Damghan University PressIranian Journal of Astronomy and Astrophysics2322-492412420260216Criticality in Nonconformal Plasmas at Finite Temperature and Density299310206610.22128/ijaa.2026.3168.1234ENLeila ShahkaramiSchool of Physics, Damghan University, Damghan 36716-45667, Iran.Journal Article20251128We study the thermodynamics of an asymptotically AdS black hole in a holographic Einstein-Maxwell-dilaton model describing a nonconformal plasma at finite temperature and chemical potential. Using a logarithmic warp factor and working in the grand canonical ensemble, we analyze the temperature-horizon relation, entropy density, grand potential, pressure, energy density, and trace anomaly, demonstrating the emergence of a first-order phase transition at low chemical potentials, a critical end point, and a smooth crossover at higher chemical potentials. Thermodynamic response functions, including the heat capacity and charge susceptibility, exhibit finite jumps at the first-order transition, divergence at the critical end point, and broad maxima in the crossover region. These results collectively establish a consistent and QCD-like phase structure in this holographic framework.https://ijaa.du.ac.ir/article_2066_303441c47ed9af1738894c23079e19a6.pdfDamghan University PressIranian Journal of Astronomy and Astrophysics2322-492412420260314Evolutionary Structure of Magnetized Accretion Flow Incorporating Saturated Thermal Conduction311322207610.22128/ijaa.2026.3224.1243ENKazem FagheiSchool of Physics, Damghan University, Damghan, IranMotahareh MohammadpourSchool of Physics, Damghan University, Damghan, Iran0000-0003-2941-8980Journal Article20260126This paper presents a time-dependent model for magnetized, advection-dominated accretion flows (ADAFs) that incorporates non-ideal effects, specifically resistivity and saturated thermal conduction. We apply a self-similar method to transform the full time-dependent magnetohydrodynamic (MHD) equations into a set of coupled ordinary differential equations, enabling us to investigate the evolving radial structure influenced by turbulent viscosity, magnetic diffusivity, and non-local energy transport. The resulting solutions confirm that the flow structure is inherently time-dependent. Numerical results demonstrate that increasing the efficiency of outward energy transport via saturated conduction weakens turbulence, reduces dissipation and temperature, increases density, and reduces the radial infall velocity while increasing the rotational velocity. In contrast, stronger magnetization leads to enhanced magnetic fields, lower temperatures, and faster radial inflow. We further show that the turbulence prescription parameter, which controls the pressure dependence of transport coefficients, significantly influences the balance between magnetic and thermal support. This framework offers a dynamic perspective on magnetized accretion flows, highlighting how non-ideal magnetic effects and conduction regulate the flow structure.https://ijaa.du.ac.ir/article_2076_a2dac6797bddfb77db28daa876007c0b.pdfDamghan University PressIranian Journal of Astronomy and Astrophysics2322-492412420260101Time-Series Forecasting of Geomagnetic Activity: A Data-Driven Approach for K_P Index Prediction323334210610.22128/ijaa.2026.3282.1245ENSevim RanjbarFaculty of Physics, University of Tabriz, PO Box 51666-16471, Tabriz, IranSomayeh TaranFaculty of Physics, University of Tabriz, PO Box 51666-16471, Tabriz, Iran0000-0001-7101-7449Journal Article20260225The geomagnetic index $\emph K_{\rm p}$ is a fundamental metric for quantifying geomagnetic activity and understanding solar-terrestrial physics. Accurately modeling and predicting its non-linear fluctuations remains a significant challenge in the study of space climate. In this research, we propose a robust statistical approach using a Long Short-Term Memory (LSTM) neural network for the time-series forecasting of the $\emph K_{\rm p}$ index. Our models are trained and evaluated on a comprehensive dataset spanning a 25-year period from 1999 to 2024, utilizing high-resolution solar wind data sourced from the NASA OMNIWeb database. We incorporate eight critical physical parameters as input features, including three-dimensional magnetic field components and plasma properties. A critical aspect of sequential modeling is determining the optimal historical context. Therefore, we systematically evaluate the impact of various input time windows (6, 12, 24, 48, 72, and 120 hours) on the predictive performance of the network. Our empirical analysis reveals that the 6-hour input window yields the highest precision, achieving an $R^2$ score of 0.7233 and minimizing the root mean square error (RMSE). These results indicate that short-term solar memory contains the most relevant dynamical features for forecasting geomagnetic variations. This study highlights the effectiveness of LSTM architectures in capturing the short-term dynamical evolution of the Earth's magnetic environment, offering valuable insights for future data-driven research in space physics.https://ijaa.du.ac.ir/article_2106_db843717858b34217e32c8f1e20697aa.pdf