Search Results (3,439)

Aggregate masonry buildings in historic urban centers constitute tangible testimony of collective identity and historical continuity. They encompass both simple terraced configurations and more intricate clusters, which are inherently vulnerable to earthquake-induced damage, due to their typological features and the transformations that occurred in the course of time. Strategies aimed at the protection and valorization of such typical architectural heritage should be based on the recognition of their peculiarities, so that the intangible values embedded within the historic fabric can be preserved. A simplified approach able to identify the effect of facade layout on the vulnerability of terraced buildings was validated on a historical center struck by the Central Italy earthquake. It is based on the evaluation of vulnerability factors derived by the application of a multi-level procedure on a large scale, which integrates data on typological and structural aspects, as well as on the condition state and previous interventions. In the center in question, the evidence of prevalent shear damage in the continuous frontage of the buildings facing the main street suggested the in-depth analysis of the facade’s characteristics, and its relationship with the main direction of the seismic swarm. Starting from a preliminary abacus of twelve vulnerability factors, 16 archetypes of facades at increasing vulnerability defined by a combination of the most significant geometrical features of building aggregates were identified. These virtual models encompass typical features that can be found in similar buildings in different contexts, thus enabling preventive actions based on parametric assessment. Full article

Cell towers play a key role in providing telecommunications infrastructure, especially in remote mountainous regions. This paper presents an approach to the efficient design of 42-metre-high cell towers intended to install high-power equipment in remote mountainous regions of the Carpathians (750 m above sea level). The region requires rapid deployment of many standardized towers adapted to geographical features. The main design challenges were the limited space available for the base, the impact of extreme weather conditions, and the need for a fast project implementation due to the critical importance of ensuring stable communication. Special methodological attention is given to how the transition between pyramidal and prismatic segments in cell tower shafts influences overall structural performance. The effect of this geometric boundary on structural efficiency and material usage has not been addressed in previous studies. A dedicated investigation shows that positioning the transition at a height of 33 m yields the best compromise between stiffness and weight, minimizing a generalized penalty function that accounts for both the horizontal displacement of the tower top and its total mass. Modal analysis confirms that the chosen configuration maintains a natural frequency of 1.68 Hz, ensuring a safe margin from resonance. For the final analysis of the behavior of towers with elements of different cross-sectional shapes, finite element modeling was used for a detailed numerical study of their structural and performance characteristics. This allowed us to assess the impact of geometric constraints of structures and take into account the most unfavorable combinations of static and dynamic loads. The study yields a concise rule of thumb for towers with compact foundations, namely that the pyramidal-to-prismatic transition should be placed at roughly 78–80% of the total tower height. Full article

This article presents a novel approach to looking at steady-state thermoelastic boundary value problems in isotropic elastic plates with curvilinear holes using a complex variable approach and rational conformal mappings. The physical domain with a non-circular opening is mapped conformally to the unit disk. A thermoelastic potential combines the temperature distribution, which is determined by the Laplace equation with Neumann boundary conditions. Gaursat functions, which are shown as truncated power series, show the complicated stress and displacement fields. They are found by putting boundary constraints at certain collocation points. This procedure presents us with a linear system that can be solved using the least squares method. The method is applied in an annular shape that is exposed to a radial temperature gradient. This experiment shows how changes at the boundary affect the distribution of stress. According to numerical simulations, stress distributions are more uniform when boundaries are smoother, but stress concentrations increase with the size of geometric disturbances. The suggested approach remarkably captures the way geometry and thermal effects interact in two-dimensional thermoelasticity. It is a reliable tool for researching intricate, heated elastic domains. Full article

The growing demand for precision and consistency in the forging industry has heightened the need for predictive simulation tools. While extensive research has focused on parameters such as flow stress, die wear, billet fracture, and residual stresses, the phenomenon of billet buckling, especially during cold upset forging, remains underexplored. Most existing models address only elastic buckling for slender billets using classical approaches like Euler and Rankine-Gordon formulae, which are not suitable for inelastic deformation in shorter billets. This study presents a numerical model developed to analyze inelastic buckling during cold forging and to determine associated stresses and deflection characteristics. The model was validated through finite element simulations across a range of billet geometries (10–40 mm diameter, 120 mm length), materials (aluminum, stainless steel, and copper), and friction coefficients (µ = 0.12, 0.16, and 0.35). Stress distributions were evaluated against die stroke, with particular emphasis on the influence of strain hardening and geometry. The results showed that billet geometry and strain-hardening exponent significantly affect buckling behavior, whereas friction had a secondary effect, mainly altering overall stress levels. A nonlinear regression approach incorporating material properties, geometric parameters, and friction was used to formulate the numerical model. The developed model effectively estimated buckling stresses across various conditions but could not precisely predict buckling points based on stress differentials. This work contributes a novel framework for integrating material, geometric, and process variables into stress prediction during forging, advancing defect control strategies in industrial metal forming. Full article

