Search Results (4,259)

 Porcine epidemic diarrhea virus (PEDV) continues to circulate globally, causing substantial economic losses to the swine industry. Historically, PEDV strains are classified into the classical G1, epidemic G2, and S-INDEL genotypes. Among these genotypes, the highly virulent and prevalent G2 genotype has been extensively studied. However, recent clinical outbreaks in China necessitate a reevaluation of the epidemiological and evolutionary dynamics of circulating strains. This study analyzed 37 newly sequenced S genes and public sequences to characterize the genetic variations of S-INDEL strains. Our analysis revealed that S-INDEL strains are endemic throughout China, with a phylogenetic analysis identifying two distinct clades: clade 1, comprising early endemic strains, and clade 2, representing a recently dominant, geographically restricted lineage in China. While inter-genotypic recombination has been documented, our findings also demonstrate that intra-genotypic and intra-clade recombination events contributed significantly to the emergence of clade 2, distinguishing its evolutionary pattern from clade 1. A comparative analysis identified 22 clade-specific amino acid changes, 11 of which occurred in the D0 domain. Notably, mutations at positively selected sites—113 and 114 within the D0 domain, a domain associated with pathogenicity—were specific to clade 2. A phylodynamic analysis indicated Germany as the epicenter of S-INDEL dispersal, with China acting as a sink population characterized by localized transmission networks and frequent recombination events. These results demonstrate that contemporary S-INDEL strains, specifically clade 2, exhibit unique recombination patterns and mutations potentially impacting virulence. Continuous surveillance is essential to assess the pathogenic potential of these evolving recombinant variants and the efficacy of vaccines against them.  Full article

Omnidirectional mobile robots have gained extensive application across diverse fields due to their exceptional maneuverability and adaptability in confined spaces. However, structural and systemic uncertainties significantly compromise motion accuracy. To enhance motion control precision, this paper proposes a sliding mode control (SMC) method integrated with a radial basis function (RBF) neural network. The approach aggregates model uncertainties, nonlinear dynamics, and unknown disturbances into a composite disturbance term. An RBF neural network is employed to approximate this disturbance, with compensation embedded within the SMC framework. An online adaptive law for neural network optimization is derived using the Lyapunov stability theorem, thereby improving the disturbance rejection capability. Comparative simulations and experiments validate the proposed method against modern control strategies. Results demonstrate superior tracking performance and robustness, significantly enhancing trajectory tracking accuracy for the MY3 wheeled omnidirectional mobile robot. Full article

In this paper, a novel online detection system is designed to enhance accuracy and operational efficiency in the outbound logistics of automotive components after production. The system consists of a scanning portal system and an improved YOLOv12-based detection algorithm which captures images of automotive parts passing through the scanning portal in real time. By integrating deep learning, the system enables real-time monitoring and identification, thereby preventing misdetections and missed detections of automotive parts, in this way promoting intelligent automotive part recognition and detection. Our system introduces the A2C2f-SA module, which achieves an efficient feature attention mechanism while maintaining a lightweight design. Additionally, Dynamic Space-to-Depth (Dynamic S2D) is employed to improve convolution and replace the stride convolution and pooling layers in the baseline network, helping to mitigate the loss of fine-grained information and enhancing the network’s feature extraction capability. To improve real-time performance, a GFL-MBConv lightweight detection head is proposed. Furthermore, adaptive frequency-aware feature fusion (Adpfreqfusion) is hybridized at the end of the neck network to effectively enhance high-frequency information lost during downsampling, thereby improving the model’s detection accuracy for target objects in complex backgrounds. On-site tests demonstrate that the system achieves a comprehensive accuracy of 97.3% and an average vehicle detection time of 7.59 s, exhibiting not only high precision but also high detection efficiency. These results can make the proposed system highly valuable for applications in the automotive industry. Full article

