Search Results (397)

Bearing fault diagnosis is critical in rotating machinery, and collecting and analyzing vibration signals from faulty bearings is a widely employed method in fault diagnosis. To efficiently extract the information of periodic pulse from complex signals and accurately identify fault characteristic frequencies, this paper proposes a BSNA (Bispectral Slice Negentropy Analysis) method. This method leverages the nonlinear characteristics of bispectral analysis and the sensitivity of negentropy measures to transform one-dimensional signals into two-dimensional spectra. By utilizing the demodulation capability of the time-frequency modulation bispectrum, it highlights the relationship between resonance bands and modulation frequency, while maximizing the preservation of critical fault information and minimizing the impact of interference signals. The fault information contained in the slices is subsequently quantified using the CSNE (correlation spectral negentropy), which effectively captures the magnitude of periodic pulse energy. By calculating the CSNE of each modulation frequency slice and visualizing it, the energy distribution of periodic pulses within each slice can be effectively observed. The feasibility of this method in rolling bearing fault diagnosis has been validated through simulation analysis and experimental comparison. This approach enables the accurate identification of fault characteristic frequency and its harmonics, thereby significantly enhancing the accuracy and robustness of fault diagnosis, particularly in complex and noisy background environments. Full article

Satellite precipitation products offer great potential for acquiring reliable precipitation data in data-sparse areas, yet they have inherent uncertainties and errors as indirect observations. This study evaluated the accuracy of multi-source satellite precipitation products from daily and precipitation magnitude perspectives and discussed the spatiotemporal variation in their inversion errors. Based on ground rainfall observations, satellite products, and environmental factors, a Transformer-based multi-source precipitation fusion method was proposed, with its effectiveness preliminarily analyzed for daily precipitation in the Jingle River Basin. The main conclusions are as follows: (1) Compared with the observed precipitation data, the GSMaP_Gauge satellite remote sensing precipitation product showed the closest agreement with the observations, ranking first in all indicators except the Probability of Detection (POD). The MSWEP satellite remote sensing precipitation product followed in performance, while the CHIRPS satellite product performed the poorest. Satellite products showed distinct error characteristics across seasons and rainfall intensities, as well as general overestimation of light rain frequency and insufficient heavy rain capture; however, these products also showed better detection capability in flood seasons. Error spatial distribution was consistent with topography, vegetation coverage, and temperature. (2) Verification demonstrated that the Transformer fusion algorithm effectively reduced relative bias and improved correlation with ground data. The scheme which incorporated environmental factors outperformed the other, which only considered precipitation characteristics, achieving higher estimation accuracy and fusion stability. Full article

Accurate indoor localization in 5G remains challenging due to multipath propagation, signal blockage, and limited bandwidth in frequency range 1 (FR1). This study evaluates attention-based recurrent neural networks for two-dimensional user equipment (UE) localization using only positioning reference signal (PRS) magnitude data. We compare five models on the xG-Loc dataset (InF-DH scenario at 3.5 GHz, 5 MHz bandwidth): a simple GRU (M1), a deeper GRU with dropout (M2), a GRU optimized via Optuna (M3), a stacked GRU with multi-head attention (M4), and a bidirectional GRU with attention (M5). Model performance is quantified using the area above the cumulative distribution function (CDF) curve (AAC) metric, where lower values indicate better localization accuracy. Attention-based models significantly outperform baselines, and M4 achieves the lowest AAC of 6.71 (17% reduction versus M1’s 8.09), while M5 attains an AAC of 6.90. Statistical analysis confirms that M4 and M5 significantly outperform M3 (ANOVA, p < 0.000001). Optimal performance emerges with moderate numbers of time steps (TS ≈ 500 to 2500), with performance plateauing and degrading at higher values. These findings demonstrate that attention mechanisms substantially enhance 5G indoor localization accuracy using only PRS magnitudes, and that automated hyperparameter optimization improves model robustness. Full article

