Search Results (6,416)

As a key precursor of tropospheric ozone and secondary particulate matter, nitrogen oxides (NO_x) exert significant impacts on air quality. Traffic emissions represent a dominant source of near-surface NO_x. The widespread adoption of new energy vehicles (NEVs) has progressively transformed the automobile fleet composition, leading to measurable reductions in NO_x emissions. This study developed a NO_x emission inventory model to quantify the impact of NEV penetration on emission trends in Guangdong (2013–2022), under the assumption that the emission shares of internal combustion engine vehicles (ICEVs) and NEVs have no significant change in adjacent years. Results demonstrate that total vehicular NO_x emissions peaked in 2019 at 55.69 × 10⁴ tons (a 16.6% increase from 2018), followed by a consistent decline. ICEVs exhibited a declining emission share from 0.037 × 10⁴ tons/year in 2013 to 0.022 × 10⁴ tons/year in 2019—a 40.5% reduction, attributable to progressive technological advancements. Following a marginal increase (2019–2021), the emission share declined significantly to 0.019 × 10⁴ tons/year in 2022. In contrast, NEVs contributed to emissions reduction, with maximal mitigation observed in 2021 (−0.241 × 10⁴ tons). ICEVs initially demonstrated emission reductions (2014–2017), succeeded by a transient increase (11.7 × 10⁴ tons through 2021) before resuming decline in 2022. The NEV-driven mitigation effect intensified progressively from 2018 to 2021, with modest attenuation in 2022. Full article

Soil organic matter (SOM) is a fundamental indicator of soil health and a major component of the global carbon cycle; its accurate quantification is essential for sustainable agriculture. Conventional chemical assays yield only point-based soil measurements and miss the spatial distribution of soil elements; airborne hyperspectral remote sensing has emerged as a promising approach for the quantitative measurement and characterization of SOM. Inversion models translate hyperspectral data into quantitative SOM estimates. However, existing models rely solely on a single preprocessing pathway, limiting their ability to fully exploit available spectral information. We address these limitations by developing a marginal contribution-driven spectral fusion network (MC-SFNet) that conducts feature-level fusion of heterogeneous preprocessing outputs within a physics-guided deep architecture. Moreover, the combination of data-driven fusion and the Kubelka–Munk (KM) model yields more physically interpretable spectral features, advancing beyond prior purely data-driven methods. We validated MC-SFNet on a self-constructed remote sensing, high-throughput hyperspectral dataset comprising 200 black soil samples from Northeastern China (400–1000 nm, 256 bands). Experimental results show that our network reduces the RMSE by 10.7% relative to the prevailing generalized hyperspectral soil-inversion model. The proposed method provides a novel preprocessing pathway for forthcoming airborne high-throughput hyperspectral missions to extract soil-specific spectral information more effectively and further enhance large-scale SOM retrieval accuracy. Full article

A two-year field study evaluated biodegradable plastic film (BPF; thicknesses: 0.006, 0.008, and 0.010 mm) versus polyethylene film (PE; 0.010 mm) and no-mulch control on sugarcane yield and weed suppression. Key results demonstrated that 0.010 mm BPF significantly enhanced sugarcane emergence (CV = 5.07% in ratoon), reduced weed biomass by 70%, and increased perennial yield by 3.83% (+5.6 t ha⁻¹), while PE film decreased yield by 3.80%. Regression analysis identified the effective stem number, plant height, and stem diameter as primary yield predictors (R² = 0.996). Logistic models revealed that film mulching duration >119 days was critical for achieving high yields (>122.2 t ha⁻¹) and sustained weed control (R² = 0.81). These findings establish 0.010 mm BPF as an optimal sustainable alternative to PE film for enhancing sugarcane productivity. Full article

As micro–nano power devices have evolved towards high frequency, high voltage, and a high level of integration, the issue of thermal resistance at heterointerfaces has become increasingly prominent, posing a key bottleneck that limits device performance and reliability. This paper presents a systematic review of the current state of research and future challenges related to interface thermal resistance in heterostructures within micro and nano power devices. First, based on phonon transport theory, we conducted an in-depth analysis of the heat transfer mechanisms at typical heterointerfaces, such as metal–semiconductor and semiconductor–semiconductor, and novel low-dimensional materials. Secondly, a comprehensive review of current interface thermal resistance characterization techniques is provided, including the application and limitations of advanced methods such as time domain thermal reflection and Raman thermal measurement in micro- and nano-scale thermal characterization. Finally, in response to the application requirements of semiconductor power devices, future research directions such as atomic-level interface engineering, machine learning-assisted material design, and multi-physics field collaborative optimization are proposed to provide new insights for overcoming the thermal management bottlenecks of micro–nano power devices. Full article

