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Carbon diffusivity in austenite is an important value in heat treatment, such as annealing and normalizing, for controlling the overall properties of steels. However, it is still a challenge to improve the model to more accurately predict the carbon diffusivity and analyze the features. Here, machine learning methods were employed to precisely predict carbon diffusivity and provide specific insights into prediction mechanisms of features. Shapley additive explanation method was employed to analyze the feature mechanisms. A total of 263 utilizable datasets were collected from the literature and analyzed, and three outliers were discarded. We then searched for hyperparameters using fivefold cross-validation and a grid search. Random forest regression (RFR) was selected based on the determination coefficient. The RFR was validated with an empirical equation using training, testing, and additional datasets. The importance and mechanisms of input features such as temperature, carbon concentration, Cr, Ni, Si, Al, Mn, and Mo were discussed using the calculation results of the Shapley value. Temperature had the greatest influence on carbon diffusivity, followed by carbon concentration, Cr, Si, Mo, Ni, Al, and Mn. Temperature, carbon concentration, Mo, Ni, and Mn increased carbon diffusivity but Cr, Si, and Al decreased carbon diffusivity.

The tempering of low-alloy steels is important for controlling the mechanical properties required for industrial fields. Several studies have investigated the relationships between the input and target values of materials using machine learning algorithms. The limitation of machine learning algorithms is that the mechanism of how the input values affect the output has yet to be confirmed despite numerous case studies. To address this issue, we trained four machine learning algorithms to control the hardness of low-alloy steels under various tempering conditions. The models were trained using the tempering temperature, holding time, and composition of the alloy as the inputs. The input data were drawn from a database of more than 1900 experimental datasets for low-alloy steels created from the relevant literature. We selected the random forest regression (RFR) model to analyze its mechanism and the importance of the input values using Shapley additive explanations (SHAP). The prediction accuracy of the RFR for the tempered martensite hardness was better than that of the empirical equation. The tempering temperature is the most important feature for controlling the hardness, followed by the C content, the holding time, and the Cr, Si, Mn, Mo, and Ni contents.

In this study, the average grain size was evaluated from a microstructure image using a convolutional neural network. Since the grain size in a microstructure image can be directly measured and verified in the original image, unlike the chemical composition or mechanical properties of material, it is more appropriate to validate the training results quantitatively. An analysis of microstructure images, such as grain size, can be performed manually or using image analysis software; however, it is expected that the analysis would be simpler and faster with machine learning. Microstructure images were created using a phase-field simulation, and machine learning was carried out with a convolutional neural network model. The relationship between the microstructure image and the average grain size was not judged by classification, as the goal was to have different results for each microstructure using regression. The results showed high accuracy within the training range. The average grain sizes of experimental images with explicit grain boundary were well estimated by the network. The mid-layer image was analyzed to examine how the network understood the input microstructure image. The network seemed to recognize the curvatures of the grain boundaries and estimate the average grain size from these curvatures.

Oiketicoides gohadoensis Roh & Lee, sp. nov. is described as new to science. The morphology of male adult, including genitalia, is described, and DNA barcodes for precise identification of the species are provided. A parasitoid, Neophryxe psychidis Townsend, 1916 (Diptera, Tachinidae) of O. gohadoensis is also reported for the first time in Korea, together with its DNA barcode sequence.

Multiphase, ultra-fine-grained transformation-induced plasticity (MP UFG TRIP) steel containing 6 mass pct Mn was obtained by cold rolling and intercritical annealing of an initially fully martensitic microstructure. UFG microstructures with an average grain size less than 300 nm were obtained. The amount of austenite in the microstructures, speculated to be formed by diffusionless transformation, was controlled by changing the intercritical temperature. The tensile properties were strongly influenced by the volume amount and the stability of the reversely transformed austenite. The MP UFG TRIP steel was characterized by pronounced localization of the deformation. The deformation band properties were analyzed in detail.

