Search Results (55)

This research focuses on analyzing the determinants of financial performance for the companies included in the Standard & Poor’s 500 index over the period from 2014 to 2023. To guide managerial decisions aimed at enhancing company performance, this study examines, as key drivers, the main financial indicators, core corporate governance characteristics, and U.S. public governance indicators. The investigation begins with a retrospective review of the specialized literature, highlighting the findings of previous studies in the field and providing the basis for selecting the variables used in the present empirical analysis. The research method employed is fixed-effects panel-data regression. The dependent variables are financial performance measures, such as the EBITDA margin, EBIT margin, net profit margin, and ROA. This study’s main results show that the price-to-book ratio, liquidity, sales growth, CEO duality, board gender diversity, ESG score, and U.S. regulatory quality exert a positive influence on financial performance. In contrast, the price-to-earnings ratio, net debt, capital intensity, R&D intensity, weighted average cost of capital, board independence, and the COVID-19 pandemic crisis have a negative impact on the financial performance of U.S. companies. The findings of this investigation could serve as benchmarks for supporting managerial decisions at the company level regarding the improvement of their financial performance. Full article

This study investigates the ionization balance of Sn ions in an electron beam ion trap (EBIT). Highly charged Sn ions are produced via collisions with a quasi-monochromatic electron beam, and the charge state distribution is analyzed using a Wien filter. Significant Sn¹⁵⁺ production occurs at electron energies below the ionization potential of Sn¹⁴⁺ (379 eV). Calculations attribute this to electron-impact ionization from metastable Sn¹⁴⁺ states. Full article

The increasing requirement for distillates, accompanied by higher quantities of heavy crude oil in world production, has positioned gas oil hydrocracking as one of the most significant processes in refineries. In the petrochemical industry, hydrocracking is an essential process that converts heavy hydrocarbons into lighter and more valuable products such as LPG (liquefied petroleum gas), diesel, kerosene, light naphtha, and heavy naphtha. This method uses hydrogen and a catalyst to break down the gas oil feedstock through hydrogenation and hydrocracking reactions. However, the gas oil hydrocracking process faces significant technical, economic, and financial obstacles that must be overcome to reveal its full potential. In this study, a computer-assisted technical–economic evaluation and an evaluation of the technical–economic resilience of the gas oil hydrocracking process at an industrial scale was carried out. Twelve technical–economic and three financial indicators were evaluated to identify this type of process’s current commercial status and to analyze possible economic performance parameter optimizations. The economic indicators listed include gross profit (GP), profitability after taxes (PAT), economic potential (EP), cumulative cash flow (CCF), payback period (PBP), depreciable payback period (DPBP), return on investment (ROI), internal rate of return (IRR), net present value (NPV), annual cost/revenues (ACR), break-even point (BEP), and on-stream efficiency at the BEP. On the other hand, the financial indicators proposed by the methodology are earnings before taxes (EBT), earnings before interest and taxes (EBIT), and earnings before interest, taxes, depreciation, and amortization (EBITDA). The technical–economic resilience of the process was also evaluated, considering the costs of raw materials, the market prices of the products, and processing capacity. The gas oil hydrocracking plant described, with a useful life of 20 years and a processing capacity of 1,937,247.91 tonnes per year, achieved a gross profit (GP) of USD 58.97 million and a return after tax (PAT) of USD 39.77 million for the first year, operating at maximum capacity. The results indicated that the process is attractive under a commercial approach, presenting a net present value (NPV) of USD 68.87 million at the end of the last year of operation and a cumulative cash flow (CCF) of less than one year⁻¹ (0.34 years⁻¹) for the first year at full processing capacity, which shows that in this process, variable costs have more weight on the economic indicators than fixed costs. Full article

In this work, the visible lines of ${\mathrm{W}}^{14+}$ ions in the wavelength range of 400–650 nm are investigated experimentally and theoretically. The experiments were performed in a low-energy electron beam ion trap. The simulated spectra of ${\mathrm{W}}^{14+}$ ions (Nd-like) were obtained from atomic structure computations in combination with a collisional–radiative model. Overall, there is a reasonable similarity between the measurements and the results of the simulations, and most of the twelve observed spectral lines associated with ${\mathrm{W}}^{14+}$ were tentatively identified. Full article

Using a high-resolution grating spectrometer on the Livermore EBIT-I electron beam ion trap, we have measured three $n=3,4\to n=1$ K-shell emission lines in lithium-like O$5^{+}$, which are situated near the O VIII Lyman-$\alpha$ lines at 19 Å. Two of the resulting wavelengths agree well with the wavelengths of these lines we reported earlier, but the wavelength of the third line does not. In contrast, our new wavelengths now fully agree with those from resonant photo-absorption experiments on the PETRA III synchrotron facility. Full article

