Background: Zinc-doped hydroxyapatite has been proposed as a graft biomaterial for bone regeneration. However, the effect of zinc on osteoconductivity is still controversial, since the release and resorption of calcium, phosphorus, and zinc in graft-implanted defects have rarely been studied. Methods: Microspheres containing alginate and either non-doped carbonated hydroxyapatite (cHA) or nanocrystalline 3.2 wt% zinc-doped cHA (Zn-cHA) were implanted in critical-sized calvarial defects in Wistar rats for 1, 3, and 6 months. Histological and histomorphometric analyses were performed to evaluate the volume density of newly formed bone, residual biomaterial, and connective tissue formation. Biomaterial degradation was characterized by transmission electron microscopy (TEM) and synchrotron radiation-based X-ray microfluorescence (SR-μXRF), which enabled the elemental mapping of calcium, phosphorus, and zinc on the microsphere-implanted defects at 6 months post-implantation. Results: The bone repair was limited to regions close to the preexistent bone, whereas connective tissue occupied the major part of the defect. Moreover, no significant difference in the amount of new bone formed was found between the two microsphere groups. TEM analysis revealed the degradation of the outer microsphere surface with detachment of the nanoparticle aggregates. According to SR-μXRF, both types of microspheres released high amounts of calcium, phosphorus, and zinc, distributed throughout the defective region. The cHA microsphere surface strongly adsorbed the zinc from organic constituents of the biological fluid, and phosphorus was resorbed more quickly than calcium. In the Zn-cHA group, zinc and calcium had similar release profiles, indicating a stoichiometric dissolution of these elements and non-preferential zinc resorption. Conclusions: The nanometric size of cHA and Zn-cHA was a decisive factor in accelerating the in vivo availability of calcium and zinc. The high calcium and zinc accumulation in the defect, which was not cleared by the biological medium, played a critical role in inhibiting osteoconduction and thus impairing bone repair.

In the field of current controllers for power converters and electric motor units, conventional figures of merit (FMs) such as mean squared error (MSE), integral of time multiplied by absolute error (ITAE), or total harmonic distortion (THD) have traditionally been used. The stochastic nature of these FMs introduces variability, which often results in inaccurate comparisons of the controllers' performance. To address this issue, a parametric statistical methodology has recently been proposed. However, it presents certain limitations, such as the assumption of normality. In response to this, the adoption of a non-parametric methodology is suggested in this work, which promises a precise evaluation of the efficiency of current controllers. In this study, we demonstrate that, under parametric approach, when the assumption of normality is violated, there is a significant increase in Type I error. Furthermore, we show that the non-parametric Mann-Whitney U test offers greater sensitivity compared to its parametric counterpart under these circumstances. Thus, the newly proposed methodology aims to optimize the decision-making process in designing high-performance current controllers for applications in power converters and electric motors. This allows for design decisions grounded in rigorous and statistically-based evaluations. The effectiveness of this methodology is confirmed through its application to a real dataset, enhancing its practicality and contributing to a deeper understanding of the subject matter.

This study introduces a hybrid numerical-analytical methodology for interpreting, for the first time, both the amplitude and phase of Synchrotron Infrared Nanospectroscopy (SINS) spectra of hydroxyapatite (HA) thin films. The approach combines the Self-Referenced Interferometry Model (SRIM) with the Kramers-Kronig relations (KKR). HA films with thicknesses of 100 nm and 600 nm were fabricated via magnetron sputtering and characterized using Fourier-transform infrared (FTIR) spectroscopy to identify vibrational modes of the phosphate group. These frequencies were used as initial parameters for Lorentzian-based SRIM fitting of the SINS amplitude spectrum. Both the integral and differential forms of KKR were applied to the fitted amplitude spectrum to reconstruct the phase, which was subsequently validated against experimental SINS data. The differential form further enabled analytical decomposition of the phase into individual oscillator and non-resonant contributions, revealing features not evident in the amplitude alone. The strong agreement between experimental and modeled spectra demonstrates the reliability of FTIR-guided SRIM fitting combined with KKR analysis. This methodology effectively bridges far-field and near-field infrared spectroscopy, showing that HA spectral responses comply with causality and analyticity. Moreover, it supports accurate phase reconstruction even under low-intensity signal conditions, indicating broad applicability to other nanostructured materials. Altogether, this work establishes a robust framework for interpreting SINS spectra and advances phase-resolved diagnostics and hybrid modeling strategies for nanoscale vibrational analysis.

Laser Speckle Imaging (LSI) is a non-invasive optical technique used to assess biological activity by detecting dynamic variations in speckle patterns. These patterns exhibit statistical symmetry in static regions, while biological activity induces symmetry breaking that can be captured through the Graphic Absolute Value of Differences (GAVD), producing the activity map $I_{GAVD}$. This work evaluates the effect of four contrast enhancement algorithms: Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), Multiscale Morphological Contrast Enhancement (MMCE), and Multiscale Top-Hat Transform with an Open-Close Close-Open (OCCO) filter, applied to intermediate LSI images, with the final activity map used for quantitative evaluation. Each method represents a distinct enhancement paradigm: HE and CLAHE are histogram-based techniques for global and local contrast adjustment, whereas MMCE and OCCO-MTH are morphological approaches that emphasize structural preservation and local detail enhancement. The dataset consisted of images of Phaseolus vulgaris (SP) and Lactuca sativa (SL) seeds. Evaluation was conducted through expert visual inspection and quantitative analysis using contrast, entropy, spatial frequency (SF), structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and contrast improvement ratio (CIR). All metrics were computed on $I_{GAVD}$ activity maps, which reflect bioactivity through the disruption of statistical symmetry. Non-parametric statistical tests (Friedman, aligned Friedman, and Quade) revealed that CLAHE and MMCE significantly improved image quality compared to the original images ($p<0.05$). Among the evaluated algorithms, CLAHE increased global contrast by approximately 25% and entropy by 6% relative to the original speckle frames, enhancing the visibility of bioactive regions. MMCE achieved the highest bioactivity contrast ratio (CIR = 0.64), while OCCO-MTH provided the best structural fidelity (SSIM = 0.91) and noise suppression (PSNR = 30.7 dB). These results demonstrate that suitable contrast enhancement can substantially improve the interpretability of LSI activity maps without altering acquisition hardware. This finding is particularly relevant for experimental applications aiming to maximize information quality without modifying acquisition hardware.