Marennine is a natural blue pigment produced by the diatom Haslea ostrearia which presents potentially useful bioactivities such as antioxidant and antibacterial activities. In this study, a new marennine refining strategy, employing solid-phase extraction and ionotropic gelation techniques, was applied to marennine-rich culture supernatant (blue water, BW) from batch cultures of H. ostrearia. The objective of this study was to assess the impact of this refining process on the quality of marennine extracts. Different fractions were obtained, which were characterized through quality indicators based on physicochemical parameters (carbohydrate content, UV–Visible, Attenuated Total Reflectance-Fourier Transform Infrared spectroscopy) and bioactivities (DPPH scavenging and Vibrio growth inhibition assay on different strains). The results showed that marennine refining allowed the improvement of its quality. Compared to the extract obtained using the commonly employed ultrafiltration approach, marennine extract’s DPPH scavenging efficiency was doubled, and its capacity to inhibit the growth of pathogenic Vibrio bacteria was preserved. Altogether, this study confirmed the benefits of the chosen refining strategy on both preserving and increasing the potency of marennine extracts from H. ostrearia.
While national governments and parliaments adopt policies to speed up the development of renewable energies, the local implementation of renewable energy projects often faces economic, ecological, and social challenges. The existing literature primarily focuses on conditions which foster local acceptance and policy integration to explain the (non-)realization of renewable energy projects. In contrast, property rights have not yet been explicitly considered as conditions. This study applies the institutional resource regime approach to investigate the joint impact of property rights and public policies on the local implementation of renewable energy projects. Drawing on a comparison of twelve cases which encompass solar, wind, biomass, and geothermal energy projects in three countries, the results show the importance of property rights within the project area, and the insufficiency of public policies alone to facilitate successful renewable energy projects. More specifically, public landownership and revenue sharing increase the chances of project success, as this entails the involvement of local democratic institutions and supports the resilience of project promotors against political contestation.
The growing reliance on power electronics introduces new challenges requiring detailed time-domain analyses with fast and accurate circuit simulation tools. Currently, commercial time-domain simulation software are mainly relying on physics-based methods to simulate power electronics. Recent work showed that data-driven and physics-informed learning methods can increase simulation speed with limited compromise on accuracy, but many challenges remain before deployment in commercial tools can be possible. In this paper, we propose a physics-informed bidirectional long-short term memory neural network (BiLSTM-PINN) model to simulate the time-domain response of a closed-loop dc-dc boost converter for various operating points, parameters, and perturbations. A physics-informed fully-connected neural network (FCNN) and a BiLSTM are also trained to establish a comparison. The three methods are then compared using step-response tests to assess their performance and limitations in terms of accuracy. The results show that the BiLSTM-PINN and BiLSTM models outperform the FCNN model by more than 9 and 4.5 times, respectively, in terms of median RMSE. Their standard deviation values are more than 2.6 and 1.7 smaller than the FCNN's, making them also more consistent. Those results illustrate that the proposed BiLSTM-PINN is a potential alternative to other physics-based or data-driven methods for power electronics simulations.
X-ray computed tomography (XCT) scanning is routinely conducted on board the drilling vessel Chikyu for scientific expeditions. A rapid visualization method, developed during International Ocean Discovery Program (IODP) Expedition 405, unwraps XCT images to enable the early characterization of sediment heterogeneities and identification of geological structures at the visual core description stage.
Habitat selection studies in large mammals typically rely on photo-interpreted forest maps to link the telemetry locations of individuals to environmental data. Such forest maps mainly provide information on forest composition, age, and disturbance history but say little about the structure of stands. In contrast, airborne LiDAR (Light Detection and Ranging) can provide 3D metrics of vegetation structure, but its application to the study of wildlife-habitat relationships remains limited. We aim to determine if combining these products could improve our capacity to understand the habitat selection patterns of large mammal species representative of the eastern Canadian boreal forest: caribou (Rangifer tarandus caribou), moose (Alces alces americana) and eastern coyote (Canis latrans). We built resource selection functions with mixed logistic regressions to characterize habitat selection patterns, using telemetry data and the different sources of information on forest composition and structure. We evaluated model performance with a k-fold cross-validation. Our results suggest that integrating LiDAR data with forest maps substantially improves the ability to characterize habitat selection patterns across species, though benefits varied with periods and study areas. For example, vegetation structure, mostly detailed by LiDAR data, was the main determinant for caribou and eastern coyotes in the snow-covered period, whereas forest composition, described in the forest maps, was most important to characterize habitat selection patterns for moose in both periods. These differences may be partly attributed to contrasting compositions between northernmost and southernmost forests in our study area (i.e. province of Quebec), species ecology, as well as potential temporal discrepancies between data sources. When used appropriately, combining LiDAR with traditional forest maps provides richer ecological insight and a more comprehensive characterization of habitat selection patterns of large boreal mammals. Based on an average population-level description of habitat selection patterns, this integrated approach can guide practitioners to preserve sparse understory to support caribou, promote complex shrub structure for moose, and limit dense cover that may favor eastern coyotes.
