Network revenue management game in the rail freight industry

PhD ThesisThe study aims to design the optimal track access tariff to coordinate the relationship between an Infrastructure Manager (IM) and a Freight Operating Company (FOC) in a vertical separated railway system. In practice, the IM takes advantage of leader position in determining the prices to unilaterally maximise its profits without the collaboration with the FOC, which leads to a sub-optimal situation. The interaction between the IM and the FOC is modelled as a network-based Stackelberg game. First, a rigorous bilevel optimisation model is presented that determines the best prices for an IM to maximise its profits without any collaboration with the FOC. The lower level of the bilevel model contains binary integer variables representing the FOC’s choices on the itineraries, which is a challenging optimisation problem not resolved in the literature. The study proposes a uniquely designed solution method involving both gradient search and local search to successfully solve the problem. Secondly, an inverse programming model is developed to determine the IM’s prices to maximise the system profit and achieve global optimality. A Fenchel cutting plane based algorithm is developed to solve the inverse optimisation model. Thirdly, a government subsidy based pricing mechanism is designed. To identify the optimal amount of subsidy, a double-layer gradient search and local search method is developed. The proposed mechanism can lead to the global optimality and ensure that the IM and the FOC are better off than the above two scenarios. Numerical cases based on the data from the UK rail freight industry are conducted to validate the models and algorithms. The results reveal that both the optimal prices obtained via inverse optimisation and the subsidy contract outperform the non-cooperation case in the current industrial practice; and that the cooperation between the IM and the FOC in determining track access tariff is better than non-cooperation

NIGCOMSAT-1R Satellite-Based Augmentation System(SBAS) test bed trial

In October, 2016 China Great Wall Industry Corporation and Spacestar Technology Company Ltd of China entered into cooperative agreement with Nigerian Communications Satellite Ltd on a Satellite-Based Augmentation System (SBAS) performance test and assessment of the Nigerian Communications Satellite (NIGCOMSAT-1R) augmentation System exploiting NIGCOMSAT-1R L Band. This paper examines test bed experimentation conducted in conjunction with partners to validate functional requirements, performance validation of units, sub-systems and systems of both the SBAS payload and ground infrastructure before a pilot project demonstration of capabilities and proof-of-concept nationwide and extension to parts and regions of Afric

Perturbations of spinning black holes in dynamical Chern-Simons gravity I. Slow rotation equations

The detection of gravitational waves resulting by the LIGO-Virgo-Kagra collaboration has inaugurated a new era in gravitational physics, providing an opportunity to test general relativity and its modifications in the strong gravity regime. One such test involves the study of the ringdown phase of gravitational waves from binary black-hole coalescence, which can be decomposed into a superposition of quasinormal modes. In general relativity, the spectra of quasinormal modes depend on the mass, spin, and charge of the final black hole, but they can be influenced by additional properties of the black hole, as well as corrections to general relativity. In this work, we employ the modified Teukolsky formalism developed in a previous study to investigate perturbations of slowly rotating black holes in a modified theory known as dynamical Chern-Simons gravity. Specifically, we derive the master equations for the $\Psi_0$ and $\Psi_4$ Weyl scalar perturbations that characterize the radiative part of gravitational perturbations, as well as for the scalar field perturbations. We employ metric reconstruction techniques to obtain explicit expressions for all relevant quantities. Finally, by leveraging the properties of spin-weighted spheroidal harmonics to eliminate the angular dependence from the evolution equations, we derive two, radial, second-order, ordinary differential equations for $\Psi_0$ and $\Psi_4$, respectively. These equations are coupled to another radial, second-order, ordinary differential equation for the scalar field perturbations. This work is the first attempt to derive a master equation for black holes in dynamical Chern-Simons gravity using curvature perturbations. The master equations can be numerically integrated to obtain the quasinormal mode spectrum of slowly rotating black holes in this theory, making progress in the study of ringdown in dynamical Chern-Simons gravity.Comment: 40 pages, 2 figures and 1 tabl

Physics-Augmented Data-EnablEd Predictive Control for Eco-driving of Mixed Traffic Considering Diverse Human Behaviors

Data-driven cooperative control of connected and automated vehicles (CAVs) has gained extensive research interest as it can utilize collected data to generate control actions without relying on parametric system models that are generally challenging to obtain. Existing methods mainly focused on improving traffic safety and stability, while less emphasis has been placed on energy efficiency in the presence of uncertainties and diversities of human-driven vehicles (HDVs). In this paper, we employ a data-enabled predictive control (DeePC) scheme to address the eco-driving of mixed traffic flows with diverse behaviors of human drivers. Specifically, by incorporating the physical relationship of the studied system and the Hankel matrix update from the generalized behavior representation to a particular one, we develop a new Physics-Augmented Data-EnablEd Predictive Control (PA-DeePC) approach to handle human driver diversities. In particular, a power consumption term is added to the DeePC cost function to reduce the holistic energy consumption of both CAVs and HDVs. Simulation results demonstrate the effectiveness of our approach in accurately capturing random human driver behaviors and addressing the complex dynamics of mixed traffic flows, while ensuring driving safety and traffic efficiency. Furthermore, the proposed optimization framework achieves substantial reductions in energy consumption, i.e., average reductions of 4.83% and 9.16% when compared to the benchmark algorithms

