for the modified qZS network control, and a motor side predictive control subsystem. The control system employs a new switching sequence selection scheme for SVM modulation to further reduce PMSM torque ripples. Experimental results are provided to validate the theoretical outcomes