Search Results (3,296)

Symmetry and structural balance play a fundamental role in the collective behavior of networked agent systems (NASs). In particular, cluster formation tracking, representing the emergence and maintenance of symmetric group structures, has attracted significant attention due to its wide applications in robotics and autonomous systems. However, most existing approaches assume autonomous leaders, which may not be applicable in scenarios where human intervention is required. With this in mind, this paper addresses the cluster formation tracking problem for NASs with collision avoidance, where the leader receives control inputs from a human-in-the-loop (HiTL), making the leader a non-autonomous system. A distributed control protocol is developed so that followers can track the trajectories of their designated leaders using only relative information from neighboring agents. Sufficient conditions are established to guarantee collision-free cluster formation tracking, and Lyapunov-based analysis is employed to prove the asymptotic convergence of the subgroup tracking errors. In the proposed framework, human intervention is incorporated through external commands applied to the leaders, which makes the leader dynamics non-autonomous while preserving the distributed nature of the follower controllers. Simulation studies on a 13-agent network with three subgroups show that all followers achieve the desired time-varying cluster formations under HiTL-driven leader motions, with convergence times ranging from 4.21 s to 5.12 s. Moreover, the final tracking errors of all followers are reduced below $9.07\times {10}^{-5}$, while the minimum pairwise distances within each subgroup remain strictly above the prescribed safety threshold. These quantitative results verify both the effectiveness of the proposed protocol and the practical feasibility of integrating HiTL commands into collision-free cluster formation tracking. Full article

Multi-UAV collaborative mapping in communication-constrained indoor environments is often hampered by a trade-off between overall map refinement and the timely completion of safety-relevant shared regions. In high-density or unmapped areas, network congestion can delay the updates that matter most for close-proximity coordination, because standard bandwidth allocation does not distinguish between general map refinement and hotspot-related spatial data. To address this issue, we propose a resilient scheduling framework that prioritizes globally useful map updates while improving safety-relevant hotspot completeness under unreliable links. At its core is a Safety Reserve allocation strategy for “hotspot” submaps—areas where UAV trajectories overlap or approach unknown frontiers. By enforcing this reserve, the system directs a limited uplink budget to hotspot-related updates earlier during congestion. To remain useful under packet loss, we implement a prefix-decodable hierarchical data structure over a lightweight stateless protocol, allowing immediate fusion of valid partial updates. The framework identifies hotspots using feedback from a Lambda-Field risk model and a truncated least squares solver with graduated non-convexity (TLS–GNC) pose-graph optimizer. Experiments on S3DIS and ScanNet under partition-based two-agent emulation show that the proposed method improves hotspot-band completeness and progressive mapping quality over the tested baselines, especially under packet loss. Full article

This study proposes a four-dimensional Historical Building Defect Information Modeling (HBDIM) framework designed to support the documentation, diagnosis, and conservation of deteriorated historic stucco elements. The framework integrates multi-source digital documentation techniques, including terrestrial laser scanning (TLS), high-resolution photogrammetry, and automated total station measurements with laboratory-based material diagnostics to create a unified digital environment for defect detection and conservation assessment. The approach was applied to the Baron Empain Palace in Egypt as a representative case study of complex architectural heritage affected by material deterioration. Within the HBDIM workflow, point cloud processing and defect-oriented information modeling were used to identify and spatially localize deterioration features such as cracking, erosion, and material loss. Laboratory investigations—including computed tomography (CT), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray fluorescence (XRF)—were conducted to evaluate the effectiveness of calcium hydroxide nanoparticle consolidation treatments and to relate microstructural material behavior to spatially mapped defects within the digital model. Mechanical testing demonstrated a significant improvement in material performance, with treated stucco samples exhibiting an average compressive strength increase of approximately 69.06% compared to untreated specimens. The results demonstrate that integrating digital documentation, defect-oriented modeling, and material diagnostics within a four-dimensional framework provides a robust platform for linking geometric deterioration patterns with material-level conservation performance. By embedding diagnostic data and treatment outcomes within a temporally structured digital model, the HBDIM approach supports preventive conservation strategies, long-term monitoring, and data-driven decision-making in sustainable heritage management. Full article

