Search Results (303)

Background/Objective: The H-HT represents a critical vulnerability for patient–family caregiver dyads. This study developed and content-validated a middle-range nursing theory, the Adaptarte Model, designed to guide dyadic adaptation during the H-HT within the Latin American healthcare context. Methods: A sequential exploratory multimethod design was executed in two phases. Phase 1 integrated three evidence streams: clinical practice insights, a JBI-guided scoping review, and two focus groups with transitional care professionals. Qualitative content analysis and iterative consensus refined the model’s core concepts, assumptions, and propositions. Phase 2 evaluated the model’s content, structure, functionality, and projection using an international panel of eleven Latin American experts meeting strict eligibility criteria. Data were analyzed using Lawshe’s Content Validity Ratio (CVR) modified by Tristán (cutoff = 0.58) and the overall Content Validity Index (CVI). Reporting followed PRISMA-ScR and GRAMMS guidelines. Results: Expert consensus confirmed the essential model components. Item-level CVR values ranged from 0.90 to 0.99, yielding an overall CVI of 0.96, while external functionality and conceptual projection achieved an average rating of 0.88. Conclusions: The Adaptarte Model demonstrates high content validity and structural clarity, establishing a rigorous theoretical foundation for subsequent empirical research. Rather than being ready for immediate clinical implementation, it provides a structured blueprint for prospective protocol development. Systematic empirical testing and longitudinal studies are now imperative to evaluate its clinical utility and drive future healthcare transformations. The scoping review protocol was prospectively registered on the Open Science Framework (OSF) URL (accessed on 23 September 2024). Full article

Accurate discrimination among static eccentricity fault (SEFs), dynamic eccentricity fault (DEFs), and mixed eccentricity fault (MEFs) in permanent-magnet-synchronous motors remains difficult because these conditions produce similar stator-current patterns. This paper proposes an STFT–CNN–LSTM-based Eccentricity Fault Diagnosis System (STFT-CL-EFDS) for classifying Normal, SEF, DEF, and MEF conditions using only three-phase stator currents. Six signal transformations were initially compared, after which the three leading representations—STFT, DWT, and CWT—were evaluated with seven neural-network architectures. Sensitivity and ablation analyses selected a 500-sample observation window and a compact log-magnitude STFT representation over the nominal 0–1 kHz band. Following full-schedule retraining, the proposed model achieved 99.57% accuracy and a 99.57% weighted F1-score, with recalls of 100.00%, 98.66%, 100.00%, and 99.53% for Normal, SEF, DEF, and MEF, respectively. It exceeded the strongest machine-learning benchmark, STFT–MLP, by 8.42 percentage points in accuracy and 8.52 percentage points in weighted F1-score. On an independent unseen test bench, the proposed model provided the most balanced response across the three fault types and ultimately converged to the correct class in every case, although temporary DEF–MEF confusion remained. These results demonstrate the effectiveness of STFT-CL-EFDS for current-only multiclass PMSM eccentricity diagnosis. The main contributions of this study are as follows: (1) a systematic comparison of six signal-transformation methods, namely Fast Fourier Transform (FFT), STFT, Discrete Wavelet Transform (DWT), Continuous Wavelet Transform (CWT), Hilbert–Huang Transform (HHT), and Variational Mode Decomposition (VMD), to determine their suitability for eccentricity fault diagnosis; (2) a comparative evaluation of seven neural-network architectures, including CNN, LSTM, CNN–LSTM, DNN, TCN, ModernTCN, and TimesNet, using the three best-performing transformation methods, namely STFT, CWT, and DWT; and (3) the development of a unified current-only STFT-CL-EFDS that combines STFT-based time–frequency representation with convolutional feature extraction and temporal-sequence learning for the classification of Normal, SEF, DEF, and MEF conditions. Full article

Multiphase swirling flows in confined spaces induce highly destructive, nonlinear fluid–structure interaction (FSI) vibrations. Understanding the underlying physical mechanisms is critical for ensuring the safety of industrial operations. This study proposes a mesoscopic multiscale framework coupling the Multi-Relaxation Time Lattice Boltzmann Method with Large Eddy Simulation (MRT-LBM-LES) and the Flügge thin-walled cylindrical shell equations to analyze two-way FSI responses. Variational Mode Decomposition (VMD) and the Hilbert–Huang Transform (HHT) are employed to decouple non-stationary broadband excitation signals. The macroscopic topological evolution of the swirling air core—from initial depression to critical breakthrough—is accurately captured. Dynamic mapping reveals a strict time-domain phase-locking mechanism between macroscopic flow instability and microscopic high-frequency structural excitation caused by cavitation bubble collapse. Furthermore, a dimensionless cross-scale energy cascade index is defined to quantify energy transfer. Results indicate that while higher discharge flow rates delay the critical breakthrough, they trigger a delayed, high-amplitude step mutation in the energy cascade, amplifying the global cumulative excitation energy by nearly 75%. Notably, the dominant high-frequency excitation consistently converges within a narrow band of 760 Hz to 790 Hz, independent of flow rate variations. These findings provide a theoretical foundation for unsteady excitation source localization and targeted vibration reduction in complex industrial pipeline networks. Full article

