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The quantitative diagnosis of rolling bearings is essential to automating maintenance decisions. Over recent years, Lempel–Ziv complexity (LZC) has been widely used for the quantitative assessment of mechanical failures as one of the most valuable indicators for detecting dynamic changes in nonlinear signals. However, LZC focuses on the binary conversion of 0–1 code, which can easily lose some effective information about the time series and cannot fully mine the fault characteristics. Additionally, the immunity of LZC to noise cannot be insured, and it is difficult to quantitatively characterize the fault signal under strong background noise. To overcome these limitations, a quantitative bearing fault diagnosis method based on the optimized Variational Modal Decomposition Lempel–Ziv complexity (VMD-LZC) was developed to fully extract the vibration characteristics and to quantitatively characterize the bearing faults under variable operating conditions. First, to compensate for the deficiency that the main parameters of the variational modal decomposition (VMD) have to be selected by human experience, a genetic algorithm (GA) is used to optimize the parameters of the VMD and adaptively determine the optimal parameters [k, α] of the bearing fault signal. Furthermore, the IMF components that contain the maximum fault information are selected for signal reconstruction based on the Kurtosis theory. The Lempel–Ziv index of the reconstructed signal is calculated and then weighted and summed to obtain the Lempel–Ziv composite index. The experimental results show that the proposed method is of high application value for the quantitative assessment and classification of bearing faults in turbine rolling bearings under various operating conditions such as mild and severe crack faults and variable loads.

Angular contact ball bearings are common basic components in rotating machinery. During the operation of the bearing, the rolling slips, resulting in contact sliding friction between it and the raceway, which in turn causes wear in the rolling element and increase in the radial clearance of the bearing. The increase in clearance also affects the stiffness of the bearing, which in turn affects the natural frequency and fatigue life of the bearing. At present, there are few studies on the influence of bearing wear (variation of clearance) on life. In this paper, the finite element model based on the theory of contact mechanics is established for the angular contact ball bearing with medium- and high-speed rotation, and the mechanical properties and fatigue life influenced by the internal action of the bearing are analyzed. The effects of radial load and deflection angle on the mechanical properties and fatigue life of the bearing are also studied. Based on the analysis results of bearing contact mechanical properties and clearance changes, the calculation method of bearing life under rolling element wear is established. The influence of the variation of clearance and preload clearance on bearing life is analyzed, and the optimal preload is obtained. The research results of this paper can provide a theoretical basis for optimizing the installation of angular contact ball bearings, reasonably determining the service conditions, and prolonging the service life of bearings, which is necessary for engineering practice.

The dynamic characteristics of the pumped storage unit (pump-turbine runner) make it highly susceptible to vibrations. Previous studies seldom addressed the clearance variation due to runner vibration, largely because of two challenges: the integration of the moving grid with the clearance and governing equations, and the intricacy of factoring in the entire shaft system's influence on vibration. By employing user-defined functions (UDF), kinematic equations for the pump-turbine runner's rotation and translation were formulated and integrated with computational fluid dynamics results. This paper introduces a computational method to simulate clearance-induced vibration displacement. The study examines the impact of clearance vibration displacement on the pump-turbine's transient flow field and runner vibrations under various operating conditions. Notably, deviations from the rated load condition led to an expansion-contraction trajectory of the runner axis, culminating in chaotic behaviour in the \"S\" characteristic zone. The correlation between runner vibrations and pressure fluctuations strengthened with the clearance displacement model, especially in narrow labyrinth seal clearances. Simulations also provided a refined estimation of the secondary rotational speed increase during load rejection. The method's reliability is confirmed through field test data comparison, offering a fresh perspective to identify the vibration characteristics of the pump-turbine runner.

The present paper studied the effects of calcination temperatures (200–800 °C) on the appearance, mineral composition, and active SiO2 content in attapulgite and investigated the effects of attapulgite before and after calcination on the chemically bonded water content, the degree of reaction of cement paste, and the mechanical properties such as the flexural strength, compressive strength, and splitting-tensile strength of cement mortar. The results indicate that the calcination temperature changes the mineral composition of attapulgite, thereby affecting the hydration activity of cement-based materials. The attapulgite calcined at 500 °C (AT500) has the best enhancement on the hydration activity of cement-based materials. The calcination at 500 °C is most beneficial to the dissolution of SiO2, and the content of SiO2 reaches 20.96%. The contents of chemically bonded water in the samples incorporated with calcined attapulgite reduced and that of the samples incorporated with AT500 at 28 d is the same as that of the control group. The reaction degree of AT500 is 78.61% at 28 days. Calcined attapulgite clay can reduce the energy consumption of the cement industry and promote the sustainable development of attapulgite clay.