The rapid development of the modern power system puts forward high requirements for economic dispatch, and the defects of the traditional centralized economic dispatch method with low security and poor optimization effect have been difficult to adapt to the development of power system. Therefore, finding an economic dispatch method that reduces electricity generation costs and CO₂ emissions is important. This study establishes a multi-region distributed optimization model and combines the multi-region distributed optimization model with a multi-gradient optimization algorithm to propose a multi-region distributed multi-gradient whale optimization algorithm (MRDMGWOA). In this study, MRDMGWOA is simulated on the IEEE 39 system and 118 system, and its performance is compared with other heuristic algorithms. The results show that: (1) in the IEEE 39 system, MRDMGWOA reduces the power generation cost and CO₂ emission by 17% and 22%, respectively, and reduces the computation time by 16.14 s compared with the centralized optimization; (2) in the IEEE 118 system, the two metrics are further optimized, with a 20% and 17% reduction in the cost and emission, respectively, and an improvement in the computational efficiency by 45.46 s; (3) in the spacing, hypervolume, and Euclidian metrics evaluation, MRDMGWOA outperforms other algorithms; (4) compared with the existing DMOGWO and DMOMFO, the computation time of MRDMGWOA is reduced by 177.49 s and 124.15 s, respectively, and the scheduling scheme obtained by MRDMGWOA is more optimal than DMOGWO and DMOMFO. Full article

Pork is a major global source of animal protein, and improving both its production efficiency and meat quality is a central goal in modern animal agriculture and food systems. This study investigated post-inflection-point growth patterns in two genetically distinct pig breeds—the lean-type Yorkshire pig (YP) and the fatty-type Qingyu pig (QYP)—with the aim of elucidating breed-specific characteristics that influence pork quality and yield. Comprehensive evaluations of carcass traits, meat quality attributes, nutritional composition, and gene expression profiles were conducted. After the growth inflection point, carcass traits exhibited greater variability than meat quality traits in both breeds, though with distinct patterns. YPs displayed superior muscle development, with the longissimus muscle area (LMA) increasing rapidly before plateauing at ~130 kg, whereas QYPs maintained more gradual but sustained muscle growth. In contrast, intramuscular fat (IMF)—a key determinant of meat flavor and texture—accumulated faster in YPs post inflection but plateaued earlier in QYPs. Correlation and clustering analyses revealed more synchronized regulation of meat quality traits in QYPs, while YPs showed greater trait variability. Gene expression patterns aligned with these phenotypic trends, highlighting distinct regulatory mechanisms for muscle and fat development in each breed. In addition, based on the growth curves, we calculated the peak age at which the growth rate declined in lean-type and fat-type pigs, which was approximately 200 days for YPs and around 270 days for QYPs. This suggests that these ages may represent the optimal slaughter times for the respective breeds, balancing both economic efficiency and meat quality. These findings provide valuable insights for enhancing pork quality through precision management and offer theoretical guidance for developing breed-specific feeding strategies, slaughter timing, and value-added pork production tailored to consumer preferences in the modern food market. Full article

Manganese (Mn) excess and low pH often coexist in some citrus orchard soils. Little information is known about the underlying mechanism by which raising pH reduces Mn toxicity in citrus plants. ‘Sour pummelo’ (Citrus grandis (L.) Osbeck) seedlings were treated with 2 (Mn2) or 500 (Mn500) μM Mn at a pH of 3 (P3) or 5 (P5) for 25 weeks. Raising pH mitigated Mn500-induced increases in Mn, iron, copper, and zinc concentrations in roots, stems, and leaves, as well as nitrogen, phosphorus, potassium, calcium, magnesium, sulfur, copper, iron, and zinc distributions in roots, but it mitigated Mn500-induced decreases in nitrogen, phosphorus, potassium, calcium, magnesium, sulfur, and boron concentrations in roots, stems, and leaves, as well as nutrient imbalance. Raising pH mitigated Mn500-induced necrotic spots on old leaves, yellowing of young leaves, decreases in seedling growth, leaf chlorophyll concentration, and CO₂ assimilation (A_CO2), increase in root dry weight (DW)/shoot DW, and alterations of leaf chlorophyll a fluorescence (OJIP) transients and related indexes. Further analysis indicated that raising pH ameliorated Mn500-induced impairment of nutrient homeostasis, leaf thylakoid structure by iron deficiency and competition of Mn with magnesium, and photosynthetic electron transport chain (PETC), thereby reducing Mn500-induced declines in A_CO2 and subsequent seedling growth. These results validated the hypothesis that raising pH reduced Mn toxicity in ‘Sour pummelo’ seedlings by (a) reducing Mn uptake, (b) efficient maintenance of nutrient homeostasis under Mn stress, (c) reducing Mn excess-induced impairment of thylakoid structure and PEPC and inhibition of chlorophyll biosynthesis, and (d) increasing A_CO2 and subsequent seedling growth under Mn excess. Full article