Background and Objectives: To evaluate the magnitude, axis and age-related changes in corneal astigmatism in patients before cataract surgery. Materials and Methods: In this retrospective, cross-sectional, and observational study, data from 2152 eyes that underwent phacoemulsification were evaluated. Keratometric values were obtained using the IOL Master 500 device. The frequency, magnitude and axis of corneal astigmatism were determined. The astigmatism axis was categorized as with the rule (WTR), against the rule (ATR), and oblique astigmatism. Quantitative analysis was performed using the power vector method (J0 and J45). The distribution and characteristics of corneal astigmatism data according to age were analyzed. Results: The mean age of the patients was 70.56 ± 8.88 years (range 40–94 years) and 1010 (46.9%) were males. Mean corneal astigmatism, J0 and J45 values were 0.96 ± 0.72, 0.05 ± 0.51, 0.01 ± 0.30 diopters (D), respectively. The most common range of magnitudes was 0.50–0.99 D with 38.8%, followed by <0.50 D (25.3%), 1.00–1.49 D (20.3%), and 1.50–1.99 D (8.7%). The cubic regression curve showed a U-shaped nonlinear relationship between age and corneal astigmatism (p < 0.001). The most common type of astigmatism was WTR with 43.4%, followed by ATR with 37.5% and oblique astigmatism with 19.1%. With the increase in age, the astigmatism axis gradually changed from WTR to ATR. There was a linear trend in the rate of these types of astigmatism across age groups (p < 0.05). Additionally, in patients under 65 years of age, WTR astigmatism was negatively correlated with age, while in patients 65 years of age and older, ATR astigmatism was positively correlated with age (r = −0.217, p < 0.001; r = 0.153, p < 0.001, respectively). Linear regression analyses revealed that the J0 value decreased significantly with age, whereas J45 showed no significant relationship. Specifically, J0 decreased by 0.014 D per year of age (95% confidence interval [CI], 0.011–0.016; p < 0.001). Conclusions: The results obtained in this study may provide information to guide surgeons in the management of astigmatism and the choice of toric intraocular lens in cataract surgery. Full article

Ionic polymer–metal composite (IPMC) sensors generate voltages or currents when subjected to deformation. The magnitude and time constant of the electrical response vary significantly with ambient humidity and water content. However, most conventional physical models focus solely on cation dynamics and do not consider water dynamics. In addition to cation dynamics, Zhu’s model explicitly incorporates the dynamics of water. Consequently, Zhu’s model is considered one of the most promising approaches for physical modeling of IPMC sensors. This paper presents exact analytical solutions to Zhu’s model of IPMC sensors for the first time. The derivation method transforms Zhu’s model into the frequency domain using Laplace transform-based analysis together with linear approximation, and subsequently solves it as a boundary value problem of a set of linear ordinary differential equations. The resulting solution is expressed as a transfer function. The input variable is the applied bending deformation, and the output variables include the open-circuit voltage or short-circuit current at the sensor terminals, as well as the distributions of cations, water molecules, and electric potential within the polymer. The obtained transfer functions are represented by irrational functions, which typically arise as solutions to a system of partial differential equations. Furthermore, this paper presents analytical approximations of the step response of the sensor voltage or current by approximating the obtained transfer functions. The steady-state and maximum values of the time response are derived from these analytical approximations. Additionally, the relaxation behavior of the sensor voltage is characterized by a key parameter newly derived from the analytical approximation presented in this paper. Full article

The biological effects of radiofrequency electromagnetic fields (RF-EMFs) remain an unresolved scientific issue with important societal relevance, particularly in the context of the global deployment of fifth-generation (5G) wireless technologies. The skin is continuously exposed to both RF-EMFs and ultraviolet (UV) radiation, a well-established inducer of oxidative stress and DNA damage, making it a relevant model for assessing combined environmental exposures. In this study, we investigated whether post-exposure to 5G RF-EMFs (3.5 and 28 GHz) modulates ultraviolet A (UVA)-induced genotoxic stress in human keratinocytes (HaCaT) and murine melanoma (B16) cells. Post-UV RF-EMF exposure significantly reduced DNA damage markers, including phosphorylated histone H2AX (γH2AX) foci formation (by approximately 30–50%) and comet tail moments (by 60–80%), and suppressed intracellular reactive oxygen species (ROS) accumulation (by 56–93%). These effects were accompanied by selective attenuation of p38 mitogen-activated protein kinase (MAPK) phosphorylation (reduced by 55–85%). The magnitude of molecular protection was comparable to that observed with N-acetylcysteine treatment or pharmacological inhibition of p38 MAPK. In contrast, RF-EMF exposure did not reverse UV-induced reductions in cell viability or alterations in cell cycle distribution, indicating that its protective effects are confined to early molecular stress-response pathways rather than downstream survival outcomes. Together, these findings demonstrate that 5G RF-EMFs can facilitate recovery from UVA-induced molecular damage via redox-sensitive and p38-dependent mechanisms, providing mechanistic insight into the interaction between modern telecommunication frequencies and UV-induced skin stress. Full article

