Experimental study of a thin thermally grown oxide layer in thermal barrier coatings based on the SWT-BP algorithm and terahertz technology.

As a promising nondestructive testing (NDT) technique with a very adaptive physical modeling of wave transmission process, terahertz technology is used for the detection and characterization of nonpolar materials and the evaluation of layered and/or defective structures. THz-TDS can also be used to perform spectroscopic analysis and detect structural defects in thermal barrier coatings (TBCs) of aero-engines. Although it is generally difficult to measure the structure of the thin oxide layer of the thermal barrier coatings whose thickness is generally lower than 30 µm (the current axial resolution of the THz-TDS cannot exceed 30 µm). We were able to complete the detection of the oxide layer within 1-29 µm through simulation by using the SWT-BP algorithm. In this study, the analysis was performed on real-world samples, the fitting degree of the SWT-BP algorithm reached 0.77, and the minimum prediction error was less than 0.1 µm. The paper also put forward some improvement measures about the experimental results.

Thin thermally grown oxide thickness detection in thermal barrier coatings based on SWT-BP neural network algorithm and terahertz technology.

Terahertz time-domain spectroscopy is a contactless and nondestructive testing technique that is often used to measure the thickness of layered materials. However, the technique presents limited thickness detection resolution, especially in the thin thermally grown oxide (TGO) of thermal barrier coatings whose thickness is below 30 µm. In this study, an SWT-BP algorithm combining a stationary wavelet transform (SWT) and a backpropagation (BP) neural network was proposed, and the regression coefficient of SWT-detailed results was 0.92. The prediction results were in good agreement with the real-time results; it demonstrated that the proposed algorithm was able to achieve a thickness prediction of up to 1-29 µm of the TGO. The proposed algorithm is suitable for thin thickness detection of the TGO.

Evaluation and Prediction of Low-Carbon Economic Efficiency in China, Japan and South Korea: Based on DEA and Machine Learning.

Addressing global climate change has become a broad consensus in the international community. Low-carbon economic development, as an effective means to address global climate change issues, has been widely explored and practiced by countries around the world. As major carbon emitting countries, there has been much focus on China, Japan and South Korea, and it is of practical significance to study their low-carbon economic development. To further measure their trend of low-carbon economic development, this paper firstly constructs a low-carbon economic efficiency evaluation index system and uses the Slack Based Measure (SBM) model. This is a kind of data envelopment analysis (DEA) method, with undesirable output based on global covariance to measure the low-carbon economic efficiency of 94 provincial-level administrative divisions (PLADs) in China, Japan, and South Korea from 2013 to 2019. Subsequently, this paper uses 10 mainstream machine learning models and combining them with Grid Search with Cross Validation (GridSearchCV) methods, selects the machine learning model with the best prediction effect. The model predicts the low-carbon economic efficiency of PLADs in China, Japan, and South Korea from 2020 to 2024 based on the parameter configuration for the best prediction effect. Finally, according to the research results, this paper proposes targeted advice for regionalized cooperation on low-carbon economic development in China, Japan, and South Korea to jointly address global climate change issues.

A Study of Carbon Emission Efficiency in Chinese Provinces Based on a Three-Stage SBM-Undesirable Model and an LSTM Model.

As a major carbon-emitting country, there is an urgent need for China to reduce carbon emissions. Studying the carbon emission efficiency of each province helps us to learn about the characteristics and evolution of regional carbon emissions, which is important for proposing effective and targeted measures to achieve the carbon peaking and carbon neutrality goals. This paper measures the carbon emission efficiency of 30 Chinese provinces from 2006 to 2019 based on a three-stage SBM-undesirable model and explores external drivers using stochastic frontier models. The results of the SBM-undesirable model show that the inter-provincial carbon emission efficiency is unevenly distributed and shows a big difference. From the results of the stochastic frontier model analysis, external drivers such as the intensity of finance in environmental protection, the level of economic development, the industrial structure, the level of urbanization, the degree of openness and the level of science as well as technology innovation all have an impact on the emission efficiency. In terms of LSTM model prediction, the model shows an excellent fitting effect, which provides a possible path for carbon emission efficiency prediction. Finally, based on the empirical results and the actual situation of each province in China, this paper proposes relevant feasible suggestions.

Differential diagnosis of acute and chronic colitis in mice by optical coherence tomography.

Background: The differential diagnosis of acute and chronic colitis remains a common clinical problem. Optical coherence tomography (OCT) is a non-invasive, high-resolution imaging technique that can be used to measure morphological changes in the intestinal wall and estimate intestinal inflammation. We aimed to conduct an ex vivo experiment on a mouse model investigate the value of OCT as a tool for the differential diagnosis of acute and chronic colitis. Methods: Mice were administered dextran sulfate sodium salt (DSS) to construct acute and chronic colitis models. Acutely- and chronically-affected intestinal walls were scanned by OCT, and then the scanned colonic tissue samples were stained with hematoxylin and eosin (HE). Structural and morphological changes indicating inflammation in the intestinal wall were evaluated in the HE sections and OCT images using different parameters. The parameters were used in one-way analysis of variance (ANOVA) to screen for a differential diagnosis of acute or chronic colitis. Results: For the HE sections, the angle of the mucosal folds, length of the basilar part, and submucosal height and area were statistically significant parameters in the comparisons between the mice with acute colitis and the control-group mice (P<0.05). In the comparisons between chronic colitis mice and control-group mice, the angle of the mucosal folds, length of the basilar part, submucosal height and area, muscularis thickness, submucosal height + muscularis thickness, and mucosal thickness were statistically significant parameters (P<0.05). Finally, in the comparisons between acute colitis mice and those with chronic colitis, the angle of the mucosal folds, submucosal height and area, muscularis thickness, submucosal height + muscularis thickness, and mucosal thickness were statistically significant parameters (P<0.05). For the OCT images, only the length of the basilar part and submucosal height + muscularis thickness were statistically significant parameters between the acute colitis mice and control-group mice (P<0.05). The length of the basilar part and submucosal height + muscularis thickness were statistically significant between chronic colitis mice and control-group mice (P<0.05). In the comparisons between acute colitis mice and those with chronic colitis, only submucosal height + muscularis thickness was a statistically significant parameter (P<0.05). Conclusions: Certain intestinal wall parameters in OCT can be used to make a differential diagnosis between acute and chronic colitis possible. This study contributes to constructing a potential diagnostic system for evaluating colorectal inflammation using OCT.