Search Results (4,688)

Distributed independent pump-controlled systems enhance excavator energy efficiency by reducing throttling losses, but sustained high-load and high-speed operation often forces a single pump into low-efficiency regions. To overcome this limitation, this study proposes a dual-pump load-sharing architecture that enables load sharing and dynamic cooperative operation of two pumps within the high-efficiency region. An adaptive closed-loop GOA-SQP optimization strategy is also developed for real-time pump speed distribution. The proposed strategy combines the global exploration capability of the Grasshopper Optimization Algorithm (GOA) with the rapid local convergence characteristics of Sequential Quadratic Programming (SQP). Unlike conventional serial hybrid optimization methods, a bidirectional efficiency-feedback mechanism is introduced to dynamically coordinate global exploration and local refinement processes in real time. Furthermore, a local perturbation and re-explosion mechanism is incorporated to suppress premature convergence, enhance population diversity, and reduce redundant iterations. Experimental validation on an excavator test platform under four-quadrant conditions shows that the proposed system improves mechanical, volumetric, and overall pump efficiencies by 14.22, 4.57, and 18.79 percentage points, respectively, and reduces total energy consumption by 8.83%. Compared with GOA, SQP, GA-SQP, and PSO-SQP, the proposed GOA-SQP algorithm reduces solution time by 39.15% and improves optimization accuracy by 0.5%. The proposed architecture and optimization strategy offer a novel and effective solution for further improving the energy efficiency of distributed independent pump-controlled excavator systems. Full article

Path planning for cargo unmanned aerial vehicles (UAVs) in continuous indoor–outdoor heterogeneous environments poses a critical challenge: promoting payload stability under sharp turns and abrupt altitude variations while maintaining navigational efficiency. To address this issue, this paper proposes a hybrid A*–APF path planning framework that embeds trajectory smoothness optimization directly into the planning process rather than treating it as a post-processing step. An improved A* algorithm is developed by incorporating a trajectory smoothness term into its cost function to penalize sharp turns during global path generation. The resulting path is further refined using an enhanced artificial potential field (APF) method with virtual target points and multi-field force synthesis to mitigate local minima. In addition, the Ramer–Douglas–Peucker algorithm is employed to remove redundant waypoints, and a trajectory generation module based on B-spline interpolation and minimum snap optimization is introduced to produce smooth and dynamically feasible trajectories. Numerical simulation results demonstrate that, in indoor warehouse environments, the proposed method reduces the average turning angle by 88.4% (to 23.1°) compared with the standard A* algorithm while maintaining a comparable path length of 135.11 m. In large-scale outdoor urban scenarios, it achieves a path smoothness of 0.0124 with an average turning angle of 40.0°, substantially outperforming the Genetic Algorithm (104.6°) and Particle Swarm Optimization (83.5°) on turning angle while delivering competitive computation times of 0.52–1.51 s. An ablation study confirms that the improved A* and enhanced APF components each contribute independently to turning angle reduction and local minima avoidance, respectively, and that their integration yields the optimal balance across all metrics. These results indicate the proposed framework’s effectiveness for UAV-based last-mile delivery in scenarios requiring seamless indoor–outdoor transitions under payload stability constraints. Full article

This paper examines the relationship between Federal Open Market Committee (FOMC) communication surprises, global risk sentiment, and net portfolio debt inflows to twelve major emerging market economies over the period 2000–2024. Exploiting a high-frequency U.S. Monetary Policy Event-Study Database, we estimate panel fixed-effects regressions and local projections at quarterly frequency. We find that global risk sentiment, proxied by the VIX, is a robust and persistent driver of emerging market capital flows, while Fed communication surprises are statistically insignificant in normal times and in the 2022–2024 tightening cycle. A striking exception is the 2013 taper tantrum—the episode of severe capital outflow pressure triggered by Chairman Bernanke’s May 2013 congressional testimony signalling a possible tapering of asset purchases. Regime interaction tests reveal a large, highly significant negative effect of communication surprises on flows during this episode alone, with no comparable effect in 2022. Local projections confirm that the taper tantrum generated a sharp initial outflow followed by partial reversal, while VIX effects are contemporaneous but not persistent. We empirically test for market learning, finding that reduced sensitivity to Fed communication reflects a discrete recalibration after the 2013 shock rather than a gradual learning process. Regarding capital flows, the taper tantrum is clearly the exception, not the rule. Full article