Implications of pleiotropy: challenges and opportunities for mining Big Data in biomedicine

Pleiotropy arises when a locus influences multiple traits. Rich GWAS findings of various traits in the past decade reveal many examples of this phenomenon, suggesting the wide existence of pleiotropic effects. What underlies this phenomenon is the biological connection among seemingly unrelated traits/diseases. Characterizing the molecular mechanisms of pleiotropy not only helps to explain the relationship between diseases, but may also contribute to novel insights concerning the pathological mechanism of each specific disease, leading to better disease prevention, diagnosis and treatment. However, most pleiotropic effects remain elusive because their functional roles have not been systematically examined. A systematic investigation requires availability of qualified measurements at multilayered biological processes (e.g., transcription and translation).The rise of Big Data in biomedicine, such as high-quality multi-omics data, biomedical imaging data and electronic medical records of patients, offers us an unprecedented opportunity to investigate pleiotropy. There will be a great need of computationally efficient and statistically rigorous methods for integrative analysis of these Big Data in biomedicine. In this review, we outline many opportunities and challenges in methodology developments for systematic analysis of pleiotropy, and highlight its implications on disease prevention, diagnosis and treatment

Interferometer Response to Geontropic Fluctuations

We model vacuum fluctuations in quantum gravity with a scalar field, characterized by a high occupation number, coupled to the metric. The occupation number of the scalar is given by a thermal density matrix, whose form is motivated by fluctuations in the vacuum energy, which have been shown to be conformal near a light-sheet horizon. For the experimental measurement of interest in an interferometer, the size of the energy fluctuations is fixed by the area of a surface bounding the volume of spacetime being interrogated by an interferometer. We compute the interferometer response to these "geontropic" scalar-metric fluctuations, and apply our results to current and future interferometer measurements, such as LIGO and the proposed GQuEST experiment.Comment: 17 pages, 6 figure

Optimal battery thermal management for electric vehicles with battery degradation minimization

The control of a battery thermal management system (BTMS) is essential for the thermal safety, energy efficiency, and durability of electric vehicles (EVs) in hot weather. To address the battery cooling optimization problem, this paper utilizes dynamic programming (DP) to develop an online rule-based control strategy. Firstly, an electrical–thermal-aging model of the LiFePO4 battery pack is established. A control-oriented onboard BTMS model is proposed and verified under different speed profiles and temperatures. Then in the DP framework, a cost function consisting of battery aging cost and cooling-induced electricity cost is minimized to obtain the optimal compressor power. By exacting three rules ”fast cooling, slow cooling, and temperature-maintaining” from the DP result, a near-optimal rule-based cooling strategy, which uses as much regenerative energy as possible to cool the battery pack, is proposed for online execution. Simulation results show that the proposed online strategy can dramatically improve the driving economy and reduce battery degradation under diverse operation conditions, achieving less than a 2.18% difference in battery loss compared to the offline DP. Recommendations regarding battery cooling under different real-world cases are finally provided

Isospectrality breaking in the Teukolsky formalism

General relativity, though the most successful theory of gravity, has been continuously modified to resolve its incompatibility with quantum mechanics and explain the origin of dark energy or dark matter. One way to test these modified gravity theories is to study the gravitational waves emitted during the ringdown of binary mergers, which consist of quasinormal modes. In several modified gravity theories, the even- and odd-parity gravitational perturbations of non-rotating and slowly rotating black holes have different quasinormal mode frequencies, breaking the isospectrality of general relativity. For black holes with arbitrary spin in modified gravity, there were no avenues to compute quasinormal modes except numerical relativity, until recent extensions of the Teukolsky formalism. In this work, we describe how to use the modified Teukolsky formalism to study isospectrality breaking in modified gravity. We first introduce how definite-parity modes are defined through combinations of Weyl scalars in general relativity, and then, we extend this definition to modified gravity. We then use the eigenvalue perturbation method to show how the degeneracy in quasinormal mode frequencies of different parity is broken in modified gravity. To demonstrate our analysis, we also apply it to some specific modified gravity theories. Our work lays the foundation for studying isospectrality breaking of quasinormal modes in modified gravity for black holes with arbitrary spin.Comment: 29 page