Search Results (2,481)

Energy generation from renewable sources has increased exponentially worldwide, particularly wind energy, which is converted into electricity through wind turbines. The growing demand for renewable energy has driven the development of horizontal-axis wind turbines with larger dimensions, as the energy captured is proportional to the area swept by the rotor blades. In this context, the dynamic loads typically observed in wind turbine towers include vibrations caused by rotating blades at the top of the tower, wind pressure, and earthquakes (less common). In offshore wind farms, wind turbine towers are also subjected to dynamic loads from waves and ocean currents. Vortex-induced vibration can be an undesirable phenomenon, as it may lead to significant adverse effects on wind turbine structures. This study presents a two-dimensional transient model for a rigid body anchored by a torsional spring subjected to a constant velocity flow. We applied a coupling of the Fourier pseudospectral method (FPM) and immersed boundary method (IBM), referred to in this study as IMERSPEC, for a two-dimensional, incompressible, and isothermal flow with constant properties—the FPM to solve the Navier–Stokes equations, and IBM to represent the geometries. Computational simulations, solved at an aspect ratio of $\varphi =4.0$, were analyzed, considering Reynolds numbers ranging from $Re=150$ to Re = 1000 when the cylinder is stationary, and $Re=250$ when the cylinder is in motion. In addition to evaluating vortex shedding and Strouhal number, the study focuses on the characterization of space–time symmetry during the galloping response. The results show a spatial symmetry breaking in the flow patterns, while the oscillatory motion of the rigid body preserves temporal symmetry. The numerical accuracy suggested that the IMERSPEC methodology can effectively solve complex problems. Moreover, the proposed IMERSPEC approach demonstrates notable advantages over conventional techniques, particularly in terms of spectral accuracy, low numerical diffusion, and ease of implementation for moving boundaries. These features make the model especially efficient and suitable for capturing intricate fluid–structure interactions, offering a promising tool for analyzing wind turbine dynamics and other similar systems. Full article

This study presents a novel approach to FTIR spectroscopy at low temperatures under atmospheric pressure. The work aimed to confirm the efficiency of a fundamentally new cryogenic setup that enables material research under the specified conditions. The new technique combines a nitrogen-based cryogenic capillary cooling system with precise temperature monitoring via a PID controller, along with DRIFT spectroscopy for hydrocarbon materials. New fundamental data were obtained on the properties and behavior of hydrocarbon compounds such as methanol, kerosene, and ethanol. The IR spectra of these samples contain key characteristic vibrations of hydrocarbon functional groups, which demonstrate the effective operability of the cryogenic device. A detailed description of the setup and measurement technique is provided, along with a thorough comparison of the results with data from other authors. The application scope of the cryogenic device, the relevance of the research, and potential future developments are also discussed. Full article

The inspiration behind this specific research is based on addressing the growing need to improve road safety via the application of the Internet of Things (IoT) system. Although several investigations have discovered the possibility of IoT-based accident recognition, recent research remains fragmented, usually concentrating on outdated science or specific use cases. This study aims to fill that gap by carefully examining and conducting a comparative analysis of 101 peer-reviewed articles published between 2008 and 2025, with a focus on IoT systems for accident recognition techniques. The review categorizes approaches depending on the sensor used, incorporation frameworks, and recognition techniques. The study examines numerous sensors, such as Global System for Mobile Communications/Global Positioning System (GSM/GPS), accelerometers, vibration, and many other superior sensors. The research shows the constraints and advantages of existing techniques, concentrating on the significance of multi-sensor utilization in enhancing recognition precision and dependability. Findings indicate that, although substantial improvements have been made in the use of IoT-based systems for accident recognition, problems such as substantial implementation costs, weather conditions, and data precision issues persist. Moreover, the research acknowledges deficiencies in standardization, as well as the requirement for strong communication systems to enhance the responsiveness of emergency services. As a result, the study suggests a plan for upcoming developments, concentrating on the incorporation of IoT-enabled infrastructure, sensor fusion approaches, and artificial intelligence. This study improves knowledge by offering an extensive viewpoint on IoT-based accident recognition, providing insights for upcoming research, and suggesting policies to facilitate implementation, eventually enhancing worldwide road safety. Full article

