Search Results (6,694)

The glass-forming ability of short-chain alkanes remains a fundamental challenge in condensed matter physics. This study investigates the structural properties of n-hexane (C₆H₁₄) and decane (C₁₀H₂₂) under two distinct cooling regimes using Raman spectroscopy: fast cooling (~50–100 K/s via contact freezing on a copper substrate at 77 K) and conventional cooling (~1–5 K/s). Despite employing rapid cooling protocols, both alkanes underwent crystallization without forming amorphous phases. n-Hexane formed a defective crystalline structure characterized by broad spectral bands (FWHM ~40–45 cm⁻¹) and diffuse phase transitions in the 180–200 K range, while decane exhibited highly ordered crystalline structures with sharp spectral features (FWHM ~15–20 cm⁻¹) and abrupt transitions at 220–240 K. Quantitative analysis of characteristic Raman bands (skeletal deformations, C-C stretching, and C-H stretching vibrations) revealed fundamental differences in crystallization mechanisms related to molecular chain length. The study demonstrates that contact freezing methods are fundamentally incapable of achieving the extreme cooling rates (>10⁴ K/s) and ultra-thin film conditions (<1 μm) necessary for alkane vitrification. These findings establish spectroscopic diagnostic criteria for distinguishing between defective and well-ordered crystalline structures and define the limitations of conventional cryogenic techniques for glass formation in alkanes. Full article

Packed granular materials absorb sound. In previous studies, granular materials sized a few millimeters and samples of grain size as a powder were studied; however, the grain sizes in between have not been addressed. In this study, the sound absorption coefficients of materials ranging from granular materials with a grain size d = 4 mm to powder materials with d = 0.05 mm were analyzed theoretically and experimentally. In addition, five packing types were studied: four types of regular packing and random packing. For these packing structures, the propagation constants and characteristic impedances were substituted within a one-dimensional transfer matrix for sound wave propagation, from which the normal-incidence sound absorption coefficient was calculated. Furthermore, our analysis accounted for particle longitudinal vibrations due to sound pressure. According to analyses of cross-sectional CT images considering tortuosity, the theoretical values for random packing tended to be close to the experimental values for d = 0.8 mm and smaller. For random packing structures with d = 0.3 mm or smaller, the experimental values were closer to the theoretical values for simple cubic lattice than the theoretical values for random packing. Full article

Mitigating ground vibrations from sources like vehicles and construction operations poses significant challenges, often relying on computationally intensive numerical methods such as Finite Element Methods (FEM) or Boundary Element Methods (BEM) for analysis. This study addresses these limitations by developing and evaluating Machine Learning (ML) methodologies for the rapid and accurate prediction of Insertion Loss (IL), a critical parameter for assessing the effectiveness of open trenches as vibration barriers. A comprehensive database was systematically generated through high-fidelity numerical simulations, capturing a wide range of geometric, elastic, and physical configurations of a stratified geotechnical system. Three distinct ML strategies—Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forests (RF)—were initially assessed for their predictive capabilities. Subsequently, a Meta-RF stacking ensemble model was developed, integrating the predictions of these base methods. Model performance was rigorously evaluated using complementary statistical metrics (RMSE, MAE, NMAE, R), substantiated by in-depth statistical analyses (normality tests, Bootstrap confidence intervals, Wilcoxon tests) and an analysis of input parameter sensitivity. The results clearly demonstrate the high efficacy of Machine Learning (ML) in accurately predicting IL across diverse, realistic scenarios. While all models performed strongly, the RF and the Meta-RF stacking ensemble models consistently emerged as the most robust and accurate predictors. They exhibited superior generalization capabilities and effectively mitigated the inherent biases found in the ANN and SVM models. This work is intended to function as a proof-of-concept and offers promising avenues for overcoming the significant computational costs associated with traditional simulation methods, thereby enabling rapid design optimization and real-time assessment of vibration mitigation measures in geotechnical engineering. Full article

Diesel engines remain the foundation for obtaining mechanical energy in sectors where autonomy and reliability are required; however, predictive diagnostics under real-world conditions remain challenging. The purpose of this scoping review is the investigation and systematization of published scientific data on the application of vibration methods for monitoring the technical condition of diesel engines in industrial or controlled laboratory conditions. Based on numerous results of publication analysis, sensor configurations, diagnosed components, signal analysis methods, and their application for assessing engine technical condition are considered. As methods for determining vibration parameters, time-domain and frequency-domain analysis, adaptive decompositions, and machine and deep learning algorithms predominate; high accuracy is more often achieved under controlled conditions, while confirmations of robustness on industrial installations are still insufficient. Key limitations for the application of vibration monitoring methods include the multicomponent and non-stationary nature of signals, a high level of noise, requirements for sensor placement, communication channel limitations, and the need for on-site processing; meanwhile, the assessment of torsional vibrations remains technically challenging. It is concluded that field validations of vibroacoustic data, the use of multimodal sensor platforms, noise-immune algorithms, and model adaptation to the specific environment are necessary, taking into account fuel quality, transient conditions, and climatic factors. Full article

