Slope of Orderable Dehn Filling of Two-Bridge Knots

In this paper, we study the Riley polynomial of double twist knots with higher genus. Using the solution of the Riley polynomial, we compute the range of rational slope $r$ such that $r$-filling of the knot complement has left-orderable fundamental group. Further more, we make a conjecture about left-orderable surgery slopes of two-bridge knots.Comment: 21 pages, 7 figure

The Economic Consequences of Financial Misreporting: Evidence from Employee Responses

This study investigates the economic consequences of financial misreporting arising from employee responses. Specifically, we examine two employee reactions: (1) withdrawing their human capital and (2) reducing holding of employer stock, in both misreporting period and post-restatement period. We find an increase in employee turnover and a decrease in employee holding of employer stock in the post-restatement period (restatement effect) and some evidence that employees start to react in the period of misreporting (misreporting effect). We also find some evidence that the misreporting effect varies with employee tenure in the misreporting period and the restatement effect varies with the severity of misreporting in the post-restatement period. We further show that our results are not driven by labor demand, increased likelihood of executive turnover, declining stock prices, and internal control weakness disclosures, and are robust to a matched sample estimation. Overall, our study provides evidence of human capital costs of financial misreporting to misreporting firms, shedding new light on the negative consequences of accounting failures

Orderability of homology spheres obtained by Dehn filling

In my thesis, I study left-orderability of $\mathbb{Q}$-homology spheres. I use $\widetilde{PSL_2\mathbb{R}}$ representations as a tool. First, I showed this tool has its limitations by constricting a series of $\mathbb{Z}$-homology spheres with potentially left-orderable fundamental groups but no non trivial $\widetilde{PSL_2\mathbb{R}}$ representations. However, this tool is still useful in most cases. With $\widetilde{PSL_2\mathbb{R}}$ representations, I construct the holonomy extension locus of a $\mathbb{Q}$-homology solid torus which is an analog of its translation extension locus. Using extension loci, I study $\mathbb{Q}$-homology 3-spheres coming from Dehn fillings of $\mathbb{Q}$-homology solid tori and construct intervals of orderable Dehn fillings

Economic Deregulation And Corporate Dividend Policy

I investigate the evolution of corporate dividend policy in response to the changing operating environment induced by economic deregulation from the 1970s-1990s. Specifically, I examine the impact of deregulation on the firm\u27s propensity to pay dividends, dividend payout ratio, the sensitivity of corporate dividend policy to current and past earnings, changes in the information content of dividends, and changes in corporate financing behavior along the deregulation process. Empirical results reveal that economic deregulation per se does not have significant impacts on firms\u27 propensity to pay dividends. However, it seems that firms reduce dividend payout along the deregulation process and adjust their payout ratio closer to that of non-regulated firms. I also find that deregulated firms\u27 dividend policy becomes more sensitive to past and current earnings following deregulation. In addition, deregulated firms become more active in external financing activities in the new operating environment, which subjects them to more frequent and closer monitoring of financial markets. The findings are in general consistent with predictions of the agency theory of dividends. However, the empirical results fail to provide support to hypotheses based on the information content theory and the clientele theory of dividends in the setting of economic deregulation

A Comprehensive Indoor Environment Dataset from Single-family Houses in the US

The paper describes a dataset comprising indoor environmental factors such as temperature, humidity, air quality, and noise levels. The data was collected from 10 sensing devices installed in various locations within three single-family houses in Virginia, USA. The objective of the data collection was to study the indoor environmental conditions of the houses over time. The data were collected at a frequency of one record per minute for a year, combining over 2.5 million records. The paper provides actual floor plans with sensor placements to aid researchers and practitioners in creating reliable building performance models. The techniques used to collect and verify the data are also explained in the paper. The resulting dataset can be employed to enhance models for building energy consumption, occupant behavior, predictive maintenance, and other relevant purposes

