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Warm-sector heavy rainfall along the south China coast poses significant forecasting challenges due to its localized nature and prolonged duration. To improve the prediction of such high-impact weather events, high-resolution numerical weather prediction (NWP) models are increasingly used to more accurately represent topographic effects. However, as these models' grid spacing approaches the scale of convective processes, they enter a "gray zone", where the models struggle to fully resolve the turbulent eddies within the atmospheric boundary layer, necessitating partial parameterization. The appropriateness of applying convection parameterization (CP) schemes within this gray zone remains controversial. To address this, scale-aware CP schemes have been developed to improve the representation of convective transport. Among these, the multi-scale Kain–Fritsch (MSKF) scheme enhances the traditional Kain–Fritsch (KF) scheme, incorporating modifications that facilitate its effective application at spatial resolutions as high as 2 km. In recent years, there has been an increase in the application of machine learning (ML) models across various domains of atmospheric sciences, including efforts to replace conventional physical parameterizations with ML models. This work introduces a multi-output bidirectional long short-term memory (Bi-LSTM) model intended to replace the scale-aware MSKF CP scheme. This multi-output Bi-LSTM model is capable of simultaneously predicting the convection trigger while also modeling the associated convective tendencies and precipitation rates with a high performance. Data for training and testing the model are generated using the Weather Research and Forecast (WRF) model over south China at a horizontal resolution of 5 km. Furthermore, this work evaluates the performance of the WRF model coupled with the ML-based CP scheme against simulations with the traditional MSKF scheme. The results demonstrate that the Bi-LSTM model can achieve high accuracy, indicating the promising potential of ML models to substitute the MSKF scheme in the gray zone. [ABSTRACT FROM AUTHOR]

NaTi2(PO4)3 (NTP) is considered a promising anode material for sodium-ion batteries due to its open and stable framework structure. However, its poor conductivity prevents its direct application as an anode material. In this paper, a dual strategy of La3+ doping and carbon coating is proposed to improve the conductivity of NTP. The results show that the NTP-La0.06@C electrode doped with 6.00% La and 5.00 wt% of carbon coating has excellent electrochemical performance, and the coulombic efficiency is up to 95.31% in the first cycle. The initial specific capacity is 103.05 mAh·g−1 with a capacity retention of 97.30% after 1000 cycles at 10C rate. Even after 2000 cycles, there is still a specific capacity of 98.62 mAh·g−1 with a capacity retention of 92.71%, and as well as the NTP-La0.06@C has a higher Na+-insert diffusion coefficient of 3.19×10−11 cm2·s−1 than the pristine NTP@C of 2.35×10−11 cm2·s−1 during charging. Therefore, the La3+ doping and carbon coating strategy shows great potential in improving the performance of NaTi2(PO4)3 anode materials. [ABSTRACT FROM AUTHOR]

A solar heat pump based on the photovoltaic photothermal (PV/T) module is a new technology that can improve the photovoltaic efficiency and recovery of waste heat in photovoltaic conversion. The comprehensive efficiency of a system can thus be greatly improved. At present, there is little research on the simulation of a solar heat pump based on the Simulink 2018 software. In this paper, PV/T modules are modeled and simulated using the Simulink software based on the typical meteorological parameters in Beijing city during summer and winter conditions. Considering the system characteristics of a simultaneous operation, the models of all components such as the compressor, condenser, evaporator, expansion valve, and heat storage tanks contained in a system are established based on the system's working principle, energy conservation equations, and some empirical formulas and then simulated. As PV/T modules are the key influencing factor for system performance, the model and simulation process are introduced in detail in this paper. The results show that the surface temperature of PV/T modules under different operating conditions are all significantly lower than that of ordinary photovoltaic panels. The average temperature of PV/T modules is only 15.2 °C, which is 20.2 °C lower than that of ordinary panels under summer conditions, and the average temperature of PV/T modules is only 0.5 °C, 17.8 °C lower than that of ordinary panels under winter conditions. The average photoelectric efficiency can be improved by 15.4% and 8.9%. And, the temperature change amplitude of PV/T modules is lower, which weakens the temperature fluctuation of the modules. The photovoltaic efficiency is therefore further increased. As a result, the power generation and photovoltaic efficiency are both improved significantly. [ABSTRACT FROM AUTHOR]