To address the ill-posedness of the Hessian matrix in monocular visual-inertial SLAM (Simultaneous Localization and Mapping) caused by unobservable depth of feature points, which leads to convergence difficulties and reduced robustness, this paper proposes a Pre-Dog-Leg feature optimization method based on an adaptive preconditioner. First, we propose a multi-candidate initialization method with robust characteristics. This method effectively circumvents erroneous depth initialization by introducing multiple depth assumptions and geometric consistency constraints. Second, we address the pathology of the Hessian matrix of the feature points by constructing a hybrid SPAI-Jacobi adaptive preconditioner. This preconditioner is capable of identifying matrix pathology and dynamically enabling preconditioning as a strategy. Finally, we construct a hybrid adaptive preconditioner for the traditional Dog-Leg numerical optimization method. To address the issue of degraded convergence performance when solving pathological problems, we map the pathological optimization problem from the original parameter space to a well-conditioned preconditioned space. The optimization equivalence is maintained by variable recovery. The experiments on the EuRoC dataset show that the method reduces the number of Hessian matrix conditionals by a factor of 7.9, effectively suppresses outliers, and significantly improves the overall convergence time. From the analysis of trajectory error, the absolute trajectory error is reduced by up to 16.48% relative to RVIO2 on the MH_01 sequence, 20.83% relative to VINS-mono on the MH_02 sequence, and up to 14.73% relative to VINS-mono and 34.0% relative to OpenVINS on the highly dynamic MH_05 sequence, indicating that the algorithm achieves higher localization accuracy and stronger system robustness. Full article

Autonomous commercial vehicles often mount multiple LiDARs to enlarge their field of view, but conventional calibration is labor-intensive and prone to drift during long-term operation. We present an online self-calibration method that combines a ground plane motion constraint with a virtual RGB–D projection, mapping 3D point clouds to 2D feature/depth images to reduce feature extraction cost while preserving 3D structure. Motion consistency across consecutive frames enables a reduced-dimension hand–eye formulation. Within this formulation, the estimation integrates geometric constraints on $SE\left(3\right)$ using Lagrange multiplier aggregation and quasi-Newton refinement. This approach highlights key aspects of identifiability, conditioning, and convergence. An online monitor evaluates plane alignment and LiDAR–INS odometry consistency to detect degradation and trigger recalibration. Tests on a commercial vehicle with six LiDARs and on nuScenes demonstrate accuracy comparable to offline, target-based methods while supporting practical online use. On the vehicle, maximum errors are 6.058 cm (translation) and 4.768° (rotation); on nuScenes, 2.916 cm and 5.386°. The approach streamlines calibration, enables online monitoring, and remains robust in real-world settings. Full article

As a core component of large marine engines, the compressor delivers robust and efficient power for propulsion. This study focuses on assessing and quantifying the uncertainty in the aerodynamic performance of a transonic rotor under various operating conditions, with the aim of investigating the impact of blade manufacturing variability on performance. Monte Carlo simulation (MCS) and sensitivity analysis were initially employed to identify parameters that significantly influence airfoil performance. Subsequently, a non-intrusive polynomial chaos (NIPC) uncertainty quantification model was developed to compare the effects of tip clearance deviation and surface geometry deviation on rotor performance. The study then analyzes how the geometric deviation at the different spanwise sections affects aerodynamic performance. The results reveal that geometric deviations have a more profound influence on aerodynamic performance than blade tip clearance. The impact of geometric deviations on average pressure ratio and efficiency of the transonic compressor rotor intensifies as the air mass flow rate approaches the near-stall point, while it decreases near the choking point. Interestingly, fluctuations in pressure ratio exhibit the opposite trend. Regarding spatial distribution, deviations in the upper half of the blade span (near the tip) exert a more dramatic influence on mass flow rate and pressure ratio fluctuation. A conceivable reason is that the inlet airflow velocity increases along the radial direction of the blade, and manufacturing variations in the same magnitude produce more notable relative geometric deviations in the upper half of the blade span. Centered on the machining tolerance guidelines for transonic compressor rotors, this work recommends stricter profile tolerance requirements for the upper half of the blade span. Full article

