Search Results (2,031)

Image dehazing is an effective approach for enhancing the quality of images captured under foggy or hazy conditions. Although existing methods have achieved certain success in dehazing performance, many rely on deep network architectures, leading to high model complexity and computational costs. To address this issue, this study aims to compare and optimize existing algorithms to improve dehazing performance. For this purpose, we innovatively propose a multi-scale feature-coordinated composite loss mechanism, integrating perceptual loss, Mean Squared Error, and L1 regularization to optimize two dehazing methods: AOD-Net and DehazeFormer. Extensive experiments demonstrate significant performance improvements under the multi-objective loss mechanism. For AOD-Net, the PSNR increased by 22.40% (+4.17 dB), SSIM by 3.62% (+0.0318), VSNR by 43% (+1.54 dB), and LPIPS decreased by 56.30% (−0.1161). Similarly, DehazeFormer showed notable enhancements: the PSNR improved by 11.43% (+2.45 dB), SSIM by 0.8% (+0.008), VSNR by 2.6% (+0.23 dB), and LPIPS decreased by 5.5% (−0.0104). These results fully validate the effectiveness of the composite loss mechanism in enhancing the feature representation capability of the models. Full article

Longnan is the largest olive cultivation area in China. The unique microclimates in Longnan make it an ideal testing ground for climate-resilient cultivation strategies with broader applications across similar regions, yet predictive models linking weather to oil quality remain scarce. This study establishes a climate suitability evaluation model for olive cultivation in central Longnan based on meteorological data and olive quality data in the Fotanggou planting base. Four key climatic factors are identified: cumulative sunshine hours during the fruit coloring to ripening period, average temperature during the fruit coloring to harvesting period, number of cloudy and rainy days during the harvesting period, and relative humidity during the fruit setting to fruit enlargement period. Olive oil quality is graded into three levels (Excellent III, Good II, Fair I) based on acidity, linoleic acid, and peroxide value using K-means clustering. A climate suitability index is developed by integrating these factors, with weights determined via principal component analysis. The model is validated against an olive quality report from the Dabao planting base, showing an 80% match rate. From 1991 to 2023, 87.9% of years exhibit suitable or moderately suitable conditions, with 100% of years in the past decade (2014–2023) reaching “Good” or “Excellent” levels. This model provides a scientific basis for evaluating and predicting olive oil quality, supporting sustainable olive industry development in Longnan. This model provides policymakers and farmers with actionable insights to ensure the long-term sustainability of olive industry amid climate uncertainty. Full article

Heavy metal pollution, particularly copper contamination, threatens the ecological environment and human survival. In response to this pressing environmental issue, the development of innovative remediation strategies has become imperative. Bioremediation technology is characterized by remarkable advantages, including its ecological friendliness, cost-effectiveness, and operational efficiency. In our previous research, Rossellomorea sp. ZC255 demonstrated substantial potential for environmental bioremediation applications. This study investigated the removal characteristics and underlying mechanism of strain ZC255 and revealed that the maximum removal capacity was 253.4 mg/g biomass under the optimal conditions (pH 7.0, 28 °C, and 2% inoculum). The assessment of the biosorption process followed pseudo-second-order kinetics, while the adsorption isotherm may fit well with both the Langmuir and Freundlich models. Cell surface alterations on the Cu(II)-treated biomass were observed through scanning electron microscopy (SEM). Cu(II) binding functional groups were determined via Fourier transform infrared spectroscopy (FTIR) analysis. Simultaneously, the genomic analysis of strain ZC255 identified multiple genes potentially involved in heavy metal resistance, transport, and metabolic processes. These studies highlight the significance of strain ZC255 in the context of environmental heavy metal bioremediation research and provide a basis for using strain ZC255 as a copper removal biosorbent. Full article

