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Developing a steady state wear equation for AA7050 hybrid composites/steel interface at elevated temperature
In this research work, an attempt was made to reinforce AA7050/5Gr composites with multi-walled carbon nanotube (MWCNT) of varying weight percentages processed through stir casting route. SEM with EDS mapping revealed that the particles were uniformly distributed over the composites. Hardness reduces with increasing MWCNT weight percentage owing to the inverse hall petch effect and increment in void content. A third-body abrasion, which happens when the CNT in the aluminium matrix material detaches from the surface and erodes material from the composites pin as well as the counter face, causes the wear resistance to rise with the addition of CNT particles. A mechanically mixed layer, which avoids direct metal-to-metal contact and thus increases wear resistance, was created at the abraded surface and at high temperature, where the reduction of wear rate was due to the development of oxide protective layer. A steady-state wear equation for the contacting surface at high temperature (R = 1/Y ?(ln W/2X)) for AA7050 hybrid compositessteel interface was developed. The enhancement in wear resistance was directly proportional to the proportion of ferrous content present on the surface, which was confirmed on the elemental analysis. Pock marks, micropits, craters and cracks were the features observed on the worn surface morphology, whereas delamination and plasticisation were the observed modes of wear mechanism. 2023 Informa UK Limited, trading as Taylor & Francis Group. -
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An Investigation of Multifractality and Herd Behaviour in Indian Capital Market During Macro-Political Events : An Empirical Evidence Through Econophysics Approach
The financial markets worldwide exhibit several complex and dynamic features in them. Among them, Multifractality is one of the most significant features of complex systems, and it has been identified and examined in the financial markets in recent years. Besides, studies in the past confirm that there exists a linkage between multifractality and herding behaviour in financial markets during extreme events. The current study attempts to investigate the presence of Multifractality caused by herding behaviour in the segments of the Indian capital market during the macro-political events. For this, the macro- political events were classified into three broad categories pre-scheduled events, intensified geopolitical events and uncertain macro-political events. Further, two major segments of the Indian capital market, namely, the equity and the Forex segment, were examined. The study employed the Multifractal Detrended Fluctuation Analysis approach to examine the Multifractality caused by herding behaviour during macro-political events. In addition, the study also measured the volatility surface and quantified the information uncertainty present in the selected segments of the Indian capital market. The findings suggest that the macro-political events impact the multifractality and herding behaviour in the examined segments of the Indian capital market. However, the degree of the multifractality caused by the herding behaviour traced in the market segments is event-specific. It differs based on the type of macro-political event. The overall analysis suggests that the pre-scheduled macro-political event's impact was higher for both equity and forex segments of the Indian capital market. Further, a high degree of multifractality caused by herding behaviour was traced in the Nifty segments during the intensified geopolitical events. On the other hand, uncertain macro-political events had no impact on the multifractality caused by the herding behaviour in equity and forex segments. The study results provide some significant implications for various market participants for investment decision-making and portfolio risk diversification during the macro-political events in India. -
Social support and help-seeking worldwide
Social support has long been associated with positive physical, behavioral, and mental health outcomes. However, contextual factors such as subjective social status and an individuals cultural values, heavily influence social support behaviors (e.g., perceive available social support, accept support, seek support, provide support). We sought to determine the current state of social support behaviors and the association between these behaviors, cultural values, and subjective social support across regions of the world. Data from 6,366 participants were collected by collaborators from over 50 worldwide sites (67.4% or n = 4292, assigned female at birth; average age of 30.76). Our results show that individuals cultural values and subjective social status varied across world regions and were differentially associated with social support behaviors. For example, individuals with higher subjective social status were more likely to indicate more perceived and received social support and help-seeking behaviors; they also indicated more provision of social support to others than individuals with lower subjective social status. Further, horizontal, and vertical collectivism were related to higher help-seeking behavior, perceived support, received support, and provision of support, whereas horizontal individualism was associated with less perceived support and less help-seeking and vertical individualism was associated with less perceived and received support, but more help-seeking behavior. However, these effects were not consistently moderated by region. These findings highlight and advance the understanding of how cross-cultural complexities and contextual distinctions influence an individual's perception, processing, and practice of social support embedded in the changing social landscape. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
