Search Results (15,672)

Habitat loss and fragmentation threaten forest-dependent ungulates in subtropical mountain systems, yet integrative assessments linking habitat quality and landscape configuration remain limited. Here, we evaluated habitat suitability and identified core habitat patches for the endangered black muntjac (Muntiacus crinifrons) in Tongboshan National Nature Reserve using an Analytic Hierarchy Process–Habitat Suitability Index (AHP–HSI) framework integrated with camera-trap validation and landscape pattern analysis. Vegetation-related indicators (NDVI and vegetation type) were the dominant suitability drivers, and highly suitable habitats accounted for 62.9% of the reserve (8646.97 ha), forming three major forest blocks with low disturbance levels. Camera-trap detections (n = 58) showed strong concordance with model predictions (98.28% within moderately suitable or higher classes). Landscape metrics revealed contrasting spatial configurations between overall high-suitability habitats and optimal core patches, indicating that demographic source areas are embedded within fragmented peripheral mosaics. Medium patches and forested ridges may function as potential stepping stones and corridors facilitating movement across habitat clusters. These findings highlight the importance of maintaining functional connectivity and mitigating edge disturbances in buffer and experimental zones to ensure long-term population persistence and effective protected-area management for forest ungulates. Full article

Single-cell RNA sequencing (scRNA-seq) provides a comprehensive understanding of cellular complexity; however, its requirement for fresh or frozen samples limits its flexibility. To overcome this limitation to effectively leverage clinical samples, Chromium Fixed RNA Profiling on formalin-fixed paraffin-embedded (FFPE) tissue blocks (scFFPE-seq) was developed to perform single-nucleus RNA sequencing from nuclei isolated from FFPE. In this study, we utilized fresh tissue samples from colon, ileum, and skin to assess the viability of scFFPE-seq compared to these fresh samples. We were able to recover unique cell types from challenging FFPE tissues and validated scFFPE-seq findings through Hematoxylin and Eosin (H&E) images. The results demonstrated that scFFPE-seq effectively captured the single-cell transcriptome in FFPE tissues, obtaining comparable cell abundance, cell type annotation, and pathway characterization to those in fresh tissues. Overall, the study presents strong evidence of the potential of scFFPE-seq to enhance scientific knowledge by enabling the generation of high-quality, sensitive single-nucleus RNA-seq data from preserved tissue samples. This technique unlocks the vast archives of FFPE samples for extensive retrospective genomic studies. Full article

The transient receptor potential vanilloid 4 channel (TRPV4) is thought to play a pivotal role in pulmonary arterial circulation. The present study hypothesizes that TRPV4 activation increases nitric oxide (NO) release and activates calcium-activated potassium of intermediate conductance (KCa3.1) in pulmonary arteries. Pulmonary arteries were isolated from wild-type mice (wt) and mice deficient in KCa3.1 channels (Kcnn4⁻/⁻) and mounted for simultaneous NO concentration and relaxation measurements. Human small pulmonary arteries were isolated and mounted in microvascular myographs for isometric tension recordings. Acetylcholine-induced increases in NO and relaxation of pulmonary arteries were slightly decreased in pulmonary arteries from Kcnn4⁻/⁻ versus wt mice. An activator of TRPV4 channels, GSK1016790A, increased NO and relaxation to the same degree in pulmonary arteries from wt and Kcnn4⁻/⁻ mice. A blocker of TRPV4 channels, HC06704, inhibited increases in NO concentration with no effect on acetylcholine (ACh) relaxation in pulmonary arteries from wt mice, but blocked increases in NO concentration and relaxation in pulmonary arteries from Kcnn4⁻/⁻ mice and responses to GSK1016790A in pulmonary arteries from wt and Kcnn4⁻/⁻ mice. Concentration-dependent relaxations induced by an inhibitor of sarcoplasmic Ca-ATPase, cyclopiazonic acid, were blocked in the presence of an inhibitor of NO synthase and a blocker of KCa3.1 channels, TRAM-34, in pulmonary arteries from wt mice, but were unaltered in the presence of TRAM-34 in arteries from Kcnn4⁻/⁻ mice, or the presence of a blocker of TRPV4 channels. In small human pulmonary arteries, ACh and sodium nitroprusside (SNP) induced concentration-dependent relaxations, blocked by endothelial cell removal, in the presence of an inhibitor of NO synthase and the KCa3.1 channel blocker TRAM-34. GSK1016790A induced relaxation of human pulmonary arteries with endothelium, but failed to relax arteries without endothelium. The findings suggest that TRPV4 channels are involved in endothelium-dependent relaxation and likely regulate pulmonary vascular tone by modulating NO release. Full article

