Search Results (193)

Grains and their derivatives play a crucial role as staple foods for the global population. Identifying grains in the food chain that are free from mycotoxin contamination is essential. Researchers have explored various traditional detection methods to address this concern. However, as grain consumption becomes increasingly time-sensitive and dynamic, traditional approaches face growing limitations. In recent years, emerging techniques—particularly molecular-based vibrational spectroscopy methods such as visible–near-infrared (Vis–NIR), near-infrared (NIR), Raman, mid-infrared (MIR) spectroscopy, and hyperspectral imaging (HSI)—have been applied to assess fungal contamination in grains and their products. This review summarizes research advances and applications of vibrational spectroscopy in detecting mycotoxins in grains from 2019 to 2025. The fundamentals of their work, information acquisition characteristics and their applicability in food matrices were outlined. The findings indicate that vibrational spectroscopy techniques can serve as valuable tools for identifying fungal contamination risks during the production, transportation, and storage of grains and related products, with each technique suited to specific applications. Given the close link between grain-based foods and humans, future efforts should further enhance the practicality of vibrational spectroscopy by simultaneously optimizing spectral analysis strategies across multiple aspects, including chemometrics, model transfer, and data-driven artificial intelligence. Full article

The subject of ship structural dynamics has faced new technological obstacles due to scientific and technological advancements, and one of the main concerns in related sectors is how to effectively reduce the vibration levels of different ships. This article focuses on the application scenarios of ship floating raft isolation systems, establishing a wave propagation model for acoustic black hole (ABH) structures based on the idea of the ABH effect. Then, a transfer matrix model for serially connected ABH structures is derived, which serves as a basis for subsequent structural designs. Second, the finite element method is used to study the energy distribution and vibration characteristics of a symmetrically distributed periodic non-uniform multi-level ABH structure. Meanwhile, it examines its bandgap distribution under a one-dimensional periodic arrangement and then investigates the vibration properties of non-uniform multi-level ABH thin-plate constructions with different periods from the perspective of engineering applications. Moreover, parameter optimization studies of non-uniform multi-level ABH structures with finite periods are carried out with an emphasis on engineering applications. The first step is to use the design space to determine the range of values for the parameters that need to be optimized. The hyper Latin cubic sampling method is then employed to select samples, and the EI criterion and PSO optimization algorithm are applied to add new samples to improve the Kriging surrogate model’s accuracy. When the optimal structural parameters have been determined, they are applied to the raft rib plate to verify the isolation effect of the non-uniform multi-level ABH structure by analyzing the vibration level difference at specific raft positions before and after embedding it. Full article

As the urban underground natural gas pipeline network expands, the explosion risk arising from methane accumulation in drainage ditches due to pipeline leakage has increased severely. A two-dimensional numerical model—9.7 m in length (including a 1-m obstacle section), 0.1 m in diameter, and with a water volume fraction of 0.2—was developed to address the flexible boundary characteristics of urban underground ditches. The investigation examined the influence of methane concentration on explosion propagation characteristics. Results indicated that, at a methane concentration of 11%, the peak pressure attained 157.9 kPa, and the peak temperature exceeded 3100 K—all of which were significantly higher than the corresponding values at 10%, 13%, and 16% concentrations. Explosion-induced water motion exerted a cooling effect that inhibited heat and pressure transfer, while obstacles imposed partial restrictions on flame propagation. Temporal profiles of temperature and pressure exhibited three distinct stages: “initial stability–rapid rise–attenuation”. Notably, at a methane concentration of 16%, the water column formed by fluid vibration demonstrated a pronounced cooling effect, causing faster decreases in measured temperatures and pressures compared to other concentrations. Full article

Spiral bevel gears are used in a wide range of industries, such as automotive and aerospace, to transfer power between intersecting axes. However, a certain level of vibration is always present in the systems, primarily due to the complex dynamic forces generated during the meshing of the gear teeth affected by the tooth profile. To address these challenges, this research developed a comprehensive dynamic model with eight degrees of freedom, capturing both translational and rotational movements of the system’s components. The study focused on evaluating the effects of two different tooth profile modifications, namely topology and flank modifications, on the vibration characteristics of the system. The system comprised a spiral bevel gear pair with mesh stiffness in forward rotation. The results highlighted that optimizing the tooth profile and minimizing tooth surface deviation significantly reduce vibration amplitudes and improve dynamic stability. These findings not only enhance the performance and lifespan of spiral bevel gears but also provide a robust foundation for the design and optimization of advanced gear systems in industrial applications, ensuring higher efficiency and reliability. In this paper, it was observed that some modifications led to a 68% reduction in vibration levels. Additionally, three modifications helped improve the vibrational behavior of the system, preventing chaotic behavior, which can lead to system failure, and transforming the system’s behavior into periodic motion. Full article

