Search Results (2,210)

In the process of plant protection for fruit trees using rotary-wing UAVs, challenges such as droplet drift, insufficient canopy penetration, and low agrochemical utilization efficiency remain prominent. Among these, the uncertainty in the spatio-temporal distribution of downwash airflow is a key factor contributing to non-uniform droplet deposition and increased drift. To address this issue, we developed a wind field numerical simulation model based on an improved hierarchical leaf density model to clarify the spatio-temporal characteristics of downwash airflow, the scale of turbulence regions, and their effects on internal canopy airflow under varying flight altitudes and different rotor speeds. Field experiments were conducted in orchards to validate the accuracy of the model. Simulation results showed that the average error between the simulated and measured wind speeds inside the canopy was 8.4%, representing a 42.11% reduction compared to the non-hierarchical model and significantly improving the prediction accuracy. The coefficient of variation (CV) was 0.26 in the middle canopy layer and 0.29 in the lower layer, indicating a decreasing trend with an increasing canopy height. We systematically analyzed the variation in turbulence region scales under different flight conditions. This study provides theoretical support for optimizing UAV operation parameters to improve droplet deposition uniformity and enhance agrochemical utilization efficiency. Full article

The widespread adoption of the Internet of Things (IoT) has driven major advancements in wireless communication, especially in rural and suburban areas where low population density and limited infrastructure pose significant challenges. Accurate Path Loss (PL) prediction is critical for the effective deployment and operation of Wireless Sensor Networks (WSNs) in such environments. This study explores the use of Convolutional Neural Networks (CNNs) for PL modeling, utilizing a comprehensive dataset collected in a smart campus setting that captures the influence of terrain and environmental variations. Several CNN architectures were evaluated based on different combinations of input features—such as distance, elevation, clutter height, and altitude—to assess their predictive accuracy. The findings reveal that CNN-based models outperform traditional propagation models (Free Space Path Loss (FSPL), Okumura–Hata, COST 231, Log-Distance), achieving lower error rates and more precise PL estimations. The best performing CNN configuration, using only distance and elevation, highlights the value of terrain-aware modeling. These results underscore the potential of deep learning techniques to enhance IoT connectivity in sparsely connected regions and support the development of more resilient communication infrastructures. Full article

Mineral imbalances in livestock can critically impair growth, immunity, and productivity. Yaks inhabiting the Qinghai–Tibetan Plateau face unique environmental challenges, including high-altitude-induced nutrient variability. This study investigated the status of mineral elements and their correlations with altitude in healthy yak calves across five regions in Ganzi Prefecture, located at elevations ranging from 3100 to 4100 m. Hair and serum samples from 35 calves were analyzed for 11 essential elements (Na, K, Ca, Mg, S, Cu, Fe, Mn, Zn, Co, and Se). The results revealed widespread deficiencies. Key deficiencies were identified: hair Na and Co were significantly below references value (p < 0.05), and Se was consistently deficient across all regions, with deficiency rates ranging from 35.73% to 56.57%. Serum Mg and Cu were generally deficient (Mg deficiency > 26% above 3800 m). S, Mn (low detection), and Co were also suboptimal. Serum selenium deficiency was notably severe in lower-altitude areas (≤59.07%). Significant correlations with altitude were observed: hair sodium levels decreased with increasing altitude (r = −0.72), while hair manganese (r = 0.88) and cobalt (r = 0.65) levels increased. Serum magnesium deficiency became more pronounced at higher elevations (r = 0.58), whereas selenium deficiency in serum was more severe at lower altitudes (r = −0.61). These findings indicate prevalent multi-element deficiencies in yak calves that are closely linked to altitude and are potentially influenced by soil mineral composition and feeding practices, as suggested by previous studies. The study underscores the urgent need for region-specific nutritional standards and altitude-adapted mineral supplementation strategies to support optimal yak health and development. Full article