This paper presents experimental testing results which describe the response of modern residential loads and electric vehicle (EV) chargers to various voltage magnitude, frequency, and phase angle disturbances. The purpose of these tests is to replicate real life network conditions and assist Network Service Providers and the Australian Energy Market Operator in identifying and predicting potential power variation and system stability issues caused by load behaviour during power system transient phenomena. By examining the behaviour of typical loads connected to distribution networks, a deeper understanding of their response can be achieved, enabling the refinement of composite load models that are compatible with the Western Electricity Coordinating Council dynamic composite load model (CMPLDW) structure presently used for dynamic studies. The performance of a wide range of common appliances found in residential settings, such as refrigerators, microwave ovens, air conditioners, direct-on-line motor-based appliances, and EV chargers, has been evaluated. The results obtained from these tests offer valuable insights into the behaviour of different load types and illustrate differing performances from established model parameters, identifying the need to refine existing CMPLDW models. The results also support the reclassification of several appliances within the composite load model, motivate the introduction of a dedicated EV charger component, and empower network operators to improve the modelling of modern power network responses. Full article

In this work, a substrate-integrated waveguide (SIW) filtering power divider with a modified complementary split-ring resonator (CSRR) is reported. Firstly, by integrating the meander-shaped slots with the conventional CSRR, the proposed inner-meander-slot CSRR (IMSCSRR) can enlarge the total length of the defected slot and increase the width of the split, thus enhancing the equivalent capacitance and inductance. In this way, the fundamental resonant frequency of the IMSCSRR can be effectively decreased without enlarging the circuit size, which can generally help to reduce the physical size by over 35%. Subsequently, to further reduce the circuit size, two IMSCSRRs are separately loaded on the top and bottom metal covers to constitute a broadside-coupled IMSCSRR, which is combined with the SIW. To verify the efficacy of the proposed SIW-IMSCSRR unit cell, a two-way filtering power divider is implemented. It combines the band-selection function of a filter and the power-distribution property of a power divider, thereby enhancing system integration and realizing size compactness. Experimental results show that the proposed filtering power divider achieves a center frequency of 3.53 GHz, a bandwidth of about 320 MHz, an in-band insertion loss of (3 + 1.3) dB, an in-band isolation of over 21 dB, and a size reduction of about 30% compared with the design without broadside-coupling, as well as good magnitude and phase variations. All the results indicate that the proposed filtering power divider achieves a good balance between low loss, high isolation, and compact size, which is suitable for system integration applications in microwave scenarios. Full article

Nanoaperture optical tweezers (NOTs) were used to analyze conalbumin in various forms. By analyzing the power spectrum of the NOT-transmitted laser signal, differences between iron and iron-free conalbumin were observed; the corner frequency extrapolated to zero-laser power was significantly larger in magnitude for conalbumin with iron, which was interpreted as coming from the enhanced electrostatic interactions close to the surface of the nanoaperture. Conalbumin in a diluted, but otherwise unprocessed, egg white sample showed the same behavior as purified iron-free conalbumin. Dynamic two-state transitions in the NOT signal were observed for iron-free conalbumin and conalbumin in egg white samples. We used this to determine the dominant state as a function of temperature, with one state showing a maximum occupancy around 30.4 °C. Deconvolution of the probability distribution function was used to find the energy landscape associated with this two-state transition. This work shows the potential of NOTs to see variations with metal ion binding, including conformational dynamics related to the binding at timescales not accessible to other methods. Full article

