Short-term forecasting of electric energy consumption has become a critical issue for companies selling and buying electricity because of the fluctuating and rising trend of its price. Forecasting tools based on Artificial Intelligence have proved to provide accurate and reliable prediction, especially Neural Networks, which have been widely used and have become one of the preferred ones. In this work, two of them, Long Short-Term Memories and Gated Recurrent Units, have been used along with a preprocessing algorithm, the Empirical Mode Decomposition, to make up a hybrid model to predict the following 24 hourly consumptions (a whole day ahead) of a hospital. Two different datasets have been used to forecast them: a univariate one in which only consumptions are used and a multivariate one in which other three variables (reactive consumption, temperature, and humidity) have been also used. The results achieved show that the best performances were obtained with the multivariate dataset. In this scenario, the hybrid models (neural network with preprocessing) clearly outperformed the simple ones (only the neural network). Both neural models provided similar performances in all cases. The best results (Mean Absolute Percentage Error: 3.51% and Root Mean Square Error: 55.06) were obtained with the Long Short-Term Memory with preprocessing with the multivariate dataset.

European Union Allowances (EUAs) are rights to emit CO₂ that may be sold or bought by enterprises. They were originally created to try to reduce greenhouse gas emissions, although they have become assets that may be used by financial intermediaries to seek for new business opportunities. Therefore, forecasting the time evolution of their price is very important for agents involved in their selling or buying. Neural Networks, an artificial intelligence paradigm, have been proved to be accurate and reliable tools for time series forecasting, and have been widely used to predict economic and energetic variables; two of them are used in this work, the Multilayer Preceptron (MLP) and the Long Short-Term Memories (LSTM), along with another artificial intelligence algorithm (XGBoost). They are combined with two preprocessing tools, decomposition of the time series into its trend and fluctuation and decomposition into Intrinsic Mode Functions (IMF) by the Empirical Mode Decomposition (EMD). The price prediction is obtained by adding those from each subseries. These two tools are combined with the three forecasting tools to provide 20 future predictions of EUA prices. The best results are provided by MLP-EMD, which is able to achieve a Mean Absolute Percentage Error (MAPE) of 2.91% for the first predicted datum and 5.65% for the twentieth, with a mean value of 4.44%.

We posit that the adsorption kinetics of surfactants critically influence cavity dynamics during the rapid formation of interfaces on submillisecond timescales. We propose that standard surfactants such as sodium dodecylsulfate (SDS), while significantly reducing surface tension at equilibrium, do not significantly alter cavity dynamics. In contrast, ultrafast surfactants such as Surfynol, with adsorption rates that match or exceed the rate of new interface formation, would allow for the formation of deeper cavities that last longer. We conducted experiments using a submillimeter jet impacting a liquid pool much larger than the jet dimensions, containing surfactant that create transient cavities which retract and close within milliseconds. Two surfactants with distinct adsorption kinetics were compared: sodium dodecylsulfate (SDS) and Surfynol 465. The induced dynamics, retraction and closure of the cavity, due to their characteristic submillisecond times, have been captured using ultrafast photography. Our experiments reveal the difference between the effects of sodium dodecylsulfate (SDS), a surfactant with moderately fast adsorption kinetics, and Surfynol 465, a surfactant with ultrafast adsorption kinetics. For SDS, the collapse pathway is nearly indistinguishable from that of pure water, suggesting negligible dynamic surface tension reduction. In contrast, Surfynol allows the emergence of deeper cavities that persist longer in the liquid pool. To quantify these effects, we applied a harmonic oscillator model that describes cavity retraction in the deep seal regime. The model fits the experimental data well, and the extracted damping values correlate with the expected dynamic surface tensions.

We propose using a dielectric beveled nozzle for electrospray and electrohydrodynamic jet printing. This nozzle stabilizes the liquid ejection of low-conductivity liquids, considerably reducing the minimum flow rate below which the flow becomes unstable. This translates into a significant reduction of the minimum jet diameter. Due to its dielectric character, electrochemical reactions occurring in metallic beveled nozzles (e.g., hypodermic needles) do not occur, preserving the purity of the liquid. This property makes this nozzle appropriate for Electrospray Ionization Mass Spectrometry or bioplotting. We illustrate the capabilities of this new technique by conducting (i) electrospray experiments with Newtonian liquids and (ii) electrohydrodynamic jet printing experiments with viscoelastic fluids. Jets with diameters around 1 μm are produced with low-conductivity liquids such as octanol and glycerin. Viscoelastic threads a few micrometers in diameter are gently deposited on a moving substrate to print out uniform lines tens of nanometers in height. Due to the strong stabilizing effect of the beveled nozzle, the minimum flow rate and jet diameter were much smaller than the respective values obtained with the cylindrical capillary in the electrospray and electrohydrodynamic jet printing experiments. The proposed technique opens new routes for electrospray and electrohydrodynamic jet printing.