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A comprehensive analysis of various structural parameters of Indian coals with the aid of advanced analytical tools
An exhaustive structural analysis was carried out on three Indian coals (ranging from sub-bituminous to high volatile bituminous coal) using a range of advanced characterization tools. Detailed investigations were carried out using UVVisible spectroscopy, X-ray diffraction, scanning electron microscopy coupled energy dispersive spectroscopy, Raman spectroscopy and Fourier transform infrared spectroscopy. The X-ray and Raman peaks were deconvoluted and analyzed in details. Coal crystallites possess turbostratic structure, whose crystallite diameter and height increase with rank. The H/C ratio plotted against aromaticity exhibited a decreasing trend, confirming the graphitization of coal upon leaching. It is also found that, with the increase of coal rank, the dependency of I20/I26 on La is saturated, due to the increase in average size of sp2 nanoclusters. In Raman spectra, the observed G peak (1585cm?1) and the D2 band arises from graphitic lattices. In IR spectrum, two distinct peaks at 2850 and 2920cm?1 are attributed to the symmetric and asymmetric CH2 stretching vibrations. The intense peak at ~1620cm?1, is either attributed to the aromatic ring stretching of C=C nucleus. 2016, The Author(s). -
A comprehensive examination of factors influencing intention to continue usage of health and fitness apps: a two-stage hybrid SEM-ML analysis
This research developed a theoretical framework based on the uses and gratification theory to investigate the intention to continue usage of Health and Fitness Apps (HFAs). In addition, this study explored how health valuation moderates the relationship between determinants and users intention to continue usage. A total of 447 HFA users data was collected from Delhi NCR, India through a purposive sampling technique. Partial least square-structure equation modeling was used to test the role of potential predictors influencing users behavioral intention to continue. The machine learning algorithms were employed to identify the features of importance. The results revealed that system quality, networkability, recordability, and task technology fit have a positive influence on hedonic motivation and utilitarian motivation. While information quality influences hedonic motivation but does not affect utilitarian motivation. Health valuation positively moderates the relationship between information quality, system quality, and networkability to intention to continue usage. We also observed that hedonic motivation emerged as a key predictor of users intention to continue usage of HFAs. The results would possibly offer useful recommendations for HFA developers, marketers, and health policymakers. The quality of fitness apps should be the primary concern of app developers. Furthermore, gamification can be incorporated into HFAs as it may influence the users hedonic motivations. The research contributes by developing a uses and gratification theory tailored for the HFAs. Additionally, this research incorporates hedonic and utilitarian motivation as mediating variables and health valuation as a moderator. 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
A Comprehensive Investigation of Blockchain Technology's Role in Cyber Security
In recent years, blockchain has become an extremely trending technology, capable of solving a variety of problems. One of these domains is cybersecurity, where blockchain technology has a huge scope. To dive deeper into this topic, we first need to understand the cybersecurity domain, the need for this field, and how it has become crucial to the current Information-Technology industry. Once we have a good understanding of the field of cybersecurity, we next focus on blockchain technology, its basic working process, and what makes it a trending infrastructural technology in today's world. The basic idea about the field of cybersecurity and blockchain technology can help us understand how the two different fields can be integrated to solve several problems in the cybersecurity domain. Eventually, we discuss the pros and cons of blockchain technology in cybersecurity and how the integration of the two different fields can make a difference. This study aims to explore various possibilities where blockchain technology can be utilized in several applications to solve a variety of problems in the field of cybersecurity. 2023 IEEE. -
A comprehensive investigation of ethyl 2-(3-methoxybenzyl) acrylate substituted pyrazolone analogue: Synthesis, computational and biological studies
