Search Results (1,331)

In the context of the global response to climate change and the active promotion of energy transformation, a number of low-carbon policies coupled with the development of synergies to help power system transformation is an important initiative. However, the insufficient articulation of the green power market, tradable green certificate (TGC) market, and carbon emission trading (CET) mechanism, and the ambiguous policy boundaries affect the trading decisions made by its market participants. Therefore, this paper systematically analyses the composition of the main players in the electricity-CET-TGC markets and their relationship with each other, and designs the synergistic mechanism of the electricity-CET-TGC markets, based on which, it constructs the optimal profit model of the thermal power plant operators, renewable energy manufacturers, power grid enterprises, power users and load aggregators under the electricity-CET-TGC markets synergy, and analyses the behavioural decision-making of the main players in the electricity-CET-TGC markets as well as the electric power system to optimise the trading strategy of each player. The results of the study show that: (1) The synergistic mechanism of electricity-CET-TGC markets can increase the proportion of green power grid-connected in the new type of power system. (2) In the selection of different environmental rights and benefits products, the direct participation of green power in the market-oriented trading is the main way, followed by applying for conversion of green power into China certified emission reduction (CCER). (3) The development of independent energy storage technology can produce greater economic and environmental benefits. This study provides policy support to promote the synergistic development of the electricity-CET-TGC markets and assist the low-carbon transformation of the power industry. Full article

Cyborg insects offer a biologically powered solution for locomotion control, but conventional methods typically rely on invasive electrical stimulation. Here, we introduce a noninvasive, phototaxis-based strategy to steer walking Endebius florensis beetles using light-emitting diode (LED) stimuli. Electroretinogram recordings revealed spectral sensitivity to blue, green, and yellow light, with reduced response to red. Behavioral assays demonstrated robust positive phototaxis to blue light and negative phototaxis to yellow. Using these findings, we built a wireless microcontroller-based backpack emitting directional blue light to induce steering. The beetles reliably turned toward the activated light, achieving angular deflections over 60° within seconds. This approach enables repeatable, trauma-free insect control and establishes a new paradigm for biohybrid locomotion systems. Full article

Hydrogen energy holds immense potential to address the global energy crisis and environmental challenges. However, its large-scale application is severely hindered by the lack of efficient hydrogen storage materials. This study systematically investigates the H₂ adsorption properties of intrinsic C₂₆ fullerene and Li-decorated C₂₆ fullerene using density functional theory (DFT) calculations. The results reveal that Li atoms preferentially bind to the H_5-5 site of C₂₆, driven by significant electron transfer (0.90 |e|) from Li to C₂₆. This electron redistribution modulates the electronic structure of C₂₆, as evidenced by projected density of states (PDOS) analysis, where the p orbitals of C atoms near the Fermi level undergo hybridization with Li orbitals, enhancing the electrostatic environment for H₂ adsorption. For Li-decorated C₂₆, the average adsorption energy and consecutive adsorption energy decrease as more H₂ molecules are adsorbed, indicating a gradual weakening of adsorption strength and signifying a saturation limit of three H₂ molecules. Charge density difference and PDOS analyses further demonstrate that H₂ adsorption induces synergistic electron transfer from both Li (0.89 |e| loss) and H₂ (0.01 |e| loss) to C₂₆ (0.90 |e| gain), with orbital hybridization between H s orbitals, C p orbitals, and Li orbitals stabilizing the adsorbed system. This study aimed to provide a comprehensive understanding of the microscopic mechanism underlying Li-enhanced H₂ adsorption on C₂₆ fullerene and offer insights into the rational design of metal-decorated fullerene-based systems for efficient hydrogen storage. Full article

