Search Results (17,606)

The semi-structured vineyard environments contain numerous irregular obstacles, posing stringent requirements on the navigational safety and trajectory tracking accuracy of mobile robots. To address this challenge, this study first optimizes the A* algorithm at the global planning layer by incorporating a composite turning-cost evaluation model and a heuristic dynamic weighting strategy, thereby effectively enhancing search efficiency and path smoothness. Building upon this, a local planning method is further developed by integrating an adaptive sampling mechanism with high-order interpolation-based kinematic continuity constraints and a heading-rate-driven velocity smoothing strategy. This enables the robot to maintain a safe clearance from obstacles in dynamic environments, thereby significantly enhancing the smoothness of obstacle avoidance maneuvers. Both simulation and field experiment results demonstrate that the improved global planning algorithm reduces the number of critical turning points and the total turning angle by up to 18.0%. Across three typical path scenarios, the proposed fusion method reduces the robot’s positional deviation by up to 21.8% and the heading angle deviation by up to 29.6%, while concurrently increasing the safe clearance from obstacles by 42.0%. These findings suggest that the proposed framework establishes a viable algorithmic foundation for improving the navigation accuracy, obstacle avoidance stability, and operational safety. Full article

Injecting CO₂ into gas reservoirs is a crucial approach for enhancing natural gas recovery and achieving CO₂ geological storage, where the gas–gas diffusion behavior between CO₂ and CH₄ directly influences gas mixing efficiency. Direct observation of the spatiotemporal evolution of concentration fields during diffusion remains insufficient. In this study, a gas–gas diffusion experimental system capable of multi-time and multi-space stratified sampling within a high-temperature high-pressure PVT cell was established based on real reservoir fluid compositions. Non-equilibrium diffusion experiments were conducted under different pressures, different initial CO₂ mole fractions, and different diffusion times. A diffusion model was developed according to Fick’s second law. The results suggest that the gas column can be divided into a natural gas zone, a transition zone, and a CO₂ zone by the dimensionless concentration gradient threshold. At 5 MPa, the transition zone width expands rapidly within the first 4 h (dimensionless width increases from 0 to 0.6902), after which growth slows. Increasing pressure significantly inhibits diffusion, reducing transition zone width and prolonging equilibration time. Rising initial CO₂ concentration also suppresses diffusion mixing, particularly in the later stage. Component profile analysis confirms that, under high pressures and high CO₂ concentrations, the diffusion flux across the interface is weakened. Compared to CH₄, the diffusion equilibration time of CO₂ is shorter and more sensitive to pressure changes. The obtained diffusion coefficients (CH₄: 2.92 × 10⁻⁸ to 4.79 × 10⁻⁸ m²/s; CO₂: 3.91 × 10⁻⁸ to 6.08 × 10⁻⁸ m²/s) are on the order of 10⁻⁸ m²/s, consistent with bulk-phase PVT literature data, validating the reliability of the experimental method and inversion model. This study lays an experimental foundation for predicting multi-component gas mass transfer under conditions of CO₂-enhanced gas recovery and CO₂ geological storage. Full article

In this paper, we investigate high-order soliton solutions of the Kundu–Eckhaus equation using the Dbar method. As a key technique in the field of integrable systems, the Dbar method occupies an important position in the study of exact solutions due to its unique advantages. Exact solutions for first-order to fifth-order solitons are presented under the zero-background condition. The nonlinear dynamics of the solitons are also discussed, including their interaction behaviors. The results are also depicted graphically in both 3D and 2D for different values of associated parameters. Full article

