Search Results (2,290)

Moving beyond the limits of purely geometric or descriptive documentation, the study conceives the digital models as a structured information system capable of coherently and queryably organising both the formal-typological and the interpretative-historical dimensions of heritage. The methodology is developed within the framework of the European Horizon MSCA project Hephaestus, which investigates cross-border Cultural Heritage Routes (CHRs) and historic fortification systems in the Adriatic and Baltic basins. The paper focuses on Adriatic CHR, through the selection, organisation, and interrelation of a distributed corpus of fortified architectures, articulated according to historical phases, territorial clusters, typological classes, and multilevel relationships. The study adopts an approach centered on HBIM models and ontological frameworks, implemented through complementary top-down and bottom-up processes. The results show the possibility of structuring HBIM-derived data within an ontology-based framework capable of linking, within a single information system, architectural elements, fortified systems, and territorial entities across heterogeneous case studies. The application to differentiated contexts highlights the ability of the models to adapt to different scales and levels of complexity, supporting querying, comparison, and multi-level interpretation of heritage. The variety of sources and contexts enables the methodology to be tested across heterogeneous historical and typological scenarios, strengthening its applicability and robustness within a multiscalar information structure. Full article

Temporal Knowledge Graph Inference (TKGI) is a cornerstone for intelligent decision-making in dynamic scenarios, but existing models face critical bottlenecks, including inadequate complex-context modeling, a lack of entity importance quantification, insufficient novel-event reasoning accuracy, and weak domain adaptability. To address these issues, this study proposes a semantics-enhanced model (LLM-DSaR) integrating Large Language Models (LLMs), temporal attention networks, and optimized contrastive learning. Specifically, a two-stage LLM semantic enhancement (LLM1 + LLM2) framework first generates structured semantic analysis reports via adaptive prompt engineering, and then extracts domain-specific semantic embeddings from the last-layer hidden states through pooling and linear projection, which are further fused with TransE-based structural embeddings; meanwhile, LLM2 mitigates data sparsity in novel-event reasoning; a dynamic weight fusion (DWF) framework adaptively assigns feature weights to achieve deep feature synergy; an LLM-enhanced contrastive-learning module strengthens event clustering and discrimination. Experiments on five public datasets and a self-constructed Robotics Temporal Knowledge Graph (RTKG) show LLM-DSaR outperforms 16 baselines: on RTKG, its MRR is 10.35 percentage points higher than GCR, and Hits@10 reaches 88.87%. Ablation experiments validate core modules’ effectiveness, confirming LLM-DSaR adapts to professional scenarios like robot maintenance prediction, providing a novel technical paradigm for complex-domain TKG reasoning. Full article

To mitigate the high measurement costs in electromagnetic compatibility (EMC) assessment, this paper proposes a robust active learning framework for fast 3D field reconstruction with sparse sampling. A novel “Four-Vote” query criterion is proposed to guide intelligent sample selection, which integrates Shannon entropy, committee variance, spatial density, and clustering-based representativeness, all derived from a heterogeneous radial basis function (RBF) committee. Furthermore, an adaptive polynomial degree adjustment mechanism is implemented to ensure stability in data-scarce 3D environments. Validated through full-wave HFSS simulations, the proposed method significantly outperforms traditional sampling strategies in both 2D and 3D scenarios, achieving high-fidelity field reconstruction with minimal sampling points. This framework provides an efficient solution for rapid spatial field mapping and EMC fault diagnosis in practical engineering scenarios. Full article

