Machine-learning representations of gait waveforms are increasingly used in movement analysis, but their added value over conventional biomechanical descriptors remains uncertain, particularly in pediatric cohorts. We evaluated whether implicit neural representations (INRs) provide useful encodings of pediatric gait waveforms for reconstruction and developmental modeling. The full cohort comprised 78 healthy children and adolescents, of whom 73 had waveform files conforming to the predefined data structure required for the repeated cross-validation analyses. Selected waveforms were modeled using several INR architectures, and reconstruction quality was assessed using R2, RMSE, and MAE. For the INR feature-ablation analyses, one architecture was assigned to each waveform class based exclusively on reconstruction performance, independently of the age targets. Side-specific, bilateral-asymmetry, signal-specific, and descriptor-family feature sets were evaluated using ten repeats of five-fold cross-validation. A separate matched benchmark in the same 73 participants compared compact Fourier-MLP-derived features with conventional biomechanical descriptors and a fold-wise raw-waveform PCA baseline. In the INR ablation analyses, combining side-specific and bilateral-asymmetry features achieved R2=0.700±0.017 for chronological-age regression, and Energy/AUC descriptors were the strongest individual feature family (R2=0.696±0.031). For exploratory three-class age-group classification (4–7, 8–12, and 13–18 years), the combined INR representation reached a balanced accuracy of 0.749 ± 0.034. In the matched representation benchmark, configurations containing conventional biomechanical descriptors performed best. The biomechanics-only baseline achieved regression R2=0.837±0.070 and balanced accuracy =0.798±0.109, whereas biomechanics+PCA achieved the highest mean regression performance (R2=0.842±0.066); this difference was not statistically significant. Adding INR-derived features did not improve regression performance and significantly reduced the evaluated classification metrics. INRs should therefore be considered complementary continuous waveform representations rather than replacements for established biomechanical descriptors.
The development of effective antioxidants has evolved from descriptive analysis toward a precise, mechanism-driven discipline targeting the molecular “redox switch”. This review synthesizes the critical advances reported since 2021, focusing on how the interplay between polyphenolic architecture and electronic descriptors, such as bond dissociation enthalpy and ionization potential, governs radical scavenging through the HAT, SET, and SPLET pathways. We evaluate the dual influence of metal coordination, where interactions can either enhance antioxidant stability through σ bond polarization or trigger pro-oxidant transitions via ligand-to-metal charge transfer. Central to this progress is the integration of computational models (DFT, QSAR) with advanced synchrotron methodologies (XAS, STXM, SR-FTIR, and SAXS), which provide element-specific validation of antioxidant behavior and subcellular oxidative mapping within complex matrices. Furthermore, we highlight how these molecular insights inform formulation engineering, specifically the development of organic nanocarriers and hybrid delivery systems, such as metal–phenolic networks, that shield therapeutic cargo from degradation and govern release in challenging physiological environments. These fundamental studies provide an essential physicochemical basis for medicine by enabling a better understanding and the rational design of antioxidant drugs, dietary supplements, and antioxidant strategies.
Motivated by the imperative for enhanced wear resistance in high-temperature applications such as aerospace, this paper presents a comprehensive investigation into the temperature-dependent wear mechanisms of sparkplasma-sintered CoCrFeMnNi high-entropy alloy (HEA) composites reinforced with titanium carbide (TiC). Five compositions containing 0-50 wt% TiC were evaluated by X-ray diffraction, scanning electron microscopy, electron backscatter diffraction, mechanical testing, and elemental mapping. The 15 wt% TiC composite (H15) exhibited the optimal combination of hardness and compressive yield strength. Ball-on-plate wear tests against Si3N4 counterbodies were performed from room temperature (RT) to 900 degrees C, demonstrating superior wear resistance across this broad temperature range with wear rates of 10-6 to 10-5 mm3/Nm. At RT-300 degrees C, mild abrasive and oxidative wear prevailed, controlled by uniform dispersion of TiC and Cr7C3 particles. At 600 degrees C, a discontinuous oxide-carbide layer forms on the wear surface, while a fine-grained layer develops in the subsurface region. This dual structure effectively suppresses delamination, enabling moderate oxide adhesion to the Si3N4 ball while maintaining a low wear rate. At 900 degrees C, a dense, continuous oxide glaze, comprising Mn-rich and Cr-rich sublayers, acted as a self-lubricating barrier, reducing wear-track dimensions by over 60 % compared to non-TiC HEA and lowering the coefficient of friction. These findings provide the first end-to-end mapping of tribological regimes in TiC reinforced HEAs, offering design guidelines for oxide-glaze engineering in advanced wear-resistant alloys.
