Search Results (1,350)

This study investigates the multi-objective path planning problem for unmanned surface vehicles (USVs), aiming to optimize both travel distance and energy consumption in maritime environments with obstacles, sea winds, and ocean currents. The proposed method accounts for practical constraints, including collision avoidance, kinematic boundaries, and speed limitations. The problem is formulated as a nonlinear multi-objective optimization model with generalized constraints and is solved using an improved particle swarm optimization algorithm enhanced by a vector-weighted fusion strategy. The algorithm adaptively balances exploration and exploitation to obtain diverse Pareto-optimal solutions. Simulation results under varying environmental conditions, along with real-world sea trials, validate the effectiveness of the proposed approach. The outcomes demonstrate that the method enables USVs to generate energy-efficient, smooth trajectories while maintaining robustness and adaptability, offering practical value for intelligent marine navigation. Full article

The increased use of prefabricated assembly technology promotes the transformation of urban subway construction in the lightweight direction, in which the closed cavity thin-walled component is increasingly widely used in underground structures due to its excellent material efficiency benefits. In order to investigate the effect of closed cavity thin-walled components, numerical models of a seven-ring solid structure and cavity structure were constructed based on the four-block prefabricated metro station of Qingdao Metro Line 9, Chengzi Station. This study considers the longitudinal effect between rings and compares the nonlinear force and deformation characteristics of both structures under the load of self-weight and use stage. The study indicates that incorporating closed cavities within structures reduces internal forces in most sections while increasing principal strain, displacement, and stress. As the applied load increases, the rate of internal force reduction diminishes, and the increment of displacement deformation also decreases. Shear lag effects occur in closed cavity sections, leading to a non-uniform normal stress distribution, with maximum shear stress appearing at rib intersections. The cavity location, mortise–tenon joints, and columns represent critical locations for deformation and force transmission within cavity structures. Optimization design must prioritize ensuring their deformation resistance and load-bearing capacity to enhance the overall structural integrity, safety, and reliability. Full article

Bioactive peptides have significant potential for applications in pharmaceuticals, the food industry, and cosmetics due to their wide spectrum of biological activities. However, their pronounced structural and functional heterogeneity complicates the classification and prediction of biological activity. This study uses data from the PepLab platform, comprising 2748 experimentally confirmed bioactive peptides distributed across 15 functional classes, including ACE inhibitors, antimicrobial, anticancer, antioxidant, toxins, and others. For each peptide, the amino acid sequence and key physicochemical descriptors are provided, calculated via the integrated DMPep module, such as GRAVY index, aliphatic index, isoelectric point, molecular weight, Boman index, and sequence length. The dataset exhibits class imbalance, with class sizes ranging from 14 to 524 peptides. An innovative methodology is proposed, combining descriptive statistical analysis, structural modeling via DEMATEL, and structural equation modeling with neural networks (SEM-NN), where SEM-NN is used to capture complex nonlinear causal relationships between descriptors and functional classes. The results of these dependencies are integrated into a multi-class machine learning model to improve interpretability and predictive performance. Targeted data augmentation was applied to mitigate class imbalance. The developed classifier achieved predictive accuracy of up to 66%, a relatively high value given the complexity of the problem and the limited dataset size. These results confirm that integrating structured dependency modeling with artificial intelligence is an effective approach for functional peptide classification and supports the rational design of novel bioactive molecules. Full article

The retrieval mechanism of traditional CBR methods has limitations when applied to complex multi-layer objects, mainly due to their nonlinearity and emergent properties. This paper proposes an improved case-based reasoning method with cascaded retrieval (CRCBR) to address the aforementioned issues, but the non-fixed model structure makes it difficult to determine appropriate weight calculation methods for CRCBR. To tackle this problem, we propose a dynamic entropy weight method for heterogeneous multi-attribute data, which designs separate entropy calculation methods for different data types and computes weights through a flexible secondary allocation mechanism. Finally, using seven evaluation metrics and three assessment perspectives as the evaluation framework, we validate the effectiveness and superiority of our method through comparative experiments with traditional CBR and CRCBR without dynamic entropy weighting (termed the baseline method) on real-world maritime emergency data. The experimental results show that our method surpasses traditional CBR across all three evaluation perspectives, outperforms the baseline in two perspectives, and is no worse than the baseline in the remaining perspective. Full article

