The European Physical Journal Special Topics最新文献

This study examines the radiative capture of a neutron by (^{10})Be using the Coulomb dissociation approach within the FRDWBA theory. We analyze the elastic Coulomb breakup of (^{11})Be on a (^{208})Pb target at 72 MeV/A to determine the photodisintegration cross section and radiative capture cross section. Utilizing the Maxwell-averaged velocity distribution, we calculate the resulting radiative neutron capture reaction rate for the (^{10})Be(n,(gamma))(^{11})Be reaction. Comparative analyses are conducted with experimental data, theoretical results from direct radiative capture methods, and transfer reaction calculations. Additionally, we contrast our findings with the existing (^{10})Be((alpha),(gamma))(^{14})C reaction rate and conclude the dominance of neutron capture over (alpha) capture by (^{10})Be.

In this review, we survey a number of experiments over the last few decades, that specifically study the destruction of the (^7textrm{Be}) nucleus, in search for a solution to the long standing cosmological lithium problem. The destruction of (^7textrm{Be}) by both neutrons and charged particles are discussed. However, the reduction in the abundance of the primordial (^7textrm{Li}) is found to be negligible and thus the lithium anomaly remains. The second lithium problem involving (^6textrm{Li}) is still controversial. Overall, it appears that the solution to the lithium problems may not reside in nuclear physics.

This review provides a thorough examination of recent developments in artificial intelligence analysis methods within mental and psychiatry field. By analyzing and comparing results obtained with various tools and techniques, we provide a comprehensive and systematic understanding of applications. Our main methods include meta-analysis, search queries with the keywords and network-based approach. In our analysis, we observed that terms associated with robotics, human–computer interaction, speech perception, and certain applications, such as chronic fatigue syndrome and psychological adaptation, have been gradually losing prominence. And conversely, techniques such as deep learning, virtual reality, and virtual assistance are gaining traction, and increasing interest was noted for applications involving autistic spectrum disorders, mild cognitive impairments, and psychiatric research areas. The structured and organized presentation of information, along with the accompanying visualizations and diagrams, makes it a valuable resource for scientists and researchers working in the domains of artificial intelligence.

The dynamics of electrical activity of the brain in various physiological states are traditionally studied by analyzing dominant rhythms separately. However, in recent years the concept of cross-communication of different cortical rhythms has also been discussed. Using this concept, we study the effects of 1-day sleep deprivation on the coordination of rhythm pairs and compare two methods for assessing their cross-correlations: the Pearson correlation coefficient (PCC) and detrended cross-correlation analysis (DCCA) with its extended version. We show that the latter approach may reveal differences in electrocorticogram (ECoG) signals for a larger number of pairs.

This article aims to determine the convergence and error bounds for the fully discrete solutions of the cancer invasion model using two-step backward difference scheme (BDF2) in time and Galerkin finite element approximation in space. The existence and uniqueness of a solution is affirmed. We establish error estimates with optimal order convergence rates for full discretization. Finally, some numerical tests are used to authenticate the scheme’s competency and accuracy.

Automated seizure detection is a major challenge in the context of epilepsy diagnostics. There are numerous approaches to this task, but most of them share the same problem—the trade-off between recall and precision, i.e. decent recall is often accompanied by low precision. This ultimately leads to a high number of false positive seizure detections, which in its turn impede automated diagnostics. The purpose of this study is to develop a method to lower the number of false positive predictions in seizure detection task when applied to real EEG recordings. We propose the cascade approach which combines the idea of iterative refinement algorithms and powerful neural networks. The method is tested on unrefined dataset, that includes EEG recordings of epileptic patients from the hospital. Time-frequency analysis based on continuous wavelet transform is used for EEG preprocessing and feature extraction. To provide predictions the approach implements convolutional neural networks. The proposed approach consists of two steps: in the first step a model is trained to provide initial predictions and then in the second step another model is trained with the knowledge of the first model’s errors. We evaluate the performance of the approach with the confusion matrix metrics adjusted to the specifics of the epilepsy diagnostics task. We show that the number of false positive predictions decreases by an order of magnitude with the use of the proposed method. We theorize about possible application of this approach within a clinical decision support system.

Intermittency near the boundary of generalized synchronization in unidirectionally coupled chaotic systems with different topology of attractors is studied. On–off intermittency is shown to be observed both in the case of the influence of the system with a simple topology of attractor on the system with a complex topology and, vice versa, in the case of the influence of the system with a complex topology of attractor on the system with a simple topology. Unidirectionally coupled Rössler and Lorenz systems and radiotechnical generators with 1.5 number of degrees of freedom have been considered as sample systems. Mechanisms resulting in the on–off intermittency appearance have been discussed.

In time-dependent traveling-wave convection, it has been confirmed that small particles of low Stokes number ((textrm{St}ll) 1) form three-dimensional closed structures known as the particle accumulation structures (PASs). We focus on coherent structures formed in high aspect-ratio thermocapillary liquid bridges ((Gamma =) 2.5), where oscillatory convection due to the so-called hydrothermal wave instability of (m_{textrm{HTW}}=) 1 stably emerges, and carry out experimental explorations into the volume ratio dependence of the emergence of coherent structures. Variation in the volume ratio induces the presence of coherent structures with different azimuthal wave numbers, that is, different spatial structures. Among the particles added to the liquid bridge, we perform spatio-temporal tracking of particles to quantify the correlation between the behavior of particles and the spatial structures of coherent structures with different rational wave numbers.