Functionally graded (FG) beams have been used more and more in highly designed structures under dynamic loading due to their graded mechanical properties and excellent performance. Their transient response on complex elastic foundations is, however, not easily predicted due to the material heterogeneity, shear deformation, rotary inertia, and coupled effect of the foundation parameters. The purpose of this study is thus to propose an accurate and efficient computational model for the dynamic analysis of FG Timoshenko beams supported by biparametric Vlasov foundations under harmonic excitation. The formulation takes into account the space-varying material properties, Timoshenko shear deformation, rotary inertia, and coupled Winkler–shear interaction of the Vlasov foundation. The governing equations are numerically solved in space with the high-order spectral element method (SEM) and in time with the Hilber–Hughes–Taylor (HHT) scheme. The resulting framework is used to study the transient displacement and vibration response with respect to the excitation frequency, material gradation index, and stiffness and damping properties of the foundation. The numerical results prove that the results converge quickly in space and time and also indicate that the dynamic response is significantly affected by the interaction between the gradation of material and the parameters of the foundation. The displacement amplitude, resonance behavior, and vibration characteristics are significantly altered by any variations in the gradation index and foundation characteristics. The results obtained with the proposed formulation are in good agreement with those available from the benchmark solutions, thus validating the correctness and reliability of the formulation. The SEM–HHT methodology offers a reliable, precise, and low-computational-cost solution for transient analysis of FG Timoshenko beams on biparametric Vlasov foundations under harmonic excitation. The proposed framework offers a powerful predictive tool for vibration analysis, response control, and design of advanced FG beam systems that can be applied in aerospace, marine, smart infrastructure, and other high-performance engineering structures. Full article

Secure IoT and smart grid networks depend on reliable hardware operation to maintain continuous service and system availability. Physical-layer abnormalities such as partial discharge (PD) can weaken infrastructure components and disrupt connected systems before conventional monitoring methods detect the problem. PD is one of the earliest indicators of abnormal hardware activity in electrical infrastructure. If it is not detected in time, it can damage equipment, reduce system reliability, and increase the risk of service interruption in intelligent network environments. Existing detection methods often struggle with PD signals because these signals are non-stationary, vary over time, and frequently contain noise. This limits reliable physical-layer threat detection in secure IoT and smart grid networks. This study presents an integrated physical-layer threat-detection framework for secure IoT and smart grid networks that combines adaptive HHT-based signal decomposition with multimodal deep learning for early hardware threat identification. The framework first applies the Hilbert–Huang Transform (HHT) to decompose PD signals and extract time–frequency features that describe discharge behavior. A convolutional neural network with an attention-based fusion mechanism then learns patterns from electrical and acoustic signals. The model classifies hardware condition into normal operation, early abnormal activity, and critical discharge states associated with potential hardware threats. The framework is evaluated using two public datasets: the Dataset of Partial Discharge and Noise Signals and the Partial Discharge Localization (PD-Loc) dataset available through the IEEE DataPort. Experimental evaluation shows that the proposed framework achieves 97.8% detection accuracy, a 97.0% F1-score, and an average AUC of 0.98. The framework maintains 94.6% accuracy under severe noise conditions (10 dB SNR) and performs inference in approximately 12 ms per sample. Furthermore, component-wise analysis further shows that HHT-based feature extraction improves detection accuracy from 91.8% to 95.6%, while multimodal learning increases the final accuracy to 97.8%. Full article

