Search Results (77)

Accurately obtaining aircraft aerodynamic parameters is essential for improving flight performance, optimizing design and control strategies, and ensuring flight safety. In this study, the improved Northern Goshawk Optimization (SPNGO) algorithm is used to optimize the kernel parameters and regularization coefficients of the Kernel Extreme Learning Machine (KELM). To address the defects of the original NGO algorithm, such as insufficient global optimization ability and being prone to falling into local optimums, two improvement strategies are proposed. The enhanced SPNGO algorithm is verified by 14 benchmark test functions, and the proposed SPNGO-KELM model is evaluated using open-source F-16 nonlinear simulation data for longitudinal aerodynamic parameter identification. The results demonstrate its effectiveness under the considered simulation conditions, while further validation with real flight-test data is required before application to actual flight environments. Comparative analysis with KELM, NGO-KELM, SSA-KELM, and WOA-KELM models shows that a single KELM is difficult to achieve high-precision aerodynamic parameter identification, and other comparison models have obvious fitting deviations in non-steady-state and strong nonlinear regions. Notably, the SPNGO-KELM model achieves the best identification performance, with a determination coefficient (R²) of 0.96537 and a mean absolute percentage error (MAPE) as low as 3.1574%. Its comprehensive identification accuracy is 1.81% to 37.98% higher than that of the comparison models, and it can effectively suppress error oscillations in nonlinear regions. Experimental results show that the proposed algorithm has excellent identification accuracy, generalization ability, and anti-interference performance. Full article

Coverage optimization of wireless sensor networks (WSNs) faces challenges such as uneven node distribution and vulnerability to coverage blind spots. This paper introduces and improves the Northern Goshawk Optimization (NGO) algorithm: the Logistic chaotic map is adopted to initialize the population for enhanced ergodicity, a nonlinear dynamic weight is introduced to balance global exploration and local exploitation, and a Gaussian–Lévy hybrid mutation mechanism is integrated to strengthen the ability to escape from local optima. Experiments on standard test functions show that the improved algorithm (INGO) can stably approach the theoretical optimal values for both unimodal and multimodal functions. The convergence speed and solution accuracy are significantly superior to those of the original NGO, with a smaller standard deviation and stronger robustness. INGO is applied to the coverage optimization of 2D and 3D WSNs, with coverage rate as the fitness function, and the optimal node deployment coordinates are output through iterative optimization. Simulation results show that INGO achieves a best coverage rate of 98.32% in the 2D scenario, which is 7.72 percentage points higher than the 90.6% of NGO. In the 3D scenario, the best coverage rate reaches 72.32%, 6.78 percentage points higher than the 65.54% of NGO. Meanwhile, INGO yields more uniform node deployment and effectively reduces coverage blind spots. Its convergence curve is smooth and oscillation-free in the late iteration stage, and the stability is significantly better than that of NGO. With proper settings of population size and iteration times, INGO can achieve better coverage performance, providing a reliable technical solution for the efficient deployment of wireless sensor networks in complex environments. Full article

In recent years, the application scenarios of Unmanned Aerial Vehicles (UAVs) have become increasingly widespread. As core components of UAVs, rotor blades’ health status is directly related to flight safety. Aiming at issues such as insufficient feature extraction, weak noise resistance, and low diagnostic accuracy in the crack fault diagnosis of UAV rotor blades, this study proposes a one-dimensional deep network integrating multi-scale convolution, a multi-path residual module, BiLSTM, and a self-attention mechanism, referred to as 1dMC-MPR-SABinet. Taking the triaxial (X, Y, Z) vibration signals of rotor blades as input, the method integrates a multi-scale convolution module and a multi-path residual module, models the bidirectional temporal dependencies of signals through Bi-LSTM, and is combined with a self-attention mechanism to enhance the capture of subtle fault features. Meanwhile, it adopts the Northern Goshawk Optimization algorithm to optimize hyperparameters, thereby improving stability in noisy environments. Experiments are validated based on a self-collected fault vibration dataset, with precision, recall, and F1-score as evaluation metrics. The results show that the proposed model achieves a diagnostic accuracy of 99.37% under noise-free conditions without NGO-based hyperparameter optimization, representing a maximum improvement of 6.50% over the comparative models. Under a strong noisy condition with SNR = 1, the base model achieves 91.95% accuracy, while after NGO-based hyperparameter optimization, the model performance is further improved, with the precision, recall, and F1-score reaching 97.64%, 97.78%, and 97.01%, respectively. Ablation experiments and generalization experiments further verify the rationality and effectiveness of the proposed architecture. Full article

