Search Results (7,537)

Non-absorbable surgical meshes are key biomedical materials used for tissue reinforcement, designed for durability, biocompatibility, and mechanical stability in clinical applications. The mechanical properties of these meshes, such as tensile strength, elasticity, and porosity, are crucial for their long-term performance and integration with host tissue. In the context of Industry 4.0/5.0, emphasis is placed on integrating intelligent technologies, such as real-time data acquisition and advanced computational modeling, to improve the design and production of surgical meshes. Computational models simulate the mechanical behavior of meshes under physiological conditions, enabling precise optimization of their material properties and design. In this article, we propose potential artificial intelligence (AI)-based approaches for future research, such as machine learning (ML), for analyzing large datasets from computational and experimental studies to identify optimal mesh configurations. The direction of tensile loading significantly influences the mechanical response of the mesh. Transversely stretched specimens demonstrated higher maximum failure forces and greater fatigue resistance than longitudinally stretched specimens, both in sutured and unsutured conditions. Suturing the mesh to biological tissue significantly reduced its mechanical strength and stiffness, demonstrating a weakening effect at the mesh-tissue interface. Cyclic loading revealed a gradual decrease in strength in all specimens, suggesting fatigue, but transversely stretched meshes maintained higher forces for >1000 cycles than longitudinally stretched meshes. The observed differences in mechanical behavior can be attributed to the anisotropic mesh structure and mechanical suturing effects, which introduce stress concentrations and structural discontinuities. These results emphasize the importance of considering both directionality and surgical technique when selecting and implementing mesh implants. Both AI-based models achieved scores above 80%, demonstrating their clinical utility and the potential for development toward prediction accuracy above 85–90% in clinical settings. Future research should incorporate AI-based computational models to improve predictive capabilities, ultimately leading to the development of more effective, patient-specific surgical meshes. Full article

This study presents a predictive maintenance framework for hybrid electric vehicles (HEVs) based on emissions behaviour under laboratory-simulated driving conditions. Vehicle speed, road gradient, and ambient temperature were selected as the principal input variables affecting emission levels. Using simulated datasets, three machine learning model, specifically Linear Regression, Multilayer Perceptron (MLP), as well as Random Forest, were trained and evaluated. Within that set, the Random Forest model demonstrated the best performance, achieving an R² score of 0.79, Mean Absolute Error (MAE) of 12.57 g/km, and root mean square error (RMSE) of 15.4 g/km, significantly outperforming both Linear Regression and MLP. A MATLAB-based graphical interface was developed to allow real-time classification of emission severity using defined thresholds (Normal ≤ 150 g/km, Warning ≤ 220 g/km, Critical > 220 g/km) and to provide automatic maintenance recommendations derived from the predicted emissions. Scenario-based validation confirmed the system’s ability to detect emission anomalies, which might function as early indicators of mechanical degradation when interpreted relative to operating conditions. The proposed framework, developed using laboratory-simulated datasets, provides a practical, interpretable, and accurate solution for emissions-based predictive maintenance. Although the results demonstrate feasibility, the framework should be further confirmed with real-world on-road data prior to large-scale use. Full article

The acoustical modelling of multilayered systems is crucial for researchers and engineers aiming to evaluate and control the behaviour of complex media and to determine their internal properties. In this work, we first develop a forward model describing the propagation of acoustic waves through various types of materials, including fluids, solids, and poroelastic media. The model relies on the classical theoretical frameworks of Thomson and Haskell for non-porous layers, while Biot’s theory is employed to describe wave propagation in poroelastic materials. The propagation is mathematically treated using the transfer matrix method, which links the acoustic displacement and stress at the extremities of each layer. Appropriate boundary conditions are applied at each interface to assemble all local matrices into a single global matrix representing the entire multilayer system. This forward model allows the calculation of theoretical transmission coefficients, which are then compared to experimental measurements to validate the approach proposed. Secondly, this modelling framework is used as the basis for solving inverse problems, where the goal is to retrieve unknown internal parameters, such as mechanical or acoustic properties, by minimizing the discrepancy between simulated and experimental transmission spectra. This inverse problem approach is essential in non-destructive evaluation applications, where direct measurements are often unfeasible. Full article

