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This paper presents an original probabilistic photovoltaic (PV) power forecasting model for the day-ahead hourly generation in a PV plant. The probabilistic forecasting model is based on 12 deterministic models developed with different techniques. An optimization process, ruled by a genetic algorithm, chooses the forecasts of the deterministic models in order to achieve the probability distribution function (PDF) for the PV generation in each one of the daylight hours of the following day in a parametric approach. The PDFs, which constitute the probabilistic forecasts, are a mixture of normal distributions, each one centred in the forecasts of the selected deterministic models. The genetic algorithm chooses the deterministic forecasts, the variance of the normal distributions and their weights in the mixture. In a case study the proposed model achieves better forecasting results than the obtained with the conditional quantile regression method applied to the same data used to develop the deterministic forecasting models.

The integration of renewable energies, specifically solar energy, in electric distribution systems is increasingly common. For an optimal operation, it is very important to forecast the final net demand of the power distribution network, considering the variability of solar energy combined with the variability of the electric energy consumption habits of population. This paper presents the methodology followed to forecast the net demand in a power distribution substation. Two approaches are considered, the net demand direct prediction, and the indirect prediction with the forecasts of PV power generation and load demand. Artificial Neural Network (ANN) based models and autoregressive models with exogenous variables (ARX) are used to predict the net demand, directly and indirectly, for the 24 hours of the day-ahead. The methodology is applied to a medium voltage distribution substation and the direct and indirect forecasts are compared.

Mutant p53 proteins result from missense mutations in the TP53 gene, the most mutated in human cancer, and have been described to contribute to cancer initiation and progression. Therapeutic strategies for targeting mutant p53 proteins in cancer cells are limited and have proved unsuitable for clinical application due to problems related to drug delivery and toxicity to healthy tissues. Therefore, the discovery of efficient and safe therapeutic strategies that specifically target mutant p53 remains challenging. In this study, we generated gold nanoparticles (AuNPs) chemically modified with low molecular branched polyethylenimine (bPEI) for the efficient delivery of gapmers targeting p53 mutant protein. The AuNPs formulation consists of a combination of polymeric mixed layer of polyethylene glycol (PEG) and PEI, and layer-by-layer assembly of bPEI through a sensitive linker. These nanoparticles can bind oligonucleotides through electrostatic interactions and release them in the presence of a reducing agent as glutathione. The nanostructures generated here provide a non-toxic and powerful system for the delivery of gapmers in cancer cells, which significantly downregulated mutant p53 proteins and altered molecular markers related to cell growth and apoptosis, thus overcoming chemoresistance to gemcitabine.

An imine-based covalent organic framework (COF) decorated in its cavities with uracil groups has shown selective recognition towards adenine in water. These results show how the confinement of the base-pair inside the COF's pores allows a remarkable selective recognition in aqueous media.

Herein, we present a new collaborative clinical simulation (CCS) model for the development of medical competencies by medical students. The model is a comprehensive compendium of published considerations and recommendations on clinical simulation (CS) and computer-supported collaborative learning (CSCL). Currently, there are no educational models combining CS and CSCL. The CCS model was designed for the acquisition and assessment of clinical competencies; working collaboratively and supported by technology, small groups of medical students independently design and perform simulated cases. The model includes four phases in which the learning objectives, short case scenarios, materials, indices, and the clinical simulation are designed, monitored, rated and debriefed.

Incidence of pulmonary congenital malformations is studied among 46,966 babies born in a twelve year period, with special reference to 27 cases of pulmonary hypoplasia. 59.25% of these newborns were males and only 51.89% of them were born at term with normal weight. 74.0% of mortality took place in the first day of life. Hypoplasia was bilateral in 59.25% of cases and associated to other malformations in 96.29%.