This study establishes a broadband coupling model of "gas-insulated substation (GIS)-grounding grid-soil" using Current Distribution, Electromagnetic Fields, Grounding and Soil Structure Analysis (CDEGS) software to mitigate the risks associated with transient ground potential rise (TGPR) during GIS switching operations, thereby protecting equipment insulation and ensuring personnel safety through simulation calculations. Simulations were predominantly performed considering parameters such as soil resistivity, switch position, metal enclosure height above ground, metal bracket cross-section, non-uniform grounding grid configuration, and vertical grounding electrodes. Subsequently, a quantitative analysis was conducted on the distribution characteristics of transient ground potential rise under different conditions. Research findings suggest that TGPR caused by GIS switching operations is predominantly localized around bushing grounding locations. It increases considerably with rising soil resistivity and greater metal enclosure-to-ground distances, although the rate of increase diminishes in later phases. At the same time, expanding the cross-sectional area of metal brackets effectively diminishes peak values and enhances the distribution of potential. Furthermore, employing appropriately spaced grounding grid layouts serves as an effective supplementary measure to suppress TGPR. These findings offer important guidance for the design of GIS grounding systems in practical engineering applications.
Outsourcing encrypted data to the cloud demands efficient and confidentiality-preserving search techniques, yet existing Searchable Symmetric Encryption (SSE) schemes suffer from severe access and search pattern leakage, and the problem is more intractable in multi-data-owner scenarios where cross-dataset privacy-preserving search remains unsolved. This paper proposes PSSE-MDO (Privacy-Preserving SSE with Pattern Obfuscation for Multiple Data Owners), the first SSE scheme that achieves robust access and search pattern hiding for multi-data-owner settings with constant client-side overhead. Our core innovation is a cryptographic protocol enabling users to issue a single constant-sized search token (trapdoor) for searching across all data owners' encrypted indexes. The scheme integrates Inner-Product Predicate Encryption (IPE) and Somewhat Homomorphic Encryption (SHE) to probabilistically obfuscate search results for each query. A key experimental result shows that the client-side cost approximately 120 ms for search token generation—remains constant regardless of the number of data owners, in contrast to existing pattern-hiding schemes with linearly scaling costs. This validates PSSE-MDO's practicality for real-world large-scale collaborative cloud systems, marking a significant step toward practical privacy-preserving search in federated cloud environments.
paper presents an adaptive & micro;quasilogarithmic gradient quantization framework aimed at reducing memory and computational demands during deep neural network training. The approach employs a companding function with a dynamically adjusted & micro; parameter that adapts to the statistical properties of gradients. Two quantization strategies are developed: a switching & micro;-quantizer that toggles between low-bit uniform and high-bit quasilogarithmic modes based on gradient variance, and a hybrid & micro;-quantizer that statically applies uniform quantization to small gradients and quasilogarithmic to larger ones. Experiments on multiple-layer perceptron (MLP) and convolution neural network (CNN) models trained on CIFAR-10 show that both quantizers retain classification accuracy close to full-precision (FP32) baselines while significantly reducing gradient reconstruction error (RMSE). The hybrid variant consistently achieves better validation accuracy, lower RMSE, and faster convergence than the switching scheme. These results highlight the potential of hybrid quasilogarithmic quantization as an efficient and scalable solution for training deep models in memory or bandwidth constrained environments.
This paper proposes an integrated thermal management framework for battery electric vehicle (BEV) propulsion systems, combining five coordinated strategies into a unified control architecture. The approach includes thermal Model Predictive Control (TMPC), residual heat recovery from the motor and inverter, variable-speed actuation of the pump-fan-chiller, a "battery-first" thermal distribution logic, and HVAC-assisted thermal preconditioning. A coupled electro-thermal RC network model is developed forAa liquid-cooled PMSM traction motor, automotive inverter, NCM battery pack, and cooling circuit, with thermal limits and derating laws aligned to OEM practice. Dynamic simulations on the WLTP Class 3b cycle are performed for four ambient temperatures (-10, 0, 25, 40 degrees C), capturing transient temperatures, temperature-dependent losses, heat flows, and auxiliary energy demand. Results show that all components remain well below their thermal limits, with no derating activation. The analysis demonstrates that range degradation in non-nominal climates is driven primarily by HVAC energy demand and battery charge-acceptance limitations, while propulsion losses remain nearly insensitive to ambient temperature. The proposed framework highlights the central role of intelligent, integrated thermal management in maximizing efficiency, safety, and real-world BEV range.
the era of multi-cloud environments, the challenge for end users or organizations is to select the most appropriate cloud service provider (CSP) due to the complexity and variability of service-level offerings by various CSPs. In this paper, we propose a MAGIQ-TOPSIS-Fuzzy-logic based Cloud Service Provider Ranking (MTF-CSPR) model, which provides aAmore robust and intelligent decision-making system in CSP selection. The proposed model addresses both qualitative and quantitative evaluation criteria by capturing and processing the inherent vagueness of linguistic assessments through fuzzy logic. MAGIQ is employed to derive optimal weights for evaluation criteria that reflect their relative importance towards the alternatives. TOPSIS is applied to rank CSPs where the ideal and anti-ideal solutions are derived, and then based on CSP's proximity to the ideal solution, the ranks are derived. The proposed model ensures accuracy, scalability, and context-awareness for the CSP ranking by integrating subjective expert opinions with objective performance metrics. Experimental evaluation demonstrates that the proposed MTF-CSPR model provides more consistent and accurate rankings compared to conventional standalone approaches, as well as the other ranking models under consideration, providing better decision-support for enterprises to adopt the hybrid or multi-cloud strategies.
