Search Results (302)

To improve semantic segmentation performance for complex urban remote sensing images with multi-scale object distribution, class similarity, and small object omission, this paper proposes MFPI-Net, an encoder–decoder-based semantic segmentation network. It includes four core modules: a Swin Transformer backbone encoder, a diverse dilation rates attention shuffle decoder (DDRASD), a multi-scale convolutional feature enhancement module (MCFEM), and a cross-path residual fusion module (CPRFM). The Swin Transformer efficiently extracts multi-level global semantic features through its hierarchical structure and window attention mechanism. The DDRASD’s diverse dilation rates attention (DDRA) block combines convolutions with diverse dilation rates and channel-coordinate attention to enhance multi-scale contextual awareness, while Shuffle Block improves resolution via pixel rearrangement and avoids checkerboard artifacts. The MCFEM enhances local feature modeling through parallel multi-kernel convolutions, forming a complementary relationship with the Swin Transformer’s global perception capability. The CPRFM employs multi-branch convolutions and a residual multiplication–addition fusion mechanism to enhance interactions among multi-source features, thereby improving the recognition of small objects and similar categories. Experiments on the ISPRS Vaihingen and Potsdam datasets show that MFPI-Net outperforms mainstream methods, achieving 82.57% and 88.49% mIoU, validating its superior segmentation performance in urban remote sensing. Full article

This study proposes a Swin-ReshoUnet architecture with a three-level enhancement mechanism to address inefficiencies in multi-scale feature extraction and gradient degradation in deep networks for high-precision seismic exploration. The encoder uses a hierarchical convolution module to build a multi-scale feature pyramid, enhancing cross-scale geological signal representation. The decoder replaces traditional self-attention with ORCA attention to enable global context modeling with lower computational cost. Skip connections integrate a residual channel attention module, mitigating gradient degradation via dual-pooling feature fusion and activation optimization, forming a full-link optimization from low-level feature enhancement to high-level semantic integration. Simulated and real dataset experiments show that at decimation ratios of 0.1–0.5, the method significantly outperforms SwinUnet, TransUnet, etc., in reconstruction performance. Residual signals and F-K spectra verify high-fidelity reconstruction. Despite increased difficulty with higher sparsity, it maintains optimal performance with notable margins, demonstrating strong robustness. The proposed hierarchical feature enhancement and cross-scale attention strategies offer an efficient seismic profile signal reconstruction solution and show generality for migration to complex visual tasks, advancing geophysics-computer vision interdisciplinary innovation. Full article

Verticillium wilt is characterized by chlorosis in leaves and is a devastating disease in eggplant. Early diagnosis, prior to the manifestation of symptoms, enables targeted management of the disease. In this study, we aim to detect early leaf wilt in eggplant leaves caused by Verticillium dahliae by integrating multispectral imaging with machine learning and deep learning techniques. Multispectral and chlorophyll fluorescence images were collected from leaves of the inbred eggplant line 11-435, including data on image texture, spectral reflectance, and chlorophyll fluorescence. Subsequently, we established a multispectral data model, fusion information model, and multispectral image–information fusion model. The multispectral image–information fusion model, integrated with a two-dimensional convolutional neural network (2D-CNN), demonstrated optimal performance in classifying early-stage Verticillium wilt infection, achieving a test accuracy of 99.37%. Additionally, transfer learning enabled us to diagnose early leaf wilt in another eggplant variety, the inbred line 14-345, with an accuracy of 84.54 ± 1.82%. Compared to traditional methods that rely on visible symptom observation and typically require about 10 days to confirm infection, this study achieved early detection of Verticillium wilt as soon as the third day post-inoculation. These findings underscore the potential of the fusion model as a valuable tool for the early detection of pre-symptomatic states in infected plants, thereby offering theoretical support for in-field detection of eggplant health. Full article

