Search Results (53)

In this paper, a series of electromagnetically-induced-transparent (EIT) spectra of different Rydberg states, controlled by microwaves, in rubidium (Rb) thermal vapor are presented. The novel evolution regularity for different Rydberg states can be found by experimentally detected transmitted EIT spectra, which can reveal the primary quantum number of different Rydberg states and how to influence microwave control spectroscopy evolution regularity, and which can pave the way in order to address the challenge of selecting Rydberg states for applications in Rydberg microwave field detection. This is helpful for the development of measuring standards of the microwave field in Rydberg states. Full article

Understanding the influence of environmental factors on cotton performance is crucial for enhancing yield and fiber quality in the context of climate change. This study investigates genotype-by-environment (G×E) interactions in cotton, using data from 250 recombinant inbred lines (CCRI70 RILs) cultivated across 14 diverse environments in China’s major cotton cultivation areas. Our findings reveal that environmental effects predominantly influenced yield-related traits (boll weight, lint percentage, and the seed index), contributing to 34.7% to 55.7% of their variance. In contrast fiber quality traits showed lower environmental sensitivity (12.3–27.0%), with notable phenotypic plasticity observed in the boll weight, lint percentage, and fiber micronaire. Employing six machine learning models, Random Forest demonstrated superior predictive ability (R² = 0.40–0.72; predictive Pearson correlation = 0.63–0.86). Through SHAP-based interpretation and sliding-window regression, we identified key environmental drivers primarily active during mid-to-late growth stages. This approach effectively reduced the number of influential input variables to just 0.1–2.4% of the original dataset, spanning 2–9 critical time windows per trait. Incorporating these identified drivers significantly improved cross-environment predictions, enhancing Random Forest accuracy by 0.02–0.15. These results underscore the strong potential of machine learning to uncover critical temporal environmental factors underlying G×E interactions and to substantially improve predictive modeling in cotton breeding programs, ultimately contributing to more resilient and productive cotton cultivation. Full article

The planting area of the maize–soybean strip intercropping system has been increasing annually in the Hexi Corridor oasis irrigation area of China. However, long-term irrational water resource utilization and the excessive mono-application of fertilizers have led to significantly low water and nitrogen use efficiency in this cropping system. To explore the sustainable production model of high yield and high water–nitrogen productivity in maize–soybean strip intercropping, we established three irrigation levels (low: 60%, medium: 80%, and sufficient: 100% of reference crop evapotranspiration) and three nitrogen application levels (low: maize 230 kg ha⁻¹, soybean 29 kg ha⁻¹; medium: maize 340 kg ha⁻¹, soybean 57 kg ha⁻¹; and high: maize 450 kg ha⁻¹, soybean 85 kg ha⁻¹) for maize and soybean, respectively. Three irrigation levels without nitrogen application served as controls. The effects of different water–nitrogen combinations on multiple indicators of the maize–soybean strip intercropping system, including yield, water–nitrogen productivity, and quality, were analyzed. The results showed that the irrigation amount and nitrogen application rate significantly affected the kernel quality of maize. Specifically, the medium nitrogen and sufficient water (N2W3) combination achieved optimal performance in crude fat, starch, and bulk density. However, excessive irrigation and nitrogen application led to a reduction in the content of lysine and crude protein in maize, as well as crude fat and crude starch in soybean. Appropriate irrigation and nitrogen application significantly increased the yield in the maize–soybean strip intercropping system, in which the N2W3 treatment had the highest yield, with maize and soybean yields reaching 14007.02 and 2025.39 kg ha⁻¹, respectively, which increased by 2.52% to 138.85% and 5.37% to 191.44% compared with the other treatments. Taking into account the growing environment of the oasis agricultural area in the Hexi Corridor and the effects of different water and nitrogen supplies on the yield, water–nitrogen productivity, and kernel quality of maize and soybeans in the strip intercropping system, the highest target yield can be achieved when the irrigation quotas for maize and soybeans are set at 100% ET0 (reference crop evapotranspiration), with nitrogen application rates of 354.78~422.51 kg ha⁻¹ and 60.27~71.81 kg ha⁻¹, respectively. This provides guidance for enhancing yield and quality in maize–soybean strip intercropping in the oasis agricultural area of the Hexi Corridor, achieving the dual objectives of high yield and superior quality. Full article

