[The case of the death of a child due to perforation of the walls of the esophagus and aorta caused by a foreign body - an electric battery].

The results of a detailed investigation of a casuistic case of fatal perforation of the walls of the esophagus and aorta with subsequent acute blood loss as a result of prolonged presence of a foreign body - an electric battery in the esophagus of a child are presented.

Towards ML-Based Diagnostics of Laser-Plasma Interactions.

The power of machine learning (ML) in feature identification can be harnessed for determining quantities in experiments that are difficult to measure directly. However, if an ML model is trained on simulated data, rather than experimental results, the differences between the two can pose an obstacle to reliable data extraction. Here we report on the development of ML-based diagnostics for experiments on high-intensity laser-matter interactions.

ML-Based Analysis of Particle Distributions in High-Intensity Laser Experiments: Role of Binning Strategy.

When entering the phase of big data processing and statistical inferences in experimental physics, the efficient use of machine learning methods may require optimal data preprocessing methods and, in particular, optimal balance between details and noise. In experimental studies of strong-field quantum electrodynamics with intense lasers, this balance concerns data binning for the observed distributions of particles and photons. Here we analyze the aspect of binning with respect to different machine learning methods (Support Vector Machine (SVM), Gradient Boosting Trees (GBT), Fully-Connected Neural Network (FCNN), Convolutional Neural Network (CNN)) using numerical simulations that mimic expected properties of upcoming experiments.

Employing machine learning for theory validation and identification of experimental conditions in laser-plasma physics.

The validation of a theory is commonly based on appealing to clearly distinguishable and describable features in properly reduced experimental data, while the use of ab-initio simulation for interpreting experimental data typically requires complete knowledge about initial conditions and parameters. We here apply the methodology of using machine learning for overcoming these natural limitations. We outline some basic universal ideas and show how we can use them to resolve long-standing theoretical and experimental difficulties in the problem of high-intensity laser-plasma interactions.

Synchronous regimes in ensembles of coupled Bonhoeffer-van der Pol oscillators.

We study synchronous behavior in ensembles of locally coupled nonidentical Bonhoeffer-van der Pol oscillators. We show that, in a chain of N elements not less than 2;{N-1}, different coexisting regimes of global synchronization are possible, and we investigate wave-induced synchronous regimes in a chain and in a lattice of coupled nonidentical Bonhoeffer-van der Pol oscillators.