Search Results (45)

Collagen products are widely marketed for skin improvement. This study evaluated the efficacy of VERISOL B in relation to key skin aging parameters. In a double-blind, placebo-controlled trial, 66 women (aged 35–55) were randomized to receive either 2.5 g of bovine-derived bioactive collagen peptides (SCPs) (n = 33) or a placebo (n = 33) daily for 8 weeks. Their eye wrinkle volume, skin elasticity, and hydration were objectively measured at baseline (X₀), 4 weeks (X₄), and 8 weeks (X₈). Additionally, the SCPs’ impact on type I collagen, elastin, and proteoglycan biosynthesis was assessed in human dermal fibroblasts. The SCP supplementation significantly (p < 0.05) reduced their eye wrinkle volume and improved their skin elasticity and hydration within 4 weeks. After 8 weeks of treatment, the positive effects were even more pronounced for all of the clinical parameters measured (p < 0.05). The fibroblast experiments confirmed the SCPs’ stimulatory impact on dermal metabolism (p < 0.05). In conclusion, oral SCP supplementation effectively reduced wrinkles and enhanced skin elasticity and hydration, likely by promoting extracellular matrix biosynthesis. Full article

The interface between old and new concrete is a critical component in many construction practices, including concrete pavements, bridge decks, hydraulic dams, and buildings undergoing rehabilitation. Despite various treatments to enhance bonding, this interface often remains a weak layer that compromises overall structural performance. Traditional design methods typically oversimplify the interface as a homogeneous or empirically adjusted factor, resulting in significant uncertainties. This paper introduces a novel framework for quantifying the anisotropic properties of old–new concrete interfaces using X-ray computed tomography (CT) and finite element-based numerical homogenization. The elastic coefficient matrix reveals that specimens away from the interface exhibit higher values in both normal and shear directions, with normal direction values averaging 33.15% higher and shear direction values 39.96% higher than those at the interface. A total of 10 sampling units along the interface were collected and analyzed to identify the “weakest vectors” in normal and shear directions. The “weakest vectors” at the interface show consistent orientations with an average cosine similarity of 0.62, compared with an average cosine similarity of 0.23 at the non-interface, which demonstrates directional features. Conversely, the result of average cosine similarity at the interface shows randomness that originates from the anisotropy of materials. The average angle between normal and shear stresses was found to be 88.64°, indicating a predominantly orthogonal relationship, though local stress distributions introduced slight deviations. These findings highlight the importance of understanding the anisotropic properties of old–new concrete interfaces to improve design and rehabilitation practices in concrete and structural engineering. Full article

The fractional Brownian field is often used to reproduce the fractal properties of complex heterogeneous media, which closely represent real-world geological materials. Studying elastic wave transport in this type of heterogeneous media is essential for advancing knowledge in geophysics, seismology, and rock mechanics. In this paper, we numerically investigate the wavefield evolution and arrival behavior of elastic wave propagation in a two-dimensional fractional Brownian field characterized by the standard deviation (σ) and the Hurst exponent (H). Using a high-fidelity finite element model, we quantify the influence of these parameters on wavefront morphology, wave arrival synchronization, and energy decay. Our results reveal that increased matrix heterogeneity with higher σ and lower H values leads to pronounced wavefront roughness, asynchronous arrival phenomena, and increscent energy decay, attributed to enhanced scattering and modulus variability. For smaller H values, rougher modulus distributions scatter wave energy more intensely, producing more coda waves and distorted wavefronts, while smoother fields with larger H fields promote smoother wave propagation. Higher σ amplifies these effects by increasing modulus variability, resulting in more attenuated wave energy and substantial wavefield disturbance. This study contributes to a quantitative understanding of how fractal heterogeneity modulates wave transport and energy attenuation in random media. Our findings hold practical significance for geophysical exploration and seismic tomography, as well as aiding in subsurface imaging and structural evaluation within fractured or stratified rock formations. Full article