Background In ecosystem carbon cycle studies, distinguishing between CO 2 emitted by roots and by microbes remains very difficult because it is mixed before being released into the atmosphere. Currently, no method for quantifying root and microbial respiration is effective. Therefore, this study investigated the relationship between soil respiration and underground root biomass at varying distances from the tree and tested possibilities for measuring root and microbial respiration. Methods Soil respiration was measured by the closed chamber method, in which acrylic collars were placed at regular intervals from the tree base. Measurements were made irregularly during one season, including high temperatures in summer and low temperatures in autumn; the soil’s temperature and moisture content were also collected. After measurements, roots of each plot were collected, and their dry matter biomass measured to analyze relationships between root biomass and soil respiration. Results Apart from root biomass, which affects soil’s temperature and moisture, no other factors affecting soil respiration showed significant differences between measuring points. At each point, soil respiration showed clear seasonal variations and high exponential correlation with increasing soil temperatures. The root biomass decreased exponentially with increasing distance from the tree. The rate of soil respiration was also highly correlated exponentially with root biomass. Based on these results, the average rate of root respiration in the soil was estimated to be 34.4% (26.6~43.1%). Conclusions In this study, attempts were made to differentiate the root respiration rate by analyzing the distribution of root biomass and resulting changes in soil respiration. As distance from the tree increased, root biomass and soil respiration values were shown to strongly decrease exponentially. Root biomass increased logarithmically with increases in soil respiration. In addition, soil respiration and underground root biomass were logarithmically related; the calculated root-breathing rate was around 44%. This study method is applicable for determining root and microbial respiration in forest ecosystem carbon cycle research. However, more data should be collected on the distribution of root biomass and the correlated soil respiration.

We propose an empirical equation to predict the martensite start temperatures of highly alloyed steels containing more than 3 wt.% of Ni or Cr or 2 wt.% of Mo, W, or Co. The martensite start temperature calculated by the proposed equation was in good agreement with experimental data owing to not only the derivation from experimental data of alloy steels with a wide range of chemical compositions but also the interaction term between carbon and carbide-forming alloying elements.

Since previous equations fail to predict MS temperature of high carbon ferrous alloys, we first propose an equation for prediction of MS temperature of ferrous alloys containing > 2 wt pct C. The presence of carbides (Fe3C and Cr-rich M7C3) is thermodynamically considered to estimate the C concentration in austenite. Especially, equations individually specialized for lean and high Cr alloys very accurately reproduce experimental results. The chemical driving force for martensitic transformation is quantitatively analyzed based on the calculation of T0 temperature.

Due to its excellent processability, thermal conductivity and high corrosion resistance, the copper tube applied to the heat exchanger is joined by the brazing process. In order to improve the performance of heat exchangers, it is essential to inspect the joint quality of copper tubes, but it is difficult to identify defects in tube-shaped joints without cutting. To solve this problem, this study proposes a new detection method based on the Convolutional Neural Network (CNN) model to detect joint defects that occur in brazing joints of copper tubes. In the experiment, a brazing joint using high-frequency induction heating was performed on a 12.71 mm diameter copper tube, which is mainly used in heat exchangers, and the joint failure was judged based on the penetration depth of the filler material measured by vertically cutting the joined copper tube. In addition, thermal image data having a data structure of 80 × 80 pixels per frame was collected to be used as data to determine whether the brazing joint is defective in real time. Finally, using the collected thermal image data, we developed a CNN model with a structure that applies different hyperparameters to determine whether or not the joint of copper tube is defective. The selected CNN model produced an f1 score of 0.991 and a recall of 99.73%, and formed the basis for developing a system that identifies defects in brazing joints of copper tubes through thermal image data obtained in real time.

This study examines environmental susceptibilities in coastal regions by integrating geospatial indices to evaluate flood susceptibility, drought severity, and crop water stress. A data fusion framework was developed using the composite coastal flood susceptibility index (CCFSI), composite drought index (CDI), and crop water stress index (CWSI), validated against 70 ground truth points. The analysis revealed that the “Very Low Flood” covered the largest area of 131,244 hectares, while the “Very High Flood” occupied 277.04 hectares. In drought severity, the “Very High Drought” dominated with 122,306 hectares, whereas the “Low Drought” spanned just 1.56 hectares. For crop water stress, the “Very Low CWSI” encompassed 93,116.8 hectares, and the “Very High CWSI” covered 10,791.9 hectares, highlighting the variations in susceptibility across regions. Validation metrics showed high reliability, with F1 scores of 0.91 for flood prediction and 0.92 for drought prediction. The composite maps highlighted key susceptibilities, with the “High Flood-High Drought” covering 3471.35 hectares, the “High Drought-High CWSI” spanning 13,162.60 hectares, and the “High Flood-High CWSI” limited to 12.51 hectares, emphasizing the areas most affected by combined environmental stressors. The proposed framework is scalable, adaptable, and provides valuable tools for policymakers to enhance climate resilience and sustainable resource management globally.