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Objective: We present a novel method for detecting atrial fibrillation (AFib) by analyzing Lead II electrocardiograms (ECGs) using a unique set of features., Methods: For this purpose, we used specific signal processing techniques, such as proper orthogonal decomposition, continuous wavelet transforms, discrete cosine transform, and standard cross-correlation, to extract 48 features from the ECGs. Thus, our approach aims to more effectively capture AFib signatures, such as beat-to-beat variability and fibrillatory waves, than traditional metrics. Moreover, our features were designed to be physiologically interpretable. Subsequently, we incorporated an XGBoost-based ECG classifier to train and evaluate it using the publicly available 'Training' dataset of the 2017 PhysioNet Challenge, which includes 'Normal,' 'AFib,' 'Other,' and 'Noisy' ECG categories., Results: Our method achieved an accuracy of 96 % and an F1-score of 0.83 for 'AFib' detection and 80 % accuracy and 0.85 F1-score for 'Normal' ECGs. Finally, we compared our method's performance with the top-classifiers from the 2017 PhysioNet Challenge, namely ENCASE, Random Forest, and Cascaded Binary. As reported by Clifford et al., 2017, these three best performing models scored 0.80, 0.83, 0.82, in terms of F1-score for 'AFib' detection, respectively., Conclusion: Despite using significantly fewer features than the competition's state-of-the-art ECG detection algorithms (48 vs. 150-622), our model achieved a comparable F1-score of 0.83 for successful 'AFib' detection., Significance: The interpretable features specifically designed for 'AFib' detection results in our method's adaptability in clinical settings for real-time arrhythmia detection and is even effective with short ECGs (<10 heartbeats)., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Dengue virus (DENV) that caused an outbreak in Dhaka, Bangladesh during 2018 was analysed phylogenetically. DENV samples were classified into type 2-Cosmopolitan genotype (54%) and type 3-genotype I (46%), indicating co-circulation of two DENV types and resurgence of type 3 associated with genotype replacement., (© 2019 The Author(s).)

At concentrations above the critical micelle concentration, surfactants can significantly enhance the solubilization of residual nonaqueous phase liquids (NAPL) and, for this reason, are the focus of research on surfactant-enhanced aquifer remediation (SEAR). As a consequence of their amphiphilic nature, surfactants may also partition to various extents between the organic and aqueous phases, thereby affecting SEAR performance. We report here on the observation and analysis of the effect of surfactant partitioning on the dissolution kinetics of residual perchloroethylene (PCE) by aqueous solutions (1000 mg/L) of the non-ionic surfactant Triton X-100 in a model porous medium. For this fluid system, batch equilibration experiments showed that the surfactant partitions strongly into the NAPL (NAPL-water partition coefficient equal to 12.5). Dynamic interfacial tension (IFT) measurements were employed to study surfactant diffusion and interfacial adsorption. The dynamic IFT measurements were consistent with partitioning of the surfactant between the two liquid phases. PCE dissolution experiments, conducted in a transparent glass micromodel using an aqueous surfactant solution, were contrasted to experiments using clean water. Surfactant partitioning was observed to delay significantly the onset of micellar solubilization of PCE, an observation reproduced by a numerical model. This effect is attributed to the reduction of surfactant concentration in the immediate vicinity of the NAPL-water interface, which accompanies transport of the surfactant into the NAPL. Accordingly, it is suggested that both the rate and the extent of diffusion of the surfactant into the NAPL affect the onset of and the driving force for micellar solubilization. While many surfactants do not readily partition in NAPL, this possibility must be considered when selecting non-ionic surfactants for the enhanced solubilization of residual chlorinated solvents in porous media.