Search Results (1,266)

Amidst the intensification of global climate change and the increasing impacts of human activities, ecosystem patterns and processes have undergone substantial transformations. The distribution and evolutionary dynamics of mountain ecosystems have become a focal point in ecological research. The Surkhan River Basin is located in the transitional zone between the arid inland regions of Central Asia and the mountain systems, where its unique physical and geographical conditions have shaped distinct patterns of vertical zonation. Utilizing Landsat imagery, this study applies a hierarchical classification approach to derive land cover classifications within the Surkhan River Basin. By integrating the NDVI (normalized difference vegetation index) and DEM (digital elevation model (30 m SRTM)), an “NDVI-DEM-Land Cover” scatterplot is constructed to analyze zonation characteristics from 1980 to 2020. The 2020 results indicate that the elevation boundary between the temperate desert and mountain grassland zones is 1100 m, while the boundary between the alpine cushion vegetation zone and the ice/snow zone is 3770 m. Furthermore, leveraging DEM and LST (land surface temperature) data, a potential energy analysis model is employed to quantify potential energy differentials between adjacent zones, enabling the identification of ecological transition areas. The potential energy analysis further refines the transition zone characteristics, indicating that the transition zone between the temperate desert and mountain grassland zones spans 1078–1139 m with a boundary at 1110 m, while the transition between the alpine cushion vegetation and ice/snow zones spans 3729–3824 m with a boundary at 3768 m. Cross-validation with scatterplot results confirms that the scatterplot analysis effectively delineates stable zonation boundaries with strong spatiotemporal consistency. Moreover, the potential energy analysis offers deeper insights into ecological transition zones, providing refined boundary identification. The integration of these two approaches addresses the dimensional limitations of traditional vertical zonation studies, offering a transferable methodological framework for mountain ecosystem research. Full article

To meet the requirements of the high spatiotemporal three-dimensional (3D) airflow field within the glide path corridor during carrier-based aircraft/unmanned aerial vehicles (UAVs) landings, this paper proposes a prediction method for high spatiotemporal resolution 3D ship airwake along the glide path by integrating computational fluid dynamics (CFD), backpropagation (BP) neural network, and Doppler wind lidar (DWL). Firstly, taking the conceptual design aircraft carrier model as the research object, CFD numerical simulations of the ship airwake within the glide path region are carried out using the Poly-Hexcore grid and the detached eddy simulation (DES)/the Reynolds-averaged Navier–Stokes (RANS) turbulence models. Then, using the high spatial resolution ship airwake along the glide path obtained from steady RANS computations under different inflow conditions as a sample dataset, the BP neural network prediction models were trained and optimized. Along the ideal glide path within 200 m behind the stern, the correlation coefficients between the predicted results of the BP neural network and the headwind, crosswind, and vertical wind of the testing samples exceeded 0.95, 0.91, and 0.82, respectively. Finally, using the inflow speed and direction with high temporal resolution from the bow direction obtained by the shipborne DWL as input, the BP prediction models can achieve accurate prediction of the 3D ship airwake along the glide path with high spatiotemporal resolution (3 m, 3 Hz). Full article

This paper presents a novel design for precise vertical landing of drones based on the detection of three phase shifts in the range of ±180°. The design has three inputs to which the signal transmitted from an oscillator located at the landing point arrives with different delays. The circuit increases the aerial tracking volume relative to that achieved by detectors with theoretical unambiguous detection ranges of ±90°. The phase shift measurement circuit uses an analog phase detector (mixer), detecting a maximum range of ±90°and a double multiplication of the input signals, in phase and phase-shifted, without the need to fulfill the quadrature condition. The calibration procedure, phase detector curve modeling, and calculation of the input signal phase shift are significantly simplified by the use of an automatic gain control on each branch, dwhich keeps input amplitudes to the analog phase detectors constant. A simple program to determine phase shifts and guidance instructions is proposed, which could be integrated into the same flight control platform, thus avoiding the need to add additional processing components. A prototype has been manufactured in C band to explain the details of the procedure design. The circuit uses commercial circuits and microstrip technology, avoiding the crossing of lines by means of switches, which allows the design topology to be extrapolated to much higher frequencies. Calibration and measurements at 5.3 GHz show a dynamic range greater than 50 dB and a non-ambiguous detection range of ±180°. These specifications would allow one to track the drone during the landing maneuver in an inverted cone formed by a surface with an 11 m radius at 10 m high and the landing point, when 4 cm between RF inputs is considered. The errors of the phase shifts used in the landing maneuver are less than ±3°, which translates into 1.7% losses over the detector theoretical range in the worst case. The circuit has a frequency bandwidth of 4.8 GHz to 5.6 GHz, considering a 3 dB variation in the input power when the AGC is limiting the output signal to 0 dBm at the circuit reference point of each branch. In addition, the evolution of phases in the landing maneuver is shown by means of a small simulation program in which the drone trajectory is inside and outside the tracking range of ±180°. Full article

