Development of a dynamic pressure standard of low amplitudes and frequencies.

The height of the gravitational wave is an influential magnitude in the estimation of its energy content, a very important parameter in the design of maritime structures such as piers and breakwaters, however, there are reasonable doubts among researchers regarding the quality of its measurement. The objective of the present work was to demonstrate that a stationary calibrated pressure transmitter allows the indirect calculation of the wave height with satisfactory accuracy despite the fact that it is a dynamic event as long as its frequency range is low, as is the case with the gravitational waves of the Costa Rican Central Pacific Ocean: between 0.05 Hz and 0.39 Hz. In the absence of a primary pattern of periodic pressure disturbances, an alternate path was developed based on the characterization of the parameters of the differential equation characteristic of a pressure measurement system in a shock tube prototype from normal shock wave theory and subsequent verification in a periodic disturbance generator that its attenuation and delay are practically negligible at the frequency of 0,1 Hz. The effect of the different geometry of the chamber of the pressure measurement system used in the sea was evaluated pneumatically by comparison with the dynamic pressure standard in the prototype of the periodic disturbance generator, while the effect of the compressibility of the fluid was evaluated in water in the wave channel of the Laboratory of Maritime Engineering, Rivers and Estuaries of the University of Costa Rica. The results show that the pressure measured from the least-squared adjustment coefficients of an electric current transmitter obtained by stationary calibration is acceptable to estimate the climatology of the gravitational wave characteristic of the Costa Rican Central Pacific Ocean with maximum errors of 136 mm in waves of height up to 1,4 m.

Comparative Evaluation of Artificial Neural Networks and Data Analysis in Predicting Liposome Size in a Periodic Disturbance Micromixer

Artificial neural networks (ANN) and data analysis (DA) are powerful tools for supporting decision-making. They are employed in diverse fields, and one of them is nanotechnology; for example, in predicting silver nanoparticles size. To our knowledge, we are the first to use ANN to predict liposome size (LZ). Liposomes are lipid nanoparticles used in different biomedical applications that can be produced in Dean-Forces-based microdevices such as the Periodic Disturbance Micromixer (PDM). In this work, ANN and DA techniques are used to build a LZ prediction model by using the most relevant variables in a PDM, the Flow Rate Radio (FRR), and the Total Flow Rate (TFR), and the temperature, solvents, and concentrations were kept constant. The ANN was designed in MATLAB and fed data from 60 experiments with 70% training, 15% validation, and 15% testing. For DA, a regression analysis was used. The model was evaluated; it showed a 0.98147 correlation coefficient for training and 0.97247 in total data compared with 0.882 obtained by DA.