Search Results (110)

Understanding alpine plants’ survival and reproduction is crucial for their conservation in climate change. Based on 423 valid distribution points, this study utilizes the MaxEnt model to predict the potential habitat and distribution dynamics of Leontopodium nanum under both current and future climate scenarios, while clarifying the key factors that influence its distribution. The primary ecological drivers of distribution are altitude (2886.08 m–5576.14 m) and the mean temperature of the driest quarter (−6.60–1.55 °C). Currently, the suitable habitat area is approximately 520.28 × 10⁴ km², covering about 3.5% of the global land area, concentrated mainly in the Tibetan Plateau, with smaller regions across East and South Asia. Under future climate scenarios, low-emission (SSP126), suitable areas are projected to expand during the 2050s and 2070s. High-emission (SSP585), suitable areas may decrease by 50%, with a 66.07% reduction in highly suitable areas by the 2070s. The greatest losses are expected in the south-eastern Tibetan Plateau. Regarding dynamic habitat changes, by the 2050s, newly suitable areas will account for 51.09% of the current habitat, while 68.26% of existing habitat will become unsuitable. By the 2070s, newly suitable areas will rise to 71.86% of the current total, but the loss of existing areas will exceed these gains, particularly under the high-emission scenario. The centroid of suitable habitats is expected to shift northward, with migration distances ranging from 23.94 km to 342.42 km. The most significant shift is anticipated under the SSP126 scenario by the 2070s. This study offers valuable insights into the distribution dynamics of L. nanum and other alpine species under the context of climate change. From a conservation perspective, it is recommended to prioritize the protection and restoration of vegetation in key habitat patches or potential migration corridors, restrict overgrazing and infrastructure development, and maintain genetic diversity and dispersal capacity through assisted migration and population genetic monitoring when necessary. These measures aim to provide a robust scientific foundation for the comprehensive conservation and sustainable management of the grassland ecosystem on the Qinghai–Tibet Plateau. Full article

This article presents a comparison of two qualitative case studies. The first case study is a partnership group involving two urban secondary school teachers working with one engineer and one education faculty member where they implemented several science, technology, engineering, and mathematics (STEM) lessons over the course of an academic year. The second case study is a partnership group involving undergraduate college students working together to build a data collection device attached to a high-altitude balloon to answer a scientific question or solve an engineering problem and translate the project into engaging lessons for a K-12/secondary student audience. The studies employed a socio-cultural theoretical framework as the lens to examine the individuals’ perspectives, experiences, and engineering meaning-making processes, and to consider what these meant to the partnership itself. The methods included interviews, focus groups, field notes, and artifacts. The analysis involved multi-level coding. The findings indicated that the strength of the partnership (pre, little p, or big P) among participants influenced the strength of the secondary engineering lessons. The partnership growth implications in terms of K-12/secondary and collegiate engineering education included the engineering lesson strength, partnership, and engineering project sustainability The participant partnership meanings revolved around lesson creation, incorporating engineering ideas into the classroom, increasing communication, and increasing secondary students’ learning, while tensions arose from navigating (not quite negotiating) roles as a team. A call for attention to school–university partnerships and the voices heard in engineering partnership building are included since professional skills are becoming even more important due to advances in artificial intelligence (AI) and other technologies. Full article

Visual tracking from the unmanned aerial vehicle (UAV) perspective has been at the core of many low-altitude remote sensing applications. Most of the aerial trackers follow “tracking-by-detection” paradigms or their temporal-context-embedded variants, where the only visual appearance cue is encompassed for representation learning and estimating the spatial likelihood of the target. However, the variation of the target appearance among consecutive frames is inherently unpredictable, which degrades the robustness of the temporal context-aware representation. To address this concern, we advocate extra visual motion exhibiting predictable temporal continuity for complete temporal context-aware representation and introduce a dual-stream tracker involving explicit heterogeneous visual tracking experts. Our technical contributions involve three-folds: (1) high-order temporal context-aware representation integrates motion and appearance cues over a temporal context queue, (2) bidirectional cross-domain refinement enhances feature representation through cross-attention based mutual guidance, and (3) consistent decision-making allows for anti-drifting localization via dynamic gating and failure-aware recovery. Extensive experiments on four UAV benchmarks (UAV123, UAV123@10fps, UAV20L, and DTB70) illustrate that our method outperforms existing aerial trackers in terms of success rate and precision, particularly in occlusion and fast motion scenarios. Such superior tracking stability highlights its potential for real-world UAV applications. Full article