The ultimate goal of research on seismic mitigation technologies is engineering application. However, current studies primarily focus on the application of dampers in planar structures, while actual engineering structures are three-dimensional (3D) in nature. A type of damper, making up tuned mass dampers (TMDs) and inerters, has excellent vibration mitigation performance and needs brackets to connect to structures. In this work, a coupled dynamic model of an energy dissipation system (EDS) comprising a TMD, an inerter, a bracket, and a 3D building structure is presented, along with analytical solutions for stochastic seismic responses. The main work is as follows. Firstly, based on D’Alembert’s dynamics principle, the seismic dynamic equations of an EDS considering a realistic damper and a 3D structure are formulated. The general dynamic equations governing the bidirectional horizontal motion of the EDS are further derived using the dynamic finite element technique. Secondly, analytical expressions for spectral moments and variances of seismic responses are obtained. Finally, four numerical examples are presented to investigate the following: (1) verification of the proposed response solutions, showing that the calculation time of the proposed method is approximately 1/500 of that of the traditional method; (2) examination of spatial effects in 3D structures under unidirectional excitation, revealing that structural seismic responses in the direction along the earthquake ground motion is approximately 10⁴ times that in the direction perpendicular to the ground motion; (3) investigation of the spatial dynamic characteristics of a 3D structure subjected to unidirectional seismic excitation, showing that the bracket parameters significantly affect the damping effects on an EDS; and (4) application of the optimization method for the damper’s parameters that considers system dynamic reliability and different weights of the damper’s parameters as constraints, indicating that the most economical damping parameters can achieve a reduction in displacement spectral moments by 30–50%. The proposed response solutions and parameter optimization technique provide an effective approach for evaluating stochastic seismic responses and optimizing damper parameters in large-scale and complex structures. Full article

We investigated a novel natural dye-sensitized solar cell (DSSC) utilizing gadolinium ruthenate pyrochlore oxide Gd₂Ru₂O₇ (GRO) as a photoanode and compared its performance to the TiO₂-Gd₂Ru₂O₇ (TGRO) combined-layer configuration. The films were fabricated using the spin-coating technique, resulting in spherical grains with an estimated mean diameter of 0.2 µm, as observed via scanning electron microscopy (SEM). This innovative photoactive gadolinium ruthenate pyrochlore oxide demonstrated strong absorption in the visible range and excellent dye adhesion after just one hour of exposure to natural dye. X-ray diffraction confirmed the presence of the pyrochlore phase, where Raman spectroscopy identified various vibration modes characteristic of the pyrochlore structure. Incorporating Gd₂Ru₂O₇ as the photoanode significantly enhanced the overall efficiency of the DSSCs. The device configuration FTO/compact-layer/Gd₂Ru₂O₇/Hibiscus-sabdariffa/electrolyte(I⁻/I₃⁻)/Pt achieved a high efficiency of 9.65%, an open-circuit voltage (Voc) of approximately 3.82 V, and a current density of 4.35 mA/cm² for an active surface area of 0.38 cm². A mesoporous TiO₂-based DSSC was fabricated under the same conditions for comparison. Using impedance spectroscopy and cyclic voltammetry measurements, we provided evidence of the mechanism of conductivity and the charge carrier’s contribution or defect contributions in the DSSC cells to explain the obtained Voc value. Through cyclic voltammetry measurements, we highlight the redox activities of hibiscus dye and electrolyte (I⁻/I₃⁻), which confirmed electrochemical processes in addition to a photovoltaic response. The high and unusual obtained Voc value was also attributed to the presence in the photoanode of active dipoles, the layer thickness, dye concentration, and the nature of the electrolyte. Full article

The Zhurong rover of the Tianwen-1 mission has detected sulfates in its landing area. The analysis of these sulfates provides scientific evidence for exploring past hydration conditions and atmospheric evolution on Mars. As a non-contact technique with long-range detection capability, Laser-Induced Breakdown Spectroscopy (LIBS) is widely used for elemental identification on Mars. However, quantitative analysis of anionic elements using LIBS remains challenging due to the weak characteristic spectral lines of evaporite salt elements, such as sulfur, in LIBS spectra, which provide limited quantitative information. This study proposes a quantitative analysis method for sulfur in sulfate-containing Martian analogs by leveraging spectral line correlations, full-spectrum information, and prior knowledge, aiming to address the challenges of sulfur identification and quantification in Martian exploration. To enhance the accuracy of sulfur quantification, two analytical models for high and low sulfur concentrations were developed. Samples were classified using infrared spectroscopy based on sulfur content levels. Subsequently, multimodal deep learning models were developed for quantitative analysis by integrating LIBS and infrared spectra, based on varying concentrations. Compared to traditional unimodal models, the multimodal method simultaneously utilizes elemental chemical information from LIBS spectra and molecular structural and vibrational characteristics from infrared spectroscopy. Considering that sulfur exhibits distinct absorption bands in infrared spectra but demonstrates weak characteristic lines in LIBS spectra due to its low ionization energy, the combination of both spectral techniques enables the model to capture complementary sample features, thereby effectively improving prediction accuracy and robustness. To validate the advantages of the multimodal approach, comparative analyses were conducted against unimodal methods. Furthermore, to optimize model performance, different feature selection algorithms were evaluated. Ultimately, an XGBoost-based feature selection method incorporating prior knowledge was employed to identify optimal LIBS spectral features, and the selected feature subsets were utilized in multimodal modeling to enhance stability. Experimental results demonstrate that, compared to the BPNN, SVR, and Inception unimodal methods, the proposed multimodal approach achieves at least a 92.36% reduction in RMSE and a 46.3% improvement in R². Full article

