A tutorial on the internet of things: from a heterogeneous network integration perspective

The days when the Internet was the only focus of the information society have already gone, and innovative network paradigms such as IoT, cloud computing, smartphone networks, social networks, and industrial networks are gaining popularity and establishing themselves as indispensable ingredients of the future smart universe. Among them, IoT is the most widespread one, envisioned to involve all things in the world. However, its potential will never be fully explored before the complete formation of cyberspace, where humans, computers, and smart objects are pervasively interconnected. Therefore, one of the most important development trends of IoT is its integration with existing network systems. In this tutorial, we provide a detailed analysis of this issue. In particular, the latest achievements, technical solutions, and influential ongoing projects are described, and possible visions and open challenges are also discussed

Diverging effects of subjective prospect values of uncertain time and money

Studies from behavioral economics show that the subjective prospect value of money has diminishing sensitivity to losses/gains, represented by an S-shape, and this has been applied in representing the subjective prospect value of time in many transportation studies such as travel behavior modeling and network equilibrium. In this study, we demonstrate that the prospect value of time has an increasing sensitivity to losses/gains and can be represented by an Ϩ-shape, which contrasts that of money. We further explain the rationality of this surprising finding based on psychological and behavioral theories and discuss extensive practical implications. The correlations between sensitivities to gains and losses in terms of magnitude are revealed as well to shed light on potential underlying correlated behavioral principles. Substantial loss-aversion features are observed in the empirical analysis, supporting endowment effects. Implications of the findings on decision-making and other areas that utilize time as a key indicator have been discussed. The findings may revolutionize many research areas that utilize time as a key indicator such as transportation engineering

H2S, the potentially novel gasotransmitter during experimental cerebral ischemia

Ph.DDOCTOR OF PHILOSOPH

Development of Lattice Boltzmann Method for Compressible Flows

Ph.DDOCTOR OF PHILOSOPH

Multi-microjoule GaSe-based mid-infrared optical parametric amplifier with an ultra-broad idler spectrum covering 4.2-16 {\mu}m

We report a multi-microjoule, ultra-broadband mid-infrared optical parametric amplifier based on a GaSe nonlinear crystal pumped at ~2 {\mu}m. The generated idler pulse has a flat spectrum spanning from 4.5 to 13.3 {\mu}m at -3 dB and 4.2 to 16 {\mu}m in the full spectral range, with a central wavelength of 8.8 {\mu}m. The proposed scheme supports a sub-cycle Fourier-transform-limited pulse width. A (2+1)-dimensional numerical simulation is employed to reproduce the obtained idler spectrum. To our best knowledge, this is the broadest -3 dB spectrum ever obtained by optical parametric amplifiers in this spectral region. The idler pulse energy is ~3.4 {\mu}J with a conversion efficiency of ~2% from the ~2 {\mu}m pump to the idler pulse.Comment: 5 pages, 5 figure

Data and Code Disclosure and Sharing Policy of communications in transportation research

202309 bcvcVersion of RecordNot mentionPublishe

Data-driven interpretation on interactive and nonlinear effects of the correlated built environment on shared mobility

Understanding the usage demand of shared mobility systems in different areas of a city and its determinants is crucial for planning, operation and management of the systems. This study leverages an unbiased data-driven approach called accumulated effect analysis for examining the complex (nonlinear and interactive) effects of correlated built environment factors on the usage of shared mobility. Special research emphasis is given to unraveling the complex effects using an unbiased and data-driven approach that can overcome the impacts of correlations among built environment factors. Based on empirical analysis of synthetic data and a field dataset about dockless bike sharing systems (DLBS), results demonstrate that the method of partial dependency analysis prevalent in the relevant literature, will result in biases when investigating the effects of correlated built environment factors. In comparison, accumulated local effect analysis can appropriately interpret the effects of correlated built environment factors. The main effects of many built environment factors on the usage of DLBS present nonlinear and threshold patterns, quantitively revealed by accumulated local analysis. The approach can reveal complex interaction effects between different built environment factors (e.g., commercial service and education facility, and metro station coverage and living facility) on the usage of DLBS as well. The interactions among two built environment factors could even change with the values of the factors rather than invariant. The outcomes offer a new approach for revealing complex influences of different built environment factors with correlations as well as in-depth empirical understandings regarding the usage of DLBS

ROBUST OPTIMIZATION OF STOCHASTIC HYBRID JOB-SHOP SCHEDULING WITH MULTIPROCESSOR TASK

Due to the large number of uncertainties in the production workshop, the actual performance of the scheduling scheme deviated significantly from the theoretical value. In order to enhance its anti-jamming capability, this paper developed the robust optimization of stochastic hybrid job-shop scheduling with multiprocessors tasks. Firstly, predictable uncertainties were abstracted into processing time variations and described by scenario analysis in the modeling process. Secondly, based on the analysis of the advantages and disadvantages of traditional robust optimization models, a new Expected Cmax and the Worst scenario Model (ECWM) was proposed. The model improved the single-index robust optimization model and avoided the disadvantage that the Max Regret Model is computationally intensive. Finally, the effectiveness of ECWM is verified by simulation experiments. The results show that the scheduling obtained by ECWM has good average performance and anti-risk ability, which indicates that the model achieves a good balance in scheduling performance enthusiasm and risk resistance