Earnings Management and Long-Run Stock Underperformance of Private Placements

The study investigates whether private placement issuers manipulate their earnings around the time of issuance and the effect of earnings management on the long-run stock performance. We find that managers of U.S. private placement issuers tend to engage in income-increasing earnings management in the year prior to the issuance of private placements. We further speculate that earnings management serves as a likely source of investor over-optimism at the time of private placements. To support this speculation, we find evidence suggesting that the income-increasing accounting accruals made at the time of private placements predict the post-issue long-term stock underperformance. The study contributes to the large body of literature on earnings manipulation around the time of securities issuance

Window Dressing in Reported Earnings

The article discusses the use of the term window dressing, a wide range of techniques for auditing, by audit clients to enhance the financial position of an entity through manipulated disclosures. The term refers to the reporting practices adopted by firms to distort earnings by changing the way stakeholders perceived the financial figures. A research suggests that firms must engage in the type of manipulative behavior for the purpose of economic incentives

Convergence of Adaptive Finite Element Approximations for Nonlinear Eigenvalue Problems

In this paper, we study an adaptive finite element method for a class of a nonlinear eigenvalue problems that may be of nonconvex energy functional and consider its applications to quantum chemistry. We prove the convergence of adaptive finite element approximations and present several numerical examples of micro-structure of matter calculations that support our theory.Comment: 24 pages, 12 figure

Enhancement of magnetic anisotropy barrier in long range interacting spin systems

Magnetic materials are usually characterized by anisotropy energy barriers which dictate the time scale of the magnetization decay and consequently the magnetic stability of the sample. Here we present a unified description, which includes coherent rotation and nucleation, for the magnetization decay in generic anisotropic spin systems. In particular, we show that, in presence of long range exchange interaction, the anisotropy energy barrier grows as the volume of the particle for on site anisotropy, while it grows even faster than the volume for exchange anisotropy, with an anisotropy energy barrier proportional to $V^{2-\alpha/d}$, where $V$ is the particle volume, $\alpha \leq d$ is the range of interaction and $d$ is the embedding dimension. These results shows a relevant enhancement of the anisotropy energy barrier w.r.t. the short range case, where the anisotropy energy barrier grows as the particle cross sectional area for large particle size or large particle aspect ratio.Comment: 7 pages, 6 figures. Theory of Magnetic decay in nanosystem. Non equilibrium statistical mechanics of many body system

Coil combination using linear deconvolution in k-space for phase imaging

Background: The combination of multi-channel data is a critical step for the imaging of phase and susceptibility contrast in magnetic resonance imaging (MRI). Magnitude-weighted phase combination methods often produce noise and aliasing artifacts in the magnitude images at accelerated imaging sceneries. To address this issue, an optimal coil combination method through deconvolution in k-space is proposed in this paper. Methods: The proposed method firstly employs the sum-of-squares and phase aligning method to yield a complex reference coil image which is then used to calculate the coil sensitivity and its Fourier transform. Then, the coil k-space combining weights is computed, taking into account the truncated frequency data of coil sensitivity and the acquired k-space data. Finally, combining the coil k-space data with the acquired weights generates the k-space data of proton distribution, with which both phase and magnitude information can be obtained straightforwardly. Both phantom and in vivo imaging experiments were conducted to evaluate the performance of the proposed method. Results: Compared with magnitude-weighted method and MCPC-C, the proposed method can alleviate the phase cancellation in coil combination, resulting in a less wrapped phase. Conclusions: The proposed method provides an effective and efficient approach to combine multiple coil image in parallel MRI reconstruction, and has potential to benefit routine clinical practice in the future

Investigation of Partial Discharge in Solid Dielectric under DC Voltage

A partial discharge, or PD, is defined as an electrical discharge that is localized within only a part of the insulation between two separated conductors. Recent research on PD mainly focuses on the study of PD characteristics under AC voltage. Compared with DC, PD under AC is more serious and can be easily detected in terms of PD number. As the results of these concentrated research, the understanding of PD under AC condition has been significantly improved and features extracted from PD measurements have been used to diagnose the insulation condition of many power apparatus. Recently, rapid development in HVDC transmission and power grids connection, and widely applied DC cable and gas-insulated switchgear because of their benefit in long distance usage lead to an increasing concern about PD under DC. However, available study for the condition is little and related research is therefore necessary and essential for understanding the lifetime and reliability of apparatus. <br/

Machine Learning meets Number Theory: The Data Science of Birch-Swinnerton-Dyer

Empirical analysis is often the first step towards the birth of a conjecture. This is the case of the Birch-Swinnerton-Dyer (BSD) Conjecture describing the rational points on an elliptic curve, one of the most celebrated unsolved problems in mathematics. Here we extend the original empirical approach, to the analysis of the Cremona database of quantities relevant to BSD, inspecting more than 2.5 million elliptic curves by means of the latest techniques in data science, machine-learning and topological data analysis. Key quantities such as rank, Weierstrass coefficients, period, conductor, Tamagawa number, regulator and order of the Tate-Shafarevich group give rise to a high-dimensional point-cloud whose statistical properties we investigate. We reveal patterns and distributions in the rank versus Weierstrass coefficients, as well as the Beta distribution of the BSD ratio of the quantities. Via gradient boosted trees, machine learning is applied in finding inter-correlation amongst the various quantities. We anticipate that our approach will spark further research on the statistical properties of large datasets in Number Theory and more in general in pure Mathematics

Thermodynamic analysis of a novel fossil-fuel–free energy storage system with a trans-critical carbon dioxide cycle and heat pump

This paper presents and analyzes a novel fossil-fuel–free trans-critical energy storage system that uses CO2 as the working fluid in a closed loop shuttled between two saline aquifers or caverns at different depths: one a low-pressure reservoir and the other a high-pressure reservoir. Thermal energy storage and a heat pump are adopted to eliminate the need for external natural gas for heating the CO2 entering the energy recovery turbines. We carefully analyze the energy storage and recovery processes to reveal the actual efficiency of the system. We also highlight thermodynamic and sensitivity analyses of the performance of this fossil-fuel–free trans-critical energy storage system based on a steady-state mathematical method. It is found that the fossil-fuel–free trans-critical CO2 energy storage system has good comprehensive thermodynamic performance. The exergy efficiency, round-trip efficiency, and energy storage efficiency are 67.89%, 66%, and 58.41%, and the energy generated of per unit storage volume is 2.12 kW·h/m3, and the main contribution to exergy destruction is the turbine reheater, from which we can quantify how performance can be improved. Moreover, with a higher energy storage and recovery pressure and lower pressure in the low-pressure reservoir, this novel system shows promising performance