To address node redundancy and coverage holes in Wireless Sensor Network (WSN) deployment, this paper proposes an Improved Northern Goshawk Optimization (INGO) algorithm with multiple enhancements. It integrates a Diverse Chaotic Map Initialization Strategy (DCMIS) into the standard Northern Goshawk Optimization (NGO) for Diverse, uniform initial populations and improved global exploration. A Bidirectional Population Evolution Dynamics (BPED) mechanism follows the pursuit-and-evasion phase, applying asymmetric logic—elite guidance and selective replacement of weak individuals—to escape local optima and accelerate global convergence. Simulations reveal uniform grid topologies and an average coverage ratio of 91.90% with INGO, outperforming Northern Goshawk Optimization (NGO), Artificial Bee Colony (ABC), Improved Wild Horse Optimizer (IWHO), and the Firefly Algorithm (FA). INGO also achieves 100.00% connectivity, eliminating isolated nodes and ensuring reliable full-network communication. These results indicate that INGO achieves higher coverage and full connectivity under the studied simulation setting, demonstrating its effectiveness for WSN deployment optimization. Full article

Kazakhstan is a major migration corridor for raptors in Central Asia, yet the migratory connectivity of these species remains poorly documented. We analysed 60 years of ringing data (1966–2025) for three species: Eurasian Sparrowhawk (Accipiter nisus), Northern Goshawk (Astur gentilis), and Shikra (Tachyspiza badia). In total, 5785 individuals were ringed, and 38 recoveries of Kazakhstan-ringed birds were obtained (0.66%). Because recoveries for Goshawk (n = 2) and Shikra (n = 1) are extremely limited, quantitative analyses were restricted to Sparrowhawk recoveries (n = 35), while the other two species are reported descriptively as case records. For Sparrowhawks, migration distances reached 1947 km (mean = 975 km) and did not differ detectably among age classes. Most ringing effort occurred at Shakpak Pass (94.7%), and recoveries indicate connectivity between Kazakhstan, Western Siberia and wintering areas in Central Asia and northern India. Among recovered dead birds (n = 25), shooting (n = 10) and powerline electrocution or collision (n = 3) were frequently reported causes. These long-term ring recoveries provide baseline information on movement connectivity and threats for Central Asian accipiters and highlight the value of sustained monitoring at migration bottlenecks. Full article

Accurate prediction of reference evapotranspiration (ET₀) using integrated deep learning approaches with limited meteorological data is highly significant for efficient water resource utilization and management in arid regions. Nevertheless, parameter optimization is frequently overlooked in current research, leading to unsatisfactory estimation accuracy that cannot meet practical application requirements. To overcome this limitation, a CNN–BiLSTM–attention hybrid model is constructed by combining the powerful feature-extraction capability of CNN and excellent sequence-processing performance of BiLSTM, followed by the integration of an attention mechanism. Five metaheuristic algorithms, namely the osprey optimization algorithm (OOA), grey wolf optimization (GWO), whale optimization algorithm (WOA), particle swarm optimization (PSO), and northern goshawk optimization (NGO), are adopted to optimize the key parameters of the proposed model. The developed hybrid models are then applied to ET₀ estimation in Linze County, China. The results demonstrate that the error indices of these models vary within the ranges of MAPE [14.28%, 14.48%], MAE [0.4270, 0.4482], RMSE [0.5596, 0.5844], and NMSE [0.0490, 0.0577]. Overall, the OOA–CNN–BiLSTM–attention model exhibited the most robust and consistent estimation performance across multiple evaluation metrics among the investigated models. Full article

