Search Results (363)

The sustainable development of smart regions directly depends on the dependability and resilience of critical energy grids (CEGs) and their key components—electrotechnical systems (ETSs). Accurate and reliable assessment of ETS dependability is a critical factor in ensuring the sustainability of CEGs. The article presents a triplet-based decomposition model for complex electrotechnical systems, in which the system is represented as three-component blocks (triplets). The aim of the study is to reduce the dimensionality of the state space and simplify the Markov analysis of system sustainability and dependability under destructive impacts. Based on this model, the TRICAM method for triplet clustering and aggregation has been developed, combining structural decomposition and functional aggregation within a single stage of analysis. Unlike quasi-lumping methods, TRICAM implements a formalized three-state aggregation with clearly defined rules and preservation of the Markov structure, which simultaneously performs aggregation and structural decomposition and enables hierarchical model construction. The method maintains uniformity in the number of aggregated states and a fixed model dimensionality, eliminating exponential growth of the state space and simplifying implementation. TRICAM is particularly suitable for flat or hierarchically structured symmetric systems, making it an effective tool for the engineering analysis of electrotechnical systems. The results of the study demonstrate the potential contribution of the proposed method for ETS-CEG to regional sustainable development by reducing the risks associated with inaccurate and non-verifiable assessment of ETS dependability. Full article

This study investigates credit unions’ expansion into non-core lending and its association with risk and financial resilience. Using US credit union call report data from 1994 to 2024, we measure the share of purchased loans, lease receivables, and loans held for sale in non-core lending. We document robust conditional, within-credit-union associations that point to a clear risk trade-off. Credit unions with higher non-core exposure grow faster in terms of loans and membership but exhibit weaker financial buffers, including lower net worth ratios and weaker economic solvency, alongside higher delinquency. Decomposition tests indicate that loans held for sale are most strongly associated with adverse buffer and asset quality patterns, while purchased loans and lease receivables display smaller and less uniform relationships. Scale interactions suggest that these associations are generally weaker for larger institutions for both membership and assets. Post-COVID estimates indicate that the baseline relationships are broadly stable, while the growth link is becoming stronger. Full article

This paper introduces modulation spaces with variable smoothness and integrability, defined via frequency-uniform decomposition operators and mixed Lebesgue-sequence spaces. Since the conventional dyadic decomposition is replaced by a uniform one, a new theoretical foundation is required. Therefore, we first introduce a new sequence of functions and establish some important results related to these functions, which are fundamental to our analysis. We then demonstrate that the definition of these modulation spaces is independent of the choice of basis functions. Furthermore, we establish several embedding theorems and prove the completeness properties of these spaces. Full article

Visual cues in urban street environments shape residents’ perceived safety, and these perceptions often differ across social groups. Using Shanghai as a case study, this research focuses on two vulnerable populations: older adults and migrants. In the context of rapid urban transformation and increasingly fine-grained governance, perceived safety not only reflects environmental experience but also relates to whether different social groups can receive equitable perceptual support and access to opportunities for public-space use. We trained a deep learning model and rated perceived safety using over 160,000 street-level images, integrated with demographic census data at the neighborhood level, to systematically examine inequalities in visual environment perception and underlying group-specific mechanisms. However, existing studies have largely relied on small-sample surveys or average-effect analyses, and systematic evidence remains limited that can simultaneously characterize city-scale inequalities in perceived safety, disparities in group exposure, and group-specific mechanisms, while translating findings into actionable guidance for targeted governance. Firstly, we quantified spatial inequality in perceived safety using the Gini coefficient and the Theil T index. Decomposition results indicate that the remaining disparity is primarily associated with between-group differences linked to social structure. Nonparametric tests and multiple linear regression further identified significant interactions between demographic characteristics (the share of older adults and the migrant proportion) and visual environmental features, confirming group-differentiated responses to comparable streetscape conditions. In addition, we developed a priority governance index that combines perceived safety scores with vulnerability indicators to spatially identify neighborhoods requiring targeted interventions. Results suggest relatively low overall spatial inequality in perceived safety at the city scale, while decomposition analyses reveal clear group-structured disparities between central and peripheral areas and between local residents and migrants. Migrants are more frequently concentrated in neighborhoods with lower perceived safety. Priority intervention areas are primarily older-adult communities in central districts and migrant settlements in peripheral areas, characterized by lower perceived safety and higher demographic vulnerability. These findings underscore the need to shift urban renewal from uniform improvements toward differentiated strategies that account for perceptual equity and social identity. Our main contribution is not the development of a new network architecture but the alignment of image-based perception estimates with demographic vulnerability at the neighborhood scale. By combining inequality decomposition with tests of interaction mechanisms, we provide governance-relevant evidence for identifying priority intervention areas and advancing fine-grained renewal decisions oriented toward visual justice. Full article