In recent years, with the wide application of underwater vehicles, the vibration and noise problems generated during their operation have attracted great attention from the academic community. Compared with the field of traditional mechanical noise, research on vibration control of the noise that is transiently excited underwater still has significant deficiencies in terms of its theoretical depth and systematicity. In this paper, we take transient noise control for underwater vehicles as the engineering entry point; systematically explain the vibration mechanisms and dynamic characteristics of underwater double-layered cylindrical shell structures; and discuss the vibration transmission paths and the development trends in the control technology in depth. This study mainly includes the following contents. Firstly, the vibration response mechanisms of underwater double-layered cylindrical shells are sorted through a bibliometric analysis, and the evolution laws for plate–shell structures and the vibration transmission paths for single–double-shell structures are summarized systematically; secondly, the multi-path vibration transmission characteristics of double-layered cylindrical shells are analyzed based on energy transfer theory, and the contribution to transient noise through different transmission paths is quantitatively evaluated; and thirdly, the vibration transmission characteristics of active control, passive control, and hybrid control are evaluated systematically in terms of the dimensions of the control mechanism. Then, the engineering applicability of active, passive, and hybrid control technologies is systematically reviewed. Finally, combined with the development of new intelligent materials and adaptive algorithms, the prospective outlook for vibration control technology for shell structures under transient excitation conditions is presented. Full article

This paper focuses on the use of advanced optimization design strategies to improve the performance and service life of engine pistons, with emphasis on enhancing their stiffness, strength, and dynamic characteristics. As a core component of the engine, the structural design and optimization of the piston are of great significance to its efficiency and reliability. First, a three-dimensional (3D) model of the piston was constructed and imported into ANSYS Workbench for finite element modeling and high-quality meshing. Based on the empirical formula, the actual working environment temperature and heat transfer coefficient of the piston were accurately determined and used as boundary conditions for thermomechanical coupling analysis to accurately simulate the thermal and deformation state under complex working conditions. Dynamic characteristic analysis was used to obtain the displacement–frequency curve, providing key data support for predicting resonance behavior, evaluating structural strength, and optimizing the design. In the optimization stage, five geometric dimensions are selected as design variables. The deformation, mass, temperature, and the first to third natural frequencies are considered as optimization goals. The response surface model is constructed by means of the design of the experiments method, and the fitted model is evaluated in detail. The results show that the models are all significant. The adequacy of the model fitting is verified by the “Residuals vs. Run” plot, and potential data problems are identified. The “Predicted vs. Actual” plot is used to evaluate the fitting accuracy and prediction ability of the model for the experimental data, avoiding over-fitting or under-fitting problems, and guiding the optimization direction. Subsequently, the sensitivity analysis was carried out to reveal the variables that have a significant impact on the objective function, and in-depth analysis was conducted in combination with the response surface. The multi-objective genetic algorithm (MOGA), screening, and response surface methodology (RSM) were, respectively, used to comprehensively optimize the objective function. Through experiments and analysis, the optimal solution of the MOGA algorithm was selected for implementation. After optimization, the piston mass and deformation remained relatively stable, and the working temperature dropped from 312.75 °C to 308.07 °C, which is conducive to extending the component life and improving the thermal efficiency. The first to third natural frequencies increased from 1651.60 Hz to 1671.80 Hz, 1656.70 Hz to 1665.70 Hz, and 1752.90 Hz to 1776.50 Hz, respectively, significantly enhancing the dynamic stability and vibration resistance. This study integrates sensitivity analysis, response surface models, and genetic algorithms to solve multi-objective optimization problems, successfully improving piston performance. Full article

