Search Results (198)

The continuous advancement of fire protection technologies has necessitated the development of comprehensive safety standards, leading to an increasingly diversified and specialized regulatory landscape. This has made it difficult for fire protection professionals to quickly and accurately locate the required fire safety standard information. In addition, the lack of effective integration and knowledge organization concerning fire safety standard entities has led to the severe fragmentation of fire safety standard information and the absence of a comprehensive “one map”. To address this challenge, we introduce FT-FLAT, an innovative CNN–Transformer fusion architecture designed specifically for fire safety standard entity extraction. Unlike traditional methods that rely on rules or single-modality deep learning, our approach integrates TextCNN for local feature extraction and combines it with the Flat-Lattice Transformer for global dependency modeling. The key innovations include the following. (1) Relative Position Embedding (RPE) dynamically encodes the positional relationships between spans in fire safety texts, addressing the limitations of absolute positional encoding in hierarchical structures. (2) The Multi-Branch Prediction Head (MBPH) aggregates the outputs of TextCNN and the Transformer using Einstein summation, enhancing the feature learning capabilities and improving the robustness for domain-specific terminology. (3) Experiments conducted on the newly annotated Fire Safety Standard Entity Recognition Dataset (FSSERD) demonstrate state-of-the-art performance (94.24% accuracy, 83.20% precision). This work provides a scalable solution for constructing fire safety knowledge graphs and supports intelligent information retrieval in emergency situations. Full article

Lattice-based metamaterials have attracted much attention due to their excellent mechanical properties. Nevertheless, designing lattice materials with desired properties is still challenging, as their mesoscopic topology is extremely complex. Herein, the bidirectional evolutionary structural optimization (BESO) method is adopted to design lattice structures with maximum bulk modulus and elastic isotropy. Various lattice configurations are generated by controlling the filter radius during the optimization processes. Afterwards, the optimized lattices are fabricated using Stereo Lithography Appearance (SLA) printing technology. Experiments and numerical simulations are conducted to reveal the mechanical behavior of the topology-optimized lattices under quasi-static compression, which are compared with the traditional octet-truss (OT) and body-centered cubic (BCC) lattice structures. The results demonstrate that the topology-optimized lattices exhibited superior mechanical properties, including modulus, yield strength, and specific energy absorption, over traditional OT and BCC lattices. Moreover, apart from the elastic modulus, the yield stress and post-yield stress of the topology-optimized lattice structures with elastically isotropic constraints also present lower dependence on the loading direction. Accordingly, the topology optimization method can be employed for designing novel lattice structures with high performance. Full article

Grid-forming (GFM) energy storage can be utilized as a backup power source for the power grid to ensure the security of the power grid. GFM energy storage can also enhance the strength of the power grid and improve its stability. However, the GFM energy storage inherits the characteristics of the synchronous generator. Low-frequency oscillations may occur in GFM energy storage, which affect the stable operation of the power system. This paper proposed an additional damping control strategy for GFM energy storage to address the low-frequency oscillation. Firstly, this paper builds the state-space small-signal mathematical model of the GFM energy storage grid-connected system to analyze the participation factors of the low-frequency oscillation mode and clarify the key control parameters affecting the GFM energy storage grid-connected system the low-frequency oscillation. Then, this paper proposed an additional damping control strategy to increase the damping ratio of the low-frequency oscillation mode and improve the stability of the GFM energy storage grid-connected system. Finally, semi-physical experiments verified the effectiveness of the proposed additional damping control strategy. Full article

The coherency matrix serves as a valuable tool for explaining the intricate details of various terrain targets. However, a significant challenge arises when analyzing ground targets with similar scattering characteristics in polarimetric synthetic aperture radar (PolSAR) target decomposition. Specifically, the overestimation of volume scattering (OVS) introduces ambiguity in characterizing the scattering mechanism and uncertainty in deciphering the scattering mechanism of large oriented built-up areas. To address these challenges, based on the generalized five-component decomposition (G5U), we propose a hierarchical extension of the G5U method, termed ExG5U, which incorporates orientation and phase angles into the matrix rotation process. The resulting transformed coherency matrices are then subjected to a five-component decomposition framework, enhanced with four refined volume scattering models. Additionally, we have reformulated the branch conditions to facilitate more precise interpretations of scattering mechanisms. To validate the efficacy of the proposed method, we have conducted comprehensive evaluations using diverse PolSAR datasets from Gaofen-3, Radarsat-2, and ESAR, covering varying data acquisition timelines, sites, and frequency bands. The findings indicate that the ExG5U method proficiently captures the scattering characteristics of ambiguous regions and shows promising potential in mitigating OVS, ultimately facilitating a more accurate portrayal of scattering mechanisms of various terrain types. Full article

