Search Results (366)

From the perspective of human vibration perception, reducing vibration stimuli transmitted to occupants is essential for improving ride comfort and reducing fatigue. Intelligent dampers, as key actuators in semi-active suspension systems, provide adjustable damping capabilities for vibration control. This article combines them with biomimetic control principles to study the vibration control of semi-active suspension. The effects of damper forward and inverse models, damping force ranges, and time delays on suspension performance were analyzed. The results show that a function prediction-based damper model, a damping force range below 0.2 times and above 1.4 times the passive curve, and a 10 ms delay could balance vibration reduction and economy. Particle swarm optimization is used to optimize LQR control parameters for different road grades and typical speeds. Inspired by the adaptive behavior of chameleons, graded weights are assigned according to road characteristics, with greater emphasis on comfort on Grade A and B roads and driving stability on Grade C and D roads. The results show that proper matching of damper models and parameter constraints can fully exploit the adjustable damping capability of smart dampers. These findings provide a theoretical basis for designing and optimizing semi-active suspension control strategies. Full article

Accurate remaining useful life (RUL) prediction of rolling bearings was essential for condition-based maintenance because bearing service degradation was primarily governed by progressive rolling-contact fatigue at the rollingelement–raceway interface, whereas vibration signals provided measurable responses to this degradation rather than being its physical cause. However, reliable RUL prediction remained challenging because vibration measurements were noisy, nonlinear, stage-dependent, and sensitive to operating-condition shifts. In this study, a health-indicator-guided temporal-attention framework was developed for bearing RUL prediction using public run-to-failure vibration datasets. The novelty of this work lay in integrating degradation-consistent health indicator construction, sliding-window life-cycle representation, and HI-guided temporal attention into a unified and interpretable prediction framework. First, degradation-sensitive vibration features were extracted and fused into a compact health indicator (HI) to represent the progressive deterioration trend. Then, sliding-window sequences were generated and processed by a Transformer-based temporal-attention network, through which long-range temporal dependencies were captured and higher weights were assigned to informative degradation segments near stage transitions and late-life acceleration. Experiments on the XJTU-SY and IMS datasets showed that the proposed method improved prediction stability, reduced late-life error amplification, and achieved better performance than baseline variants without HI or temporal attention. Ablation analysis confirmed that HI construction mitigated cross-stage drift, whereas temporal attention enhanced transition sensitivity during accelerated degradation. Robustness and cross-domain tests further indicated that the method maintained acceptable degradation-following behavior under noise perturbations and operating-condition changes, although explicit domain-adaptation mechanisms were still required for strongly shifted target domains. Full article

MgCr₂O₄ nanospinel samples were synthesized using a modified Pechini method, followed by controlled calcination. The resulting materials were evaluated in terms of crystal structure, particle morphology, and optical and electronic properties. Their oxidative activity towards the degradation of organic dyes was investigated via photocatalysis, Fenton-like, and photon-Fenton-like processes. Various analytical techniques were employed to characterize the samples, including X-ray diffraction (XRD) with Rietveld refinements, infrared (IR) spectroscopy, UV–Vis spectroscopy, colorimetry, and transmission and high-resolution transmission electron microscopy (TEM/HRTEM). Structural characterization revealed that MgCr₂O₄ crystallized after calcination at 600 °C, and Rietveld refinements confirmed cubic Fd-3m symmetry. IR spectra confirmed the short-range order through the presence of vibrational modes assigned to CrO₆^2- octahedra. UV–Vis spectroscopy indicated mixed Cr valences (Cr³+/Cr⁶+) for samples calcined at temperatures below 900 °C, with Cr⁶+ eliminated at higher temperatures, confirmed by electron paramagnetic resonance (EPR) spectroscopy. This suggests that an oxidation reaction occurred due to oxygen vacancies in the lattice. Optical bandgap (Eg) increased with temperature. Samples calcined at low temperatures were dark green and became more saturated at temperatures above 900 °C, suggesting photoresponse to visible light, as indicated by the Eg values. The oxidative activity of the nanospinels in degrading the dyes methylene blue (MB) and rhodamine B (RhB) under visible light depended on the nature of the dye, the catalyst concentration, and the use of H₂O₂ in the process to improve the formation of hydroxyl radicals (•OH), as confirmed by photohydroxylation of terephthalic acid (TA). The highest degradation rate was observed in the photo-Fenton-like process, with 96% and 97% degradation of RhB and MB dyes in 60 min, reaching a kinetic rate constant (${\mathit{K}}_{app}$) of 0.055 min⁻¹ and 0.051 min⁻¹, respectively. This study highlights the importance of controlling various parameters to promote the formation of reactive oxygen species (ROS) required for oxidative degradation by nanospinels. Full article