Smart structures with topological configurations that integrate perception and actuation have complex geometric features. The simplification of these features can lead to deviations in dynamic characteristics, making it difficult to establish an accurate dynamic model. Uncertainties, such as material nonlinearity, hysteresis in elastic deformation, and external disturbances, affect the trajectory tracking accuracy of the smart structure’s actuation function. This paper proposes a modeling method that combines finite element unit bodies and orthogonal characteristic mode reduction to construct an accurate dynamic model of the smart structure and design an adaptive backstepping controller. Nonlinear dynamic equations are derived through a finite element analysis of the structure, and the orthogonal characteristic mode reduction method is employed to reduce computational complexity while ensuring model accuracy. An adaptive backstepping controller is designed to mitigate model uncertainties and achieve precise trajectory tracking control. Simulation and experimental results demonstrate that the proposed method can effectively handle the nonlinearity and modeling errors of smart structures, achieving high-precision trajectory tracking and verifying the accuracy of the dynamic model as well as the robustness of the controller. Full article

The rhizosphere microbiome plays an important role in carbon- and nitrogen-cycling in soil and in the stress response of plants. It also affects the function of the ammonium transporter (AmtB) that senses nitrogen levels inside and outside the cells of the associative nitrogen-fixing bacterium GXGL-4A. However, the potential mechanism of the interaction between the AmtB deletion mutant of GXGL-4A (∆amtB) and microorganisms in the rhizosphere of plants under low-nitrogen stress is still unclear. As revealed by transcriptome analyses, mutation of the amtB gene in GXGL-4A resulted in a significant up-regulation of many functional genes associated with nitrogen fixation and transportation at transcription level. The application of ∆amtB changed the nitrogen level in the rhizosphere of cucumber seedlings and reshaped the microbial community structure in the rhizosphere, enriching the relative abundance of Actinobacteriota and Gemmatimonadota. Based on bacterial functional prediction analyses, the metabolic capacities of rhizobacteria were improved after inoculation of cucumber seedlings with the original strain GXGL-4A or the ∆amtB mutant, resulting in the enhancement of amino acids, lipids, and carbohydrates in the cucumber rhizosphere, which promoted the growth of cucumber plants under a low-nitrogen stress condition. The results contribute to understanding the biological function of gene amtB, revealing the regulatory role of the strain GXGL-4A on cucumber rhizosphere nitrogen metabolism and laying a theoretical foundation for the development of efficient nitrogen-fixing bacterial agents for sustainable agricultural production. Full article

The southeastern Yunnan region in the southwestern Nanpanjiang Basin is one of the most important manganese enrichment zones in China. Manganese mineralization is mainly confined to marine mud–sand–carbonate interbeds of the Middle Triassic Ladinian Falang Formation (T₂f), which contains several medium to large deposits such as Dounan, Baixian, and Yanzijiao. However, the geological processes that control manganese mineralization in this region remain insufficiently understood. Understanding the tectonic evolution of the basin is therefore essential to unravel the mechanisms of Middle Triassic metallogenesis. This study investigates how rift-related tectonic activity influences manganese ore formation. This study integrates global gravity and magnetic field models (WGM2012, EMAG2v3), audio-frequency magnetotelluric (AMT) profiles, and regional geological data to investigate ore-controlling structures. A distinct gravity low–magnetic high belt is delineated along the basin axis, indicating lithospheric thinning and enhanced mantle-derived heat flow. Structural interpretation reveals a rift system with a checkerboard pattern formed by intersecting NE-trending major faults and NW-trending secondary faults. Four hydrothermal plume centers are identified at these fault intersections. AMT profiles show that manganese ore bodies correspond to stable low-resistivity zones, suggesting fluid-rich, hydrothermally altered horizons. These findings demonstrate a strong spatial coupling between hydrothermal activity and mineralization. This study provides the first identification of the internal rift architecture within the Nanpanjiang Basin. The basin-scale rift–graben system exerts first-order control on sedimentation and manganese metallogenesis, supporting a trinity model of tectonic control, hydrothermal fluid transport, and sedimentary enrichment. These insights not only improve our understanding of rift-related manganese formation in southeastern Yunnan but also offer a methodological framework applicable to similar rift basins worldwide. Full article

