Search Results (84,257)

Real-time, accurate and detailed monitoring of urban green space is of great significance for constructing the urban ecological environment and maximizing ecological benefits. Although high-resolution remote sensing technology provides rich ground object information, it also makes the surface information of urban green spaces more complex. Existing classification methods often struggle to meet the requirements of classification accuracy and the automation demands of high-resolution images. This study utilized GF-7 remote sensing imagery to construct an urban green space classification method for Beijing. The study used the YOLO v8 model as the framework to conduct a fine classification of urban green spaces within the Fifth Ring Road of Beijing, distinguishing between evergreen trees, deciduous trees, shrubs and grasslands. The aims were to address the limitations of insufficient model fit and coarse-grained classifications in existing studies, and to improve vegetation extraction accuracy for green spaces in northern temperate cities (with Beijing as a typical example). The results show that the overall classification accuracy of the trained YOLO v8 model is 89.60%, which is 25.3% and 28.8% higher than that of traditional machine learning methods such as Maximum Likelihood and Support Vector Machine, respectively. The model achieved extraction accuracies of 92.92%, 93.40%, 87.67%, and 93.34% for evergreen trees, deciduous trees, shrubs, and grasslands, respectively. This result confirms that the combination of deep learning and high-resolution remote sensing images can effectively enhance the classification extraction of urban green space vegetation, providing technical support and data guarantees for the refined management of green spaces and “garden cities” in megacities such as Beijing. Full article

We experimentally investigate the structural, magnetic, transport, and electronic properties of two d⁵ iridate double perovskite materials La₂BIrO₆ (B = Mg, Zn). Notably, despite similar crystallographic structure, the two compounds show distinctly different magnetic behaviors. The M = Mg compound shows an antiferromagnetic-like linear field-dependent isothermal magnetization below its transition temperature, whereas the M = Zn counterpart displays a clear hysteresis loop followed by a noticeable coercive field, indicative of ferromagnetic components arising from a non-collinear Ir spin arrangement. The local structure studies authenticate perceptible M/Ir antisite disorder in both systems, which complicates the magnetic exchange interaction scenario by introducing Ir-O-Ir superexchange pathways in addition to the nominal Ir-O-B-O-Ir super-superexchange interactions expected for an ideally ordered structure. While spin–orbit coupling (SOC) plays a crucial role in establishing insulating behavior for both these compounds, the rotational and tilting distortions of the IrO₆ (and MO₆) octahedral units further lift the ideal cubic symmetry. Finally, by measuring the Ir-L₃ edge resonant inelastic X-ray scattering (RIXS) spectra for both the compounds, giving evidence of spin–orbit-derived low-energy inter-J-state (intra t_2g) transitions (below ~1 eV), the charge transfer (O 2p → Ir 5d), and the crystal field (Ir t_2g → e_g) excitations, we put forward a qualitative argument for the interplay among effective SOC, non-cubic crystal field, and intersite hopping in these two compounds. Full article

With the rapid development of Internet of Vehicles (IoV) technology, massive data are continuously integrated into intelligent transportation systems, making efficient computing resource allocation a critical challenge for enhancing network performance. Due to the dynamic and real-time characteristics of IoV tasks, existing static offloading strategies fail to effectively cope with the complexity caused by network fluctuations and vehicle mobility. To address this issue, this paper proposes a task offloading algorithm based on the dynamic adjustment of the parallel factor in directed acyclic graphs (DAG), referred to as Dynamic adjustment of Parallel Factor (DPF). By leveraging edge computing, the proposed algorithm adaptively adjusts the parallel factor according to the dependency relationships among subtasks in the DAG, thereby optimizing resource utilization and reducing task completion time. In addition, the algorithm continuously monitors network conditions and vehicle states to dynamically schedule and offload tasks according to real-time system requirements. Compared with traditional static strategies, the proposed method not only significantly reduces task delay but also improves task success rates and overall system efficiency. Extensive simulation experiments conducted under three different task load conditions demonstrate the superior performance of the proposed algorithm. In particular, under high-load scenarios, the DPF algorithm achieves markedly better task completion times and resource utilization compared to existing methods. Full article

