Search Results (641)

Reliable spatial georeferencing of mobile mapping platforms is a fundamental prerequisite for high-fidelity urban remote sensing products such as 3D point clouds and digital twins. However, in deep urban canyons, severe signal occlusion and multipath effects reduce visible GNSS satellites, causing ambiguity resolution (AR) failure and degraded observation geometry for UGV-borne systems. Conventional Vehicle-to-Vehicle (V2V) cooperation offers limited improvement due to symmetric ground-level occlusion. To overcome this, we propose an asymmetric GNSS/UWB fusion method that introduces Unmanned Aerial Vehicles (UAVs) as high-altitude dynamic spatial anchors to reconstruct the 3D observation geometry. Two contributions are presented: (i) an asymmetric heterogeneous stochastic model coupling carrier-to-noise ratio (C/N0) and elevation angle to handle the quality disparity between air and ground sensor links, preventing multipath contamination of high-fidelity UAV observations; and (ii) a dynamic baseline constrained least-squares algorithm integrating Ultra-Wideband (UWB) ranging to stabilize GNSS positioning under high-dynamic relative motion. Validated through high-fidelity simulations and field experiments, the method achieves a 98.2% AR success rate and sub-decimeter 3D accuracy under extreme occlusion (≤3 visible satellites), while urban-canyon tests demonstrate 100% positioning availability across all evaluated epochs and reduce the 95th-percentile 3D error from 7.25 m to 0.19 m under the tested single-UAV/single-UGV configuration. The framework supports smart city modeling, 3D reconstruction, and infrastructure monitoring. Full article

Xizang is characterized by high altitude, low air pressure, strong atmospheric transparency, and complex terrain, while sparse ground stations coexist with continuously available remotely sensed data, and systematic studies on SSR bias correction and meteorological influences under plateau conditions remain limited. This study focuses on a short-term spring case at one urban observation site in Lhasa, using observations collected from 4 to 30 April 2025 to investigate the bias correction of remotely sensed surface solar radiation (SSR) and the influence of meteorological factors. Ground observations and Himawari-8 remotely sensed data were first spatially and temporally matched and preprocessed. Spearman correlation analysis was then used to select key input variables. Support vector regression, random forest, XGBoost, and multiple linear regression models were subsequently developed, followed by a Stacking ensemble model for bias correction. Finally, local sensitivity analysis was conducted to examine the local response of the correction model to selected meteorological variables at a representative baseline point. The results showed that the correlation coefficient between remotely sensed SSR and ground-observed SSR was 0.88 ($p<0.001$). The Stacking ensemble model achieved the best performance, with a test set $R^2$ of 0.8796, an MAE of 118.54 W/m², and an RMSE of 152.41 W/m². Local sensitivity analysis showed that a +10 hPa perturbation in air pressure increased the model output by 173.45 W/m², while a +10 °C perturbation in air temperature increased the output by 23.76 W/m². This study provides a reference for improving the accuracy of remotely sensed SSR and for solar resource assessment in plateau regions. Full article

This study investigates the hydroclimatic impacts of climate change on the Göynük Stream Basin, a snow-fed tributary within the Euphrates River Basin, utilizing flow, precipitation, and temperature data from 1975 to 2022. The Göynük Stream Basin is characterized by high-altitude, harsh continental conditions, with its flow regime heavily influenced by snowmelt, rendering it particularly sensitive to climate change. Employing a suite of trend analysis methods, including Mann–Kendall, Spearman Rho, Theil–Sen, Şen-Innovative Trend Analysis (ITA), and Innovative Polygon Trend Analysis (IPTA), the research evaluated annual and seasonal data from one stream and four meteorological stations across multiple significance levels (90%, 95%, 99%). Unlike conventional hydroclimatic studies based solely on monotonic trend detection, this study integrates classical trend tests, innovative trend approaches, temporal regime-based analysis (RAPS), and machine learning techniques within a unified assessment framework to evaluate both hydroclimatic variability and runoff predictability under climate change conditions. Key findings indicate a significant decline in annual flow rates by approximately 9.37%, with a notable decrease in maximum flow rates evidenced by a negative trend slope of −0.2726 m³/s/year. While precipitation trends were generally decreasing, temperature data exhibited significant increases, especially during winter and spring. Seasonal analysis revealed substantial flow reductions in summer and autumn, coupled with an earlier timing of the annual maximum flow, shifting from mid-May to late March/early April, suggesting earlier snowmelt. The study concludes that the Göynük Stream Basin is experiencing increasing hydroclimatic pressures attributable to climate change. These insights are crucial for water resource management and serve as a guideline for similar snow-fed sub-basins within the broader Euphrates River Basin. Furthermore, the integration of a machine learning approach, utilizing meteorological and seasonal data, demonstrated strong monthly runoff prediction capabilities with NRMSE of 4.11% and R² equal to 0.951. Feature importance analysis highlighted seasonality and temperature as primary predictive factors. However, a marked decline in model accuracy after 2011 was observed, indicating a non-stationarity in the hydroclimatic system, likely driven by climate change impacts and underscoring the need for adaptive management strategies. Full article