Maternal stress during lifetime and pregnancy may influence offspring epigenetic age, impacting long-term health. We conducted a systematic review and meta-analysis of associations between maternal stress and epigenetic aging markers: telomere length (TL) and DNA methylation (DNAm) age acceleration. The systematic search was performed according to PRISMA guidelines and registered on PROSPERO (ref. CRD42023474640). Fixed and random effect meta-analyses were carried out, stratified by stress type and marker type (TL, DNAm). Sixteen studies met inclusion criteria; 12 were meta-analyzed (10 TL, 2 DNAm). Due to high heterogeneity, restricted maximum likelihood meta-analysis suggested significant inverse associations between maternal stress and offspring TL. Perceived stress was associated with shorter TL (p-value = 7 × 10⁻⁴, β = −0.085, 95%CI [−0.135, −0.036]), as was lifetime stress/trauma (p-value = 0.01, β = −0.209, 95%CI [−0.370, −0.049]). In contrast, maternal stress showed no significant associations with DNAm age acceleration (p-value = 0.32). Both perceived maternal stress and maternal stress were associated with shorter offspring TL, suggesting that stress exposure across the maternal lifespan influences offspring biological aging markers. No significant association was observed with DNAm-based aging clocks. Further studies with larger sample sizes and more homogeneous settings are needed to confirm and expand upon our observations. Full article

Archaeological sites characterized by the coexistence of extensive above-ground terrain and hypogeum structures present major challenges for accurate and comprehensive geospatial documentation. Conventional survey approaches—such as static terrestrial laser scanning (TLS), total-station measurements, and aerial photogrammetry—often suffer from operational constraints, particularly in the presence of narrow underground spaces, low or absent illumination, harsh environmental conditions, and restrictions on UAV deployment. Additional complexity arises when both surface and subterranean elements must be consistently georeferenced to a common global reference system, especially where establishing a traditional topographic–geodetic control network is impractical. Within the framework of the EIMAWA Egyptian–Italian Mission conducted by the University of Milano since 2018, the Geomatics group of the University of Bologna designed and implemented a multi-scale multi-technique 3D documentation workflow, with a prominent role assumed by Simultaneous Localization and Mapping (SLAM) mobile laser scanning. The approach was supported by GNSS measurements providing centimetric accuracy. SLAM was employed to document both the surface necropolis and multiple hypogeal tombs, enabling rapid acquisition of dense three-dimensional data in environments where traditional techniques are limited. All datasets were integrated within a unified reference system, resulting in a coherent, multi-layered spatial dataset representing both landscape and underground spaces. The results demonstrate that SLAM can produce dense point clouds that document at few-centimetric level accuracy and continuously both above- and below-ground contexts. Quantitative analyses of the co-registration and mutual alignment of multiple SLAM datasets confirm a high degree of internal consistency, further enhanced through post-processing refinement. Overall, the experience indicates that this solution represents a practical and reliable technique for complex archaeological surveying. Full article

Organic dye-, pharmaceutical-, and heavy metal-contaminated water are emerging environmental issues, and thus there is a requirement for the development of efficient and sustainable purification methods. Semiconductor (SmC) material-based photocatalysis using TiO₂ and Fe₂O₃ nanostructures is considered a promising field for pollutant degradation due to its chemical stability, nontoxicity, and ability to perform photocatalytic degradation using light irradiation. Understanding the thermal, optical, and charge transport properties governing their photocatalytic activity requires advanced characterisation methods. In this context, photothermal (PT) techniques provide powerful tools for probing non-radiative processes and energy transport in photocatalytic materials. The photocatalytic activity of these materials strongly depends on their structural, optical, thermal, and electronic properties. These properties can be enhanced through several modification strategies, including metal and non-metal doping (e.g., C, N, Cu, Ag, Au), surface modification, forming a complex with SiO₂, and the formation of Fe₂O₃–TiO₂ heterostructure nanocomposites. In this review, a comprehensive overview is provided of TiO₂ and Fe₂O₃-based nanocomposites with a specific focus on characterisation techniques for photothermal characterisation techniques, including thermal lens spectroscopy (TLS), beam deflection spectrometry (BDS), and photoacoustic spectroscopy (PAS), for determining thermal diffusivity, thermal conductivity, bandgap energy, carrier lifetime, surface roughness, porosity, etc., which are related to photocatalytic activity. The properties of these nanocomposites are correlated with photocatalytic activity for pollutant degradation using these nanocomposites. The challenges faced while using these nanocomposites for pollutant degradation are also discussed, along with future prospects for designing efficient photocatalysts for water purification applications. Full article

