Search Results (5,978)

With global climate change, urbanization, and agricultural resource limitations, precision agriculture and crop monitoring are crucial worldwide. Integrating multi-source remote sensing data with deep learning enables accurate crop mapping, but selecting optimal network architectures remains challenging. To improve remote sensing-based fruit planting classification and support orchard management and rural revitalization, this study explored feature selection and network optimization. We proposed an improved CF-EfficientNet model (incorporating FGMF and CGAR modules) for fruit planting classification. Multi-source remote sensing data (Sentinel-1, Sentinel-2, and SRTM) were used to extract spectral, vegetation, polarization, terrain, and texture features, thereby constructing a high-dimensional feature space. Feature selection identified 13 highly discriminative bands, forming an optimal dataset, namely the preferred bands (PBs). At the same time, two classification datasets—multi-spectral bands (MS) and preferred bands (PBs)—were constructed, and five typical deep learning models were introduced to compare performance: (1) EfficientNetB0, (2) AlexNet, (3) VGG16, (4) ResNet18, (5) RepVGG. The experimental results showed that the EfficientNetB0 model based on the preferred band performed best in terms of overall accuracy (87.1%) and Kappa coefficient (0.677). Furthermore, a Fine-Grained Multi-scale Fusion (FGMF) and a Condition-Guided Attention Refinement (CGAR) were incorporated into EfficientNetB0, and the traditional SGD optimizer was replaced with Adam to construct the CF-EfficientNet architecture. The results indicated that the improved CF-EfficientNet model achieved high performance in crop classification, with an overall accuracy of 92.6% and a Kappa coefficient of 0.830. These represent improvements of 5.5 percentage points and 0.153, compared with the baseline model, demonstrating superiority in both classification accuracy and stability. Full article

This study investigates geothermal waters in the Xinjiang region through hydrogeochemical methods, including cluster analysis, ionic ratios, and isotopic analysis. Cluster analysis categorized the geothermal water samples into three distinct groups (G1, G2, and G3). The predominant hydrochemical facies are SO₄-HCO₃-Na, SO₄-Cl-Na, and Cl-Na types, whose formation is controlled by multiple factors. Evidence from molar ratios of major ions suggests that geothermal waters in Group G1 are predominantly governed by water–rock interactions, whereas Groups G2 and G3 are mainly influenced by evaporative concentration. Hydrogen and oxygen isotopic signatures confirm that meteoric water serves as the primary recharge source for these geothermal waters. The spatial correlation between regional tectonic features and most geothermal discharge points demonstrates a consistent relationship between geothermal water occurrence and structural distribution in Xinjiang. Additionally, a conceptual circulation model is proposed wherein meteoric water undergoes deep circulation following local recharge, ascends along fault zones under tectonic pressure, and mixes with shallow groundwater. This research primarily elucidates the hydrogeochemical characteristics and recharge mechanisms of geothermal resources in Xinjiang, thereby providing a scientific basis for their future development and utilization. Full article

Under global warming, drought frequency and its severity have risen notably, posing considerable challenges to vegetation growth. Central Asia (CA), recognized as the largest non-zonal arid zone globally, features dryland ecosystems that are particularly vulnerable to drought stress. This research examines how plant life in CA reacts to prolonged dry spells by analyzing multiple datasets, including drought indices and satellite-derived NDVI measurements, spanning four decades (1982–2022). This study also delves into the compound impact of drought, revealing how its influence on vegetation unfolds through both cumulative stress and delayed ecological responses. Based on the research results, the vegetation coverage in CA exhibited a notable rising tendency from 1982 to 1998. Specifically, it increased at a rate of 4 × 10⁻³ per year (p < 0.05). On the other hand, the direction of this trend shifted to a downward one during the period from 1999 to 2022. During this latter phase, the vegetation coverage decreased at a rate of −4 × 10⁻³ per year (p > 0.05). Vegetation changes in the study area underwent a fundamental reversal around 1998, shifting from widespread greening during 1982–1998 to persistent browning during 1999–2022. Specifically, 98.6% of the region underwent pronounced summer drought stress, which triggered a substantial rise in vegetation browning. The vegetation response to the accumulated and lagged effects of drought varied across seasons, with summer exhibiting the strongest sensitivity, followed by spring and autumn. The lagged effect of drought predominantly influences the vegetation during the growing season and spring, affecting 59.44% and 79.27% of CA, respectively. In contrast, the accumulated effect of drought is more prominent in summer and autumn, affecting 54.92% and 56.52% of CA. These insights offer valuable guidance for ecological restoration initiatives and sustainable management of dryland ecosystems. Full article