This paper presents a symmetry-guided multi-elite Gekko Japonicus Algorithm, termed MIGJA, for global optimization and multi-threshold image segmentation. The method modifies the original GJA from three aspects. In the movement stage, a success-rate feedback mechanism is used to adapt the Lévy-flight probability and step-size coefficient according to recent search behavior, allowing the population to switch more flexibly between exploration and exploitation. In the guidance stage, several elite individuals are combined to form a weighted collaborative center, which reduces the excessive dependence on a single best solution and provides a more balanced search direction. In the reconstruction stage, historical memory and differential information are introduced into the tail reconstruction process to help inferior or stagnant individuals move out of local regions during the later search phase. The proposed MIGJA is tested on the CEC2017 and CEC2020 benchmark suites and further applied to Otsu-based multi-threshold image segmentation. The numerical results show that MIGJA performs competitively in terms of convergence accuracy and stability. According to the Friedman mean-rank results, MIGJA ranks first in all test settings, with mean-rank reductions of about 78.8–84.5% compared with the original GJA and 66.7–76.1% compared with the strongest competitor. In the segmentation experiments, MIGJA also obtains favorable objective function values and image quality metrics, including PSNR, FSIM, and SSIM. These findings suggest that the proposed algorithm is suitable for both benchmark optimization and multi-threshold image segmentation tasks. Full article

Coiling temperature is a pivotal process parameter for hot-rolled strip steel, which directly determines the microstructure and mechanical properties of final products. Affected by the coupling of multiple process variables, coiling temperature presents strong nonlinearity and complex time-varying characteristics. Traditional heat transfer mechanism models, Random Forest (RF), Extreme Learning Machine (ELM) and single Long Short-Term Memory (LSTM) networks fail to fully explore the deep correlation among variables. In addition, their hyperparameters are generally selected by manual trial-and-error, leading to unsatisfactory prediction accuracy and poor robustness in practical production. To address the above limitations, this paper proposes a novel prediction model named WOA-CNN-GRU-SE, where the Whale Optimization Algorithm (WOA) is adopted for parameter optimization. Firstly, Convolutional Neural Network (CNN) is utilized to extract local coupling features from various working condition parameters. Secondly, the Squeeze-and-Excitation (SE) attention mechanism is applied to adaptively recalibrate channel weights, which enhances key features closely related to temperature variation and suppresses redundant interference information. Afterwards, Gated Recurrent Unit (GRU) is employed to conduct in-depth learning of temporal features. Furthermore, WOA is used to globally optimize critical hyperparameters, including learning rate, the number of GRU hidden units and L2 regularization coefficient, so as to eliminate the drawbacks of manual parameter tuning. Comparative experiments are conducted on actual production data from a hot rolling line. The results demonstrate that the proposed model outperforms CNN-GRU, CNN-GRU-SE, LSTM, RF and ELM in prediction performance. Its hit rate reaches 92.56% within the industrial error range of ±6 °C. This model effectively realizes accurate prediction of coiling temperature under complex working conditions and possesses great application potential in industrial practice. Full article

Long-term time series forecasting requires models that can represent smooth global trends and irregular local fluctuations without assigning the same capacity to both. Existing decomposition-based neural forecasters often separate signals but continue to process the resulting components with architecturally homogeneous encoders, which can overfit simple trends and underfit nonlinear residuals. This paper proposes MLPFormer, a hybrid MLP–Transformer framework for component-specific forecasting. The input sequence is decomposed by a moving-average filter into trend and residual terms. A shallow channel-wise MLP encodes the low-frequency trend, providing a low-capacity inductive bias for smooth structures, whereas a Transformer encoder with depthwise separable convolution models high-frequency residual dynamics and cross-variable dependencies. The two paths are fused and decoded by parallel linear and nonlinear prediction heads. Experiments on nine public benchmarks, with results reported as the mean over three random seeds, show average reductions of 11.8% in MSE and 8.1% in MAE relative to the mean of seven competitive baselines spanning Transformer-based, linear/MLP-based, and RNN-based paradigms published between 2023 and 2025. Ablation studies confirm that the gains arise from matching encoder capacity to component complexity rather than from increasing parameter count. MLPFormer provides a practical hybrid artificial intelligence design for forecasting tasks in which trend and residual structures coexist. Full article

