Protection of Human Umbilical Vein Endothelial Cells against Oxidative Stress by MicroRNA-210

Oxidative stress induces endothelial cell apoptosis and promotes atherosclerosis development. MicroRNA-210 (miR-210) is linked with apoptosis in different cell types. This study aimed to investigate the role of miR-210 in human umbilical vein endothelial cells (HUVECs) under oxidative stress and to determine the underlying mechanism. HUVECs were treated with different concentrations of hydrogen peroxide (H2O2), and cell viability was evaluated using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay and ATP assay. To evaluate the role of miR-210 in H2O2-mediated apoptosis, gain-and-loss-of-function approaches were used, and the effects on apoptosis and reactive oxygen species (ROS) level were assayed using flow cytometry. Moreover, miR-210 expression was detected by quantitative reverse transcriptase polymerase chain reaction (qRT-PCR), and expression of the following apoptosis-related genes was assessed by qRT-PCR and Western blot at the RNA and protein level, respectively: caspase-8-associated protein 2 (CASP8AP2), caspase-8, and caspase-3. The results showed that H2O2 induced apoptosis in HUVECs in a dose-dependent manner and increased miR-210 expression. Overexpression of miR-210 inhibited apoptosis and reduced ROS level in HUVECs treated with H2O2. Furthermore, miR-210 downregulated CASP8AP2 and related downstream caspases at protein level. Thus, under oxidative stress, miR-210 has a prosurvival and antiapoptotic effect on HUVECs by reducing ROS generation and downregulating the CASP8AP2 pathway

A Capacity Optimization Method for a Hybrid Energy Storage Microgrid System Based on an Augmented ε- Constraint Method

In general, microgrids have a high renewable energy abandonment rate and high grid construction and operation costs. To improve the microgrid renewable energy utilization rate, the economic advantages, and environmental safety of power grid operation, we propose a hybrid energy storage capacity optimization method for a wind–solar–diesel grid-connected microgrid system, based on an augmented ε- constraint method. First, the battery is coupled with a seasonal hydrogen energy storage system to establish a hybrid energy storage model that avoids the shortcomings of traditional microgrid systems, such as a single energy storage mode and a small capacity. Second, by considering the comprehensive cost and carbon emissions of the power grid within the planning period as the objective function, the abandonment rate of renewable energy as the evaluation index, and the electric energy storage and seasonal hydrogen energy storage system operating conditions as the main constraints, the capacity allocation model of the microgrid can be constructed. Finally, an augmented ε- constraint method is implemented to optimize the model above; the entropy–TOPSIS method is used to select the configuration scheme. By comparative analysis, the results show that the optimization method can effectively improve the local absorption rate of wind and solar radiation, and significantly reduce the carbon emissions of microgrids

A bi-layer optimization method of the grid-connected microgrid based on the multi-strategy of the beluga whale algorithm

Endeavoring to enhance the penetration rate of renewable energy sources, concurrently ensuring economic and operational stability, this study proposes a novel bi-layer optimization method of the wind–solar-storage AC/DC microgrid (MG). First, by incorporating a superordinate electric and seasonal hydrogen hybrid energy storage system (E&SHESS), the topology structure of the microgrid is established. Subsequently, to rectify the intrinsic limitations of the conventional beluga whale optimization (BWO) algorithm, this paper proposes a multi-strategy hybrid improvement to BWO (MHIBWO). This innovative improvement integrates an MTent strategy, a step size adjustment mechanism, and a crisscross strategy. Then, constructing a bi-layer iterative model based on the topology, annual net income and grid-connected friendliness are introduced as optimization objectives for the outer and inner layers, respectively, utilizing MHIBWO and CPLEX for resolution. Through a nested iteration of the two layers, the model outputs the capacity scheme with the best performance of economy and stability. Finally, the simulation unequivocally demonstrated the superiority of MHIBWO and the model proposed. In addition, based on the real data of the Elia power station, the validity of the method in operation is tested using the fuzzy C-means algorithm (FCMA) to extract and aggregate typical days, thereby presenting a sophisticated solution for the field of microgrids optimization configuration

MAGNETIC FIELD DESIGN OF THE BAPS HIGH PRECISION QUADRUPOLE MAGNET

Abstract The Beijing Advanced Photon Source (BAPS) is a high performance light source planned to be constructed in China. High precision small aperture quadrupole magnets are required in the BAPS storage ring, which need extremely high mechanical accuracy. Instead of the conventional manufacture method, the coils are comprised of several U-shaped solid copper sheets. So two-piece structure of the iron core can be adopted to reduce assembly error and improve the poles symmetry. Design considerations, 2D and 3D magnetic field calculations are presented in detail, and the needed mechanical precision is estimated according to the error field analysis

Table1_A bi-layer optimization method of the grid-connected microgrid based on the multi-strategy of the beluga whale algorithm.DOCX