The convergence of Internet of Things (IoT), embedded microcontrollers, and robotics has significantly transformed industrial and service applications under the Industry 5.0 paradigm. IoT-enabled automation not only reduces human intervention but also improves system efficiency, safety, and adaptability across multiple domains. The growing integration of automation technologies in manufacturing lines has significantly reduced human intervention while improving productivity and operational safety. Robotic arms play a crucial role in modern industrial environments, particularly for repetitive, hazardous, or precision-demanding tasks. This study presents a cost-effective robotic arm system for product selection, sorting and processing in automated production lines. The system operates in both automatic and manual modes and utilizes an ESP32-based controller, radio frequency identification (RFID) modules, and low-cost sensors to identify and transport products on a conveyor. A mobile, IoT-enabled interface provides remote real-time monitoring and control, while integrated safety mechanisms, current-voltage protections, and emergency stop circuitry enhance operational reliability. Using cost-effective components to reduce total cost, the system has been successfully validated through experiments to reduce labor dependency and operational errors, proving its scalability and economic viability for industrial automation. Compared to similar systems, this study presents an Industry 5.0 approach for low-cost IoT-based automated production lines. Full article

Introduction: The success of metal–ceramic restorations depends on the mechanical and adhesive properties of the metal–ceramic interface. With the emergence of additive manufacturing technologies such as selective laser melting (SLM), there is growing interest in comparing these methods with conventional casting. This pilot study aimed to generate hypothesis-forming data on how fabrication method (casting and 3D printing) and alumina sandblasting with two particle sizes (125 μm and 250 μm) influence flexural performance of Co-Cr metal–ceramic systems within the standardized ISO 9693 framework. Materials and Methods: Rectangular Co-Cr alloy specimens were manufactured using two techniques: conventional casting and 3D printing via SLM. Each group was divided based on the sandblasting particle size. After ceramic application in accordance with ISO 9693:2012, samples underwent a three-point bending test using a universal testing machine (Instron 8872) to assess the displacement force required to fracture the ceramic layer. Five specimens were tested per group, and mean values and standard deviations were calculated. Data were statistically analyzed using two-way ANOVA followed by Tukey’s HSD post hoc test (p < 0.05). Results: Cast samples exhibited significantly higher displacement strength than printed ones. Among all groups, the cast samples sandblasted with 250 μm particles (CCT_250) showed the best performance (mean: 12.48 ± 0.91 N), while the 3D-printed group treated with 125 μm particles (CCP_125) showed the lowest strength (mean: 7.24 ± 0.65 N). Larger abrasive particles (250 μm) improved bond strength in both fabrication techniques. Two-way ANOVA revealed significant main effects of manufacturing method (F(1,16) = 13.63, p = 0.002, η² = 0.46) and particle size (F(1,16) = 6.17, p = 0.024, η² = 0.28), with no interaction between factors. Conclusions: Both the manufacturing method and the sandblasting protocol significantly influence the flexural performance of Co-Cr ceramic systems. Conventional casting combined with 250 μm particle sandblasting ensures the highest ceramic adhesion, while SLM-printed substrates may require additional surface treatments to improve bonding efficiency. Complementary surface treatments such as bonding agents or chemical oxidation may enhance the metal–ceramic bond in SLM-fabricated frameworks. Full article

Highly sensitive bioinspired cutaneous receptors are essential for realistic human-robot interaction. This study presents a biomimetic tactile sensor morphologically modeled after the Meissner corpuscle, designed for high dynamic sensitivity achieved using a coiled configuration. Our proposed electrolytic polymerization technique with magnet-responsive hybrid fluid (HF) was employed to fabricate soft, elastic rubber sensors with embedded coiled electrodes. The coiled configuration, optimized by electrolytic polymerization, exhibited high responsiveness to dynamic motions including pressing, pinching, twisting, bending, and shearing. The mechanism of the haptic property was analyzed by electrochemical impedance spectroscopy (EIS), revealing that reactance variations define an equivalent electric circuit (EEC) whose resistance (R_p), capacitance (C_p), and inductance (L_p) change with applied force; these changes correspond to mechanical deformation and the resulting variation in the sensor’s built-in voltage. The roll-type Meissner-inspired sensor demonstrated fast-adapting behavior and broadband vibratory sensitivity, indicating its potential for high-performance tactile and auditory sensing. These findings confirm the feasibility of electrolytically polymerized hybrid fluid rubber as a platform for next-generation bioinspired haptic interfaces. Full article