The Al-bearing scorodite from the Hemerdon Ball Mine (HBM) was studied using electron microscopy and microprobe analysis, single-crystal X-ray diffraction, infrared, and Raman spectroscopy. The crystal chemistry formula of Al-bearing scorodite is expressed as Fe³⁺_0.87Al³⁺_0.16(As_0.97O₄)·H₂O. The calculated d-spacings and unit-cell parameters of Al-bearing scorodite are slightly affected by the substitution of Al for Fe in the octahedral sites. The Al-bearing scorodite HBM crystalizes in the Pbca space group with the following unit-cell lattice parameters: a = 8.92882(14) Å; b = 10.02217(14) Å; c = 10.30525(15) Å; V(Å) = 922.18(2) and Z = 8. The lattice structure becomes slightly distorted by the formation of the Fe,Al-OH octahedron, which leads to a compression of the newly formed octahedron along the a* ^ b* direction and an expansion of the Fe-OH octahedron along the c* direction. The incorporation of Al³⁺ has a strong effect on the tilting angle of the Fe,Al-OH octahedron in the b* ^ c* crystallographic direction. The refined structure suggests that Al³⁺ occupies the octahedral sites alongside Fe³⁺, leading to a distortion of the Fe,Al-OH octahedron. Infrared and Raman spectroscopy exhibit a doublet at 820 and 800 cm⁻¹, and at 810 and 800 cm⁻¹ ascribed to the Fe,Al-O-OAsO₃ group. The 799–800 cm⁻¹ Raman region is assigned to the Fe–O–As group (at 798 and 803 cm⁻¹), whereas the 810–814 cm⁻¹ region is ascribed to a band resulting from the AsO₄³⁻ [υ₁ (A₁) symmetric stretching vibrational modes], indicative of the Fe,Al–OH–As group in both Al-bearing scorodite and mansfieldite. Full article

Traditional handmade papers from China, Japan, and Korea, including Xuan paper, Washi, and Hanji, are difficult to distinguish visually because they share cellulose-rich compositions and similar appearances. This study applied near-infrared hyperspectral imaging (NIR-HSI) and machine-learning classifiers to identify selected traditional handmade papers by country and product type. Spectra in the 1250–1700 nm region were analyzed using k-nearest neighbors, support vector machines, and artificial neural networks. The models achieved high classification performance, with F1-scores of up to 1.000, and Y-scrambling confirmed that the results were not attributable to random class assignment. SHAP analysis identified important wavelength regions near 1256, 1360, 1404, 1449, 1537, 1576, 1635, and 1685 nm, which were associated with C–H, O–H, phenolic, hydrogen-bonded polysaccharide, and lignin-related vibrations. These bands varied among paper groups and provided chemically meaningful information for classification, while SAM visualization revealed pixel-level spectral similarity. These results show that NIR-HSI provides a compact, nondestructive, and interpretable approach for classifying selected East Asian handmade papers. Full article

m-Aminostyrene (MAS) is a key molecular scaffold with an electron-donating amino group and a conjugating vinyl group, exhibiting significant potential in photonic materials and biological applications due to its rotamerism and photoinduced behavior. Despite its importance, a comprehensive, rotamer-resolved investigation of its vibronic and cationic spectroscopic properties is lacking. Here, we report a high-resolution study on the cis and trans rotamers of jet-cooled MAS using two-color resonant enhanced multi-photon ionization (2C-REMPI), UV-UV hole-burning (HB), and mass-analyzed threshold ionization (MATI) spectroscopies, combined with density functional theory (DFT) calculations. The HB technique unambiguously resolves the vibronic spectra of each rotamer, overcoming the limitations of previous one-color REMPI studies. The excitation energies (S₁ ← S₀) are determined to be 30,416 cm⁻¹ (cis) and 30,932 cm⁻¹ (trans). The MATI spectra yield precise adiabatic ionization energies (AIEs) of 61,569 cm⁻¹ (cis) and 61,274 cm⁻¹ (trans). A comprehensive assignment of vibrational modes in both the S₁ and D₀ states is provided, revealing distinct mode activities and frequency shifts between the two rotamers. A propensity for Δν = 0 upon ionization is observed, indicating high geometrical similarity between the S₁ and D₀ states. This work provides a crucial spectroscopic blueprint for understanding the electronic and vibrational structure of MAS rotamers, with implications for the design of functionalized styrene-based molecular systems. Full article