The spray-applied pipe lining (SAPL) method, extensively employed in the trenchless rehabilitation of reinforced concrete pipes (RCPs) due to its operational versatility, remains constrained by an incomplete understanding of the failure behavior of rehabilitated pipelines, thereby impeding optimal design strategies. This study proposes an analytical approach to evaluate the structural performance of pipes with fiber-reinforced mortar lining, with a particular focus on interface failure and its consequences. Two RCPs with an inner diameter of 1000 mm, repaired with 34 mm and 45 mm centrifugally sprayed fiber-reinforced mortar liners, were subjected to three-edge-bearing (TEB) tests. The elastic limit loads of the two pipes were 57% and 39% of their pre-rehabilitation conditions, while the ultimate loads were 45% and 69%. A thicker liner exhibits a greater susceptibility to interface failure, leading to wider cracks around the elastic stage during loading. Once the interface failure occurs, load redistribution allows the liner to resist further cracking and sustain higher capacity, demonstrating enhanced bearing performance. Critical factors influencing the failure process were analyzed to inform design optimization, revealing that improving the interface takes precedence, followed by thickness design. Full article

The objective of this study was to evaluate the effects of dietary supplementation with different levels of Rhodotorula mucilaginosa (0.0 g/kg, 0.1 g/kg, 1.0 g/kg, and 10.0 g/kg) on resistance to Aeromonas veronii infection in red claw crayfish (Cherax quadricarinatus) (initial body weight of 0.13 ± 0.06 g). The investigation combined a 56-day feeding trial with a subsequent 7-day infection challenge to assess cumulative mortality, immune and antioxidant enzyme activities, and the relative expression of immune-related genes. During the A. veronii infection test, the cumulative mortalities for the 0.1 g/kg, 1.0 g/kg, and 10.0 g/kg groups were 44.44%, 38.89%, and 38.89%, respectively, all significantly lower (p < 0.05) than that of the control group (58.33%). Compared with the control group, after infection with A. veronii, the activities of acid phosphatase, alkaline phosphatase, catalase, and superoxide dismutase in the hepatopancreas and alkaline phosphatase, lysozyme in the hemolymph of red claw crayfish in the 1.0 g/kg group significantly increased (p < 0.05). The activities of aspartate aminotransferase and alanine aminotransferase in the hemolymph of red claw crayfish in the 1.0 g/kg group significantly decreased (p < 0.05). The relative expression levels of serine protease inhibitor, crustacean hyperglycemic hormone, anti-lipopolysaccharide factor, and superoxide dismutase genes in the hepatopancreas of red claw crayfish in the 1.0 g/kg group were significantly upregulated (p < 0.05). In conclusion, R. mucilaginosa could significantly improve the antibacterial ability of red claw crayfish against A. veronii. In this experimental context, the ideal addition level of R. mucilaginosa is determined to be 1.0 g/kg. Full article

