ZnO nanorods (NRs) films, nitrogen-doped (ZnO:N), and ZnO doped with nitrogen and decorated with silver nanostructures (ZnO:N-Ag) NRs films were vertically supported on undoped and N doped ZnO seed layers by a wet chemical method. The obtained films were characterized structurally by X-ray diffraction. Morphological and elemental analysis was performed by scanning electron microscopy, including an energy dispersive X-ray spectroscopy facility and their optical properties by Ultraviolet-Visible Spectroscopy. Analysis performed in the NRs films showed that the nitrogen content in the seed layer strongly affected their structure and morphology. The mean diameter of ZnO NRs ranged from 70 to 190 nm. As the nitrogen content in the seed layer increased, the mean diameter of ZnO:N NRs increased from 132 to 250 nm and the diameter dispersion decreased. This diameter increase occurs simultaneously with the incorporation of nitrogen into the ZnO crystal lattice and the increase in the volume of the unit cell, calculated using the X-ray diffraction patterns and confirmed by a slight shift in the XRD angle. The diffractograms indicated that the NRs have a hexagonal wurtzite structure, with preferential growth direction along the c axis. The SEM images confirmed the presence of metallic silver in the form of nanoparticles dispersed on the NRs films. Finally, the degradation of methyl orange (MO) in an aqueous solution was studied by UV-vis irradiation of NRs films contained in the bulk of aqueous MO solutions. We found a significant enhancement of the photocatalytic degradation efficiency, with ZnO:N-Ag NRs film being more efficient than ZnO:N NRs film, and the latter better than the ZnO NRs film.

This paper presents a new algorithm to solve the optimal reconfiguration problem in distribution networks, using the algorithm called Improved Simulated Annealing combined with Hybrid Cooling (ISA-HC) and Selective Space Search, which leverages the capabilities of the Open Distribution System Simulator (OpenDSS) software and the selective space search concept to enhance performance and reduce the search space. The ISA-HC algorithm determines an adequate starting point for the temperature and initial solution according to the size of the system. For adequate cooling, a three-stage cooling approach was employed to achieve effective cooling, combining two methods widely used in the literature. Overall, the ISA-HC algorithm is a promising method for solving the optimal reconfiguration problem in distribution networks. The algorithm was tested on the systems of 5, 33, 69, and 94 buses and compared to other existing methods in the literature. The results show that the proposed method is more robust and efficient, providing better convergence and reliably achieving good quality global solutions.

This paper proposes a hybrid machine learning framework for detecting electricity fraud within the broader context of Non-Technical Losses (NTLs) in power-distribution systems. The framework combines unsupervised anomaly detection using Isolation Forest with supervised classification through XGBoost, exploiting the complementary strengths of both algorithms. Using real consumption data from a Peruvian utility, the approach integrates domain-informed feature engineering to capture behavioral, temporal, and contextual indicators of irregular usage. To address the extreme class imbalance inherent to fraud datasets, the SMOTETomek hybrid resampling technique was applied, enhancing minority-class representation and decision boundary clarity. Experimental results achieved high predictive performance on the test set (AUC-ROC = 0.999, F1-score = 0.77) using an optimized decision threshold of 0.6. Moreover, SHAP-based interpretability analysis identified extreme monthly variations, prolonged low-consumption periods, and tariff category as key behavioral predictors of fraudulent activity. The robustness of the proposed framework was further validated through a 5-fold cross-validation procedure during the training phase, ensuring consistent performance across different data partitions. Overall, the proposed framework demonstrates not only robust and explainable performance but also practical operational value, providing utilities with a scalable data-driven tool to optimize inspection strategies and maximize recovery of non-technical losses.

This paper presents a new algorithm to solve the optimal reconfiguration problem in distribution networks, using the algorithm called Improved Simulated Annealing combined with Hybrid Cooling (ISA-HC) and Selective Space Search, which leverages the capabilities of the Open Distribution System Simulator (OpenDSS) software and the selective space search concept to enhance performance and reduce the search space. The ISA-HC algorithm determines an adequate starting point for the temperature and initial solution according to the size of the system. For adequate cooling, a three-stage cooling approach was employed to achieve effective cooling, combining two methods widely used in the literature. Overall, the ISA-HC algorithm is a promising method for solving the optimal reconfiguration problem in distribution networks. The algorithm was tested on the systems of 5, 33, 69, and 94 buses and compared to other existing methods in the literature. The results show that the proposed method is more robust and efficient, providing better convergence and reliably achieving good-quality global solutions