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Mine Waste-Based Next Generation Bricks: A Case Study of Iron Ore Tailings, Red Mudand GGBS Utilization in Bricks
Utilization of mine wastes as a building material in the construction industry surmises to environmental and sustainable concepts in civil engineering.The potential environmental threat posed by mining wastes, as well as a growing societal awareness of the need to effectively treat mining wastes, has elevated the subject importance.The present research proposes a method of producing bricks that is both cost effective and environmentally benign. The research is based on the geopolymerization, known to save energy by obviating high-temperature kiln firing and lowering greenhouse gas emissions. The methodology encompasses the mixing of red mud and iron ore tailings in the range of 90% to 50% with a decrement of 10% with GGBS in the range of 10% to 50% with an increment of 10%. The raw materials and the developed composites have been tested as per Indian and ASTM standards.In addition to tests pertaining to the physical and mechanical properties, XRF, XRD, and SEM tests have been performed for examining various related issues. Based on the result analysis, the compressive strength values showed noticeable differences in case of IOT and red mud bricks with IOT-based bricks showing better compressive strengths. 2021 M. Beulah et al. -
Hybrid fruit-fly optimization algorithm with k-means for text document clustering
The fast-growing Internet results in massive amounts of text data. Due to the large volume of the unstructured format of text data, extracting relevant information and its analysis becomes very challenging. Text document clustering is a text-mining process that partitions the set of text-based documents into mutually exclusive clusters in such a way that documents within the same group are similar to each other, while documents from different clusters differ based on the content. One of the biggest challenges in text clustering is partitioning the collection of text data by measuring the relevance of the content in the documents. Addressing this issue, in this work a hybrid swarm intelligence algorithm with a K-means algorithm is proposed for text clustering. First, the hybrid fruit-fly optimization algorithm is tested on ten unconstrained CEC2019 benchmark functions. Next, the proposed method is evaluated on six standard benchmark text datasets. The experimental evaluation on the unconstrained functions, as well as on text-based documents, indicated that the proposed approach is robust and superior to other state-of-the-art methods. 2021 by the authors. Licensee MDPI, Basel, Switzerland. -
Maximizing Bifunctionality for Overall Water Splitting by Integrating H2 Spillover and Oxygen Vacancies in CoPBO/Co3O4 Composite Catalyst
In the pursuit of utilizing renewable energy sources for green hydrogen (H2) production, alkaline water electrolysis has emerged as a key technology. To improve the reaction rates of overall water electrolysis and simplify electrode manufacturing, development of bifunctional electrocatalysts is of great relevance. Herein, CoPBO/Co3O4 is reported as a binary composite catalyst comprising amorphous (CoPBO) and crystalline (Co3O4) phases as a high-performing bifunctional electrocatalyst for alkaline water electrolysis. Owing to the peculiar properties of CoPBO and Co3O4, such as complementing Gibbs free energy values for H-adsorption (?GH) and relatively smaller difference in their work functions (??), the composite exhibits H2 spillover (HS) mechanism to facilitate the hydrogen evolution reaction (HER). The outcome is manifested in the form of a low HER overpotential of 65 mV (at 10 mA cm?2). Moreover, an abundant amount of surface oxygen vacancies (Ov) are observed in the same CoPBO/Co3O4 composite that facilitates oxygen evolution reaction (OER) as well, leading to a mere 270 mV OER overpotential (at 10 mA cm?2). The present work showcases the possibilities to strategically design non-noble composite catalysts that combine the advantages of HS phenomenon as well as Ov to achieve new record performances in alkaline water electrolysis. 2024 The Author(s). Small Science published by Wiley-VCH GmbH. -
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Herbal healing traditions: A study of folk medicines used by traditional healers of Sonamukhi block, Bankura district, West Bengal, India
This ethnobotanical study aims to dive into the traditional medicinal practices used by tribals in Sonamukhi Block, Bankura district, West Bengal, India. Focusing on the use of medicinal herbs, the study carefully investigates the intergenerational wisdom kept by traditional healers, who play an important role in healthcare administration. Through conversations with traditional healers and patients, a thorough list of medicinal plants used to treat various diseases has been developed. The study demonstrates that traditional healers successfully use these medicinal plants to produce herbal medications, offering insights into the painstaking process of herbal medicine preparation that was explored in depth during the investigation. These findings highlight the significance of joint efforts to preserve indigenous knowledge and encourage the incorporation of traditional healing techniques into contemporary healthcare. The research study focused on the complicated junction of scientific methodologies and traditional beliefs, indicating the possibility for effective collaboration between scientific institutions and indigenous populations to improve healthcare practices. The studies' thorough examination of medicinal plant usage and herbal medication manufacturing emphasizes the importance of preserving, recording, and using this unique traditional knowledge for the benefit of world healthcare. 2024 Horizon e-Publishing Group. All rights reserved. -
