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Study of multilayer flow of two immiscible nanofluids in a duct with viscous dissipation
Numerical simulations for the mixed convective multilayer flow of two different immiscible nanofluids in a duct with viscous heating effects were performed in this study. The left and right faces of the duct are maintained to be isothermal, while other side faces are insulated. The mathematical governing system for each layer consists of an incompressibility condition equation, the Navier-Stokes momentum equation, and the conservation of energy equation. At the interface of the immiscible layer, the continuity of velocity, shear stress, temperature, and heat flux are considered. The dimensionless equations governing each layer were numerically integrated using the finite difference method and the Southwell-over-relaxation method. A mesh independence test is conducted. Furthermore, a parametric study is performed to analyze how the different nanoparticle volume fractions and viscous heating affect the transport characteristics of engine oil-copper and mineral oil-silver nanofluids. The study also examined the effects of various types of nanoparticles and base fluids. The results demonstrated that heat transport could be efficiently controlled by considering the viscous heating aspect. Moreover, the effects of different nanoparticles on heat transport were found to be more significant than those of base fluids. Finally, a point-wise comparison of our numerical results demonstrates a good agreement with existing studies in the literature. 2023 Author(s). -
Influence of manufacturing process on distribution of MWCNT in aluminium alloy matrix and its effect on microhardness
Nano composites are finding increased focus and their influence on improving the matrix properties are very attractive. But the success is fully dependent on the uniform distribution and dispersion of nano reinforcements in the matrix. Manufacturing process was found to have greater role in distribution of the reinforcements. The liquid processing and solid processing like SPS and hot coining found to have different effect on the matrix due to the nature of reinforcements. Current study focussed on the microstructure study using Back scattered images and the microhardness with and without reinforcements. MWCNT was occupying the particle boundary. Hot coining was found to distribute MWCNT on the particle surface as well as on the particle boundary. Clustering was absent and resulted in improved hardness in comparison with casting as well as spark plasma sintering. 2018 Trans Tech Publications, Switzerland. -
Ban or boon: Consumer attitude towards plastic bags ban
In Tamil Nadu, the state government has imposed a ban on plastic bags two years ago. This has created a major impact of the day to day life of common people. Though it has positive effect on the environment, the common public had different perception as a consumer. This paper aimed at studying the consumer attitude towards the ban on plastic bags. A descriptive research design adopted to address the various dimension of consumer perception towards the ban on plastic ban. A sample size of 400 respondents was selected on the basis of systematic random sampling technique to collect data through structured questionnaire. For conducting the survey, consumers of retail shops in urban and rural places were chosen as target respondents. The collected data were analyzed with the help of statistical tools such as ANOVA, t-Test, Correlation, Linear Regression and Structural equation modelling and the interpretation reported. The result revealed that only 34 percentage of respondent were aware the environmental impact of plastic bags. About 71 percentage of consumers reported that they have faced difficulties in their day to day life due to plastic ban. 2021 American Institute of Physics Inc.. All rights reserved. -
Insuretech: Saviour of insurance sector in India
Technology in finance has propelled financial literacy and inclusiveness and may give the insurance sector an edge to reach its potential consumers. The current study aimed to identify the role of Fintech in transforming the insurance sector and improving the penetration rate in India. With the descriptive research design, the study collects the primary data through a survey technique targeting the general public and personnel in the insurance sector as a study population. A conceptual model is proposed to understand the interlink between consumer attitude towards Insurance, factors influencing their decision, and the role of Fintech in bridging the gap in insurance penetration. This study focuses on three areas, namely health insurance, life insurance, and vehicle insurance. The study's findings reveal that the insurtech will significantly improve the efficiency of the insurance sector which will result in significant financial performance. 2024 Srinesh Thakur, Anvita Electronics, 16-11-762, Vijetha Golden Empire, Hyderabad. -
Exploration of digital image tampering detection using CNN with modified particle swarm optimization in deep learning
