Search Results (153)

Accurate non-contact human height estimation is vital for large-scale growth monitoring in schools but remains challenging for monocular RGB sensors due to scale ambiguity and keypoint jitter. This study proposes a robust temporal SegPose framework for high-precision height measurement in unconstrained outdoor playground environments. We develop a multi-task deep learning model using a MobileNetV4 backbone and a novel Height-Aware Boundary Refinement (HABR) module, which utilizes nose-spatial priors to refine cranial vertex localization. To resolve scale issues, a three-way orthogonal calibration system is established using existing playground marking lines and goalposts to dynamically estimate ground plane metric factors. A linear Kalman filter is integrated to smooth keypoint trajectories, suppressing gait-induced oscillations and reducing high-frequency jitter by 64.92%. Validated on a dataset of 95 volunteers (53 males, 42 females) at distances of 6–12 m, the proposed system achieves a mean absolute error (MAE) of 1.42 cm and a mean absolute percentage error (MAPE) of 0.84%, significantly outperforming recent Transformer-based state-of-the-art methods. The framework operates at 42.7 FPS, ensuring real-time performance while adhering to a privacy-preserving protocol that decouples biometric records from individual identities. These results demonstrate that our framework effectively overcomes boundary ambiguity and distance-dependent resolution loss, providing a reliable, efficient, and ethical solution for automated physical health assessments in educational settings. Full article

Accurate land-use polygon mapping for forest-oriented natural resource monitoring requires both image-based class prediction and the reconciliation of heterogeneous geospatial semantics. In operational mapping, land survey, forestry survey, and natural resource monitoring datasets often differ in classification systems, management objectives, boundary rules, and mapping scales, causing semantic conflicts when overlaid or forced into one-to-one categories. To address this issue, this study proposes a topology-aware land-use polygon-mapping framework that integrates multi-source semantic representation learning, semantically guided relation learning, and topology-constrained polygon optimization. In the experiments, remote sensing imagery provides visual evidence, the Third National Land Survey (TNLS) and forestry survey (FS) datasets are encoded as source-specific auxiliary semantic priors, and the natural resource integrated monitoring (NRIM) data serve only as reference labels for training and evaluation. Rather than resolving cross-source conflicts using predefined rules, the framework learns a unified land-use representation, transforms semantic boundary cues into a vertex-edge topology graph, and reconstructs GIS-compatible polygons through topology-constrained polygon optimization. In a representative forest–agricultural landscape, the method achieved an mIoU of 79.86%, an APLS of 56.82%, and a TOPO-F1 of 54.56%. These results suggest that learnable semantic harmonization and topology-aware polygon generation can improve the semantic consistency and vector reliability of land-use products for forest and natural resource monitoring. Full article

Amphibious robots must reconcile two distinct mechanical requirements within a compact locomotion architecture. Terrestrial operation requires limb structures with sufficient load-bearing capacity, contact stability, and bending resistance, whereas aquatic operation benefits from a larger projected area for drag-based thrust generation. Conventional amphibious platforms often address these requirements by combining separate land and water propulsion modules, which increases structural redundancy, system mass, and hydrodynamic resistance. To reduce this conflict at the structural level, this study proposes a bioinspired origami morphing limb based on a modified Yoshimura pattern. The limb transforms between a closed cylindrical configuration for terrestrial support and an unfolded planar configuration for aquatic paddling. A vertex-splitting topology and thick-panel geometric constraints are introduced to suppress the bifurcation instability associated with the zero-thickness Yoshimura vertex, thereby obtaining a deterministic single-degree-of-freedom folding path suitable for robotic actuation. A screw-theory-based kinematic model is established to relate the active driving angle to the passive folding angle, and geometric parameter analysis is used to connect the folding state with load-bearing and paddling morphologies. A quadruped amphibious robot prototype is fabricated using rigid polylactic acid panels and flexible thermoplastic polyurethane hinges. Prototype-level observations qualitatively demonstrate reversible transformation within the tested operating range and show walking, crawling, rolling, water-entry, and underwater locomotion modes. Full article