In the Internet of Vehicles (IoV), zero-day attacks pose a significant security threat. These attacks are characterized by unknown patterns and limited sample availability. Traditional anomaly detection methods often fail because they rely on oversimplified assumptions, hindering their ability to model complex normal IoV behavior. This limitation results in low detection accuracy and high false alarm rates. To overcome these challenges, we propose a novel zero-day attack detection framework based on Feature-Enhanced Inverse Mapping (IM-ZDD). The framework introduces a two-stage process. In the first stage, a feature enhancement module mitigates data scarcity by employing an innovative multi-generator, multi-discriminator Conditional GAN (CGAN) with dynamic focusing loss to generate a large-scale, high-quality synthetic normal dataset characterized by sharply defined feature boundaries. In the second stage, a learning-based inverse mapping module is trained exclusively on this synthetic data. Through adversarial training, the module learns a precise inverse mapping function, thereby establishing a compact and expressive representation of normal behavior. During detection, samples that cannot be effectively mapped are identified as attacks. Experimental results on the F2MD platform show IM-ZDD achieves superior accuracy and a low false alarm rate, yielding an average AUC of 98.25% and F1-Score of 96.41%, surpassing state-of-the-art methods by up to 4.4 and 10.8 percentage points. Moreover, with a median detection latency of only 3 ms, the framework meets real-time requirements, providing a robust solution for zero-day attack detection in data-scarce IoV environments. Full article

SARS-CoV-2 variants have demonstrated distinct epidemiological patterns and clinical presentations throughout the COVID-19 pandemic. Understanding variant-specific differences at the respiratory epithelium is crucial for understanding their pathogenesis. Here, we utilized human nasal organoid air–liquid interface (HNO-ALI) cell cultures to compare the viral replication kinetics, innate immune response, and epithelial damage of six different strains of SARS-CoV-2 (B.1.2, WA, Alpha, Beta, Delta, and Omicron). All variants replicated efficiently in HNO-ALIs, but with distinct replication kinetic patterns. The Delta variant exhibited delayed replication kinetics, achieving a steady state at 6 days post-infection compared to 3 days for other variants. Cytokine analysis revealed robust pro-inflammatory and chemoattractant responses (IL-6, IL-8, IP-10, CXCL9, and CXCL11) in WA1, Alpha, Beta, and Omicron infections, while Delta significantly dampened the innate immune response, with no significant induction of IL-6, IP-10, CXCL9, or CXCL11. Immunofluorescence and H&E analysis showed that all variants caused significant ciliary damage, though WA1 and Delta demonstrated less destruction at early time points (3 days post-infection). Together, these data show that, in our HNO-ALI model, the Delta variant employs a distinct “stealth” strategy characterized by delayed replication kinetics and epithelial cell innate immune evasion when compared to other variants of SARS-CoV-2, potentially explaining a mechanism that the Delta variant can use for its enhanced transmissibility and virulence observed clinically. Our findings demonstrate that variant-specific differences at the respiratory epithelium could explain some of the distinct clinical presentations and highlight the utility of the HNO-ALI system for the rapid assessment of emerging variants. Full article