The objective of this study is to define groundwater management zones for a complex deformed and fissured Precambrian karst aquifer, which underlies one of the most important agricultural areas in the semi-arid region of Irecê, Bahia, Brazil. It is an unconfined aquifer, hundreds of meters thick, resulting from a large sequence of carbonates piled up by thrust faults during tectonic plate collisions. Groundwater recharge and flow in this aquifer are greatly influenced by karst features, through the high density of sinkholes and vertical wells. Over the past four decades, population and agricultural activities have increased in the region, resulting in unsustainable groundwater withdrawal and, at the same time, water quality degradation. Therefore, it is important to develop legal and environmental management strategies. This work proposes the division of the karst area into three well-defined management zones by mapping karst structures, land use, and urban occupation, as well as the concentrations of chloride and nitrate in the region’s groundwater. Zone 1 in the north possesses the lowest levels of karstification, anthropization, and contamination, while zone 2 in the central region has the highest levels and zone 3 in the south ranging in-between (except for stronger karstification). The delimitation of management zones will contribute to the development and implementation of optimized zone-specific groundwater preservation and restoration strategies. Full article

When people overextend their step length, it leads to an increase in vertical movement and braking force. The overextension elevates landing impacts, which may increase pain in the knees or lower back. The objective of this study was to examine the effects of soft-landing walking techniques in a 90 min, instructor-led group class for older adults. To evaluate a landing impact, an accelerometer measurement system (Descente LTD., Tokyo, Japan) was used to measure a participant 10 meter (m) of walking. Assessment outcomes included the average number of steps, step length, upward acceleration which reflects the landing impact, and survey questions. A total of 223 older adults (31 men, 192 women, mean age 74.4 ± 5.7 years) completed the walking lesson. Following the lesson, participants decreased their step lengths and reduced upward acceleration, along with an increased step count. The number of steps increased, and a positive correlation (r = 0.73, p < 0.01) was observed between the rate of change in step length and upward acceleration. Over 95% of participants gave high marks for practicality and understanding the accelerometer measurements. The information derived from this study will provide valuable insight into the effectiveness of soft-landing techniques as a promotion of a healthy walking program for older adults. Full article

Tropical rainforests are rapidly disappearing due to human activities, particularly land-use changes, resulting in a heterogeneous mosaic of landscapes that substantially contribute to global terrestrial biodiversity loss. We investigated how changes in land-use affect species richness, composition, and functional guilds of ground-dwelling ants within various land-use systems at a local scale in the Amazonian rainforest. Our focus was to respond to the following: (i) How do local species richness and community composition reflect differences among land-use systems? (ii) Are ground-dwelling ants, especially specialists, negatively impacted by intensified land-use changes? We surveyed 55 sites representing five land-use systems: primary forest, secondary forest, forest corridor, selective logging, and Eucalyptus plantation. We registered 150 ant species, and species richness ranged from 43 to 94. Richness varies according to the land-use systems, likely influenced by differences in habitat structural complexity both vertically and horizontally. Ant species composition and guilds distribution also varied among land-use systems studied. Environments characterized by reduced structural complexity or higher disturbed levels, such as Eucalyptus plantations, tend to support lower resource availability, which may lead to decreased species richness. However, the surrounding matrix appears to play a key role in maintaining regional biodiversity, as evidenced by the absence of differences in ground-dwelling ants diversity across all land-use systems studied. Full article

