Search Results (3,178)

This study investigates the glycosylation patterns of serum IgG antibodies in relation to COVID-19 infection and vaccination, highlighting the potential of specific glycan profiles as biomarkers for immune responses. Using Spearman correlation analysis, distinct associations among glycan levels and various clinical laboratory parameters were identified, revealing complex, non-linear interactions that influence immune dynamics. Significant differences were observed in sialylated glycan profiles across patient groups, indicating that vaccination and natural infection elicit unique immune mechanisms and suggesting that vaccination induces favorable glycosylation changes. Notably, high-mannose glycans were found to correlate with other glycan types, underscoring their critical role in the immune response and suggesting their potential as biomarkers to differentiate between infection- and vaccination-induced immunity. The findings suggest that understanding these glycosylation dynamics may enhance diagnostic and therapeutic strategies, providing valuable tools for differentiating between immune responses elicited by infection and vaccination. Overall, this study contributes to the understanding of glycosylation’s impact on immune function in the context of COVID-19, emphasizing the importance of specific glycan markers, such as sialylated and high-mannose structures, in clinical applications. Full article

Obesity is a major global health concern, being associated with insulin resistance and multiple metabolic disorders. Gremlin 1 (GREM1), a bone morphogenetic protein (BMP) antagonist, is increasingly recognized as a key regulator of adipose tissue dysfunction and impaired thermogenesis in obesity. Orlistat, a lipase inhibitor that reduces dietary fat absorption, is one of the most commonly used pharmacological agents for obesity management. White tea has demonstrated antioxidant and anti-obesity properties in experimental models. The aim of this study was to evaluate the effects of white tea on metabolic parameters (HOMA-IR, BMP4, Gremlin1) and GREM1 expression in rats made obese by a high-fat diet (HFD). A total of 40 male Sprague-Dawley rats were randomized into five groups: a standard diet group (STD); a high-fat diet group (HFD); an HFD + orlistat group (ORL); an HFD + 50 mg/kg white tea group (WT50); and an HFD + 150 mg/kg white tea group (WT150). Obesity was induced by feeding the rats a 45% high-fat diet for 3 weeks. Serum insulin, glucose and HOMA-IR levels were measured. Levels of GREM1 and BMP4 in serum and retroperitoneal adipose tissue were assessed. White tea supplementation significantly reduced weight gain and HOMA-IR compared to the HFD group. GREM1 mRNA expression in visceral adipose tissue decreased markedly in the WT50 and WT150 groups (p = 0.002 and p = 0.017, respectively). Serum GREM1 levels were significantly lower in the white tea-treated groups than in the HFD group (p = 0.011). Tissue BMP4 levels were only significantly reduced in the WT50 group (p = 0.005), indicating a non-linear dose–response pattern. There was a negative correlation between serum BMP4 levels and weight gain (rho = –0.440, p = 0.015). White tea was associated with improvements in metabolic parameters in an HFD-induced obesity model. These observations suggest a potential association between white tea bioactives and adipose tissue-related molecular pathways implicated in obesity. Given the short intervention duration and the exploratory design of this animal study, the findings should be interpreted with caution. Full article

Early fault vibration signals of rolling bearings are non-stationary and nonlinear, with weak fault signatures easily masked by noise. Traditional denoising methods (e.g., wavelet thresholding, empirical mode decomposition (EMD)) struggle to accurately extract effective features. Although variational mode decomposition (VMD) overcomes mode mixing, its core parameters rely on empirical selection, making it prone to local optima and limiting its denoising performance. To address this critical issue, this study aims to propose a hybrid model with adaptive parameter optimization and efficient denoising capabilities, enhancing the signal-to-noise ratio (SNR) and feature discriminability of early fault signals in rolling bearings. The novelty of this work is reflected in three aspects: (1) An improved edge strategy whale optimization algorithm (IEWOA) is proposed, incorporating six enhancements to balance global exploration and local exploitation. Using the minimum average envelope entropy as the objective function, the IEWOA achieves adaptive global optimization of VMD parameters. (2) A hybrid framework of “IEWOA-VMD + dataset-specific wavelet thresholding for secondary denoising” is constructed. The optimized VMD first decomposes signals to separate noise and effective components, followed by secondary denoising, ensuring both adaptable signal decomposition and precise denoising. (3) Comprehensive validation is conducted across five models using two public datasets (Case Western Reserve University (CWRU) and Paderborn Universität (PU)). Key findings demonstrate that the proposed method achieves a root-mean-square error (RMSE) as low as 0.00013–0.00041 and a Normalized Cross-Correlation (NCC) of 0.9689–0.9798, significantly outperforming EEMD, traditional VMD, and VMD optimized by single algorithms. The model effectively suppresses noise interference, preserves the fundamental and harmonic components of fault features, and exhibits strong robustness under different loads and fault types. This work provides an efficient and reliable signal preprocessing solution for early fault diagnosis of rolling bearings. Full article