Thanks to the inherent advantages, including being green, broadband, and high security, visible light communication (VLC), as one powerful enabling technology for 6G and beyond the Internet of Things (IoT), has received ever-increasing discussion and attention. In order to improve the quality of VLC links and extend their coverage, various intelligent reflecting surfaces (IRSs) have been massively discussed and optimized into the VLC field. Apparently, the current research works are merely limited to the investigation of well-known Lambertian source-based, IRS-assisted VLC. Consequently, there is a lack of targeted analysis and evaluation of the diversity of beam configurations for light-emitting diodes (LEDs) and the potential non-Lambertian IRS-assisted VLC links. To fill the above research gap of this VLC branch, this article focuses on introducing the innovative LED non-Lambertian beams into typical IRS-assisted VLC systems to construct novel IRS-assisted non-Lambertian VLC links. The investigation results indicate that compared to the baseline Lambertian IRS-assisted VLC scheme, the proposed representative non-Lambertian IRS-assisted VLC schemes could provide up to 22.22 dB and 14.08 dB signal-to-noise ratio gains for side and corner receiver positions, respectively. Moreover, this article quantitatively evaluates the impact of the initial azimuth angle (i.e., beam azimuth orientation) of asymmetric non-Lambertian optical beams on the performance of IRS-assisted VLC and the relevant fundamental characteristics. Full article

Coal resources serve as a fundamental pillar for global economic development and remain the dominant energy source in China. To improve coal resource utilization and assess the impact hazards related to strip filling mining, this study selects the No. 3-3 coal seam of a mine in Tuokexun as its engineering context. By integrating theoretical investigation and numerical modeling, a comparative evaluation was performed between the conventional mining approach and the strip filling mining technique in terms of impact hazard. The results reveal that during the first phase of strip filling mining—characterized by a high filling ratio—the level of impact hazard remains minimal. Relative to the traditional method, the peak advance abutment pressure during the second phase of strip filling mining is reduced by as much as 17.8%. Moreover, significant reductions are observed in stress concentration, deformation intensity, and the extent of plastic zone propagation along the retreat roadway. Under the conventional method, the influence range is approximately 70 m, whereas under strip filling mining, it decreases to about 60 m. These insights confirm that strip filling mining can effectively diminish impact-related hazards and enhance the safety of underground coal extraction operations. Full article

To investigate the geographical and species differences regarding mineral element content of camel milk, this research used camel milk from the Tacheng, Altay, and Ili regions of Xinjiang and cow milk, goat milk, and horse milk from the Tacheng region as subjects. The contents of 22 mineral elements were measured using inductively coupled plasma mass spectrometry (ICP-MS) and inductively coupled plasma optical emission spectrometry (ICP-OES). The results showed that the contents of macro elements Ca, P, K, and Na in camel milk were significantly higher than those in other milk sources (p < 0.01). The contents of trace elements such as Se, Sr, and Ni were very significantly higher than those in other milk sources (p < 0.01). The content of 12 mineral elements in camel milk was very significantly higher than in other types of milk (p < 0.01). Principal component analysis (PCA) and factor analysis emphasized the relationship between element distribution and different milk sources, and the linear discriminant analysis (LDA) model could identify the species type of milk. Geographical analysis indicated that trace elements such as Sr, Ni, and Cr were highly significantly enriched in Tacheng camel milk (p < 0.01). The established LDA model achieved traceability of the geographical origin of Xinjiang camel milk. This research reveals the mineral nutritional advantages of camel milk and its geographical differentiation patterns, providing theoretical support for exploring the functional properties of camel milk and for identifying species and regions through minerals. It is important to promote the upgrading of the specialty dairy product industry. Full article

