Search Results (2,645)

Cardiac magnetic resonance imaging (CMR) is widely used for the diagnosis and functional assessment of cardiovascular diseases because of its noninvasive nature and excellent soft-tissue contrast. However, accelerated cine magnetic resonance imaging (cine MRI) acquisition usually relies on undersampling, which may lead to noise, aliasing artifacts, and detail loss in reconstructed images. To address this issue, we propose a wavelet-guided dynamic Transformer with diffusion priors for cardiac cine MRI reconstruction. Specifically, a diffusion model is introduced into a reduced latent feature space to generate high-frequency prior features with only 8 reverse sampling steps, thereby enhancing detail recovery while maintaining moderate computational cost. In addition, a wavelet-guided dynamic Transformer is designed to capture low-frequency structural information and temporal dependencies across adjacent frames. By combining wavelet-domain decomposition, diffusion priors, and dynamic spatiotemporal modeling, the proposed framework improves reconstruction quality while preserving temporal consistency. Experimental results on multiple cardiac cine MRI datasets show that the proposed method achieves superior reconstruction accuracy and temporal consistency over several competing approaches, while maintaining a favorable balance between computational efficiency and reconstruction performance. These findings indicate that the proposed framework is an effective and robust solution for accelerated cardiac cine MRI reconstruction. Full article

This paper examines the technological dimension of “handwritten time” a distinctive mode of existence of the Russian Avant-garde. By the mid-1930s, the avant-garde’s stylistic confrontation with Socialist Realism had effectively expelled it from the contemporary literary process, artificially arresting its development—an instance of “unfinished modernity.” The article offers a detailed analysis of the technology of self-archiving (“lit-recycling”) developed by Aleksei Kruchyonykh: a deliberately chosen strategy of uncensored writing oriented toward an implicit reader of the future. The conscious refusal to complete the conventional publishing cycle, together with the systematic archiving of materials, generated a new pragmatics of the Russian avant-garde, enabling continued work under conditions of total censorship. The study considers both the strengths of this pragmatics of self-isolation and its unavoidable costs, above all the rupture of author–reader communication. Drawing on workbooks and diary notebooks from the 1930s, it reconstructs an archiving technology that had fully matured by that decade: the balance between draft and fair copy, as well as the mechanisms of auto-communication and self-censorship. Each stage of textual work is shown to acquire a specific function within a single technological continuum. Special attention is paid to contemporary methods for reconstructing the avant-garde’s creative records. The article reconstructs successive versions of Kruchyonykh’s poems (“Irina in the Fog,” “Trash,” “All Dead Poets…,” “Mind You!,” “Grumbling,” etc.), and cites diaries and handwritten books. It also foregrounds Kruchyonykh’s “prophetic” texts—those marked by a premonition of the coming great war—which conclude his diary and creative notebooks of the 1930s. Full article

Due to the high sensitivity of proton exchange membrane fuel cells (PEMFCs) to feed gas contamination through balance of plant (BOP) materials, in situ qualification plays a crucial role to secure performance, durability, and economic viability. To be able to deliver verified and accurate qualification results it is necessary to analyze the test method in detail and to perform repetitions on certain measurements. This work focuses on validation of an in situ material qualification test method in terms of measuring precision on a previously developed test bench and statistical significance of collected data. As a statistical approach t-test was used to calculate confidence intervals based on a sample size of 15 reference measurements with the same parameters and setup but variable membrane electrode assemblies (MEAs). The results show substantial reduction in confidence intervals with growing measurement’s sample size clearly quantifying accuracy of the analyzed methodology. The precision of the test method, as indicated by the calculated confidence intervals of irreversible voltage loss is approximately 1.21 mV, corresponding to a relative deviation of about 0.17% with respect to the calculated mean value across all steady-state phases (SSPs). This approach also provides an insight into the natural degradation behavior of the tested MEAs. The calculated effects can serve as a basis for design of experiments (DOE) in future test series. Full article