Vibrational spectroscopy, including Raman and near-infrared techniques, enables the non-destructive evaluation of starch gelatinization, head rice yield, and aroma-active volatile compounds in parboiled rice subjected to varying soaking and drying conditions. Raman and NIR spectra were collected for rice samples processed under different conditions and integrated with reference analyses to develop and validate partial least squares regression and artificial neural network models. The optimized PLSR model demonstrated strong predictive performance, with R² values of 0.9406 and 0.9365 for SG and HRY, respectively, and residual predictive deviations of 3.98 and 3.75 using Raman effective wavelengths. ANN models reached R² values of 0.97 for both SG and HRY, with RPDs exceeding 4.2 using NIR effective wavelengths. In the aroma compound analysis, p-Cymene exhibited the highest predictive accuracy, with R² values of 0.9916 for calibration, and 0.9814 for cross-validation. Other volatiles, such as 1-Octen-3-ol, nonanal, benzaldehyde, and limonene, demonstrated high predictive reliability (R² ≥ 0.93; RPD > 3.0). Conversely, farnesene, menthol, and menthone showed poor predictability (R² < 0.15; RPD < 0.4). Principal component analysis revealed that the first principal component explained 90% of the total variance in the Raman dataset and 71% in the NIR dataset. Hotelling’s T² analysis identifies influential outliers and enhances model robustness. Optimal processing conditions for achieving maximum HRY and SG values were determined at 65 °C soaking for 180 min, followed by drying at 70 °C. This study underscores the potential of integrating vibrational spectroscopy with machine learning techniques and targeted wavelength selection for the high-throughput, accurate, and scalable quality evaluation of parboiled rice. Full article

Potassium (K), a critical macronutrient for the growth and development of Korla fragrant pear (Pyrus sinkiangensis Yu), plays a pivotal regulatory role in sugar-acid metabolism. Furthermore, K exhibits a highly specific response in near-infrared (NIR) spectroscopy compared to elements such as nitrogen (N) and phosphorus (P). Given its fundamental impact on fruit quality parameters, the development of rapid and non-destructive techniques for K determination is of significant importance for precision fertilization management. By measuring leaf potassium content at the fruit setting, expansion, and maturity stages (decreasing from 1.60% at fruit setting to 1.14% at maturity), this study reveals its dynamic change pattern and establishes a high-precision prediction model by combining near-infrared spectroscopy (NIRS) with machine learning algorithms. “Near-infrared spectroscopy coupled with machine learning can enable accurate, non-destructive monitoring of potassium dynamics in Korla pear leaves, with prediction accuracy (R²) exceeding 0.86 under field conditions.” We systematically collected a total of 9000 leaf samples from Korla fragrant pear orchards and acquired spectral data using a benchtop near-infrared spectrometer. After preprocessing and feature extraction, we determined the optimal modeling method for prediction accuracy through comparative analysis of multiple models. Multiplicative scatter correction (MSC) and first derivative (FD) are synergistically employed for preprocessing to eliminate scattering interference and enhance the resolution of characteristic peaks. Competitive adaptive reweighted sampling (CARS) is then utilized to screen five potassium-sensitive bands, specifically in the regions of 4003.5–4034.35 nm, 4458.62–4562.75 nm, and 5145.15–5249.29 nm, among others, which are associated with O-H stretching vibration and changes in water status. A comparison between random forest (RF) and BP neural network indicates that the MSC + FD–CARS–BP model exhibits the optimal performance, achieving coefficients of determination (R²) of 0.96% and 0.86% for the training and validation sets, respectively, root mean square errors (RMSE) of 0.098% and 0.103%, a residual predictive deviation (RPD) greater than 3, and a ratio of performance to interquartile range (RPIQ) of 4.22. Parameter optimization revealed that the BPNN model achieved optimal stability with 10 neurons in the hidden layer. The model facilitates rapid and non-destructive detection of leaf potassium content throughout the entire growth period of Korla fragrant pears, supporting precision fertilization in orchards. Moreover, it elucidates the physiological mechanism by which potassium influences spectral response through the regulation of water metabolism. Full article