Priority effects—the phenomenon where early-arriving species influence the establishment, growth, and reproduction of later-arriving species during community assembly—play a critical role in determining the invasion success of exotic species. However, how priority effects are influenced by nitrogen (N) availability remains understudied. The invasive species Bidens frondosa has rapidly expanded its range in China over the past few years. Yet it remains unclear how priority effects in B. frondosa versus native species may mediate invasion success, as well as how nutrient levels may alter these effects. Addressing these questions is essential for understanding the mechanisms driving B. frondosa invasion and for developing effective management strategies. In a greenhouse experiment, we manipulated the planting order of B. frondosa and its native congener B. biternata, then measured the growth and competitiveness of B. frondosa and B. biternata in both control and N addition treatments. Planting order greatly impacted the growth and competitiveness of both B. frondosa and B. biternata. Early arrival had more positive effects on B. frondosa than B. biternata, while late arrival more strongly inhibited B. frondosa than B. biternata. For B. frondosa, priority effects lessened with nitrogen addition, but the opposite occurred for B. biternata. Thus, priority effects may favor B. frondosa invasion, while late arrival, particularly under nitrogen addition, may curb its spread. As such, sowing early-germinating native species represents a useful management strategy for controlling B. frondosa invasions. Full article

In deepwater drilling operations, inhibiting methane hydrate (MH) formation is critical for wellbore flow assurance. This study synthesized a zwitterionic polymer NDAD and evaluated its hydrate inhibition performance through high-pressure reactor tests, magnetic resonance imaging (MRI), and molecular simulations. Results demonstrate that NDAD at concentrations of 1.0 wt% extends MH formation time by 4.9 times compared to conventional inhibitor PVCap. Notably, NaCl (10–15 wt%) synergistically enhances inhibition efficiency by inducing NDAD chain stretching to form physical gel networks, increasing solution viscosity by 98%. This gel structure obstructs methane–water diffusion, prolonging hydrate induction time. Response surface methodology (RSM) identifies NDAD dosage as the primary control factor for inhibition efficacy. Molecular simulations confirm that NDAD inhibits hydrate formation through dual pathways: (i) competitive hydration by ionic groups disrupting water cage structures and (ii) gel networks imposing mass transfer resistance to methane diffusion. Full article

Background: Lumbar spondylolisthesis (LS) is a common spinal disorder characterized by the forward displacement of the vertebra. Early detection is challenging due to asymptomatic presentation in the early stages. This study develops and validates an AI-based deep learning model for the early, high-precision diagnosis of LS using lumbar X-ray images. Methods: A total of 3300 lateral lumbar X-ray images were collected from Beijing Tongren Hospital, and an external dataset of 1100 images was used for validation. The images were randomly divided into the training, validation, and test sets. The model uses semantic segmentation to precisely segment vertebral bodies and calculate distances between vertebrae to identify and grade LS using the Meyerding classification. Model performance was compared to other algorithms and clinical experts. Results: The model achieved F1 Scores of 0.92 and 0.91 on the hospital and external datasets, respectively, outperforming other methods. It showed diagnostic accuracies of 96.1% and 94.4%, exceeding the performance of physicians (90.6% and 89.3%). These results highlight the potential of AI in improving diagnostic accuracy and clinical decision-making. Conclusions: Our deep learning model demonstrates high accuracy and reliability in diagnosing LS, providing a valuable tool for early detection and better patient outcomes. Future work will involve expanding the dataset and validating the model in clinical settings. Full article

This study presents an integrated workflow for the characterization of fault-controlled fractured–vuggy reservoirs, demonstrated through a comprehensive analysis of the TP12CX fault zone in the Tahe Oilfield. The methodology establishes a four-element structural model—comprising the damage zone, fault core, vuggy zone, and cavern system—coupled with a multi-attribute geophysical classification scheme integrating texture contrast, deep learning, energy envelope, and residual impedance attributes. This framework achieves a validation accuracy of 91.2%. A novel structural element decomposition–integration approach is proposed, combining deterministic structural reconstruction with facies-constrained petrophysical modeling to quantify reservoir properties. The resulting models identify key heterogeneities, including caverns (Φ = 17.8%, K = 587 mD), vugs (Φ = 3.5%, K = 25 mD), and fractures (K = 1400 mD), with model reliability verified through production history matching. Field application of an optimized nitrogen foam flooding strategy, guided by this workflow, resulted in an incremental oil recovery of 3292 tons. The proposed methodology offers transferable value by addressing critical challenges in karst reservoir characterization, including seismic resolution limits, complex heterogeneity, and late-stage development optimization in fault-controlled carbonate reservoirs. It provides a robust and practical framework for enhanced oil recovery in structurally complex carbonate reservoirs, particularly those in mature fields with a high water cut. Full article