This study investigates the effects of low nitrogen (N) and phosphorus (P) stress on the growth and yield of nine barley (Hordeum vulgare L.) genotypes (1267-2, 1749-1, 1149-3, 2017Y-2, 2017Y-16, 2017Y-17, 2017Y-18, 2017Y-19, and XBZ17-1-61), all of which are spring two-rowed hulled barley types from the Economic Crops and Beer Material Institute, Gansu Academy of Agricultural Sciences. Data were collected over two consecutive growing seasons (2021–2022) at Huangyang Town (altitude 1766 m, irrigated desert soil with 1.71% organic matter, 1.00 g·kg⁻¹ total N, 0.87 g·kg⁻¹ total P in 0–20 cm plough layer) to elucidate the correlation between stress tolerance traits and yield performance. Field experiments were conducted under two treatment conditions: no fertilization (NP0) and normal fertilization (180 kg·hm⁻² N and P, NP180). Growth indicators (plant height, spike length, spikelets per unit area, etc.) and quality indicators (proportion of plump/shrunken grains, 1000-grain weight, protein, starch content) were measured, and data were analyzed using correlation analysis, principal component analysis, and structural equation modeling. The results revealed that low N and P stress significantly impacted quality indicators, such as the proportion of plump and shrunken grains, while having a minimal effect on growth indicators like plant height and spike length. Notably, the number of spikelets per unit area emerged as a critical factor positively influencing yield. Among the tested genotypes, 1749-1, 1267-2, 1149-3, 2017Y-16, 2017Y-18, 2017Y-19, and XBZ17-1-61 exhibited superior yield performance under low N and P stress conditions, indicating their potential for breeding programs focused on stress resilience. Included among these, the 1749-1 line showed the best overall performance and consistent results across both years. Full article

Resettlement communities in Qinghai are located in cold, high-altitude regions with dry climates and strong solar radiation. Although not extremely cold, the moderate heating demand aligns well with high solar availability, making passive design highly effective for reducing energy use. This study investigates solar-optimized spatial configurations that enhance passive energy performance while addressing functional settlement needs. Through parametric modeling and climate-responsive simulations, four key spatial parameters are examined: building spacing, courtyard depth, density, and volumetric ratio. The findings highlight the dominant role of front–rear spacing in solar access, with optimal values at 3–4 m for single-story and 5–10 m for two-story buildings, balancing radiation gain and land use efficiency. Courtyard depths under 2.7 m significantly limit south façade exposure due to shading from the opposite courtyard wall under low-angle winter sun. This reduction results in the south façade attaining only 55.7–79.6% of the solar radiation acquisition by an unobstructed south façade (the baseline). Meanwhile, clustered orientations reduce inter-building shading losses by 38–42% compared to dispersed layouts. A three-tiered design framework is proposed: (1) macro-scale solar orientation zoning, (2) meso-scale spacing tailored to building height, and (3) micro-scale courtyard modulation for low-angle winter radiation. Together, these strategies provide practical, scalable guidelines for energy-efficient, climate-responsive settlement design in the alpine regions of Qinghai. Full article

The yak (Bos grunniens) is a key species in high-altitude rangelands of Asia. Despite their ecological and economic importance, yak production faces persistent challenges, including low milk yields, vulnerability to climate changes, emerging diseases, and a lack of systematic breeding programs. This review presents the genomic, physiological, and environmental dimensions of yak biology and husbandry. Genes such as EPAS1, which encodes hypoxia-inducible transcription factors, underpin physiological adaptations, including enlarged cardiopulmonary structures, elevated erythrocyte concentrations, and specialized thermoregulatory mechanisms that enable their survival at elevations of 3000 m and above. Copy number variations (CNVs) and single nucleotide polymorphisms (SNPs) present promising markers for improving milk and meat production, disease resistance, and metabolic efficiency. F1 and F2 generations of yak–cattle hybrids show superior growth and milk yields, but reproductive barriers, such as natural mating or artificial insemination, and environmental factors limit the success of these hybrids beyond second generation. Infectious diseases, such as bovine viral diarrhea and antimicrobial-resistant and biofilm-forming Enterococcus and E. coli, pose risks to herd health and food safety. Rising ambient temperatures, declining forage biomass, and increased disease prevalence due to climate changes risk yak economic performance and welfare. Addressing these challenges by nutritional, environmental, and genetic interventions will safeguard yak pastoralism. This review describes the genes associated with different yak traits and provides an overview of the genetic adaptations of yaks (Bos grunniens) to environmental stresses at high altitudes and emphasizes the need for conservation and improvement strategies for sustainable husbandry of these yaks. Full article