Although the genus Rhododendron is globally distributed and rich in bioactive constituents, the metabolomic landscapes of most species remain unexplored, hampering elucidation of their adaptive strategies and pharmaceutical potential. Objectives: This study sought to construct comprehensive metabolic atlases of four representative yet understudied Rhododendron species—R. triflorum, R. faucium, R. nivale, and R. strigillosum—and to quantify inter-specific metabolic divergence by UPLC-MS/MS-based, widely targeted metabolomics. Methods: The petals of four Rhododendron species were freeze-dried, pulverised, and extracted with 70% methanol (containing an internal standard). Metabolites were separated on an SB-C18 column (2.1 × 100 mm, 1.8 µm) using a 0–95% acetonitrile gradient (flow rate 0.35 mL min⁻¹, 40 °C) and analysed by tandem mass spectrometry. Reliable quantification was ensured by molecular weight database matching, ion source standardisation, and quality control (QC), achieving a coefficient of variation (CV) < 15%. Principal component analysis (PCA) and optimised partial least squares discriminant analysis (OPLS-DA) were performed on standardised data with unit variance. Results: A total of 3705 metabolites were confidently identified, dominated by flavonoids (870), terpenoids (572), phenolic acids (394), and amino-acid derivatives (332). PCA and OPLS-DA models revealed clear species-specific clustering (R²Y ≥ 0.98, Q² ≥ 0.95; permutation test p < 0.01). Comparative analysis yielded 1495 significantly differential metabolites; R. triflorum exhibited the highest cumulative abundance, followed by R. faucium, R. nivale, and R. strigillosum. KEGG enrichment highlighted “metabolic pathways” as the most significantly over-represented, together with flavonoid biosynthesis, phenylpropanoid metabolism, and terpenoid backbone biosynthesis. Conclusions: The study delivers the first high-coverage metabolomic reference for four neglected Rhododendron species, evidencing profound inter-specific metabolic differentiation centred on flavonoids, terpenoids, and phenolic acids. The data provide a robust foundation for understanding molecular adaptation to alpine environments and for accelerating targeted drug discovery from Rhododendron resources. Full article

Quantifying dynamic urban resilience is critical for climate adaptation. This study assesses the spatiotemporal resilience of 6838 flood-affected communities across 39 Chinese cities using high-resolution human activity data. By establishing a multi-phase framework, we extract six metrics characterizing resistance and recovery trajectories. Results reveal a distinct resilience paradox: coastal cities, despite suffering deeper instantaneous shocks from typhoons, exhibit superior adaptive capacity compared to inland cities, which face chronic recovery deficits under rainstorm stress. Unsupervised clustering identifies 12 distinct resilience phenotypes, ranging from brittle collapse to adaptive growth. Structural analysis confirms a Matthew Effect where functional diversity and economic vitality enable resource-rich communities to bounce forward, while peripheral areas remain trapped in vulnerability. These findings underscore the need for resilience-based regeneration policies that prioritize spatial justice and resource optimization over static engineering standards. Full article

The existing fruit tree yield prediction methods mainly rely on fruit period images or long-term meteorological and soil data, which make it difficult to meet the needs of early yield prediction. In addition, the flowering period images contain complex spatial distribution and severe overlap between flowers, which makes it challenging to directly extract stable structural indicators related to yield. Most existing research has focused on simple statistical indicators such as the number of flowers, while the spatial clustering structure of flowers and their relationship with yield have not been fully explored. Therefore, this article proposes an early apple yield prediction based on flowering stage image thinning simulation characteristics. In this study, blossom images and fruit maturity yield data from 100 apple trees were collected, with flower mask images extracted through standardized image processing. First, the traditional DBSCAN clustering algorithm was enhanced by integrating a KDTree acceleration structure and an adaptive multi-scale mechanism, forming the adaptive multi-scale clustering algorithm (AMS-DBSCAN) to achieve efficient identification of flower clusters and individual flowers. Based on this, two flower thinning simulation strategies based on density and spatial uniformity were designed to model artificial thinning rules and construct multi-dimensional, interpretable phenotypic features. Then, the original statistical features were fused with strategy-generated features and optimized using Lasso. We compared multiple models including XGBoost, BPNN, and SVR for yield prediction. The experimental results showed that XGBoost achieved good predictive performance under the hybrid feature set (R² = 0.856, RMSE = 3.098), which was further improved to R² = 0.900 after feature optimization with Lasso. The results demonstrate that the proposed method enables reliable early yield estimation, providing a new reference for precision management and early decision-making in fruit tree cultivation. Full article

Rapid urbanization-induced extreme rainstorms severely disrupt social functions. Previous research often focused on “de-densification” strategies, which are difficult to adapt to high-density Sponge City Stormwater Management Units (SMUs) that carry core development functions. This study uses Shenzhen as a case study, utilizing Keep movement big data as a “social sensor” for system function perception and introducing the Socio-Ecological-Technological Systems (SETS) theory to construct a “recovery (RCN)–resistance (MI)” binary assessment framework. Through systematic clustering and hierarchical regression models, the driving mechanisms of blue landscape patterns, topography, road networks, and the built environment on social behavioral resilience are systematically parsed. The results show: (1) Road network morphology dominates resistance, while multi-dimensional elements collaborate for recovery. Resistance (MI) is primarily dominated by macro road network detour resistance (TPD2000, β = 0.956), while recovery depends on the synergistic support of blue space interspersion (Blue_IJI), topography, and micro-circulation road networks. (2) Green infrastructure fails in the model due to efficiency bottlenecks, empirical evidence of weakened regulation caused by green space fragmentation in ultra-high-density environments. (3) Low-density, eco-centric built environments provide dual synergistic gains for resilience. Based on this, a “Bidirectional Socio-Ecological Resilience Needs Pyramid” model is constructed, identifying four governance types such as the “Synergistic Balanced Type”. This study provides a quantitative basis for the transition from administrative control to precise morphological governance in high-density cities. Full article

