Search Results (4,565)

The Hough transform (HT) is widely used in computer vision, tomography, and neural networks. Numerous algorithms for HT computation have been proposed, making their systematic comparison essential. However, existing comparative methodologies are either non-universal and limited to certain HT formulations or task-oriented, relying on application-specific criteria that do not fully capture algorithmic properties. This paper introduces a novel unified methodology for the systematic comparison of HT algorithms. It evaluates key characteristics, including computational complexity, accuracy, and auxiliary space complexity, while explicitly accounting for the property of self-adjointness. The methodology integrates both implementation-level and theoretical considerations related to the interpretation of HT as a discrete approximation of the Radon transform. A set of mathematically justified evaluation functions, not previously described in the literature, is proposed to support our methodology. Importantly, the methodology is universal, applicable across diverse HT paradigms, encompasses pattern-based and Fourier-based fast HT (FHT) algorithms, and offers a comprehensive alternative to existing task-specific methodologies. Its application to several state-of-the-art FHT algorithms ($FHT2DT$, $FHT2SP$, $ASD2$, $KHM$, and Fast Slant Stack) yields new experimentally confirmed theoretical insights, identifies $ASD2$ as the most balanced algorithm, and provides practical guidelines for algorithm selection. In particular, the methodology reveals that for image sizes up to 3000, the maximum normalized computational complexity increases as follows: $FHT2DT$ (1.1), $ASD2$ (15.3), and $KHM$ (30.6), while the remaining algorithms exhibit at least 1.1 times higher values. The maximum orthotropic approximation error equals 0.5 for $ASD2$, $KHM$, and Fast Slant Stack; lies between 0.5 and 1.5 for $FHT2SP$; and reaches 2.1 for $FHT2DT$. In terms of worst-case normalized auxiliary space complexity, the lowest values are achieved by $FHT2DT$ (2.0), Fast Slant Stack (4.0, lower bound), and $ASD2$ (6.8), with all other algorithms requiring at least 8.2 times more memory. Full article

Externally excited synchronous machines (EESMs) are a rare-earth-free solution for traction applications. However, variable field excitation and magnetic coupling increase control complexity. Efficient validation of the resulting control functionalities can be carried out using hardware-in-the-loop (HIL) testing, which requires high-fidelity real-time simulation models. This paper presents a semi-analytical, discrete-time EESM model tailored for HIL applications. Nonlinear magnetic saturation and magnetic coupling are captured using an inverted flux–current characteristic combined with a rotating coordinate transformation, which improves resource utilization. Spatial harmonics are included through a Fourier decomposition of the angle-dependent inverse characteristics. Additionally, different loss mechanisms are considered to accurately represent the physical behavior of the machine. The model is parameterized using finite element analysis (FEA) results from a 100$\mathrm{k}$$\mathrm{W}$ salient-pole EESM. It is implemented on a field-programmable gate array to achieve real-time capability at a simulation frequency of $2.5$$\mathrm{M}$$\mathrm{Hz}$. Validation results for the typical operating range show deviations below $0.1$ compared to detailed FEA results, demonstrating accurate real-time simulation of the electromagnetic behavior. Full article

This paper aims to present a non-destructive, optimized variational mode decomposition (VMD)-based ground-penetrating radar (GPR) method developed for identifying void defects in reinforced concrete (RC) structures. This study also presents an enhanced framework for defect detection in RC by integrating advanced spectrum analysis with deep learning techniques. A GPR investigation was conducted on an RC bridge deck with known structural defects to generate a representative dataset reflecting both intact and void-defective conditions. In addition to conventional spectral techniques such as fast Fourier transform (FFT), spectrogram, and scalogram, an optimized variational mode decomposition (VMD) method was implemented. The VMD approach decomposes GPR signals into intrinsic mode functions, enabling refined feature extraction beyond traditional spectral methods and allowing clear differentiation between intact and defective signals. The limited availability and quality of GPR small datasets have restricted the application of a functional 1D-CNN which generally requires at least several hundred datasets. To address this challenge, a data augmentation strategy is adopted. FFT-based features were successfully utilized to train a one-dimensional convolutional neural network (1D-CNN) for automated defect identification. The results demonstrate that both the advanced spectrum-based approach and the hybrid framework combining spectral analysis with deep learning significantly improve defect detection performance. Overall, the proposed methodology provides an effective and intelligent solution to support timely, data-driven decision-making for maintenance and safety assurance of bridge infrastructure. Full article

