Description of the idealized bed roughness effects on tracer transport in water flumes by applying the strange attractor multifractal analysis

River hydrodynamicsTurbulent open channel flow and transport phenomen

OWA-based aggregation operations in multi-expert MCDM model

This paper presents an analysis of multi-expert multi-criteria decision making (ME-MCDM) model based on the ordered weighted averaging (OWA) operators. Two methods of modeling the majority opinion are studied as to aggregate the experts' judgments, in which based on the induced OWA operators. Then, an overview of OWA with the inclusion of different degrees of importance is provided for aggregating the criteria. An alternative OWA operator with a new weighting method is proposed which termed as alternative OWAWA (AOWAWA) operator. Some extensions of ME-MCDM model with respect to two-stage aggregation processes are developed based on the classical and alternative schemes. A comparison of results of different decision schemes then is conducted. Moreover, with respect to the alternative scheme, a further comparison is given for different techniques in integrating the degrees of importance. A numerical example in the selection of investment strategy is used as to exemplify the model and for the analysis purpose

Weighted‐selective aggregated majority‐OWA operator and its application in linguistic group decision making

This paper focuses on the aggregation operations in the group decision-making model based on the concept of majority opinion. The weighted-selective aggregated majority-OWA (WSAM-OWA) operator is proposed as an extension of the SAM-OWA operator, where the reliability of information sources is considered in the formulation. The WSAM-OWA operator is generalized to the quanti- fied WSAM-OWA operator by including the concept of linguistic quantifier, mainly for the group fusion strategy. The QWSAM-IOWA operator, with an ordering step, is introduced to the individual fusion strategy. The proposed aggregation operators are then implemented for the case of alternative scheme of heterogeneous group decision analysis. The heterogeneous group includes the consensus of experts with respect to each specific criterion. The exhaustive multicriteria group decision-making model under the linguistic domain, which consists of two-stage aggregation processes, is developed in order to fuse the experts' judgments and to aggregate the criteria. The model provides greater flexibility when analyzing the decision alternatives with a tolerance that considers the majority of experts and the attitudinal character of experts. A selection of investment problem is given to demonstrate the applicability of the developed model

Using 222Rn to identify and quantify groundwater inflows to the Mundo River (SE Spain)

Groundwater discharge to the Mundo River (SE, Spain) has been investigated from 2011 to 2013 by means of 222Rn activities in river water and groundwater. Starting nearby the river source, some 50km of river channel have been studied. The Mundo River is located in the water stressed region of the Segura River Basin. Identifying and quantifying groundwater discharge to rivers is essential for the Hydrological Plan of the Segura Basin Authority. Four main areas of groundwater discharge to the river have been identified by means of 222Rn. Moreover, groundwater fluxes have been quantified using radon activities and, when possible, have been validated with chloride mass balances. The uncertainty range (±2σ) of all water balances has also been assessed. Groundwater discharge (QGW) values estimated by radon mass balances (RMB) and chloride mass balances (CMB) were similar in the river tracts and/or dates in which surface inputs from tributaries were null or negligible. This adds confidence to the QGW values estimated by RMB in the reaches were CMB could not be performed due to the existence of ungauged surface inputs, as is the case of the upper basin of the Mundo River, as well as to the applicability of the method to similar situations. Quantification of groundwater discharge allowed identifying Ayna zone as the main gaining reach of the studied area, with up to 29,553±8667m3day-1 in year 2011. Overall, the total QGW estimated by means of RMB for the studied area was 8-16% of the total river flow. The results are coherent with the meteorological conditions of the study period (average rainfall around 450mm/y) and also with the undisturbed situation of the aquifers discharging to the Mundo River in the considered area.Fil: Ortega Ormaechea, Lucia. Universidad Politécnica de Cartagena; España. Universidad Nacional del Centro de la Provincia de Buenos Aires. Rectorado. Instituto de Hidrología de Llanuras-Sede Azul. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Instituto de Hidrología de Llanuras-Sede Azul; ArgentinaFil: Manzano, M.. Universidad Politécnica de Cartagena; EspañaFil: Custodio, E.. Universidad Politecnica de Catalunya; EspañaFil: Hornero, J.. Instituto Geológico y Minero de España; EspañaFil: Rodríguez Arévalo, J.. Centro de Estudios y Experimentación de Obras Públicas; Españ

Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy

The aim of this study was to analyse the electroencephalogram (EEG) background activity of Alzheimer’s disease (AD) patients using the Multiscale Entropy (MSE). The MSE is a recently developed method that quantifies the regularity of a signal on different time scales. These time scales are inspected by means of several coarse-grained sequences formed from the analysed signals. We recorded the EEGs from 19 scalp electrodes in 11 AD patients and 11 age-matched controls and estimated the MSE profile for each epoch of the EEG recordings. The shape of the MSE profiles reveals the EEG complexity, and it suggests that the EEG contains information in deeper scales than the smallest one. Moreover, the results showed that the EEG background activity is less complex in AD patients than control subjects. We found significant difference

Assessing the contribution of understory sun-induced chlorophyll fluorescence through 3-D radiative transfer modelling and field data

A major international effort has been made to monitor sun-induced chlorophyll fluorescence (SIF) from space as a proxy for the photosynthetic activity of terrestrial vegetation. However, the effect of spatial heterogeneity on the SIF retrievals from canopy radiance derived from images with medium and low spatial resolution remains uncharacterised. In images from forest and agricultural landscapes, the background comprises a mixture of soil and understory and can generate confounding effects that limit the interpretation of the SIF at the canopy level. This paper aims to improve the understanding of SIF from coarse spatial resolutions in heterogeneous canopies by considering the separated contribution of tree crowns, understory and background components, using a modified version of the FluorFLIGHT radiative transfer model (RTM). The new model is compared with others through the RAMI model intercomparison framework and is validated with airborne data. The airborne campaign includes high-resolution data collected over a tree-grass ecosystem with the HyPlant imaging spectrometer within the FLuorescence EXplorer (FLEX) preparatory missions. Field data measurements were collected from plots with a varying fraction of tree and understory vegetation cover. The relationship between airborne SIF calculated from pure tree crowns and aggregated pixels shows the effect of the understory at different resolutions. For a pixel size smaller than the mean crown size, the impact of the background was low (R2 > 0.99; NRMSE 0.2). This study demonstrates that using a 3D RTM model improves the calculation of SIF significantly (R2 = 0.83, RMSE = 0.03 mW m−2 sr−1 nm−1) when the specific contribution of the soil and understory layers are accounted for, in comparison with the SIF calculated from mixed pixels that considers only one layer as background (R2 = 0.4, RMSE = 0.28 mW m−2 sr−1 nm−1). These results demonstrate the need to account for the contribution of SIF emitted by the understory in the quantification of SIF within tree crowns and within the canopy from aggregated pixels in heterogeneous forest canopies

The Relevance of Heart Rate Fluctuation When Evaluating Atrial Substrate Electrical Features in Catheter Ablation of Paroxysmal Atrial Fibrillation

[EN] Coronary sinus (CS) catheterization is critical during catheter ablation (CA) of atrial fibrillation (AF). However, the association of CS electrical activity with atrial substrate modification has been barely investigated and mostly limited to analyses during AF. In sinus rhythm (SR), atrial substrate modification is principally assessed at a global level through P-wave analysis. Cross-correlating CS electrograms (EGMs) and P-waves' features could potentiate the understanding of AF mechanisms. Five-minute surface lead II and bipolar CS recordings before, during, and after CA were acquired from 40 paroxysmal AF patients. Features related to duration, amplitude, and heart-rate variability of atrial activations were evaluated. Heart-rate adjustment (HRA) was applied. Correlations between each P-wave and CS local activation wave (LAW) feature were computed with cross-quadratic sample entropy (CQSE), Pearson correlation (PC), and linear regression (LR) with 10-fold cross-validation. The effect of CA between different ablation steps was compared with PC. Linear correlations: poor to mediocre before HRA for analysis at each P-wave/LAW (PC: max. +18.36%, p = 0.0017, LR: max. +5.33%, p = 0.0002) and comparison between two ablation steps (max. +54.07%, p = 0.0205). HRA significantly enhanced these relationships, especially in duration (P-wave/LAW: +43.82% to +69.91%, p < 0.0001 for PC and +18.97% to +47.25%, p < 0.0001 for LR, CA effect: +53.90% to +85.72%, p < 0.0210). CQSE reported negligent correlations (0.6-1.2). Direct analysis of CS features is unreliable to evaluate atrial substrate modification due to CA. HRA substantially solves this problem, potentiating correlation with P-wave features. Hence, its application is highly recommended.This research received partial financial support from public grants DPI2017-83952-C3, PID2021-00X128525-IV0, PID2021-123804OB-I00. and TED2021-129996B-I00 of the Spanish Government 10.13039/501100011033 jointly with the European Regional Development Fund (EU), SB-PLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha. and AICO/2021/286 from Generalitat Valenciana.Vraka, A.; Moreno-Arribas, J.; Gracia-Baena, JM.; Hornero, F.; Alcaraz, R.; Rieta, JJ. (2022). The Relevance of Heart Rate Fluctuation When Evaluating Atrial Substrate Electrical Features in Catheter Ablation of Paroxysmal Atrial Fibrillation. Journal of Cardiovascular Development and Disease. 9(6):1-16. https://doi.org/10.3390/jcdd90601761169