The article presents a comprehensive determination and analysis of the dynamic accuracy of the AC traction network–pantograph interface using an equivalent lumped-parameter RLC model derived from a distributed-parameter representation of the traction line. The study investigates the system’s response to representative excitation signals: step, sinusoidal, and multi-harmonic, where the root mean square value of the voltage error at the network–pantograph interface is adopted as the main performance indicator. A novel contribution of this work lies in determining the upper bound on the dynamic error (UBDE) for input signals constrained by realistic physical limitations: initially by magnitude and duration, and subsequently extended with an additional rate of change constraint. In the first case, an iterative optimization procedure is applied to determine the constrained excitation and its corresponding error, while in the extended case, the problem of maximizing the dynamic error energy is solved numerically using a genetic algorithm. In both formulations, the objective is to identify extreme, physically admissible excitation waveforms that represent the most unfavorable dynamic scenarios for voltage reproduction within the traction network–pantograph RLC interface. The results obtained in this study are of both theoretical and practical significance. They allow the identification of frequency ranges and resonance conditions that intensify dynamic errors, support the design of compensation and filtering strategies, and enable the assessment of the system robustness to fast disturbances and supply voltage distortions. From a theoretical point of view, the article introduces a unified methodology for the determination and evaluation of dynamic errors and their worst-case upper estimates under realistic signal constraints, providing a foundation for future research on control design, optimization, and voltage quality requirements in AC traction power systems. Full article

This study investigates the aerodynamic and aeroacoustics behavior of automotive cooling modules in both conventional internal combustion engine (ICE) vehicles and electric vehicles (EVs), with a particular focus on installation effects. Numerical simulations based on the Lattice Boltzmann Method (LBM) are conducted to analyze noise generation mechanisms and flow characteristics across four configurations. The study highlights the challenges of adapting classical cooling module components to EV setups, emphasizing the influence of heat exchanger (HE) placement and duct geometry on noise levels and flow dynamics. The results show that the presence of the HE smooths the upstream flow, improves rotor loading distribution and disrupts long, coherent vortical structures, thereby reducing tonal noise. However, the additional resistance introduced by the HE leads to increased rotor loading and enhanced leakage flow through the shroud-rotor gap. Despite these effects, the overall sound pressure level (OASPL) remains largely unchanged, maintaining a similar magnitude and dipolar directivity pattern as the configuration without the HE. In EV modules, the inclusion of ducts introduces significant flow disturbances and localized pressure fluctuations, leading to regions of high flow rate and rotor loading. These non-uniform flow conditions excite duct modes, resulting in troughs and humps in the acoustic spectrum and potentially causing resonance at the blade-passing frequency, which increases the amplitude in the lower frequency range. Analysis of the loading force components reveals that rotor loading is primarily driven by thrust forces, while duct loading is dominated by lateral forces. Across all configurations, fluctuations at the leading and trailing edges of the rotor are observed, originating from the blade tip and extending to approximately mid-span. These fluctuations are more pronounced in the EV module, identifying it as the dominant source of pressure disturbances. The numerical results are validated against experimental data obtained in the anechoic chamber at the University of Sherbrooke and show good agreement. The relative trends are accurately predicted at lower frequencies, with slight over-prediction, and closely match the experimental data at mid-frequencies. Full article

A cascade deep learning approach is proposed for optimizing the design and control of a dual-frequency induction heating system used in semiconductor manufacturing. The system is composed of two independent power inductors, fed at different frequencies, to achieve a homogeneous temperature profile along a graphite susceptor surface, crucial for enhancing layer quality and integrity. The optimization process considers both electrical (current magnitudes and frequencies) and geometrical parameters of the coils, which influence the power penetration and subsequent temperature distribution within the graphite disk. A two-step procedure based on deep neural networks (DNNs) is employed. The first step, namely optimal design, identifies the optimal operating frequencies and geometrical parameters of the two coils. The second step, namely optimal control, determines the optimal current magnitudes. The DNNs are trained using a database generated through finite element (FE) analysis. This deep learning-based cascade approach reduces computational time and multiphysics simulations compared to classical methods by reducing the dimensionality of parameter mapping. Therefore, the proposed method proves to be effective in solving high-dimensional multiphysics inverse problems. From the application point of view, achieving thermal uniformity (±7% fluctuation at 1100 °C) improves layer quality, increases efficiency, and reduces operating costs of epitaxy reactors. Full article

