This paper proposes an adaptive Overcurrent (OC) relay protection scheme that combines load forecasting and clustering-based operating state identification to dynamically adjust relay settings. A Long Short-Term Memory (LSTM) model predicts feeder current, allowing the system to proactively select appropriate relay setting groups for different load conditions. The method is validated on the IEEE 33-bus distribution network. Results show that the forecasting model achieves an Mean Absolute Percentage Error (MAPE) of 2.94 %, while the relay settings derived from predicted loads closely match those obtained from actual data. The difference in operating time is negligible between forecast-based and actual coordination and a safety protection mechanism is employed to deal with the forecast or classification errors by switching to a safe backup protection group. These results demonstrate the effectiveness of the proposed approach in maintaining reliable protection performance under changing operating conditions through SCADA, and are activated using IEC 61,850 protocols and GOOSE-based group switching mechanisms. The time-domain analysis shows a worst-case timing deviation below 41 ms, well within the 300 ms Coordination Time Interval (CTI) requirement.
In order to coordinate EVs along with renewable energy, it is necessary to have accurate forecasting, adaptive control, and a secure energy exchange. The hybrid framework that integrates Transformer forecasting with multi-agent reinforcement learning (MARL) suggested in this paper appears to highly suitable for the intended application. In fact, the Transformer encoder is the one responsible for the accurate predictions of EV load and renewable generation, while MARL policies give the required decentralization and dynamic coordination. Blockchain acts as a safety net for the transactions and a sign of trustworthiness for the prosumers, with IoT-level compression playing the role of latency eliminator in densely packed EV networks. Testing results show that predictive reliability is 96.9%, balancing is 32.7%, efficiency is 26.4%, and latency is 24 ms. Based on the comparison with ML baselines, the framework is 6.8% more accurate, 7.5% more balancing is achieved, 5.9% of the cost optimization is improved, and therefore, the EV–renewable integration is not only scalable but also resilient.
This study introduces an AI-driven integrated framework for predicting and optimizing the performance of turbo air classifiers, addressing the limited application of advanced intelligence techniques in fine-particle processing. A turbo air classifier was examined using three operational inputs, rotor speed (561–1739 rpm), primary air flow (98.87–351.13 m3/h), and secondary air flow (6–74 m3/h), to predict two key performance indicators: cut size (CS) and classification accuracy index (CAI). Multilayer perceptron neural networks (MLPNNs) were optimized using modified particle swarm optimization (MPSO), marine predators algorithm (MPA), and gray wolf optimizer (GWO). MPSO-MLPNN yielded the best CS predictions (R > 0.999), while GWO-MLPNN achieved the most accurate CAI predictions (R > 0.99). Pareto-based multi-objective bat algorithm (MOBA) was then applied to minimize CAI while constraining CS within 15–18 μm and 18–21 μm. The Pareto results revealed a clear trade-off: CAI decreased from ∼2.30 to ∼1.65 as CS increased slightly in the fine separation regime and stabilized at ∼1.58–1.60 for coarser separation. Optimal conditions showed that fine separation requires high rotor speed with moderate–high airflow, whereas coarser, energy-efficient operation is achievable with lower rotor speeds and high airflow.
Reactive oxygen and nitrogen species (RONS) constitute a unifying molecular axis across various cancer therapy modalities and are primary regulators of regulated cell death (RCD). Generally, cancer cells function under high oxidative stress to maintain proliferation, making them vulnerable to therapeutic approaches that push RONS levels above their survival threshold. The purpose of this review is to consolidate mechanistic evidence linking redox modulation to therapeutic efficacy. We analyzed current literature regarding standard and emerging anticancer modalities, including radiotherapy, proton therapy, FLASH therapy, chemotherapy, cold atmospheric plasma, photodynamic therapy, and engineered nanoplatforms. We specifically examined the molecular mechanisms by which these therapies induce mitochondrial ROS accumulation and trigger distinct cell death pathways. Our literature review indicates that these diverse modalities achieve tumor selectivity by increasing mitochondrial ROS beyond cytotoxic limits. When combined strategically, they further promote tumor-specific oxidative stress, maximizing therapeutic efficacy while minimizing damage to healthy tissues. We also highlight the critical biosafety considerations and regulatory frameworks necessary for the safe clinical translation of these RONS-based treatments. Redox-modulating strategies can address critical challenges, including chemoradiation resistance, metabolic rewiring, and the persistence of cancer stem cells. We propose that RONS-centered therapeutic design represents a viable strategy to improve the efficacy of contemporary cancer treatments by combining redox biology with cutting-edge therapeutic engineering. This graphical abstract depicts how various cancer treatment modalities cause RONS-mediated oxidative stress, hence activating different cell death pathways in cancer cells.
Bimetallic nanoparticles (BMNPs) composed of coinage metals are receiving substantial attention due to their high stability, versatility, and biocompatibility compared with single-metal nanoparticles. Among these, silver-copper (Ag-Cu) BMNPs stand out as a particularly promising class of materials. The combination of silver and copper produces synergism, enabling them to function as an efficient material for several applications at a relatively low cost compared with other coinage metals such as gold and platinum. Considering the emergence of Ag-Cu BMNPs in material science, this review provides an overview of the synthesis methods, including physical, chemical, and biological methods, outlining their advantages, limitations, and practical considerations. The wide range of their potential applications is also examined, encompassing catalysis, medicine, agriculture, biosensing, electronics and optical technologies, and surface-enhanced Raman spectroscopy. Special emphasis is given on how factors such as atomic arrangement, mixing behavior, particle size, shape, and surface characteristics influence their performance. Overall, Ag-Cu BMNPs emerge as cost-effective, adaptable, and high-performance nanomaterials with considerable promise for addressing contemporary technological and environmental challenges.
