Search Results (14,733)

Federated Learning (FL) is a machine learning technique that preserves data privacy and security by training models directly on decentralized edge network devices. This generates substantial communication overhead due to the repeated exchange of model updates across numerous edge network devices. Quantization has tackled this challenge by reducing communication overhead and computational costs by quantizing model updates. Although selecting the most suitable quantization level to balance communication efficiency and model accuracy is challenging, failing to achieve this balance results in excessive compression, leading to accuracy degradation due to the lossy nature of the quantization technique. This challenge was tackled in this paper via a Quantile-based lossless compression method named Pcodec, which implements lossless compression in the FL context. Pcodec is a Quantile-based lossless compression algorithm designed for numerical data that utilizes mode identification with delta encoding and binning, where binning groups similar values into entropy-coded bins and stores the exact offset within each bin, thus achieving high compression ratios and efficient processing speed. Using MNIST and CIFAR-10 datasets and models such as CNN and ResNet18, we demonstrate that Pcodec achieves up to 58.19% size reduction with no accuracy loss compared to standard quantization methods. The experiments showed that the proposed Quantile-based compression approach in FL reduces up to 2.81× the communication overhead between each server and edge network device while maintaining the accuracy. In comparison to quantization, the Quantile approach reduced the communication overhead by 2.74×, tackling the main challenge of FL context by reducing communication overhead with a remarkably high compression ratio while maintaining the model’s accuracy. Full article

This paper investigates the temporal evolution of high-speed compressible fluids governed by the two-dimensional Euler equations in irregular flow fields using the Fourier Neural Operator (FNO). We reconstruct the irregular flow field point set into sequential format compatible with FNO input requirements, and then embed temporal bundling technique within a recurrent neural network (RNN) for multi-step prediction. We further employ a composite loss function to balance errors across different physical quantities. Experiments are conducted on three different types of irregular flow fields, including orthogonal and non-orthogonal grid configurations. Then we comprehensively analyze the physical component loss curves, flow field visualizations, and physical profiles. On non-orthogonal grids, our method consistently achieves improvements in both computational efficiency and error compared to other baseline models. Results demonstrate that our approach achieves high accuracy, as evidenced by maximum relative $L_2$ errors of $(0.75\%,0.56\%,0.35\%)$ for ($p,T,$$\parallel \mathbf{u}\parallel$) respectively (where p, T, and $\parallel \mathbf{u}\parallel$ denote pressure, temperature, and velocity magnitude), and offers substantial improvements in computational efficiency over traditional numerical methods. Within this data-driven context, the method accurately and efficiently simulates the temporal evolution of high-speed compressible flows in irregular domains. Full article

This paper studies observer-based stabilization of a normalized incommensurate fractional-order discrete-time SI benchmark model for computer-virus propagation. The model is formulated with Caputo-like fractional-difference operators and allows the susceptible and infected compartments to have different memory orders. In contrast with a predictive malware-forecasting model, the proposed system is explicitly treated as a dimensionless benchmark for qualitative analysis and control design. To clarify how the benchmark can be connected to empirical cybersecurity data, the revised formulation includes a calibration and fractional-order selection procedure based on normalized infection telemetry, admissible parameter sets, and loss minimization. The incommensurate orders are therefore interpreted as identifiable modeling parameters, not as arbitrary constants. The plant, observer, and control laws are formulated on the integer update grid, and the memory terms are implemented through the equivalent Volterra-type convolution representation. A nonlinear Luenberger-type observer is proposed under infected-state measurements, which is justified as a detectability-based cyber-monitoring configuration rather than a full observability assumption. The observer gain design, the full-state feedback design, and the observer-based output-feedback design are derived from first-order linearized incommensurate fractional-order models. The resulting criteria are expressed through characteristic-root conditions associated with linear incommensurate Caputo-type fractional-order difference systems. The scope of the theoretical claims is made explicit: the results provide local linearized-design guarantees and do not establish global or semi-global nonlinear stabilization. The nonlinear residuals, measurement-noise channel, incomplete-measurement formulation, and limitations of the linearized characteristic-root approach are stated explicitly so that the numerical section can assess robustness, sensitivity, and the effective region of attraction of the nonlinear closed loop. Full article

