Search Results (1,457)

Background: Some widely used wildland fire behavior models, like the Fire Area Simulator (FARSITE), propagate fire fronts by computing the front-normal velocity (spread rate) as a function of local inputs and the front-normal direction. Such models are sometimes observed to cause the collapse of crown fires into sharp wedge shapes that eliminate heading fire behavior. Aims: We set out to document this phenomenon and, more generally, understand the relationships between fire shapes and spread rate functions. Methods: The phenomenon is studied both mathematically and through simulation experiments. Non-smooth fire fronts are theorized mathematically by an Eikonal partial differential equation ($H(x,\tau ,D\tau )=1$), where the unknown $\tau \left(x\right)$ is the time-of-arrival function and the Hamiltonian $H(x,t,p)$ is positively homogeneous and possibly non-convex in p; convex analysis is used to study viscosity solutions in constant conditions. Results: We show that a fire spread model preserves the smoothness of fire fronts if and only if it is equivalent to using the Huygens principle. Nontrivially, this is equivalent to a convexity criterion on the inverse spread rate profile, which is then the polar dual of the Huygens wavelet; this corresponds to Hamiltonian–Lagrangian duality. The relevance of smoothness-destroying models to crown fire is debated. Exact analytical formulas are derived for fire growth in constant conditions. Conclusions: Our understanding of fire spread models is improved by solving the spread equations in more general ways than previously known. In particular, the collapse of heading crown fires into sharp shapes is now explained. Smoothness-destroying spread models cannot be simulated by algorithms based on travel time like cellular automata; their general well-definedness remains an open question. Fire modelers can use these findings to guide their search for improved crown fire models, and more generally to verify the accuracy of numerical implementations. Full article

This paper presents GenPluSSS, a plugin that adds the visualization of homogeneous and heterogeneous, optically thick, translucent materials on the Blender 3D modeling tool. The working principle of this plugin is based on the GenSSS method, which combines Genetic Algorithm (GA) and Singular Value Decomposition (SVD)-based subsurface scattering representation. The proposed plugin has been implemented using the Mitsuba renderer, an open-source rendering system, and has been validated on measured subsurface scattering datasets. Experimental results demonstrate that the proposed plugin visualizes homogeneous and heterogeneous subsurface scattering effects accurately with compact data representation while maintaining computational efficiency and achieving competitive rendering times compared to dipole-based and SVD-based approaches. In addition, conceptual and quantitative comparisons with recent neural subsurface scattering methods are presented in terms of rendering speed, peak memory usage, material support, and hardware dependency. The proposed framework brings measured subsurface scattering methods into practical rendering workflows within open-source content creation environments. Full article

Intrusion detection serves as a core technology for securing heterogeneous edge networks, including IoT, industrial edges, and 5G networks. However, existing federated learning-based intrusion detection systems suffer from environmental heterogeneity, limited sample availability, and severe class imbalance—issues that result in inefficient resource allocation and compromised detection performance against rare attacks. In this paper, we propose a novel lightweight intrusion detection model for heterogeneous edge networks, named FedNIDS-CNN, which is based on dynamic distillation-aided federated learning with a CNN backbone. In the data preprocessing phase, a two-level class balancing strategy integrating nearest-neighbor interpolation augmentation and adaptive synthetic sampling is employed to ensure distortion-free sample synthesis. For feature and model optimization, principal component analysis (PCA) is used to reduce the dimensionality of traffic features, while a lightweight 1D-CNN is adopted as the base model to alleviate computational overhead on edge devices. During federated training and knowledge aggregation, a dynamic weight distillation loss mechanism is designed to enhance the model’s ability to recognize minority-class attacks. Meanwhile, the federated framework supports client-side local training and server-side weighted soft-label aggregation, enabling effective knowledge fusion across heterogeneous models. Experimental results on the CICIDS2017 dataset demonstrate that the proposed method achieves an accuracy of 98.55% and an F1-score of 98.40%. Benefiting from the soft-label transmission and parameter-free aggregation design, the framework gets rid of the constraint of homogeneous model architecture and natively supports heterogeneous network models and edge devices with different computing capabilities. It also significantly reduces communication traffic and per-round training latency, confirming its excellent real-time performance and applicability in resource-constrained edge environments. Full article

