Search Results (11,105)

Background/Objectives: To identify existing evidence on strategies for standardising nursing handovers in paediatric hospital settings, given their impact on communication, safety, and quality of care. International bodies such as the WHO and The Joint Commission recommend standardisation as a key measure to reduce patient safety incidents. Methods: A scoping review was conducted in December 2022 using Medline, Cochrane Library, Scopus, and CINAHL databases. The search strategy included documents published between 2012 and 2022, in Spanish, English, Catalan, French, and/or Portuguese. We screened according to inclusion criteria (professional nurses and hospitalisation) and exclusion criteria (intensive care and medical professionals) and tabulated the results according to concurrent themes. The PRISMA-ScR guidelines were followed. Results: A total of 308 records were identified. After screening, 25 full-text articles were assessed for eligibility. Following quality appraisal, six were excluded for not meeting predefined criteria, resulting in 19 studies included in the final synthesis. The evidence mapped shows that most structured communication tools have been developed or validated in adult or medical contexts, with limited evaluation in paediatric nurse-to-nurse inpatient settings. Standardised structured communication tools used in hospital settings include SBAR, I-PASS, and Flex 11, while assessment instruments such as the Handoff CEX Scale and Handover Evaluation Scale have been applied to evaluate handover quality. Conclusions: Structured communication tools may contribute to improving information transfer and perceived quality of handover; however, paediatric nurse-specific evidence remains limited and frequently derives from non-nursing or adult contexts. Further adaptation and validation in paediatric inpatient nursing settings are required. Full article

Rapid and accurate mapping of earthquake-triggered landslides from satellite imagery is critical for emergency response and hazard assessment, yet remains challenging due to irregular boundaries, extreme size variations, and atmospheric noise. This paper proposes BiFusion-LDSeg, a novel bi-directional fusion enhanced latent diffusion framework that synergistically combines CNN-Transformer architectures with generative diffusion models for robust landslide segmentation. The framework introduces three key innovations: (1) a dual-encoder with Bi-directional Attention Gates (Bi-AG) enabling sophisticated cross-modal feature calibration between local CNN textures and global Transformer context; (2) a conditional latent diffusion process operating in learned low-dimensional landslide shape manifolds, reducing computational complexity by 100× while enabling inference with only 10 sampling steps versus 1000+ in standard diffusion models; and (3) a boundary-aware progressive decoder employing multi-scale reverse attention mechanisms for precise boundary delineation. Comprehensive experiments on three earthquake datasets from Sichuan Province, China (Lushan Mw 7.0, Jiuzhaigou Mw 6.5, Luding Mw 6.8) demonstrate superior performance, outperforming state-of-the-art methods by 7–13% in IoU and 5–7% in DSC across all three datasets. The framework exhibits exceptional noise robustness, strong cross-dataset generalization, and inherent uncertainty quantification, enabling reliable deployment for post-earthquake landslide inventory mapping at regional scales. Full article

Precision weeding is crucial for maximizing crop yields and minimizing herbicide use. However, deploying standard deep learning models in agriculture faces challenges due to the high morphological diversity of weeds and the computational constraints of edge devices. Hence, this study proposes MCS-YOLO, a lightweight detection model based on the YOLOv8 architecture. First, a channel-level Mamba module is integrated into the backbone to model long-range feature dependencies and enhance global texture representation. The LMAB module employs parallel depthwise separable convolutions with varying receptive fields and coordinate attention to improve multi-scale weed discrimination. To mitigate feature blurring and misalignment during upsampling, the LCAU module adopts dynamic offset sampling beyond fixed interpolation methods. Finally, the SCS-Head integrates dual-branch depthwise separable convolution with channel shuffling to reduce parameter redundancy while preserving efficient feature expression. Experimental results on the Weed-Crop dataset demonstrate that MCS-YOLO achieves 76.4% mAP@50 and 38.3% mAP@50–95, outperforming YOLOv8s by 3.1% and 1.5%, respectively. Furthermore, the parameter count is reduced by 20.7%, from 11.13 M to 8.83 M, and GFLOPs are reduced by 39.6%, from 28.5 to 17.2. These results confirm that MCS-YOLO effectively balances a lightweight design with high detection accuracy, offering a viable solution for real-time weed detection and automated weeding on embedded agricultural platforms. Full article