Tomicus yunnanensis, a notorious forest pest in southwest China, primarily employs infochemicals to coordinate mass attacks that overcome host tree defenses. However, secondary visual cues, particularly detection of host color changes, also aid host location. This study characterized the compound eye structure and vision of T. yunnanensis using electron microscopy and phototaxis tests. The apposition eye contains 224–266 ommatidia, with asymmetry between left and right. Quadrilateral facets occupy the dorsal third, while hexagonal facets dominate elsewhere. Each ommatidium comprises a large corneal lens, an acone-type crystalline cone from four cone cells, and an open-type rhabdom formed by eight retinular cells (R7–R8 centrally, R1–R6 peripherally), surrounded by two primary and at least seventeen secondary pigment cells. Dark/light adaptation alters cone size/shape and rhabdom cross-sectional area/outline (without pigment granule movement) to regulate light reaching the photoreceptors. Behavioral observations showed peak flight activity occurs between 7:00–11:00 AM, with no nighttime activity. Phototaxis tests revealed females are highly sensitive to 360 nm, 380 nm, and 700 nm wavelengths, while males exhibit high sensitivity to 360 nm and 400 nm. This work enhances knowledge on the integration of visual and olfactory sensory information in beetles for host location and non-host avoidance. Full article

Carbon fiber fabric reinforced composite laminates are widely used in the automotive and aerospace components, which are prone to suffering low velocity impacts. In this paper, helicoidal layups of fabrics inspired by the Bouligand type structure of the dactyl clubs of mantis shrimp are proposed to improve the impact resistance of carbon fiber fabric reinforced composite laminates. Low velocity impact tests and finite element simulation are carried out to investigate the effect of the rotation angle of helicoidal layups on the impact damage behaviors of composite laminates, including impact force response, energy absorption characteristics and damage mechanism. Results show that the simulation results of impact force–time response, absorbed energy–time response, and damage characteristics show good agreements with the experimental results. With the increase in impact energy, the maximum value of impact force, the absorbed energy and the energy absorption ratio for all specimens are all increased. Under all impact energies, the impact damage of specimens with helicoidal layups are lower than that of specimen QI1 (rotation angle of 0°), indicating that the helical layup of woven carbon fabric can sufficiently enhance the impact resistance of the composite material. Furthermore, the impact resistance of specimen HL2 (rotation angle of 12.8°) is the best, because it demonstrates the lowest impact damage and highest impact force under all energies. This work provides a bionic design guideline for the high impact performance of carbon fiber fabric reinforced composite laminate. Full article

Edge computing builds relevant services and applications on the edge server near the user side, which enables a faster service response. However, the lack of large-scale hardware resources leads to weak defense for edge devices. Therefore, proactive defense security mechanisms, such as Intrusion Detection Systems (IDSs), are widely deployed in edge computing. Unfortunately, most of those IDSs lack causal analysis capabilities and still suffer the threats from Advanced Persistent Threat (APT) attacks. To effectively detect APT attacks, we propose a heterogeneous graph neural networks threat detection model based on the provenance graph. Specifically, we leverage the powerful analysis and tracking capabilities of the provenance graph to model the long-term behavior of the adversary. Moreover, we leverage the predictive power of heterogeneous graph neural networks to embed the provenance graph by a node-level and semantic-level heterogeneous mutual attention mechanism. In addition, we also propose a provenance graph reduction algorithm based on the semantic similarity of graph substructures to improve the detection efficiency and accuracy of the model, which reduces and integrates redundant information by calculating the semantic similarity between substructures. The experimental results demonstrate that the prediction accuracy of our method reaches 99.8% on the StreamSpot dataset and achieves 98.13% accuracy on the NSL-KDD dataset. Full article