To improve the efficiency and output power of the twin-screw expander used in natural gas pressure energy recovery, a hybrid NGO-SDERIME algorithm is proposed for structural optimization, with the structural parameters of the male and female rotors selected as the optimization design variables. First, the enhanced Rime Ice Optimization (RIME) algorithm is adopted to perform hybrid improvement on the Northern Goshawk Optimization (NGO) algorithm; then, the stability and superiority of the proposed hybrid algorithm are verified by using a suite of benchmark test functions; finally, the algorithm is applied to the structural optimization of the twin-screw expander, followed by numerical simulation and experimental verification. The results indicate that, compared with other existing algorithms, the proposed NGO-SDERIME hybrid algorithm shows excellent convergence and strong optimization performance. After optimization using this algorithm, the output power of the screw expander increases by 5.5%, the high-speed leakage area is significantly reduced, the isentropic efficiency improves from 75.1% to 78.1%, and the average mass flow rate increases from 18.42 t/h to 18.72 t/h. Full article

Today’s distribution networks are becoming increasingly complex, necessitating highly accurate and robust fault diagnosis methods. Traditional methods based on impedance or traveling waves often lack flexibility and precision in these dynamic environments. This study proposes a hybrid approach based on the synergy between machine learning (ML) techniques and a recent metaheuristic, the Northern Goshawk Optimizer (NGO). Fault location is performed using a cubic spline interpolation model. Classification is handled by a decision tree, while fault resistance—a key parameter that significantly influences diagnostic performance—is optimized using the NGO algorithm. The effectiveness of the proposed method is evaluated through a series of experiments conducted on the IEEE 34-bus test network. These experiments encompass various fault scenarios (single line-to-ground, line-to-line, double line-to-ground, and three-phase faults) as well as voltage and load variation conditions. Fault resistance values considered in the study are 0, 10, 50 and 100 ohms. The results highlight the robustness and efficiency of the hybrid approach, achieving an accuracy rate of up to 99.999% in fault location. This level of performance enables reliable identification of both the fault location and the affected line. Full article

To address the peak-fluctuating cooling load of ocean-going fishing vessels and the dependency of traditional refrigeration systems on fuel-driven power, this study proposes an exhaust waste-heat-driven ammonia water absorption–compression hybrid refrigeration system. The proposed system was thermodynamically analyzed and simulated based on the principles of heat and mass transfer. Considering the full-cycle cooling demand, an objective optimization model with the goal of minimizing the total operating cost was established and solved using the Northern Goshawk Optimization (NGO) algorithm. Using real data from a fishing company, a voyage cycle of Lu Huang Yuan Yu 105 was selected as a case study. Results showed that NGO outperformed the Genetic Algorithm and Particle Swarm Optimization, achieving the smallest cooling deficit and faster convergence. Compared with the independent compression refrigeration system, the hybrid system reduced the cooling deficit by 9.7%, improved cooling capacity by over 35% during voyage, 5% during fishing, and 2% during processing, while lowering fuel consumption by 10% and efficiently utilizing exhaust heat. Sensitivity analysis identified optimal ranges for ammonia concentration and circulation ratio and highlighted the significant influence of cooling water temperature on system performance. This study provides a valuable reference for the design and optimization of low-grade waste-heat-driven hybrid refrigeration systems in maritime applications. Full article

