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Novel heterocyclic thiosemicarbazones derivatives as colorimetric and "turn on" fluorescent sensors for fluoride anion sensing employing hydrogen bonding
(Chemical Equation Presented) Two novel heterocyclic thiosemicarbazone derivatives have been synthesized, and characterized, by means of spectroscopic and single crystal X-ray diffraction methods. Their chromophoric-fluorogenic response towards anions in competing solvent dimethyl sulfoxide (DMSO) was studied. The receptor shows selective recognition towards fluoride anion. The binding affinity of the receptors with fluoride anion was calculated using UV-visible and fluorescence spectroscopic techniques. 2013 Elsevier B.V. All rights reserved. -
An Efficient Detection and Prediction of Intrusion in Smart Grids Using Artificial Neural Networks
In recent years, fraud identification on Internet of Things (IoT) devices has been essential to obtaining better results in all fields, such as smart cities, smart grids, etc. As a result, there are more IoT devices in the smart grid's power management sectors, and apart from these identifications, intrusion into the smart grid is very difficult. Hence, to overcome this, a proposed intrusion detection system in a smart grid using an artificial neural network (ANN) has been used to detect the intrusion and improve the prediction rate, and it has been very effective on various faults injected into the smart grids in ranges and seasons. As per the simulation result, the proposed method shows better results as compared to a conventional neural network (CNN) with respect to the root mean square error in terms of weekly, monthly, and seasonal terms of 0.25%, 0.15%, and 0.26%, respectively. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Evaluation of tourism infrastructure around the ancient marvels of Mahabalipuram and Pattadakal
India is rich in culture and well-known for its mythological knowledge. It has plenty of architectural marvels recognized by UNESCO and maintained by the Archaeological Survey of India (ASI). For this chapter, two of the cultural UNESCO sites, Mahabalipuram and Pattadakal, have been taken as the scope of the chapter. The introduction section talks about the history of the monuments and provides a brief overview of the tourism infrastructure. The following section, evaluation of tourism infrastructure, aims to analyze Mahabalipuram and Pattadakal's tourism infrastructure using four parameters: transportation and connectivity, accommodation, gastronomical facilities, and tourist amenities. An observational study of the sites was conducted between December 2023 and January 2024. This is followed by the recommendations provided by the authors to improve the tourism infrastructure in these sites and, finally, the chapter's conclusion and future scope for researchers. 2024, IGI Global. All rights reserved. -
Effects of dark matter on the upper bound mass of neutron stars
Observations have indicated that we do not see neutron stars (NS) of mass near the theoretical upper limit as predicted. Here we invoke the role of dark matter (DM) particles in star formation, and their role in lowering the mass of remnants eventually formed from these stars. Massive stars can capture DM particles more effectively than the lower mass stars, thus further softening the equation of state of the remnant neutron stars. We also look at the capture of DM particles by the NS, which could further soften the upper mass limit of NS. The admixture of DM particles would be higher at earlier epochs (high z). 2020 Elsevier B.V. -
Revisiting Cournot Duopoly Model An Experimental Study
Journal of the Institute for Research in Social Sciences and Humanities, Vol-6 (1&2), pp. 151-170. ISSN-0973-3353 -
Speculative investment decisions in cryptocurrency: a structural equation modelling approach
Cryptocurrency markets are inclined towards speculative usage due to the inherent high risk of financial loss and the potential for substantial gains during transaction completion. In response to this phenomenon, this study represents the inaugural effort to explore the influence of variables such as subjective norms, domain knowledge, impulsive investment tendencies, and self-control on decisions related to speculative investments. Utilising structural equation modelling with a dataset of 367 responses in India, the study is the first of its kind. The research reveals that subjective norms and domain knowledge play a significant role in influencing impulsive investment and self-control. Additionally, impulsive investment exhibits significant associations with decisions involving speculative investments. This insight underscores the complexity wherein individuals, despite exercising self-control, may still engage in speculative decisions that lead to adverse consequences. The findings have practical implications for investors and regulators, offering valuable insights into investment behaviours within the cryptocurrency realm. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
The Role of Major-Sport Event Cricket with Respect to Consumer Perception and Attitude Towards Ambush Marketing
International Journal of Business and Management Invention, Vol-2 (10), pp. 76-81. ISSN-2319-801X -
Can co-creating in CSR initiatives influence loyal customers? Evidence from the banking industry
Consumers are increasingly pressing organizations to adapt meaningful corporate social responsibility (CSR) initiatives and seek avenues for co-creation. Study to investigate if CSR can help co-creation significantly contributes to the competitive advantage of banks. Many previous pieces of research have recognized CSR as a strategic imperative that may help businesses to build consumer loyalty. To address gaps in the literature based on the social identity theory and theory of social exchange, this article investigates the impact of CSR on consumer loyalty while considering the mediation effect of co-creation. The data were collected in India, and the sample contained 520 customers of banks. Partial least squares-structural equation modeling was used to test the hypothesis. The study findings show that CSR, directly and indirectly, impacts consumer loyalty through co-creation. The current study's findings aid banking institutions in determining how to design and implement strategies based on CSR and co-creation that could eventually result in consumer loyalty. 2023 ERP Environment and John Wiley & Sons Ltd. -
