QRS Complex Detection based on Multilevel Thresholding and Peak-to-Peak Interval Statistics

Heart beats are important aspects of the study of heart diseases in medical science as they provide vital information on heart disorders and diseases or abnormalities in the heart rhythm. Each heart beat provides a QRS complex in the electrocardiogram (ECG) which is centered at the R-peak. The analysis of ECG is hindered by low-frequency noises, high-frequency noise, interference from P and T waves, and change in QRS morphology. Therefore, it is a major challenge to detect the QRS complexes using automatic detection algorithms.This thesis aims to present three new peak detection algorithms based on a statistical analysis of the ECG signal. In the first algorithm, a novel method of segmentation and statistical false peak elimination is proposed. The second algorithm uses different levels of adaptive thresholds to detect true peaks while the third algorithm combines and modifies the two proposed algorithms to provide better efficiency and accuracy in QRS complex detection. The proposed algorithms are tested on the MIT-BIH arrhythmia and provides better detection accuracy in comparison to several state-of-the-art methods in the field. To evaluate the performance of the proposed method, the merits of evaluation consider the number of false positives and negatives. A false positive (FP) is the result of a noise peak being detected and a false negative (FN) occurs when a beat is not detected at all. The methods emphasize better detection algorithms that detect peaks efficiently and automatically without eliminating the high-frequency noise completely and hence reduces the overall computational time

Mechanisms of Acid Catalyzed Dissociation of Trisbenzoylacetonatocobalt(III) in Acetone-Water

14-1

Kinetics & Mechanism of Acid-catalysed Tris(I-amidino-O-alkylurea)cobalt(III) Dissociation of Complexes

480-48

Applications of deep learning in disease diagnosis of chest radiographs: A survey on materials and methods

Recent advances in deep learning have given rise to high performance in image analysis operations in healthcare. Lung diseases are of particular interest, as most can be identified using non-invasive image modalities. Deep learning techniques such as convolutional neural networks, convolution autoencoders, and graph convolutional networks have been implemented in several pulmonary disease identification applications, e.g., lung nodule classification, Covid-19, and pneumonia detection. Various sources of medical images such as X-rays, computed tomography scans, magnetic resonance imaging, and positron emission tomography scans make deep learning techniques favorable to identify lung diseases with great accuracy. This paper discusses state-of-the-art methods that use deep learning on various medical imaging modalities to detect and classify diseases in the lungs. A description of a few publicly available databases is included in this study, along with some distinct deep learning techniques developed in recent times. Furthermore, several challenges and open research areas for pulmonary disease diagnosis using deep learning are discussed. The objective of this work is to direct researchers in the field of diagnosis of lung diseases

A review on remanufacturing, reuse, and recycling in supply chain—Exploring the evolution of information technology over two decades

Remanufacturing, reuse, and recycling in supply chain (RRR & Supply chain) are recently gaining significant attention from researchers. This bibliometric study aims to make overall research scenarios on RRR & Supply chain by analyzing the published papers. For a collection of exclusive papers in this area, we rely on the well-known trustworthy database SCOPUS. The bibliometric study is done by the contents of publication and citation structure of influential papers, leading authors, institutions, and countries, citation structure of top ten journals, country analysis in the area of RRR & Supply chain research. Present study reveals that S.M. Gupta has the highest number of publications, the USA is the most influential country, the renowned ‘Journal of Cleaner Production’ has published the greatest number of papers, and the Institute ‘Huazhong University of Science and Technology’ has the highest number of publications in this field. Through data mining this work revealed that closed loop supply chain, industry, waste, review, policy, circular economy, and sustainability are mostly used keywords while highly cited articles frequently used keywords like supply chain management; closed-loop supply chain; reverse logistics; article; logistics; manufacture; costs; decision making; environmental impact; and waste management. This study will help to fill the research gap and show a roadmap for future research direction in this area