Background/Objectives: The accurate and efficient segmentation of brain tumors from 3D MRI data remains a significant challenge in medical imaging. Conventional deep learning methods, such as convolutional neural networks and transformer-based models, frequently introduce significant computational overhead or fail to effectively represent multi-scale features. Methods: This paper presents a lightweight deep learning framework that uses adaptive discrete wavelet decomposition and iterative axial attention to improve 3D brain tumor segmentation. The wavelet decomposition module effectively captures multi-scale information by breaking it down into frequency sub-bands, thereby the mitigating detail loss often associated with standard downsampling methods. Ablation studies confirm that this module enhances segmentation accuracy, particularly in preserving the finer structural details of tumor components. Simultaneously, the iterative axial factorization attention reduces the computational burden of 3D spatial modeling by processing attention sequentially along individual axes, preserving long-range interdependence while consuming minimal resources. Results: Our model performs well on the BraTS2020 and FeTS2022 datasets with average Dice scores of 85.0% and 88.1%, with our competitive results using only 5.23 million parameters and 9.75 GFLOPs. In comparison to state-of-the-art methods, it effectively balances accuracy and efficiency, making it suitable for resource-constrained clinical applications. Conclusions: This study underscores the advantages of integrating frequency-domain analysis with optimized attention mechanisms, paving the way for scalable, high-performance medical image segmentation algorithms with broader clinical diagnostic applications. Full article

In order to ensure the safety of methane/hydrogen, regular SiO₂ powder was modified. Fe(C₅H₅)₂/SiO₂ composite dry powder (CDP) was selected as the explosion-suppression material. Explosion-suppression experiments and numerical simulations were adopted to investigate the inhibition effect of 0% (${\mathit{X}}_{{\mathrm{H}}_2}$ = 0%) and 20% (${\mathit{X}}_{{\mathrm{H}}_2}$ = 20%) hydrogen doping ratios. The flame structure, flame propagation speed, and maximum explosion pressure are depicted to compare the inhibition effect of different mass fractions (X_Fe(C5H5)2 = 0–6%). The results showed that CDP significantly reduced the flame propagation velocity and maximum explosion pressure of ${\mathit{X}}_{{\mathrm{H}}_2}$ = 0%. The best effect was observed when 6% Fe(C₅H₅)₂ was added, with the velocity reduced to 9.241 m/s. The maximum explosion pressure was reduced to 0.518 MPa, and the effect was relatively weak for ${\mathit{X}}_{{\mathrm{H}}_2}$ = 20%, with the maximum pressure reduced to 0.525 MPa. In addition, the key radical production and temperature sensitivity showed that Fe(C₅H₅)₂ altered the molar fractions of the major species and increased the consumption of •H, •O, and •OH. As the mass fraction of Fe(C₅H₅)₂ increased, the steady-state concentrations of •H, •O, and •OH in the system showed a significant decreasing trend. This phenomenon originated from the two-step synergistic mechanism of Fe(C₅H₅)₂ inhibiting radical generation and accelerating radical consumption. This study provides insight into the process of Fe(C₅H₅)₂/SiO₂ composite dry powder inhibition and renders theoretical guidance for the explosion protection of methane/hydrogen. Full article

Arabinogalactan proteins (AGPs) constitute a diverse class of hydroxyproline-rich glycoproteins implicated in various aspects of plant growth and development. However, their functional characterization in cotton (Gossypium spp.) remains limited. As a globally significant economic crop, cotton serves as the primary source of natural fiber, making it essential to understand the genetic mechanisms underlying its growth and development. This study aims to perform a comprehensive genome-wide identification and characterization of the AGP gene family in Gossypium spp., with a particular focus on elucidating their structural features, evolutionary relationships, and functional roles. A genome-wide analysis was conducted to identify AGP genes in Gossypium spp., followed by classification into distinct subfamilies based on sequence characteristics. Protein motif composition, gene structure, and phylogenetic relationships were examined to infer potential functional diversification. Subcellular localization of a key candidate gene, GhAGP50, was determined using fluorescent protein tagging, while gene expression patterns were assessed through β-glucuronidase (GUS) reporter assays. Additionally, hormonal regulation of GhAGP50 was investigated via treatments with methyl jasmonate (MeJA), abscisic acid (ABA), indole-3-acetic acid (IAA), and gibberellin (GA). A total of 220 AGP genes were identified in Gossypium spp., comprising 19 classical AGPs, 28 lysine-rich AGPs, 55 AG peptides, and 118 fasciclin-like AGPs (FLAs). Structural and functional analyses revealed significant variation in gene organization and conserved motifs across subfamilies. Functional characterization of GhAGP50, an ortholog of AGP18 in Arabidopsis thaliana, demonstrated its role in promoting epidermal hair formation in leaves and stalks. Subcellular localization studies indicated that GhAGP50 is targeted to the nucleus and plasma membrane. GUS staining assays revealed broad expression across multiple tissues, including leaves, inflorescences, roots, and stems. Furthermore, hormonal treatment experiments showed that GhAGP50 expression is modulated by MeJA, ABA, IAA, and GA, suggesting its involvement in hormone-mediated developmental processes. This study presents a comprehensive genome-wide analysis of the AGP gene family in cotton, providing new insights into their structural diversity and functional significance. The identification and characterization of GhAGP50 highlight its potential role in epidermal hair formation and hormonal regulation, contributing to a deeper understanding of AGP functions in cotton development. These findings offer a valuable genetic resource for future research aimed at improving cotton growth and fiber quality through targeted genetic manipulation. Full article