The potential damage to aviation products caused by vibration and shock during road transportation has long been overlooked, despite structural failure under dynamic loading emerging as a critical technical challenge affecting product reliability. For aviation components, both stress and vibration analysis are essential prerequisites prior to formal assembly. This study investigates a symmetric vertical tail, a common aviation structure, employing an innovative model group analysis method to characterize its dynamic stress and strain distributions under real transportation conditions. Experimental measurements of vibration acceleration and impact loads during transport served as input data for constructing a numerical model based on stress and vibration theory. The model elucidates the mechanical responses of the tail in both modal and vibrational states, enabling effectively evaluation of dynamic vibrations on the tail and its critical subcomponents during road transport. The findings provide actionable insights for optimizing aviation component packaging design, mitigating vibration-induced damage, and enhancing transportation safety. Full article

In the artificial cultivation of Morchella sextelata (Black Morel), exogenous nutrient bags (ENBs) commonly employ wheat grains as the primary substrate raw material. However, this approach is costly and runs counter to the “non-grain” development direction advocated by the edible mushroom industry. Under controlled field conditions, twelve self-made formulations were set up and compared with a conventional market formulation to comprehensively analyze their impacts on the agronomic traits, yield, soil physicochemical properties, and economic benefits of M. sextelata fruiting bodies. The research findings indicate that the nutrient bag formulations have a significant effect on soil available nutrients. Specifically, the contents of alkali-hydrolysable nitrogen (AN) and available potassium (AK) exhibit a significantly negative correlation with M. sextelata yield (r = −0.60, p < 0.05; r = −0.72, p < 0.01, respectively). Among all the treatment groups, the KY1 formulation (comprising 30% wheat grains, 5% rice bran, 60% corncobs, 2% rice husks, 1% lime, and 1% gypsum) achieved the highest yield of 915.13 kg per 667 m², which was 16.1% higher than that of the control group. The net economic benefit per unit area (667 m²) reached CNY 75,282.15, representing a 20.7% increase compared to the traditional wheat grains-based formulation. In conclusion, partially substituting wheat grains with rice bran in ENBs can not only reduce reliance on staple food resources but also enhance yield and economic efficiency. Due to the differences in cultivated strains and environmental conditions, the impact on morel yield is substantial; therefore, the results of this study need further validation through pilot trials. Full article

In recent years, synthetic aperture radars (SARs) have been widely applied in various fields due to their all-weather, day-and-night global imaging capabilities. As one of the most common types of antennas, the reflector antenna offers some advantages for spaceborne radars, including low cost, lightweight, high gain, high radiation efficiency, and low sidelobes. Consequently, spaceborne SAR systems based on reflector antennas exhibit significant potential. This paper reviews the main types and characteristics of reflector antennas, with particular attention to the structural configurations and feed arrangements of deployable reflector antennas in spaceborne SAR applications. Additionally, some emerging techniques, such as digital beamforming, staggered SAR, and SweepSAR based on reflector antennas, are examined. Finally, future development directions in this field are discussed, including high-resolution wide-swath imaging and advanced antenna deployment schemes. Full article

Sclerotinia sclerotiorum, known as a typical necrotrophic pathogenic fungus, exhibits a complex pathogenic mechanism. Research on S. sclerotiorum has primarily focused on oxalic acid, pathogenicity-related enzymes, and secreted proteins. In this study, we identified a transcription factor, SsSR (S. sclerotiorum Sterol-Related transcription factor), which regulates S. sclerotiorum infection by modulating virulence through ergosterol biosynthesis. We characterized the transcriptional activity of SsSR and its downstream target gene, SsCYP51. SsSR undergoes phosphorylation induced by the host plant, subsequently regulating the expression of SsCYP51. The deletion of SsSR or SsCYP51 does not affect the growth or acid production of S. sclerotiorum, but it leads to a reduction in ergosterol, significantly diminishing virulence and impairing the stress tolerance of the hyphae. In summary, this study identifies a transcription factor, SsSR, that specifically regulates the virulence of S. sclerotiorum. SsSR upregulates the expression of SsCYP51 through phosphorylation during the infection phase, leading to the synthesis of ergosterol, which enhances hyphal stress tolerance and thereby promotes infection. Full article