Copper (Cu), silver (Ag), and copper–silver (Cu–Ag) powders were synthesized using ultrasonic spray pyrolysis (USP) combined with hydrogen-assisted reduction in order to examine how key processing parameters influence particle characteristics. The effects of reduction temperature, gas atmosphere, and precursor molar ratio on particle morphology, size distribution, and elemental composition were systematically investigated. Aqueous precursor solutions of copper nitrate trihydrate and silver nitrate were atomized in a USP reactor and thermally treated under hydrogen-containing or argon atmospheres at temperatures between 500 and 700 °C. The resulting powders were characterized by scanning electron microscopy (SEM), particle size analysis using ImageJ, and energy-dispersive X-ray spectroscopy (EDS). The results showed that both temperature and gas atmosphere strongly affected particle formation. Hydrogen-assisted synthesis promoted efficient reduction and high metal purity but was associated with increased particle coalescence, whereas argon atmospheres yielded finer and more uniform particles through thermally driven decomposition. In the case of Cu–Ag powders, the precursor molar ratio played a decisive role in particle stability. A 1:1 Cu:Ag ratio produced uniform particles with reduced susceptibility to surface oxidation, while Ag-rich compositions (1:3 Cu:Ag) showed increased agglomeration and partial oxidation after synthesis. Overall, this study demonstrates that careful adjustment of gas atmosphere, synthesis temperature, and precursor composition enables control over the morphology and compositional stability of Cu, Ag, and Cu–Ag powders produced by USP. These findings provide practical guidance for the scalable preparation of mono- and bimetallic metal powders for applications in electronics, catalysis, and energy-related technologies. Full article

Isogeometric analysis (IGA) is a numerical methodology for solving differential equations by employing basis functions that preserve the exact geometry of the domain. This approach is based on a class of mathematical functions known as NURBS (Non-Uniform Rational B-Splines). Representing a domain with NURBS entities often requires multiple patches, especially for complex geometries. Bivariate NURBS, defined as tensor products, enforce global refinements within a patch and, in multi-patch models, these refinements are propagated to other model patches. The use of T-Splines with extraordinary points offers a solution to this issue by enabling local refinements through unstructured meshes. The analysis of T-Spline models is performed using a Bézier extraction technique that relies on extraction operators that map Bézier functions to T-Spline functions. When generating a T-Spline model, careful attention is required to ensure that all T-Spline functions are linearly independent—a necessary condition for IGA—in order to form T-Splines that are suitable for analysis. In this sense, this work proposes a methodology to automate the generation of bidimensional unstructured meshes for IGA through T-Splines with extraordinary points. An algorithm for generating unstructured finite element meshes, based on domain decomposition of quadrilateral patches, is adapted to construct T-Spline models. Validation models demonstrate the methodology’s flexibility in generating locally refined isogeometric models. Full article

CO₂ laser processing has emerged as an efficient dry-finishing technique capable of inducing controlled chemical and morphological transformations in cotton and denim textiles. The strong mid-infrared absorption of cellulose enables localised photothermal heating, leading to selective dye decomposition, surface oxidation, and micro-scale ablation while largely preserving the bulk fabric structure. These laser-driven mechanisms modify colour, surface chemistry, and topography in a predictable, parameter-dependent manner. Low-fluence conditions predominantly produce uniform fading through fragmentation and oxidation of indigo dye; in comparison, moderate thermal loads promote the formation of carbonyl and carboxyl groups that increase surface energy and enhance wettability. Higher fluence regimes generate micro-textured regions with increased roughness and anchoring capacity, enabling improved adhesion of dyes, coatings, and nanoparticles. Compared with conventional wet processes, CO₂ laser treatment eliminates chemical effluents, strongly reduces water consumption and supports digitally controlled, Industry 4.0-compatible manufacturing workflows. Despite its advantages, challenges remain in standardising processing parameters, quantifying oxidation depth, modelling thermal behaviour, and assessing the long-term stability of functionalised surfaces under real usage conditions. In this review, we consolidate current knowledge on the mechanistic pathways, processing windows, and functional potential of CO₂ laser-modified cotton substrates. By integrating findings from recent studies and identifying critical research gaps, the review supports the development of predictable, scalable, and sustainable laser-based cotton textile processing technologies. Full article

