Search Results (6,144)

Digital Beamforming SAR (DBF-SAR) provides high-resolution wide-swath imaging capability, yet it is affected by inter-channel amplitude, phase and time-delay errors induced by temperature variations and random error factors. Since all elevation channel data are weighted and summed by the DBF module in real time, conventional record-then-compensate approaches cannot meet real-time processing requirements. To resolve the problem, a real-time calibration architecture for Intermediate Frequency DBF (IFDBF) is presented in this paper. The Field-Programmable Gate Array (FPGA) implementation estimates amplitude errors through simple summation, time-delay errors via a simple counter, and phase errors via single-bin Discrete-Time Fourier Transform (DTFT). The time-delay and phase error information are converted into single-tone frequency components through Dechirp processing. The proposed method deliberately employs a reduced-length DTFT implementation to achieve enhanced delay estimation range adaptability. The method completes calibration within tens of PRIs (under 1 s). The proposed method is analyzed and validated through a spaceborne simulation and X-band 16-channel DBF-SAR experiments. Full article

This paper investigates the surrounding control problem of multiple unmanned surface vehicles (USVs) against false data injection (FDI) attacks and proposes a learning-based prescribed performance control (PPC) integrated with a dynamic event-triggering (DET) mechanism. First, a predefined-time observer (PTO) is designed to estimate the injected false data. Then, the constrained surrounding tracking error of multi-USVs is first formulated based on an exponential prescribed performance function. To facilitate the control law design, the constrained surrounding problem is transformed into an unconstrained space using a hyperbolic tangent function. Based on adaptive dynamic programming (ADP) and the DET mechanism, a prescribed performance time-varying surrounding control scheme is developed. Finally, the effectiveness and superiority of the proposed control strategy are demonstrated through rigorous theoretical analysis and simulation experiments, while Zeno behavior in the event-triggered mechanism is excluded. Full article

Background: Studies have shown that humans can rapidly learn the shape of new objects or adjust their behavior when encountering novel situations. Research on visual cognition in the brain further indicates that the ventral visual pathway plays a critical role in core object recognition. While existing studies often focus on microscopic simulations of individual neural structures, few adopt a holistic, system-level perspective, making it difficult to achieve robust few-shot learning capabilities. Method: Inspired by the mechanisms and processes of the ventral visual stream, this paper proposes a computational model with a macroscopic neural architecture for few-shot learning. We reproduce the feature extraction functions of V1 and V2 using a well-trained Vision Transformer (ViT) and model the neuronal activity in V4 and IT using two neural fields. By connecting these neurons based on Hebbian learning rules, the proposed model stores the feature and category information of the input samples during support training. Results: By employing a scale adaptation strategy, the proposed model emulates visual neural mechanisms, enables efficient learning, and outperforms state-of-the-art few-shot learning algorithms in comparative experiments on real-world image datasets, demonstrating human-like learning capabilities. Conclusion: Experimental results demonstrate that our ventral-stream-inspired machine-learning model achieves effective few-shot learning on real-world datasets. Full article

Limited depth of field in modern optical imaging systems often results in partially focused images. Multi-focus image fusion (MFF) addresses this by synthesizing an all-in-focus image from multiple source images captured at different focal planes. While deep learning-based MFF methods have shown promising results, existing approaches face significant challenges. Convolutional Neural Networks (CNNs) often struggle to capture long-range dependencies effectively, while Transformer and Mamba-based architectures, despite their strengths, suffer from high computational costs and rigid input size constraints, frequently necessitating patch-wise fusion during inference—a compromise that undermines the realization of a true global receptive field. To overcome these limitations, we propose MLP-MFF, a novel lightweight, end-to-end MFF network built upon the Pyramid Fusion Multi-Layer Perceptron (PFMLP) architecture. MLP-MFF is specifically designed to handle flexible input scales, efficiently learn multi-scale feature representations, and capture critical long-range dependencies. Furthermore, we introduce a Dual-Path Adaptive Multi-scale Feature-Fusion Module based on Hybrid Attention (DAMFFM-HA), which adaptively integrates hybrid attention mechanisms and allocates weights to optimally fuse multi-scale features, thereby significantly enhancing fusion performance. Extensive experiments on public multi-focus image datasets demonstrate that our proposed MLP-MFF achieves competitive, and often superior, fusion quality compared to current state-of-the-art MFF methods, all while maintaining a lightweight and efficient architecture. Full article