Understanding the complex interplay between environmental factors and fruit quality development requires sophisticated analytical approaches linking cellular architecture to environmental conditions. This study introduces a novel application of dual-resolution X-ray computed tomography (CT) for the non-destructive characterization of apple internal tissue architecture in relation to fruit growth, thereby advancing beyond traditional methods that are primarily focused on postharvest analysis. By extracting detailed three-dimensional structural parameters, we reveal tissue porosity and heterogeneity influenced by crop load, maturity timing and canopy position, offering insights into internal quality attributes. Employing correlation analysis, Principal Component Analysis, Canonical Correlation Analysis, and Structural Equation Modeling, we identify temperature as the primary environmental driver, particularly during early developmental stages (45 Days After Full Bloom, DAFB), and uncover nonlinear, hierarchical effects of preharvest environmental factors such as vapor pressure deficit, relative humidity, and light on quality traits. Machine learning models (Multiple Linear Regression, Random Forest, XGBoost) achieve high predictive accuracy (R² > 0.99 for Multiple Linear Regression), with temperature as the key predictor. These baseline results represent findings from a single growing season and require validation across multiple seasons and cultivars before operational application. Temporal analysis highlights the importance of early-stage environmental conditions. Integrating structural and environmental data through innovative visualization tools, such as anatomy-based radar charts, facilitates comprehensive interpretation of complex interactions. This multidisciplinary framework enhances predictive precision and provides a baseline methodology to support precision orchard management under typical agricultural variability. Full article

This study establishes a comprehensive structural isomorphism between Conway’s Game of Life and the entrepreneurial process, analysing the latter as a complex adaptive system governed by non-linear dynamics rather than linear predictability. Through a rigorous qualitative approach based on a systematic literature review and abductive inference, the research identifies and correlates four fundamental dimensions: uncertainty, adaptability, growth, and sustainability. Transcending traditional metaphorical comparisons, this paper introduces a novel mathematical model that modifies Conway’s deterministic logic by incorporating an «Agency» variable (A). This critical addition quantifies how an entrepreneur’s internal capabilities can counterbalance environmental pressures (neighbourhood density) to determine survival thresholds, effectively transforming the simulation into a «Game of Life with Agency» where participants actively influence their viability potential (Ψ). The analysis explicitly correlates specific algorithmic configurations with real-world business phenomena: high-entropy initial states («The Soup») mirror early-stage market uncertainty where outcomes are probabilistic; «gliders» represent the necessity of strategic pivoting and continuous displacement for survival; and «oscillators» symbolise dynamic sustainability through rhythmic equilibrium rather than static permanence. Furthermore, the study validates the «Gosper Glider Gun» pattern as a model for scalable, generative growth. By bridging abstract systems theory with managerial practice, the research positions these simulations as «mental laboratories» for decision-making. The findings theoretically validate iterative methodologies like the Lean Startup and conclude that successful entrepreneurship operates on the «Edge of Chaos», providing a rigorous framework for navigating high stochastic uncertainty. Full article