Lightweight day-ahead photovoltaic (PV) forecasting models encounter a significant technical challenge: under resource-constrained deployment conditions, it is difficult to simultaneously address weather-regime heterogeneity, maintain model interpretability, and preserve adaptability as operating conditions evolve. To address this issue, we propose a multimodal short-term photovoltaic (PV) forecasting method that integrates weather-mode partitioning using the Clustering Using Representatives (CURE) algorithm with a self-updating Batch-Lasso model. First, the meteorological-PV dataset is partitioned along two dimensions by combining seasonal grouping with CURE clustering within each season, producing representative weather modes and enhancing the fidelity of weather pattern classification. Second, to extract informative predictors from high-dimensional meteorological inputs while maintaining interpretability, we formulate per-mode Lasso regression and adopt the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) to efficiently solve for the sparse regression coefficients. Third, we introduce a batch-based self-update and correction mechanism with rollback verification, enabling the mode-specific models to be refreshed as new historical data become available while preventing performance degradation. Compared with representative machine learning baselines, the proposed method maintains competitive accuracy with substantially lower computational and storage overhead, enabling high-frequency and energy-efficient inference on resource-constrained terminals, thereby reducing operational burdens and computational energy costs and better meeting the deployment needs of sustainable energy systems under heterogeneous weather conditions. Full article

The rapid growth of Internet of Things (IoT) and Industrial IoT applications has intensified the demand for low-latency and reliable computation support for deadline-constrained periodic real-time tasks. While unmanned aerial vehicles (UAVs) enabling mobile edge computing (MEC) can reduce latency by bringing compute closer to data sources, terrestrial MEC deployments often suffer from limited coverage and poor adaptability to spatially heterogeneous demand. In this paper, we study a multiple-UAV-assisted MEC system serving cluster-based IoT networks, where cluster heads generate deadline-constrained periodic tasks for offloading under strict deadlines. To ensure practical feasibility in dense urban environments, we benchmark UAV mobility using a realistic 3D collision avoidance navigation graph with shortest-path execution, rather than assuming unconstrained continuous UAV motion in free space. On top of this benchmark, we systematically compare three multi-agent reinforcement learning (MARL) paradigms for joint navigation and periodic task offloading: (i) continuous 3D control MARL that outputs motion commands directly; (ii) discrete graph-based MARL that selects collision-free shortest paths; and (iii) asynchronous macro-action MARL. Using a high-fidelity 3D digital twin of San Francisco, we evaluate these paradigms under a unified protocol in terms of offloading success, end-to-end latency, and energy consumption. The results reveal clear performance trade-offs induced by realistic 3D collision avoidance constraints and provide actionable insights for designing UAV-assisted MEC systems supporting periodic real-time task offloading. Full article

4D millimeter-wave radar provides high-precision ranging capability and exhibits strong robustness under adverse weather and low-visibility conditions, but its point clouds are relatively sparse and suffer from severe elevation-angle measurement noise. Monocular cameras, by contrast, provide rich semantic information and high recall, yet are fundamentally limited by scale ambiguity. To exploit the complementary characteristics of these two sensors, this paper proposes a radar-camera fusion 3D multi-object tracking framework that does not rely on complex 3D annotated data. First, on the radar signal-processing side, a Gaussian distribution-based adaptive angle compression method and IMU-based velocity compensation are introduced to effectively suppress measurement noise, and an improved DBSCAN clustering scheme with recursive cluster splitting and historical static-box guidance is employed to generate high-quality radar detections. Second, a disparity-domain metric depth recovery method is proposed. This method uses filtered radar points as sparse metric anchors, performs robust fitting with RANSAC, and applies Kalman filtering for temporal smoothing, thereby converting the relative depth output of the visual foundation model Depth Anything V2 into metric depth. Finally, a hierarchical fusion strategy is designed at both the detection and tracking levels to achieve stable cross-modal state association. Experimental results on a self-collected dataset show that the proposed method achieves an overall MOTA of 77.93%, outperforming single-modality baselines and other comparison methods by 11 to 31 percentage points. This study provides an effective solution for low-cost and robust environment perception in complex dynamic scenarios. Full article