This study investigates the impact of polyvinyl alcohol (PVA) fibers on the mechanical properties and durability of high-performance shotcrete (HPS). Results demonstrate that PVA fibers have a dual impact on the performance of HPS. Positively, PVA fibers enhance the tensile strength and toughness of shotcrete due to their intrinsic high tensile strength and fiber-bridging effect, which significantly improves the material’s splitting tensile strength, deformation resistance, and toughness, and the splitting tensile strength and peak strain have been found to be increased by up to 30.77% and 31.51%, respectively. On the other hand, the random distribution and potential agglomeration of PVA fibers within the HPS matrix can lead to increased air-void formations. This phenomenon raises the volume content of large bubbles and increases the average bubble area and diameter, thereby elevating the pore volume fraction within the 500–1200 μm and >1200 μm ranges. Therefore, these microstructural changes reduce the compactness of the HPS matrix, resulting in a decrease in compressive strength and elastic modulus. The compressive strength exhibited a reduction ranging from 10.44% to 15.11%, while the elastic modulus showed a decrease of between 8.09% and 12.67%. Overall, the PVA-HPS mixtures with different mix proportions demonstrated excellent frost resistance, chloride ion penetration resistance, and carbonation resistance. The electrical charge passed ranged from 133 to 370 C, and the carbonation depth varied between 2.04 and 6.12 mm. Although the incorporation of PVA fibers reduced the permeability and carbonation resistance of shotcrete, it significantly mitigated the loss of tensile strength during freeze–thaw cycles. The findings offer insights into optimizing the use of PVA fibers in HPS applications, balancing enhancements in tensile properties with potential impacts on compressive performance. Full article

The buried hills of the Archean metamorphic rocks in the Bozhong Depression of the Bohai Bay Basin are the main gas-bearing strata, with burial depths ranging from 4000 m to 5500 m. However, metamorphic rocks have internal structural characteristics, such as diverse mineral components, oriented arrangement of mineral particles, complex pore connectivity, variable crystal structures, orthogonal development of multiple sets of fractures, and uneven fluid filling. Compared with conventional reservoirs, they have obvious heterogeneity and anisotropy characteristics. Traditional rock physics modeling methods are no longer suitable for predicting the elastic and anisotropic parameters of metamorphic reservoirs. Therefore, we introduced a vector mixed random medium model to calculate the effect of the oriented arrangement of metamorphic rock minerals on the modulus of the rock matrix and introduced a metamorphic factor to describe the impact of metamorphic recrystallization and alteration metasomatism on the elastic modulus of the rock matrix. Practical applications have shown that the new, improved rock physics modeling method can better estimate the S-wave velocity and anisotropy parameters in wells compared to traditional rock physics modeling methods, providing a reliable basis for predicting fractured reservoirs in metamorphic rock at buried hills. Full article

This paper analyses mechanical property prediction through Machine Learning for continuous fiber-reinforced polymer matrix composites printed using the novel Material Extrusion Additive Manufacturing technique. The composite is formed by a nylon-based matrix and continuous fiber (carbon, Kevlar, or fiberglass). From the literature, the elastic modulus and tensile strength were taken along with printing parameters like fiber content, fiber fill type, matrix lattice, matrix fill density, matrix deposition angle, and fiber deposition angle. Such data were fed to several supervised learning algorithms: Ridge Regression, Bayesian Ridge Regression, Lasso Regression, K-Nearest Neighbor Regression, CatBoost Regression, Decision Tree Regression, Random Forest Regression, and Support Vector Regression. The Machine Learning analysis confirmed that fiber content is the most influential parameter in elasticity (E) and strength ($\sigma$). The results show that the K-Nearest Neighbors and CatBoost provided the closest predictions for E and $\sigma$ compared to the other models, and the tree-based model presented the narrowest error distribution. The computational metrics point to a size versus prediction time tradeoff between these two best predictors, and adopting the prediction time as the most relevant criterion leads to the conclusion that the CatBoost model can be considered, when compared to the others tested, the most appropriate solution to work as a predictor in the task at hand. Full article

