Search Results (465)

This study proposes a joint denoising method based on intelligent optimization variational mode decomposition (VMD) and normalized least mean square error (NLMS). Experiments show that this method has good adaptability to non-stationary weak signals (such as medical ultrasonic Doppler signals), effectively separating signal components through VMD’s multi-scale decomposition and combining with NLMS’s adaptive filtering mechanism to suppress local noise. However, in scenarios with strong transient interference (such as mechanical vibration noise), the deviation in modal number selection of VMD leads to a decrease in decomposition efficiency; under low sampling rate conditions (<20 kHz), the steady-state convergence speed of NLMS is reduced by approximately 35%. Therefore, the universality of this method in complex noise environments requires further verification. This study provides a new theoretical perspective for non-stationary signal processing, but parameter optimization needs to be combined with specific noise characteristics in practical engineering applications. Full article

Background and Objectives: Nasopharyngeal carcinoma (NPC) displays marked geographic and histopathological heterogeneity, and prognostic determinants in non-endemic regions remain incompletely defined. This study aimed to evaluate the impact of clinicopathological characteristics and treatment modalities on survival outcomes among patients with stage II–IVA NPC treated with curative intent at a single tertiary cancer center. Materials and Methods: A retrospective analysis was conducted on 81 consecutive patients with histologically confirmed NPC treated between 2000 and 2022. Demographic, clinical, and treatment parameters were extracted from institutional records. Survival outcomes—including disease-free survival (DFS), locoregional recurrence-free survival (LRFS), distant metastasis-free survival (DMFS), cancer-specific survival (CSS), and overall survival (OS)—were estimated using the Kaplan–Meier method and compared using the log-rank test. Prognostic variables identified in univariate analysis were further assessed by multivariable Cox proportional hazards regression (Cox’s model). Results: The cohort included 59 men (72.8%) and 22 women (27.2%), with a median age of 50.8 years (range, 19–78). Most patients presented with locally advanced disease (T3–T4, 53.1%; N2, 60.5%; stage III–IVA, 87.7%). Non-keratinizing undifferentiated carcinoma (World Health Organization [WHO] type III) was the predominant histology (71.6%), followed by the non-keratinizing differentiated subtype (17.3%). Median DFS and OS were 94.6 and 139.4 months, respectively. According to the univariate analysis, histological subtypes and a family history of cancer were significantly associated with DFS, whereas comorbid systemic disease showed an unexpected association with longer DMFS. The multivariable Cox model identified the histological subtype as an independent predictor of disease recurrence (HR = 2.23, 95% CI: 1.00–4.94; p = 0.049). For OS, both histological subtype (HR = 2.40, 95% CI: 1.10–5.25; p = 0.029) and age at diagnosis (HR = 1.05, 95% CI: 1.02–1.09; p = 0.005) were independent adverse prognostic factors. Conclusions: In this long-term, single-center study from a non-endemic region, histological subtype emerged as the most powerful determinant of prognosis, significantly influencing both DFS and OS. Patients with non-keratinizing undifferentiated (WHO type III) carcinoma demonstrated superior outcomes compared with those with differentiated histology. Additionally, increasing age at diagnosis was independently associated with poorer OS. In contrast, inflammatory and nutritional biomarkers, the Pan-Immune–Inflammation Value (PIV) and the Prognostic Nutritional Index (PNI), showed no prognostic significance. These findings underscore the continued prognostic relevance of histopathologic classification and age and highlight the need for large-scale, standardized studies integrating Epstein–Barr virus (EBV) status and host-related factors in non-endemic NPC populations. Full article

