@article{PreethamSataStannariusPuzyrev2025, author = {Preetham Sata, Sai and Stannarius, Ralf and Puzyrev, Dmitry}, title = {Machine learning for prediction of dynamical clustering in granular gases}, series = {EPJ Web of Conferences}, volume = {340}, journal = {EPJ Web of Conferences}, editor = {Alam, M. and Das, S.L. and Bose, M. and Murthy, T.G. and Chikkadi, V. and Singh, A. and Luding, S.}, publisher = {EDP Sciences}, doi = {10.1051/epjconf/202534012012}, year = {2025}, abstract = {Continuously excited dense granular gases in microgravity can develop spatial inhomogeneities of the particle distribution. Dynamical clustering is a phenomenon where a significant share of particles concentrate in strongly overpopulated regions. It is caused by a complex interplay between the energy influx and dissipation in collisions. The overall packing fraction, container geometry, and excitation parameters influence the gas-cluster transition. We perform Discrete Element Method (DEM) simulations for frictional spheres in a cuboid container and apply statistical criteria to the packing fraction profiles. Machine learning (ML) methods are used to study the dependence of the gas-cluster transition on system parameters. It is a promising alternative to predict the state of the system without the need for the time-consuming DEM simulations. We identify the best models for predicting the dynamical clustering of frictional spheres in a specific experimental geometry.}, language = {en} } @article{PtashnikMikhailovYastrebovetal.2017, author = {Ptashnik, Sergey V. and Mikhailov, Anatoliy K. and Yastrebov, Alexander V. and Petrov, Peter K. and Liu, Wei and McN Alford, Neil and Hirsch, Soeren and Kozyrev, Andrey B.}, title = {Ferroelectric thin film acoustic devices with electrical multiband switching ability}, series = {Scientific Reports}, volume = {7}, journal = {Scientific Reports}, number = {15289}, doi = {10.1038/s41598-017-14895-8}, year = {2017}, language = {en} } @article{PuzyrevTorstenHarthetal.2024, author = {Puzyrev, Dmitry and Torsten, Torsten and Harth, Kirsten and Stannarius, Ralf}, title = {Cooling of a granular gas mixture in microgravity}, series = {npj microgravity}, volume = {10}, journal = {npj microgravity}, number = {1}, publisher = {Springer}, address = {Brandenburg an der Havel}, doi = {10.1038/s41526-024-00369-5}, pages = {1 -- 9}, year = {2024}, abstract = {Granular gases are fascinating non-equilibrium systems with interesting features such as spontaneous clustering and non-Gaussian velocity distributions. Mixtures of different components represent a much more natural composition than monodisperse ensembles but attracted comparably little attention so far. We present the observation and characterization of a mixture of rod-like particles with different sizes and masses in a drop tower experiment. Kinetic energy decay rates during granular cooling and collision rates were determined and Haff's law for homogeneous granular cooling was confirmed. Thereby, energy equipartition between the mixture components and between individual degrees of freedom is violated. Heavier particles keep a slightly higher average kinetic energy than lighter ones. Experimental results are supported by numerical simulations.}, language = {de} } @article{PuzyrevTrittelHarthetal.2025, author = {Puzyrev, Dmitry and Trittel, Torsten and Harth, Kirsten and Sata, Sai Preetham and Stannarius, Ralf and Cruz Hidalgo, Ra{\´u}l}, title = {AI-aided visual data analysis for granular gases: Complex particles, high density, and other challenges}, series = {EPJ Web of Conferences}, volume = {340}, journal = {EPJ Web of Conferences}, editor = {Alam, M. and Das, S.L. and Bose, M. and Murthy, T.G. and Chikkadi, V. and Singh, A. and Luding, S.}, publisher = {EDP Sciences}, doi = {10.1051/epjconf/202534012013}, year = {2025}, abstract = {Microgravity experiments with three-dimensional (3D) granular gases, i.e., ensembles of freemoving macroscopic particles which collide inelastically, produce large amounts of stereo video footage which require processing and analysis. The main steps of data treatment are particle detection, 3D matching and tracking in stereoscopic views, and quantification of ensemble statistical properties such as, e.g. mean kinetic energy or collision processes. Frequent overlapping or clustering of particles and their complicated movement patterns require smart software solutions. In recent years, Artificial Intelligence/Machine Learning (AI/ML) methods were successfully used for analysis of granular systems. We have applied such techniques to the granular gases of rod-like particles and developed a software tool which enables a full cycle of semi-automatic experimental data analysis. The approach is now tested on more complex, non-convex particles, shaped as 3D crosses (hexapods). Another challenge is optical analysis of dense granular gases, where individual particles cannot be tracked. We present a preliminary result of application of an ML method for number density profiles extraction in VIP-Gran experiment with dense ensemble of rod-like particles.}, language = {en} } @article{RedkaMukhinGrishkanichetal.2019, author = {Redka, D. and Mukhin, N. and Grishkanich, A. and Terukov, E. and Hirsch, S{\"o}ren and Ignateva, L.