Aiming at diverse urban tree structures and difficulties in vegetation point cloud extraction and utilization, this study proposed single-tree-scale biological parameter estimation methods for urban scenarios to enhance point cloud’s application value in urban greening management. For single-tree segmentation, it constructed a method based on the constraints of the trees’ geometric features and combined the gravitational modeling characteristics, called the CGF-CG single-tree segmentation method. This method (i) combines clustering and principal direction analysis to extract trunk points, (ii) introduces canopy segmentation based on trunk positions, (iii) optimizes edge point attributes via a gravitational model. Based on CGF-CG’s accurate results, an improved random forest method for single-tree biological parameter (IRF-BP) estimation (aboveground biomass, carbon storage, leaf area index, living vegetation volume) was proposed: (i) correlation analysis with variable screening, (ii) adaptive feature selection and pigeon-inspired optimization to enhance model generalization, (iii) adopting Shapley Additive Explanations (SHAP) to improve interpretability. Based on these, a complete model for different tree species was constructed. Validation showed that CGF-CG exhibited negligible over-segmentation and under-segmentation in the selected study areas, with overall average precision, recall, and F1-score over 98.5%. Additionally, on the selected overall region, the overall mF1 score, mPTP, and mPTR of our method are 99.13%, 99.15%, and 99.12%, respectively, which are superior to Forestmetrics, lidR, PyCrown, and DBSCAN methods. IRF-BP performed well, with a highest R² of 0.81 and a lowest mean absolute percentage error of 7.5%, effectively surpassing the performance of traditional models such as RFR, GBR, KNN, and XGB. In summary, results provided theoretical and technical support for urban green resource management and evaluation. Full article

With the continuous expansion of maritime trade scale, ports have put forward increasingly higher requirements for transshipment efficiency. Container twist lock disassembly is a key link in the loading and unloading process, and its automation level has a significant impact on the ship’s berthing time at the port. Aiming at the demand of automated disassembly for high-precision 3D vision, this paper proposes a multi-view point cloud local registration method for twist lock recognition. First, Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) is used to extract the keyhole region with the highest overlap in multi-view point clouds, reducing the interference from non-overlapping structures. Then, a two-stage strategy of “coarse registration + fine registration” is adopted: initial alignment is achieved through Random Sample Consensus (RANSAC), and the Iterative Closest Point (ICP) algorithm is improved by combining adaptive distance threshold and normal consistency constraint to complete fine registration. Experimental results show that the proposed method outperforms the global registration scheme in both accuracy and efficiency: the Root Mean Square Error (RMSE) is reduced to 2.15 mm, the Relative Mean Distance (RMD) is reduced to 1.81 mm, and the registration time is approximately 2.41 s. Compared with global registration, the efficiency is improved by 44.2%, which can meet the real-time requirements of continuous operation at automated terminals for the perception link and the time constraints for subsequent manipulator control. The research results preliminarily verify the application potential of this method in the scenario of automated twist lock disassembly. Full article

Search and rescue (SAR) operations in mountainous terrain present significant challenges due to complex environments, time-critical decisions, and the need for effective human–AI collaboration. Existing approaches typically employ either fully autonomous systems that lack adaptability to varying task requirements, or fixed human–AI authority allocations that fail to leverage the distinct strengths of humans and AI across different mission phases. This paper proposes Phase-Aware Hierarchical Reinforcement Learning (PAHRL), a novel framework that dynamically allocates decision-making authority between human operators and AI agents based on identified task phases. First, we formulate the mountain SAR problem as a three-phase task structure: Wide Search (WS), Target Confirmation (TC), and Rescue Coordination (RC), and examine the consistency of this decomposition through unsupervised clustering analysis, supported by bootstrap stability (ARI = 0.983 ± 0.083) and multiple clustering metrics. Second, we design an adaptive authority mechanism with four levels (L1: Human-Led to L4: Full-Auto) that automatically adjusts human involvement based on current phase characteristics and environmental uncertainty estimates. Third, we introduce a priority-based task execution module that ensures efficient resource allocation across multiple rescue objectives while respecting authority constraints. Extensive experiments demonstrate that PAHRL outperforms baseline methods, achieving a 20.9% higher success rate compared to standard PPO (59.0% vs. 48.8%) and 66.7% improvement over heuristic approaches. PAHRL maintains 96.9% precision even under 60% noise conditions with only 0.09 false rescues per episode. Ablation studies further reveal that phase awareness serves as a critical robustness mechanism; removing phase detection causes complete mission failure under noisy conditions. These results evaluate that phase-aware dynamic authority allocation significantly enhances both efficiency and robustness in human–AI collaborative SAR missions. While demonstrated in a proof-of-concept simulation with computational human models, validation with real operators and more complex environments remains essential before operational deployment. Full article

