Search Results (3,752)

The criminal law assessment of consensual sexting between minors requires interpretation within a child-rights framework that accounts for children’s evolving capacities and the ultima ratio principle of criminal law. Although child self-generated sexual images and videos (CSGIV) may, in many jurisdictions, conceptually fall within the scope of offences relating to child pornography or child sexual abuse material (CSAM), consensual peer-to-peer sharing typically lacks the classical elements of sexual exploitation. This article provides a structured comparative overview of how the criminal law systems of the twenty-seven European Union (EU) Member States regulate consensual minor-to-minor sexting, identifying three regulatory models and assessing their compatibility with child-rights standards. The research is based on a structured comparative legal analysis drawing on the report and country reports of the second monitoring round of the Lanzarote Committee, complemented by a primary analysis of the relevant criminal law provisions of the Member States. The analytical framework relies on a coding manual developed by the authors along thematic dimensions. The findings identify three regulatory models: systems that provide explicit differentiation and safeguards; systems that formally criminalise the conduct but operate with implicit mitigation; and systems that entail a broad risk of criminalisation. The analysis reveals considerable normative fragmentation and demonstrates that the absence of explicit differentiation may expose forms of adolescent self-expression to criminal liability. The article concludes that, to comply with child-rights standards, explicit normative safeguards and a consistent application of the exceptional character of criminal law are required. Full article

The traditional monitoring of forest musk deer behavior primarily relies on direct human observation or the post hoc playback analysis of ordinary surveillance videos. This approach is not only time-consuming and labor-intensive but also highly subjective, easily leading to missing or misjudged critical behavioral information. Moreover, it is difficult to achieve real-time monitoring and anomaly warning. These limitations severely constrain the efficiency of the large-scale artificial breeding of forest musk deer and the effective advancement of wild population conservation. Thus, this study proposes a forest musk deer behavioral recognition method based on an improved YOLOv8-Pose. A forest musk deer behavior image dataset covering four typical behaviors was constructed, and 18 keypoints were systematically annotated. This study designs a Dilated Spatial Pyramid Pooling-Fast (DILATED-SPPF) module and a Multi-scale Depthwise Separable Context Mixer (MDSC-Mixer) module, and integrates them into YOLOv8-Pose. Experimental results show that the improved model outperforms the original YOLOv8-Pose and comparison models such as YOLOv11/v12-Pose on key metrics of object detection (Box-mAP50 0.929, Box-mAP50-95 0.814) and pose estimation (Pose-mAP50 0.879, Pose-mAP50-95 0.565). This study further develops a visual interactive interface that intuitively presents detection results and skeleton structures. This work provides a high-precision, low-cost automated behavior analysis tool for the artificial breeding and wild conservation of forest musk deer with significant application value for enhancing the intelligence level of endangered species protection. Full article

Cat behavior recognition in unconstrained videos is important for animal welfare monitoring and veterinary assessment, yet remains challenging because behavior cues are often carried by highly deformable and intermittently visible parts such as the head and tail. This study aims to improve clip-level cat behavior recognition under unstable part visibility in real-world videos. We propose PMTNet, a part-centric temporal network for cat behavior recognition under unstable part visibility. The framework first detects the cat body, head, and tail using a DEIM-based detector, then selects a detector according to video-domain continuity and stability, and finally models behavior from ROI appearance features and explicit geometric motion cues. The framework was developed and evaluated using a part-detection dataset of 4000 training images and 500 validation images, together with a cat behavior dataset of 1283 video clips across five categories. In the best-performing setting, PMTNet achieved 93.1% Top-1 Accuracy and 90.9% Macro-F1. Ablation studies further suggest that detector choice in the video domain, complementary part cues, and missing-aware fusion all contribute to the final recognition performance. On the present dataset, PMTNet also outperformed representative end-to-end video recognition baselines. These results support the use of part-centric temporal modeling for cat behavior recognition in unconstrained real-world videos. Full article

Existing self-supervised methods have achieved significant success in ground-level autonomous driving scenarios, but applying them directly to Unmanned Aerial Vehicle (UAV) videos remains challenging. On the one hand, rapid pose changes in UAVs often lead to oblique-view imaging, making it difficult for conventional methods to handle the perspective distortion in oblique imagery. On the other hand, complex UAV viewpoints may cause depth blurring in low-texture regions. To address these challenges, we propose a lightweight self-supervised monocular depth estimation method for UAV scenarios. By utilizing a Dynamic Direction-Aware Module (DDaM), the network adaptively adjusts the sampling grid to correct distorted features during feature extraction, while enhancing its ability to capture features at different spatial locations. Furthermore, to mitigate the loss of spatial information caused by multiple downsampling operations, we integrate a Coordinate Attention Mechanism into the encoder. This mechanism captures features along two separate spatial axes, preserving the spatial coordinates of object boundaries. Our experiments demonstrate that the synergy between DDaM and the Coordinate Attention Mechanism enables the prediction of more accurate object boundaries and richer local details. To validate the effectiveness and practical applicability of the proposed method, we conduct experiments on both the MidAir synthetic dataset and the UAVid real-world dataset. The results show that, compared with current baseline methods, our approach maintains competitive performance while requiring the fewest parameters. Full article

