Search Results (17)

The determination of an optimal nitrogen (N) fertilization rate is critical for the sustainable large-scale cultivation of Cinnamomum camphora var. linaloolifera for essential oil production. Both suboptimal and excessive nitrogen inputs can adversely affect plant sustainable development and essential oil biosynthesis, underscoring the necessity of precise nutrient management. This study investigated the effects of five N application rates (0, 45, 90, 135, and 180 kg·hm⁻²) on vegetative growth, essential oil yield, and quality. Growth parameters, including plant height, basal diameter, specific leaf area (SLA), and essential oil yield and yield rate. Oil composition was characterized via gas chromatography–mass spectrometry (GC-MS). The application of 90 kg·hm⁻² N significantly enhanced plant height (74.31%), basal diameter (54.95%), SLA (20.91%), and biomass (181.8%) relative to the nitrogen-free control. Nitrogen uptake was concentrated in foliar tissues, accounting for 82.8% of total plant nitrogen accumulation. This fertilization rate also maximized essential oil yield (9.15 g·plant⁻¹) and yield rate (2.44%), reflecting increases of 178.9% and 24.49%, respectively. Linalool was the predominant oil constituent (89.84–91.81%), with its highest concentration observed at the 90 kg·hm⁻² treatment. At this rate, the relative abundance of oxygenated compounds increased by 0.97%, while hydrocarbon content decreased by 0.62%, indicating a qualitative improvement in oil composition. The findings reveal a threshold response to nitrogen input, wherein rates exceeding 90 kg·hm⁻² did not confer further benefits and may reduce efficiency. Collectively, these results suggest that a nitrogen application rate of 90 kg·hm⁻² optimally enhances vegetative growth, nitrogen assimilation, and both the quantitative and qualitative traits of essential oils in C. camphora var. linaloolifera. Full article

Snow cover variations significantly affect the stability of regional water supply and terrestrial ecosystems in arid northwest China. This study comprehensively evaluates snow resource changes since 2000 by integrating multisource remote sensing datasets and analyzing four key indicators: snow cover area (SCA), snow phenology (SP), snow depth (SD), and snow water equivalent (SWE). The results reveal a slight downtrend in SCA over the past two decades, with an annual decline rate of 7.13 × 10³ km². The maximum SCA (1.28 × 10⁶ km²) occurred in 2010, while the minimum (7.25 × 10⁵ km²) was recorded in 2014. Spatially, SCA peaked in December in the north and January in the south, with high-altitude subregions (Ili River Basin (IRB), Tarim River Region (TRR), North Kunlun Mountains (NKM), and Qaidam Basin (QDB)) maintaining stable summer snow cover due to low temperatures and high precipitation. Analysis of snow phenology indicates a significant shortening of snow cover duration (SCD), with 62.40% of the study area showing a declining trend, primarily driven by earlier snowmelt. Both SD and SWE exhibited widespread declines, affecting 75.09% and 84.85% of the study area, respectively. The most pronounced SD reductions occurred in TRR (94.44%), while SWE losses were particularly severe in North Tianshan Mountains (NTM, 94.61%). The total snow mass in northwest China was estimated at 108.95 million tons, with northern Xinjiang accounting for 66.24 million tons (60.8%), followed by southern Xinjiang (37.44 million tons) and the Hexi Inland Region (5.27 million tons). Consistency analysis revealed coherent declines across all indicators in 55.56% of the study area. Significant SD and SCD reductions occurred in TRR and Tuha Basin (THB), while SWE declines were widespread in NTM and IRB, driven by rising temperatures and decreased snowfall. The findings underscore the urgent need for adaptive strategies to address emerging challenges for water security and ecological stability in the region. Full article

The widespread deployment of unmanned aerial vehicles (UAVs) in modern warfare has profoundly increased the complexity and dynamic nature of aerial combat. To address the limitations of traditional UAV combat intention recognition methods, which rely on the “complete information” assumption and struggle to adapt effectively to dynamic adversarial environments, this paper proposes a deep learning-based UAV air combat intention recognition model (BLAC). The BLAC model establishes dynamic temporal feature mappings through a bidirectional long short-term memory network (BL) and innovatively incorporates a cross-attention mechanism (A) paired with contrastive learning (C) to improve model performance. To mitigate battlefield information uncertainty, the BLAC model implements cubic spline interpolation for numerical features and proximity-based imputation for non-numerical features, effectively resolving data loss challenges. The experimental results demonstrate that the BLAC model achieves superior intention recognition accuracy compared to mainstream models, maintaining over 91% accuracy even under 30% data loss conditions. These outcomes confirm the robustness and adaptability of the model in dynamic combat environments. This research not only provides an efficient framework for UAV combat intention recognition under information uncertainty but also offers valuable theoretical and practical insights for advancing intelligent command and control systems. Full article

