Research of microstructure and permeability evolution of coal following LN2 treatment elucidate the process of cryogenic fracturing due to environmentally friendly behavior in comparison with conventional hydraulic fracturing. The evolution of the 2D microstructure of bituminous coal before and after LN2 treatment was examined using a high-resolution camera. The image processing was implemented using functions from the OpenCV Python library that are sequentially applied to digital images of original coal samples. The images were converted into binary pixel matrices to identify cracks and to evaluate the number of cracks, crack density, total crack area, and average crack length. Results were visualized using Seaborn and Matplotlib Python libraries. There were calculations of total crack area (TCA), total number of cracks (TNC), crack density (CD), the average length of cracks (Q2), first (Q1) and third (Q3) quartiles in fracture length statistics. Our findings demonstrate a progressive increase in the Total Crack Area (δTCA) with longer freezing times and an increased number of freezing–thawing cycles. In contrast, the change in crack density (δCD) was generally unaffected by freezing time alone but exhibited a significant increase after several freezing–thawing cycles. Among the freezing times investigated, the highest crack density (CD) value of 300 m−1 was achieved in FT60, while the lowest CD value of 31.25 m−1 was observed in FT90 after liquid nitrogen (LN2) treatment. Additionally, the FTC4 process resulted in a 50% augmentation in the number of cracks, whereas the FTC5 process tripled the number of small cracks.

Nowadays, it is common for people to take photographs of every beverage, snack, or meal they eat and then post these photographs on social media platforms. Leveraging these social trends, real-time food recognition and reliable classification of these captured food images can potentially help replace some of the tedious recording and coding of food diaries to enable personalized dietary interventions. Although Central Asian cuisine is culturally and historically distinct, there has been little published data on the food and dietary habits of people in this region. To fill this gap, we aim to create a reliable dataset of regional foods that is easily accessible to both public consumers and researchers. To the best of our knowledge, this is the first work on the creation of a Central Asian Food Dataset (CAFD). The final dataset contains 42 food categories and over 16,000 images of national dishes unique to this region. We achieved a classification accuracy of 88.70% (42 classes) on the CAFD using the ResNet152 neural network model. The food recognition models trained on the CAFD demonstrate the effectiveness and high accuracy of computer vision for dietary assessment.