A sustainable way to recycle used tires and improve the functionality of asphalt pavements is through the use of crumb rubber modified asphalt (CRMA). However, its application during high-temperature construction raises environmental and occupational health concerns due to the release of significant quantities of odorous and potentially harmful gases. Therefore, this study selected α-Amyl cinnamic aldehyde (ACA) as a deodorant and added it to CRMA at proportions of 0.5%, 1.0%, 1.5%, and 2.0% to prepare DCRMA. A number of common tests, such as softening point, ductility, penetration, Brookfield rotational viscosity, and segregation analysis, were used to evaluate the basic characteristics of the modified asphalt. A self-developed asphalt fume monitoring device was used to quantitatively analyze the changes in VOCs, H₂S gas concentration, and solid particle content in the asphalt fumes to assess the deodorization effect of ACA on CRMA. Furthermore, the deodorization mechanism of ACA on CRMA was explored in depth using microscopic methods, such as fluorescence microscopy (FM) and Fourier transform infrared spectroscopy (FTIR). The findings demonstrated that ACA can increase the softening point and viscosity of CRMA while decreasing its penetration and ductility. The storage stability was optimal at a 1.0% ACA addition. Additionally, as the ACA content increased, the concentrations of VOCs, H₂S gas, and solid particles in the asphalt fumes continued to decrease. FM results indicated that when the ACA content did not exceed 1.0%, it promoted the swelling degree of CR in the asphalt. FTIR results showed that ACA can reduce the characteristic peak intensity of CRMA. This study offers important technical references and practical support for the environmentally friendly use of CRMA. Full article

The physicochemical and bioactive properties of polysaccharides from blue honeysuckle (Lonicera caerulea L.) berries were comprehensively investigated. Fourier transform infrared spectroscopy confirmed that both samples were acid heteropolysaccharides. High-performance liquid chromatography detailed a monosaccharide profile of arabinose, galacturonic acid, rhamnose, galactose, and glucose in specific molar ratios. High-performance gel permeation chromatography further revealed variations in molecular weight and distribution among the samples. Functionally, the polysaccharides exhibited significant in vitro antioxidant capacity. For the first time, the polysaccharides are shown to inhibit pancreatic lipase, in addition to α-amylase and α-glucosidase, demonstrating potent inhibitory activity with low IC₅₀ values (2.80 ± 0.12 mg/mL). This bioactivity, particularly toward lipase, was correlated with structural properties such as monosaccharide profile and molecular weight. These results highlight the potential of these polysaccharides as functional food ingredients for managing hyperglycemia and obesity and provide novel insights into their structure–activity relationships. Full article

In order to achieve rapid and qualitative detection of soluble heavy metal ions, nitrogen-doped fluorescent carbon quantum dots (N-CQDs) were synthesized using chitin extracted from shrimp and crab shells as the carbon source. The structural, morphological, and optical properties of the synthesized N-CQDs were systematically characterized using transmission electron microscopy (TEM), field emission scanning electron microscopy (FE-SEM), Fourier transform infrared spectroscopy (FTIR), Raman, X-ray photoelectron spectroscopies (XPS), ultraviolet-visible (UV-Vis) absorption spectroscopy and fluorescence spectroscopy. The resulting N-CQDs exhibited a carbonization yield of 54.46% and a fluorescence quantum yield of 34.33%. Their morphology, structure and optical properties were thoroughly characterized using a range of analytical techniques. The synthesized N-CQDs exhibited excellent fluorescence properties, and remarkable stability. When applied for metal ion detection, the N-CQDs displayed a distinct and selective fluorescence quenching response exclusively toward Fe³⁺ ions. The detection limit for Fe³⁺ at room temperature was 4.04 μmol/L. Furthermore, due to the inherent nitrogen present in the acetyl amino groups of chitin, nitrogen doping was achieved without the need for external dopants during the hydrothermal synthesis process. Owing to their high stability, low cost and low toxicity, the N-CQDs synthesized in this study provide a promising fluorescence sensing platform with excellent selectivity for Fe³⁺ detection, achieved through precise control of surface functional groups. Full article