Under cyclic moving load action, tensile-dominant structures are prone to crack initiation due to cumulative damage effects. The presence of cracks leads to structural stiffness degradation and nonlinear redistribution of dynamic characteristics, thereby compromising str18uctural integrity and service performance. The current research on the dynamic behavior of cracked structures predominantly focuses on transient analysis through high-fidelity finite element models. However, the existing methodologies encounter two critical limitations: computational inefficiency and a trade-off between model fidelity and practicality. Thus, this study presents an innovative analytical framework to investigate the dynamic response of cracked simply supported beams subjected to moving loads. The proposed methodology conceptualizes the cracked beam as a system composed of multiple interconnected sub-beams, each governed by the Euler–Bernoulli beam theory. At crack locations, massless rotational springs are employed to accurately capture the local flexibility induced by these defects. The transfer matrix method is utilized to derive explicit eigenfunctions for the cracked beam system, thereby facilitating the formulation of coupled vehicle–bridge vibration equations through modal superposition. Subsequently, dynamic response analysis is conducted using the Runge–Kutta numerical integration scheme. Extensive numerical simulations reveal the influence of critical parameters—particularly crack depth and location—on the coupled dynamic behavior of the structure subjected to moving loads. The results indicate that at a constant speed, neither crack depth nor position alters the shape of the beam’s vibration curve. The maximum deflection of beams with a 30% crack in the middle span increases by 14.96% compared to those without cracks. Furthermore, crack migration toward the mid-span results in increased mid-span displacement without changing vibration curve topology. For a constant crack depth ratio (γ_i = 0.3), the progressive migration of the crack position from 0.05 L to 0.5 L leads to a 26.4% increase in the mid-span displacement (from 5.3 mm to 6.7 mm). These findings highlight the efficacy of the proposed method in capturing the complex interactions between moving loads and cracked concrete structures, offering valuable insights for structural health monitoring and assessment. Full article

This study systematically investigates for the first time the effects of dual-site co-cultivation on spectral characteristics and trace element enrichment in marine-cultured Akoya pearls from Beihai, China. Akoya pearls were cultured over a one-year period, with the final 40-day stage designated as the terminal phase. During this period, two experimental groups of pearl oysters were established: Group Y remained in Beihai for continued local cultivation and harvest, while Group B was transferred to Weihai, Shandong Province, for terminal-stage farming under different thermal conditions. A series of comparative analyses were performed using Fourier-transform infrared (FTIR) spectroscopy, ultraviolet-visible (UV-Vis) spectroscopy, Raman spectroscopy, X-ray fluorescence (XRF), and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The FTIR results revealed distinct differences between the two groups in the distribution of amide and polysaccharide functional groups, particularly around 1643 cm⁻¹ and 1100 cm⁻¹. The UV-Vis spectra of Group B displayed characteristic absorption bands at 430 nm and 460 nm, associated with the organic matrix of the nacre. Raman spectroscopy further indicated a higher abundance of organic-related vibrational features in Group B. Additionally, both XRF and LA-ICP-MS analyses consistently showed significant differences in the concentrations and distributions of trace elements, particularly copper (Cu), cobalt (Co), and zinc (Zn). The findings demonstrate that the dual-site co-cultivation mode significantly impacts both the organic composition and trace element enrichment patterns in seawater Akoya pearls. This research provides valuable references for optimizing environmental parameters in pearl cultivation processes. Full article

As a key component of unmanned aerial vehicles (UAVs), the vibrational characteristics of the airframe critically impact flight safety and imaging quality. These vibrations, often generated by motor-propeller systems or aerodynamic forces, can lead to structural fatigue during flight or cause image blur in payloads like cameras. To analyze the dynamic performance of the six-rotor UAV frame, this paper develops a state-space model based on linear state-space theory, structural dynamics principles, and modal information. The Direct Current (DC) gain method is employed to reduce the number of modes, followed by frequency response analysis on the reduced modes to derive the frequency–domain transfer function between the excitation input and response output points. The contribution of each mode to the overall frequency response is evaluated, and the frequency response curve is subsequently plotted. The results indicate that the model achieves a 73-fold speed improvement with an error rate of less than 13%, thereby validating the accuracy of the six-rotor UAV frame state-space model. Furthermore, the computational efficiency has been significantly enhanced, meeting the requirements for vibration simulation analysis. The dynamic analysis approach grounded in state-space theory offers a novel methodology for investigating the dynamic performance of complex structures, enabling efficient and precise analysis of frequency response characteristics in complex linear systems such as electric vehicle (EV) battery modules and motor systems. By treating EV components as dynamic systems with coupled mechanical–electrical interactions, this method contributes to the reliability and safety of sustainable transportation systems, addressing vibration challenges in both UAVs and EVs through unified modeling principles. Full article