This study examined the integraftion of airborne Light Detection and Ranging (LiDAR), Simultaneous Localization and Mapping (SLAM)-based handheld LiDAR, and iPhone LiDAR to inspect forest road networks following forest operations. The goal is to overcome the challenges posed by dense canopy cover and ensure accurate and efficient data collection and mapping. Airborne data were collected using the DJI Matrice 300 RTK UAV equipped with a Zenmuse L2 LiDAR sensor, which achieved a high point density of 285 points/m² at an altitude of 80 m. Ground-level data were collected using the BLK2GO handheld laser scanner (HPLS) with SLAM methods (LiDAR SLAM, Visual SLAM, Inertial Measurement Unit) and the iPhone 13 Pro Max LiDAR. Data processing included generating DEMs, DSMs, and True Digital Orthophotos (TDOMs) via DJI Terra, LiDAR360 V8, and Cyclone REGISTER 360 PLUS, with additional processing and merging using CloudCompare V2 and ArcGIS Pro 3.4.0. The pairwise comparison analysis between ALS data and each alternative method revealed notable differences in elevation, highlighting discrepancies between methods. ALS + iPhone demonstrated the smallest deviation from ALS (MAE = 0.011, RMSE = 0.011, RE = 0.003%) and HPLS the larger deviation from ALS (MAE = 0.507, RMSE = 0.542, RE = 0.123%). The findings highlight the potential of fusing point clouds from diverse platforms to enhance forest road mapping accuracy. However, the selection of technology should consider trade-offs among accuracy, cost, and operational constraints. Mobile LiDAR solutions, particularly the iPhone, offer promising low-cost alternatives for certain applications. Future research should explore real-time fusion workflows and strategies to improve the cost-effectiveness and scalability of multisensor approaches for forest road monitoring. Full article

Fin-stabilized guided rockets exhibit ballistic characteristics such as low initial velocity, high flight altitude, and long flight duration, which render their impact point accuracy and flight stability highly susceptible to the influence of wind. In this paper, the four-dimensional nonlinear angular motion equations describing the changes in attack angle and the law of axis swing of a dual-spin rocket are established, and the phase trajectory and equilibrium point stability characteristics of the nonlinear angular motion system under windy conditions are analyzed. Aiming at the problem that the equilibrium point of the angular motion system cannot be solved analytically with the change in wind speed, a phase trajectory projection sequence method based on the Poincaré cross-section and stroboscopic mapping is proposed to analyze the effect of wind on the angular motion bifurcation characteristics of a dual-spin rocket. The possible instability of angular motion caused by nonlinear aerodynamics under strong wind conditions is explored. This study is of reference significance for the launch control and aerodynamic design of guided rockets in complex environments. Full article

Viticis Fructus (VF), a fruit known for its unique flavor profile and various health benefits, demonstrates substantial quality variations depending on its area of production. Traditional methods of production area verification based on internal compound analysis are hampered by a number of technical limitations. This investigation systematically characterized the cuticular wax composition of VF sample from a diverse variety of production areas. Quantitative analyses were conducted to evaluate the spatial distribution patterns of the wax constituents. Significant regional variations were observed: Anhui sample exhibited the highest total wax content (21.39 μg/cm²), with n-alkanes dominating at 76.67%. High-latitude regions showed elevated triterpenoid acid levels, with maslinic acid (0.53 μg/cm²) and ursolic acid (0.34 μg/cm²) concentrations exceeding those of their low-latitude counterparts by four- and three-fold, respectively. Altitudinal influence manifested in long-chain alcohol accumulation, as triacontanol reached 0.87 μg/cm² in high-altitude sample. Five key biomarkers demonstrated direct quality correlations: eicosanoic acid, n-triacontane, dotriacontanol, β-amyrin, and α-amyrin. This study established three novel origin identification protocols: single-component quantification, multi-component wax profiling, and wax ratio analysis. This work not only reveals the latitudinal dependence of VF wax composition, but also provides a scientific framework for geographical authentication. Our findings advance wax-based quality evaluation methodologies for fruit products, offering practical solutions for production area verification challenges in food raw materials. Full article

