Does a change in debt structure matter in earnings management? the application of nonlinear panel threshold test

In this study, we apply Hansen¡¦s (1999) nonlinear panel threshold test, the most powerful test of its kind, to investigate the relationship between debt ratio and earnings management of 474 selected Taiwan-listed companies during the September 2002 - June 2005 period. Rather than a fixed positive relation that is determined from the OLS, our empirical results strongly suggest that when a firm¡¦s debt ratio exceeds 46.79% and 62.17%, its debt structure changes, which in turn leads to changes in earnings management. With an increase in debt ratio, managers tend to manage earnings to a greater extent and at a higher speed. In other words, the threshold effect of debt on the relationship between debt ratio and earnings management generates an increasingly positive impact. These empirical results provide concerned investors and authorities with an enhanced understanding of earnings management, as manipulated by managers confronted with different debt structures.

Are countries with higher levels of mental health cases experience higher divorce rates?

This paper aims to determine if spouses’ mental health can be a factor affecting the divorce rate of marriage. A regression analysis is carried out to determine how the percentage of mental health cases in a country’s population affects the divorce rates of a country, while controlling the effects of labour force participation and income. The data from the selected 20 countries are collected from reputable world organizations selected. The results obtained from the regression analysis show that mental health has a marginally significant association with divorce rate and the association between income index and divorce rate is statistically significant

Constraining the position of the knee in the galactic cosmic ray spectrum with ultra-high-energy diffuse γ\gamma-rays

The diffuse $\gamma$-ray emission was measured up to $957$ TeV by the Tibet-AS$\gamma$ experiment recently. Assuming that it is produced by the hadronic interaction between cosmic ray nuclei and the interstellar medium, it requires that the cosmic ray nuclei should be accelerated well beyond PeV energies. Measurements of the cosmic ray spectra for different species show diverse results at present. The Tibet experiments showed that the spectrum of proton plus helium has an early knee below PeV. If this is correct, the diffuse $\gamma$-ray emission would suggest an additional component of Galactic cosmic rays above PeV energies. This second component may originate from a source population of so-called PeVatrons revealed by recent ultra-high energy $\gamma$-ray observations, and could contribute to the cosmic ray fluxes up to the energy of the second knee. On the other hand, the KASCADE measurement showed that the knee of protons is higher than PeV. In this case, the diffuse $\gamma$-rays observed by Tibet-AS$\gamma$ can be well accounted for by only one cosmic ray component. These two scenarious (ie. the Tibet and KASCADE knees) could be distinguished by the spectral structures of diffuse $\gamma$-rays and cosmic ray nuclei. Future measurements of spectra of individual nuclei by HERD and LHAASO experiments and diffuse $\gamma$-rays by LHAASO can jointly constrain these two scenarios.Comment: 9 pages,4 figures. accepted by Ap

A Novel Multi-Task Learning Empowered Codebook Design for Downlink SCMA Networks

Sparse code multiple access (SCMA) is a promising code-domain non-orthogonal multiple access (NOMA) scheme for the enabling of massive machine-type communication. In SCMA, the design of good sparse codebooks and efficient multiuser decoding have attracted tremendous research attention in the past few years. This paper aims to leverage deep learning to jointly design the downlink SCMA encoder and decoder with the aid of autoencoder. We introduce a novel end-to-end learning based SCMA (E2E-SCMA) design framework, under which improved sparse codebooks and low-complexity decoder are obtained. Compared to conventional SCMA schemes, our numerical results show that the proposed E2E-SCMA leads to significant improvements in terms of error rate and computational complexity

FedBA: Non-IID Federated Learning Framework in UAV Networks

With the development and progress of science and technology, the Internet of Things(IoT) has gradually entered people's lives, bringing great convenience to our lives and improving people's work efficiency. Specifically, the IoT can replace humans in jobs that they cannot perform. As a new type of IoT vehicle, the current status and trend of research on Unmanned Aerial Vehicle(UAV) is gratifying, and the development prospect is very promising. However, privacy and communication are still very serious issues in drone applications. This is because most drones still use centralized cloud-based data processing, which may lead to leakage of data collected by drones. At the same time, the large amount of data collected by drones may incur greater communication overhead when transferred to the cloud. Federated learning as a means of privacy protection can effectively solve the above two problems. However, federated learning when applied to UAV networks also needs to consider the heterogeneity of data, which is caused by regional differences in UAV regulation. In response, this paper proposes a new algorithm FedBA to optimize the global model and solves the data heterogeneity problem. In addition, we apply the algorithm to some real datasets, and the experimental results show that the algorithm outperforms other algorithms and improves the accuracy of the local model for UAVs

XRCC1, but not APE1 and hOGG1 gene polymorphisms is a risk factor for pterygium.

PurposeEpidemiological evidence suggests that UV irradiation plays an important role in pterygium pathogenesis. UV irradiation can produce a wide range of DNA damage. The base excision repair (BER) pathway is considered the most important pathway involved in the repair of radiation-induced DNA damage. Based on previous studies, single-nucleotide polymorphisms (SNPs) in 8-oxoguanine glycosylase-1 (OGG1), X-ray repair cross-complementing-1 (XRCC1), and AP-endonuclease-1 (APE1) genes in the BER pathway have been found to affect the individual sensitivity to radiation exposure and induction of DNA damage. Therefore, we hypothesize that the genetic polymorphisms of these repair genes increase the risk of pterygium.MethodsXRCC1, APE1, and hOGG1 polymorphisms were studied using fluorescence-labeled Taq Man probes on 83 pterygial specimens and 206 normal controls.ResultsThere was a significant difference between the case and control groups in the XRCC1 genotype (p=0.038) but not in hOGG1 (p=0.383) and APE1 (p=0.898). The odds ratio of the XRCC1 A/G polymorphism was 2.592 (95% CI=1.225-5.484, p=0.013) and the G/G polymorphism was 1.212 (95% CI=0.914-1.607), compared to the A/A wild-type genotype. Moreover, individuals who carried at least one C-allele (A/G and G/G) had a 1.710 fold increased risk of developing pterygium compared to those who carried the A/A wild type genotype (OR=1.710; 95% CI: 1.015-2.882, p=0.044). The hOGG1 and APE1 polymorphisms did not have an increased odds ratio compared with the wild type.ConclusionsXRCC1 (Arg399 Glu) is correlated with pterygium and might become a potential marker for the prediction of pterygium susceptibility