Search Results (708)

Circadian rhythm regulates several physiological functions, and influences endocrine and metabolic responses in mammals, with cortisol acting as important modulator of this mechanism. Cortisol secretion is affected by both internal and external factors and is intensified under stress conditions. The response to surgical stress is consistently observed after surgical procedures, such as ovariohysterectomy (OVH). Therefore, this study aimed to evaluate the influence of the circadian rhythm on the surgical stress response following elective OVH in healthy bitches. Twenty patients weighing between 10 and 20 kg were hospitalized 48 h before surgery and remained hospitalized for 48 h postoperative. The animals were randomly allocated into two groups and underwent OVH either in the morning (6–8 h—a.m., GAM) or at night (18–20 h—p.m., GPM). Surgical procedures were standardized with respect to the surgical team, technique applied and duration; this was carried out in order to induce a comparable level of surgical stress. Physical parameters (systolic blood pressure, heart rate, respiratory rate and rectal temperature) and laboratorial analyses (cortisol, leukogram, protein thiols, no protein thiols, vitamin C, ferric reducing ability of plasma and thiobarbituric acid reactive substances) were assessed immediately before surgery and at 2, 4, 6, 12, 24 and 48 h, as well as 14 days postoperatively. No significant changes in cortisol profile were detected. However, significant alteration in the respiratory rate, rectal temperature, time to first urine, and lipid peroxidation were observed in the GPM group, suggesting that surgeries performed at night induce greater disturbances in homeostasis than those performed in the morning. Full article

In the analysis and design of wind turbines, aeroelastic analysis is required that considers elastic structure and control in addition to aerodynamic characteristics. In recent years, with the increase in size and reduction in the cost of wind turbines, problems have emerged that cannot be addressed with the standard analysis methods. The accuracy of the Blade Element and Momentum (BEM) theory, which is the most common aerodynamic analysis and design theory, is reduced in conditions where three-dimensional effects such as radial flow are not negligible. Furthermore, full-model Computational Fluid Dynamics (CFD), which is commonly used for complex aerodynamic problems, is not applicable for the design calculation of wind turbines considering itscomputational demands. To address these challenges, the Actuator Line Model (ALM) can be utilized as practical load analysis methods that account for structural elasticity, control, and fluctuating winds—offering a level of fidelity between both approaches. However, the conventional ALM does not account for the stall delay (SD) observed in the inboard section of rotor. In this study, an ALM based on CFD is developed by incorporating Snel’s stall delay model, which was developed for BEM. Additionally, the use of the NREL 5 MW reference wind turbine rotor results in the load distribution of the inboard section of this developed model to be comparable to that of the full-model CFD; however, a similar observation is not made for the conventional BEM. Full article

The development of anisotropic gold nanostructures supporting localized surface plasmon (LSP) resonances in the near-infrared (NIR) biological window is of great interest for diagnostic and therapeutic nanotechnologies. Here, we report gold open-shell nanoparticles (AuOSNs), a symmetry-broken nanoshell architecture exhibiting strong NIR surface-enhanced Raman scattering (SERS) activity. AuOSNs were fabricated via a surfactant-free strategy combining bottom-up silica sphere assembly with a simple top-down gold deposition process, without using highly cytotoxic surfactants such as cetyltrimethylammonium bromide (CTAB). Boundary element method (BEM) simulations revealed that the asymmetric open-shell geometry induces NIR LSP resonances with pronounced electromagnetic field localization near the opening edges, depending on excitation configuration. Consistent with these predictions, extinction spectra of AuOSNs dispersed in water showed an LSP resonance peak at ~793 nm, close to the 785 nm excitation wavelength for SERS. In aqueous dispersion, AuOSNs modified with 4-mercaptobenzoic acid (4-MBA) exhibited strong SERS activity with enhancement factors of ~10⁶. Furthermore, polyethylene glycol (PEG)-modified MBA/AuOSNs showed negligible cytotoxicity in vitro. SERS imaging confirmed that PEG/MBA/AuOSNs enable visualization of HeLa cells via characteristic MBA SERS signals. These results demonstrate that surfactant-free AuOSNs provide a biocompatible platform for NIR-excited SERS sensing and cellular imaging, highlighting their potential in plasmonic bioimaging applications. Full article