Earnings management is a widespread phenomenon in practice, with researchers therefore focusing on trying to understand what motives and factors lead to companies engaging in earnings management. In addition to internal financial and macroeconomic factors, the influence of institutional factors including corporate social responsibility (CSR) has been widely studied in recent years. In Lithuania, there have been no studies on the manipulation of accounting information in socially responsible companies. Therefore, this study aims to identify the impact of CSR on the application of earnings management in the context of internal financial and macroeconomic factors. The results of this study are significant as they not only enable assessing the impact of social responsibility on the application of earnings management in Lithuanian companies, but also the influence of macroeconomic factors such as the gross domestic product (GDP), inflation, foreign direct investment (FDI), average wages, and unemployment, as well as internal financial factors such as leverage, returns on assets (RoA), and the profitability of EBIT. The results show that CSR reduces the use of earnings management, regardless of whether it is accrual-based or real earnings management. Additionally, this analysis demonstrates that, among the internal financial factors, leverage carries the most substantial influence. The higher a company’s leverage, the more inclined that company is to use earnings management. Exploring the impact of macroeconomic indicators, it was found that the GDP, inflation, and unemployment rate have a statistically significant impact on the use of earnings management, albeit only if the firm uses accrual-based earnings management and adopts a profit-enhancing strategy. Full article

A group of EUV lines of H- and He-like ions of C provides excellent wavelength calibrations for a position-sensitive multichannel detector at a high-resolution spectrograph. We have exploited this setting for a series of spectra of highly charged Eu ions recorded at the Livermore SuperEBIT electron beam ion trap. A variation in the electron beam energy results in spectra with correspondingly staggered highest Eu ion charge states ranging from Na- through to Ni-like Eu ions. A number of spectral features can be identified from the literature, but the majority of line identifications need guidance from computations of simulated spectra on the basis of collisional-radiative models. For ions with more than two electrons in the valence shell, the typical computational results are of a markedly lower accuracy. We have applied the Flexible Atomic Code (FAC), which is capable of handling all our measured ions with reasonable accuracy. We look into the systematics of the deviation of the computed transition energies from the measured ones as a function of the electron number. Full article

The prediction of bankruptcy risk poses a formidable challenge in the fields of economics and finance, particularly within the healthcare industry, where it carries significant economic implications. The burgeoning field of healthcare electronic commerce, continuously evolving through technological advancements and changing regulations, introduces additional layers of complexity. We collected financial data from 1265 U.S. healthcare industries to predict bankruptcy based on 40 financial ratios using multi-class classification machine learning models across various industry subsectors and market capitalizations. The exceptionally high post-tuning accuracy rates, exceeding 90%, along with high-performance metrics solidified the robustness and exceptional predictive capability of the gradient boosting model in bankruptcy prediction. The results also demonstrate the power and sensitivity of financial ratios in predicting bankruptcy based on financial ratios. The Altman models highlight the return on investment (ROI) as the most important parameter for predicting bankruptcy risk in healthcare industries. The Ohlson model identifies return on assets (ROA) as an important ratio specifically for predicting bankruptcy risk within industry subsectors. Furthermore, it underscores the significance of both ROA and the enterprise value to earnings before interest and taxes (EV/EBIT) ratios as important parameters for predicting bankruptcy based on market capitalization. Recognizing these ratios enables proactive decision making that enhances resilience. Our findings contribute to informed risk management strategies, allowing for better management of healthcare industries in crises like those experienced in 2022 and even on a global scale. Full article