This study presents a closed-form analytical solution for large-deformation pressure-induced stress and displacement fields in thick-walled, functionally graded (FG) hyperelastic polyvinyl chloride (PVC) cylinders subjected to internal pressure. The formulation inherently satisfies incompressibility—an aspect not guaranteed by standard finite element methods (FEMs)—and provides explicit expressions for all stress and deformation components. Using a Mooney–Rivlin model with an exponential–logarithmic gradation law, the study examines bi-layer and tri-layer configurations under varying property-changing scenarios. The governing equations are reduced to a single nonlinear scalar relation for the radial mapping constant, ensuring computational efficiency. Analytical predictions demonstrate excellent agreement with FEM results (errors < 1%) and recover homogeneous limits, and demonstrate that continuous gradation significantly reduces stress concentrations compared to discrete layering. The proposed model offers an efficient tool for designing pressure-resistant FG hyperelastic components for engineering applications such as pipes, hoses, biomedical devices, and protective casings. Full article

Dynamic ON–OFF server control is widely used to reduce energy consumption in data centers. We study ON–OFF control in a queueing system with an s-staggered setup policy, which limits the number of servers that can simultaneously enter the setup state. Despite its potential energy benefits, analytical results for this policy remain limited, particularly for large-scale systems with infinite buffers. This paper presents a generating-function-based analysis of the s-staggered setup queueing model and derives exact expressions for the stationary queue-length distribution. We enhance the conventional generating-function approach by reformulating the non-homogeneous part of the underlying Markov chain, thereby reducing the number of computational states and improving scalability. The proposed algorithm requires approximately half the computational effort of the conventional generating-function approach when s is small relative to system capacity. Numerical experiments demonstrate that the algorithm can efficiently handle large-scale systems and provides insights into the energy–performance trade-off. Full article

Welding aluminum (Al) to titanium (Ti) is particularly challenging because of the large differences in their melting points and the tendency to form cavities and brittle intermetallic compounds. Such issues can be mitigated in friction stir welding (FSW) by understanding the underlying mechanisms of microstructural evolution and tensile fracture behavior. In the present study, FSW was carried out on commercially pure Al and commercially pure Ti. X-ray micro-computed tomography results show that the distribution of Ti fragments depends on their morphology, with fine particles (volume 10³–10⁴ µm³) being distributed homogeneously, while large flakes (10⁷–10⁹ µm³) are concentrated near the joint interface. A three-dimensional analysis of Ti fragment distribution was performed to clarify material flow and particle dispersion within the weld nugget. EDS (Energy-Dispersive Spectroscopy) and EPMA (Electron Probe Microanalysis) composition mapping confirmed the formation of AlTi and Al₃Ti intermetallic phases, with Al₃Ti as the dominant phase (consistent with its lower Gibbs free energy of formation). Because Al is the primary element in the matrix and undergoes the highest degree of deformation, its microstructural evolution in Al was examined using Electron Backscatter Diffraction (EBSD). Grain refinement in Al was attributed to continuous dynamic recrystallization (CDRX). Mechanical mixing and intermetallic formation increased the hardness of the weld, while the tensile response corresponded to a joint efficiency of approximately 77%, alone with an 11% improvement in elongation over base Al. The study further establishes a correlation among Ti particle distribution, local microstructural evolution, and the tensile response of the joint. Fractographic analysis indicates a bimodal fracture mechanism, and failure occurred away from the joint interface, indicating a strong joint. To interpret this behavior, a spring-based model was proposed to relate the fracture location and tensile deformation to the spatial variation in microstructure across the welded zones. This approach provides a conceptual framework that is extendable to other dissimilar material systems with spatially varying microstructures. Full article