Accurate Unmanned Aerial Vehicle (UAV) position estimation is the cornerstone of urban low-altitude safety management systems. Time Difference of Arrival (TDOA) and Remote Identification (Remote ID) are widely used surveillance technologies with complementary characteristics. TDOA provides high-rate updates but suffers from geometry-induced horizontal–vertical anisotropy and multipath effects, while Remote ID supplies absolute state information yet struggles with intermittent sampling and packet loss. Existing fusion schemes typically address these issues in isolation: sequential filtering manages asynchrony but assumes Gaussian noise, robust estimators suppress outliers at the cost of discarding valid data, and coupled-filter architectures allow vertical anomalies to contaminate horizontal estimates through the Kalman gain cross-coupling. No prior framework jointly handles structural TDOA altitude jumps, stochastic Remote ID timing jitter, and the geometric anisotropy between estimation subspaces within a single coherent pipeline. To bridge this gap, we propose a Hybrid Conditional Kalman Filter (HCKF) framework comprising three integrated modules. First, a kinematics-based temporal alignment module maps asynchronous measurements onto a uniform timeline and predicts missing samples, resolving cross-modal time mismatches. Second, a measurement quality evaluation mechanism detects TDOA altitude steps via robust two-layer stratification and scores Remote ID timing irregularity through a confidence mapping, converting these anomalies into dynamic covariance adjustments and weight caps without discarding observations. Third, a Subspace-Decoupled Fusion strategy exploits the physical insight that TDOA horizontal precision derives from hyperbolic intersection geometry, whereas its vertical estimates suffer from weak observability due to near-coplanar ground-station deployment . By applying entropy-guided weighting in the horizontal plane and a conditional Remote ID-dominant rule in the vertical axis, this design prevents cross-dimensional error propagation. The framework was validated using three real-world flight missions at distinct altitudes (255 m, 345 m, and 440 m) totaling 13.51 km of flight distance, with RTK serving as ground truth. HCKF reduces the Root Mean Square Error by over 40% relative to single-source baselines (95% bootstrap confidence interval: [35.2%, 48.7%]), and paired Wilcoxon signed-rank tests confirm statistically significant improvement ($p<0.01$) over standard EKF, Covariance Intersection, and Iterative CI across all three tracks. Full article

Post-exercise release of cardiac biomarkers reflects physiological adaptations of the myocardium to exercise; however, data on their kinetics after exhaustive exercise under hypoxia remain scarce. We determined the kinetics of cardiac biomarker changes following a single bout of exercise to volitional exhaustion under normoxia and moderate normobaric hypoxia (2000 m and 3000 m a.s.l.) in trained (n = 12; VO_2max 64.2 ± 2.9 mL·kg⁻¹·min⁻¹) and untrained (n = 12; VO_2max 44.1 ± 7.4 mL·kg⁻¹·min⁻¹) men. Participants performed a graded exercise test (GXT) followed by a constant-workload exercise test (CXT) at the lactate threshold under three conditions (FiO₂ = 20.9%, 16.5%, 14.4%). Venous blood was sampled at rest, immediately post-exercise, and at 2, 6, and 24 h of recovery for determination of cardiac troponin T (cTnT) and I (cTnI), myoglobin (Mb), creatine kinase MB isoform (CK-MB), heart-type fatty acid-binding protein (H-FABP), ischemia-modified albumin (IMA), and N-terminal pro-B-type natriuretic peptide (NT-proBNP) by ELISA. Exhaustive exercise induced significant elevations in all biomarkers, peaking at 2–6 h post-exercise and largely returning to resting values by 24 h. Moderate normobaric hypoxia did not augment the cardiac biomarker response; rather, it attenuated the increases in Mb, NT-proBNP, and IMA, likely due to earlier peripheral fatigue and lower absolute mechanical work. The inhibitory effect of hypoxia on cTnI release was observed exclusively in trained men, suggesting an interaction between training-related cardiac adaptations and the hypoxic stimulus. These findings support the safety of high-intensity exercise at simulated altitudes of 2000–3000 m a.s.l. Full article