Service industry alchemy: A symphony of digital innovations in customer engagement
The emergence of digitization, automation, and artificial intelligence has transformed service delivery, allowing businesses to increase productivity, tailor client experiences, and provide cutting-edge solutions. The delivery, use, and accessibility of services are changing in various service sectors due to innovations. Among them, healthcare, education, and finance have received considerable attention in recent years. To synthesize prior research on innovations in the service industry, the chapter attempts a thematic, sentiment, and bibliometric analysis of the research domain. For the analysis, data was extracted from the Scopus database and was filtered by application of inclusion-exclusion, with the use of NVivo and Bibliometric software VOS viewer. Most productive and influential articles, authors, journals, and affiliations were recognized. Thematic mapping and trend analysis revealed past and present research subdomains that were used for the prediction of future research agendas. 2024, IGI Global. All rights reserved. -
Analysing the Ascendant Trend of Veganism: A Comprehensive Study on the Shift towards Sustainable Dietary Choices
Background: Veganism has become a prominent social and culinary movement due to concerns about animal welfare, environmental sustainability, and ones own health. Vegans strive to consume only plant-based meals in order to lessen the suffering of animals, stop the environmental damage caused by the animal agriculture sector, and enhance their own health. Objective: This chapter aspires to understand various dynamics of consumer consciousness towards veganism through social media analysis (Twitter) and research opinions. Materials and Methods: This chapter used a qualitative approach and a three-part methodology. Firstly, a literature review examines the impact of veganism on human health, ethical needs and sustainable food choices. Secondly, the authors extracted tweets and analysed them using data visualisation software- NVivo with the essential parameters being themes, sentiment, world map, and word cloud. Results: Sentiment analysis explained consumer perception towards veganism as a storming blackball result of 36.1 present positive insights. Word map analysis describes veganism as a global phenomenon. The third part analysed the Scopus research data and identified food, diet and meat as major themes in veganism. The Scopus database sentimental analysis also re-emphasised the growing positive insights towards it. Conclusion: This study highlighted the significance of veganism as a sustainable dietary choice for addressing urgent global issues while promoting a thoughtful and compassionate approach to eating. It is also emerging as a powerful tool for positive change in preserving and promoting biodiversity. 2024 selection and editorial matter, Mourade Azrour, Jamal Mabrouki, Azidine Guezzaz, Sultan Ahmad, Shakir Khan and Said Benkirane; individual chapters, the contributors. -
Decoding Retail Realities: Traditional Retailers' Outlook on Sales Erosion to Modern Retail Economy
The traditional retail landscape in Indian metropolises has changed significantly in the last several decades, mostly due to the modern retail economy's growth, including corporate chain stores and e-commerce sites. Small merchants have been gradually displaced as a result of this paradigm shift, which has been exacerbated by changed Foreign Direct Investment (FDI) laws that have brought significant money into the Indian market, as well as a rise in consumer disposable income and the wave of digitalization. This study explores small merchants' consequences as they contend with the growing power of organized retail and e-commerce behemoths. Despite earlier research studies mostly focusing on the organized trade's exponential rise due to changing customer behavior, this paper fills this gap by illuminating the traditional retailers perspective towards the contemporary retail landscape and highlighting the threats to small businesses with a traditional focus. The study uses empirical analysis using tools like SPSS and SEM models to examine the initial troubles faced by small retailers of fast-moving consumer goods (FMCG), highlighting the difficulties they face in competing with the powerful forces of deep discounting, massive sales events, and evolving consumer tastes. This exploratory research analyzed the undermining factors like utilitarian and hedonic, purchasing patterns, menaces, hindrances, pecuniary and location as reasons for the retail paradigm from traditional to modern trade. The outcome emphasized that utilitarian factors like ambience, experience, status, variety, payment modes, single-store distribution and assortment are the drivers behind the explosion of traditional trade by the modern trade in retail economy. 2024 The Author(s). -
The Metamorphic Influence of Cause-Related Marketing: Empowering Consumers as Catalysts for Societal Transformation