Improving the flight trajectory in climb phases, such as in the continuous climb operation, has the potential to reduce fuel consumption. In this paper, we propose an approach that combines a neural network and genetic algorithms to determine the fuel-optimal vertical climb profile under a given flight envelope. As a case study, this method was utilized for the climb phase of a Boeing 787. The results indicate that, from a fuel-consumption perspective, a steep climb with a climb rate of approximately 3000 ft/min to the cruising altitude is desirable. This implies that staying at a high altitude for a long time is effective in reducing fuel consumption. Plotting the vertical profile on the map as a case study of climb trajectory for Narita International Airport indicates that the profile is possible with a vertical separation of 1000 ft with arrival traffic and overflight around the airport. Finally, we discuss the limitations of the optimization method and future challenges. Full article

Intensified human activities and changes in land-use patterns have led to numerous eco-environmental challenges. A comprehensive understanding of the eco-environmental effects of land-use transitions and their driving mechanisms is essential for developing scientifically sound and sustainable environmental management strategies. However, existing studies often lack a comprehensive analysis of these mechanisms due to methodological limitations. This study investigates the eco-environmental effects of land-use transitions in the Poyang Lake Region over the past 30 years from the perspective of the production-living-ecological space (PLES) framework. Additionally, a geographically explainable artificial intelligence (GeoXAI) framework is introduced to further explore the mechanisms underlying these eco-environmental effects. The GeoXAI framework effectively addresses the challenges of integrating nonlinear relationships and spatial effects, which are often not adequately captured by traditional models. The results indicate that (1) the conversion of agricultural space to forest and lake spaces is the primary factor contributing to eco-environmental improvement. Conversely, the occupation of forest and lake spaces by agricultural and residential uses constitutes the main driver of eco-environmental degradation. (2) The GeoXAI demonstrated excellent performance by incorporating geographic variables to address the absence of spatial causality in traditional machine learning. (3) High-altitude and protected water areas are more sensitive to human activities. In contrast, geographic factors have a greater impact on densely populated urban areas. The results and methodology presented here can serve as a reference for eco-environmental assessment and decision-making in other areas facing similar land-use transformation challenges. Full article

This paper proposes an integrated tree model architecture and a low-cost data construction method based on an improved Stacking strategy. It systematically analyzes the importance of safety indicators for mountainous sharp bends in plateau regions and conducts safety evaluation and optimization-strategy research for ten typical sharp-bend road segments in Tibet. In response to the challenges of traditional data collection in Tibet’s unique geographical and policy constraints, we innovatively use drone aerial video as the data source, integrating Tracker motion trajectory analysis, SegFormer road segmentation, and CAD annotation techniques to construct a dataset covering multi-dimensional features of “human–vehicle–road–environment” for mountainous plateau sharp-bend highways. Compared with similar studies, the cost of this dataset is significantly lower. Based on the strong interpretability of tree models and the excellent generalization ability of ensemble learning, we propose an improved Stacking strategy tree model structure to interpret the importance of each indicator. The Spearman correlation coefficient and TOPSIS algorithm are used to conduct safety evaluation for ten sharp-bend roads in Tibet. The results show that the output of the improved Stacking strategy and the sensitivity analysis of the three tree models indicate that curvature variation rate and acceleration are the most significant factors influencing safety, while speed and road width are secondary factors. The study also provides a safety ranking for the ten selected sharp-bend roads, offering a reference for the 318 Quality Improvement Project. From the perspective of indicator importance, curvature variation rate, acceleration, vehicle speed, and road width are crucial for the safety of mountainous plateau sharp-bend roads. It is recommended to implement speed limits for vehicles and widen the road-bend radius. The technical framework constructed in this study provides a reusable methodology for safety assessment of high-altitude roads in complex terrains. Full article

Ophiocordyceps sinensis, is an entomopathogenic fungus renowned for its medicinal properties, thriving in the frigid and high-altitude regions of the Qinghai–Tibet plateau. Given the limited availability of wild resources and the increasing recognition of their medicinal value, the cultivation of O. sinensis was initiated. However, there is a paucity of research investigating the disparities in their quality. This study evaluated the primary physiological indicators of both wild and cultivated O. sinensis. It also employed proteome and untargeted metabolome approaches to elucidate the differences in quality and underlying mechanisms between the two types. The results revealed that the contents of key representative components, including polysaccharide, crude protein, adenosine, and mannitol, were higher in wild O. sinensis than in cultivated O. sinensis. A total of 499 differentially expressed proteins (DEPs), including 117 up-regulated and 382 down-regulated DEPs, were identified in wild and cultivated O. sinensis. Additionally, 369 up-regulated differentially accumulated metabolites (DAMs) and 737 down-regulated DAMs were also identified. Wild O. sinensis had higher relative levels of lysophospholipid metabolites, while cultivated O. sinensis had higher relative levels of aldehydes and carboxylic acids. Correlation analysis revealed that different habitats altered 47 pathways shared between the proteome and metabolome, including carbohydrate metabolism and energy metabolism. β-glucosidase and α-galactosidase play essential roles in carbohydrate catabolism and may indirectly influence amino acid synthesis through energy metabolic pathways. The differential expression of polyamine oxidase (PAO) could reflect variations in polyamine metabolism and ammonia production between wild and cultivated O. sinensis. These variations may consequently affect nitrogen homeostasis and the biosynthesis of nitrogen-containing compounds, ultimately leading to differences in nutritional quality. In conclusion, these findings offer a novel perspective on the applications of O. sinensis and serve as a reference for the targeted development of cultivated O. sinensis. Full article