Passiflora edulis propagation relies extensively on grafting, yet the optimization of pruning strategies for scion quality remains empirically guided. This study elucidates the physiological and molecular mechanisms underlying scion quality across five leaf retention treatments (0%, 25%, 50%, 75%, and unpruned control). The 50% partial leaf retention (50% PLR) treatment optimally promoted axillary bud development in passion fruit through coordinated physiological and molecular adaptations. This treatment significantly outperformed other treatments in terms of both bud sprouting rate and growth parameters (including length and diameter). Physiological analyses demonstrated transient auxin accumulation coupled with synchronized antioxidant system activation, maintaining redox homeostasis. Transcriptomic profiling identified upregulation of genes in the auxin signaling pathway and cytokinin activators, while dormancy-related genes were suppressed. These findings establish 50% PLR as an optimal threshold that balances photosynthetic capacity with hormonal regulation, providing a science-based strategy to standardize grafted seedling production, while enhancing scion quality for grafting efficiency. Full article

Lightning strikes pose a significant threat to the reliability of power distribution networks, with cascading effects on energy sustainability and community resilience. This paper proposes a lightning disaster risk prediction model for distribution networks, designing a lightning strike hazard matrix to classify historical fault records and incorporating future multi-source heterogeneous data to predict lightning-induced fault hazard levels and enhance the sustainability of grid operations. To address spatiotemporal imbalances in data distribution, we first propose diagnostic threshold settings for low-frequency elements alongside a method for calculating hazard diagnostic criteria. This approach systematically integrates high-hazard, low-frequency factors into risk analyses. Second, we introduce an adaptive weight optimization algorithm that dynamically adjusts risk factor weights by quantifying their contributions to overall system risk. This method overcomes the limitations of traditional frequency-weighted approaches, ensuring more robust hazard assessment. Experimental results demonstrate that, compared to baseline models, the proposed model achieves average improvements of 21%/8.3% in AUROC, 30.2%/47.4% in SE, and 20.5%/8.1% in CI, empirically validating its superiority in risk prediction and engineering applicability. Full article

In this work, the feasibility of simulating the thermal inactivation of Brochothrix thermosphacta in beef during heating processing based on hyperspectral imaging (HSI) in the wavelength range of 400–1000 nm was investigated. The Weibull and modified Gompertz kinetic models for the thermal inactivation of B. thermosphacta in beef heated in the range 40–60 °C were developed based on the full wavelength, featured spectral variables, and their principal component scores of HSI information, respectively. Notably, the specific wavebands at 412 nm and 735 nm showed a strong correlation with the surviving B. thermosphacta population during the beef heating process. The partial least squares regression models had a satisfactory ability in quantifying B. thermosphacta in beef, with an R_v² and RMSE of 0.826 and 0.341 log CFU/g, respectively. Furthermore, the Weibull model coupled with the HSI at 735 nm was suitable for kinetic modeling of the thermal inactivation of B. thermosphacta in beef, with an R² value of 0.937. Consequently, this work suggests the potential of the HSI technique for quantifying and monitoring microbes in meat during heating and can be applied for the thermal inactivation kinetic modeling of microorganisms. Full article

In recent years, the rapid development of moving platforms, especially unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), has promoted their widespread applications in various fields such as precision agriculture and formation flight. In these applications, for accurate real-time kinematic positioning between two moving platforms, receiver autonomous integrity monitoring (RAIM) is necessary to assure the reliability of the obtained relative positioning. However, the existing carrier phase-based RAIM (CRAIM) algorithms are mainly a direct extension of pseudorange-based RAIM (PRAIM), whose availability is also a major challenge in signal-harsh environments. Learning from the integrated system between Global Navigation Satellite System (GNSS) and INS and based on a multiple hypothesis solution separation (MHSS) algorithm, we have developed an improved CRAIM algorithm, which combines Beidou Navigation Satellite System (BDS) and INS to offer integrity information for real-time kinematic relative positioning between two moving platforms in challenging environments. To achieve more robust and efficient fault detection and exclusion (FDE) results, an algorithm of observation-domain outlier detection combined with MHSS (OOD-MHSS) is also proposed. In this algorithm, the kinematic relative positioning method with INS addition is performed first, then, based on double-difference (DD) phase observations with known integer ambiguities and the OOD-MHSS method, the integrity monitoring information can be provided for the kinematic relative positioning between two moving platforms. To assess the performance of the OOD-MHSS and the improved CRAIM algorithm, a series of kinematic experiments between different platforms was analyzed and discussed. The results show that the improved CRAIM algorithm can perform effective FDE and provide reliable integrity information, which offers centimeter-level relative position solutions with decimeter-level protection levels (PLs) (integrity budget: 1 × 10⁻⁵/h). Both observation outlier detection and INS improve the continuity and availability of kinematic relative positioning and the PLs in horizontal and vertical directions. The PL values have been improved by up to 24.3%, and availability has reached 96.67% in harsh urban areas. This is of great significance for applications requiring higher precision and integrity in kinematic relative positioning. Full article