Search Results (3,751)

Low-emission zones (LEZs) require cities to define policy rules, predict effects before deployment, and verify outcomes afterwards, yet mobility, emissions, meteorology, exposure, data governance, and public communication are commonly handled in separate systems. This paper presents envair360, a Physical Intelligence architecture and a four-stage, evidence-gated LEZ methodology connecting project definition, baseline feasibility, digital-twin design, deployment, and verified impact closure. A design-science method is combined with an operational case study of Cartagena, Spain, because the research object is both a socio-technical artefact and a context-dependent municipal deployment. The technology chain is selected to bridge complementary scales and functions: SUMO for link- and vehicle-level traffic, WRF and CHIMERE for meteorology and regional chemistry, MUNICH and street-canyon parameterisation for computationally tractable street resolution, model-output calibration anchored to measurements, and FIWARE/NGSI-LD for governed context exchange. The manuscript distinguishes city observations, peer-reviewed component validation, demonstrated platform capabilities, and policy or engineering targets. A Murcia component study reports lower hourly than daily agreement after deep-learning calibration (NO${}_2$: $r=0.79$ hourly and $0.94$ daily; O${}_3$: $r=0.85$ hourly and $0.97$ daily), illustrating the importance of temporal aggregation and transfer limits. Digitisation of the prior Madrid ozone-density figure indicates modal shifts of approximately +32.0 and +27.8 source-axis units at two stations; the supplied source does not permit a numerical NO${}_x$ bias estimate. A separate six-city export audit covers 24,384 records and 4064 street segments and demonstrates a common model-output schema, not predictive validation. In Cartagena, project documentation reports elevated PM${}_{10}$/PM${}_{2.5}$, urban heat and solar-radiation stress, and a plausible role for dry-climate dust resuspension, supporting a superblock-oriented LEZ proposal with a long-term 30% vehicular CO${}_2$ reduction target. The paper’s specific contribution is the governed orchestration, evidence taxonomy, quality gates, reproducible lineage, explicit policy-scenario representation, and portable city-onboarding protocol; it does not claim that the individual scientific models, the Cartagena deployment, or the cited project targets originated in this manuscript. Full article

Accurate retinal vessel segmentation supports quantitative vascular analysis in assessing ocular and systemic diseases. Yet, its clinical scalability is constrained by limited pixel-level annotations and domain shift across heterogeneous fundus datasets. Thus, this study proposes a domain-adaptive retinal vessel segmentation model (DA-VesselNet), a weakly supervised approach that transfers vessel-segmentation knowledge from annotated source datasets to the unannotated Retinal Fundus Multi-Disease Image Dataset (RFMiD). The model was trained on DRIVE, CHASE_DB1, and FIVES, and adapted to RFMiD. The ResNet50 encoder with an attention-gated U-Net decoder, confidence-aware pseudo-label supervision, and perturbation consistency regularisation were used for the training process. Results on the held-out CHASE_DB1 indicated that DA-VesselNet achieved a Dice score of 0.5778, an Intersection over Union (IoU) of 0.4082, and an Area Under the Curve (AUC) of 0.9518. On 200 held-out FIVES test images with different pathological features, it achieved a Dice score of 0.7447 and an AUC of 0.9733, outperforming the source-only baseline model. To assess adaptation independently of source-adjacent data, the model was further tested on STARE and HRF, two domains excluded entirely from source training. Adaptation improved Dice by 0.0469 on STARE and 0.0056 on HRF with AUC gains of 0.0140 and 0.0121, respectively. Ablation analysis identified source-anchored supervision as the dominant contributor to performance. The adapted model was subsequently applied to generate vessel pseudo-labels for the RFMiD target domain, providing a structural resource for future vessel-informed analysis. These findings demonstrate that DA-VesselNet offers a scalable solution for creating clinically relevant pseudo-labelled vessels in fundus imaging with limited annotations. Full article

Point cloud fusion is crucial in geospatial analysis, combining data from multiple sources (e.g, LiDAR and photogrammetry) to provide a more complete and accurate environmental representation. However, integrating airborne hybrid sensors or cross-source point clouds remains challenging due to variations in geometric accuracy, data precision, gaps, and sensor attributes. Despite recent advancements, these challenges remain and are among the most demanding aspects in geospatial data processing for remote sensing applications. We propose a new point cloud fusion algorithm that leverages local plane constraints to achieve advanced semantic consistency. The proposed method dynamically fits local planes to the target point clouds, enabling robust alignment of source points to these planes. Evaluation on two real-world datasets demonstrates significant gains in accuracy and preservation of geometric details. Our algorithm also improves the accuracy of downstream tasks such as semantic segmentation. In our experiment, the overall accuracy for the Dudelange dataset increases from 48.5% to 80.1%, and that for the Dublin dataset increases from 72.9% to 88.0%. While challenges persist with sparse and noisy datasets, experimental results highlight the effectiveness of the proposed method, offering valuable insights for maximizing the potential of cross-source point cloud data. Full article