The accurate short-term forecasting (PV) of power is crucial for grid stability control, energy trading optimization, and renewable energy integration in smart grids. However, PV generation is extremely variable and non-linear due to environmental fluctuations, which challenge the conventional forecasting models. This study proposes a hybrid deep learning architecture, Wavelet Transform–Transformer–Temporal Convolutional Network–Efficient Channel Attention Network–Gated Recurrent Unit (WT–Transformer–TCN–ECANet–GRU), to capture the overall temporal complexity of PV data through integrating signal decomposition, global attention, local convolutional features, and temporal memory. The model begins by employing the Wavelet Transform (WT) to decompose the raw PV time series into multi-frequency components, thereby enhancing feature extraction and denoising. Long-term temporal dependencies are captured in a Transformer encoder, and a Temporal Convolutional Network (TCN) detects local features. Features are then adaptively recalibrated by an Efficient Channel Attention (ECANet) module and passed to a Gated Recurrent Unit (GRU) for sequence modeling. Multiscale learning, attention-driven robust filtering, and efficient encoding of temporality are enabled with the modular pipeline. We validate the model on a real-world, high-resolution dataset of a Moroccan university building comprising 95,885 five-min PV generation records. The model yielded the lowest error metrics among benchmark architectures with an MAE of 209.36, RMSE of 616.53, and an R² of 0.96884, outperforming LSTM, GRU, CNN-LSTM, and other hybrid deep learning models. These results suggest improved predictive accuracy and potential applicability for real-time grid operation integration, supporting applications such as energy dispatching, reserve management, and short-term load balancing. Full article

The impacts of the COVID-19 pandemic and post-pandemic recovery in urban communities in the Arctic have been substantial, but their gendered aspects remain largely unknown. The goal of this study was to enhance the understanding of the gender-based impact on women in the urban areas of Alaska by exploring strengths and constraints to resilience in the social and economic domains of gender equality during and in the aftermath of the COVID-19 pandemic. Drawing on grounded theory methodology, this study is based on 29 in-depth, semi-structured interviews. The study methodology utilized a conceptual framework that integrated deficit-based and strength-based analytical perspectives. The paper implemented a voice-centered approach that drew on thematic interviews conducted with women in Anchorage and Nome. Alaska’s urban women demonstrated resilience rooted in self-empowerment and community caregiving. This was reflected in their critical re-evaluation of social and economic gendered structures, a reassessment of priorities in family and social relationships, and the mobilization of support networks. These acts of reflection and care transformed into processes of constructing new meanings of life during dramatic events and became a source of personal strength. The crisis also enabled a re-evaluation of entrenched gender dynamics and women’s ability to challenge gendered divisions in both the workplace and at home. Despite signs of resilience, the pandemic signified a setback for gender equality. It exacerbated pre-existing gender disparities within households, disrupted established pre-pandemic social support networks, increased unpaid domestic labor and a motherhood penalty, and deepened unemployment and income gaps. To further adapt to post-pandemic conditions, women need empowerment and greater representation in decision-making roles, which are critical to strengthening resilience in both the social and economic domains of gender equality. Full article

This study introduces a novel family of probability distributions, termed the Pi-Power Logistic-G family, constructed through the application of the Pi-power transformation technique. By employing the Weibull distribution as the baseline generator, a new and flexible model, the Pi-Power Logistic Weibull distribution, is formulated. Particular emphasis is given to this specific member of the family, which demonstrates a rich variety of hazard rate shapes, including J-shaped, reverse J-shaped, and monotonic increasing patterns, thereby highlighting its adaptability in modeling diverse types of lifetime data. The paper examines the fundamental properties of this distribution and applies the method of maximum likelihood estimation (MLE) to determine its parameters. A Monte Carlo simulation was performed to assess the performance of the estimation method, demonstrating that both Bias and mean square error decline as the sample size increases. The utility of the proposed distribution is further highlighted through its application to real-world engineering datasets. Using model selection metrics and goodness-of-fit tests, the results demonstrate that the proposed model outperforms existing alternatives. In addition, a Bayesian approach was used to estimate the parameters of both datasets, further extending the model’s applicability. The findings of this study have significant implications for the fields of reliability modeling, survival analysis, and distribution theory, enhancing methodologies and offering valuable theoretical insights. Full article