The aim of this study was to evaluate the influence of altitudinal grassland systems on forage antioxidant properties and the nutritional composition of beef produced in Caraș-Severin County, Romania. We hypothesised that cattle raised at higher altitudes would produce beef with a superior nutritional profile, characterised by a more favourable lipid composition and enhanced antioxidant-related characteristics. Samples of fresh grass and hay were gathered from three representative areas: plain (Sacu, 154 m), hill (Văliug, 550 m), and mountain (Cozia, 1130 m). The beef samples were represented by two categories of commercially important muscles: Longissimus thoracis (loin) and Semimembranosus (topside), sourced from animals raised in each location. The proximate composition of forage samples indicated substantially higher levels of fatty acids, protein, and ash in mountain grasslands compared to lowland regions (p < 0.05). The total polyphenol content (TPC) and antioxidant activity (DPPH test) revealed a similar pattern, with the strongest antioxidant activity (lowest IC₅₀ value) recorded for Cozia hay (GHC) samples. The composition of beef was additionally influenced by the production area. Samples derived from mountainous regions exhibited elevated protein concentrations, moderate intramuscular fat levels, and enhanced mineral composition in comparison to samples from plain areas. Fatty acid analysis revealed that mountain-sourced beef had significantly reduced levels of saturated fatty acids (SFA) and elevated concentrations of monounsaturated fatty acids (MUFA) and polyunsaturated fatty acids (PUFA), including the nutritionally beneficial n-3 fatty acids and conjugated linoleic acid (CLA). Principal component analysis distinctly classified beef samples based on production method, with mountain-origin samples indicating better lipid properties and enhanced antioxidant-related variables. The findings demonstrate that natural grasslands at higher altitudes may enhance both the bioactive quality of fodder and the nutritional value of beef. Mountain pasture systems are a sustainable approach for producing high-quality beef with enhanced lipid composition and increased market value. Full article

On-site withstand voltage testing is essential for evaluating insulation performance and detecting defects in UHV AC equipment; however, existing safety distance criteria are mainly based on empirical experience or extrapolated from low-altitude and lower-voltage conditions, limiting their applicability. To address this issue, a systematic framework for safety distance calculation and altitude correction is developed. The selection principles and circuit configuration of the test system are analyzed to clarify the constraints between power capacity and tuning under high-voltage, large-capacity conditions. Based on air-gap discharge characteristics, a minimum safety distance model is established for the 1000 kV main transformer with respect to grounded structures and personnel. Meteorological factors and proximity effects are further incorporated to propose correction methods and on-site zoning strategies. Results indicate that a baseline safety distance of approximately 10 m is appropriate at altitudes up to 1000 m, and the model captures the nonlinear degradation of insulation strength in long air gaps at higher altitudes. A case study at 3620 m yields a minimum safety distance of 16.4 m, providing a quantitative basis for safe UHV AC on-site testing under varying altitude conditions. Full article

To elucidate the effects of nitrogen (N) addition on soil carbon (C), N, and phosphorus (P) cycling in high-altitude orchards on the Qinghai–Tibet Plateau, a three-year field experiment was conducted at an altitude of 3000 m with four N application rates (0, 150, 300, and 450 kg N ha⁻¹, designated as CK, N150, N300, and N450, respectively). We determined soil physicochemical properties, 12 soil enzyme activities, and metagenomic characteristics, and further adopted partial least squares path modeling (PLS-PM) for data analysis and mechanism exploration. The results were as follows: (1) The N300 treatment yielded the maximum C-hydrolase activities and soil organic carbon content, with a 40.6% increase in soil organic carbon compared with the CK group. (2) The N450 treatment resulted in a 365.4% increase in soil nitrate content and significantly reduced the soil pH (from 6.32 to 5.86). Such environmental filtering significantly decreased the relative abundance of Nitrospirota and its core denitrification genes, including nosZ and narI. (3) Continuous N input induced secondary soil P limitation, leading to a more than 90% increase in phosphatase activities under the N450 treatment. Pseudomonadota activated soil P sources by enriching the functional potential of the phn gene cluster. Furthermore, the PLS-PM analysis revealed a significant negative statistical association between P-cycling enzymes and N-cycling functional potential (p < 0.01). This statistical linkage supports the observation of divergent metabolic responses among different element cycles. In conclusion, under the specific experimental conditions tested, an optimal N application rate of 300 kg N ha⁻¹ is recommended to balance agricultural productivity and soil ecological health. The microbiome of alpine apple orchards responds to elevated N input through metabolic trade-offs, namely reducing the functional potential for denitrification and enhancing the P recycling system. These findings provide vital molecular evidence to guide fertilizer reduction, optimize nutrient management, and promote the sustainable development of high-altitude agroecosystems. Full article