Three-dimensional (3D) sonar overcomes the limitations of traditional measurement methods regarding imaging coverage and accuracy, making it indispensable for underwater structure monitoring. However, complex underwater environments often introduce significant noise into 3D sonar data, degrading monitoring performance. To address this, we propose a geometry-based filtering method. First, Total Least Squares (TLS) is employed to construct local spatial features, which guides a region-growing segmentation based on normal vector attributes. Subsequently, the resulting clusters are refined using these local geometric characteristics. Finally, statistical filtering is applied to eliminate residual outliers from a local to a global scale. Experimental results demonstrate that the proposed method achieves F1 scores of 78.65% and 84.49% in outlier removal, effectively suppressing noise while preserving structural integrity. Full article

Background/Objectives: Endometrial cancer (EC) is a multifactorial disease influenced by metabolic, hormonal, and environmental factors. Trace and macroelements play a critical role in cellular homeostasis, oxidative stress, and tumor progression; however, their relationship with EC grading and clinical characteristics remains insufficiently understood. Methods: This study evaluated the concentrations of selected macro- and trace elements (Na, K, Ca, P, Mg, Mn, Cu, Zn, Be, As, Cr, Mo, Ti, Tl, Pb) in patients with endometrial cancer (G1–G3) and a control group (C). Elemental analysis was performed using inductively coupled plasma optical emission spectrometry (ICP-OES). Associations between elemental concentrations and clinicopathological variables, including age, body mass index (BMI), menopausal status, diabetes, and smoking, were assessed using appropriate statistical tests, including ANOVA with Tukey’s post hoc analysis and Student’s t-test. Multivariate regression analysis was performed to identify independent predictors of elemental alterations. Results: Significant differences in elemental concentrations were observed across EC grading. Higher-grade tumors were associated with increased levels of Ca, P, Mg, and Mn, while Na and K showed a decreasing trend with tumor progression. No statistically significant differences were observed for Zn, Ti, Tl, or Pb across histological grades. Stratified analyses demonstrated that clinical and metabolic factors had a limited and selective impact on elemental profiles. Age and BMI were associated with minor variations in selected elements, whereas menopausal status, diabetes, and smoking showed predominantly non-significant or inconsistent effects. Multivariate analysis identified histological grade as the primary determinant of elemental alterations, while other variables exhibited weaker or element-specific associations. Conclusions: Elemental homeostasis in endometrial cancer is primarily associated with tumor progression rather than systemic metabolic or lifestyle factors. Changes in Ca-, P-, Mg-, and Mn-related pathways may reflect tumor-driven metabolic reprogramming, whereas most trace elements remain relatively stable. These findings suggest that elemental profiling may provide insight into EC biology, although its clinical utility requires further investigation. Full article

This work presents a simulation-based approach to support the planning of Terrestrial Laser Scanning (TLS) surveys for landslide monitoring. Starting from an approximate digital model of the slope, the method estimates the spatial distribution of positional error induced by scanner characteristics, laser beam divergence and, critically, by the incidence angle between the laser beam and the local surface normal. Because complex morphologies cause rapid local variations in incidence angle, neighbouring points may exhibit markedly different error magnitudes, making a direct classification of raw error values insufficient to delineate homogeneous areas. To address this, a multidimensional variable is defined for each simulated point, combining position, estimated error, distance from the scanner and incidence angle. After dimensionality reduction through PCA, the dataset is clustered using K-means with a sufficiently large number of clusters to preserve spatial resolution. Each cluster is associated with a representative error level, and clusters are then merged into broader error classes that delineate zones of comparable expected precision. The procedure is repeated for alternative scanner positions, enabling a comparative evaluation of achievable accuracy across the slope and the identification of areas requiring multiple scans. The method provides a quantitative, reproducible framework to guide TLS station selection and optimize survey design in complex morphological settings. Full article