The need for Structural Health Monitoring is evident in order to ensure the safety of civil infrastructure. The goal of vibration monitoring is to derive the eigenfrequencies, mode shapes and damping of a structure. A change in the eigenfrequency over time can indicate deterioration or damage in a structure. The amplitude can be used to calculate the damping ratio. As the damping ratio is amplitude-dependent, it is important to correctly determine the amplitude values. This study focuses on the amplitude correctness of high-rate Global Navigation Satellite System (GNSS) receiver data. In an experiment with controlled oscillations with a shaker and a Laser Triangulation Sensor (LTS) as a reference, the vibration amplitudes derived by GNSS measurements were analyzed, using time-frequency techniques like Short Time Fourier Transform (STFT) and Wavelet Transform (WT). We demonstrate that vibrations in the millimeter range can be derived from the measurements of satellites orbiting 20,000 km above Earth. However, the amplitudes of the determined frequencies show systematic errors up to 60% when compared to independent reference measurements. We introduce a correction method to reduce this error by applying a frequency-dependent correction function. Full article

Plant growth regulation is orchestrated by complex hormonal networks involving gibberellin and auxin signaling pathways. In this study, a comprehensive in silico approach was employed to comparatively evaluate the plant growth regulators (PGRs) forchlorfenuron (CPPU) and strigol (STG) against two key proteins from Arabidopsis thaliana: Gibberellin 3-beta-dioxygenase 2 (GA3Ox2), a rate-limiting enzyme in the biosynthesis of bioactive gibberellins, and the auxin signaling repressor IAA7. These targets were specifically selected because they represent critical regulatory nodes in two major hormonal pathways controlling plant growth: GA3Ox2 governs the final steps of gibberellin activation, while IAA7 modulates auxin-responsive gene expression through its interaction with Auxin Response Factors. Therefore, their combined analysis enables the evaluation of potential regulatory effects of PGRs on both gibberellin biosynthesis and auxin-mediated transcriptional control. Molecular docking analyses revealed that both ligands exhibited higher binding affinity toward GA3Ox2 than IAA7, with STG showing slightly more favorable binding energies (−7.91 kcal/mol for GA3Ox2 and −5.43 kcal/mol for IAA7) compared to CPPU (−7.18 and −4.79 kcal/mol, respectively). These results suggest a structural preference of both PGRs toward the gibberellin biosynthetic pathway. To further assess complex stability under near-physiological conditions, 100 ns molecular dynamics (MD) simulations were conducted using the CHARMM36m force field. Despite its slightly lower docking scores, CPPU demonstrated greater conformational stability, lower RMSD fluctuations, and more persistent hydrogen bonding patterns, particularly in complexes with IAA7. In contrast, STG induced more pronounced conformational rearrangements, although it promoted slightly more compact protein conformations in certain systems. Fourier-transform infrared (FTIR) spectroscopy supported the computational findings by confirming the presence of key functional groups responsible for hydrogen bonding and hydrophobic interactions. Collectively, the results indicate that although STG exhibits higher initial binding affinity, CPPU forms more dynamically stable complexes with both proteins. These findings suggest that CPPU may represent a more robust candidate for sustained modulation of auxin and gibberellin signaling pathways in plant growth regulation. Full article

