Search Results (3,655)

Reliable and up-to-date measurements of public space remain scarce in small and medium-sized towns (SMSTs), where conventional geospatial datasets are often outdated, inconsistent, or inaccessible. This study presents a low-cost and fully reproducible computational pipeline that integrates nadir RGB imagery captured by a DJI Mini 3 UAV with a lightweight instance-segmentation model (Ultralytics YOLOv12-seg) and GIS-based post-processing to derive class-specific surface indicators at the neighborhood scale. The workflow consists of four components: autonomous UAV acquisition over three representative zones of Sesquilé, Colombia; planar mosaic generation and georeferencing using ad hoc ground control points; fine-tuning of a YOLOv12-seg model trained on locally annotated images; and transformation of predicted masks into OSM and GeoPackage geometries for metric analysis. The trained model achieved stable convergence with mask mAP50 ≈ 0.85 and mAP50–95 ≈ 0.70, supported by balanced precision–recall behavior across classes. Spatial outputs exhibit coherent morphological contrasts between the analyzed zones. Buildings occupy 48.17% of the mapped area, vegetation 25.88%, and transport- and plaza-related public space (roadways, sidewalks, and hardscape areas) 25.95%. These proportions capture a clear gradient from a dense urban core to less consolidated peripheral sectors. Results demonstrate that very-high-resolution UAV imagery, combined with open-source deep-learning tools and structured GIS post-processing, can reliably produce operational public-space indicators for SMSTs at low cost. The methodology provides an accessible and scalable framework for evidence-based urban assessment in municipalities with limited technical and financial resources. Full article

Background/Objectives: Reproducible evaluation of aerosol dispersibility remains a key challenge in the development of dry powder inhalers (DPIs), where small variations in particle cohesion, morphology, or device resistance can lead to large differences in aerodynamic performance. In passive DPIs, the forces required for powder fluidization and aerosolization arise from the interaction of patient inspiratory airflow with device geometry and must overcome strong interparticle cohesive forces to enable effective lung delivery. Cascade impaction is the gold standard for determining aerodynamic particle size distribution (APSD), but its low throughput and experimental burden limit its utility for systematic formulation and device screening. Prior studies have explored laser diffraction-based particle sizing under varying dispersion energies as indirect metrics of powder dispersibility. Here, we extend this approach by introducing a mathematically rigorous, distribution-based framework that applies the first-order Wasserstein distance (Earth Mover’s Distance) to quantify relative dispersibility with respect to a material-specific maximally dispersed reference state. Methods: Mannitol, trehalose, and inulin were spray-dried under matched conditions to generate model dry powders. Particle size distributions were measured by laser diffraction (Sympatec HELOS/R) using both a RODOS dry dispersion module to define a maximally dispersed reference state and an INHALER module to generate aerosols under clinically relevant dispersion conditions spanning multiple device resistances and pressure drops. For each condition, the Wasserstein-1 distance (W₁) was computed between cumulative volume-based size distributions obtained under reference and inhaler-based dispersion. Cascade impaction was used as an orthogonal method to characterize aerodynamic performance under a representative dispersion condition. Results: W₁ captured formulation-, device-, and flow-dependent differences in dispersibility that were not readily separable by visual inspection of particle size distributions alone. Crystalline mannitol exhibited the largest and most flow-rate-dependent W₁ values, whereas amorphous trehalose and polymeric inulin showed smaller W₁ values with distinct, non-monotonic pressure responses that depended on device resistance. W₁ qualitatively aligned with cascade impaction metrics, exhibiting a positive association with mass median aerodynamic diameter and an inverse association with fine particle fraction, while also demonstrating that efficient dose emission can occur despite incomplete deagglomeration. Conclusions: This study establishes the Wasserstein distance as a physically interpretable, formulation-agnostic metric for quantifying aerosol dispersibility relative to a material-specific reference state. This framework enables systematic comparison of dispersion efficiency across devices and operating conditions using standard laser diffraction data and provides a reproducible basis for mechanistic optimization of DPI formulations and inhaler designs. Full article