In this study, we successfully synthesized ethyl 2-(3-methoxybenzyl) acrylate-substituted pyrazolones derivative (EMH) through the reaction of Baylis-Hillman acetate with pyrazolones. We conducted comprehensive screenings to evaluate its invitro antifungal, antibacterial, and antioxidant properties. The molecule demonstrated notable in vitro antifungal and antibacterial activities attributed to the presence of anisole, enhancing absorption rates through increased lipid solubility and improving pharmacological effects. Structure-activity relationship (SAR) studies supported these findings. Additionally, insilico studies delved into the molecular interactions of the synthesized molecule with DNA Gyrase, Lanosterol 14 alpha demethylase, and KEAP1-NRF2 proteins, revealing strong binding interactions at specific sites. Furthermore, we employed ab-initio techniques to theoretically estimate the photophysical properties of the compounds. Ground state optimization, dipole moment, and HOMO-LUMO energy levels were calculated using the DFT-B3LYP-6-31G(d) basis set. The theoretical HOMO-LUMO values indicated high electronegativity and electrophilicity index. NBO analysis confirmed the presence of intermolecular ONH hydrogen bonds resulting from the interaction of the lone pair of oxygen with the anti-bonding orbital. Overall, our results suggest that anisole-substituted pyrazolones derivatives exhibit promising applications in both photophysical and biological domains. 2024 -
A comprehensive investigation of the effect of mineral additives to bituminous concrete
Research efforts to employ sustainable materials for road construction have been on the rise in recent years. In particular, the use of polymers as additives in asphalt mix has been actively explored by several researchers. Bituminous pavementsnormally constructed in India, have increasing number of premature failures, due to increase in traffic density and noteworthy variations in road temperatures. The modified binders have proven to improve numerous properties of bituminous surfaces such as temperature susceptibility, fatigue life, creep, resistance to permanent deformation and rutting. The present study has focussed on the experimental investigations conducted to evaluate the influence of mineral additives, such as wollostonite and Rice Husk Ash (RHA) on Indirect Tensile Strength (ITS) and Tensile Strength Ratio (TSR) of bituminous concrete (BC)maintaining uniformity of aggregate properties.The results establish that the bituminous concrete blends modified using rice husk ash at 20% and wollostonite at 8%, with hydrated lime are most suitable for practical applications. 2021 Elsevier Ltd. All rights reserved. -
A comprehensive investigation on machine learning techniques for diagnosis of down syndrome
Down Syndrome is a chromosomal disease which causes many physical and cognitive disabilities. Down Syndrome patients are more vulnerable than any other patient. Medical experts started knowing it now with keen awareness. In recent years it has become a field of interest for many researchers, medical experts and social organisation. For the researchers it is an area of interest where very little work is done and a lot to be explored. Machine Learning consists of different processing levels like pre-processing, segmentation, feature selection and classification. Each level contains a vast set of techniques like filters, segmentation algorithms and classifiers. Machine Learning is one of the most popular algorithm, which is used to automate the decision making process with higher rate of accuracy in less time with least error rate. Machine Learning proved its significance with highest rate of accuracy in decision making and problem solving in almost all the fields but automated decision making in medical science is still a challenge. This paper reviews the different works done in the field of Down Syndrome using Machine Learning applied on different medical images, and the techniques like pre-processing, segmentation, feature selection and classification. The aim of this research work is to analyse and identify the Machine Learning methodologies that works efficiently to detect Down Sundrome. 2017 IEEE. -
A comprehensive literature review on financial inclusion /
Asian Journal Of Research In Banking And Financial, Vol.7, Issue 8, pp.119-133, ISSN: 2249-7323. -
A comprehensive LR model for predicting banks stock performance in Indian stock market
The study focusses on developing a Logistic Regression model to distinguish between Good and Poor Performance of Bank-stocks which are traded in Indian stock market with regard to the financial ratios. The study- sample comprises of financialratios of 40 nationalised and private banks, for a period of six years. The study ascertains and scrutinizes eleven financial ratios that can categorize the Banksbroadly into two categories as good or poor, up to the accuracy level of 78 percent, based on their rate of return. First, the study predicts the performance of banks by using financial ratios and tries to build the goodness of fit by using Logistic Regression approach. The study also emphasizes that this model can enrich an investors ability to forecast the price of various stocks. However, the paper confers the real-world implications of Logistic Regression model to envisage the performance of Banks in the stock market. The study reveals that the model could be useful to potential investors, fund managers, and investment companies to improve their strategies and to select the out-performing Bank-stocks. Serials Publications Pvt. Ltd. -