In the present study, a thorough examination of nanoindentation-based mechanical properties of a refractory MoTaNbVW high-entropy alloy (RHEA) was conducted. Basic mechanical properties, such as the indentation modulus of elasticity, indentation hardness, and indentation-absorbed elastic energy, were assessed by means of different input testing variables, such as the loading speed and indentation depth. The obtained results were discussed in terms of the elasto-plastic behavior of the affected material by the indentation process and material volume. Detailed analysis of the RHEA alloy’s nanoindentation creep behavior was also assessed. The effect of testing parameters such as preset indentation depth, loading speed, and holding—at the creep stage—time were selected for their impact. The results were explained in terms of the availability of mobile dislocations to accommodate creep deformation. Crucial parameters, such as maximum shear stress developed during testing (τ_max), critical volume for dislocation nucleation (Vcr), and creep deformation stress exponent n, were taken into consideration to explain the observed behavior. Additionally, in all cases of mechanical property examination and in order to identify those input testing parameters—in case—that have the most severe effect, an extensive statistical analysis was conducted using four different methods, namely ANOVA, correlation matrix analysis, Random Forest analysis, and Partial Dependence Plots. It was observed that in most of the cases, the statistical treatment of the obtained testing data was in agreement with the microstructural and metallurgical observations and postulates. Full article

Computational approaches could provide a viable and cost-effective alternative to expensive experiments for accurately evaluating the nonlinear constitutive behavior of cementitious nanocomposite materials. In the present study, the mechanical properties of cement paste reinforced with multi-wall carbon nanotubes (MWCNTs) are examined experimentally and numerically. A multiscale computational approach is adopted in order to verify the experimental results. For this scope, a random sequential adsorption algorithm was developed to generate non-overlapping matrix-inclusion three-dimensional (3D) representative volume elements (RVEs), considering the inclusions as straight elements. Nonlinear finite element analyses (FEA) were performed, and the homogenized elastic and inelastic mechanical properties were computed. The use of a multiscale computational approach to accurately evaluate the nonlinear constitutive behavior of cementitious materials has rarely been explored before. For this purpose, the RVEs were analyzed both in pure tension and compression. Young’s modulus as well compressive and tensile strength results were compared and eventually matched the experimental values. Moreover, the effect of MWCNTs on the nonlinear stress–strain behavior of reinforced cement paste was noted. Subsequently, three-point bending tests were conducted, and the stress–strain behavior was verified with FEA in the macro scale. The numerical modeling reveals a positive correlation between the concentration of MWCNTs and improved mechanical properties, assuming ideal dispersion. However, it also highlights the impact of practical limitations, such as imperfect dispersion and potential defects, which can deteriorate the mechanical properties that are observed in the experimental results. Among the different cases studied, that with a 0.1 wt% MWCNTs/CP composite demonstrated the closest agreement between the numerical model and the experimental measurements. The numerical model achieved the best accuracy in estimating the Young’s modulus (underestimation of 13%), compressive strength (overestimation of 1%), and tensile strength (underestimation of 6%) compared to other cases. Overall, these numerical findings contribute significantly to understanding the mechanical behavior of the nanocomposite material and offer valuable guidance for optimizing cement-based composites for engineering applications. Full article

Polymer composites containing magnetic fillers are promising materials for a variety of applications, such as in energy storage and medical fields. To facilitate the engineering design of respective components, a comprehensive understanding of the mechanical behavior of such inhomogeneous and potentially highly anisotropic materials is important. Therefore, the authors created magnetic composites by compression molding. The epoxy polymer matrix was modified with a commercial-grade thickening agent. Isotropic magnetic particles were added as the functional filler. The microstructural morphology, especially the filler distribution, dispersion, and alignment, was characterized using microscopy techniques. The mechanical properties of the composites were experimentally characterized and studied by stochastic finite element analysis (SFEA). Modeling was conducted employing four cases to predict the elastic modulus: fully random distribution, randomly aligned distribution, a so-called “rough” interface contact, and a bonded interface contact. Results from experiments and SFEA modeling were compared and discussed. Full article