Background: Rice bacterial leaf blight, caused by the Gram-negative bacterium Xanthomonas oryzae pv. Oryzae (Xoo), significantly impacts rice production. To address this disease, research efforts have focused on discovering and utilizing novel disease-resistant genes and examining their functional mechanisms. Methods and Results: In this study, a variety of bacterial strains were utilized. CX28-3, AX-11, JC12-2, and X10 were isolated from the high-altitude japonica rice-growing region on the Yunnan Plateau. Additionally, PXO61, PXO86, PXO99, and PXO339, sourced from the International Rice Research Institute (IRRI), were included in the analysis. To evaluate the resistance characteristics of Haonuoyang, artificial leaf cutting and inoculation methods were applied. Results indicated that Haonuoyang exhibited broad-spectrum resistance. Additionally, to explore the genetic mechanisms of resistance, the TFAIII-type transcription factor OsZFPH was cloned from Haonuoyang using PCR amplification. The subcellular localization method identified the precise location of the OsZFPH gene within the cell. The expression of OsZFPH was induced by Xoo stress. The overexpression of OsZFPH resulted in increased activities of enzymes, including SOD, CAT, and POD, while silencing the gene led to reduced enzyme activities. Furthermore, the hormones SA (salicylic acid), JA (jasmonic acid), and GA (gibberellin) were shown to positively regulate the gene expression. Protein interactions with OsZFPH were verified through a yeast two-hybrid system and BiFC technology. Hap5, which aligned with the sequence of Haonuoyang, was found to belong to a haplotype consisting of Jingang 30, 40 resequenced rice varieties, 18 Oryza rufipogon, and 29 Oryza granulata. Conclusions: The findings of this study emphasize the vital role of OsZFPH in rice resistance to bacterial leaf blight. The identification of broad-spectrum resistance in Haonuoyang and the understanding of OsZFPH gene functions provide valuable insights for the future development of rice varieties with improved resistance to this destructive disease. Full article

In mineral, environmental, and engineering explorations, we frequently encounter geological bodies with varied sizes, depths, and conductivity contrasts with surround rocks and try to interpret them with single survey data. The conventional three-dimensional (3-D) inversions significantly rely on the size of the grids, which should be smaller than the smallest geological target to achieve a good recovery to anomalous electric conductivity. However, this will create a large amount of unknowns to be solved and cost significant time and memory. In this paper, we present a multi-scale (MS) stochastic inversion scheme based on shearlet transform for airborne electromagnetic (AEM) data. The shearlet possesses the features of multi-direction and multi-scale, allowing it to effectively characterize the underground conductivity distribution in the transformed domain. To address the practical implementation of the method, we use a compressed sensing method in the forward modeling and sensitivity calculation, and employ a preconditioner that accounts for both the sampling rate and gradient noise to achieve a fast stochastic 3-D inversion. By gradually updating the coefficients from the coarse to fine scales, we obtain the multi-scale information on the underground electric conductivity. The synthetic data inversion shows that the proposed MS method can better recover multiple geological bodies with different sizes and depths with less time consumption. Finally, we conduct 3-D inversions of a field dataset acquired from Byneset, Norway. The results show very good agreement with the geological information. Full article

This study demonstrates the design and field implementation of an innovative servo concrete bracing system in foundation pit excavation. The bracing system comprises concrete struts, revised purlins, and hydraulic jacks, and its field performance is evaluated in a deep foundation pit project in Shanghai, China. The field measurements demonstrate that the servo bracing system effectively reduces the maximum lateral displacement of the retaining wall by up to 31%. Moreover, the servo jacks modify the wall’s flexural behavior by introducing local inflection points at certain depths and driving the displacement peak upward. Furthermore, the system’s performance varies with strut configuration, and servo forces influence not only the corresponding acting strut but also the adjacent struts’ behavior, implying that the monitoring scope should be expanded when applying the servo bracing system in actual engineering. This study provides a meaningful technical reference for future servo concrete bracing system applications in foundation pit engineering. Full article