With the increasing requirements for “Green Mine” construction, Cemented Tailings Backfill (CTB) has emerged as the preferred strategy for solid waste management and ground pressure control in underground metal mines. However, full tailings, characterized by wide particle size distribution and high fine-grained content, exhibit complex physicochemical properties that lead to significant non-linear behavior in slurry rheology and strength evolution, posing challenges for accurate prediction using traditional empirical formulas. Addressing the issues of significant strength fluctuations and difficulties in mix proportion optimization in a specific lead–zinc mine, this study systematically conducted physicochemical characterizations, slurry sedimentation and transport performance evaluations, and mechanical strength tests. Through multi-factor coupling experiments, the synergistic effects of cement type, cement-to-tailings (c/t) ratio, slurry concentration, and curing age on backfill performance were elucidated. Quantitative results indicate that solids mass concentration is the critical factor determining transportability. Concentrations exceeding 68% effectively mitigate segregation and stratification during the filling process while maintaining optimal fluidity. Regarding mechanical properties, the c/t ratio and concentration show a significant positive correlation with Uniaxial Compressive Strength (UCS). For instance, with a 74% concentration and 1:4 c/t ratio, the 3-day strength increased by 1.4 times compared to the 68% concentration, with this increment expanding to 2.0 times by 28 days. Furthermore, a comparative analysis of four cement types revealed that 42.5# cement offers superior techno-economic indicators in terms of reducing binder consumption and enhancing early-age strength. This research not only establishes an optimized mix proportion scheme tailored to the operational requirements of the lead–zinc mine but also provides a quantitative scientific basis and theoretical framework for the material design and safe production of CTB systems incorporating high fine-grained full tailings. Full article

To improve the accuracy and robustness of lithium-ion battery state of charge (SOC) estimation, this paper proposes a generalized mixture maximum correlation-entropy criterion-based adaptive extended Kalman filter (GMMCC-AEKF) algorithm, addressing the performance degradation of the traditional extended Kalman filter (EKF) under non-Gaussian noise and inaccurate initial conditions. Based on the GMMCC theory, the proposed algorithm introduces an adaptive mechanism and employs two generalized Gaussian kernels to construct a mixed kernel function, thereby formulating the generalized mixture correlation-entropy criterion. This enhances the algorithm’s adaptability to complex non-Gaussian noise. Simultaneously, by incorporating adaptive filtering concepts, the state and measurement covariance matrices are dynamically adjusted to improve stability under varying noise intensities and environmental conditions. Furthermore, the use of statistical linearization and fixed-point iteration techniques effectively improves both the convergence behavior and the accuracy of nonlinear system estimation. To investigate the effectiveness of the suggested method, experiments for SOC estimation were implemented using two lithium-ion cells featuring distinct rated capacities. These tests employed both dynamic stress test (DST) and federal test procedure (FTP) profiles under three representative temperature settings: 40 °C, 25 °C, and 10 °C. The experimental findings prove that when exposed to non-Gaussian noise, the GMMCC-AEKF algorithm consistently outperforms both the traditional EKF and the generalized mixture maximum correlation-entropy-based extended Kalman filter (GMMCC-EKF) under various test conditions. Specifically, under the 25 °C DST profile, GMMCC-AEKF improves estimation accuracy by 86.54% and 10.47% over EKF and GMMCC-EKF, respectively, for the No. 1 battery. Under the FTP profile for the No. 2 battery, it achieves improvements of 55.89% and 28.61%, respectively. Even under extreme temperatures (10 °C, 40 °C), GMMCC-AEKF maintains high accuracy and stable convergence, and the algorithm demonstrates rapid convergence to the true SOC value. In summary, the GMMCC-AEKF confirms excellent estimation accuracy under various temperatures and non-Gaussian noise conditions, contributing a practical approach for accurate SOC estimation in power battery systems. Full article

To address the issues of insensitivity to high-impedance ground faults and difficulty in identifying reflected wavefronts in single-ended traveling-wave fault location methods for asymmetric ground faults in high-voltage AC transmission lines, this paper proposes a single-ended fault location method based on the modal amplitude ratio and deep learning. First, based on the dispersion characteristics of traveling waves, an approximate formula is derived between the fault distance and the amplitude ratio of the sum of the initial transient voltage traveling-wave 1-mode and 2-mode to 0-mode at the measurement point. Simulation verifies that the fault distance x from the measurement point at the line head is unaffected by transition resistance and fault inception angle, and that a nonlinear positive correlation exists between the distance x and the modal amplitude ratio. The multi-scale wavelet modal maximum ratio of the sum of 1-mode and 2-mode to 0-mode is used to characterize the amplitude ratio. This ratio serves as the input for a Residual Bidirectional Long Short-Term Memory (BiLSTM) network, which is optimized using the Dung Beetle Optimizer (DBO). The DBO-Res-BiLSTM model fits the nonlinear mapping between the fault distance x and the amplitude ratio. Simulation results demonstrate that the proposed method achieves high location accuracy. Furthermore, it remains robust against variations in fault type, location, transition resistance, and inception angle. Full article