The expansion of urban rail transit has exacerbated environmental issues related to low-frequency noise (LFN), yet the impact of geometric symmetry breaking on structure-borne noise remains underexplored. This study aims to quantify the mechanism by which cross-sectional asymmetry influences the vibro-acoustic coupling of viaducts. A 2.5D Hybrid Finite Element-Boundary Element Method (FEM-BEM) was employed to model a parametric box girder under eccentric track loading, and the numerical framework was validated against analytical benchmarks. The “Modal Symmetry Index” (MSI) and “Acoustic Asymmetry Indicator” (AAI) were defined to evaluate the effects of the asymmetry parameter ($\alpha$) on sound field distribution. Numerical results reveal a nonlinear “V-shaped” relationship between geometric asymmetry and acoustic directivity. While severe asymmetry ($\alpha >0.15$) exacerbates noise deflection via flexural–torsional coupling, a critical “self-balance zone” exists. Specifically, moderate asymmetry ($\alpha \approx 0.07$) effectively neutralizes load eccentricity, reducing the AAI from 1.5 dB (in strictly symmetric designs) to nearly 0 dB. Robustness analysis under right-side loading conditions further confirms a “reverse deflection” phenomenon, verifying that the proposed self-balance design minimizes directional sensitivity. These findings challenge the traditional assumption that geometric symmetry is acoustically optimal. A “competition–compensation” mechanism is identified, suggesting that deliberate, slight geometric asymmetry can serve as an effective passive noise control strategy for viaducts. Full article

Accurate and rapid prediction of sensor responses is critical for real-time measurement systems, digital twin implementations, and sensor design optimization. Traditional numerical methods such as Finite Element Method (FEM) and Computational Fluid Dynamics (CFD) provide high-fidelity solutions but suffer from prohibitive computational costs, limiting their applicability in time-sensitive applications. This paper presents a novel framework utilizing Fourier Neural Operators (FNO) as surrogate models for fast multi-physics sensor response prediction across thermal, acoustic, and flow measurement domains. Unlike conventional neural networks that learn finite-dimensional mappings, FNO learns operators between infinite-dimensional function spaces by parameterizing the integral kernel in Fourier space, enabling resolution-invariant predictions with remarkable computational efficiency. We demonstrate the framework’s efficacy through three comprehensive case studies: (1) thermal sensor response prediction achieving $R^2>0.98$ with 8300× speedup over FEM, (2) acoustic sensor array modeling with mean absolute error below 0.5 dB and 4000× speedup over BEM, and (3) flow sensor characterization with velocity field prediction accuracy exceeding 97% and 31,000× speedup over CFD. The proposed FNO-based surrogate models are trained on simulation datasets generated from high-fidelity numerical solvers and validated against simulation holdout data for all three case studies, with additional experimental validation conducted for the thermal sensor case. Results indicate that FNO architectures effectively capture the underlying physics governing sensor behavior while reducing inference time from minutes to milliseconds. The framework enables real-time sensor calibration, uncertainty quantification, and design optimization, opening new possibilities for intelligent measurement systems and Industry 4.0 applications. We also investigate the spectral characteristics of FNO predictions, addressing the inherent low-frequency bias through a hybrid architecture combining FNO with local convolutional layers. The primary contributions of this work include: (1) the first systematic application of FNO-based surrogate modeling specifically tailored for sensor response prediction across multiple physics domains, (2) a novel H-FNO architecture that combines spectral operators with local convolutions to mitigate spectral bias in sensor applications, and (3) comprehensive validation including both simulation and experimental data for practical deployment. This work establishes FNO as a powerful tool for accelerating sensor simulation and advancing the field of AI-enhanced instrumentation and measurement. Full article