A Comprehensive Methodical Strategy for Forecasting Anticipated Time of Delivery in OnlineFood Delivery Organizations
Determining the cost of shipping has long been a cornerstone of urban logistics, but today's effective outcomes need acceptable precision. Around the globe, internet-based meal ordering and distribution services have surpassed public expectations; for example, in India, platform-to-consumer distributions and delivery of food and drinks reached an astounding amount of more than 290 million transactions in 2023. Businesses are required to provide customers with precise details on the time it will take for their food to be delivered, starting from the moment the purchase is placed until it reaches the customer's door. Customers won't place orders if the result measure is greater than the actual delivery date, but a greater number of consumers are going to contact the customer service line if the period of waiting falls shorter than their actual shipment period. This study's primary goals are to identify critical variables that affect the availability of nutritious food inspiring leaders as well as to provide an approach for making accurate predictions. Analyzing and contrasting the primary effects and challenges of distribution and shipping in the nation's many different sectors. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A comprehensive molecular docking-based study to identify potential drug-candidates against the novel and emerging severe fever with thrombocytopenia syndrome virus (SFTSV) by targeting the nucleoprotein
Severe fever with thrombocytopenia syndrome (SFTS) is a newly emerging haemorrhagic fever that is caused by an RNA virus called Severe fever with Thrombocytopenia Syndrome virus (SFTSV). The disease has spread globally with a case fatality rate of 30%. The nucleoprotein (N) of the virus has a pivotal role in replication and transcription of RNA inside the host. Considering that no specific treatment regime is suggested for the disease, N protein may be regarded as the potential candidate drug target. In the present study, in silico molecular docking was performed with 130 compounds (60 natural compounds and 70 repurposed synthetic drugs) against the N protein. Based on the binding affinity (kcal mol?1), we selected Cryptoleurine (?10.323kcalmol?1) and Ivermectin (?10.327kcalmol?1) as the top-ranked ligands from the natural compounds and repurposed synthetic drugs groups respectively, and pharmacophore analysis of these compounds along with other high performing ligands revealed that two aromatic and one acceptor groups could strongly interact with the target protein. Finally, molecular dynamic simulations of Cryptoleurine and Ivermectin showed stable interactions with the N protein of SFTSV. To conclude, Cryptoleurine and Ivermectin can be considered as a potential therapeutic agent against the infectious SFTS virus. Graphical abstract: (Figure presented.) The Author(s) under exclusive licence to Archana Sharma Foundation of Calcutta 2024. -
A comprehensive novel model for network speech anomaly detection system using deep learning approach
Network Intrusion Detection System (NIDS) is the key technology for information security, and it plays significant role for classifying various attacks in the networks accurately. An NIDS gains an understanding of normal and anomalous behavior by examining the network traffic and can identify unknown and new attacks. Analyzing and Identifying unfamiliar attacks are one of the big challenges in Network IDS research. A huge response has been given to deep learning over the past several years and novelty in deep learning techniques are also improved regularly. Deep learning based Network Intrusion Detection approach is highly essential for improved performance. Nowadays, Machine learning algorithms made a revolution in the area of human computer interaction and achieved significant advancement in imitating human brain exactly. Convolutional Neural Network (CNN) is a powerful learning algorithm in deep learning model for improving the machine learning ability in order to achieve high attack classification accuracy and low false alarm rate. In this article, an overview of deep learning methodologies for commonly used NIDS such as Auto Encoder (AE), Deep Belief Network (DBN), Deep Neural Network (DNN), Restricted Boltzmann Machine (RBN). Moreover, the article introduces the most recent work on network anomaly detection using deep learning techniques for better understanding to choose appropriate method while implementing NIDS through widespread literature analysis. The experimental results designate that the accuracy, false alarm rate, and timeliness of the proposed CNN-NIDS model are superior than the traditional algorithms. 2020, Springer Science+Business Media, LLC, part of Springer Nature. -
A Comprehensive Research on Deep Learning Based Routing Optimization Algorithms in Software Defined Networks