Does perceived corporate social responsibility improve customer engagement? - An empirical evidence from Indian banks
In recent years, banks are trying to embed their corporate social responsibility (CSR) and societal outreach initiatives into their strategic process to improve their competitive advantage and performance. A previous study reveals that CSR initiatives and efforts of the banks will likely to positively influence the customers attitudes toward that bank and generate favourable behavioural outcomes. This study will provide a deeper investigation of whether the perceived CSR discriminate against the customer engagement level in the bank. This paper attempts to measure the discriminating power of CSR towards customer engagement. Maignan and Ferrells (2004) scale was used to ascertain the corporate social responsibility, and for measuring customer engagement, the Gallop scale (2001) was used. Primary data was collected through a simple random sampling technique from 612 customers across different banks. The discriminant analysis was carried out to find out the discriminating power of CSR towards customer engagement. Discriminating function model results exactly predicting customer engagement level based on the CSR initiatives. The findings are supportive and helpful for the banks in formulating effective CRM Strategy to satisfy and engage their customer at a high level through effectively articulated CSR plans and policies. 2024 Inderscience Publishers. All rights reserved. -
HHO-Based Vector Quantization Technique for Biomedical Image Compression in Cloud Computing
In the present digital era, the exploitation of medical technologies and massive generation of medical data using different imaging modalities, adequate storage, management, and transmission of biomedical images necessitate image compression techniques. Vector quantization (VQ) is an effective image compression approach, and the widely employed VQ technique is Linde-Buzo-Gray (LBG), which generates local optimum codebooks for image compression. The codebook construction is treated as an optimization issue solved with utilization of metaheuristic optimization techniques. In this view, this paper designs an effective biomedical image compression technique in the cloud computing (CC) environment using Harris Hawks Optimization (HHO)-based LBG techniques. The HHO-LBG algorithm achieves a smooth transition among exploration as well as exploitation. To investigate the better performance of the HHO-LBG technique, an extensive set of simulations was carried out on benchmark biomedical images. The proposed HHO-LBG technique has accomplished promising results in terms of compression performance and reconstructed image quality. 2023 World Scientific Publishing Company. -
Extensive long-term verbal memory training is associated with brain plasticity
The human brain has a remarkable capacity to store a lifetime of information through visual or auditory routes. It excels and exceeds any artificial memory system in mixing and integrating multiple pieces of information encoded. In this study, a group of verbal memory experts was evaluated by multiple structural brain analysis methods to record the changes in the brain structure. The participants were professional Hindu pandits (priests/scholars) trained in reciting Vedas and other forms of Hindu scriptures. These professional Vedic priests are experts in memorization and recitation of oral texts with precise diction. Vedas are a collection of hymns. It is estimated that there are more than 20,000 mantras and shlokas in the four Vedas. The analysis included the measurement of the grey and white matter density, gyrification, and cortical thickness in a group of Vedic pandits and comparing these measures with a matched control group. The results revealed an increased grey matter (GM) and white matter (WM) in the midbrain, pons, thalamus, parahippocampus, and orbitofrontal regions in pandits. The whole-brain corelation analysis using length ofpost-training teachingduration showed significant correlation with the left angular gyrus. We also found increased gyrification in the insula, supplementary motor area, medial frontal areas, and increased cortical thickness (CT) in the right temporal pole and caudate regions of the brain. These findings, collectively, provide unique information regarding the association between crucial memory regions in the brain and long-term practice of oral recitation of scriptures from memory with the proper diction that also involved controlled breathing. 2021, The Author(s). -
Potential of banana based cellulose materials for advanced applications: A review on properties and technical challenges
