Nurzhan Ussipov

Toward more realistic machine-learning inference of the dense-matter equation of state from supernova gravitational waves

Almat Akhmetali, Y. S. Abylkairov, Marat Zaidyn, A. Sakan et autres

Gravitational waves from core-collapse supernovae offer a unique probe of the equation of state (EOS) of dense nuclear matter. For rapidly rotating stars, previous machine-learning studies demonstrated promising EOS classification accuracy. However, these analyses relied on several simplifying assumptions. In this work, …

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Toward More Realistic Machine-Learning Inference of the Dense-Matter Equation of State from Supernova Gravitational Waves

Almat Akhmetali, Y. Sultan Abylkairov, Marat Zaidyn, Aknur Sakan et autres

Gravitational waves from core-collapse supernovae offer a unique probe of the equation of state (EOS) of dense nuclear matter. For rapidly rotating stars, previous machine-learning studies demonstrated promising EOS classification accuracy. However, these analyses relied on several simplifying assumptions. In this work, …

Evolution of fractality in centrally concentrated young clusters

Almat Akhmetali, Adilkhan Assilkhan, Mordecai-Mark Mac Low, Nurzhan Ussipov et autres

We investigate the structural evolution of young star clusters forming within centrally concentrated molecular clouds. Our simulations use the Torch framework, which integrates the FLASH magnetohydrodynamics code with the AMUSE environment, enabling a self-consistent treatment of gas dynamics, star formation, stellar evolution, …

Evaluating machine learning-based routing algorithms on various wireless network topologies (Retraction Notice)

Dana Turlykozhayeva, Sауаt Akhtanov, Dauren Zhexebay, Nurzhan Ussipov et autres

Wireless networks are crucial to modern communication infrastructure, supporting a wide range of applications from personal use to industrial operations. The rapid growth of mobile devices, IoT, and advanced technologies like 5G has heightened the demand for scalable, efficient, and reliable wireless …

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The role of fractal dimension in wireless mesh network performance

Marat Zaidyn, Sayat Akhtanov, Dana Turlykozhayeva, Symbat Temesheva et autres

Wireless mesh networks (WMNs) depend on the spatial distribution of nodes, which directly influences connectivity, routing efficiency, and overall network performance. Conventional models typically assume uniform or random node placement, which inadequately represent the complex, hierarchical spatial patterns observed in practical deployments. …

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Probing Supernovae through gravitational wave entropy

Aknur Sakan, Nurzhan Ussipov, Ernazar B. Abdikamalov, Almat Akhmetali et autres

We study an entropy-based framework to analyze gravitational-wave signals from core-collapse supernovae. We use waveforms generated by numerical simulations and analyze them in both the time domain and the time-frequency domain using short-time Fourier and continuous wavelet transforms. From each representation, we …

Machine learning-based classification of variable stars using phase-folded light curves

Almat Akhmetali, Alisher Zhunuskanov, Timur A. Namazbayev, Marat Zaidyn et autres

Classifying variable stars is crucial for advancing our understanding of stellar evolution and dynamics. As large-scale surveys generate increasing volumes of light curve data, the demand for automated and reliable classification techniques continues to grow. Traditional methods often rely on manual feature …

The Role of Fractal Dimension in Wireless Mesh Network Performance

Marat Zaidyn, Sayat Akhtanov, Dana Turlykozhayeva, Symbat Temesheva et autres

Wireless mesh networks (WMNs) depend on the spatial distribution of nodes, which directly influences connectivity, routing efficiency, and overall network performance. Conventional models typically assume uniform or random node placement, which inadequately represent the complex, hierarchical spatial patterns observed in practical deployments. …

Luminis Stellarum et Machina: Applications of Machine Learning in Light Curve Analysis

Almat Akhmetali, Alisher Zhunuskanov, Aknur Sakan, Marat Zaidyn et autres

The rapid advancement of observational capabilities in astronomy has led to an exponential growth in the volume of light curve (LC) data, creating both opportunities and challenges for time-domain astronomy. Traditional analytical methods often struggle to fully extract the scientific value of …

A routing algorithm for wireless mesh network based on information entropy theory

Dana Turlykozhayeva, Sayat Akhtanov, Z. Zh. Zhanabaev, Nurzhan Ussipov et autres

Abstract Nowadays, wireless mesh networks (WMNs) are rapidly spreading around the world due to their competitive advantages. Beyond this, their adaptability is evident in supporting a wide range of applications: from powering broadband home networks and educational programs to driving healthcare advancements, …

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Evaluating machine learning-based routing algorithms on various wireless network topologies

Dana Turlykozhayeva, Sayat Akhtanov, Dauren Zhexebay, Nurzhan Ussipov et autres

Wireless networks are crucial to modern communication infrastructure, supporting a wide range of applications from personal use to industrial operations. The rapid growth of mobile devices, IoT, and advanced technologies like 5G has heightened the demand for scalable, efficient, and reliable wireless …

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The Analysis of the ZnO/Por-Si Hierarchical Surface by Studying Fractal Properties with High Accuracy and the Behavior of the EPR Spectra Components in the Ordering of Structure

Tatyana Seredavina, Rashid Zhapakov, Danatbek Murzalinov, Yu. M. Spivak et autres

A hierarchical surface that includes objects with different sizes, as a result of creating local fields, initiates a large number of effects. Micropores in the composition of macropores, as well as nanoclusters of the substance, were detected by scanning electron and atomic …

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