The Price of Being International: Exploring Early Career Outcome Differences between International and Domestic Master’s Recipients from U.S. Institutions

Based on neo-racism theory, this study seeks to examine whether international master’s recipients (IMR) who graduated from U.S. institutions have significant early career outcome differences as compared to domestic master’s recipients (DMR) in terms of major-job match, annual earnings and job satisfaction. By analyzing combined datasets of National Survey of Recent College Graduates (2001, 2003, 2006, 2008, 2010), this study attempts to examine employment outcome differences between international and domestic master’s recipients from three perspectives. First, this study tries to answer whether international status has a significant effect on international master’s recipients’ career outcomes relative to domestic master’s recipients. In addition, this study examines the extent to which the effect of international status on career outcomes differs by field of study, Carnegie classification and institutional control. Further, the present study investigates whether country of origin plays a significant role in shaping international master’s recipients’ early career outcomes by comparing career outcomes of international master’s recipients born in China and India to domestic master’s recipients born in the U.S. After controlling for demographic, educational and job market characteristics, this study found that international master’s recipients were two times more likely than domestic master’s recipients to hold jobs that are related to their master’s degree, faced a 6% earnings disadvantage as compared to domestic master’s recipients, and did not have significant difference in job satisfaction as compared to domestic master’s recipients. The subgroup analysis found that international status effect on employment outcomes differs by field of study, Carnegie classification and institutional control. In particular, all else being equal, international master’s recipients with engineering majors faced a 12.24% annual salary loss as compared to their domestic engineering peers in the U.S. job market. International master’s recipients who graduated from Research II institutions in Carnegie classifications had a significant 14.15% earnings loss as compared to their domestic master’s recipients from the same institutions, all else being equal. In terms of country of origin effect on career outcomes, this study found that international master’s recipients from India performed much better in the U.S. job market than international master’s recipients from China in annual earnings and job satisfaction

Robust Quadratic Stabilizability and H

This paper mainly discusses the robust quadratic stability and stabilization of linear discrete-time stochastic systems with state delay and uncertain parameters. By means of the linear matrix inequality (LMI) method, a sufficient condition is, respectively, obtained for the stability and stabilizability of the considered system. Moreover, we design the robust H∞ state feedback controllers such that the system with admissible uncertainties is not only quadratically internally stable but also robust H∞ controllable. A sufficient condition for the existence of the desired robust H∞ controller is obtained. Finally, an example with simulations is given to verify the effectiveness of our theoretical results

Model-free Reinforcement Learning for H2/H∞{H_{2}/H_{\infty}} Control of Stochastic Discrete-time Systems

This paper proposes a reinforcement learning (RL) algorithm for infinite horizon $\rm {H_{2}/H_{\infty}}$ problem in a class of stochastic discrete-time systems, rather than using a set of coupled generalized algebraic Riccati equations (GAREs). The algorithm is able to learn the optimal control policy for the system even when its parameters are unknown. Additionally, the paper explores the effect of detection noise as well as the convergence of the algorithm, and shows that the control policy is admissible after a finite number of iterations. The algorithm is also able to handle multi-objective control problems within stochastic fields. Finally, the algorithm is applied to the F-16 aircraft autopilot with multiplicative noise

Study on H

This paper studies the H- index problem. We obtain a necessary and sufficient condition of H- index larger than γ>0. A generalized differential equation is introduced and it is proved that its solvability and the feasibility of the H- index are equivalent. We extend the deterministic cases to the stochastic models. Our results can be used to fault detection filter analysis. Finally, the effectiveness of the proposed results is illustrated by an example

Robust Stability, Stabilization, and H

This paper studies robust stability, stabilization, and H∞ control for a class of nonlinear discrete time stochastic systems. Firstly, the easily testing criteria for stochastic stability and stochastic stabilizability are obtained via linear matrix inequalities (LMIs). Then a robust H∞ state feedback controller is designed such that the concerned system not only is internally stochastically stabilizable but also satisfies robust H∞ performance. Moreover, the previous results of the nonlinearly perturbed discrete stochastic system are generalized to the system with state, control, and external disturbance dependent noise simultaneously. Two numerical examples are given to illustrate the effectiveness of the proposed results

Stochastic H∞ Control for Discrete-Time Singular Systems with State and Disturbance Dependent Noise

This paper is concerned with the stochastic H∞ state feedback control problem for a class of discrete-time singular systems with state and disturbance dependent noise. Two stochastic bounded real lemmas (SBRLs) are proposed via strict linear matrix inequalities (LMIs). Based on the obtained SBRLs, a state feedback H∞ controller is presented, which not only guarantees the resulting closed-loop system to be mean square admissible but also satisfies a prescribed H∞ performance level. A numerical example is finally given to illustrate the effectiveness of the proposed theoretical results

A Perfect Decomposition Model for Analyzing Transportation Energy Consumption in China

Energy consumption in transportation industry is increasing. Transportation has become one of the fastest energy consumption industries. Transportation energy consumption variation and the main influencing factors of decomposition contribute to reduce transportation energy consumption and realize the sustainable development of transportation industry. This paper puts forwards an improved decomposition model according to the factors of change direction on the basis of the existing index decomposition methods. Transportation energy consumption influencing factors are quantitatively decomposed according to the transportation energy consumption decomposition model. The contribution of transportation turnover, transportation structure and transportation energy consumption intensity changes to transportation energy consumption variation is quantitatively calculated. Results show that there exists great energy-conservation potential about transportation structure adjustment, and transportation energy intensity is the main factor of energy conservation. The research achievements enrich the relevant theory of transportation energy consumption, and help to make the transportation energy development planning and carry out related policies