Vibrations induced by marine environmental loads can compromise the operational performance of offshore platforms and, in severe cases, result in structural instability or overturning. This study proposes a biomimetic nonlinear X-shaped vibration isolation system (NXVIS) to suppress earthquake-induced vibration response in offshore platforms. Compared with traditional passive vibration isolators, the key innovations of the NXVIS include: (1) the proposed NXVIS can be tailored to different load requirements and resonant frequencies to accommodate diverse offshore platforms and environmental loads; (2) By adjusting isolator parameters (e.g., link length and spring stiffness, etc.), the anti-vibration system can achieve different types of nonlinear stiffness and a large-stroke quasi-zero stiffness (QZS) range, enabling ultra-low frequency (ULF) vibration control without compromising load capacity. To evaluate the effectiveness of the designed NXVIS for vibration suppression of jacket offshore platforms under seismic loads, numerical analysis was performed on a real offshore platform subjected to seismic loads. The results show that the proposed nonlinear vibration isolation solution significantly reduces the dynamic response of deck displacement and acceleration under seismic loads, demonstrating effective low-frequency vibration control. This proposed NXVIS provides a novel and effective method for manipulating beneficial nonlinearities to achieve improved anti-vibration performance. Full article

As a large-scale flexible structure, the free-vibration characteristics of a horizontal single-axis solar tracking bracket (HSSTB) hold significance for its dynamic optimization design. However, due to material fabrication, construction processes, and harsh field service environments, structural parameters such as the elastic modulus inevitably exhibit uncertainty, leading to discrepancies between actual free-vibration characteristics and design values. This study considers the randomness of the steel elastic modulus and conducts a global sensitivity analysis of a real-life five-column HSSTB. First, the Kriging method is employed to build a surrogate model to describe the natural frequencies of the HSSTB and its stochastic parameters, which enables efficient evaluation of the statistical characteristics of the HSSTB’s natural frequencies. Further, the Sobol indices are utilized to quantify the influence of parameter randomness on the natural frequencies. The results indicate that the mean values of the first five natural frequencies are slightly lower than the design values. The first, fourth, and fifth natural frequencies of the five-column HSSTB are predominantly influenced by the middle three columns, while the second and third natural frequencies are more susceptible to the two edge columns. Full article

This paper presents a mathematics-focused AI iterative prediction framework that combines Extreme Gradient Boosting (XGBoost) for nonlinear function approximation with nonlinear autoregressive model with exogenous inputs (NARXs) for time-series modeling, applied to analyzing the Remaining Useful Life (RUL) and availability of Unmanned Aerial Vehicle (UAV) Brushless DC (BLDC) motors. The framework integrates nonlinear regression, temporal recursion, and survival analysis into a unified system. The dataset includes five UAV motor types, each recorded for 10 min at 20 Hz, totaling approximately 12,000 records per motor for validation across these five motor types. Using grouped K-fold cross-validation by motor ID, the framework achieved mean absolute error (MAE) of 4.01 h and root mean square error (RMSE) of 4.51 h in RUL prediction. Feature importance and SHapley Additive exPlanation (SHAP) analysis identified temperature, vibration, and HI as key predictors, aligning with degradation mechanisms. For availability assessment, survival metrics showed strong performance, with a C-index of 1.00 indicating perfect risk ranking and a Brier score at 300 s of 0.159 reflecting good calibration. Additionally, Conformalized Quantile Regression (CQR) enhanced interval coverage under diverse operating conditions, providing mathematically guaranteed uncertainty bounds. The results demonstrate that this framework improves both accuracy and interpretability, offering a reliable and adaptable solution for UAV motor prognostics and maintenance planning. Full article

Diagnosing female infertility is a complex and time-consuming task due to the large number of factors affecting the patient’s fertility, which results in the need to perform many tests to determine the cause in each case accurately. In recent years, the use of spectroscopic methods has been explored for their potential to identify spectral markers of female infertility through analysis of follicular fluid (FF). This article aims to serve as a review and presentation of the research performed in the field of female infertility diagnostics using NMR and vibrational spectroscopy in the analysis of FF samples. Full article