Border management serves as a crucial control checkpoint for governments to regulate the quality and safety of imported food. In 2020, the first-generation ensemble learning prediction model (EL V.1) was introduced to Taiwan’s border food management. This model primarily assesses the risk of imported food by combining five algorithms to determine whether quality sampling should be performed on imported food at the border. In this study, a second-generation ensemble learning prediction model (EL V.2) was developed based on seven algorithms to enhance the “detection rate of unqualified cases” and improve the robustness of the model. In this study, Elastic Net was used to select the characteristic risk factors. Two algorithms were used to construct the new model: The Bagging-Gradient Boosting Machine and Bagging-Elastic Net. In addition, F_β was used to flexibly control the sampling rate, improving the predictive performance and robustness of the model. The chi-square test was employed to compare the efficacy of “pre-launch (2019) random sampling inspection” and “post-launch (2020–2022) model prediction sampling inspection”. For cases recommended for inspection by the ensemble learning model and subsequently inspected, the unqualified rates were 5.10%, 6.36%, and 4.39% in 2020, 2021, and 2022, respectively, which were significantly higher (p < 0.001) compared with the random sampling rate of 2.09% in 2019. The prediction indices established by the confusion matrix were used to further evaluate the prediction effects of EL V.1 and EL V.2, and the EL V.2 model exhibited superior predictive performance compared with EL V.1, and both models outperformed random sampling. Full article

Psychological stress exerts its effects mainly through the release of corticotropin releasing hormone (CRH), which activates inflammatory pathways in skin (inter alia), resulting in redness, extracellular matrix degradation, loss of skin elasticity and firmness, and the appearance of wrinkles—namely, accelerated skin aging. In order to propose a solution to this neurogenic aging phenomenon, we report here on studies using a myricitrin-rich extract of Cistus incanus, a Mediterranean shrub used in traditional medicine for the treatment of inflammatory and other diseases. These studies include a CRH receptor (CRH-R1) blocking assay; in vitro inflammatory cytokine reduction under CRH stimulation, and ex vivo NF-kB inhibition; and a double-blind clinical trial performed on highly stressed panelists, evaluating skin inflammation and wrinkling (active formulation vs. placebo control, applied split-face following a computer-generated randomization scheme; 36 subjects recruited and randomized, 30 analyzed; no adverse effects recorded; EMA/INFARMED registration #118505, internally funded). The results show that this extract can effectively block the CRH-R1 receptor, preventing NF-κB activation and the production of related pro-inflammatory cytokines. In a clinical setting, this same extract delivered significant anti-inflammatory and anti-aging effects. Taken together, these results demonstrate the value of this extract as a cosmetic active to counter neurogenic inflammation and skin aging. Full article

Background: Current guideline-based implantable cardioverter-defibrillator (ICD) implants fail to meet the demands for precision medicine. Machine learning (ML) designed for survival analysis might facilitate personalized risk stratification. We aimed to develop explainable ML models predicting mortality and the first appropriate shock and compare these to standard Cox proportional hazards (CPH) regression in ICD recipients. Methods and Results: Forty-five routine clinical variables were collected. Four fine-tuned ML approaches (elastic net Cox regression, random survival forests, survival support vector machine, and XGBoost) were applied and compared with the CPH model on the test set using Harrell’s C-index. Of 887 adult patients enrolled, 199 patients died (5.0 per 100 person-years) and 265 first appropriate shocks occurred (12.4 per 100 person-years) during the follow-up. Patients were randomly split into training (75%) and test (25%) sets. Among ML models predicting death, XGBoost achieved the highest accuracy and outperformed the CPH model (C-index: 0.794 vs. 0.760, p < 0.001). For appropriate shock, survival support vector machine showed the highest accuracy, although not statistically different from the CPH model (0.621 vs. 0.611, p = 0.243). The feature contribution of ML models assessed by SHAP values at individual and overall levels was in accordance with established knowledge. Accordingly, a bi-dimensional risk matrix integrating death and shock risk was built. This risk stratification framework further classified patients with different likelihoods of benefiting from ICD implant. Conclusions: Explainable ML models offer a promising tool to identify different risk scenarios in ICD-eligible patients and aid clinical decision making. Further evaluation is needed. Full article