The design of efficient and robust metaheuristic algorithms remains a fundamental challenge in addressing complex optimization problems, particularly those involving high dimensionality, nonlinearity, and multimodal landscapes where traditional methods often struggle. To overcome these difficulties and enhance search effectiveness and adaptability, this paper proposes the Enhanced Red-billed Blue Magpie Optimizer (ERBMO), an improved variant of the recently introduced Red-billed Blue Magpie Optimizer (RBMO). ERBMO integrates three synergistic enhancement mechanisms: Diversity-adaptive weight updating, Periodic pattern search, and Evolutionary probabilistic combinatorial mutation. These components are specifically designed to strengthen population diversity, mitigate premature convergence, and achieve a dynamic balance between exploration and exploitation throughout the optimization process. Comprehensive evaluations demonstrate the superior performance of ERBMO. On the CEC2017 benchmark suite, ERBMO achieves the highest Friedman rankings across all tested dimensions, with average ranks of 1.667 (30D), 1.433 (50D), and 1.133 (100D), consistently outperforming the original RBMO which ranked second. On the CEC2022 benchmark suite, ERBMO again secures the top overall rankings for both 10D (1.833) and 20D (1.833), surpassing nine state-of-the-art algorithms including ESC and RBMO. Ablation study results confirm the effectiveness of each proposed strategy, as the complete ERBMO achieves superior Friedman rankings (2.282 for 10D, 2.140 for 20D) compared to variants with any single strategy removed. Furthermore, when compared against classical algorithms (PSO, DE, CMA-ES) and their advanced variants (SaDE, LSHADE) on CEC2022, ERBMO obtains the best overall rankings (3.550 for 10D, 3.089 for 20D). When applied to four real-world engineering design problems—speed reducer, pressure vessel, step-cone pulley, and hydrostatic thrust bearing—ERBMO consistently ranks first, achieving optimal or near-optimal solutions with superior robustness. The superior performance across both benchmark and practical problems highlights the effectiveness and reliability of the proposed improvements. This work presents a framework for engineering optimization and metaheuristic algorithm design. The source code of ERBMO is publicly available at: https://github.com/x5865/Enhanced-Red-billed-Blue-Magpie-Optimizer-ERBMO- .

AMPA-subtype glutamate receptors (AMPARs) mediate excitatory synaptic transmission. AMPAR ion channels exhibit multiple subconductance states that tune neuronal responses to glutamate. GluA4 is the rarest subunit in the brain but is enriched in interneurons and the cerebellum. Rising evidence points to GluA4 AMPARs in the development of neurological diseases, but the structural mechanisms of GluA4 function remain enigmatic. Here, we show the distinct features of GluA4 that tune AMPAR function. We find that GluA4 AMPARs have a canonical “Y” shaped architecture where local dimer pairs are domain-swapped between the amino terminal domain (NTD) and ligand binding domain (LBD), both of which comprise the extracellular domain. All four LBDs are glutamate bound yet open the GluA4 ion channel by asymmetric hinging in all four channel helices. We observe that the glutamate-saturated LBD has conformational plasticity, which tunes the ion channel gate below. These data provide a framework for understanding channel subconductance, outline the distinct properties of GluA4, expand our understanding of conformational plasticity in AMPARs, and will inform therapeutic design. Fast excitatory neurotransmission is mediated by AMPA receptors. Here, the authors discover that the GluA4 AMPA receptor subunit has distinct structural properties and conformational plasticity that tune its activation to glutamate.

Deep learning has been used in inverse synthetic aperture radar (ISAR) imaging to improve resolution performance, but there still exist some problems: the loss of weak scattering points, over-smoothed imaging results, and the universality and generalization. To address these problems, an ISAR resolution enhancement method of exploiting a generative adversarial network (GAN) is proposed in this paper. We adopt a relativistic average discriminator (RaD) to enhance the ability of the network to describe target details. The proposed loss function is composed of feature loss, adversarial loss, and absolute loss. The feature loss is used to get the main characteristics of the target. The adversarial loss ensures that the proposed GAN recovers more target details. The absolute loss is adopted to make the imaging results not over-smoothed. Experiments based on simulated and measured data under different conditions demonstrate that the proposed method has good imaging performance. In addition, the universality and generalization of the proposed GAN are also well verified.