Backgrounds/Objectives: Malignant pleural effusion (MPE) is a frequent complication of advanced cancer, and talc pleurodesis via video-assisted thoracoscopic surgery (VATS) represents a standard palliative treatment with high efficacy. However, evidence regarding synchronous bilateral pleurodesis in patients with bilateral MPE is limited. This study evaluates the feasibility, safety, and outcomes of a single-stage bilateral VATS talc pleurodesis approach. Materials and Methods: We retrospectively analyzed patients undergoing synchronous bilateral VATS talc poudrage between 2000 and 2025 at a single tertiary cancer center. Inclusion criteria included adult patients with bilateral MPE, expandable lungs, and suitability for surgery. Preoperative assessment involved imaging and multidisciplinary evaluation. Perioperative data, complications, mortality, and recurrence rates at 30 days and 3 months were collected. Survival and pleural effusion-free survival were estimated using the Kaplan–Meier method. Results: Thirty patients were included (median age 63.2 years). The most common primary tumors were breast (43%), lung (30%), and ovarian cancer (17%). Mean operative time was 78.6 min, with no intraoperative complications. Mean hospital stay was 6 days. Postoperative morbidity included atrial fibrillation (13%) and respiratory failure (6.6%), both managed conservatively. Thirty-day mortality was 3%. Pleural effusion recurrence occurred in 6.6% at 3 months and 10% at 7 months. Mean follow-up was 9.7 months. Conclusions: Synchronous bilateral VATS talc pleurodesis is a feasible and safe procedure in selected patients with bilateral MPE with acceptable morbidity. Further prospective studies are needed to confirm these findings and refine patient selection. Full article

In a context of greenhouse-gas-reduction for climate-change mitigation, Optical Gas Imaging (OGI) is cited by US and EU regulations as a key technology for detecting methane leaks in the oil and gas industry. The paper outlines the principles of OGI, covering specificity of both high-performance cooled cameras and cost-effective thermal infrared uncooled cameras. It explains camera design, the optical-radiometric theory of contrast and sensitivity, and provides a comprehensive description of the key performance indicators (KPIs) such as NETD, NECL, and MDLR; together with parameters that influence them. These theoretical concepts are supported by measurements taken under laboratory conditions and outdoors, with wind and complex scenes. Finally, video-processing methods for visualizing gas leaks are presented, showing how they increase visual sensitivity and reduce the user’s cognitive load. Full article

This study investigates how different rural landscape types shape visual attention and physiological responses, with the aim of informing more targeted rural landscape renewal. Four typical rural landscape types in the suburbs of Hefei, China, were examined: Flat Farmland (FF), Hilly Forest (HF), Developed Plain (DP), and Water-network Lowland (WNL). All four study villages are project villages in the suburban area of Hefei where rural revitalization is currently being advanced. This study therefore treats them as empirical cases within the context of rural revitalization in China, using them to examine perceptual differences among rural landscape types and their implications for rural landscape renewal. A two-stage research design was adopted to balance field realism and laboratory control. In the first stage, 40 representative scene images were selected by combining field video records with fluctuations in on-site skin conductance response (SCR). In the second stage, laboratory experiments were conducted while participants viewed the selected images, during which eye-tracking, skin conductance, and heart rate data were recorded simultaneously. These measures were used to characterize visual attention allocation and autonomic physiological responses across different rural landscape types, rather than to directly measure landscape preference. For Area of Interest (AOI) analysis, each image was coded into six landscape element categories: vegetation, buildings, roads, sky, vernacular buildings, and water bodies. The results revealed significant typological differences in overall visual search patterns and autonomic responses. Gaze hotspots were concentrated on identifiable targets and boundary regions in the foreground and midground, whereas the sky attracted relatively limited attention. FF primarily emphasized vernacular buildings and farmland boundaries, HF emphasized settlement interfaces and spatial transition nodes, DP emphasized road junctions and facilities along routes, and WNL emphasized water bodies and water–land interface zones. These findings suggest that a two-stage multimodal design can provide supporting evidence for understanding type-specific perceptual responses and can support more targeted strategies for rural landscape renewal. Full article

