Search Results (755)

Characterization of propeller blade vibrations is essential to ensure aerodynamic performance, minimize noise emissions, and maintain structural integrity in aerospace and unmanned aerial vehicle applications. Conventional high-fidelity finite-element and fluid–structure simulations yield precise modal predictions but incur prohibitive computational costs, limiting rapid design exploration. This paper introduces a data-driven surrogate modeling framework based on a feedforward neural network to predict natural vibration frequencies of propeller blades with high accuracy and a dramatically reduced runtime. A dataset of 1364 airfoil geometries was parameterized, meshed, and analyzed in ANSYS 2024 R2 across a range of rotational speeds and boundary conditions to generate modal responses. A TensorFlow/Keras model was trained and optimized via randomized search cross-validation over network depth, neuron counts, learning rate, batch size, and optimizer selection. The resulting surrogate achieves $R^2>0.90$ and $NRMSE<0.08$ for the second and higher-order modes, while reducing prediction time by several orders of magnitude compared to full finite-element workflows. The proposed approach seamlessly integrates with CAD/CAE pipelines and supports rapid, iterative optimization and real-time decision support in propeller design. Full article

This paper presents a low-cost, modular ankle–foot prosthesis that integrates an S-shaped compliant foot with a parallel spring–short-stroke actuator branch to balance energy return, impact attenuation, and rapid personalization. The design follows an FDM-oriented CAD/CAE workflow using PETG and interchangeable modules (foot, ankle unit, pylon adapter). Finite-element analyses of heel-strike, mid-stance, and toe-off load cases, supported by bench checks, show strain localization in intended flexural regions, a minimum safety factor of 15 for the housing, and peak-stress reduction after geometric refinements (increased transition radii and local ribs). The modular layout simplifies servicing and allows quick tuning of stiffness and damping without redesigning the load-bearing structure. The results indicate an engineeringly realistic path toward accessible prosthetics and provide a basis for subsequent upgrades toward semi-active control and sensor-assisted damping. Full article

Using additive manufacturing for the design of inserts in injection moulding (IM) offers advantages in product development and customization. However, challenges related to operating temperature and mechanical resistance remain. This article presents a systematic screening methodology to evaluate the suitability of materials for specific applications. Ten commercial Material Extrusion (MEX) filaments were selected to produce test samples. Moldex3D simulation software was employed to model the IM process using two thermoplastics and to determine the temperature and pressure conditions that the printed inserts must withstand. Simulation results were critically interpreted and cross-referenced with the experimental material characterisations to evaluate material suitability. Nine of the ten MEX materials were suitable for IM with LDPE, and five with PP. Dimensional assessments revealed that six insert solutions required further post-processing for assembly, while three did not. All of the selected materials successfully survived 10 injection cycles without encountering any significant issues. The simulation results were validated by comparing temperature data from a thermal imaging camera during IM, revealing only minor deviations. The study concludes that combining targeted material characterization with CAE simulation provides an effective and low-cost strategy for selecting MEX filaments for injection moulding inserts, supporting rapid tooling applications in niche production. Full article

This study aims to gather a sustainable understanding of additive manufacturing and other Manufacturing 4.0 approaches like horizontal and vertical integration and cloud computing techniques with a focus on industrial applications. The DfAM will apply 4.0 tools to gather product feasibility and execution, with CAE—FEM analysis and CAM. This publication focuses on the redesign of a vehicle suspension arm. The main objective is to apply innovative design techniques that optimize component performance while minimizing cost and time. The IDeS method and TRIZ methodology were used, resulting in a composite element, aiming to make the FDM-sourced process a viable option, with a weight reduction of more than 80%, with less material consumption and, hence, less vehicle energy consumption. The part obtained is holistically sustainable as it was obtained by reducing the overall labor used and material/scrap generated, and the IDES data sharing minimized rework and optimized the overall production time. Full article

