With the progressive aging of the population, bone fractures are an increasing major health concern. Diverse strategies are being studied to reduce the recovery times using nonaggressive treatments. Electrical stimulation (either endogenous or externally applied electric pulses) has been found to be effective in accelerating bone cell proliferation and differentiation. However, the direct insertion of electrodes into tissues can cause undesirable inflammation or infection reactions. As an alternative, magnetoelectric heterostructures (wherein magnetic fields are applied to induce electric polarization) could be used to achieve electric stimulation without the need for implanted electrodes. Here, we develop a magnetoelectric platform based on flexible kapton/FeGa/P-(VDF-TrFE) (flexible substrate/magnetostrictive layer/ferroelectric layer) heterostructures for remote magnetic-field-induced electric field stimulation of human osteoblast cells. We show that the use of flexible supports overcomes the clamping effects that typically occur when analogous magnetoelectric structures are grown onto rigid substrates (which preclude strain transfer from the magnetostrictive to the ferroelectric layers). The study of the diverse proliferation and differentiation markers evidence that in all the stages of bone formation (cell proliferation, extracellular matrix maturation, and mineralization), the electrical stimulation of the cells results in a remarkably better performance. The results pave the way for novel strategies for remote cell stimulation based on flexible platforms not only in bone regeneration but also in many other applications where electrical cell stimulation may be beneficial (e.g., neurological diseases or skin regeneration).

This research presents a novel methodology based on data analysis for improving voltage stability in transmission systems. The proposal aims to determine a single distributed generator’s optimal location and sizing using the Fast Voltage Stability Index (FVSI) as the primary metric under $N-1$ contingency conditions. The developed strategy systematically identifies the most critical transmission lines close to instability through a frequency analysis of the FVSI in the base case and across multiple contingency scenarios. Subsequently, the weak buses associated with the most critical line are determined, on which critical load increases are simulated. The Distributed Generator (DG) sizing and location parameters are then optimized through a statistical analysis of the inflection point and the rate of change of the FVSI statistical parameters. The methodology is validated in three case studies: IEEE systems with 14, 30, and 118 buses, demonstrating its scalability and effectiveness. The results show significant reductions in FVSI values and notable improvements in voltage profiles under stress and contingency conditions. For example, in the 30-bus IEEE system, the average FVSI for all contingency scenarios was reduced by 26% after applying the optimal solution. At the same time, the voltage profiles even exceeded those of the base case. This strategy represents a significant contribution, as it is capable of improving the stability of the electrical power system in all $N-1$ contingency scenarios with overload at critical nodes. Using a single DG as a low-cost and highly effective corrective measure, the proposed approach outperforms conventional solutions through statistical analysis and a data-centric approach.

Voltage stability assessment under transmission contingencies is traditionally performed using severity-based indices evaluated on isolated outage scenarios. While effective for identifying extreme events, such approaches provide limited insight into which transmission corridors structurally govern voltage-stress behavior across the full contingency space. This paper introduces a persistence-based diagnostic framework for voltage stability assessment under exhaustive $N-1$ line contingencies, using the Fast Voltage Stability Index (FVSI) as a base indicator. Rather than ranking lines by instantaneous severity, the proposed methodology identifies dominant transmission lines—defined as those attaining the maximum FVSI in each convergent contingency—and aggregates these outcomes statistically to quantify dominance persistence, conditional severity, and dispersion. A dominance concentration metric ($k_{90}$) is introduced to measure how many transmission corridors are sufficient to explain the majority of dominant voltage-stress events. The framework is applied to IEEE 14, 30, and 118-bus benchmark systems under exhaustive $N-1$ enumeration. Results reveal a clear phenomenon of dominance collapse: as system size increases, dominant voltage-stress outcomes concentrate onto an extremely small set of transmission corridors. While IEEE 14 exhibits partial dominance dispersion ($k_{90}=2$), both IEEE 30 and IEEE 118 demonstrate near-total dominance collapse ($k_{90}=1$), where a single corridor governs more than 90% of dominant FVSI events. The proposed approach is fully deterministic, scalable, and independent of control or optimization assumptions, making it well-suited for planning-stage screening, monitoring prioritization, and pre-filtering of large-scale contingency studies. By shifting voltage stability analysis from severity-only screening to persistence-based structural diagnosis, this work provides new insight into vulnerability concentration in modern transmission networks.