This study assesses indicative technical correspondences and divergences between Francisco de Zurbarán’s painting practices and those observed in the seventeenth-century Óbidos workshop (Baltazar Gomes Figueira and Josefa d’Óbidos). We focus on the composition and function of priming layers, the shadow-to-light painting sequence, and pigment/binder usage. A multi-analytical approach was employed: portable X-ray Fluorescence (XRF), Optical Microscopy on polished cross-sections (OM), Scanning Electron Microscopy in backscattered mode with Energy-Dispersive X-ray analysis (SEM-BSE/EDS), Micro-Confocal Raman Spectroscopy (µ-Raman), and Micro-Fourier Transform Infrared Spectroscopy (µ-FTIR). Rather than treating single pigments as diagnostic, we compare patterns of application and stratigraphic behaviour—notably a two-layer priming, in which a finer, Fe-rich upper layer is actively used to build shadows, and a consistent exploitation of the priming as a value layer in a shadow-to-light sequence. Materials largely overlap, while priming compositions differ, plausibly reflecting local resources. Given the small corpus (two works by Zurbarán, one by Baltazar, and one by Josefa), conclusions are presented as indicative and contextualized within Iberian workshop practice. Full article

Umbral theory, formulated in its modern version by S. Roman and G. C. Rota, has been reconsidered in more recent times by G. Dattoli and collaborators with the aim of devising a working computational tool in the framework of special function theory. Concepts like the umbral image and umbral vacuum have been introduced as pivotal elements of the discussion which, albeit effective, lack generality. This article is directed towards endowing the formalism with a rigorous formulation within the context of formal power series with complex coefficients $(\mathbb{C}⟦t⟧,\partial )$. The new formulation is founded on the definition of the umbral operator $\mathfrak{u}$ as a functional in the “umbral ground state” subalgebra of analytically convergent formal series $\phi \in \mathbb{C}\left\{t\right\}$. We consider in detail some specific classes of umbral ground states $\phi$ and analyse the conditions for analytic convergence of the corresponding umbral identities, defined as formal series resulting from the action on $\phi$ of operators of the form $f\left(\zeta {\mathfrak{u}}^{\mu }\right)$ with $f\in \mathbb{C}\left\{t\right\}$ and $\mu ,\zeta \in \mathbb{C}$. For these umbral states, we exploit the Gevrey classification of formal power series to establish a connection with the theory of Borel–Laplace resummation, allowing us to make rigorous sense of a large class of—even divergent—umbral identities. As an application of the proposed theoretical framework, we introduce and investigate the properties of new umbral images for the Gaussian trigonometric functions, which emphasise the trigonometric-like nature of these functions and enable defining the concept of a “Gaussian Fourier transform”, a potentially powerful tool for applications. Full article

To promote the high-value utilization of fly ash (FA) and address the prolonged setting time and limited strength associated with conventional single-alkali activation, this study proposes a synergistic dual-alkali activation strategy using Ca(OH)₂ and Na₂SiO₃ in combination with microwave-assisted curing for low-calcium fly ash. Samples containing varying amounts of Ca(OH)₂ were systematically characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), compressive strength testing, and pore structure analysis. The results show that Ca(OH)₂ facilitates the formation of calcium aluminosilicate hydrate (C-A-S-H) gel, while Na₂SiO₃ sustains the alkaline environment and enhances the dissolution of SiO₂ and Al₂O₃ from FA. The dual-alkali synergistic system, when coupled with microwave treatment, markedly refines the pore structure, increases the degree of polymerization, and improves compressive strength from 0.5 MPa to 1.7 MPa with increasing Ca(OH)₂ content. In addition, the prepared fly ash-based geopolymer (FABG) demonstrates pronounced pH-buffering capacity in acidic environments and exhibits antibacterial activity, primarily attributable to its sustained release of alkalinity. This work highlights that integrating dual-alkali activation with microwave curing can simultaneously enhance microstructural development, chemical stability, and functional performance in low-calcium FA systems, thereby offering a viable route for the development of sustainable and multifunctional green building materials derived from industrial solid waste. Full article