Purpose: In a market where prices and quality are fiercely competitive, companies have overflowed the market with a number of suitable brands. In the competitive business world of today, marketing tactics must always evolve to meet changing times and circumstances. Researchers have been motivated to discover the underlying aspects driving cause-related marketing strategy due to its global acceptability. This research elucidated the concept of cause-related marketing and emphasized the elements that motivate consumers to engage in such initiatives and influence their choice of products. Design/Methodology/Approach: The study was conducted with the help of a questionnaire sent to 480 respondents, out of which 432 questionnaires were found to be complete. Furthermore, the study examined the significance of each factor and its impact on decision-making using the confirmatory factor SEM model to analyze the data. Findings: The study revealed that Commitment was the utmost preferred attribute for the preference of cause-related marketing products. Practical Implications: A competitive edge may be obtained through cause-related marketing. Companies may provide financial support, increased awareness, and motivated actions for significant causes by making the most of their resources and efforts. This collaborative effort between companies and consumers has the potential to improve society significantly. Originality: The confirmatory factor SEM model has been used in this work to address urgent problems, new trends, or important information gaps. 2024, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Natural Language Processing on Diverse Data Layers through Microservice Architecture
With the rapid growth in Natural Language Processing (NLP), all types of industries find a need for analyzing a massive amount of data. Sentiment analysis is becoming a more exciting area for the businessmen and researchers in Text mining NLP. This process includes the calculation of various sentiments with the help of text mining. Supplementary to this, the world is connected through Information Technology and, businesses are moving toward the next step of the development to make their system more intelligent. Microservices have fulfilled the need for development platforms which help the developers to use various development tools (Languages and applications) efficiently. With the consideration of data analysis for business growth, data security becomes a major concern in front of developers. This paper gives a solution to keep the data secured by providing required access to data scientists without disturbing the base system software. This paper has discussed data storage and exchange policies of microservices through common JavaScript Object Notation (JSON) response which performs the sentiment analysis of customer's data fetched from various microservices through secured APIs. 2020 IEEE. -
Database aware memory reconstruction for object oriented programming paradigm
Data storage is a big challenge in front of industries and researchers when its growing enormously. Traditional data storage strategy was fulfilling the business needs till the data was in structured format. But now due to Internet of Things (IoT) compatible devices unstructured data is more than structured one. In such cases traditional data storage strategy won't work efficiently. Initially data storage devices used to store the data irrespective of its logical storage. It means the record was stored either in array format or block format. Such type of storage was not matching physical and logical structure. Logically, structured data is generated as an attribute of particular entity, but physically it gets stored in a sequential memory storage either as file or as memory block. Object Based Storage pattern(OBS) stores the data in the way object gets generated by the programmer and accordingly memory is allocated for that object. Object contains all the data related to that particular entity which makes it easy to retrieve it back. Current study includes comparative advantages, operations and study of different service providers of object-based storage. We also focused on the current need of object-based storage structure for big data analysis and cloud computing. International Journal of Scientific and Technology Research. All rights reserved. -
Modelling the nexus of macro-economic variables with WTI Crude Oil Price: A Machine Learning Approach
Crude oil price shocks have a significant impact on aggregate macroeconomic indices like GDP, interest rates, investment, inflation, unemployment, and currency rates, according to empirical evidence. Various factors like GDP, CPI, and Gold prices show a considerable impact on the Crude old prices. The correlation analysis between these variables can help the machine learning model to find the highly impacting factor of the target variable. The advanced machine learning algorithms can be used to find the most relevant variable impacting the crude oil price followed by predicting the crude oil price. Time series analysis algorithms can forecast the crude oil prices for the specific period ahead. In the current study it was observed that US dollar and CPI show a high impact on Crude oil prices. The study has implemented six machine learning algorithms out of which the ARIMAX was found to be the most efficient model. VAR and ARIMA models are used to successfully forecast the crude oil prices for the next 5 years. From the current research, a machine learning model has been obtained as an outcome of the study, which will help economists in the future to understand the dynamics of crude oil prices driver and forecast it for the near future. 2022 IEEE. -
