In recent years, advancements in deep Convolutional Neural Networks (CNNs) have brought about a paradigm shift in the realm of image super-resolution (SR). While augmenting the depth and breadth of CNNs can indeed enhance network performance, it ofte...
Efficient and precise colorectal polyp segmentation has significant implications for screening colorectal polyps. Although network variants derived from the Transformer network have high accuracy in segmenting colorectal polyps with complex shapes, t...
In order to investigate the cross-scale effects of the interaction between the hard and soft segments of stiff polyurethane foam on the material’s mesoscopic pore structure and macroscopic compression characteristics in various negative-tempera...
This paper analyses how local energy and climate actions can affect the use of water and land resources locally, nationally and globally. Each of these resource systems is linked to different Sustainable Development Goals (SDGs); we also explore rela...
Multivariate time series forecasting requires modeling complex and evolving spatio-temporal dependencies as well as frequency-domain patterns; however, the existing Transformer-based approaches often struggle to effectively capture dynamic inter-seri...
Stereomatching plays an essential role in 3D reconstruction using very-high-resolution (VHR) remote sensing images. However, it still faces unignorable challenges due to the multi-scale objects in large scenes and the multi-modality probability distr...
An information-theoretic approach for detecting interactions and informationtransfer between two systems is extended to interactions between dynamical phenomenaevolving on different time scales of a complex, multiscale process. The approach isdemonst...
Over decades, cities have undergone rapid urbanization and uncontrolled urban growth. The need for future cities to operate as adaptable complex systems has generated an interest in the self-organizing resilient city. The main aim of the study is to...
Anti-UAV detection is paramount for safeguarding airspace security. However, existing methodologies often exhibit low detection accuracy due to their inability to adaptively address target scale variations and complex backgrounds. To enhance detectio...
Steel surface defect classification is critical for industrial quality control, yet existing methods struggle to balance accuracy and efficiency for real-time deployment in vision-based sensor systems. This paper presents MobileSteelNet, a lightweigh...
To enhance the accuracy of product recognition in non-store retail sales and address misidentification and missed detection caused by occlusion in densely placed goods, we propose an improved YOLOv8-based network: Dense-YOLO. We first introduce an en...
Eighty-five percent of United States citizens live in urban areas. However, research surrounding the resilience and sustainability of complex urban systems focuses largely on coastal megacities (>1 million people). Midsize cities differ from their...
In elite endurance sports, marginal differences in finishing times drive ongoing equipment improvement to enhance athlete performance. In cross-country skiing, researchers, since the 1930s, have faced the challenge of minimising the resistance caused...
Accurate and reliable detection of landslides plays a crucial role in disaster prevention and mitigation efforts. However, due to unfavorable environmental conditions, uneven surface structures, and other disturbances similar to those of landslides,...
The Affinity for Technology Interaction (ATI) scale has been widely used to assess the tendency to engage in technology. To enhance the scale’s applicability and facilitate cross-cultural research, it is essential to provide translations of the...
The accurate prediction of drug–target interactions is essential for drug discovery and development. However, current models often struggle with two challenges. First, they fail to model the directional flow and positional sensitivity of protei...
Road crack detection is one of the important issues in the field of traffic safety and urban planning. Currently, road damage varies in type and scale, and often has different sizes and depths, making the detection task more challenging. To address t...
Video emotion recognition faces significant challenges due to the strong spatiotemporal coupling of dynamic expressions and the substantial variations in cross-scale motion patterns (e.g., subtle facial micro-expressions versus large-scale body gestu...
Aiming at the problems that the semantic representation of information extracted by the shallow layer of the current remote sensing image scene classification network is insufficient, and that the utilization rate of primary visual features decreases...
Radar signal intra-pulse modulation recognition can be addressed with convolutional neural networks (CNNs) and time–frequency images (TFIs). However, current CNNs have high computational complexity and do not perform well in low-signal-to-noise...
Constructing ecological networks (ENs) is an effective measure to mitigate the conflict between urban development and ecological conservation. However, existing simulating methods lack adequate consideration of human ecological demands and the spatia...
Unmanned Aerial Vehicles provide a cost-effective solution for water environment monitoring, yet detecting floating litter remains challenging due to small target scales, complex geometries, and severe surface interferences. To bridge the data defici...
The integrated assessment of watershed ecosystems is increasingly critical for sustainable water resource management amid global environmental change. Multi-source data integration—encompassing in situ monitoring, remote sensing, and model-base...
Resilience thinking has strongly influenced how people understand and pursue sustainability of linked social-ecological systems. Resilience thinking highlights the need to build capacity and manage general system properties in a complex, constantly c...
China’s grain production has transformed from absolute shortage to a current structural oversupply. High-intensity production introduced further challenges for the eco-environment, smallholder livelihood, and the man-land interrelationship. Driven by...