The field of image processing is crucial for many different applications, including forensic evidence, insurance claims, medical imaging, bio-informatics, artifact collection and more. In many sectors nowadays, digital photographs are regarded as a trustworthy source of information. The manipulation of such photographs leads to a variety of issues. The study presents a method using convolutional neural networks (CNN) combined with modified particle swarm optimization (MPSO) to improve the accuracy of tampering detection. This advancement contributes to improved reliability in fields requiring image authenticity verification, such as forensics and media. The design includes the collection of a dataset comprising both original and tampered images for training and testing the model. A dataset, such as the Media Integration and Communication Center (MICC) dataset, is utilized, which includes various images that have been altered through different tampering techniques. This dataset serves as the foundation for training the CNN and evaluating its performance The findings indicate that the proposed MPSO_CNN method outperforms traditional techniques in terms of precision, accuracy, recall, and F-measure, demonstrating its effectiveness in identifying tampered images. The results highlight the significance of using advanced deep learning techniques for reliable image authenticity verification. 2025, Institute of Advanced Engineering and Science. All rights reserved. -
LSTM-MGTO: a novel early breast cancer detection using long short term memory based modified gorilla troops optimization algorithm
One of the most prevalent and severe tumors in women, breast cancer, remains a major global health issue despite a notable increase in incidence over the last ten years. It is the second leading cause of cancer-related death among women. Identifying breast cancer in its early stages has the potential to save lives; however, current screening techniques for the illness require several laboratory procedures involving medical experts. Automated solutions with rapid and reliable diagnostic capabilities are needed to minimize human error and expedite breast cancer diagnosis. The projected accuracy of cancer diagnosis remains far from matching the precision offered by existing approaches, even with the research on automated systems for the disease being studied. This work suggests a long short-term memory-based modified Gorilla troop optimization (LSTM-MGTO) method for breast cancer classification in order to address these issues. The Mastectomy Koibra Dataset (BCCD) and Wisconsin Diagnostic Mastectomy (WDBC) datasets were used to test the suggested methods. First, the proposed system employs contrast-limited adaptive histogram equalization (CLAHE) to enhance the quality of digital mammograms. Furthermore, employ a semantic deep learning (SDL) model to extract features. After the feature selection process, a recursive feature elimination technique was implemented to determine the crucial WDBC and BCCD characteristics that are relevant to breast cancer detection. Moreover, recommend a modified U-Net architecture for partitioning in both unmapped and guided contexts. The experimental findings indicate that the newly developed partitioning model surpasses existing advanced techniques, yielding superior results in both Dice and IoU score evaluations. On the WDBC and BCCD datasets, the suggested U-Net segmentation produces maximum Dice scores of 97.65% and 96.24%, respectively. Additionally, the model obtained the greatest IoU scores of 95.43% and 90.65% on the WDBC and BCCD datasets, respectively, according to the experimental findings. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2025. -
Recruitment Analytics: Hiring in the Era of Artificial Intelligence
Introduction: Traditional recruitment system relied heavily on the applicants curriculum vitae (CV). This system, besides becoming redundant, has proved to be a futile exercise leading to the hiring of candidates that eventually turn out to be misfits. CVs were the only source of candidates data available for the recruiters a few years back. Face-to-face interviews was considered to be the ultimate solution for hiring suitable candidates. However, evidence suggests that interview scores and job performances do not complement each other. Advancement in artificial intelligence (AI) has introduced several techniques in the recruitment process. Purpose: This chapter underscores the drawbacks of the traditional recruitment process. Evidence suggests that the traditional recruitment process is prone to subjectivity and is time-consuming. Surprisingly, despite the disadvantages, the integration of AI into the recruitment process is still slow. This chapter highlights the need to harness AI and the advantage technology could bring to the recruitment process. Some of the techniques that are garnering attention and widely used by organisations, such as chatbots, gamification, virtual employment interviews, and resume screening are described to enable the readers to understand with less effort. Chatbots and gamification techniques are described through process flow charts. We also describe the various types of interviews that could be conducted through virtual platforms and the modality by which the resume screening technique operates. Today, we are at a juncture wherein it is pertinent to acknowledge the superiority of technology-driven processes over traditional ones. This chapter will help the readers to understand the modus operandi to implement chatbots, gamification, virtual interviews and online resume screening techniques besides their advantages. Scope: Although chatbots, resume screening, virtual interviews, and gamification are used in other areas, too, such as training and development, marketing, etc., in this chapter, we restrict solely to employee recruitment processes. Methodology: Scoping review is used to examine the existing literature from various databases such as Google Scholar, IEEE, Proquest, Emerald, Elsevier, and JSTOR databases are used for extracting relevant articles. Findings: Automation and analytics in recruitment and selection remove bias which is otherwise increasingly found in manual hiring processes. Also, previous studies have observed that candidates engage in impression management tactics in traditional face-to-face interviews. However, through automated