This work presents a comprehensive study of weakly B-symmetric warped product manifolds ${\left(WBS\right)}_n$, a natural extension of several classical curvature-restricted geometries including B-flat, B-parallel, and B-recurrent manifolds. We begin by formulating the fundamental properties of the B-tensor $B(X,Y)=aS(X,Y)+brg(X,Y)$, where S is the Ricci tensor, r the scalar curvature, and $a,b$ are smooth non-vanishing functions. The warped product structure is then exploited to obtain explicit curvature identities for base and fiber manifolds under various geometric constraints. Detailed characterizations are established for Einstein conditions, Codazzi-type tensors, cyclic parallel tensors, and the behavior of geodesic vector fields. The weakly B-symmetric condition is analyzed through all possible projections of vector fields, leading to sharp criteria describing the interaction between the warping function and curvature. Several applications are discussed in the context of Lorentzian geometry, including perfect fluid and generalized Robertson–Walker spacetimes in general relativity. These results not only unify different curvature-restricted frameworks but also reveal new geometric and physical implications of warped product manifolds endowed with weak B-symmetry. Full article

This work establishes definitive conditions for the inheritance of categorical completeness and cocompleteness by categories of internal group objects. We prove that while the completeness of $\mathbf{Grp}\left(\mathcal{C}\right)$ follows unconditionally from the completeness of the base category $\mathcal{C}$, cocompleteness requires $\mathcal{C}$ to be regular, cocomplete, and admit a free group functor left adjoint to the forgetful functor. Explicit limit and colimit constructions are provided, with colimits realized via coequalizers of relations induced by group axioms over free group objects. Applications demonstrate cocompleteness in topological groups, ordered groups, and group sheaves, while Lie groups serve as counterexamples revealing necessary analytic constraints—particularly the impossibility of equipping free groups on non-discrete manifolds with smooth structures. Further results include the inheritance of regularity when the free group functor preserves finite products, the existence of internal hom-objects in locally Cartesian closed settings, monadicity for locally presentable $\mathcal{C}$, and homotopical extensions where model structures on $\mathbf{Grp}\left(\mathcal{M}\right)$ reflect those of $\mathcal{M}$. This framework unifies classical category theory with geometric obstruction theory, resolving fundamental questions on exactness transfer and enabling new constructions in homotopical algebra and internal representation theory. Full article

Experimental measurement of dose distributions is a pivotal step in the quality assurance of radiotherapy treatments, especially for those relying on high delivery accuracy such as hadron therapy. This study investigated the response of a polymer gel dosimeter to determine its suitability in performing geometric beam characterizations for hadron therapy under high-quenching conditions. Different extraction energies of proton and carbon ion beams were considered. Gel dose–response linearity and long-term stability were confirmed through optical measurements. Gel phantoms were irradiated with pencil beams and analyzed via magnetic resonance imaging. A multi-echo T₂-weighted sequence was used to reconstruct depth–dose profiles and transversal distributions acquired by the gels, which were benchmarked against reference data. As expected, a response-quenching effect in the Bragg peak region was noted. Nonetheless, the studied gel formulation proved reliable in acquiring the geometric characteristics of the beams, even without correcting for the quenching effect. Indeed, depth–dose distributions acquired by the gels showed an excellent agreement with measured particle range with respect to reference values, with mean discrepancies of 0.5 ± 0.2 mm. Single-spot transverse FWHM values at increasing depths also presented an average agreement within 1 mm with values determined with radiochromic films, thus supporting the excellent spatial resolving capabilities of the dosimetric gel. Full article

Isothermal hot compression tests were performed on an Al-4.8Cu-0.25Mg-0.32Mn-0.17Si alloy using a Gleeble-3500 thermomechanical simulator within the temperature range of 350–510 °C and strain rate range of 0.001–10 s⁻¹, achieving a true strain of 0.9. The constitutive equation and hot processing maps were established to predict the flow behavior of the alloy. The hot deformation mechanisms were investigated through microstructural characterization using inverse pole figure (IPF), grain boundary (GB), and grain orientation spread (GOS) analysis. The results demonstrate that both dynamic recovery (DRV) and dynamic recrystallization (DRX) occur during hot deformation. At high lnZ values (high strain rates and low deformation temperatures), discontinuous dynamic recrystallization (DDRX) dominates. Under middle lnZ conditions (low strain rate or high deformation temperature), both continuous dynamic recrystallization (CDRX) and DDRX are the primary mechanisms. Conversely, at low lnZ values (low strain rates and high temperatures), CDRX and geometric dynamic recrystallization (GDRX) become predominant. The DRX process in the Al-Cu-Mg alloy is controlled by the deformation temperature and strain rate. Full article