Thyroid cancer (THCA) remains a prevalent endocrine malignancy, with limited molecular markers for accurate diagnosis and targeted therapy. This study aimed to identify key biomarkers and therapeutic targets for THCA using integrative bioinformatics and experimental validation. We used differential gene expression analysis, gene enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning algorithms to identify potential biomarkers. Receiver operating characteristic (ROC) curve analysis, immunohistochemical data from the Human Protein Atlas (HPA), quantitative real-time PCR (qPCR), and immune infiltration analysis were used for further validation. Additionally, ROC and multivariate Cox regression analyses were conducted to quantitatively evaluate the diagnostic and prognostic performance of candidate genes. Four candidate biomarkers (VEGFA, TYK2, NRP1, and C5AR1) were identified, but only C5AR1 showed statistical significance in ROC and prognostic analyses. Specifically, C5AR1 demonstrated a strong diagnostic value (AUC = 0.873, 95% CI: 0.822–0.924) and was significantly associated with poorer overall survival (hazard ratio [HR] = 2.41, 95% CI: 1.15–5.06, p  = 0.021). Further qPCR and immune infiltration analyses confirmed that C5AR1 expression was associated with immune cell infiltration, potentially influencing THCA progression. This study identifies C5AR1 as a key biomarker in THCA, suggesting its role in immune-related tumor progression. These findings indicate statistical associations rather than causal mechanisms, highlighting the need for further experimental validation. The results provide a foundation for targeted immunotherapy strategies in THCA treatment.

Squeeze film damper (SFD) is widely used in the vibration suppression of aeroengine rotor systems, but will cause complex motions of the rotor system under specific operating conditions. In this paper, a lumped-mass dynamic model of the high-pressure rotor system in an aeroengine is established, and the nonlinear stiffness and damping formula of SFD are introduced into the above model. The vibration responses of the rotor system under different rotating speeds and with different unbalances are investigated numerically, and the influence of SFD on the rotor system vibration and the change of suppression ability are compared and analyzed. The results show that in the case of high speed, together with a small unbalance, the rotor system will perform a complex vibration or a bistable vibration due to SFD. If the unbalance is properly increased under the same case of high speed, the vibration of the rotor becomes single-harmonic and the bistable vibration disappears. The research results can provide a helpful reference for analyzing complex vibration mechanisms of the rotor system with SFD and achieving an effective vibration suppression through unbalance regulation.

Background Acute ischemic stroke (AIS) remains a major determinant for both mortality and enduring functional impairment. High density lipoprotein cholesterol (HDL) and Uric acid (UA) have opposing effects on oxidative stress and vascular protection. The UA/HDL ratio (UHR) may reflect systemic oxidative–lipid imbalance. This study examined whether admission UHR is linked to unfavorable prognosis among individuals with AIS. Methods Between October 2022 and September 2024, 822 consecutive AIS patients admitted to Bethune Hospital of Shanxi were retrospectively analyzed. Inclusion required first-ever AIS confirmed by imaging findings within 72 h. The principal endpoint was the functional status assessed at 90 days. Baseline demographics, clinical features, and laboratory values were collected. To account for confounding factors, the association of UHR with outcomes was analyzed through restricted cubic spline (RCS) models, multivariable logistic regression and subgroup analyses. Results Among 822 patients, 262 (31.9%) had unfavorable outcomes. These patients exhibited higher UA, lower HDL, and elevated UHR. Multivariable analysis confirmed that UHR was independently associated with unfavorable outcomes (adjusted OR: 1.0021; P  = 0.0013). Membership in the top UHR quartile conferred an approximately twofold higher risk compared with the bottom quartile (adjusted OR = 1.97). RCS models demonstrated a linear positive association, with consistent findings across subgroups defined by stroke severity, sex, smoking, alcohol use, hypertension, and diabetes. Conclusions Admission UHR is independently linked to unfavorable prognosis among individuals with AIS and holds promise as a simple, economical tool for early risk stratification and personalized management. Its use could support timely interventions and personalized management, contributing to improved patient outcomes and efficient allocation of healthcare resources. Prospective research is needed to validate its prognostic value and guide targeted interventions.