The exponential growth of candidate data from large-scale radio pulsar surveys has created a pressing need for efficient and accurate classification methods. This paper presents a novel hybrid pulsar candidate recognition algorithm that integrates diagnostic plot images and structured numerical features using a multi-scale DenseNet framework. The proposed model combines convolutional neural networks (CNNs) for extracting spatial patterns from pulsar diagnostic plots and feedforward neural networks (FNNs) for processing scalar features such as SNR, DM, and pulse width. By fusing these multimodal representations, the model achieves superior classification performance, particularly in class-imbalanced settings standard to pulsar survey data. Evaluated on a synthesized dataset constructed from FAST and HTRU survey characteristics, the model demonstrates robust performance, achieving an F1-score of 0.904 and AUC-ROC of 0.978. Extensive ablation and cross-validation analyses confirm the contribution of each data modality and the model’s generalizability. Furthermore, the system maintains low inference latency (4.2 ms per candidate) and a compact architecture (~2.3 million parameters), indicating potential for real-time deployment once validated on real observational datasets. The proposed approach offers a scalable and interpretable multimodal framework for automated pulsar classification and provides a foundation for future validation and potential integration into observatories such as FAST and the Square Kilometre Array (SKA). Full article

The autonomous operation of unmanned gyroplanes is constrained by the limited fidelity of aerodynamic models and control challenges posed by unique flight characteristics. To address these issues, a comprehensive methodology for unmanned gyroplane modeling and autonomous flight control is proposed. High-fidelity aerodynamic models were developed through a modified parameter identification structure, and the longitudinal and lateral modal characteristics of the prototype gyroplane were subsequently analyzed. Targeting the control coupling, delayed pitch response, and throttle-airspeed nonlinearities, a novel autonomous flight control strategy is proposed for unmanned gyroplanes. Precise energy management and longitudinal-lateral decoupling were achieved through feedforward trim compensation, pitch-damping augmentation, and coordinated allocation of throttle and rotor tilt. Comparative analysis verified the high accuracy of the identified aerodynamic models, with the coefficient of determination between measured and simulated attitude responses exceeding 0.92. Furthermore, flight tests were conducted on an unmanned gyroplane prototype, including climb and descent maneuvers, climb to level flight transitions, and turning trajectory tracking. The results show that the proposed autonomous control strategy achieves precise tracking of altitude, airspeed, and trajectory, with airspeed errors remaining within 1.5 m/s. Full article