For cement pavements on vast road networks, cracking has become one of the principal distresses threatening structural integrity and traffic safety. This study introduces DCP-TransUNet, a model featuring a new hybrid encoder that enhances the continuity of crack extraction under complex conditions through a DSE-CNN module and a CLMA-Transformer block. To further strengthen learning and interpretability for challenging crack imagery, a PPA bottleneck module is designed to capture additional discriminative features. Experimental results indicate strong performance: on the public dataset, DCP-TransUNet achieves mIoU 79.12%, Recall 87.96%, F1 87.06%, and Precision 86.21%; on the private dataset, it attains mIoU 68.83%, Recall 74.42%, F1 77.57%, and Precision 81.67%. Compared with other models, these outcomes demonstrate the method’s accuracy and effectiveness for crack segmentation. Full article

Segmenting white matter tracts in diffusion-weighted magnetic resonance imaging (dMRI) is of vital importance for brain health analysis. It remains a challenging task due to the intersection and overlap of tracts (i.e., multiple tracts coexist in one voxel) and the data complexity of dMRI images (e.g., 4D high spatial resolution). Existing methods that demonstrate good performance implement direct volumetric tract segmentation by performing on individual 2D slices. However, this ignores 3D contextual information, requires additional post-processing, and struggles with the boundary handling of 3D volumes. Therefore, in this paper, we propose an efficient 3D direct volumetric segmentation method for segmenting white matter tracts. It has three key innovations. First, we propose to deeply interleave convolutions and transformer blocks into a U-shaped network, which effectively integrates their respective strengths to extract spatial contextual features and global long-distance dependencies for enhanced feature extraction. Second, we propose a novel channel-wise transformer, which integrates depth-wise separable convolution and compressed contextual feature-based channel-wise attention, effectively addressing the memory and computational challenges of 4D computing. Moreover, it helps to model global dependencies of contextual features and ensures each hierarchical layer focuses on complementary features. Third, we propose to train a fully symmetric network with gradually sized volumetric patches, which can solve the challenge of few 3D training samples and further reduce memory and computational costs. Experimental results on the largest publicly available tract-specific tractograms dataset demonstrate the superiority of the proposed method over the current state-of-the-art methods. Full article

This study aimed to evaluate the technical and environmental feasibility of producing concrete paver blocks and pervious concrete paver blocks by incorporating marble waste to evaluate its filler effect within the cementitious matrix. The methodology included the characterization of marble waste, the production of test specimens with the control (0%), 10%, 20%, and 30% of cement replacement, and the execution of performance tests, supplemented by statistical analyses. The results indicated that marble waste replacement significantly impacted the properties. In terms of pervious concrete paver block permeability, the highest rates were observed in the control and 30% treatments. For water absorption, concrete paver blocks showed higher values at a maximum of 20%, while pervious concrete paver blocks maintained statistically analogous values for 10% and 20%. Regarding compressive strength, the concrete paver block formulation with 10% marble waste was statistically compatible with the control. It is concluded that the incorporation of marble waste into concrete and pervious concrete paver blocks is environmentally advantageous as it valorizes an industrial waste. However, mix design optimization is essential, given that excessive replacement (above 10%) resulted in a reduction in compressive strength. Full article

The transition from centralized power systems to decentralized infrastructures with a high share of renewable energy sources calls for reliable settlement in P2P electricity trading across “smart” regions. Blockchain platforms can enhance transparency and facilitate automated settlement; however, double-spend attacks still pose a threat to transaction finality and, consequently, undermine trust in the payment layer. This paper quantifies this risk through a probabilistic analysis of classical double-spend scenarios for Proof-of-Work (PoW) and Proof-of-Stake (PoS) blockchains augmented with periodic checkpoints, which render the chain history prior to the latest checkpoint effectively irreversible. We develop attack models for both consensus mechanisms and derive explicit formulas for the attacker’s success probability as a function of the adversarial share, the spacing between checkpoints, and the number of confirmation blocks. On this basis, we compute the minimum confirmation depth needed to satisfy a predefined risk threshold. Numerical evaluation using the derived expressions shows that checkpoints consistently reduce double-spend probability relative to checkpoint-free baselines; in the evaluated settings, the reduction reaches up to 44% and becomes more pronounced as the adversarial share increases. Finally, the analysis yields practical guidance for energy trading applications: accept a payment after the computed number of confirmations when it fits within a single checkpoint interval; otherwise, treat finality as reaching the next checkpoint. Full article