Climate change introduces severe non-stationarity and high-frequency noise into hydro-meteorological data. This noise degrades the predictive accuracy of traditional data-driven streamflow models. We propose a physics-inspired data enhancement framework coupling the CEEMDAN-based Hilbert-Huang Transform (HHT) with Stochastic Resonance (SR). We applied this framework to the Lanzhou section of the upper Yellow River. HHT isolates the dominant characteristic frequency of the basin’s streamflow system at 0.0026 cycles/day. Using this frequency as a target, we constructed a Bayesian-optimized SR system. The system converts the energy of high-frequency meteorological noise into low-frequency periodic components, facilitating frequency alignment between the meteorological inputs and the hydrological response. We evaluated the SR-enhanced meteorological inputs across three machine learning architectures: Random Forest, XGBoost, and LSTM. All algorithms demonstrated an improved performance. The SR-LSTM model achieved a Nash-Sutcliffe Efficiency (NSE) of 0.91 ± 0.03. This represents a 19% improvement over the baseline LSTM score of 0.79 ± 0.02. The SR-LSTM demonstrated robust accuracy during extreme hydrological events; it achieved a high-flow NSE of 0.89 and effectively mitigated the common peak-underestimation issue by constraining relative peak magnitude errors to approximately −5.08%. Overall, this study presents a practical data enhancement approach for streamflow forecasting under complex climatic conditions. Full article

The blasting-induced environmental impact of tunneling is a major concern in drill and blast excavation practice, particularly in urban areas. The present paper carries out comprehensive numerical modeling to study the vibration attenuation at the soil surface away from the blasting source as well as the resulting interactions between a historical structure and the surrounding soil, with particular attention to the effects of a millisecond delay. Special attention is given to the interpretation of the role of the local site effects in terms of the frequency-dependent changes of the vibration attenuation mechanism and the response of the historical structure. The velocity responses along the ground surface generally exhibit higher-frequency suppression and low-frequency amplification for both instantaneous blasting and millisecond delay blasting cases in the layered soil–rock site. The millisecond delay blasting can effectively avoid excessive vibration velocity and thus reduce the vibration amplitude at the ground surface by 60–70% (compared with instantaneous blasting), with the predominant frequency mainly concentrated in the high frequence band of 400–500 Hz. The empirical formulae for predicting the vibration attenuation along the scale distance in a soil–rock site has been proposed for both instantaneous blasting and millisecond delay blasting. Through the HHT spectral analyses of the velocity response of the historical structure, it is seen that the difference of structure properties between the wood-frame tower and the base masonry structure has a remarkable influence on the structural vibration. The numerical results can provide a reliable reference for the practical blasting scheme and the systematic study of the dynamic responses of historical structures subjected to blasting-induced vibrations. Full article

Electrocardiogram (ECG) arrhythmia classification remains critical for automated cardiac diagnosis, yet feature extraction methods are frequently adopted without systematic comparative evaluation. This study presents a controlled comparative analysis of four signal processing techniques—Mel-Frequency Cepstral Coefficients (MFCC), Discrete Wavelet Transform (DWT), Hilbert–Huang Transform (HHT), and Synchrosqueezing Wavelet Transform (SSWT)—for ECG feature extraction. Using the MIT-BIH Arrhythmia Database with ANSI/AAMI EC57:1998 standard mapping, we trained Cascade Forest classifiers on each feature set under identical preprocessing and SMOTE-based class balancing conditions to ensure a fair comparison. DWT features achieved superior performance (accuracy: 98.79%, macro-F1: 92.93%, precision: 94.39%) compared to MFCC (88.30% macro-F1), SSWT (84.54% macro-F1), and HHT (83.59% macro-F1), particularly for clinically challenging minority arrhythmia classes. However, DWT’s performance advantage incurred substantial computational cost (10,050 s), while MFCC provided competitive results with a 62% lower computational burden. These findings provide evidence-based guidance for feature extraction method selection in interpretable ECG classification systems, demonstrating critical performance-efficiency trade-offs relevant to clinical deployment contexts. Full article

Gray-Box (GB) models are being considered for risk monitoring of Small Modular Reactors (SMRs). Their effectiveness is linked to the proper selection of the model parameters. This paper proposes a systematic methodology for identifying the most influential parameters of a GB model for estimating safety-critical variables of an SMR during normal operation and accident scenarios. The GB integrates a reduced-order physics-based model (White-Box, WB) with a data-driven (Black-Box, BB) model that corrects the outputs of the WB using the condition-monitoring data collected by sensors positioned onto the SMR. The proposed method combines signal decomposition, specifically the Hilbert–Huang Transform (HHT), and global sensitivity analysis (SA), based on first-order Kucherenko indices, to quantify the contribution of non-stationary, correlated GB input parameters to the variability of the safety-critical output parameters of interest. The proposed approach is applied to the Small Modular Dual Fluid Reactor (SMDFR), and the obtained results demonstrate its effectiveness in identifying informative and physically interpretable features, reducing complexity and computational burden to enable real-time risk monitoring. Full article

