Search Results (257)

Mangroves play a crucial role in ecosystems, and the accurate classification and real-time monitoring of mangrove species are essential for their protection and restoration. To improve the segmentation performance of mangrove UAV remote sensing images, this study performs species segmentation after the super-resolution (SR) reconstruction of images. Therefore, we propose SwinNET, an SR reconstruction network. We design a convolutional enhanced channel attention (CEA) module within a network to enhance feature reconstruction through channel attention. Additionally, the Neighborhood Attention Transformer (NAT) is introduced to help the model better focus on domain features, aiming to improve the reconstruction of leaf details. These two attention mechanisms are symmetrically integrated within the network to jointly capture complementary information from spatial and channel dimensions. The experimental results demonstrate that SwinNET not only achieves superior performance in SR tasks but also significantly enhances the segmentation accuracy of mangrove species. Full article

CO₂ transport is a crucial part of CCUS. Nonetheless, due to the physical property differences between CO₂ and natural gas and oil, CO₂ pipeline transport is distinct from natural gas and oil transport. Gaseous CO₂ transportation has become the preferred scheme for transporting impurity-containing CO₂ tail gas in purification plants due to its advantages of simple technology, low cost, and high safety, which are well suited to the scenarios of low transportation volume and short distance in purification plants. The research on its physical property and state parameters is precisely aimed at optimizing the process design of gaseous transportation so as to further improve transportation efficiency and safety. Therefore, it has important engineering practical significance. Firstly, this paper collected and analyzed the research cases of CO₂ transport both domestically and internationally, revealing that phase state and physical property testing of CO₂ gas containing impurities is the basic condition for studying CO₂ transport. Subsequently, the exhaust gas captured by the purification plant was captured after hydrodesulfurization treatment, and the characteristics of the exhaust gas components were obtained by comparing before and after treatment. By preparing fluid samples with varied CO₂ content and conducting the flash evaporation test and PV relationship test, the compression factor and density of natural gas under different temperatures and pressures were obtained. It is concluded that under the same pressure in general, the higher the CO₂ content, the smaller the compression factor. Except for pure CO_2, the higher the CO₂ content, the higher the density under constant pressure, which is related to the content of C₂ and heavier hydrocarbon components. At the same temperature, the higher the CO₂ content, the higher the viscosity under the same pressure; the lower the pressure, the slower the viscosity growth slows down. The higher the CO₂ content at the same temperature, the higher the specific heat at constant pressure. With the decrease in temperature, the CO₂ content reaching the highest specific heat at the identical pressure gradually decreases. Finally, BWRS, PR, and SRK equations of state were utilized to calculate the compression factor and density of the gas mixture with a molar composition of 50% CO₂ and the gas mixture with a molar composition of 100% CO₂. Compared with the experimental results, the most suitable equation of state is selected as the PR equation, which refers to the parameter setting of critical nodes of CO₂ gas transport. Full article

As a high-market-value sector, the consumer electronics industry is particularly vulnerable to reputational damage from surface defects in shipped products. However, the high level of automation and the short product life cycles in this industry make defect sample collection both difficult and inefficient. This challenge has led to a severe shortage of publicly available, comprehensive datasets dedicated to surface defect detection, limiting the development of targeted methodologies in the academic community. Most existing datasets focus on general-purpose object categories, such as those in the COCO and PASCAL VOC datasets, or on industrial surfaces, such as those in the MvTec AD and ZJU-Leaper datasets. However, these datasets differ significantly in structure, defect types, and imaging conditions from those specific to consumer electronics. As a result, models trained on them often perform poorly when applied to surface defect detection tasks in this domain. To address this issue, the present study introduces a specialized optical sampling system with six distinct lighting configurations, each designed to highlight different surface defect types. These lighting conditions were calibrated by experienced optical engineers to maximize defect visibility and detectability. Using this system, 14,478 high-resolution defect images were collected from actual production environments. These images cover more than six defect types, such as scratches, plain particles, edge particles, dirt, collisions, and unknown defects. After data acquisition, senior quality control inspectors and manufacturing engineers established standardized annotation criteria based on real-world industrial acceptance standards. Annotations were then applied using bounding boxes for object detection and pixelwise masks for semantic segmentation. In addition to the dataset construction scheme, commonly used semantic segmentation methods were benchmarked using the provided mask annotations. The resulting dataset has been made publicly available to support the research community in developing, testing, and refining advanced surface defect detection algorithms under realistic conditions. To the best of our knowledge, this is the first comprehensive, multiclass, multi-defect dataset for surface defect detection in the consumer electronics domain that provides pixel-level ground-truth annotations and is explicitly designed for real-world applications. Full article