Disruptive color on animals’ bodies can reduce the risk of being caught. This study explores the camouflaging effect of disruptive color when applied to military targets. Disruptive and non-disruptive color patterns were placed on the target surface to form simulation materials. Then, the simulation target was set in woodland-, grassland-, and desert-type background images. The detectability of the target in the background was obtained by collecting eye movement indicators after the observer observed the background targets. The influence of background type (local and global), camouflage pattern type, and target viewing angle on the disruptive-color camouflage pattern was investigated. This study aims to design eye movement observation experiments to statistically analyze the indicators of first discovery time, discovery frequency, and first-scan amplitude in the target area. The experimental results show that the first discovery time of mixed disruptive-color targets in a forest background was significantly higher than that of non-mixed disruptive-color targets (t = 2.54, p = 0.039), and the click frequency was reduced by 15% (p < 0.05), indicating that mixed disruptive color has better camouflage effectiveness in complex backgrounds. In addition, the camouflage effect of mixed disruptive colors on large-scale targets (viewing angle ≥ 30°) is significantly improved (F = 10.113, p = 0.01), providing theoretical support for close-range reconnaissance camouflage design. Full article

Precise detection of canopy parameters is vital as it offers essential information for pest management in orchards. Among these parameters, leaf area density stands out as a key indicator of orchard canopies. A detection algorithm for leaf area density was proposed, and a leaf area density detection system for orchard canopies was designed based on the algorithm. By processing the point cloud data acquired by using LiDAR together with the algorithm, the total leaf area of the fitted leaves was calculated. Through an orthogonal regression experiment conducted on a laboratory-simulated canopy, this research established a mathematical calculation model ($R^2$ = 0.96) for determining the leaf area density of an orchard canopy. The leaf area density of an orchard canopy can be calculated using the total leaf area of the fitted leaves and an established mathematical model. To assess the accuracy of the detection system, both laboratory-simulated canopy experiments and real orchard canopy experiments were conducted. The results revealed that the absolute value of the mean relative error in the laboratory-simulated canopy experiments was 11.58%, and the absolute value of the mean relative error in the orchard canopy experiments was 16.75%. The research results have confirmed the feasibility of the LiDAR point cloud data processing algorithm. Furthermore, this algorithm can provide theoretical support for the subsequent development of intelligent plant protection equipment in orchards. Full article

As urban populations grow and energy demands escalate, it is increasingly challenging for existing building electrical infrastructure in densely populated areas to meet contemporary energy requirements. Traditional grid expansion methods often impose prohibitive economic costs and environmental impacts. Photovoltaic-battery (PVB) systems emerge as a sustainable alternative to enhance building energy self-sufficiency while addressing transformer capacity constraints. This study develops a multi-objective optimization methodology for PVB system configuration in retrofit applications, introducing the transmission limit ratio (TLR) metric to quantify grid interaction capacity. Taking a residential building as a case study, the constraints on configuration variables under insufficient transformer capacity are obtained through simulation. Applying the NSGA-II algorithm, optimal configurations are identified for economic and environmental scenarios. In terms of configuration, a PVB system, 0.743 PV penetration, 205 kWh battery is the best optimal configuration for an economic operation scenario, while 1.356 PV penetration and 201 kWh battery is the best for an environmental operation scenario, when the TLR is 0.8. The analysis demonstrates PV penetration’s critical role in scenario transition, while battery capacity primarily ensures system stability across TLR variations. This methodology provides practical insights for engineers in optimizing sustainable energy systems within existing infrastructure constraints, particularly relevant for high-density urban environments. Full article

Ceramic surfaces are directly related to product quality and safety in industry, and any minor defects may affect performance. Therefore, surface defect detection has important practical significance. Traditional detection methods have limitations, while deep learning methods bring new opportunities. Although there have been many studies on ceramic surface detection, most of them focus on traditional image processing methods or single-angle deep learning applications. This article proposes a detection scheme that combines multi-perspective image acquisition and improved deep learning models for complex environments in industrial production lines, with a particular focus on small-sample, imbalance, and small-target defects. In ceramic defect detection, defects are often diverse, small in size, and difficult to collect, which can lead to insufficient model training and low recognition accuracy when using deep learning methods for defect detection. In addition, industrial production requires the high real-time performance of detection systems, which must respond quickly while ensuring accuracy to meet efficient and stable quality control requirements. Therefore, data imbalance, small samples, small targets, and real-time issues are particularly critical in ceramic defect detection. This article first introduces the basic steps and current situation of data preparation. It then explores solutions to the imbalanced-sample problem in ceramic surface defect detection using methods such as data augmentation, sample distribution optimization, network structure improvement, and loss function design. Additionally, it reviews the small-sample problem in ceramic surface defect detection through approaches like data augmentation, transfer learning, unsupervised learning, and network structure optimization. This article also elaborates on methods to enhance the detection accuracy of small-target defects on ceramic surfaces, including adding attention mechanisms, improving features, and optimizing network structures. Finally, it discusses improvements in the real-time performance of model defect detection from two perspectives: enhancing lightweight models and integrating and optimizing network modules. This article summarizes solutions for implementing ceramic surface defect-detection technology and explores future research directions in this field. Full article