The operational reliability of gas-insulated switchgear/gas-insulated transmission lines (GIS/GIL) is critically threatened by internal metallic particles, which serve as primary triggers for insulation degradation. Conventional partial discharge (PD) detection methods often lack sensitivity during the early stages of particle movement. To overcome these limitations, this study aims to develop a novel non-intrusive defect diagnosis methodology based on the analysis of mechanical vibration signals. The coupled particle motion model integrating the electrostatic field, particle tracking, and multibody dynamics has been established. This model reveals the dynamic law that metallic particles migrate toward the conductor and undergo charge polarity reversal after collision, with a maximum speed of 2.7 m/s. Meanwhile, the peak vibration acceleration excited by the collision is calculated as 0.02 m/s². Accordingly, the high-voltage experimental platform with the full-scale prototype is built to simulate the actual operating conditions of the power grid. With the particle defects set inside the prototype, vibration signals are collected by using an accelerometer, and the measured peak vibration acceleration is 0.017 m/s². Finally, a defect diagnosis method based on the Hilbert–Huang Transform (HHT) and correlation coefficient analysis is proposed. This method uses Empirical Mode Decomposition (EMD) to extract the IMF4 component of the signal in the vicinity of the 1000 Hz frequency band. When particle defects occur, the correlation coefficient between the IMF4 component and the original signal exceeds 0.7668. This vibration-based monitoring technique provides an alternative for the condition-based maintenance of GIS/GIL, offering significant engineering value for enhancing the safety and reliability of power transmission infrastructure. Full article

Background/Objectives: Environmental conditions in natural and cultivation-managed habitats strongly influence plant physiology and medicinal quality. However, the molecular mechanisms underlying metabolic differentiation in Anoectochilus roxburghii remain poorly understood. This study aimed to elucidate the metabolic and transcriptional differences between wild and cultivated A. roxburghii and to identify the regulatory mechanisms driving habitat-associated variation in metabolite profiles. Methods: We applied integrated non-targeted metabolomics and transcriptomics to compare metabolic profiles and gene expression in the leaves and stems of 15-month-old wild and cultivated A. roxburghii plants. Gene–metabolite correlation analysis was performed to identify coordinated correlation networks associated with key biosynthetic pathways. Results: Our analyses revealed clear differences in metabolite composition and transcriptional patterns between habitat types, suggesting distinct strategies of metabolic resource allocation. Wild plants showed significant enrichment of amino acids and other primary metabolites, whereas cultivated plants accumulated higher levels of flavonoids. Gene–metabolite correlation analysis indicated that multiple flavonoid metabolites were closely associated with key structural genes, including F3H, C12RT1, and HHT1, forming a tightly connected correlation network. In addition, several transcription factor families, including MYB, bHLH, WRKY, and AP2/ERF, showed strong correlations with genes involved in the flavonoid pathway, suggesting that flavonoid accumulation in cultivated plants may be associated with coordinated transcriptional control. Conclusions: Taken together, these findings suggest that habitat conditions are associated with differences in metabolic networks and resource allocation in A. roxburghii. This work provides new insight into the metabolic plasticity of this medicinal plant and highlights potential factors associated with molecular mechanisms that may contribute to variation in medicinal quality. Full article

In the deep and narrow valleys of southwestern China, free-surface spillways are widely adopted as auxiliary flood-discharge structures in water conservancy projects. Owing to the high water head upstream, tunnels are often plagued by problems including excessive velocity, cavitation damage, and insufficient downstream energy dissipation. Previous studies have demonstrated that the installation of novel lateral deflectors in tunnels can effectively regulate local flow patterns while providing additional energy dissipation capacity. In this study, physical model experiments combined with numerical simulations were employed to further compare the energy dissipation characteristics of lateral deflectors. The turbulent characteristics, the energy dissipation process, and the evolution of vortex structures were systematically analyzed based on turbulent kinetic energy, turbulence dissipation rate, fluctuating pressure coefficient, and Hilbert–Huang transform (HHT) spectral analysis. The results show that the novel lateral deflector significantly enhances local turbulence intensity and turbulent kinetic energy, promoting the conversion of mean kinetic energy into turbulent kinetic energy and its rapid dissipation within a shorter distance. Spectral energy reaches its peak in the jet impingement region, accompanied by a marked increase in high-frequency components, indicating an intensified energy transfer from large-scale vortices to small-scale vortices. These findings suggest that the novel deflector can serve as an effective internal energy dissipator in free-surface tunnels with shorter turbulent region and more local turbulence. Full article