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

The plant cuticle, a protective barrier against external stresses, and abscisic acid (ABA), a key phytohormone, are crucial for plant growth and stress responses. Heterologous expression of AtNCED3 in plants has been widely studied. In this research, by comparing the japonica rice cultivar Zhonghua 10 and its AtNCED3 over-expressing lines during the vegetative growth stage through multiple methods, we found that AtNCED3 over-expression increased leaf ABA content, enhanced epidermal wax and cutin accumulation, modified wax crystal density, and thickened the cuticle. These changes reduced leaf epidermal permeability and the transpiration rate, thus enhancing drought tolerance. This study helps understand the role of endogenous ABA in rice cuticle synthesis and its mechanism in plant drought tolerance, offering potential for genetic improvement of drought resistance in crops. Full article

This study aimed to clarify the effects of jujube–cotton intercropping on cotton yield and photosynthetic characteristics, providing a theoretical basis for its application in the oasis irrigation areas of southern Xinjiang and offering practical recommendations to local farmers for increasing economic benefits. The effects were investigated from 2020 to 2023 using Zhongmian 619 cotton and juvenile jujube trees. Changes in leaf area index (LAI), transpiration rate (Tr), stomatal conductance (Gs), net photosynthetic rate (Pn), intercellular CO₂ concentration (Ci), yield, and economic benefits were evaluated over the years. The results showed that (1) a positive correlation was observed between LAI and the photosynthetic characteristics of cotton. Compared to monoculture cotton, intercropped cotton exhibited lower Pn, Gs, and Tr, and at the peak boll stage, monoculture cotton had significantly higher photosynthetic characteristics, indicating that intercropping affected cotton photosynthesis. (2) From 2020 to 2023, the land equivalent ratio (LER) of jujube–cotton intercropping remained above 1, with overall yield and economic benefit surpassing those of monoculture cotton and jujube, particularly in 2023 when the yield increased by 55.35%. (3) A significant positive correlation was found between cotton yield and LAI. In conclusion, jujube–cotton intercropping enhances photosynthesis, improving yield, economic benefits, and land use efficiency. Full article

To investigate the corrosion resistance of 60Si2MnA spring steel coated with Zn-Al in a domestic atmospheric environment containing harmful salts, the corrosion environmental factors (temperature, humidity, deposited salts, and pH) were obtained through field research. The deliquescence and weathering behavior of harmful salts were studied using impedance methods to establish their characteristic curves. Additionally, a self-designed salt deposition test apparatus was employed to conduct accelerated atmospheric corrosion tests under constant salt deposition (10 g/m²) and controlled temperature and humidity conditions (20 °C/75% RH and 40 °C/75% RH) over different corrosion periods. The results show that noticeable red rust appeared on the samples after one month of corrosion. As the temperature increased, the consumption of the coating accelerated. XRD and Raman analyses reveal that the main corrosion products of the coating materials were ZnO, Zn(OH)₂, and Zn₅(CO₃)₂(OH)₆, while the red rust primarily consisted of iron oxides and hydroxides. In the early stages of corrosion, the self-corrosion current density was relatively low due to the protective effects of the coating and the corrosion product layer, indicating good corrosion resistance. However, in the later stages, the integrity of the coating and the corrosion product layer deteriorated, leading to a significant increase in the self-corrosion current density and a decline in corrosion resistance. This study provides a data foundation for understanding the corrosion behavior of Zn-Al-coated spring steel in atmospheric environments and offers theoretical insights for developing more corrosion-resistant coatings and optimizing anti-corrosion measures. Full article

Modular-assembled antennas represent an effective solution for the challenge of building super-large antennas in orbit. To investigate the impact of errors in modular-assembled antennas on their radiation characteristics, this study proposes definitions for these errors and presents methods for addressing them during the engineering design phase. The sources of errors in the modular antenna units are identified, and formulas for the error contributions of each module are derived. Based on this error analysis, a relationship between the errors of individual modules and the overall assembled antenna is established, along with an analytical expression for the antenna’s error. The influence of various error terms on the radiation characteristics of the assembled antenna is then examined. Simulations of the antenna’s radiation performance have been conducted, and the results demonstrate that changes in the antenna’s error patterns correlate with variations in its radiation characteristics. These findings provide valuable insights for guiding the engineering design of modular-assembled antennas. Full article