Leaf senescence is a precisely regulated developmental process that is critical for grapevine growth and yield, which is easily influenced by environmental factors. High temperature is a major factor that accelerates senescence rapidly, adversely affects photosynthetic performance, severely hindering fruit nutrient metabolism and growth. This study investigated chlorophyll fluorescence and physiological traits in grape (Vitis vinifera L.) leaves at different senescence stages under natural high-temperature conditions in Turpan. Measurements included chlorophyll content, MDA levels, antioxidant enzyme activities, and chlorophyll fluorescence parameters. The results showed that (1) young leaves exhibited higher and more sustained chlorophyll content but were prone to wilting, whereas older leaves showed accelerated chlorosis and functional decline; (2) high temperature severely impaired PSII function, inhibiting electron transport and photochemical efficiency, reflected in increased ABS/RC, TR_o/RCC, and DI_o/RC, and decreased F_v/F_m, F_v/F_o, and PI_abs; (3) POD, SOD, CAT and MDA levels initially increased then decreased, correlating with photosynthetic changes and leaf age; and (4) young leaves maintained stronger photosynthetic capability and physiological resilience than older ones. Although partial recovery occurred after temperature reduction, photosynthetic and antioxidant activities did not fully revert. This suggests persistent heat-induced functional decline and accelerated senescence, providing insights for understanding heat-induced leaf senescence and developing strategies for cultivating grapevines. Full article

The increasing complexity of urban traffic networks has highlighted the potential of Multi-Agent Reinforcement Learning (MARL) for Traffic Signal Control (TSC). However, most existing MARL methods assume homogeneous observation and action spaces among agents, ignoring the inherent heterogeneity of real-world intersections in topology and signal phasing, which limits their practical applicability. To address this gap, we propose HAPS-PPO (Heterogeneity-Aware Policy Sharing Proximal Policy Optimization), a novel MARL framework for coordinated signal control in heterogeneous road networks. HAPS-PPO integrates two key mechanisms: an Observation Padding Wrapper (OPW) that standardizes varying observation dimensions, and a Dynamic Multi-Strategy Grouping Learning (DMSGL) mechanism that trains dedicated policy heads for agent groups with distinct action spaces, enabling adequate knowledge sharing while maintaining structural correctness. Comprehensive experiments in a high-fidelity simulation environment based on a real-world road network demonstrate that HAPS-PPO significantly outperforms Fixed-time control and mainstream MARL baselines (e.g., MADQN, FMA2C), reducing average delay time by up to 44.74% and average waiting time by 59.60%. This work provides a scalable and plug-and-play solution for deploying MARL in realistic, heterogeneous traffic networks. Full article

Livestock represent a significant source of methane (CH₄) emissions, particularly in pastoral regions. However, in Xinjiang—a pivotal pastoral region of China—the spatiotemporal patterns of livestock CH₄ emissions remain poorly characterized, constraining regional mitigation actions. Here, a detailed CH₄ emissions inventory for livestock in Xinjiang spanning the period 2000–2020 is compiled. Eight livestock categories were covered, gridded livestock maps were developed, and the dynamic emission factors were built by using the IPCC 2019 Tier 2 approaches. Results indicate that the CH₄ emissions increased from ~0.7 Tg in 2000 to ~0.9 Tg in 2020, a 28.5% increase over the past twenty years. Beef cattle contributed the most to the emission increase (59.6% of total increase), followed by dairy cattle (35.7%), sheep (13.9%), and pigs (4.3%). High-emission hotspots were consistently located in the Ili River Valley, Bortala, and the northwestern margins of the Tarim Basin. Temporal trend analysis revealed increasing emission intensities in these regions, reflecting the influence of policy shifts, rangeland dynamics, and evolving livestock production systems. The high-resolution map of CH₄ emissions from livestock and their temporal trends provides key insights into CH₄ mitigation, with enteric fermentation showing greater potential for emission reduction. This study offers the first long-term, high-resolution CH₄ emission inventory for Xinjiang, providing essential spatial insights to inform targeted mitigation strategies and enhance sustainable livestock management in arid and semi-arid ecosystems. Full article