Amid ongoing societal development, tourists’ travel behavior patterns have been undergoing substantial transformations, and understanding their evolution has emerged as a key area of scholarly interest. Taking Beijing as a case study, this research aims to uncover the spatiotemporal evolution patterns of tourist flows and their underlying driving mechanisms. Based on digital footprint relational data, a dual-perspective analytical framework—“tourist perception–tourist flow network”—is constructed. By integrating the center-of-gravity model, social network analysis, and regression models, the study systematically examines the dynamic spatial structure of tourist flows in Beijing from 2012 to 2024. The findings reveal that in the post-pandemic period, Beijing tourists place greater emphasis on the cultural connotation and experiential aspects of destinations. The gravitational center of tourist flows remains relatively stable, with core historical and cultural blocks retaining strong appeal, though a slight shift has occurred due to policy influences and emerging attractions. The evolution of the spatial network structure reveals that tourism flows have become more dispersed, while the influence of core scenic spots continues to intensify. Government policy orientation, tourism information retrieval, and the agglomeration of tourism resources significantly promote the structure of tourist flows, whereas the general level of tourism resources exerts no notable influence. These findings offer theoretical insights and practical guidance for the sustainable development and regional coordination of tourism in Beijing, and provide a valuable reference for the spatial restructuring of urban tourism in the post-COVID-19 era. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder primarily characterized by memory loss, cognitive decline, and structural brain atrophy. Substantial sex differences have been observed in its incidence, clinical trajectory, and response to treatment. Women are disproportionately affected, exhibiting faster progression and more severe cognitive impairment. Exercise has emerged as a promising non-pharmacological intervention to mitigate AD-related decline, yet growing evidence reveals that its benefits vary by sex. This review synthesizes current findings from human and animal studies, focusing on how exercise impacts AD differently in males and females. In women, exercise is more strongly associated with improvements in cognitive function, neurotrophic support, and emotional regulation. In men, benefits tend to involve structural preservation and oxidative adaptations. Underlying mechanisms include differential hormonal profiles, inflammatory responses, and neuroplastic signaling pathways. These findings underscore the need to consider sex as a biological variable in AD research. Developing sex-specific exercise strategies may enhance therapeutic outcomes and support more individualized approaches in AD prevention and care. Full article

Avian influenza viruses (AIVs) pose significant risks to occupational populations engaged in poultry farming, livestock handling, and live poultry market operations due to frequent exposure to infected animals and contaminated environments. This review synthesizes evidence on AIV exposure patterns and risk factors through a comprehensive analysis of viral characteristics, host dynamics, environmental influences, and human behaviors. The main routes of transmission include direct animal contact, respiratory contact during slaughter/milking, and environmental contamination (aerosols, raw milk, shared equipment). Risks increase as the virus adapts between species, survives longer in cold/wet conditions, and spreads through wild bird migration (long-distance transmission) and live bird trade (local transmission). Recommended control measures include integrated animal–human–environment surveillance, stringent biosecurity measures, vaccination, and education. These findings underscore the urgent need for global ‘One Health’ collaboration to assess risk and implement preventive measures against potentially pandemic strains of influenza A viruses, especially in light of undetected mild/asymptomatic cases and incomplete knowledge of viral evolution. Full article

Real-time quality monitoring using molten pool images is a critical focus in researching high-quality, intelligent automated welding. To address interference problems in molten pool images under complex welding scenarios (e.g., reflected laser spots from spatter misclassified as porosity defects) and the limited interpretability of deep learning models, this paper proposes a multi-granularity spatiotemporal representation learning algorithm based on the hybrid enhancement of handcrafted and deep learning features. A MobileNetV2 backbone network integrated with a Temporal Shift Module (TSM) is designed to progressively capture the short-term dynamic features of the molten pool and integrate temporal information across both low-level and high-level features. A multi-granularity attention-based feature aggregation module is developed to select key interference-free frames using cross-frame attention, generate multi-granularity features via grouped pooling, and apply the Convolutional Block Attention Module (CBAM) at each granularity level. Finally, these multi-granularity spatiotemporal features are adaptively fused. Meanwhile, an independent branch utilizes the Histogram of Oriented Gradient (HOG) and Scale-Invariant Feature Transform (SIFT) features to extract long-term spatial structural information from historical edge images, enhancing the model’s interpretability. The proposed method achieves an accuracy of 99.187% on a self-constructed dataset. Additionally, it attains a real-time inference speed of 20.983 ms per sample on a hardware platform equipped with an Intel i9-12900H CPU and an RTX 3060 GPU, thus effectively balancing accuracy, speed, and interpretability. Full article