Owing to its excellent crack resistance and durability, High-Performance Fiber-Reinforced Concrete (HPFRC) has been extensively applied in engineering structures exposed to extreme loading conditions. The Mode I dynamic fracture strength of HPFRC under high-strain-rate conditions exhibits significant strain-rate sensitivity and nonlinear response characteristics. However, existing experimental methods for strength measurement are limited by high costs and the absence of standardized testing protocols. Meanwhile, conventional data-driven models for strength prediction struggle to achieve both high-precision prediction and physical interpretability. To address this, this study introduces a dynamic fracture strength prediction method based on a feature-weighted linear ensemble (FWL) mechanism. A comprehensive database comprising 161 sets of high-strain-rate test data on HPFRC fracture strength was first constructed. Key modeling variables were then identified through correlation analysis and an error-driven feature selection approach. Subsequently, six representative machine learning models (KNN, RF, SVR, LGBM, XGBoost, MLPNN) were employed as base learners to construct two types of ensemble models, FWL and Voting, enabling a systematic comparison of their performance. Finally, the predictive mechanisms of the models were analyzed for interpretability at both global and local scales using SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) methods. The results demonstrate that the FWL model achieved optimal predictive performance on the test set (R² = 0.908, RMSE = 2.632), significantly outperforming both individual models and the conventional ensemble method. Interpretability analysis revealed that strain rate and fiber volume fraction are the primary factors influencing dynamic fracture strength, with strain rate demonstrating a highly nonlinear response mechanism across different ranges. The integrated prediction framework developed in this study offers the combined advantages of high accuracy, robustness, and interpretability, providing a novel and effective approach for predicting the fracture behavior of HPFRC under high-strain-rate conditions. Full article

Background/Objectives: MB09 is a denosumab biosimilar to the reference products (RPs) Xgeva and Prolia. A population pharmacokinetic (popPK) meta-analysis was conducted to characterize the denosumab PK profile and to support MB09 biosimilarity. Methods: Pooled denosumab PK data from one phase I study [255 healthy adult men receiving a single 35 mg subcutaneous (SC) dose] and one phase III study (555 postmenopausal women with osteoporosis receiving two 60 mg SC doses, one every six months) were used. A one-compartment model with first-order absorption and elimination and parallel non-linear saturable clearance was used. Body weight was included on clearance as a structural covariate and treatment was tested as a covariate on all PK parameters. PK biosimilarity was assessed at 35 mg dose. Results: For a 70 kg subject, the apparent clearance and central volume of distribution for denosumab were 0.123 L/day [95% confidence interval (CI): 0.114, 0.132] and 9.33 L (95% CI: 9.11, 9.55), respectively. The Michaelis constant was 0.124 ng/mL and the maximum rate for the non-linear clearance was 0.139 ng/day. Model-based bioequivalence criteria were met for RP Xgeva, European and US-sourced, versus MB09 for a dose of 60 mg SC. The mean area under the plasma concentration curve (AUC) resultant from the simulation of MB09 120 mg SC was similar to the published mean AUC observed for Xgeva 120 mg SC every four weeks. Conclusions: This analysis provides a valuable assessment of denosumab PK characteristics and elucidates in more detail how the MB09 PK profile compares to the denosumab RPs, supporting the totality of evidence on MB09 biosimilarity. Full article

In multi-pass welding processes, achieving a uniform and smooth weld surface is crucial for mechanical performance and dimensional accuracy. However, the complex nonlinear relationships between welding parameters and weld bead geometry present significant challenges for traditional optimization methods. This study proposes an intelligent prediction and optimization framework that integrates a backpropagation (BP) neural network with an improved hill-climbing algorithm to enhance weld surface smoothness in automated multi-bead overlay welding. Experimental data collected under varying arc voltages, wire feed rates, and welding speeds were used to train the neural network. The improved hill-climbing algorithm adaptively adjusts weights and biases in the BP model to overcome issues of local minima and slow convergence. Comparative results demonstrate that the proposed method significantly outperforms conventional BP approaches in terms of prediction accuracy and convergence efficiency. Furthermore, optimal welding parameters identified by the model yield smoother weld surfaces, reducing the need for post-processing. This work provides a novel solution for intelligent control and real-time optimization in advanced welding systems. Full article