This study introduces enhanced artificial intelligence (AI)-assisted terrain-aided navigation (TAN) for a sophisticated jet trainer, building upon our prior researchby incorporating real-flight test validation. The proposed TAN integrates a high-performance terrain server, a digital elevation model, and an efficient line-of-sight algorithm to facilitate terrain-aided navigation. The system utilizes an advanced search algorithm in conjunction with two filter designs, including adaptive filters that dynamically optimize navigation precision and operational efficiency. A significant development is the AI model’s capacity to independently alternate between the resource-intensive search algorithm and a set of filters, thereby maintaining navigational accuracy while facilitating in-flight execution without supplementary hardware requirements. Comprehensive Monte Carlo calculations, validated by flight test instrumentation (FTI) data, indicate that the proposed TAN consistently facilitates low-altitude navigation across diverse operational settings. The incorporation of actual flight data not only substantiates the system’s efficacy but also offers novel perspectives on practical implementation obstacles and improvements. These findings signify an advancement in autonomous terrain-aided navigation, connecting simulation with actual flight performance. 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

Scabies and tungiasis are skin-related neglected tropical diseases (NTDs) associated with poverty and poor living conditions. We performed an ecological study covering a state in northeast Brazil to identify socio-economic and environmental factors associated with the occurrence of severe scabies and severe tungiasis, respectively. Data on disease occurrence on the municipality level were derived from a previous study based on online questionnaires. A total of 47 (26.0%) of the 181 state’s municipalities reported severe tungiasis, and 113 (62.4%) severe scabies. Municipalities with occurrence of severe tungiasis were characterized by higher annual rainfalls (median = 883 mm vs. 741 mm; p = 0.037), higher minimum temperatures (median = 23.4 °C vs. 22.7 °C; p = 0.002), higher aridity indices indicating more humid climates (median = 45.1 vs. 50.6; p = 0.019), lower altitudes (median = 88.8 m vs. 201 m; p < 0.001), higher mean air humidity (66.5% vs. 63%; p = 0.018), and better socioeconomic indices (Municipal Human Development Index [MHDI]—median = 0.616 vs. 0.611; p = 0.048/MHDI Longevity—mean = 0.769 vs. 0.759; p = 0.007/Municipal Development Index [MDI]—median = 27.5 vs. 21.8; p < 0.001). Municipalities with predominant luvisol soil characteristics had a lower risk for severe tungiasis (RR = 0.46; 95% CI = 0.27–0.79; p = 0.003), whereas municipalities with predominant gleysols had a significantly higher risk (RR = 2.44; 95% CI = 1.43–4.15; p = 0.010). Municipalities with occurrence of severe scabies were characterized by significantly higher annual rainfalls (median = 804 mm vs. 708 mm; p = 0.001), higher minimum temperatures (23.1 °C vs. 22.3 °C; p < 0.001), higher aridity index (median = 48.2 vs. 41.9; p = 0.014), higher air humidity (65.9% vs. 61%; p = 0.001), lower altitudes (median = 153 m vs. 246 m; p = 0.003), and better socio-economic indicators (MHDI—median = 0.616 vs. 608; p= 0.012/MHDI Education—mean = 0.559 vs. 0.541; p = 0.014/MDI—median = 24.3 vs. 21.1; p = 0.005). In multivariate regression analysis, MDI remained significantly associated with the presence of severe tungiasis in the final model (RR = 1.04; 95% CI: 1.02–1.05; p < 0.001) and the presence of severe scabies with minimum temperature (RR = 1.13; 95% CI: 1.04–1.24; p = 0.003) and aridity index (RR = 1.01; 95% CI: 1.00–1.01; p = 0.004). Our study underscores the importance of environmental and socioeconomic factors for the occurrence of severe scabies and tungiasis in a semi-arid climatic context, offering a perspective for identification of high-risk areas, and providing evidence for the control of skin NTDs withina One Health approach. Full article