This study proposes a reproducible and accessible methodological framework for non-planar path generation to enable scaffold biofabrication on irregular anatomical surfaces replicating the native morphology of human tissue. By integrating a simulation-based trajectory optimization system with a robotic arm, lattice paths are generated using an intersection-based method with parallel planes. This method is processed by intersecting the anatomical object with orthogonal planes, allowing for the creation of paths that conform to complex geometries. The proposed approach relies on widely available and commonly used tools, such as MATLAB, avoiding the need for highly specialized software. Thus, a MATLAB-based kinematic model computes optimal end-effector trajectories, while a coaxial nozzle facilitates the simultaneous extrusion of an alginate-based biomaterial. The proposed method ensures smooth trajectory execution, achieving positional standard deviation within the reproducibility threshold of the robotic arm for an optimal path discretization density. Unlike conventional planar methods, the optimized approach achieves positional accuracy within the robotic arm’s reproducibility threshold while demonstrating superior geometric conformity on complex anatomical patches. The approach successfully fabricates scaffolds with controlled deposition on anatomical patches, demonstrating improved geometric conformity over traditional planar methods. This method provides a pathway for patient-specific scaffold fabrication, supporting advances in tissue engineering and regenerative medicine. Full article

With engineering architecture being shifted to meet the requirements of sustainable development, the need for optimized design solutions places precise engineering methods at the core of the contemporary industrial transition toward data-driven strategies. A timely conversion to lightweight components in drivetrain systems has led to the prominent use of high-strength polymer gears, establishing them as a critical point of interest in the field of power transmission. However, as the conversion to polymer gears relies on expensive and time-consuming laboratory testing, there is a standstill in evaluating the structural properties specific to polymer gear design. In addition, one of the major concerns in the development of polymer-based gear drives is linked with their operational performance and dynamic response under fault conditions influenced by surface wear. To address these difficulties, a framework for surface wear prediction is developed, enabling precise design optimization for specific drivetrain requirements. Computations of wear progression over multiple duty cycles are built upon the mathematical background of Archard’s wear theory, while internal changes in gear contact pressure distribution are constructed on Winkler’s surface model. The framework provides an innovative support for polymer gear systems, as it imports the three-dimensional (3D) scanning data of gear geometry, therefore enabling the analysis of actual flank surfaces with designated surface modifications and manufacturing errors. The framework’s effectiveness, confirmed by experimental validation, demonstrates a superior estimation of contact parameters and overall performance compared to traditional design methods, highlighting scalable solutions that contribute to ongoing industrial engineering objectives. Full article

Background: Dipeptidyl peptidase-4 (DPP4) is a key metabolic enzyme involved in postprandial glucose regulation through incretin hormone modulation, making it an important target in nutrition and metabolic health research. Although dietary and plant-derived bioactive compounds have been reported to influence DPP4, exploration of the food-associated chemical space remains limited by its size and diversity. Methods: Here, we present an integrated computational framework combining machine learning, molecular docking, and molecular dynamics simulations to prioritize dietary and supplemental compounds with potential interaction capacity toward DPP4. Supervised classification models were trained on a curated DPP4 bioactivity dataset and evaluated using scaffold-based partitioning to ensure chemically realistic generalization. Results: The top-performing random forest model achieved robust performance across independent splits (mean AUC 0.889 ± 0.017; average precision 0.959 ± 0.010) and was applied to screen 69,574 food-derived compounds. Model interpretation identified recurring heteroaromatic and polar substructural features associated with predicted interaction propensity. Structure-based screening further prioritized seven food-derived compounds, including lipid-associated coenzyme A derivatives, which occupied the canonical DPP4 binding site with favorable docking scores (−13.12 to −12.06 kcal/mol). Extended molecular dynamics simulations (500 ns) demonstrated stable binding geometries, compact hydrogen-bond networks, and consistent engagement of key DPP4 residues, including Glu205, Glu206, Arg125, and Tyr631. Conclusions: Overall, our study provides a scalable computational strategy for identifying bioactive dietary and supplemental compounds with potential relevance to metabolic regulation. The framework supports nutraceutical research and functional food development by enabling targeted experimental investigation of diet–enzyme interactions. Full article

In the long-duration stratospheric operation of High-Altitude Platform Stations (HAPSs), strict management of the limited solar energy balance is a decisive factor determining mission success. However, existing planar approximation models ignore self-shading and incidence angle losses associated with curved surfaces. In this study, we propose a novel framework that catalogs the airframe geometry as a 4-tensor, achieving both physical rigor and computational speed. This method is a thousand times faster than ray tracing methods, and successfully reproduces the minute output fluctuations observed in actual flight data. Notably, in the winter solstice analysis, when the energy balance is most severe, the planar model overestimates power generation by approximately 25% during level flight and by approximately 12% even during turning maneuvers. Quantifying this discrepancy in environments with minimal energy margins is essential for mitigating the risk of airframe loss and formulating feasible operational plans. Full article