Revealing the trade-offs, synergies, and driving mechanisms among land use functions is essential for mitigating conflicts between functions, optimizing territorial spatial patterns, and providing policy support for regional sustainable development. Taking the Central Yunnan Urban Agglomeration as a case study, this study adopts a grid-based evaluation unit and employs a multi-model fusion approach to systematically analyze the interaction mechanisms among land use functions. By integrating the Pearson correlation method and root mean square deviation (RMSD) model, the trade-off and synergy relationships and their spatiotemporal evolution were quantitatively assessed. The XGBoost–SHAP model and optimized parameter-based geographical detector (OPGD) were introduced to identify the nonlinear characteristics and interaction effects of influencing factors on land use function trade-offs and synergies. In addition, a geographically weighted regression (GWR) model was used to explore spatial heterogeneity in these effects. The results indicate that (1) from 2010 to 2020, the overall synergy between production and ecological functions (PF&EF) in the urban agglomeration was enhanced, while trade-offs between production and living functions (PF&LF) intensified, and the trade-off intensity between living and ecological functions (LF&EF) decreased. Significant spatial heterogeneity exists among land use function interactions: PF&EF and PF&LF trade-offs are concentrated in the central and eastern parts of the urban agglomeration, while LF&EF trade-offs are more scattered, mainly occurring in highly urbanized and ecologically sensitive areas; (2) the dominant factors influencing land use function trade-offs and synergies include precipitation, slope, land use intensity, elevation, NDVI, Shannon diversity index (SHDI), distance to county centers, and distance to expressways; (3) these dominant factors exhibit strong nonlinear effects and significant threshold responses in shaping trade-offs and synergies among land use functions; and that (4) compared with the OLS model, the GWR model demonstrated higher fitting accuracy. This reveals that the impacts of natural, socio-economic, and landscape pattern factors on land use function interactions are characterized by pronounced spatial heterogeneity. Full article

Wheel slipping during trajectory tracking presents significant challenges for wheeled mobile robots (WMRs), degrading accuracy and stability on low-friction or dynamic terrain. Effective control requires addressing unknown slipping parameters while balancing tracking precision and energy efficiency. To address this challenge, a control framework integrating a sliding mode observer (SMO), an improved particle swarm optimization (PSO) algorithm, and a linear quadratic regulator (LQR) is proposed. First, a dynamic model incorporating longitudinal slipping is established. Second, an SMO is designed to estimate the slipping ratio in real-time, with chattering suppressed using a low-pass filter. Finally, an improved PSO algorithm featuring a nonlinear cosine-decreasing inertia weight strategy optimizes the LQR weighting matrices (Q/R) online to both minimize tracking errors and control energy consumption. Simulations including both circular and sine wave trajectories demonstrate that the SMO achieves rapid and accurate slipping ratio estimation, while the PSO-optimized LQR significantly enhances tracking accuracy, achieves smoother control inputs, and maintains stability under varying slipping conditions. Full article

To address the challenges of low diagnostic accuracy arising from the non-stationary and nonlinear time-varying characteristics of acoustic signals in rolling bearing fault diagnosis, as well as their susceptibility to noise interference, this paper proposes a fault diagnosis method based on a Weighted Average Algorithm–Feature Mode Decomposition (WAA-FMD) and a Local–Global Adaptive Multi-scale Attention Mechanism (LGAF)–Swin Transformer. First, the WAA is utilized to optimize the key parameters of FMD, thereby enhancing its signal decomposition performance while minimizing noise interference. Next, a bilateral expansion strategy is implemented to extend both the time window and frequency band of the signal, which improves the temporal locality and frequency globality of the time–frequency diagram, significantly enhancing the ability to capture signal features. Ultimately, the introduction of depthwise separable convolution optimizes the receptive field and improves the computational efficiency of shallow networks. When combined with the Swin Transformer, which incorporates LGAF and adaptive feature selection modules, the model further enhances its perceptual capabilities and feature extraction accuracy through dynamic kernel adjustment and deep feature aggregation strategies. The experimental results indicate that the signal denoising performance of WAA-FMD significantly outperforms traditional denoising techniques. In the KAIST dataset (NSK 6205: inner raceway fault and outer raceway fault) and the experimental dataset (FAG 30205: inner raceway fault, outer raceway fault, and rolling element fault), the accuracies of the proposed model reach 100% and 98.62%, respectively, both exceeding that of other deep learning models. In summary, the proposed method demonstrates substantial advantages in noise reduction performance and fault diagnosis accuracy, providing valuable theoretical insights for practical applications. Full article

