Search Results (4,651)

Cotton is a globally strategic crop that plays a crucial role in sustaining national economies and livelihoods. To address the challenges of accurate cotton field extraction in the complex planting environments of Xinjiang’s Alaer reclamation area, a cotton field identification model was developed that integrates multi-source satellite remote sensing data with machine learning methods. Using imagery from Sentinel-2, GF-1, and Landsat 8, we performed feature fusion using principal component, Gram–Schmidt (GS), and neural network techniques. Analyses of spectral, vegetation, and texture features revealed that the GS-fused blue bands of Sentinel-2 and Landsat 8 exhibited optimal performance, with a mean value of 16,725, a standard deviation of 2290, and an information entropy of 8.55. These metrics improved by 10,529, 168, and 0.28, respectively, compared with the original Landsat 8 data. In comparative classification experiments, the endmember-based random forest classifier (RFC) achieved the best traditional classification performance, with a kappa value of 0.963 and an overall accuracy (OA) of 97.22% based on 250 samples, resulting in a cotton-field extraction error of 38.58 km². By enhancing the deep learning model, we proposed a U-Net architecture that incorporated a Convolutional Block Attention Module and Atrous Spatial Pyramid Pooling. Using the GS-fused blue band data, the model achieved significantly improved accuracy, with a kappa coefficient of 0.988 and an OA of 98.56%. This advancement reduced the area estimation error to 25.42 km², representing a 34.1% decrease compared with that of the RFC. Based on the optimal model, we constructed a digital map of continuous cotton cropping from 2021 to 2023, which revealed a consistent decline in cotton acreage within the reclaimed areas. This finding underscores the effectiveness of crop rotation policies in mitigating the adverse effects of large-scale monoculture practices. This study confirms that the synergistic integration of multi-source satellite feature fusion and deep learning significantly improves crop identification accuracy, providing reliable technical support for agricultural policy formulation and sustainable farmland management. Full article

Change detection technology holds significant importance in disciplines such as urban planning, land utilization tracking, and hazard evaluation, as it can efficiently and accurately reveal dynamic regional change processes, providing crucial support for scientific decision-making and refined management. Although deep learning methods based on computer vision have achieved remarkable progress in change detection, they still face challenges including reducing dynamic background interference, capturing subtle changes, and effectively fusing multi-temporal data features. To address these issues, this paper proposes a novel change detection model called OFNet. Building upon existing Siamese network architectures, we introduce an optical flow branch module that supplements pixel-level dynamic information. By incorporating motion features to guide the network’s attention to potential change regions, we enhance the model’s ability to characterize and discriminate genuine changes in cross-temporal remote sensing images. Additionally, we innovatively propose a dual-domain attention mechanism that simultaneously models discriminative features in both spatial and frequency domains for change detection tasks. The spatial attention focuses on capturing edge and structural changes, while the frequency-domain attention strengthens responses to key frequency components. The synergistic fusion of these two attention mechanisms effectively improves the model’s sensitivity to detailed changes and enhances the overall robustness of detection. Experimental results demonstrate that OFNet achieves an IoU of 83.03 on the LEVIR-CD dataset and 82.86 on the WHU-CD dataset, outperforming current mainstream approaches and validating its superior detection performance and generalization capability. This presents a novel technical method for environmental observation and urban transformation analysis tasks. Full article