Buildings are a significant component of urban space and are essential to smart cities, catastrophe monitoring, and land use planning. However, precisely extracting building polygons from remote sensing images remains difficult because of the variety of building designs and intricate backgrounds. This paper proposes an end-to-end polygon dynamic adjustment algorithm (PDAA) to improve the accuracy and geometric consistency of building contour extraction by dynamically generating and optimizing polygon vertices. The method first locates building instances through the region of interest (RoI) to generate initial polygons, and then uses four core modules for collaborative optimization: (1) the feature enhancement module captures local detail features to improve the robustness of vertex positioning; (2) the contour vertex tuning module fine-tunes vertex coordinates through displacement prediction to enhance geometric accuracy; (3) the learnable redundant vertex removal module screens key vertices based on a classification mechanism to eliminate redundancy; and (4) the missing vertex completion module iteratively restores missed vertices to ensure the integrity of complex contours. PDAA dynamically adjusts the number of vertices to adapt to the geometric characteristics of different buildings, while simplifying the prediction process and reducing computational complexity. Experiments on public datasets such as WHU, Vaihingen, and Inria show that PDAA significantly outperforms existing methods in terms of average precision (AP) and polygon similarity (PolySim). It is at least 2% higher than existing methods in terms of average precision (AP), and the generated polygonal contours are closer to the real building geometry. Values of 75.4% AP and 84.9% PolySim were achieved on the WHU dataset, effectively solving the problems of redundant vertices and contour smoothing, and providing high-precision building vector data support for scenarios such as smart cities and emergency response. Full article

The impacts of the solar eclipse that occurred on 8 April 2024 over the United States on various atmospheric parameters are investigated. We analyzed surface and vertical profiles of temperature and humidity to understand how this eclipse affected the atmosphere from the ground to the stratosphere. Our findings show a significant response throughout the atmospheric range. The eclipse caused a decrease in shortwave radiation, leading to cooler Earth surfaces and a subsequent drop in surface temperature. This cooling effect also resulted in high relative humidity and lower wind speeds at the surface. Furthermore, GPS radio occultation data from COSMIC-2 revealed a decrease in tropospheric temperature and increase in stratospheric temperature during the eclipse. We also observed a reduction in both the temperature and height of the tropopause. The uniqueness of the present investigations lies in delineating the solar eclipse’s effects on the land and ocean. Our analysis indicates that land regions experienced a more pronounced temperature change compared to ocean regions. Full article

Habitat loss and land-use intensification are major threats to biodiversity in the Brazilian Atlantic Forest, particularly for bat assemblages that provide key ecosystem services. In this study, we examined how landscape composition (forest and pasture cover) and local forest structure influence the richness and abundance of phyllostomid bats across 20 forest fragments in southern Bahia. Bat sampling was conducted using mist nets, and forest structure was quantified using tree measurements and vertical foliage stratification. We applied structural equation modeling to test the direct and indirect effects of landscape and local variables. Our results show that forest cover has both direct and indirect positive effects on bat diversity, mediated by improved forest structure. In contrast, increased pasture cover negatively affected forest structure and was weakly associated with bat diversity. The most abundant species were generalist frugivores, such as Carollia perspicillata and Rhinophylla pumilio. These findings highlight the importance of maintaining forest cover and structural complexity to support bat diversity in agroforestry-dominated landscapes. Conservation strategies that integrate habitat protection with sustainable land-use practices are crucial to maintaining biodiversity and the ecological functions provided by bats in this globally threatened biome. Full article