Energy storage is considered a crucial unit in utilizing renewable energy sources, and compressed air energy storage (CAES) provides a cost-effective solution. It offers the benefits of zero pollution, a long lifespan, low maintenance costs, and minimal environmental impact. In order to increase the possibilities of compressed air energy storage for vehicle power, the performance of the expander needs to be studied. First, a CAES unit test bench is established. Then, the volumetric flow rate, rotational speed, torque, and output power are examined. Additionally, the isentropic exhaust temperature, pressure, and gas consumption rate of the scroll expander are analyzed. Finally, analyzing the economic feasibility of the CAES unit entails running the unit under varied driving conditions. Results reveal that the pressure of the input expander is high, which will lead to greater torque, greater peak power, and a greater temperature drop, but the gas in the air tank will also run out quickly. The peak power of the scroll expander does not occur at the maximum volume flow rate, rotation speed, and torque. The basic investment of the CAES unit mainly depends on the peak output power. [ABSTRACT FROM AUTHOR]

With the increasing demand for logistics in modern society, how to achieve low-cost and efficient logistics delivery has become an urgent research topic. A hybrid evolutionary JAYA algorithm (H-JAYA) based on global optimization was designed to address the complex path planning problem of electric vehicles. This algorithm introduces a reverse learning mechanism to calculate the current optimal and worst individuals, while using differential perturbation mechanism and sine cosine operator to update the individual's position. In addition, the study used the H-JAYA algorithm to construct a corresponding mathematical model for the optimization problem of electric vehicle paths. The results showed that in the three examples, the H-JAYA algorithm tested the optimal curve convergence speed, and it tended to stabilize after about 30 iterations. Meanwhile, in the RCDP5001 example, the total cost of the H-JAYA algorithm reached the lowest value of 623 yuan. The H-JAYA algorithm has significant advantages in solving the distribution path problem of electric vehicles, and can be well applied to practical logistics distribution, providing effective technical support for modern e-commerce logistics planning. [ABSTRACT FROM AUTHOR]

High‐resolution and accurate prediction of near‐surface weather parameters based on numerical weather prediction (NWP) models is essential for many downstream and real‐world applications. Traditional dynamical or statistical downscaling methods are insufficient to derive high‐resolution data from operational NWP forecasts, making it essential to devise new approaches. In recent years, an increasing number of researchers have explored the implementations of deep learning (DL) based models for spatial downscaling, motivated by the similarity between the super‐resolution (SR) problem in computer vision (CV) and downscaling. Furthermore, while transformer‐based models have become state‐of‐the‐art models for many SR tasks, they are rarely applied for downscaling of weather forecasts or climate projections. This study adapted transformer‐based models such as SwinIR and Uformer to downscale the temperature at 2 m (T2m$$ {T}_{2\mathrm{m}} $$) and wind speed at 10 m (WS10m$$ W{S}_{10\mathrm{m}} $$) over Eastern Inner Mongolia, encompassing the area from 39.6–46°N latitude and 111.6–118°E longitude. We used high‐resolution forecast (HRES) data from the European Centre for Medium‐range Weather Forecast (ECMWF) with a spatial resolution of 0.1° as the input and gridded observation data from the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) at a spatial resolution of 0.01° as the target. Given that the models use observation data rather than a coarse‐grained version of forecast data as the target, they accomplish both bias correction and spatial downscaling. The results demonstrate that the performance of SwinIR and Uformer is superior to that of two convolutional neural network (CNN) based models (UNet and RCAN). Additionally, we introduced a novel module to extract features of varying resolution from the high‐resolution topography data and applied a multiscale feature fusion module to merge features of different scales, contributing to further enhancement of Uformer's performance. [ABSTRACT FROM AUTHOR]