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Soft Computing Approach for Student Dropouts in Education System
The education system has increased the number of dropouts in the coming years, decreasing the number of educated people. Education system refers to a group of institutions like ministries of education, local education bodies, teacher training institutes, universities, colleges, schools, and more whose primary purpose is to provide education to all the people, especially young people and children in educational settings. The research aims to improve the student dropout rate in the education system by focusing on students performance and feedback. The students dropout rate can be calculated based on complexity, credits, attendance, and different parameters. This study involves the extensive study that inculcates student dropout with their performance and other parameters with soft computing approaches. There are various soft computing approaches used in the education system. The approaches and techniques used are sequential pattern mining, sentimental analysis, text mining, outlier decision, correlation mining, density estimation, etc. The approaches and techniques will be beneficial to calculating and decreasing the rate of dropout of students in the education system. The research will make a unique contribution to improved education by calculating the dropout rate of students. In particular, we argue that the dropout rate is increasing, so soft computing techniques can be the solution to improvise/reduce the dropout rate. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Application of Regression Analysis of Student Failure Rate
The education sector has been rapidly growing and is currently facing several challenges. One such challenge is identifying students who are at risk of failing, as this can help educators provide targeted interventions to improve student performance. Machine learning models have been developed to predict the probability of student failure based on various student performance metrics to address this issue. In this paper, we present a regression-based model that predicts the probability of student failure using student performance metrics such as attendance, previous academic performance, and demographic information. The model was trained on a dataset of students and achieved high accuracy in predicting the probability of student failure. While the model performs well in predicting the probability of student failure, there is always room for improvement. Possible enhancements to the model include feature engineering, ensemble learning, hyperparameter tuning, deep learning, and interpretability. These enhancements can improve the models accuracy, stability, and transparency, leading to better predictions and targeted interventions for at-risk students. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Unraveling the Interplay Between Biodiversity and Heavy Metal Content in Elookkaras Aquatic and Terrestrial Ecosystems
Background and Objective: There exists a notable correlation between biodiversity and the concentration of heavy metals, particularly concerning their role in bioremediation efforts. This study was about the heavy metal content in the aquatic and terrestrial ecosystem of Eloorkkara, located in the Kadungalloor Grama Panchayat of Kerala, India. Materials and Methods: Sampling was systematically carried out across all four seasons in order to capture the fluctuations in seasonal disturbances. Eight samples each of groundwater, river water, aquatic soil and terrestrial soil were randomly collected from the study area. Additionally, three dominant plant species from both aquatic and terrestrial habitats were carefully selected for analysis. Utilizing Inductively Coupled Plasma Mass Spectrometry (ICP-MS), the samples underwent thorough analysis to measure the levels of Cr, Cu, As, Cd, Pb, Zn, Fe, Ni and Co concentrations. Results: Indicate significant differences in heavy metal concentrations across various plant species and throughout seasonal changes, emphasizing the complex processes involved in metal accumulation. Terrestrial ecosystems exhibited higher species richness compared to aquatic ecosystems. Areas with high biodiversity tended to have lower metal concentration suggesting a potential mitigating effect of diverse ecosystems and areas with poor diversity had higher heavy metal concentration suggesting the vulnerability of degraded ecosystems. Conclusion: The research highlights the crucial role of biodiversity in influencing the absorption and dispersion of heavy metals within ecosystems. These findings carry significant implications for environmental management and conservation efforts aimed at curbing heavy metal pollution and safeguarding biodiversity in Elookkara and analogous environments. 2024 Chandni Asha Syamlal and D. Sayantan. -
Accumulation of heavy metals (Cr, Cu, As, Cd, Pb, Zn, Fe, Ni, Co) in the water, soil and plants collected from Edayar Region, Ernakulam, Kerala, India
