Search Results (6,032)

The digital preservation of built cultural heritage requires precise documentation techniques capable of capturing complex architectural geometries often affected by occlusions and data voids. This study presents a robust multi-sensor fusion workflow integrating Terrestrial Laser Scanning (TLS) and Unmanned Aerial Vehicle (UAV) photogrammetry for the 3D reconstruction of the Hasaköy (Sasima) Church in Niğde, Türkiye. To address the limitations of traditional registration methods, specifically the susceptibility of the Iterative Closest Point (ICP) algorithm to local minima in datasets with partial overlaps, this study proposes a fine-tuning approach based on the Multi-population Based Differential Evolution (MDE) algorithm. The methodology employs a coarse-to-fine strategy, initiating with Fast Point Feature Histogram (FPFH) extraction and RANSAC (Random Sample Consensus) for global alignment, followed by TR-ICP, MDE, PSO, and Aquila Optimizer (AO) evaluation, computational-time analysis, FPFH-radius sensitivity testing, and 6-DoF transformation decomposition to characterize both accuracy and operational cost. In the 30-run fine-tuning evaluation, MDE reduced the mean bidirectional trimmed RMSE from 0.4152 m for TR-ICP to 0.3726 m. With a population parameter of 10, MDE retained a low median RMSE of 0.3718 m, while PSO exhibited a wider stochastic tail under the same bounded 6-DoF search budget. AO produced a higher mean bidirectional trimmed RMSE of 0.5233 m. The decimeter-scale bidirectional RMSE should be interpreted as a cross-source, partial-overlap distance metric rather than sensor precision; the overlapping facade objective was approximately 2.4–2.8 cm, and the UAV block was independently controlled with a 1.34 cm GCP RMSE. This study establishes a transparent and reproducible framework for heritage documentation, supporting the faithful digital preservation of endangered monuments with complex typologies. Full article

This paper addresses the current development status of a innovative direct high-pressure electrolyser (DHPEL, operating up to 700 bar) and its integration into a microgrid system in which solar energy constitutes the primary energy source and a hybrid energy storage system, comprising a battery and hydrogen, is employed. The DHPEL under development enables the direct production and storage of hydrogen at high pressures, thereby obviating the need for intermediate mechanical compression. In combination with standardized pressure vessels (300–350 bar) or the increasingly widespread use of CFRP-based high-pressure storage tanks (up to 700 bar), the DHPEL concept represents a technically and economically attractive option for microgrids with hybrid energy storage. The hybrid storage concept is based on functional differentiation between the storage media: the battery is intended to act predominantly as a buffer or short-term storage unit, and the hydrogen is designated for long-term energy storage. In principle, this configuration facilitates an autonomous energy supply relying exclusively on renewable energy sources; this is achieved by enabling the surplus solar energy generated in summer to be converted into hydrogen and subsequently utilized in winter. A rule-based energy-management algorithm is presented, prioritizing hydrogen production from surplus energy during the summer period and aiming to minimize interaction with the public electricity grid. This is particularly relevant for high-latitude regions, such as Germany, where solar irradiation is significantly lower in winter than in summer. A quasi-optimal sizing of all components in the microgrid, along with a realistic techno-economic assessment of the overall system, is performed using an energy-management model implemented in Simulink and utilised with realistic boundary conditions. A case study utilizing realistic solar generation and empirically derived electrical load profiles demonstrates the technical and economic viability of seasonal energy shifting from summer to winter (resulting in an autarky degree exceeding 1) within an economically acceptable cost range. Full article

In transient stability preventive control of power systems, time-domain simulation is computationally intensive. In addition, the initial operating feature data often contain abundant redundant and irrelevant information. These factors may adversely affect the assessment performance of machine learning models. To address these issues, a transient stability preventive control method based on the sample convolution and interaction network (SCINet) is proposed. First, a feature selection algorithm based on the orthogonal maximal information coefficient and information gain (OMICIG) is developed to extract the key operating features of the system. Second, the SCINet model is employed to learn the nonlinear mapping relationship between the selected key operating features and the transient stability index (TSI). Then, the trained SCINet model is embedded into the transient stability constrained optimal power flow (TSCOPF) model as a surrogate transient stability constraint. In this way, the complicated computation associated with nonlinear differential-algebraic equations (DAE) in the conventional TSCOPF model is avoided. Furthermore, an improved dung beetle optimizer (IDBO) algorithm is used to iteratively solve the resulting model, thereby deriving a preventive control strategy that ensures transient stability while maintaining system operating economy. Finally, simulation studies on the New England 10-machine 39-bus and the IEEE 118-bus system demonstrate the effectiveness of the proposed method. Full article

