Search Results (176)

Background: Repeated high-intensity intervals under normoxic (NOR) and hypoxic (HYP) conditions is a training strategy used by athletes. Although different protocols have been used, the effect of longer recovery between repetitions is unclear. In addition, information on the effect of repeated high-intensity intervals on HYP in women is scarce. Aims: To analyse the differences between sexes and between conditions (NOR and HYP) in Repeated Wingate (RW) performance and neuromuscular fatigue in triathletes. Methods: A total of 12 triathletes (men: n = 7, 23.00 ± 4.04 years; women: n = 5, 20.40 ± 3.91) participated in this randomised, blinded, crossover study. In two separate sessions over seven days, participants performed 3 × 30” all out with 7′ of recovery in randomised NOR (fraction of inspired oxygen: ≈20%; ≈300 m altitude) and HYP (fraction of inspired oxygen: ≈15.5%; ≈2500 m altitude) conditions. Before and after RW, vertical jump tests were performed to assess neuromuscular fatigue. Oxygen saturation, power, perceived exertion, muscle soreness and heart rate parameters were assessed. Results: Significant differences were reported between sexes in the parameters of vertical jump, oxygen saturation, RW performance and heart rate (p < 0.05). However, between conditions (NOR and HYP), only differences in oxygen saturation were reported (p < 0.05). No significant differences were reported between conditions (NOR and HYP) in RW performance, neuromuscular fatigue, muscle soreness and perception of exertion. Conclusions: A 3 × 30” RW protocol with 7′ recovery in HYP could have no negative consequences on performance, neuromuscular fatigue and perception of exertion in triathletes compared to NOR, independently of sex. Full article

Trajectory planning is crucial for solar aircraft endurance. The multi-wing morphing solar aircraft can enhance solar energy acquisition through wing deflection, which simultaneously incurs aerodynamic losses, complicating energy coupling and challenging existing planning methods in efficiency and long-term optimization. This study presents an energy-optimal trajectory planning method based on Hierarchical Reinforcement Learning for morphing solar-powered Unmanned Aerial Vehicles (UAVs), exemplified by a Λ-shaped aircraft. This method aims to train a hierarchical policy to autonomously track energy peaks. It features a top-level decision policy selecting appropriate bottom-level policies based on energy factors, which generate control commands such as thrust, attitude angles, and wing deflection angles. Shaped properly by reward functions and training conditions, the hierarchical policy can enable the UAV to adapt to changing flight conditions and achieve autonomous flight with energy maximization. Evaluated through 24 h simulation flights on the summer solstice, the results demonstrate that the hierarchical policy can appropriately switch its bottom-level policies during daytime and generate real-time control commands that satisfy optimal energy power requirements. Compared with the minimum energy consumption benchmark case, the proposed hierarchical policy achieved 0.98 h more of full-charge high-altitude cruise duration and 1.92% more remaining battery energy after 24 h, demonstrating superior energy optimization capabilities. In addition, the strong adaptability of the hierarchical policy to different quarterly dates was demonstrated through generalization ability testing. Full article

Accurate estimation of potential evapotranspiration (PET) is of paramount importance for water resource management, especially in Mediterranean mountainous environments that are often data-scarce and highly sensitive to climate variability. This study evaluates the performance of four machine learning (ML) regression algorithms—Support Vector Regression (SVR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and K-Nearest Neighbors (KNN)—in predicting daily PET using limited meteorological data from a high-altitude in Central Greece. The ML models were trained and tested using easily available meteorological inputs—temperature, relative humidity, and extraterrestrial solar radiation—on a dataset covering 11 years (2012–2023). Among the tested configurations, RFR showed the best performance (R² = 0.917, RMSE = 0.468 mm/d, MAPE = 0.119 mm/d) when all the above-mentioned input variables were included, closely approximating FAO56–PM outputs. Results bring to light the potential of machine learning models to reliably estimate PET in data-scarce conditions, with RFR outperforming others, whereas the inclusion of the easily estimated extraterrestrial radiation parameter in the ML models training enhances PET prediction accuracy. Full article