We study double-circulant codes over a class of semi-local rings arising from the idempotent construction $R={\mathbb{Z}}_{p^2}+u{\mathbb{Z}}_{p^2}$, where $u^2=u$, and p is an odd prime. Although both algebraic settings considered admit this presentation, they correspond to two distinct rings depending on whether the additional relation $pu=0$ is imposed or not. These two configurations induce different ideal lattices and symmetry properties, which play a decisive role in the structure and enumeration of codes. Exploiting the Chinese Remainder Theorem, we describe self-dual and linear complementary dual (LCD) double-circulant codes in a unified, componentwise manner. Exact enumeration formulas are derived by reducing the corresponding duality constraints to norm equations over finite fields and unramified Galois extensions of ${\mathbb{Z}}_{p^2}$. We further construct explicit ${\mathbb{F}}_p$-linear Gray maps from $R^{2n}$ to ${\mathbb{F}}_p^{6n}$ in the degenerate case $pu=0$ and to ${\mathbb{F}}_p^{8n}$ in the standard case $pu\ne 0$, and show that these maps preserve self-duality and the LCD property. Assuming a standard primitive-root hypothesis on the code length, as predicted by Artin’s primitive root conjecture, we establish asymptotic existence bounds for the Gray images of both LCD and self-dual double-circulant codes via a probabilistic argument. The degenerate case $pu=0$ yields a shorter Gray expansion and a stronger self-dual entropy threshold, while the case $pu\ne 0$ leads to a larger self-dual ensemble with distinct asymptotic characteristics. Full article

Monocular camera localization in known environments is a critical task for applications like autonomous navigation, augmented reality, and robotic positioning, requiring precise spatial awareness. Unlike localization in unknown environments, which builds maps in real time, this leverages pre-existing data for higher accuracy. This review comprehensively analyzes monocular camera localization methods in known environments, categorizing them into 2D-2D feature matching, 2D-3D feature matching, and regression-based approaches. It consolidates foundational techniques and recent advancements, providing inter-class and intra-class performance comparisons on mainstream datasets. Key findings show that 2D-3D methods generally offer the highest accuracy, especially in structured outdoor environments, due to robust use of 3D spatial information. However, recent scene coordinate regression methods, such as ACE and ACE++, achieve comparable or superior performance in indoor scenes with more efficient pipelines. This review highlights challenges and proposes future directions: (1) synthetic data generation to meet deep learning demands, while addressing domain gaps; (2) improving generalization to unseen scenes and reducing retraining; (3) multi-sensor fusion for enhanced robustness; (4) exploring transformer-based and graph neural network architectures; (5) developing lightweight models for real-time performance on resource-constrained devices. This review aims to guide researchers and practitioners in method selection and identify key research directions. Full article

Invasive freshwater fishes often display high trophic plasticity, facilitating their establishment and persistence in novel environments. This study examined the feeding ecology, growth patterns, and trophic role of the invasive black bullhead Ameiurus melas in the eutrophic Gruža Reservoir (Central Serbia), with emphasis on ontogenetic dietary shifts and potential ecological impact. Diet composition was analyzed in 103 individuals representing three age classes using traditional diet indices, Costello graphical analysis, self-organizing maps (SOMs), and the Indicator Value (IndVal). Chironomidae, Protozoa, and fish eggs were the dominant dietary components across age classes, although their relative importance varied ontogenetically. Younger individuals exhibited a more generalized feeding strategy, whereas older fish showed increased specialization on benthic prey. SOM-IndVal analyses revealed prey taxa associated with specific feeding patterns at the individual level, identifying Diptera as an indicator prey not detected by population-level indices. Length–weight relationships indicated negative allometric growth (b < 3) across all age classes, consistent with a diet dominated by low-energy prey. These feeding patterns may contribute to altered benthic processes, reduced native fish recruitment, and reinforcement of eutrophic conditions. Overall, the results highlight the pronounced trophic flexibility and ecological plasticity of A. melas, supporting its invasive success in degraded freshwater ecosystems. Full article

This paper develops a structural and constructive theory of right generalized quasi-polycyclic (GQPC) codes over the finite chain ring $R={\mathbb{F}}_q+u{\mathbb{F}}_q$ with $u^2=0$, extending the existing field-based GQPC framework to a ring-theoretic setting. Right GQPC codes over R are modeled as $R\left[x\right]$-submodules of direct products of polycyclic ambient algebras $R\left[x\right]/⟨x^{e_i}-{\alpha }_i\left(x\right)⟩$, induced by vectors ${\alpha }_i\in R^{e_i}$, thereby unifying right quasi-polycyclic and generalized quasi-cyclic codes over $R.$ Under explicit and verifiable factorization conditions on the defining polynomials, we establish a Chinese Remainder Theorem decomposition that reduces right GQPC codes to collections of shorter codes over finite chain-ring extensions of R. This decomposition yields a characterization of ρ-generator right GQPC codes and leads to a canonical normalized generating set with an upper-triangular structure. As a consequence, we obtain an explicit rank formula in terms of the diagonal generator polynomials, together with an effective normalization algorithm. To demonstrate the coding-theoretic impact of the framework, we combine these structural results with a distance-compatible Gray map $\Phi :R\to {\mathbb{F}}_q^2$ and construct new q-ary linear codes from 2-generator right GQPC codes of index 2 over R. For $q=9$ and $q=3$, the resulting Gray images attain optimal or near-optimal parameters with respect to the best-known bounds, confirming that right GQPC codes over ${\mathbb{F}}_q+u{\mathbb{F}}_q$ constitute a robust and effective ring-based source of high-quality linear codes. Full article