The accumulation of heavy metals in the environment is a significant concern due to their potential toxicity and persistence. This study investigates the levels of heavy metal contamination in the water, soil and plants of the Edayar region in Ernakulam, Kerala, India. The region has experienced industrialization and urbanization, leading to concerns about heavy metal pollution. The study aims to assess the concentrations of chromium (Cr), copper (Cu), arsenic (As), cadmium (Cd), lead (Pb), zinc (Zn), iron (Fe), nickel (Ni) and cobalt (Co) in water, soil, aquatic and terrestrial plants. Samples were collected from various locations within the Edayar region, and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) was conducted to quantify heavy metal concentrations. The findings of this study will contribute to the assessment of heavy metal pollution in the Edayar region. Plants with a high diversity index were taken for analysis from both aquatic and terrestrial habitats. Scoparia dulcis L. seems to specialize in metal accumulation, possibly for protective purposes. Synedrella nodiflora Gaertn demonstrates adaptability to metal-rich environments through robust metal uptake and tolerance mechanisms. Alternanthera philoxeroides (Mart.) Griseb, on the other hand, appears to have developed mechanisms to manage heavy metal exposure. The results indicate significant levels of heavy metal contamination across all samples, with the highest concentrations detected in soil, followed by water and plants. Chromium and lead levels in soil exceeded the permissible limits set by international standards, posing potential risks to human health and the ecosystem. The accumulation patterns in plants varied, with higher bioaccumulation factors observed for zinc and copper, suggesting their preferential uptake. This study highlights the urgent need for remediation strategies and continuous monitoring to mitigate the impact of heavy metal pollution in the Edayar region. The results will help in understanding the environmental impact of human activities. Copyright: The Author(s). -
Seasonal study on the Aquatic and Terrestrial Habitat of Edayar region, Ernakulam, Kerala, India
This study examines the plant diversity and physicochemical characteristics of both aquatic and terrestrial ecosystems in the industrialized region of Edayar, Kadungalloor, Ernakulam, Kerala, India. The research is conducted seasonally, encompassing the four seasons of Kerala: southwest monsoon, northeast monsoon, winter season and summer season. Edayar is home to approximately 400 industries. The main objective of this study is to assess the plant diversity with a specific focus on herb and macrophyte diversity, in the Edayar region, along with analyzing the physicochemical properties of soil and water. Random sampling using quadrat techniques is employed to collect data on species diversity. Diversity indices, such as the Simpson Index and Shannon-Wiener index are utilized to analyze the recorded species diversity. Scoparia dulcis L. among herb species and Eichhornia crassipes (Mart.) Solms among macrophytes were found dominating in all the seasons. The results for the physico-chemical analysis of water and soil were found approaching the threshold of standard limits.The findings provide valuable insights into plant diversity and ecological dynamics of the Edayar region, which have been significantly impacted by industrial activities. The outcomes serve as a basis for the development and implementation of effective conservation and management strategies to mitigate potential ecological risks associated with industrial activities in the region. 2024 World Researchers Associations. All rights reserved. -
Mind-Set In Mathematics Learning : Role of Teacher-Student Interaction on Student Engagement, Wellbeing and Achievemrent
Mathematics learning is an integral part of the school curriculum. Children learn basic concepts in mathematics and then gradually reach the abstract level. Challenges in mathematics learning are largely observed after grade seven. Students may show disinterest towards the subject due to several reasons including past learning experiences, teacher-student interaction (TSI), anxiety and self-efficacy levels. If the students cannot connect what they learn, it impacts their interaction in the classroom, and it acts as a reason for losing interest. The literature review reveals the importance of mathematics anxiety, self-efficacy, and utility value and contribute to the construct of mind-set. Student engagement is influenced by mind-set in mathematics learning and TSI, and predicts achievement and wellbeing. The study adopts a mixed-method design with the qualitative study aiming to support the quantitative study and strengthen the validity of the results. The quantitative study sample consists of 774 eighth graders from various English medium schools in Bengaluru, Karnataka. The qualitative phase seeks to determine the students' perception of mathematics learning through their classroom experiences among 17 students using semi-structured interviews. The tested conceptual model shows an excellent fit. It shows mind-set in mathematics affects TSI, influences student engagement and leads to student-wellbeing. There was no indirect effect for the achievement and other variables. The findings related to the open-ended questions indicate the importance of teachers and content. There is a lack of understanding among students about the practical application of the learning content. The thematic analysis results provided five main themes: student attributes, teacher attributes, classroom environment, content-related and utility value. Integration of the findings leads to the importance of TSI and student engagement in the mathematics classroom. Also, the connection between variables related to mathematics learning and student wellbeing. The results of the study have important implications for developing engaging pedagogies. -
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Isolation and characterization of plant growth promoting bacteria (PGPB) from the rhizosphere of Spinacea oleracea L.