This study investigates the morphometric and anthropogenic controls governing the occurrence and spatial distribution of tributary–junction fans (TJFs) along the Chaudière River, Québec, Canada. Using GIS-based morphometric analysis, field validation, and multivariate statistics (PCA, CART, LDA), 142 tributary watersheds were analyzed, of which 41 display fan-shaped depositional features. Basin relief, drainage density, contributing area, and slope–area coupling emerge as the dominant predictors of TJF development, delineating an intermediate energy domain where sediment supply and transport capacity become balanced enough to allow partial geomorphic coupling at confluence nodes. CART analysis identified approximate slope and area thresholds (slope < 9°, area > 20 km²; 66% accuracy), while LDA achieved 76%, indicating that morphometry provides useful but incomplete predictive power. These moderate performances reflect the additional influence of event-scale hydrological forcing and unquantified Quaternary substrate heterogeneity typical of postglacial terrain. Beyond morphometry, anthropogenic disturbance exerts a secondary but context-dependent influence, with moderately disturbed watersheds (10–50% altered) showing higher frequencies of fans than both highly engineered (>50%) and minimally disturbed (<10%). This pattern suggests that land-use modification can locally reinforce or offset morphometric predisposition by altering sediment-routing pathways. Overall, TJFs function as localized sediment-storage buffers that may be periodically reactivated during high-magnitude floods. The combined effects of basin geometry, land-use pressures, and hydroclimatic variability explain their spatial distribution. The study provides an indicative, process-informed framework for evaluating sediment connectivity and depositional thresholds in cold-region fluvial systems, with implications for geomorphic interpretation and hazard management. Full article

With the rapid development of high-speed and heavy-haul railways in cold regions, understanding the influence of external loads on the frost heave behavior of subgrade fillings is of great importance. In this study, one-dimensional frost heave experiments were conducted on Group A coarse-grained soil under different static and dynamic loading conditions. The effects of upper plate freezing temperature, load magnitude, load frequency, and water supply (closed and open systems) were systematically investigated. The variations in internal temperature, frozen depth, frost-heave deformation, and post-freezing water content distribution were analyzed. The results indicate that external loads exert a certain inhibitory effect on frost-heave deformation of coarse-grained soil. Upper plate freezing temperature and load magnitude were identified as key factors influencing frost heave behavior, while load frequency had little effect. With decreasing upper plate freezing temperature, both frozen depth and frost-heave deformation increased. The frost heave ratio decreased with increasing load magnitude. Compared with fine-grained soil, coarse-grained soil exhibited shorter frost heave growth periods (about 3–5 h) and less pronounced ice lens formation. In addition, the water content in the frozen portion increased slightly, while that in the unfrozen portion decreased. These findings provide valuable insights for evaluating the stability of railway subgrades and optimizing design parameters in cold-region engineering. Full article

Near-fault ground motions exhibit significant characteristics such as velocity pulses, rupture directivity, and strong vertical components, which pose serious threats to structural safety. However, near-fault ground motion records remain scarce and have not been adequately accounted for in current seismic design codes. This paper proposes a data-driven simulation method for non-stationary near-fault ground motions based on Stationary Wavelet Transform (SWT) combined with Hilbert’s instantaneous frequency estimation. First, to address the baseline drift issue commonly observed in measured seismic motions, a baseline correction technique combining the least squares method and the Iwan method is proposed to enhance the reliability of seismic time histories. Subsequently, statistical distributions of velocity pulses and vertical-to-horizontal (V/H) acceleration ratios, along with their relationships with fault distance and magnitude, are analyzed based on more than 900 ground motion records. The results show that these near-fault motions generally contain pronounced long-period components, which will have significant implications for the seismic response of long-period structures. Additionally, unidirectional pulses dominate in near-fault records. Among the 107 selected long-period pulse records, unidirectional pulses account for 69.2%. Based on this, seismic motions are decomposed using SWT, and stochastic reconstruction is performed, combined with multivariate response spectrum matching to optimize the generation of near-fault time histories consistent with the target spectrum. Compared with the results obtained without optimization, the proposed method reduces the mean square error by about 40% or more, demonstrating a clear improvement in accuracy and reliability. This method provides reliable seismic input support for seismic analysis and performance-based design of bridges in near-fault regions. Full article