For microbiological diagnostics, accurately counting and segmenting microbial colonies is extremely important. However, manual methods are labor-intensive and yield inconsistent results. We develop a hybrid model using swarm intelligence, combining a convolutional transformer with nested skip connections and global context with channel and spatial attention. Parameter tuning is supported by a variety of swarm optimization algorithms (e.g., Particle Swarm Optimization, Quantum-behaved Particle Swarm Optimization, and Differential Evolution Particle Swarm Optimization). Morphological refinement, including a further watershed transform, an attention graph, and post-processing, enhances colony boundaries by separating them. Grad-CAM++, Integrated Gradients, and temperature scaling provide a transparent and trustworthy model through explainability and post hoc calibration. The proposed model was extensively tested on the Microbial Colony Recognition and Circular Bacterial Colony Datasets, achieving a Dice score of 94.2%, an Intersection over the Union of 88.6%, and a mean absolute counting error of 2.7 colonies. These results significantly outperform several baseline models, including U-Net (88.1%), U-Net++ (89.7%), Attention U-Net (90.6%), and Swin-Unet (91.4%). Statistically significant improvements were confirmed (p < 0.01). A cross-dataset analysis demonstrates the framework’s robustness and cross-domain applicability, and positions it as a trustworthy, explainable automated model for assessing microbial colonies in laboratory and clinical settings. Full article

Aiming at the issues of complex waveforms and low retracking accuracy in coastal satellite altimetry, this paper proposes a complete data processing workflow comprising Fully Focused Synthetic Aperture Radar (FFSAR) waveform processing, waveform classification, denoising, and retracking. Based on actual Sentinel-3A waveforms offshore of Hong Kong, a simulated dataset containing 35,409 waveforms across 17 categories was constructed. A Wave-Transformer classifier based on the Transformer architecture is proposed, achieving 89.16% accuracy with F1-scores above 78% for all categories. Differentiated strategies are adopted for different waveform types: a 3$\sigma$riterion for abnormal peaks, the Dijkstra algorithm for multi-peak waveforms, sub-waveform secondary retracking for trailing noise, and a Modified-Adaptive model for sharp waveforms. Multi-metric evaluation shows that UFSAR and FFSAR outperform PLRM in data validity, retracking success rate, and MQE. In this study, within 10 km of the coast, the Root Sum Square (RSS) of FFSAR sea surface height (SSH) is 4.31 cm lower than that of UFSAR. Validation against tide gauge data shows FFSAR achieves a correlation coefficient of 0.82 and an RMSE of 6.20 cm, superior to UFSAR (0.76, 7.55 cm) and PLRM (0.74, 9.49 cm). Full article

The fatigue failure of railway axles is directly associated with the operational safety of trains. As 25CrMo4 steel is commonly employed for high-speed train axles, precise evaluation of its fatigue life is essential for transportation reliability. This study compared six machine learning models following hyperparameter optimization via a differential evolution algorithm. The DE-optimized Gaussian process regression (DE-GPR) model exhibited superior predictive performance, achieving a coefficient of determination (R²) of 0.8020 and a root mean square error (RMSE) of 0.1250 on the most significant outer test fold. Furthermore, an interpretable analysis of the model utilized a combination of SHapley Additive exPlanations (SHAP) and partial dependence plots (PDP) to elucidate feature importance. The results indicate that the applied stress level is the predominant feature affecting fatigue life predictions and that it slightly interacts with surface residual stress and full width at half maximum to influence the predicted fatigue life. This study can provide valuable insights into the fatigue life assessment and process optimization of 25CrMo4 steel components. Full article