To address knowledge gaps in urban wetlands’ role in sustaining avian diversity along migration corridors, this study systematically surveyed three Yancheng wetland parks with a distinct habitat gradient. Monthly surveys were conducted from January to December 2024 using fixed-width line transects and point counts, with three 300 m transects set in each park and all birds within 50 m of the transect line recorded, and Shannon–Wiener, Simpson, Pielou’s Evenness, and Margalef Richness indices were employed for quantitative analysis. A total of 83 bird species across 16 orders and 41 families were documented, including the National Class I Protected and Endangered Oriental Stork and three Class II nationally protected species (Black-winged Kite, Crested Goshawk, Common Kestrel). Fengyi Lake Park, with 71 species, served as a critical migratory waterbird hub. Yandu Wetland Park sustained community stability through high habitat heterogeneity, supporting specialized breeders, and Dongfang Wetland Park, with 34 urban adaptor-dominated species, provided key autumn pulsed resources for frugivores and granivores. This study identifies habitat heterogeneity as the primary driver of avian community differentiation and highlights that the ecological functions of urban wetlands are contingent on multi-habitat complementarity. We, therefore, advocate for prioritizing the construction of heterogeneous habitat structures in urban wetland planning, enhancing functional complementarity and connectivity among distinct wetland types, and preserving the continuity of migratory bird habitat corridors along the East Asian-Australasian Flyway. These findings furnish robust scientific evidence and actionable guidance for regional green space planning and biodiversity conservation. Full article

Background/Objectives: Non-invasive samples offer an attractive alternative to logistically challenging invasive approaches in wildlife genetic studies but often contain low-quality host DNA that limits downstream analyses. Here, we assessed the applicability of moulted Eurasian goshawk feathers as a DNA source for whole-genome re-sequencing. Methods: We analysed 75 moulted feathers collected opportunistically from breeding territories. Each feather was measured from tip to tip, and its condition was visually assessed. Whole-genome re-sequencing was performed with a target coverage of 13× using 150 bp paired-end reads. Results: Feathers yielded an average of 7.19 ± 10.93 ng/μL DNA. DNA yield was positively correlated with feather size and the presence of blood traces in the calamus. On average, feather samples performed well, producing 208.7 ± 59.82 million reads, of which 82.69 ± 27.15% aligned to the reference genome, resulting in 83.58 ± 19.02% of the genome being covered at least once. After quality filtering, 10.34 ± 3.11 million biallelic single-nucleotide variants remained, of which 457,745 were common variants (MAF > 0.05). Larger feathers in good condition, with higher DNA yields and blood traces in the calamus, tended to perform better throughout the re-sequencing workflow. Nevertheless, approximately 22.7% of samples failed due to high missing data or poor genotype quality. Conclusions: Performance varied substantially even among samples with similar characteristics, indicating that improved sample selection incorporating direct measures of host DNA quality may be beneficial. Despite these challenges, moulted feathers represent a readily available DNA source for genome-wide re-sequencing of medium- to large-sized raptor species. Full article

To achieve accurate voltage sag prediction and early warning, thereby improving power quality, a hybrid voltage sag prediction framework is proposed by integrating Kernel Entropy Component Analysis (KECA) with an improved Northern Goshawk Optimization (NGO) algorithm for hyperparameter tuning of a CNN-LSTM model. First, to address the limitations of the original NGO, such as proneness to falling into local optima and high randomness of the initial population distribution, a refraction-opposition-based learning mechanism is introduced to enhance population diversity and expand the search space. Furthermore, a sine–cosine strategy (SCA) with nonlinear weight coefficients is integrated into the exploration phase to dynamically adjust the search step size, optimizing the balance between global exploration and local exploitation, thereby boosting convergence speed and accuracy. The improved algorithm (SCNGO) is then utilized to optimize the hyperparameters of the CNN-LSTM model. Second, KECA is applied to voltage-sag-related data to extract key features and eliminate redundant information, and the resulting dimensionally reduced data are fed as input to the SCNGO-CNN-LSTM model to further improve prediction performance. Experimental results demonstrate that the SCNGO-CNN-LSTM model outperforms other comparative models significantly across multiple evaluation metrics. Compared with NGO-CNN-LSTM, GWO-CNN-LSTM, and the original CNN-LSTM, the proposed method achieves a mean squared error (MSE) reduction of 53.45%, 44.68%, and 66.76%, respectively. The corresponding root mean squared error (RMSE) is decreased by 25.33%, 18.61%, and 36.92%, while the mean absolute error (MAE) is reduced by 81.23%, 77.04%, and 86.06%, respectively. These results confirm that the proposed framework exhibits superior feature representation capability and significantly improves voltage sag prediction accuracy. Full article