Hyperfine structure constants have many applications, but are often hard to calculate accurately due to large and canceling contributions from different terms of the hyperfine interaction operator, and also from different closed and spherically symmetric core subshells that break up due to electron correlation effects. In multiconfiguration calculations, the wave functions are expanded in terms of configuration state functions (CSFs) built from sets of one-electron orbitals. The orbital sets are typically enlarged within the layer-by-layer approach. The calculations are energy-driven, and orbitals in each new layer of correlation orbitals are spatially localized in regions where the weighted total energy decreases the most, overlapping and breaking up different closed core subshells in an irregular pattern. As a result, hyperfine structure constants, computed as expectation values of the hyperfine operators, often show irregular or oscillating convergence patterns. Large orbital sets, and associated large CSF expansions, are needed to obtain converged values of the hyperfine structure constants. We analyze the situation for the states of the $\{2s^{2}2p^3,2s^{2}2p^{2}3p,2s^{2}2p^{2}4p\}$ odd and $\{2s^{2}2p^{2}3s,2s2p^4,2s^{2}2p^{2}4s,2s^{2}2p^{2}3d\}$ even configurations in N I, and show that the convergence with respect to the increasing sets of orbitals is radically improved by introducing separately optimized orbital sets targeted for describing the spin- and orbital-polarization effects of the $1s$ and $2s$ core subshells that are merged with, and orthogonalized against, the ordinary energy-optimized orbitals. In the layer-by-layer approach, the spectroscopic orbitals are kept frozen from the initial calculation and are not allowed to relax in response to the introduced layers of correlation orbitals. To compensate for this lack of variational freedom, the orbitals are transformed to natural orbitals prior to the final calculation based on single and double substitutions from an increased multireference set. The use of natural orbitals has an important impact on the states of the $2s^{2}2p^{2}3s$ configuration, bringing the corresponding hyperfine interaction constants in closer agreement with experiment. Relying on recent progress in methodology, the multiconfiguration calculations are based on configuration state function generators, cutting down the time for spin-angular integration by factors of up to 50, compared to ordinary calculations. Full article

The X-ray spectra of L-shell transitions in Neon-like Xenon ion (Xe⁴⁴⁺) have been precisely measured at the Shanghai Electron-Beam Ion Trap using a high-resolution crystal spectrometer. Focusing on the line-intensity ratio of the 3F {2p⁶-(2p⁵_1/23s_1/2)_J=1} and 3D {2p⁶-(2p⁵_3/23d_5/2)_J=1} lines (3F/3D), our measurements have achieved remarkable precision improvements over the previous studies. These spectra have been simulated using the collisional-radiative model (CRM) within the Flexible Atomic Code, showing good agreement with the measurements. The previously reported discrepancies, approximately ranging from 10% to 20%, have been significantly reduced in this work to below 1.4% for electron-beam energies exceeding 6 keV and to around 7% for lower energies. Furthermore, our analysis of population fluxes of the involved levels reveals a very high sensitivity of the 3F line to radiation cascades. This suggests that the current CRM, which conventionally excludes interionic population transfer processes, may underestimate the population of the upper level of the 3F line and the cascade-related higher levels, thus explaining the remaining discrepancies. These findings provide a solid foundation for further minimizing these discrepancies and are crucial for understanding the atomic structure and plasma model of these ions. Full article

The biogas sector in Europe and Italy is attracting growing investment, combining agricultural activity, the circular economy, and renewable energy production. Firms in the sector widely use debt capital and, for this reason, there is a need to evaluate the structure of investments, financing, and debt service capacity calculated by applying interest coverage ratios (ICRs). ICRs are widely used by banks in granting loans, and calculation of ICRs allows managers and policy makers to correctly evaluate firms’ performance in the sector. In this research, based on a sample of 160 observations, the structure of investments and sources of financing of firms in the biogas sector, operating in northern Italy, are analyzed. ICRs are calculated with different approaches to establish which ICRs provide the most reliable results in the application. The research analyzes the correlations and highlights significant differences between ICRs. The research highlights some important findings: (a) the NWC is negative in 109 out of 160 observations and, therefore, constitutes a source of financing in the majority of observations; (b) ICRs based on EBITDA and CF are above the threshold value of “1” in 143 and 145 observations, respectively, while ICRs based on EBIT, OCF, and UFCF are above the threshold value of “1” in 132, 133, and 122 observations, respectively. The research allows the conclusion that the ICRs based on EBITDA and CF tend to overestimate results; ICRs based on EBIT, OCF and UFCF are preferable, and can therefore be applied by managers, banks, and policy makers and be used as debt covenants. For the calculation of the repayment of the NFP, the research has highlighted that ICRs in which the cost of the debt is deducted from the numerator are preferable. The research can thus be usefully applied and expanded to other territories, or by considering a larger sample with the aim of inferring conclusions of general validity. Full article