This study proposes an experimental data-driven hybrid prediction framework for hydraulic turbine performance using a Particle Swarm Optimization-enhanced Extreme Learning Machine (PSO-ELM). The performance of three hydraulic turbines, namely Pelton, Kaplan, and Francis turbines, was experimentally investigated under different jet-opening and guide-vane conditions. The experimental results showed that the Pelton turbine (PT) achieved its highest efficiency at low jet opening, whereas the Kaplan and Francis turbines performed better at higher guide-vane openings. The measured data includes 36 tests, which were then used to evolve and evaluate hybrid ML models for predicting hydraulic power and efficiency. Jet-opening or guide-vane position (25%, 50%, 75% and 100%) and rotational speed were used as input variables, while brake power and efficiency were used as output variables. The proposed PSO-ELM model was compared with other optimized ELM models, including Genetic Algorithms Extreme Learning Machine (GA-ELM), Differential Evolution Extreme Learning Machine (DE-ELM), and Whale Optimization Algorithm Extreme Learning Machine (WOA-ELM), as well as Particle Swarm Optimization–Adaptive Neuro-Fuzzy Inference System (PSO-ANFIS) and Particle Swarm Optimization–Multi-Layer Perceptron (PSO-MLP) models. The suggested method presents a hopeful structure for tackling the difficulties linked to performance evaluation, thus enabling a more dependable and effective use of energy resources. The main findings validate that a PSO-based structure reaching an R² value of 0.997 is more efficient in predictive tool performance optimization for hydropower systems. Full article

This study analyzed the spatial distribution characteristics of the foot-end trajectory of a robotic leg mechanism during different gait phases. Based on this analysis, a task space partition-based dimensional parameter optimization method was proposed. To further evaluate the spatial distribution of the high-performance transmission regions after optimization, a box-counting dimension and lacunarity were introduced as supplementary characterization indices. First, according to the functional requirements of different gait phases, the task space of the mechanism is partitioned into stance, mid-swing, and swing-transition regions. A unified kinematic model and singularity criterion are then established for the planar five-bar mechanism, and mechanism performance indices for different task regions are constructed based on the Jacobian matrix to characterize the force and velocity transmission capabilities of the mechanism, as well as its singularity margin. A genetic algorithm is used to perform dimensional synthesis optimization of the mechanism parameters. Furthermore, a task space transmission performance field is introduced, and the area ratio, box-counting dimension, and lacunarity of regions with high performance are used to characterize the spatial structure of high-performance transmission regions before and after optimization. Finally, a series of theoretical calculations and physical experiments are conducted to verify that the differential characteristics of the mechanism have a significant influence on both its static and dynamic performance. The experimental results show that the optimized mechanism achieves lower normalized objective values in all task regions and outperforms the reference mechanism in load capacity, static power consumption, positioning accuracy, and trajectory consistency. The maximum static load capacity reaches 1.29 times that of the reference mechanism, while the static power consumption is reduced to approximately one half of that of the reference mechanism. Full article

Constrained optimization plays a central role in transport and logistics location problems, such as depot siting under geometric or infrastructure-related constraints. In practice, the classification of constrained extrema by classical second-order methods, typically based on bordered Hessians and the explicit manipulation of the total differentials of the constraint functions, can be cumbersome and error-prone, especially in engineering-oriented applications. In this paper, we present algorithmic procedures for the classification of constrained extrema in low-dimensional problems (2D and 3D), with applications to transport location models. The proposed approach does not avoid the use of constraint derivatives, since first-order constraint information is necessary for any local constrained classification procedure. Rather, it avoids the explicit manipulation of the total differentials of the constraints during the application phase. The required constraint information is incorporated through first-order partial derivatives evaluated at the stationary point, leading to simple algebraic test coefficients derived from the second derivatives of the Lagrangian. The procedures apply to regular non-degenerate cases and require only the solution of Fermat-type systems together with the evaluation of low-order determinants. Their practical relevance is illustrated through a transport depot location problem with geometric constraints, showing how the proposed approach can provide a transparent and effective decision-support tool for transport and logistics engineering. Full article