The Dujiangyan–Siguniangshan mountain rack railway project is China’s first mountain rail transit. Most of its lines are located in mountainous areas and close to natural ecological protection areas, which have strict restrictions on the vibration and noise of train operation. At the same time, the vibration of mountain rack railway trains is also an important factor affecting the safety and riding comfort of trains. However, due to the multi-source vibration of gear teeth, wheels, rails, and suspensions, it is difficult to clearly define the vibration characteristics and vibration transmission path of the train, which has a serious impact on its vibration noise suppression and optimization. To this end, this study proposed a set of evaluation methods for the vibration characteristics and transfer paths of mountain rack trains based on a combination of dynamics and operational transfer path analysis (OTPA). Considering the interaction between the dynamic behaviors of the primary and secondary suspensions, the gear tooth contact behavior, the wheel–rail contact behavior and the dynamic behaviors of the track system, a dynamic model of a mountain rack train based on the finite element method was established, and the effectiveness of the model was verified through field experiments. On this basis, the OTPA method was used to establish a vibration transfer path model between the secondary suspension and the center of mass of the car body, and it was used to analyze the vibration mechanism and transfer path of the train body at the rated speed (20 km/h) and the limited speed (30 km/h). This study is of great significance for suppressing the vibration noise of mountain rack trains, reducing the impact on the ecological environment and improving ride comfort. Full article

Noises on the field can affect the electromechanical system characteristics in the bearing fault diagnostic process. This paper presents a deep learning-based fault-diagnosis model DRSN–GCE (Deep Relative Shrinkage Network with Gated Convolutions and Enhancements), which is designed to deal with noise and improve noise resistance. In the first step, the data are preprocessed by adding different noises with different ratios of signal to noise and different frequencies to the vibration signals. This simulates the field noise environments. The continuous wavelet transformation (CWT), which converts the time-series signal from one dimension to a time-frequency two-dimensional image, provides rich data input for the deep learning model. Secondly, a convolutional gated layer is added to the deep residual network (DRSN), which suppresses the noise interference. The residual connection structure has also been improved in order to improve the transfer of features. In complex signals, the Gated Convolutional Shrinkage Module is used to improve feature extraction and suppress noise. The experiments on the Case Western Reserve University bearing dataset show that the DRSN–GCE exhibits high diagnostic accuracy and strong noise immunity in various noise environments such as Gauss, Laplace, Salt-and-Pepper, and Poisson. DRSN–GCE is superior to other deep learning models in terms of noise suppression, fault detection accuracy, and rolling bearing fault diagnoses in noisy environments. Full article

Flow-induced vibration (FIV) characteristics are key factors in enhancing heat transfer. However, challenges such as insufficient heat transfer enhancement and the fatigue strength of the tube bundle persist in the context of improving the heat transfer in elastic tube bundle heat exchangers. This study proposes a novel passive heat transfer enhancement paradigm for elastic tube bundles based on externally induced self-excited oscillations of fluid. By constructing a non-contact energy transfer system, the external oscillation energy is directed into the elastic tube bundle heat exchanger, achieving dynamic stress buffering and breaking through the steady-state flow heat transfer boundary layer. A three-dimensional fluid–structure interaction numerical model is established using Star CCM+2021.3 (16.06.008) to conduct a comparative analysis of the flow characteristics and heat transfer performance between the original structure without an oscillator and the improved structure equipped with a fluid oscillator. The results indicate that the improved structure, through the periodic unsteady jet induced by the fluid oscillator, significantly enhances the turbulence intensity of the shell-side fluid, with the turbulent kinetic energy increasing by over 50%. The radial flow area is notably expanded, thereby reducing the thermal resistance of the boundary layer. At cooling fluid velocities of 6 to 9 m/s, the heat transfer capability of the improved structure is enhanced by more than 50%. Compared with the original structure, the new structure, due to the loading of an external oscillation structure, causes the cold air to present a periodic up and down jet phenomenon. This jet phenomenon, on the one hand, increases the heat exchange area between the cold air and the outer surface of the tube bundle, thereby enhancing the heat exchange capacity. On the other hand, the large-area impact of the fluid reduces the thickness of the boundary layer, lowers the thermal resistance and thereby enhances the heat exchange capacity. Furthermore, this improved structure buffers the mechanical vibrations through self-excited oscillations of the fluid medium, ensuring that the stress levels in the tube bundle remain below the fatigue threshold, effectively mitigating the failure risks associated with traditional active vibration strategies. Full article