Serological testing for SARS-CoV-2-specific antibodies, particularly those targeting the nucleocapsid protein, plays a key role in assessing past infection and estimating population-level seroprevalence. The seroprevalence of nucleocapsid antibodies against SARS-CoV-2 was evaluated in 1048 blood donors using the Elecsys Anti-SARS-CoV-2 electrochemiluminescence immunoassay. Participants completed a questionnaire to assess risk factors, symptoms during SARS-CoV-2 infection and vaccination status. The overall SARS-CoV-2 seroprevalence was 89.69%. Seroprevalence was not significantly associated with gender or age. In multivariate logistic regression, most investigated risk factors showed no significant association with seroprevalence. However, residence area and vaccination status were independently associated with SARS-CoV-2 seropositivity. Donors from rural areas had significantly higher odds of seropositivity (aOR = 1.68; 95% CI: 1.01–2.79; p = 0.045) compared to those from urban areas. Unvaccinated individuals were more likely to test positive for SARS-CoV-2 compared to vaccinated participants (aOR: 2.59; 95% CI: 1.35–4.99; p = 0.004). After three years of the COVID-19 pandemic, the prevalence of SARS-CoV-2 among blood donors was remarkably high, indicating that the vast majority of this population group had been exposed to the virus. This study highlights the risk factors for SARS-CoV-2 infection and the differences in antibody prevalence between vaccinated and unvaccinated individuals. Our findings underscore the pivotal role of vaccination in controlling the pandemic and provide valuable insights for policymakers in designing targeted strategies to curb future SARS-CoV-2 transmission. Full article

Due to their excellent physicochemical properties, the nanoparticles (NPs) have been utilized in various potential applications, including environmental remediation, energy storage, and nanomedicine. In this work, the ultrasonic and manual stirring approaches were used to integrate yttrium oxide (Y₂O₃) nanoparticles (NPs) into reduced graphene oxide (RGO) and carbon nanotubes (CNTs) to enhance their photocatalytic and anticancer properties. Pure Y₂O₃NPs, Y₂O₃/RGO NCs, and Y₂O₃/CNTs NCs were characterized using different analytical techniques, such as XRD, SEM, EDX with Elemental Mapping, FTIR, UV-Vis, PL, and DLS to investigate their improved structural, surface morphological, chemical bonding, optical, and surface charge properties. XRD data confirmed the successful integration of Y₂O₃into RGO and CNTs, with minor changes in crystallite sizes. SEM images with EDX analysis revealed that Y₂O₃NPs were uniformly distributed on RGO and CNTs, reducing aggregation. Chemical bonding and interactions between Y₂O₃and carbon materials were investigated using Fourier Transform Infrared (FTIR) analysis. UV and PL results suggest that the optical studies showed a shift in absorption peaks upon integration with RGO and CNTs. This indicates enhanced light absorption and modifications to the band gap between (3.79–4.40 eV) for the obtained samples. In the photocatalytic experiment, the degradation efficiency of bromophenol blue (BPB) dye for Y₂O₃RGO NCs was up to 87.3%, outperforming pure Y₂O₃NPs (45.83%) and Y₂O₃/CNTs NCs (66.78%) after 120 min of UV irradiation. Additionally, the MTT assay demonstrated that Y₂O₃/RGO NCs exhibited the highest anticancer activity against MG-63 bone cancer cells with an IC50 value of 45.7 µg/mL compared to Y₂O₃CNTs NCs and pure Y₂O₃NPs. This work highlights that Y₂O₃/RGO NCs could be used in significant applications, including environmental remediation and in vivo cancer therapy studies. Full article

Multilayer heterostructures of [(BiFeO₃)_m/(SrTiO₃)_n]_p were synthesized on ITO-coated quartz substrates via pulsed laser deposition, with varying thickness ratios (r = n/m) and periodicities (p = 1–3). Structural, electrical, and piezoelectric properties were systematically investigated using X-ray diffraction, AFM, and PFM. The BiFeO₃ layers crystallized in a distorted rhombohedral phase (R3c), free of secondary phases. Compared to single-layer BiFeO₃ films, the multilayers exhibited markedly lower leakage current densities and enhanced piezoelectric response. Electrical conduction transitioned from space-charge-limited current at low fields (E < 100 kV/cm) to Fowler–Nordheim tunneling at high fields (E > 100 kV/cm). Optimal performance was achieved for r = 0.30, p = 1, with minimal leakage (J = 8.64 A/cm² at E = 400 kV/cm) and a peak piezoelectric coefficient (d₃₃ = 55.55 pm/V). The lowest coercive field (Ec = 238 kV/cm) occurred in the configuration r = 0.45, p = 3. Saturated hysteresis loops confirmed stable ferroelectric domains. These findings demonstrate that manipulating layer geometry in [(BiFeO₃)_m/(SrTiO₃)_n]_p stacks significantly enhances functional properties, offering a viable path toward efficient, lead-free piezoelectric nanodevices. Full article