The relevance of this study is determined by the need for a scientifically grounded assessment of environmental risks associated with rocket launches and by the necessity of ensuring environmental safety in areas potentially affected by space activities. Comprehensive monitoring of rocket-stage impact zones and adjacent populated areas is especially important because pollutant distribution depends on natural, climatic, and spatial factors. This study assesses the environmental impact of the “Soyuz-2.1a” launch with the “Progress MS-29” cargo spacecraft in Kazakhstan using integrated field monitoring, laboratory analysis, and geoinformation methods. The work should be interpreted as a single-event environmental monitoring assessment, while historical monitoring data from 2020–2023 were used only as a retrospective comparative background for the U-25 impact area and were not included in the main BACI statistical analysis. The study covered the launch site, adjacent populated areas, and the U-25 stage impact zone. A before–after control-impact (BACI) design with distance stratification and consideration of wind direction was applied to identify post-launch changes. Measurements below the limit of detection and limit of quantification were processed using censored-data methods, including Regression on Order Statistics (ROS) and the Kaplan–Meier estimator. Spatial analysis was used to generate concentration fields, contour maps, and risk zones, revealing an anisotropic distribution of environmental stress in the downwind sector. An integrated hazard quotient (HQ) metric was applied to compare air, water, and soil conditions on a unified scale. The results indicate that the post-launch impact was localized and time-limited, with the greatest sensitivity observed in the soil component of the U-25 zone during the early post-launch period. Atmospheric air and water indicators remained within regulatory limits in populated areas. The proposed approach combines BACI monitoring, censored-data analysis, spatial modeling, and GIS-based visualization, providing a reproducible framework for the environmental assessment of rocket-stage impact areas. The practical recommendations include staged post-launch monitoring, temporary restriction of access to high-stress zones, primary reclamation of contaminated soil, and the use of WebGIS tools to support environmental decision-making. Full article

Background/Objectives: Efficient patient flow in urgent urology services is critical to timely care delivery, yet workflow bottlenecks in specialty clinics remain underexplored. This study aimed to identify workflow bottlenecks, evaluate patient flow and staff attitudes, and explore clinician readiness for digital decision-support in a high-volume NHS emergency urology walk-in clinic. Methods: A two-week observational study was conducted at an emergency urology service in London. Time-stamped pathway data were collected for 80 patient journeys to identify total clinic duration. Differences associated with investigation ordering and senior escalation were analyzed using t-tests. Clinicians (n = 34) completed a questionnaire assessing perceptions of AI, and nursing staff provided qualitative feedback on operational pressures. Results: Mean total clinic journey time was 2 h 42 min, with the post-assessment phase accounting for 64% of total duration. Investigation ordering was the principal source of delay: patients undergoing investigations remained significantly longer in clinic than those who did not (3 h 17 min vs. 2 h 15 min, p < 0.05), and doctor-to-discharge time more than doubled (2 h 20 min vs. 1 h 2 min, p < 0.005). Senior escalation did not significantly prolong patient flow. Staff surveys demonstrated moderate trust in and comfort with AI as a decision-support tool. Nursing feedback highlighted inappropriate attendances, limited staffing, and workspace constraints as key stressors. Discussion: Delays were primarily driven by investigation ordering rather than senior review, identifying investigation timing as a potential target for future pathway optimisation. Conclusions: Investigation-related delays were the dominant workflow bottleneck. While no AI system was deployed in this study, these findings provide empirical groundwork to inform the design and prospective evaluation of AI-supported triage in specialty acute care settings. Full article

To address the challenge of exorbitant computational costs in the reliability analysis of complex steel structures, which stems from the impact of multiple sources of uncertainty throughout their entire lifecycle, this paper presents a comparative evaluation of the explicit reconstruction of the Limit State Function (LSF) using SVM combined with Hyperparameter Optimization (HPO) for structural reliability analysis under constrained computational budgets. Although traditional Monte Carlo simulation (MCS) exhibits high accuracy, it requires a substantial number of finite element calculations, rendering it difficult to satisfy the efficiency requirements of engineering projects. Conversely, the first-order and second-order reliability methods (FORM/SORM) offer high computational efficiency but rely on explicit limit state functions, posing challenges for their direct application to complex structural systems. Thus, this study initially acquires response samples of the structure under various combinations of random variables through a limited number of finite element analyses (FEA). Subsequently, it employs an SVM to develop a highly accurate equivalent explicit limit state function, which serves as a substitute for the original implicit limit state function. Finally, it integrates Monte Carlo simulation to efficiently evaluate the structure’s failure probability and reliability index. Meanwhile, to tackle the problem of SVM model performance being highly susceptible to hyperparameters, this study presents a comparative analysis of four strategies: Bayesian Optimization (BO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Random Search (RS), aiming to identify the optimal parameter combination and improve the model’s generalization capability. Through verification with four progressive examples, including linear, nonlinear, truss, and multistory frame structures, the results demonstrate that the proposed method can accurately characterize the nonlinearity of structural responses. The obtained failure probabilities and reliability indices are in close agreement with those obtained from the direct Monte Carlo simulation (MCS) and existing research. Moreover, while maintaining computational accuracy, the method significantly reduces computational costs, thereby providing an efficient and practical solution for structural reliability analysis in engineering practice. Full article