Machine learning approach in the development of building occupant personas

The user persona is a communication tool for designers to generate a mental model that describes the archetype of users. Developing building occupant personas is proven to be an effective method for human-centered smart building design, which considers occupant comfort, behavior, and energy consumption. Optimization of building energy consumption also requires a deep understanding of occupants' preferences and behaviors. The current approaches to developing building occupant personas face a major obstruction of manual data processing and analysis. In this study, we propose and evaluate a machine learning-based semi-automated approach to generate building occupant personas. We investigate the 2015 Residential Energy Consumption Dataset with five machine learning techniques - Linear Discriminant Analysis, K Nearest Neighbors, Decision Tree (Random Forest), Support Vector Machine, and AdaBoost classifier - for the prediction of 16 occupant characteristics, such as age, education, and, thermal comfort. The models achieve an average accuracy of 61% and accuracy over 90% for attributes including the number of occupants in the household, their age group, and preferred usage of heating or cooling equipment. The results of the study show the feasibility of using machine learning techniques for the development of building occupant persona to minimize human effort.Comment: 12 pages, 4 figure

gem-Dibromocyclopropanes and enzymatically derived cis-1,2-dihydrocatechols as building blocks in alkaloid synthesis

The application of the title building blocks, the 6,6-dibromobicyclo[3.1.0]hexanes and the cis-1,2-dihydrocatechols, to the total synthesis of crinine and lycorinine alkaloids is described.We thank the Australian Research Council and the Institute of Advanced Studies for generous financial support

Numerical analysis of vibration-isolating effect of vibration-isolating slot under buried pipe subjected to millisecond blasting

Research on vibration-isolating effects of vibration-isolating slot on buried pipe can be done by numerical method, without being disturbed by external environmental factors. It has measured data without relatively high experiment cost and analyzed the influence of some key parameters according to the results of numerical simulation. The results show that the vibration speed of the pipeline with vibration-isolating slot tends to have a larger decrease than those without vibration-isolating slot. What’s more, the homogeneous explosive charge is discrepant in different working conditions, but the vibration-isolating ratio is similar in the vibration-isolating slot with same structure parameter. The millisecond blasting is hardly affected by total explosive charge. But the blasting seismic intensity is influenced by explosive charge in each stage directly

Experimental and numerical simulation study of perforation effect of steel pipes subject to the impact loadings of ASC and LSC jets

The perforation effect of steel pipes subjected to the circular-shaped charge (ASC) and linear-shaped charge (LSC) jet were studied by experimental research, and the explicit nonlinear dynamic finite element computer code LS-DYNA was adapted to study the nonlinear responses of the steel pipes, which subjected to the impact of the two different jets, using Lagrangian-Eulerian coupling method. The deformation process and the stress of the steel pipes were described and analyzed, and the simulation results are in good agreement with the experiment data. The studies indicated that under the impact of ASC jet, the steel pipe got a circular incision and a deformation process of local perforation, flocculent shear lip forming and axial shock. Under the impact of LSC jet, the steel pipe got a ship-type incision and a deformation process of coupling of local perforation and dent, whole bending and radial shock. The formation of flocculent shear lip attributes to the radial stress concentration. Under the impact of LSC jet, the whole bending leads to the axial stretch and tearing of the cut tip, and there is a bigger radial plastic deformation area than the damage effect for the impact of ASC jet

Multi-Level Data-Driven Battery Management: From Internal Sensing to Big Data Utilization

Battery management system (BMS) is essential for the safety and longevity of lithium-ion battery (LIB) utilization. With the rapid development of new sensing techniques, artificial intelligence and the availability of huge amounts of battery operational data, data-driven battery management has attracted ever-widening attention as a promising solution. This review article overviews the recent progress and future trend of data-driven battery management from a multi-level perspective. The widely-explored data-driven methods relying on routine measurements of current, voltage, and surface temperature are reviewed first. Within a deeper understanding and at the microscopic level, emerging management strategies with multi-dimensional battery data assisted by new sensing techniques have been reviewed. Enabled by the fast growth of big data technologies and platforms, the efficient use of battery big data for enhanced battery management is further overviewed. This belongs to the upper and the macroscopic level of the data-driven BMS framework. With this endeavor, we aim to motivate new insights into the future development of next-generation data-driven battery management