In this study, first-principles calculations were employed to analyze the effect of Si and O doping on the electronic structure of the Fe/Zn interface, aiming to reveal the mechanism underlying the degradation of its interfacial stability. Through detailed analysis of bond population, charge density, differential charge density, as well as total density of states (TDOS) and partial density of states (PDOS), the following findings were obtained: After Si and O doping, the charge distribution at the Fe/Zn interface exhibits local aggregation or sparsity. The differential charge density shows a redistribution of charges, and the charge density in the Fe-Zn bonding region changes. In terms of density of states, the contribution of orbitals related to Fe and Zn atoms to the density of states near the Fermi level is altered. The hybridization between the orbitals of Si/O atoms and those of Fe/Zn atoms affects the electronic interaction. Comprehensive analysis indicates that the degradation of Fe/Zn interfacial stability caused by Si and O doping is mainly attributed to the following factors: it modifies the chemical bonding, induces lattice distortion which generates internal stress, enhances the inhomogeneity of charge distribution, and weakens the bonding force between Fe and Zn atoms. This research provides a theoretical basis for the performance regulation of related material systems. Full article

Egocentric hand gesture recognition is vital for natural human–computer interaction in augmented and virtual reality (AR/VR) systems. However, most deep learning models struggle to balance accuracy and efficiency, limiting real-time use on wearable devices. This paper introduces a Two-Stream Temporal Shift Module Transformer Fusion Network (TSMTFN) that achieves high recognition accuracy with low computational cost. The model integrates Temporal Shift Modules (TSMs) for efficient motion modeling and a Transformer-based fusion mechanism for long-range temporal understanding, operating on dual RGB-D streams to capture complementary visual and depth cues. Training stability and generalization are enhanced through full-layer training from epoch 1 and MixUp/CutMix augmentations. Evaluated on the EgoGesture dataset, TSMTFN attained 96.18% top-1 accuracy and 99.61% top-5 accuracy on the independent test set with only 16 GFLOPs and 21.3M parameters, offering a 2.4–4.7× reduction in computation compared to recent state-of-the-art methods. The model runs at 15.10 samples/s, achieving real-time performance. The results demonstrate robust recognition across over 95% of gesture classes and minimal inter-class confusion, establishing TSMTFN as an efficient, accurate, and deployable solution for next-generation wearable AR/VR gesture interfaces. Full article

A detailed investigation of the structure–property relationships of three-antennary oligoglycines in aqueous solutions is performed. Two representatives of these substances are investigated: CH₃C(-CH₂-NH-Gly₅)₃ and CH₃C(-CH₂-NH-Gly₇)₃. The aim is to clarify the effect of molecular peculiarities and the concentration of the oligoglycines on bulk-solution performance and on adsorption-layer properties at the solution–air interface. This study is focused on the clarification of the conditions for the onset of bulk and interfacial supramolecular species in the aqueous environment. The presence of oligoglycine antennae attached to a common carbon-atom center allows the formation of highly coordinated intra- and intermolecular ‘click-clack’ interactions and presumes the possibility for the development of extended H-bonded networks, e.g., in the form of Polyglycine II motifs. A combined study protocol, including dynamic light scattering, profile analysis tensiometry, and microscopic thin-liquid-film techniques, is applied. The results allow the drawing of essential conclusions about the possible coupling mechanism of bulk and interfacial phenomena. The outcomes give grounds to advance the following hypothesis: due to the synchronized action of noncovalent interactions, three types of tectomeric structures may appear—dimers, gel-like elements, and disk-like supramolecular entities. Options for fine-tuning of the tectomer formation in aqueous solutions are presented, and possible application routes are outlined. Full article