Nanofiltration (NF) is considered a competitive purification method for acidic stream treatments. However, conventional thin-film composite NF membranes degrade under acid exposures, limiting their applications in industrial acid treatment. For example, wet-process phosphoric acid contains impurities of multivalent metal ions, but NF membrane technologies for impurity removal under harsh conditions are still immature. In this work, we develop a novel strategy of acid-resistant nanofiltration membranes based on interfacial polymerization (IP) of polyethyleneimine (PEI) and cyanuric chloride (CC) with the s-triazine ring. The IP process was optimized by orthogonal experiments to obtain positively charged PEI-CC membranes with a molecular weight cut-off (MWCO) of 337 Da. We further applied it to the approximate industrial phosphoric acid purification condition. In the tests using a mixed solution containing 20 wt% P₂O₅, 2 g/L Fe³⁺, 2 g/L Al³⁺, and 2 g/L Mg²⁺ at 0.7 MPa and 25 °C, the NF membrane achieved 56% rejection of Fe, Al, and Mg and over 97% permeation of phosphorus. In addition, the PEI-CC membrane exhibited excellent acid resistance in the 48 h dynamic acid permeation experiment. The simple fabrication procedure of PEI-CC membrane has excellent acid resistance and great potential for industrial applications. Full article

Road density is a key indicator of human activity, causing habitat loss and fragmentation. Soil fungi, essential for ecosystem functioning, are sensitive bioindicators. Yet their responses to road density in urban green spaces are poorly characterized. Here, we analyzed the composition of the dominant fungal community, examined both the direct and indirect effects of road density on soil fungal communities, and identified specialist species. Focusing on Shanghai, China, a rapidly urbanizing city, we considered both edaphic factor and the road network. Through machine learning and Spearman correlation regression analyses, we quantified the relative importance of road density and edaphic factor in shaping fungal community composition and employed occupancy-specificity modeling to identify specialist taxa. Our results revealed that Ascomycota, Basidiomycota, Zygomycota, Rozellomycota, Chytridiomycota, and Glomeromycota were the dominant phyla, accounting for 93% of the retrieved ITS sequences. Road density was found to be the primary driver of fungal community composition, followed by soil lead and potassium concentrations. Notably, opportunistic pathogens (Acremonium spp.) correlated positively with road density (p < 0.001). Specialist species in high-density areas were primarily pathotrophic fungi, while saprotrophic fungi dominated in low-density areas. These findings highlight the need for urban planning strategies to mitigate the ecological impact of road density. Full article

The role of rhizosphere bacteria in facilitating plant invasion is increasingly acknowledged, yet the influence of specific microbial functional traits remains insufficiently understood. This study addresses this gap by isolating two bacterial strains, Bacillus sp. ScRB44 and Pseudomonas sp. ScRB22, from the rhizosphere of the invasive weed Solidago canadensis. We assessed their nitrogen utilization capacity and indoleacetic acid (IAA) production capabilities to evaluate their ecological functions. Our three-stage experimental design encompassed strain promotion, nutrient stress, and competition phases. Bacillus sp. ScRB44 demonstrated robust IAA production and significantly improved the nitrogen utilization efficiency, significantly enhancing S. canadensis growth, especially under nutrient-poor conditions, and promoting a shift in biomass allocation toward the roots, thereby conferring a competitive advantage over native species. Conversely, Pseudomonas sp. ScRB22 exhibited limited functional activity and a negligible impact on plant performance. These findings underscore that the ecological impact of rhizosphere bacteria on invasive weeds is closely linked to their specific growth-promoting functions. By enhancing stress adaptation and optimizing resource allocation, certain microorganisms may facilitate the establishment of invasive weeds in adverse environments. This study highlights the significance of microbial functional traits in invasion ecology and suggests novel approaches for microbiome-based invasive weed management, with potential applications in agricultural soil health improvement and ecological restoration. Full article