In this work, we introduce and study a generalization for r-uniform hypergraphs of complement-reducible graphs, the so-called co-graphs. The operations for r-join-hypergraphs are the binary disjoint union of two given r-join-hypergraphs and the r-nary join, which inserts all possible hyperedges of cardinality r, each including exactly one vertex from each of r given r-join-hypergraphs. We characterize the primal graphs of r-join-hypergraphs as special co-graphs and give some properties of r-join-hypergraphs. This allows us to give a method that decides whether an r-uniform input hypergraph H is an r-join-hypergraph and, in the case of an affirmative answer, finds a decomposition tree for H in polynomial time. This task proved to be challenging. We show specific formulas for computing various hypergraph parameters for r-uniform hypergraphs defined by the binary disjoint union of two r-uniform hypergraphs and the r-nary join of r r-uniform hypergraphs. The parameters considered are the size of a largest stable set, the size of a largest co-stable set, the size of a largest independent set, the size of a largest co-independent set, the size of a smallest vertex cover, the size of a smallest 2-transversal, the size of a largest matching, the size of a smallest dominating set, the chromatic number, the strong chromatic number, and the upper chromatic number. This yields $\mathcal{O}(n·r)$-time algorithms to compute these values on r-join-hypergraphs on n vertices given by a decomposition tree. Of particular interest is the development of an efficient algorithm to compute the size of a largest matching. In addition, we infer relations among the considered parameters restricted to r-join-hypergraphs. Our methods generalize and reprove a number of results that are known for co-graphs. Full article

How did exchanges between the architecture, engineering, and construction sectors of postcolonial states challenge dominant patterns of North–South technology transfer, and actively begin to construct the substantive scaffolding of an alternative international order? This article proposes a theoretical framework for understanding South–South exchange as a distinct spatial practice in postcolonial architectural history, recovering previously unexplored patterns of circulation—construction technologies, professional expertise, contracting capacity—that connected allegedly peripheral geographies spanning from Southeast Asia and Africa to the Caribbean and Southeast Europe. It draws on Doreen Massey’s concept of ‘power geometry’, Anna Tsing’s theorization of ‘friction’, and Édouard Glissant’s ‘archipelagic thinking’ to develop three analytical movements: Geometries, Powers, and Archipelagic Relations. Together these map the lateral circulation of expertise, technology, and labor among postcolonial nations operating outside the North-South and East–West axes around which twentieth-century architectural history has largely been organized. The paper argues that the prevailing diffusionist model resembles a two-body gravitational system that forecloses the possibility of lateral force, and treats South–South exchanges instead as orbital anomalies, persistent deviations from predicted paths that reveal an unseen distributed gravitational field. Rather than organizing around a metropolitan vertex, this field constitutes what computational geometry calls an ‘unstructured mesh’: contingent, uncentered, and variable in its local density. The mesh’s topology traces across a range of exchanges drawn from the forthcoming edited volume South–South: Non-Alignment and Cooperation in the Construction of the Global South, which brings together contributions from a dozen scholars of postcolonial architecture. The case studies include triangulations in which prefabrication technology traveled between Yugoslavia, Cuba, and Angola, and professional expertise looped between India, Africa and Europe. The motivational fields and frictions that drove and transformed each exchange are examined in turn. The article culminates in a reading of the Non-Aligned Movement (NAM) and the Afro-Asian Housing Organisation (AAHO) through Glissant’s concepts of relation and opacity—the right of postcolonial actors to participate in exchange without becoming fully legible to Cold War systems that demanded every alignment be declared. By challenging prevailing narratives that position the ‘Global South’ primarily as recipient rather than generator of innovation, the article contributes to a decolonial architectural historiography that shows how the political project of Non-Alignment was substantively materialized in the production of the built environment. Recovering these alternate forms of architectural flow demands a South–South historiographical orientation as a corrective to the field’s persistent centeredness, and offers historical insight no less relevant today for rethinking the potential, and the challenges, of contemporary transnational cooperation. Full article

