The European Physical Journal Special Topics最新文献

Urban water systems continue to face a major problem with water leakage, which results in substantial waste, shortages, damage to infrastructure, and monetary losses. While deep learning models have been effective in locating and identifying leaks, overfitting may result from their complexity over several training epochs. By including an attention mechanism, prominent features are given priority, improving model performance without compromising simplicity. Furthermore, layer normalization reduces problems in long short-term memory networks such as exploding gradients. Notable F1-scores are achieved by the proposed approach, demonstrating strong performance in both leak detection and localization tasks. Performance analysis under three different conditions for leak detection task such as source adaptation, target adaptation and adversarial simulation have shown an increase with scores of 91.59, 86.25 and 82.51 yielding 8.2%, 8.7% and 6.8% of improvement in F1-score, respectively. Similarly, performance analysis under three different conditions for leak localization task such as source adaptation, target adaptation and adversarial simulation has shown an increase with scores of 89.86, 84.39 and 80.77, yielding 7.4%, 8.5% and 8.6% of improvement in F1-score, respectively. Also, analysis using Wasserstein distance indicates reduced covariate shift through significant increase in accuracy (around 6.5%–9.5%, respectively), which is essential for adapting to varying water demand scenarios. The effectiveness of the proposed approach in urban water management is underscored by these results, emphasizing its potential for enhancing resource conservation and infrastructure sustainability.

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.