Cotton is a crucial cash crop widely valued for its fiber. It is an important source of natural fiber and has diverse applications. Improving fiber quality is of significant economic and agricultural importance. Purple acid phosphatases (PAPs) are multifunctional enzymes critical for plant cell wall biosynthesis, root architecture modulation, low-phosphorus stress adaptation, and salt/ROS stress tolerance. In this study, a comprehensive genome-wide analysis of the PAP gene family was performed for four cotton species (G. hirsutum, G. barbadense, G. raimondii, and G. arboreum) to explore its potential role in improving fiber quality. A total of 193 PAP genes were identified in these species, revealing several conserved domains that contribute to their functional diversity. Phylogenetic analysis showed that the cotton PAP2 genes exhibited high homology with NtPAP12, a cell wall synthesis-related gene. Using cotton varieties with contrasting fiber thickness (EZ60, micronaire 4.5 vs. CCRI127, micronaire 3.5), qRT-PCR analysis demonstrated significantly higher expression levels of GhPAP2.2, GhPAP2.6, GhPAP2.8, and GhPAP2.9 in EZ60 fibers during 20–25 DPA compared to CCRI127. These results highlight the potential influence of PAP genes on cotton fiber development and provide valuable insights for improving fiber quality in cotton breeding. Full article

Geological hazards in densely vegetated mountainous regions are challenging to detect due to terrain concealment and the limitations of traditional visualization methods. This study introduces the LiDAR image highlighting algorithm (LIHA), a novel approach for enhancing micro-topographical features in digital elevation models (DEMs) derived from airborne LiDAR data. By analogizing terrain profiles to non-stationary spectral signals, LIHA applies locally estimated scatterplot smoothing (Loess smoothing), wavelet decomposition, and high-frequency component amplification to emphasize subtle features such as landslide boundaries, cracks, and gullies. The algorithm was validated using the Mengu landslide case study, where edge detection analysis revealed a 20-fold increase in identified micro-topographical features (from 1907 to 37,452) after enhancement. Quantitative evaluation demonstrated LIHA’s effectiveness in improving both human interpretation and automated detection accuracy. The results highlight LIHA’s potential to advance early geological hazard identification and mitigation, particularly when integrated with machine learning for future applications. This work bridges signal processing and geospatial analysis, offering a reproducible framework for high-precision terrain feature extraction in complex environments. Full article