Smart beta investing in India portfolio construction implementation and evaluation
The smart beta strategies, having marked their footprint in the developed markets in the last decades on the backdrop of the failure of active investing, are capturing emerging markets such as India recently. In this regard, the study attempts to examine the performance of smart beta strategies in long-only, multifactor, and alternative indexing frameworks in India. The study builds alternatively weighted (AW) univariate portfolios. Firstly, the cap-weighted (CW) single-factor portfolios are built. Subsequently, the portfolios are alternatively weighted and compared to the CW portfolio. Next, the CW multifactor portfolios are built and compared with singlefactor portfolios. Finally, the AW multifactor portfolios are built and newlinecompared with CW multifactor portfolios. All the portfolios are tested for their significant performance relative to the risk-free rate, market, and alpha under factor models. The portfolios were constructed from the constituents of NIFTY 500, adjusting for survivorship bias. The sample period spanned over 21 years from 01/10/2000 to 31/09/2021. The hypotheses were tested using the One-Sample T-test or Wilcoxon Signed Rank test for the difference in return, based on return distribution, and the Wald test for the difference in alpha and exposure using the Seemingly Unrelated Regression framework. The portfolios were constructed and analyzed using Python. We find mixed evidence of factor presence; the factor portfolios built on market data such as Illiquid, Winner, Stable, and Size offered better performance than those built on fundamental data such as Value, Strong, and Conservative. The Integrated portfolio does not differ from Mixed and single-factor portfolios, except for underperformance against the Illiquid portfolio. The alternative weighting offered mixed performance at single and multifactor levels. -
Smart Beta Investing in India : Portfolio Construction, Implementation, and Evaluation
The smart beta strategies, having marked their footprint in the developed markets in the last decades on the backdrop of the failure of active investing, are capturing emerging markets such as India recently. In this regard, the study attempts to examine the performance of smart beta strategies in long-only, multifactor, and alternative indexing frameworks in India. The study builds alternatively weighted (AW) univariate portfolios. Firstly, the cap-weighted (CW) single-factor portfolios are built. Subsequently, the portfolios are alternatively weighted and compared to the CW portfolio. Next, the CW multifactor portfolios are built and compared with singlefactor portfolios. Finally, the AW multifactor portfolios are built and newlinecompared with CW multifactor portfolios. All the portfolios are tested for their significant performance relative to the risk-free rate, market, and alpha under factor models. The portfolios were constructed from the constituents of NIFTY 500, adjusting for survivorship bias. The sample period spanned over 21 years from 01/10/2000 to 31/09/2021. The hypotheses were tested using the One-Sample T-test or Wilcoxon Signed Rank test for the difference in return, based on return distribution, and the Wald test for the difference in alpha and exposure using the Seemingly Unrelated Regression framework. The portfolios were constructed and analyzed using Python. We find mixed evidence of factor presence; the factor portfolios built on market data such as Illiquid, Winner, Stable, and Size offered better performance than those built on fundamental data such as Value, Strong, and Conservative. The Integrated portfolio does not differ from Mixed and single-factor portfolios, except for underperformance against the Illiquid portfolio. The alternative weighting offered mixed performance at single and multifactor levels. -
Factor investing: evidence of long-only factor portfolios from the Indian market
The study examines the performance of long-only factor portfolios in the Indian market. An extended 8-factors model and well-known factor models are used to analyse the exposure and risk-adjusted performance of factor portfolios. The results reveal a mixed portfolio performance: market-driven factors like illiquid, winner, stable, and small offered better performance than those based on fundamental data like value, strong, and conservative. While the market factor is the primary return driver, the SMB and HML factors are the other standard return drivers. The portfolios showed exposure to the specific factor they are constructed upon, except for the strong portfolio. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
Impact of COVID-19 on NSE Sectoral Indices
The paper examined the level of impact of COVID-19 on different sectors of the Indian economy using NSE sectoral indices under the event study methodology. Multiple events and event windows were considered to examine the sectoral impact of the pandemic on different events and at various stages of the outbreak. Amongst the specific events considered, the lockdown announcement had a significant impact on sectoral indices, whereas the stimulus package announcement had limited impact and the 1st COVID-19 case had no impact on sectoral indices. The impact was intensive in the prelockdown period leading to the intensive lockdown, there was not much impact in other phases. The overall impact of the COVID-19 outbreak was significantly negative on Banks, Financial Services, and Realty indices, and positive on Pharma and FMCG indices. The media index showed no significant impact of the pandemic. Indian Institute of Finance. -