Well cementing is an important step in oil and gas development. It uses cement to seal the formation and the casing, preventing fluid leakage. However, when conducting offshore oil well cementing operations, deep-water formations are usually weakly consolidated soils, and it is difficult to form a good cementation between the cement and formation. Therefore, enhancing the strength of the formation is one of the effective measures. This study uses the microbial-induced carbonate precipitation technology to cement sandy formations containing clay minerals. The triaxial tests were conducted to evaluate the consolidation effectiveness in the presence of three clay minerals: montmorillonite, illite, and kaolinite. X-ray computed tomography was utilized to characterize microscopic pore parameters, while thermogravimetric analysis, X-ray diffraction, and surface potential measurements were applied to analyze the mechanisms of clay minerals affecting microbial consolidation. The results showed that microbial mineralization mainly affects the cohesion of the samples. The cohesion of the montmorillonite sample increased from 20 kPa to 65.4 kPa, an increase of up to 3.27 times. The other two samples (illite and kaolinite) had increases of only 0.33 times and 1.82 times. Although the strength of the montmorillonite sample increased the most, unexpected large pores appeared with a diameter of over 120 µm, accounting for 7.1%. This is mainly attributed to the mineral expansion property. The expansion of the minerals will trap more microorganisms in the sample, thereby generating more calcium carbonate. And it also reduced the gaps between sand particles, creating favorable conditions for the connection of calcium carbonate. Although the surface charge of the minerals also affects the attachment of microorganisms, all three minerals have negative charges and a difference of no more than 0.84 mV (pH = 9). Therefore, the expansion property of the minerals is the dominant factor affecting the mechanical and microstructure of the sample. Full article

Integrated sensing and communication (ISAC) has emerged as a transformative technology for intelligent transportation systems. Index modulation (IM), recognized for its high robustness and energy efficiency (EE), has been successfully incorporated into ISAC systems. However, most existing IM-based ISAC schemes overlook the spatial multiplexing potential of millimeter-wave channels and remain confined to single-hop vehicle-to-vehicle (V2V) setups, failing to address the challenges of energy consumption and noise accumulation in real-world multi-hop V2V networks with complex road topologies. To bridge this gap, we propose a spatial scattering modulation-based ISAC (ISAC-SSM) scheme and introduce it to multi-hop V2V networks. The proposed scheme leverages the sensed positioning information to select maximum signal-to-noise ratio relay vehicles and employs a detect-amplify-and-forward (DAF) protocol to mitigate noise propagation, while utilizing sensed angle data for Doppler compensation to enhance communication reliability. At each hop, the transmitter modulates index bits on the angular-domain spatial directions of scattering clusters, achieving higher EE. We initially derive a closed-form bit error rate expression and Chernoff upper bound for the proposed DAF ISAC-SSM under multi-hop V2V networks. Both theoretical analyses and Monte Carlo simulations have been made and demonstrate the superiority of DAF ISAC-SSM over existing alternatives in terms of EE and error performance. Specifically, in a two-hop network with 12 scattering clusters, compared with DAF ISAC-conventional spatial multiplexing, DAF ISAC-maximum beamforming, and DAF ISAC-random beamforming, the proposed DAF ISAC-SSM scheme can achieve a coding gain of 1.5 dB, 2 dB, and 4 dB, respectively. Moreover, it shows robust performance with less than a 1.5 dB error degradation under 0.018 Doppler shifts, thereby verifying its superiority in practical vehicular environments. Full article