In this paper, a viscoelastic (VE) tuned inerter damper (TID) that replaces conventional stiffness and damping elements with a cost-effective VE element is proposed to achieve a target-based improvement of seismically protected buildings. The semi-analytical solution of the optimal tuning frequency ratio of the VE TID is presented based on a two-degree-of-freedom (2-DOF) system, accounting for inherent structural damping disturbances, and then is extended to a MDOF system via an effective mass ratio. The accuracy of the semi-analytical solution is validated by comparing the numerical solution. Finally, numerical analyses on a viscoelastically damped building and a base-isolated building with optimally designed VE TIDs under historical earthquakes are performed. The numerical results validate the target-based improvement capability of the VE TID with a modest mass ratio in avoiding large strokes or deformation of existing dampers and isolators, and further reducing the specific mode vibration. Full article

Grazing is a free-range farming model commonly practiced in low-external-input agricultural systems. The widespread use of veterinary antibiotics in livestock farming has led to significant environmental accumulation of antibiotic residues and antibiotic resistance genes (ARGs), posing global health risks. This study investigated the antibiotic residues, bacterial community, ARG profiles, and mobile genetic elements (MGEs) in cattle feces from three provinces in western China (Ningxia, Xinjiang, and Inner Mongolia) under grazing modes. The HPLC-MS detection showed that the concentration of tetracycline antibiotics was the highest in all three provinces. Correlation analysis revealed a significant negative correlation between antibiotic residues and the diversity and population abundance of intestinal microbiota. However, the abundance of ARGs was directly proportional to antibiotic residues. Then, the Sankey analysis revealed that the ARGs in the cattle fecal samples were concentrated in 15 human pathogenic bacteria (HPB) species, with 9 of these species harboring multiple drug resistance genes. Metagenomic sequencing revealed that carbapenemase-resistant genes (bla_KPC and bla_VIM) were also present in considerable abundance, accounting for about 10% of the total ARGs detected in three provinces. Notably, Klebsiella pneumoniae strains carrying bla_CTX-M-55 were detected, which had a possibility of IncFII plasmids harboring transposons and IS19, indicating the risk of horizontal transfer of ARGs. This study significantly advances the understanding of the impact of antibiotic residues on the fecal microbiota composition and ARG profiles in grazing cattle from northwestern China. Furthermore, it provides critical insights for the development of rational antibiotic usage strategies and comprehensive public health risk assessments. Full article

Objectives: Drug-drug cocrystals with improved properties can be used to facilitate the development of synergistic therapeutic combinations. The goal of the present study is to obtain novel drug-drug cocrystals involving two anti-glioma agents, temozolomide (TMZ) and myricetin (MYR). Methods: The novel TMZ-MYR cocrystal was prepared via slurry and solvent evaporation techniques and characterized by X-ray diffraction, thermal analysis, infrared spectroscopy, and dynamic vapor sorption measurements. The stability, compaction, and dissolution properties were also evaluated. Results: Crystal structure analysis revealed that the cocrystal lattice contains two TMZ molecules, one MYR molecule, and four water molecules, which are linked by hydrogen bonding interactions to produce a three-dimensional network. The cocrystal hydrate exhibited favorable stability and tabletability compared to pure TMZ. A dissolution study showed that the maximum solubility of MYR in the cocrystal (176.4 μg/mL) was approximately 6.6 times higher than that of pure MYR·H₂O (26.9 μg/mL), while the solubility of TMZ from the cocrystal (786.7 µg/mL) was remarkably lower than that of pure TMZ (7519.8 µg/mL). The solubility difference between MYR and TMZ was diminished from ~280-fold to ~4.5-fold. Conclusions: Overall, the TMZ-MYR cocrystal optimizes the stability and tabletability of TMZ and the dissolution behavior of both drugs, offering a promising approach for synergistic anti-glioma therapy with improved clinical potential. Full article