This study introduces a new synthesis algorithm for triply periodic minimal surfaces based on determining the equilibrium configuration of elastic membranes constrained at their boundaries. Beyond the methodology itself and its computational efficiency, the scientific relevance of this work lies in the 66 surfaces with these characteristics that it enabled to generate. Leveraging their continuous and highly regular geometry, these surfaces were used to define novel shell-based lattices, the mechanical behavior of which was investigated numerically and experimentally through both static and dynamic analyses. The computational models demonstrated high predictive accuracy, with numerical results deviating by less than 10% from the experimental data. Across the new geometries, the surface-area-to-volume ratio ranged from $1.8$ to $4.8$${\mathrm{cm}}^{-1}$. At infill coefficients of 10%, 20%, and 30%, the structures exhibited a wide range of stiffness and anisotropic behaviors, with equivalent elastic modulus spanning from 0.02% to 25% that of the base material and Zener indices from $4.67\times {10}^{-2}$ to $11.8$. Ultimately, the study revealed a clear influence of cell geometry on stress concentration and modal response. Full article

This article presents a comprehensive and analytically explicit study of optimal discrete quantization on spherical geometries equipped with the geodesic metric. Focusing on highly symmetric configurations on the unit sphere ${\mathbb{S}}^2$, we investigate three explicit models of discrete uniform distributions and derive closed-form expressions for their optimal quantizers and corresponding mean square quantization errors. (I) For N equally spaced points on the equator, we obtain exact error formulas for both divisible and non-divisible cases $n\nmid N$, demonstrating that optimal Voronoi cells form contiguous arcs with midpoint representatives. (II) For two antipodally symmetric small circles at latitudes $\pm {\varphi }_0$, each with M longitudes, we prove a no-cross-circle Voronoi phenomenon, establish symmetry-preserving optimality, and derive finite-sum error formulas together with sharp curvature-dependent bounds and asymptotics. (III) For a single small circle at latitude ${\varphi }_0$, we obtain analogous exact error formulas and show that curvature reduces distortion by a factor of ${cos}^2{\varphi }_0$, while preserving the $n^{-2}$ decay rate. Across all models, we rigorously establish the “block midpoint principle”: optimal Voronoi cells on a circle are contiguous azimuthal blocks, and their optimal representatives are the corresponding azimuthal midpoints. Numerical tables and illustrative figures highlight curvature effects and compare divisible and non-divisible cases. An algorithmic appendix provides pseudocode and a small, commented Python implementation to facilitate reproducibility. Written with didactic clarity while maintaining full mathematical rigor, this work bridges geometric intuition and analytic precision, providing explicit benchmark models that illuminate curvature effects and support further developments in quantization on curved manifolds. Full article

The calibration of suspension geometry involves highly nonlinear kinematic relationships and leads to challenging optimization landscapes that are difficult to explore efficiently with classical local methods. Quadratic Unconstrained Binary Optimization (QUBO) provides a unified discrete formulation that enables the use of a wide range of metaheuristic solvers, but its practical behavior in realistic engineering-inspired problems remains insufficiently benchmarked. This paper presents a computational benchmarking study of classical, parallel, and hybrid metaheuristic solvers applied to a QUBO-formulated double wishbone suspension geometry problem. A symbolic multi-body kinematic model is constructed and discretized into a large-scale QUBO instance capturing camber and caster tracking objectives across multiple roll conditions. Using a fixed low-resolution binary encoding, we systematically evaluate solver performance in terms of objective value, runtime, and time-to-solution trade-offs. The benchmark includes standard simulated annealing and tabu search, parallel simulated annealing, population-based annealing, bandit-controlled hybrid heuristics, and continuous-relaxation-based ADMM methods with and without annealing refinement. Extensive experiments conducted on a Euro-HPC pre-exascale system demonstrate that parallel and hybrid solvers can achieve substantial reductions in wall-clock time—often exceeding two orders of magnitude—while attaining objective values comparable to classical simulated annealing. The results reveal clear trade-offs between solution quality and computational efficiency, and highlight how solver structure influences performance on large QUBO instances derived from symbolic engineering models. Rather than focusing on final design optimality, this study provides a reproducible reference benchmark and practical insights into solver behavior for QUBO-based engineering optimization problems. Full article