In this paper, a 2D mesoscale finite element (FE) numerical model of mortar, considering the influence of the ITZ, was proposed to evaluate the corrosion of mortar in sodium sulfate. On the mesoscale, the corroded mortar was regarded as a three-phase composite material composed of sand, cement paste, and an interface transition zone (ITZ). Firstly, the volume fractions and mechanical parameters (elastic modulus, Poisson’s ratio, and strength) of the mesoscale phases were obtained. Then, the cement paste and the ITZ were combined to form an equivalent matrix by homogenization methods, and the calibrated constitutive relations of the equivalent matrix were established. Subsequently, a two-dimensional (2D) random circular aggregate (RCA) model and a 2D random polygonal aggregate (RPA) model of corroded mortar were established using the random aggregate model. The failure process of corroded mortar specimens under uniaxial compression was simulated by the mesoscale FE numerical model. Comparing the simulation results with the measured stress–strain curves of the uniaxial compression test, it was found that the simulation results of the 2D RP model were closer to the experimental results than those of the 2D RC model. Meanwhile, the numerical simulation results were in good agreement with the experimental results, and the error values of peak stress between the simulation results and the measured results were within 7%, which showed that the 2D mesoscale FE model could accurately predict the results of a uniaxial compression test of a mortar specimen under sulfate attack. Full article

Engineering structures are often assembled from parts with different materials. When uncertainty quantification techniques are applied, the curse of dimensionality increases the computational complexity. Here, a stochastic Galerkin method for planar elasticity allowing for multiple regions with independent uncertain materials is introduced. The method allows for efficient solution of linear systems both in fully assembled and matrix-free formulations. The selection of the stochastic basis polynomials is performed using a priori knowledge of the decay of the random fields. The statistical quantities of interest are the expected solution and variance, both of which can be computed efficiently after the Galerkin system has been solved. Analysis of the results indicates that the proposed method is highly efficient in terms of both computational resource requirements and discretization of the stochastic dimensions. The results were verified with Monte Carlo and quasi-Monte Carlo methods. Full article

Green mandarins are widely consumed unripe as mandarin oranges (Citrus unshiu Marcov.), which exhibit anti-inflammatory and anti-wrinkle effects by inhibiting the production of inflammatory cytokines and matrix metalloproteinase. A randomized, double-blind, placebo-controlled clinical study was performed to verify the skin improvement efficacy and safety of green mandarin extract (PTE). For the standardization of PTE, narirutin was set as a marker compound, and PTE with a constant narirutin content was prepared for the study. After randomizing subjects with periorbital wrinkles, they were orally administered PTE (300 mg/day) or a placebo for 12 weeks. Periorbital wrinkles were measured using PRIMOS^CR SF. Skin elasticity, moisture content, transepidermal water loss, and gloss were also measured. In the study results, the depth, volume, and skin roughness of the periorbital wrinkles were significantly improved compared to the control group (p = 0.011, 0.009, and 0.004, respectively). The survey confirmed that the skin condition improved after PTE consumption for 12 weeks. No adverse reactions associated with PTE were observed during the study period. Thus, the results demonstrate that PTE effectively improves UV-induced skin wrinkles. Therefore, it is considered that PTE has sufficient value as a functional food ingredient that can prevent skin aging. Full article