The Educational Competition Optimizer (ECO) shows promise on simple tasks but struggles with high-dimensional and complex landscapes due to rigid stage division and limited search operators. This paper proposes a Hybrid Strategy Enhanced ECO (HSECO) featuring: (i) a self-adaptive parameter evolution mechanism for individual-level flexibility, (ii) a multi-operator adaptive selection scheme switching between learning and differential evolution strategies based on real-time feedback, and (iii) an archive-assisted diversity preservation module to mitigate premature convergence. HSECO is validated on CEC2017, CEC2020 and CEC2022, and a continuous engineering benchmark. Statistical tests confirm its superiority over nine State-of-the-Art and parameter-free algorithms in accuracy, convergence speed, and robustness. Ablation and diversity analyses verify its balanced exploration–exploitation dynamics. Finally, HSECO is applied to a three-dimensional UAV path-planning problem, where path length, altitude variation, and turning smoothness are integrated into a single fitness function using a weighted-sum formulation. Therefore, from a metaheuristic optimization perspective, the UAV case is treated as a single-objective constrained optimization problem rather than a Pareto-based multi-objective problem. Experimental results show that HSECO obtains shorter, safer, and smoother trajectories with lower overall weighted fitness. Full article

The safe and stable operation of air-insulated switchgear (AIS) in high-altitude and low-pressure environments is significantly affected by partial discharge (PD), which accelerates insulation aging and may threaten power system reliability. Therefore, effective online monitoring and fault diagnosis methods are of considerable engineering importance. This paper proposes a PD-type recognition method based on the concentration ratio of two characteristic decomposition gases, CO and NO₂. First, a hybrid numerical model coupling fluid dynamics and plasma chemistry was established to simulate the microscopic decomposition mechanism of air discharge. The simulation results indicate that CO and NO₂ are relatively stable and detectable among the considered air-discharge products and that their generation is promoted by increased average electron energy under low-pressure conditions. Subsequently, an experimental platform was developed to simulate three typical insulation defects, namely point discharge, air-gap discharge, and surface discharge, under different simulated altitudes. Quantitative analysis using Fourier-transform infrared spectroscopy and gas chromatography revealed clear correlations between defect type and gas concentration characteristics. Based on these results, a diagnostic criterion was established under the tested conditions: a CO/NO₂ concentration ratio less than 1 indicates the epoxy-resin-based surface discharge model, whereas a ratio greater than 1 indicates point discharge or air-gap discharge. The latter two types can be further distinguished according to the time-dependent increasing trend of the ratio for air-gap discharge. Finally, based on the observed diffusion characteristics of these gases in the laboratory switchgear model, a low-cost online detection prototype using semiconductor gas sensors was developed. Laboratory validation using three typical single-defect models showed that the proposed method achieved 100% recognition accuracy when sufficient time-series data were available. However, further field validation is required before large-scale industrial application. The proposed CO/NO₂ ratio method provides a potential low-cost auxiliary diagnostic approach for AIS insulation monitoring, particularly under high-altitude and low-pressure conditions. Full article

This paper proposes a novel Bidirectional Beetle-Informed RRT* (BBI-RRT*) Connect algorithm to enhance the safety and path planning efficiency of 6-DOF robotic manipulators operating in the complex high-altitude environment of angle-steel towers. By digitally reconstructing the tower environment through model registration, the algorithm establishes an accurate foundation for subsequent path planning. A bidirectional beetle antennae search mechanism is employed to guide node sampling, effectively accelerating the convergence rate of the algorithm. To ensure the generation of feasible path, a multi-constraint objective function is designed to balance path length, smoothness, and operability. Additionally, an Informed RRT* process is integrated to refine the path within an adaptive 3D ellipsoid, achieving global path optimization. Both simulation tests on the Unity platform and real-world experiments are conducted to validate the effectiveness and superiority of the proposed algorithm. Full article