Biocompatibility, biodegradability, and toxicity issues of synthetic polymers have propelled the search for environmentally friendly and non-toxic alternatives. In this context, biobased materials have gained much popularity due to their non-toxic, biodegradable, and sustainable nature. Bananas are considered as one of such natural material which fulfil the requirements to be tailored as a biocompatible biopolymer. Banana derived wastes can be used for extraction of commercially important biopolymers like starch, cellulose, nanocellulose and their subsequent utilization in wide variety of applications. Banana derived biopolymers and their bio composites and widely used for medical applications such as wound healing, fabrication of bone plates, cellulose based gate dielectrics, and capacitors for insulin pumps, and pacemakers. In addition, banana based nanocellulose can be used in tissue engineering, biosensing, drug delivery, bioimaging, wound healing, enzyme immobilization and preparation of tablets for oral administration. Moreover, banana-based polymers can be employed in applications such as food packaging, biofuel production, and production of multilayered papers. Considering the potential applications of banana-based nanomaterials, this review work is framed to understand the process of extraction of starch, cellulose, nanocellulose and biopolymers from banana derived wastes with specific emphasis on their extraction methods and composite preparation methods. In addition, it discusses in detail the promising and potential applications of the derived materials in health and environmental sectors. The presented review is a comprehensive discussion on banana-based waste conversion strategies to produce value added products useful in medical and environmental applications. 2023 The Author(s) -
Advanced Sentiment Analysis: From Lexicon-Enhanced BERT to Dimensionality Reduction Using NLP
Social media platforms serve as vital connections for communication, generating massive quantities of data that represent an array of perspectives. Efficient sentiment analysis is necessary for understanding public opinion, particularly in domains such as product reviews and socio-political discussion. This paper develops a novel sentiment analysis model that is customized for social media data by integrating machine learning algorithms, language processing techniques with part-of-speech tagging, and dimensionality reduction methods. The model will improve sentiment analysis performance by tackling challenges like noise and data domain variations. To further improve sentiment representation, it includes convolutional neural networks (CNNs), BERT embeddings, N-grams, and sentiment lexicons. The model's effectiveness is determined on a variety of datasets, which enhances sentiment analysis in social media discussion. This paper goes beyond sentiment analysis in code-mixed, multilingual text and highlights the importance of careful data before treatment and an extensive variety of ML algorithms. This study attempts to explain the nuances of sentiment analysis and its use in social media discussions through methodical research. 2024 IEEE. -
Strategic Management During a Pandemic
The COVID- 19 pandemic changed world dynamics, working scenarios, as well as professional and emotional dimensions. The virus has emerged as a significant threat for the continuity of business. Keeping the gravity of the problem in mind, companies must understand the need for change and must now update their strategy to account for pandemics. The next pandemic may be more severe than the current one, meaning that organizations need to devise mechanisms and business models to fight with these situations and maintain business continuity. They should not only look forward to saving plants, machinery and infrastructure, but also concentrate on employee welfare, customer engagement and satisfaction during this crisis time. The book will not only present the evidence of various effective solutions to run a business in the time of a pandemic, but also put forward the new models and practices of business being followed by people at the time of crisis. It aims to create a bridge between existing business models and proposed business solutions, focusing on existing theories and most importantly case studies from the recent happenings. This rich collection of chapters will provide insights regarding the business challenges, opportunities and practices during pandemic situations like COVID- 19, making it particularly valuable to researchers, academics and students in the fields of strategic management, leadership and disaster management. 2022 selection and editorial matter, Vikas Kumar and Gaurav Gupta. -
Energy-Efficient Cluster in Wireless Sensor Network Using Life Time Delay Clustering Algorithms
Through Wireless Sensor Networks (WSN) formation, industrial and academic communities have seen remarkable development in recent decades. One of the most common techniques to derive the best out of wireless sensor networks is to upgrade the operating group. The most important problem is the arrangement of optimal number of sensor nodes as clusters to discuss clustering method. In this method, new client nodes and dynamic methods are used to determine the optimal number of clusters and cluster heads which are to be better organized and proposed to classify each round. Parameters of effective energy use and the ability to decide the best method of attachments are included. The Problem coverage find change ability network route due to which traffic and delays keep the performance to be very high. A newer version of Gravity Analysis Algorithm (GAA) is used to solve this problem. This proposed new approach GAA is introduced to improve network lifetime, increase system energy efficiency and end delay performance. Simulation results show that modified GAA performance is better than other networks and it has more advanced Life Time Delay Clustering Algorithms-LTDCA protocols. The proposed method provides a set of data collection and increased throughput in wireless sensor networks. 2022 CRL Publishing. All rights reserved. -
HMOSHSSA: a novel framework for solving simultaneous clustering and feature selection problems