The gradual deterioration of fresco pictorial information presents a formidable obstacle for conservators dedicated to protecting humanity’s shared cultural legacy. Currently, scholars in the field of mural conservation predominantly focus on image segmentation techniques as a vital tool for facilitating mural restoration and protection. However, the existing image segmentation methods frequently fall short of delivering optimal segmentation results. To address this issue, this study introduces a novel mural image segmentation approach termed NDFNGO, which integrates a nonlinear differential learning strategy, a decay factor, and a Fractional-order adaptive learning strategy into the Northern Goshawk Optimization (NGO) algorithm to enhance segmentation performance. Firstly, the nonlinear differential learning strategy is incorporated to harness the diversity and adaptability of differential tactics, thereby augmenting the algorithm’s global exploration capabilities and effectively improving its ability to pinpoint optimal segmentation threshold regions. Secondly, drawing on the properties of nonlinear functions, a decay factor is proposed to achieve a more harmonious balance between the exploration and exploitation phases. Finally, by integrating historical individual data, the Fractional-order adaptive learning strategy is employed to reinforce the algorithm’s exploitation capabilities, thereby further refining the quality of image segmentation. Subsequently, the proposed method was evaluated through tests on twelve mural image segmentation tasks. The results indicate that the NDFNGO algorithm achieves victory rates of 95.85%, 97.9%, 97.9%, and 95.8% in terms of the fitness function metric, PSNR metric, SSIM metric, and FSIM metric, respectively. These findings demonstrate the algorithm’s high performance in mural image segmentation, as it retains a significant amount of original image information, thereby underscoring the superiority of the technology proposed in this study for addressing this challenge. Full article

Rice husk ash (RHA) offers an eco-friendly way to improve concrete. Owing to the complex mix design of RHA concrete, accurately predicting its strength remains a challenge. This study addresses this need by compiling a dataset of 291 compressive strength records for RHA concrete. Using seven key input variables (e.g., cement, water, and RHA content), three novel hybrid models were developed by integrating the XGBoost algorithm with advanced metaheuristic optimizers: Northern Goshawk Optimization (NGO), Arctic Puffin Optimization (APO), and Catch Fish Optimization Algorithm (CFOA). These hybrid models were compared against classic Random Forest (RF), and Support Vector Regression (SVR), and unoptimized XGBoost models. The results demonstrated that all hybrid models significantly outperformed the unoptimized classic models. The APO–XGBoost model achieved the highest prediction accuracy on the testing set (RMSE = 3.5462, R² = 0.9579 on testing set), followed by CFOA–XGBoost and NGO–XGBoost. Cement content was revealed to be the most influential parameter on compressive strength, as determined by a sensitivity analysis, ahead of both water and coarse aggregate content. This research confirms the superiority of metaheuristic-optimized hybrid models for predicting the strength of RHA concrete, providing a reliable data-driven tool to support its mix design and promote its application in sustainable construction. Full article

To address the urgent need for rapid detection and precise segmentation of oil spill incidents, a cascaded processing framework integrating the YOLOv11 model with an enhanced Northern Goshawk Optimization (NGO) algorithm is proposed. This method effectively utilizes the advantages of deep learning and metaheuristic algorithms. Firstly, the YOLOv11 model was used for preliminary localization and segmentation of oil spill target regions in marine radar images. Subsequently, an improved NGO algorithm based on adaptive weighting factors, Levy flight perturbation, and pinhole imaging perturbation was used to finely segment the region, balancing processing efficiency and accuracy requirements. The experimental results showed that the cascade architecture proposed effectively balances the problems of false detection and missed detection. Compared with other methods, the marine radar oil film detection method based on YOLOv11 combined with improved NGO exhibited strong adaptability in complex scenes. Multiple indicators, such as accuracy, precision, recall, specificity, and Dice similarity coefficient, indicate that this method has good performance in marine radar oil spill detection tasks. Full article