The progress in biopolymers and their composites as advanced materials for wound healing has revolutionized therapeutic approaches for skin regeneration. These materials can effectively integrate their inherent biocompatibility and biodegradability with the enhanced mechanical strength and customizable properties of polymers and functional additives. This review presents a detailed investigation of the design principles, classifications, and biomedical applications of biopolymeric composites, focusing on their capabilities to promote angiogenesis, exhibit antimicrobial activities, and facilitate controlled drug delivery. By overcoming the challenges of conventional wound dressings, such as inadequate exudate management, mechanical fragility, and cytotoxicity, these composites provide dynamic, stimuli-responsive platforms that can adapt to the wound microenvironment. This study further highlights innovative advances in nanoparticle-assisted reinforcement, fiber-based scaffolds, and multi-stimuli responsive smart delivery systems. Finally, the future perspective illustrates how the challenges related to long-term physiological stability, scalable manufacturing, and clinical implementation can be addressed. Overall, this article delivers a comprehensive framework for understanding the transformative impact of biopolymeric composites in next-generation wound care. Full article

The airborne 2D-S optical array probe has worked for more than ten years and has collected a large number of cloud particle images. However, existing detection methods cannot detect cloud particles with high precision due to the size differences of cloud particles and the occurrence of particle fragmentation during imaging. So, this paper proposes a novel cloud particle detection method. The key innovation is an adaptive anchor SSD module, which overcomes existing limitations by generating anchor points that adaptively align with cloud particle size distributions. Firstly, morphological transformations generate multi-scale image information through repeated dilation and erosion operations, while removing irrelevant artifacts and fragmented particles for data cleaning. After that, the method generates geometric and mass centers across multiple scales and dynamically merges these centers to form adaptive anchor points. Finally, a detection module integrates a modified SSD with a ResNet-50 backbone for accurate bounding box predictions. Experimental results show that the proposed method achieves an mAP of 0.934 and a recall of 0.905 on the test set, demonstrating its effectiveness and reliability for cloud particle detection using the 2D-S probe. Full article

For addressing the technical challenges of photon denoising and water level extraction in ICESat-2 satellite-based water monitoring applications, this paper proposes an innovative solution integrating Gaussian function fitting with Hough transform. The method first employs histogram Gaussian fitting to achieve coarse denoising of water body regions. Subsequently, a probability attenuation model based on elevation differences between adjacent photons is constructed to accomplish refined denoising through iterative optimization of adaptive thresholds. Building upon this foundation, the Hough transform technique from image processing is introduced into photon cloud processing, enabling robust water level extraction from ICESat-2 data. Through rasterization, discrete photon distributions are converted into image space, where straight lines conforming to the photon distribution are then mapped as intersection points of sinusoidal curves in Hough space. Leveraging the noise-resistant characteristics of the Hough space accumulator, the interference from residual noise photons is effectively eliminated, thereby achieving high-precision water level line extraction. Experiments were conducted across five typical water bodies (Qinghai Lake, Long Land, Ganquan Island, Qilian Yu Islands, and Miyun Reservoir). The results demonstrate that the proposed denoising method outperforms DBSCAN and OPTICS algorithms in terms of accuracy, precision, recall, F1-score, and computational efficiency. In water level estimation, the absolute error of the Hough transform-based line detection method remains below 2 cm, significantly surpassing the performance of mean value, median value, and RANSAC algorithms. This study provides a novel technical framework for effective global water level monitoring. Full article

Cultural heritage institutions, such as museums and galleries, today face the challenge of managing an increasing volume of unsolicited visitor feedback generated across online platforms. This study offers a practical and scalable methodology that transforms 5856 multilingual Google reviews from 59 globally ranked museums and galleries into actionable insights through sentiment analysis, correlation diagnostics, and guided Latent Dirichlet Allocation. By addressing the limitations of prior research, such as outdated datasets, monolingual bias, and narrow geographical focus, the authors analyze a current and diverse set of recent reviews to capture a timely and globally relevant perspective on visitor experiences. The adopted guided LDA model identifies 12 key topics, reflecting both operational issues and emotional responses. The results indicate that while visitors generally express overwhelmingly positive sentiments, dissatisfaction tends to be concentrated in specific service areas. Correlation analysis reveals that longer, emotionally rich reviews are more likely to convey stronger sentiment and receive peer endorsement, highlighting their diagnostic significance. From a practical perspective, the methodology empowers professionals to prioritize improvements based on data-driven insights. By integrating quantitative metrics with qualitative topics, this study supports operational decision-making and cultivates a more empathetic and responsive data management mindset for museums. The reproducible and adaptable nature of the pipeline makes it suitable for cultural institutions of various sizes and resources. Ultimately, this work contributes to the field of cultural informatics by bridging computational precision with humanistic inquiry. That is, it illustrates how intelligent analysis of visitor reviews can lead to a more personalized, inclusive, and strategic museum experience. Full article