Pipelines play a vital role in material transportation within industrial settings. This review synthesizes detection technologies for early-stage small gas leaks from pipelines in the industrial sector, with a focus on acoustic-based methods, optical gas imaging (OGI), and multimodal fusion approaches. It encompasses detection principles, inherent challenges, mitigation strategies, and the state of the art (SOTA). Small leaks refer to low flow leakage originating from defects with apertures at millimeter or submillimeter scales, posing significant detection difficulties. Acoustic detection leverages the acoustic wave signals generated by gas leaks for non-contact monitoring, offering advantages such as rapid response and broad coverage. However, its susceptibility to environmental noise interference often triggers false alarms. This limitation can be mitigated through time-frequency analysis, multi-sensor fusion, and deep-learning algorithms—effectively enhancing leak signals, suppressing background noise, and thereby improving the system’s detection robustness and accuracy. OGI utilizes infrared imaging technology to visualize leakage gas and is applicable to the detection of various polar gases. Its primary limitations include low image resolution, low contrast, and interference from complex backgrounds. Mitigation techniques involve background subtraction, optical flow estimation, fully convolutional neural networks (FCNNs), and vision transformers (ViTs), which enhance image contrast and extract multi-scale features to boost detection precision. Multimodal fusion technology integrates data from diverse sensors, such as acoustic and optical devices. Key challenges lie in achieving spatiotemporal synchronization across multiple sensors and effectively fusing heterogeneous data streams. Current methodologies primarily utilize decision-level fusion and feature-level fusion techniques. Decision-level fusion offers high flexibility and ease of implementation but lacks inter-feature interaction; it is less effective than feature-level fusion when correlations exist between heterogeneous features. Feature-level fusion amalgamates data from different modalities during the feature extraction phase, generating a unified cross-modal representation that effectively resolves inter-modal heterogeneity. In conclusion, we posit that multimodal fusion holds significant potential for further enhancing detection accuracy beyond the capabilities of existing single-modality technologies and is poised to become a major focus of future research in this domain. Full article

The large-scale trajectory simulation serves as a fundamental basis for the mission planning of a rocket-powered vehicle swarm. However, the traditional flight trajectory calculation method for a rocket-powered vehicle, which employs strict dynamic and kinematic models, often struggles to meet the temporal requirements of mission planning. To address the challenges of timely computation and intelligent optimization, a segmented training strategy, derived from the domain knowledge of the multi-stage flight characteristics of a rocket-powered vehicle, is integrated into the deep neural network (DNN) method. A high-precision trajectory prediction model that fuses multi-DNN is proposed, which can rapidly generate high-precision trajectory data without depending on accurate dynamic models. Based on the determination of the characteristic parameters derived from rocket-powered trajectory theory, a homemade dataset is constructed through a traditional computation method and utilized to train the DNN model. Extensive and varying numerical simulations are given to substantiate the predictive accuracy, adaptability, and stability of the proposed DNN-based method, and the corresponding comparative tests further demonstrate the effectiveness of the segmented strategy. Additionally, the real-time computational capability is also confirmed by computing the simulation of generating full trajectory data. Full article

Desertification poses critical threats to agricultural productivity and socio-economic stability, particularly in vulnerable regions like Thatta and Badin districts of Sindh, Pakistan. Traditional monitoring methods lack the accuracy and temporal resolution needed for effective early warning systems. This study presents a novel Spatio-Temporal Desertification Predictor (STDP) framework that integrates deep learning with next-generation satellite imaging for time-series desertification forecasting. The proposed encoder–decoder architecture combines Convolutional Neural Networks (CNNs) for spatial feature extraction from high-resolution satellite imagery with modified Long Short-Term Memory (LSTM) networks enhanced by multi-head attention to capture temporal dependencies. Environmental variables are fused through an adaptive data integration layer, and hyperparameter optimization is employed to enhance model performance for edge computing deployment. Experimental validation on a 15-year satellite dataset (2010–2024) demonstrates superior performance with MSE = 0.018, MAE = 0.079, and $R^2=0.94$, outperforming traditional CNN-only, LSTM-only, and hybrid baselines by 15–20% in prediction accuracy. The framework forecasts desertification trends through 2030, providing actionable signals for environmental management and policy-making. This work advances the integration of AI with satellite-based Earth observation, offering a scalable path for real-time environmental monitoring in IoT and edge computing infrastructures. Full article

