Search Results (42,880)

Multi-sensor-based human activity recognition (HAR) models trained with deep learning often exhibit limited generalization when applied to data collected under conditions different from those seen during training. To alleviate this issue, we present an adversarial domain adaptation framework that incorporates contrastive group construction to promote class-aware feature alignment. Specifically, augmented and perturbed sample groups are generated in both source and target domains and optimized through contrastive learning objectives, allowing the feature extractor to compact semantically similar representations while separating dissimilar ones without relying on target-domain annotations. This joint design preserves semantic structure while reducing cross-domain distribution discrepancies, resulting in representations that are both domain-invariant and discriminative. Experiments conducted on the Opportunity dataset validate the effectiveness of the proposed approach, demonstrating consistent performance gains over representative unsupervised domain adaptation methods. Full article

As population aging accelerates, age-related declines in visual sensitivity and attentional control make older drivers more vulnerable to suboptimal in-vehicle interface designs. Head-up displays (HUDs) are intended to reduce gaze shifts by overlaying information within the forward field of view, yet empirical evidence remains limited on how specific HUD presentation strategies reshape older drivers’ visual attention allocation. Grounded in theories of visual attention and cognitive load, this study systematically investigates three design variables that are increasingly common in contemporary HUDs (including AR-HUDs): (1) dynamic versus static navigation cues, (2) pedestrian warning strategies under different lighting conditions, and (3) the spatial placement of high-priority information. We first conducted a formative user study to define variables and operationalizations, and then carried out three within-subject driving-simulator experiments using controlled HUD stimuli and eye tracking. Objective gaze measures (e.g., fixation count, total fixation duration, and time to first fixation) were combined with subjective preference ratings to characterize attentional capture, search efficiency, and potential attentional costs. Findings reveal a robust trade-off: continuously changing navigation cues enhance attentional capture but can also increase attentional “stickiness,” unnecessarily consuming older drivers’ limited attentional resources. In pedestrian hazard tasks, real-time overlay warnings that were spatially aligned with the hazard significantly improved visual localization under low-light conditions, outperforming early warnings and multi-stage strategies. Across tasks and layout conditions, the central HUD region showed a stable attentional advantage—placing critical information centrally elicited greater visual attention and stronger subjective preference. These results provide mechanistic evidence for how HUD parameters modulate older drivers’ attention and yield actionable implications for prioritization, temporal pacing of dynamic navigation cues, and a “center-first” layout strategy to guide age-friendly HUD design. Full article

Out-of-hospital cardiac arrest (OHCA) remains a leading cause of sudden death worldwide, with wide variation in reported incidence and outcomes driven by heterogeneity in registries, emergency medical services (EMS) organization, and case definitions. Despite substantial advances in resuscitation systems, survival after EMS-treated OHCA generally remains below 10%, and outcomes are critically time dependent. Delays in emergency call activation, bystander cardiopulmonary resuscitation (CPR), and—most importantly—early defibrillation are associated with a rapid decline in return of spontaneous circulation and favorable neurological recovery. This narrative review synthesizes current evidence and implementation strategies aimed at reducing “time-to-CPR” and “time-to-shock,” with a specific focus on public-access defibrillation (PAD) as a tool to mitigate avoidable delay. Randomized trials and large registry studies consistently demonstrate that automated external defibrillator (AED) use before EMS arrival is a key determinant of survival in patients with shockable rhythms. However, the real-world effectiveness of PAD remains limited by suboptimal AED placement, restricted 24/7 accessibility, low public awareness, and underutilization driven by fear and lack of confidence. We compare different PAD delivery models—including EMS-based, police and first-responder-based, and fully integrated community systems—and summarize evidence supporting targeted, high-yield AED deployment and cost-effectiveness. In addition, we review emerging strategies to reduce avoidable delay and strengthen the early links of the chain of survival, such as school-based training programs, smartphone- and SMS-based citizen-responder networks, improved dispatch recognition of cardiac arrest (including artificial intelligence–supported tools), and drone-enabled AED delivery. Across these approaches, patient benefit critically depends on system integration, alert performance, and true AED accessibility. Finally, we describe the Italian “Progetto Vita” experience as a community-integrated model explicitly designed to minimize avoidable delay through widespread AED deployment, lay responder training, and real-time integration with EMS. We conclude by outlining future priorities, including the development of robust national OHCA registries and scalable solutions for the high burden of cardiac arrests occurring at home, such as population-level deployment of low-cost, ultra-portable AEDs. Full article