With artificial intelligence (AI) rapidly increasing in popularity and presence in everyday life, new applications utilizing AI are being explored across virtually all domains, from banking and healthcare to cybersecurity to generative AI for images, voice, and video content creation. With that trend comes an inherent need for increased AI capabilities. One cornerstone of AI applications is the ability of generative AI to consume documents and utilize their content to answer questions, generate new content, correlate it with other data sources, and more. No longer constrained to text alone, we now leverage multimodal AI models to help us understand visual elements within documents, such as images, tables, figures, and charts. Within this realm, capabilities have expanded exponentially from traditional Optical Character Recognition (OCR) approaches towards increasingly utilizing complex AI models for visual content analysis and understanding. Modern approaches, especially those leveraging AI, are now focusing on interpreting more complex diagrams such as flowcharts, block diagrams, Unified Modeling Language (UML) diagrams, electrical schematics, and timing diagrams. These diagram types combine text, symbols, and structured layout, making them challenging to parse and comprehend using conventional techniques. This paper presents a historical analysis and comprehensive survey of scientific literature exploring this domain of visual understanding of complex technical illustrations and diagrams. We explore the use of deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based architectures. These models, along with OCR, enable the extraction of both textual and structural information from visually complex sources. Despite these advancements, numerous challenges remain, however. These range from hallucinations, where the content extraction system produces outputs not grounded in the source image, which leads to misinterpretations, to a lack of contextual understanding of diagrammatic elements, such as arrows, grouping, and spatial hierarchy. This survey focuses on five key diagram types: flowcharts, block diagrams, UML diagrams, electrical schematics, and timing diagrams. It evaluates the effectiveness, limitations, and practical solutions—both traditional and AI-driven—that aim to enable the extraction of accurate and meaningful information from complex diagrams in a way that is trustworthy and suitable for real-world, high-accuracy AI applications. This survey reveals that virtually all approaches struggle with accurately extracting technical diagram information. It also illustrates a path forward. Pursuing research to further improve their accuracy is crucial for supporting and enabling various applications, including complex document question answering and Retrieval Augmented Generation (RAG), document-driven AI agents, accessibility applications, and automation. Full article

Mushrooms are among the most important indoor-grown horticultural cash crops. Recent increases in consumption are driven by shifts toward healthier diets and a growing vegan population. Mushroom spawn is one of key factors that influence consistency, quality, and the yield of mushrooms. Many studies of mushroom spawn have been published but the performance of mushroom spawn under different conditions has not been summarised. Comprehensive literature searches were conducted to identify the effects of spawn on biological efficiency, and 40 publications were included in this systematic review. Most of the studies were conducted on oyster mushroom (Pleurotus spp.), and grain spawn was popularly used when studying mushroom. Spawn type and rate were demonstrated to affect mycelium growth, which directly influenced mushroom yield. The use of liquid spawn increased mycelium growth, reduced spawn running time, and enhanced mushroom yield. Most studied cases used spawn rates of 3–5% and the biological yield efficiency (BE) of Pleurotus spp. was varied from 5.18 to 173.38% if using grain spawn. The BEs of Hericicum erinacea and Volvariella volvacea inoculated with grain spawn were lower at 22.3–44.4% and 9.42–15.79%, respectively. Recently developed stick and block spawn types seem to be promising spawn with a BE ranging from 68.65 to 70.94%. Full article