Neck pain is increasingly associated with exposure-dependent dysfunction linked to digitally mediated behaviors, prolonged near-work, sustained postures, and reduced movement variability, whereas cervical assessment remains dominated by static imaging and brief in-clinic examination. This narrative review evaluates why current diagnostic approaches remain poorly suited to the dynamic nature of many contemporary cervical disorders and examines segmental cervical motion as a clinically relevant but insufficiently observed functional target. Evidence from static imaging, dynamic radiographic methods, laboratory motion analysis, wearable inertial sensing, markerless video, and digital measure validation frameworks is synthesized to assess both current capabilities and translational limitations. Dynamic radiographic methods can characterize intervertebral motion with high anatomical specificity, but they are not suitable for scalable longitudinal monitoring. By contrast, wearable and video-based approaches are more compatible with real-world assessment, yet they capture external head–neck kinematics rather than vertebral-level kinematics directly and remain constrained by indirect observability, soft-tissue artifact, and inference uncertainty. On this basis, the review proposes a four-layer framework for continuous non-invasive cervical functional assessment based on sensing, representation, inference, and clinical interpretation, in which segmental cervical behavior is treated as a latent segment-informed functional construct inferred from multimodal external signals and periodically anchored to sparse reference-grade imaging anchors. Segmental motion signatures are consequently positioned as candidate digital measures for longitudinal cervical monitoring, provided that their development is supported by rigorous analytical and clinical validation, explicit uncertainty reporting, and demonstrated incremental clinical value. Full article

Tensor completion is commonly formulated by minimizing a convex combination of nuclear norms of mode-wise unfolding matrices. Although effective, the non-negative weight constraint can limit the flexibility of mode balancing, especially when different modes contribute unequally to the reconstruction. In this paper, we propose a tensor completion model based on a linear combination of nuclear norms, where the weights are allowed to take signed values under a normalization constraint. To implement this model, we develop an ADMM-based algorithm, termed FlexHaLRTC, which extends the standard singular value thresholding update to handle both shrinkage for positive weights and expansion for negative weights. Experiments on color image inpainting and video completion show that the proposed method achieves competitive PSNR, SSIM, and RSE results, with more noticeable gains in high-missing-rate settings. Full article

In video synthetic aperture radar (Video SAR), target motion causes defocusing, making it impossible to determine the target’s real-time position using reflected echoes. However, the shadows formed by the target occluding ground reflections can accurately characterize the target’s real-time position. To address challenges such as varying shadow scales, low contrast with the moving background, and susceptibility to clutter interference, this paper proposes a shadow detection network called DSENet to enhance the detail and semantic features of shadows. First, to enhance shadow features and reduce sampling loss during backbone network feature extraction, we design a detailed information enhancement (DIE) module to achieve lossless downsampling and effectively preserve the detailed features of the shadowed target. Second, we propose a semantic spatial feature aggregation (SSFA) module to enhance global semantic space feature extraction, improve the contextual feature representation of the target’s shadow region, and provide robust semantic space prior information for the model. Finally, we designed a detailed semantic fusion (DSF) module to improve the neck network’s ability to fuse shadow details and semantic features in video SAR images, further enhancing the model’s localization performance for target shadow features and achieving accurate localization of moving targets in video SAR. Comparative and ablation experiments validate the effectiveness and superiority of the proposed method. Experimental results on the Sandia National Laboratories (SNL) public dataset demonstrate that DSENet is efficient and performs excellently, achieving a P of 92.4% and an F1 score of 83.1%. Full article

As urbanization picks up pace and the public demand for security keeps climbing, video surveillance systems have emerged as a vital tool for maintaining social stability and safeguarding public safety. Person Re-Identification (Re-ID), as one of the core technologies in intelligent monitoring, mainly aims to accurately match pedestrian identities across cameras without overlapping fields of view. However, in practical applications, occlusion remains a primary challenge that severely degrades Re-ID performance. Especially in high-density crowds, pedestrians are often partially or completely obscured by other objects or individuals, resulting in incomplete image information and impaired feature representation, which significantly reduces recognition accuracy and reliability. Aiming at the problems of excessive reliance on external pose estimation models and asymmetric information matching in occluded Re-ID, this paper proposes a transformer-based pedestrian background decoupling network. The algorithm achieves foreground–background separation and multi-scale feature matching through the synergy of three modules. Meanwhile, a two-stage training strategy is adopted: the first stage optimizes the decoupling module to ensure clean feature separation, while the second stage jointly fine-tunes the correlation module to enhance matching accuracy. Extensive experimental results show that the proposed algorithm outperforms existing methods. Full article