The Tibetan goat (Capra hircus) exhibits remarkable adaptations to high-altitude hypoxia, yet the molecular mechanisms remain unclear. This study integrates RNA-seq, WGCNA, and machine learning to explore gene-environment interactions (G × E) in hypoxia adaptation. Fibroblasts from the Tibetan goat and Yunling goat were cultured under hypoxic (1% O₂) and normoxic (21% O₂) conditions, respectively. This identified 68 breed-specific (G), 100 oxygen-responsive (E), and 620 interaction-driven (I) Differentially Expressed Genes (DEGs). The notably higher number of interaction-driven DEGs compared to other effects highlights transcriptional plasticity. We defined two gene sets: Environmental Stress Genes (n = 632, E ∪ I) and Genetic Adaptation Genes (n = 659, G ∪ I). The former were significantly enriched in pathways related to oxidative stress defense and metabolic adaptation, while the latter showed prominent enrichment in pathways associated with vascular remodeling and transcriptional regulation. CTNNB1 emerged as a key regulatory factor in both gene sets, interacting with CASP3 and MMP2 to form the core of the protein–protein interaction (PPI) network. Machine learning identified MAP3K5, TGFBR2, RSPO1 and ITGB5 as critical genes. WGCNA identified key modules in hypoxia adaptation, where FOXO3, HEXIM1, and PPARD promote the stabilization of HIF-1α and metabolic adaptation through the HIF-1 signaling pathway and glycolysis. These findings underscore the pivotal role of gene–environment interactions in hypoxic adaptation, offering novel perspectives for both livestock breeding programs and biomedical research initiatives. Full article

Forest carbon sinks have faced significant challenges with the accelerating warming trend in the 21st century. Net primary productivity (NPP) serves as a critical indicator of the carbon cycle in forest ecosystems and is intricately influenced by both human activities and climate change. This study focuses on the subtropical Southern Forests of China as the research object, using the Wuyi Mountains as a representative study area. The positive and negative contributions of ecologically oriented human activities driven by China’s forestry construction over the past few decades were investigated along with potential extreme climate factors affecting the forest NPP from an altitude gradient perspective and regional-scale forest NPP changes from a novel viewpoint. MODIS NPP, climate, and land use data, along with a vegetation type transfer matrix and statistical methods, were utilized for this purpose. The results are summarized as follows. (1) From 2000 to 2022, NPP in the Wuyi Mountains exhibited a high distribution pattern in the northeastern and southern areas and a low distribution pattern in the central region, with a weak overall increase and an average annual growth increment of only 0.11 gC·m⁻²·year⁻¹. NPP increased with altitude, with a mean growth rate of 5.0 gC·m⁻²·hm⁻¹. Notably, the growth rate of NPP was most pronounced in the altitude range below 298 m in both temporal and vertical dimensions. (2) In the context of China’s long-term Forestry Ecological Engineering Projects and Natural Forest Protection Projects, as well as climate warming, the transformation of vegetation types from relatively low NPP types to high NPP types in the Wuyi Mountains has resulted in a total NPP increase of 211.58 GgC over the past 23 years. Specifically, only the altitude range below 298 m showed negative vegetation type transformation, leading to an NPP decrease of 119.44 GgC. The expansion of urban and built-up lands below 500 m over the 23-year period reduced NPP by 147.92 GgC. (3) The climatic factors inhibiting NPP in the Wuyi Mountains were extreme nighttime high temperatures from June to September, which significantly weakened the NPP of evergreen broadleaf forests above 500 m in elevation. This inhibitory effect still resulted in a reduction of 127.36 GgC in the NPP of evergreen broadleaf forests within this altitude range, despite a cumulative increment in the area of evergreen broadleaf forests above 500 m over the past 23 years. In conclusion, the growth in NPP in the southern inland subtropical regions of China slowed after 2000, primarily due to the significant rise in nighttime extreme high temperatures and the expansion of human-built areas in the region. This study provides valuable data support for the adaptation of subtropical forests to climate change. Full article

