Search Results (704)

The synthesis of “green ammonia” from “green hydrogen” represents a critical pathway for renewable energy integration and industrial decarbonization. This study investigates the green ammonia synthesis process using an axial–radial fixed-bed reactor equipped with three catalyst layers. A simplified two-dimensional physical model was developed, and a multiscale simulation approach combining computational fluid dynamics (CFD) with physics-informed neural networks (PINNs) employed. The simulation results demonstrate that the majority of fluid flows axially through the catalyst beds, leading to significantly higher temperatures in the upper bed regions. The reactor exhibits excellent heat exchange performance, ensuring effective preheating of the feed gas. High-pressure zones are concentrated near the top and bottom gas outlets, while the ammonia mole fraction approaches 100% near the bottom outlet, confirming superior conversion efficiency. By integrating PINNs, the prediction accuracy was substantially improved, with flow field errors in the catalyst beds below 4.5% and ammonia concentration prediction accuracy above 97.2%. Key reaction kinetic parameters (pre-exponential factor k₀ and activation energy Ea) were successfully inverted with errors within 7%, while computational efficiency increased by 200 times compared to traditional CFD. The proposed CFD–PINN integrated framework provides a high-fidelity and computationally efficient simulation tool for green ammonia reactor design, particularly suitable for scenarios with fluctuating hydrogen supply. The reactor design reduces energy per unit ammonia and improves conversion efficiency. Its radial flow configuration enhances operational stability by damping feed fluctuations, thereby accelerating green hydrogen adoption. By reducing fossil fuel dependence, it promotes industrial decarbonization. Full article

To address the issues of burr formation, structural deformation, and tearing in the conventional machining of Nomex honeycomb composites, this study aims to clarify the mechanisms by which ultrasonic vibration-assisted cutting enhances machining quality. A multi-scale analysis framework is developed to examine the effects of ultrasonic vibration on fiber distribution, cell-level shear response, and the overall cutting mechanics. At the microscale, analyses show that ultrasonic vibration mitigates stress concentrations, thereby shortening fiber length. At the mesoscale, elastic buckling and plastic yielding models show that ultrasonic vibration lowers shear strength and modifies the deformation. A macro-scale comparison of cutting behavior with and without ultrasonic vibration was conducted. The results indicate that the intermittent contact effect induced by vibration significantly reduces cutting force. Specifically, at an amplitude of 40 μm, the cutting force decreased by approximately 29.7% compared to the condition without ultrasonic vibration, with an average prediction error below 8.6%. Compared to conventional machining, which causes the honeycomb angle to deform to approximately 130°, ultrasonic vibration preserves the original 120° geometry and reduces burr length by 36%. These results demonstrate that ultrasonic vibration effectively reduces damage through multi-scale interactions, offering theoretical guidance for high-precision machining of fiber-reinforced composites. Full article

The two-stage proportional valve is a key control component in heavy-duty equipment, where its signal-flow characteristics critically influence operational performance. This study proposes an innovative flow characteristic regulation method using variable-gain feedback grooves. Unlike conventional throttling notch optimization, the core mechanism actively adjusts pilot–main valve mapping through feedback groove shape and area gain adjustments to achieve the desired flow curves. This approach avoids complex throttling notch issues while retaining the valve’s high dynamics and flow capacity. Mathematical modeling elucidated the underlying mechanism. Subsequently, trapezoidal and composite feedback grooves are designed and investigated via simulation. Finally, composite feedback groove spools tailored to construction machinery operating conditions are developed. Comparative experiments demonstrate the following: (1) Pilot–main mapping inversely correlates with area gain; increasing gain enhances micro-motion control, while decreasing gain boosts flow gain for rapid actuation. (2) This method does not significantly increase pressure loss or energy consumption (measured loss: 0.88 MPa). (3) The composite groove provides segmented characteristics; its micro-motion flow gain (2.04 L/min/0.1 V) is 61.9% lower than conventional valves, significantly improving fine control. (4) Adjusting groove area gain and transition point flexibly modifies flow gain and micro-motion zone length. This method offers a new approach for high-performance valve flow regulation. Full article

