Lateral Stability Control of Four-Wheel Independent Drive Electric Vehicles Based on Model Predictive Control

Four-wheel independent drive electric vehicle was used as the research object to discuss the lateral stability control algorithm, thus improving vehicle stability under limit conditions. After establishing hierarchical integrated control structure, we designed the yaw moment decision controller based on model predictive control (MPC) theory. Meanwhile, the wheel torque was assigned by minimizing the sum of consumption rates of adhesion coefficients of four tires according to the tire friction ellipse theory. The integrated simulation platform of Carsim and Simulink was established for simulation verification of yaw/rollover stability control algorithm. Then, we finished road experiment verification of real vehicle by integrated control algorithm. The result showed that this control method can achieve the expectation of effective vehicle tracking, significantly improving the lateral stability of vehicle

Effects of caffeine, tea polyphenol and daidzein on the pharmacokinetics of lansoprazole and its metabolites in rats

O objetivo deste estudo foi avaliar os efeitos da cafeína, do polifenol do chá e da daidzeína na farmacocinética do lansoprazol e de seus metabólitos. Administraram-se, intragastricamente, aos ratos cafeína (30 mg·kg-1, uma vez ao dia), polifenol do chá(400 mg·kg-1, uma vez ao dia) ou daidzeína (13,5 mg·kg-1, uma vez ao dia), por 14 dias, seguindo-se a administração de lansoprazol (8 mg·kg-1) no 15º. dia. As concentrações plasmáticas do lansoprazol e de seus dois metabólitos primários, 5-hidroxilansoprazol e sulfona de lansoprazol, foram determinadas por cromatografia líquida de alta eficiência acoplada com espectrometria de massas (CLAE-EM/EM). O polifenol do chá elevou, significativamente, a Área Sob a Curva (ASC) do lansoprazol de 680,29 ± 285,99 para 949,76 ± 155,18 μg/L.h e reduziu a da sulfona de lansoprazol de 268,82 ± 82,37 para 177,72 ± 29,73 μg/L.h. A daidzeína aumentou a ASC do lansoprazol de 680,29 ± 285,99 para 1130,44 ± 97,6 μg/L.h e reduziu a da sulfona de lansoprazol de 268,82 ± 82,37 para 177,72 ± 29,73 μg/L.h. A farmacocinética do 5-hidroxilansoprazol permaneceu intacta na presença de polifenol do chá ou daidzeína. A cafeína não afetou a farmacocinética do lansoprazol e de seus metabólitos. Os resultados sugerem que o polifenol do chá e a daidzeína podem inibir o metabolismo in vivo do lansoprazol por supressão da CYP3A.The aim of this study was to evaluate the effects of caffeine, tea polyphenol and daidzein on the pharmacokinetics of lansoprazole and its metabolites. Rats were intragastrically administered caffeine (30 mg·kg-1, once per day), tea polyphenol (400 mg·kg-1, once per day) or daidzein (13.5 mg·kg-1, once per day) for 14 days, followed by an intragastric administration of lansoprazole (8 mg·kg-1) on the 15th day. The plasma concentrations of lansoprazole and its two primary metabolites, 5-hydroxylansoprazole and lansoprazole sulfone, were determined by high-performance liquid chromatography coupled with tandem mass spectrometry (HPLC-MS/MS). Tea polyphenol significantly elevated the Area Under the Curve (AUC) of lansoprazole from 680.29 ± 285.99 to 949.76 ± 155.18 μg/L.h and reduced that of lansoprazole sulfone from 268.82 ± 82.37 to 177.72 ± 29.73 μg/L.h. Daidzein increased the AUC of lansoprazole from 680.29 ± 285.99 to 1130.44 ± 97.6 μg/L.h and decreased that of lansoprazole sulfone from 268.82 ± 82.37 to 116.23 ± 40.14 μg/L.h. The pharmacokinetics of 5-hydroxylansoprazole remained intact in the presence of tea polyphenol or daidzein. Caffeine did not affect the pharmacokinetics of lansoprazole and its metabolites. The results imply that tea polyphenol and daidzein may inhibit the in vivo metabolism of lansoprazole by suppressing CYP3A

Biomass Gasification: An Overview of Technological Barriers and Socio-Environmental Impact

Biomass gasification has been regarded as a promising technology to utilize bioenergy sustainably. However, further exploitation of biomass gasification still needs to overcome a significant number of technological and logistic challenges. In this chapter, the current development status of biomass gasification, especially for the activities in China, has been presented. The biomass characters and the challenges associated with biomass collection and transportation are covered and it is believed that biomass gasification coupled with distributed power generation will be more competitive in some small communities with large amount of local biomass materials. The technical part of biomass gasification is detailed by introducing different types of gasifiers as well as investigating the minimization methods of tar, which have become more and more important. In fact, applying biomass gasification also needs to deal with other socio-environmental barriers, such as health concerns, environmental issues and public fears. However, an objective financial return can actually accelerate the commercialization of biomass gasification for power and heat generation, and in the meantime, it will also contribute to other technical breakthroughs

InstMove: Instance Motion for Object-centric Video Segmentation

Despite significant efforts, cutting-edge video segmentation methods still remain sensitive to occlusion and rapid movement, due to their reliance on the appearance of objects in the form of object embeddings, which are vulnerable to these disturbances. A common solution is to use optical flow to provide motion information, but essentially it only considers pixel-level motion, which still relies on appearance similarity and hence is often inaccurate under occlusion and fast movement. In this work, we study the instance-level motion and present InstMove, which stands for Instance Motion for Object-centric Video Segmentation. In comparison to pixel-wise motion, InstMove mainly relies on instance-level motion information that is free from image feature embeddings, and features physical interpretations, making it more accurate and robust toward occlusion and fast-moving objects. To better fit in with the video segmentation tasks, InstMove uses instance masks to model the physical presence of an object and learns the dynamic model through a memory network to predict its position and shape in the next frame. With only a few lines of code, InstMove can be integrated into current SOTA methods for three different video segmentation tasks and boost their performance. Specifically, we improve the previous arts by 1.5 AP on OVIS dataset, which features heavy occlusions, and 4.9 AP on YouTubeVIS-Long dataset, which mainly contains fast-moving objects. These results suggest that instance-level motion is robust and accurate, and hence serving as a powerful solution in complex scenarios for object-centric video segmentation.Comment: Accepted to CVPR 202