Abnormality Detection in ECG Signal applying Poincare and Entropy-based Approaches

Detection of abnormality in heart is of major importance for early and appropriate clinical medication. In this work, we have proposed two models for detection of abnormality in ECG signals. The normal ECG signals are closely repetitive in nature to a large extent, whereas ECG signals with abnormalities tend to differ from cycle to cycle. Hence, repetitive plot like the Poincare is efficient to detect such non-repetitiveness of the signal; thereby, indicating abnormalities. Hence, we have used Poincare plot to develop the two proposed models. One of the models uses direct analysis of the binary image of the plot to detect the difference in retracing, between the healthy and unhealthy samples. The other model uses entropy of the Poincare plot to detect the difference in randomness of plots between the two classes. Most importantly, we have used only lead II ECG signal for analysis. This ensures ease of computation as it uses signal of only a single lead instead of the 12 leads of the complete ECG signal. We have validated the proposed models using ECG signals from the ‘ptb database’. We have observed that the entropy analysis of the Poincare plots gives the best results with 90% accuracy of abnormality detection. This high accuracy of classification, combined with less computational burden enables its practical implementation for the development of a real life abnormality detection schem

Observation of γγ → ττ in proton-proton collisions and limits on the anomalous electromagnetic moments of the τ lepton

The production of a pair of τ leptons via photon–photon fusion, γγ → ττ, is observed for the f irst time in proton–proton collisions, with a significance of 5.3 standard deviations. This observation is based on a data set recorded with the CMS detector at the LHC at a center-of-mass energy of 13 TeV and corresponding to an integrated luminosity of 138 fb−1. Events with a pair of τ leptons produced via photon–photon fusion are selected by requiring them to be back-to-back in the azimuthal direction and to have a minimum number of charged hadrons associated with their production vertex. The τ leptons are reconstructed in their leptonic and hadronic decay modes. The measured fiducial cross section of γγ → ττ is σfid obs = 12.4+3.8 −3.1 fb. Constraints are set on the contributions to the anomalous magnetic moment (aτ) and electric dipole moments (dτ) of the τ lepton originating from potential effects of new physics on the γττ vertex: aτ = 0.0009+0.0032 −0.0031 and |dτ| < 2.9×10−17ecm (95% confidence level), consistent with the standard model

A Case Series on Unusual Neck Masses

Neck masses can be defined as any abnormal swelling or growth from the level of base of skull to clavicle. They can be benign or malignant so a thorough investigation is necessary to reach to a final diagnosis. Here we report a case series of three unusual neck masses presenting to the Out patient Department of Otorhinolaryngology and Head and Neck Surgery in R. G. Kar Medical College, a tertiary care hospital of Kolkata in a span of 1.5 years. The rarity of the etiology behind the neck masses makes this case series unique

Revisiting Recommendation Systems

Recommender systems are applied in a multitude of spheres and have a significant role in reduction of information overload on those websites that have the features of voting. Therefore, it is an urgent need for them to adapt and respond to immediate changes in user preference. To overcome the shortcomings of each individual approach to design the recommender systems, a myriad of ways to coalesce different recommender systems are proposed by researchers. In this chapter, the authors have presented an insight into the design of recommender systems developed, namely content-based and collaborative recommendations, their evaluation, their lacunae, and some hybrid models to enhance the quality of prediction.</jats:p

GPU-Based Level Set Method for MRI Brain Tumor Segmentation Using Modified Probabilistic Clustering

The level set method (LSM) has been widely used in image segmentation due to its intrinsic nature which allows handling complex shapes and topological changes easily. We propose a new level set algorithm, which is based on probabilistic c mean objective function which incorporates intensity inhomogeneity in image and robust to noise. The computational complexity of the proposed LSM is greatly reduced by using highly parallelizable lattice Boltzmann method (LBM). So the proposed algorithm is effective and highly parallelizable. The proposed LSM is implemented using Experimental results demonstrate the performance of the proposed method. </jats:p

Band-structure tunability via the modulation of excitons in semiconductor nanostructures: manifestation in photocatalytic fuel generation

Understanding the energetics of electron transfer at the semiconductor interface is crucial for the development of solar harvesting technologies, including photovoltaics, photocatalysis, and solar fuel systems. However, modern artificial photosynthetic materials are not efficient and limited by their fast charge recombination with high binding energy of excitons. Hence, reducing the exciton binding energy can increase the generation of charge carriers, which improve the photocatalytic activities. Extensive research has been dedicated to improving the exciton dissociation efficiency through rational semiconductor design via heteroatom doping, vacancy engineering, the construction of heterostructures, and donor-pi-acceptor (D-pi-A) interfaces to extend the charge carrier migration, promoting the dissociation of excitons. Consequently, functionalized photocatalysts have demonstrated remarkable photocatalytic performances for solar fuel production under visible light irradiation. This review provides the fundamental aspects of excitons in semiconductor nanostructures, having a high binding energy and ultrafast exciton formation together with promising photo-redox properties for solar to fuel conversion application. In particular, this review highlights the significant role of the excitonic effect in the photocatalytic activity of newly developed functional materials and the underlying mechanistic insight for tuning the performance of nanostructured semiconductor photocatalysts for water splitting, CO2 reduction, and N-2 fixation reactions