Apples are cultivated over a large global area with high yields, and efficient robotic harvesting requires accurate detection and localization, particularly in complex orchard environments where occlusion by leaves and fruits poses substantial challenges. To address this, we proposed a YOLO-CSB model-based method for apple detection and localization, designed to overcome occlusion and enhance the efficiency and accuracy of mechanized harvesting. Firstly, a comprehensive apple dataset was constructed, encompassing various lighting conditions and leaf obstructions, to train the model. Subsequently, the YOLO-CSB model, built upon YOLO11s, was developed with improvements including the integration of a lightweight CSFC Block to reconstruct the backbone, making the model more lightweight; the SEAM component is introduced to improve feature restoration in areas with occlusions, complemented by the efficient BiFPN approach to boost detection precision. Additionally, a 3D positioning technique integrating YOLO-CSB with an RGB-D camera is presented. Validation was conducted via ablation analyses, comparative tests, and 3D localization accuracy assessments in controlled laboratory and structured orchard settings, The YOLO-CSB model demonstrated effectiveness in apple target recognition and localization, with notable advantages under leaf and fruit occlusion conditions. Compared to the baseline YOLO11s model, YOLO-CSB improved mAP by 3.02% and reduced the parameter count by 3.19%. Against mainstream object detection models, YOLO-CSB exhibited significant advantages in detection accuracy and model size, achieving a mAP of 93.69%, precision of 88.82%, recall of 87.58%, and a parameter count of only 9.11 M. The detection accuracy in laboratory settings reached 100%, with average localization errors of 4.15 mm, 3.96 mm, and 4.02 mm in the X, Y, and Z directions, respectively. This method effectively addresses complex occlusion environments, enabling efficient detection and precise localization of apples, providing reliable technical support for mechanized harvesting. Full article

Frequent drilling fluid lost circulation in the Kuqa foreland area of the Tarim Oilfield severely constrains drilling efficiency and safety. The complex formation structures and diverse lost circulation types in this region are compounded by a lack of systematic classification in existing studies and weak correlation between mechanism analysis and field plugging measures, leading to a deficiency in quantitative decision-making for lost circulation prevention and control. Based on lithology analysis, loss zone pressure differential calculation, well log interpretation, and core observations, this study establishes an integrated “formation–lithology–pressure” diagnostic and classification method for lost circulation. A systematic classification framework comprising five types of lost circulation channels and mechanisms was developed. Based on this, the dominant lost circulation types and characteristics of three typical vertical formations in the Kuqa foreland were clarified: ① The supra-salt sandy conglomerate formations (e.g., Q1x, N2k) are dominated by permeability loss, where the loss rate (V) and bottomhole pressure differential (ΔP) exhibit a strong positive correlation (V ∝ ΔP). On-site application of graded bridging plugging formulations achieved a first-attempt success rate of ≥90%. ② The salt–gypsum formations (E1-2km) are primarily characterized by induced fracture loss, with a weak correlation between V and ΔP and dynamic fracture opening/closing behavior. Conventional rigid plugging materials showed limited effectiveness, resulting in a first-attempt success rate of <50%. ③ The K1bs formation is dominated by vertically developed natural fracture loss, where V and ΔP also demonstrate a strong positive correlation. In a specific Keshen block, a power-law relationship between the fracture aperture (W) and loss rate was established (W = 0.26·V^0.62, R² = 0.98), providing a basis for predicting fracture aperture and optimizing plugging formulations, with a plugging success rate of ≥80%. The classification system and quantitative criteria developed in this study effectively link lost circulation mechanisms, dynamic characteristics, and engineering countermeasures, offering theoretical support and a decision-making framework for optimizing lost circulation prevention and control measures and improving success rates in the Kuqa foreland area. Full article

Deep learning–based pansharpening methods for remote sensing have advanced rapidly in recent years. However, current methods still face three limitations that directly affect reconstruction quality. Content adaptivity is often implemented as an isolated step, which prevents effective interaction across scales and feature domains. Dynamic multi-scale mechanisms also remain constrained, since their scale selection is usually guided by global statistics and ignores regional heterogeneity. Moreover, frequency and spatial cues are commonly fused in a static manner, leading to an imbalance between global structural enhancement and local texture preservation. To address these issues, we design three complementary modules. We utilize the Adaptive Convolution Unit (ACU) to generate content-aware kernels through local feature clustering, thereby achieving fine-grained adaptation to diverse ground structures. We also develop the Multi-Scale Receptive Field Selection Unit (MSRFU), a module providing flexible scale modeling by selecting informative branches at varying receptive fields. Meanwhile, we incorporate the Frequency–Spatial Attention Unit (FSAU), designed to dynamically fuse spatial representations with frequency information. This effectively strengthens detail reconstruction while minimizing spectral distortion. Specifically, we propose the Hybrid Attention Fusion Network (HAFNet), which employs the Hybrid Attention-Driven Residual Block (HARB) as the fundamental utility to dynamically integrate the above three specialized components. This design enables dynamic content adaptivity, multi-scale responsiveness, and cross-domain feature fusion within a unified framework. Experiments on public benchmarks confirm the effectiveness of each component and demonstrate HAFNet’s state-of-the-art performance. Full article