Human coronaviruses are a group of viruses that continue to threaten human health. In this study, we investigated the antiviral activity of 4-hydroxychalcone (4HCH), a chalcone derivative, against human coronavirus HCoV-OC43. We found that 4HCH significantly inhibited the cytopathic effect, reduced viral protein and RNA levels in infected cells, and increased the survival rate of HCoV-OC43-infected suckling mice. Mechanistically, 4HCH targets the early stages of viral infection by binding to the epidermal growth factor receptor (EGFR) and inhibiting the EGFR/AKT/ERK1/2 signaling pathway, thereby suppressing viral replication. Additionally, 4HCH significantly reduced the production of pro-inflammatory cytokines and chemokines in both HCoV-OC43-infected RD cells and a suckling mouse model. Our findings demonstrate that 4HCH exhibits potent antiviral activity both in vitro and in vivo, suggesting its potential as a therapeutic agent against human coronaviruses. This study highlights EGFR as a promising host target for antiviral drug development and positions 4HCH as a candidate for further investigation in the treatment of coronavirus infections. Full article

The aim of this study was to investigate the effects of the crop load on the berry and wine composition of Marselan grapes. Thus, the appropriate crop load for Marselan wine grapes in Ningxia was determined based on the shoot density and the number of clusters per shoot. Marselan grapes from the Gezi Mountain vineyard, located at the eastern foot of Helan Mountain in the Qingtongxia region of Ningxia, were selected as the research material to conduct a combination experiment with four levels of shoot density and three levels of cluster density. The analysis of the berry and wine chemical composition was combined with a wine sensory evaluation to determine the optimal crop load levels. Crop load regulation significantly affected both the grape berry composition and the basic physicochemical properties of the resulting wine. Low crop loads improved metrics such as the berry weight and soluble solids content. A low shoot density facilitated the accumulation of organic acids, flavonols, and hydroxybenzoic acids in wine. Moderate crop loads were conducive to anthocyanin synthesis—the total individual anthocyanins content in the 10–20 shoots per meter of the canopy treatment group ranged from 116% to 490% of the control group—whereas excessive crop loads hindered its accumulation. Crop load management significantly influenced the aroma composition of wine by regulating the content of sugars, nitrogen sources, and organic acids in grape berries, thereby promoting the synthesis of esters and the accumulation of key aromatic compounds, such as terpenes. This process optimized pleasant flavors, including fruity and floral aromas. In contrast, wines from the high crop load and control treatments contained lower levels of these aroma compounds. Compounds such as ethyl caprylate and β-damascenone were identified as potential quality markers. Overall, the wine produced from vines with a crop load of 30 clusters (15 shoots per meter of canopy, 2 clusters per shoot) received the highest sensory scores. Appropriate crop load management is therefore critical to improving the chemical composition of Marselan wine. Full article

As the global energy system accelerates its transition toward a low-carbon economy, renewable energy sources (RESs), such as wind and photovoltaic power, are rapidly replacing traditional fossil fuels. These RESs are becoming a critical element of deeply decarbonized power systems (DDPSs). However, the inherent non-stationarity, multi-scale volatility, and uncontrollability of RES output significantly increase the risk of source–load imbalance, posing serious challenges to the reliability and economic efficiency of power systems. Scenario generation technology has emerged as a critical tool to quantify uncertainty and support dispatch optimization. Nevertheless, conventional scenario generation methods often fail to produce highly credible wind and solar output scenarios. To address this gap, this paper proposes a novel renewable energy scenario generation method based on a multi-resolution diffusion model. To accurately capture fluctuation characteristics across multiple time scales, we introduce a diffusion model in conjunction with a multi-scale time series decomposition approach, forming a multi-stage diffusion modeling framework capable of representing both long-term trends and short-term fluctuations in RES output. A cascaded conditional diffusion modeling framework is designed, leveraging historical trend information as a conditioning input to enhance the physical consistency of generated scenarios. Furthermore, a forecast-guided fusion strategy is proposed to jointly model long-term and short-term dynamics, thereby improving the generalization capability of long-term scenario generation. Simulation results demonstrate that MDDPM achieves a Wasserstein Distance (WD) of 0.0156 in the wind power scenario, outperforming DDPM (WD = 0.0185) and MC (WD = 0.0305). Additionally, MDDPM improves the Global Coverage Rate (GCR) by 15% compared to MC and other baselines. Full article