This study develops a quantitative risk assessment framework that explicitly incorporates site-dependent variability in NATM (New Austrian Tunneling Method) tunnel construction projects. The underlying motivation is that identical risk factors can exhibit substantially different risk levels depending on project-specific site conditions. Conventional risk assessment approaches, which rely primarily on probability and impact ratings, are inherently limited in their ability to capture such variations across different project environments. To address this gap, key site condition factors affecting NATM tunnel construction were systematically identified and integrated into the existing risk assessment framework through a structured scoring and weighting process. Eight site condition factors were selected based on an extensive review of domestic and international literature, underground safety evaluation reports, tunnel design standards, geotechnical information databases, standard cost data, and expert consultation. These factors—Geotechnical Condition, Construction Schedule Float, Construction Budget Contingency, Spoil Bank Location, Likelihood of Civil Petitions, Underground Water Level, Environmental (Noise, Vibration), and Site Accessibility (Traffic Constraints)—were each quantified using a five-level scale ranging from 0.6 (very favorable) to 1.4 (very unfavorable). Subsequently, a composite site condition index was derived by combining the assigned scores with corresponding weights, and this index was incorporated as an adjustment coefficient into the conventional risk scoring system. The results demonstrate that, when the composite site condition index is considered, both the final risk magnitude and management priority vary depending on site-specific conditions, even for identical risk factors. This indicates that the proposed framework provides a more refined representation of actual project environments than traditional probability–impact-based approaches. The model can also serve as an effective decision-support tool for developing risk mitigation strategies tailored to specific site characteristics. Accordingly, the proposed model enhances the accuracy of risk assessment in tunnel projects and facilitates the rational identification of critical risks requiring prioritized management. However, because certain evaluation criteria rely on expert judgment, further validation through diverse real-world case studies and improvements to the objectivity of the evaluation framework remain necessary. Full article

Torsional resonance is a common phenomenon in engineering vehicle drivelines. To avoid the influence of resonance on the driveline, it is typical to modify the frequency. However, traditional frequency modification methods cannot precisely achieve expected frequencies while keeping others unchanged. They often cause frequency ‘overflow’ and fail to account for the influence of modal damping on drivelines. To address the issues above, a passive modification method is proposed to modify the natural frequencies of engineering vehicle drivelines, considering modal damping. In this paper, the dynamic equations for gears and shafts are derived by a lumped-parameter model that employs the Lagrange method to establish a reasonably equivalent model as a serial-parallel system consisting of (moment of inertia)-(torsional spring)-(torsional damper) with free boundary conditions. Additionally, the passive structural modification for the partial eigenvalue assignment (PEVAPSM) method is employed to modify the specified partial natural torsional frequencies to realizable expected values, while others remain unchanged. The modal damping of the original driveline is modified based on the orthogonal decomposition method. Finally, the practical applicability of the method proposed in this paper is demonstrated through a specific example. Results indicate that the PEVAPSM method has been successfully extended and supplemented from a theoretical translational system, ignoring modal damping, to a practical torsional system considering modal damping to modify natural frequencies of the structure. The improved PEVAPSM method enables to precisely determine the moment of inertia and modal damping of gears in the driveline, preventing resonance with other structures at the same frequency. It offers valuable guidance for studying the torsional vibration characteristics of engineering vehicle drivelines. Full article

In the classical Stochastic Subspace Identification (SSI) method, mode selection is primarily based on frequency stability, damping stability, and mode shape similarity using the Modal Assurance Criterion (MAC). However, these criteria are often insufficient for reliable modal identification in high-noise environments. This study advances beyond the classical approach by introducing a multi-criteria optimization framework for mode evaluation. In addition to the conventional frequency and damping assessments utilized in the classical SSI method, the proposed approach incorporates a range of supplementary structural metrics. These include Density, Cosine Similarity Difference (CSD), Damping Stability (DS), Spatial Roughness (SR), Mode Shape Complexity (MSC), Signal Energy Coherence (SEC), and Normalized Modal Difference (NMD). These metrics are computed within specifically optimized windows on the stabilization diagram. By integrating spatial, phase, and energy-based characteristics of mode shapes alongside traditional metrics such as the MAC, the method enables a more comprehensive and robust mode selection process that surpasses the limitations of relying solely on frequency and damping stability. Compared to the classical SSI, the optimized window approach provides a significant advantage by enabling the reliable selection of consistent modes by considering the continuity and multi-criteria coherence of modes across window transitions. As a result, the elimination of noise modes and the reliable separation of structural modes are established on a more systematic basis. To achieve this, a two-stage optimization strategy is implemented: the first stage determines the optimal frequency window width and minimum mode count threshold, while the second stage utilizes a Multi-Criteria Decision Making (MCDM) framework based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm to assign optimized weights to the structural metrics and rank the candidate windows accordingly. As a result, the ideal frequency window is identified based on its TOPSIS score and subsequently validated using the MAC, confirming that the selected window corresponds to reliable structural modes. The framework is validated using long-term in situ measurements from a Roller Compacted Concrete (RCC) dam operating under significant environmental and operational noise. The dataset comprises continuous, high-resolution (200 Hz) vibration recordings collected between 1 July 2023 and 30 October 2024. While the calendar duration is limited to several weeks, the uninterrupted 24 h measurements yield a high-density time-series dataset with substantial information content, enabling a statistically meaningful and robust evaluation of modal identification performance under real-world and noisy conditions. The results reveal that relying solely on traditional selection criteria such as pole density and the MAC can often lead to the identification of spurious modes, particularly in noisy environments. In contrast, the proposed TOPSIS-based multi-criteria decision-making framework incorporates a broader range of structural indicators, balancing frequency, damping, spatial, and energy-related metrics to enhance the consistency and reliability of mode selection. This approach proved effective even under high-noise conditions, successfully distinguishing true structural modes from artificial ones. Application of the TOPSIS method to RCC dam data revealed consistent fundamental frequencies at approximately 5–10 Hz, 10 Hz, and 15 Hz, confirming its robustness and suitability for complex structural monitoring tasks. Full article

