Multi-Particle Tracking in Complex Plasmas Using a Simplified and Compact U-Net.

Detecting micron-sized particles is an essential task for the analysis of complex plasmas because a large part of the analysis is based on the initially detected positions of the particles. Accordingly, high accuracy in particle detection is desirable. Previous studies have shown that machine learning algorithms have made great progress and outperformed classical approaches. This work presents an approach for tracking micron-sized particles in a dense cloud of particles in a dusty plasma at Plasmakristall-Experiment 4 using a U-Net. The U-net is a convolutional network architecture for the fast and precise segmentation of images that was developed at the Computer Science Department of the University of Freiburg. The U-Net architecture, with its intricate design and skip connections, has been a powerhouse in achieving precise object delineation. However, as experiments are to be conducted in resource-constrained environments, such as parabolic flights, preferably with real-time applications, there is growing interest in exploring less complex U-net architectures that balance efficiency and effectiveness. We compare the full-size neural network, three optimized neural networks, the well-known StarDist and trackpy, in terms of accuracy in artificial data analysis. Finally, we determine which of the compact U-net architectures provides the best balance between efficiency and effectiveness. We also apply the full-size neural network and the the most effective compact network to the data of the PK-4 experiment. The experimental data were generated under laboratory conditions.

Complex plasma research under microgravity conditions.

The future of complex plasma research under microgravity condition, in particular on the International Space Station ISS, is discussed. First, the importance of this research and the benefit of microgravity investigations are summarized. Next, the key knowledge gaps, which could be topics of future microgravity research are identified. Here not only fundamental aspects are proposed but also important applications for lunar exploration as well as artificial intelligence technology are discussed. Finally, short, middle and long-term recommendations for complex plasma research under microgravity are given.

Cold Atmospheric Plasma Reduces Vessel Density and Increases Vascular Permeability and Apoptotic Cell Death in Solid Tumors.

Cold atmospheric plasma (CAP) has demonstrated promising anti-cancer effects in numerous in vitro and in vivo studies. Despite their relevance for the treatment of solid tumors, effects of CAP on tumor vasculature and microcirculation have only rarely been investigated. Here, we report the reduction of vessel density and an increase in vascular permeability and tumor cell apoptosis after CAP application. Solid tumors in the chorioallantoic membrane of chicken embryos were treated with CAP and evaluated with respect to effects of CAP on embryo survival, tumor size, and tumor morphology. Furthermore, intratumoral blood vessel density, apoptotic cell death and the tumor-associated microcirculation were investigated and compared to sham treatment. Treatment with CAP significantly reduced intratumoral vessel density while increasing the rate of intratumoral apoptosis in solid tumors. Furthermore, CAP treatment increased vascular permeability and attenuated the microcirculation by causing vessel occlusions in the tumor-associated vasculature. These effects point out the potential of CAP as a promising and yet underrated therapeutic modality for addressing the tumor vasculature in the treatment of solid tumors.

Crystallization process of a three-dimensional complex plasma.

Characteristic timescales and length scales for phase transitions of real materials are in ranges where a direct visualization is unfeasible. Therefore, model systems can be useful. Here, the crystallization process of a three-dimensional complex plasma under gravity conditions is considered where the system ranges up to a large extent into the bulk plasma. Time-resolved measurements exhibit the process down to a single-particle level. Primary clusters, consisting of particles in the solid state, grow vertically and, secondarily, horizontally. The box-counting method shows a fractal dimension of d_{f}≈2.72 for the clusters. This value gives a hint that the formation process is a combination of local epitaxial and diffusion-limited growth. The particle density and the interparticle distance to the nearest neighbor remain constant within the clusters during crystallization. All results are in good agreement with former observations of a single-particle layer.

Absence of thermophoretic flow in relativistic heavy-ion collisions as an indicator for the absence of a mixed phase.

If a quark-gluon plasma is formed in relativistic heavy-ion collisions, there may or may not be a mixed phase of quarks, gluons, and hadronic clusters when the critical temperature is reached in the expansion of the fireball. If there is a temperature gradient in the fireball, the hadronic clusters, embedded in the heat bath of quarks and gluons, would be subjected to a thermophoretic force. It is shown that, even for small temperature gradients and short lifetimes of the mixed phase, thermophoresis would lead to a flow essentially stronger than the observed one. The absence of this strong flow provides support for a rapid or sudden hadronization mechanism without a mixed phase.