The high-pressure spherical gas storage tank in a wind tunnel energy storage and gas supply system is a critical pressure-bearing component of the wind tunnel operation system. The linearized stress in its critical control region is a key parameter for structural safety assessment. Therefore, investigating and evaluating the linearized stress and its associated uncertainty in this region is essential for enhancing operational safety. In this study, a three-dimensional finite element model of the spherical tank was developed, and the critical control region was identified through stress linearization. The operating internal pressure, working temperature, and shell wall thickness were treated as random input variables. Based on the stress linearization results, the stability of the critical control location was assessed. For physically homogeneous intervals, a non-intrusive polynomial chaos expansion surrogate model was constructed, and a conditional uncertainty propagation model for the linearized stress was established. Compared with the Monte Carlo and GUM methods, the non-intrusive polynomial chaos expansion method achieves substantially higher computational efficiency while producing consistent evaluation results. The uncertainty analysis shows that the operating internal pressure is the dominant contributor to the uncertainty of the linearized stress, followed by the effective wall thickness of the spherical shell. In contrast, the working temperature has a minor effect, and the interactions among the input variables are weak. Full article

Cement clinker production is a thermal- and emissions-intensive process requiring high-temperature heat for drying, calcination, and sintering. This review provides a process-based assessment of refuse-derived fuel (RDF), solid recovered fuel (SRF), tire-derived fuel (TDF), and biomass as partial substitutes for coal and petcoke in modern dry-process cement kilns. The study synthesized the evidence from plant-scale trials, pilot and laboratory experiments, process modeling, computational fluid dynamics, emissions studies, life-cycle assessment (LCA), techno-economic analysis (TEA), and regional case studies to evaluate alternative fuels across fuel properties, kiln-zone suitability, process stability, clinker quality, emissions performance, and environmental outcomes. The review shows that stable co-processing generally requires fuels with net calorific values above 14 MJ kg⁻¹ and moisture contents below 15%, although TDF can provide 26–33 MJ kg⁻¹ and sustain high-energy kiln duty when sulfur, zinc, and steel residues are controlled. RDF, SRF, and biomass require pre-processing, homogenization, calibrated dosing, and continuous fuel-quality monitoring to limit incomplete burnout, deposit formation, volatile circulation, and clinker-quality variation. LCA studies show that 20% RDF thermal substitution can reduce global warming potential by about 3.3–4.2%, increasing to approximately 6.7% when avoided landfill methane credits are included. Modern abatement systems can maintain particulate matter at about 10–30 mg Nm⁻³ and PCDD/F below 0.1 ng TEQ Nm⁻³ under stable operation. The review concludes that alternative fuels are quality-dependent co-processing options whose mitigation role is complementary to clinker-factor reduction, energy-efficiency improvement, low-clinker binders, electrified heating, oxy-fuel calcination, and carbon capture. Full article

Binary Waring decomposition seeks to express a homogeneous binary form as a minimal sum of powers of linear forms. In the binary setting, Sylvester’s theorem gives a classical algebraic route for rank determination and parameter recovery through structured Hankel/catalecticant matrices. Although this procedure is exact and interpretable in ideal arithmetic, practical rank identification may become unstable when the input coefficients are contaminated by noise or when the underlying roots are close to degenerate configurations. This paper develops a data-driven rank inference framework coupled with certified Sylvester reconstruction for robust binary Waring decomposition. The proposed method first converts the coefficient sequence into a Hankel-aware graph that captures recurrence-induced dependencies among polynomial coefficients. A graph neural network is then used to infer plausible rank candidates from this structured representation. Instead of accepting a single prediction directly, the framework performs explicit Sylvester reconstruction and algebraic residual verification for candidate ranks. To further improve decision reliability, a lightweight meta-verification module integrates reconstruction residuals, model confidence scores, and stability-related indicators to select the most credible rank. Experiments on large-scale synthetic binary forms show that the proposed meta-guided variant improves rank identification and verified reconstruction success relative to the one-shot hybrid solver under low-to-moderate noise while maintaining the transparency and auditability of classical symbolic–numeric computation. Additional stress tests indicate that performance can degrade under shifted sampling regimes; so, the method should be interpreted as a robust decision layer within the modeled problem class rather than as unconstrained real-world validation. Full article

The microstructure of metal matrix composites is inherently governed by fabrication routes and processing parameters, yet technological and physical constraints often prevent the realization of intended structural designs. In particle-reinforced composites produced via casting, interactions between the solidification front and inclusions frequently lead to agglomeration, segregation, and hence, a non-uniform distribution of the inclusions concentration. To mitigate these effects, post-processing techniques such as Friction Stir Processing offering particular promise for cast materials by refining microstructures and enhancing phase homogeneity. This study addresses these challenges by application of Fourier transform analysis to characterize stochastic inclusion distributions. Building on the Windows Washing method, we extend its application to heterogeneous media with varying inclusion concentrations. Through computer simulations and experimental analysis of real composites, we demonstrate that discrete Fourier transform can reveal hidden stochastic periodicity. The proposed framework provides a pathway toward improved predictive models and optimization strategies for metal matrix composites processing and performance. Full article