Background: Serum cystatin C is a promising biomarker for vascular risk, yet its nonlinear dose–response relationships and prognostic value in general populations remain unclear, particularly for stroke-specific outcomes. Methods: This study utilized data from the National Health and Nutrition Examination Survey (NHANES) conducted in 1999–2002 cycles. A total of 11,610 participants were included in the primary analysis examining the cross-sectional association between cystatin C and stroke morbidity, using multivariate logistic regression models and odds ratios (ORs). Analyses utilized complete-case data (n = 11,610 for morbidity; n = 11,598 for mortality). Subsequently, 11,598 adults were retained for mortality endpoint analyses, which focused on the longitudinal association between cystatin C and stroke mortality, using cause-specific weighted multivariable Cox models and ratios (HRs). Restricted cubic splines identified nonlinear thresholds, and piecewise regression quantified risk gradients. Models were adjusted for sociodemographic/clinical/behavioral confounders. Results: Serum cystatin C exhibited a nonlinear dose–response relationship with stroke morbidity (p for nonlinear < 0.001), with an inflection point at 1.24 mg/L; below this threshold, each 0.1 mg/L increase conferred 13.84-fold higher odds (95% CI: 7.11–27.03, p < 0.001). For mortality, nonlinear thresholds were identified at 1.24 mg/L for all-cause/cause-specific mortality (HR = 6.73–10.60 per 0.1 mg/L increase, p < 0.001) and 1.81 mg/L for stroke-specific mortality. Conversely, cerebrovascular mortality demonstrated a linear association (HR = 1.43 per 1 mg/L increase, p = 0.008), though cystatin C independently predicted risk (HR = 1.38/continuous, p = 0.034 in fully adjusted models). Conclusions: This study identifies serum cystatin C as an independent predictor after full adjustment of stroke morbidity and all-cause and cardio-cerebrovascular mortality. Consequently, cystatin C emerges as a dual-purpose biomarker for early vascular injury detection in subclinical populations and integrated mortality risk stratification. Future research should validate these thresholds in prospective neuroimaging-confirmed cohorts and investigate interventions targeting cystatin C pathways to optimize preventive strategies. Full article

The rapid and accurate identification of power system transient stability status is a fundamental prerequisite for ensuring the secure and reliable operation of large-scale power grids. With the increasing complexity and heterogeneity of modern power system components, system nonlinearity has grown significantly, rendering traditional time-domain simulation and direct methods unable to meet accuracy and efficiency requirements simultaneously. To further improve the prediction accuracy of power system transient stability and provide more refined assessment results, this paper integrates deep learning with power system transient stability and proposes a transient stability assessment of power systems built upon a deep spatio-temporal feature extraction network method. First, a spatio-temporal feature extraction module is constructed by combining an improved graph attention network with a residual bidirectional temporal convolutional network, aiming to capture the spatial and bidirectional temporal characteristics of transient stability data. Second, a classification module is developed using the Kolmogorov–Arnold network to establish the mapping relationship between spatio-temporal features and transient stability states. This enables the accurate determination of the system’s transient stability status within a short time after fault occurrence. Finally, a weighted cross-entropy loss function is employed to address the issue of low prediction accuracy caused by the imbalanced sample distribution in the evaluation model. The feasibility, effectiveness, and superiority of the proposed method are validated through tests on the New England 10-machine 39-bus system and the NPCC 48-machine 140-bus system. Full article

