Due to the significant price differences among different types of edible oils, expensive oils like olive oil are often blended with cheaper edible oils. This practice of adulteration in edible oils, aimed at increasing profits for producers, poses a major concern for consumers. Furthermore, adulteration in edible oils can lead to various health issues impacting consumer well-being. In order to meet the requirements of fast, non-destructive, universal, accurate, and reliable quality testing for edible oil, the oblique-incidence reflectivity difference (OIRD) method combined with machine learning algorithms was introduced to detect a variety of edible oils. The prediction accuracy of Gradient Boosting, K-Nearest Neighbor, and Random Forest models all exceeded 95%. Moreover, the contribution rates of the OIRD signal, DC signal, and fundamental frequency signal to the classification results were 45.7%, 34.1%, and 20.2%, respectively. In a quality evaluation experiment on olive oil, the feature importance scores of three signals reached 63.4%, 18.9%, and 17.6%. The results suggested that the feature importance score of the OIRD signal was significantly higher than that of the DC and fundamental frequency signals. The experimental results indicate that the OIRD method can serve as a powerful tool for detecting edible oils.

This paper presents a supervisory system that considers converter efficiency for local microgrids of commercial buildings to solve the uncertainty problem of the sources and loads while also optimizing local microgrid operating costs and maintaining power supply quality for commercial buildings. The supervisory system includes an energy management layer and a power management layer. In the energy management layer, a long-term optimization approach is used to reduce the operating costs by considering the dynamic converter efficiency. In the power management layer, a real-time power optimization method is structured to deal with the uncertainty problem of the sources and loads, and to ensure that the direct current bus power is balanced while also guaranteeing the power quality by considering the dynamic converter efficiency. Four cases are proposed for the supervisory system, and these cases are simulated in MATLAB/Simulink under three typical weather conditions: cloud, sunshine, and rain. The comparison of simulation results for cases 1 and 2 illustrates the impact of converter efficiency on energy coordination in microgrids. The simulation results of cases 3 and 4 verify that the performance—in terms of the power supply quality and the operating costs—of the proposed microgrid supervisory system considering dynamic converter efficiency outperforms that of the microgrid supervisory system considering fixed converter efficiency.

Due to the significant price difference, expensive oils like olive oil are often blended with cheaper edible oils. This practice of adulteration in edible oils, aimed at increasing profits for producers, poses a major concern for consumers. Furthermore, adulteration in edible oils can lead to various health issues impacting consumer well-being. In order to meet the requirements of fast, non-destructive, universal, accurate and reliable quality testing for edible oil, the oblique-incidence reflectivity difference (OIRD) method combined with machine learning algorithms was introduced to detect a variety of edible oils. The prediction accuracy of Gradient Boosting, K-Nearest Neighbor, and Random Forest models exceeded 95%. Experimental results indicate that the OIRD method can serve as a powerful tool for detecting edible oils.

The increasing global demand for renewable energy has significantly accelerated the development of floating offshore wind turbine (FOWT) platforms, particularly as wind energy resources expand into deeper waters. This paper provides a comprehensive overview of the evolution of floating platforms, examining traditional designs such as spar, barge, semi-submersible, and tension leg platforms (TLP), along with recent advancements in hybrid and nature-inspired structures. Key structures innovations, including the integration of wave energy converter and biomimetic designs, are highlighted for their contributions to enhanced stability, cost efficiency, and energy output. Furthermore, the paper also reviews recent advancements in platform optimization techniques, including genetic algorithms, structural and analytical optimization methods, which improve platform stability and hydrodynamic performance. Finally, various attitude control and stability regulation methods, such as active and semi-active control strategies, as well as data-driven and model predictive approaches, are explored for their potential to improve stability and energy efficiency under dynamic environmental conditions. This review aims to provide valuable insights into the ongoing advancements and challenges in FOWT platform design, offering guidance for future research and large-scale deployment.