Fermentation is a foundational process that has historically underpinned the development of global civilizations. By extending the shelf life of perishable ingredients while enhancing flavor, nutrition, and bioactive properties, fermentation has provided the food security necessary for societies to flourish. Traditionally, these processes utilized locally available raw materials—such as milk, cereals, fruits, and vegetables—to produce a diverse array of non-alcoholic, alcoholic, and functional foods. This review explores the evolution of prominent ancient fermentation products and the contemporary movement to revive their authentic sensory profiles, including unique aromas and textures. Furthermore, it examines the transition from traditional artisanal methods to modern industrial production, where the use of standardized starter cultures and precise process parameters ensures product uniformity for the global market while employing precision fermentation to improve traditional fermentation products. By bridging ancestral wisdom with modern food science, this review highlights the enduring relevance of fermentation in the current food landscape. Full article

Monocular depth estimation for ${360}^{\circ }$ omnidirectional images is essential for immersive scene understanding and 3D reconstruction but remains challenging due to the non-uniform geometric distortions introduced by equirectangular projection (ERP). In particular, ERP suffers from latitude-dependent sampling bias, where the effective receptive field of standard convolution varies with the spherical latitude, leading to inconsistent feature representation and degraded depth prediction in high-distortion regions. To address this problem, this paper proposes a distortion-aware bi-projection fusion framework that integrates the global contextual continuity of ERP with the locally distortion-reduced geometric representation of cube map projection (CMP). The core component of the proposed framework is a Multi-scale Coordinate-Transformer Fusion (MCTF) module, which combines convolutional feature mixing, Transformer-based global context modeling, and Coordinate Attention-based spatial recalibration. By explicitly encoding vertical coordinate information into the fusion process, MCTF adaptively recalibrates feature responses according to latitude-dependent distortion patterns. Extensive experiments on the 3D60, Matterport3D, and Stanford2D3D benchmarks demonstrate that the proposed method consistently outperforms state-of-the-art omnidirectional depth estimation approaches. On the 3D60 dataset, our method reduces RMSE by 21.2% compared with the ERP baseline and achieves a 30% RMSE reduction in polar regions, where ERP distortion is most severe. These results validate the effectiveness of coordinate-aware feature calibration for robust ${360}^{\circ }$ monocular depth estimation. Full article

In high-pier bridge construction, the quality and accuracy of embedded rebar placement are critical to ensuring structural safety and durability. However, conventional manual inspection methods are inefficient, subjective and pose significant safety risks in high-altitude operations. These methods are unable to comprehensively inspect all pier columns on a daily basis, and frequently result in delays in acceptance that necessitate rework. In order to address these challenges, the current study proposes a smart vision-based inspection framework for the automatic and high-precision quality assessment of rebar under long-distance imaging conditions. This approach allows quality inspectors to remotely predict and evaluate the embedment quality of rebars from a safe distance. Notably, this work introduces a novel dual-source coordinate fusion mechanism that integrates improved instance segmentation with corner detection for global-to-local precision enhancement, representing an original contribution to rebar placement inspection in complex high-pier scenarios. The framework integrates an improved YOLOv8-CD segmentation model and a corner detection algorithm through a dual-source coordinate fusion mechanism, achieving an integration of global rebar detection and local feature enhancement. The YOLOv8-CD model, when optimised, features the Convolutional Block Attention Module (CBAM) integrated into the backbone, with the objective of enhancing recognition accuracy for small targets. Additionally, a Dilation-Wise Residual (DWR) module has been inserted before the neck C2f layer for the purpose of strengthening multi-scale feature extraction. The process of perspective correction and pixel-to-actual-length conversion coefficienting is performed in order to achieve a millimetre-level measurement of the rebar spacing and diameter. Empirical validation through real high-pier construction scenes demonstrates that the proposed framework attains a detection accuracy of 98.82%, surpassing conventional YOLO-based and single-source methodologies. The experimental results demonstrate that this framework is able to detect objects at longer distances, and to maintain its performance when the target is at a greater distance than that which was used for training. The proposed approach is expected to provide an efficient, safe, and quantitative solution for intelligent bridge construction quality monitoring, offering valuable insights for the future development of smart construction and structural health inspection systems. Full article

