We study the effects of non-linear interactions of hydrodynamic shock waves in the solar atmosphere and their influence on the resonant oscillations at the cut-off frequency. The shock waves originate from randomly generated wave packets with broad frequency distributions, as opposed to previous studies which start with monochromatic wave trains, single pulses or, at the most, regular waves trains with stochastically changing periods. We consider only one-dimensional (vertical) adiabatic or isothermal hydrodynamics using a numerical code based on a modified flux corrected transport algorithm. The results are analyzed by applying Fourier methods, i.e. power and phase difference spectra
Systematic reviews on the grading of STS using MRI are lacking. This review analyses the role of different MRI features in inferring the histological grade of STS.
On discute de l'etat d'equilibre d'un disque fin en rotation isotherme et en auto-gravitation. Pour cela on resout l'equation integrodifferentielle des perturbations dont on discute les solutions. On considere l'influence de l'exposant polytrope et l'effet de la sphere entourante sur la stabilite
Les equations d'equilibre d'un disque infiniment mince a symetrie axiale rotatif avec une auto-gravitation et une pression polytrope 2D sont analysees. L'utilisation de methodes d'approximation pour la configuration d'equilibre sont etudiees
Soft tissue sarcomas (STS) are a heterogeneous group of rare malignant tumors. Tumor grade might be underestimated in biopsy due to intratumoral heterogeneity. This mini-review aims to present the current state of predicting malignancy grades of STS through radiomics, machine learning, and deep learning on magnetic resonance imaging (MRI). Several studies investigated various machine-learning and deep-learning approaches in T2-weighted (w) images, contrast-enhanced (CE) T1w images, and DWI/ADC maps with promising results. Combining semantic imaging features, radiomics features, and deep-learning signatures in machine-learning models has demonstrated superior predictive performances compared to individual feature sources. Furthermore, incorporating features from both tumor volume and peritumor region is beneficial. Especially random forest and support vector machine classifiers, often combined with the least absolute shrinkage and selection operator (LASSO) and/or synthetic minority oversampling technique (SMOTE), did show high area under the curve (AUC) values and accuracies in existing studies.
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