Articles you may be interested inDielectric controlled excited state relaxation pathways of a representative push-pull stilbene: A mechanistic study using femtosecond fluorescence up-conversion technique J. Chem. Phys. 138, 084308 (2013); 10.1063/1.4792933Cisstilbene isomerization: Temperature dependence and the role of mechanical friction

Viscosity and solvent dependence of lowbarrier processes: Photoisomerization of cisstilbene in compressed liquid solventsThe photoisom~rization .of stilbene has been studied in low viscosity liquid alkanes and in the gas ph~e. The bamer cross1Og process shows no evidence of a low-friction or energy controlled regton e~en at the lowest liquid visc~siti~s studied. We present evidence that the barrier crossing may be 10 the energy controlled regton 10 the thermal vapor. We discuss the influence of intramolecular.vibrati~nal energ! transfe~ on the observed dynamics and note that entropy effects should be conSIdered 10 companng expenmental data with theoretical models.

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Rice lodging severely affects harvest yield. Traditional evaluation methods and manual on-site measurement are found to be time-consuming, labor-intensive, and cost-intensive. In this study, a new method for rice lodging assessment based on a deep learning UNet (U-shaped Network) architecture was proposed. The UAV (unmanned aerial vehicle) equipped with a high-resolution digital camera and a three-band multispectral camera synchronously was used to collect lodged and non-lodged rice images at an altitude of 100 m. After splicing and cropping the original images, the datasets with the lodged and non-lodged rice image samples were established by augmenting for building a UNet model. The research results showed that the dice coefficients in RGB (Red, Green and Blue) image and multispectral image test set were 0.9442 and 0.9284, respectively. The rice lodging recognition effect using the RGB images without feature extraction is better than that of multispectral images. The findings of this study are useful for rice lodging investigations by different optical sensors, which can provide an important method for large-area, high-efficiency, and low-cost rice lodging monitoring research.

Microfluidic-based organs-on-chips (OoCs) are a rapidly developing technology in biomedical and chemical research and have emerged as one of the most advanced and promising in vitro models. The miniaturization, stimulated tissue mechanical forces, and microenvironment of OoCs offer unique properties for biomedical applications. However, the large amount of data generated by the high parallelization of OoC systems has grown far beyond the scope of manual analysis by researchers with biomedical backgrounds. Deep learning, an emerging area of research in the field of machine learning, can automatically mine the inherent characteristics and laws of “big data” and has achieved remarkable applications in computer vision, speech recognition, and natural language processing. The integration of deep learning in OoCs is an emerging field that holds enormous potential for drug development, disease modeling, and personalized medicine. This review briefly describes the basic concepts and mechanisms of microfluidics and deep learning and summarizes their successful integration. We then analyze the combination of OoCs and deep learning for image digitization, data analysis, and automation. Finally, the problems faced in current applications are discussed, and future perspectives and suggestions are provided to further strengthen this integration.