The degradation of ‘black soil beach’ (BSB) ecosystems in the Three-River-Source region, characterized by widespread bald patches and severe soil erosion, poses a critical threat to regional ecological security and sustainable pastoralism. This study aims to elucidate the spatial distribution patterns and driving factors of bald patches in BSB degraded grasslands within the Guoluo Tibetan Autonomous Prefecture, providing a scientific basis for targeted restoration strategies. Utilizing multi-source remote sensing data (Landsat 8–9 OLI, UAV imagery, and Google Earth), we employed the Multiple Endmember Spectral Mixture Analysis (MESMA) method to identify bald patches, combined with the landscape pattern index and spatial autocorrelation to quantify their spatial heterogeneity. Geographical detector analysis was applied to assess the influence of natural and anthropogenic factors. The results indicate the following: (1) The patches are bounded by the Yellow River, showing a distribution pattern of ‘high in the west and low in the east’. The total area of patches reached 32,222.11 km², accounting for 43.43% of the total area of Guoluo Prefecture, among which Maduo County and Dari County had the highest degradation rate. (2) With the aggravation of degradation, the patch density of each county increased first and then decreased, while the aggregation index and landscape shape index continued to decrease. (3) Spatial autocorrelation of bare patches strengthens with degradation severity (Moran’s I index 0.6543→0.7999). LISA identified two clusters: the high–high agglomeration area in the north of Maduo–Dari and the low–low agglomeration area in the southeast of Jiuzhi–Banma, revealing the spatial heterogeneity of the degradation process. (4) The spatial distribution pattern of bare patches was mainly affected by the annual average precipitation and actual stocking capacity, and the synergistic effect was significantly higher than that of a single factor. The combination of a 4491–4708 m high altitude area, 0–5° gentle slope zone, and soil texture (clay 27–31%, silt 43–100%) has the highest degradation risk. This multi-factor coupling effect explains the limitations of traditional single factor analysis and provides a new perspective for accurate repair. Full article

Multi-object tracking (MOT) is a key intermediate task in many practical applications and theoretical fields, facing significant challenges due to complex scenarios, particularly in the context of drone-based air-to-ground military operations. During drone flight, factors such as high-altitude environments, small target proportions, irregular target movement, and frequent occlusions complicate the multi-object tracking task. This paper proposes a cross-scene multi-object tracking (CST) method to address these challenges. Firstly, a lightweight object detection framework is proposed to optimize key sub-tasks by integrating multi-dimensional temporal and spatial information. Secondly, trajectory prediction is achieved through the implementation of Model-Agnostic Meta-Learning, enhancing adaptability to dynamic environments. Thirdly, re-identification is facilitated using Dempster–Shafer Theory, which effectively manages uncertainties in target recognition by incorporating aircraft state information. Finally, a novel dataset, termed the Multi-Information Drone Detection and Tracking Dataset (MIDDTD), is introduced, containing rich drone-related information and diverse scenes, thereby providing a solid foundation for the validation of cross-scene multi-object tracking algorithms. Experimental results demonstrate that the proposed method improves the IDF1 tracking metric by 1.92% compared to existing state-of-the-art methods, showcasing strong cross-scene adaptability and offering an effective solution for multi-object tracking from a drone’s perspective, thereby advancing theoretical and technical support for related fields. Full article

The phyllosphere microbiome of aquatic macrophytes constitutes an integral component of freshwater ecosystems, serving crucial functions in global biogeochemical cycling and anthropogenic pollutant remediation. In this study, we examined the assembly mechanisms of epiphytic bacterial communities across four phylogenetically diverse macrophyte species (Scirpus validus, Hippuris vulgaris, Nymphoides peltatum, and Myriophyllum spicatum) inhabiting Ningwu Mayinghai Lake (38.87° N, 112.20° E), a vulnerable subalpine freshwater system in Shanxi Province, China. Through 16S rRNA amplicon sequencing, we demonstrate marked phyllospheric microbiome divergence, as follows: Gammaproteobacteria dominated S. validus, H. vulgaris and N. peltatum, while Alphaproteobacteria dominated in M. spicatum. The nitrate, nitrite, and pH value of water bodies and the chlorophyll, leaf nitrogen, and carbon contents of plant leaves are the main driving forces affecting the changes in the β-diversity of epiphytic bacterial communities of four plant species. The partitioning of assembly processes revealed that deterministic dominance governed S. validus and M. spicatum, where niche-based selection contributed 67.5% and 100% to community assembly, respectively. Conversely, stochastic processes explained 100% of the variability in H. vulgaris and N. peltatum microbiomes, predominantly mediated by dispersal limitation and ecological drift. This investigation advances the understanding of microbial community structural dynamics and diversity stabilization strategies in aquatic macrophyte-associated microbiomes, while establishing conceptual frameworks between plant–microbe symbiosis and the ecological homeostasis mechanisms within vulnerable subalpine freshwater ecosystems. The empirical references derived from these findings offer novel perspectives for developing conservation strategies aimed at sustaining biodiversity equilibrium in high-altitude lake habitats, particularly in the climatically sensitive regions of north-central China. Full article