As the years pass by, there is an increase in abiotic stress conditions around the environment that directly or indirectly affect agriculture around the world. Therefore, there is a dire need to increase the sustainability of plants. Plant Growth Promoting Bacteria (PGPB) play an important role in maintaining the physiology and growth of plants under various stress conditions. This study looks into the isolation and characterization of different PGPB from Spinacia oleracea L. and their tolerance against salinity and commonly used commercial pesticides against the Spinacia family. The techniques used are isolation by serial dilution, 16sRna sequencing, characterization of different PGPB assays for confirmation such as ammonia production, catalase test, phosphate solubilisation, potassium solubilization, siderophore production, indole-3-acetic acid production, biofilm formation assay, halotolerance and tolerance study using Minimal Inhibitory Concentration (MIC). PGPB were isolated and characterized from Spinacia oleracea L., which was under an abiotic stress environment. Isolates were Bacillus clarus, Bacillus licheniformis, Paenibacillus alvei SJ6 and Paenibacillus alvei SJ8, having quantities as high as 78.10.004 mgL-1 phosphate solubilization, 43.8 mgL?1 of indole-3-acetic acid production, 14.5660.011 psu of siderophore production and 0.62 0.027 mol mL?1 of ammonia production. All isolates also had considerable amounts of halotolerance up to 10%, whereas Bacillus licheniformis had 12.5% halotolerance. The bacterial isolates had considerable tolerance against commonly used commercial pesticides against green leafy vegetables such as chlorpyriphos + cypermethrin combination and fungicides such as mancozeb. Therefore, this study looks into the isolation of potential plant growth promoting bacteria that have considerable amount of halotolerance and pesticide tolerance. 2025 World Researchers Associations. All rights reserved. -
Emoji Sentiment Analysis of User Reviews on Online Applications Using Supervised Machine Learning
Analyzing the sentiment behind emojis can provide valuable insights into the emotional context and user sentiment associated with textual content. To conduct a comparative analysis of diverse supervised machine learning models that can achieve the highest level of accuracy in Emoji Sentiment Analysis is the purpose of this research. Five machine learning models used in this research are K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), Logistic Regression, Naive Bayes, and Random Forest. The experimental process resulted in ANN and KNN models giving an accuracy of 92%. The ANN model shows its proficiency in effectively managing large datasets. ANN also supports fault tolerance. The KNN model refrains from conducting calculations during the training phase and only constructs a model when a query is executed on the dataset. This characteristic makes KNN particularly well-suited for data mining. Both ANN and K-NN excelled in the experimental study due to these distinctive attributes. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Impact of values and psychographic traits on compulsive buying behaviour in fast fashion brands
The Fast Fashion is emerging as a rapidly growing category in the Indian garment retailsector. Fast Fashion provides the latest trendy fashionable affordable clothing to consumersmultiple times in a year. The pressure of fast fashion influences the buying behavior ofconsumers resulting in compulsive purchasing tendencies (Johnson & Attmann, 2009). Theresearch study focuses on the compulsive buying behavior of Fast Fashion brand consumers.In India, Fast Fashion is emerging as an important and growing category in the garment retail. Because of rapid fashion cycles, what is ???in??? is always changing, causing consumers to feel pressure to continually update their wardrobes (Cwerner, 2001).Compulsive buying consumers buy more frequrntly without controlling their the urge to purchase(Muller, et al., 2015). The significant percentage of fast fashion buyers qulify as ???excessive shoppers??? and this international phenomenon is spreading around the world. (Greenpeace International, 2017). Shopping for clothes reflect broader values (Tatzel, 1982) and for the retailers to be successful the knowledge of consumers about culturally-defined values is important (Hyllegard et al., 2005).The extensive review of literature shows that there is a lack of research both in academic and marketing aspects regarding Compulsive buying behaviours of Fast Fashion consumer with respect to value psychographic traits. The objective of the research study is to understand the effect of Values on Compulsive Buying Behaviour of Fast Fashion Brands and to analyse the mediating vi effect of Psychographic Traits (Fast Fashion involvement, Fashion???Consciousness and Innovativeness) on Compulsive Buying Behaviour.The study used List of value (LOV) Scale to find the impact of consumer values on compulsive buying behaviour. The psychographic traits are used as intervening variable. The survey is conducted for participants aged 18-25 yrs. Analysis of data is done using Factor analysis, Anova, Multiple Regression. The study will help marketer of Fast Fashion clothing to frame their product and communication strategy in such a way that it appeals to the consumers with required psychographic traits and values. -
Unveiling the Indian REIT narrative-qualitative insights intoretail investors perspectives
Purpose: The present study delves into the causes of relatively lower retail participation in the Indian REIT market. Specifically, it investigates investors' attitudes and perceptions towards REITs as a unique asset class. This paper provides a comprehensive understanding of the perception and factors influencing Indian retail investors' reluctance to participate in the REIT market. Design/methodology/approach: Qualitative research was conducted through semi-structured interviews to gather insights from non-investors in REITs. The data were transcribed and analyzed using content analysis techniques. Finally, coding techniques were used to identify broad study themes. Findings: According to the study results, many retail investors are unfamiliar with REITs. Even among those knowledgeable about REITs and with a favorable view, it is not commonly seen as a feasible investment option due to its early stage, unattractive returns and limited number of REITs. Practical implications: Developed countries have established REIT markets, while it is still in its infancy in developing countries such as India. Financial advisors, fund houses and the media should focus on educating investors to increase awareness. Originality/value: The study is the first qualitative investigation into the perception of retail investors to understand the reasons for lower retail engagement in the Indian REIT market. 2024, Emerald Publishing Limited. -