Efficient RFID spatial exploration in dynamic warehouse environments is challenging due to occlusions, sensing geometry constraints, and the weak coupling between information acquisition and navigation decisions. Many existing inventory robots treat RFID sensing as a passive data source during exploration, without explicitly optimizing sensing pose or prioritizing inventory-driven frontiers, which can result in incomplete coverage and redundant traversal. This paper presents ArmTenna, an articulated mobile robotic platform that formulates RFID inventory exploration as an active perception problem. The system integrates dual 4-DOF robotic arms carrying directional UHF RFID antennas and a 2-DOF neck-mounted RGB-D camera, enabling adaptive interrogation of candidate regions. We propose a multi-modal frontier exploration framework that combines newly detected EPC tags, average RSSI values, and vision-based product detections into a composite utility function for goal selection. By embedding articulated antenna control directly into the frontier evaluation loop, the robot tightly couples sensing geometry with exploration decisions. Experimental validation with 150 tagged items across three separated warehouse zones shows that ArmTenna achieves up to 97% map coverage, compared to 72% for a baseline platform, while reducing missed-tag regions. These results demonstrate that integrating active sensing pose control with multi-modal frontier evaluation provides an effective and scalable solution for RFID-driven warehouse inventory automation. Full article

The rapid growth of e-commerce has intensified operational and sustainability challenges in urban last-mile delivery, necessitating routing methods that perform reliably under realistic traffic and spatial conditions. This study evaluates three routing algorithms, Nearest Neighbor (NN), Clarke–WrightSavings (CWS), and Ant Colony Optimization (ACO), using 1764 real-world Amazon delivery stops grouped into ten operational clusters in the Nashville metropolitan area. Travel distances and times were obtained through the Google Maps Distance Matrix API in driving mode to reflect actual road network structure and typical traffic conditions. Substantial performance differences were observed across algorithms and cluster configurations. NN achieved a strong performance in compact clusters (18.43 miles and 58.48 min in Cluster 4) but performed poorly in dispersed clusters (82.44 miles and 196.48 min in Cluster 9), reflecting high sensitivity to spatial dispersion. In contrast, CWS consistently reduced travel distance and time across clusters, achieving the shortest observed route (18.50 miles and 47.82 min in Cluster 10). Relative to ACO, CWS reduced travel distance by up to 42% (Cluster 9) and reduced travel time by over 45% in high-dispersion clusters. ACO exhibited the highest variability, with distances reaching 98.77 miles and travel times exceeding 218 min. Multi-criteria evaluation using efficiency ratios, distributional analysis, performance quadrant visualization, and a Composite Performance Index (CPI) confirmed the dominance of CWS. CPI scores of 1.00 (CWS), 0.78 (NN), and 0.00 (ACO) reflected balanced spatial and temporal efficiency under identical traffic-informed inputs. The results demonstrate that deterministic savings-based routing provides superior stability, efficiency, and scalability in semi-static urban delivery systems. However, the present study did not benchmark the evaluated algorithms against state-of-the-art exact TSP solvers (e.g., Concorde, LKH) or more recent metaheuristics such as Genetic Algorithms or Variable Neighborhood Search. The objective was to provide a controlled empirical comparison under consistent traffic-informed cost matrices rather than to establish global optimality bounds. Consequently, while the findings strongly support the relative superiority of the Clarke–Wright Savings approach within the evaluated framework, future research incorporating advanced exact and hybrid optimization methods would further contextualize algorithmic performance. Full article

