The development and constant improvement of accurate predictive models of electricity generation from photovoltaic systems provide valuable planning tools for designers, producers, and self-consumers. In this research, an adaptive neuro-fuzzy inference model (ANFIS) was developed, which is an intelligent hybrid model that integrates the ability to learn by itself provided by neural networks and the function of language expression, how fuzzy logic infers, and an ANFIS model optimized by the particle swarm algorithm, both with a predictive capacity of about eight months. The models were developed using the Matlab^® software and trained with four input variables (solar radiation, module temperature, ambient temperature, and wind speed) and the electrical power generated from a photovoltaic (PV) system as the output variable. The models’ predictions were compared with the experimental data of the system and evaluated with rigorous statistical metrics, obtaining results of RMSE = 1.79 kW, RMSPE = 3.075, MAE = 0.864 kW, and MAPE = 1.47% for ANFIS, and RMSE = 0.754 kW, RMSPE = 1.29, MAE = 0.325 kW, and MAPE = 0.556% for ANFIS-PSO, respectively. The evaluations indicate that both models have good predictive capacity. However, the PSO integration into the hybrid model allows for improving the predictive capability of the behavior of the photovoltaic system, which provides a better planning tool.

In this manuscript, distinct approaches were used in order to obtain the best electrical power estimation from photovoltaic systems located at different selected places in Mexico. Multiple Linear Regression (MLR) and Gradient Descent Optimization (GDO) were applied as statistical methods and they were compared against an Adaptive Neuro-Fuzzy Inference System (ANFIS) as an intelligent technique. The data gathered involved solar radiation, outside temperature, wind speed, daylight hour and photovoltaic power; collected from on-site real-time measurements at Mexico City and Hermosillo City, Sonora State. According to our results, all three methods achieved satisfactory performances, since low values were obtained for the convergence error. The GDO improved the MLR results, minimizing the overall error percentage value from 7.2% to 6.9% for Sonora and from 2.0% to 1.9% for Mexico City; nonetheless, ANFIS overcomes both statistical methods, achieving a 5.8% error percentage value for Sonora and 1.6% for Mexico City. The results demonstrated an improvement by applying intelligent systems against statistical techniques achieving a lesser mean average error.

We report the fabrication of a stable Si/SiO₂ core-shell network using hot-wire chemical vapor deposition on a silicon substrate at a relatively low substrate temperature of 200 °C. Structural investigations using transmission electron microscopy and X-ray diffraction confirm the presence of nanocrystalline silicon and silicon dioxide quantum dots in the form of a core-shell network embedded in the amorphous SiO_x matrix, while selected area electron diffraction confirms the formation of a core-shell structure. The core-shell structure exhibits a bright white emission that can be seen with the unaided eye at room temperature without any post-annealing treatments, and the observed photoemission does not alter in color or intensity after prolonged laser exposure. Additional measurements are performed while varying the laser power and optical gain is found in the as-deposited material. Intense stable white luminescence is observed and shows the prospective for various optical and biological applications in the future.

Bis(ortho-substituted aryl)amines were arylated on the nitrogen atom with various haloarenes in high yields using the palladium catalyst, which was generated from palladium(II) acetate and tri(tert-butyl)phosphine.