Hydrological reservoirs are complex systems in which storage variations integrate climate forcing, catchment response, releases, withdrawals, evaporation, and monitoring procedures. This study presents an information-theoretic characterization of storage dynamics in five semiarid reservoirs in Paraíba, Brazil. The main analytical layer is the complexity–entropy causality plane (CECP), computed from daily storage increments by permutation entropy and Martín-Plastino-Rosso statistical complexity. CECP was estimated for fixed periods and for sliding windows of 120 observations, with sensitivity tests for embedding dimension, delay, and window length. The workflow also benchmarks CECP distance against conventional descriptors, quantifies ties and zero increments, and tests window overlap. As physical context, monotonic elevation-storage curves were reconstructed for 2009–2014, 2015–2019, and 2020–2026, and storage differences at equivalent water levels were quantified by bootstrap confidence intervals. The reservoirs occupied a high-entropy, low-to-moderate-complexity region of the CECP, but their distances from the maximum-entropy/minimum-complexity vertex differed across reservoirs and periods. Sliding windows revealed temporal mobility that was hidden by fixed-period summaries, especially in Engenheiro Arcoverde, Jatobá I, and Mãe d’Água. Rankings remained strongly concordant when window overlap decreased from 94.2% to 0% (Spearman $\rho =0.943$), although absolute coordinates were sensitive to the treatment of reported plateaus. Elevation-storage shifts provided an independent structural context: negative shifts were compatible with possible useful-capacity reduction, although not uniquely attributable to sedimentation. The results show that CECP descriptors can reveal ordinal organization and regime mobility in reservoir storage increments, while $V\left(H\right)$ curves supply the physically interpretable storage-capacity context. The combined evidence prioritizes Engenheiro Arcoverde and Mãe d’Água for bathymetric, curve history, and operational verification. The proposed workflow is therefore an exploratory information-theoretic screening tool for data-limited reservoir monitoring, not a substitute for bathymetric validation. Full article

We introduce a minimal relational network model in which superconducting-like behavior emerges as a collective phase of constrained connectivity and phase coherence, without assuming microscopic electrons, phonons, or material-specific interactions. The model is formulated as a concrete instantiation of a previously introduced axiomatic relational–informational framework for emergent geometry and effective spacetime, in which geometry and effective forces arise from constrained information flow rather than from a background manifold. Mathematically, this construction is realized on a finite weighted graph with binary edge-activation variables and compact vertex phase variables, sampled through a Gibbs ensemble generated by an additive informational action. The system is represented as a finite weighted graph with weighted edges encoding transport or informational costs, augmented by dynamically activated low-cost channels and compact phase degrees of freedom defined at vertices. The effective edge costs induce a weighted shortest-path metric, providing an operational notion of emergent relational geometry. Using Monte Carlo simulations on two-dimensional periodic lattices, we show that the same informational action supports three distinct emergent regimes: a normal resistive phase, a fragile low-temperature-like superconducting phase characterized by noise-sensitive coherence, and a noise-robust high-temperature-like superconducting phase in which global phase coherence persists under substantial fluctuations. These regimes are identified using purely relational observables with direct graph-theoretic and statistical-mechanical interpretation, including percolation of low-cost channels, phase correlation functions, an operational phase stiffness (helicity modulus), and a geometric diagnostic based on relational ball growth. In particular, we extract an effective geometric dimension from the scaling of low-cost accessibility balls, using a ball-growth relation of the form $⟨B\left(r\right)⟩ ~ r^{d_{eff}}$, revealing a clear monotonic hierarchy between normal, fragile superconducting, and noise-robust superconducting—like regimes. This demonstrates that superconducting-like behaviour in the present framework corresponds not only to percolation and phase alignment, but also to a qualitative reorganization of relational geometry. Robustness is tested via finite-size comparison between 8 × 8, 12 × 12 and 16 × 16 lattice realizations. Within this framework, normal and superconducting-like behavior arise from the same underlying relational mechanism and differ only in the structural stability of connectivity, coherence, and geometric accessibility under fluctuations. The aim of this work is structural rather than material-specific: we do not reproduce detailed experimental phase diagrams or microscopic pairing mechanisms, but identify minimal relational conditions under which low-dissipation, phase-coherent transport can emerge as a generic organizational regime of constrained relational systems. Full article

This article introduces novel methodologies, coordinate systems, and procedures in computational geometry that further develop a Euclidean-based relationalistic framework. The objective is to describe tools using object-oriented relational elements with symmetry, anchored to a fixed point in a relational model, that generate structured point sets serving as blueprints for geometric figures and physical structures representing their source objects. Geometric operations and transformations construct ratio figures and ordered proportional structures. Using discrete $\mathbb{N}$-Euclidean geometry, two relational coordinate systems are introduced—polar-vertex coordinates and radial coordinates—both formed through discrete geometric operations. A relational unit circle of fixed magnitude is defined by a 4::1 proportional equivalence between radius and angular ratios, independent of real-number or arc-length geometry. Euclid’s theory of proportion is extended from static abstract magnitudes to symmetry-driven geometric construction, and a square-pyramid geometric blueprint is produced from an Earth ratio figure with accurate dimensional magnitudes. The findings reveal a novel commensurability between the radius of a circle and the side length of a square using a shared fixed point coupled via a 3:4:5 Pythagorean-triple triangle, introducing the concept of ordered proportions. Full article