The growing environmental and economic impacts of carbon-based fuels have accelerated the search for sustainable alternatives, with hydrogen (H₂) emerging as a clean and efficient energy carrier. Sodium borohydride (NaBH₄) is a promising hydrogen storage compound, due to its high hydrogen content (10.6 wt%) and stability under ambient conditions. However, its hydrolysis with water proceeds slowly without an effective catalyst. In this study, gold nanoparticle-decorated spherical carbon (AuSC) composites were synthesized and evaluated as catalysts for NaBH₄ hydrolysis. The spherical carbon support, prepared via a glucose-mediated route, provided a high-surface-area and conductive matrix that dispersed and stabilized Au nanoparticles, preventing agglomeration. Catalyst morphology and composition were characterized using XRD, TEM, SEM, and EDS analyses. The AuSC catalyst exhibited excellent catalytic activity, producing 21.8 mL of H₂ at pH 7, 303 K, and 835 μmol NaBH₄. The activation energy (E_a) was determined to be 51.6 kJ mol⁻¹, consistent with a heterolytic B–H bond cleavage mechanism at the Au–C interface. The TON (2.82 × 10⁴) and TOF (1.41 × 10⁴ h⁻¹) values confirmed high intrinsic catalytic efficiency. These results demonstrate that Au-decorated spherical carbon composites are efficient, stable, and promising catalysts for hydrogen generation from NaBH₄ hydrolysis under mild conditions. Full article

Mechanical energy is a plentiful, environmentally friendly, and sustainable energy source in the natural world. In this work, we successfully use friction to transform mechanical energy into ZnO and ZnO/Nd₂O₃ (1, 2, 3, 4 and 5 mol%) tribocatalysts. Under magnetic stirring, the catalyst particles and the polytetrafluoroethylene (PTFE)-sealed magnetic bar rubbed against one another, transferring electrons across the contact interface. While the PTFE absorbed the electrons, holes were simultaneously left on the catalyst. Because of their potent oxidative power, the holes in the valence band of sol–gel catalysts can efficiently oxidize organic pollutants, much like photocatalysis. In the absence of light, the tribocatalytic tests showed that ZnO and ZnO/Nd₂O₃ flowers could remove antibiotics (Doxycycline) when magnetized. We could further improve the tribocatalytic performance by adjusting the quantity of rare earth elements (1, 2, 3, 4 and 5 mol%), stirring speed, and magnetic rod type. Besides creating a green tribocatalysis method for organic pollutants’ oxidative purification, this work provides a possible pathway for transforming environmental mechanical energy into chemical energy, which may be applied to environmental remediation and sustainable energy. Full article

The eyelid skin represents a unique anatomical and immunological interface between the external environment and the ocular surface. Due to its structural delicacy, dense vascularization, and continuous exposure to microbial and environmental antigens, it is a primary target of inflammatory and autoimmune processes. This review aims to synthesize current molecular insights into eyelid skin inflammation, with particular emphasis on autoimmune mechanisms. We discuss autoimmune diseases such as ocular cicatricial pemphigoid, pemphigus, discoid and systemic lupus erythematosus, and thyroid-associated orbitopathy, focusing on the roles of T helper cell subsets, pro-inflammatory cytokines (IL-1β, IL-6, IL-17, TNF-α), and autoantibody-mediated complement activation. We further address the contribution of the periocular microbiome and meibomian gland dysfunction. Diagnostic advances, including confocal microscopy, in vivo molecular imaging, and tear proteomics, are highlighted alongside emerging targeted therapies such as biologics and small molecules directed at IL-17, TNF-α, and B-cell activity. Finally, we propose future perspectives for precision medicine approaches, integrating omics technologies and microbiome-based therapies to advance personalized management of eyelid skin inflammation. Full article

