The impact of technological innovation on the green digital economy and development strategies.

To investigate the interplay among technological innovation, industrial structure, production methodologies, economic growth, and environmental consequences within the paradigm of a green economy and to put forth strategies for sustainable development, this study scrutinizes the limitations inherent in conventional deep learning networks. Firstly, this study analyzes the limitations and optimization strategies of multi-layer perceptron (MLP) networks under the background of the green economy. Secondly, the MLP network model is optimized, and the dynamic analysis of the impact of technological innovation on the digital economy is discussed. Finally, the effectiveness of the optimization model is verified by experiments. Moreover, a sustainable development strategy based on dynamic analysis is also proposed. The experimental results reveal that, in comparison to traditional Linear Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB) models, the optimized model in this study demonstrates improved performance across various metrics. With a sample size of 500, the optimized model achieves a prediction accuracy of 97.2% for forecasting future trends, representing an average increase of 14.6%. Precision reaches 95.4%, reflecting an average enhancement of 19.2%, while sensitivity attains 84.1%, with an average improvement of 11.8%. The mean absolute error is only 1.16, exhibiting a 1.4 reduction compared to traditional models and confirming the effectiveness of the optimized model in prediction. In the examination of changes in industrial structure using 2020 data to forecast the output value of traditional and green industries in 2030, it is observed that the output value of traditional industries is anticipated to decrease, with an average decline of 11.4 billion yuan. Conversely, propelled by the development of the digital economy, the output value of green industries is expected to increase, with an average growth of 23.4 billion yuan. This shift in industrial structure aligns with the principles and trends of the green economy, further promoting sustainable development. In the study of innovative production methods, the green industry has achieved an increase in output and significantly enhanced production efficiency, showing an average growth of 2.135 million tons compared to the average in 2020. Consequently, this study highlights the dynamic impact of technological innovation on the digital economy and its crucial role within the context of a green economy. It holds certain reference significance for research on the dynamic effects of the digital economy under technological innovation.

Identification of the Natural Product Rotihibin†A as a TOR Kinase Signaling Inhibitor by Unbiased Transcriptional Profiling.

Bioactive natural products are important starting points for developing chemical tools for biological research. For elucidating their bioactivity profile, biological systems with concise complexity such as cell culture systems are frequently used, whereas unbiased investigations in more complex multicellular systems are only rarely explored. Here, we demonstrate with the natural product Rotihibin A and the plant research model system Arabidopsis thaliana that unbiased transcriptional profiling enables a rapid, label-free, and compound economic evaluation of a natural product's bioactivity profile in a complex multicellular organism. To this end, we established a chemical synthesis of Rotihibin A as well as that of structural analogues, followed by transcriptional profiling-guided identification and validation of Rotihibin A as a TOR signaling inhibitor (TOR=target of rapamycin). These findings illustrate that a combined approach of transcriptional profiling and natural product research may represent a technically simple approach to streamline the development of chemical tools from natural products even for biologically complex multicellular biological systems.

Dose Conversion Coefficients Based on Taiwanese Reference Phantoms and Monte Carlo Simulations for Use in External Radiation Protection.

Reference phantoms are widely applied to evaluate the radiation dose for external exposure. However, the frequently used reference phantoms are based on Caucasians. Dose estimation for Asians using a Caucasian phantom can result in significant errors. This study recruited 40 volunteers whose body sizes are close to the average Taiwanese population. Magnetic resonance imaging was performed to obtain the organ volume for construction of the Taiwanese reference man (TRM) and Taiwanese reference woman (TRW). The dose conversion coefficients (DCC) resulting from photo beams in anterior-posterior, posterior-anterior, right-lateral, left-lateral, and isotropic irradiation geometries were estimated. In the anterior-posterior geometry, the mean DCC differences among organs between the TRM and ORNL phantom at 0.1, 1, and 10 MeV were 7.3%, 5.8%, and 5.2%, respectively. For the TRW, the mean differences from the ORNL phantom at the three energies were 10.6%, 7.4%, and 8.3%. The DCCs of the Taiwanese reference phantoms and the ORNL phantom presented similar trends in other geometries. The torso size of the phantom and the mass and geometric location of the organ have a significant influence on the DCC. The Taiwanese reference phantoms can be used to establish dose guidelines and regulations for radiation protection from external exposure.

Construction of Taiwanese Adult Reference Phantoms for Internal Dose Evaluation.

In the internal dose evaluation, the specific absorbed fraction (SAF) and S-value are calculated from the reference phantom based on Caucasian data. The differences in height and weight between Caucasian and Asian may lead to inaccurate dose estimation. In this study, we developed the Taiwanese reference phantoms. 40 volunteers were recruited. Magnetic resonance images (MRI) were obtained, and the contours of 15 organs were drawn. The Taiwanese reference man (TRM) and Taiwanese reference woman (TRW) were constructed. For the SAF calculation, the differences in the self-absorption SAF (self-SAF) between the TRM, TRW, and Oak Ridge National Laboratory (ORNL) adult phantom were less than 10% when the difference in organ mass was less than 20%. The average SAF from liver to pancreas of TRM was 38% larger than that of the ORNL adult phantom, and the result of TRW was 2.02 times higher than that of the ORNL adult phantom. For the S-value calculation, the ratios of TRW and ORNL adult phantom ranged from 0.91 to 1.57, and the ratios of TRM and ORNL adult phantom ranged from 1.04 to 2.29. The SAF and S-value results were dominantly affected by the height, weight, organ mass, and geometric relationship between organs. By using the TRM and TRW, the accuracy of internal dose evaluation can be increased for radiation protection and nuclear medicine.