The health status of rolling bearings is critical to the normal operation of rotating machinery. To effectively extract vibration signal features and accurately identify different fault types, a novel method based on enhanced composite multi-scale slope entropy (ECMSE) and a honey badger algorithm-optimized kernel extreme learning machine (HBA–KELM) is proposed. Specifically, ECMSE integrates high-order differences into the composite multi-scale framework to capture high-frequency information while preserving low-frequency characteristics, thereby enhancing the discriminability of time-series representations. Meanwhile, an average coarse-graining strategy is incorporated to achieve a more comprehensive characterization of the signals. The extracted features are then input into the HBA–KELM classifier for fault identification. Experiments conducted on two public and private rolling bearing datasets demonstrate that our method achieves superior performance in distinguishing different fault types and damage levels compared with several existing approaches. Full article

Al-Sc alloys are widely applied in aerospace and automotive lightweighting owing to the excellent performance imparted by nano-sized Al₃Sc precipitates. Accurate simulation of the full-cycle precipitation kinetics is critical for optimizing aging heat treatment processes, but the traditional Lifshitz-Slyozov-Wagner (LSW) theory is only applicable to the coarsening stage, while the conventional Kampmann-Wagner-Numerical (KWN) model suffers from severe numerical diffusion and fails to correct errors caused by discontinuous precipitate size distributions. To address these issues, a high-resolution population dynamics model based on the Van Leer limiter was established in this study, which is an improved KWN model that simultaneously considers interfacial energy transition during nucleation and coarsening and the effect of precipitate volume fraction on particle growth rate. Isothermal aging precipitation of Al₃Sc in Al-0.2 wt.% Sc and Al-0.3 wt.% Sc alloys at 350 °C was systematically simulated, and key kinetic parameters including nucleation rate, critical nucleation radius, average precipitate radius, and normalized size distribution were calculated. The results show that the simulated average radius and normalized size distribution are in excellent agreement with experimental data, and the model accurately captures the plateau characteristic of average radius evolution during aging. Increasing Sc content significantly shortens the nucleation-growth stage and advances the onset of coarsening by approximately one order of magnitude. Compared with the LSW theory, the proposed model achieves second-order accuracy in smooth regions and suppresses spurious oscillations in discontinuous regions, fully reproducing the incubation, nucleation-growth, and coarsening stages of precipitation. This high-resolution model provides reliable theoretical support for the aging process optimization of Al-Sc alloys and offers an effective numerical method for precipitation kinetics simulation of other dilute binary alloys. Full article

Reliable solar energy harvesting demands advanced control strategies capable of maintaining thermal precision despite inherent environmental unpredictability. This research addresses the critical challenge of temperature regulation in the solar furnace system, which is hindered by severe non-linearities and stochastic environmental uncertainties. The study aims to transition Fractional-Order PID (FOPID) control from theoretical design to reliable industrial application by accounting for the Uncertain Design Vector (UDV) during the tuning phase. A Reliability-Based Design Optimization (RBDO) framework is proposed, utilizing a hybrid Multi-Objective Imperialist Competitive Algorithm (MOICA) integrated with Monte Carlo Analysis (MCAR). This approach simultaneously optimizes the Maximum Sensitivity (Ms), the integral of Time-weighted Absolute Error (ITAE) and their sensitivities, while ensuring physical realizability through the FOPID structure. Crucially, the simulation results demonstrate that the RBDO-tuned FOPID design achieves optimal performance levels comparable to deterministic methods while significantly reducing the overall system sensitivity by 35% to 55% compared to both deterministic and literature-based methods (GA-FOPID and PSO-FOPID). The study concludes that integrating probabilistic reliability into multi-objective metaheuristics provides a robust control strategy for high-temperature solar facilities, effectively mitigating the performance degradation caused by real-world parameter fluctuations and ensuring consistent operational stability. Full article