Background: Optimal nutrition and training regimens are essential for athletes to maximize performance and recovery. Probiotic supplementation, through the modulation of the gut microbiota, and omega-3 fatty acids, known for their anti-inflammatory properties, may enhance physiological adaptations when combined with targeted training. This study evaluated the effects of probiotics and omega-3 supplementation, alongside ultra-short race pace training (USRPT), on performance metrics in competitive sprint swimmers. Methods: In this double-blind, placebo-controlled study, 60 male sprint swimmers (age: 19.2 ± 3.6 years; height: 182.2 ± 5.2 cm; weight: 81.6 ± 4.4 kg) with a minimum of five years of training experience, were randomly assigned to six groups (n = 10 per group): (1) Control (CON), (2) USRPT only, (3) Placebo + USRPT (PLA + USRPT), (4) Probiotics + USRPT (PRO + USRPT), (5) Omega-3 + USRPT (OMEGA + USRPT), and (6) Probiotics + Omega-3 + USRPT (PRO + OMEGA + USRPT). Over the eight-week intervention, the participants in PRO + USRPT consumed one multi-strain probiotic capsule daily (4.5 × 10¹¹ CFU) and a placebo capsule. Those in OMEGA + USRPT ingested 1000 mg of fish oil after lunch (500 mg EPA and 180 mg DHA per capsule) paired with a placebo capsule. The combined supplementation group (PRO + OMEGA + USRPT) received both probiotic and omega-3 capsules. The PLA + USRPT group consumed two starch capsules daily. The USRPT protocol was implemented across all the training groups, where the swimmers performed 17 sets of 25 m and 12.5 m sprints based on weekly recorded race times. Performance assessments included pre- and post-test measurements of sprint times (50 m and 100 m freestyle), vertical jump tests (both in water and on dry land), and other strength and endurance metrics (reaction time, agility T-test, sprint index, fatigue index, and velocity). Results: The combined intervention of probiotics and omega-3 with USRPT produced the greatest improvements in performance. The PRO + OMEGA + USRPT group reduced 50 m freestyle time by 1.92% (p = 0.002, pEta² = 0.286) and 100 m freestyle time by 2.48% (p = 0.041, pEta² = 0.229), demonstrating significant Time × Group interactions consistent with a synergistic effect. Additionally, the sprint index improved (pEta² = 0.139, p = 0.013) and reaction time decreased (pEta² = 0.241, p = 0.009) in the combined group, indicating enhanced anaerobic capacity and neuromuscular responsiveness compared to single interventions. Conclusions: This study suggests that combining probiotics and omega-3 supplementation with USRPT leads to synergistic improvements in sprint swimming performance, enhancing anaerobic power and recovery beyond what is achieved with individual interventions. This integrated approach may provide a practical strategy for competitive swimmers seeking to optimize their performance. Future studies should incorporate mechanistic markers, longer intervention durations, and diverse athlete populations to clarify further and extend these findings. Full article

Urbanization has profoundly transformed land surface morphology and amplified thermal environmental modifications, culminating in intensified urban heat island (UHI) phenomena. Local climate zones (LCZs) provide a robust methodological framework for quantifying thermal heterogeneity and dynamics at local scales. Our study investigated the Changsha–Zhuzhou–Xiangtan urban agglomeration (CZXA) as a case study and systematically examined spatiotemporal patterns of LCZs and land surface temperature (LST) from 2002 to 2019, while elucidating mechanisms influencing urban thermal environments and proposing optimized cooling strategies. Key findings demonstrated that through multi-source remote sensing data integration, long-term LCZ classification was achieved with 1,592 training samples, maintaining an overall accuracy exceeding 70%. Landscape pattern analysis revealed that increased fragmentation, configurational complexity, and diversity indices coupled with diminished spatial connectivity significantly elevate LST. Rapid development of the city in the vertical direction also led to an increase in LST. Among seven urban morphological parameters, impervious surface fraction (ISF) and pervious surface fraction (PSF) demonstrated the strongest correlations with LST, showing Pearson coefficients of 0.82 and −0.82, respectively. Pearson coefficients of mean building height (BH), building surface fraction (BSF), and mean street width (SW) also reached 0.50, 0.55, and 0.66. Redundancy analysis (RDA) results revealed that the connectivity and fragmentation degree of LCZ_8 (COHESION8) was the most critical parameter affecting urban thermal environment, explaining 58.5% of LST. Based on these findings and materiality assessment, the regional cooling model of “cooling resistance surface–cooling source–cooling corridor–cooling node” of CZXA was constructed. In the future, particular attention should be paid to the shape and distribution of buildings, especially large, openly arranged buildings with one to three stories, as well as to controlling building height and density. Moreover, tailored protection strategies should be formulated and implemented for cooling sources, corridors, and nodes based on their hierarchical significance within urban thermal regulation systems. These research outcomes offer a robust scientific foundation for evidence-based decision-making in mitigating UHI effects and promoting sustainable urban ecosystem development across urban agglomerations. Full article