Observed rainfall erosivity risk (ORE) index is defined as the erosivity risk in the event of extreme rainfall events. ORE measures the kinetic energy of raindrops generated during a period of maximum precipitation intensity with the formula $\mathrm{O}\mathrm{R}\mathrm{E}=\left(\mathrm{E}\mathrm{D}·\mathrm{T}\mathrm{E}\mathrm{I}\right)/10$, where ED = erosivity density, TEI = total erosivity index, and ORE is measured in MJ mm ha⁻¹ h⁻¹ yr⁻¹. The goal of this study is to model ORE, estimate its spatiotemporal variability, and predict (PRE) and simulate ORE for the state of Sinaloa (1969–2018). Five indices of rainfall erosivity were calculated: the modified Fournier index, precipitation concentration index, ED, TEI, and rainfall erosivity factor. The nonparametric trend in ORE was calculated. Using multiple nonlinear regressions (MNR), PRE (dependent variable) was calculated as a function of cumulative annual, annual average, seasonal average, and seasonal cumulative rainfall (independent variables). To simulate PRE, cumulative distribution functions, adjusted return periods (ARPs), and the 99th percentile were used. ORE ranged from 51.39 MJ mm ha⁻¹ h⁻¹ yr⁻¹ in 1970 (Culiacán) to 92679.40 MJ mm ha⁻¹ h⁻¹ yr⁻¹ in 1998 (Sta. C. de Alaya). The only year that had very high ORE at all nine stations was 1998. The only significant trend was ORE = 34.64 MJ mm ha⁻¹ h⁻¹ yr⁻¹ (Culiacán). The nine PRE models were significantly predictive (Spearman correlation > 0.280). Guatenipa, Rosario, and Siqueros registered very high PRE, since one to eight extreme erosivity events per century are predicted on average. A new methodology is proposed for calculating ORE and PRE, which can be used to develop alternatives for managing and protecting agricultural land in the state considered “the breadbasket of Mexico”. Full article

Vegetation indices (VIs) enable rapid, non-destructive biochemical monitoring in olive fruits, yet their performance across diverse germplasm and ripening stages remains systematically uncharacterized. This exploratory screening systematically evaluated 87 VIs for predicting chlorophyll and polyphenol content across 31 cultivars at four ripening stages, prioritizing genetic diversity to establish species-level biochemical–spectral relationships through integration of hyperspectral data (380–1080 nm) with biochemical analyses. Modified Chlorophyll Absorption Ratio Index 3 (MCARI 3) and Transformed Chlorophyll Absorption Ratio Index (TCARI) achieved 91 strong correlations (|r| ≥ 0.9) across 124 cultivar-stage combinations. High-performing indices incorporated 550 nm with red/red-edge bands (670–710 nm) and non-linear formulations. Moderate inter-cultivar variability indicated that cultivar-specific calibrations may be necessary. Principal component analysis captured the totality of variance, revealing three biochemical clusters, high-chlorophyll cultivars (n = 5; 91.8 and 7385.6 mg kg⁻¹ chlorophyll/polyphenols, respectively), typical-range cultivars (n = 22; 126.6 and 4016.8 mg kg⁻¹), and elite cultivars (n = 5; 790.4 and 5799.8 mg kg⁻¹), demonstrating VIs’ capacity for cultivar discrimination. Chlorophyll degradation exhibited conserved patterns, supporting universal tracking models. Conversely, polyphenol dynamics displayed marked genotype-dependency, with cultivars showing positive, negative, or minimal variation, yielding non-significant population-level effects, despite robust cultivar-specific trends. Full article