The development of biodegradable scaffolds for load-bearing bone tissue engineering (BTE) presents a fundamental multi-criteria optimization challenge, requiring a simultaneous balance among mechanical performance, biological integration, and degradation kinetics. These criteria are inherently conflicting: composite formulations with the highest compressive strength frequently exhibit suboptimal porosity, while those with superior osteoconductivity often lack sufficient load-bearing capacity. To address this challenge rigorously, this study establishes a hybrid Fuzzy Analytic Hierarchy Process–Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy AHP-TOPSIS) framework to evaluate and rank five clinically relevant biodegradable polymer–ceramic composite candidates: PLA/Hydroxyapatite (PLA/HA), PCL/Hydroxyapatite (PCL/HA), PLGA/Bioactive Glass (PLGA/BG), PLA/Carbon Nanotubes (PLA/CNT), and PLA/Magnesium (PLA/Mg). Quantitative property data were systematically extracted from ten peer-reviewed experimental studies published between 2021 and 2025, and converted into Triangular Fuzzy Numbers (TFNs) to explicitly model inter-study variability arising from differences in fabrication methods, filler loading, and testing conditions. Fuzzy AHP analysis identified Compressive Strength (w = 25.2%) and Cell Viability (w = 21.5%) as the dominant decision criteria for load-bearing cortical bone repair. The Fuzzy TOPSIS ranking identified PLA/HA as the optimal composite candidate (Closeness Coefficient, CCᵢ = 0.677), demonstrating the superior multi-criteria balance required for cortical bone repair applications. Although PLA/CNT achieved the highest mechanical strength, it was outranked due to lower osteoconductivity and elevated cytotoxicity uncertainty at high nanotube concentrations (CCᵢ = 0.544). Sensitivity analysis across five distinct weighting scenarios confirmed the robustness of PLA/HA as the primary candidate. These findings provide a validated, replicable computational blueprint for evidence-based scaffold material selection, with direct implications for reducing the burden of costly trial-and-error experimentation in BTE research. Full article

Deep neural networks are vulnerable to adversarial examples, even in hard-label black-box settings where only the top-1 prediction is available. To address the challenges of high-dimensional optimization under limited query budgets, we propose two query-efficient attack methods: Adam-OPT, which integrates Adam-based adaptive optimization into the ray-search framework to stabilize and accelerate zeroth-order gradient updates; Prior-Adam-OPT, which further incorporates transfer-based priors from surrogate models to enhance gradient estimation. Adam-OPT leverages historical gradient information and per-parameter adaptive updates to improve convergence, while Prior-Adam-OPT constructs a prior-guided orthogonal search basis that combines surrogate and random directions, enhancing both gradient accuracy and query efficiency. Our approach demonstrates superior performance across CIFAR-10, ImageNet, and zero-shot CLIP models, consistently reducing perturbation magnitudes and improving attack efficiency compared to state-of-the-art hard-label attacks. Ablation studies highlight the importance of the number of vectors used for gradient estimation and the quality of surrogate models, showing that combining adaptive optimization with transfer-based priors provides a scalable and robust framework for generating high-quality adversarial examples in challenging black-box scenarios. Full article

Protein molecularly imprinted polymers (MIPs), as artificial antibodies, are promising for protein separation due to their low cost, easy preparation, and high stability, but their performance is limited by poor mass transfer, imprecise imprinting, and single interaction modes. Herein, dendritic mesoporous silica nanoparticles (DMSNs) were used as the support, and a self-designed multifunctional poly(ionic liquid) macromonomer (p(VIMCD-co-VAIM-co-VSIM-co-VVIM)) served as the functional monomer to achieve directional anchoring of cytochrome C (Cyt-C). Surface-imprinted microspheres (DMSNs@MPS@PILs-MIPs) were prepared via free-radical copolymerization for Cyt-C recognition. The DMSNs possessed interconnected mesoporous channels, good dispersibility, an average particle size of ~80 nm, and a specific surface area of 267.97 m²/g. Ionic liquid monomers were synthesized via alkylation, and the macromonomer was constructed through a two-step method. Molecular dynamics simulations and spectroscopic characterization revealed the macromonomer-stabilized Cyt-C conformation, with interactions dominated by van der Waals forces. The DMSNs@MPS@PILs-MIPs featured a thin imprinted layer (~5 nm) to reduce mass-transfer resistance. Adsorption studies showed Cyt-C adsorption followed Langmuir and pseudo-second-order models, with a maximum capacity of 383.14 mg/g and an imprinting factor of 2.17. Only 12% capacity loss occurred after repeated cycles, indicating robust regeneration stability. This study provides a feasible strategy for constructing protein surface-imprinted polymers based on multifunctional synergistic interactions and conformational stabilization. Full article