Background/Objectives: Quantitative computed tomography (CT) metrics are widely used to assess pulmonary involvement and to predict short-term severity in coronavirus disease 2019 (COVID-19). However, it remains unclear whether baseline artificial intelligence (AI)-based quantitative high-resolution computed tomography (HRCT) metrics of pneumonia burden provide incremental prognostic value for in-hospital composite adverse outcomes beyond routine clinical factors, or whether these imaging-derived markers carry any exploratory signal for long-term severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reinfection among hospitalized patients. Most existing imaging studies have focused on diagnosis and acute-phase prognosis, leaving a specific knowledge gap regarding AI-based quantitative HRCT correlates of early deterioration and subsequent reinfection in this population. To evaluate whether combining deep learning-derived, quantitative, HRCT features and clinical factors improve prediction of in-hospital composite adverse events and to explore their association with long-term reinfection in patients with COVID-19 pneumonia. Methods: In this single-center retrospective study, we analyzed 236 reverse-transcription polymerase chain reaction (RT-PCR)-confirmed COVID-19 patients who underwent baseline HRCT. Median follow-up durations were 7.65 days for in-hospital outcomes and 611 days for long-term outcomes. A pre-trained, adaptive, artificial-intelligence-based, prototype model (Siemens Healthineers) was used for pneumonia analysis. Inflammatory lung lesions were automatically segmented, and multiple quantitative metrics were extracted, including opacity score, volume and percentage of opacities and high-attenuation opacities, and mean Hounsfield units (HU) of the total lung and opacity. Patients were stratified based on receiver operating characteristic (ROC)-derived optimal thresholds, and multivariable Cox regression was used to identify predictors of the composite adverse outcome (intensive care unit [ICU] admission or all-cause death) and SARS-CoV-2 reinfection, defined as a second RT-PCR-confirmed episode of COVID-19 occurring ≥90 days after initial infection. Results: The composite adverse outcome occurred in 38 of 236 patients (16.1%). Higher AI-derived opacity burden was significantly associated with poorer outcomes; for example, opacity score cut-off of 5.5 yielded an area under the ROC curve (AUC) of 0.71 (95% confidence interval [CI] 0.62–0.79), and similar performance was observed for the volume and percentage of opacities and high-attenuation opacities (AUCs up to 0.71; all p < 0.05). After adjustment for age and comorbidities, selected HRCT metrics—including opacity score, percentage of opacities, and mean HU of the total lung (cut-off −662.38 HU; AUC 0.64, 95% CI 0.54–0.74)—remained independently associated with adverse events. Individual predictors demonstrated modest discriminatory ability, with C-indices of 0.59 for age, 0.57 for chronic obstructive pulmonary disease (COPD), 0.62 for opacity score, 0.63 for percentage of opacities, and 0.63 for mean total-lung HU, whereas a combined model integrating clinical and imaging variables improved prediction performance (C-index = 0.68, 95% CI: 0.57–0.80). During long-term follow-up, RT-PCR–confirmed reinfection occurred in 18 of 193 patients (9.3%). Higher baseline CT-derived metrics—particularly opacity score and both volume and percentage of high-attenuation opacities (percentage cut-off = 4.94%, AUC 0.69, 95% CI 0.60–0.79)—showed exploratory associations with SARS-CoV-2 reinfection. However, this analysis was constrained by the very small number of events (n = 18) and wide confidence intervals, indicating substantial statistical uncertainty. In this context, individual predictors again showed only modest C-indices (e.g., 0.62 for procalcitonin [PCT], 0.66 for opacity score, 0.66 for the volume and 0.64 for the percentage of high-attenuation opacities), whereas the combined model achieved an apparent C-index of 0.73 (95% CI 0.64–0.83), suggesting moderate discrimination in this underpowered exploratory reinfection sample that requires confirmation in external cohorts. Conclusions: Fully automated, deep learning-derived, quantitative HRCT parameters provide useful prognostic information for early in-hospital deterioration beyond routine clinical factors and offer preliminary, hypothesis-generating insights into long-term reinfection risk. The reinfection-related findings, however, require external validation and should be interpreted with caution given the small number of events and limited precision. In both settings, combining AI-based imaging and clinical variables yields better risk stratification than either modality alone. Full article

Offshore wind turbine support structures are in harsh and unsteady marine environments, and their dynamic characteristics could change gradually after long-term service. To better understand the status and improve remaining life estimation, it is essential to conduct in situ measurement and update the numerical models of these support structures. In this paper, the modal properties of a 5.5 MW offshore wind turbine were first identified by a widely used operational modal analysis technique, frequency-domain decomposition, given the acceleration data obtained from eight sensors located at four different heights on the tower. Then, a finite element model was created in MATLAB R2020a and a set of model parameters including scour depth, foundation stiffness, hydrodynamic added mass and damping coefficients was updated in a Bayesian inference frame. It is found that the posterior distributions of most parameters significantly differ from their prior distributions, except for the hydrodynamic added mass coefficient. The predicted natural frequencies and damping ratios with the updated parameters are close to those values identified with errors less than 2%. But relatively large differences are found when comparing some of the predicted and identified mode shape coefficients. Specifically, it is found that different combinations of the scour depth and foundation stiffness coefficient can reach very similar modal property predictions, meaning that model updating results are not unique. This research demonstrates that the Bayesian inference framework is effective in constructing a more accurate model, even when confronting the inherent challenge of non-unique parameter identifiability, as encountered with scour depth and foundation stiffness. Full article