Direction of arrival (DOA) estimation is a fundamental problem in array signal processing, with extensive applications in radar, communications, sonar, and other fields. Traditional DOA estimation methods, such as MUSIC and ESPRIT, rely on eigenvalue decomposition or spectral peak search, which suffer from high computational complexity and performance degradation under conditions of low signal-to-noise ratio (SNR), coherent signals, and array imperfections. Cylindrical arrays offer unique advantages for omnidirectional sensing due to their circular structure and three-dimensional coverage capability; however, their nonlinear array manifold increases the difficulty of estimation. This paper proposes a circular-attention residual network (CA-ResNet) for DOA estimation using uniform cylindrical arrays. The proposed approach achieves high accuracy and robust angle estimation through phase difference feature extraction, a multi-scale residual network, an attention mechanism, and a joint output module. Simulation results demonstrate that the proposed CA-ResNet method delivers superior performance under challenging scenarios, including low SNR (−10 dB), a small number of snapshots (L = 5), and multiple sources (1 to 4 signal sources). The corresponding root mean square errors (RMSE) are 0.21°, 0.45°, and below 1.5°, respectively, significantly outperforming traditional methods like MUSIC and ESPRIT, as well as existing deep learning models (e.g., ResNet, CNN, MLP). Furthermore, the algorithm exhibits low computational complexity and a small parameter size, highlighting its strong potential for practical engineering applications and robustness. Full article

Direction-of-arrival (DOA) estimation is fundamental in array signal processing, yet classical algorithms suffer from significant performance degradation under low signal-to-noise ratio (SNR) conditions and require computationally intensive eigenvalue decomposition. This study presents a systematic comparative analysis of two backbone networks, a convolutional neural network (CNN) and long short-term memory (LSTM) for DOA estimation, addressing two critical research gaps: the lack of a mechanistic understanding of architecture-dependent performance under varying conditions and insufficient validation using real measured data. Both networks are trained using cross-spectral density matrices (CSDMs) from simulated uniform linear array (ULA) signals. Under baseline conditions (1° classification interval), both CNN and LSTM methods reach an accuracy (ACC) above 98%, in which the error is ±1° for CNN and ±2° for LSTM, only existing in the end-fire direction. Key findings reveal that LSTM maintains above 90% accuracy down to −20 dB SNR, demonstrating superior noise robustness, whereas CNN exhibits better angular resolution. Four performance boundaries are identified: optimal performance is achieved at half-wavelength element spacing; SNR crossover occurs at −20 dB below which accuracy drops sharply; the snapshot threshold of 32 marks the transition from snapshot-deficient to snapshot-sufficient conditions; the array size of 8 is the turning point for the performance variation rate. Comparative analysis against traditional methods demonstrates that deep learning approaches achieve superior resolution ability, batch processing efficiency, and noise robustness. Critically, models trained exclusively on single-target simulated data successfully generalize to multi-target experimental data from the Shallow Water Array Performance (SWAP) program, recovering primary target trajectories without domain adaptation. These results provide concrete engineering guidelines for architecture selection and validate the sim-to-real generalization capability of CSDM-based deep learning approaches in underwater acoustic environments. Full article