Waveguide-like propagation in elongated underground environments—utility corridors, logistics tunnels—generates dense multipath that can cause the earliest or strongest resolvable channel impulse response (CIR) component to originate from a specular reflection rather than the direct line-of-sight (LOS) path. In the single-anchor CIR-tap-based implementations common to practical ultra-wideband (UWB) systems, baseline estimators such as phase-difference-of-arrival (PDOA) and MUSIC rely on selecting a single dominant CIR component, producing large angle-of-arrival (AoA) errors whenever the selected path is a reflection. We propose a multipath credibility selection (MCS) AoA estimator, MCS-AoA, that does not require explicit LOS/NLOS classification. The algorithm scores each resolvable CIR component with four credibility factors—amplitude significance, time-of-flight (TOF) consistency, inter-baseline phase–geometry agreement, and cross-baseline coherence—and fuses retained candidates into a credibility-weighted spatial covariance matrix for 2D MUSIC search. Field experiments on a custom five-channel coherent UWB platform compare MCS-AoA against six baselines—PDOA, MUSIC, MVDR/Capon, TLS-ESPRIT, PwMUSIC, and DNN-AoA. In an underground corridor (5–40 m), MCS-AoA achieves an azimuth/elevation MAE of 1.00°/1.46°, outperforming all baselines (PDOA: 2.26°/2.49°; MUSIC: 1.76°/2.40°; next-best PwMUSIC: 1.44°/2.17°); in a logistics tunnel (5–80 m), it achieves a 1.19° overall azimuth MAE. Simulations corroborate these gains, with a 0.71° azimuth RMSE at 80 m (69.3% reduction over PDOA) and 86.6% of estimates falling within 1°. Full article

The need for new feed ingredients that could reduce methane (CH₄) emissions from dairy cattle while maintaining rumen function is essential for sustainable milk production. This study aimed to evaluate the CH₄ mitigation potential of selected microalgae and macroalgae, along with an agro-industrial by-product, using two feeding strategies, and hypothesized that lipid- and polyphenol-rich materials would reduce CH₄ production in an inclusion-dependent manner. An in vitro batch culture study (24 h) was conducted to evaluate microalgae (Euglena gracilis and Aurantiochytrium spp.), macroalgae (Undaria pinnatifida), and an agro-industrial by-product (grape marc) either as feed additives (5%) or as a partial replacement of the concentrate mixture (30%, 50%, and 70%) in a basal diet consisting of 50% Klein grass hay and 50% concentrate mixture. As a feed additive, grape marc stands out for its potential to reduce CH₄ yield by about 43.3% without adversely affecting digestibility, pH, or total volatile fatty acid concentrations. When used as feed replacements, Euglena-, Aurantiochytrium-, and grape marc-based feeds reduced CH₄ yield at the highest replacement levels (50 and 70%); however, these effects were accompanied by decreased total gas production and volatile fatty acid concentrations, indicating reduced fermentation activity. Meanwhile, at a 30% replacement level, they showed promising efficiency as alternative feeds. Overall, CH₄ mitigation depends more strongly on inclusion strategy rather than feed type. Lipid-rich microalgae showed potential as concentrate replacements up to 30%, whereas grape marc was most effective as a feed additive for reducing CH₄ emissions. Full article

Background: Real-time and accurate analysis of breast ultrasounds is crucial for diagnosis but remains challenging due to issues like low image contrast and operator dependency. This study aims to address these challenges by developing an integrated framework for real-time lesion detection and tracking. Methods: The proposed system combines Contrast-Limited Adaptive Histogram Equalization (CLAHE) for image preprocessing, a transfer learning-enhanced YOLOv11 model following a continual learning paradigm for cross-center generalization in for lesion detection, and a novel Detection-Based Tracking (DBT) approach that integrates Kernelized Correlation Filters (KCF) with periodic detection verification. The framework was evaluated on a dataset comprising 11,383 static images and 40 ultrasound video sequences, with a subset verified through biopsy and the remainder annotated by two radiologists based on radiological reports. Results: The proposed framework demonstrated high performance across all components. The transfer learning strategy (TL12) significantly improved detection outcomes, achieving a mean Average Precision (mAP) of 0.955, a sensitivity of 0.938, and an F1 score of 0.956. The DBT method (KCF + YOLO) achieved high tracking accuracy, with a success rate of 0.984, an Intersection over Union (IoU) of 0.85, and real-time operation at 54 frames per second (FPS) with a latency of 7.74 ms. The use of CLAHE preprocessing was shown to be a critical factor in improving both detection and tracking stability across diverse imaging conditions. Conclusions: This research presents a robust, fully integrated framework that bridges the gap between speed and accuracy in breast ultrasound analysis. The system’s high performance and real-time efficiency underscore its strong potential for clinical adoption to enhance diagnostic workflows, reduce operator variability, and improve breast cancer assessment. Full article

