Charging load forecasting of electric vehicles based on sparrow search algorithm‐improved random forest regression model

Abstract In order to solve the problem that the current charging load forecasting accuracy is not high, it is difficult to simulate the actual charging load distribution of Electric Vehicles (EVs), and it is impossible to reasonably predict the future load, a charging load forecasting model based on Sparrow Search Algorithm (SSA) improved Random Forest Regression (RFR) is proposed. The SSA is used to enhance the ability of global optimization and local exploration. Combined with the advantages of the RFR model, such as low generalization error, fast convergence speed, and few adjustment parameters, the SSA was used to optimize the parameters of the decision tree number and the number of split nodes in the RFR, and the optimal value of the parameters is obtained, so as to obtain the optimal performance of the RFR. Firstly, based on the concept of travel chain and conditional probability distribution, the user's travel habits are described. Monte Carlo simulation method was used to simulate the driving, parking, and charging behaviours of a large number of EVs in different regions, so as to obtain the charging load of EVs in different regions. Then, a charging load forecasting model based on SSA improved RFR is established. Monte Carlo simulation results are used as sample data to predict the charging load of EVs in different regions. Finally, taking a certain area as an example, the experimental results show that the charging load prediction model based on Sparrow Search Algorithm improved Random Forest Regression (SSA‐RFR) can accurately predict the charging load of EVs in different regions, and the charging load of different regional types is obviously different. Compared with the RFR model and other literature models, the SSA‐RFR model has better prediction accuracy, which verifies the feasibility and superiority of SSA‐RFR model in EVs charging load prediction

Directing Induced Pluripotent Stem Cell Derived Neural Stem Cell Fate with a Three-Dimensional Biomimetic Hydrogel for Spinal Cord Injury Repair

Current treatment approaches for spinal cord injuries (SCIs) are mainly based on cellular transplantation. Induced pluripotent stem cells (iPSCs) without supply constraints and ethical concerns have emerged as a viable treatment option for repairing neurological disorders. However, the primarily limitations in the neuroregeneration field are uncontrolled cell differentiation, and low cell viability caused by the ischemic environment. The mechanical property of three-dimensional (3D) hydrogel can be easily controlled and shared similar characteristics with nerve tissue, thus promoting cell survival and controlled cell differentiation. We propose the combination of a 3D gelatin methacrylate (GelMA) hydrogel with iPSC-derived NSCs (iNSCs) to promote regeneration after SCI. In vitro, the iNSCs photoencapsulated in the 3D GelMA hydrogel survived and differentiated well, especially in lower-moduli hydrogels. More robust neurite outgrowth and more neuronal differentiation were detected in the soft hydrogel group. To further evaluate the in vivo neuronal regeneration effect of the GelMA hydrogels, a mouse spinal cord transection model was generated. We found that GelMA/iNSC implants significantly promoted functional recovery. Further histological analysis showed that the cavity areas were significantly reduced, and less collagen was deposited in the GelMA/iNSC group. Furthermore, the GelMA and iNSC combined transplantation decreased inflammation by reducing activated macrophages/microglia (CD68-positive cells). Additionally, GelMA/iNSC implantation showed striking therapeutic effects of inhibiting GFAP-positive cells and glial scar formation while simultaneously promoting axonal regeneration. Undoubtedly, use of this 3D hydrogel stem cell-loaded system is a promising therapeutic strategy for SCI repair