Orbital orientation evolution of massive binary black holes at the centres of non-spherical galaxies

At the centre of a spherical and kinematically isotropic galaxy, the orientation of a massive binary black hole (BBH) orbit (i.e., the direction of the BBH orbital angular momentum) undergoes a random walk. If the stars in a spherical system have a non-zero total angular momentum, the BBH orbital orientation evolves towards aligning with the total stellar angular momentum direction. In this paper, we show that a triaxial galaxy has an alignment-erasing effect, that is, the alignment of the BBH orientations towards the galaxy rotation axis can be decreased significantly or erased. We also show that in a non-rotating axisymmetric galaxy, the BBH orbital orientation evolves towards the axisymmetric axis and precesses about it in a retrograde direction. Our results provide a step towards understanding the spin orientations of the final merged BH (and hence probable orientation of any jet produced) within its host galaxy, and may help to constrain the recoiling velocity of the merged BH arose from gravitational wave radiation as well.Comment: 16 pages, 9 figures, MNRAS accepte

Development of parametric eco-driving models for fuel savings: A novel parameter calibration approach

The existing conventional traffic flow models aims to simulate human-driven following vehicles in real world. In this era of emerging transport solutions, controlling or intervening traffic flow to achieve high fuel efficiency along with good driving safety and travel efficiency becomes a reality. As such, it is worth exploring the possibility of developing eco-driving models to optimise vehicle movements for fuel consumption minimisation, while maintaining safety and efficiency. In this study, we propose a modified genetic algorithm (GA) based calibration method that enables the calibrated parametric traffic flow (car following) models to simulate or control vehicles in an eco-driving manner. By developing a novel objective function for the GA method based on the widely-used VT-Micro fuel consumption model, the proposed method can calibrate model parameters towards improving fuel efficiency. Besides, by subtly using heavy fuel consumptions as a surrogate index to represent low travel efficiency or dangerous driving strategies, the modified GA method with the novel objective function can guide the calibrated model towards achieving complete eco-driving requirements. Experimental simulation results further indicate that traffic flow models calibrated by the modified GA-based method can also alleviate traffic disturbances and oscillations in a more effective manner

Accurate and fast two-step phase shifting algorithm based on principle component analysis and Lissajous ellipse fitting with random phase shift and no pre-filtering

To achieve high measurement accuracy with less computational time-in-phase shifting interferometry, a random phase-shifting algorithm based on principal component analysis and Lissajous ellipse fitting (PCA& LEF) is proposed. It doesn't need pre-filtering and can obtain relatively accurate phase distribution with only two phase shifted interferograms and less computational time and is suitable for different background intensity, modulation amplitude distributions and noises. Moreover, it can obtain absolutely accurate result when the background intensity and modulation amplitude are perfect and can partly suppress the effect of imperfect background intensity and modulation amplitude. Last but not least, it removes the restriction that PCA needs more than three interferograms with welldistributed phase shifts to subtract relatively accurate mean. The simulations and experiments verify the correctness and feasibility of PCA& LEF. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing AgreementNational Natural Science Foundation of China (NSFC) [11304034]; Department of Science and Technology of Jilin Province [20190701018GH]; Education Department of Jilin Province [JJKH20190691KJ]; State Key Laboratory of Applied OpticsOpen access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Multimodal Neurophysiological Representations of High School Students’ Situational Interest: A Machine Learning Approach

Interest plays a vital role in students’ learning performance. Accurately measuring situational interest in the classroom environment is important for understanding the learning mechanism and improving teaching. However, self-report measurements frequently encounter issues of subjectivity and ambiguity, and it is hard to collect dynamic self-report scales without disturbance in the naturalistic environment. Thanks to the development of neuroscience and portable biosensors, it has become possible to represent psychological states with neurophysiological features in the classroom environment. In this study, multimodal neurophysiological signals, including electroencephalograph (EEG), electrodermal activity (EDA), and photoplethysmography (PPG), were applied to represent situational interest under both laboratory (Study 1) and naturalistic (Study 2) paradigms. A total of 33 features were extracted, and 7 different statistical indicators were calculated for each of them across all the epochs. Among these features, 47 in Study 1 and 49 in Study 2 demonstrated significant correlation with self-report situational interest. Employing a machine learning model, the analysis yielded a mean absolute error (MAE) of 0.772 and mean squared error (MSE) of 0.883 for the dataset in Study 1. However, the model was not robust on data from Study 2. These findings offer empirical support for the conceptual framework of situational interest, demonstrate the potential of neurophysiological data in educational assessments, and also highlight the challenges in naturalistic paradigm

Essays in Financial Economics

This thesis addresses various contractual solutions to firms' financial distress. The first chapter presents a unified framework for analyzing the holdout problem, a pervasive economic phenomenon in which value creation is hindered by the incentive to free-ride on other agents' participation. The framework nests many classic applications, such as takeovers and debt restructuring, and highlights the role of the commitment power: The holdout problem can be resolved using contingent contracts with commitment, e.g., by a unanimity rule if the principal can commit to calling off the deal when anyone holds out. In contrast, a lack of commitment substantially alters the optimal offers depending on the payoff sensitivities of the existing contracts, which explains the absence of the unanimity rule despite its efficacy and cross-sectional heterogeneity in offers. (E.g., senior debt is used in debt restructuring but not in takeovers.) Furthermore, I show that stronger partial commitment can backfire via renegotiation, exacerbating the holdout problem. This non-monotonicity reconciles contradictory empirical findings on the use of CACs in the sovereign debt market and sheds light on various policies. Lastly, the paper shows stronger investor protection could facilitate instead of hinder restructuring under limited commitment.The second chapter considers the role of institutions in financial distress. Distress can be mitigated by filing for bankruptcy (which is costly) or preempted by restructuring (which is impeded by a collective action problem). We find that bankruptcy and restructuring are complements, not substitutes: Reducing bankruptcy costs facilitates restructuring rather than crowding it out. So does making bankruptcy more debtor-friendly, under a condition that can be written in terms of a few easily observable sufficient statistics. The model gives new perspectives on relief policies (e.g., subsidies to bankrupt firms) and on legal debates (e.g., the absolute priority rule). The third chapter examines the optimal design of liquidity insurance contracts for firms that experience a quality shock concurrent with the liquidity shock, both of which cannot be verified and hence contracted upon. Due to the incompleteness of these contracts, firms cannot receive full liquidity insurance: If a firm is fully insured, it has little incentive to stop inefficient projects, as creditors bear the costs. Therefore, the optimal contract involves limited insurance and requires co-investment with internal cash. Interestingly, my findings challenge the classical theory that low-pledgeability firms rely more on liquidity insurance. Instead, I show that a lack of pledgeability prevents these firms from obtaining more liquidity insurance. In fact, I demonstrate a positive relationship between liquidity insurance and pledgeability, which sheds light on the seemingly paradoxical fact that smaller firms that need liquidity insurance the most are the least insured and face the highest risk of revocation. Furthermore, my results rationalize the common cash-related covenants in credit lines