Precise medical image segmentation plays a vital role in disease diagnosis and clinical treatment. Although U-Net-based architectures and their Transformer-enhanced variants have achieved remarkable progress in automatic segmentation tasks, they still face challenges in complex medical imaging scenarios, particularly around simultaneously modeling fine-grained local details and capturing long-range global contextual information, which limits segmentation accuracy and structural consistency. To address these challenges, this paper proposes a novel medical image segmentation framework termed DA-TransResUNet. Built upon a ResUNet backbone, the proposed network integrates residual learning, Transformer-based encoding, and a dual-attention (DA) mechanism in a unified manner. Residual blocks facilitate stable optimization and progressive feature refinement in deep networks, while the Transformer module effectively models long-range dependencies to enhance global context representation. Meanwhile, the proposed DA-Block jointly exploits local and global features as well as spatial and channel-wise dependencies, leading to more discriminative feature representations. Furthermore, embedding DA-Blocks into both the feature embedding stage and skip connections strengthens information interaction between the encoder and decoder, thereby improving overall segmentation performance. Experimental results on the LiTS2017 dataset and Sliver07 dataset demonstrate that the proposed method achieves incremental improvement in liver segmentation. In particular, on the LiTS2017 dataset, DA-TransResUNet achieves a Dice score of 97.39%, a VOE of 5.08%, and an RVD of −0.74%, validating its effectiveness for liver segmentation. Full article

Frequent extreme rainfall events in northwestern China have made loess–mudstone composite slopes highly susceptible to progressive failure, posing serious threats to infrastructure and public safety. This study investigates the deformation–failure mechanisms and evolutionary characteristics of such slopes under rainfall infiltration by integrating indoor physical model tests with long-term SBAS-InSAR time-series deformation monitoring. The physical model experiments reveal pronounced hydro-mechanical heterogeneity within the composite slope: surface fissures act as preferential flow paths, the mudstone interface exerts a significant water-blocking effect, and hydrological responses differ markedly between shallow and deep layers. The wetting front exhibits a distinct dual-layer migration pattern, characterized by rapid lateral expansion in the shallow layer and delayed advancement in the deep layer. Rainfall infiltration induces a progressive failure process, evolving from toe infiltration softening and mid-slope local erosion to differential crest erosion and ultimately overall sliding, forming a typical failure pattern of frontal creeping, central shearing, and rear tensile deformation. SBAS-InSAR results indicate that the natural landslide experienced a similar long-term progressive evolution, developing from shallow, localized deformation to deep-seated and slope-wide acceleration under multi-year rainfall. Despite differences in spatial deformation patterns influenced by natural microtopography, the failure stages and dominant deformation zones identified by both approaches show strong consistency. The combined results demonstrate that rainfall-induced suction decay, interface softening, pore water pressure accumulation, and stress redistribution jointly control the progressive instability of loess–mudstone slopes. This study highlights the effectiveness of integrating physical modeling and InSAR monitoring for elucidating rainfall-induced landslide mechanisms and provides scientific insights for hazard assessment and mitigation in composite-structure slopes. Full article

Transformer-based deep learning techniques have recently shown outstanding potential in remote sensing scene classification (RSSC), benefiting from their ability to capture global semantic relationships and contextual dependencies. However, effectively utilizing the raw image and global semantic information while simultaneously taking into account detailed features and multi-scale spatial relationships remains a major challenge. Therefore, this paper proposes a novel FG-Swin KANsformer model that integrates frequency domain and gradient prior information from raw images with the Kolmogorov–Arnold Network (KAN) to enhance nonlinear feature modeling. The FG-Swin KANsformer consists of three key components: the Discrete Cosine Transform (DCT) module, the gradient-spatial feature extraction (GSFE) module, and the Swin Transformer module integrated with KAN. In the feature embedding phase, the DCT module extracts frequency domain features, while the GSFE module uses multi-scale convolutions and Sobel operators to extract spatial structures and gradient information at different scales, thereby enhancing the utilization of the original image’s frequency domain and gradient prior information. In the Swin Transformer feature modeling phase, the conventional multilayer perceptron (MLP) in Swin Transformer Blocks is replaced by KAN, which decomposes complex multivariate functions into univariate compositions, thereby improving nonlinear representation capacity and enhancing feature discrimination. The thorough experiments on three distinct public remote sensing (RS) datasets demonstrate that FG-Swin KANsformer exhibits outstanding performance. Full article