Against the backdrop of the “Dual Carbon” strategy and global shipping digitalization, data elements have emerged as the key enabling factor and predictive correlate of coastal port smartness. Using panel data for seven coastal provinces/municipalities and eight coastal ports in China from 2017 to 2024, this paper constructs a “base-supply-flow-use” data element development index (DEDI) and a “WSR” coastal port smartness index (CPSI), employing VHSD-EM dynamic model, random forest algorithm, and partial effect model to examine the association patterns, nonlinear responses, and differentiated enhancement pathways between data elements and port smartness. Findings reveal: (1) CPSI and DEDI exhibit a high positive correlation with narrowing regional disparities; (2) CPSI shows stepwise spatial differentiation, with Shanghai and Ningbo-Zhoushan Ports leading, while Guangdong demonstrates “data advancement but smartness lag”; (3) in the random forest model, the predictive contribution of DEDI to CPSI is 13.586%, which ranks behind digital inclusive finance and openness level but is higher than regional economic strength and innovation output. The combined predictive contribution of the DEDI main effect and its interaction terms reaches 32.567%; (4) the univariate partial effect of DEDI on predicted CPSI followed a three-stage nonlinear pattern of initial accumulation, accelerated improvement around a threshold of DEDI ≈ 0.215, and diminishing marginal effects at higher levels; and (5) the joint partial effects of DEDI with digital inclusive finance, economic development, fiscal transportation expenditure, and innovation output showed clear dimensional and regional heterogeneity. Accordingly, four policy pathways are proposed: constructing a full-chain data element system, enabling synergistic empowerment of data and supporting elements, formulating regionally differentiated catch-up strategies, and strengthening the dual-wheel support of digital inclusive finance and opening-up—all aimed at advancing the development of world-class ports. Full article

Continuous dynamic monitoring of coastline changes is essential for revealing the evolutionary laws and spatiotemporal characteristics of coastal systems. In this study, we employed AlphaEarth Foundations (AEF) data and Sentinel-2 imagery to investigate coastline and land use changes in the Pearl River Estuary (PRE) region over the period 2017–2023. The Random Forest (RF) algorithm was adopted to extract coastlines and classify coastal land-use types, after which their spatiotemporal evolution was quantitatively analyzed. The results demonstrate that the classification performance of AEF data is significantly better than that of Sentinel-2 imagery, with the average overall accuracy and Kappa coefficient exceeding 92% and 89%, respectively. The PRE coastline shows an evolutionary pattern of “overall contraction accompanied by regional differentiation”: its total length first increased and then decreased, peaking at 1029.05 km in 2019, representing a cumulative net reduction of 7.54 km over the 2017–2023 period. Meanwhile, land use expansion driven by reclamation resulted in a cumulative net increase of 25.26 km². Aquaculture ponds (AP) constitute the dominant type of newly reclaimed land, accounting for more than 50%, while the expansion of impervious surface (IS) accounts for 24.52%. This study provides novel insights and a scientific basis for the refined management of coastlines, sustainable land use planning, and coastal-marine ecological protection in the Pearl River Estuary and similar regions worldwide. Full article

In this paper, a composite control framework integrating feedback linearization, an extended state observer, and an adaptive fractional-order sliding-mode controller is presented for autonomous underwater vehicles operating under uncertain hydrodynamics and external disturbances. The proposed algorithm, named adaptive fractional-order sliding-mode control with extended state observer, aims to enhance trajectory-tracking accuracy, disturbance rejection, and robustness against model uncertainties beyond what is offered by conventional active disturbance rejection control and integer-order sliding-mode control. First, a fractional-order sliding surface with an extended state observer is introduced to estimate and compensate lumped disturbances, where the fractional operator provides intrinsic filtering and memory effects to reduce chattering. Second, an adaptive exponential reaching law with smooth switching is formulated to overcome the trade-off between convergence speed and chattering, and a Levant differentiator is employed for sensorless velocity estimation. Finally, the uniform ultimate boundedness of the closed-loop system is proved via Lyapunov stability theory. Comparative simulation studies on step, sinusoidal, and circular trajectories under external disturbances, measurement noise, and 50% parametric uncertainties demonstrate that the proposed controller achieves zero overshoot, suppresses position fluctuations by 97%, and reduces root mean square tracking errors by 38–70% relative to conventional methods, confirming its superior performance. Full article