Widespread species may exhibit considerable genetic variation among populations due to their extensive distribution ranges, and may even give rise to new species in remote areas. Integrative species delimitation via multiple types can provide a robust framework for accurate species identification and rapid discovery of cryptic diversity. The subgenus Tliponius (Hemiptera: Coreidae: Homoeocerus) has several species and three broadly distributed species across China. In this study, we selected as many geographical sample sites of widely distributed species as possible and conducted species identification based on integrated taxonomy of morphological, mitochondrial and SNP data for 28 individuals within Tliponius. Our results revealed a cryptic lineage previously subsumed under the polytypic H. unipunctatus in Yunnan Province and described as Homoeocerus (Tliponius) dianensis Liang, Li & Bu sp. nov. The presence of seven distinct species within Tliponius was supported by species delimitation and divided into two clades: (H. dilatatus + (H. marginellus + (H. unipunctatus + H. dianensis sp. nov.))) and (H. yunnanensis + (H. laevilineus + H. marginiventris). Based on our findings, extensive sampling of widespread species is highly important for the accuracy of species delimitation and the discovery of cryptic species. Full article

Lanthanide rare earth elements possess significant promise for material applications owing to their distinctive optical and magnetic characteristics, as well as their versatile coordination capabilities. This study introduced a lanthanide-functionalized magnetic nanocellulose composite (NNC@Fe₃O₄@La(OH)₃) for effective phosphorus/nitrogen (P/N) ligand separation. The hybrid material employs the adaptable coordination geometry and strong affinity for oxygen of La³⁺ ions to show enhanced DNA-binding capacity via multi-site coordination with phosphate backbones and bases. This study utilized cellulose as a carrier, which was modified through carboxylation and amination processes employing deep eutectic solvents (DES) and polyethyleneimine. Magnetic nanoparticles and La(OH)₃ were subsequently incorporated into the cellulose via in situ growth. NNC@Fe₃O₄@La(OH)₃ showed a specific surface area of 36.2 m²·g⁻¹ and a magnetic saturation intensity of 37 emu/g, facilitating the formation of ligands with accessible La³⁺ active sites, hence creating mesoporous interfaces that allow for fast separation. NNC@Fe₃O₄@La(OH)₃ showed a significant affinity for DNA, with adsorption capacities reaching 243 mg/g, mostly due to the multistage coordination binding of La³⁺ to the phosphate groups and bases of DNA. Simultaneously, kinetic experiments indicated that the binding process adhered to a pseudo-secondary kinetic model, predominantly dependent on chemisorption. This study developed a unique rare-earth coordination-driven functional hybrid material, which is highly significant for constructing selective separation platforms for P/N-containing ligands. Full article

This paper proposes a hierarchical cooperative tracking control method for multi-missile formations under dynamic event-triggered mechanisms, addressing parameter uncertainties and saturated overload constraints. The proposed hierarchical structure consists of a reference-trajectory generator and a trajectory-tracking controller. The reference-trajectory generator considers communication and collaboration among multiple interceptors, imposes saturation constraints on virtual control inputs, and generates reference trajectories for each receptor, effectively suppressing aggressive motions caused by overload saturation. On this basis, a radial basis function neural network (RBFNN) combined with a sliding-mode disturbance observer is adopted to estimate unknown external disturbances and unmodeled dynamics, and the finite-time convergence of the disturbance observer is proved. A tracking controller is then designed to ensure precise tracking of the reference trajectory by missile. This approach not only reduces communication and computational burdens but also effectively avoids Zeno behavior, enhancing the practical feasibility and robustness of the proposed method in engineering applications. The simulation results verify the effectiveness and superiority of the proposed method. Full article