Energy resources are abundant in high-altitude regions, and the construction of power transmission and transformation projects has important value. However, harsh natural environments can increase project costs. To address the issue of insufficient accuracy caused by the impact of extreme weather factors on cost predictions for power transmission and transformation projects in high-altitude regions, this paper first constructs a four-dimensional influencing factor system covering climate and environment, engineering scale, material consumption, and technological economy. On this basis, a hybrid deep-learning model combining an improved whale optimization algorithm (IWOA) and a convolutional neural network (CNN) is then proposed. The model improves the training accuracy of CNNs and avoids falling into local optima through the use of an SGDM optimizer, the L2 regularization method, and the Bayesian optimization method. Nonlinear convergence factors and adaptive weights are introduced to enhance the WOA’s ability to optimize the CNN’s learning rate. The case analysis results show that, compared with the comparison model, the proposed IWOA-CNN model exhibits a better convergence performance and fitting effect in the training set and a better prediction effect on the test set. Its mean absolute percentage error is as low as 1.51%, which is 10.1% lower than the optimal comparison model. The root mean square error is reduced to 5.07, and the sum of squared errors is reduced by 72.4%, demonstrating high prediction accuracy. The comparative analysis of scenarios further confirms the crucial role of climate environment; that is, the prediction accuracy of models containing a climate dimension is improved by 51.6% compared to models without such a climate dimension, indicating that the nonlinear impact of low temperatures, frozen soil, and other characteristics of high-altitude regions on costs cannot be ignored. The research results of this paper enrich the method system and application scenarios for the cost prediction for power transmission and transformation projects and provide theoretical reference for engineering predictions in other complex geographical environments. Full article

The aerial measurement of wind speed and direction is important for the development of the low-altitude economy, meteorology, climate research, and renewable energy systems. Existing UAV-based wind measurements, whether instrument-based or flight-dynamic-based, consistently exhibit bias and significant errors, limiting their reliability for precise wind estimation. This study introduces a machine learning (ML) approach to improve the accuracy of the wind speed and direction estimation using UAVs. The proposed method leverages data from sensors onboard UAV platforms, combined with advanced ML algorithms trained on ground-truth measurements obtained through high-resolution LiDAR systems. The experiments reveal that incorporating a 10 s smoothing window yields a root mean square error (RMSE) value of 0.39 m/s for the wind speed (horizontal) and an even lower bias (≤0.069 m/s) when using a 60 s smoothing window, representing a marked improvement over traditional techniques. These results are particularly promising at longer smoothing windows (>50 s), where the ML-based approach achieves superior accuracy compared to LiDAR measurements. The findings underscore the potential of integrating machine learning with UAV-based wind measurement systems to achieve higher precision and reliability in wind characterization. Full article

Background: Respiratory pathologies, such as COVID-19 and bronchitis, pose significant challenges for high-level athletes, particularly during demanding altitude training camps. Metabolomics offers a promising approach for early detection of such pathologies, potentially minimizing their impact on performance. This study investigates the metabolic differences between athletes with and without respiratory illnesses during an altitude training camp using urine samples and multivariate analysis. Methods: Twenty-seven elite rowers (15 males, 12 females) participated in a 12-day altitude training camp at 1850 m. Urine samples were collected daily, with nine athletes developing respiratory pathologies (8 COVID-19, 1 bronchitis). Nuclear Magnetic Resonance spectroscopy was used to analyze the samples, followed by data processing with Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA), allowing to use Variable Importance in Projection (VIP) scores to identify key metabolites contributing to group separation. Results: The PLS-DA model for respiratory illness showed good performance (R² = 0.89, Q² = 0.35, p < 0.05). Models for altitude training achieved higher predictive power (Q² = 0.51 and 0.72, respectively). Metabolites kynurenine, N-methylnicotinamide, pyroglutamate, propionate, N-formyltryptophan, tryptophan and glucose were significantly highlighted in case of respiratory illness while trigonelline, 3-hydroxyphenylacetate, glutamate, creatine, citrate, urea, o-hydroxyhippurate, creatinine, hippurate and alanine were correlated to effort in altitude. This distinction confirms that respiratory illness induces a unique metabolic profile, clearly separable from hypoxia and training-induced adaptations. Conclusions: This study highlights the utility of metabolomics in identifying biomarkers of respiratory pathologies in athletes during altitude training, offering the potential for improved monitoring and intervention strategies. These findings could enhance athlete health management, reducing the impact of illness on performance during critical training periods. Further research with larger cohorts is warranted to confirm these results and explore targeted interventions. Full article

The increasing popularity of drones for Internet of Things (IoT) applications has led to significant research interest in autonomous navigation within unknown and dynamic environments. Researchers are utilizing supervised learning techniques that rely on image datasets to train drones for autonomous navigation, which are typically used for rescue, surveillance, and medical aid delivery. Current datasets lack data that allow drones to navigate in a 3D environment; most of these data are dedicated to self-driving cars or navigation inside buildings. Therefore, this study presents an image dataset for training drones for 3D navigation. We developed an algorithm to capture these data from multiple worlds on the Gazebo simulator using a quadcopter. This dataset includes images of obstacles at various flight altitudes and images of the horizon to assist a drone in flying at an appropriate altitude, which allows it to avoid obstacles and prevents it from flying unnecessarily high. We used deep learning (DL) to develop a model to classify and predict the image types. Eleven experiments performed with the Gazebo simulator using a drone and a convolution neural network (CNN) proved the database’s effectiveness in avoiding different types of obstacles while maintaining an appropriate altitude and the drone’s ability to navigate in a 3D environment. Full article

