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Oxalis triangularis ‘Purpurea’ has significant ornamental value in landscaping. There is a critical necessity to elucidate the gene functions of O. triangularis ‘Purpurea’ and dissect the molecular mechanisms governing key ornamental traits. However, a reliable genetic transformation method remains elusive. In this study, our investigation revealed that various transformation parameters, including recipient material (petioles), pre-culture time (2–5 days), acetosyringone (AS) concentration (100–400 μM), Agrobacterium concentrations (OD600 = 0.4–1.0), infection time (5–20 min), and co-culture time (2–5 days), significantly impacted the stable genetic transformation in O. triangular ‘Purpurea’. Notably, the highest genetic transformation rate was achieved from the leaf discs pre-cultured for 3 days, treated with 200 μM AS infected with Agrobacterium for 11 min at OD600 of 0.6, and subsequently co-cultured for 3 days. This treatment resulted in a genetic transformation efficiency of 9.88%, and it only took 79 days to produce transgenic plants. Our transformation protocol offers advantages of speed, efficiency, and simplicity, which will greatly facilitate genetic transformation for O. triangular ‘Purpurea’ and gene function studies.

Highly accurate trajectory prediction models can achieve route optimisation and save airspace resources, which is a crucial technology and research focus for the new generation of intelligent air traffic control. Aiming at the problems of inadequate extraction of trajectory features and difficulty in overcoming the short-term memory of time series in existing trajectory prediction, a trajectory prediction model based on a convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) network combined with dual attention and genetic algorithm (GA) optimisation is proposed. First, to autonomously mine the data association between input features and trajectory features as well as highlight the influence of important features, an attention mechanism was added to a conventional CNN architecture to develop a feature attention module. An attention mechanism was introduced at the output of the BiLSTM network to form a temporal attention module to enhance the influence of important historical information, and GA was used to optimise the hyperparameters of the model to achieve the best performance. Finally, a multifaceted comparison with other typical time-series prediction models based on real flight data verifies that the prediction model based on hyperparameter optimisation and a dual attention mechanism has significant advantages in terms of prediction accuracy and applicability.