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We investigate the effective oxidation state and local environment of yttrium in photochromic YHO thin film structures produced by e-beam evaporation, along with their chemical structure and optical properties. Transmission electron microscopy images reveal the oxidized yttrium hydride thin film sample exhibiting a three-layered structure. X-ray photoelectron spectroscopy (XPS) measurements manifest that the oxidation state of yttrium is modified, dependent on the film's composition/depth. Furthermore, Ion beam analysis confirms that this variability is associated with a composition gradient within the film. X-ray absorption spectroscopy at the Y K-edge reveals that the effective oxidation state of yttrium is approximately +2.5 in the transparent/bleached state of YHO. Spectroscopic ellipsometry investigations showed a complex non-linear optical depth profile of the related sample confirming the dominant phase of YHO and the presence of Y2O3 and Y towards the middle of the film. The first evidence of (n; k) dispersion curves for e-beam sputtered photochromic YHO thin films are reported for transparent and dark states. [ABSTRACT FROM AUTHOR]

The Li2O-Al2O3-SiO2 (LAS) glass system has high commercial values due to its very low thermal expansion coefficients and its very high chemical resistance among other properties. In this study, the preparation of LAS-based glass–ceramics (G-Cs) is reported, and the influence of P2O5 as a nucleating agent for crystallizing the amorphous glass structure at different rates into this glass composition, on the physical, structural, and mechanical properties is presented. The influece of different crytalization temperature on the crystal size and micreostructure as well as the radiation absorption abilities of the G-Cs were also investigated. The G-Cs were prepared using the melt and quench method and subjected to different temperature regimes to crystallise. The crystallinity of the prepared samples was examined with the aid of X-ray Diffraction analysis, while their chemical composition was probed using energy dispersive spectroscopy (EDS). The mass attenuation coefficients (MACs) of the samples were estimated for 15 keV–15 MeV photons using the well-known WinXCom software. Lithium silicate with the main phase formulation Li0.75Al0.75Si2.25O6 was identified as the main crystalline phase within the residual glass phase. The increase in P2O5 content slightly diminishes the photon absorption abilities of the G-Cs. However, the G-Cs showed better photon absorption prowess compared to some notable shielding materials. The G-Cs are thus important shielding materials useful for radiation control purposes. [ABSTRACT FROM AUTHOR]

The software bill of materials (SBOM) emerged in 2018 as an important component in software security and software supply chain management. SBOM is an inventory presented as a list of the components that make up software. In recent years, whether software products contain vulnerabilities is a phenomenon that should be checked regularly by the users of that product. This paper deals with the systematic identification and vulnerability analysis of software components based on the concept of software bill of materials. The fact that a software product itself does not contain vulnerabilities does not mean that the software product is secure. Even if software projects do not contain any vulnerabilities when examined alone, there may be vulnerabilities in their components. Vulnerabilities in the dependencies or components of the product may be sufficient for cyber attackers to exploit that product. Minimizing the damage caused by vulnerabilities in software components is the basis of cyber security efforts. In this study, the necessity of automatically generating software bill of materials in software development/deployment environments and performing vulnerability analysis on this bill of materials is demonstrated and a suitable model is proposed. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate correlation between serum iron parameters and liver T2* value in hemodialysis patients with iron overload due to parenteral iron therapy. Materials and Methods: We evaluated 30 hemodialysis patients using a multiecho T2*‐weighted MRI sequence. Age, sex, duration of dialysis, iron and erythropoietin doses taken in the past year, and serum iron parameters were recorded. Liver T2* values were averaged from three distinct liver regions. A T2* value of 33 ± 7 ms is considered normal. Declines below 24, 21, and 14 ms signify iron overload grades 1, 2, and 3, respectively. Results: There was no statistically significant difference comparing the measurements of 3 different ROIs (p > 0.05). A total of 23 patients (76.6%) had iron overload. Serum ferritin levels of patients with iron overload were significantly higher than those without iron overload (687.25 [186.5–1489] ng/mL vs. 371.25 [127.5–542.5] ng/mL, p = 0.008). No linear correlation was observed between age, dialysis duration, serum iron metrics, medication doses, and T2* values. Likewise, no significant differences were found among patients based on iron overload status or its grades concerning these parameters. Conclusion: While standard serum markers might overlook iron overload, elevated ferritin levels are promising. MRI reliably detects iron overload in patients receiving parenteral iron. [ABSTRACT FROM AUTHOR]

Indoor localization involves pinpointing the location of an object in an interior space and has several applications, including navigation, asset tracking, and shift management. However, this technology has not yet been perfected, and many methods, such as triangulation, Kalman filters, and machine learning models have been proposed to address indoor localization problems. Unfortunately, these methods still have a large degree of error that makes them ill-suited for difficult cases in real-time. In this study, we propose a hybrid model for Bluetooth low energy-based indoor localization. In this model, the triangulation method is combined with several machine learning methods (naïve Bayes, k-nearest neighbor, logistic regression, support vector machines, and artificial neural networks) that are optimized and tested in three different environments. In the experiment, the proposed model performed similarly to the solo triangulation model in easy and medium cases; however, the proposed model obtained a much smaller degree of error for hard cases than either solo triangulation or machine learning models alone. [ABSTRACT FROM AUTHOR]

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