As a commercially valuable aquaculture species, rainbow trout (Oncorhynchus mykiss) urgently require solutions to growth inhibition associated with reproductive development. To elucidate the function of the cell cycle regulator Cyclin E genes (CCNE1 and CCNE2) in this process, we cloned the genes and analyzed their relative expression across various tissues and gonadal developmental stages. Using RNA interference (RNAi) and overexpression in RTG2 cells, we examined the effects of CCNE on cell viability, proliferation, and meiotic gene expression. Results showed that the open reading frame lengths of CCNE1 and CCNE2 were 1230 bp and 1188 bp, encoding 408 and 395 amino acids, respectively. Both proteins contain two conserved cyclin boxes, exhibit high structural similarity, and are phylogenetically most closely related to Oncorhynchus tshawytscha and Oncorhynchus kisutch. Expression and localization analyses revealed that CCNE1 was highly expressed in the ovary, while CCNE2 was highly expressed in the testis. Both proteins were expressed during fertilized egg development and key gonadal stages (at 13, 21, and 35 months post-fertilization). CCNE expression positively correlated with RTG2 cell viability and proliferation, with immunofluorescence confirming that CCNE is localized in the nucleus. Knockdown or overexpression of CCNE induced the differential expression of reproductive-related genes and key meiotic regulators. These findings suggest that CCNE1 and CCNE2 balance meiosis and gamete development through specific regulatory mechanisms, and their dysregulation may be a key factor underlying meiosis inhibition and reproductive development abnormalities. Full article

Yeast and its derivatives, including yeast extract and yeast cell wall, are well established as safe and environmentally sustainable feed additives that significantly improve animal production performance and health. Their incorporation into swine production serves as an innovative nutritional strategy aimed at improving growth performance, bolstering health status, and enhancing immune function in pigs. As a versatile microorganism, yeast generates a variety of bioactive compounds through fermentation, such as amino acids, vitamins, enzymes, and growth factors, which collectively contribute to improved growth and overall health in pigs. This review consolidates current research on the utilization of yeast and yeast derivatives in swine production, highlighting their biological functions and practical implications within the industry. Full article

As widely used peroxidase-like nanozymes, Fe-based nanozymes still suffer from an unclear reaction mechanism, which limits their further application. In this work, through alkaline treatment and then the replacement or occupation of strong acid sites by isolated Fe species, porous ZSM-5-confined atomic Fe species nanozymes with separated medium acid sites (Al-OH) and isolated Fe-O₄ sites were prepared. And the structure and the state of Fe-O₄ confined by ZSM-5 were determined by AC-HAADF-STEM, XPS, and XAS. In the oxidation of 3, 3′, 5, 5′-tetramethylbenzidine (TMB) by the hydrogen peroxide (H₂O₂) process, the heterolysis of H₂O₂ to ∙OH mainly occurs at the isolated Fe-O₄ sites, and then the generated ∙OH can spill over to the Al-OH sites to oxidize the adsorbed TMB. The synergistic effect between Fe-O₄ sites and medium acid sites can significantly benefit the catalytic performance of Fe-based nanozymes. Full article

This study combines machine vision technology and deep learning models to rapidly assess the activity of anaerobic ammonium oxidation (Anammox) granular sludge. As a highly efficient nitrogen removal technology for wastewater treatment, the Anammox process has been widely applied globally due to its energy-saving and environmentally friendly features. However, existing sludge activity monitoring methods are inefficient, costly, and difficult to implement in real-time. In this study, we collected and enhanced 1000 images of Anammox granular sludge, extracted color features, and used machine learning and deep learning training methods such as XGBoost and the ResNet50d neural network to construct multiple models of sludge image color and sludge denitrification efficiency. The experimental results show that the ResNet50d-based neural network model performed the best, with a coefficient of determination (R²) of 0.984 and a mean squared error (MSE) of 523.38, significantly better than traditional machine learning models (with R² up to 0.952). Additionally, the experiment demonstrated that under a nitrogen load of 2.22 kg-N/(m³·d), the specific activity of Anammox granular sludge reached its highest value of 470.1 mg-N/(g-VSS·d), with further increases in nitrogen load inhibiting sludge activity. This research provides an efficient and cost-effective solution for online monitoring of the Anammox process and has the potential to drive the digital transformation of the wastewater treatment industry. Full article

Mine water treatment and emissions have become important factors that restrict the comprehensive benefits of coal enterprises and local economic development, and the use of the deep well recharge method can address the specific conditions of mine surge water. This paper takes the actual situation of coal mine water treatment as an example and innovatively carries out dynamic tests for the Ordovician limestone aquifers deep in the mine. Intermittent reinjection test shows that under the same reinjection time, the water level recovery rate during the intermittent period is fast at first and then slow. Moreover, the recovery speed of the water level buried depth slows down with the increase in the reinjection time, which reveals the characteristics of the water level rising rapidly and recovering quickly during the reinjection of the reservoir. The average formation water absorption index is 420.81 m³/h·MPa. The water level buried depth of the long-term reinjection test showed three stages (rapid rise, slow rise, and stable stages), and the water level buried depth was raised to 1.52 m at its highest. Monitoring data from the surrounding 5 km area showed that reinjection did not affect aquifer water levels, verifying the excellent storage capacity of the deep Ordovician fissure-karst aquifer. The variability of well loss under pumping and injection conditions was comparatively analyzed, and the well loss produced by the recharge test was 4.06 times higher than that of the pumping test, which provided theoretical support for the calculation of hydrogeological parameters to eliminate the influence of well loss. This study deepens the understanding of Ordovician limestone aquifers in deep mine water, providing a reference for cheap mine water treatment and sustainable groundwater management in similar mine areas. Full article