This study explores the viability of ultra-high-performance concrete (UHPC) as a structural material for compressed air storage (CAES) systems, combining comprehensive experimental testing and numerical simulations. Scaled (1:20) CAES tanks were designed and tested experimentally under controlled pressure conditions up to 4 MPa (580 psi), employing strain gauges to measure strains in steel cylinders both with and without UHPC confinement. Finite element models (FEMs) developed using ANSYS Workbench 2024 simulated experimental conditions, enabling detailed analysis of strain distribution and structural behavior. Experimental and numerical results agreed closely, with hoop strain relative errors between 0.9% (UHPC-confined) and 1.9% (unconfined), confirming the numerical model’s accuracy. Additionally, the study investigated the role of a rubber interface layer integrated between the steel and UHPC, revealing its effectiveness in mitigating localized stress concentrations and enhancing strain distribution. Failure analyses conducted using the von Mises criterion for steel and the Drucker–Prager criterion for UHPC confirmed adequate safety factors, validating the structural integrity under anticipated operational pressures. Principal stresses from numerical analyses were scaled to real-world operational pressures. These thorough results highlight that incorporating rubber enhances the system’s structural performance. Full article

Recently, the EU approved RENURE-criteria materials to be used as substitutes for synthetic N fertilisers. Several studies have been performed on the agronomic efficacy and potential environmental impacts of different bio-based fertilisers (BBFs) from biomass recovery, including the RENURE-criteria materials. But information is lacking about their effectiveness under abiotic stress conditions like salinity and drought. The predictions for climate change-induced increased drought and soil salinisation for the European soils have also increased, making it inevitable to understand BBF performance in these impending situations. Two RENURE-criteria top-priority materials (ammonium nitrate (AN) and ammonium sulphate (AS) and another commercially used BBF—an evaporator concentrate (CaE)) were evaluated in a pot trial growing spinach under salinity and drought stress with a reference ‘no stress’ condition to examine crop growth, nutrient uptake, and nitrogen fertiliser replacement value (NFRV). Agronomically, BBFs performed at par with the synthetic fertiliser (SF) under unstressed and salt-stressed conditions, whereas, under drought stress, BBFs outperformed the SF treatment. AS exhibited the highest yield and nutrient uptake, displaying an NFRV of 3.1 and 1.8 under no-stress and salt-stress conditions, respectively. Salt stress did not negatively impact the crops grown in this trial, potentially due to the higher potassium content in the system, which alleviated the possible negative impacts of high sodium. This study delves into the agronomic response, without evaluating crop physiological changes, and, hence, should be taken as a preliminary step into further investigation of observed elemental interactions (that could be potentially driving stress mitigation) while also examining the crop physiology during the duration of stress. Full article

Pathogens such as Toxoplasma gondii, lentiviruses (e.g., CAE), Hypoderma spp., Neospora caninum, Mycoplasma spp., and pestiviruses are important for goat farming in Lithuania; however, data on their prevalence remain limited. To address this gap, a multi-pathogen study was conducted between 2021 and 2024 using selected ELISA kits (ID.vet, Innovative Diagnostics, France). A total of 380 blood samples were collected from 30 goat herds across different regions of Lithuania; the sample size varied depending on the pathogen. Serum samples were tested for antibodies, and seroprevalence was calculated for each pathogen. The highest seroprevalence was detected for T. gondii (38.9%, 143/368) and CAE virus (19.5%, 74/380). Antibodies to Mycoplasma spp. (0.3%, 1/368), Hypoderma spp. (3.8%, 7/184), and N. caninum (0.5%, 2/368) were detected only sporadically, while no antibodies to Border disease virus or Q fever were identified. Mixed infections were found in 7.6% of samples. Chi-square analysis showed that co-infections with toxoplasmosis and CAE occurred more frequently than expected (χ² = 19.05, p < 0.001). Herd size was significantly associated only with CAE seroprevalence (χ² = 7.913, df = 1, p < 0.05). Overall, toxoplasmosis and CAE were identified as the most epidemiologically relevant infections in the Lithuanian goat population. Full article

