With the rapid development of new power systems, medium- and low-voltage distribution networks have become increasingly complex. This leads to low accuracy and poor generalization in traditional localization methods, which cannot meet the requirements for rapid self-healing and safe operation in intelligent distribution networks. To overcome the drawbacks of existing fault localization techniques—such as limited fault data, inadequate extraction of temporal information, multi-source data heterogeneity, and the need for lightweight models—a novel collaborative fault localization approach based on Wasserstein Generative Adversarial Network with Gradient Penalty and edge federated learning is proposed. The improved Wasserstein Generative Adversarial Network is used to generate high-quality fault samples and extract temporal features. Adaptive weighting and a region-weighted module are introduced into the Federated Averaging algorithm to improve global aggregation accuracy. This study utilizes the IEEE 123 simulation dataset and the on-site measured dataset as verification data, which consist of over 10,000 simulation electrical samples and 326 real single-phase grounding fault samples, respectively. According to the results, the Fréchet Inception Distance is 12.36 for fault sample generation, while the Peak Signal-to-Noise Ratio reaches 27.94 dB. The proposed method achieves a localization accuracy of 94.85%. In cases of high resistance, arc faults, load variation, and noise, the localization accuracies reach 82.70%, 84.06%, 89.37%, and 82.25%, respectively. The proposed model also performs better than existing methods in terms of parameter size, inference time, and bandwidth consumption. The proposed model outperforms existing methods in terms of fault sample generation, localization accuracy, anti-interference capability, and lightweight performance. Moreover, it does not require the uploading of raw measurement data, thereby balancing data privacy protection with the deployment capability of edge terminals. It is highly suitable for complex on-site operating conditions and provides technical support for rapid and accurate fault localization, on-site troubleshooting, and self-healing in medium- and low-voltage distribution networks.
The preservation of the perceptual quality of host images is the aim of Steganography techniques by concealing the information within digital media. Designing embedding strategies that maintain high visual fidelity while ensuring reliable payload recovery remains a key challenge in practical data hiding systems. This paper presents a transform-domain steganography framework that integrates discrete wavelet decomposition, block-based frequency analysis, and quantisation-driven embedding. The proposed approach embeds payload information in singular values extracted from the mid-band region of DCT blocks obtained from the LH subband of the wavelet domain. The embedding process is guided by a quantization index modulation scheme, while redundancy mechanisms and multi-carrier embedding are employed to improve extraction stability. The evaluation is performed on the BOSSBase image dataset, which provides a diverse collection of natural images for steganographic experiments. Experimental results achieve the high PSNR values ranging from 50 to 56 dB, structural similarity values 0.998533 while embedding indicates better imperceptibility of stego images. The robustness of the framework was further examined under JPEG compression. Under no attack conditions, the method achieved a BER of zero and maintained moderate extraction reliability in compression-induced perturbations in the coefficients. An ablation analysis was conducted to study the influence of the quantization step, repetition factor, and carrier selection on system performance. The observed trends confirm that the proposed embedding strategy provides a balanced compromise between visual imperceptibility and extraction robustness. The results demonstrate that the TTKS framework offers an effective solution for high-fidelity image steganography in transform-domain environments.
The analysis of Herschel Bulkley Papanastasiou flow over a stretching surface is analyzed in this research work. This fluid model makes the jump condition in yield stress continuous by mathematical means and hence convergence in numerical computation becomes easy. This fluid model changes velocity, heat and mass transfer as well as their relevant parameters such as friction factor, Nusselt number, Sherwood number and couple stress. This research is significant for energy generation, manufacturing processes and biological transports. The governing (nonlinear partial differential) equations which are formulated from momentum, micro-rotation, energy and diffusion of mass equations are transformed to ordinary differential equations using similarity transformations. The governing (nonlinear partial differential) equations, which are momentum, micro-rotation, energy, and mass diffusion equations, have been simplified through similar transformations. The implicit finite difference method (FDM) has been utilized for the transformation of ODEs. The calculation and analysis of the steady laminar boundary layer flow of regularized Herschel-Bulkley micropolar fluids over a stretching sheet in the case of viscous dissipation and chemical reaction have been done. The validation of the current solution is carried out by comparing it with the MATLAB bvp5c code with some changes. Thickness of the boundary layer in the axial velocity not necessarily smaller than temperature or concentration depending on the values of the field variables. Influence of the flow, heat, and concentration parameters and several slip boundary conditions on the dimensionless velocities, temperature and concentration as well as on the physical quantities such as friction factor, Nusselt number, Sherwood number and couple stress are shown in Tables and graphs. Comparisons have been made between the present and available, exact, analytical and numerical works. Agreement is very good between the available, exact, analytical and numerical works.
Diphtheria remains a vaccine-preventable disease of public-health importance, and recurrent outbreaks continue to be reported in settings where vaccination coverage and access to health care are limited. This study develops a deterministic compartmental model of diphtheria transmission that incorporates post-exposure acquired immunity. The treatment of post-exposure immunity is the distinguishing feature of the model. Exposed individuals who clear the pathogen subclinically without active infection acquire immunity, which confers lasting protection, at a constant rate. This differs from earlier diphtheria models, in which individuals with strong immunity remain at risk of reinfection. Our treatment of post-exposure immunity represents the acquisition of protective immunity following subclinical exposure. The elasticity of the reproduction number with respect to post-exposure immunity is smaller than that of the other controls, but the mechanism markedly reduces the exposed population, and its effect on the exposed class is larger than its elasticity would suggest, because it acts directly on that class rather than through the threshold quantity. The results indicate that isolation and primary vaccination are the dominant controls and act through complementary routes, and that post-exposure immunity, although of secondary importance for the reproduction number, has a substantial effect on the exposed population and on the progression from exposure to active disease. These findings support the development of post-exposure vaccination as well as an integrated strategy combining sustained vaccination and booster programmes with prompt detection and isolation of infectious cases.
A high-precision, non-contact measurement method for ruby balls is proposed by integrating annular dark-field imaging with multi-scale adaptive edge detection. Multi-scale gradient-response fusion and entropy-guided local thresholding are used to improve outer-contour extraction under non-uniform contrast and noise. Cubic-spline interpolation provides sub-pixel edge localization, while RANSAC screening and geometric-distance circle fitting reduce the influence of outliers. For a 5 mm ruby-ball sample, the method achieved a mean absolute error of 1.6 μm and a standard deviation of 0.7 μm. At an SNR of 20 dB, the measurement error remained at 2.4 μm, compared with 6.5 μm for the Hessian-matrix method and 11.8 μm for the conventional pixel-level regionprops method. Experiments on ruby, ceramic, and cemented-carbide balls with diameters of 3–12 mm further demonstrated stable performance across different materials, sizes, and imaging conditions. The proposed framework therefore provides a robust approach for the non-contact dimensional inspection of precision spherical components.