Endeavoring to enhance the penetration rate of renewable energy sources, concurrently ensuring economic and operational stability, this study proposes a novel bi-layer optimization method of the wind–solar-storage AC/DC microgrid (MG). First, by incorporating a superordinate electric and seasonal hydrogen hybrid energy storage system (E&SHESS), the topology structure of the microgrid is established. Subsequently, to rectify the intrinsic limitations of the conventional beluga whale optimization (BWO) algorithm, this paper proposes a multi-strategy hybrid improvement to BWO (MHIBWO). This innovative improvement integrates an MTent strategy, a step size adjustment mechanism, and a crisscross strategy. Then, constructing a bi-layer iterative model based on the topology, annual net income and grid-connected friendliness are introduced as optimization objectives for the outer and inner layers, respectively, utilizing MHIBWO and CPLEX for resolution. Through a nested iteration of the two layers, the model outputs the capacity scheme with the best performance of economy and stability. Finally, the simulation unequivocally demonstrated the superiority of MHIBWO and the model proposed. In addition, based on the real data of the Elia power station, the validity of the method in operation is tested using the fuzzy C-means algorithm (FCMA) to extract and aggregate typical days, thereby presenting a sophisticated solution for the field of microgrids optimization configuration.</p

Efficient Spatial-Temporal Information Fusion for LiDAR-Based 3D Moving Object Segmentation

Accurate moving object segmentation is an essential task for autonomous driving. It can provide effective information for many downstream tasks, such as collision avoidance, path planning, and static map construction. How to effectively exploit the spatial-temporal information is a critical question for 3D LiDAR moving object segmentation (LiDAR-MOS). In this work, we propose a novel deep neural network exploiting both spatial-temporal information and different representation modalities of LiDAR scans to improve LiDAR-MOS performance. Specifically, we first use a range image-based dual-branch structure to separately deal with spatial and temporal information that can be obtained from sequential LiDAR scans, and later combine them using motion-guided attention modules. We also use a point refinement module via 3D sparse convolution to fuse the information from both LiDAR range image and point cloud representations and reduce the artifacts on the borders of the objects. We verify the effectiveness of our proposed approach on the LiDAR-MOS benchmark of SemanticKITTI. Our method outperforms the state-of-the-art methods significantly in terms of LiDAR-MOS IoU. Benefiting from the devised coarse-to-fine architecture, our method operates online at sensor frame rate. The implementation of our method is available as open source at: https://github.com/haomo-ai/MotionSeg3D.Comment: Accepted by IROS2022. Code: https://github.com/haomo-ai/MotionSeg3

Strength behaviors of CH₄ hydrate-bearing silty sediments during thermal decomposition

Predicting the mechanical response of methane hydrate-bearing sediments prior to and during gas production enable appropriate design and anticipate risk due to extraction process of methane from deep-ocean and permafrost setting. In this study, a series of triaxial drained shear tests followed by hydrate dissociation were performed on artificial hydrate-bearing silty sediments at given porosity and stress conditions. The peak strength of HBSS increases exponentially with hydrate saturation, which signifies proportional loss of strength due to hydrate dissociation by thermal decomposition. The peak strength of partially dissociated sediments is slightly lower than the strength of sediments with similar hydrate saturation freshly formed. The enhancement effect of CH4 hydrate on the strength behaviors of HBSS would be more obvious under higher effective confining pressures. The peak strength increase of HBSS was not only due to the increase in cohesion component but also frictional component for a given hydrate saturation and porosity. Thermal decomposition of HBSS is governed directly by its hydrate saturation rather than the confining stress, although with higher confining stress the dissipation of the released gas is affected by the permeability of the sediments thus slightly prolonging the dissociation process.</p

Fabrication of carboxymethyl cellulose and graphene oxide bio-nanocomposites for flexible nonvolatile resistive switching memory devices

Nowadays the development of natural biomaterials as promising building polymers for flexible, biodegradable, biocompatible and environmentally friendly electronic devices is of great interest. As the most common natural polymers, cellulose and its derivatives have the potential to be applied in the devices owing to the easy processing, nontoxicity and biodegradability. Here, write-once-read-many-times resistive switching devices based on biodegradable carboxymethyl cellulose-graphene oxide (CMC-GO) nanocomposite are demonstrated for the first time. The hybridization sites formed by the gelation of CMC and GO molecules contribute to the excellent memory behaviors. When compared with devices base on pure GO and CMC, the device with the Al/CMC-GO/Al/SiO2 structure exhibits brilliant write-once-read-many-times (WORM) switching characteristics such as high ON/OFF current ratio of ˜105, low switching voltage of 2.22 V, excellent stability and durability. What's more, the device shows high flexibility and good resistive switching behaviors even with soft PET substrate (Al/CMC-GO/Al/PET structure). This newly designed cellulose-graphene oxide-based polymer nanocomposites are quite cheap and easy processed for large scale manufacturing of memory devices and can further contribute to future biodegradable data storage applications such as portable stretchable displays, wearable electronics and electronic skins in the coming age of artificial intelligence