To ensure a reliable electrical isolation point in power systems, the seismic performance assessment of disconnect switches is of critical importance for maintaining operational continuity under earthquake excitations. In this study, a hybrid method combining a convolutional neural network (CNN) and a long short-term memory (LSTM) network is proposed for the seismic intelligent classification and response prediction of disconnect switches. Unlike conventional approaches that rely on finite element simulations or shake table tests with high computational costs, the proposed method learns directly from raw ground motion records. The CNN component is designed to capture local frequency characteristics of input ground motions, enabling automatic classification into low-, medium-, or high-frequency categories. Subsequently, category-specific LSTM models are established to map the ground motion time series to multi-dimensional performance indicators of the disconnect switch. These indicators include top absolute accelerations, bottom shear forces, and relative deformations of porcelain posts. A training set comprising 102 ground motion records is constructed based on numerical simulations of a validated simplified model, while another testing set comparing 21 ground motion records are employed to validate the performance of predicted models. Training and validation results demonstrate that the CNN achieves a great classification accuracy. The LSTM predictions show good agreement with the computed time-history responses, with errors of root-mean-square responses generally within 10%. The proposed method provides a rapid, data-driven alternative to traditional seismic analysis, significantly reducing computational time while preserving prediction fidelity. It also enables the parallel prediction of multiple coupled performance indicators, which is not readily achievable by existing single-output surrogate models. Full article

This paper presents an analysis of heat transfer in a thick-walled steel plate. During the experimental studies, one surface of the plate was heated, while the other, parallel to it, was cooled with water. Based on experimental studies, a curve of the heat transfer coefficient (HTC) was obtained and used in numerical tests. The solution to the inverse heat conduction problem (IHCP) for the plate presented in this paper is based on the finite element method and variational calculus. The computational model uses the quasi-Newton BFGS algorithm. Numerical tests examined the influence of the location of the temperature measurement point and the time step on the stability and accuracy of the IHCP solution. The quality of the solution was evaluated using norms describing the average and maximum errors of the calculated boundary conditions. The analysis results in the determination of the most favorable time step for various locations of the measurement point. Full article

In performance-based earthquake engineering (PBEE), fragility curves hold significant importance for a reliable risk assessment of the existing reinforced concrete (RC) structures. Fragility curves require numerous incremental nonlinear dynamic analyses, which are highly time-consuming and computationally intensive. However, predicting the fragility curve parameters of RC structures by a machine learning algorithm could effectively reduce this cost. In this study, machine learning (ML)-based numerical analyses were performed in order to predict the fragility curve parameters of the existing RC structures, considering rapidly observable structural parameters by street survey. The construction date, story number, plan irregularities, soft story, and damage states are the main variables that are considered in this study. Hence, a dataset comprising the results of 620 structural fragility analyses was compiled from the existing literature. Key fragility parameters, namely the median and standard deviation, are predicted using several machine learning algorithms, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Artificial Neural Networks (ANNs). The performance of the proposed models is evaluated using R², RMSE, and MAE metrics under a five-fold cross-validation scheme. Furthermore, nonlinear dynamic analyses are conducted on a representative set of structural models to validate the machine learning predictions. The results indicate that the ANN model achieves the highest predictive accuracy, followed by ensemble tree-based methods, demonstrating the capability of machine learning approaches to effectively capture complex nonlinear relationships between seismic input parameters and structural response. The proposed framework significantly reduces computational effort while maintaining reliable prediction accuracy, offering an efficient tool for seismic risk assessment and fragility estimation of existing structures. Full article