Solar-driven irrigation, a promising clean technology for agricultural water conservation, is constrained by mismatched photovoltaic (PV) pump outflow and irrigation demand, alongside unstable PV output. While compressed-air energy storage (CAES) shows mitigation potential, existing studies lack systematic explorations of pump water-lifting characteristics and supply capacity under coupled meteorological and air pressure effects, limiting its practical promotion. This study focuses on a solar-coupled compressed-air energy storage regulated sprinkler irrigation system (CAES-SPSI). Integrating experimental and theoretical methods, it establishes dynamic flow models for three DC diaphragm pumps considering combined PV output and outlet back pressure, introduces pressure loss and drop coefficients to construct a nozzle pressure dynamic model via calibration and iteration, and conducts a 1-hectare corn field case study. The results indicate the following: pump flow increases with PV power and decreases with outlet pressure (model deviation < 9.24%); nozzle pressure in pulse spraying shows logarithmic decline; CAES-SPSI operates 10 h/d, with hourly water-lifting capacity of 0.317–1.01 m³/h and daily cumulation of 6.71 m³; and the low-intensity and long-duration mode extends irrigation time, maintaining total volume and optimal soil moisture. This study innovatively incorporates dynamic air pressure potential energy into meteorological-PV coupling analysis, providing a universal method for quantifying pump flow changes, clarifying CAES-SPSI’s water–energy coupling mechanism, and offering a design basis for its agricultural application feasibility. Full article

Qarhan Salt Lake, located in the Qaidam Basin of northwestern China, is a highland lake characterized by diverse surface features, including salt lakes, salt crusts, and saline-alkali lands. Investigating the distribution and dynamic variations of salt crusts is essential for mineral resource development and regional ecological monitoring. To this end, the surface of the study area was categorized into several types according to micro-geomorphological characteristics. Polarimetric synthetic aperture radar (PolSAR), which provides rich scattering information, is well suited for distinguishing these surface categories. To achieve more accurate classification of salt crust types, the scattering differences among various types were comparatively analyzed. Stable samples were further selected using unsupervised Wishart clustering with reference to field survey results. Besides, to address the weak inter-class separability among different salt crust types, this paper proposes a PolSAR classification method tailored for salt crust discrimination by integrating unsupervised feature learning, attention-based feature optimization, and global context modeling. In this method, convolutional autoencoder (CAE) is first employed to learn discriminative local scattering representations from original polarimetric features, enabling effective characterization of subtle scattering differences among salt crust types. Vision Transformer (ViT) is introduced to model global scattering relationships and spatial context at the image-patch level, thereby improving the overall consistency of classification results. Meanwhile, the attention mechanism is used to bridge local scattering representations and global contextual information, enabling joint optimization of key scattering features. Experiments on fully polarimetric Gaofen-3 and dual-polarimetric Sentinel-1 data show that the proposed method outperforms the best competing method by $2.34\%$ and $1.17\%$ in classification accuracy, respectively. In addition, using multi-temporal Sentinel-1 data, recent temporal changes in salt crust distribution are identified and analyzed. Full article

The floor acoustic package is a crucial component of a vehicle’s overall acoustic insulation system, and its performance directly influences the interior sound field distribution and acoustic comfort. Conventional investigations of acoustic package performance primarily rely on experimental testing and computer-aided engineering (CAE) simulations. However, these methods often suffer from limited accuracy control, high computational cost, and low efficiency. In contrast, data-driven modeling approaches have recently demonstrated strong potential in addressing these challenges. In this paper, a Squeeze-and-Excitation Residual Network (SE-ResNet) is proposed to predict and analyze the sound insulation performance of vehicle floor systems based on the original structural and material parameters of acoustic package components. By replacing the conventional CAE process with a data-driven framework, the proposed method enhances prediction accuracy and computational efficiency. With the lowest recorded RMSE of 0.4048 dB across the 200–8000 Hz spectrum, the SE-ResNet model ranks first in overall performance. It substantially outperforms the SE-CNN (0.9207 dB) and also shows a clear advantage over both the SE-LSTM (0.4591 dB) and the ResNet (0.4593 dB). Validation using the acoustic package data of a new vehicle model further confirms the robustness of the proposed approach, yielding an overall RMSE = 0.4089 dB and CORR = 0.9996 on the test dataset. These results collectively demonstrate that the SE-ResNet-based method presents a promising and robust solution for forecasting the sound insulation performance of vehicle floor systems. Moreover, the proposed framework offers methodological and technical support for the data-driven prediction and analysis of other vehicle noise and vibration problems. Full article