Novel Entropy-Based Metrics for Long-Term Atrial Fibrillation Recurrence Prediction Following Surgical Ablation: Insights from Preoperative Electrocardiographic Analysis

[EN] Atrial fibrillation (AF) is a prevalent cardiac arrhythmia often treated concomitantly with other cardiac interventions through the Cox-Maze procedure. This highly invasive intervention is still linked to a long-term recurrence rate of approximately 35% in permanent AF patients. The aim of this study is to preoperatively predict long-term AF recurrence post-surgery through the analysis of atrial activity (AA) organization from non-invasive electrocardiographic (ECG) recordings. A dataset comprising ECGs from 53 patients with permanent AF who had undergone Cox-Maze concomitant surgery was analyzed. The AA was extracted from the lead V1 of these recordings and then characterized using novel predictors, such as the mean and standard deviation of the relative wavelet energy (RWEm and RWEs) across different scales, and an entropy-based metric that computes the stationary wavelet entropy variability (SWEnV). The individual predictors exhibited limited predictive capabilities to anticipate the outcome of the procedure, with the SWEnV yielding a classification accuracy (Acc) of 68.07%. However, the assessment of the RWEs for the seventh scale (RWEs7), which encompassed frequencies associated with the AA, stood out as the most promising individual predictor, with sensitivity (Se) and specificity (Sp) values of 80.83% and 67.09%, respectively, and an Acc of almost 75%. Diverse multivariate decision tree-based models were constructed for prediction, giving priority to simplicity in the interpretation of the forecasting methodology. In fact, the combination of the SWEnV and RWEs7 consistently outperformed the individual predictors and excelled in predicting post-surgery outcomes one year after the Cox-Maze procedure, with Se, Sp, and Acc values of approximately 80%, thus surpassing the results of previous studies based on anatomical predictors associated with atrial function or clinical data. These findings emphasize the crucial role of preoperative patient-specific ECG signal analysis in tailoring post-surgical care, enhancing clinical decision making, and improving long-term clinical outcomes.This research has received financial support from public grants PID2021-123804OB-I00, PID2021- 00X128525-IV0, and TED2021-130935B-I00 of the Spanish Government, 10.13039/501100011033, in conjunction with the European Regional Development Fund (EU), SBPLY/21/180501/000186, from Junta de Comunidades de Castilla-La Mancha, and AICO/2021/286 from Generalitat Valenciana. Pilar Escribano holds the 2020-PREDUCLM-15540 scholarship co-financed by the European Social Fund (ESF) operating program 2014 2020 of Castilla-La Mancha.Escribano, P.; Ródenas, J.; García, M.; Hornero, F.; Gracia-Baena, JM.; Alcaraz, R.; Rieta, JJ. (2024). Novel Entropy-Based Metrics for Long-Term Atrial Fibrillation Recurrence Prediction Following Surgical Ablation: Insights from Preoperative Electrocardiographic Analysis. Entropy. 26(1). https://doi.org/10.3390/e2601002826

On the application of the auto mutual information rate of decrease to biomedical signals

The auto mutual information function (AMIF) evaluates the signal predictability by assessing linear and non-linear dependencies between two measurements taken from a single time series. Furthermore, the AMIF rate of decrease (AMIFRD) is correlated with signal entropy. This metric has been used to analyze biomedical data, including cardiac and brain activity recordings. Hence, the AMIFRD can be a relevant parameter in the context of biomedical signal analysis. Thus, in this pilot study, we have analyzed a synthetic sequence (a Lorenz system) and real biosignals (electroencephalograms recorded with eyes open and closed) with the AMIFRD. We aimed at illustrating the application of this parameter to biomedical time series. Our results show that the AMIFRD can detect changes in the non-linear dynamics of a sequence and that it can distinguish different physiological conditions