recruitment processes, the influence of these tactics can be eliminated. AI-based virtual interviews reduce human bias. It also helps recruiters to hire talents across the globe. Gamification improves the candidates perception of the work and work environments. Through gamified techniques, the recruiters can understand whether a candidate possesses the required job skills. Chatbots are an interactive technique that can respond to interviewees queries. Resume screening techniques can save the recruiters time by screening and selecting the most appropriate candidates from a large pool. Hence, the chosen candidates alone can be referred to the next stage of the recruitment cycle. AI improves the efficiency of the recruitment process. It reduces mundane tasks. It saves time for the human resources (HR) team. 2023 by V. R. Uma, Ilango Velchamy and Deepika Upadhyay. -
Examining women's purchase pattern of casual footwear in accordance with their attitudes and interests
Purpose: The present study examines the association between the choices of casual footwear attributes of women in accordance with their behavioral pattern. Design/Methodology/Approach: Data was collected from 2365 women through a questionnaire that comprised of two sections. The first section comprised of 50 AIO statements based on which the respondents were profiled according to their behavioural patterns. The second section comprised of selected footwear and store attributes. The consumers were profiled into eleven clusters using factor analysis. The regression scores were used to assign the respondents to the respective components that were extracted through factor analysis. Reliability Test and KMO Test were conducted to check the reliability and adequacy of the sample size. Further, only those variables that qualified the collinearity test were alone subject to regression analysis. Through ANOVA test, it was observed that significant differences existed among the consumers within the clusters. Therefore, the AIO statements were considered as independent variables that were regressed against ten selected footwear attributes. Findings: The Results indicated that consumers with different behaviors had varied preferences towards footwear attributes. Practical Implications: The results of the study indicate that the manufacturers in the footwear sector should revisit their existing strategies and target the consumers on the basis of their behavior as the proliferation of the unorganized sector is very high in this sector. Original Value: There are innumerable literatures that focus on trade policies followed in the footwear market in international countries, treatment of workers in the footwear industry, therapeutic use of footwear, supply chain patterns etc., but hardly any significant study that explores the consumers' behaviour and their association towards their footwear preferences has been conducted. Behavioral segmentation has been used in many other products like apparels, insurance, real estate etc., but not in the footwear sector. The present study is an attempt to fill this gap. -
Overt dependence of health insurance industry on healthcare system
A vast majority of the population in the developing economies remains uninsured. Moreover, the informal sector that employs a larger section of the society is untouched by any of the government scheme. In this study, we use health belief model to examine the factors that induce willingness to buy health insurance among the illness and the non-illness group. A cross-sectional study was conducted on 1,339 participants above 20 years of age of which 351 had contracted illness in the past and 988 had not. Data was collected using questionnaire from four highly populated districts in India. The questionnaire was developed based on the constructs of health belief model. The data was statistically analysed. Kendalls Tau-b correlation technique was used to explore the relationship between perceived vulnerability and product aversion. Logistic regression was used to find out the odds at which each independent variable, categorised based on the health belief model, contributes to willingness to buy. The model was able to predict 15% of the variance for willingness-to-buy among the illness and 27% among the non-illness groups. Findings suggest that the perceived vulnerability reduced product aversion among the illness group. Mere presence of primary and super-specialty hospitals was not sufficient for the illness group to subscribe for health insurance. Income perceptions emerged as a significant predictor among the illness group. Presence of well-established hospital, income perceptions, and subjective norms were significant predictors among the non-illness group. The growth of the health insurance industry largely depends upon the presence of well-established hospitals. In the absence of adequate healthcare facilities, attempts by the insurers to promote insurance covers will become futile. Insurers should also consider alternate segmentation patterns albeit the present socio-demographic pattern, as the health risk experience differs among individuals. Asian Academy of Management and Penerbit Universiti Sains Malaysia, 2021. -
Investigation of Cervical Cancer Detection from Whole Slide Imaging