The Three Gorges Reservoir Area (TGRA) is an important ecological barrier and cultural intermingling zone in the upper reaches of the Yangtze River, and its traditional villages carry unique information about natural changes and civilisational development, but face the challenges of conservation and development under the impact of modernisation and ecological pressure. This study takes 112 traditional villages in the TGRA that have been included in the protection list as the research objects, aiming to construct a cultural landscape identification framework for the traditional villages in the TGRA. Through field surveys, landscape feature assessments, GIS spatial analysis, and multi-source data analysis, we systematically analyse their cultural landscape type systems and spatial differentiation characteristics, and then reveal their cultural landscape types and spatial differentiation patterns. (1) The results of the study show that the spatial distribution of traditional villages exhibits significant altitude gradient differentiation—the low-altitude area is dominated by traffic and trade villages, the middle-altitude area is dominated by patriarchal manor villages and mountain farming villages, and the high-altitude area is dominated by ethno-cultural and ecologically dependent villages. (2) Slope and direction analyses further reveal that the gently sloping areas are conducive to the development of commercial and agricultural settlements, while the steeply sloping areas strengthen the function of ethnic and cultural defence. The results indicate that topographic conditions drive the synergistic evolution of the human–land system in traditional villages through the mechanisms of agricultural optimisation, trade networks, cultural defence, and ecological adaptation. The study provides a paradigm of “nature–humanities” interaction analysis for the conservation and development of traditional villages in mountainous areas, which is of practical value in coordinating the construction of ecological barriers and the revitalisation of villages in the reservoir area. Full article

With the rise of the low-altitude urban traffic system, urban air mobility (UAM) has developed rapidly. As a critical component of the UAM system, the strategic layout of vertiports helps divert ground traffic pressure. To satisfy various demand patterns, different vertiport levels are needed, so we focus on the hierarchical vertiport location problem. Considering the capacity limitation, a median location model is established to minimize vertiport construction cost, passenger commuting cost, and penalty cost. For the nonlinear term in the objective function, the Big-M method is employed. Based on the reformulated model, we improve the branch-and-bound algorithm (LVBB) to solve it, where the Lagrange relaxation method is used to decompose the large-scale problem into parallel subproblems and compute the lower bound, and the variable neighborhood search algorithm is used to obtain the upper bound. Numerical experiments are performed in the 11 administrative districts of Nanjing, China. The results demonstrate that the proposed location scheme effectively balances vertiport construction cost and passenger commuting cost while satisfying capacity limitations. It also significantly reduces commuting time to improve passenger satisfaction. This scheme can offer strategic guidance for infrastructure planning in UAM. Full article

Breeding programs often overlook the use of root traits. Therefore, we investigated the relevance of sorghum root traits in explaining its adaptation to combined drought and heat stress (CDHS). Six (i.e., three pre-release lines + three checks) sorghum genotypes were established at two low-altitude (i.e., <600 masl) locations with a long-term history of averagely very high temperatures in the beginning of the summer season, under two management (i.e., CDHS and well-watered (WW)) regimes. At each location, the genotypes were laid out in the field using a randomized complete block design (RCBD) replicated two times. Root trait data, namely root diameter (RD), number of roots (NR), number of root tips (NRT), total root length (TRL), root depth (RDP), root width (RW), width–depth ratio (WDR), root network area (RNA), root solidity (RS), lower root area (LRA), root perimeter (RP), root volume (RV), surface area (SA), root holes (RH) and root angle (RA) were gathered using the RhizoVision Explorer software during the pre- and post-flowering stage of growth. RSA traits differentially showed significant (p < 0.05) correlations with grain yield (GY) at pre- and post-flowering growth stages and under CDHS and WW conditions also revealing genotypic variation estimates exceeding 50% for all the traits. Regression models varied between pre-flowering (p = 0.013, R² = 47.15%, R² Predicted = 29.32%) and post-flowering (p = 0.000, R² = 85.64%, R² Predicted = 73.30%) growth stages, indicating post-flowering as the optimal stage to relate root traits to yield performance. RD contributed most to the regression model at post-flowering, explaining 51.79% of the 85.64% total variation. The Smith–Hazel index identified ICSV111IN and ASAREACA12-3-1 as superior pre-release lines, suitable for commercialization as new varieties. The study demonstrated that root traits (in particular, RD, RW, and RP) are linked to crop performance under CDHS conditions and should be incorporated in breeding programs. This approach may accelerate genetic gains not only in sorghum breeding programs, but for other crops, while offering a nature-based breeding strategy for stress adaptation in crops. Full article

