RESUMEN
Hydrogen energy and biomass energy are green and sustainable forms that can solve the energy crisis all over the world. Electrocatalytic water splitting is a marvelous way to produce hydrogen and biomass platform molecules can be added into the electrolyte to reduce the overpotential and meanwhile are converted into some useful organics, but the key point is the design of electrocatalyst. Herein, ultralow noble metal Ru is doped into NiS2 to form RuO2@NiS2 heterojunction. Amongst them, the 0.06 RuO2@NiS2 has low overpotentials of 363 mV for OER and 71 mV for HER in 1 m KOH, which are superior to the RuO2 and Pt/C. Besides, the 0.06 RuO2@NiS2 shows a low overpotential of 173 mV in 1 m KOH+0.1 m glycerol, and the glycerol is oxidized to glyceraldehyde and formic acid via the high Faraday efficiency GlyOR process, and the splitting voltage is only 1.17 V. In addition, the 0.06 RuO2@NiS2 has a low overpotential of 206 mV in 1 m KOH+0.1 m glucose, and the glucose is converted to glucaric acid, lactic acid, and formic acid. This work has a "one stone three birds" effect for the production of hydrogen, low splitting voltage, and high-value-added biomass chemicals.
RESUMEN
Lane detection is a vital component of intelligent driving systems, offering indispensable functionality to keep the vehicle within its designated lane, thereby reducing the risk of lane departure. However, the complexity of the traffic environment, coupled with the rapid movement of vehicles, creates many challenges for detection tasks. Current lane detection methods suffer from issues such as low feature extraction capability, poor real-time detection, and inadequate robustness. Addressing these issues, this paper proposes a lane detection algorithm that combines an online re-parameterization ResNet with a hybrid attention mechanism. Firstly, we replaced standard convolution with online re-parameterization convolution, simplifying the convolutional operations during the inference phase and subsequently reducing the detection time. In an effort to enhance the performance of the model, a hybrid attention module is incorporated to enhance the ability to focus on elongated targets. Finally, a row anchor lane detection method is introduced to analyze the existence and location of lane lines row by row in the image and output the predicted lane positions. The experimental outcomes illustrate that the model achieves F1 scores of 96.84% and 75.60% on the publicly available TuSimple and CULane lane datasets, respectively. Moreover, the inference speed reaches a notable 304 frames per second (FPS). The overall performance outperforms other detection models and fulfills the requirements of real-time responsiveness and robustness for lane detection tasks.