To address issues such as attitude instability, insufficient adaptability, and poor operational quality of precision operation equipment caused by complex terrain conditions in hilly orchards, this study designed an electric carrier Self-Leveling Platform based on the 3-RRS parallel configuration. Focusing on the stability requirements of the operation plane, an automatic leveling control strategy was proposed with the constant center height of the moving platform as an additional constraint condition. Based on the inverse kinematics solution of the 3-RRS Parallel Mechanism, the analytical mapping relationship between the fuselage attitude and the compensation angle of the leveling leg crank was derived, and based on this, the working space of the Self-Leveling Platform and the maximum compensation angles of the moving platform in the pitch and roll directions were calculated. Key structural parameters were optimized using a multi-objective genetic algorithm, followed by the completion of a 3D model design and modal simulation analysis to verify the effectiveness of the structural design. Finally, leveling performance tests were conducted on a prototype. The results showed that the platform can achieve omnidirectional automatic leveling, with a maximum leveling time of 1.593 s and a maximum steady-state error of 0.62° under typical slope and load conditions. Analysis of variance results further indicated that there are significant differences in the leveling performance of the 3-RRS parallel configuration of the Self-Leveling Platform in the pitch and roll directions, demonstrating anisotropic characteristics. This study provides an effective solution for attitude stability control of orchard operation equipment in hilly areas and offers theoretical reference and technical support for the application of the 3-RRS parallel configuration in the agricultural equipment field. Full article

With the large-scale integration of wind power into the grid, the accuracy of wind farm cluster power prediction has become a key factor for the sustainability of modern power systems. Reliable ultra-short-term forecasts support the secure dispatch of high-penetration renewable energy, reduce wind curtailment, and improve the low-carbon and economical operation of power systems. Aiming at the problem of significant differences in wind turbine characteristics, this paper proposes a prediction method based on an improved density-based spatial clustering of applications with noise (DBSCAN) and a hybrid deep learning model. First, the wind speed signal is decomposed at multiple scales using successive variational modal decomposition (SVMD) to reduce non-stationarity. Subsequently, the DBSCAN parameters are optimized by the fruit fly optimization algorithm (FOA), and dimensionality reduction is performed by principal component analysis (PCA) to achieve efficient clustering of wind turbines. Next, the representative turbines with the highest correlation are selected in each cluster to reduce computational complexity. Finally, the SVMD-TCN-BiGRU-MSA-GJO hybrid model is constructed, and long-term dependence is extracted using a temporal convolutional network (TCN); the temporal features are captured by bidirectional gated recurrent units (BiGRUs); the feature weights are optimized by a multi-head self-attention mechanism (MSA), and the hyper-parameters are, in turn, optimized by golden jackal optimization (GJO). The experimental results show that this method reduces the MAE, RMSE, and MAPE by 14.02%, 12.9%, and 13.84%, respectively, and improves R² by 3.9% on average compared with the traditional model, which significantly improves prediction accuracy and stability. These improvements enable more accurate scheduling of wind power, lower reserve requirements, and enhanced stability and sustainability of power system operation under high renewable penetration. Full article

To address the challenges in identifying effective fault features and achieving sufficient diagnostic accuracy and robustness in variable-speed printing press bearings, where complex mixed-condition vibration signals exhibit non-stationarity, strong nonlinearity, ambiguous time-frequency characteristics, and overlapping fault features across multiple operating conditions, this paper proposes an adaptive optimization signal decomposition method combined with dual-modal time-series and image deep feature fusion for variable-speed multi-condition bearing fault diagnosis. First, to overcome the strong parameter dependency and significant noise interference of traditional adaptive decomposition algorithms, the Crested Porcupine Optimization Algorithm is introduced to adaptively search for the optimal noise amplitude and integration count of ICEEMDAN for effective signal decomposition. IMF components are then screened and reorganized based on correlation coefficients and variance contribution rates to enhance fault-sensitive information. Second, multidimensional time-domain features are extracted in parallel to construct time-frequency images, forming time-sequence-image bimodal inputs that enhance fault representation across different dimensions. Finally, a dual-branch deep learning model is developed: the time-sequence branch employs gated recurrent units to capture feature evolution trends, while the image branch utilizes SE-ResNet18 with embedded channel attention mechanisms to extract deep spatial features. Multimodal feature fusion enables classification recognition. Validation using a bearing self-diagnosis dataset from variable-speed hybrid operation and the publicly available Ottawa variable-speed bearing dataset demonstrates that this method achieves high-accuracy fault identification and strong generalization capabilities across diverse variable-speed hybrid operating conditions. Full article