Early cancer detection is critical in enhancing a patient's clinical results. Cervical cancer detection from a large number of whole slide images generated regularly in a clinical setting is a complex and time-consuming task. As a result, we require an efficient and accurate model for early cancer diagnosis, especially cervical cancer as it can be fully prevented if detected in an early stage. This study focuses on in-depth writing on current methodologies for cervical cancer segmentation and characterization from the whole cervical slide. It combines the state of their specialty's performance measurement with the quantitative evaluation of cutting-edge techniques. Numerous publications over the last eleven years (2011-2022) clearly outline various cervical imaging methods over multiple blocks. And this review shows different types of algorithms used in each processing stage of detection. The study clearly indicates the advancements in the automation field and the necessity of the same. Published under licence by IOP Publishing Ltd. -
A Data Mining approach on the Performance of Machine Learning Methods for Share Price Forecasting using the Weka Environment
It is widely agreed that the share price is too volatile to be reliably predicted. Several experts have worked to improve the likelihood of generating a profit from share investing using various approaches and methods. When used in reality, these methods and algorithms often have too low of a success rate to be helpful. The extreme volatility of the marketplace is a significant contributor. This article demonstrates the use of data mining methods like WEKA to study share prices. For this research's sake, we have selected a HCL Tech share. Multilayer perceptron's, Gaussian Process and Sequential minimal optimization have been employed as the three prediction methods. These algorithms that develop optimal rules for share market analysis have been incorporated into Weka. We have transformed the attributes of open, high, low, close and adj-close prices forecasted share for the next 30 days. Compare actual and predicted values of three models' side by side. We have visualized 1step ahead and the future forecast of three models. The Evaluation metrics of RMSE, MAPE, MSE, and MAE are calculated. The outcomes achieved by the three methods have been contrasted. Our experimental findings show that Sequential minimal optimization provided more precise results than the other method on this dataset. 2023 IEEE. -
Reliable monitoring security system to prevent MAC spoofing in ubiquitous wireless network
Ubiquitous computing is a new paradigm in the world of information technology. Security plays a vital role in such networking environments. However, there are various methods available to generate different Media Access Control (MAC) addresses for the same system, which enables an attacker to spoof into the network. MAC spoofing is one of the major concerns in such an environment where MAC address can be spoofed using a wide range of tools and methods. Different methods can be prioritized to get cache table and attributes of ARP spoofing while targeting the identification of the attack. The routing trace-based technique is the predominant method to analyse MAC spoofing. In this paper, a detailed survey has been done on different methods to detect and prevent such risks. Based on the survey, a new proposal of security architecture has been proposed. This architecture makes use of Monitoring System (MS) that generates frequent network traces into MS table, server data and MS cache which ensures that the MAC spoofing is identified and blocked from the same environment. 2019, Springer Nature Singapore Pte Ltd. -
A Review on Flood Prediction Algorithms and A Deep Neural Network Model for Estimation of Flood Occurrence
Flood occurs as often as possible happens due to many environmental changes in our planet in the present years. The occurrence and damages caused by flood is very high. Major cause of flood is due to heavy rainfall which in turn increases the water level of the rivers and other water bodies. The various factors that play a major role in the occurrence of rainfall are rise in temperature, humidity level, dew point, pressure in and around the area of concern, wind speed, etc. In order to reduce the number of victims due to flood it is necessary to have a system to predict flood occurrence. In this paper, we classify and analyzed the various prediction algorithms which show usage of Deep Neural Network produces better results. In addition, a design model has been proposed to predict the flood by training the Deep Neural Network with the above-mentioned factors. 2020, Asian Research Association. All rights reserved. -
Jabbar Patel filmmaking - An auteur theory approach /
Films are a product of the director‟s mind. Through films we convey and understand certain messages by the use of certain symbols and metaphors that reoccur in our surrounding. Studies show that directors have their own individual style or pattern in which they prefer to portray certain elements in the movie. -
Understanding the secular evolution of NGC 628 using UltraViolet Imaging Telescope
Secular and environmental effects play a significant role in regulating the star-formation rate and hence the evolution of the galaxies. Since ultraviolet (UV) flux is a direct tracer of the star formation in galaxies, the UltraViolet Imaging Telescope (UVIT) onboard AstroSat enables us to characterize the star-forming regions in a galaxy with its remarkable spatial resolution. In this study, we focus on the secular evolution of NGC 628, a spiral galaxy in the local Universe. We exploit the resolution of UVIT to resolve up to ?63 pc in NGC 628 for identification and characterization of the star-forming regions. We identify 300 star-forming regions in the UVIT far-UV image of NGC 628 using ProFound and the identified regions are characterized using Starburst99 models. The age and mass distribution of the star-forming regions across the galaxy supports the inside-out growth of the disc. We find that there is no significant difference in the star-formation properties between the two arms of NGC 628. We also quantify the azimuthal offset of the star-forming regions of different ages. Since we do not find an age gradient, we suggest that the spiral density waves might not be the possible formation scenario of the spiral arms of NGC 628. The headlight cloud present in the disc of the galaxy is found to be having the highest star-formation rate density (0.23 Myr-1 kpc-2) compared to other star-forming regions on spiral arms and the rest of the galaxy. 2022 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