Orthotropic materials are increasingly employed in advanced thermal systems due to their direction-dependent heat transfer characteristics. Accurate numerical modeling of heat conduction in such media remains challenging, particularly for 3D geometries with nonlinear boundary conditions and internal heat generation. In this study, conventional boundary element method (BEM) and isogeometric boundary element method (IGABEM) formulations are developed and compared for steady-state orthotropic heat conduction problems. A coordinate transformation is adopted to map the anisotropic governing equation onto an equivalent isotropic form, enabling the use of classical Laplace fundamental solutions. Volumetric heat generation is incorporated via the radial integration method (RIM), preserving the boundary-only discretization, while nonlinear Robin boundary conditions are treated using variable condensation and a Newton–Raphson iterative scheme. The performance of both methods is evaluated using a hollow ellipsoidal benchmark problem with available analytical solutions. The results demonstrate that IGABEM provides higher accuracy and smoother convergence than conventional BEM, particularly for higher-order discretizations, which is owing to its exact geometric representation and higher continuity. Although IGABEM involves additional computational overhead due to NURBS evaluations, both methods exhibit similar quadratic scaling with respect to the degrees of freedom. Full article

The horizontal-axis tidal turbine is a representative device for harnessing ocean tidal energy, and the structural optimization of its blades is crucial for enhancing the power capture efficiency. In this work, the twist and chord distributions of the blade are determined using an improved Blade Element Momentum (BEM) approach, in which tip and hub loss factors are employed to enhance the modeling accuracy, and these results are employed to construct a parametric model of the original rotor. Due to its simplified assumptions and inability to capture three-dimensional flow effects, computational fluid dynamics (CFD) simulations were carried out to evaluate the hydrodynamic performance and flow analysis of the designed rotor. Further, the orthogonal test method was used to optimize the hydraulic performance of the rotor. Three optimization parameters, namely hub diameter, airfoil type, and maximum airfoil thickness, were set with three levels. Based on the orthogonal design scheme, nine rotor configurations were generated, and their energy capture characteristics and flow fields were subsequently evaluated through numerical simulations. The analysis indicates that the choice of airfoil exerts the strongest impact on the rotor’s energy capture efficiency, while the influences of maximum airfoil thickness and hub diameter follow in descending order. Consequently, the optimized rotor adopts a NACA63-415 airfoil with a reduced maximum thickness of 0.9 T₀ and an intermediate hub diameter of 15%R, achieving a power coefficient of 0.445 at the design tip-speed ratio of 4, corresponding to a 3.08% improvement compared with the original design. Flow field analysis demonstrates that the optimized geometry promotes a more uniform spanwise pressure distribution and effectively suppresses flow separation, thereby enhancing the overall hydrodynamic efficiency. Full article

This paper presents the experimental validation of a computational kinematic model for a passive Stewart–Gough platform equipped with modified Cardan joints. The introduced joint geometry significantly increases structural stiffness but invalidates the standard spherical joint assumption commonly used in hexapod kinematic formulations. To address this, we develop an efficient numerical optimization-based framework that solves both the direct and inverse kinematics without relying on simplified joint models. Furthermore, to enable autonomous and absolute pose measurement, each cylindrical leg joint of the platform is equipped with a LinACE™ absolute linear encoder. This sensor integration transforms the passive mechanism into an IoT-enabled reconfigurable fixture capable of internally sensing, tracking, and recalling its own configuration. The direct kinematics are computed iteratively using a homogeneous transformation formulation and benchmarked against analytical models derived for ideal spherical joints. Experimental results demonstrate sub-millimeter accuracy in pose estimation, confirming the validity of the proposed kinematic model and highlighting the suitability of the sensor-equipped hexapod for industrial flexible fixturing applications. Full article

This paper outlines the design and testing of a horizontal-axis tidal turbine (HATT) at a scale of 1:20, employing numerical simulations and experimental validation. The design employed an in-house code based on the Blade Element Momentum (BEM) theory. As reliable lift and drag coefficients for this scale are not present in the literature due to the low Reynolds number of the airfoil, Computational Fluid Dynamics (CFD) simulations were conducted to generate accurate polar diagrams for the NACA 4412 airfoil. The turbine was then 3D-printed and the rotor tested in a subsonic wind tunnel at various fixed rotational speeds to determine the power coefficient. Fluid dynamic similarity was achieved by matching the Reynolds number and tip-speed ratio in air to their values in water. Three-dimensional CFD simulations were also performed, yielding turbine efficiency results that agreed fairly well with the experimental data. However, both the experimental and numerical simulation results indicated a higher power coefficient than that predicted by BEM theory. The CFD results revealed the presence of radial velocity components and vortex structures that could reduce flow separation. The BEM model does not capture these phenomena, which explains why the power coefficient detected by experiments and numerical simulations is larger than that predicted by the BEM theory. Full article