Evaluating the productivity contribution of individual fracturing stages in deep coalbed methane (CBM) horizontal wells remains a critical challenge, hindering the optimization of stimulation designs. This study systematically integrates dual-phase (aqueous and gaseous) fracturing tracer monitoring with machine learning algorithms to address this issue. Based on large-scale field applications across ten deep CBM horizontal wells in the Changqing mining area of the Ordos Basin, comprising 132 monitored stages, quantitative production profile data were interpreted. Three distinct gas production archetypes—Homogeneous, Heel-Dominated, and Heterogeneous—were identified, each governed by specific geomechanical and stratigraphic controls. Pearson correlation analysis and Random Forest feature importance ranking were employed to decouple the hierarchical influence of geological parameters (Class I coal intersection length, trajectory position, coal thickness) and engineering parameters (proppant volume, pumping rate, fluid volume). A power-law correlation between Class I coal length and initial gas productivity was quantified (R² = 0.71). For the first time, an economically viable “differentiated fracturing scale window” tailored to coal petrophysical classes and wellbore trajectory positions was defined. Subsequently, a machine learning-assisted geology-engineering closed-loop optimization methodology was established, using tracer data as a dynamic feedback bridge to iteratively refine fracturing designs. This research provides a reliable technical approach and practical template for enhancing single-well productivity and recovery efficiency in deep unconventional gas reservoirs. Full article

To improve the reliability and path quality of three-dimensional UAV path planning in complex mountainous environments, this paper proposes an Adaptive Elite Differential Gold Rush Optimizer (AEDGRO). The main novelty of AEDGRO lies in the coordinated integration of three enhancement mechanisms into the original Gold Rush Optimizer: chaotic good-point initialization for improving initial population coverage, adaptive elite differential mining for strengthening exploitation around promising regions, and stagnation-aware Gaussian–Cauchy mutation for escaping local optima. A UAV path-planning model is constructed by considering path length, altitude fluctuation, trajectory smoothness, terrain collision avoidance, threat-region avoidance, and UAV safety clearance. The experimental results on the IEEE CEC2017 benchmark suite show that AEDGRO obtains the best Friedman average ranking of 1.63, outperforming the original GRO with a ranking of 4.80. In the UAV path-planning experiments, AEDGRO achieves the lowest mean fitness value of 235.69 and the smallest standard deviation of 7.55, indicating better path quality and stronger robustness than the compared algorithms. The generated trajectories are smoother and can effectively avoid mountainous terrain and threat regions. These results demonstrate that AEDGRO has clear advantages in global optimization accuracy, convergence stability, and UAV path-planning applicability. Full article

The blockchain-based Automated Market Maker (AMM) mechanism establishes a multilateral trading market for multi-source homogeneous data assets. Its advantage lies in realizing algorithmic dynamic pricing and automated circulation through decentralized liquidity pools, effectively avoiding the single-point failure issues and pricing inefficiencies associated with traditional centralized platforms, while significantly improving the trading efficiency and value conversion potential of data assets. However, in high-frequency, large-scale, multilateral data trading scenarios, these AMM liquidity pools face intensified Impermanent Loss (IL) that cannot be easily addressed by conventional risk mitigation approaches, necessitating domain-specific tailored solutions. To address this issue, our study proposes a blockchain on-chain liquidity pool-based data trading market model. Through mathematical modeling and simulation experiments, we quantify how trader behavioral characteristics, including price sensitivity differentials, heterogeneous trading frequencies, and trading size variations, impact the value of AMM liquidity pool. On this basis, we propose a Behavior-Aware Real-time Trading-driven Impermanent Loss optimization method (BART-IL), which uses multi-factor scoring to dynamically sequence trades, generating low-impermanent-loss execution paths to mitigate risks for Liquidity Providers (LPs). Experimental results demonstrate that BART-IL reduces IL for LPs, capping maximum loss at 25.6% in large-scale trading scenarios and achieving over 40% loss reduction in high-frequency-dominant markets. Accordingly, the method substantially lowers the overall risk of data trading. This research addresses the adaptability bottleneck of AMM mechanisms for non-standard assets. By integrating innovations in mechanism design and algorithm optimization, we construct a low-cost blockchain-based decentralized data trading framework with enhanced fairness, offering important implications for ensuring the robustness and attractiveness of data trading platforms. Full article