Background: African swine fever (ASF) is a highly contagious and often deadly disease that poses a major threat to swine production worldwide. The lack of a commercially available vaccine underscores the critical need for innovative immunization strategies to combat ASF. Methods: Six ASFV antigenic proteins (K78R, A104R, E120R, E183L, D117L, and H171R) were fused with the Lactiplantibacillus plantarum WCFS1 surface anchor LP3065 (LPxTG motif) to generate recombinant Lactiplantibacillus plantarum NC8 (rNC8) strains. The surface expression was confirmed using immunofluorescence and Western blotting assays. Additionally, the dendritic cell-targeting peptides (DCpep) were co-expressed with each antigen protein. Mice were immunized at a dosage of 10⁹ colony-forming units (CFU) per strain per mouse via intragastric (I.G.), intranasal (I.N.), and intravenous (I.V.) routes. The bacterial mixture was heat-inactivated by boiling for 15 min to destroy viable cells while preserving antigenic structures. I.V. administration caused no hypersensitivity, confirming the method’s safety and effectiveness. Results: Following I.G. administration, rNC8-E120R, rNC8-E183L, rNC8-K78R, and rNC8-A104R induced significant levels of secretory immunoglobulin A (sIgA) in fecal samples, whereas rNC8-H171R and rNC8-D117L failed to induce a comparable response. Meanwhile, rNC8-D117L, rNC8-K78R, and rNC8-A104R also elicited significant levels of sIgA in bronchoalveolar lavage fluid (BALF). Following I.N. immunization, rNC8-E120R, rNC8-K78R, and rNC8-A104R significantly increased sIgA levels in both fecal and BALF immunization. In contrast, I.V. immunization with heat-inactivated rNC8-K78R and rNC8-A104R induced robust serum IgG titers, whereas the remaining antigens elicited minimal or insignificant responses. Flow cytometry analysis revealed expanded CD3⁺CD4⁺ T cells in mice immunized via the I.N. and I.G. and CD3⁺CD4⁺ T cells only in those immunized via the I.N. route. Th1 responses were also significant in the sera of mice immunized via the I.G. and I.N. routes. Conclusions: The rNC8 multiple-antigen cocktail elicited strong systemic and mucosal immune responses, providing a solid foundation for the development of a probiotic-based vaccine against ASF. Full article

This study investigates the influence of different surface treatments (namely, mechanical abrasion and solvent cleaning with isopropyl alcohol and acetone) on the adhesive bonding performance of polylactic acid (PLA) substrates produced by Fused Deposition Modeling (FDM). Pull-off tests revealed that the isopropanol-cleaned specimens achieved the highest bond strength, with an average pull-off value exceeding 8.5 MPa, compared to approximately 5.6 MPa for untreated PLA. Conversely, acetone cleaning resulted in the lowest performance (about 3.5 MPa), while mechanical abrasion yielded intermediate values of about 6 MPa. FTIR analysis confirmed that no chemical reactions occurred, although acetone and abrasion induced significant physical surface changes, unlike isopropanol, which acted as an effective cleaning agent. These findings demonstrate that surface cleanliness plays a dominant role over morphological alterations in enhancing the adhesion of PLA-based 3D-printed joints. Full article

Electrocardiogram (ECG) and phonocardiogram (PCG) signals are widely used in the early prevention and diagnosis of cardiovascular diseases (CVDs) due to their ability to accurately reflect cardiac conditions from different physiological perspectives and their ease of acquisition. Currently, some studies have explored the joint use of ECG and PCG signals for disease screening, but few studies have considered the trade-off between classification performance and energy consumption in model design. In this study, we propose a multimodal CVDs detection framework based on Spiking Neural Networks (SNNs), which integrates ECG and PCG signals. A differential fusion strategy at the signal level is employed to generate a fused EPCG signal, from which time–frequency features are extracted using the Adaptive Superlets Transform (ASLT). Two separate Spiking Convolutional Neural Network (SCNN) models are then trained on the ECG and EPCG signals, respectively. A confidence-based dynamic decision-level (CDD) fusion strategy is subsequently employed to perform the final classification. The proposed method is validated on the PhysioNet/CinC Challenge 2016 dataset, achieving an accuracy of 89.74%, an AUC of 89.08%, and an energy consumption of 209.6 $\mathsf{\mu }$J. This method not only achieves better balancing performance compared to unimodal signals but also realizes an effective balance between model energy consumption and classification effect, which provides an effective idea for the development of low-power, multimodal medical diagnostic systems. Full article