Multimodal medical image fusion provides vital anatomical and pathological details for clinical diagnosis. However, existing diffusion algorithms often struggle with prolonged inference times and local structure loss. To address these critical issues in applied medical imaging, we propose an accelerated edge-aware diffusion model with spatial refinement. This framework utilizes a coarse-to-fine collaborative architecture. It first extracts structural priors via edge-enhanced data blocks and a non-uniform time-step accelerated sampling strategy. During refinement, a spatially adaptive non-convex variational module employs a Nesterov accelerated alternating direction method of multipliers for pixel-level correction to efficiently remove diffusion artifacts and sharpen anatomical boundaries. We conduct extensive comparative experiments against the vanilla diffusion baseline and state-of-the-art deep learning paradigms. Qualitative and quantitative evaluations on clinical datasets demonstrate the superior balanced performance of our model. The framework delivers highly natural visual representations, effectively merging sharp skeletal contours from computed tomography with rich soft tissue textures from magnetic resonance imaging while preventing unnatural over-sharpening. Additionally, it demonstrates outstanding performance across comprehensive statistical metrics, reflecting exceptional image fidelity, robust global contrast, and precise structural preservation. Furthermore, the model reduces inference time by approximately 42% compared to the baseline. Ultimately, this framework strikes an optimal balance between superior image fusion quality and computational efficiency, offering enhanced visual representations with potential utility for clinical image processing under limited resources. Full article

To address micro-break and edge-chipping defects in ferrite magnetic sheets, as well as the difficulty of balancing detection accuracy and deployment cost under complex grinding-texture interference, this paper proposes FMSD-YOLO12, an efficient and lightweight defect detection model based on YOLOv12. The proposed method follows a task-oriented design for three coupled challenges in ferrite magnetic sheet inspection, namely texture-interfered feature extraction, cross-scale feature inconsistency, and lightweight yet accurate defect localization. Specifically, a Spatially Re-weighted Convolution (SR-Conv) is introduced into the C3k2 backbone module to suppress repetitive grinding-texture noise and enhance the response contrast of subtle defect regions. A Context and Spatial Feature Calibration Network (CSFCN) is further developed to improve semantic consistency and spatial alignment during multi-scale feature fusion. In addition, a Lightweight Shared Detail-Enhanced Convolutional Detection head (LSDECD) is designed to strengthen weak-edge localization while reducing parameter redundancy through re-parameterization. Experimental results show that, with a comparable number of parameters, FMSD-YOLO12 improves mAP@50 by 2.40%, mAP@75 by 3.71%, and mAP@50-95 by 3.03% on the magnetic sheet defect dataset. These results demonstrate that the proposed model achieves a favorable balance between detection accuracy and computational efficiency for irregular defect detection under complex industrial backgrounds. Full article

Vehicle detection is a fundamental perception task in intelligent transportation systems and autonomous driving. Although state-of-the-art detectors achieve competitive performance under normal conditions, their robustness degrades substantially under adverse conditions such as rain, fog, low illumination, and sensor noise. To address this challenge, we propose RD-DETR, a vehicle detector that incorporates reaction–diffusion mechanisms into deep feature learning. The RDNet backbone adopts a pyramid-based enhancement strategy in which shallow layers preserve fine-grained texture details while deep layers employ reaction–diffusion-inspired dynamics to suppress noise and enhance target representations. The Phase-Guided Spatial Attention (PGSA) module leverages phase-related structural cues that are relatively less sensitive to global illumination and contrast variations, helping recover vehicle boundaries when appearance cues become unreliable under adverse imaging conditions. The Content-Aware Adaptive Fusion Module (CA-AFM) dynamically aggregates multi-scale features according to scene complexity, improving detection across diverse traffic scenarios. Experiments on BDD100K and DAWN show that RD-DETR yields mAP@0.5 improvements of 3.2 and 4.0 percentage points over RT-DETR, respectively, while reducing model parameters by 27.6%, indicating a favorable balance between accuracy and efficiency under the evaluated settings. Full article