A country-based review in COVID-19 related research developments

The COVID-19 pandemic has turned our life topsy-turvy. It has bought a massive change in all sectors around the world. A great number of research papers have already been published accounting for various aspects of the COVID-19 issue, owing to the ever-increasing interest in this hot area. The essential data is gathered using the well-known and dependable search engine SCOPUS. We looked at research papers, journals, and reviews from 25 leading countries to highlight a comprehensive study of research output through COVID-19 papers. This study focuses on the top authors, leading articles, and journals from various nations, the percentage of published papers in various fields, and the top collaborative research work from different authors and countries. USA, UK, China, Italy, and India have all made a significant contribution to COVID-19 research. The USA is the leading country followed by UK and China but for H-index China is in the best position. The highest number of papers has been developed in the area of "medicine". The Harvard Medical School of the UK contributed the highest number of papers followed by the University of Toronto of Canada. Professor K. Dhama of India has published the highest number of papers while C. Huang of China received the highest number of citations. It also highlights that several authors have differing opinions on the efficacy of taking the medicine remdesivir. Our research provides a complete and comprehensive image of the virus’s current research status, or in other words, a roadmap of the present research status

Analyzing a socially responsible closed-loop distribution channel with recycling facility

Abstract This paper deals with a closed-loop distribution channel consisting of a socially responsible manufacturer, multiple retailers and a third party collector. In reality, collection of used products (plastic, glass, metal) by a third party collector is more common than the collection through retailers. This is because retailers generally faces difficulties  such as lack of space and manpower. Aligned with many closed loop supply chains, this paper assumes that the third party operates the reverse channel by collecting the used products. The third party collects used products, segregates recyclable items and sends them to the manufacturer for further use. The manufacturer not only shows social responsibility to the stakeholders and shareholders, but also collects the used products from the third party and recycles them to new products. Considering profit maximizing motives of the channel members, the paper examines the effect of manufacturer’s degree of social responsibility on the collection activity of the third party. Under manufacturer Stackelberg game setting, it is found that product recycling is directly proportional  to the manufacturer’s corporate social responsibility (CSR) concerns and there must be a threshold of recycling for the optimal benefit that can be acquired through CSR practice. The proposed model is illustrated by a numerical example and a sensitivity analysis reveals nature of the parameters

Syntheses, crystal structures, spectral studies, and DFT calculations of two new square planar Ni(II) complexes derived from pyridoxal-based Schiff base ligands

<div><p>Two new complexes based on a Schiff base derived from pyridoxal N,N-dimethylethylenediamine (HL¹) and N,N-diethylethylenediamine (HL²), [Ni(L¹)SCN] (<b>1</b>) and [Ni(L²)SCN] (<b>2</b>), have been synthesized and structurally characterized by single-crystal X-ray diffraction along with other physical techniques, including elemental analyses, IR spectra, cyclic voltammetry, UV–vis, and luminescence studies. X-ray studies suggest that in both the complexes nickel lies in a slightly distorted square planar environment occupied by the tridentate ONN ligand and an isothiocyanate moiety. Density functional theory computations have been carried out to characterize the complexes.</p></div

Heterometallic Cu^II–Dy^III Clusters of Different Nuclearities with Slow Magnetic Relaxation

The synthesis, structures, and magnetic properties of two heterometallic Cu^II–Dy^III clusters are reported. The first structural motif displays a pentanuclear Cu^II₄Dy^III core, while the second one reveals a nonanuclear Cu^II₆Dy^III₃ core. We employed <i>o</i>-vanillin-based Schiff base ligands combining <i>o</i>-vanillin with 3-amino-1-propanol, H₂vap, (2-[(3-hydroxy-propylimino)-methyl]-6-methoxy-phenol), and 2-aminoethanol, H₂vae, (2-[(3-hydroxy-ethylimino)-methyl]-6-methoxy-phenol). The differing nuclearities of the two clusters stem from the choice of imino alcohol arm in the Schiff bases, H₂vap and H₂vae. This work is aimed at broadening the diversity of Cu^II–Dy^III clusters and to perceive the consequence of changing the length of the alcohol arm on the nuclearity of the cluster, providing valuable insight into promising future synthetic directions. The underlying topological entity of the pentanuclear Cu₄Dy cluster is reported for the first time. The investigation of magnetic behaviors of <b>1</b> and <b>2</b> below 2 K reveals slow magnetic relaxation with a significant influence coming from the variation of the alcohol arm affecting the nature of magnetic interactions