The classification of variable stars is essential for understanding stellar evolution and dynamics. With the growing volume of light curve data from extensive surveys, there is a need for automated and accurate classification methods. Traditional methods often rely on manual feature extraction and selection, which can be time-consuming and less adaptable to large datasets. In this work, we present an approach using a convolutional neural network (CNN) to classify variable stars using only raw light curve data and their known periods, without the need for manual feature extraction or hand-selected data preprocessing. Our method utilizes phase-folding to organize the light curves and directly learns the variability patterns crucial for classification. Trained and tested on the Optical Gravitational Lensing Experiment (OGLE) dataset, our model demonstrates an average accuracy of 88% and an F1 score of 0.89 across five well-known classes of variable stars. We also compared our classification model with the Random Forest (RF) classifier and showed that our model gives better results across all of the classification metrics. By leveraging CNN, our approach does not need manual feature extraction and can handle diverse light curve shapes and sampling cadences. This automated, data-driven method offers a powerful tool for classifying variable stars, enabling efficient processing of large datasets from current and future sky surveys.

This study developed a machine learning-based methodology to classify gravitational wave (GW) signals from black hole-neutron star (BH-NS) mergers by combining convolutional neural network (CNN) with conditional information for feature extraction. The model was trained and validated on a dataset of simulated GW signals injected to Gaussian noise to mimic real world signals. We considered all three types of merger: binary black hole (BBH), binary neutron star (BNS) and neutron star-black hole (NSBH). We achieved up to 96% correct classification of GW signals sources. Incorporating our novel conditional information approach improved classification accuracy by 10% compared to standard time series training. Additionally, to show the effectiveness of our method, we tested the model with real GW data from the Gravitational Wave Transient Catalog (GWTC-3) and successfully classified ~90% of signals. These results are an important step towards low-latency real-time GW detection.

Over the past decade, wireless mesh networks (WMNs) have seen significant advancements due to their simple deployment, cost-effectiveness, ease of implementation, and reliable service coverage. However, despite these advantages, the placement of nodes in WMNs presents a critical challenge that significantly impacts their performance. This issue is recognized as an NP-hard problem, underscoring the necessity of development optimization algorithms, such as heuristic and metaheuristic approaches. This motivated us to develop the Maximum Entropy Genetic Algorithm (MEGA) to address the issue of mesh router node placement in WMNs. To assess the proposed method, we conducted experiments across various scenarios with different settings, focusing on key metrics such as network connectivity and user coverage. The simulation results showed the comparative performance of MEGA in relation to other prominent algorithms, such as the Coyote Optimization Algorithm (COA), Firefly Algorithm (FA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), revealing MEGA’s effectiveness and usability in determining optimal locations for mesh routers.

In this work, a mathematical model based on the Einstein–Smoluchowski stochastic equation describing the output voltage of gas sensors is presented. The Ornstein–Uhlenbeck stochastic process was used to analyze the noise spectrum of laser gas sensors, as a result of which it was proved that the output signal has the form of a Lorentz spectrum. The model allows us to determine the frequency spectrum of the output voltage, taking into account the diffusion and relaxation processes of the gas. Experimental studies have shown changes in time series and correlation parameters for various gases (air, methane, ethanol, ammonia). The proposed model can serve as a basis for creating digital twins of sensors and developing self-calibration systems. The analytical approach establishes a relationship between stochastic fluctuations caused by molecular diffusion and the measurable spectral characteristics of the sensor output. Characteristic parameters of the Lorentzian spectrum, including spectral density, relaxation time, and correlation properties, were evaluated for different gaseous media. Comparative analysis demonstrates that each gas exhibits distinct noise signatures determined by its physical and diffusion properties, enabling reliable discrimination without introducing additional sensing elements. The proposed methodology improves the understanding of fluctuation-induced processes in laser gas sensors and provides a theoretical framework for noise-based gas identification. The obtained results may contribute to the development of intelligent gas sensing systems, real-time signal processing algorithms, and adaptive monitoring platforms for environmental, industrial, and safety applications.

Due to the importance of stellar evolution, new theories and approaches to the study of stars are constantly being developed. This study presents novel results obtained through the application of the fluctuation-dissipation theorem. According to this theorem, fluctuations within a system give rise to dissipation in the form of thermal equilibrium. The spectral correlation function of fluctuations is related to the degree of photon dissipation. In this work, the evolutionary stage of stars is determined by analyzing the relationship between dissipation and fluctuation in complex FS CMa-type systems.

Theoretical foundations are presented for the application of information–entropy methods from statistical physics to the determination of stellar evolutionary stages. A balance equation involving normalized conditional information and entropy is proposed. The conditional information is defined as the difference between the entropy of the phase space and the conditional probability entropy. A correspondence is demonstrated between theoretical predictions and observational data from stellar emission spectra with respect to their evolutionary classification. The proposed methodology is further applied to the analysis of complex FS CMa-type objects, which exhibit dusty and gaseous structures with components at different evolutionary stages. In this context, the conditional information derived from asymmetric spectral lines is shown to be consistent with the theoretical criteria for the evolutionary status of single, binary, and unclassified stars.