Hereditary renal tubulopathies are rare monogenic disorders caused by defects in tubular transport mechanisms that impair the handling of electrolytes, water, and acid–base balance along the nephron. While each tubulopathy is individually uncommon, their collective burden is clinically relevant, as these disorders can severely affect quality of life and predispose to nephrolithiasis, dehydration episodes, and progression to chronic kidney disease. Advances in molecular genetics have identified more than 70 genes involved in renal tubular physiology; however, a substantial proportion of these cases remain genetically unresolved, and marked phenotypic heterogeneity complicates diagnosis and management. This narrative review provides an integrated overview of the main transport systems operating in the different tubular segments of the nephron—proximal tubule, thick ascending limb of the loop of Henle, distal convoluted tubule and collecting duct—summarizing the tubulopathies associated with each segment and discussing in greater detail representative inherited disorders that illustrate the clinical consequences of their dysfunction. We highlight current diagnostic challenges and limitations of existing therapeutic strategies and discuss novel diagnostic approaches as well as emerging treatment options. Improved genetic diagnosis, validation of candidate biomarkers, and the development of novel therapeutic strategies will be essential to advance precision medicine and improve outcomes for patients with inherited renal tubulopathies. Full article

LiDAR is widely used in robotics because it provides reliable range data for navigation and mapping. On a small embedded robot, however, there is a practical conflict between scan resolution and reaction speed. Dense scans provide better environmental detail, but they take too long for fast obstacle avoidance, whereas sparse scans are faster but can miss obstacles if the spacing between adjacent rays is too large. This paper presents an Interleaved Sparse–Dense Scanning method for a servo-actuated single-point time-of-flight LiDAR mounted on an embedded mobile robot. A dense nested pan–tilt sweep is used for three-dimensional mapping, while a sparse forward scan is inserted between dense rows for obstacle detection and motion control. A geometric model is derived to relate sensing range, beam spacing, and minimum detectable object width. That model is then linked to zone-based safety constraints and to the distance the robot can travel before the next obstacle update. For the robot used in this study, the resulting sparse configuration is a 7-point forward scan over a 180${}^{\circ }$ field of view. Experiments in a real indoor environment showed that this configuration reliably detected target blocking obstacles and reduced decision latency by 6.2 times compared with waiting for a complete dense scan before each navigation update. The proposed method provides a practical balance between reactive obstacle avoidance and useful 3D mapping on a low-cost embedded platform, while making the system’s timing and safety limits explicit. Full article

Morocco is among the regions in the Mediterranean basin most exposed to the impacts of climate variability and change. This increasing exposure requires a detailed and rigorous analysis of regional atmospheric dynamics to better understand the mechanisms behind recent climate trends. This study aims to examine the variability of circulation weather types (CWTs) at a regional scale over the period 1980–2019, within a geographical area bounded by latitudes 20° to 40° N and longitudes 10° to 22.5° W. The analysis is based on data from the NCEP-DOE Reanalysis 2, including mean sea level pressure (MSLP) and geopotential height at 500 hPa (Z500), with a spatial resolution of 2.5° in both latitude and longitude. The adopted methodology identifies daily CWT using a principal component analysis (PCA) in S-mode with Varimax rotation (PCAV), followed by the evaluation of their monthly distributions and temporal trends. The analysis highlights a marked trend toward increased atmospheric configurations conducive to hot conditions during the dry season, associated with the intensification and northward shift in the Saharan thermal low. This dynamic is reinforced by the increased frequency of ridges or high geopotential heights at 500 hPa, which transport warm tropical air toward the region. Moreover, the study reveals a notable decrease in the frequency of upper-level troughs at 500 hPa during the wet season. These upper-level troughs play a crucial role in cyclogenesis and the delivery of precipitation. These findings indicate a shift toward a regional atmospheric dynamic unfavorable to Morocco’s hydric balance, characterized by more frequent and intense summer heat and worsening winter drought. Full article

