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Wind speed affects aviation performance, clean energy production, and other applications. By accurately predicting wind speed, operational delays and accidents can be avoided, while the efficiency of wind energy production can also be increased. This paper initially overviews the definition, characteristics, sensors capable of measuring the feature, and the relationship between this feature and wind speed for all Quality Indicators (QIs). Subsequently, the feature importance of each QI relevant to wind-speed prediction is assessed, and all QIs are employed to predict horizontal wind speed. In addition, we conduct a comparison between the performance of traditional point-wise machine learning models and temporally correlated deep learning ones. The results demonstrate that the Bidirectional Long Short-Term Memory (BiLSTM) neural network yielded the highest level of accuracy across three metrics. Additionally, the newly proposed set of QIs outperformed the previously utilised QIs to a significant degree.

This study theoretically proposed a novel surface plasmon resonance biosensor by incorporating emerging two dimensional material blue phosphorus and graphene layers with plasmonic gold film. The excellent performances employed for biosensing can be realized by accurately tuning the thickness of gold film and the number of blue phosphorus interlayer. Our proposed plasmonic biosensor architecture designed by phase modulation is much superior to angular modulation, providing 4 orders of magnitude sensitivity enhancement. In addition, the optimized stacked configuration is 42 nm Au film/2-layer blue phosphorus /4-layer graphene, which can produce the sharpest differential phase of 176.7661 degrees and darkest minimum reflectivity as low as 5.3787 × 10−6. For a tiny variation in local refractive index of 0.0012 RIU (RIU, refractive index unit) due to the binding interactions of aromatic biomolecules, our proposed biosensor can provide an ultrahigh detection sensitivity up to 1.4731 × 105 °/RIU, highly promising for performing ultrasensitive biosensing application.