This exploratory study assessed the vehicle- and route-dependent response of five multipoint injection passenger vehicles to two commercial fuel additives marketed as octane-related gasoline additives under real-world high-altitude driving conditions in Quito, Ecuador. The tests were conducted on one urban route and one rural/peripheral route using base gasoline with a nominal octane index of RON 85, RON 85 gasoline with Additive A, and RON 85 gasoline with Additive B. Fuel economy and CO₂-related indicators were obtained through the OBD-II port using the Torque Pro application; therefore, the reported values were interpreted as electronic control unit-based estimates rather than direct gravimetric fuel consumption or laboratory emissions measurements. The revised analysis used OBD-derived trip-average fuel economy as the primary response variable. The mixed-effects model showed a significant effect of route on fuel economy (p < 0.001) and a significant fuel condition × route interaction (p = 0.0089), while the main effect of fuel condition was not statistically significant (p = 0.0699). Additive B increased the mean OBD-derived trip-average fuel economy on the urban route from 11.56 to 12.60 km·L⁻¹, but reduced it on the rural route from 13.46 to 12.65 km·L⁻¹. At the vehicle level, the previously extreme Vehicle 3 response was revised to a more plausible increase from 11.03 to 13.64 km·L⁻¹ (+23.68%) when trip-average fuel economy was used. Since the actual RON/MON values and physicochemical properties of the final fuel blends were not experimentally measured, the observed responses cannot be attributed exclusively to octane number enhancement. Overall, the findings indicate that commercial additive performance was vehicle- and route-dependent rather than universally beneficial. This field-based assessment supports evidence-informed decision-making for sustainable mobility and aligns with SDG 16 and SDG 17 through transparent technical evaluation and academic collaboration. Full article

Traditional linear models in urban informatics struggle to capture the complex, non-linear spatial non-stationarity inherent in metropolitan housing markets. To overcome these constraints, this study introduces a data-driven computational framework integrating a Spatio-Temporal Graph Convolutional Network (STGCN) with gradient-based Explainable Artificial Intelligence (XAI) and Geographically Weighted Regression (GWR). This framework is empirically tested using 217,598 apartment transactions in Busan, the Republic of Korea, augmented with high-resolution micro-demographic grids and Digital Elevation Model (DEM) topographical data. Utilizing unsupervised K-Means clustering, the region is spatially stratified into a dense Urban Core and a dispersed Suburban Periphery. The STGCN demonstrates overwhelming predictive superiority ($R^2=0.802$) over the traditional Spatial Error Model ($R^2=0.437$). Crucially, gradient-based XAI and localized GWR coefficients successfully unspool the deep learning “black box,” visualizing hyper-localized economic realities that global linear models obscure. The analysis expose stark regional market segmentation driven by environmental topography, mathematically quantifying non-linear dynamics such as coastal high-floor premiums, severe mountainous altitude penalties, and latent urban reconstruction premiums. Ultimately, this research bridges the gap between predictive computational power and spatial economic interpretability, offering a robust informatics framework for equitable urban planning. Full article

Replacing traditional hydrocarbon fuel in aircraft turbine engines with hydrogen fuel contributes, in line with current trends, to reducing harmful carbon dioxide emissions and enabling increased flight altitude. Given the high research costs of full-scale turbine engines, research on miniature turbojet engines, due to their availability and relatively low modification costs, can play a significant role in better understanding and developing concepts for adapting existing hydrocarbon-based fuel systems to hydrogen fuel. This article presents the results of a comprehensive numerical analysis of the hydrogen combustion process—illustrating changes in its location and structure—for multiple variants of design changes to the combustion chamber of the miniature GTM-140 turbojet engine, primarily involving appropriate shaping of airflows through the holes in the glow tube and the location of the hydrogen injection point. Based on this analysis, a modernized combustion chamber geometry was proposed, which should ensure a stable hydrogen combustion process that is safe for the thermal resistance of the structural material—and structurally comparable to the baseline Jet-A1 hydrocarbon fuel combustion process. The obtained results can give ground for the construction and experimental testing of a hydrogen-powered turbine engine. Full article

Mining areas in high-altitude cold and arid mountains exhibit heterogeneous land-cover types, large spatial extent, and fragmented boundaries, which makes large-area monitoring difficult with manual interpretation. This study proposes CS-DeepLabV3+, an enhanced semantic segmentation framework built upon DeepLabV3+ for 1-m optical imagery in the Kunlun Mountains. A contextual modeling block is inserted between the encoder output and the atrous spatial pyramid pooling module to strengthen long-range dependency modeling under complex backgrounds. In the decoder, a channel attention block is applied to fused features to suppress redundant responses and improve separability among confusing categories. Experiments on a self-built dataset (Kunlun-Set) demonstrate improved boundary delineation and region consistency for typical mining-related classes (e.g., tailings ponds, stockpiles, and industrial yards). CS-DeepLabV3+ achieved an 81.83% mean intersection-over-union on the test set, outperforming the DeepLabV3+ baseline by 3.52 percentage points. Ablation studies verify that contextual modeling and channel recalibration provide complementary gains. Full article