Machine learning for causal inference, particularly at the individual level, has attracted intense interest in many domains. Existing techniques focus on controlling differences in distribution between treatment groups in a data-driven manner, eliminating the effects of confounding factors. However, few of the current methods adequately discuss the difference in treatment group sizes. Two approaches, a direct and an indirect one, deal with potential missing data for estimating individual treatment with binary treatments and different treatment group sizes. We embed the two methods into certain frameworks based on the domain adaption and representation. We validate the performance of our method by two benchmarks in the causal inference community: simulated data and real-world data. Experiment results verify that our methods perform well. Full article

Metal sulfides in waste rocks and tailings are susceptible to serious soil and water contamination due to the generation of acid mine drainage (AMD) during stockpiling. The hydrometallurgical process is one of the most essential heavy metal remediation technologies through harmless disposal and resource utilization of the waste sulfides. However, atmospheric hydrometallurgy of sulfides still faces great challenges due to low leaching efficiency and high cost. In this work, we proposed a cooperative leaching system (Fe₂(SO₄)₃-O₃) and investigated the oxidative dissolution process of sphalerite (ZnS). Under the optimal conditions, the extracted zinc reached 97.8%. Reactive oxygen species (ROS) (·OH, ¹O₂ and ·O₂⁻) were identified in the radical quenching experiments. The dissolution of sphalerite did not show passivation due to the ozone’s capability to oxidize the sulfur in sphalerite to sulfate. In addition, stirring rate, O₃ inlet concentration, and Fe₂(SO₄)₃ concentration had a significant effect on the dissolution of sphalerite. Meanwhile, the apparent activation energy was 24.11 kJ/mol based on kinetic fitting, which indicated that the controlling step of the reaction was mainly a diffusion process. This work demonstrated the cooperative effect of sphalerite leaching in the O₃-Fe₂(SO₄)₃ system and provided a theoretical reference for efficient and atmospheric dissolution of sphalerite. Full article

The surface-to-borehole transient electromagnetic (SBTEM) method can provide images at higher resolution for deep earth because its receivers are close to targets. However, as usually the boreholes distribute sparsely, the limited EM data can result in an “equivalent trap” in SBTEM inversions, i.e., the data are well-fitted, but the model is not properly recovered. To overcome this non-unique problem, we propose a dual-mesh three-dimensional (3D) SBTEM inversion scheme. We first use a coarse mesh to obtain a rough resistivity distribution near the borehole, and then we map the coarse mesh attribute into a fine one and capture details from the fine mesh inversion. We test our method on both synthetic data and survey data acquired in Daye, Hubei Province, China. Numerical experiments show that our dual-mesh inversion strategy can better recover the location and resistivity of targets. Full article

For the mineral exploration in complex terrain areas, the semi-airborne transient electromagnetic (SATEM) technology is one of the most powerful methods due to its high efficiency and low cost. However, since the mainstream SATEM systems only observe the component dBz/dt and the data are usually processed by simple interpretation or one-dimensional (1D) inversion, their resolutions are too low to accurately decipher the fine underground structures. To overcome these problems, we proposed a novel 3D forward and inversion method for the multi-component SATEM system. We applied unstructured tetrahedron grids to finely discretize the model with complex terrain, subsequently we used the vector finite element method to calculate the SATEM responses and sensitivity information, and finally we used the quasi-Newton method to achieve high-resolution underground structures. Numerical experiments showed that the 3D inversion could accurately recover the location and resistivities of the underground anomalous bodies under the complex terrain. Compared to a single component data, the inversion of the multi-component data was more accurate in describing the vertical boundary of the electrical structures, and preferable for high-resolution imaging of underground minerals. Full article

The traditional three-dimensional (3D) magnetotelluric (MT) forward modeling using Krylov subspace algorithms has the problem of low modeling efficiency. To improve the computational efficiency of 3D MT forward modeling, we present a novel geometric multigrid algorithm for the finite element method. We use the vector finite element to discretize Maxwell’s equations in the frequency domain and apply the Dirichlet boundary conditions to obtain large sparse complex linear equations for the solution of EM responses. To improve the convergence of the solution at low frequencies we use the divergence correction to correct the electric field. Then, we develop a V-cycle geometric multigrid algorithm to solve the linear equations system. To demonstrate the efficiency and effectiveness of our geometric multigrid method, we take three synthetic models (COMMEMI 3D-2 model, Dublin test model 1, modified SEG/EAEG salt dome model) and compare our results with the published ones. Numerical results show that the geometric multigrid algorithm proposed in this paper is much better than the commonly used Krylov subspace algorithms (such as SOR-GMRES, ILU-BICGSTAB, SOR-BICGSTAB) in terms of the iteration number, the solution time, and the stability, and thus is more suitable for large-scale 3D MT forward modeling. Full article