As aviation operations extend into complex natural environments, dust particles present significant challenges to flight stability and safety, particularly in dust-prone regions like southern Xinjiang. This study employs high-fidelity computational fluid dynamics (CFD) simulations, combined with the SST turbulence model and the Lagrangian discrete phase model, to analyze the aerodynamic response of the NACA 0012 airfoil at varying wind speeds (5, 15, and 30 m/s) and angles of attack (3°, 8°, and 12°). The results indicate that, at low speeds and moderate to high angles of attack, dust particles reduce lift by over 70%, primarily due to boundary layer instability, weakened suction-side pressure, and premature flow separation. Higher wind speeds slightly delay flow separation, but cannot counteract the disturbances caused by the particles. At higher angles of attack, drag increases by more than 60%, driven by wake expansion, shear dissipation, and delayed pressure recovery. Pitching moment frequently reverses from negative to positive, reflecting a forward shift in the aerodynamic center and a loss of pitching stability. An increase in dust concentration amplifies these effects, leading to earlier moment reversal and more abrupt stall behavior. These findings underscore the urgent need to improve aircraft design, control, and safety strategies for operations in dusty environments. Full article

Background and Objectives: Avian influenza poses a continuous public health threat, particularly to individuals with occupational exposure to poultry such as farm workers, live animal market employees, and processing plant staff. This study aimed to investigate the systemic metabolic effects of such exposure and to identify potential biomarkers for early detection and health risk assessment. Materials and Methods: An untargeted liquid chromatography–mass spectrometry (LC-MS)-based metabolomics approach was applied to analyze serum samples from occupationally exposed individuals and healthy controls. Multivariate statistical analysis, pathway enrichment, and topology analysis were performed to identify significantly altered metabolites and metabolic pathways. The least absolute shrinkage and selection operator (LASSO) algorithm was employed to select key metabolites. Results: Multivariate statistical analysis revealed a clear separation between the exposure group and control, suggesting distinct metabolic profiles between the two populations. Pathway analysis indicated significant alterations in alanine, aspartate, and glutamate metabolism, as well as tryptophan metabolism, which are closely linked to immune regulation, energy metabolism, and host–pathogen interactions. LASSO feature selection and subsequent manual verification identified 17 key metabolites with strong discriminative power. Furthermore, lipidomic profiling revealed a pronounced increase in lysophosphatidylcholine (LPC) levels and a concurrent decrease in phosphatidylcholine (PC) species in exposed individuals. Conclusions: This study reveals metabolic disruptions associated with occupational avian influenza exposure and identifies potential serum biomarkers related to immune and lipid metabolism. These findings provide novel insights into host responses to avian influenza exposure and may support early detection and health risk assessment in high-risk occupational populations. Full article

The prediction of thin-bedded, favorable sand bodies within the Triassic Baikouquan Formation fan delta on the western slope of the Mahu Sag is challenging due to their strong spatial heterogeneity. To address this, we propose an integrated workflow that synergizes seismic sedimentology with geologically constrained seismic inversion. This study leverages well logging, core data, and 3D seismic surveys. Initially, seismic attribute analysis and stratal slicing were employed to delineate sedimentary microfacies, revealing that the fan delta front subfacies comprises subaqueous distributary channels, interdistributary bays, and distal bars. Subsequently, the planform distribution of these microfacies served as a critical constraint for the Seismic Waveform Indicative Inversion (SWII), effectively enhancing the resolution for thin sand body identification. The results demonstrate the following: (1). Two NW-SE trending subaqueous distributary channel systems, converging near the BAI65 well, form the primary reservoirs. (2). The SWII, optimized by our workflow, successfully predicts high-quality sand bodies with a cumulative area of 159.2 km², primarily located in the MAXI1, AIHU10, and AICAN1 well areas, as well as west of the MA18 well. This study highlights the value of integrating sedimentary facies boundaries as a geological constraint in seismic inversion, providing a more reliable method for predicting heterogeneous thin sand bodies and delineating future exploration targets in the Mahu Sag. Full article