In this paper we present the design of a tolerance analysis-based closed-loop system and a compensation framework applied to high-precision linear Delta robots. It considers the modelling of static and dynamic errors propagation arising from the structural tolerances and the end-effector’s positioning. This approach is combined with a closed-loop control system implemented using high-resolution optical encoders. The model is applied to the ROMI robot, a high-precision experimental Delta robot designed for microsurgical applications. Our simulation results reveal a theoretical home position error (the centre of the robot’s platform) of 1.9 mm, which is effectively compensated through kinematic calibration and a tolerance analysis-based closed-loop system. The proposed framework is evaluated experimentally through proof-of-concept experiments mimicking a microsurgical resection task conducted on a human peripheral nerve sample. The results from executing micrometre scale parallelogram and circular trajectories showed error reduction rates of 92.3% and 51.2% respectively, after five trajectory iterations. These findings confirm that manufacturing-induced errors can be consistently compensated using the proposed methodology, thus eliminating the need for ultra-high-precision machined components. This work establishes a practical and scalable pathway for designing more affordable high-precision robotic systems suitable for microsurgical and other high-precision applications. Full article

Lignin valorization is a central objective of modern biorefinery research. This study investigates the catalytic depolymerization of two technical lignins, kraft lignin from beech hardwood and natron lignin from annual plants, via two complementary routes: analytical catalytic pyrolysis (Py-GC/MS, 300–600 °C) and hydrogenolysis (250–310 °C, Ru/C, isopropanol/H₂). In Py-GC/MS experiments, noble-metal catalysts on carbon supports (Ru/C, Pd/C, RuPd/C) were screened. Relative compound distributions revealed phenolic derivatives as the dominant products, with Ru/C yielding the highest conversion for lignin from annual plants at 500 °C and Pd/C proving most selective for hardwood lignin at 400 °C. Hydrogenolysis was optimized through a five-level, three-factor central composite design, varying temperature, residence time, and catalyst loading. Lignin conversion ranged from 64 to 83 wt% and bio-oil yield from 69 to 89 wt%. A regression model identified optimal conditions at 295 °C, 32 min, and 17 wt% Ru/C. Catalyst regeneration via solvent washing, H₂O₂ oxidation, and controlled thermal treatment resulted in only an 8% decrease in lignin conversion. The results demonstrate that lignin origin, catalyst type, and depolymerization pathway jointly govern product selectivity, highlighting clear strategies for targeted phenolic compound production. Full article

In photovoltaic remote sensing image segmentation tasks, fully supervised methods can achieve high accuracy. However, the high cost of pixel-level annotation significantly limits their scalability in large-scale scenarios. To overcome this annotation bottleneck, this paper proposes a zero-shot cross-modal segmentation framework based on the visual-language pre-trained foundation model (CLIP). This approach harnesses CLIP’s cross-modal knowledge transfer capabilities to achieve precise extraction of photovoltaic targets without requiring any downstream training. This paper first introduces the Layer-wise Augmented Residual Attention (LARA) mechanism to enhance fine-grained detail representation in the feature space. Subsequently, a Cross-modal Semantic Attribution Module (CMSA) is designed to generate precise activation maps by leveraging image-text alignment gradient information. Finally, the Confidence-Aware Refinement Strategy (CARS) replaces the conventional training-based denoising process, directly producing high-quality binary segmentation masks through adaptive thresholding. Comparative experiments were conducted to evaluate the proposed method against various baselines using several public datasets with varying resolutions in Jiangsu Province including Unmanned Aerial Vehicles imagery, Beijing-2, Gaofen-2, and a self-created Sentinel-2 imagery covering multiple countries. Notably, the proposed method achieved an IoU of 70.3% on the Gaofen-2 PV03 dataset with a spatial resolution of approximately 0.3 m and 50.8% on the self-created Sentinel-2 PV_Sentinel-2 dataset with a spatial resolution of 10 m. Experimental results demonstrate that our proposed approach maintains excellent cross-domain generalisation capabilities while reducing annotation costs, thereby providing an efficient and viable technical pathway for the automated monitoring of large-scale photovoltaic facilities. Full article