This paper revisits Problem A of the TEAM 35 benchmark from the viewpoint of robustness estimation under manufacturing uncertainty. Rather than treating the original extremal-position-based sensitivity metric as the formulation to be improved, it is used only as a baseline for comparison with other metrics. In this work, robustness is evaluated as the largest degradation of the nominal magnetic-field homogeneity objective observed over prescribed sets of admissible manufacturing perturbations. In addition to turn-position uncertainties, the present study also includes uncertainty in the excitation current density. While turn-position errors affect each turn individually, current-density uncertainty affects the error contributions of all turns simultaneously through a common term. This common-mode excitation uncertainty represents an extension of the original benchmark formulation and is one of the paper’s main focal points. Several Design of Experiments (DoE) methodologies, as well as search-based robustness estimation strategies, are compared in terms of error in estimated robustness and computational demand. The results show that the original extremal-position-based approximation can substantially underestimate the sampled robustness of the nominal field-homogeneity objective. Including current-density uncertainty further increases the discrepancy between the original metric and the sampled robustness estimates. Full article

The rapid growth in the electric vehicle sector has increased demand for advanced battery thermal management systems (BTMSs) with high heat-dissipation capacity and temperature uniformity. Immersion cooling using dielectric fluids has recently been recognized as a promising alternative technology to conventional indirect liquid cooling methods. This study investigates the thermal and hydrodynamic behaviour of a sixteen-lithium-ion cell battery (LIB) module immersed in low-viscosity dielectric fluids using three-dimensional computational fluid dynamics simulations. In this context, a total of twenty dielectric fluids are evaluated using the ANSYS Fluent solver, with particular emphasis on the effects of key thermophysical properties, including viscosity, density, specific heat capacity, and thermal conductivity. The simulation findings reveal that mineral oil and PAO4 yield the lowest maximum LIB cell temperatures, with a reduction of approximately 4 K compared to the least effective dielectric fluids, such as undecane and cumene. Moreover, in terms of temperature uniformity, mineral oil, Novec 7000, and PAO4 exhibit the most homogeneous temperature distributions among the twenty dielectric fluids. In addition, they show an improvement in the temperature uniformity index of approximately 32.4% compared with the least effective dielectric fluid, cumene. On the other hand, mineral oil and PAO4 generate significantly higher pressure drops because of their relatively high viscosities, which increases hydraulic resistance and pumping power requirements. These findings demonstrate that excellent thermal performance does not necessarily correspond to optimal overall thermo-hydraulic behaviour. Overall, the results confirm that immersion-BTMS performance is governed by a complex interaction between dielectric fluid thermophysical properties and flow behaviour, highlighting the importance of coupled thermo-hydraulic optimization in the selection of dielectric fluids for next-generation immersion-cooled battery systems. Full article

Sodium caseinate (SC)-stabilized emulsions are highly susceptible to flocculation and phase separation near the protein isoelectric point (pI), limiting their application in acidified food systems. In this study, high acyl gellan gum (HA) was introduced to construct pH-responsive protein–polysaccharide complexes to modulate the interfacial assembly and stability of SC emulsions. Results demonstrated that HA interacts with SC primarily through electrostatic attraction and multi-site hydrogen bonding. This interaction induces protein conformational rearrangement and, as evidenced by combined structural and computational analyses, facilitates the assembly of a denser, interconnected composite network. The formation of HA–SC complexes significantly enhanced interfacial adsorption, reduced oil–water interfacial tension. Rheological and microrheological analyses revealed the composite system formed an elasticity-dominated weak gel network, restricting droplet mobility and suppressing aggregation. Consequently, HA–SC emulsions exhibited markedly improved pH tolerance and physical stability compared to SC-only emulsions, particularly near the pI, evidenced by reduced droplet size, lower Turbiscan stability indices, and more homogeneous microstructures. Crucially, utilizing a well-defined mechanistic model of fixed HA and SC concentrations, this study quantitatively links molecular interactions, interfacial network reconstruction, and macroscopic emulsion stability across a broad pH continuum. Rank-correlation analysis of pH-resolved descriptors shows the molecular charge state co-varies monotonically with the interfacial network and macroscopic stability, and is inversely coupled to droplet mobility. These findings provide new insights into protein–polysaccharide interfacial engineering, establishing the essential physical-stability foundation for the future rational design of acid-tolerant food emulsions and functional delivery systems. Full article