Localized ionization enhancements (LIEs) in altitude range corresponding to the D-region ionosphere, disrupting Very-Low-Frequency (VLF) signal propagation. This case study focuses on Lightning-induced Electron Precipitation (LEP), analyzing amplitude and phase variations in VLF signals recorded in Belgrade, Serbia, from worldwide transmitters. Due to the localized, transient nature of Energetic Electron Precipitation (EEP) events and the path-dependence of VLF responses, research relies on event-specific case studies to model reflection height and sharpness via numerical simulations. Findings show LIEs are typically under 1000 × 500 km, with varying internal structure. Accumulated case studies and corresponding data across diverse conditions contribute to a broader understanding of ionospheric dynamics and space weather effects. These findings enhance regional modeling, support aerosol–electricity climate research, and underscore the value of VLF-based ionospheric monitoring and collaboration in Europe. Full article

Xylosalsola chiwensis (Popov) Akhani & Roalson is listed in the Red Data Book of Kazakhstan as a rare species with a limited distribution, occurring in small populations in Kazakhstan, Uzbekistan, and Turkmenistan. The aim of this study is to deepen the understanding of the ecological conditions of its habitats, the floristic composition of its associated plant communities, the species’ morphological and anatomical characteristics, and its molecular phylogeny, as well as to identify the main threats to its survival. The ecological conditions of the X. chiwensis habitats include coastal sandy plains and the slopes of chinks and denudation plains with gray–brown desert soils and bozyngens on the Mangyshlak Peninsula and the Ustyurt Plateau at altitudes ranging from −3 to 270 m above sea level. The species is capable of surviving in arid conditions (less than 100 mm of annual precipitation) and under extreme temperatures (air temperatures exceeding 45 °C and soil surface temperatures above 65 °C). In X. chiwensis communities, we recorded 53 species of vascular plants. Anthropogenic factors associated with livestock grazing, industrial disturbances, and off-road vehicle traffic along an unregulated network of dirt roads have been identified as contributing to population decline and the potential extinction of the species under conditions of unsustainable land use. The morphometric traits of X. chiwensis could be used for taxonomic analysis and for identifying diagnostic morphological characteristics to distinguish between species of Xylosalsola. The most taxonomically valuable characteristics include the fruit diameter (with wings) and the cone-shaped structure length, as they differ consistently between species and exhibit relatively low variability. Anatomical adaptations to arid conditions were observed, including a well-developed hypodermis, which is indicative of a water-conserving strategy. The moderate photosynthetic activity, reflected by a thinner palisade mesophyll layer, may be associated with reduced photosynthetic intensity, which is compensated for through structural mechanisms for water conservation. The flow cytometry analysis revealed a genome size of 2.483 ± 0.191 pg (2n/4x = 18), and the phylogenetic analysis confirmed the placement of X. chiwensis within the tribe Salsoleae of the subfamily Salsoloideae, supporting its taxonomic distinctness. To support the conservation of this rare species, measures are proposed to expand the area of the Ustyurt Nature Reserve through the establishment of cluster sites. Full article

Low-altitude drones, which are unimpeded by traffic congestion or urban terrain, have become a critical asset in emergency rescue missions. To address the current lack of emergency rescue data, UAV aerial videos were collected to create an experimental dataset for action classification and localization annotation. A total of 5082 keyframes were labeled with 1–5 targets each, and 14,412 instances of data were prepared (including flight altitude and camera angles) for action classification and position annotation. To mitigate the challenges posed by high-resolution drone footage with excessive redundant information, we propose the SlowFast-Traffic (SF-T) framework, a spatio-temporal sequence-based algorithm for recognizing traffic accident rescue actions. For more efficient extraction of target–background correlation features, we introduce the Actor-Centric Relation Network (ACRN) module, which employs temporal max pooling to enhance the time-dimensional features of static backgrounds, significantly reducing redundancy-induced interference. Additionally, smaller ROI feature map outputs are adopted to boost computational speed. To tackle class imbalance in incident samples, we integrate a Class-Balanced Focal Loss (CB-Focal Loss) function, effectively resolving rare-action recognition in specific rescue scenarios. We replace the original Faster R-CNN with YOLOX-s to improve the target detection rate. On our proposed dataset, the SF-T model achieves a mean average precision (mAP) of 83.9%, which is 8.5% higher than that of the standard SlowFast architecture while maintaining a processing speed of 34.9 tasks/s. Both accuracy-related metrics and computational efficiency are substantially improved. The proposed method demonstrates strong robustness and real-time analysis capabilities for modern traffic rescue action recognition. Full article