Pumped storage power stations provide essential benefits to power grids by cutting peak loads, filling valleys, and boosting renewable energy integration rates. They serve as the foundation for developing a new power system based on renewable energy. Pump turbines are currently evolving to provide higher heads, larger capacities, and higher rotating speeds. The performance and dependability of its basic components have a direct impact on the safety and stability of unit operation. Based on this, this research looks into the modal characteristics and structural aspects of essential stationary components, such as the pump-turbine head cover. By comparing the mechanical performance of two different structural designs (Design A and Design B), Design B features an overall thickness 1.5 times that of Design A and incorporates an upper flange structure. Its design aims to enhance the component’s resistance to bending and deformation, optimize stress distribution while reducing peak stress values, and improve modal characteristics. This approach elevates the overall structural performance of the fixed components to accommodate the complex operating conditions of ultra-high-head pumped storage units. It was discovered that Design B had greater bending and deformation resistance than Design A, as well as better stress distribution and lower maximum stress values. This study further indicates that variations in structural design lead to significant differences in modal characteristics and overall structural performance. In particular, the thicknesses of the head cover’s main body and stiffening ribs are critical parameters that govern the modal behavior and structural properties of stationary components. These insights provide critical technical guidance for optimizing the design of stationary parts, such as the head cover, in pumped storage power plant units. Full article

The frame of the six-row self-propelled packaging cotton picker serves as the primary load-bearing structure. During operation, the frame is subjected to multiple vibration signals, which are further intensified by coupling effects. These vibrations negatively impact the machine’s operational stability and overall performance. In this study, vibration source tests were designed to collect dynamic response data, enabling systematic analysis of excitation mechanisms and vibration characteristics. Furthermore, a comprehensive analytical approach integrating finite element simulation with experimental analysis was employed to optimize the layout of the vibration sources on the frame. Finally, the frame was validated through modal testing, with multiple measurement points arranged at the interfaces between the frame and the vibration source for vibration tests and time–frequency domain analysis. The results indicate that the final optimized dimensional parameters of the frame were determined as follows: X1 = 1575 mm, X2 = 805 mm, and X3 = 275 mm. Furthermore, time–frequency domain analysis reveals that the natural frequency of the rack designed in this study is effectively separated from the dominant excitation frequency band. This design feature successfully mitigates the risk of resonance, thereby fulfilling the intended performance objectives. Full article

This study investigates the vibration and noise characteristics of oil-immersed power transformers, with a particular focus on the influence of transformer oil on structural dynamics and acoustic emission. The research integrates multi-physics modelling, finite-element simulation, and field measurements to analyze the vibration transmission paths from the core and windings to the tank wall. A fluid–structure interaction (FSI) model is developed to account for the damping effect of insulating oil, and a correction factor is introduced to adjust modal parameters. Simulation results reveal that oil significantly enhances vibration propagation, especially in the vertical direction, while structural ribs and clamping configurations affect local vibration intensity. Noise simulations show that magnetostriction is the dominant source of audible sound, with harmonic components sensitive to load and voltage variations. Experimental validation using a portable sound level meter confirms the simulation trends and highlights the spatial variability of acoustic pressure. The findings provide a theoretical and practical basis for optimizing sensor placement and developing voiceprint-based diagnostic tools for transformer condition monitoring. Full article