Discovering an optimal routing in Software Defined Networks (SDNs) is challenging due to several factors like scalability issues, interoperability, reliability, poor configuration of controllers and security measures. The compromised SDN controller attacks at the control plane layer, packet losses in the topology and end-to-end delay are the most security risk factors in SDNs. To overcome this, in most of the existing researches, Deep Reinforcement Learning (DRL) algorithm with various optimization techniques was implemented for optimal routing in SDN by providing link weights to balance the end-to-end delay and packet losses. DRL used Deterministic Policy Gradient (DPG) method which acts as an actor-critic reinforcement learning agent that searches for an optimal policy to minimize the expected cumulative long-term reward. However, discovering an optimal routing with efficient security measures is still a major challenge in SDNs. This research proposes a detailed review of routing optimization algorithms in SDN using Deep Learning (DL) methods which supports the researchers in accomplishing a better solution for future research. 2023 IEEE. -
A comprehensive review of AI based intrusion detection system
In today's digital world, the tremendous amount of data poses a significant challenge to cyber security. The complexity of cyber-attacks makes it difficult to develop efficient tools to detect them. Signature-based intrusion detection has been the common method used for detecting attacks and providing security. However, with the emergence of Artificial Intelligence (AI), particularly Machine Learning, Deep Learning and ensemble learning, promising results have been shown in detecting attacks more efficiently. This review discusses how AI-based mechanisms are being used to detect attacks effectively based on relevant research. To provide a broader view, the study presents taxonomy of the existing literature on Machine Learning (ML), Deep learning (DL), and ensemble learning. The analysis includes 72 research papers and considers factors such as the algorithm and performance metrics used for detection. The study reveals that AI-based intrusion detection methods improve accuracy, but researchers have primarily focused on improving performance for detecting attacks rather than individual attack classification. The main objective of the study is to provide an overview of different AI-based mechanisms in intrusion detection and offer deeper insights for future researchers to better understand the challenges of multi-classification of attacks. 2023 -
A Comprehensive Review of Linear Regression, Random Forest, XGBoost, and SVR: Integrating Machine Learning and Actuarial Science for Health Insurance Pricing
Actuarial science and data science are being studied as a fusion using Industry 4.0 technologies such as the Internet of Things, artificial intelligence, big data, and machine learning (ML) algorithms. When analyzing earlier components of actuarial science, it could have been more accurate and quick, but when later stages of AI and ML were integrated, the algorithms weren't up to the standard, and actuaries experienced some accuracy concerns. The company requires actuaries to be precise with analysis to acquire reliable results. As a result of the large amount of data these companies collect, a choice made manually may turn out to be incorrect. We will, therefore, examine alternative models in this article as part of the decision-making process. Once we have chosen the best path of action, we will use our actuarial expertise to evaluate the risk associated with specific charges features. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A comprehensive review of microplastic pollution in freshwater and marine environments
Water popularly termed the The Elixir of Life is now polluted beyond control in several regions. Microplastics, the tiny contaminants have found their way into all walks of life. They have also been found to be present in human blood, multiple organs, and even breast milk. There is an abundance of microplastics in the air we breathe, the food we eat, and the water we drink. Curbing them has to start with a ban of all forms of primary microplastics, and single use plastics with preference being given to biodegradable alternatives. India in particular banned single use plastics in 2022, which put an end to several commonly used plastic items being replaced with biodegradables. Paint is one of the largest contributors to microplastics, followed by textile industry, cosmetic, pharmaceutical industry, packaging industry are all top contributors to microplastics. The wastewater treatment plants aren't designed to filter microplastics from the source and this results in microplastics polluting all water resources. Though several novel techniques for microplastic segregation exist such as sieving, filtration, density separation, visual sorting, alkali digestion exist, they aren't fully employed as the initial process of microplastic segregation from waste is still in question. 2024 The Author(s) -
A Comprehensive Review of Small Building Detection in Collapsed Images: Advancements and Applications of Machine Learning Algorithms