In recent years, deformation disasters in mine shafts have occurred frequently, posing a threat to mine safety. The nonlinear coupling relationship between shaft surrounding rock deformation and rock mass mechanical parameters is a key criterion for surrounding rock stability. However, existing machine learning prediction methods are rarely applied to shaft deformation, and issues such as poor accuracy and generalization of single models remain. To address this, the study proposes a feature-weighted Stacking ensemble model, which considers 15 feature variables; using RMSE, MAE, R², and inter-model MAPE correlation as evaluation metrics, GBDT, XGBoost, KNN, and MLP are selected as base learners, with Lasso linear regression as the meta-learner. Prediction errors are corrected by weighting the outputs of base learners based on prediction accuracy. Experiments show that, using MAPE as the evaluation metric, the improved model reduces the error by 2.59% compared with the best base learner KNN, by 6.83% compared with XGBoost, and by 0.18% more than the traditional Stacking algorithm, making it suitable for predicting weak surrounding rock shaft deformation under multi-feature conditions. Full article

Seismic action significantly affects the mechanical properties and failure characteristics of bridge pile foundations, soft rocks, and sediments. This study, by integrating shaking table tests, numerical simulations, and on-site monitoring, systematically analyzed the influence mechanisms of seismic intensity, sediment characteristics, and pile foundation layout on structural responses. Tests show that the 2.5-layer rock–sand pile exhibits nonlinear bearing degradation under seismic force: when the seismic acceleration increases from 0 to 100 m/s², the bearing capacity of the pile foundation decreases by 55.3%, and the settlement increases from 3.2 mm to 18.5 mm. When the acceleration is ≥2 m/s², the cohesion of the sand layer is destroyed, causing a semi-liquefied state. When it is ≥10 m/s², the resistance loss reaches 80%. The increase in pore water pressure leads to dynamic settlement. When the seismic acceleration is greater than 50 m/s², the shear modulus of the sand layer drops below 15% of its original value. The thickness of the sediment has a nearly linear relationship with the reduction rate of the bearing capacity. When the thickness increases from 0 to 1.4 cm, the reduction rate rises from 0% to 55.3%. When the thickness exceeds 0.8 cm, it enters the “danger zone”, and the bearing capacity decreases nonlinearly with the increase in thickness. The particle size is positively correlated with the reduction rate. The liquefaction risk of fine particles (<0.1 mm) is significantly higher than that of coarse particles (>0.2 mm). The load analysis of the pile cap shows that when the sediment depth is 140 cm, the final bearing capacity is 156,187.2 kN (reduction coefficient 0.898), and the maximum settlement is concentrated at the top point of the pile cap. Under the longitudinal seismic load of the pile group, the settlement growth rate of the piles containing sediment reached 67.16%, triggering the dual effect of “sediment–earthquake”. The lateral load leads to a combined effect of “torsional inclination”, and the stress at the top of the non-sediment pile reaches 6.41MPa. The seismic intensity (PGA) is positively correlated with the safety factor (FS) (FS increases from 1.209 to 37.654 when 10 m/s²→100 m/s²), while sediment thickness (h) is negatively correlated with FS (FS decreases from 2.510 to 1.209 when 0.05 m→0.20 m). The research results reveal the coupled control mechanism of sediment characteristics, seismic parameters, and pile foundation layout on seismic performance, providing key parameters and an optimization basis for bridge design in high-intensity areas. Full article