Heortia vitessoides Moore (Lepidoptera: Pyralidae), the dominant outbreak defoliator of Aquilaria sinensis (Myrtales: Thymelaeaceae, the agarwood-producing tree), poses a severe threat to the sustainable development of the agarwood industry. Current research has preliminarily revealed its biological traits and gene functions. However, significant gaps persist in integrating climate adaptation mechanisms, control technologies, and host interaction networks across disciplines. This review systematically synthesizes the multidimensional mechanisms underlying H. vitessoides outbreaks through the logical framework of “Fundamental Biology of Outbreaks—Environmental Drivers—Control Strategies—Molecular Regulation—Host Defense.” First, we integrate the biological characteristics of H. vitessoides with its climatic response patterns, elucidating the ecological pathways through which temperature and humidity drive population outbreaks by regulating development duration and host resource availability. Subsequently, we assess the efficacy and limitations of existing control techniques (e.g., pheromone trapping, Beauveria bassiana application), highlighting the critical bottleneck of insufficient mechanistic understanding at the molecular level. Building on this, we delve into the molecular adaptation mechanisms of H. vitessoides. Specifically, detoxification genes (e.g., HvGSTs1) and temperature stress-responsive genes (e.g., HvCAT, HvGP) synergistically enhance stress tolerance, while chemosensory genes mediate mating and host location behaviors. Concurrently, we reveal the host defense strategy of A. sinensis, involving activation of secondary metabolite defenses via the jasmonic acid signaling pathway and emission of volatile organic compounds that attract natural enemies—an “induced resistance–natural enemy collaboration” mechanism. Finally, we propose future research directions: deep integration of gene editing to validate key targets, multi-omics analysis to decipher the host–pest–natural enemy interaction network, and development of climate–gene–population dynamics models. These approaches aim to achieve precision control by bridging molecular mechanisms with environmental regulation. This review not only provides innovative pathways for managing H. vitessoides but also establishes a paradigm for cross-scale research on pests affecting high-value economic forests. Full article

Objectives: This study aims to elucidate the potential molecular mechanism of Sinomenine (SIN) in treating renal injury in Diabetic Nephropathy (DN) through network pharmacology, molecular docking, and in vivo validation. Materials and Methods: db/db mice were used as a DN model to evaluate the therapeutic effects of SIN on body weight, blood glucose levels, renal function, and histopathology. Network pharmacology and molecular docking were integrated to predict the potential molecular mechanisms of SIN in DN treatment. Subsequently, in vivo validation was performed on db/db mice using ELISA, Western blotting, RT-qPCR, immunofluorescence, and immunohistochemistry. Results: Firstly, we found that SIN (62.4 mg/kg) improved general conditions and renal function in db/db mice, alleviating renal pathological damage. Network pharmacology analysis identified IL-1β, IL-6, and TNF-α as key targets of SIN in DN. SIN reduced IL-1β, IL-6, and TNF-α levels by inhibiting the cGAS/STING signaling pathway and its downstream p-TBK1, p-IRF3, and NF-κB expression. Conclusions: SIN alleviates inflammatory injury in DN, potentially through the cGAS/STING pathway. Full article

The operational reliability of industrial cooling systems is critically compromised by the crystallization of ammonium chloride (NH₄Cl) in the terminal sections of heat exchangers and at air-cooler inlets. This study systematically investigated the deposition characteristics of NH₄Cl particles in hydrogenation air coolers, along with the factors influencing this process, using a combination of experimental analyses and CFD-DEM coupled simulations. Numerical simulations indicated that gas velocity is the primary factor that governs the NH₄Cl deposition behavior, whereas the NH₄Cl particle size significantly affects the deposition propensity. Under turbulent conditions, larger particles (>300 μm) exhibit a greater deposition tendency due to increased inertial effects. A power-law equation (R² > 0.75) fitted to the experimental data effectively predicts the variations in the deposition rates across tube bundles. This study offers a theoretical foundation and predictive framework for optimizing anti-clogging design and maintenance strategies in industrial air coolers. Full article