Peanut (Arachis hypogaea L.) is a globally important oilseed cash crop, yet its limited genetic diversity and unique reproductive biology present persistent challenges for conventional crossbreeding. Traditional breeding approaches are often time-consuming and inadequate, mitigating the pace of cultivar development. Essential for double fertilization and programmed cell death (PCD), DUF679 membrane proteins (DMPs) represent a membrane protein family unique to plants. In the present study, a comprehensive analysis of the DMP gene family in peanuts was conducted, which included the identification of 21 family members. Based on phylogenetic analysis, these genes were segregated into five distinct clades (I–V), with AhDMP8A, AhDMP8B, AhDMP9A, and AhDMP9B in clade IV exhibiting high homology with known haploid induction genes. These four candidates also displayed significantly elevated expression in floral tissues compared to other organs, supporting their candidacy for haploid induction in peanuts. Subcellular localization prediction, confirmed through co-localization assays, demonstrated that AhDMPs primarily localize to the plasma membrane, consistent with their proposed roles in the reproductive signaling process. Furthermore, chromosomal mapping and synteny analyses revealed that the expansion of the AhDMP gene family is largely driven by whole-genome duplication (WGD) and segmental duplication events, reflecting the evolutionary dynamics of the tetraploid peanut genome. Collectively, these findings establish a foundational understanding of the AhDMP gene family and highlight promising targets for future applications in haploid induction-based breeding strategies in peanuts. Full article

The modern Chinese architectural heritage combines sturdy Western materials with delicate Chinese styling, mainly adopting brick-timber structural systems that are highly vulnerable to fire damage. The study assesses the fire spread characteristics of the First Faculty House, a 20th-century architectural heritage located at a university in China. The assessment is carried out by analyzing building materials, structural configuration, and fire load. By using FDS (Fire Dynamics Simulator (PyroSim version 2022)) and SketchUp software (version 2023) for architectural reconstruction and fire spread simulation, explores preventive measures to reduce fire risks. The result show that the total fire load of the building amounts to 1,976,246 MJ. After ignition, flashover occurs at 700 s, accompanied by a sharp increase in the heat release rate (HRR). The peak ceiling temperature reaches 750 °C. The roof trusses have critical structural weaknesses when approaching flashover conditions, indicating a high potential for collapse. Three targeted fire protection strategies are proposed in line with the heritage conservation principle of minimal visual and functional intervention: fire sprinkler systems, fire retardant coating, and fire barrier. Simulations of different strategies demonstrate their effectiveness in mitigating fire spread in elongated architectural heritages with enclosed ceiling-level ignition points. The efficacy hierarchy follows: fire sprinkler system > fire retardant coating > fire barrier. Additionally, because of chimney effect, for fire sources located above the ceiling and other hidden locations need to be warned in a timely manner to prevent the thermal plume from invading other sides of the ceiling through the access hole. This research can serve as a reference framework for other Modern Chinese Architectural Heritage to develop appropriate fire mitigation strategies and to provide a methodology for sustainable development of the Chinese architectural heritage. Full article