Octopus arms combine a compliant continuum body with distributed suckers, providing a biological reference for soft manipulators that require large deformation and stable local contact. Inspired by this functional organization, this study presents an octopus-inspired two-segment pneumatic soft manipulator with passive suction cups at the distal end. The manipulator consists of a cylindrical proximal segment, a tapered distal segment, and a thermoplastic polyurethane (TPU) suction-cup array. The proximal segment provides structural support and global bending, whereas the tapered distal segment improves local compliance and contact posture adjustment near the target surface. Each segment contains three independently driven pneumatic chambers arranged at 120° intervals, enabling spatial bending through differential pressurization. The distal suction cups are not connected to an active vacuum source; instead, attachment is assisted by mechanical pressing, partial air expulsion from the cup cavity, and elastic recovery of the cup lip. Finite element simulations were conducted to examine pressure-driven bending of the soft arm and deformation of the suction cups under equivalent sealing loads. A piecewise constant curvature model was established to estimate the posture and reachable workspace of the two-segment manipulator. A prototype was fabricated and tested on a pneumatic control platform. Within the pressure range of 50–200 kPa, both segments exhibited increasing bending angles with increasing input pressure; at 200 kPa, the maximum observed bending angles were approximately 70° for the proximal segment and 87° for the distal segment. Distal-segment tests demonstrated passive contact holding on a brown glass bottle and a black roll of electrical tape. Coordinated actuation further produced compound bending and twisting postures. These results show that the proposed design translates the functional division of octopus arms into a modular pneumatic soft manipulator with controllable spatial deformation and passive distal contact support. Full article

Modern remote sensing images (RSIs) provide increasingly fine spatial detail, making pronounced scale variations and complex spatial distributions of land-cover classes more apparent and thereby increasing the difficulty of semantic segmentation. Recent remote sensing semantic segmentation methods therefore have increasingly adopted Transformer- and Mamba-based models to improve global contextual modeling. However, these models often suffer from intraclass inconsistency and interclass feature confusion, leading to fragmented object structures and imprecise boundary delineation. Accordingly, we develop SPFMamba, an architecture built around Mamba that combines semantic prompting with frequency-adaptive fusion, thereby improving global context modeling and fine-detail representation. During feature reconstruction, we propose a semantic prompt global–local Mamba (SPGLM) block to jointly model global semantic information and local spatial cues. Its parallel semantic prompt global and multidirectional local perception branches promote semantically coherent and spatially continuous feature distributions, thereby effectively preserving the structural integrity of ground objects. To further alleviate cross-level semantic discrepancies and strengthen the representation of small-scale targets, we design a high-frequency adaptive fusion module (HFAFM). It first refines high-frequency responses in shallow layers to retain small-scale object structures and boundary cues. Subsequently, deep semantic priors guide local cross-attention, allowing low-level spatial cues to be selectively integrated with deeper semantic representations. Evaluations across ISPRS Vaihingen, ISPRS Potsdam, and OpenEarthMap datasets show that SPFMamba delivers favorable segmentation accuracy with only 17.85 M parameters, while showing improved preservation of object structures and fine spatial details in qualitative comparisons. Full article

This article aims to analyze the determining factors behind the persistence of barriers to financial inclusion in Morocco. It also seeks to highlight the relationship between several economic, social, cultural, regulatory and institutional constraints on the access and use of financial services by underserved populations. Thus, our methodology is exclusively quantitative and relies on a survey conducted through a questionnaire targeting a sample of 562 individuals from diverse socio-economic backgrounds. The collected data were analyzed using the partial least squares method (SmartPLS). The study reveals the importance of improving financial education, digital infrastructure, and regulatory flexibility to overcome financial exclusion. It also underscores the need to strengthen trust in financial institutions, adapt financial products to user needs, and implement inclusive public policies that promote financial empowerment for all segments of the population. Full article