The enhancement of enzyme activity has garnered significant attention in biotransformation processes and applications. This enhancement is achieved through the use of specific nanomaterials (NMs) with unique physicochemical characteristics responsive to external stimuli. In this study, an enzyme–Fe₃O₄ nano-biocatalytic system (NBS) was developed to enable real-time activation of enzymatic catalysis under alternating magnetic field (AMF) and near-infrared (NIR) irradiation using dual-functional Fe₃O₄ magnetic nanoparticles (MNPs). When exposed to an AMF, Fe₃O₄ MNPs generate molecular vibrations through mechanisms such as Néel or Brown relaxation while acting as a photothermal agent in response to NIR irradiation. The synergistic effect of AMF and NIR irradiation significantly enhanced energy transfer between the enzyme and Fe₃O₄ MNPs, resulting in a maximum 4.3-fold increase in enzyme activity. Furthermore, the system reduced aldol reaction time by 66% (from 4 h to 1.5 h) while achieving 90% product yield. Additionally, factors such as nanoparticle size and NIR power were found to play a critical role in the efficiency of this real-time regulation strategy. The results also demonstrate that the enzyme–Fe₃O₄ nanocomposites (NCs) significantly enhanced catalytic efficiency and reduced the reaction time for aldol reactions. This study demonstrates an efficient NBS controlled via the synergistic effects of AMF and NIR irradiation, enabling spatiotemporal control of biochemical reactions. This work also provides a breakthrough strategy for dynamic biocatalysis, with potential applications in industrial biomanufacturing, on-demand drug synthesis, and precision nanomedicine. Full article

Building vibration induced by running trains or other loads is related to soil vibration, building coupling loss, and the building floor’s amplification factor. In order to predict the vibration response of the proposed building caused by various loads, the propagation law of Rayleigh wave in saturated foundation soil and the refraction and transmission coefficients of Rayleigh wave between saturated soil and building structure are analyzed by using the theoretical analysis method, and the building coupling loss coefficient is obtained. The dynamic equation of the building’s structural vibration is established, and the analytical expression of the floor amplification factor is derived. A frame structure building is selected as the specific research object. Based on the Kirchhoff thin plate theory, the three-dimensional frame structure characteristic matrix of the building is obtained, and the vertical displacement values of each floor of the building under the action of the Ricker pulse load are calculated. The results are compared with the results in the literature, which verifies the effectiveness and accuracy of the transfer function method proposed in this study and fills the gap of insufficient research on the analysis of displacement transfer loss in soil structure interaction (SSI) in the literature. Full article

To address the challenge of low diagnostic accuracy in rolling bearing fault diagnosis under varying operating conditions, this paper proposes a novel method integrating the synchronized wavelet transform (SWT) with an enhanced Vision Transformer architecture, referred to as ResCAA-ViT. The SWT is first applied to process raw vibration signals, generating high-resolution time–frequency maps as input for the network model. By compressing and reordering wavelet transform coefficients in the frequency domain, the SWT enhances time–frequency resolution, enabling the clear capture of instantaneous changes and local features in the signals. Transfer learning further leverages pre-trained ResNet50 parameters to initialize the convolutional and residual layers of the ResCAA-ViT model, facilitating efficient feature extraction. The extracted features are processed by a dual-branch architecture: the left branch employs a residual network module with a CAA attention mechanism, improving sensitivity to critical fault characteristics through strip convolution and adaptive channel weighting. The right branch utilizes a Vision Transformer to capture global features via the self-attention mechanism. The outputs of both branches are fused through addition, and the diagnostic results are obtained using a Softmax classifier. This hybrid architecture combines the strengths of convolutional neural networks and Transformers while leveraging the CAA attention mechanism to enhance feature representation, resulting in robust fault diagnosis. To further enhance generalization, the model combines cross-entropy and mean squared error loss functions. The experimental results show that the proposed method achieves average accuracy rates of 99.96% and 96.51% under constant and varying load conditions, respectively, on the Case Western Reserve University bearing fault dataset, outperforming other methods. Additionally, it achieves an average diagnostic accuracy of 99.25% on a real-world dataset of generator non-drive end bearings in wind turbines, surpassing competing approaches. These findings highlight the effectiveness of the SWT and ResCAA-ViT-based approach in addressing complex variations in operating conditions, demonstrating its significant practical applicability. Full article