Accurately identifying small buildings in images of collapses is essential for disaster assessment and urban planning. In the context of collapsed images, this study provides an extensive overview of the methods and approaches used for small building detection. The investigation covers developments in machine learning algorithms, their uses, and the consequences for urban development and disaster management. This work attempts to give a brief grasp of the difficulties, approaches, and potential paths in the field of small building detection from collapsed imaging through a thorough investigation of the body of existing literature. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A comprehensive review of the Indian retail industry growth /
International Journal of Social And Allied Research, Vol.7, Issue 2, pp.49-53, ISSN No: 2319-3611. -
A Comprehensive Review on Antibacterial, Anti-Inflammatory and Analgesic Properties of Noble Metal Nanoparticles
Their health industry is facing challenges due to a rise in mortality rates brought on by various multi-drug-resistant bacterial strains. As a result, new and improvedantibacterial drugs are urgently needed. Similarly, when some unwanted foreign pathogensenter the cellular premises to disturb its homeostasis, inflammation develops as an immune reaction. However, these immune responses also become a double-edged sword when the inflammatory reaction lasts for a long time, and pain is also linked to inflammatory responses. Inflammation and pain are both signs of tissue injury. Pain is,by definition, an unpleasant experience that ultimately interferes with their normalwell-being. Hence, bacterial infection, inflammation, and pain need medical assistance to maintain homeostasis. Conventional medicines possess so many repercussive effects, which then demand a replacement with a less toxic and more efficient modern drug. In their review article, for the first time, they present recent advancements in biomedical applications such as the antimicrobial, anti-inflammatory, and analgesic properties of noble metal nanoparticles. Noble metals have limited availability in the earth's crust. Hence, their physicochemical characterizations and applications are greatly limited. Still, there are some interesting research findings that offer a significant ray of hope for the health sector all over the world. 2023 Wiley-VCH GmbH. -
A comprehensive review on application of atomic force microscopy in Forensic science
The primary objective of forensic investigation of a case is to recognize, identify, locate, and examine the evidence. Microscopy is a technique that provides crucial information for resolving a case or advancing the investigation process by analyzing the evidence obtained from a crime scene. It is often used in conjunction with suitable analytical techniques. Various microscopes are employed; scanning probe microscopes are available in diverse forensic analyses and studies. Among these, the atomic force microscope (AFM) is the most commonly used scanning probe technology, offering a unique morphological and physico-chemical perspective for analyzing multiple pieces of evidence in forensic investigations. Notably, it is a non-destructive technique capable of operating in liquid or air without complex sample preparation. The article delves into a detailed exploration of the applications of AFM in the realms of nanomechanical forensics and nanoscale characterization of forensically significant samples. 2024 Elsevier Ltd and Faculty of Forensic and Legal Medicine -
A comprehensive review on energy management strategy of microgrids
Renewable energy resources are a one-stop solution for major issues that include drastic climate change, environmental pollution, and the depletion of fossil fuels. Renewable energy resources, their allied storage devices, load supplied, non-renewable sources, along with the electrical and control devices involved, form the entity called microgrids. Energy management systems are essential in microgrids with more than one energy resource and storage system for optimal power sharing between each component in the microgrid for efficient, reliable and economic operation. A critical review on energy management for hybrid systems of different configurations, the diverse techniques used, forecasting methods, control strategies, uncertainty consideration, tariffs set for financial benefits, etc. are reviewed in this paper. The novelty of reformer based fuel cells, which generates hydrogen on demand, thereby eliminating the requirement of hydrogen storage and lowest carbon footprint is discussed for the first time in this paper. The topics requiring extended research and the existing gap in literature in the field of energy management studies are presented in the authors perspective, which will be helpful for researchers working in the same specialization. Papers are segregated based on multiple aspects such as the configuration, in particular, grid-tied, islanded, multi microgrids, the control strategies adopted besides the identification of limitations/factors not considered in each work. Moreover, at the end of each section, the literature gap related to each category of segregated group is identified and presented. 2023 The Author(s)