Subdiffusion on graphs is often modeled by time-fractional diffusion equations; yet, its structural and dynamical consequences remain unclear. We show that subdiffusive transport on graphs is a memory-driven process generated by a random time change that compresses operational time, produces long-tailed waiting times, and breaks Markovianity while preserving linearity and mass conservation. While the subordination representation and complete monotonicity properties of the Mittag-Leffler function are classical, we develop a graph-based synthesis in which Mittag-Leffler dynamics admit an exact convex, mass-preserving representation as a superposition of Laplacian semigroups evaluated at rescaled times. This perspective reveals fractional diffusion as ordinary diffusion acting across multiple intrinsic time scales and enables new structural and dynamical interpretations of graphs. This framework uncovers heterogeneous, vertex-dependent memory effects and induces transport biases absent in classical diffusion, including algebraic relaxation, degree-dependent waiting times, and early-time asymmetries between sources and neighbors. These features define a subdiffusive geometry on graphs, enabling the recovery of global shortest paths, in contrast to the graph exploration of diffusive geometry, while simultaneously favoring high-degree regions. Finally, we show that time-fractional diffusion can be interpreted as a singular limit of multi-rate diffusion, in an appropriate asymptotic sense. Full article

We investigate a generalization of complement reducible graphs, called co-graphs, for r-uniform hypergraphs. The operations of r-co-hypergraphs are the disjoint union of two given r-co-hypergraphs and the join operation, which inserts all hyperedges of cardinality r between the non-empty vertex subsets of two given r-co-hypergraphs. We show that the primal graphs of r-co-hypergraphs are special co-graphs and that r-co-hypergraphs are closed under complementation of r-uniform hypergraphs. This leads to a method that can determine whether an input hypergraph H is an r-co-hypergraph. If the answer is positive, we find a decomposition tree for H in polynomial time. We give specific formulas for how to compute several hypergraph parameters for r-uniform hypergraphs defined by the disjoint union of two r-uniform hypergraphs and the join of two r-uniform hypergraphs. The considered parameters are the size of a largest stable set, the size of a largest co-stable set, the size of a largest independent set, the size of a largest co-independent set, the size of a smallest vertex cover, the size of a smallest 2-transversal, the size of a smallest dominating set, the strong chromatic number, and the upper chromatic number. This leads to $\mathcal{O}\left(n\right)$ time algorithms to compute these values on r-co-hypergraphs on n vertices given by a decomposition tree. Further, we conclude relations for the considered parameters restricted to r-co-hypergraphs. Our methods generalize and re-prove several results known for co-graphs. Full article

Accurate energy forecasting is essential for maintaining power system reliability, integrating renewable generation, and ensuring market stability. Although machine learning has improved forecasting accuracy, its operational deployment depends on Machine Learning Operations (MLOps) platforms that automate and scale the entire lifecycle of energy data pipelines. However, the capabilities of existing MLOps platforms for energy forecasting have not been systematically compared. This study adopts a PRISMA-informed review process to identify relevant end-to-end MLOps platforms for energy forecasting and then maps their documented capabilities using an established energy forecasting pipeline lifecycle as the reference structure. A total of 256 records were screened across vendor documentation, open-source repositories, and academic literature, of which 13 MLOps platforms were selected for comparative capability analysis. Platform capabilities are organised and presented across an end-to-end lifecycle covering project setup and governance, data ingestion and management, model development and experimentation, deployment and serving, and monitoring and feedback. Commercial platforms such as Amazon SageMaker and Google Vertex AI generally provide stronger end-to-end integration and production readiness, while open-source platforms such as Kubeflow and ClearML offer modular flexibility that typically requires additional integration effort to achieve end-to-end operation. The mapping identifies four priority areas where platform support remains limited, namely (i) governance workflow automation, (ii) automated data quality validation, (iii) feature management, and (iv) deployment and monitoring support under nonstationary conditions. These findings indicate that platform selection for energy forecasting should be treated as a lifecycle capability decision, balancing end-to-end integration, operational assurance, and long-term flexibility. Full article