In real-life scenarios, information about the number of clusters is unknown. Due to this, clustering algorithms are unable to generate the valuable partitions. Beside this, the appropriate and optimal number of features is also required to produce the good quality clusters. The selection of optimal number of clusters and feature is a challenging task in the clustering. To resolve these problems, an automatic multi-objective-based clustering approach called HMOSHSSA is proposed in this paper. In HMOSHSSA, the spotted hyena and salp swarm algorithms are hybridized to obtain a better trade-off between these algorithms intensification and diversification capabilities. Two novel concepts for encoding and threshold setting are incorporated in the HMOSHSSA. The encoding scheme is used to choose the optimal number of clusters and features during the optimization process. The variance of dataset is used for setting the threshold values for both clusters and features. A novel fitness function is proposed to improve the optimization process. The suggested algorithms performance is evaluated using eight well-known real-world datasets. The statistical significance of HMOSHSSA is measured through t-tests. Results reveal that the proposed approach is able to detect the optimal number of clusters and features from a given dataset without user intervention. This approach is also deployed for solving microarray data analysis and image segmentation problems. HMOSHSSA outperformed the other considered algorithms in terms of performance measures. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
A critical assessment of technical advances in pharmaceutical removal from wastewater A critical review
Use of pharmaceutical products has seen a tremendous increase in the recent decades. It has been observed that more than thirty million tons of pharmaceuticals are consumed worldwide. The used pharmaceutical products are not completely metabolized in human and animal body. Therefore, they are excreted to the environment and remain there as persistent organic chemicals. These compounds emerge as toxic contaminants in water and affect the human metabolism directly or indirectly. This literature review is an endeavour to understand the origin, applications and current advancement in the removal of pharmaceuticals from the environment. It discusses about the pharmaceuticals used in medical applications such diagnosis and disease treatment. In addition, it discusses about the recent approaches applied in pharmaceutical removal including microbial fuel cells, biofiltration, and bio nanotechnology approaches. Moreover, the challenges associated with pharmaceutical removal are presented considering biological and environmental factors. The review suggest the potential recommendations on pharmaceutical removal. 2023 The Authors -
Commercialization potential of agro-based polyhydroxyalkanoates biorefinery: A technical perspective on advances and critical barriers
The exponential increase in the use and careless discard of synthetic plastics has created an alarming concern over the environmental health due to the detrimental effects of petroleum based synthetic polymeric compounds. Piling up of these plastic commodities on various ecological niches and entry of their fragmented parts into soil and water has clearly affected the quality of these ecosystems in the past few decades. Among the many constructive strategies developed to tackle this global issue, use of biopolymers like polyhydroxyalkanoates as sustainable alternatives for synthetic plastics has gained momentum. Despite their excellent material properties and significant biodegradability, polyhydroxyalkanoates still fails to compete with their synthetic counterparts majorly due to the high cost associated with their production and purification thereby limiting their commercialization. Usage of renewable feedstocks as substrates for polyhydroxyalkanoates production has been the thrust area of research to attain the sustainability tag. This review work attempts to provide insights about the recent developments in the production of polyhydroxyalkanoates using renewable feedstock along with various pretreatment methods used for substrate preparation for polyhydroxyalkanoates production. Further, the application of blends based on polyhydroxyalkanoates, and the challenges associated with the waste valorization based polyhydroxyalkanoates production strategy is elaborated in this review work. 2023 Elsevier B.V. -
Design considerations of an inductive sensor for segmented mirror telescopes
The Segmented mirror technology has become natural choice for any optical telescope larger than 8 meter in size, where small mirror segments are aligned and positioned with respect to each other to an accuracy of few tens of nanometer. Primary mirror control system with the help of edge sensor and soft linear actuator maintains that alignment which changes due to gravity and wind loading. For any segmented mirror telescope edge-sensor plays very critical role. It should have very high spatial resolution (few nanometer), large range, multidimensional sensing, high temporal stability as well as immunity towards relative change in temperature and humidity. Though capacitive sensors are widely used for this purpose, however, their inherent sensitivity towards humidity and dust make them unsuitable for telescopes operating at humid low altitude regions. Whereas, inductance based sensors, working on the principal of mutual inductance variation between two planar inductor coils, produce promising results in such a situation. Looking at stringent requirements, design and development of a planar inductive sensor is a challenge. As a first step toward sensor development, we have explored the design aspects of it. The inductive coils are first simulated and analyzed using electromagnetic FEA software for different coil parameters. The design considerations include optimization of coil parameters such as geometry of coils, trace densities, number of turns, etc. and operational requirements such as number of degree of freedoms to be sensed, range of travel, spatial resolution, as well as required sensitivity. The simulation results are also verified through experimentation. In this first paper we report the design and analysis results obtained from FEA simulations. 2018 SPIE.