Education is a complex and multidisciplinary field. Effective personalization in education is grounded in both educational theory and hybrid AI practice. Personalization is typically driven by explicit, structured knowledge; however, the effective automated extraction of implicit knowledge from educational data is also of great importance. This research analyzes and classifies the knowledge required for personalization, as well as the effective technologies for its representation and storage in both human-readable and machine-processable forms. As a result, we propose a conceptual model of a layered, hybrid knowledge base architecture grounded in mathematical logic, designed to structure knowledge for supporting personalization in intelligent educational systems. Systems of mapped ontologies constitute a core component of the proposed architecture. The proposed architecture extends the well-known intelligent tutoring systems architecture by incorporating new types of knowledge as well as structural and organizational elements and by providing a detailed description of their interrelationships and integration mechanisms. It is important to make easier and effective development of ontologies for usage in knowledge models, integrated in practical e-learning systems. The proposed conceptual model also promotes ontology reuse, thereby reducing the time, effort, and cost associated with ontology development and evolution. To enhance ontology development and usage through effective reuse, we propose a structured organization of metadata for describing all components of hybrid AI-driven knowledge bases. This metadata framework can support the development of an ontology that facilitates the discovery, selection, and reuse of appropriate ontologies, rules, mappings, and tools stored in specialized knowledge repositories for educational purposes. Full article

Edge, fog, and cloud continuum architectures that interconnect resource-constrained devices, intermediate edge servers, and remote cloud data centers face persistent challenges in handling heterogeneous and latency-sensitive workloads while reducing energy consumption and improving resource utilization. Classical task offloading approaches either rely on static heuristics, which lack adaptability to dynamic conditions, or on metaheuristic optimizers, which often incur high computational overhead and centralized coordination. This paper proposes LITO, a lemur-inspired task offloading algorithm for edge, fog, and cloud continuum systems that models the infrastructure as a social system in which computing nodes assume distinct roles that mirror lemur social hierarchies. Building on an abstracted model of lemur group behavior, LITO incorporates two key lemur-inspired mechanisms: an energy-aware task assignment mechanism based on sun basking, a thermoregulation behavior in which lemurs seek favorable warm spots, mapped here to selecting energetically efficient execution nodes, and a cooperative scheduling policy based on huddling, group clustering under stress, mapped here to sharing load among overloaded nodes. These mechanisms are combined with a continual supervised policy-learning layer with contextual bandit feedback that refines offloading decisions from online feedback. The resulting multi-objective formulation jointly minimizes energy consumption and deadline violations while maximizing resource utilization and throughput under high-load conditions in the edge and fog segment of the continuum. Simulations under diverse workload regimes and task complexities show that LITO outperforms representative multi-objective offloading baselines in terms of energy consumption, resource utilization, latency, Service Level Agreement (SLA) violations, and throughput in congested scenarios. Full article

Reliable urban stormwater design under nonstationary rainfall is becoming increasingly important, yet quantitative links between floods occurrence, updated design requirements, and associated costs remain limited. This study (i) characterizes the evolution and impacts of flood-related events in São Paulo State, Brazil (1991–2024), and (ii) quantifies how nonstationary rainfall projections (CMIP6 SSP2-4.5 and SSP5-8.5) affect culvert sizing and construction costs across municipalities for standardized hypothetical catchments, across multiple return periods and future horizons. Observations indicate an increase in flood occurrence, from an average of 7.8 events per year in the 1990s to 72.9 events per year in the 2010s. In the immediate future (2015–2055), SSP2-4.5 projected costs remain close to the baseline for most municipalities (for return periods ≤ 25 years, 68% show increases up to 10%), whereas for the distant future (2056–2100) 86% exceed 10% under SSP5-8.5. However, under SSP5-8.5 in the immediate future (2015–2055), and for RP > 25 years, approximately 46% of municipalities exceed 10% additional costs. Design discharges generally rise by 9–43% in the immediate future, with stronger increases under SSP5-8.5 toward the late century. Mapping required hydraulic area to commercially available nominal sizes discretizes upgrades and creates threshold behavior, with larger basins crossing size classes more often. These findings challenge the assumption of stationary design and support the adoption of nonstationary adaptation strategies to reduce the long-term probability of structural failure. Full article