Prolonged expression of gene-editing components in CRISPR-modified plants can interfere with phenotypic analysis of target traits, increase the risk of off-target mutations, and lead to unnecessary metabolic burden. To mitigate these issues in peanut (Arachis hypogaea L.), callus-specific promoters were screened to restrict Cas9 expression to the callus stage, minimizing its activity in regenerated plants. In this study, six callus-specific genes in peanut were identified by mining RNA sequencing datasets and validating their expression profiles using quantitative reverse transcriptase PCR. The promoters of Arahy.H0FE8D, Arahy.WT3AEF, Arahy.I20Q6X, Arahy.ELJ55T, and Arahy.N9CMH4 were cloned and assessed for their expression activity. Beta-glucuronidase (GUS) histochemical staining confirmed that all five promoters were functional in peanut callus. Further investigation revealed their ability to drive cytosine base editing via a deaminase-nCas9 fusion protein, with all promoters successfully inducing precise base substitutions in peanut. Notably, P_Ah-H0FE8D, P_Ah-WT3AEF, P_Ah-ELJ55T, and P_Ah-N9CMH4 exhibited comparable or higher editing efficiencies than the commonly used cauliflower mosaic virus 35S promoter. These findings provide valuable tools for improving the biosafety of CRISPR-based genome editing in peanut breeding programs. Full article

In this study, the ultrasonic-assisted extraction of deep eutectic solvents (UADES) for tea polysaccharides was optimized, and their physicochemical properties and antioxidant activities were analyzed. The optimal DES comprised choline chloride (CC) and ethylene glycol (EG) in a molar ratio of 1:3, with a water content of 40%. The optimized condition was an extraction temperature of 61 °C, an ultrasonic power of 480 W, and an extraction time of 60 min. The UADES extraction rate of polysaccharides (ERP) was 15.89 ± 0.13%, significantly exceeding that of hot water (HW) extraction. The polysaccharide content in the UADES-extracted tea polysaccharides (UADESTPs) was comparable to that of hot-water-extracted tea polysaccharides (HWTPs) (75.47 ± 1.35% vs. 74.08 ± 2.51%); the UADESTPs contained more uronic acid (8.35 ± 0.26%) and less protein (12.91%) than HWTP. Most of the UADESTPs (88.87%) had molecular weights (Mw) below 1.80 × 10³ Da. The UADESTPs contained trehalose, glucuronic acid, galactose, xylose, and glucose, with molar ratios of 8:16:1:10. The free radical scavenging rate and total reducing power of the UADESTPs were markedly superior to those of the HWTPs. Moreover, the UADESTPs had a better alleviating effect on H₂O₂-induced oxidative injury in HepG2 cells. This study develops an eco-friendly and efficient extraction method for tea polysaccharides, offering new insights for the development of tea polysaccharides. Full article

Au-Ag alloy nanoshells (ANSs) were synthesized via chemical reduction, exhibiting superior plasmonic photocatalytic performance. TEM revealed uniform hollow structures (~80 nm), while EDS mapping confirmed homogeneous Au-Ag distribution throughout the shell. According to EDX analysis, the alloy contained 71.40% Ag by weight. XRD verified the formation of a substitutional solid solution without phase separation. The photocatalytic activity was evaluated using p-nitrothiophenol (PNTP) to 4,4′-dimercapto-azobenzene (DMAB) conversion monitored by SERS. UV-Vis spectroscopy showed LSPR peaks of ANSs between Au and Ag NPs, confirming effective alloying. Kinetic studies revealed that ANSs exhibited reaction rates 250–351 times higher than those of Au NPs and 5–10 times higher than those of Ag NPs. This resulted from the synergistic catalysis of Au-Ag and enhanced electromagnetic fields. ANSs demonstrated dual functionality as SERS substrates and photocatalysts, providing a foundation for developing multifunctional nanocatalytic materials. Full article