Accurate mine pressure prediction is essential for understanding roof–support interaction and supporting intelligent monitoring in fully mechanized longwall mining. In underground production, hydraulic-support pressure sensors provide continuous pressure sequences that reflect the mechanical response of the support–roof system. However, these sequences are affected by local mining disturbances, missing records, abnormal zero-value segments, nonstationary fluctuations, and periodic weighting, which make future pressure forecasting challenging. To address this issue, an LSTM–Transformer hybrid model is proposed for hydraulic-support pressure forecasting. The LSTM module extracts local nonlinear pressure-evolution features from recent historical windows, whereas the Transformer module captures temporal dependencies and periodic pressure patterns through global sequence modeling. Support-wise experiments were conducted using field monitoring data from Yili No. 1 Mine, and the pressure sequence of each support was processed independently to avoid mixing information from different support locations. In the representative test case, the proposed model achieved an R² of 0.971 and reduced the MAE to 0.471 MPa, while improving the phase consistency of predicted pressure peaks. Further analysis indicates that sufficient historical data coverage is necessary to capture complete pressure-evolution cycles, and that the 25-step forecasting case maintains stable accuracy for short-term mine pressure estimation. These findings demonstrate the feasibility of the proposed approach for hydraulic-support pressure prediction under the monitored conditions of the studied working face. Full article

To address the limitation that existing Visual Simultaneous Localization and Mapping (VSLAM) methods fail under complex and variable illumination conditions due to the inability to extract sufficient feature points, this paper proposes a robust V SLAM localization method based on multi-level adaptive image enhancement. First, the method employs dynamic brightness compensation to preprocess the original image, initially improving the global brightness distribution. Second, through RGB-to-HSV color space conversion, the brightness V-channel is separated to eliminate the interference of color information in the enhancement process. On this basis, to overcome the limitation of the existing CLAHE algorithm that relies on a fixed clipping threshold and cannot adapt to the local brightness distribution and texture complexity of different image regions, we propose an improved adaptive-threshold CLAHE algorithm based on local statistical characteristics, providing a stable image foundation for feature extraction. Meanwhile, to handle the interference of moving objects in dynamic environments, we incorporate a YOLOv5 object detection thread into the ORB-SLAM3 framework to remove feature points on dynamic objects. This detection module works in synergy with the multi-level image enhancement module, further improving localization robustness in dynamic scenarios. Extensive experiments on the public EuRoC and TUM datasets demonstrate that our method reduces the root mean square error of absolute trajectory error by 29.60% compared to ORB-SLAM3, with a reduction of up to 97.85% on high-dynamic sequences. Our method achieves better localization accuracy and robustness under complex illumination conditions, offering a new solution for visual localization in challenging illumination scenarios. Full article

To address the inherent limitations of the traditional Animated Oat Optimization Algorithm (AOO), including poor uniformity of initial random population distribution and insufficient dynamic balance between global exploration and local exploitation, this paper proposes an Enhanced Animated Oat Optimization Algorithm (EAOO) incorporating multi-strategy improvements. First, the Sinusoidal chaotic map is introduced to replace the original random initialization method. Leveraging the ergodicity and uniformity of chaotic sequences, the spatial distribution of the population is optimized, and the diversity of the initial population is significantly enhanced. Second, a nonlinear disturbance factor is embedded into the position update of leaders during both the exploration and exploitation phases, enabling dynamic and adaptive adjustment of the search range. This effectively balances the algorithm’s capabilities in global exploration and local exploitation. Finally, an adaptive t-distribution mutation operator, combined with a dynamic selection strategy, is integrated. The degrees of freedom are adaptively adjusted throughout the iterative process, allowing the algorithm to switch between global escape and local fine-search modes, thereby overcoming the premature convergence deficiency of the original algorithm. Simulation and comparative experiments are conducted based on the CEC2017 and CEC2020 benchmark function suites. Systematic evaluations are carried out from multiple perspectives, including optimization accuracy, convergence speed, and statistical significance. The experimental results demonstrate that the proposed EAOO achieves superior comprehensive performance across various complex function types—including unimodal, multimodal, hybrid, and composite functions—exhibiting higher optimization accuracy, faster convergence speed, and stronger robustness. Statistical tests further confirm the significant performance differences between EAOO and the compared algorithms. Furthermore, EAOO is applied to two typical constrained engineering optimization problems: welded beam design and pressure vessel design. The simulation results show that EAOO yields better structural design parameters and lower manufacturing costs, demonstrating outstanding practical value and broad application prospects in solving high-dimensional, nonlinear, constrained engineering optimization problems. Full article