Pomegranate blackheart disease, as an internal disease affecting the global pomegranate industry, is difficult to identify externally and urgently requires non-destructive detection methods for rapid diagnosis. This study established discriminative models for blackheart disease severity in pomegranates by using near-infrared (NIR) spectroscopy and soft X-ray imaging techniques. The results showed that the optimal NIR-based discriminative model, constructed with a Random Forest (RF) algorithm based on spectra preprocessed by the second-derivative (D2) denoising and a Competitive Adaptive Reweighted Sampling (CARS) algorithm, achieved a prediction set accuracy of 86.00%; the optimal soft X-ray imaging-based discriminative model, built with an RF algorithm using textural features extracted from images preprocessed by median filtering and a Contrast-Limited Adaptive Histogram Equalization (CLAHE) algorithm combined with gray-level co-occurrence matrix (GLCM) and gray-gradient co-occurrence matrix (GGCM) algorithms, reached a prediction set accuracy of 93.10%. In terms of model performance, the model based on soft X-ray imaging exhibited superior performance. Both techniques possess distinct advantages and limitations yet enable non-destructive detection of pomegranate blackheart disease. Further technical optimizations in the future could provide enhanced support for the healthy development of the pomegranate industry. Full article

Among the scour protection measures for pile foundations, the use of solidified mud has demonstrated effective protection against scour. However, research on the mechanical integrity of this protective measure is relatively scarce. Therefore, a series of experiments were performed on cement-solidified soil and Enzyme-Induced Carbonate Precipitation (ECIP) solidified soil to analyze fluidity, disintegration, and unconfined compressive strength, along with an analysis of influencing parameters. Test results show the following: for cement-solidified soil, fluidity decreases with higher cement content, while its disintegration rate decreases with more cement and its unconfined compressive strength increases with a longer curing time and higher cement content. For ECIP-solidified soil, fluidity decreases with higher soy powder concentration but increases with higher binder solution concentration. ECIP’s initial disintegration rate increases with binder concentration, but after 7 days curing, its disintegration rate decreases with both higher binder concentration and higher soy powder concentration. ECIP’s strength increases with higher soy powder concentration. Crucially, both types of solidified soil exhibit decreased unconfined compressive strength with higher initial water content. The research results can provide a reference for the construction of solidified soil in the field of scour protection. Full article

High permeance combined with high salt/dye separation efficiency is a prerequisite for achieving zero-liquid-discharge treatment of saline textile wastewater by membrane technology. Thin-film nanocomposite (TFN) membranes incorporating porous nanoparticles offer a promising route to overcome the permeability–selectivity trade-off of conventional polymer membranes. In this study, a vacuum-assisted method was used to co-blend ZIF-67 and SiO₂ nanoparticles, while branched polyethyleneimine (PEI) served as a cross-linking bridge, resulting in a high-performance TFN membrane for salt/dye separation. Acting as a molecular connector, PEI coordinated with ZIF-67 through metal–amine complexation and simultaneously formed hydrogen bonds with surface hydroxyl groups on SiO₂, thereby linking ZIF-67 and SiO₂. The resulting membrane exhibited good hydrophilicity and excellent dye separation performance (water flux = 359.8 L m⁻² h⁻¹ bar⁻¹; Congo Red rejection = 99.2%) as well as outstanding selectivity in dye/salt mixtures (Congo Red/MgCl₂ selectivity of 1094). The optimal ZIF@SiO₂-PEI membrane maintained stable dye rejection over a wide range of trans-membrane pressures, initial concentrations, and pH values. These results reveal the huge potential of applying the ZIF@SiO₂-PEI TFN membranes for resource recovery in sustainable textile wastewater systems. Full article