Accurate forecasting in heterogeneous spatiotemporal environments requires models that are both generalizable and interpretable, while also preserving cross-scale symmetry between temporal and spatial patterns. Existing deep learning approaches often...
The cross-media water entry problem widely exists in fields such as ocean engineering and aerospace. The highly non-stationary characteristics of the cross-media water entry process significantly influence the structural strength and ballistic stabil...
Leveraging thermal infrared imagery to complement RGB spatial information is a key technology in industrial sensing. This technology enables mobile devices to perform scene understanding through RGB-T semantic segmentation. However, existing networks...
Accurate classification of breast cancer histopathology images is critical for early diagnosis and treatment planning. Yet, conventional deep learning models face significant challenges under limited annotation scenarios due to their reliance on larg...
Surface defect detection is essential for ensuring the quality and safety of steel products. While Transformer-based methods have achieved state-of-the-art performance, they face several limitations, including high computational costs due to the quad...
Socio-ecological systems are complex, dynamic structures driven by cross-scale interactions between climate, disturbance and subsistence strategies. We synthetize paleoecological data to explore the emergence and evolution of anthropogenic landscapes...
Recognizing the significance of medicinal plants for rural livelihoods and primary healthcare, this paper attempted to analyze institutional interplays in medicinal plants management in Bangladesh. It assessed the governing process of natural resourc...
Defect inspection of Printed Circuit Board (PCB) is essential for maintaining the safety and reliability of electronic products. With the continuous trend toward smaller components and higher integration levels, identifying tiny imperfections on dens...
Small object detection remains a challenge in the remote sensing field due to feature loss during downsampling and interference from complex backgrounds. A novel network, termed SEMA-YOLO, is proposed in this paper as an enhanced YOLOv11-based framew...
Complex systems science has contributed to our understanding of ecology in important areas such as food webs, patch dynamics and population fluctuations. This has been achieved through the use of simple measures that can capture the difference betwee...
Ensuring the safety of transmission lines necessitates effective insulator defect detection. Traditional methods often need more efficiency and accuracy, particularly for tiny defects. This paper proposes an innovative insulator defect recognition me...
This review paper explores the field of mesoscale to microscale modeling over complex terrain as it traverses multiple so-called gray zones. In an attempt to bridge the gap between previous large-scale and small-scale modeling efforts, atmospheric si...
Employing the multi-scale strategy in hyperspectral image (HSI) classification enables the exploration of complex land-cover structures with diverse shapes. However, existing multi-scale methods still have limitations for fine feature extraction and...
Semantic segmentation of remote sensing images is a fundamental task in geospatial analysis and Earth observation research, and has a wide range of applications in urban planning, land cover classification, and ecological monitoring. In complex geogr...
The efficiency of wetlands in removing nutrients from streams and rivers can be accurately evaluated using diatom-inferred total phosphorus (DI-TP), as DI-TP integrates the effects of various environmental factors. However, studies assessing the effi...
Unmanned aerial vehicle (UAV) forest fire detection is vital for forest safety. However, early-stage UAV fire scenarios often involve small targets, weak smoke signals, and strict onboard resource constraints, which pose significant challenges to exi...
Colorectal cancer, as a malignant tumor with a high incidence rate worldwide, relies on the precise segmentation of polyps during colonoscopy for its early diagnosis. However, clinical colonoscopy images often face challenges such as low contrast, bl...
Maritime ship detection faces challenges due to complex object poses, variable target scales, and background interference. This paper introduces YOLO-PFA, a novel SAR ship detection model that integrates multi-scale feature fusion and dynamic alignme...
Recently, Vision Transformers (ViTs) have been actively applied to fine-grained visual recognition (FGVR). ViT can effectively model the interdependencies between patch-divided object regions through an inherent self-attention mechanism. In addition,...
Understanding how observational scale affects the interactions and spatial distributions of ecosystem services is important for effective ecosystem assessment and management. We conducted a case study in the Ussuri watershed, Northeast China, to expl...
Anomaly detection methods play a crucial role in automated quality control within modern manufacturing systems. In this context, unsupervised methods are increasingly favored due to their independence from large-scale labeled datasets. However, exist...
We are in a rapidly changing world where new dynamics are stressing the knowledge-action landscape: a greater understanding that cross-scale interactions are critical; increasing pressure to more fully address issues of equity in sustainable developm...
For the problems of inaccurate recognition and the high missed detection rate of existing mask detection algorithms in actual scenes, a novel mask detection algorithm based on the YOLO-GBC network is proposed. Specifically, in the backbone network pa...
The high computational complexity of transformer-based detectors leads to slow inference speeds. RT-DETR demonstrates improved performance in these aspects, yet there remains room for enhancement. To achieve more comprehensive feature learning and be...
Enhancing low-light images is crucial in computer vision applications. Most existing learning-based models often struggle to balance light enhancement and color correction, while images typically contain different types of information at different le...
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