Sugarcane bagasse (SCB) is a lignocellulosic byproduct with low biodegradability, limiting its potential for biological processes such as biogas production. The objective of this study was to evaluate whether a short-term biological pretreatment with the cellulolytic bacterium Proteus mirabilis KC94 could enhance SCB hydrolysis, improve nutrient balance, and increase biomethane potential (BMP). Three treatments were compared: untreated bagasse (UB), sterilized bagasse (SB), and KC94-pretreated bagasse (PB). Glucose release was highest in PB (61.83 ± 0.8 mg/mL), indicating enhanced cellulose degradation in PB relative to UB (53.19 ± 0.9 mg/mL) and SB (44.00 ± 0.5 mg/mL). Elemental analysis revealed a more balanced nutrient profile in PB, characterized by optimal carbon and nitrogen levels, and reduced sulfur content, indicating microbial assimilation and potential biological desulfurization. Scanning electron microscopy revealed pronounced structural disruption, increased porosity, and fiber delamination in PB, confirming the efficacy of KC94-mediated lignocellulosic pretreatment. BMP assays conducted over a 31-day incubation period revealed that PB produced the highest cumulative methane yield (99 ± 0.7 mL CH₄/g VS), representing 19% and 25% increases over UB and SB, respectively. PB biomethanation was also faster compared to the other two substrates. These findings demonstrate the novelty of a 5-day bacterial pretreatment strategy, which significantly improves lignocellulosic hydrolysis and methane yield. Specifically, P. mirabilis KC94 pretreatment increased glucose release by 16–40% and cumulative methane yield by 19–25% compared to untreated and sterilized controls. This cost-effective and environmentally friendly approach highlights the potential of P. mirabilis KC94 to valorize sugarcane bagasse, advancing sustainable energy recovery and circular bioeconomy practices. Full article

This study focuses on the optimization of modern extraction techniques for selected by-product materials, including apple, lemon, and tangerine peels, and onion skins, using artificial neural network (ANN) models. The extraction methods included ultrasound-assisted extraction (UAE) and microwave-assisted extraction (MAE) with water as the extractant, as well as maceration (MAC) with natural deep eutectic solvents (NADES). Key parameters, such as total phenolic content (TPC), total flavonoid content (TFC), and antioxidant activities, including reducing power (EC₅₀) and free radical scavenging capacity (IC₅₀), were evaluated to compare the efficiency of each method. Among the techniques, UAE outperformed both MAE and MAC in extracting bioactive compounds, especially from onion skins and tangerine peels, as reflected in the highest TPC, TFC, and antioxidant activity. UAE of onion skins showed the best performance, yielding the highest TPC (5.735 ± 0.558 mg CAE/g) and TFC (1.973 ± 0.112 mg RE/g), along with the strongest antioxidant activity (EC₅₀ = 0.549 ± 0.076 mg/mL; IC₅₀ = 0.108 ± 0.049 mg/mL). Tangerine peel extracts obtained by UAE also exhibited high phenolic content (TPC up to 5.399 ± 0.325 mg CAE/g) and strong radical scavenging activity (IC₅₀ 0.118 ± 0.099 mg/mL). ANN models using multilayer perceptron architectures with high coefficients of determination (r² > 0.96) were developed to predict and optimize the extraction results. Sensitivity and error analyses confirmed the robustness of the models and emphasized the influence of the extraction technique and by-product type on the antioxidant parameters. Principal component and cluster analyses showed clear grouping patterns by extraction method, with UAE and MAE showing similar performance profiles. Overall, these results underline the potential of UAE- and ANN-based modeling for the optimal utilization of agricultural by-products. Full article