Facial Expression Recognition (FER) is a research topic of great practical significance. However, existing FER methods still face numerous challenges, particularly in the interaction between spatial and global information, the distinction of subtle expression features, and the attention to key facial regions. This paper proposes a lightweight Global-Aware Spatial (GAS) Attention module, designed to improve the accuracy and robustness of FER. This module extracts global semantic information from the image via global average pooling and fuses it with local spatial features extracted by convolution, guiding the model to focus on regions highly relevant to facial expressions (such as the mouth and eyes). This effectively suppresses background noise and enhances the model’s ability to perceive subtle expression variations. In addition, we further introduce a Squeeze-and-Excitation (SE) Attention module into the dual-branch architecture to adaptively adjust the channel-wise weights of features, emphasizing critical region information and enhancing the model’s discriminative capacity. Based on these improvements, we develop the Ada-DF++ network model. Experimental results show that the improved model achieves test accuracies of 89.21%, 66.14%, and 63.75% on the RAF-DB, AffectNet (7cls), and AffectNet (8cls) datasets, respectively, outperforming current state-of-the-art methods across multiple benchmarks and demonstrating the effectiveness of the proposed approach for FER tasks. Full article

Multimodal image fusion is an efficient information integration technique, with infrared and visible light image fusion playing a critical role in tasks such as object detection and recognition. However, obtaining images from different modalities with high-precision registration presents challenges, such as high equipment performance requirements and difficulties in spatiotemporal synchronization. This paper proposes an image augmentation and registration method based on an improved NeRF (neural radiance field), capable of generating multimodal augmented images with spatially precise registration for both infrared and visible light scenes, effectively addressing the issue of obtaining high-precision registered multimodal images. Additionally, three image fusion methods—MS-SRIF, PCA-MSIF, and CNN-LPIF—are used to fuse the augmented infrared and visible images. The effects and applicable scenarios of different fusion algorithms are analyzed through multiple indicators, with CNN-LPIF demonstrating superior performance in the fusion of visible and infrared images. Full article

This study investigates the processability—via Fused Deposition Modeling (FDM) 3D printing—and mechanical performance of biocomposites based on polylactic acid (PLA), polybutylene succinate (PBS), and their 50/50 wt% blend, each reinforced with hemp shive at 3 and 5 wt%. Blending PLA with PBS represents a straightforward and encouraging strategy to enhance both the printability and mechanical properties of the individual resins, expanding the range of their potential applications. The addition of hemp shive—a by-product of hemp processing—not only enhances the biodegradability of the composites but also improves their thermo-mechanical performance, as well as aligning with circular economy principles. The rheological characterization, performed on all the systems, evidenced that the PLA/PBS blend possesses viscoelastic properties well suited for FDM, enabling smooth extrusion through the nozzle, good shape stability after deposition, and effective interlayer adhesion. Moreover, the constrain effect of hemp shives within the polymer matrix reduced the extrudate swell, a key factor affecting the dimensional accuracy of the printed parts. Optimal processing conditions were identified at a nozzle temperature of 190 °C and a printing speed of 70 mm/s, providing a favorable compromise between print quality, final performances and production efficiency. From a mechanical perspective, the PLA/PBS blend exhibited an 8.6-fold increase in elongation at break compared to neat PLA, and its corresponding composite showed a ductility nearly three times higher than the PLA-based counterpart’s. In conclusion, the findings of this study provide new insights into the interplay between material formulation, rheological behavior and printing conditions, supporting the development of sustainable, hemp-reinforced biocomposites for additive manufacturing applications. Full article