Acute myocardial ischemia (AMI) is a life-threatening cardiovascular disorder characterized by excessive oxidative stress and persistent inflammatory cascades, yet safe multi-target natural therapeutic agents remain scarce. Cinnamomum migao is a well-known Miao ethnic medicine used for cardiovascular conditions, yet its cardioprotective effects and mechanisms remain largely unclear. This study investigated the efficacy and underlying mechanism of C. migao ethyl acetate extract (MGE) against AMI. MGE significantly improved the viability of H9c2 cardiomyocytes subjected to OGD/R injured. UPLC-MS/MS and molecular networking identified 30 constituents in MGE, among which sesquiterpenoids predominated. In ISO-induced AMI rats, MGE dose-dependently mitigated myocardial injury, as reflected by reduced ST-segment elevation, serum CK-MB and LDH levels, and alleviated histopathological damage. MGE enhanced SOD and CAT activities, decreased MDA content, and inhibited the secretion of TNF-α, IL-6 and IL-1β. Mechanistically, MGE downregulated the expression of NOX4, HIF-1α, p38 MAPK and NF-κB p65, while activating the Nrf2/HO-1 pathway. Oxyphyllenone A and magnodelavin C were identified as key active sesquiterpenoids that stably bound to IL-17 and TNF. Collectively, MGE alleviates AMI injury via anti-oxidative, anti-hypoxic, and anti-inflammatory effects through modulation of the HIF-1α/Nrf2/NF-κB/MAPK axis downstream of oxidative stress, with sesquiterpenoids serving as its key bioactive components. This work provides robust experimental evidence supporting C. migao as a promising natural antioxidant candidate for the prevention and treatment of AMI. Full article

The post-earthquake damage state of reinforced concrete (RC) double-column piers directly affects bridge traffic capacity and emergency response efficiency. To improve the interpretability of damage assessment, this study proposes a Structural–Visual Integrated Evaluation (SVIE) framework that combines structural response analysis with image-based damage evidence. Structural responses from quasi-static tests are used to define four baseline damage states: intact-to-slight, moderate, severe, and critical damage. An improved DeepLabv3+ model is then applied to 315 global-scene images for end-to-end semantic segmentation of background, concrete spalling, and reinforcement exposure. The extracted visual evidence is used to verify its consistency with the baseline structural states. On the test set, the model effectively identified concrete spalling regions, achieving an IoU, F1-score, Precision, and Recall of 78.86%, 88.18%, 89.93%, and 86.49%, respectively. For reinforcement exposure, although IoU and Recall were relatively low because of sample scarcity and small-target characteristics, Precision reached 70.40%, indicating that detected regions can provide supplementary evidence for severe local damage. The consistency analysis showed that the morphology of visual damage was generally compatible with the progression of structural damage states. The results provide a laboratory-based proof of concept for a mechanically grounded and visually interpretable framework for rapid post-earthquake assessment of RC double-column piers. Full article

Infrared–visible image fusion aims to integrate thermal target information from infrared images and structural texture information from visible images into a single informative image. Existing deep fusion methods still face challenges in preserving fine textures, maintaining structural consistency, and balancing complementary information under low-light conditions. To address these issues, this paper proposes MRCDFusion, an unsupervised infrared–visible image fusion network based on residual conditional diffusion and Transformer refinement. Specifically, a shared dense encoder is used to extract modality-specific and cross-modal complementary features from infrared and visible images. A Modality-Level Attention Module (MLAM) is then introduced to aggregate strong responses from infrared and visible features and construct modality-aware condition features for guiding the diffusion process. Instead of generating fused features from scratch, the proposed method adopts a base-plus-residual diffusion strategy, in which base features preserve global structures and residual diffusion enhances local details. A deterministic noise strategy is further introduced to improve inference reproducibility. The diffusion-enhanced features are refined by a window Transformer and depthwise separable convolutions, followed by gated feature fusion and progressive image reconstruction. Experiments are conducted primarily on the low-light LLVIP dataset, while FMB, TNO, and RoadScene are used for zero-shot cross-dataset evaluation without additional fine-tuning. The results show that MRCDFusion achieves particularly strong performance in gradient- and edge-related metrics while remaining competitive in visual information fidelity and cross-modal correlation metrics. Ablation studies verify the effectiveness of the main components, and downstream detection and auxiliary segmentation experiments further demonstrate the potential utility of the fused representations for subsequent visual perception tasks. Full article

