Fractional Quantum Hall States of the A Phase in the Second Landau Level.

A proposal of the existence of an Anomalous phase (A phase) [Das et al., Phys. Rev. Lett. 131, 056202 (2023)PRLTAO0031-900710.1103/PhysRevLett.131.056202] at the experimental range of moderate Landau-level-mixing strength has recently been made for the 5/2 state. We here report that the gapped A phase is generic to the sequence of spin-polarized fractional quantum Hall states with filling fractions ν=n/(nm-1) and ν=1-n/(nm-1), (n≥1,m≥3), that exhausts almost all the observed states and also predicts some states in the second Landau level for GaAs systems. Our proposed trial wave functions for all these states have remarkably high overlaps with the corresponding exact ground states and can support non-Abelian quasiparticle excitations with charge e/[2(nm-1)]. By analyzing edge modes, we predict experimentally verifiable thermal Hall conductance 2.5(π^{2}k_{B}^{2}T/3h) for all the states in these sequences.

Anomalous Reentrant 5/2 Quantum Hall Phase at Moderate Landau-Level-Mixing Strength.

A successful probing of the neutral Majorana mode in recent thermal Hall conductivity measurements opines in favor of the particle-hole symmetric Pfaffian (PH-Pf) topological order, contrasting the theoretical predictions of Pfaffian or anti-Pfaffian phases. Here we report a reentrant anomalous quantized phase that is found to be gapped in the thermodynamic limit, distinct from the conventional Pfaffian, anti-Pfaffian, or PH-Pf phases, at an intermediate strength of Landau level mixing. Our proposed wave function consistent with the PH-Pf shift in spherical geometry rightly captures the topological order of this phase, as its overlap with the exact ground state is very high and it reproduces low-lying entanglement spectra. A unique topological order, irrespective of the flux shifts, found for this phase, possibly corroborates the experimentally found topological order.

Fetal Health Classification from Cardiotocograph for Both Stages of Labor-A Soft-Computing-Based Approach.

To date, cardiotocography (CTG) is the only non-invasive and cost-effective tool available for continuous monitoring of the fetal health. In spite of a marked growth in the automation of the CTG analysis, it still remains a challenging signal processing task. Complex and dynamic patterns of fetal heart are poorly interpreted. Particularly, the precise interpretation of the suspected cases is fairly low by both visual and automated methods. Also, the first and second stage of labor produce very different fetal heart rate (FHR) dynamics. Thus, a robust classification model takes both stages into consideration separately. In this work, the authors propose a machine-learning-based model, which was applied separately to both the stages of labor, using standard classifiers such as SVM, random forest (RF), multi-layer perceptron (MLP), and bagging to classify the CTG. The outcome was validated using the model performance measure, combined performance measure, and the ROC-AUC. Though AUC-ROC was sufficiently high for all the classifiers, the other parameters established a better performance by SVM and RF. For suspicious cases the accuracies of SVM and RF were 97.4% and 98%, respectively, whereas sensitivity was 96.4% and specificity was 98% approximately. In the second stage of labor the accuracies were 90.6% and 89.3% for SVM and RF, respectively. Limits of agreement for 95% between the manual annotation and the outcome of SVM and RF were (-0.05 to 0.01) and (-0.03 to 0.02). Henceforth, the proposed classification model is efficient and can be integrated into the automated decision support system.

A machine learning pipeline to classify foetal heart rate deceleration with optimal feature set.

Deceleration is considered a commonly practised means to assess Foetal Heart Rate (FHR) through visual inspection and interpretation of patterns in Cardiotocography (CTG). The precision of deceleration classification relies on the accurate estimation of corresponding event points (EP) from the FHR and the Uterine Contraction Pressure (UCP). This work proposes a deceleration classification pipeline by comparing four machine learning (ML) models, namely, Multilayer Perceptron (MLP), Random Forest (RF), Naïve Bayes (NB), and Simple Logistics Regression. Towards an automated classification of deceleration from EP using the pipeline, it systematically compares three approaches to create feature sets from the detected EP: (1) a novel fuzzy logic (FL)-based approach, (2) expert annotation by clinicians, and (3) calculated using National Institute of Child Health and Human Development guidelines. The classification results were validated using different popular statistical metrics, including receiver operating characteristic curve, intra-class correlation coefficient, Deming regression, and Bland-Altman Plot. The highest classification accuracy (97.94%) was obtained with MLP when the EP was annotated with the proposed FL approach compared to RF, which obtained 63.92% with the clinician-annotated EP. The results indicate that the FL annotated feature set is the optimal one for classifying deceleration from FHR.

Cardiotocograph-based labor stage classification from uterine contraction pressure during ante-partum and intra-partum period: a fuzzy theoretic approach.

Computerized techniques for Cardiotocograph (CTG) based labor stage classification would support obstetrician for advance CTG analysis and would improve their predictive power for fetal heart rate (FHR) monitoring. Intrapartum fetal monitoring is necessary as it can detect the event, which ultimately leads to hypoxic ischemic encephalopathy, cerebral palsy or even fetal demise. To bridge this gap, in this paper, we propose an automated decision support system that will help the obstetrician identify the status of the fetus during ante-partum and intra-partum period. The proposed algorithm takes 30 min of 275 Cardiotocograph data and applies a fuzzy-rule based approach for identification and classification of labor from 'toco' signal. Since there is no gold standard to validate the outcome of the proposed algorithm, the authors used various statistical means to establish the cogency of the proposed algorithm and the degree of agreement with visual estimation were using Bland-Altman plot, Fleiss kappa (0.918 ± 0.0164 at 95% CI) and Kendall's coefficient of concordance (W = 0.845). Proposed method was also compared against some standard machine learning classifiers like SVM, Random Forest and Naïve Bayes using weighted kappa (0.909), Bland-Altman plot (Limits of Agreement 0.094 to 0.0155 at 95% CI) and AUC-ROC (0.938). The proposed algorithm was found to be as efficient as visual estimation compared to the standard machine learning algorithms and thus can be incorporated into the automated decision support system.