Comparative Study of Machine Learning Models for Bee Colony Acoustic Pattern Classification on Low Computational Resources.

In precision beekeeping, the automatic recognition of colony states to assess the health status of bee colonies with dedicated hardware is an important challenge for researchers, and the use of machine learning (ML) models to predict acoustic patterns has increased attention. In this work, five classification ML algorithms were compared to find a model with the best performance and the lowest computational cost for identifying colony states by analyzing acoustic patterns. Several metrics were computed to evaluate the performance of the models, and the code execution time was measured (in the training and testing process) as a CPU usage measure. Furthermore, a simple and efficient methodology for dataset prepossessing is presented; this allows the possibility to train and test the models in very short times on limited resources hardware, such as the Raspberry Pi computer, moreover, achieving a high classification performance (above 95%) in all the ML models. The aim is to reduce power consumption and improves the battery life on a monitor system for automatic recognition of bee colony states.

Digital holographic interferometry applied to the investigation of ignition process.

We use the digital holographic interferometry (DHI) technique to display the early ignition process for a butane-air mixture flame. Because such an event occurs in a short time (few milliseconds), a fast CCD camera is used to study the event. As more detail is required for monitoring the temporal evolution of the process, less light coming from the combustion is captured by the CCD camera, resulting in a deficient and underexposed image. Therefore, the CCD's direct observation of the combustion process is limited (down to 1000 frames per second). To overcome this drawback, we propose the use of DHI along with a high power laser in order to supply enough light to increase the speed capture, thus improving the visualization of the phenomenon in the initial moments. An experimental optical setup based on DHI is used to obtain a large sequence of phase maps that allows us to observe two transitory stages in the ignition process: a first explosion which slightly emits visible light, and a second stage induced by variations in temperature when the flame is emerging. While the last stage can be directly monitored by the CCD camera, the first stage is hardly detected by direct observation, and DHI clearly evidences this process. Furthermore, our method can be easily adapted for visualizing other types of fast processes.

Mooney-Rivlin Parameter Determination Model as a Function of Temperature in Vulcanized Rubber Based on Molecular Dynamics Simulations.

The automotive industry is entering a digital revolution, driven by the need to develop new products in less time that are high-quality and environmentally friendly. A proper manufacturing process influences the performance of the door grommet during its lifetime. In this work, uniaxial tensile tests based on molecular dynamics simulations have been performed on an ethylene-propylene-diene monomer (EPDM) material to investigate the effect of the crosslink density and its variation with temperature. The Mooney-Rivlin (MR) model is used to fit the results of molecular dynamics (MD) simulations in this paper and an exponential-type model is proposed to calculate the parameters C1(T) and C2T. The experimental results, confirmed by hardness tests of the cured part according to ASTM 1415-88, show that the free volume fraction and the crosslink density have a significant effect on the stiffness of the EPDM material in a deformed state. The results of molecular dynamics superposition on the MR model agree reasonably well with the macroscopically observed mechanical behavior and tensile stress of the EPDM at the molecular level. This work allows the accurate characterization of the stress-strain behavior of rubber-like materials subjected to deformation and can provide valuable information for their widespread application in the injection molding industry.