When addressing high-dimensional complex optimization problems, the vanilla Dung Beetle Optimizer (DBO) suffers from slow convergence, frequent stagnation in local optima, and progressive degradation of population diversity. To overcome the above inherent defects, this paper proposes a multi-strategy hybrid improved DBO variant named the SWDBO, which incorporates three targeted enhancement modules. First, an adaptive population proportion strategy is developed to dynamically adjust the population sizes of rolling beetles, brood beetles, small beetles and thief beetles throughout iterations. More individuals are allocated for extensive global exploration at the early evolutionary stage, while more search agents are reserved for delicate local exploitation in later iterations, which maintains stable population diversity over the entire optimization process. Second, the bubble-net encircling and spiral predation mechanisms of the Whale Optimization Algorithm (WOA) are embedded into the position update formula of rolling beetles. This integration strengthens fine local search performance and accelerates the overall convergence rate. Third, a modified seagull optimization operator combined with Lévy random perturbation is introduced into the position updating rule of thief beetles. This improved jump mechanism optimizes individual movement trajectories and enables the algorithm to effectively escape local optimal traps. Numerical experiments are implemented on the 100-dimensional benchmark functions of CEC2017 and CEC2020. Moreover, the proposed SWDBO is validated on three classical constrained engineering optimization tasks, including three-bar truss design, ten-bar truss design and cantilever beam sizing optimization. Wilcoxon rank-sum tests statistically verify significant performance disparities between the SWDBO and competing optimizers. For the three structural engineering cases, the design solutions obtained by the SWDBO produce lighter structural mass while satisfying all constraint requirements. Overall experimental evidence proves that the proposed multi-strategy improvement framework can efficiently tackle high-dimensional numerical optimization and constrained engineering design problems, and the SWDBO exhibits prominent performance in balancing global exploration and local exploitation. Full article

Modern agricultural systems heavily rely on carbon-intensive energy inputs, emphasizing the urgent need to assess and optimize specific crop supply chains from thermodynamic and environmental perspectives. This study presents a comprehensive cumulative assessment of the energy, exergy, and carbon emissions of onion production in Türkiye, utilizing mass, energy, and entropy balance formulations combined with field-survey data from Adıyaman province. The results indicate that the total cumulative energy consumption is 722.28 MJ/ton, with nitrogen fertilizer contributing 61%. The thermodynamic analysis reveals that nitrogen fertilizer, irrigation water, and diesel fuel drive a cumulative exergy consumption of 465.83 MJ/ton, while irrigation water dominates the carbon emission at 20.65 kg CO₂/ton. Based on these streams, integrated sustainability indicators specifically the Cumulative Degree of Perfection (CDP) and the Renewability Index (RI) were calculated under conventional and solar modernization scenarios. A renewable energy scenario incorporating photovoltaic-powered irrigation and electrified machinery substantially enhanced thermodynamic perfection and process renewability, increasing CDP from 3.33 to 6.22 and RI from 0.70 to 0.84. These findings offer actionable insights for scaling local solar-driven modernization to mitigate grid dependency and support global Sustainable Development Goals (SDGs) by reducing fossil-fuel integration. Full article

Innovation districts have emerged as a prominent urban strategy to catalyse knowledge-based development and economic growth. Yet their contribution to sustainability and an understanding of what they entail beyond economic growth remains underexplored. This paper proposes a conceptual framework rooted in urban monitoring to identify and synthesise the contributions of innovation districts (or similar development strategies) into the city’s sustainable development. Specifically, it targets the initial processes of intention and objective setting, where different and conflicting policies overlap to define definite city-level objectives. Using Oslo as a single embedded case study, the paper further proposes a methodology for identifying and localising urban sustainability indicators (USIs) based on the city’s three innovation districts: Oslo Science City, Hovinbyen Circular Oslo, and Punkt Oslo. This methodology integrates global frameworks, such as the UN Sustainable Development Goals and the Global Urban Monitoring Framework, with local priorities and stakeholder perspectives through a combined top-down and bottom-up approach. The process resulted in a set of 40 indicators across five sustainability domains—social, environmental, economic, cultural, and governance—revealing governance as a critical enabler for sustainable implementation. While most indicators stem from theoretical conceptions of innovation districts, governance indicators rather reflect local experiences and institutional dynamics. Full article