Neonatal calves’ diarrhea, which can be severe enough to cause death, has a significant impact on the global cattle industry. In this study, alfalfa polysaccharides and seaweed polysaccharides were found to significantly improve the diarrhea condition in neonatal calves. To explore the underlying mechanisms, further microbiomic and metabolomic analyses were conducted. This study investigated the impact of alfalfa polysaccharides and seaweed polysaccharides on growth performance, serum metabolites, gut microbiota, and metabolomics in neonatal Holstein calves. A total of 24 newborn calves were randomly assigned to three groups, with 8 calves per treatment group. The control (CON) group was fed a basal diet, the alfalfa polysaccharide (AP) group received a basal diet supplemented with alfalfa polysaccharides (4 g/calf/day), and the seaweed polysaccharide group (SP) received a basal diet supplemented with seaweed polysaccharides (4 g/calf/day). These polysaccharides were plant extracts. Compared to the CON group, the results indicated that SP significantly enhanced the body weight, height, chest circumference, and average daily gain of Holstein calves (p < 0.05), while also reducing the diarrhea rate and improving manure scoring (p < 0.05). Compared to the CON, AP also reduced the diarrhea rate (p < 0.05). In terms of serum biochemistry, supplementation with AP and SP increased serum alkaline phosphatase (ALP) and insulin-like growth factor 1 (IGF-1) levels compared to the CON group (p < 0.05). Both AP and SP elevated serum catalase (CAT) and Total Antioxidant Capacity (T-AOC) levels, indicating enhanced antioxidant status (p < 0.05). Regarding immune responses, supplementation with AP and SP significantly increased serum complement component 3 (C3) and immunoglobulin M (IgM) levels, while significantly reducing pro-inflammatory cytokines interleukin-18 (IL-18), tumor necrosis factor alpha (TNF-α), and interferon-gamma (IFN-γ) compared to the CON group (p < 0.05). Microbiota analysis revealed that AP modulated the abundance of Firmicutes, while SP influenced the abundance of Prevotella and Succiniclasticum. AP and SP differentially influenced intestinal metabolites compared to the CON group, leading to enrichment in pathways related to immunity, antibacterial, and anti-inflammatory functions. These pathways included the biosynthesis of alkaloids from ornithine, lysine, and nicotinic acid, glucocorticoid and mineralocorticoid receptor canothersis/antagonists, secondary metabolite biosynthesis, and alkaloid biosynthesis from histidine and purine, thus alleviating intestinal inflammation. Therefore, by supplementing with AP and SP, the diarrhea rate in calves was reduced, and the immune function of Holstein calves was enhanced, while simultaneously promoting a higher relative abundance of beneficial gut bacteria and suppressing the relative abundance of pathogenic bacteria. Additionally, gut pathways associated with immune response and inflammation were modulated by AP and SP. This study provided valuable insights and theoretical underpinnings for the use of AP and SP in preventing diarrhea in neonatal calves. Full article

The Sedongpu Basin is characterized by frequent glacial debris movements and glacial hazards. To accurately monitor and research these glacier hazards, Sentinel-1 Synthetic Aperture Radar images observed between 2014 and 2022 were collected to extract surface motion using SBAS-POT technology. The acquired temporal surface deformation and multiple optical remote sensing images were then jointly used to analyze the characteristics of the long-term glacier movement in the Sedongpu Basin. Furthermore, historical meteorological and seismic data were collected to analyze the mechanisms of multiple ice avalanche chain hazards. It was found that abnormal deformation signals of glaciers SDP1 and SDP2 could be linked to the historical ice avalanche disaster that occurred around the Sedongpu Basin. The maximum deformation rate of SDP1 was 74 m/a and the slope cumulative deformation exceeded 500 m during the monitoring period from 2014 to 2022, which is still in active motion at present; for SDP2, a cumulative deformation of more than 300 m was also detected over the monitoring period. Glaciers SDP3, SDP4, and SDP5 have been relatively stable until now; however, ice cracks are well developed in SDP4 and SDP5, and ice avalanche events may occur if these ice cracks continue to expand under extreme natural conditions in the future. Therefore, this paper emphasizes the seriousness of the ice avalanche event in Sedongpu Basin and provides data support for local disaster management and disaster prevention and reduction. Full article