Real-time object detection in soccer videos presents significant challenges due to the dynamic nature of matches, varying object scales, and the stringent requirement for efficient processing. In this work, we define real-time detection as that which achieves inference speeds of at least 30 frames per second (FPS), which is the minimum requirement for smooth video processing and live broadcast applications. While transformer-based detectors have achieved remarkable accuracy, their quadratic computational complexity limits their real-time applications. In this paper, we propose SoccerDETR, a novel real-time detection framework that integrates MobileMamba-based visual state space models with an efficient transformer encoder for soccer object detection. Our approach introduces four key innovations: (1) a MobileMamba backbone leveraging selective state space modeling to achieve linear computational complexity while maintaining global receptive fields; (2) a Semantic-aware Dynamic Feature Fusion Module (SDFM) that adaptively aggregates multi-scale features through progressive semantic injection; (3) a Spatial-Channel Synergistic Attention (SCSA) mechanism that explores the synergistic effects between spatial and channel attention for enhanced feature representation; and (4) a Separable Dynamic Decoder that employs dynamic convolution attention to replace traditional cross-attention, significantly reducing computational overhead. Additionally, we design a Scale-Aware Focal Loss (SAFL) that addresses the class imbalance and scale variation problems inherent in soccer scenarios. Extensive experiments on the Soccana and SoccerNet datasets demonstrate that SoccerDETR achieves state-of-the-art performance with 94.2% mAP@50 on Soccana and 91.8% mAP@50 on SoccerNet, while maintaining real-time inference speed of 78 FPS on a single NVIDIA RTX 4090 GPU with a batch size of 1 and an input resolution 640 × 640. Our method outperforms existing approaches by 2.3–5.7% in mAP while being 1.5–3.2× faster, demonstrating the effectiveness of state space models for efficient sports video object detection. Comprehensive ablation studies validate the effectiveness of each proposed component, and cross-dataset experiments demonstrate strong generalization capability. Full article

Detecting rare ground-level obstacles (e.g., large boulders) in dense boreal forests from low-altitude UAV RGB imagery is challenging due to limited annotated data, strong background clutter, and expensive field labeling. This paper evaluates two complementary synthetic-data augmentation pipelines for low-data forestry object detection: segmentation-guided diffusion inpainting, where SegFormer-derived forest-floor masks constrain Stable Diffusion inpainting to plausible insertion regions, and simulator-based generation in Unreal Engine 5 with controlled domain randomization and automatic annotations. We conduct a ten-fold cross-validation study on a real UAV dataset of 64 images and report both accuracy and stability across folds. Compared to real-only training (mean mAP50 ≈ 0.579; mAP50-95 ≈ 0.350), inpainting improves mean performance (mAP50 ≈ 0.647; mAP50-95 ≈ 0.435) while substantially reducing cross-fold variance and lifting the worst-case fold from 0.301 to 0.619 in mAP50. Simulator augmentation yields slightly lower mean accuracy (mAP50 ≈ 0.546; mAP50-95 ≈ 0.344) but markedly improves robustness by mitigating collapse on difficult splits (minimum mAP50 0.496 vs. 0.301). These results indicate that carefully curated generative augmentation can reduce failure risk and improve generalization in extremely data-limited forestry detection settings without additional field data collection. Full article

This study investigates the gel characteristics of alkali-activated materials (AAMs) synthesized using wood ash (WA), and metakaolin (MK) as solid precursors. The research explores the influence of precursor type and sodium hydroxide (NaOH) concentrations in the alkali activator solution on the resulting physicochemical, microstructural, mechanical, and radiological properties of gels. The alkaline activators were prepared by mixing sodium hydroxide solutions (6 M and 12 M) with a sodium silicate (water glass) solution at a volume ratio of 1.5. The physicochemical characteristics of raw materials and AAMs were thoroughly analyzed using X-ray fluorescence (XRF), Diffuse Reflectance Infrared Fourier Transform (DRIFT) spectroscopy, X-ray diffraction (XRD), and scanning electron microscopy (SEM) with EDS elemental mapping. FTIR analysis confirmed the formation of an amorphous gels geopolymer network. XRD revealed the presence of characteristic crystalline phases (quartz, calcite) within an amorphous matrix. Mechanical properties, such as compressive strength, depended on precursor type and alkali molarity: metakaolin (12 M) reached ~14 MPa, while wood ash showed ~4 MPa (6 M) and ~0.5 MPa (12 M) due to high CaO, low Si and Al, and unfavorable SiO₂/Al₂O₃ (5.71) and Na₂O/Al₂O₃ (3.19) ratios. Furthermore, this research estimates radiological doses by quantifying radionuclide content via gamma-spectrometry. Alkali activation significantly reduced radiological hazard parameters, with radium equivalent activity (Ra_eq) decreasing to 238.0 Bq/kg and the external hazard index (H_ex) to 0.643 for A₁₂MK, while the annual effective dose rate for A₁₂WA was only 0.265 nSv/y-all values remaining well below the recommended safety limit of 370 Bq/kg (≤1 mSv/y). The decrease in activity concentration index (I_γ), Ra_eq, and H_ex with increasing NaOH concentration indicates effective radionuclide immobilization within the geopolymer matrix, confirming the suitability of these alkali-activated materials for safe use in construction from a radiation protection perspective. Full article