This contribution examines the use of ICT in mosaic heritage to enhance the user experience in heritage interpretation. The study focuses on the digital graphic reconstruction of a Roman mosaic featuring animal emblems. As the original mosaic is no longer physically accessible, the reconstruction relied on the systematic collection, comparison, and analysis of diverse visual sources, including archival photographs, historical drawings, and related documentation; photography and drone capture; digital assembly of borders of hand-drawn mosaic elements; and systematic assembly and completion of repetitions in representative mosaic motifs and observational color and design refinement of animal emblems. The article outlines the mosaic’s schematic layout and the key reconstruction steps, resulting in a digital representation developed within the limits imposed by the available evidence. The outcome demonstrates that combining historical research and digital graphics can effectively support the preservation and communication of cultural heritage. Finally, an augmented reality application for interactive presentation of the reconstructed mosaic is introduced, enabling users to explore both the reconstruction process and the interpretive meanings of individual mosaic elements, thereby enhancing engagement and understanding. Full article

Monocular 3D reconstruction remains a persistent challenge for autonomous driving systems in Degraded Visual Environments (DVEs) with extreme glare and low illumination, such as highway tunnels, due to the lack of reliable texture cues. This paper proposes a physics-aware deep learning framework that overcomes these limitations by fusing polarization sensing with conventional intensity imaging. Unlike traditional end-to-end data-driven fusion strategies, we propose a Modality-Aligned Parameter Injectionstrategy. By remapping the weight space of the input layer, this strategy achieves a smooth transfer of the pre-trained Vision Transformer (i.e., MiDaS) to multi-modal inputs. Its core advantage lies in the seamless integration of four-channel polarization geometric information while fully preserving the pre-trained semantic representation capabilities of the backbone network, thereby avoiding the overfitting risk associated with training from scratch on small-sample data. Furthermore, we design a Reliability-Aware Gating mechanism that dynamically re-weights appearance and geometric cues based on intensity saturation and the physical validity of polarization signals as measured by the Degree of Linear Polarization (DoLP). We validate the proposed method on our self-constructed POLAR-GLV benchmark, a real-world dataset collected specifically for high dynamic range tunnel scenarios. Extensive experiments demonstrate that our method consistently outperforms intensity-only baselines, reducing geometric reconstruction error by 24.2% in high-glare tunnel exit zones and 10.0% at tunnel entrances. Crucially, compared to multi-stream fusion architectures, these performance gains come with negligible additional computational cost, making the framework highly suitable for resource-constrained onboard inference environments. Full article

Mentorship represents a promising approach for sport organizations interested in supporting the positive youth development, retention, and well-being of girls in sport. Despite growing interest in mentorship as a youth development strategy, limited research explores how sport organizations understand and deliver mentorship programming for girl-identifying youth in Canada. The purpose of this study was to examine how Canadian sport organizations conceptualize mentorship and implement it to support girls’ developmental and sporting experiences. Using a qualitative research design, we conducted semi-structured interviews with Canadian sport organizational leads (N = 9) and analyzed available program resources (e.g., evaluation reports, program manuals). Reflexive thematic and document analysis revealed three findings: (a) how organizational staff understand mentorship for girls in sport; (b) how mentorship is delivered in practice; (c) system-level barriers and recommendations that shape mentorship (programming). Findings contribute to sport and youth-focused scholarship by illustrating how sport organizations shape mentoring as a developmental experience for girls in sport and by pointing to gender-responsive, co-designed mentorship frameworks as priority areas for future research. Practically, this research underscores the importance of investing in relational capacity and evidence-based mentorship models to better support girls’ sport experiences in Canada. Full article