This study presents the results of experimental studies on the thermal conductivity of specimens made from selected pure polymer filaments manufactured with the use of FFF 3D-printing technology. The tested samples were made of polylactic acid (PLA), polyethylene terephthalate glycol (PET-G), and acrylonitrile butadiene styrene (ABS). In particular, the effects of the infill patterns and infill density on the tested samples were examined in order to characterize the influence of these parameters on the materials’ effective thermal conductivity. Honeycomb and grid infill patterns of the tested samples with infill densities of 40%, 60%, 80%, and 100% were examined. The influence of temperature on thermal conductivity was studied as well. Thermal conductivity was measured using the guarded heat flow method, according to the ASTM E1530 standard within the defined temperature ranges of 20–60 °C for ABS and PET-G and 20–50 °C for PLA material. Samples of the tested materials were manufactured with the use of the Fused Filament Fabrication method (FFF), and filaments with a uniform black color were used. The obtained results were analyzed in terms of thermal conductivity variation after samples’ infill pattern and infill density modifications, which provides extended thermal property characterization of the polymeric filaments adopted for 3D printing. Full article

Electricity demand forecasting is essential for smart grid management, yet it presents challenges due to the dynamic nature of consumption trends and regional variability in usage patterns. While federated learning (FL) offers a privacy-preserving solution for handling sensitive, region-specific data, traditional FL approaches struggle when local datasets are limited, often leading models to overfit noisy peak fluctuations. Additionally, many regions exhibit stable, periodic consumption behaviors, further complicating the need for a global model that can effectively capture diverse patterns without overfitting. To address these issues, we propose Federated Hybrid Graph Attention Network with Two-step Optimization for Electricity Consumption Forecasting (FedHMGAT), a hybrid modeling framework designed to balance periodic trends and numerical variations. Specifically, FedHMGAT leverages a numerical structure graph with a Gaussian encoder to model peak fluctuations as dynamic covariance features, mitigating noise-driven overfitting, while a multi-scale attention mechanism captures periodic consumption patterns through hybrid feature representation. These feature components are then fused to produce robust predictions. To enhance global model aggregation, FedHMGAT employs a two-step parameter aggregation strategy: first, a regularization term ensures parameter similarity across local models during training, and second, adaptive dynamic fusion at the server tailors aggregation weights to regional data characteristics, preventing feature dilution. Experimental results verify that FedHMGAT outperforms conventional FL methods, offering a scalable and privacy-aware solution for electricity demand forecasting. Full article

Steady-State Visual Evoked Potentials (SSVEPs) have emerged as an efficient means of interaction in brain–computer interfaces (BCIs), achieving bioinspired efficient language output for individuals with aphasia. Addressing the underutilization of frequency information of SSVEPs and redundant computation by existing transformer-based deep learning methods, this paper analyzes signals from both the time and frequency domains, proposing a stacked encoder–decoder (SED) network architecture based on an xLSTM model and spatial attention mechanism, termed SED-xLSTM, which firstly applies xLSTM to the SSVEP speller field. This model takes the low-channel spectrogram as input and employs the filter bank technique to make full use of harmonic information. By leveraging a gating mechanism, SED-xLSTM effectively extracts and fuses high-dimensional spatial-channel semantic features from SSVEP signals. Experimental results on three public datasets demonstrate the superior performance of SED-xLSTM in terms of classification accuracy and information transfer rate, particularly outperforming existing methods under cross-validation across various temporal scales. Full article

Rotating machinery is essential to modern industrial systems, where rolling bearings play a critical role in ensuring mechanical stability and operational efficiency. Failures in bearings can result in serious safety risks and significant financial losses, which highlights the need for accurate and robust methods for diagnosing bearing faults. Traditional diagnostic methods relying on single-source data often fail to fully leverage the rich information provided by multiple sensors and are more prone to performance degradation under noisy conditions. Therefore, this paper proposes a novel bearing fault diagnosis method based on a multi-level fusion framework. First, the Symmetrized Dot Pattern (SDP) method is applied to fuse multi-source signals into unified SDP images, enabling effective fusion at the data level. Then, a combination of RepLKNet and Bidirectional Gated Recurrent Unit (BiGRU) networks extracts multi-modal features, which are then fused through a cross-attention mechanism to enhance feature representation. Finally, information entropy is utilized to assess the reliability of each feature channel, enabling dynamic weighting to further strengthen model robustness. The experiments conducted on public datasets and noise-augmented datasets demonstrate that the proposed method significantly surpasses other single-source and multi-source data fusion models in terms of diagnostic accuracy and robustness to noise. Full article