The current study focuses on axial ultrasonic vibration-assisted micro-milling as an advanced technique to improve the machining performance of Ti6Al4V, a material whose difficult-to-cut properties present a significant barrier to manufacturing the high-quality micro-components essential for aerospace and biomedical applications. A full factorial design was employed to evaluate the influence of feed-per-tooth (f_z), axial depth-of-cut (a_p), and ultrasonic vibration on cutting forces, surface roughness, burr formation, and tool wear. Experimental results demonstrate that ultrasonic assistance significantly reduces cutting forces by 20.09% and tool wear by promoting periodic tool–workpiece separation and improving chip evacuation. However, it increases surface roughness due to the formation of uniform micro-dimples, which may enhance tribological properties. Burr dimensions were primarily governed by feed-per-tooth, with higher feeds minimizing burr size. The study provides actionable insights into optimizing machining parameters for cutting Ti6Al4V, highlighting the trade-offs between force reduction, surface texture, and burr control. These findings contribute to advancing ultrasonic-assisted micro-milling for industrial applications, namely aerospace and biomedical applications requiring high precision and extended tool life. Full article

With the comprehensive digitalization and electrification of aircraft, electromechanical actuation systems (EAS) have been increasingly applied. However, EAS are affected by various nonlinear factors, such as friction and mechanical backlash, which can compromise system stability and control accuracy, thereby reducing the operational lifespan of the EAS. This study focuses on these two nonlinear factors and proposes a hybrid control approach to mitigate their effects. In the speed loop of the EAS, a Super-Twisting sliding mode controller combined with a generalized proportional–integral observer (GPIO) is designed, while in the position loop, a hybrid controller integrating a radial basis function (RBF) neural network with sliding mode control is implemented. Leveraging the advantages of numerical analysis in SIMULINK and dynamic simulation in ADAMS, a co-simulation framework is established to evaluate the hybrid control algorithm under nonlinear effects. Furthermore, a control test bench for the control surface transmission system is constructed to analyze the dynamic and static performance of the system under different control strategies and input commands. The experimental results show that, compared with the PID control, the hybrid control method reduces the steady-state error and vibration amplitude of the step response displacement by 51% and 75%, respectively, and decreases the amplitude of speed fluctuations by 75%. For the sinusoidal response, the displacement lag is reduced by 76%, and the amplitude of speed fluctuations is reduced by 50%. Full article

Bellows compensators are critical components in pipeline systems, designed to absorb thermal expansions, vibrations, and pressure reflections. Ensuring their operational reliability requires accurate prediction of the stress–strain state (SSS) and stability under internal pressure. This study presents a comprehensive mathematical model for analyzing corrugated bellows compensators, formulated as a boundary value problem for a system of partial differential equations (PDEs) within the Kirchhoff–Love shell theory framework. Two numerical approaches are developed and compared: a finite difference method (FDM) applied to a reduced axisymmetric formulation to ordinary differential equations (ODEs) and a finite element method (FEM) for the full variational formulation. The FDM scheme utilizes a second-order implicit symmetric approximation, ensuring stability and efficiency for axisymmetric geometries. The FEM model, implemented in Ansys 2020 R2, provides high fidelity for complex geometries and boundary conditions. Convergence analysis confirms second-order spatial accuracy for both methods. Numerical experiments determine critical pressures based on the von Mises yield criterion and linearized buckling analysis, revealing the influence of geometric parameters (wall thickness, number of convolutions) on failure mechanisms. The results demonstrate that local buckling can occur at lower pressures than that of global buckling for thin-walled bellows with multiple convolutions, which is critical for structural reliability assessment. The proposed combined approach (FDM for rapid preliminary design and FEM for final verification) offers a robust and efficient methodology for bellows design, enhancing reliability and reducing development time. The work highlights the importance of integrating rigorous PDE-based modeling with modern numerical techniques for solving complex engineering problems with a focus on structural integrity and long-term performance. Full article

Background/Objectives: Activities of daily living (ADL) are critical for independence after stroke, yet many survivors remain functionally limited. Vibration therapy (VT), including whole-body and focal modalities, has been proposed as an adjunct to enhance recovery, but effects on ADL remain unclear. This study aimed to evaluate the overall effectiveness of VT on ADL and to identify moderating factors. Methods: A systematic review and meta-analysis were conducted following PRISMA 2020 guidelines. Thirteen controlled trials (12 RCTs, 1 nRCT) involving VT in stroke were included. Standardized mean differences (Hedges’ g) were synthesized using random-effects models. Meta-regression and subgroup analyses examined moderators such as session number, vibration parameters, stroke stage, and ADL subdomains. Risk of bias was assessed with RoB 2 and ROBINS-I. Results: VT produced a small but significant effect on ADL (Hedges’ g = 0.19; 95% CI: 0.06–0.33; p = 0.008), though significance was lost after adjustment for publication bias. Heterogeneity was moderate (I² = 34%). Session number was the only significant moderator (p = 0.045), explaining ~24% of variance, with the greatest benefit in the 13–24 session range (g = 0.34; 95% CI: 0.05–0.63). Subgroup analysis showed improvement in physical function/mobility (g = 0.32; p = 0.048), but not in self-care or quality-of-life outcomes. Other parameters were not significant moderators. Conclusions: VT confers modest benefits for ADL after stroke, particularly in mobility-related domains. Session number appears clinically important, with 13–24 sessions suggesting an optimal dose window. Full article

