建立针对双狭窄的动脉模型的计算流体动力学（CFD）的患者特异性有限元分析（FEA）模型涉及时间和努力，限制医生在时间关键时间医疗应用中快速响应的能力。这些问题可能通过培训深度学习（DL）模型来解决，以使用由具有不同配置的简化双韵动脉模型的CFD模拟产生的数据集来学习和预测血流特性。当通过从IVUS成像的实际双狭窄的动脉模型进行血液流动模式时，揭示了狭窄的颈部几何形状的正弦逼近，这些颈部几何形状被广泛用于先前的研究作品，未能有效地代表真实的效果收缩。结果，提出了一种收缩颈的新型几何表示，其就广义简化模型而言，这始终是前者的假设。动脉腔直径和流量参数的顺序变化沿着船长的长度呈现使用LSTM和GRU DL模型的机会。然而，对于短长度的倍增血液动脉的小数据集，基本神经网络模型优于大多数流动性质的专用RNN。另一方面，LSTM对预测具有大波动的流动性能更好，例如在血管的长度上变化血压。尽管在数据集中的船舶的所有属性训练和测试方面具有良好的整体准确性，但GRU模型在所有情况下为单个血管流预测的表现不佳。结果还指向任何模型中每个属性的单独优化的超级参数，而不是旨在通过单一的HyperParameters来实现所有输出的整体良好性能。

Jamdani is the strikingly patterned textile heritage of Bangladesh. The exclusive geometric motifs woven on the fabric are the most attractive part of this craftsmanship having a remarkable influence on textile and fine art. In this paper, we have developed a technique based on the Generative Adversarial Network that can learn to generate entirely new Jamdani patterns from a collection of Jamdani motifs that we assembled, the newly formed motifs can mimic the appearance of the original designs. Users can input the skeleton of a desired pattern in terms of rough strokes and our system finalizes the input by generating the complete motif which follows the geometric structure of real Jamdani ones. To serve this purpose, we collected and preprocessed a dataset containing a large number of Jamdani motifs images from authentic sources via fieldwork and applied a state-of-the-art method called pix2pix to it. To the best of our knowledge, this dataset is currently the only available dataset of Jamdani motifs in digital format for computer vision research. Our experimental results of the pix2pix model on this dataset show satisfactory outputs of computer-generated images of Jamdani motifs and we believe that our work will open a new avenue for further research.