The aromatic structure and the rich nitrogen content of polymers based on covalent triazine-based frameworks (CTF) and their unique hydrophilic-lipophilic-balanced adsorption properties make them promising candidates for an adsorbent that can be used for sample pretreatment. Herein, a new covalent triazine-based framework (CTF-DBF) synthesized by a Friedel–Crafts reaction was used for the determination of the content of nucleotides in commercial infant formula. It was shown that the synthetic materials had an amorphous microporous structure, a BET surface area of up to 595.59 m²/g, and 0.39 nm and 0.54 nm micropores. The versatile adsorption properties of this material were evaluated by quantum chemistry theory calculations and batch adsorption experiments using five nucleotides as probes. The quantum chemistry results demonstrated that CTF-DBF can participate in multiple interactions with nucleotides. All the analyses performed present good linearity with R² > 0.9993. The detection limits of targets ranged from 0.3 to 0.5 mg/kg, the spiked recoveries were between 85.8 and 105.3% and the relative standard deviations (RSD, n = 6) were between 1.1 and 4.5%. All these results suggest that this versatile CTF-DBF has great potential for sample pretreatment. Full article

We reported the second- and third-order temporal interference of two non-degenerate pseudo-thermal sources in a nitrogen-vacancy center (NV⁻). The relationship between the indistinguishability of source and path alternatives is analyzed at low temperature. In this article, we demonstrate the switching between three-mode bunching and frequency beating effect controlled by the time offset and the frequency difference to realize optical demultiplexer. Our experimental results suggest the advanced technique achieves channel spacing and speed of the demultiplexer of about 96% and 17 ns, respectively. The proposed demultiplexer model will have potential applications in quantum computing and communication. Full article

Even though mechanical activation is a significant pretreatment technology for the efficient extraction of metals from mineral resources, its effects on the bioleaching of sphalerite and marmatite are rarely discussed. In this study, mechanical activation pretreatment using various grinding media and grinding times was conducted, and particle size distribution, morphology, X-ray diffraction (XRD) and energy dispersive spectrometry (EDS) analyses, as well as batch bioleaching experiments, were carried out. The results suggest that #C conditions (corundum jar with zirconia balls) were more efficient than #S conditions (stainless steel jar with stainless steel balls) for the grinding of both sphalerite and marmatite. Mechanical activation significantly improved the bioleaching of sphalerite; however, it inhibited that of marmatite, possibly due to the formation of reactive oxygen species (ROS). The optimum grinding conditions for the bioleaching of sphalerite and marmatite are proposed. Full article

Alkali-treated titanium (Ti) with a porous, homogeneous, and uniform nanonetwork structure (TNS) that enables establishment of a more rapid and firmer osteointegration than titanium has recently been reported. However, the mechanisms underlying the enhanced osteogenic activity on TNS remains to be elucidated. This study aimed to evaluate the surface physicochemical properties of Ti and TNS, and investigate osteoinduction and osteointegration in vivo. Surface characteristics were evaluated using scanning electron microscopy (SEM), scanning probe microscopy (SPM), and X-ray photoelectron spectrometry (XPS), and the surface electrostatic force of TNS was determined using solid zeta potential. This study also evaluated the adsorption of bovine serum albumin (BSA) and human plasma fibronectin (HFN) on Ti and TNS surfaces using quartz crystal microbalance (QCM) sensors, and apatite formation on Ti and TNS surfaces was examined using a simulated body fluid (SBF) test. Compared with Ti, the newly developed TNS enhanced BSA and HFN absorbance capacity and promoted apatite formation. Furthermore, TNS held less negative charge than Ti. Notably, sequential fluorescence labeling and microcomputed tomography assessment indicated that TNS screws implanted into rat femurs exhibited remarkably enhanced osteointegration compared with Ti screws. These results indicate that alkali-treated titanium implant with a nanonetwork structure has considerable potential for future clinical applications in dentistry and orthopedics. Full article