Background and Objectives: Acetabular fracture surgery is associated with substantial perioperative blood loss and prolonged operative time. Routine preoperative pelvic computed tomography (CT) carries information about body composition that is not currently exploited for risk stratification. We tested whether (i) CT-defined pelvic sarcopenia is associated with lower preoperative haemoglobin and (ii) preoperative subcutaneous fat cross-sectional area (CSA) is independently associated with operative time, after adjustment for surgical approach, age, fracture complexity and sarcopenia status. Materials and Methods: In this single-centre retrospective cohort study, 48 adults (37 men, 11 women; mean age 40.2 ± 16.5 years) who underwent open reduction and internal fixation (ORIF) for unilateral acetabular fractures between 2016 and 2024 were included. Pelvic muscle and subcutaneous fat CSAs were measured on the contralateral side of preoperative CT images using ImageJ. Sarcopenia was defined as an internal, cohort-relative classification based on the sex-specific bottom tertile of psoas CSA. Normality was assessed by Shapiro–Wilk testing; Pearson or Spearman correlation was used accordingly, and the 36 pairwise correlations were controlled with the Benjamini–Hochberg false-discovery-rate procedure. The multivariable model used ordinary least squares regression with heteroscedasticity-consistent (HC3) standard errors and a median quantile-regression robustness check. Results: Sarcopenic patients (n = 17) had significantly lower preoperative haemoglobin (12.63 ± 1.24 vs. 14.00 ± 1.53 g/dL; p = 0.002; Cohen’s d = 0.96). The absolute perioperative haemoglobin drop was numerically smaller in the sarcopenic group (ΔHb 1.64 ± 0.91 vs. 2.46 ± 1.87 g/dL) but did not reach statistical significance (p = 0.079); estimated blood loss (p = 0.258) and transfusion requirement (p = 0.567) did not differ between groups. Pelvic muscle CSAs correlated positively with preoperative haemoglobin (all q < 0.05 after Benjamini–Hochberg correction). In the multivariable model (F[6, 41] = 3.71, p = 0.005; adjusted R² = 0.26; all variance inflation factors 1.06–1.26), subcutaneous fat CSA (B = +0.25 min/cm², p = 0.004) and the modified Stoppa approach (vs. Kocher–Langenbeck; +65 min, p = 0.001) were independently associated with operative time. Conclusions: In this exploratory retrospective cohort, routine preoperative pelvic CT contained two body-composition signals that may warrant prospective evaluation: pelvic sarcopenia, which was associated with lower baseline haemoglobin, and subcutaneous adiposity, which was associated with longer operative time in the primary regression model. Both signals require confirmation—the sarcopenia–bleeding relationship was not statistically significant, and the subcutaneous fat association was attenuated under robust inference. These findings are hypothesis-generating; prospective multicentre validation with height-normalised sarcopenia thresholds and body mass index is required before clinical implementation. Full article

Surface faulting poses an earthquake-related hazard with direct consequences for infrastructure and high-risk facilities. Probabilistic Fault Displacement Hazard Analysis (PFDHA) is widely used to estimate the annual frequency of exceedance (AFOE) of specific displacement values at sites on or near active faults. This approach requires numerous input parameters related to fault characterization and coseismic displacement distribution, yet few studies have examined how these parameter choices affect hazard results. Thus, we conduct an analysis following a One-At-a-Time (OAT) strategy, in which a single parameter is varied with respect to three kinematic-specific baselines. We explored the PFDHA outputs obtained allied to the broadly adopted regression models and scaling laws available in the literature up to 2023. We compared the hazard curves obtained for principal faulting from each calculation to a baseline parametrization, and we computed the percentage difference in AFOE, given a displacement amount, with respect to such a baseline. We obtained values in the interval −100% to +200%, computed within the displacement interval adopted for the hazard calculation, attesting that empirical regressions contribute significantly to hazard curve variations. Our sensitivity study could inform operative choices by practitioners and provides insights for optimizing data acquisition efforts in fault displacement hazard assessments. Full article

To meet the requirement for high-precision dynamic positioning of fully actuated vessels under wave-frequency disturbances, and to achieve clock-synchronous triggering for system analysis, decision-making and reliable communication, this paper proposes a minimum-time simultaneous triggering (MTST) scheme based on a modified self-adaptive observer. Firstly, the concepts of result-dependent event (RDE) and conflict are introduced to describe the internal coupling characteristics of the system and the continuous actuation behavior under the superposition of triggering signals. Then, for the estimation of yaw perturbation, a self-adaptive parameter algorithm is employed in the modified observer, whose stability is subsequently proven. To reduce channel occupancy during the cooperative transmission of distributed triggering signals, a multi-port scheme is proposed, including RDE, and a corresponding controller is designed. Furthermore, to avoid the computational explosion phenomenon and estimate complex nonlinear unknown terms, the dynamic surface method and radial basis function neural network are used in filtering and function approximation, respectively. Finally, theoretical derivations show that the multi-port processing ensures the stability of all system nodes without the Zeno phenomenon. Meanwhile, the MTST scheme also maintains system stability while effectively eliminating both the Zeno phenomenon and signal conflict. Numerical simulation results reveal that compared with the multi-port event-triggering (MET) scheme, the MTST scheme achieves performance improvements of 9.76%, 0.37%, and 43.15% in tracking precision, energy efficiency, and control smoothness, respectively, which demonstrates its prominent advantages in event-triggered control systems. While improving positioning accuracy, the scheme exhibits a slight slowdown in heading-direction convergence and introduces a heavier communication load. These characteristics reflect a fundamental trade-off: the MTST scheme provides superior control performance at the cost of an increased triggering frequency and greater communication overhead. Full article