As the core abrasive in chemical mechanical polishing (CMP) processes, the morphology, size uniformity, and chemical reactivity of CeO₂ nanoparticles (NPs) are crucial factors determining the surface precision and yield of devices. In this work, a KNO₃–LiNO₃ eutectic molten salt was used as the reaction medium. By systematically adjusting key processing parameters (such as the type of cerium source, the species and dosage of surfactants, and calcination conditions), the regulatory effects of these factors on particle growth mechanisms were clarified. This adjustment enabled the controlled synthesis of spherical CeO₂ NPs with customized morphology, particle size, and surface defect states. The multi-stage reaction process of the precursor during calcination was identified by applying thermal analysis techniques, including TG-DSC and TG-FTIR. This process includes dehydration, ion exchange, and thermal decomposition. Microstructural analysis shows that the type and dosage of the cerium source and template agent significantly affect the uniformity of particle size and spherical morphology. Moreover, by using an optimized process with a heating rate of 6 °C/min and maintaining at 400 °C for 3 h, spherical CeO₂ NPs with an average particle size of 60 nm, uniform size distribution, and high sphericity were successfully synthesized via a single-step calcination process. Based on these findings, a further proposal was put forward regarding a crystal growth mechanism mediated by micelle-directed assembly and oriented attachment. This method only requires a single calcination step, has mild reaction conditions, and involves a simple process without the need for specialized equipment—features that show great potential for scalable production. It provides both a theoretical basis and experimental support for the controlled preparation of high-performance CeO₂ abrasives. Full article

Underwater acoustic energy transmission (UAET) is critical for sustaining long-term operations of underwater platforms, but its efficiency is constrained by the limited aperture of underwater receivers—requiring acoustic energy to be concentrated within a pre-defined target angular domain. Existing array-weighting methods face inherent limitations: traditional window-based techniques optimize mainlobe–sidelobe trade-offs rather than target-specific energy concentration, while intelligent algorithms suffer from high computational cost, quasi-optimality, and poor reproducibility. To address these gaps, this study proposes an array beam energy aggregation optimization method based on Rayleigh quotient eigenvalues for UAET. First, a rigorous mathematical model of the acoustic energy concentration problem was established: by defining a target-domain energy operator matrix ${\mathrm{R}}_{\mathsf{\Theta }}$ with a Toeplitz–sinc structure (Hermitian positive definite), the energy-focusing problem was transformed into a tractable linear algebra problem. Second, the optimization objective of maximizing target-domain energy was formulated as a generalized Rayleigh quotient maximization problem, where the optimal amplitude weights correspond to the eigenvector of the maximum eigenvalue of ${\mathrm{R}}_{\mathsf{\Theta }}$—solved via Cholesky whitening and eigenvalue decomposition to ensure theoretical optimality and low computational complexity. Comprehensive validations were conducted via simulations and underwater physical experiments. Simulations on 1D uniform linear arrays and 2D 4-layer circular ring arrays showed that the proposed method outperformed traditional weighting methods and PSO in target angular energy concentration: for the 16-element linear array, its energy radiation efficiency in the 30° domain was 14% higher than classical methods (Blackman weighting). Underwater physical tests further confirmed its superiority: for the 4-layer circular ring array at 1 m, the acoustic energy efficiency in the 30° target domain reached 21.5% higher than Blackman weighting. Additionally, the method exhibited strong adaptivity (dynamic weight adjustment with target angular width) and scalability (performance improvement with array size), meeting UAET’s real-time and reliability requirements. This work provides a theoretically optimal and engineering-feasible solution for directional acoustic energy transfer in underwater environments, offering valuable insights for UAET system design. Full article

Transitional attack represents a pivotal tactic in modern firefighting, whose efficacy is profoundly contingent upon the impact characteristics of water streams and their subsequent distribution patterns. This study integrates computational fluid dynamics (CFD) simulations with experimental validation to develop a momentum decomposition model for jet impingement on a ceiling. The model analyzes the dominant mechanisms of tangential spread and normal rebound on water distribution and optimizes water application strategies. Theoretical analysis reveals that upon ceiling impact, the normal velocity component of the stream undergoes rapid attenuation, causing the flow to be predominantly governed by tangential diffusion. This phenomenon results in an asymmetrically elliptical ground distribution, characterized by a significant concentration of water volume at the terminus of the diffusion path, while wall boundaries induce further water accumulation. A comparative analysis of the stream impact process and water distribution demonstrates a high degree of concordance between experimental and simulation results, thereby substantiating the reliability of the proposed model. Numerical simulations demonstrate that an increased jet angle markedly improves both coverage area and flux density. Higher water pressure enhances jet kinetic energy, leading to improved distribution uniformity. Appropriately extending the horizontal projection distance of the water jet further contributes to broadening the effective coverage. The parametric combination of a 49° jet angle, water pressure of 0.2–0.25 MPa, and a relative horizontal distance of 1.5–2.0 m is identified as optimal for overall performance. This research provides a scientific foundation and practical operational guidelines for enhancing the efficiency and safety of the transitional attack methodology. Full article