To explore the spectral characteristics of near-field vibration signals from multi-hole millisecond-delay blasting in open-pit mines and the modulation effect of delay time on blasting energy distribution, field blasting vibration tests with multi-gradient delays were conducted taking an open-pit coal mine in Xinjiang as the engineering background. Particle Swarm Optimization (PSO) optimized Variational Mode Decomposition (VMD) and Hilbert-Huang Transform (HHT) were introduced for the refined processing and frequency band energy ratio analysis of the measured signals, and field vibration control tests were subsequently carried out. The results show that compared with the traditional Empirical Mode Decomposition (EMD), the PSO-optimized VMD can effectively overcome the mode aliasing phenomenon. By extracting the high-frequency Intrinsic Mode Function (IMF7) that characterizes the instantaneous detonation impulse, the actual delay time was successfully inverted to be 10.47 ms. The inter-hole delay time significantly affects the time-frequency distribution of vibration energy. Under the 25 ms delay condition, the energy ratio of the high-frequency band is the highest, and the low-frequency energy accumulation degree is the lowest, which is most conducive to shortening the vibration duration and accelerating energy attenuation. Control tests further confirmed that adopting a 17 ms delay in the near-slope area can effectively control the peak particle velocity (PPV) in the near field, while adopting a 23 ms delay in the middle and far areas can further reduce the low-frequency energy concentration. The research results demonstrate a dynamic matching strategy for millisecond delays based on spatial distance differences, which has important guiding significance for realizing safe and efficient blasting vibration control in open-pit mines. Full article

This study investigates the effectiveness of various feature extraction methods applied to vibration signals for the automatic detection of production defects in CNC (Computerised Numerical Control) milling machines. A dataset consisting of real-world data collected from CNC machines equipped with accelerometers was used. The objective of the study is to compare three main groups of techniques: time-domain analysis (TDA), frequency-domain analysis (FDA), and time–frequency-domain analysis (TFA). The findings indicate that basic TDA features lack the necessary sensitivity to accurately distinguish between Good Processing (GP) and Bad Processing (BP) states. Frequency-domain methods, such as the Fast Fourier Transform (FFT), median frequency calculation, and the Welch periodogram, provide better insights but still have limitations. The most effective results are obtained with TFA methods, particularly Empirical Mode Decomposition (EMD) and the Hilbert–Huang Transform (HHT), which reveal deeper signal characteristics. Following the feature optimisation studies, it was determined that a combination of four features—FMED, IMF2, IMF5 and WPT26—yielded the optimal performance, with an accuracy of 91.48%. The incorporation of a fifth feature resulted in information saturation within the model and did not improve performance. This study makes a novel contribution to literature by conducting an in-depth investigation into the most effective feature extraction and selection techniques for achieving robust discrimination between GP and BP productions using vibration signals in CNC milling processes. Conclusively, TFA features, supported by advanced signal processing, offer a strong basis for reliable, automated defect detection in CNC milling operations. Full article

Background/Objectives: Dementia is a progressive neurodegenerative disorder for which accurate and timely diagnosis remains a major clinical challenge. Electroencephalography (EEG) offers a noninvasive and cost-effective means of capturing neurophysiological alterations, motivating the development of reliable EEG-based automated diagnostic frameworks. This study aims to systematically examine how different time–frequency representations (TFRs) affect dementia classification performance within a unified multi-channel EEG image fusion framework. Methods: Resting-state, eyes-closed EEG recordings from 88 subjects, including Alzheimer’s disease, frontotemporal dementia, and cognitively normal controls, were preprocessed and segmented. Channel-wise signals were converted into two-dimensional time–frequency images using Short-Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT), Hilbert–Huang Transform (HHT), Wigner–Ville Distribution (WVD), or Constant-Q Transform (CQT). Images from 19 EEG channels were fused into a structured grid and classified using pretrained convolutional neural networks, including MobileNetV2, ResNet-50, and InceptionV3. Results: Results indicate that classification performance is highly dependent on the chosen TFR. The STFT-based representation combined with InceptionV3 achieved the highest accuracy, reaching 98.8% with random splitting and 84.3% with subject-wise splitting, outperforming previous studies. CQT also showed competitive performance, whereas HHT and WVD were less effective. Gradient-weighted class activation mapping provided interpretable visualization of physiologically relevant EEG channel contributions. Conclusions: The proposed framework demonstrates the importance of structured multi-channel fusion and systematic TFR evaluation for robust and interpretable EEG-based dementia classification and serves as a foundation for future cross-dataset validation. Full article