Self-Supervised Learning (SSL) has attracted increasing attention in remote sensing image understanding because it can learn transferable representations from unlabeled images. However, two issues remain insufficiently examined in collaborative SSL for remote sensing. First, when high-ratio masking removes entire small objects or structurally informative regions, the remaining visible patches may provide insufficient evidence for semantically coherent reconstruction. Second, heterogeneous self-supervised objectives may exhibit different loss scales, gradient magnitudes, and convergence behaviors such that fixed coefficients can produce uneven branch contributions during training. To address these issues, this paper proposes Context-Consistency Enhanced Collaborative Self-Supervised Learning (C²DSSL) for remote sensing image understanding. C²DSSL introduces a teacher–student context-consistency constraint, in which multi-scale reconstruction features from the complete teacher observation serve as contextual targets for the student network when reconstructing the corresponding masked observation. In addition, gradient-sensitive dynamic weighting uses temporally smoothed loss-gradient magnitudes as empirical signals to adjust the relative contributions of heterogeneous self-supervised objectives. Under the evaluated settings, adding the context-consistency constraint improves KNN representation evaluation, UCMerced classification, Potsdam semantic segmentation, and DOTA oriented object detection, while maintaining the Baseline performance on the Million-AID subset. Dynamic weighting shows task-dependent effects, including a performance gain on the Million-AID subset classification task when combined with CCL. Full article

In conventional CNC machining, the workpiece clamping pose is registered with a preset CAD model under multiple geometric constraints to establish the machining reference frame. The tool path, generated from this model, is subsequently used to produce components of identical geometry. However, this paradigm proves inadequate when a final shape must accommodate morphological variations specific to each individual blank. Manual grinding, as an alternative, is not only inefficient and hazardous but also relies heavily on subjective quality assessment. To address these challenges, we propose an adaptive local-region milling strategy tailored for blanks with similar yet non-identical surface morphologies, enabling the finished geometry to adjust dynamically to each workpiece. Under conditions of under-constrained clamping, visual positioning is first employed to automatically locate the target regions. Line laser scanning is then conducted over the planned area to acquire high-density point clouds. Through segmentation, points lying outside the region to be machined are extracted, from which a theoretical post-machining surface is reconstructed. Milling toolpaths are subsequently planned based on this reconstructed model to compensate for surface variations across different blanks. Experimental validation on a three-axis CNC milling machine demonstrates that the proposed adaptive strategy effectively replaces manual grinding by removing the bulk of the machining allowance from locally variant surfaces. With the reconstructed model serving as the reference, 77.1 percent of the machining errors fall below 0.055 mm. These results confirm that the method yields a smooth and level surface finish, thereby meeting the fundamental requirements for such adaptive machining tasks. Full article

Glomerulosclerosis is not an independent disease entity but rather a common histopathological endpoint shared by diverse glomerular diseases and chronic kidney disorders. It represents the final common pathway of progressive glomerular injury and is a major determinant of chronic kidney disease (CKD) progression. Accordingly, this review focuses on the shared cellular and molecular mechanisms that drive glomerulosclerosis, together with current diagnostic strategies, mechanism-based therapeutic approaches, and future directions toward precision nephrology. We begin by summarizing the structural and functional features of the glomerulus and outlining the key molecular mechanisms that drive sclerosis, including podocyte loss, mesangial matrix expansion, endothelial dysfunction, inflammation, and fibrosis-related signaling pathways. The clinical and pathological classifications of glomerular diseases associated with glomerulosclerosis—ranging from primary and secondary focal segmental glomerulosclerosis (FSGS) to diabetic, hypertensive, and immune-mediated forms—are discussed in detail. We further review current diagnostic approaches, including renal biopsy patterns, emerging biomarkers, and novel imaging and digital pathology tools that enable earlier and more precise assessment of disease severity. Recent advances in treatment are summarized across conservative, immunologic, and targeted therapeutic categories, with emphasis on SGLT2 inhibitors, endothelin receptor antagonists, immunomodulatory agents, and antifibrotic compounds. Finally, we highlight rapidly evolving therapeutic and technological advances, including podocyte-directed interventions, APOL1-targeted therapies, RNA- and gene-based therapeutics, and artificial intelligence-driven precision nephrology. These emerging approaches are reshaping the management paradigm of glomerular diseases by enabling molecular disease stratification and personalized treatment strategies. Despite substantial progress, significant unmet needs remain, particularly concerning early detection, individualized treatment selection, and the marked heterogeneity of disease mechanisms across patient populations. Overall, this review provides an integrated framework for understanding glomerulosclerosis as a shared pathological endpoint of diverse glomerular diseases and highlights emerging mechanism-based therapeutic strategies that support the transition toward precision nephrology. Full article