Background: Medically unexplained oral symptoms/syndromes (MUOS), such as Burning Mouth Syndrome and Persistent Idiopathic Facial Pain, present significant management challenges due to the lack of standardized treatments and high-level evidence. While pharmacotherapy is often employed, real-world data on treatment adherence—a pragmatic proxy for effectiveness and tolerability—remain sparse, especially in Japan. This study aimed to describe the real-world prescribing patterns of antidepressants and dopamine receptor partial agonists (DPAs) for MUOS and retrospectively investigate their association with treatment continuation. Methods: This retrospective observational study analyzed data from patients initiating pharmacotherapy for MUOS at a specialized clinic in Japan (April 2021–March 2023). We used Cox proportional hazards models to evaluate treatment continuation for amitriptyline monotherapy and antidepressant–aripiprazole adjunctive therapy. The primary outcome was the time to discontinuation. Dosage effects were modeled using B-splines to capture nonlinearity. Results: Among 702 MUOS patients who started pharmacotherapy, 493 received amitriptyline as the first prescription, and 108 received aripiprazole as an adjunctive therapy. For amitriptyline monotherapy, a nonlinear relationship was observed between dosage and discontinuation risk, with a relatively lower hazard around 25 mg/day across age groups. In the antidepressant–aripiprazole adjunctive group, the overall hazard ratio for discontinuation was higher (HR = 4.75, p < 0.0005) compared to non-adjunctive therapy, likely due to indication bias reflecting more treatment-resistant cases. However, within the aripiprazole adjunctive group, a U-shaped relationship was identified between maximum aripiprazole dosage and discontinuation risk, with the lowest hazard (HR ≈ 0.30) observed at approximately 1.7–1.8 mg/day. Mild side effects such as drowsiness, dry mouth, constipation, tremor, insomnia, and weight gain were noted, but no severe adverse events occurred. Conclusions: This real-world data analysis suggests specific dosage ranges (amitriptyline ≈ 25 mg/day; aripiprazole augmentation ≈ 1.7–1.8 mg/day) are associated with longer treatment continuation in MUOS patients. Treatment continuation reflects a crucial balance between symptom relief and tolerability, essential for managing these chronic conditions. It is critical to emphasize that these findings are descriptive and observational, derived from a specialized setting, and do not constitute prescriptive recommendations. They highlight the importance of individualized dosing. Definitive evidence-based strategies require validation through prospective randomized controlled trials. Full article

Stains, defects, and snow accumulation constitute three prevalent photovoltaic (PV) anomalies; each exhibits unique color and thermal signatures yet collectively curtail energy yield. Existing detectors typically sacrifice accuracy for speed, and none simultaneously classify all three fault types. To counter the identified limitations, an enhanced YOLOv11 framework is introduced. First, the hue-saturation-value (HSV) color model is employed to decouple hue and brightness, strengthening color feature extraction and cross-sensor generalization. Second, an outlook attention module integrated into the backbone precisely delineates micro-defect boundaries. Third, a mix structure block in the detection head encodes global context and fine-grained details to boost small object recognition. Additionally, the bounded sigmoid linear unit (B-SiLU) activation function optimizes gradient flow and feature discrimination through an improved nonlinear mapping, while the gradient-weighted class activation mapping (Grad-CAM) visualizations confirm selective attention to fault regions. Experimental results show that overall mean average precision (mAP) rises by 1.8%, with defect, stain, and snow accuracies improving by 2.2%, 3.3%, and 0.8%, respectively, offering a reliable solution for intelligent PV inspection and early fault detection. Full article

The mechanical performance of laser-welded Ti6Al4V alloy joints is governed by multiple process parameters with complex interplay, leading to nonlinear correlations, that complicate the quest for optimal parameters. In this paper, a reverse engineering model for process parameters was developed using backpropagation (BP) neural networks, targeting mechanical properties as the optimization objective for inverse parameter design. The BP neural network was enhanced via differential evolution tuning, achieving significant improvements in both mechanical property prediction and process parameter inversion. The prediction model demonstrated a relative error of approximately 3%, whereas the inverse model exhibited an error of about 6% under varying process conditions. A novel hybrid BP-WC model was then proposed by fusing weight coefficients from both the prediction and inverse models. This model reduced the inverse error of process parameters to 3%, providing a robust framework for efficient parameter optimization in laser welding for Ti6Al4V alloy. Full article