Images captured under weak illumination typically suffer from low brightness and contrast, severe color distortion, and significant noise contamination, which not only degrade human visual perception but also hinder the performance of high-level vision tasks. Low-light image enhancement aims to improve visual quality and provide favorable conditions for subsequent image processing. To address the challenges of non-uniform illumination and loss of details in dark regions, we propose a two-stage denoising diffusion model (two-stage DDM). Specifically, we design a convolution-based Retinex decomposition module to achieve fast and robust image decomposition, followed by a two-stage diffusion-based denoising process that further enhances global image details, brightness, and contrast. In addition, we introduce a feature enhancement module to strengthen the representational capacity of the reflectance component. To evaluate the robustness and generalization ability of the proposed model, extensive experiments are conducted on the LOLv1, LOLv2-real, and LSRW datasets. Experimental results demonstrate that the proposed two-stage DDM achieves competitive performance with state-of-the-art methods, producing enhanced images with more natural and spatially uniform brightness, along with noticeably improved visual quality. Full article

This study systematically investigates the influence of laser power (1000 W, 1400 W, 1800 W) on the microstructure and properties of Ni25 alloy coatings prepared by laser cladding to optimize process parameters for enhanced comprehensive performance. Through the analysis of multi-dimensional characterization, it is found that the laser power significantly changes the thermal cycle, thus determining the evolution of microstructure. At 1000 W, a fine dendritic structure with dispersed hard phases (BNi₃, BFe₃Ni₃, CrB₂, Cr₇C₃) yielded the highest hardness (442.52 HV) but poor wear (volume loss: 0.3346 mm³) and corrosion resistance (Icorr: 2.75 × 10⁻⁴ A·cm⁻²) due to microstructural inhomogeneity. The 1400 W coating, featuring a uniform γ-Ni dendrite/eutectic network and increased B solid solubility, achieved an optimal balance with the lowest wear rate (0.0685 mm³), superior corrosion resistance (Icorr: 2.34 × 10⁻⁵; A·cm⁻²), and a stable friction coefficient (0.816), despite lower hardness (342.00 HV). At 1800 W, grain coarseness and Cr₇C₃ decomposition led to blocky hard phases, recovering hardness (415.36 HV) and reducing the friction coefficient (0.757), but resulting in intermediate wear and corrosion resistance. This study demonstrates that the uniformity and continuity of the microstructure are the key determinants governing the comprehensive service properties of the laser cladding layer, with their importance outweighing a single hardness index. 1400 W is identified as the optimal laser power, providing critical insights for fabricating high-performance Ni25 coatings in demanding service environments. Full article

We study statistical dependence in the frequency domain using the integral probability metric (IPM) framework. We propose the uniform Fourier dependence measure (UFDM) defined as the uniform norm of the difference between the joint and product-marginal characteristic functions. We provide a theoretical analysis, highlighting key properties, such as invariances, monotonicity in linear dimension reduction, and a concentration bound. For the estimation of the UFDM, we propose a gradient-based algorithm with singular value decomposition (SVD) warm-up and show that this warm-up is essential for stable performance. The empirical estimator of UFDM is differentiable, and it can be integrated into modern machine learning pipelines. In experiments with synthetic and real-world data, we compare UFDM with distance correlation (DCOR), Hilbert–Schmidt independence criterion (HSIC), and matrix-based Rényi’s $\alpha$-entropy functional (MEF) in permutation-based statistical independence testing and supervised feature extraction. Independence test experiments showed the effectiveness of UFDM at detecting some sparse geometric dependencies in a diverse set of patterns that span different linear and nonlinear interactions, including copulas and geometric structures. In feature extraction experiments across 16 OpenML datasets, we conducted 160 pairwise comparisons: UFDM statistically significantly outperformed other baselines in 20 cases and was outperformed in 13. Full article