Background: Substituted hydroxyapatites (HAps) are widely used in oral-care formulations for enamel repair; however, head-to-head comparisons among commercial grades remain limited. Objective: To compare four commercial HAps: A (Kal-HAp), B (FL-HAp), C (FL-HAp-SC), and D (microRepair^®, a biomimetic Zn–carbonate-substituted HAp) and to evaluate their ability to form an enamel-like coating in vitro. Methods: We characterized the powders by X-ray diffraction (crystalline phase, Landi crystallinity index), FTIR-ATR (phosphate/carbonate bands), SEM/EDS (morphology, surface Ca/P), and DLS (particles size, ζ-potential). In vitro, human enamel sections were treated with 5% slurries in artificial saliva; surface coverage was quantified by image analysis on SEM. Results: All commercial materials analyzed in this work were composed of HAp. Differences were observed between HApin terms of crystallinity-range [2 Theta 8.0–60.0°], carbonate substitution (ATR [carbonate group evaluated −870 cm⁻¹]), and particle size (DLS [in a range 0.1–10,000 nm], Z-mean [mV]). On enamel, all samples form a hydroxyapatite layer; coverage differed between groups ([A] 28.83 ± 7.35% vs. [B] 31.11 ± 3.12% vs. [C] 57.20 ± 33.12% vs. [D] 99.90 ± 0.12%), with the biomimetic Zn-carbonate-substituted HAp showing the highest coverage, and the post-treatment Ca/P ratio approached values similar to those of dental enamel. Conclusions: Complementary physic-chemical signatures (crystallinity, carbonate substitution, and morphology) relate to enamel-surface coverage in vitro, providing evidence base for selecting HAp grades for enamel-repair formulations, which is a practical implication for product design. Full article

Detailed description of phase composition and geochemistry of the Muschelkalk carbonate rocks of the Upper Silesian Province in Poland were presented in this article. The tests were carried out to determine mineralogical features and geochemical properties. The samples were collected from the formations of the Lower Muschelkalk (Gogolin Unit), Middle Muschelkalk (Diplopore Dolomite Unit) and Upper Muschelkalk (Tarnowice Unit, Boruszowice Unit). The following research methods were used: macroscopic description, X-Ray Diffraction, Fourier transform infrared spectroscopy, X-Ray Fluorescence and Atomic spectrometry with plasma intensification. The following carbonate phases were identified: a low-Mg calcite, a high-Mg calcite, a proto-dolomite, an ordered dolomite and a huntite. The results of XRD analysis allowed the determination of the chemical formulas of the mineral phases. XRF and ICP AES analyses allowed to establish the content of following trace elements: Sr, Ba, Al, Si, Fe, Mn, K, Na, S, Cl, Ti, Cr, Ni, Zn, Rb, Zr, Pb, As, V, Be, B, Co, Cu, Br, Mo and Cd. Apart from Sr and Ba, they are not fundamental components of carbonate rocks. They indicate the presence of minerals such as silicates, aluminosilicates, oxides and sulfides. Full article