Soils in arid regions are characterized by elevated salinity levels. During the process of soil moisture evaporation, salts are transported with water to the surface, resulting in the formation of salt crusts. Although these crusts significantly impact soil moisture evaporation, there is a paucity of systematic quantitative research concerning their formation mechanisms, dynamic evolution patterns, and effects on evaporation. To elucidate the mechanisms by which salt crusts influence soil moisture evaporation, this study conducted evaporation experiments utilizing brine soil columns. Various thicknesses of sand mulching (1 cm, 2 cm, 3 cm, 4 cm, 5 cm, and 6 cm) were applied to the top of the soil columns to generate different forms of NaCl salt crusts. Observations of soil column water evaporation rates, salt crust coverage (SCC), and salt crust morphology were conducted to analyze the effects of salt crust formation on soil water evaporation. The results indicate that the morphology and coverage of NaCl salt crusts significantly influence soil moisture evaporation. A crusty salt crust with high coverage impedes soil moisture evaporation; a patchy salt crust with moderate coverage may promote evaporation; the absence of a crust on the surface has a relatively weak effect on soil moisture evaporation. Nevertheless, the development of ‘salt trees’ within the soil profile can increase soil evaporation. These findings challenge the conventional understanding that “salt inhibits evaporation,” providing essential mechanistic parameters for accurately quantifying evaporation fluxes in saline soils and enhancing regional water cycle models, particularly the module related to atmosphere–soil water vapor exchange. Full article

Background: The study of non-Newtonian fluids in thin channels is crucial for advancing technologies in microfluidic systems and targeted industrial coating processes. Nanofluids, which exhibit enhanced thermal properties, are of particular interest. This paper investigates the complex flow and heat transfer characteristics of a Sutterby nanofluid (SNF) within a thin channel, considering the combined effects of magnetohydrodynamics (MHD), Brownian motion, and bioconvection of microorganisms. Analyzing such systems is essential for optimizing design and performance in relevant engineering applications. Method: The governing non-linear partial differential equations (PDEs) for the flow, heat, concentration, and bioconvection are derived. Using lubrication theory and appropriate dimensionless variables, this system of PDEs is simplified into a more simplified system of ordinary differential equations (ODEs). The resulting nonlinear ODEs are solved numerically using the boundary value problem (BVP) Midrich method in Maple software to ensure accuracy. Furthermore, data for the Nusselt number, extracted from the numerical solutions, are used to train an artificial neural network (ANN) model based on the Levenberg–Marquardt algorithm. The performance and predictive capability of this ANN model are rigorously evaluated to confirm its robustness for capturing the system’s non-linear behavior. Results: The numerical solutions are analyzed to understand the variations in velocity, temperature, concentration, and microorganism profiles under the influence of various physical parameters. The results demonstrate that the non-Newtonian rheology of the Sutterby nanofluid is significantly influenced by Brownian motion, thermophoresis, bioconvection parameters, and magnetic field effects. The developed ANN model demonstrates strong predictive capability for the Nusselt number, validating its use for this complex system. These findings provide valuable insights for the design and optimization of microfluidic devices and specialized coating applications in industrial engineering. Full article

The gut microbiota of insects, shaped by extensive coevolution, plays a crucial role in host adaptability and ecological expansion. The fall armyworm (Spodoptera frugiperda J.E. Smith), a highly polyphagous and migratory invasive pest, infests more than 350 plant species worldwide, resulting in substantial crop losses and increased reliance on pesticide applications. In this study, we employed 16S rRNA high-throughput sequencing to investigate diet-induced alternations in the gut microbial communities of larvae fed corn, rice, or an artificial diet. The results showed that diet significantly influenced microbial diversity, with rice-fed larvae exhibiting the highest richness. Firmicutes, Proteobacteria, and Cyanobacteria were the predominant bacterial phyla. Genus-level analysis revealed notable diet-dependent shifts, with Enterobacter and other genera (e.g., Ochrobactrum and Allorhizobium–Neorhizobium–Pararhizobium–Rhizobium) only detected in plant-fed groups. Additionally, current findings suggest that gut microbial genera are more prevalent when S. frugiperda larvae feed on rice plants than on corn plants or an artificial diet and are closely linked to their metabolic activities. Dominant microbial genera are expected to support essential metabolic processes and exhibit increased abundance on rice. These results indicate that the gut microbiome of S. frugiperda is diet-driven reorganization, potentially facilitating its polyphagy. This study extends the current understanding by elucidating the specific gut microbial taxa and their putative metabolic associations that are responsive to diet in S. frugiperda, thereby providing a theoretical basis for its polyphagous capability and underscoring microbiota-based strategies for sustainable pest management. Full article