This study presents a comprehensive multifractal characterization of full-scale pore structures in middle- to high-rank coal reservoirs from the Western Guizhou–Eastern Yunnan Basin and establishes a permeability prediction model integrating fractal heterogeneity and pore throat parameters. Eight coal samples were analyzed using mercury intrusion porosimetry (MIP), low-pressure gas adsorption (N₂/CO₂), and multifractal theory to quantify multiscale pore heterogeneity and its implications for fluid transport. Results reveal weak correlations (R² < 0.39) between conventional petrophysical parameters (ash yield, volatile matter, porosity) and permeability, underscoring the inadequacy of bulk properties in predicting flow behavior. Full-scale pore characterization identified distinct pore architecture regimes: Laochang block coals exhibit microporous dominance (0.45–0.55 nm) with CO₂ adsorption capacities 78% higher than Tucheng samples, while Tucheng coals display enhanced seepage pore development (100–5000 nm), yielding 2.5× greater stage pore volumes. Multifractal analysis demonstrated significant heterogeneity (Δα = 0.98–1.82), with Laochang samples showing superior pore uniformity (D₁ = 0.86 vs. 0.82) but inferior connectivity (D₂ = 0.69 vs. 0.71). A novel permeability model was developed through multivariate regression, integrating the heterogeneity index (Δα) and effective pore throat diameter (D₁₀), achieving exceptional predictive accuracy. The strong negative correlation between Δα and permeability (R = −0.93) highlights how pore complexity governs flow resistance, while D₁₀’s positive influence (R = 0.72) emphasizes throat size control on fluid migration. This work provides a paradigm shift in coal reservoir evaluation, demonstrating that multiscale fractal heterogeneity, rather than conventional bulk properties, dictates permeability in anisotropic coal systems. The model offers critical insights for optimizing hydraulic fracturing and enhanced coalbed methane recovery in structurally heterogeneous basins. Full article

Induction in pineapples requires the targeted delivery of specific chemical solutions into the plant’s central core to enable batch management, a task currently reliant on manual operation. This study addressed this challenge by analyzing the physical characteristics of pineapple plants and establishing a perception-based mathematical model for core position localization. An integrated hardware–software system was developed, complemented by a human–machine interface for real-time operational monitoring. Comprehensive experiments were conducted to evaluate the spraying accuracy, nozzle response time, and prototype performance. The results demonstrate that the actuation system—comprising solenoid valves, pumps, and flowmeters—achieved an average spraying error of 2.72%. The average nozzle opening/closing time was 0.111 s; with a standard operating speed of 0.5 m/s, a delay compensation distance of 55.5 mm was implemented. In human–machine comparative trials, the automated system outperformed manual spraying in both efficiency and stability, with average errors of 7.1% and 6.4%, respectively. The system reduced chemical usage by over 67,500 mL per hectare while maintaining a miss-spray rate of 5–6%. Both two-tailed tests revealed extremely significant differences (p < 0.001). These findings confirm that the developed solution meets the operational requirements for pineapple floral induction, offering significant improvements in precision and resource efficiency. Full article

The rapid advancement of unmanned aerial vehicle (UAV) technology has enabled the coordinated operation of multi-UAV systems, offering significant applications in agriculture, logistics, environmental monitoring, and disaster relief. In agriculture, UAVs are widely utilized for tasks such as ecological restoration, crop monitoring, and fertilization, providing efficient and cost-effective solutions for improved productivity and sustainability. This study addresses the collaborative task allocation problem for multi-UAV systems, using ecological grassland restoration as a case study. A multi-objective, multi-constraint collaborative task allocation problem (MOMCCTAP) model was developed, incorporating constraints such as UAV collaboration, task completion priorities, and maximum range restrictions. The optimization objectives include minimizing the maximum task completion time for any UAV and minimizing the total time for all UAVs. To solve this model, a deep reinforcement learning-based seagull optimization algorithm (DRL-SOA) is proposed, which integrates deep reinforcement learning with the seagull optimization algorithm (SOA) for adaptive optimization. The algorithm improves both global and local search capabilities by optimizing key phases of seagull migration, attack, and post-attack refinement. Evaluation against five advanced swarm intelligence algorithms demonstrates that the DRL-SOA outperforms the alternatives in convergence speed and solution diversity, validating its efficacy for solving the MOMCCTAP. Full article

Invertase (INV) irreversibly converts sucrose to glucose and fructose during processes such as differentiation and organ development in plants, especially during the development of trichomes. Systematic identification and analysis of INVs in Salicaceae remain limited. Here, INV genes in Populus deltoides and Salix suchowensis were investigated, and their chromosomal localization, collinearity, gene structures, cis-regulatory elements, and phylogenetic relationships were comprehensively analyzed. Twenty and seventeen INVs were found, respectively, in P. deltoides and S. suchowensis, most of which were derived from a common ancestor and exhibited similar chromosomal distribution and high collinearity. Orthologs between the two species showed conservation of gene structures and promoter regulatory elements. Multi-species phylogenetic analysis identified an evolutionary clade associated with seed fiber development in P. deltoides and S. suchowensis. Further evaluation of INV expression in female catkins at various stages of seed fiber formation verified the predominance of PdeVINV1, PdeVINV2, PdeVINV3, and PdeVINV4 in P. deltoides, as well as SsuVINV1 and SsuVINV2 in S. suchowensis, during critical phases of catkin fiber differentiation. These genes are likely to have significant regulatory roles in the initiation and development of catkin fiber cells. These findings provide a reference for future functional studies of INVs. Full article