Segmentation of skin lesions in dermoscopic images is critical for the accurate diagnosis of skin cancers, particularly malignant melanoma, yet it is hindered by irregular lesion shapes, blurred boundaries, low contrast, and artifacts, such as hair interference. Conventional deep learning methods, typically based on UNet or Transformer architectures, often face limitations in regard to fully exploiting lesion features and incur high computational costs, compromising precise lesion delineation. To overcome these challenges, we propose SGNet, a structure-guided network, integrating a hybrid CNN–Mamba framework for robust skin lesion segmentation. The SGNet employs the Visual Mamba (VMamba) encoder to efficiently extract multi-scale features, followed by the Dual-Domain Boundary Enhancer (DDBE), which refines boundary representations and suppresses noise through spatial and frequency-domain processing. The Semantic-Texture Fusion Unit (STFU) adaptively integrates low-level texture with high-level semantic features, while the Structure-Aware Guidance Module (SAGM) generates coarse segmentation maps to provide global structural guidance. The Guided Multi-Scale Refiner (GMSR) further optimizes boundary details through a multi-scale semantic attention mechanism. Comprehensive experiments based on the ISIC2017, ISIC2018, and PH2 datasets demonstrate SGNet’s superior performance, with average improvements of 3.30% in terms of the mean Intersection over Union (mIoU) value and 1.77% in regard to the Dice Similarity Coefficient (DSC) compared to state-of-the-art methods. Ablation studies confirm the effectiveness of each component, highlighting SGNet’s exceptional accuracy and robust generalization for computer-aided dermatological diagnosis. Full article

Coxsackievirus B3 (CV-B3) infection causes inflammatory conditions such as viral myocarditis and meningitis, and incidence rates are rising annually. While children are more likely to be affected by severe manifestations, the molecular basis of this age-dependent susceptibility is poorly understood. In this study, we used young Balb/c mice at three developmental stages (7-, 14-, and 30-day-old mice) to investigate CV-B3 pathogenesis. Our findings revealed that 7-day-old mice exhibited substantial infection susceptibility and pathological severity compared to older mice. Critically, an age-dependent analysis showed a progressive decline in the expression of CV-B3-binding Coxsackievirus and Adenovirus Receptor (CAR) splice variants (CAR1 and CAR2) at both the transcriptional and translational levels as the mice matured from 7 to 30 days. These receptor isoforms demonstrated a direct correlation with viral replication efficiency in younger hosts. Concurrently, aging was associated with a rise in non-binding CAR variants (CAR3 and CAR4). During CV-B3 infection, the abundance of CAR1/CAR2 in young mice facilitated accelerated viral proliferation, coupled with the hyperactivation of the NLRP3 inflammasome and the expansion of IL-17-producing γδT cells (γδT17 cells). This cascade triggered excessive production of proinflammatory cytokines (IL-1β, IL-18, and IL-17), culminating in pronounced inflammatory infiltrates within cardiac and cerebral tissues. These findings establish NLRP3 inflammasome dysregulation as a critical determinant of CV-B3-induced tissue damage and provide novel insights into the heightened susceptibility to CV-B infection during early life and its associated severe disease rates. Full article

To accurately assess the water quality, ecosystem status, distribution of large benthic organisms, and ecological restoration under human intervention, an analysis of benthic organisms on Caofeidian in September and November 2023 and January and May of the following year was conducted in this work. By performing CCA (canonical correspondence analysis) and cluster and correlation coefficient (Pearson) analyses, the temporal variation characteristics of benthic abundance, dominant species, community structure and biodiversity were analyzed. A total of 79 species of macro-benthic animals were found in four months, including 32 species of polychaetes, cnidarians, 1 species of Nemertean, 19 species of crustaceans, and 24 species of molluscs. The use of conventional grab-type mud collectors revealed that the Musculus senhousei dominated the survey (Y > 0.02). While only a small number of Ruditapes philippinarum were collected from bottom-dwelling species, a certain number of bottom-dwelling species (Ruditapes philippinarum and Scapharca subcrenata) were also collected during the trawl survey. Additionally, a significant population of Rapana venosa was found in the area. It is speculated that the dual effects of predation and competition are likely the primary reasons for the relatively low abundance of bottom-dwelling species. The density and biomass of macro-benthos were consistent over time, which was the highest in May, the second highest in January, and the lowest in September and November. The main environmental factors affecting the large benthic communities in the surveyed sea areas were pH, DO, NO₂⁻-N, T, SAL and PO₄³⁻-P. Combined with historical data, it was found that although the environmental condition in the Caofeidian sea area has improved, the Musculus senhousei has been dominant. In addition, the abundance of other species is much less than that of the Musculus senhousei, and the diversity of the benthic community is still reduced. Our work provides valuable data support for the management and improvement of bottom Marine pasture and promotes the transformation of Marine resources from resource plunder to a sustainable resource. Full article