The vibronic spectra of the first excited singlet state (S₁) and the cation spectra of the ground state of the cation (D₀) of jet-cooled cyclopropylbenzene (CPB) were investigated using resonance-enhanced multiphoton ionization (REMPI) and photoelectron velocity-map imaging techniques, respectively. The vibronic spectra indicated the existence of only the bisected conformer, a finding corroborated by quantum chemical calculations. The S₀ → S₁ electronic transition originated at 36,858.5 cm⁻¹, with an adiabatic ionization energy of 66,846 ± 15 cm⁻¹. Vibrational levels in both states were assigned with the assistance of theoretical geometry optimization and frequency calculations. These experimental spectra and theoretical calculations provided valuable insights into the structural and vibrational characteristics of CPB in its excited and cationic states. Full article

Background: Diabetic Foot Ulcers (DFU) are a common complication of diabetes, often leading to infections, amputations and even death if left untreated. Effective management of the Diabetic Foot (DF) requires timely detection and frequent monitoring. Current DF assessment methods, by healthcare professionals, are largely based on visual inspection of feet, together with touch, temperature, and vibration sensitivity, and pedal pulse. Methods: The paper describes a machine-learning approach for the assessment of DF from feet images, combining pre-trained convolutional neural networks (CNN) with Generative AI for dataset annotation. Specifically, the GPT-4o-mini model was used to assign risk labels (Low, Medium or High Risk) to individual foot images, following a structured designed prompt for this task. The labeled dataset was used to train and evaluate two pre-trained CNN architectures, namely, ResNet50 and VGG16. Output predictions are obtained by aggregating the prediction for each of the images of a patient. Results: The results obtained show that both ResNet50 and VGG16 achieved good overall performance, with ResNet50 showing superior results. The High Risk class achieved the highest performance. The Low and Medium Risk classes also showed good performance but were prone to confusion due to the similar features of the images belonging to those classes. Conclusions: The technical contribution of the paper is a Streamlit App, available online for public use, showcases the work. The primary scientific contribution is the demonstration of how Generative AI can be used to train common CNN and automate a highly relevant healthcare process. Full article

Chagas disease remains a major neglected parasitic illness in Latin America and other endemic regions, and benznidazole (BZN) is still the primary trypanosomacidal drug despite its incompletely understood mechanism of action. This work provides a detailed biophysical characterization of the conformational behavior and vibrational properties of benznidazole (BZN), a first-line trypanocidal drug still widely used for the treatment of Chagas disease. Using density functional theory combined with relaxed potential energy surface scans in vacuum and implicit water, two low-energy conformers (BZN1 and BZN2) were identified, separated by moderate rotational barriers and a small energy difference, indicating that both are intrinsically accessible at room temperature. For each conformer, infrared and Raman spectra were calculated and assigned via vibrational mode analysis, then compared with FT-IR and FT-Raman spectra recorded for pharmaceutical-grade polycrystalline BZN. The theoretical and experimental spectra show excellent agreement, with a Raman band in the 1350–1400 $\mathrm{c}{\mathrm{m}}^{-1}$ region emerging as a sensitive conformational marker: the experimental maximum at $1359\mathrm{c}{\mathrm{m}}^{-1}$ matches the most intense BZN1 mode, whereas the corresponding BZN2 band appears about $13\mathrm{c}{\mathrm{m}}^{-1}$ higher in frequency. This clear spectroscopic fingerprint demonstrates that the solid drug is overwhelmingly composed of the BZN1 conformer, despite the theoretical accessibility of BZN2. Overall, the study links the conformational landscape of benznidazole to its vibrational signatures and highlights Raman spectroscopy, supported by quantum chemical calculations, as a powerful tool for conformational and potential polymorphic control of this clinically important nitroimidazole. Full article