}, title = {NdYAG surface modification of ZnO for solar cells}, series = {Journal of Physics: Conference Series}, volume = {1410}, journal = {Journal of Physics: Conference Series}, publisher = {IOP Science}, doi = {10.1088/1742-6596/1410/1/012106}, pages = {012106}, year = {2019}, language = {en} } @article{RegehlyHecht2022, author = {Regehly, Martin and Hecht, Stefan}, title = {Auf dem Weg zum Replikator}, series = {Physik in unserer Zeit}, volume = {53}, journal = {Physik in unserer Zeit}, number = {3}, publisher = {Wiley-VCH GmbH}, issn = {1521-3943}, doi = {10.1002/piuz.202201637}, pages = {125 -- 131}, year = {2022}, language = {de} } @article{RudenkoGaponenkoLitvinovetal.2020, author = {Rudenko, Maryia and Gaponenko, Nikolai and Litvinov, Vladimir and Ermachikhin, Alexander and Chubenko, Eugene and Borisenko, Victor and Mukhin, Nikolay and Radyush, Yuriy and Tumarkin, Andrey and Gagarin, Alexander}, title = {Structural Dependent Eu3+ Luminescence, Photoelectric and Hysteresis Effects in Porous Strontium Titanate}, series = {Materials}, volume = {13}, journal = {Materials}, number = {24}, publisher = {MDPI}, doi = {10.3390/ma13245767}, year = {2020}, language = {en} } @article{RuzankinaMukhinMermouletal.2022, author = {Ruzankina, Iuliia and Mukhin, Nikolay and Mermoul, Ali and Parfenov, Vadim and Fron, Eduard and Ferrini, Gabriele}, title = {Surface optical sensitivity enhanced by a single dielectric microsphere}, series = {Optics Express}, volume = {30}, journal = {Optics Express}, number = {24}, publisher = {Optica}, pages = {43021 -- 43036}, year = {2022}, abstract = {Single dielectric microspheres can manipulate light focusing and collection to enhance optical interaction with surfaces. To demonstrate this principle, we experimentally investigate the enhancement of the Raman signal collected by a single dielectric microsphere, with a radius much larger than the exciting laser spot size, residing on the sample surface. The absolute microsphere-assisted Raman signal from a single graphene layer measured in air is more than a factor of two higher than that obtained with a high numerical aperture objective. Results from Mie's theory are used to benchmark numerical simulations and an analytical model to describe the isolated microsphere focusing properties. The analytical model and the numerical simulations justify the Raman signal enhancement measured in the microsphere-assisted Raman spectroscopy experiments.}, language = {en} } @article{SataStannariusPuzyrev2025, author = {Sata, Sai Preetham and Stannarius, Ralf and Puzyrev, Dmitry}, title = {Criteria for dynamical clustering in permanently excited granular gases: comparison and estimation with machine learning approaches}, series = {Granular Matter}, volume = {27}, journal = {Granular Matter}, number = {4}, publisher = {Springer}, doi = {10.1007/s10035-025-01560-5}, year = {2025}, abstract = {When granular gases in microgravity are continuously excited mechanically, spatial inhomogeneities of the particle distribution can emerge. At a sufficiently large overall packing fraction, a significant share of particles tend to concentrate in strongly overpopulated regions, so-called clusters, far from the excitation sources. This dynamical clustering is caused by a complex balance between energy influx and dissipation. The mean number density of particles, the geometry of the container, and the excitation strength influence cluster formation. A quantification of clustering thresholds is not trivial. We generate 'synthetic' data sets by Discrete Element Method simulations of frictional spheres in a cuboid container and apply established criteria to classify the local packing fraction profiles. Machine learning approaches that predict dynamic clustering from known system parameters on the basis of classical test criteria areoposed and tested. It avoids the necessity of complex numerical simulations.}, language = {en} } @article{SchumannStoilovaBortolettoetal.2025, author = {Schumann, Sophia and Stoilova, Velizara and Bortoletto, Enrico and Piva, Elisabetta and Pacchini, Sara and Mozzi, Gloria and Jechow, Andreas and Marion, Andrea and Bertotto, Daniela and Erlandson, Ann and H{\"o}lker, Franz and Greenberg, Larry and Santovito, Gianfranco}, title = {Impact of artificial light at night (ALAN) on cardiac function and acute oxidative stress physiology in migratory brown trout smolts}, series = {Environmental Pollution}, journal = {Environmental Pollution}, number = {381}, publisher = {Elsevier}, doi = {10.1016/j.envpol.2025.126593}, pages = {1 -- 9}, year = {2025}, abstract = {Light pollution poses significant ecological challenges for nocturnal animals reliant on natural light for migration, orientation, and circadian rhythms. The physiological effects of abrupt exposure to artificial light at night (ALAN) on migratory fish, such as the light experienced passing near illuminated infrastructures, remain poorly understood. This study investigates the physiological responses of brown trout (Salmo trutta) smolts to low-intensity (0.02 lx) and short-term (30 s) ALAN, simulating nocturnal migration light conditions near illuminated bridges. To evaluate the influence of social dynamics, trout were tested individually (solitary) or in groups of six. Using continuous cardiac monitoring with data storage tags, alongside analyses of oxidative stress markers and adenylate kinase (AK) activity in the heart, we identified distinct patterns of physiological responses. Solitary fish exhibited significant heart rate variability (HRV) increases following repeated ALAN exposure, suggesting impaired physiological regulation under repeated ALAN exposure. In contrast, trout in groups displayed consistently lower HRV over the entire 90-min experiment, implying that social dynamics likely influenced a sustained oxidative stress response, corroborated by increased AK activity. Oxidative stress markers further reflected social effects, with significant upregulation of key antioxidant enzymes (sod1, sod2, gpx1, gpx4) and elevated lipid peroxidation, identifying lipids as primary oxidative targets. The observed divergence between superoxide dismutase (SOD) activity and sod gene expression suggests adaptive post-transcriptional regulation to maintain redox balance under combined environmental and social stress. These findings reveal that social dynamics under ALAN can amplify physiological stress, potentially affecting migratory outcomes.}, language = {en} }