We propose a novel theoretical framework for describing photon–electron interactions and electron collision processes in a unified manner within quantum electrodynamics. Specifically, we develop a method to construct the Dirac operator in curved spacetime using only matrix representations rooted in the basis structure of four-dimensional gamma matrix algebra, without introducing vierbeins (tetrads) or independent spin connections. We realize 16 gamma matrices with two indices as $256\times 256$ matrices and embed the spacetime metric directly into the matrix elements. This reduces geometric operations such as covariantization, connection-like operations, and basis transformations to matrix products and trace calculations, yielding a unified and transparent computational scheme. The spacetime dimension remains as four, and the number “16” represents the number of basis elements of four-dimensional gamma matrix algebra ($2^4=16$). Based on the extended QED Lagrangian, vertex rules, propagators, spin sums, and traces can be handled uniformly, making it suitable for automation. As validation of this method, we analyzed four fundamental scattering processes in atomic and particle physics: (i) Compton scattering (photon–electron scattering), (ii) muon pair production ($e^{+}{e}^{-}\to {\mu }^{+}{\mu }^{-}$), (iii) Møller scattering (electron–electron collision), and (iv) Bhabha scattering (electron–positron collision). In the flat spacetime limit, we confirmed the exact reproduction of standard quantum electrodynamics (QED) results including the Klein–Nishina formula. Furthermore, trial calculations using a metric with off-diagonal components show systematic deviations from flat results near scattering angle $\theta \approx {90}^{\circ }$, suggesting that metric-induced angular dependence could in principle serve as an observable signature. The matrix representation developed in this work enables unified pipeline execution of theoretical calculations for photon interactions and charged particle collision processes, with expected applications to precision calculations in atomic and particle physics. Full article

In recent years, several quantum algorithms have been proposed for addressing combinatorial optimization problems. Among them, the Quantum Approximate Optimization Algorithm (QAOA) has become a widely used approach. However, reported limitations of QAOA have motivated the development of multiple algorithmic variants, including recursive hybrid methods such as the Recursive Quantum Approximate Optimization Algorithm (RQAOA), as well as the Quantum-Informed Recursive Optimization (QIRO) framework. In this work, we integrate the Quantum Alternating Operator Ansatz within the QIRO framework in order to improve its quantum inference stage. Both the original and the enhanced versions of QIRO are applied to the Minimum Vertex Cover problem, an NP-complete problem of practical relevance. Performance is evaluated on a benchmark of Erdös-Rényi graph instances with varying sizes, densities, and random seeds. The results show that the proposed modification leads to a higher number of successfully solved instances across the considered benchmark, indicating that refinements of the variational layer can improve the effectiveness of the QIRO framework. Full article

During ion thruster operation, electromagnetic waves propagating through the plasma plume undergo absorption and refraction effects. This paper presents a graphics processing unit (GPU) parallel ray tracing (RT) algorithm for inhomogeneous media to analyze plasma plume-induced perturbations on the radiation characteristics of a satellite reflector antenna, substantially improving computational efficiency. This algorithm performs ray path tracing in the plume, with the vertex and central rays in each ray tube assigned to dedicated GPU threads. This enables the parallel computation of electromagnetic wave attenuation, phase, and polarization. By further applying aperture integration and the superposition principle, the influence of the plume on the far-field antenna radiation patterns is efficiently analyzed. Comparison with serial results validates the accuracy of the algorithm for plume calculation, achieving approximately 319 times speed-up for 586,928 ray tubes. Within the 2–5 GHz frequency range, the plume causes amplitude attenuation of less than 3 dB. This study provides an efficient solution for real-time analysis of plume-induced interference in satellite communications. Full article

Automated machine learning (AutoML) platforms are increasingly adopted in manufacturing to support data-driven decision-making. However, systematic and reproducible evaluations of their practical applicability remain limited. This study presents a controlled benchmarking framework for comparing three selected cloud-based AutoML platforms: Google Vertex AI, Microsoft Azure ML and IBM Watsonx, in the context of raw material demand forecasting for mold manufacturing. A synthetic dataset was generated to reflect essential operational characteristics of industrial production, including batch-based manufacturing, inventory-triggered replenishment and delivery lead times. While the underlying bill of materials logic is deterministic, the interaction of production variability and inventory dynamics introduces nonlinear and time-dependent behavior. All platforms were evaluated using identical data splits, chronological cross-validation and consistent performance metrics to ensure fair comparison and prevent information leakage. Results indicate moderate predictive performance, which is attributed to embedded operational complexity. Performance differences between platforms are marginal, highlighting that practical considerations such as feature handling, deployment readiness and computational effort may be more influential than raw accuracy. Although synthetic data limit external validity, the proposed framework provides a reproducible and transparent basis for applied evaluation of AutoML platforms. Future work will incorporate real industrial data and robustness testing under non-stationary and disrupted production conditions. Full article