This paper proposes an optimized algorithm based on YOLOv11s to address the problem of insufficient detection accuracy of vehicle targets from a drone perspective due to certain scenes involving complex backgrounds, dense vehicle targets, and/or large variations in vehicle target scales due to oblique imaging. The proposed algorithm enhances the model’s local feature extraction capability through a module collaboration optimization strategy, integrates coordinate convolution to strengthen spatial perception, and introduces a small object detection head to address target size variations caused by altitude changes. Additionally, we construct a dedicated dataset for urban vehicle detection that is characterized by high-resolution images, a large sample size, and low training resource requirements. Experimental results show that the proposed algorithm achieves gains of 1.9% in precision, 6.0% in recall, 4.2% in mAP@0.5, and 3.3% in mAP@0.5:0.95 compared to the baseline network. The improved model also achieves the highest F1-score, indicating an optimal balance between precision and recall. Full article

The sustainable management of natural reserves is increasingly prioritized within the global tourism sector, especially in fragile ecosystems like the Santurbán Moorland in Colombia. As a high-altitude Andean ecosystem providing essential water resources, the Santurbán Moorland faces mounting pressures from tourism growth and mining activity. This study assesses the adoption of sustainable tourism practices among tourism service providers (TSPs) in the region and identifies key gaps to inform policy and academic interventions. A cross-sectional, mixed-methods approach was applied, integrating surveys based on the European Tourism Indicators System (ETIS) and the Global Sustainable Tourism Council (GSTC) criteria, as well as structured interviews, field observations, and document analysis. Confirmatory factor analysis identified “sustainable management” as the most robust dimension (Cronbach’s alpha = 0.953); however, no TSPs reported using renewable energy, and less than 5% of employees had received formal training in tourism. The main challenges include the lack of environmental certification, insufficient infrastructure, and limited communication of sustainability practices. Based on these findings, the study proposes targeted public policies, financial incentives, and specialized academic training to strengthen sustainable practices. The results offer insights into the challenges faced by emerging ecotourism destinations and provide strategic guidelines to support a balance between environmental conservation and local socioeconomic development. Full article

Efficient and realistic large-scale scene modeling is an important application of low-altitude remote sensing. Although the emerging 3DGS technology offers a simple process and realistic results, its high computational resource demands hinder direct application in large-scale 3D scene reconstruction. To address this, this paper proposes a novel grid-based scene-segmentation technique for the process of reconstruction. Sparse point clouds, acting as an indirect input for 3DGS, are first processed by Z-Score and a percentile-based filter to prepare the pure scene for segmentation. Then, through grid creation, grid partitioning, and grid merging, rational and widely applicable sub-grids and sub-scenes are formed for training. This is followed by integrating Hierarchy-GS’s LOD strategy. This method achieves better large-scale reconstruction effects within limited computational resources. Experiments on multiple datasets show that this method matches others in single-block reconstruction and excels in complete scene reconstruction, achieving superior results in PSNR, LPIPS, SSIM, and visualization quality. Full article

With the growing integration of the Internet of Things (IoT), low-altitude intelligent networks, and vehicular networks, smart city traffic systems are gradually evolving into an air–ground integrated intelligent monitoring framework. However, traditional centralized model training faces challenges such as high network load due to massive data transmission, energy management difficulties for mobile devices like UAVs, and privacy risks associated with non-anonymized road operation data. Therefore, this paper proposes an air–ground collaborative federated learning framework that integrates Bayesian prediction and an incentive mechanism to achieve privacy protection and communication optimization through localized model training and differentiated incentive strategies. Simulation experiments demonstrate that, compared to the Equal Contribution Algorithm (ECA) and the Importance Contribution Algorithm (ICA), the proposed method improves model convergence speed while reducing incentive costs, providing theoretical support for the reliable operation of large-scale intelligent traffic monitoring systems. Full article

Solar radiation storms, caused by high-energy solar energetic particles (SEPs) released during solar flares or coronal mass ejections (CMEs), have a substantial impact on the Earth’s environment. These storms can disrupt satellite operations, interfere with high-frequency (HF) communications, and increase the radiation exposure of high-altitude flights. To reduce these effects, the Korea Space Weather Center (KSWC) monitors and forecasts solar radiation storms using satellite data and predictive models. This paper introduces the space weather forecasting methods employed by the KSWC and the analysis approach for satellite data from GOES, SDO, the LASCO coronagraph, and STEREO. We introduce a predictive model for solar radiation storms, which is composed of two key components: (1) a machine learning model, which is trained using solar flare and CME data obtained from satellite observations, and (2) a physics-based model that incorporates the mechanisms of SEP generation through CMEs approaching the Earth. The machine learning model primarily forecasts the peak intensity of solar radiation storms based on real-time solar activity data, while the physics-informed model enhances the interpretability and understanding of the machine learning model’s predictions. The effectiveness and operability of this approach have been tested at the KSWC. Full article