Background/Objectives: Carotid perivascular adipose tissue (PVAT) density on computed tomography angiography (CTA) is a noninvasive surrogate marker of local vascular inflammation, but its relevance to stroke etiology in a homogeneous cohort of patients undergoing mechanical thrombectomy (MT) remains unclear. Methods: We retrospectively analyzed 146 consecutive patients with acute ischemic stroke treated with MT for acute middle cerebral artery (MCA) occlusion between May 2018 and August 2024. Baseline CTA was used to quantify carotid PVAT density with two 2–3 mm² circular regions of interest per internal carotid artery (ICA), placed ≥1 mm from the vessel wall. Measurements were performed bilaterally, and the ICA ipsilateral to the occluded MCA was defined as the stroke-side ICA. Etiology was classified according to the Trial of ORG 10172 in Acute Stroke Treatment (TOAST) system and grouped as large-artery atherosclerosis (LAA), cardioembolism (CE), and other/undetermined (OD/UD). Interobserver agreement was assessed using the intraclass correlation coefficient. Results: The mean age was 72.21 ± 12.39 years; 83.6% of patients achieved successful recanalization (mTICI ≥ 2b), and 47.9% had a favorable 90-day outcome (mRS ≤ 2). In the LAA subgroup (n = 38), ipsilateral PVAT density was significantly higher (less negative) than contralateral PVAT density (−64.24 ± 11.74 vs. −78.22 ± 9.13 HU; p < 0.001). Ipsilateral PVAT density differed significantly across TOAST groups (ANOVA p = 0.004), being higher in LAA than in CE (Δ = 11.19 HU; p = 0.003) and OD/UD (Δ = 9.54 HU; p = 0.004). ROC analysis showed modest discrimination for LAA versus non-LAA stroke (AUC 0.67, 95% CI 0.58–0.75), with an optimal cutoff of −79 HU (sensitivity 92.1%, specificity 40.7%). In multivariable logistic regression, higher ipsilateral PVAT density was independently associated with LAA etiology (per 1-HU increase: OR 1.048, 95% CI 1.018–1.079; p = 0.0016). PVAT density was not associated with recanalization success or 90-day functional outcome. Conclusions: In patients with acute MCA occlusion undergoing MT, higher carotid PVAT density on the stroke side was independently associated with LAA stroke etiology but had limited value for predicting MT success or short-term clinical outcome. Full article

Traditional European craft practices face dual pressures: the erosion of tacit knowledge held by aging practitioners, and the risk of cultural homogenization through uninformed digital adoption. This paper presents a comparative analysis of a structured design pilot conducted across five Representative Craft Instances (RCIs): glassblowing, tapestry, marble/silversmithing, porcelain, and woodcarving within the Horizon Europe CRAEFT project. Drawing on co-creative workshops, motion capture pipelines, physically based rendering (PBR), interactive simulation, and additive manufacturing, we analyze how context-specific digital tools performed as mediators rather than modernizers across heterogeneous craft domains. Cross-domain analysis reveals that digital tools achieve cultural legitimacy only when introduced through co-creative, practitioner-led cycles; that gesture and tacit knowledge are transferable via structured computational pipelines; and that methodological portability, not workflow replication, is the appropriate model for cross-context scaling. Implications are discussed for sustainable heritage policy, design education, and the development of craft-sensitive digital infrastructure in Europe. A cross-RCI comparative assessment matrix evaluates all five domains across seven analytical dimensions: practitioner adoption, perceived usefulness, cultural legitimacy, technical maturity, sustainability impact, transferability potential, and educational effectiveness. Finally, practitioner reflective accounts from participating designers and craftspeople are presented to ground the analytical findings empirically. Full article