Background/Objectives: Paeonia delavayi, a high-altitude-adapted medicinal and oil-producing plant, exhibits broad elevational distribution. Understanding how environmental factors regulate its growth across altitudes is critical for optimizing cultivation and exploiting its economic potential. Methods: In this study, we conducted a comprehensive Iso-Seq and RNA-seq analysis to elucidate the transcriptional profile across diverse altitudes and three seed developmental stages. Results: Using Pacbio full-length cDNA sequencing, we identified 39,267 full-length transcripts, with 80.03% (31,426) achieving successful annotation. RNA-seq analysis uncovered 11,423 and 9565 differentially expressed genes (DEGs) in response to different altitude and developmental stages, respectively. KEGG analysis indicated that pathways linked to fatty acid metabolism were notably enriched during developmental stages. In contrast, pathways associated with amino acid and protein metabolism were significantly enriched under different altitudes. Furthermore, we identified 34 DEGs related to fatty acid biosynthesis, including genes encoding pivotal enzymes like biotin carboxylase, carboxyl transferase subunit alpha, malonyl-CoA-acyl carrier protein transacylase, 3-oxoacyl-ACP reductase, 3-hydroxyacyl-ACP dehydratase, and stearoyl-ACP desaturase enoyl-ACP reductase. Additionally, ten DEGs were pinpointed as potentially involved in high-altitude stress response. Conclusions: These findings provide insights into the molecular mechanisms of fatty acid biosynthesis and adaptation to high-altitude stress in peony seeds, providing a theoretical foundation for future breeding programs aimed at enhancing seed quality. Full article

Reaction bonding (RB) using Al powder is an effective method for preparing porous ceramics with low shrinkage, high porosity, and high strength. However, it remains challenging to optimize mechanical strength and oil conductivity simultaneously for atomizer applications. Herein, aiming at addressing this issue, porous Al₂O₃ ceramics with ultra-high mechanical strength and oil conductivity were fabricated via the RB process using polymethyl methacrylate (PMMA) microspheres as the pore-forming agent. The pore structure was gradually optimized by regulating the additive amount, particle size, and particle gradation of PMMA microspheres. The bimodal pores, formed by Al oxidation-induced hollow structures (enhancing bonding force) and burnout of large-sized PMMA microspheres, significantly improved mechanical strength; meanwhile, three-dimensional interconnected pores derived from particle gradation increased the diversity and quantity of oil-conduction channels, boosting oil conductivity. Consequently, under an open porosity of 58.2 ± 0.1%, a high compressive strength of 7.9 ± 0.3 MPa (a 54.7% improvement) and an excellent oil conductivity of 2.1 ± 0.0 mg·s⁻¹ (a 46.5% improvement) were achieved. This superior performance combination, overcoming the trade-off between strength and oil conductivity, demonstrates substantial application potential in atomizers. Full article

Banana Fusarium wilt (BFW, also known as Panama disease) is a highly infectious and destructive disease that threatens global banana production, requiring early recognition for timely prevention and control. Current monitoring methods primarily rely on continuous variable features—such as band reflectances (BRs) and vegetation indices (VIs)—collectively referred to as basic features (BFs)—which are prone to noise during the early stages of infection and struggle to capture subtle spectral variations, thus limiting the recognition accuracy. To address this limitation, this study proposes a discretized enhanced feature (EF) construction method, the automated kernel density segmentation-based feature construction algorithm (AutoKDFC). By analyzing the differences in the kernel density distributions between healthy and diseased samples, the AutoKDFC automatically determines the optimal segmentation threshold, converting continuous BFs into binary features with higher discriminative power for early-stage recognition. Using UAV-based multi-spectral imagery, BFW recognition models are developed and tested with the random forest (RF), support vector machine (SVM), and Gaussian naïve Bayes (GNB) algorithms. The results show that EFs exhibit significantly stronger correlations with BFW’s presence than original BFs. Feature importance analysis via RF further confirms that EFs contribute more to the model performance, with VI-derived features outperforming BR-based ones. The integration of EFs results in average performance gains of 0.88%, 2.61%, and 3.07% for RF, SVM, and GNB, respectively, with SVM achieving the best performance, averaging over 90%. Additionally, the generated BFW distribution map closely aligns with ground observations and captures spectral changes linked to disease progression, validating the method’s practical utility. Overall, the proposed AutoKDFC method demonstrates high effectiveness and generalizability for BFW recognition. Its core concept of “automatic feature enhancement” has strong potential for broader applications in crop disease monitoring and supports the development of intelligent early warning systems in plant health management. Full article