Tsunami-induced scour around coastal embankments and nearshore structures is a primary cause of structural instability and failure. However, the hydrodynamic mechanisms by which coastal vegetation mitigates this scour remain insufficiently understood. This study employs three-dimensional numerical simulations to investigate the influence of rigid and flexible vegetation on overflow-induced scour downstream of embankments and local scour around structures under tsunami-like inundation. The simulations were conducted using Ansys Fluent 2021R2, utilizing the Volume of Fluid (VOF) method to capture the free surface and the RNG k − ε turbulence model within the Reynolds-averaged Navier–Stokes (RANS) framework. Computational geometries were reconstructed from laboratory experiments, and the model’s reliability was validated against measured water surface profiles. The results demonstrated that vegetation significantly alters flow dynamics, velocity distributions, vortex structures, and both the magnitude and patterns of bed shear stress within scour holes. Specifically, in overflow-induced scour, vegetation suppresses scour intensity by inducing backwater effects, enhancing momentum diffusion, attenuating flow impingement on the bed, and reducing peak bed shear stress. Conversely, for local scour around structures, vegetation increases upstream water depth while intensifying downstream wake vortices, leading to scour hole elongation—particularly under dense and tall vegetation. These findings offer novel insights into the hydrodynamics of vegetation-induced scour mitigation and provide guidelines for optimizing vegetation configurations to enhance the tsunami resilience of coastal infrastructure. Full article

Iron(II) complexes bearing N-heterocyclic carbene (NHC) ligands have emerged as promising earth-abundant dye sensitizers for applications in dye-sensitized solar cells (DSSCs). In this work, we present a computational study of a set of 42 Fe–NHC dyes derived from seven ligand frameworks, systematically functionalized with donor, acceptor, and donor–acceptor groups to tune or enhance their photophysical properties. The calculated geometries reveal that substitution modulates Fe–N bond lengths and ligand dihedral angles only slightly, preserving the structural integrity of the complexes. TD-DFT calculations show clear and predictable electronic trends: donor groups raise the HOMO, acceptor groups lower the LUMO, and the combined push–pull configuration produces the most pronounced HOMO–LUMO gap narrowing and largest redshifts in MLCT transitions. Key DSSC performance descriptors, including electron-injection and dye-regeneration free energies, light-harvesting efficiency, excited-state lifetimes, and hole-transport reorganization energies, collectively identify the double-acceptor and push–pull derivatives as the most promising candidates across multiple frameworks. Full article

Background: Arterial stenosis produces nonlinear changes in vascular impedance that are challenging to investigate in real time using either benchtop flow phantoms or high-fidelity computational fluid dynamics (CFD) models. Objective: This study aimed to develop and evaluate a low-cost printed circuit board (PCB) analog capable of reproducing the hemodynamic effects of progressive arterial stenosis through an R–L–C mapping of vascular mechanics. Methods: A lumped-parameter (0D) electrical network was constructed in which voltage represented pressure, current represented flow, resistance modeled viscous losses, capacitance corresponded to vessel compliance, and inductance represented fluid inertance. A variable resistor simulated focal stenosis and was adjusted incrementally to represent progressive narrowing. Input U_in, output U_out, peak-to-peak V_pp, and mean V_avg voltages were recorded at a driving frequency of 50 Hz. Physiological correspondence was established using the canonical relationships. $R={\displaystyle \frac{8\mu l}{\pi r^4}}$, $L={\displaystyle \frac{pl}{\pi r^2}}$, $C={\displaystyle \frac{3\pi r^3}{2Eh}}$, where μ is blood viscosity, ρ is density, E is Young’s modulus, and h is wall thickness. A calibration constant was applied to convert measured voltage differences into pressure differences. Results: As simulated stenosis increased, the circuit exhibited a monotonic rise in U_out and V_pp, with a precise inflection beyond mid-range narrowing—consistent with the nonlinear growth in pressure loss predicted by fluid dynamic theory. Replicate measurements yielded stable, repeatable traces with no outliers under nominal test conditions. Qualitative trends matched those of surrogate 0D and CFD analyses, showing minimal changes for mild narrowing (≤25%) and a sharp increase in pressure loss for moderate to severe stenoses (≥50%). The PCB analog uses a simplified, lumped-parameter representation driven by a fixed-frequency sinusoidal excitation and therefore does not reproduce fully characterized physiological systolic–diastolic waveforms or heart–arterial coupling. In addition, the present configuration is intended for relatively straight peripheral arterial segments and is not designed to capture the complex geometry and branching of specialized vascular beds (e.g., intracranial circulation) or strongly curved elastic vessels (e.g., the thoracic aorta). Conclusions: The PCB analog successfully reproduces the characteristic hemodynamic signatures of arterial stenosis in real time and at low cost. The model provides a valuable tool for educational and research applications, offering rapid and intuitive visualization of vascular behavior. Current accuracy reflects assumptions of Newtonian, laminar, and lumped flow; future work will refine calibration, quantify uncertainty, and benchmark results against physiological measurements and full CFD simulations. Full article