Zoo animals, harboring zoonotic gastrointestinal protozoal diseases, pose potential hazards to the safety of visitors and animal keepers. This study involved the collection and examination of 400 fresh fecal samples from 68 animal species, obtained from five zoos. The aim of this study was to determine the occurrence, genetic characteristics, and zoonotic potential of common gastrointestinal protists. PCR or nested PCR analysis was conducted on these samples to detect four specific parasites: Cryptosporidium spp., Giardia duodenalis, Enterocytozoon bieneusi, and Blastocystis spp. The overall prevalence of Cryptosporidium spp was 0.5% (2/400), G. duodenalis was 6.0% (24/400), Blastocystis spp. was 24.5% (98/400), and E. bieneusi was 13.5% (54/400). G. duodenalis, Blastocystis spp., and E. bieneusi were detected in all of the zoos, exhibiting various zoonotic genotypes or subtypes. G. duodenalis-positive samples exhibited three assemblages (D, E, and B). Blastocystis spp. subtypes (ST1, ST2, ST3, ST4, ST5, ST8, ST10, ST13, and ST14) and one unknown subtype (ST) were identified. A total of 12 genotypes of E. bieneusi were identified, including SC02, BEB6, Type IV, pigEBITS 7, Peru8, PtEb IX, D, CD9, EbpC, SCBB1, CM4, and CM7. Moreover, significant differences in the positive rates among different zoos were observed (p < 0.01). The findings indicate that zoo animals in China are affected by a range of intestinal protozoa infections. Emphasizing molecular identification for specific parasite species or genotypes is crucial for a better understanding of the zoonotic risk. Preventing and controlling parasitic diseases in zoos is not only vital for zoo protection and management but also holds significant public health implications. Full article

The V4 countries (Poland, Slovakia, Czechia, Hungary) hold significant importance within the European Union, contributing nearly 20% of the EU’s arable land and 10% of its crop output from 2018 to 2020. The study focuses on companies as organizational entities engaged in arable crop production, representing a specific segment of agricultural producers. The objective is to analyze the concentration and financial performance of these companies in the V4 countries during the period 2018–2020. Financial data from 822 companies in Hungary, 226 in Slovakia, 17 in Czechia, and 967 in Poland were processed from the EMIS database (Emerging Market Information System). In Poland and Czechia, a significant proportion of the companies were classified as micro-enterprises, while in Hungary and Slovakia, they were predominantly small farms. The Gini index indicates a high concentration of Polish farms, a medium concentration of Hungarian and Slovakian farms, and a low concentration of Czech companies. In terms of financial profitability, Hungarian and Polish companies are the most favorable, while Slovak farms are the least favorable. Polish companies exhibit significant heterogeneity, which may also be attributed to high concentration. The results suggest that higher financial profitability is associated with a lower debt rate in the capital structure. Based on the examined sample, there is no close relationship between farm size and financial efficiency in the case of companies engaged in arable crop production in the V4 countries. This study also found that as farm size increases, the profit (EBIT) tends to increase, but the magnitude of this effect varies among the companies in different countries. The study’s findings also support that other factors play a role in the development of profitability. Full article

Chest X-ray (CXR) is the most important technique for performing chest imaging, despite its well-known limitations in terms of scope and sensitivity. These intrinsic limitations of CXR have prompted the development of several artificial intelligence (AI)-based software packages dedicated to CXR interpretation. The online database “AI for radiology” was queried to identify CE-marked AI-based software available for CXR interpretation. The returned studies were divided according to the targeted disease. AI-powered computer-aided detection software is already widely adopted in screening and triage for pulmonary tuberculosis, especially in countries with few resources and suffering from high a burden of this disease. AI-based software has also been demonstrated to be valuable for the detection of lung nodules detection, automated flagging of positive cases, and post-processing through the development of digital bone suppression software able to produce digital bone suppressed images. Finally, the majority of available CE-marked software packages for CXR are designed to recognize several findings, with potential differences in sensitivity and specificity for each of the recognized findings. Full article

This paper presents an extended bio-inspired texture (E-BiT) descriptor for image texture characterization. The E-BiT descriptor combines global ecological concepts of species diversity, evenness, richness, and taxonomic indexes to effectively capture texture patterns at local and global levels while maintaining invariance to scale, translation, and permutation. First, we pre-processed the images by normalizing and applying geometric transformations to assess the invariance properties of the proposed descriptor. Next, we assessed the performance of the proposed E-BiT descriptor on four datasets, including histopathological images and natural texture images. Finally, we compared it with the original BiT descriptor and other texture descriptors, such as Haralick, GLCM, and LBP. The E-BiT descriptor achieved state-of-the-art texture classification performance, with accuracy improvements ranging from 0.12% to 20% over other descriptors. In addition, the E-BiT descriptor demonstrated its generic nature by performing well in both natural and histopathologic images. Future work could examine the E-BiT descriptor’s behavior at different spatial scales and resolutions to optimize texture property extraction and improve performance. Full article