(1) Background: This experiment investigated the effects of balancing methionine or lysine under different dietary protein levels on intestinal function and immune organ development in Wanxi white geese during the brooding period. (2) Methods: A total of 180 one-day-old Wanxi white geese were randomly divided into six groups using a 2 × 3 factorial design, with amino acid balance (lysine or methionine) and dietary crude protein levels (20%, 18%, and 16%) as experimental factors. Each group consisted of five replicates with six geese per replicate. The feeding trial lasted 28 days. (3) Results: The lysine-balanced diet significantly increased jejunal lipase activity in 14-day-old geese (p < 0.05). In contrast, the methionine-balanced diet significantly enhanced ileal trypsin, maltase, and amylase activities at 14 days, as well as ileal trypsin activity at 28 days (p < 0.05). Lysine balance markedly improved jejunal weight and muscularis thickness, ileal weight, villus height, and villus-to-crypt (V/C) ratio in 14-day-old geese, and further increased jejunal villus height and ileal crypt depth and muscularis thickness at 28 days (p < 0.05). Methionine balance significantly elevated total antioxidant capacity (T-AOC) in the jejunum and ileum of geese at both 14 and 28 days (p < 0.05). Moreover, the methionine-balanced group exhibited a significantly higher thymus index and spleen index in 28-day-old geese compared with the lysine-balanced group (p < 0.05). Notably, geese fed the 18% crude protein diet showed significantly greater follicular and medullary areas of the bursa of Fabricius at 14 days than those fed 20% or 16% protein diets (p < 0.05). (4) Conclusions: Appropriate dietary protein reduction combined with balanced lysine or methionine supplementation effectively enhances intestinal digestive function, antioxidant capacity, and immune organ development in Wanxi white geese. Among the tested treatments, a dietary protein level of 18% produced the most favorable overall outcomes during the brooding period. Full article

This study proposes a novel coplanar spiral-varying-channel space-coiled acoustic metamaterial (CSV-SCAM) for efficient low-frequency noise control in the range of approximately 200–400 Hz. By integrating continuously graded spiral channels with secondary spiral branches, the proposed structure enables multi-stage acoustic impedance matching and enhanced thermo-viscous dissipation, effectively overcoming the bulkiness and limited low-frequency efficiency of conventional porous absorbers. Finite element simulations and impedance tube experiments demonstrate that the CSV-SCAM achieves near-unity deep-subwavelength sound absorption, with a peak sound absorption coefficient exceeding 0.99 around 750–850 Hz using a thickness of only 10 mm. Furthermore, hybrid configurations composed of units with different branch numbers significantly broaden the effective absorption bandwidth by more than 20% while maintaining high absorption levels. Compared with traditional Helmholtz resonators, the proposed metamaterial exhibits superior compactness, structural robustness, and design flexibility, providing a promising solution for practical low-frequency noise mitigation in space-constrained engineering applications. Full article

The existing research on Android malware detection using graph neural networks (GNNs) has largely focused on architectural improvements, while input node feature representations have received less systematic attention. This study adopts a representation-centric approach to enhance function call graph (FCG)-based malware classification through interpretability-driven feature engineering. We propose a dual-level structural feature framework integrating local topological patterns with global graph-level properties. The initial feature set comprises 13 dimensions: five local degree profile (LDP) features and eight global structural features capturing community structure, execution flow, and connectivity patterns. To mitigate the curse of dimensionality, we apply an interpretability-driven selection using integrated gradients (IG), gradient-weighted class activation mapping (GradCAM), and Shapley additive explanations (SHAP), yielding an optimized seven-dimensional subset. Experiments on the MalNet-Tiny benchmark demonstrate that the proposed approach achieves 94.47 ± 0.25% accuracy with jumping knowledge GraphSAGE (JK-GraphSAGE), improving the LDP-only baseline by 0.32 percentage points while reducing feature dimensionality by 46%. The selected features exhibit consistent importance across four GNN architectures and multiple message-passing layers, demonstrating model-agnostic effectiveness. The results reveal that aggregation mechanisms critically influence feature utility, highlighting the necessity of interpretability-guided design for robust malware detection. This work provides a systematic methodology for feature engineering in graph-based security applications. Full article

