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.

Harmful algal blooms (HABs) produce potent neurotoxins, such as domoic acid (DA) and saxitoxin (STX), which negatively impact marine mammal health. To contextualize these threats within specific regional ecosystems, we conducted a comprehensive meta-analysis of algal toxin concentrations in stranded and non-stranded seals and sea lions reported between 1990 and 2025. Following a systematic screening of 2377 records, 15 studies met the criteria for inclusion in our systematic review and quantitative analysis. Both toxins displayed substantial variability among studies. Notably, the concentrations of DA found in feces were markedly higher than in other matrices, and feces appears to be a high-yield matrix for detecting recent exposure. Urine and serum showed moderate DA levels, while fetal fluids were among the lowest. Geographically, most studies originated from California and the Alaskan Arctic. Multi-matrix monitoring of algal toxins is required in remote or logistically challenging regions. We propose that exposure to algal and cyanobacterial neurotoxins is a testable contributor to recurrent, unexplained mass mortality events among Caspian seals (Pusa caspica) and that targeted toxicological screening within the Caspian Sea ecosystem is urgently needed.

The discrete logarithm problem is a fundamental challenge in number theory with significant implications for cryptographic protocols. In this paper, we investigate the limitations of gradient-based methods for learning the parity bit of the discrete logarithm in finite cyclic groups of prime order. Our main result, supported by theoretical analysis and empirical verification, reveals the concentration of the gradient of the loss function around a fixed point, independent of the logarithm's base used. This concentration property leads to a restricted ability to learn the parity bit efficiently using gradient-based methods, irrespective of the complexity of the network architecture being trained. Our proof relies on Boas-Bellman inequality in inner product spaces and it involves establishing approximate orthogonality of discrete logarithm's parity bit functions through the spectral norm of certain matrices. Empirical experiments using a neural network-based approach further verify the limitations of gradient-based learning, demonstrating the decreasing success rate in predicting the parity bit as the group order increases.

Understanding the accuracy limits of machine learning algorithms is essential for data scientists to properly measure performance so they can continually improve their models' predictive capabilities. This study empirically verified the error bound of the AdaBoost algorithm for both synthetic and real-world data. The results show that the error bound holds up in practice, demonstrating its efficiency and importance to a variety of applications. The corresponding source code is available at https://github.com/armanbolatov/adaboost_error_bound.