Beyond brick and mortar: determinants of retail investors investment intention in indirect real estate through REITs in India
Purpose: This research aims to identify the factors that influence the investment intention of retail investors in Indian REITs. The study incorporates the theory of planned behavior and innovation diffusion theory as the research framework, with perceived risk and mass media influence as additional constructs. Design/methodology/approach: Primary data were collected using self-administered questionnaires from 534 potential investors in India. The data were analyzed using partial least square structural equation modeling. Findings: The study showed that factors such as relative advantage, compatibility, attitude, subjective norms, perceived behavioral control and mass media significantly and positively influence investment intention in Indian REITs. However, perceived risk was found to have a negative and significant influence, while complexity did not affect investment intention. Originality/value: This is the first quantitative investigation into determining the factors influencing the investment intention of Indian retail investors on Indian REITs. 2024, Emerald Publishing Limited. -
Secure Bitcoin Transaction and IoT Device usage in Decentralized Application
In the recent years, there has been a boom in the number of connected devices due to developments in the field of Internet of things. This has also increased the requirements of security specification. The proposed method is introducing a secure information transmission system by using Blockchain technology. Blockchain is a relatively new technology which was introduced by stoshi nakamoto, which was also the basis for developing crypto currency [bitcoin]. Crypto currencies are made transparent and secure using their network architecture, which is a combo of a decentralized and distributed network. In this paper is try to exploit the same methodology used in crypto currencies to develope an IOT network, where the devices can talk to their peers in a secure manner. They explored all the different networks and features of developing a Decentralized application that is named as Dapp. 2018 IEEE. -
Label-Based Feature Classification Model for Extracting Information with Dynamic Load Balancing
Efficient extraction of information from various sources is very tedious. Achieving this requires very sophisticated feature classification model and ability of the system to adapt to changing environments of data and its random distributions with an efficient use of computational resources. Label-based feature classification model (LFCM) with dynamic load balancing is proposed to address an efficient model to extract information in data set. This technique is effective in data analysis to discover the new feature set. Label approach incorporates unique label concept and it avoids any data duplication using labels. Each data sample is assigned to only one label to improve the accuracy and effectiveness of the retrieval process. Based on the data relevancy and specific features that can be extracted using proposed algorithm, classification model and semantic representation of data in vector form minimizes the data loss, and dimensionality reduction plays a vital role in building an efficient model. Various graphs and results obtained from the experiments show an improvement of information extraction using this proposed labeled LFCM approach. This approach brings lots of real time challenges that are handled to bring accuracy factor as the main focus in this proposed system. Both classification and extraction uses different model to obtain the intended results. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Arts education, academic achievement and cognitive ability
Although art is often considered to be a means for maximizing human potential, the causes and consequences of artistic experiences are poorly understood. The present chapter reviews the relevant literature concerning the consequences of participating in the arts. It is clear that training in the arts improves performance on arts-specific tasks. For example, children who take music lessons perform better than their untrained peers on musical tasks such as perceiving musical key and harmony (Corrigall and Trainor, 2009). But training in the arts may also be associated with performance in non-arts domains. This chapter examines the possibility of four such associations, namely whether arts education is associated with academic achievement, general cognitive ability, language processing and visuospatial skills. In each case, the literature is evaluated in terms of the consistency of the findings and the evidence for claims of causation. Training in the arts and academic achievement Training in the arts is associated positively with academic achievement. For example, in a sample of Canadian high-school students, participation in musical activities in the eleventh grade predicted academic achievement in the twelfth grade (Gouzouasis, Guhn and Kishor, 2007). Other results point to similar associations between academic achievement and involvement in any type of arts-related activity. In one study that included more than 25,000 American high-school students, arts participation and school grades were recorded during the eighth, tenth and twelfth grades (Catterall, Chapleau and Iwanaga, 1999). At each point in time, students who were involved in the arts had better grades than other students. A similar positive association emerged in a meta-analysis of five correlational studies (Winner and Cooper, 2000). In a larger meta-analysis of 10 years of data from the American College Board (198898), Vaughn and Winner (2000) concluded that compared to students without arts training, students reporting any form of arts involvement (dance, drama, music and visual arts) obtained higher scores on the Scholastic Aptitude Test (SAT). This advantage for the arts group was evident for the verbal score, the mathematics score and the composite score. Students with drama lessons showed the strongest association, followed (in descending order) by students studying music, painting and dance. Even enrollment in theoretical classes (e.g., music or art history courses) was predictive of better SAT scores. Cambridge University Press 2014.