The development of high energy and power density sodium-ion batteries (SIBs) has attracted increasing interest in the last two decades due to the abundance and cost-effectiveness of sodium resources. Herein, this study developed a self-templating synthetic method to construct MoSe₂ nanosheets which were intercalated by ZnSe nanoparticles and were anchored on the in situ reduced graphene oxide layers. The thus-fabricated composites exhibited excellent Coulombic efficiency, a remarkable rate capability and an exceptionally long cycle life when being utilized as the anode in SIBs. Specifically, a reversible capacity of 265 mAh g⁻¹ was achieved at 20 A g⁻¹, which could be maintained for 6400 cycles. At an ultra-high rate of 30.0 A g⁻¹, the anode retained a capacity of 235 mAh g⁻¹ after 9500 cycles. Such a strong performance was attributed to its unique porous structure and synergistic interactions of multi-components. The underlying sodium storage mechanism was further investigated through various techniques such as in situ X-ray diffraction spectroscopy, the galvanostatic intermittent titration method, etc. Overall, this study illustrates the great potential of clad-structured multicomponent hybrids in developing high-performance SIBs. Full article

Despite the remarkable progress of large language models (LLMs) in understanding and generating unstructured text, their application in structured data domains and their multi-role capabilities remain underexplored. In particular, utilizing LLMs to perform complex reasoning tasks on knowledge graphs (KGs) is still an emerging area with limited research. To address this gap, we propose KG-EGV, a versatile framework leveraging LLMs to perform KG-based tasks. KG-EGV consists of four core steps: sentence segmentation, graph retrieval, EGV, and backward updating, each designed to segment sentences, retrieve relevant KG components, and derive logical conclusions. EGV, a novel integrated framework for LLM inference, enables comprehensive reasoning beyond retrieval by synthesizing diverse evidence, which is often unattainable via retrieval alone due to noise or hallucinations. The framework incorporates six key stages: generation expansion, expansion evaluation, document re-ranking, re-ranking evaluation, answer generation, and answer verification. Within this framework, LLMs take on various roles, such as generator, re-ranker, evaluator, and verifier, collaboratively enhancing answer precision and logical coherence. By combining the strengths of retrieval-based and generation-based evidence, KG-EGV achieves greater flexibility and accuracy in evidence gathering and answer formulation. Extensive experiments on widely used benchmarks, including FactKG, MetaQA, NQ, WebQ, and TriviaQA, demonstrate that KG-EGV achieves state-of-the-art performance in answer accuracy and evidence quality, showcasing its potential to advance QA research and applications. Full article

Ammonia is known as an alternative hydrogen supplier because of its high hydrogen content and convenient storage and transport. Hydrogen production from ammonia decomposition also provides a source of hydrogen for fuel cells. While catalysts composed of ruthenium metal atop various support materials have proven to be effective for ammonia decomposition, non-precious-metal-based catalysts are attracting more attention due to desires to reduce costs. We prepared a series of Fe, Co, Ni, Mn, and Cu monometallic catalysts and their alloys as catalysts over proton-conducting ceramics via the impregnation method as precious-metal-free ammonia decomposition catalysts. While Co and Ni showed superior performance compared to Fe, Mn, and Cu on a BaZr_0.1Ce_0.7Y_0.1Yb_0.1O_3−б (BZCYYb) support as an ammonia decomposition catalyst, the cost of Fe is much lower than that of other metals. Alloying Fe with Co can significantly increase the conversion and stability and lower the overall cost of materials. The measured ammonia decomposition rate of FeCo/BZCYYb reached 100% at 600 °C, and the ammonia decomposition rate was almost unchanged during the long-term test of 200 h, which reveals its good catalytic activity for ammonia decomposition and thermal stability. When the metallic catalyst remained unchanged, BZCYYb also exhibited better performance compared to other commonly used oxide supports. Finally, when ammonia cracked using our alloy catalyst was fed to solid oxide fuel cells (SOFCs), the peak power densities were very close to that achieved with a simulated fully cracked gas stream, i.e., 75% H₂ + 25% N₂, thus proving the effectiveness of this new type of ammonia decomposition catalyst. Full article