Background/Objectives: Simulation-based education often lacks personalization, focusing on technical competence rather than individual student profiles. This protocol describes a study designed to evaluate whether adapting gamified narratives to nursing students’ personality profiles has the potential to support academic performance in obstetrics. This study aims to validate the integration of psychometric profiling and AI as a sustainable strategy for personalized clinical training. Methods: A cluster-randomized controlled longitudinal pilot trial will be conducted at the University of Atlántico Medio. The protocol has been submitted for registration at ClinicalTrials.gov (Registration Pending). Thirty-eight second-year nursing students meeting inclusion criteria (excluding repeaters or those with prior specialized training) will be assigned by natural practice to either a control group (generic gamification) or an experimental group (gamification adapted according to Player Personality and Dynamics Scale profiles using AI-generated content). The intervention comprises four clinical simulation sessions focusing on pregnancy and childbirth, which are managed via the Wix platform. The primary outcome is academic performance, measured as “Learning Gain” (post-test scores minus pre-test scores). Secondary outcomes include student satisfaction measured via the Gameful Experience Scale. Data will be analyzed using Mann–Whitney U tests to compare overall efficacy and intragroup evolution. To minimize observer bias, knowledge assessments will utilize automated, objective scoring, and participants will be blinded to the study hypothesis. Expected Outcomes: The study aims to establish the technical and pedagogical feasibility of integrating AI-adapted narratives into nursing curricula. It is anticipated that the personalized approach will show positive trends in learning gains and engagement patterns, providing a baseline for larger multicenter trials. Conclusions: This protocol presents a framework for “Precision Education” in nursing, shifting from “one-size-fits-all” simulations to student-centered adaptive training. The use of Generative AI makes such personalization sustainable and cost-effective for health science faculties. Full article

Background/Objectives: Disorders of Gut–Brain Interaction (DGBIs), particularly irritable bowel syndrome (IBS), are frequently underdiagnosed in clinical practice, contributing to a substantial hidden burden of disease. This study aimed to quantify this “symptomatic iceberg” by comparing the prevalence of formal IBS diagnoses with a broader symptom-based case definition in a clinical cohort. Methods: We conducted a cross-sectional analysis of 194 adult subjects from a gastroenterology clinic in Western Romania. Data on demographics, clinical diagnoses, self-reported symptoms, and eating behaviors were collected. For the case–control analysis, patients with confirmed organic gastrointestinal pathology or incomplete data were excluded. The final analytical sample consisted of 52 patients classified as having a functional DGBI phenotype and 84 asymptomatic controls without organic disease, while 58 were excluded from the analysis. Results: While only 4.4% (95% CI: 2.0–9.3%) of the cohort (N = 136) had a formal IBS diagnosis, 47.8% (95% CI: 39.6–56.1%) met criteria for an IBS-compatible symptom cluster, yielding an underdiagnosis ratio of 10.8. Neuro-vegetative symptoms such as sweating (19.1%) and dizziness (11.8%) were highly prevalent. In the case–control analysis, patients with a functional DGBI phenotype had a significantly higher mean BMI compared to controls (28.15 ± 6.49 vs. 24.47 ± 4.60 kg/m²; p = 0.001). DGBI cases were less likely to report regular snacking behavior (OR = 0.36; 95% CI: 0.18–0.74; p = 0.009), suggesting behavioral adaptation. A sensitivity analysis excluding participants with CRP > 10 mg/L (n = 98) confirmed the robustness of these associations, indicating that minor systemic inflammation did not bias the primary findings. Full article