Transnational youth frequently navigate multiple languages and continually negotiate not only affiliation, but also the legitimacy of the languages they use within changing linguistic hierarchies. However, their educational experiences are often framed through fragmented classroom practices, deficit-based assessments, and nationally bounded curricular frameworks. In this paper, I respond by theorizing Fictional Linguistic Landscapes (FLL) as a transdisciplinary pedagogical approach that utilizes fiction and participatory cultural practices to position language learning as a form of semiotic design, critical inquiry, and identity (re)work. Grounded in linguistic landscape studies, multiliteracies pedagogy, and fan-based meaning-making, FLL positions learners as world-builders and allows them to experiment with visibility, hierarchy, and language(s) in safe fictional environments. This study outlines the four-phase FLL in Second Language Teaching and Learning (L2TL) cycle and provides five pedagogical design spaces to address issues of raciolinguistic valuation, deficit institutional representations, affective harm, peer-level marginalization, and translocal or return migrant identity negotiation. Rather than viewing imagination as an outcome of teaching, FLLinL2TL structures it as a necessary process for learning, linking creative production to explicit linguistic objectives and reflective justification. I conclude by discussing implications for classroom practice, teacher education, and future research on the potential of the FLLinL2TL approach in transnational education research. Full article

Terrestrial laser scanning (TLS) provides dense point clouds for landslide monitoring, yet occlusion, heterogeneous point density, and seasonal vegetation introduce noise and unstable deformation boundaries in multi-temporal change detection. To overcome the limitations of the multiscale model-to-model cloud comparison (M3C2) method under dominant downslope tangential motion and vegetation disturbance, we propose a block-wise ICP method to retrieve 3D displacement vectors. The scene is partitioned into local sub-blocks; rigid registration is performed within each sub-block, and the estimated translation is assigned to the sub-block center. A two-stage matching and quality control procedure removes under-constrained sub-blocks, enabling the direct retrieval of 3D displacement vectors and interpretable boundaries. Applied to the Longxigou landslide in Wenchuan using RIEGL VZ-2000i surveys on 1 November 2023 and 23 May 2024, the proposed method produces a more continuous displacement field and clearer boundaries than M3C2. For a tower target, manual measurements indicate a displacement of 0.41–0.63 m; our estimates are within 0.33–0.40 m, whereas M3C2 mostly falls between −0.25 and 0.25 m. In a seasonal vegetation change scene, we detect a canopy envelope expansion of approximately 0.20–0.40 m, while M3C2 shows scattered canopy responses that hinder boundary interpretation. A sensitivity analysis indicates a block-scale trade-off between boundary stability and peak preservation, motivating adaptive multi-scale blocking and uncertainty quantification. Full article

Multimodal epileptic seizure detection using physiological biosignals remains challenging due to signal noise, inter-subject variability, weak cross-modal alignment, and the limited interpretability of many machine learning models. To address these challenges, this study proposes a parameterized multimodal feature-fusion framework that unifies normalization, modality weighting, and nonlinear cross-modal interaction within a single mathematical representation. Four fusion parameters, the fusion exponent $\rho$, interaction weight $\left(\delta \right)$, normalization factor $\left(\lambda \right),$ and the cross-modal interaction term $\left(\eta \right)$, are introduced at the feature-fusion level, while all classifiers retain their original learning mechanisms. The framework is evaluated using synchronized EEG, ECG, EMG, and accelerometer signals from 120 subjects, segmented into 2 s windows at 512 Hz and analyzed using twelve classical and deep learning classifiers. Principal Component Analysis (PCA) applied to the fused feature space reveals improved class separability compared to unimodal representations, with EEG exhibiting the strongest intrinsic discrimination and peripheral modalities contributing complementary structure when fused. SHapley Additive exPlanations (SHAP) further identify entropy as the most influential feature across all modalities, followed by RMS and energy, yielding physiologically coherent attributions. Quantitative performance evaluation and ablation analysis confirm that the observed improvements arise from the proposed representation design rather than classifier-specific modifications. Unlike existing architecture-dependent fusion strategies, the proposed method introduces a mathematically parameterized feature-space formulation that enhances separability and interpretability without modifying classifier architectures, thereby establishing a representation-driven paradigm for explainable multimodal seizure detection. These results demonstrate that mathematically principled feature-space modeling can simultaneously enhance predictive performance and interpretability, providing a transparent and robust foundation for explainable multimodal seizure detection. Full article