Aiming at the challenging issue of nonlinear coupling control between cooling intensity and solidification rate in the secondary cooling zone of round billet continuous casting, this study proposes an efficient 3D temperature field modeling method that integrates hybrid coordinate systems with equal-area meshing. The model is applicable to the temperature range of 800–1520 °C during the continuous casting process. With the modeling strategies of constructing an r-θ-z hybrid coordinate system and designing a dynamic equal-area meshing method, and combined with a topological structure optimization algorithm, the geometric adaptability and numerical stability of the model are significantly improved. Based on this, an explicit-semi-implicit dual-mode finite difference solution model is developed, where the explicit scheme meets real-time online calculation requirements, and the semi-implicit scheme combined with preconditioned Gauss–Seidel iteration enables high-precision offline simulation. Furthermore, a boundary condition model incorporating adaptive mold heat flux correction and multi-mechanism heat transfer in the secondary cooling zone is established. Based on Microsoft Visual Studio 2019 (Version 16.11) C++ development, SIMD vectorization and temperature gradient threshold optimization technologies are employed, resulting in a 35% improvement in computational efficiency. Industrial validation results show that, taking 42CrMo steel with a casting speed of 0.24 m/min and a cross-section of φ600 mm as an example, the deviation between the calculated surface temperature (887 °C) and the measured value (876 °C) of the round billet in the straightening zone is only 11 °C, and the calculation error of the cold billet diameter is only 0.325% (with a calculated value of 597.548 mm and a measured average value of 599.5 mm), both meeting the accuracy requirements for engineering applications. The model breaks through the limitations of traditional empirical formulas and provides theoretical support for digital control of continuous casting processes and quality optimization of high-alloy steels. Full article

This paper investigates the inherent conflict between individual and collective interests within multi-coalition games. Unlike traditional noncooperative frameworks where players solely optimize a collective objective, our model incorporates individual player preferences, naturally formulating a constrained multi-objective game. To address this, we introduce an endogenous preference weight factor to scalarize the multi-objective problem into a tractable single-objective game. Furthermore, we propose distributed game strategies equipped with predefined-time convergence to compute the Nash equilibrium of the multi-coalition game, alongside the weight factors. Subsequently, consensus protocols and gradient descent methods are synthesized to locate the weighted Nash equilibrium of the scalarized game. The rigorous predefined-time convergence of the proposed algorithm is established via Lyapunov stability theory. Finally, numerical simulations validate the efficacy and superiority of the proposed framework. Full article

Despite the low thermal conductivity that characterizes the mechanical behavior of cementitious composites like concrete, high temperatures acting for long periods could have devastating effects on the overall integrity and stability of structures. Such damage encompasses not only the structural but also the material level, manifested as a degradation of the strength and stiffness properties together with increasing porosity and the consequent cohesion loss. Adding fibers to the cementitious matrix is a strategy that increases the fire resistance of structures, improving the fracture energy release capacity beyond the peak strength. This fact has been experimentally demonstrated in numerous publications and requires the development of advanced computational constitutive models with the aim of predicting the evolution of both elastic properties and failure behavior in fiber-reinforced concrete. In this work, a temperature-dependent, thermodynamically consistent microplane material model based on the smeared crack approach is developed to simulate the mechanical behavior of preheated steel fiber-reinforced concrete (SFRC) under residual conditions. The influence of high temperatures on the material response is evaluated in terms of stress versus crack opening displacement or crack slip curves, whereas the failure analysis in the form of discontinuous bifurcation is addressed by means of numerical analysis of the acoustic tensor, identifying the critical orientation for varying temperature levels, material properties and boundary conditions. Full article