Support Vector Machines (SVMs) are widely used in critical decision-making applications, such as precision agriculture, due to their strong theoretical foundations and their ability to construct an optimal separating hyperplane in high-dimensional spaces. However, the effectiveness of SVMs is highly dependent on the efficiency of the optimization algorithm used to solve their underlying dual problem, which is often complex and constrained. Classical solvers, such as Sequential Minimal Optimization (SMO) and Stochastic Gradient Descent (SGD), present inherent limitations: SMO ensures numerical stability but lacks scalability and is sensitive to heuristics, while SGD scales well but suffers from unstable convergence and limited suitability for nonlinear kernels. To address these challenges, this study proposes a novel hybrid optimization framework based on Open Competency Optimization and Particle Swarm Optimization (OCO–PSO) to enhance the training of SVMs. The proposed approach combines the global exploration capability of PSO with the adaptive competency-based learning mechanism of OCO, enabling efficient exploration of the solution space, avoidance of local minima, and strict enforcement of dual constraints on the Lagrange multipliers. Across multiple datasets spanning medical (diabetes), agricultural yield, signal processing (sonar and ionosphere), and imbalanced synthetic data, the proposed OCO-PSO–SVM consistently outperforms classical SVM solvers (SMO and SGD) as well as widely used classifiers, including decision trees and random forests, in terms of accuracy, macro-F1-score, Matthews correlation coefficient (MCC), and ROC-AUC. On the Ionosphere dataset, OCO-PSO achieves an accuracy of $95.71\%$, an F1-score of $0.954$, and an MCC of $0.908$, matching the accuracy of random forest while offering superior interpretability through its kernel-based structure. In addition, the proposed method yields a sparser model with only 66 support vectors compared to 71 for standard SVC (a reduction of approximately $7\%$), while strictly satisfying the dual constraints with a near-zero violation of $1.3\times {10}^{-3}$. Notably, the optimal hyperparameters identified by OCO-PSO ($\mathrm{C}=2$, $\gamma \approx 0.062$) differ substantially from those obtained via Bayesian optimization for SVC ($\mathrm{C}=10$, $\gamma \approx 0.012$), indicating that the proposed approach explores alternative yet equally effective regions of the hypothesis space. The statistical significance and robustness of these improvements are confirmed through extensive validation using 1000 bootstrap replications, paired Student’s t-tests, Wilcoxon signed-rank tests, and Holm–Bonferroni correction. These results demonstrate that the proposed metaheuristic hybrid optimization framework constitutes a reliable, interpretable, and scalable alternative for training SVMs in complex and high-dimensional classification tasks. Full article

The fault data of the aero-engine source domain are constrained by factors such as variable operating conditions, structural coupling, fault correlations, and information attenuation. Consequently, the obtained fault features often exhibit localities. This leads to significant discrepancies in fault feature distributions between the source and target domains, resulting in poor generalization capabilities and insufficient stability in aero-engine fault diagnosis. To address these issues, an aero-engine fault diagnosis method based on Domain-Adversarial Neural Network (DANN) and Feature Interaction (FI-DANN) is proposed. Firstly, a fault diagnosis network architecture is designed based on traditional DANN by incorporating a feature interaction module into its feature extractor. Secondly, the Kronecker product is employed to fully excavate nonlinear relationships between the features, thereby increasing the number of fault features to obtain higher-dimensional and more accurate fault features. Finally, based on information entropy theory, the number of interacted features is controlled through a weighted combination, ensuring that the retained features possess greater fault information content. This guarantees the strong generalization capability and high stability of the model. The experimental results show that the best fault diagnosis accuracies of Convolutional Neural Network (CNN), traditional DANN, and FI-DANN are 79.64%, 90.00%, and 99.03%, respectively, indicating that the proposed FI-DANN can effectively integrate multi-source fault information and enhance the accuracy, stability, and generalization capability of fault diagnosis models. Full article

This paper investigates the planning problem of AC-integrated wind–photovoltaic–hydro–storage (WPHS) bundled transmission systems. To effectively capture the uncertainty and interdependence in renewable power outputs, a Copula-enhanced distributionally robust optimization (DRO) framework is developed, enabling a unified treatment of stochastic and correlated renewable generation within the system planning process. First, a location and capacity planning model based on DRO for WPHS generation bases is formulated, in which a composite-norm ambiguity set is constructed to describe the uncertainty of renewable resources. Second, the Copula function is employed to characterize the nonlinear dependence structure between wind and photovoltaic (PV) power outputs, providing representative scenarios and initial probability distribution (PD) support for the construction of a bivariate ambiguity set that embeds coupling information. The resulting optimization problem is solved using the column and constraint generation (C&CG) algorithm. In addition, an evaluation metric termed the transmission corridor utilization rate (TCUR) is proposed to quantitatively assess the efficiency of external AC transmission planning schemes, offering a new perspective for the evaluation of regional power transmission strategies. Finally, simulation results validate that the proposed model achieves superior performance in terms of system economic efficiency and TCUR. Full article