This study examines the perceived impact of sensory and interactive architectural design in inclusive learning environments on the sensory–emotional responses and behavioral–academic outcomes of children with neurodevelopmental disorders—namely Autism Spectrum Disorder, Down Syndrome, and Attention-Deficit/Hyperactivity Disorder—during early childhood within the Egyptian educational context. Adopting a perception-based, non-causal analytical perspective, a descriptive–analytical, survey-based design was implemented using a validated questionnaire developed from an architectural–educational conceptual framework grounded in relevant literature. The study involved (N = 202) parents, teachers, therapists, and caregivers who evaluated the perceived influence of environmental design elements on children’s sensory responses, behavior, social interaction, and academic performance, based on observational and experiential assessments rather than objective environmental performance measurements. The results indicated high perceived impacts on sensory–emotional responses (84.8%) and behavioral–academic outcomes (82.0%). Movement–spatial attributes showed the strongest influence, followed by balanced natural lighting, calming colors, natural materials, and low-noise acoustic conditions, while natural elements and sensory gardens played a regulatory role in supporting emotional stability and social interaction. The study concludes that sensory- and emotionally responsive architectural design, when understood as a supportive component of the educational experience rather than an independent causal factor, and integrated with appropriate pedagogical practices, contributes to inclusive learning environments accommodating neurodevelopmental diversity, while informing the development of an applied, evidence-informed architectural design framework that translates perceptual–correlational findings into structured and operational design guidelines adaptable to the Egyptian educational context. Full article

This paper proposes a high-speed static random access memory (SRAM) architecture that integrates a self-refresh mechanism with a novel single error and adjacent-bit errors correction (SEABEC) scheme to enhance resilience against single-event upsets (SEUs) in radiation-prone environments. By leveraging extended Hamming coding and dynamic circuits, the design achieves a 29.1% RW speed improvement, reduces SEU cross-section by one order of magnitude, and incurs a 29.8% area overhead and a 95.2% dynamic power increase of the ECC module, leading to an overall chip area increase of ~14.2% compared to static logic-based RH SEC-DED SRAM. Radiation experiments validate superior tolerance across a LET range of 1.63–21.8 MeV·cm²/mg, demonstrating nearly doubled SEU resilience compared to conventional SEC-DED-based designs. This work balances error correction capabilities with system efficiency, making it suitable for high-reliability applications in space electronics and advanced processors. Full article

Strengthening key competencies in veterinary preclinical education, such as anatomical identification, spatial–visual reasoning, and anatomical–surgical understanding, is essential for effective preclinical learning. In this context, veterinary preclinical education is undergoing a transformation process in which traditional theoretical–practical approaches show limitations in responding to current educational demands, making it necessary to adopt innovative strategies based on active learning and simulation. This study presents a simulation-based educational approach designed to support competency development within preclinical veterinary education. Using a reproducible and low-cost workflow applied to a real canine cranial case of extra-genital transmissible venereal tumor (TVCT) with frontal bone invasion, used exclusively as a teaching scenario. Fourteen veterinary medicine students from the same institution participated in two instructional conditions: Group A received traditional theoretical instruction (including cadaveric specimens) without the use of 3D-printed models, while Group B participated in simulation-based training supported by virtual planning and a 3D-printed cranial model. Learning outcomes are assessed through structured observation and descriptive analysis. A Likert-type survey was also used to assess satisfaction and engagement among students who participated in the model-supported training, as well as to map competencies across cognitive, visual-spatial, and anatomical-surgical reasoning domains, with evaluation conducted by veterinarians with clinical and teaching experience. Descriptive observations indicated that students participating in the simulation-based training engaged in three-dimensional anatomical exploration of cranial anatomy and case-based anatomical-surgical discussion. In addition, survey responses from Group B indicated high levels of engagement and interest, as well as high perceived usefulness of the model-supported training experience. These findings suggest that simulation-based educational frameworks may offer a safe, transferable, and pedagogically valuable strategy for competency development within preclinical veterinary education. Full article