This paper discusses the influence of the periodicity of finite-element (FE) discrete models and its influence on solutions to the one-dimensional (1-D) wave equation. Numerical solutions to wave-propagation problems obtained via the displacement-based formulations of the finite-element method (FEM) often exhibit high-frequency behavior, which is frequently dismissed in the literature as undesired, spurious, and/or having no physical meaning. In this paper, we verify this notion by demonstrating that this behavior is not merely a computational anomaly but is due to the inherent periodic properties of discrete numerical models. Using Bloch’s theorem, we reveal and demonstrate how, at high frequencies, the discrete nature of FEM numerical models leads to the prevailing behavior governed by the periodic nature of the computational models. In order to illustrate this phenomenon, we investigate 1-D wave propagation in rods, leveraging the non-dispersive nature of the governing equation as a benchmark. In addition to the classical and specialized FEM, we analyze two alternative formulations: the time-domain spectral finite-element method (TD-SFEM) and a novel spline-based finite-element method (spFEM) proposed by the authors. The results obtained and presented explain qualitatively the origins of these numerical anomalies and suggest strategies to mitigate their effects, effectively shifting the periodicity-induced behavior beyond the range of physically relevant frequencies by appropriate selection of approximation polynomials. The authors demonstrate that this can be fully achieved only in the case of spFEM, for which the usable percentage of the available spectra of eigenfrequencies reaches 67%, while in the case of other FEM approaches discussed is significantly smaller as determined by numerical dispersion and the presence of frequency band gaps. Full article

Background: The precise identification of cancer subtypes through the integration of multi-omics data has emerged as a key research direction in bioinformatics. Among existing multi-omics integration methods, similarity graph-based clustering algorithms have attracted widespread interest owing to their capacity to effectively characterize the association patterns between samples. However, the majority of existing methods primarily focus on first-order relationships among samples while ignoring the prevalent high-order neighborhood relationships, and fail to fully exploit the complementary information from different omics. Methods: To address these limitations, we propose an innovative multi-omics integration framework termed MHSGTR, which integrates multi-omics data by combining Motif high-order similarity graphs and tensor regularization to identify cancer subtypes. Specifically, MHSGTR introduces Motif theory to construct a high-order similarity graph and designs a high-order graph learning term to obtain a hybrid similarity that integrates both first-order and high-order information, thereby capturing the latent high-order structural information among samples. For multi-omics data integration, we employ third-order tensor regularization constraints to explore complementary information across multi-omics data, coupled with an attention module to adaptively learn omics-specific weights for constructing a consensus similarity graph. Final clusters are derived via spectral clustering. Results: Comprehensive experiments on eight TCGA cancer datasets and a case study on adrenocortical carcinoma (ACC) demonstrate that MHSGTR achieves superior clustering performance and identifies cancer subtypes with significant biological differences, showcasing its effectiveness in robust multi-omics integration. Full article

The present paper proposes a methodology for the analysis and modelling of transient events in a data center based on real-world high-resolution voltage and current measurements. The proposed approach includes the identification of relevant events, temporal segmentation, multivariate representation, and the application of dimensionality reduction techniques to obtain compact representations of the observed dynamics. A total of eight representative transient events were identified in the available dataset. These events were characterized by short-duration disturbances of moderate magnitude, which is consistent with the operation of highly reliable infrastructures. Three main methods were evaluated: PCA/POD, Kernel PCA, and Autoencoder. The results show that all three approaches are capable of reconstructing the event dynamics with low reconstruction errors, suggesting the presence of a low-dimensional structure in the analyzed data. Among the evaluated methods, PCA/POD provided the best balance between compactness, interpretability, and computational efficiency, while Kernel PCA and Autoencoder offered advantages for representing nonlinear behaviors. The results provide case-study evidence on the feasibility of constructing reduced-order representations for the analysis and monitoring of transient events in data centers under limited-data conditions. Full article