Cochlear implants are highly successful in restoring speech perception but variability in outcomes exists. Post-surgical fibrosis and neo-ossification are thought to play a significant role, being linked to increased impedance and loss of residual hearing and posing challenges for re-implantation. Hence, there is growing interest in pharmacological interventions to limit intracochlear fibrosis and neo-ossification. While current approaches focus on steroids, studies in other organs have identified many candidate drugs. However, selection is hindered by a limited understanding of the molecular and cellular mechanisms driving fibrosis after implantation. This review introduces potential drug candidates for cochlear implant-induced fibrosis, with many targeting core fibrotic pathways such as TGF-β/SMAD, PDGF, and Wnt/β-catenin or inhibiting pro-inflammatory signalling. By drawing on lessons from other tissues, this review identifies mechanisms and therapeutic approaches adaptable to the cochlea. Understanding fibrosis across organs will guide strategies to prevent or reverse cochlear fibrosis. Their translation requires careful evaluation of local delivery, minimal ototoxicity, and effects on the electrode–tissue interface. Full article

Background: Rapidly growing mycobacteria (RGM) are microorganisms with variable pathogenicity, which can cause different clinical forms of mycobacterioses. They can form structured communities at the liquid-air interface and adhere to animate and inanimate solid surfaces, characterizing one of their most powerful mechanisms of resistance and survival, named biofilms. Objectives: Here, a novel series of sulfamethoxazole (SMTZ) Schiff bases were obtained by the condensation of the primary amine from SMTZ core with six different aldehydes to evaluate their antimicrobial and antibiofilm activities, as well as physicochemical and in silico characteristics. Methods: The compounds L1–L6 included: pyridoxal hydrochloride (L1), salicylaldehyde (L2), 3-methoxysalicylaldehyde (L3), 2-hydroxy-1-naphthaldehyde (L4), 3-allylsalicylaldehyde (L5), and 4-(diethylamino)salicylaldehyde (L6). MIC determination was performed against standard strains and seven clinical isolates. Time-kill assays, biofilm inhibition assays, atomic force microscopy, and peripheral blood mononuclear cell cytotoxicity assays were carried out. Density functional theory (DFT) calculations using quantum descriptors, Mulliken charges, Fukui functions, non-covalent interactions (NCI), and reduced density gradient (RDG), along with molecular docking calculations to DHS, LasR, and PqsR, supported the experimental trend. Results: The compounds L1–L6 showed a significant capacity to inhibit the growth of RGM, with MIC values in the range of 0.61 to 1.22 μg mL⁻¹, which are significantly lower than those observed for the parent compound SMTZ, demonstrating superior antimicrobial potency. To deepen antimicrobial activity assays, L1 was chosen for further evaluations and showed a significant ability to inhibit the growth of RGM in both planktonic and biofilm forms. In addition, atomic force microscopy views great changes in topography, electrical force, and nanomechanical properties of microorganisms. The cytotoxic assays with the peripheral blood mononuclear cell model suggest that the new compound may be considered as an antimicrobial alternative, as well as a safe substance showing selectivity indexes in the range of efficacy. Conclusions: Density functional theory (DFT) calculations were performed to obtain quantum descriptors, Mulliken charges, Fukui functions, non-covalent interactions (NCI), and reduced density gradient (RDG), which, with molecular docking calculations to DHS, LasR, and PqsR, supported the experimental trend. Full article

Polymer composite coatings are promising for tribological protection, with stable transfer films being key to their friction-reducing and anti-wear performance, yet the mechanism by which rare-earth compounds, known to enhance polymer tribological properties, regulate transfer film growth remains unclear. In this work, the tribological performance of phenolic resin (PF)-based coatings filled with lanthanum oxide (La₂O₃) and lanthanum fluoride (LaF₃) was systematically investigated. The results demonstrate that the friction coefficients of 5La₂O₃/PF and 3LaF₃/PF decrease to 0.024 and 0.031, representing a 79.66% and 73.95% reduction compared to pure PF, which compensates for the inadequacy of oil lubrication. Tribochemical analyses and characterizations of tribofilm structures confirm that complex tribochemical reactions involving rare-earth compounds occur, promoting the growth of a solid-lubricating tribofilm at the boundary lubrication interface. This work provides a theoretical foundation for the design of high-performance polymer lubricating coatings. Full article