IoT based heart monitoring and alerting system with cloud computing and managing the traffic for an ambulance in India
Global Burden of Disease Report, released in Sept 2017, shows that Cardiovascular Diseases caused 1.7 million deaths (17.8%) in 2016 and it is the leading cause of deaths in India [1]. According to the Indian Heart Association, 25% of all heart attacks happen under the age of 40. In most cases, the initial heart attacks are often ignored. Even post-diagnosis, as per government data [2], 50% of heart attack cases reach the hospital in more than 400 minutes against the ideal window time of 180 minutes; post which damage is irreversible. The delay is often attributed to delay in reaching a hospital or receiving primary aid. In India, traffic conditions also add to the grimace of the situation. Although the government is taking various measures; a holistic solution is required to minimize the delay at each of the steps like accessing the patient situation, contacting the Medical aid or making available the nearest aid possible. In this paper, we aim at providing the holistic solution using the Internet of Things technology (IOT) along with data analytics. IoT enables real-time capturing and computation of medical data from smart sensors built-in wearable devices. The amalgamation of Internet-based services with Medical Things (Smart sensors) enhance the chances of survival of patients. The proposed system analyses the inputs collected from the sensors fit with the patients prone to cardiovascular diseases to ascertain the emergency situation. In addition, to these data, the system also considers age, maximum and minimum heart rate. Based on computational results received from the input parameters, the system triggers the alert to emergency contacts such as the close relatives of the patient, doctors, the hospitals and nearby ambulance. The proposed system combines with the optimized navigation platform to guide the medical assistance to find the fastest route. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved. -
Child sexual abuse prevention involving mothers: A Quasi-experimental study with Protection Motivation Theory-based intervention
Child sexual abuse (CSA) is a worldwide problem. The safety of children is everybody's responsibility. Parental and family involvement is crucial in the CSA prevention process. Parental lack of knowledge may increase the risk of abuse among children. Educating parents is crucial as children are mostly dependent on them. The role of mothers in educating children on CSA is of relevance as they are identified as more sensitive and responsible caregivers. The present study aimed to promote knowledge and attitude towards CSA prevention among mothers through a psychoeducation program. A pre- and posttest design (n = 67) was used with an intervention based on Protection Motivation Theory (PMT). A CSA knowledge and attitude scale was developed for the assessment of the participants for the study. There were significant changes in knowledge and attitude among the participants after the intervention. The prevention of CSA will be effective with knowledge gains after the PMT-based intervention. The intervention programs that involve mothers in CSA education have benefits. The findings of this study can be helpful to incorporate public health approaches to devise evidence-based parental programs in community settings. 2022 Wiley Periodicals LLC. -
An Energy Optimized Clustering approach for Communication in Vehicular Cloud Systems
Vehicular cloud networks are considered to possess faster transitional topology and mobility thereby adhering to its features as an ad hoc network. Many times, it is difficult to monitor vehicular nodes that results in internetworking concerns as a result of power inadequacy during real computation. This leads to lots of energy wastage issues encountered during routing which degrades lifetime of nodes. Thus in this study a new clustering based energy optimization method is proposed to enhance the efficiency of vehicular communication. K-medoid cluster analysis along with dragonfly approach is applied to the system model to optimize energy. On the basis of simulation undertaken, it is recorded that the network lifetime, packets delivered, processing delay and throughput are increased using the proposed model. 2023 IEEE. -
A Comparative Analysis On Machine Learning Algorithm for Score Prediction and Proposal of Enhanced Nae Bayes
Sports attracted a lot of people to watch various games all over the world. India is not an exception. Among various games, cricket has special attention. Cricket in India contributes to the Indian economy on a large scale. Cricket is also known for the broad amount of data gathered for each team, season, and player. Hence, cricket is a perfect domain to work on various data analysis and machine learning approaches to acquire useful insights and information. In this paper, algorithms were used to enhance the output of the team in a sports league, particularly, IPL (cricket). It reflects the performance of the team on a deeper analysis of the requirements of T20 cricket. 2022 IEEE.