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Considering the importance of combustion diagnosis in industrial manufacturing and many fields, efficient, quick, and real-time multidimensional reconstruction is necessary and indispensable. Hence, focusing on the combustion field dynamic and multi-dimensional reconstruction, a modified U-ConvLSTM model was proposed to combine with the TDLAS method to resolve the real-time reconstruction and short prediction. By dividing the combustion field into space and time slices, we used discretized spatiotemporal slices to complete the 2-D distribution reconstruction and then expanded them into higher dimensions. The simulation results demonstrate that our design can effectively reconstruct different 2-D distributions, achieving a reconstruction error of less than 5%. Three-step predictions also performed well, a PSNR no less than 30 dB, and an SSIM no less than 0.75. In general, our multidimensional combustion field reconstruction method, based on the spatiotemporal discretization U-ConvLSTM model, can enhance the accuracy of combustion field reconstruction and provide short-term predictions. This work will contribute to closed-loop control in industrial fields.

Tunable diode laser absorption spectroscopy (TDLAS) technology, combined with chromatographic imaging algorithms, is commonly used for two-dimensional temperature and concentration measurements in combustion fields. However, obtaining critical temperature information from limited detection data is a challenging task in practical engineering applications due to the difficulty of deploying sufficient detection equipment and the lack of sufficient data to invert temperature and other distributions in the combustion field. Therefore, we propose a sparse projection multi-view synthesis model based on U-Net that incorporates the sequence learning properties of gated recurrent unit (GRU) and the generalization ability of residual networks, called GMResUNet. The datasets used for training all contain projection data with different degrees of sparsity. This study shows that the synthesized full projection data had an average relative error of 0.35%, a PSNR of 40.726, and a SSIM of 0.997 at a projection angle of 4. At projection angles of 2, 8, and 16, the average relative errors of the synthesized full projection data were 0.96%, 0.19%, and 0.18%, respectively. The temperature field reconstruction was performed separately for sparse and synthetic projections, showing that the application of the model can significantly improve the reconstruction accuracy of the temperature field of high-energy combustion.

High-resolution flame temperature images are essential indicators for evaluating combustion conditions. Tunable diode laser absorption spectroscopy (TDLAS) is an effective combustion diagnostic method. In actual engineering, due to the limitation of line-of-sight (LOS) measurement, TDLAS technology has the problems of small data volume and low dimensionality in measuring combustion fields, which seriously limits the development of TDLAS in combustion diagnosis. This article demonstrates a TDLAS imaging method based on a 64-pixel area array sensor to reconstruct the two-dimensional temperature field of the flame. This paper verifies the robustness of the Algebraic Reconstruction Technique (ART) algorithm through numerical simulation and studies the effects of temperature, concentration, and pressure on the second harmonic intensity based on the HITRAN database. The two-dimensional temperature field of the flame was reconstructed, and reconstruction accuracy was verified using thermocouples. The maximum relative error was 3.71%. The TDLAS detection system based on a 64-pixel area array sensor provides a way to develop high-precision, high-complexity flame temperature measurement technology.

miR-30b, which is encoded by the gene located on chromosome 8q24.22, plays an important role in a variety of diseases. In most types of tumors, miR-30b significantly inhibits the proliferation, migration, and invasion of cancer cells through the regulation of target genes. Moreover, miR-30b can inhibit the PI3K/AKT signaling pathway through the regulation of EGFR, AKT, Derlin-1, GNA13, SIX1, and other target genes, thus inhibiting the EMT process of tumor cells and promoting apoptosis. In addition, miR-30 plays a significant role in alleviating drug resistance in tumor cells. Although the use of miR-30b as a clinical diagnostic indicator or anticancer drug is still facing great difficulties in the short term, with the deepening of research, the potential application of miR-30b is emerging.