The convergence of artificial intelligence and robotic surgery is redefining the management of genitourinary cancers by enhancing diagnostic accuracy, surgical precision, and training efficiency. This narrative review explores recent advancements in artificial intelligence applications across the cancer care continuum, with a focus on prostate, kidney, and bladder malignancies. Artificial intelligence tools, particularly those based on machine learning and deep learning, have demonstrated strong performance in analyzing imaging data, segmenting tumors, predicting pathological features, and supporting clinical decision-making. Intraoperatively, artificial intelligence enables skill assessment, personalized feedback, and real-time navigation by processing data from surgical videos and robotic system sensors. Augmented reality and intraoperative modeling further enhance visualization and margin control during complex procedures. The review also discusses emerging technologies such as single-port robotic platforms, which offer advantages in confined anatomical spaces and support less invasive approaches. Additionally, the growing field of telesurgery is addressed, highlighting its feasibility for complex urologic operations across vast distances. While many of these innovations are still in early stages of clinical validation, their integration into practice has the potential to improve oncologic and functional outcomes, expand access to expert care, and foster the development of next-generation surgical strategies in urologic oncology. Full article

Children increasingly consume online video content, creating a growing need for scalable approaches to support content moderation workflows. However, directly identifying harmful or policy-violating content, such as violence, sexual content, or self-harm, remains a complex task that typically requires specialized classifiers and domain-specific annotations. In this context, sentiment analysis can provide complementary information by capturing affective signals expressed through language and visual cues. This study does not treat sentiment polarity as a direct indicator of unsafe or policy-violating content. Instead, it explores multimodal sentiment analysis as an auxiliary triage signal that may help prioritize content for human review or identify segments requiring further inspection. This paper investigates the feasibility of using large vision–language models (LVLMs) for zero-shot multimodal sentiment analysis on utterance-aligned video segments. We evaluate two LVLMs, LLaVA-OneVision-7B and Qwen2.5-VL-7B, under three input settings: text-only, vision-only, and multimodal, using a conversational TV-series dataset consisting of short utterance-level video segments and transcripts. The results show that multimodal sentiment inference can provide useful screening signals without task-specific fine-tuning, although the benefits are model-dependent. LLaVA-OneVision-7B consistently outperforms Qwen2.5-VL-7B and benefits more clearly from combining textual and visual inputs, whereas Qwen2.5-VL-7B shows limited improvement across modality settings. We also analyze the trade-off between frame sampling and image resolution. Finally, we discuss limitations related to dataset scope, annotation subjectivity, class imbalance, and the need for broader validation before real-world deployment. Full article

Magnetizing living cells with superparamagnetic iron oxide nanoparticles (SPIONs) enables their remote manipulation using external magnetic field. This lays the foundation for magnetically assembling tissue precursors within cell-friendly, proliferation-permissive environments and holds considerable promise for biomedical applications, particularly in the development of complex single- and multicellular tissue constructs for bone and organ reconstruction. However, progress in this field is limited by the lack of robust mathematical tools for accurate control of ensembles of magnetic nano- and micro-objects. In practical printing scenarios, collective behavior and unavoidable statistical heterogeneity—such as variations in SPION size and shape or deviations in cell magnetization—render traditional equation-based modeling inadequate. We developed a hybrid modeling framework integrating conventional physics-based simulations with artificial intelligence-driven image analysis. Dynamic parameters were extracted from video recordings of magnetized cells moving within model microfluidic devices exposed to well-defined magnetic fields and gradients. The AI-based analysis enabled quantitative characterization of ensemble behavior under heterogeneous conditions. The proposed framework successfully captured the collective dynamics of magnetized cell ensembles and enabled accurate control of their spatial organization under external magnetic actuation. The integration of simulation and data-driven analysis provided robust parameter identification despite statistical heterogeneity within the system. This integrated modeling approach provides a practical and effective tool for controlling the three-dimensional magnetic assembly of living cells, with strong potential for applications in tissue engineering. Full article

Persistent traffic congestion and the need for efficient traffic monitoring have increased the demand for automated vehicle-analysis systems based on CCTV footage. This study presents a CCTV-based vehicle monitoring system that integrates vehicle detection, tracking, counting, public/private vehicle class prediction, seven-category vehicle-type prediction, vehicle-color recognition, and traffic-state estimation using YOLOv12 and DeepSORT. To reduce manual annotation effort during the initial training stage, a semi-automated method for generating synthetic composite road scenes was developed by combining cropped vehicle images and road-background images. The detector was first trained on 10,000 synthetic images and then sequentially fine-tuned on real CCTV data. Four real-world traffic video clips from Metro Manila were used in the study. Three 5 min clips were used within the staged refinement workflow: the first two for iterative refinement and the third for final post-refinement evaluation of the adapted model. A separate fourth CCTV clip was reserved exclusively for blind evaluation without on-the-fly retraining. The final system achieved average accuracies of 97% for public/private vehicle class prediction, 90% for seven-category vehicle-type prediction, 82% for vehicle-color recognition, and 96.67% for vehicle counting on the final evaluation video. The results show that synthetic pretraining combined with limited real-world fine-tuning can improve performance in CCTV-based vehicle monitoring while reducing the amount of manually labeled real-world data required. The study also discusses the limitations of the current evaluation protocol and the need for broader multi-location testing. Full article