Accurate cloud and cloud-shadow segmentation is a crucial step in optical remote sensing image preprocessing, playing a significant role in subsequent applications such as land-cover classification and change detection. However, the complexity of cloud/shadow shapes and noise interference (e.g., snow and ice, buildings, complex backgrounds, and atmospheric optics) make this task challenging. Although existing deep learning methods have achieved remarkable results in cloud segmentation tasks, a better balance between computational efficiency and segmentation accuracy is still needed. Traditional deep learning models have good detail and generalization capabilities due to their local feature extraction ability and spatial invariance, but they are relatively weak in processing global context information, leading to false positives and false negatives in complex scenarios. Encoders based on state space models (such as VMamba) can effectively capture global context through long-range dependency modeling, but there is still room for optimization in computational efficiency. Additionally, complex attention mechanisms (such as CBAM) can improve feature representation capability, but the large number of parameters limits the deployment efficiency of models. This paper conducts a systematic architectural exploration of the MCloudX cloud segmentation network, seeking a balance between efficiency and accuracy from three directions: backbone network modernization, encoder efficiency optimization, and attention mechanism lightweighting. Through comprehensive ablation experiments on SPARCS and L8-Biome datasets, we systematically evaluate the independent and synergistic effects of each component and validate them on Biome_3 and SPARCS datasets. Experimental results show that the proposed optimization configuration (ResNet50+LocalMamba+ECA-Net) significantly improves computational efficiency while maintaining comparable accuracy to the baseline. We name this optimization configuration LECloud, providing valuable empirical references for future research on efficient remote sensing segmentation architectures. Full article

Ottoman mosques represent a unique synthesis of architectural elegance and structural ingenuity, where massive masonry domes are balanced on slender supports through carefully engineered load transfer systems. These monumental buildings, constructed centuries ago without modern analytical tools, continue to challenge contemporary engineers seeking to understand their behavior under seismic loading. This study presents an integrated evaluation of the structural and geotechnical performance of the 16th-century Sultan Selim Mosque in Konya, Türkiye, one of the most prominent examples of Classical Ottoman architecture. The research combines ambient vibration testing (AVT), geotechnical investigations, and finite element modeling (FEM) to assess the existing structural condition and soil–structure interaction (SSI) effects. Dynamic identification through AVT provided the modal characteristics of the mosque, which were used to calibrate a detailed three-dimensional FEM developed in ANSYS Workbench using a macro-modeling approach. The numerical analyses showed that observed deformation patterns and stress concentrations are consistent with field damage observations, indicating that differential settlements and heterogeneous subsoil stiffness are the primary factors influencing the structural response. The findings enhance understanding of the seismic behavior of monumental masonry domed structures and offer a solid basis for the evaluation and conservation of Ottoman-era architectural heritage. Full article

The interpretation of geophysical multi-attribute surveys is often subjective and complicated by large datasets, prompting the need for automated fusion methods that preserve structures and enhance anomalies. This study introduces an image fusion approach that combines the non-subsampled contourlet transform (NSCT) with the New Metric Parameter (NMP) rule to integrate multi-source polarizability and resistivity data for copper–nickel exploration. Using NSCT, source images are decomposed into multi-scale, multi-directional low- and high-frequency sub-bands. Low-frequency components are fused through dynamic weighting, while high-frequency components are merged using the NMP rule. The sensitivity to key parameters—such as low-frequency weight, grid size, and grid angle—was assessed using field data. Results indicate that NSCT + NMP fusion enhances spatial resolution and boundary definition of anomalies, effectively merging low resistivity with high polarizability signals. Quantitative field validation shows that 82.43% of the gabbroic mineralization zone has a judging coefficient below 0.45, confirming the fusion accuracy. Optimal parameter choices include dynamically adjusted low-frequency weights, a grid size that balances detail and noise suppression, and a 45° square grid for directional neutrality. This method offers a practical strategy for joint multi-physical data analysis and improved spatial recognition of mineralized bodies in exploration. Full article

In intensive farming, contactless individual pig identification is crucial for precision feeding and health monitoring. However, real-world barn conditions—such as fluctuating illumination, severe occlusions, non-rigid poses, and high inter-individual similarity—pose significant challenges. Existing models struggle to balance high accuracy with lightweight deployment. To address this, we propose YOLO-ESO, an optimized detection framework based on YOLOv10n. YOLO-ESO introduces three core innovations: (1) integrating the C2f_ODConv module into the backbone to strengthen feature learning under complex poses via dynamic convolution; (2) redesigning the neck with a Semantics and Detail Infusion (SDI) module to improve multi-scale fusion while suppressing background noise; and (3) embedding an Efficient Multi-Scale Attention (EMA) mechanism before the detection head to capture fine-grained identity cues like texture and contours. Evaluated on a real-world pig dataset, YOLO-ESO achieves an mAP@0.5 of 96.6%, an mAP@0.5:0.95 of 71.1%, and an F1 of 92.0%. YOLO-ESO surpasses state-of-the-art detectors including YOLOv8, YOLOv11, and RT-DETR, while introducing only 8.7 GFLOPs and 3.48 million parameters. Overall, the proposed YOLO-ESO provides an accurate and lightweight solution for robust individual pig identification in complex farming environments, showing strong potential for practical deployment in precision livestock farming. Full article