This study explores the application of modular design in the regeneration of community public spaces within rapidly transforming urban environments, using Haikou as a case study. The objective is to improve spatial quality and community sustainability while preserving cultural identity and community engagement. Through a mixed-methods approach involving questionnaires, GIS-based spatial analysis, and case studies, the research identifies key challenges such as fragmented layouts, limited accessibility, and insufficient green space. In response, a “policy–design–community” integration mechanism is proposed to guide bottom-up and top-down coordination. A multidimensional evaluation framework is developed to assess the effectiveness of modular interventions across functional, spatial, and cultural dimensions. The findings suggest that modular design—owing to its standardization and flexibility—enhances spatial adaptability and construction efficiency, and strengthens cultural identity and community engagement. This research provides a replicable and data-informed strategy for the renewal of public spaces in Chinese urban environments. Full article

Accurate identification of cotton leaf pests and diseases is essential for sustainable cultivation but is challenged by complex backgrounds, diverse pest morphologies, and varied symptoms, where existing deep learning models often show insufficient robustness. To address these challenges, RDL-YOLO model is proposed in this study. In the proposed model, RepViT-Atrous Convolution (RepViT-A) is employed as the backbone network to enhance local–global interaction and improve the response intensity and extraction accuracy of key lesion features. In addition, the Dilated Dense Convolution (DDC) module is designed to achieve a dynamic multi-scale receptive field, enabling the network to adapt to lesion defects of different shapes and sizes. LDConv further optimizes the effect of feature fusion. Experimental results showed that the mean Average Precision (mAP) of the proposed model reached 77.1%, representing a 3.7% improvement over the baseline YOLOv11. Compared with leading detectors such as Real-Time Detection Transformer (RT-DETR), You Only Look Once version 11 (YOLOv11), DETRs as Fine-grained Distribution Refinement (D-FINE), and Spatial Transformer Network-YOLO (STN-YOLO). RDL-YOLO exhibits superior performance, enhanced reliability, and strong generalization capabilities in tests on the cotton leaf dataset and public datasets. This advancement offers a practical technical solution for improved agricultural pest and disease management. Full article

With the accelerated digital transformation in education, the efficient integration of massive multimodal instructional resources and the support for interactive question answering (QA) remains a prominent challenge. This study introduces Multimodal Disciplinary Knowledge-Augmented Generation (MDKAG), a framework integrating retrieval-augmented generation (RAG) with a multimodal disciplinary knowledge graph (MDKG). MDKAG first extracts high-precision entities from digital textbooks, lecture slides, and classroom videos by using the Enhanced Representation through Knowledge Integration 3.0 (ERNIE 3.0) model and then links them into a graph that supports fine-grained retrieval. At inference time, the framework retrieves graph-adjacent passages, integrates multimodal data, and feeds them into a large language model (LLM) to generate context-aligned answers. An answer-verification module checks semantic overlap and entity coverage to filter hallucinations and triggers incremental graph updates when new concepts appear. Experiments on three university courses show that MDKAG reduces hallucination rates by up to 23% and increases answer accuracy by 11% over text-only RAG and knowledge-augmented generation (KAG) baselines, demonstrating strong adaptability across subject domains. The results indicate that MDKAG offers an effective route for scalable knowledge organization and reliable interactive QA in education. Full article

This work investigates the solutions of fractional integro-differential equations (FIDEs) using a unique kernel operator within the Caputo framework. The problem is addressed using both analytical and numerical techniques. First, the two-step Adomian decomposition method (TSADM) is applied to obtain an exact solution (if it exists). In the second part, numerical methods are used to generate approximate solutions, complementing the analytical approach based on the Adomian decomposition method (ADM), which is further extended using the Sumudu and Shehu transform techniques in cases where TSADM fails to yield an exact solution. Additionally, we establish the existence and uniqueness of the solution via fixed-point theorems. Furthermore, the Ulam–Hyers stability of the solution is analyzed. A detailed error analysis is performed to assess the precision and performance of the developed approaches. The results are demonstrated through validated examples, supported by comparative graphs and detailed error norm tables ($L_{\infty }$, $L_2$, and $L_1$). The graphical and tabular comparisons indicate that the Sumudu-Adomian decomposition method (Sumudu-ADM) and the Shehu-Adomian decomposition method (Shehu-ADM) approaches provide highly accurate approximations, with Shehu-ADM often delivering enhanced performance due to its weighted formulation. The suggested approach is simple and effective, often producing accurate estimates in a few iterations. Compared to conventional numerical and analytical techniques, the presented methods are computationally less intensive and more adaptable to a broad class of fractional-order differential equations encountered in scientific applications. The adopted methods offer high accuracy, low computational cost, and strong adaptability, with potential for extension to variable-order fractional models. They are suitable for a wide range of complex systems exhibiting evolving memory behavior. Full article