In order to explore the correlation between meteorological factors and power load changes, as well as the role of these factors in load forecasting, a hybrid load forecasting modeling framework based on quantile Granger causality test and bidirectional long short-term memory (QGCT-BiLSTM) is proposed. The Augmented Dickey–Fuller test (ADF) is used to test the smoothness of the influencing factor series and the load series, and the variables that passed the smoothness test are subjected to QGCT for identification of the characteristic variables with significant causal associations. Furthermore, the BiLSTM model is then constructed using the selected factors to generate load forecasts. Using real data from Fujian, China, we demonstrate that QGCT-based feature screening reduces forecasting errors by an average of 34.96%, where the RMSE, MAE and MAPE are 29.19%, 30.06% and 45.63%, respectively, thereby validating the necessity of causal factor selection. Additionally, single-factor perturbation analysis at seasonal scales quantifies load sensitivity to environmental changes, while geographic–climatic coupling mechanisms explain observed load variation patterns. The results confirm that QGCT-BiLSTM effectively isolates critical meteorological drivers and significantly enhances prediction accuracy compared to conventional approaches, achieving 20.3% lower RMSE and 16.8% lower MAE than LSTM. Full article

Pipeline leakage can induce ground surface settlements and structural responses in existing tunnels. A thorough understanding of pipeline–tunnel interactions is crucial for optimizing urban underground design and establishing construction guidelines. As urban underground spaces undergo rapid, large-scale development, their layouts have grown increasingly complex. Previous studies have mainly focused on the leakage propagation range and the resulting strata instability during tunnel excavation, while paying limited attention to the effects of pipeline leakage on existing tunnels. This study systematically investigated the mechanical response of existing tunnel structures to pipeline leakage under different layout configuration conditions using numerical modeling. A two-dimensional numerical model was developed to simulate the pipeline leakage process and its impact on adjacent tunnels. The research established a correlation between surrounding rock strength parameters and the saturation degree while examining the evolution patterns of leakage effects in various tunnel–pipeline arrangements. The analysis specifically focused on the mechanical influence of horizontal pipeline–tunnel distance, quantitatively determining the relationships among pipeline–tunnel spacing, leakage duration, and structural internal force. The horizontal pipeline–tunnel distance did not influence the development of the leakage zone above the tunnel vault but significantly altered the seepage path length and interface contact area. The complete encapsulation of the tunnel periphery by the leakage zone required progressively longer durations with increasing horizontal offsets: 16 days (0 m), 20 days (3 m), and 33 days (6 m). Corresponding circumferential contact ratios at 10 days were measured at 68.9%, 56.4%, and 30.6%, respectively. Furthermore, prolonged seepage duration led to increased ground subsidence with expanded affected areas, while the maximum settlement decreased proportionally with greater horizontal separation from the tunnel. These findings provide valuable insights for planning, designing, and maintaining “old tunnel-new pipeline” systems in urban underground development. Full article

Recycling the positive electrode materials of spent Li-ion batteries is critical for environmental sustainability and resource security. To facilitate the attainment of the goal, this study presents a novel approach for recovering valuable metals from positive electrode materials of spent lithium-ion batteries (LIBs) in an H-shaped cell containing aqueous NaCl electrolyte. The process employs hydrochloric acid that could be derived from the chlorine cycle as the leaching agent. The electrolytic device is engineered to generate a high pH gradient, thereby enhancing the leaching of metal elements and eliminating the requirement for external acid or base addition. This green recycling approach adheres to the principles of a circular economy and provides an environmentally friendly solution for sustainable battery material recycling. Full article