The challenge of accurately diagnosing mechanical failures in high-voltage circuit breakers is exacerbated by the non-stationary characteristics of vibration signals. This study proposes a Dual-Channel Convolutional Neural Network (DC-CNN) framework based on the Gramian Angular Field (GAF) transformation, which effectively captures both global and local information about faults. Specifically, vibration signals from circuit breaker sensors are firstly transformed into Gramian Angular Summation Field (GASF) and Gramian Angular Difference Field (GADF) images. These images are then combined into multi-channel inputs for parallel CNN modules to extract and fuse complementary features. Experimental validation under six operational conditions of a 220 kV high-voltage circuit breaker demonstrates that the GAF-DC-CNN method achieves a fault diagnosis accuracy of 99.02%, confirming the model’s effectiveness. This work provides substantial support for high-precision and reliable fault diagnosis in high-voltage circuit breakers within power systems. Full article

Volatile organic compounds (VOCs) are presently posing a rather considerable threat to both human health and environmental sustainability. Among these, n-butanol is commonly identified as bringing potential hazards to environmental integrity and individual health. This study presents the creation of a highly sensitive n-butanol gas sensor utilizing cobalt-doped zinc oxide (ZnO) derived from a metal–organic framework (MOF). A series of x-Co/MOF-ZnO (x = 1, 3, 5, 7 wt%) nanomaterials with varying Co ratios were generated using the homogeneous co-precipitation method and assessed for their gas-sensing performances under a low operating temperature (191 °C) and UV excitation (220 mW/cm²). These findings demonstrated that the 5-Co/MOF-ZnO sensor presented the highest oxygen vacancy (O_v) concentration and the largest specific surface area (SSA), representing the optimal reactivity, selectivity, and durability for n-butanol detection. Regarding the sensor’s response to 100 ppm n-butanol under UV excitation, it achieved a value of 1259.06, 9.80 times greater than that of pure MOF-ZnO (128.56) and 2.07 times higher than that in darkness (608.38). Additionally, under UV illumination, the sensor achieved a rapid response time (11 s) and recovery rate (23 s). As a strategy to transform the functionality of ZnO-based sensors for n-butanol gas detection, this study also investigated potential possible redox reactions occurring during the detection process. Full article

African swine fever virus (ASFV) causes global swine outbreaks, but its cellular pathogenesis is poorly understood. Using single-cell RNA data from ASFV-infected pig spleens across four timepoints, we identified macrophages as the primary viral reservoir, with infection driving lymphoid depletion and myeloid expansion. We characterized four functionally distinct macrophage subsets, including a metabolically reprogrammed SusceptibleMac population serving as the major viral niche and an AntiviralMac subset rapidly depleted during infection. Viral gene expression analysis revealed E165R as a central hub in viral replication networks, while host transcriptomics uncovered disruption of Netrin signaling pathways that may facilitate immune evasion. Pseudotime analysis revealed dynamic macrophage state transitions during infection. These findings provide a high-resolution cellular atlas of ASFV pathogenesis, revealing macrophage subset-specific responses that shape disease outcomes and identifying potential targets for therapeutic intervention. Full article