Current methods of dam breach analyses adopt a deterministic approach. Applying these methods to Concrete Faced Rockfill Dams (CFRD) is fraught with huge levels of uncertainty, especially in the context of natural hazards. The frequency and magnitude of rainfall and earthquakes are higher in today\'s world. In the literature, the complete collapse of dams is modelled on dam breach parameters that define the dam break outflow but are not related to the return periods of natural hazards. As more new dams are constructed to control the floods in intra-plate seismic regions, this study presents a novel approach to the derivation of generalized dam break parameters for CFRD based on the structural analysis of Finite Element Model (FEM) simulations for peak ground accelerations corresponding to 475 and 2475 year return periods. Furthermore, the occurrence of rainfall and earthquake for different return periods are modelled using 2D hydrodynamic simulations. Results show the significance of generalized dam breach parameters for planning and managing CFRDs during earthquakes. The study emphasizes the utilization of structural analysis outputs for the hydraulic modeling of dam breaks, which will result in more specific and accurate dam break parameters. Additionally, the study has shown that the flood risk and the severity will increase with the intensity of earthquake and rainfall magnitudes. Disaster mitigation strategies can be optimized by considering the integrated occurrence of rainfall and earthquakes based on the probability of occurrence, demonstrated using a case-study dam. Another significant outcome of the study is the effect of soil saturation condition during a dam break, which reveals that areas within 40 km of the dam breach location might be worst affected.

BACKGROUND: Overt hepatic encephalopathy (HE) should be predicted preoperatively to identify suitable candidates for transjugular intrahepatic portosystemic shunt (TIPS) instead of first-line treatment. This study aimed to construct a 3D assessment-based model to predict post-TIPS overt HE.
METHODS: In this multi-center cohort study, 487 patients who underwent TIPS were subdivided into a training dataset (390 cases from three hospitals) and an external validation dataset (97 cases from another two hospitals). Candidate factors included clinical, vascular, and 2D and 3D data. Combining the least absolute shrinkage and operator method, support vector machine, and probability calibration by isotonic regression, we constructed four predictive models: clinical, 2D, 3D, and combined models. Their discrimination and calibration were compared to identify the optimal model, with subgroup analysis performed.
RESULTS: The 3D model showed better discrimination than did the 2D model (training: 0.719 vs. 0.691; validation: 0.730 vs. 0.622). The model combining clinical and 3D factors outperformed the clinical and 3D models (training: 0.802 vs. 0.735 vs. 0.719; validation: 0.816 vs. 0.723 vs. 0.730; all p < 0.050). Moreover, the combined model had the best calibration. The performance of the best model was not affected by the total bilirubin level, Child-Pugh score, ammonia level, or the indication for TIPS.
CONCLUSIONS: 3D assessment of the liver and the spleen provided additional information to predict overt HE, improving the chance of TIPS for suitable patients. 3D assessment could also be used in similar studies related to cirrhosis.

This paper aimed to provide an innovative 2D phase space model and evaluate its performance in categorizing electroencephalogram (EEG) signals of normal and epileptic patients. The main contributions of the current study are as follows. (1) For the first time, it was proposed a new 2D model based on a 2-piece Rose Spiral Curve (RSC) in EEG analysis. (2) The trajectory patterns of the model were examined for signals of different natures, including constant, periodic, random, and EEG. (3) It was presented some benchmarks for quantifying the trajectory patterns. (4) Applying these measures, support vector machine, Naïve Bayes, AdaBoost, and K-nearest neighbor were used in the epileptic EEG classification problem to estimate the method efficiency. Bonn database, which takes account of EEG signals of healthy, in the course of an epileptic seizure occurrence, and seizure-free cases, was assessed. The results indicated that the proposed framework provided a correct rate of 100% for recognizing healthy subjects and the EEGs with seizure activity. Additionally, seizure-free brain activity was classified with an accuracy of 96.7%. To conclude, the proposed RSC model can be suitable for serving as a computer-aided diagnosis tool for epileptic seizures.

It is well known that membrane reactors are inherently two-dimensional systems in which species concentrations vary as a consequence of both the reaction and permeation across the membrane, which occurs in the direction perpendicular to that of the main gas flow. Recently, an expression for an enhanced Sherwood number was developed to describe the hydrogen concentration gradients arising in methane steam-reforming membrane reactors as a consequence of the combined effect of hydrogen production, dispersion, and permeation. Here, the analysis is developed in further detail with the aim of (i) assessing the validity of the simplifying assumptions made when developing the 1D model and (ii) identifying the operating conditions under which it is possible to employ the 1D model with the enhanced Sherwood number.

Atherosclerosis is a complex inflammatory pathology underpinning cardiovascular diseases (CVD), which are the leading cause of death worldwide. The interplay between vascular stromal cells and immune cells is fundamental to the progression and outcome of atherosclerotic disease, however, the majority of in vitro studies do not consider the implications of these interactions and predominantly use mono-culture approaches. Here we present a simple and robust methodology involving the co-culture of vascular endothelial (ECs) and smooth muscle cells (SMCs) alongside an inflammatory compartment, in our study containing THP-1 macrophages, for studying these complex interactions. Using this approach, we demonstrate that the interaction between vascular stromal and immune cells produces unique cellular phenotypes and soluble mediator profiles not observed in double-cell 2D cultures. Our results highlight the importance of cellular communication and support the growing idea that in vitro research must evolve from mono-culture systems to provide data more representative of the multi-cellular environment found in vivo. The methodology presented, in comparison with established approaches, has the advantage of being technically simple whilst enabling the isolation of pure populations of ECs, SMCs and immune cells directly from the co-culture without cell sorting. The approach described within would be applicable to those studying mechanisms of vascular inflammation, particularly in relation to understanding the impact cellular interaction has on the cumulative immune-vascular response to atherogenic or inflammatory stimuli.