RESUMO
A comprehensive understanding of the linear/nonlinear dynamic behavior of wireless microresonators is essential for micro-electromechanical systems (MEMS) design optimization. This study investigates the dynamic behaviour of a magnetoelectric (ME) microresonator, using a finite element method (FEM) and machine learning algorithm. First, the linear/nonlinear behaviour of a fabricated thin-film ME microactuator is assessed in both the time domain and frequency spectrum. Next, a data driven system identification (DDSI) procedure and simulated annealing (SA) method are implemented to reconstruct differential equations from measured datasets. The Duffing equation is employed to replicate the dynamic behavior of the ME microactuator. The Duffing coefficients such as mass, stiffness, damping, force amplitude, and excitation frequency are considered as input parameters. Meanwhile, the microactuator displacement is taken as the output parameter, which is measured experimentally via a laser Doppler vibrometer (LDV) device. To determine the optimal range and step size for input parameters, the sensitivity analysis is conducted using Latin hypercube sampling (LHS). The peak index matching (PIM) and correlation coefficient (CC) are considered assessment criteria for the objective function. The data-driven developed models are subsequently employed to reconstruct/predict mode shapes and the vibration amplitude over the time domain. The effect of driving signal nonlinearity and total harmonic distortion (THD) is explored experimentally under resonance and sub-resonance conditions. The vibration measurements reveal that as excitation levels increase, hysteresis variations become more noticeable, which may result in a higher prediction error in the Duffing array model. The verification test indicates that the first bending mode reconstructs reasonably with a prediction accuracy of about 92 percent. This proof-of-concept study demonstrates that the simulated annealing approach is a promising tool for modeling the dynamic behavior of MEMS systems, making it a strong candidate for real-world applications.
RESUMO
BACKGROUND: Protoberberine isoquinoline alkaloids are found in many plant species. They consist of a diverse class of secondary metabolites with many pharmacologically active members, such as different derivatives of berberine already patented. In the development of approximately 20-25% of all cancers, altered hedgehog (Hh) signalling is involved where the smoothened (Smo) transmembrane receptor triggers Hh signalling pathway towards Gli1 gene expression. OBJECTIVE: The current study aimed to model and verify the anti-Smo activity of berberine and its derivatives using a novel automated script. METHOD: Based on the patented inventions filed on ADMET modelling until 2016, which also predicts ADMET parameters and binding efficiency indices for all molecules, a script was developed to run automated molecular docking for a large number of small molecules. RESULTS: Berberine was found to interact with Lys395 of Smo receptor via hydrogen bonding and cation-π interactions. In addition, π-π interactions between berberine aromatic rings and two aromatic residues in the Smo transmembrane domain, Tyr394 and Phe484, were noted. Binding efficiency indices using an in silico approach to plot the Smo-specific binding potency of each ligand was performed. The mRNA level of Gli1 was studied as the outcome of Hh signalling pathway to show the effect of berberine on hedgehog signalling. CONCLUSION: This study predicted the role of berberine as an inhibitor of Smo receptor, suggesting its effectiveness in hedgehog signalling during cancer treatment.