(1) Background: Urban parks play a critical role in conserving biodiversity within city landscapes, yet the effects of fine-scale microhabitat heterogeneity remain poorly understood. This study examines how land cover and vegetation unit type within parks influence butterfly diversity. (2) Methods: From July to September 2019 and June to September 2020, adult butterflies were surveyed in 27 urban parks across Beijing. We classified vegetation into units based on vertical structure and management intensity, and then applied the patch–matrix framework and landscape metrics to quantify fine-scale heterogeneity in vegetation unit composition and configuration. Generalized linear models (GLM), generalized additive models (GAM), and random forest (RF) models were applied to identify factors influencing butterfly richness (Chao1 index) and abundance. (3) Results: In total, 10,462 individuals representing 37 species, 28 genera, and five families were recorded. Model results revealed that the proportion of park area covered by spontaneous herbaceous areas (SHA), wooded spontaneous meadows (WSM), and the Shannon diversity index (SHDI) of vegetation units were positively associated with butterfly species richness. In contrast, butterfly abundance was primarily influenced by the proportion of park area covered by cultivated meadows (CM) and overall green-space coverage. (4) Conclusions: Fine-scale vegetation patch composition within urban parks significantly influences butterfly diversity. Our findings support applying the patch–matrix framework at intra-park scales and suggest that integrating spontaneous herbaceous zones—especially wooded spontaneous meadows—with managed flower-rich meadows will enhance butterfly diversity in urban parks. Full article

Effective management of urban development requires robust decision-support tools, including land suitability analysis and its visual communication. This study introduces and evaluates seven 3D geovisualization methods—Horizontal Planes, Point Cloud, 3D Surface, Vertical Planes, 3D Graduated Symbols, Prism Map, and Voxels—for visualizing land suitability for residential development in Jihlava, Czechia. Using five raster-based data layers derived from a multi-criteria evaluation (Urban Planner methodology) across three time horizons (2023, 2028, 2033), the visualizations were implemented in ArcGIS Online and assessed by 19 domain experts via a structured questionnaire. The evaluation focused on clarity, usability, and accuracy in interpreting land suitability values, with the methods being rated on a five-point scale. Results show that the Horizontal Planes method was rated highest in terms of interpretability and user satisfaction, while 3D Surface and Vertical Planes were considered the least effective. The study demonstrates that visualization methods employing visual variables (e.g., color and transparency) are better suited for land suitability communication. The methodological contribution lies in systematically comparing 3D visualization techniques for thematic spatial data, providing guidance for their application in planning practice. The results are primarily intended for urban planners, designers, and local government representatives as supportive tools for efficient planning of future built-up area development. Full article

While space weather indices (e.g., F10.7, Dst index) are commonly employed to characterize ionospheric activity levels, the Global Mean Electron Content (GMEC) provides a more direct and comprehensive indicator of the global ionospheric state. This metric demonstrates greater potential than space weather indices for calibrating empirical ionospheric models such as IRI-2020. The COSMIC-2 constellation enables continuous, all-weather global ionospheric monitoring via radio occultation, unimpeded by land–sea distribution constraints, with over 8000 daily occultation events suitable for GMEC modeling. This study developed two lightweight GMEC models using COSMIC-2 data: (1) a POD GMEC model based on slant TEC (STEC) extracted from Level 1b podTc2 products and (2) a PROF GMEC model derived from vertical TEC (VTEC) calculated from electron density profiles (EDPs) in Level 2 ionPrf products. Both backpropagation neural network (BPNN)-based models generate hourly GMEC outputs as global spatial averages. Critically, GMEC serves as an essential intermediate step that addresses the challenges of utilizing spatially irregular occultation data by compressing COSMIC-2’s ionospheric information into an integrated metric. Building on this compressed representation, we implemented a convolutional neural network (CNN) that incorporates GMEC as an auxiliary feature to calibrate IRI-2020’s global ionospheric maps. This approach enables computationally efficient correction of systemic IRI TEC errors. Experimental results demonstrate (i) 48.5% higher accuracy in POD/PROF GMEC relative to IRI-2020 GMEC estimates, and (ii) the calibrated global IRI TEC model (designated GCIRI TEC) reduces errors by 50.15% during geomagnetically quiet periods and 28.5% during geomagnetic storms compared to the original IRI model. Full article

This study presents a cooperative unmanned aerial system (UAS) designed to enable precise autonomous landings in unstructured environments using low-cost onboard vision technology. This approach involves a carrier UAV with a stabilized RGB camera and a neural inference system, as well as a lightweight tailsitter payload UAV with an embedded grayscale vision module. The system relies on visually recognizable landing markers and does not require additional sensors. Field trials comprising full deployments achieved an 80% success rate in autonomous landings, with vertical touchdown occurring within a 1.5 m radius of the target. These results confirm that vision-based marker detection using compact neural models can effectively support autonomous UAV operations in constrained conditions. This architecture offers a scalable alternative to the high complexity of SLAM or terrain-mapping systems. Full article