Po Li

Objective: To predict the pharmacokinetic (ADMET) properties of cinnamaldehyde using molecular modeling and provide a theoretical foundation for its development as a drug candidate. Methods: The ADMET module in Discovery Studio 2.5 was employed to evaluate six key parameters: passive intestinal absorption, aqueous solubility, blood–brain barrier (BBB) penetration, cytochrome P450 2D6 (CYP2D6) inhibition, hepatotoxicity, and plasma protein binding. While acknowledging the emergence of machine learning (ML) approaches such as Support Vector Regression (SVR) and Random Forest (RF) in Computer-Aided Drug Design (CADD) , this study utilizes rule-based simulation for rapid profiling. However, we recognize that this approach has limitations in analyzing the relationship between molecular descriptors and activity compared to advanced QSAR models . Recent studies on anti-breast cancer candidate drugs have demonstrated that multi-objective optimization models combining ML algorithms (such as LightGBM, Random Forest, and XGBoost) with Particle Swarm Optimization (PSO) can achieve significant improvements in both biological activity prediction (R² = 0.743) and ADMET property optimization , highlighting the potential of integrated ML approaches in drug development. Additionally, the predicted low systemic bioavailability of cinnamaldehyde due to rapid hepatic metabolism warrants further investigation. Results: Predicted values for cinnamaldehyde generally fell within acceptable ranges for drug-likeness. However, the analysis highlighted potential limitations in systemic bioavailability due to rapid hepatic metabolism. Both cis- and trans-isomers showed similar ADMET profiles, suggesting configurational changes have minimal impact on these specific pharmacokinetic parameters within the resolution of this model.

In this paper, a new metasurface structure is designed to reduce radar cross section (RCS). In order to reduce the radar cross section (RCS), a new metasurface structure is designed. The unit adopts the polarization conversion metasurface structure, which is composed of asymmetric double arrow-shaped metal structure and etched on the dielectric substrate on the back of the metal plate. This polarization conversion metasurface (PCM) unit consists of asymmetric double arrow-shaped metal structure, and it is etched on a dielectric substrate backed with metallic plate. It can realize converting linear polarized waves to orthogonal ones in a broadband from 18.1GHz to 32.7GHz efficiently. According to the further development of the triangle chessboard layout, we also designed several new circular fan-shaped configurations, which realize RCS reduction in a wider -10dB bandwidth compared with the triangle chessboard configuration. Finally, an optimized 8-lobed circular sector configuration composed of the designed PCM unit is selected for processing and testing. It can realize the RCS reduction greater than 10dB in a wide bandwidth ranging from 13.7GHz to 27.5GHz, compared with the equal-sized metallic-backed dielectric substrate plate. The experimental results basically agree with the simulation ones. The proposed broadband circular sector structure has broad application prospects such as phased array antenna technology, electromagnetic stealth technology, and electromagnetic absorption technology.

Instantaneous frequency rate (IFR) estimation from multi-component signals can be applied in a variety of applications. Viterbi algorithm (VA) on cubic phase function (CPF) is a highly performed IFR estimator for the mono-component signal. However, for the multi-component signal, when signal components are overlapped on the time frequency rate (TFR) plane, inaccurate IFRs may be tracked due to switch problem (SP) in VA and cross-terms in CPF. To overwhelm these two challenges, a directionally smoothed pseudo CPF (DSPCPF) is firstly proposed, the algorithm assumes that the IFR curve can be approximately linear along a direction within an adequately short time window, at a time instant, while the TFR points corresponding to cross-terms are dispersed in a entire TFR plane, then, TFR points of CPF along the IFR curve are integrated to enhance the auto-terms and suppress the cross-terms. In addition, to suppress the SP, the direction matrix of DSPCPF is utilized to construct an additional directional penalty function for VA. The new VA combined with DSPCPF is applied to estimate IFRs. In this way, more accurate IFRs can be obtained, and the performance of novel algorithms are validated by several artificial multi-component frequency modulated signals.