Accurate liver segmentation in laparoscopic surgery is critical but remains challenging due to low contrast, occlusion, and irregular organ boundaries. While deep learning has advanced medical image segmentation, existing models often trade off between accuracy, computational efficiency, and boundary precision. We propose IMAU-Net, a hybrid architecture integrating a pre-trained InceptionV3 encoder with a novel bottleneck combining Multi-Core Pooling (MCP) and enhanced Atrous Spatial Pyramid Pooling (ASPP). The MCP module captures fine-to-medium spatial details through parallel multi-kernel pooling, while ASPP extracts multi-scale contextual information via dilated convolutions. Evaluated on the M2CAI dataset with 5-fold cross-validation, IMAU-Net achieves a mean Dice coefficient of 0.9179 ± 0.012 and IoU of 0.8483 ± 0.015. Furthermore, external validation on the independent CholecSeg8K dataset (250 test samples) demonstrates generalizability across different laparoscopic procedures, achieving a Dice coefficient of 0.8745 ± 0.0312 and AUC of 0.9542, with a performance degradation of only 4.3% despite domain shift between liver surgery and cholecystectomy. Comparative analysis with state of the art methods demonstrates superior performance, with computational efficiency suitable for real-time applications (45 FPS, 42.3 M parameters). The proposed architecture provides an optimal balance between accuracy and efficiency for intraoperative guidance systems. While evaluated on retrospective laparoscopic image datasets rather than real-time intraoperative workflows, the model demonstrates potential for integration into surgical guidance systems pending prospective validation. Full article

Under climate change, extreme wind events are predicted to become both more common and more severe, increasing the vulnerability of plantation forests. In February 2023, ex-tropical Cyclone Gabrielle caused widespread wind damage to radiata pine (Pinus radiata D. Don) forests across the North Island of New Zealand, providing a rare opportunity to evaluate mechanistic wind-risk modelling under extreme storm conditions. This study assessed the performance of the ForestGALES model in predicting wind damage within the Rangipo genetic accelerator trial and examined the influence of stocking and cultivation on wind vulnerability. Using detailed pre-cyclone field measurements and high-resolution unmanned aerial vehicle light detection and ranging (ULS) data, ForestGALES was parameterised for the Rangipo trial and applied at both individual-tree and stand scales. Model predictions were compared with observed post-cyclone damage using balanced area under the receiver operating characteristic curve (AUC), accounting for strong class imbalance. Wind damage was observed in 16.7% of trees, of which 10.2% showed stem breakage and 6.5% overturning. Across both spatial scales, overturning was more accurately predicted than stem breakage. At the individual-tree scale, ForestGALES showed moderate predictive skill, with balanced AUC values of 0.650 ± 0.014 for overturning, 0.595 ± 0.011 for breakage, and 0.621 ± 0.008 for total damage. Model performance was stronger at the stand scale, where discrimination was highest for overturning (AUC 0.811 ± 0.122), followed by breakage (0.693 ± 0.116) and total damage (0.623 ± 0.083). Silvicultural treatments significantly influenced predicted critical wind speeds (CWS). High-stocking treatments exhibited consistently higher CWS values and therefore greater wind firmness than low-stocking treatments, while cultivation effects were smaller but significant. Simulated reductions in stocking further demonstrated increased wind vulnerability as stocking declined, highlighting thinning as a primary determinant of wind risk. These findings demonstrate that ForestGALES can reliably discriminate wind damage at operational stand scales under extreme cyclone conditions and highlight the importance of stand structure in improving plantation resilience under increasingly storm-prone climates. Full article