Francis turbines are renowned for their high efficiency and adaptability across a wide range of head and discharge conditions. However, internal mechanical friction losses (IMFLs), resulting from rotational frictional resistance between the rotating runner and the surrounding fluid, remain a significant obstacle to further performance optimisation. This study introduced a CFD-derived integral friction torque framework, validated through theoretical analysis, that enables the spatially resolved quantification of IMFLs in Francis turbine runners. Building on this framework, a comprehensive computational approach was established to quantify IMFLs in a Francis turbine runner using a CFD-derived integral torque method combined with a theoretical verification model. Three runner configurations were analysed: the original runner model (ORM), a modified runner (RM1) with selective exit height reduction, and a modified runner (RM2) with uniform exit height reduction. Transient simulations were conducted at the best efficiency point (BEP) using the shear stress transport (SST) k–ω turbulence model and a sliding mesh approach. The numerical results were verified using the theoretical model and systematically evaluated to assess IMFL mechanisms and runner performance. The findings demonstrate that variations in runner geometry significantly influence internal frictional resistance and turbine efficiency. Compared with ORM, both RM1 and RM2 reduced the rotational friction torque, with RM2 exhibiting the greatest improvement: a 2.83% reduction in total friction resistance torque, a 14.74% reduction in total power losses, and a 1% absolute increase in efficiency. These improvements are primarily attributed to reduced wall shear stress and a more uniform pressure distribution across the runner surface. Full article

Generative pre-training via neural rendering has become a cornerstone for scaling 3D perception in autonomous driving. However, prevalent approaches relying on implicit Neural Radiance Fields (NeRFs) face two fundamental limitations: the shape-radiance ambiguity inherent in vision-centric optimization and the prohibitive computational overhead of volumetric ray marching. To address these challenges, we propose AES-Gaussian, a novel multi-modal pre-training framework grounded in the efficient 3D Gaussian Splatting (3DGS) representation. Diverging from symmetric fusion paradigms, our core innovation is an Asymmetric Encoder architecture that couples a deep semantic vision backbone with a lightweight, physics-aware LiDAR branch. In this framework, LiDAR data serve not merely for semantic extraction, but as sparse physical anchors. By employing a novel Explicit Feature Synergy mechanism, we directly inject raw LiDAR intensity and depth priors into the Gaussian decoding process, thereby rigidly constraining scene geometry in open-world environments. Extensive empirical validation on the nuScenes dataset demonstrates the superiority of our approach. AES-Gaussian achieves state-of-the-art transfer performance, yielding a substantial 7.0% improvement in NDS for 3D Object Detection and a 4.8% mIoU gain in 3D semantic occupancy prediction compared to baselines. Notably, our method reduces geometric reconstruction error by over 50% while significantly improving training and inference efficiency, attributed to the streamlined asymmetric design and rapid Gaussian rasterization. Ultimately, by enhancing both perception accuracy and system efficiency, this work contributes to the development of safer and more reliable autonomous driving systems. Full article

We develop a function theory on a three-dimensional reduced quaternionic model endowed with a projected (and, therefore, non-associative) product, together with its natural dual extension generated by a nilpotent infinitesimal unit. After introducing the associated first-order Dirac-type system, we construct explicit Cauchy kernels and prove a Cauchy–Pompeiu representation for sufficiently smooth functions with values in the dual algebra. We derive a Teodorescu-type right inverse, Liouville- and uniqueness-type principles, and residue formulas for isolated singularities. For smooth hypersurfaces, we establish Plemelj–Sokhotski boundary limits for the Cauchy transform and its dual lift. Worked examples illustrate how the reduced product interacts with boundary geometry and provide a practical route to computation. Full article