Arbuscular Mycorrhizal Fungi (AMF) can form symbiotic relationships with most plants. They can alleviate the toxic effects of heavy metals on plants. This study analyzed the effects of AMF (Diversispora versiformis, D.v.) on the physiological responses and root organic acid secretion of tomato (Solanum lycopersicum L.) under cadmium (Cd) stress, in order to elucidate how AMF enhance Cd tolerance. The results indicated that when the AMF inoculation rate of tomato seedlings ranged from 26.75% to 38.23%, the AMF treatment significantly promoted tomato growth. Cd significantly reduced the agronomic traits of tomato. However, AMF inoculation dramatically lowered the Cd level from 19.32 mg/kg to 11.54 mg/kg in tomato roots, and effectively reduced the negative effect of Cd toxicity on seedling growth. Cd stress also significantly reduced the chlorophyll fluorescence parameters, chlorophyll contents, and photosynthetic intensity parameters in seedling leaves, while the AMF treatment significantly increased these indicators. Under Cd stress, the AMF treatment significantly increased the activities of SOD, POD, and CAT, and reduced the levels of reactive oxygen species and the contents of osmotic regulatory substances in roots. Under Cd stress conditions, the AMF treatment also significantly increased the auxin level (57.24%) and reduced the abscisic acid level (18.19%), but had no significant effect on trans-zeatin riboside and gibberellin contents in roots. Cd stress markedly reduced the content of malic acid and succinic acid by 17.28% and 25.44%, respectively; however, after the AMF inoculation, these indicators only decreased by 2.47% and 2.63%, respectively. Under Cd stress, AMF could increase tomato roots’ antioxidant capacity to reduce ROS level, thereby alleviating the toxicity induced by ROS and maintaining reactive oxygen metabolism, enhancing the plant’s stress resistance. In summary, the AMF treatment enhances the osmotic regulation capacity and maintains the stability of cell membranes by reducing the levels of osmotic regulatory substances in roots. It also enhances the Cd tolerance of tomato plants by regulating the contents of root hormones and aerobic respiration metabolites, among other pathways. Therefore, inoculating plants with AMF is a prospective strategy for enhancing their adaptive capacity to Cd-polluted soils. Full article

In this work, a series of novel high-entropy alloys CoCrFeNiSiV_x (x = 0.25; 0.5; 0.75; 1.0) with an intermetallic compound structure was proposed. The effect of vanadium addition on the structure, as well as selected mechanical and corrosion properties, was investigated. In the case of the CoCrFeNiSiV_0.25 alloy, the structural analysis revealed the formation of a dual-phase structure consisting of Fe_1.812V_0.907Si_0.906-type and Fe₅Ni₃Si₂-type intermetallic phases. The increase in vanadium concentration results in the crystallization of one Fe_1.812V_0.907Si_0.906 intermetallic phase detected by the X-ray diffraction method. The increase in vanadium content had a beneficial influence on the corrosion resistance of CoCrFeNiSiV_x alloys in 3.5% NaCl. The CoCrFeNiSiV alloy exhibited the lowest corrosion current density of 0.17 μA/cm² and the highest corrosion potential of −0.228 V. The hardness of the alloys investigated increased with vanadium content, reaching 1006 HV for the equimolar alloy. In turn, the lowest friction coefficient of 0.63 ± 0.06 was obtained for the CoCrFeNiSiV_0.75 alloy. The abrasive, fatigue, and oxidative wear were identified as the main wear mechanisms. Full article

Visual inspection remains a cornerstone of quality control in welded structures, yet manual evaluations are inherently constrained by subjectivity, inconsistency, and limited scalability. This study presents WeldVGG, a deep learning-based visual inspection model designed to automate weld defect classification using radiographic imagery. The proposed model is trained on the RIAWELC dataset, a publicly available collection of X-ray weld images acquired in real manufacturing environments and annotated across four defect conditions: cracking, porosity, lack of penetration, and no defect. RIAWELC offers high-resolution imagery and standardized class labels, making it a valuable benchmark for defect classification under realistic conditions. To improve trust and explainability, Grad-CAM++ is employed to generate class-discriminative saliency maps, enabling visual validation of predictions. The model is rigorously evaluated through stratified cross-validation and benchmarked against traditional machine learning baselines, including SVC, Random Forest, and a state-of-the-art architecture, MobileNetV3. The proposed model achieves high classification accuracy and interpretability, offering a practical and scalable solution for intelligent weld inspection. Furthermore, to prove the model’s ability to generalize, a test on the GDXray was performed, yielding positive results. Additionally, a Wilcoxon signed-rank test was conducted separately to assess statistical significance between model performances. Full article