As lunar exploration missions advance, the need for safe and sustainable in situ energy systems has become increasingly critical. This study investigates the thermoelectric performance of Bi₂Te₃-based thermoelectric materials under the natural temperature variations on the lunar surface, aiming to illustrate the potential of thermoelectric generation technology in power supply for a crewed moon base. A numerical approach was employed to assess the energy conversion behavior and optimize the geometric design of a thermoelectric module couple consisting of a P-leg and N-leg. The results indicate that Bi₂Te₃-based modules exhibit promising functionality under cryogenic conditions, highlighting their potential as an in situ power source during the long lunar night. Furthermore, geometric optimization was shown to significantly enhance the overall thermoelectric performance. The present study illustrates that TEG technology offers a viable pathway toward reliable energy generation in extreme lunar environments, supporting future mission sustainability. Full article

This study proposes a novel framework to enhance inner race fault features in servo motor bearings by acquiring rotary encoder-derived instantaneous angular speed (IAS) signals, which are obtained from a servo motor encoder without requiring additional external sensors. However, such signals are often obscured by strong periodic interferences from motor pole-pair and shaft rotation order components. To address this issue, three key improvements are introduced within the cyclic blind deconvolution (CYCBD) framework: (1) a comb-notch filtering strategy based on rotation domain synchronous averaging (RDA) to suppress dominant periodic interferences; (2) an adaptive fault order estimation method using the autocorrelation of the squared envelope spectrum (SES) for robust localization of the true fault modulation order; and (3) an improved envelope harmonic product (IEHP), based on the geometric mean of harmonics, which optimizes the deconvolution filter length. These combined enhancements enable the proposed improved CYCBD (ICYCBD) method to accurately extract weak fault-induced cyclic impulses under complex interference conditions. Experimental validation on a test rig demonstrates the effectiveness of the approach in enhancing and extracting the fault-related features associated with the inner race defect. Full article

Imaging in controlled agriculture helps maximize plant growth by saving labor and optimizing resources. By monitoring specific plant traits, growers can prevent crop losses by correcting environmental conditions that lead to physiological disorders like leaf tipburn. This study aimed to identify morphometric and spectral markers for the early detection of tipburn in two Romaine lettuce (Lactuca sativa) cultivars (‘Chicarita’ and ‘Dragoon’) using an image-based system with color and multispectral cameras. By monitoring tipburn in treatments using melatonin, lettuce cultivars, and with and without supplemental lighting, we enhanced our system’s accuracy for high-resolution tipburn symptom identification. Canopy geometrical features varied between cultivars, with the more susceptible cultivar exhibiting higher compactness and extent values across time, regardless of lighting conditions. These traits were further used to compare simple linear, logistic, least absolute shrinkage and selection operator (LASSO) regression, and random forest models for predicting leaf fresh and dry weight. Random forest regression outperformed simpler models, reducing the percentage error for leaf fresh weight from ~34% (LASSO) to ~13% (RMSE: 34.14 g to 17.32 g). For leaf dry weight, the percentage error decreased from ~20% to ~12%, with an explained variance increase to 94%. Vegetation indices exhibited cultivar-specific responses to supplemental lighting. ‘Dragoon’ consistently had higher red-edge chlorophyll index (CIrededge), enhanced vegetation index, and normalized difference vegetation index values than ‘Chicarita’. Additionally, ‘Dragoon’ showed a distinct temporal trend in the photochemical reflectance index, which increased under supplemental lighting. This study highlights the potential of morphometric and spectral traits for early detection of tipburn susceptibility, optimizing cultivar-specific environmental management, and improving the accuracy of predictive modeling strategies. Full article

Hyperspectral imaging (HSI) systems often suffer from complex noise degradation during the imaging process, significantly impacting downstream applications. Deep learning-based methods, though effective, rely on impractical paired training data, while traditional model-based methods require manually tuned hyperparameters and lack generalization. To address these issues, we propose SS³L (Self-Supervised Spectral-Spatial Subspace Learning), a novel HSI denoising framework that requires neither paired data nor manual tuning. Specifically, we introduce a self-supervised spectral–spatial paradigm that learns noisy features from noisy data, rather than paired training data, based on spatial geometric symmetry and spectral local consistency constraints. To avoid manual hyperparameter tuning, we propose an adaptive rank subspace representation and a loss function designed based on the collaborative integration of spectral and spatial losses via noise-aware spectral-spatial weighting, guided by the estimated noise intensity. These components jointly enable a dynamic trade-off between detail preservation and noise reduction under varying noise levels. The proposed SS³L embeds noise-adaptive subspace representations into the dynamic spectral–spatial hybrid loss-constrained network, enabling cross-sensor denoising through prior-informed self-supervision. Experimental results demonstrate that SS³L effectively removes noise while preserving both structural fidelity and spectral accuracy under diverse noise conditions. Full article