The Planting Oysters to Strengthen the Foundation (POSF) method, as a construction technique for coastal stone structures in the Northern Song Dynasty of China (1059), has been preserved to this day. Exploring its long-term reinforcement mechanism can provide theoretical support and practical guidance for the protection and sustainable development of world marine cultural heritage. This article uses Crustacean Ash Triad Clay (CATC) from Shihu Ancient Wharf in Quanzhou as a case study and conducts a systematic investigation using XRD, Raman, SEM-EDS, FTIR, and 16S rRNA high-throughput sequencing. The results show that CATC has a core skeleton of 94.6% quartz, with potassium feldspar, dolomite, and metal compounds as auxiliary components; that its 19.04% porosity provides enrichment space for positively charged ions and tide-borne microorganisms; that electrostatic adsorption between barnacle adhesive and the material achieves physical reinforcement; and that microbial metabolism promotes dolomite formation, producing chemical reinforcement. Thus, the ternary coupling of Biology–Environment–Materials forms a BEM long-term reinforcement mechanism suitable for low-carbon construction in the ocean. Full article

Background: The incidence of dementia, especially Alzheimer’s disease (AD), is rising, with over 55 million affected globally. Therefore, this disease, for which there is no adequate treatment, is more frequent and prevalent in women. Royal jelly, a bee secretion, is known for its health benefits and contains proteins, carbohydrates, lipids, minerals, polyphenols, enzymes, and B vitamins, as well as anti-inflammatory and antioxidant properties relevant to AD. Thus, we aimed to investigate the chemical compounds in royal jelly extract and their effect on neurobehavioral changes in an AD female model. Methods: In vitro studies were used to investigate the chemical and physicochemical properties of the royal jelly extract. In vivo studies, we divided female mice into five groups (n = 25): Control (C), Alzheimer (ALZ), ALZ standard (ALZ-STD, rivastigmine 1 mg/Kg), ALZ-D1 (royal jelly 150 mg/kg), and ALZ-D2 (royal jelly 300 mg/kg). The mice received the treatments orally at 45 days. We induced the AD model by orally administering aluminum chloride at 100 mg/kg and intraperitoneally injecting D-galactose at 120 mg/kg for 45 consecutive days, after which we subjected the animals to the radial arm maze, Morris water maze, elevated plus maze, and forced swim tests. Results: Analyses showed moderate acidity and a rich bioactive profile, with flavonoids being more prevalent. Antioxidant activity tests indicated moderate efficacy, while FTIR-ATR analysis revealed the chemical complexity of royal jelly. The royal jelly extract used in the study did not induce toxicity in vivo. Notably, royal jelly improved cognitive deficits, neurodegeneration, and reduced anxiety in AD. Conclusions: The study suggests that royal jelly extract has promising neuroprotective properties and could be a viable natural therapeutic option for AD. Full article

In marine hydrodynamics, the core of the boundary element method (BEM) lies in the numerical calculation of the free-surface Green’s function. With the rise of artificial intelligence, using neural networks to fit Green’s function has become a new trend, yet most existing studies are confined to fitting Green’s function in infinite water depth. In this paper, a neural network fitting method for a finite-depth Green’s function is proposed. The classical Multilayer Perceptron (MLP) network and the emerging Kolmogorov–Arnold Network (KAN) are employed to conduct global and partition-based fitting experiments. Experiments indicate that the partition-based KAN fitting model achieves higher fitting accuracy, with most regions reaching 4D fitting precision. For large-scale data input, the average time for the model to calculate a single Green’s function value is 0.0868 microseconds, which is significantly faster than the 0.1120 s required by the traditional numerical integration method. These results demonstrate that the KAN can serve as an accurate and efficient model for finite-depth Green’s functions. The proposed KAN-based fitting method not only reduces the computational cost of numerical evaluation of Green’s functions but also maintains high prediction precision, providing an alternative approach to accelerate BEM calculations for floating body hydrodynamic analysis. Full article