Detecting ships near the coast in Synthetic Aperture Radar (SAR) images is an important task. However, achieving accurate detection in these scenarios remains a significant challenge. The complex coastal topologies and multiple scattering effects frequently induce severe shore–sea feature aliasing, which conventional end-to-end detectors struggle to untangle due to their inherent architectural conflicts between background suppression and fine-grained localization. To address this issue, we propose a two-stage generative framework named Segmentation Guidance and Enhanced Diffusion (SGED). In the first stage, an Enhanced Attention U-Net (EAU-Net) is specifically tailored for robust shore–sea separation. By integrating adaptive Signal-to-Noise Ratio (SNR) masking, lightweight Transformer bottlenecks, and an Edge-Aware Composite Loss, EAU-Net isolates the maritime search space, achieving a Dice Similarity Coefficient (DSC) of 0.9047 and an 85.30% Near-Shore Coverage Accuracy (NSCA). Building upon this refined prior, the second stage introduces a SAR-Enhanced Diffusion Detector (SAR-DDet). It constructs a structural–statistical dual-verification mechanism by embedding Pixel Difference Convolution (PDC) and Constant False Alarm Rate (CFAR) soft attention into the multi-scale features. Coupled with a Normalized Wasserstein Distance (NWD) loss and a four-step DDIM iterative denoising process, SAR-DDet effectively mitigates small-target gradient vanishing and corrects bounding box coordinate quantization errors. Experiments on a near-shore subset of the HRSID benchmark demonstrate that SGED achieves competitive performance. It achieves an mAP of 62.35%, an $A{P}_{75}$ of 74.67%, and a small-target $A{P}_S$ of 60.14%, with consistent improvements over monolithic baseline architectures on this dataset. Full article

UAV-based ship segmentation is important for berthing and unberthing monitoring, ship–berth distance estimation, and situational awareness in port waters. However, direct video mask propagation with Segment Anything Model 2 (SAM 2) remains susceptible to local contour degradation in high-resolution UAV videos containing weak berth-side boundaries, adjacent tugboats, quay-side structures, water-surface reflections, and target-scale variations. To address this problem, a constrained local boundary refinement method is proposed for target-ship segmentation. The method follows a training-free, first-frame-mask-initialized semi-supervised video object segmentation setting, with all SAM 2 parameters remaining frozen. ROI Boundary Re-Inference first enhances weak contours within local target neighborhoods. Prompt-Consensus Refinement then retains boundary candidates consistently supported by multiple structured prompt variants. Finally, Boundary-Constrained Non-Erosive Fusion restricts supplementation to a narrow neighborhood of the propagated boundary and incorporates reliable candidates without deleting the original foreground. Experiments on a self-built UAV berthing and unberthing video dataset show that the proposed method improves Boundary F@5 px from 89.68% to 94.03% and J&F from 94.08% to 96.39%. These results demonstrate that the proposed method improves target-ship boundary delineation without model training or fine-tuning. Full article

Indoor 3D referring segmentation aims to identify and segment the target object in a point cloud according to a natural language expression. Although recent advances in multimodal feature fusion have markedly improved this task, existing methods do not jointly model the structural regularities commonly observed in indoor objects, such as approximate symmetry and repetitive local patterns, in conjunction with the directional constraints conveyed by referring expressions. In this work, we formulate symmetry-aware representation as the extraction of direction-consistent structural responses from both forward and backward traversals of the same spatially ordered sequence while preserving direction-sensitive variations introduced by occlusion, point cloud incompleteness, and cluttered scene layouts. Based on this formulation, we propose PCMTRefer, a symmetry-aware text-guided Point Mamba framework for indoor 3D referring segmentation. The input point cloud is first partitioned using an octree and arranged into a spatially coherent sequence via Z-order (Morton) ordering. A bidirectional state-space encoder then aggregates complementary context from both traversal directions, yielding richer representations of regular boundaries, repetitive structures, and approximately bilateral object geometries. In parallel, an asymmetric text-to-point guidance module injects semantic cues—object categories, attributes, and spatial relationships—into point-wise features, while a Background-Relaxation Token offers an auxiliary matching channel for non-target background regions. A Gumbel-Softmax-based semantic primitive learning module further extracts discriminative cues from referring expressions and integrates language semantics with point-level geometric features through a multi-scale decoder. Experimental results on the ScanRefer benchmark show that PCMTRefer achieves an Overall Acc@0.25 of 58.56%, an Overall Acc@0.5 of 54.19%, and an mIoU of 49.97%. Multi-seed validation further confirms the statistical stability of these results, with standard deviations of less than 0.2% across three independent runs. Full article