Shock buffet is one of the critical issues affecting the aerodynamic performance, flight quality, and flight safety of large aircraft. To overcome the limitations of traditional experimental measurement methods, such as insufficient capability in capturing flow features and high cost, an integrated experimental system tailored for extreme cryogenic and high-Reynolds-number conditions is developed based on the conventional tuft technique. This system comprises “preparation of low-flow-disturbance fluorescent mini-tufts, high-efficiency large-area tuft taping, automatic generation of digital streamline, and flow topology analysis”. Furthermore, a technique for assessing the transonic shock buffet onset using dynamic flow visualization with fluorescent mini-tufts is proposed. This paper takes a typical supercritical airfoil as the research object. First, through high-precision numerical simulations, it reveals that low-energy, unstable boundary-layer separation is the core driving force for the development and maintenance of shock buffet, and that flow separation characteristics serve as an important basis for determining the shock buffet onset. Subsequently, experimental validation is conducted in a 0.3 m high-Reynolds-number transonic wind tunnel. Using a dual-excitation-band composite light source, simultaneous measurements of pressure-sensitive paint (PSP) and fluorescent mini-tuft patterns are realized. The experimental results show that under extreme conditions, characterized by a wide total temperature range of 110 K to 280 K and strong scouring at Mach numbers from 0.6 to 0.9, the fluorescent mini-tufts (approximately 0.05 mm in diameter) exhibit excellent flow-following capability without any detachment. The digitized flow patterns of the fluorescent mini-tufts, obtained via computer image recognition algorithms, clearly reveal the location and area of boundary-layer separation. The trends show good agreement with the cryogenic PSP results, providing an important reference for determining the shock buffet onset. Full article

Accurate characterization of coherent vortex structures in high-speed turbulent boundary layers presents a persistent challenge due to the flow’s high dimensionality and nonlinear dynamics. This study investigates an optimized decomposition framework that integrates modal decomposition techniques with a novel vortex identification strategy to extract dynamically significant features. The numerical solution from a previously conducted high-fidelity simulation of MVG-controlled supersonic flow serves as the testbed. Principal Component Decomposition and Non-negative Matrix Factorization are applied across multiple flow variables to evaluate their effectiveness in isolating coherent structures. The results show that, across the velocity-based cases, 3–4 modes capture 70% of the TKE with MSE about 0.1, while the Liutex case requires 14 modes but achieves a lower MSE of about 0.04. Overall, using the same number of modes yields similar reconstruction performance across all cases. The influence of various normalization and rescaling methods on decomposition performance is also examined. Optimization is guided by two primary criteria: the interpretability of spatial modes and MSE in reconstructing vortex structures. By employing low-rank matrix representations, this optimization study aims to enhance interpretability and reduce computational costs. This approach establishes a mathematically rigorous and efficient platform for analyzing vortex dynamics, achieving significant dimensionality reduction while preserving key features of turbulent transport. Full article

316LN stainless steel is widely used in critical nuclear fusion structural components due to its excellent mechanical properties and machinability. However, its high thermal expansion coefficient and low thermal conductivity promote welding distortion, while work hardening causes residual stress accumulation. Thermo-elastic–plastic finite element modeling (FEM) is the primary numerical method for predicting these effects. Yet, despite hardware advances, full-scale simulations—especially for thick plates with multi-pass welds—remain computationally expensive, hindering the balance between efficiency and accuracy. To address the inherent trade-off between welding efficiency and dimensional accuracy in multi-pass, multi-layer welding of thick-section components, this study employs MSC. Marc to develop a finite element model of a 15 mm thick butt-welded joint fabricated from 316LN stainless steel. Three distinct heat source models—instantaneous, enhanced moving, and moving element-set—are systematically implemented to simulate transient temperature fields, residual stress distributions, and welding deformation. All numerical predictions are rigorously validated against experimental measurements to comprehensively assess both accuracy and computational efficiency. Results indicate that: (i) the predicted molten pool geometries and characteristic thermal cycle profiles from all three models exhibit strong agreement with experimental observations; (ii) longitudinal residual stress distributions predicted by all models align closely with measured values; (iii) transverse residual stresses predicted by the moving element-set and enhanced moving heat sources agree well with experiments, whereas those from the instantaneous heat source show marked deviation; (iv) angular distortion predictions from the moving element-set heat source achieve over 90% conformity with experimental data, while the instantaneous heat source substantially underestimates angular distortion, and the enhanced moving heat source yields approximately 65% agreement; and (v) in terms of computational efficiency, the instantaneous heat source requires only ~40% of the computation time needed by the moving heat source. Full article