An efficient numerical approach utilizing a variational weak form, grounded in 2D elastic theory and variational principles, is proposed for analyzing the in-plane vibrational behavior of rectangular plates resting on elastically restrained boundaries. The differential and integral operators can be discretized into matrix representations employing the differential quadrature method (DQM) and Taylor series expansion techniques. The discretization of dynamics equations stems directly from a weak formulation that circumvents the need for any transformation or discretization of higher-order derivatives encountered in the corresponding strong equations. Utilizing the matrix elementary transformation technique, the displacements of boundary and internal nodes are segregated, subsequently leading to the derivation of the generalized eigenvalue problem pertaining to the free vibration analysis of the Functionally Graded Material (FGM) rectangular plate. Furthermore, the study examines the impact of the gradient parameter, aspect ratio, and elastic constraints on the dimensionless frequency characteristics of the FGM rectangular plate. Ultimately, the modal properties of an in-plane FGM rectangular plate are investigated. Full article

The objective of this study was to improve measurement accuracy in the evaluation of autonomous agricultural tractor performance by addressing external disturbances, such as sensor installation errors, vibrations, and heading-induced bias that occur during the measurement of the conventional rear three-point hitch (Rear 3-Point) system. To mitigate these disturbances, the measurement point was relocated to the cab, where external interference is comparatively minimal. However, in compliance with the ISO 12188 standard, the Rear 3-Point system must be used as the reference measurement point. Therefore, its coordinates were indirectly estimated using an extended Kalman filter (EKF) and artificial intelligence (AI)-based techniques. A hybrid model was developed in which a transformer-based AI model was trained using the Rear 3-Point coordinates predicted by EKF as the ground truth. While traditional time-series models, such as LSTM and GRU, show limitations in predicting nonlinear data, the application of an attention mechanism was found to enhance prediction performance by effectively learning temporal dependencies and vibration patterns. The experimental results show that the EKF-based estimation achieved a precision of RMSE 1.6 mm, a maximum error of 12.6 mm, and a maximum standard deviation of 3.9 mm compared to actual measurements. From the perspective of experimental design, the proposed hybrid model was able to predict the trajectory of the autonomous agricultural tractor with significantly reduced external disturbances when compared to the actual measured Rear 3-Point coordinates, while also complying with the ISO 12188 standard. These findings suggest that the proposed approach provides an effective and integrated solution for developing high-precision autonomous agricultural systems. Full article

Magnetorheological (MR) foams represent a class of smart materials with unique tunable viscoelastic properties when subjected to external magnetic fields. Combining porous structures with embedded magnetic particles, these materials address challenges such as leakage and sedimentation, typically encountered in conventional MR fluids while offering advantages like lightweight design, acoustic absorption, high energy harvesting capability, and tailored mechanical responses. Despite their potential, challenges such as non-uniform particle dispersion, limited durability under cyclic loads, and suboptimal magneto-mechanical coupling continue to hinder their broader adoption. This review systematically addresses these issues by evaluating the synthesis methods (ex situ vs. in situ), microstructural design strategies, and the role of magnetic particle alignment under varying curing conditions. Special attention is given to the influence of material composition—including matrix types, magnetic fillers, and additives—on the mechanical and magnetorheological behaviors. While the primary focus of this review is on MR foams, relevant studies on MR elastomers, which share fundamental principles, are also considered to provide a broader context. Recent advancements are also discussed, including the growing use of artificial intelligence (AI) to predict the rheological and magneto-mechanical behavior of MR materials, model complex device responses, and optimize material composition and processing conditions. AI applications in MR systems range from estimating shear stress, viscosity, and storage/loss moduli to analyzing nonlinear hysteresis, magnetostriction, and mixed-mode loading behavior. These data-driven approaches offer powerful new capabilities for material design and performance optimization, helping overcome long-standing limitations in conventional modeling techniques. Despite significant progress in MR foams, several challenges remain to be addressed, including achieving uniform particle dispersion, enhancing viscoelastic performance (storage modulus and MR effect), and improving durability under cyclic loading. Addressing these issues is essential for unlocking the full potential of MR foams in demanding applications where consistent performance, mechanical reliability, and long-term stability are crucial for safety, effectiveness, and operational longevity. By bridging experimental methods, theoretical modeling, and AI-driven design, this work identifies pathways toward enhancing the functionality and reliability of MR foams for applications in vibration damping, energy harvesting, biomedical devices, and soft robotics. Full article