Centella asiatica exhibits antioxidant, anti-inflammatory, and dermal-regenerative activities, yet the in vivo efficacy of an orally administered, dose-standardized extract against ultraviolet B (UVB)-induced photoaging has not been fully elucidated. This study investigated the protective effects of a chemically standardized C. asiatica extract (sCAE; 70 mg/g asiaticoside) in UVB-irradiated Skh:HR-1 hairless mice. Animals received oral sCAE (40 or 80 mg/kg/day) for eight weeks during repeated UVB exposure. Comprehensive assessments—including skin biophysical measurements, histological analysis, ELISA, and gene expression profiling—were performed to characterize dose-dependent responses. sCAE significantly reduced wrinkle formation, transepidermal water loss, malondialdehyde accumulation, and pro-inflammatory cytokines, while enhancing skin hydration, elasticity, antioxidant enzyme activities, and collagen expression. It also restored hyaluronic acid, ceramide, and their biosynthetic genes, and suppressed matrix metalloproteinase-1 and -9. Notably, the higher dose (80 mg/kg) consistently shifted key parameters toward normal levels, demonstrating a clear dose–response effect. These findings provide the first integrative in vivo evidence that orally administered, asiaticoside-standardized C. asiatica extract mitigates UVB-induced photoaging by concurrently improving barrier lipids, extracellular matrix integrity, inflammation, and oxidative stress, supporting its potential as a nutricosmetic agent for skin health. Full article

Background: Impaired mucus drainage from the paranasal sinuses is often associated with nasal obstruction and reduced airway function in growing patients. Orthopedic maxillary protraction and expansion techniques can enhance airway dynamics, but their underlying fluid–structure mechanisms remain insufficiently understood. Objective: To validate that the Right Angle Maxillary Protraction Appliance (RAMPA), combined with a semi-rapid maxillary expansion (sRME) intraoral device gHu-1, improves mucus drainage by enhancing nasal airflow through nasal cavity expansion. Methods: The effects of RAMPA therapy were analyzed using computational fluid dynamics (CFD) for single-phase (air) and two-phase (air–mucus) flows within the nasal cavity, employing the unsteady RANS turbulence model. Finite element method (FEM) results from prior studies were synthesized to assess changes in the center and radius of maxillary rotation induced by RAMPA-assisted sRME. A male patient (aged 8 years 7 months to 11 years 7 months) treated with extraoral RAMPA and the intraoral appliance (gHu-1) underwent pre- and post-treatment cone-beam computed tomography (CBCT) and ear, nose, and throat (ENT) evaluation. Results: FEM analysis revealed an increased radius and elevated center of maxillary rotation, producing expansion that was more parallel to the palatal plane. CFD simulations showed that nasal cavity expansion increased airflow velocity and pressure drop, enhancing the suction effect that promotes mucus clearance from the frontal sinus. Clinically, nasal passages widened, paranasal opacities resolved, and occlusal and intermolar widths improved. Conclusions: RAMPA combined with sRME improves nasal airflow and maxillary skeletal expansion, facilitating paranasal mucus clearance and offering a promising adjunctive approach for enhancing upper airway function in growing patients. Full article

Salvia rosmarinus Spenn. (rosemary) is a medicinal and aromatic plant of notable pharmacological value. This study evaluated the therapeutic properties of rosemary leaves collected from two Serbian continental (L1, L2) and one Montenegrin Mediterranean (L3) locations, harvested in November (N), March (M), and July (J). Extracts prepared with 70% methanol, 70% ethanol, and water were analyzed for chemical composition and biological activity. L3 extracts exhibited the highest polyphenolic content, with L3M methanolic extract showing the greatest total phenolic (134.60 mg GAE/g) and phenolic acid levels (211.96 mg CAE/g), and L3M ethanolic extract the highest flavonoid content (25.54 mg QE/g). LC/MS analysis identified 28 previously unreported compounds in Rosmarinus sp. extracts, revealing hydroxycinnamic acid derivatives and flavonoid O-glycosides as the main constituents in S. rosmarinus. The alcoholic extracts were rich in 1,8-cineole, camphor, borneol, terpinen-4-ol, and verbenone. L3 extracts demonstrated the strongest antioxidant and enzyme-inhibitory activities, often surpassing positive controls. L3J showed pronounced cytotoxicity against HCT-116 colorectal cancer cells (IC₅₀ = 13.08 µg/mL after 24 h incubation), while showing non-cytotoxic effects on normal human keratinocytes (IC₅₀ > 500 µg/mL). Finally, rosmarinic acid alone synergistically enhanced the cytotoxic effect of 5-fluorouracil (combination index < 0.8). This comprehensive study highlights the influence of geography, season, and solvent on phytochemical profile and bioactivity of rosemary extracts, emphasizing the therapeutic potential of distinct rosemary populations. Full article