Background: Yiqi Zengmian (YQZM) functions as an immunopotentiator by enhancing both cellular and humoral immunity. However, its pharmacodynamic active constituents, particularly those absorbed into the bloodstream, and mechanism of action remain unclear. This study aimed to investigate the immunopotentiating effects and mechanisms of YQZM in mice immunized with the BBIBP-CorV (Beijing Bio-Institute of Biological Products Coronavirus Vaccine). Methods: Serum pharmacochemistry and ultra-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS) were employed to identify bioavailable components of YQZM. The mice received the BBIBP-CorV twice on days 1 and 14, while YQZM was orally administered for 28 days. Neutralization assays and ELISA quantified antigen-specific antibodies (abs), flow cytometry (FC) and intracellular cytokine staining (ICS) were used to assess immune cell populations and their cytokines, and an enzyme-linked immunospot assay (ELISpot) quantified memory T and B cells (MBs and MTs). To identify underlying mechanisms, network pharmacology, RNA sequencing (RNA-Seq), molecular docking, Western blotting (WB), and quantitative reverse transcription PCR (RT-qPCR) were performed. Results: YQZM significantly enhanced antigen-specific antibody titers, immune cell proportions, cytokine levels, and memory lymphocyte functions. UPLC-MS/MS analysis identified 31 bioactive compounds in YQZM. KEGG enrichment analysis based on RNA-Seq and network pharmacology implicated the TLR-JAK-STAT signaling pathway in YQZM’s immune-enhancing effects. WB and RT-PCR validated that YQZM upregulated the expression of critical nodes in the TLR-JAK-STAT signaling pathway. Furthermore, molecular docking indicated that YQZM’s primary active components exhibited strong binding affinity for critical proteins. Conclusions: YQZM effectively enhances vaccine-induced innate and adaptive immunity via a multi-component, multi-target mechanism, among which the TLR-JAK-STAT signaling pathway is a validated molecular target. Full article

This study presents a novel approach to predicting ship speed based on real-time voyage observation data, aiming to enhance maritime safety and operational efficiency. Observational data from a 20,000-ton bulk carrier, including variables such as latitude, longitude, GPS orientation, wind direction, wind speed, and main engine parameters, were collected and preprocessed to mitigate noise and handle missing values. Six machine learning models—the Backpropagation (BP) Neural Network, Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), XGBoost, and LightGBM—were employed to develop predictive models. Among these, the LightGBM model demonstrated the highest prediction accuracy, achieving a Root Mean Squared Error (RMSE) of 0.188, Mean Absolute Error (MAE) of 0.149, and a coefficient of determination (R²) of 0.978. The results highlight the potential of the LightGBM model in optimizing ship navigation and improving maritime operational efficiency. These findings offer a reliable foundation for further advancements in predictive maritime technologies and route optimization. Full article

Hydrogels with conductive properties hold significant promise in the realm of flexible electronics, owing to their pliability, outstanding conductivity, and diverse functionalities. Nevertheless, the majority of conductive hydrogels are prone to being brittle and easily damaged; as such, they are not adapt to cold environments, which seriously hinders their practical applications. Therefore, hydrogels that possess both conductivity and anti-freezing, as well as moisturizing, capabilities have garnered considerable interest, and these hydrogels can work stably in harsh environments. Phytic acid (PA), which mainly exists in plant seeds, is a kind of natural compound widely existing in nature that can be recycled; it provides electrical conductivity and anti-freezing to hydrogels. Here, a highly conductive hydrogel with excellent anti-freezing and moisturizing capabilities was prepared by incorporating PA into a polyacrylamide/gelatin hydrogel. The incorporation of PA endowed the hydrogel with an excellent conductivity of 5.8 S·cm⁻¹. In addition, robust hydrogen bonding was formed between water and phytic acid molecules, and the hydrogel demonstrated remarkable anti-freezing and water retention. On this basis, hydrogels can be used for human winter sports sensing and low-temperature environmental alarm devices to provide faster rescue. This study provides a novel method for the development of hydrogels with low-temperature stability, and provides a revelation for the application of anti-freezing hydrogels in icy and snowy environments. Full article