Accurate thermospheric mass density (TMD) prediction is critical for applications in solar-terrestrial physics, spacecraft safety, and remote sensing systems. While existing deep learning (DL)-based TMD models are predominantly data-driven, their performance remains constrained by observational data limitations. This study proposes ResNet-MSIS, a novel hybrid framework that integrates prior knowledge from the empirical NRLMSIS-2.1 model into a residual network (ResNet) architecture. The incorporation of NRLMSIS-2.1 enhanced the performance of ResNet-MSIS, yielding a lower root mean squared error (RMSE) of 0.2657 × ${10}^{-12}$ kg/m³ in TMD prediction compared with 0.2750 × ${10}^{-12}$ kg/m³ from ResNet, along with faster convergence during training and better generalization on Gravity Recovery and Climate Experiment (GRACE-A) data, which was trained and validated on the CHAllenging Minisatellite Payload (CHAMP) TMD data (2000–2009, altitude of 305–505 km, avg. 376 km) under quiet geomagnetic conditions (Kp ≤ 3). The DL model was subsequently tested on the remaining CHAMP-derived TMD observations, and the results demonstrated that ResNet-MSIS outperformed both ResNet and NRLMSIS-2.1 on the test dataset. The model’s robustness was further demonstrated on GRACE-A data (2002–2009, altitude of 450–540 km, avg. 482 km) under magnetically quiet conditions, with the RMSE decreasing from 0.3352 × ${10}^{-12}$ kg/m³ to 0.2959 × ${10}^{-12}$ kg/m³, indicating improved high-altitude prediction accuracy. Additionally, ResNet-MSIS effectively captured the horizontal TMD variations, including equatorial mass density anomaly (EMA) and midnight density maximum (MDM) structures, confirming its ability to learn complex spatiotemporal patterns. This work underscores the value of merging data-driven methods with domain-specific prior knowledge, offering a promising pathway for advancing TMD modeling in space weather and atmospheric research. Full article

The ability to monitor forest areas after disturbances is key to ensure their regrowth. Problematic situations that are detected can then be addressed with targeted regeneration efforts. However, achieving this with automated photo interpretation is problematic, as training such systems requires large amounts of labeled data. To this effect, we leverage citizen science data (iNaturalist) to alleviate this issue. More precisely, we seek to generate pre-training data from a classifier trained on selected exemplars. This is accomplished by using a moving-window approach on carefully gathered low-altitude images with an Unmanned Aerial Vehicle (UAV), WilDReF-Q (Wild Drone Regrowth Forest—Quebec) dataset, to generate high-quality pseudo-labels. To generate accurate pseudo-labels, the predictions of our classifier for each window are integrated using a majority voting approach. Our results indicate that pre-training a semantic segmentation network on over 140,000 auto-labeled images yields an $F1$ score of $43.74$% over 24 different classes, on a separate ground truth dataset. In comparison, using only labeled images yields a score of $32.45$%, while fine-tuning the pre-trained network only yields marginal improvements ($46.76$%). Importantly, we demonstrate that our approach is able to benefit from more unlabeled images, opening the door for learning at scale. We also optimized the hyperparameters for pseudo-labeling, including the number of predictions assigned to each pixel in the majority voting process. Overall, this demonstrates that an auto-labeling approach can greatly reduce the development cost of plant identification in regeneration regions, based on UAV imagery. Full article

The challenge of obtaining landslide susceptibility zoning in Tibet is compounded by the high altitude, extensive range, and difficult exploration of the region. To address this issue, a novel evaluation approach based on Stacking ensemble machine learning is proposed. This study focuses on Jiacha County, adopts the slope unit as the evaluation unit, and picks up 14 evaluation factors that symbolize the topography and geomorphology, environmental and hydrological features, and basic geological features. These landslide conditioning factors were integrated into a total of 4660 Stacking ensemble learning models, randomly combined by 10 base-algorithms, including AdaBoost, Decision Tree (DT), Gradient Boosting Decision Tree (GBDT), k-Nearest Neighbors (kNNs), LightGBM, Multilayer Perceptron (MLP), Random Forest (RF), Ridge Regression, Support Vector Machine (SVM), and XGBoost. All models were trained, using the natural discontinuity method to classify landslide susceptibility, and the AUC value, the area under the ROC curve, was taken to evaluate the model. The results show that the maximum AUC values in the 9 models performing better reach 0.78 and 0.99 over the test set and the train set. Most of the areas identified as high susceptibility and above show consistency with the interpretation of the existing geological field data. Thus, the Stacking ensemble method is applicable to the landslide susceptibility situation in Jiacha County, Tibet, and can provide theoretical support for disaster prevention and mitigation work in the Qinghai–Tibet Plateau area. Full article