Rotational isomers (rotamers) of substituted aromatic molecules exhibit distinct physicochemical properties that are fundamental to understanding their reactivity and biological functions. However, resolving individual rotamers spectroscopically remains challenging due to their similar transition energies and overlapping spectral features. Herein, we report the conformer-specific identification and characterization of three stable rotamers of m-ethoxyphenol using a combination of resonance-enhanced multiphoton ionization (REMPI), hole-burning (HB) spectroscopy, and mass-analyzed threshold ionization (MATI) techniques, complemented by high-level quantum chemical calculations at the B3PW91/aug-cc-pVTZ and G4 levels of theory. The S₁ ← S₀ electronic origins of rotamers I, IV, and III were determined to be 35,966 ± 2, 36,031 ± 2, and 36,198 ± 2 cm⁻¹, respectively, while their corresponding adiabatic ionization energies (IEs) were precisely measured as 64,574 ± 5, 64,122 ± 5, and 64,994 ± 5 cm⁻¹. The vibrational spectra of both the S₁ excited state and the D₀ cationic ground state were assigned, with most active modes corresponding to in-plane benzene ring vibrations. Structural analysis reveals that the benzene ring undergoes slight expansion upon S₁ ← S₀ excitation and contraction upon D₀ ← S₁ ionization, while the overall molecular geometry remains remarkably similar across all three electronic states, a key factor underlying the excellent agreement between experimental and simulated Franck–Condon spectra. Comparison with m-methoxyphenol demonstrates that the stronger electron-donating ability of the ethoxy group leads to systematically lower excitation and ionization energies. The distinct spectroscopic fingerprints established herein provide a definitive reference for identifying specific m-ethoxyphenol rotamers in future studies of this molecule and its complexes. Full article

BACKGROUND: Overhead athletes are at increased risk of shoulder dysfunction due to repetitive, high-velocity movements that can disrupt scapular muscle activation patterns. Whole-body vibration (WBV) has been proposed as a training modality to enhance neuromuscular activation, but its effects on scapular muscle activity and activation timing remain unclear. METHODS: This randomized controlled trial investigated the effects of WBV-assisted push-up training on scapular muscle activation and onset latency in university-level overhead athletes. Forty participants were randomly assigned to a WBV group or a control group performing identical push-up exercises without vibration for four weeks. Surface electromyography was used to assess normalized muscle activation (%MVIC) and activation latency of the upper trapezius (UT), serratus anterior (SA), and lower trapezius (LT) before and after the intervention. A 2 × 2 mixed-model ANOVA was applied for statistical analysis. RESULTS: Significant time × group interactions were found for muscle activation in LT and SA (p < 0.01). The WBV group demonstrated substantially greater increases in activations in these muscles compared with the control group, with the largest improvements observed in the serratus anterior. No statistically significant between-group differences were identified for muscle onset latency (p > 0.05). CONCLUSIONS: Adding WBV to push-up training significantly enhances key scapular muscle activation in overhead athletes but does not significantly affect muscle onset latency. WBV-assisted push-ups may act as a practical, low-load strategy to improve scapular muscle recruitment and potentially reduce the risk of sports-related shoulder injuries and pain in overhead athletes. Full article

The paper presents an optimisation workflow for modelling of a periodic mechanical structure in the form of a multi-material, axisymmetric beam. The optimisation objective is to prescribe the positions and widths of selected band gaps within a target frequency range for three basic types of structural vibrations: flexural, longitudinal and torsional. The decision variables were geometric parameters of the unit cell and material properties of selected thermoplastics assigned to successive segments of the cell. The frequency characteristics of the beam were determined using the time-domain spectral finite-element method (TD-SFEM). This model was used to perform a sensitivity analysis using the Morris method, which showed the dominant influence of the beam geometry on the position and width of band gaps, with a relatively smaller role of material variability. Due to high computational costs of the global optimisation based on a FEM solver, a surrogate regression model in the form of a residual MLP network was developed to predict the positions and widths of the first five band gaps for each vibration type. The global search was carried out using a genetic algorithm (GA) with the surrogate model and then the results were refined using a deterministic goal-attainment method with a high-fidelity model. Full article