With the rapid advancement of deepfake technology, the detection of low-quality synthetic facial images has become increasingly challenging, particularly in cases involving low resolution, blurriness, or noise. Traditional detection methods often exhibit limited performance under such conditions. To address these limitations, this paper proposes a novel algorithm, YOLOv9-ARC, which is designed to enhance the accuracy of detecting low-quality fake facial images. The proposed algorithm introduces an innovative convolution module, Adaptive Kernel Convolution (AKConv), which dynamically adjusts kernel sizes to effectively extract image features, thereby mitigating the challenges posed by low resolution, blurriness, and noise. Furthermore, a hybrid attention mechanism, Convolutional Block Attention Module (CBAM), is integrated to amplify salient features while suppressing irrelevant information. Extensive experiments demonstrate that YOLOv9-ARC achieves a mean average precision (mAP) of 75.1% on the DFDC (DeepFake Detection Challenge) dataset, representing a 3.5% improvement over the baseline model. The proposed YOLOv9-ARC not only addresses the challenges of low-quality deepfake detection but also demonstrates significant improvements in accuracy within this domain. Full article

Oats (Avena sativa L.) are forage grasses moderately tolerant to saline-alkali soil and are widely used for the improvement and utilization of saline-alkali land. Using the oat varieties collected from the Qaidam Basin as experimental materials, based on the analysis data of the main agronomic traits, quality, and soil physical and chemical properties of different oat varieties at the harvest stage. The hay yield of Molasses (17,933.33 kg·hm⁻²) was the highest (p < 0.05), the plant height (113.59 cm) and crude fat (3.02%) of Qinghai 444 were the highest (p < 0.05), the fresh-dry ratio (2.62), crude protein (7.43%), and total salt content in plants (68.33 g·kg⁻¹) of Qingtian No. 1 were the highest (p < 0.05), and the Relative forage value (RFV) of Baler (122.96) was the highest (p < 0.05). In the 0–15 cm and 15–30 cm soil layers of different oat varieties, the contents of pH, EC, total salt, Ca²⁺, Mg²⁺, and HCO₃⁻ showed a decreasing trend at the harvest stage compared to the seedling stage, while the contents of organic matter, total nitrogen, Cl⁻, and SO4²⁻ showed an increasing trend. The contents of K⁺ and Na⁺ maintained a relatively balanced relationship between the seedling stage and the harvest stage in the two soil layers. Qingtian No. 1, Qingyin No. 1, and Molasses all rank among the top three in terms of production performance and soil physical and chemical properties, and they are the oat varieties suitable for cultivation in the research area. Full article

Studying the host galaxies of fast radio bursts (FRBs) is critical to understanding the formation processes of their sources and, hence, the mechanisms by which they radiate. Toward this end, we have extended the Blinkverse database version 1.0, which already included burst information about FRBs observed by various telescopes, by adding information about 92 published FRB host galaxies to make version 2.0. Each FRB host has 18 parameters describing it, including redshift, stellar mass, star-formation rate, emission line fluxes, etc. In particular, each FRB host includes images collated by FASTView, streamlining the process of looking for clues to understanding the origin of FRBs. FASTView is a tool and API for quickly exploring astronomical sources using archival imaging, photometric, and spectral data. This effort represents the first step in building Blinkverse into a comprehensive tool for facilitating source observation and analysis. Full article

Zinc finger-homeodomain (ZF-HD) transcription factors are a unique class that only exist in plants and are essential for plant growth and development, various stress responses, and quality formation and regulation. In recent years, an increasing number of reports regarding this class of transcription factors have been published, identifying their novel functions. In this paper, the evolution, structural characteristics, and subfamily classification of ZF-HD transcription factors are comprehensively introduced and the roles of the ZF-HD in abiotic and biotic stress responses, plant hormone signal transduction, and quality regulation are extensively investigated. In future studies, more efforts should be focused on the in-depth exploration of the mechanisms through which the ZF-HD could act at various stages of plant growth and development. We also determine the current research status and future directions related to the ZF-HD, with the aim of providing a comprehensive knowledge base and research insights for the further exploration of ZF-HD transcription factors in plant molecular biology. Full article