The stability of absolute gravimeters during carriage-falling body separation is crucial for improving gravitational acceleration measurement accuracy. Transmission speed accuracy of the transmission system and system vibration are core factors determining this stability, while steel belt pre-tightening force, free-fall segment acceleration, and start-up segment displacement are key parameters influencing both. In-depth analysis of their coupling clarified their roles, and two objective function models (for speed accuracy and vibration) were established, with fitting accuracies R² = 0.8976 and R² = 0.8395, respectively. Since traditional single-objective optimization fails to balance “improving speed accuracy” and “suppressing vibration”, this study proposes a multi-objective optimization method: two Nondominated Sorting Genetic Algorithm II (NSGA-II) parameter sets were designed, Hypervolume (HV) index quantified solution set quality, and Wilcoxon signed-rank test was combined to determine the optimal parameter set; comparing the Global Criterion Method and Weighted Sum Method, the former was superior (no dimensional bias) and more suitable for this study, finally screening out the optimal parameter combination. Experimental results showed that the measured transmission speed accuracy was 0.09132 m/s (16.94% lower than the orthogonal experiment’s optimal level); the measured system vibration was 0.022 m/s², falling within the orthogonal experiment’s optimal range. Consequently, separation moment stability was significantly enhanced, with its standard deviation reduced by 45% pre-optimization. This method achieves global balance in transmission system dynamic performance, providing an effective parameter optimization strategy for improving absolute gravimeter measurement accuracy. Full article

Lubricants significantly influence the performance and durability of internal combustion engines (ICEs), yet fresh oils seldom represent in-service conditions. To replicate realistic end-of-life scenarios, lubricants were artificially degraded in sufficient quantities for experimental investigation. This study introduces a methodology to evaluate the impact of altered lubricants on turbocharger dynamics under controlled laboratory conditions. A comparative analysis was performed on turbochargers operating with fresh and aged oils of varying compositions to establish correlations between lubricant properties and vibrational response. Particular attention was given to sub-synchronous phenomena and their implications for rotordynamic stability. Variations in damping and stiffness were assessed under constant pressure and temperature to support mathematical modeling of lubricant degradation and viscosity evolution. Experiments were conducted on a cold turbocharger test bench equipped with acceleration, speed, and displacement sensors, while a mobile oil control unit ensured precise regulation of inlet oil pressure and temperature. Full article

Background/Objectives: Friedreich’s ataxia (FA) is a rare neurodegenerative disorder caused by GAA repeat expansions in the FXN gene. While well-studied in larger populations, data from Southeastern Europe are limited. This study aimed to characterize the clinical and genetic features of FA in a Serbian cohort and explore genotype–phenotype correlations. Methods: A multi-center, retrospective–prospective analysis was conducted on 30 genetically confirmed FA patients. Clinical assessments included neurological, cardiological, and metabolic evaluations. GAA repeat sizes were determined in 26 patients, and correlations with clinical features were analyzed. Results: The mean age at disease onset was 9.0 ± 3.0 years, with ataxia as the initial symptom in 80% of patients. Hypertrophic cardiomyopathy was present in 73.3%, and 43.3% of patients lost ambulation within 1.5 to 15 years after symptom onset. Two patients developed diabetes, and two were diagnosed with nephrotic syndrome. Genetic analysis revealed an average GAA1 repeat length of 805 and GAA2 of 1024 alleles. Larger GAA1 expansions were associated with extensor plantar responses, while longer GAA2 repeats correlated with impaired vibration sense. Disease duration was strongly linked to multiple neurological signs and loss of ambulation. No significant correlation was found between GAA repeat length and age at onset. Conclusions: This study provides the first genotype–phenotype analysis of FA in Serbia, confirming known patterns and revealing new comorbidities, such as nephrotic syndrome. GAA repeat length influences some clinical features but does not fully predict disease onset or progression, indicating the need for broader genetic and environmental studies. Full article