The areca palm (Areca catechu L.), a medicinal tropical crop, hosts three novel viruses, areca palm necrotic ringspot virus (ANRSV), areca palm necrotic spindle-spot virus (ANSSV), and ANRSV2, which form a new genus Arepavirus in the family Potyviridae. Both viruses feature a unique tandem leader protease arrangement (HCPro1-HCPro2). To elucidate HCPro2’s role, this study identified its interaction partners in infected cells using affinity purification coupled with liquid chromatography-tandem mass spectrometry, a yeast two-hybrid system, and co-immunoprecipitation. Thirteen host proteins and five viral factors (HCPro1, 6K2, VPg, NIa-Pro, NIb) were validated as HCPro2 interactors. Among the host proteins interacting with HCPro2, the expression of five genes (NbeIF4A, NbSAMS1α, NbTEF1α, NbUEP1, and NbRan2) was upregulated under the condition of viral infection, while the expression of another five genes (NbpsbS1, NbPGK, NbchIP, NbClpC1A, and NbCysPrx) was downregulated. Functional assays showed that silencing NbeIF4A or NbPGK significantly reduced viral accumulation in Nicotiana benthamiana. These findings reveal HCPro2’s network of virus-host interaction, highlighting its critical role in viral pathogenesis. Further exploration of these interactions may clarify the evolutionary significance of tandem leader proteases and inform novel plant antiviral strategies. Full article

Plastic, a ubiquitous part of our daily lives, has become a global necessity, with annual production exceeding 300 million tons. However, the accumulation of synthetic polymers in our environment poses a pressing global challenge. To address this urgent issue, fungi have emerged as potential agents for plastic degradation. In our previous manuscript, ‘A Review of the Fungi That Degrade Plastic’, we explored the taxonomic placement of plastic-degrading fungi across three main phyla: Ascomycota, Basidiomycota, and Mucoromycota. In this review, we built upon that foundation and aimed to further explore the taxonomic relationships of these fungi in a comprehensive and detailed manner, leaving no stone unturned. Moreover, we linked metabolic activity and enzyme production of plastic-degrading fungi to their taxonomy and summarized a phylogenetic tree and a detailed table on enzyme production of plastic-degrading fungi presented here. Microbial enzymes are key players in polymer degradation, operating intra-cellularly and extra-cellularly. Fungi, one of the well-studied groups of microbes with respect to plastic degradation, are at the forefront of addressing the global issue of plastic accumulation. Their unique ability to hydrolyze synthetic plastic polymers and produce a wide range of specific enzymes is a testament to their potential. In this review, we gather and synthesize information concerning the metabolic pathways of fungi involved in the degradation of plastics. The manuscript explores the diverse range of specific enzymes that fungi can produce for plastic degradation and the major pathways of plastic metabolism. We provide a listing of 14 fungal enzymes (Esterase, Cutinase, Laccase, Peroxidases, Manganese peroxidase, Lignin peroxidase, Oxidoreductases, Urease, Protease, Lipase, Polyesterase, Dehydrogenase, Serine hydrolase, and PETase) involved in pathways for plastic degradation alongside the relevant fungi known to produce these enzymes. Furthermore, we integrate the fungi’s enzyme-producing capabilities with their taxonomy and phylogeny. Taxonomic and phylogenetic investigations have pinpointed three primary fungal classes (Eurotiomycetes, Sordariomycetes (Ascomycota), and Agaricomycetes (Basidiomycota)) as significant plastic degraders that produce the vital enzymes mentioned earlier. This paper provides a foundational resource for recognizing fungal involvement in the biodegradation of synthetic polymers. It will ultimately advance fungal biotechnology efforts to address the global issue of plastic accumulation in natural environments. Full article