Non-minimal Einstein-Yang-Mills-Higgs theory: Associated, color and color-acoustic metrics for the Wu-Yang monopole model

We discuss a non-minimal Einstein-Yang-Mills-Higgs model with uniaxial anisotropy in the group space associated with the Higgs field. We apply this theory to the problem of propagation of color and color-acoustic waves in the gravitational background related to the non-minimal regular Wu-Yang monopole.Comment: 14 pages, no figure

The Effects of Environmental Regulation on the Singapore Stock Market

This study examines the impact of environmental regulation on the Singapore stock market using the event study methodology. Several asset pricing models are used to estimate sectoral abnormal returns. Additionally, we estimate the change in systematic risk after the introduction of the carbon tax and related regulation. We conduct various robustness tests, including the Corrado non-parametric ranking test, the Chesney non-parametric conditional distribution approach, a representation of market integration, and Fama–French five-factor model. We find evidence showing that the environmental regulations tend to achieve their desired effects in Singapore in which several big polluters (including industrial metals and mining, forestry and papers, and electrical equipment and services) were negatively affected by the announcements of environmental regulations and carbon tax. In addition, our results indicate that the electricity sector, one of the biggest polluters, was negatively affected by the announcement of environmental regulations and carbon tax. We also find that environmental regulations seem to boost the performance of environmentally-friendly sectors whereby we find the alternative energy industry (focusing on new renewable energy technologies) experienced a sizeable positive reaction following the announcements of these regulations

Giant Spin Seebeck Effect through an Interface Organic Semiconductor

Interfacing an organic semiconductor C60 with a non-magnetic metallic thin film (Cu or Pt) has created a novel heterostructure that is ferromagnetic at ambient temperature, while its interface with a magnetic metal (Fe or Co) can tune the anisotropic magnetic surface property of the material. Here, we demonstrate that sandwiching C60 in between a magnetic insulator (Y3Fe5O12: YIG) and a non-magnetic, strong spin-orbit metal (Pt) promotes highly efficient spin current transport via the thermally driven spin Seebeck effect (SSE). Experiments and first principles calculations consistently show that the presence of C60 reduces significantly the conductivity mismatch between YIG and Pt and the surface perpendicular magnetic anisotropy of YIG, giving rise to enhanced spin mixing conductance across YIG/C60/Pt interfaces. As a result, a 600% increase in the SSE voltage (VLSSE) has been realized in YIG/C60/Pt relative to YIG/Pt. Temperature-dependent SSE voltage measurements on YIG/C60/Pt with varying C60 layer thicknesses also show an exponential increase in VLSSE at low temperatures below 200 K, resembling the temperature evolution of spin diffusion length of C60. Our study emphasizes the important roles of the magnetic anisotropy and the spin diffusion length of the intermediate layer in the SSE in YIG/C60/Pt structures, providing a new pathway for developing novel spin-caloric materials

The impact of dimethylformamide on the synthesis of graphene quantum dots derived from graphene oxide

Graphene quantum dots (GQDs) have garnered immense interest in recent years due to their unique optical, electrical, and chemical properties, making them promising candidates for various applications in optoelectronics, bioimaging, and sensing. However, enhancing the control over the size, surface chemistry, and optical properties of GQDs remains a significant challenge. In this study, a novel recipe was proposed to successfully synthesize various GQDs via a typical solvothermal process, which has proven to be a versatile and scalable approach. In addition to the main ingredient – graphene oxide suspension, dimethylformamide (DMF) and hydrogen peroxide serving as a cutting agent were added to the reaction mixture. This synthesis method was found to be more promising than the reference one in which DMF was replaced by double distilled water. Through systematic experimentation, we demonstrated that the addition of DMF enables the successful GQD production over a wider range of reaction times; hence, the UV absorption band and photoluminescence properties of GQDs can be better adjusted. The dependence of photoluminescence on the excitation wavelength was observed in the as-prepared materials as they were excited with a range of wavelengths from 360 to 480 nm. The obtained insights not only advance our understanding of GQD synthesis but also open up avenues for tailoring their properties for specific applications

Gamow-Teller strength distributions for double-beta-decaying nuclei within continuum-QRPA

A version of the pn-continuum-QRPA is outlined and applied to describe the Gamow-Teller strength distributions for $\beta\beta$-decaying open-shell nuclei. The calculation results obtained for the pairs of nuclei $^{116}$Cd-Sn and $^{130}$Te-Xe are compared with available experimental data.Comment: 8 pages, 3 figures, To appear in the proceedings of "Nucleus-2007: Fundamental problems of nuclear physics, atomic power engineering and nuclear technologies" Voronezh, Russia, June 25-29, 200

Corrigendum to The impact of dimethylformamide on the synthesis of graphene quantum dots derived from graphene oxide

The original article, “Khuong T. Truong, Thach H. Pham, Khai V. Tran. The impact of dimethylformamide on the synthesis of graphene quantum dots derived from graphene oxide. Chimica Techno Acta. 2023;10(4):202310405”, is available at: https://doi.org/10.15826/chimtech.2023.10.4.0

Improving Object Detection in Medical Image Analysis through Multiple Expert Annotators: An Empirical Investigation

The work discusses the use of machine learning algorithms for anomaly detection in medical image analysis and how the performance of these algorithms depends on the number of annotators and the quality of labels. To address the issue of subjectivity in labeling with a single annotator, we introduce a simple and effective approach that aggregates annotations from multiple annotators with varying levels of expertise. We then aim to improve the efficiency of predictive models in abnormal detection tasks by estimating hidden labels from multiple annotations and using a re-weighted loss function to improve detection performance. Our method is evaluated on a real-world medical imaging dataset and outperforms relevant baselines that do not consider disagreements among annotators.Comment: This is a short version submitted to the Midwest Machine Learning Symposium (MMLS 2023), Chicago, IL, US