Drilling is fundamental to the assembly of aerospace structures, where millions of fastening holes must meet stringent structural and geometric requirements. Despite significant technological advances, hole quality remains sensitive to nonlinear and stochastic interactions between mechanics, thermal effects, tribology, and structural configuration. This review consolidates recent advances in intelligent drilling, focusing on how sensors and artificial intelligence (AI) are integrated to enable process understanding, prediction, and control. In-process monitoring modalities (e.g., cutting forces/torque, vibration, acoustic emission, motor current/active power, infrared thermography, and vision) are examined with respect to signal characteristics, feature design, and modelling choices for real-time anomaly detection, tool condition monitoring, and phase/interface recognition. Predictive quality modelling of burr, delamination, roughness, and roundness is discussed across statistical learning, kernel methods, and neural and hybrid models. Offline parameter optimisation via surrogate-assisted and evolutionary algorithms is considered alongside adaptive control strategies. Practical aspects of robotic drilling and multifunctional end-effectors are highlighted as enablers of embedded sensing and feedback. Finally, cross-cutting challenges (e.g., limited, heterogeneous datasets and model generalisability across materials, tools, and geometries) are outlined, together with research directions including curated multi-sensor benchmarks, multi-source transfer learning, physics-informed machine learning, and explainable AI to support trustworthy deployment in aerospace manufacturing. Full article

Accurate identification of tool wear states is crucial for ensuring machining quality and reliability. However, non-stationary signal characteristics, feature coupling, and limited use of multimodal information remain major challenges. This study proposes a hybrid framework that integrates a Sparrow Search Algorithm–optimized Continuous Wavelet Transform (SSA-CWT) with a Cross-Modal Time–Frequency Fusion Network (TFF-Net). The SSA-CWT adaptively adjusts Morlet wavelet parameters to enhance energy concentration and suppress noise, generating more discriminative time–frequency representations. TFF-Net further fuses cutting force and vibration signals through a sliding-window multi-head cross-modal attention mechanism, enabling effective multi-scale feature alignment. Experiments on the PHM2010 dataset show that the proposed model achieves classification accuracies of 100%, 98.7%, and 98.7% for initial, normal, and severe wear stages, with F1-score, recall, and precision all exceeding 98%. Ablation results confirm the contributions of SSA optimization and cross-modal fusion. External validation on the HMoTP dataset demonstrates strong generalization across different machining conditions. Overall, the proposed approach provides a reliable and robust solution for intelligent tool condition monitoring. Full article

Acute kidney injury (AKI) occurs commonly in hospitalized patients, especially patients in intensive care units (ICUs). Medications are among the major causative factors of AKI. This narrative review addressed and updated different aspects of anti-infective-associated AKI, including amphotericin B, cidofovir, foscarnet, polymyxins, vancomycin, and aminoglycosides. There is no standard definition or operational criteria to describe anti-infective-associated AKI. Characteristically, it usually occurs during the first two weeks of treatment and is typically dose dependent. Functional resolution occurs, but kidney injury can affect renal functional reserve and increase susceptibility to future AKI events. A variety of pathophysiological mechanisms impacting glomerular, tubular, and interstitial components of the kidney are usually responsible for the development of AKI from anti-infective medications. Oxidative stress and inflammation play a pivotal role in the pathogenesis of antibiotic-related AKI. Numerous patient-related, medication-related, and co-administered-related scenarios have been demonstrated as risk factors for anti-infective-induced AKI. Apart from traditional indexes of kidney function (serum creatinine and urine output), novel biomarkers of kidney function (e.g., serum cystatin C) and damage (e.g., urinary kidney-injury molecule-1 and neutrophil gelatinase-associated lipocalin) have been noticed in recent clinical studies with promising findings. The efficiency of preventive strategies against anti-infective-associated AKI in most cases appears to be variable, relative, and modest. Close and regular monitoring of kidney function parameters is crucial during treatment with nephrotoxic antibiotics. Currently, there is no definitive treatment modalities for the management of AKI with anti-infectives. Therefore, supportive care is the mainstay of treatment. Full article