Mussels are nutrient-rich but perishable, resulting in substantial resource loss. A protease-producing strain (Bacillus velezensis Z-1, Mytilus edulis) isolated from marine sludge was used to hydrolyze mussels, producing Y-1, a hydrolysate with antioxidant activity. In this study, ultrafiltration, gel chromatography, and LC-MS/MS were employed to isolate and identify bioactive peptides from the hydrolysate. The results revealed that the hydrolysate exhibited antioxidant activity, pancreatic cholesterol esterase inhibitory activity, pancreatic lipase inhibitory activity, and α-glucosidase inhibitory activity. Molecular docking using AutoDock Tools 1.5.6 was performed to analyze the interactions of peptides with CD38 and Keap1, leading to the identification of five potentially bioactive peptides: VPPFY, IMLFP, LPFLF, FLPF, and FPRIM. These peptides formed hydrogen bonds and hydrophobic interactions with CD38 and Keap1, demonstrating strong DPPH radical scavenging and superoxide anion radical scavenging capacities. This study highlights the multifunctional bioactive potential of these peptides, offering insights into their therapeutic applications. The findings provide a novel approach for the effective utilization of mussel resources and highlight their potential application value in the development of functional foods. Full article

Crop disease identification is a pivotal research area in smart agriculture, forming the foundation for disease mapping and targeted prevention strategies. Among the most prevalent global wheat diseases, powdery mildew—caused by fungal infection—poses a significant threat to crop yield and quality, making early and accurate detection crucial for effective management. In this study, we present QY-SE-MResNet34, a deep learning-based classification model that builds upon ResNet34 to perform multi-class classification of wheat leaf images and assess powdery mildew severity at the single-leaf level. The proposed methodology begins with dataset construction following the GBT 17980.22-2000 national standard for powdery mildew severity grading, resulting in a curated collection of 4248 wheat leaf images at the grain-filling stage across six severity levels. To enhance model performance, we integrated transfer learning with ResNet34, leveraging pretrained weights to improve feature extraction and accelerate convergence. Further refinements included embedding a Squeeze-and-Excitation (SE) block to strengthen feature representation while maintaining computational efficiency. The model architecture was also optimized by modifying the first convolutional layer (conv1)—replacing the original 7 × 7 kernel with a 3 × 3 kernel, adjusting the stride to 1, and setting padding to 1—to better capture fine-grained leaf textures and edge features. Subsequently, the optimal training strategy was determined through hyperparameter tuning experiments, and GrabCut-based background processing along with data augmentation were introduced to enhance model robustness. In addition, interpretability techniques such as channel masking and Grad-CAM were employed to visualize the model’s decision-making process. Experimental validation demonstrated that QY-SE-MResNet34 achieved an 89% classification accuracy, outperforming established models such as ResNet50, VGG16, and MobileNetV2 and surpassing the original ResNet34 by 11%. This study delivers a high-performance solution for single-leaf wheat powdery mildew severity assessment, offering practical value for intelligent disease monitoring and early warning systems in precision agriculture. Full article

As a novel system, multi-dimensional space joint-observation SAR (MSJosSAR) can simultaneously acquire target information across multiple dimensions such as frequency, angle, and polarization. This capability facilitates a more comprehensive understanding of the target and enhances subsequent recognition applications. However, current research on the configuration optimization of multi-dimensional SAR systems is limited, particularly in balancing recognition requirements with observation costs. This limitation has become a major bottleneck restricting the development of MSJosSAR. Moreover, studies on the joint utilization of multi-dimensional information at the scattering center level remain insufficient, which constrains the effectiveness of target component recognition. To address these challenges, this paper proposes a configuration optimization method for MSJosSAR based on the separability of scattering mechanisms. The approach transforms the configuration optimization problem into a vector separability problem commonly addressed in machine learning. Experimental results demonstrate that the multi-dimensional configuration obtained by this method significantly improves the classification accuracy of scattering mechanisms. Additionally, we propose a feature extraction and classification method for scattering centers across frequency and angle-polarization dimensions, and validate its effectiveness through electromagnetic simulation experiments. This study offers valuable insights and references for MSJosSAR configuration optimization and joint feature information processing. Full article