The digital and ecological transition of the industrial sector requires methodological tools that integrate information modelling, performance simulation, and operational decision support. In this context, the present study introduces and tests a semi-automatic BIM-to-BEM framework to optimise human–machine interaction and support critical data interpretation through Graphical User Interfaces. The objective is to propose and validate a BIM-to-BEM workflow for an existing industrial facility to enable comparative evaluation of energy retrofit scenarios. The information model, developed through an interdisciplinary federated approach and calibrated using parametric procedures, was exported in the gbXML format to generate a dynamic, interoperable energy model. Six simulation scenarios were defined incrementally, including interventions on the building envelope, Heating, Ventilation and Air Conditioning (HVAC) systems, photovoltaic production, and relamping. Results are made accessible through dashboards developed with Business Intelligence tools, allowing direct comparison of different design configurations in terms of thermal loads and indoor environmental stability, highlighting the effectiveness of integrated solutions. For example, the combined interventions reduced heating demand by up to 32% without compromising thermal comfort, while in the relamping scenario alone, the building could achieve an estimated 300 MWh reduction in annual electricity consumption. The proposed workflow serves as a technical foundation for developing an operational and evolving Digital Twin, oriented toward the sustainable governance of building–system interactions. The method proves to be replicable and scalable, offering a practical reference model to support the energy transition of existing industrial environments. Full article

Household food waste remains a significant barrier to sustainable consumption goals. This study investigates the impact of sociodemographic characteristics influence self-reported food waste levels in Poland. A cross-sectional CAWI survey (N = 1000), based on the HFSSM, was conducted among adults responsible for purchasing household food. Associations between food waste and structural factors were analysed using χ² tests, Spearman rank correlations, and Mann–Whitney U tests. Age appears as the strongest determinant: younger respondents consistently report higher food waste, while older adults indicate markedly lower levels. Household composition is equally important—the presence and number of children significantly increase waste. Economic status is also relevant: pensioners and disability-benefit recipients report substantially less waste than employed and self-employed individuals. Net household income shows no significant effect. Education does not necessarily reduce food waste; in some comparisons, higher-educated respondents report slightly higher levels, suggesting that formal education does not automatically translate into effective food management routines. The findings highlight that food waste is primarily influenced by life stage, household structure, and daily habits rather than income or education. Interventions should focus on younger adults and families with children, emphasising practical skills such as meal planning, inventory management, appropriate storage, and the use of leftovers. Full article

Background/Objectives: The ¹⁰³Pd/^103mRh in vivo generator represents a promising Auger electron-emitting system, in which both parent and daughter radionuclides emit predominantly Auger electrons with minimal accompanying radiation. This study investigates the release dynamics of daughter radionuclides from the ¹⁰³Pd/^103mRh in vivo generator and evaluates the underlying mechanisms governing bond rupture and daughter retention. Methods: Cyclotron irradiation of rhodium foils was performed in two separate batches, followed by radionuclide separation using conventional wet chemistry and a novel dry distillation technique. The purified ¹⁰³Pd radionuclide was used to radiolabel DOTA-TATE, phthalocyanine-TATE, and DOTA-TOC chelators. The resulting complexes were immobilized on Strata-X and Strata-C18 solid-phase extraction columns. Scheduled elution experiments were conducted to quantify the release of the ^103mRh daughter radionuclide. Results: The measured ^103mRh release rates were 9.8 ± 3.0% and 9.6 ± 2.7% from Strata-X columns with DOTA-TATE and phthalocyanine-TATE, respectively, and 10.5 ± 2.7% and 12.0 ± 0.5% from Strata-X and Strata-C18 columns, respectively, with DOTA-TOC. These values are significantly lower than the ~100% release predicted based on the reported Auger electron yield of 186%. One explanation for this difference could be potential inconsistencies in decay data that may require correction; this needs further investigation. The results further demonstrated that delocalized π-electrons, introduced via phthalocyanine-based chelation, did not mitigate daughter release. Conclusions: The low observed daughter nuclide release represents a favorable characteristic for the future clinical translation of the ¹⁰³Pd/^103mRh Auger emitter pair. The findings support the conclusion that Auger electron cascades, rather than nuclear recoil energy, dominate bond rupture processes. Full article