This study introduces C-EMDNet, a nonlinear speech denoising approach that combines the adaptive decomposition capabilities of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and a deep convolutional architecture operating directly in the time-intrinsic mode function (IMF) domain. Unlike conventional enhancement methods that rely on fixed time–frequency representations, such as the short-time Fourier transform (STFT), the proposed approach interprets CEEMDAN IMFs as a morphological latent space that captures the multi-scale structure of speech. A U-Net-like network was trained to estimate mode-wise masks, enabling selective noise suppression while preserving the harmonic and formant structures. Experiments on standard noisy speech datasets show that C-EMDNet outperforms classical denoising algorithms and competitive deep learning baselines. These results highlight the promise of nonlinear morphological representations for an alternative framework speech enhancement. Full article

The urgent need to mitigate carbon dioxide (CO₂) emissions from fossil-fuel-based electricity generation has driven research into advanced materials for post-combustion carbon capture. This paper presents a modified solvothermal technique to synthesize zinc (Zn) and magnesium (Mg) based MOF-74 suitable for CO₂ capture from coal-fired power plants. The materials were synthesized through a solvothermal method using N,N-dimethylformamide (DMF) as the primary solvent, and subsequently characterized using Brunauer–Emmett–Teller (BET) surface area analysis, Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX), and thermogravimetric analysis (TGA). Both MOFs contained oxygen-containing functional groups and were thermally stable up to 430 °C and 600 °C respectively, making them ideal for carbon capture. The low-pressure N₂-BET surface areas were 55 m²/g and 24.73 m²/g. In conclusion, the Zn material had a mesoporous structure, making it more favorable for carbon capture. It was found that prolonged synthesis time weakened the MOF structure. Future work should experimentally evaluate CO₂ capture from coal-derived flue gas using Zn/Mg-MOF-74 materials, investigating adsorption behavior and kinetics through isotherm and kinetic models, while also assessing the effect of varying Zn: Mg ratios under optimized synthesis conditions. Full article

Thermal stress is an important factor affecting the long-term performance of solid insulation in GIS/GIL, and the physicochemical properties of insulating materials play a crucial role in governing surface charge dynamics. This study investigates the influence of accelerated thermal aging on the surface charge behavior of Al₂O₃-doped epoxy resin insulators. Different aging severities were applied to simulate long-term service conditions, and charge accumulation and dissipation characteristics were correlated with physicochemical evolution revealed by scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR). The results indicate that increasing aging severity reduces the charge accumulation rate while increasing the saturated surface charge density. Voltage polarity significantly influences surface charge behavior: a relatively uniform distribution is observed under positive polarity, whereas localized charge clusters are more likely to form under negative polarity. Thermal aging also accelerates the development of surface defects and increases polar functional groups, resulting in degraded insulating performance. These findings clarify the relationship between thermal aging, physicochemical evolution, and surface charge dynamics in epoxy-based insulation systems. Full article

Blended nanocomposite solid polymer electrolytes are gaining considerable attention as next-generation materials for use in flexible lithium-ion battery systems. These materials help ensure a more uniform distribution of lithium ions at the electrode–electrolyte interface, contributing to the development of a stable interfacial layer that mitigates lithium dendrite formation. In this study, solid polymer electrolytes were synthesized using a binary polymer matrix composed of polyethylene oxide (PEO) and polymethyl methacrylate (PMMA), with lithium iodide (LiI) as the ionic salt. Zirconium dioxide (ZrO₂) nanoparticles were introduced as nanofillers in varying concentrations to investigate their influence on the physical and functional characteristics of the polymer matrix. Characterization was carried out using Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR), and X-ray Diffraction (XRD). SEM images indicated that ZrO₂ nanoparticles remained well-dispersed up to 3 wt%, while higher loadings showed slight agglomeration. FTIR analysis revealed noticeable changes in absorption bands, suggesting strong interactions among polymer chains and the nanofillers. XRD data confirmed the semi-crystalline behavior of the PEO/PMMA blend system. The inclusion of ZrO₂ nanofillers enhanced the structural integrity and ionic conductivity of the polymer matrix, making them promising candidates for applications in electrochemical energy storage and advanced material interfaces. The systematic incorporation of ZrO₂ nanofillers into the PEO/PMMA matrix significantly improved the microstructural uniformity, polymer–filler interactions, and ionic transport behavior of the solid polymer electrolytes. Full article