Wireless power transfer (WPT) via coupled magnetic resonances has been in development for over a decade. However, the frequency splitting phenomenon occurs in the over-coupled region. Thus, the output power of the two-coil system achieves the maximum output power at the two splitting angular frequencies, and not at the natural resonant angular frequency. According to the maximum power transfer theorem, the impedance compensation method was adopted in many WPT projects. However, it remains a challenge to achieve the maximum output power and transmission efficiency in a fixed-frequency mode. In this study, dynamic impedance compensation for WPT was presented by utilizing the compensator within a virtual three-coil WPT system. First, the circuit model was established and transfer characteristics of a system were studied by utilizing circuit theories. Second, the power superposition of the WPT system was carefully researched. When a pair of compensating coils was inserted into the transmitter loop, the conjugate power of the compensator loop was created via magnetic coupling of the two compensating coils that insert into the transmitter loop. The mechanism for dynamic impedance compensation for wireless power transfer was then provided by investigating a virtual three-coil WPT system. Finally, the experimental circuit of a virtual three-coil WPT system was designed, and experimental results are consistent with the theoretical analysis, which achieves the maximum output power and transmission efficiency.

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Accurate land use land cover (LULC) classification is vital for the sustainable management of natural resources and to learn how the landscape is changing due to climate. For accurate and efficient LULC classification, high-quality datasets and robust classification methods are required. With the increasing availability of satellite data, geospatial analysis tools, and classification methods, it is essential to systematically assess the performance of different combinations of satellite data and classification methods to help select the best approach for LULC classification. Therefore, this study aims to evaluate the LULC classification performance of two commonly used platforms (i.e., ArcGIS Pro and Google Earth Engine) with different satellite datasets (i.e., Landsat, Sentinel, and Planet) through a case study for the city of Charlottetown in Canada. Specifically, three classifiers in ArcGIS Pro, including support vector machine (SVM), maximum likelihood (ML), and random forest/random tree (RF/RT), are utilized to develop LULC maps over the period of 2017–2021. Whereas four classifiers in Google Earth Engine, including SVM, RF/RT, minimum distance (MD), and classification and regression tree (CART), are used to develop LULC maps for the same period. To identify the most efficient and accurate classifier, the overall accuracy and kappa coefficient for each classifier is calculated throughout the study period for all combinations of satellite data, classification platforms, and methods. Change detection is then conducted using the best classifier to quantify the LULC changes over the study period. Results show that the SVM classifier in both ArcGIS Pro and Google Earth Engine presents the best performance compared to other classifiers. In particular, the SVM in ArcGIS Pro shows an overall accuracy of 89% with Landsat, 91% with Sentinel, and 94% with Planet. Similarly, in Google Earth Engine, the SVM shows an accuracy of 87% with Landsat 8 and 92% with Sentinel 2. Furthermore, change detection results show that 13.80% and 14.10% of forest areas have been turned into bare land and urban class, respectively, and 3.90% of the land has been converted into the urban area from 2017 to 2021, suggesting the intensive urbanization. The results of this study will provide the scientific basis for selecting the remote sensing classifier and satellite imagery to develop accurate LULC maps.

Insecticide synergists biochemically inhibit insect metabolic enzyme activity and are used both to increase the effectiveness of insecticides and as a diagnostic tool for resistance mechanisms. Considerable attention has been focused on identifying new synergists from phytochemicals with recognized biological activities, specifically enzyme inhibition. Jack pine (Pinus banksiana Lamb.), black spruce (Picea mariana (Mill.) BSP.), balsam fir (Abies balsamea (L.) Mill.), and tamarack larch (Larix laricina (Du Roi) Koch) have been used by native Canadians as traditional medicine, specifically for the anti-inflammatory and antioxidant properties based on enzyme inhibitory activity. To identify the potential allelochemicals with synergistic activity, ethanol crude extracts and methanol/water fractions were separated by Sephadex LH-20 chromatographic column and tested for in vitro glutathione S-transferase (GST) inhibition activity using insecticide-resistant Colorado potato beetle, Leptinotarsa decemlineata (Say) midgut and fat-body homogenate. The fractions showing similar activity were combined and analyzed by ultra pressure liquid chromatography-mass spectrometry. A lignan, (+)-lariciresinol 9'-p-coumarate, was identified from P. mariana cone extracts, and L. laricina and A. balsamea bark extracts. A flavonoid, taxifolin, was identified from P. mariana and P. banksiana cone extracts and L. laricina bark extracts. Both compounds inhibit GST activity with taxifolin showing greater activity compared to (+)-lariciresinol 9'-p-coumarate and the standard GST inhibitor, diethyl maleate. The results suggested that these compounds can be considered as potential new insecticide synergists.