Accumulation of ferrous sulfate residue (FSR) not only occupies land but also results in environmental pollution and waste of iron resource; thus, recycling of iron from FSR has attracted widespread concern. To this end, this article shows fabrication and system analysis of hematite (HM) nanoparticles from FSR via microwave-assisted reduction technology. Physicochemical properties of HM nanoparticles were investigated by multiple analytical techniques including X-ray diffraction (XRD), Fourier transform infrared spectrum (FTIR), Raman spectroscopy, scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), ultraviolet visible (UV-Vis) spectrum, vibrating sample magnetometer (VSM), and the Brunauer–Emmett–Teller (BET) method. Analytic results indicated that the special surface area, pore volume, and pore size of HM nanoparticles with the average particle size of 45 nm were evaluated to be ca. 20.999 m²/g, 0.111 cm³/g, and 0.892 nm, respectively. Magnetization curve indicated that saturation magnetization Ms for as-synthesized HM nanoparticles was calculated to be approximately 1.71 emu/g and revealed weakly ferromagnetic features at room temperature. In addition, HM nanoparticles exhibited noticeable light absorption performance for potential applications in many fields such as electronics, optics, and catalysis. Hence, synthesis of HM nanoparticles via microwave-assisted reduction technology provides an effective way for utilizing FSR and easing environmental burden. Full article

The reliable operation of rotating machinery is critical in industrial production, necessitating advanced fault diagnosis and maintenance strategies to ensure operational availability. This study employs supervised machine learning algorithms to apply multi-label classification for fault detection in rotating machinery, utilizing a real dataset from multi-sensor systems installed on a suction fan in a typical manufacturing industry. The presented system focuses on multi-modal data analysis, such as vibration analysis, temperature monitoring, and ultrasound, for more effective fault diagnosis. The performance of general machine learning algorithms such as kNN, SVM, RF, and some boosting techniques was evaluated, and it was shown that the Random Forest achieved the best classification accuracy. Feature importance analysis has revealed how specific domain characteristics, such as vibration velocity and ultrasound levels, contribute significantly to performance and enabled the detection of multiple faults simultaneously. The results demonstrate the machine learning model’s ability to retrieve valuable information from multi-sensor data integration, improving predictive maintenance strategies. The presented study contributes a practical framework in intelligent fault diagnosis as it presents an example of a real-world implementation while enabling future improvements in industrial condition-based maintenance systems. Full article

An innovative method is proposed to analyze the coupled vibration between random traffic and large-span bridges under the combined action of wind loads. The dynamic behavior of bridges subjected to these multifactorial influences is investigated through a comprehensive bridge dynamics model. Specifically, a refined full-bridge finite element model is developed to simulate the traffic–bridge coupled vibration, with wind forces applied as external dynamic loads. The effects of wind speed and vehicle speed on the coupled system are systematically evaluated using the finite element software ABAQUS 2023. To ensure computational accuracy and efficiency, the large-span nonlinear dynamic solution method is employed, integrating the Newmark-β time integration method with the Newton–Raphson iterative technique. The proposed method is validated through experimental measurements, demonstrating its effectiveness in capturing the synergistic impacts of wind and traffic on bridge dynamics. By incorporating the stochastic nature of traffic flow and combined wind forces, this approach provides a detailed analysis of bridge responses under complex loading conditions. The study establishes a theoretical foundation and practical reference for the safety assessment of large-span bridges. Full article

This study reports the discovery and structural characterization of a novel polymorph, designated as Form III, of Alclometasone dipropionate, a corticosteroid commonly used in the treatment of inflammatory dermatoses. Form III was obtained by modifying the crystallization conditions reported in prior art and was thoroughly characterized using Powder X-ray Diffraction (PXRD), Fourier Transform Infrared (FT-IR) spectroscopy, melting-point determination, Differential Scanning Calorimetry (DSC), Thermogravimetric Analysis (TGA), including its first derivative (DTG), optical microscopy, and Scanning Electron Microscopy (SEM). In parallel, pure Form II, previously observed only in mixtures with Form I, was successfully isolated and characterized using the same analytical techniques. Both forms were compared in terms of structural, thermal, and morphological properties. PXRD analysis revealed that Form III crystallizes in a triclinic system; FT-IR spectroscopy revealed unique vibrational signatures, and microscopy showed rod-like crystal morphology. The discovery of Form III expands the current understanding of the solid-state landscape of Alclometasone dipropionate and opens opportunities for the identification of new industrial purification methods for the compound. Full article