ABSTRACT Curcumin is a natural polyphenol derived from Curcuma longa with well‐documented anti‐inflammatory, antioxidant, antimicrobial, and wound‐healing properties. However, its clinical application in dermatology remains limited due to its low water solubility, low stability, and limited skin penetration. Recent advances in nanotechnology have enabled the development of curcumin delivery systems designed to improve dermal bioavailability, stability, and controlled release. Our novelty lies in integrating bibliometric analyses with a translational emphasis on food‐grade nanocarriers. This review highlights the therapeutic relevance of curcumin in major skin conditions, such as psoriasis, atopic dermatitis, acne, wound healing, burns, and skin cancer, with an emphasis on lipid‐ and polymer‐based nanoformulations, such as liposomes, niosomes, solid lipid nanoparticles, nanostructured lipid carriers, and hydrogel‐based platforms. Furthermore, the bibliometric analysis highlights a growing scientific interest in curcumin nanotherapies, although clinical evidence remains limited. In general, curcumin nanoformulations represent promising strategies for topical dermatological applications, but further clinical validation and regulatory development are required to support their application in therapeutic products.
Shape memory alloys (SMAs) are characterized by complex hysteresis behavior resulting from phase transformations between martensite and austenite, which is significantly affected by the frequency of cyclic loading. Accurate prediction of these processes is essential to ensure the reliability of structural components in various engineering applications. This paper presents an approach for predicting the hysteresis behavior of SMAs based on an ensemble Voting machine learning model. The ensemble included Random Forest, Gradient Boosting, Extra Trees, Support Vector Regressor, K-Nearest Neighbors, and a Multilayer Perceptron. The model weights were determined as the inverse of the mean squared error, which ensured a balanced contribution of each algorithm to the final prediction. The model performance was evaluated using the MAE, MSE, R², and MAPE metrics. The results demonstrated high prediction accuracy (R2 > 0.998, MAE < 0.022, MSE < 0.0008, and MAPE < 0.008) and confirmed the ability of the model to generalize across independent cycles, including extrapolated ones (251 and 300). The predicted hysteresis loops showed good agreement with the experimental curves. The obtained results confirm the effectiveness of the ensemble approach for modeling the behavior of SMAs and predicting their functional properties.
Safety devices are used to protect the drive elements of machines and mechanisms from destructive effects caused by overloads. In a certain period of time, when the torque value increases above the permissible value, they disconnect the kinematic chain of the mechanism drive, thus preventing breakage of critical machine components. Accordingly, the use of safety devices in machine and mechanism drives is essential for modern engineering. One of these devices is the toothed safety clutch (TSC), which is characterized by relative simplicity of design and excellent functional qualities. Currently, many designs of safety clutches are in operation, but each subsequent design is developed to increase its manufacturability, improve technological capabilities, operation accuracy and reliability, which was implemented in the proposed design of the safety clutch.
The present paper sets forth the findings of a numerical study of the dynamic behaviour of a high-rise frame building with different materials. This study was conducted using the finite element method in the LIRA PC environment. The aim of this study was to evaluate how different combinations of reinforced concrete, steel and wood affect the natural frequencies and mode shapes of the nine-storey building frame. The computational model utilised three-dimensional finite elements with a 10 × 10 cm mesh. A total of nine material configurations for the lower (1–3) and upper (4–9) floors were analysed. The results demonstrated a clear dependence of the dynamic behaviour on the spatial stiffness of the structure. The configurations exhibiting higher stiffness, specifically option 3a (steel on the lower floors and reinforced concrete on the upper floors), demonstrated the highest natural frequencies, suggesting the highest overall stiffness. Conversely, configurations with wood on all or the majority of floors (group 2 and option 3b) exhibited the lowest frequencies, indicative of enhanced deformability. A comparison of the frequency spectra demonstrates a substantial impact of material heterogeneity on the modal parameters. Increases in the proportion of steel or reinforced concrete in the upper floors have been shown to result in higher mid- and high-frequency natural modes. Conversely, the use of wood has been demonstrated to reduce these frequencies, thereby affecting the overall stiffness of the structure.
The present research investigates the optimization of ball burnishing (BB) process parameters to create regular lubricating grooves on multilayer connecting rod liners to prevent engine seizure. The study utilized a Taguchi L9 fractional orthogonal array to evaluate the impact of ball diameter, deforming force, and feed rate on the resulting groove widths. Statistical analysis (ANOVA) revealed that ball diameter is the primary driver of groove width variation, exhibiting a non-linear parabolic relationship where the diameter serves as a stabilizing threshold. While deformation force showed a steady linear progression in widening traces, higher feed rates were found to restrict localized plastic flow, resulting in narrower groove widths. For the bimetallic structure (steel back with AlSn20Cu coating), the research recommends tailoring forces to the specific layer—forces for the anti-friction layer and for the substrate to avoid structural destruction. Profilometry confirmed that the height of edge inflows directly correlates with groove depth, ranging from 6 to 30 μm. The optimized non-linear regression model developed in this study achieved an exceptionally high coefficient of determination (R2 = 99.84%), ensuring precise predictive accuracy. Overall, these findings provide a robust framework for researchers to enhance the durability of heavy-duty engine components through controlled surface topography.