Paper’s inherent hydrophilicity and porosity cause inadequate barrier properties, failing under high humidity/temperature. This study successfully developed a hydrophobic nanocoating agent (xLNPs-OTS) through silanization modification using D276 (lignin nanoparticles with a diameter of 276 nm) as the substrate and OTS (octadecyltrichlorosilane) as the functionalizing agent. By applying the coating to paper surfaces followed by a hot-pressing process, the paper achieved comprehensive performance enhancements, including superior water, oil, and vapor barrier properties, thermal stability, mechanical strength, frictional resistance, and self-cleaning capabilities. The Cobb 60 value of LOTSC3.5T120t30 (the coating made from the OTS silanized lignin with the coating amount of 3.5 g/m² and a hot-pressing at 120 °C for 30 min) coated paper is as low as 3.75 g/m², and can withstand hot water at 100 °C for 60 min. The Cobb 60 value of the LOTSC20T120t30 (the coating made from the OTS silanized lignin with the coating amount of 20 g/m² and a hot-pressing at 120 °C for 30 min) coated paper is reduced to 0.9 g/m², the Kit grade is 6, and all coated papers are endowed with self-cleaning features. This study advances lignin’s high-value utilization, driving sustainable packaging and supporting eco-friendly paper material development. Full article

The spatial heterodyne spectrometer is an interferometric spectrometer specifically designed for particular detection targets, capable of achieving ultra-high spectral resolution within a designated spectral range. As the demand for signal detection accuracy continues to increase, the extraction of accurate target spectra from spatial heterodyne interferograms has become increasingly important. This paper applies a deep neural network to the spectral reconstruction of specific spatial heterodyne interferograms. The spectral reconstruction model, SRDNN, was trained using CO₂ data simulated by the SCIATRAN radiative transfer model and the principles of spatial heterodyne spectroscopy. The results indicate that SRDNN has excellent CO₂ spectral reconstruction performance, with an evaluation index $R^2$ of 0.9943 and an $MSE$ of 0.00021. The average difference between the reconstructed spectra and the target spectra is only 0.371%. Furthermore, the method was further validated using experimental data obtained from a spatial heterodyne spectrometer. The remarkable spectral reconstruction results and excellent evaluation indicators once again demonstrated the universality and effectiveness of the method. Finally, the robustness of the method was studied using noisy experimental data. The results demonstrate that the method can accurately reconstruct spectra from interferograms with slight noise without requiring additional processing, simplifying the spectral reconstruction process. This work is expected to provide novel methods and effective solutions for the spectral reconstruction of specific targets detected by spatial heterodyne spectrometers. Full article

Multi-source fusion techniques have emerged as cutting-edge approaches for spatial precipitation estimation, yet they face persistent accuracy limitations, particularly under extreme conditions. Machine learning offers new opportunities to improve the precision of these estimates. To bridge this gap, we propose a hybrid artificial neural network–geographically weighted regression (ANN–GWR) model that synergizes event recognition and quantitative estimation. The ANN module dynamically identifies precipitation events through nonlinear pattern learning, while the GWR module captures location-specific relationships between multi-source data for calibrated rainfall quantification. Validated against 60-year historical data (1960–2020) from China’s Yongding River Basin, the model demonstrates superior performance through multi-criteria evaluation. Key results reveal the following: (1) the ANN-driven event detection achieves 10% higher accuracy than GWR, with a 15% enhancement for heavy precipitation events (>50 mm/day) during summer monsoons; (2) the integrated framework improves overall fusion accuracy by more than 10% compared to conventional GWR. This study advances precipitation estimation by introducing an artificial neural network into the event recognition period. Full article