Recently, nanocomposite structures have been widely designed and applied in the fields of mechanical and aerospace engineering. Classical active vibration control and structural health monitoring systems must be redesigned to account for the new dynamic behaviour of these structures, which arises from the new properties of the nanomaterials and possible discontinuities/cracks that may appear. Therefore, the paper addresses an essential problem related to the natural and coupled forced vibration of a beam containing graphene platelets as nano-fillers dispersed in an epoxy resin matrix in various ways. Additionally, the matrix discontinuity effect is taken into consideration. Both phenomena, the dispersion of nano-fillers and various discontinuities in the ply system, may cause a coupling effect in the derived Euler-Lagrange equations system of motion for axial, transverse, and torsional vibrations. Effective nanomaterial properties are determined based on the experimentally proven Voigt-Reuss model connected with a modified Halpin-Tsai micromechanical technique. Additionally, discontinuities in the matrix are represented by the two-phase model. Additionally, to simulate beam excitations in three directions by a modal shaker, the force vector is defined for axial force, torque and transverse point force. The formulated initial-boundary value problem is solved using a powerful infinite Fourier trigonometric series and the Cholesky method, utilising the expansion theorem. It provides continuous solutions to the formulated problem without the need to find optimal meshes. A comprehensive validation study is performed to confirm the accuracy of the obtained model by comparing it with results available in the literature. The influence of different distributions and weight percentages of nano-fillers and various crack systems on shifting natural frequencies and steady-state responses of the beam subjected to different modal forces is comprehensively investigated. Moreover, the Fast Fourier Transformation spectra and 2D and 3D phase portraits are determined to evaluate the sensitivity of the steady-state beam's responses to variations in nanomaterial properties and the presence of cracks. Finally, we demonstrated a coupling effect between the steady-state responses of the beam in axial, transverse, and rotary directions for different directions of acting external forces.
Normalization is a critical step in Multiple-Criteria Decision Analysis (MCDA) because it transforms heterogeneous criterion values into comparable information. This study examines normalization techniques through the lens of entropy, highlighting how criterion data structure shapes normalization behavior and ranking stability within TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). Seven widely used normalization procedures are analyzed regarding mathematical properties, sensitivity to extreme values, treatment of benefit and cost criteria, and rank reversal. Normalization is treated as a source of uncertainty in MCDA outcomes, as different schemes can produce divergent rankings under identical decision settings. Shannon entropy is employed as a descriptive measure of information dispersion and structural uncertainty, capturing the heterogeneity and discriminatory potential of criteria rather than serving as a weighting mechanism. An illustrative experiment with ten alternatives and four criteria (two high-entropy, two low-entropy) demonstrates how entropy mediates normalization effects. Seven normalization schemes are examined, including vector, max, linear Sum, and max-min procedures. For vector, max, and linear sum, cost-type criteria are treated using either linear inversion or reciprocal transformation, whereas max-min is implemented as a single method. This design separates the choice of normalization form from the choice of cost-criteria transformation, allowing a cleaner identification of their respective contributions to ranking variability. The analysis shows that normalization choice alone can cause substantial differences in preference values and rankings. High-entropy criteria tend to yield stable rankings, whereas low-entropy criteria amplify sensitivity, especially with extreme or cost-type data. These findings position entropy as a key mediator linking data structure with normalization-induced ranking variability and highlight the need to consider entropy explicitly when selecting normalization procedures. Finally, a practical entropy-based method for choosing normalization techniques is introduced to enhance methodological transparency and ranking robustness in MCDA.