Analysis of Membership Probability in Nearby Young Moving Groups with Gaia DR2
We analyze the membership probability of young stars belonging to nearby moving groups with Gaia DR2 data. The sample of 1429 stars was identified from "The Catalog of Suspected Nearby Young Moving Group Stars." Good-quality parallax and proper motion values were retrieved for 890 stars from the Gaia DR2 database. The analysis for membership probability is performed in the framework of the LACEwING algorithm. From the analysis it is confirmed that 279 stars do not belong to any of the known moving groups. We estimated the U, V, W space velocity values for 250 moving group members, which were found to be more accurate than previous values listed in the literature. The velocity ellipses of all the moving groups are well constrained within the "good box," a widely used criterion to identify moving group members. The age of moving group members are uniformly estimated from the analysis of the Gaia color-magnitude diagram with MIST isochrones. We found a spread in the age distribution of stars belonging to some moving groups, which needs to be understood from further studies. 2020. The American Astronomical Society. All rights reserved.. -
Disentangling the association of PAH molecules with star formation
Context. Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous complex molecules in the interstellar medium and are used as an indirect indicator of star formation. On the other hand, the ultraviolet (UV) emission from young massive stars directly traces the star formation activity in a galaxy. The James Webb Space Telescope (JWST), along with the UltraViolet Imaging Telescope (UVIT), opened up a new window of opportunity to better understand the properties of PAH molecules that are associated with star-forming regions. Aims. We investigate how the resolved scale properties of PAH molecules in nearby galaxies are affected by star formation. Methods. We analyzed the PAH features observed at 3.3, 7.7, and 11.3 m using F335M, F770W, and F1130W images obtained from the JWST. These images helped us identify and quantify the PAH molecules. Additionally, we used UVIT images to assess the star formation associated with these PAH-emitting regions. Our study focused on three galaxies, namely NGC 628, NGC 1365, and NGC 7496, which were selected based on the availability of both JWST and UVIT images. Bright PAH emission regions were identified in the JWST images, and their corresponding UV emission was estimated using the UVIT images. We quantified the star formation properties of these PAH emitting regions using the UVIT images. Furthermore, we investigated the relation between the star formation surface density (?SFR) and the PAH ratios to better understand the impact of star formation on the properties of PAH molecules. Results. Based on the resolved scale study of the PAH-bright regions using JWST images, we found that the fraction of ionized PAH molecules is high in the star-forming regions with high ?SFR. We observed that emission from smaller PAH molecules is higher in star-forming regions with higher ?SFR. Conclusions. Our study suggests that the PAH molecules excited by the photons from star-forming regions with higher ?SFR are dominantly smaller and ionized molecules. UV photons from the star-forming regions could be the reason for the higher fraction of the ionized PAHs. We suggest that the effect of the high temperature in the star-forming regions and the formation of smaller PAH molecules in the star-forming regions might also result in the higher emission in the F335MPAH band. The Authors 2024. -
Trust green, pay more: Decoding green brand loyalty and willingness to pay more for electric vehicles through green transparency and green perceived value
The StimulusOrganismResponse framework is applied in this study to explore the impact of Green Transparency (stimuli) and Green Perceived Value (stimuli) on Green Brand Trust (organism) and, subsequently, on Green Brand Loyalty (response) and Willingness to Pay More (response). Self-Brand Connection is examined as a moderator. An online survey was distributed to 557 EV consumers. We employed both PLS-SEM (SmartPLS 4) and CB-SEM (AMOS 29) to test the direct, mediating, and moderating effects, with CB-SEM used as a robustness check for model stability. The results show that both Green Transparency and Green Perceived Value are positive antecedents of Green Brand Trust. Green Brand Trust, in turn, positively influences Green Brand Loyalty and Willingness to Pay More and mediates the effects of the two stimuli. The results also confirm that Self-Brand Connection significantly and positively strengthens the Green Brand Trust?Green Brand Loyalty and Green Brand Trust?Willingness to Pay More relationships. This study establishes Green Brand Trust as a core green consumer behavior mechanism and identity alignment as a catalyst for Green Brand Loyalty and Willingness to Pay More, offering actionable guidance to EV brands for credibility building, customer retention, and sustainable consumption. 2026 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license. http://creativecommons.org/licenses/by-nc-nd/4.0/ -
TRANSFORMING GREEN TRANSPARENCY INTO GREEN BRAND LOYALTY AND REPURCHASE INTENTIONS: THE ROLE OF BRAND IMAGE AND CREDIBILITY AMONG ELECTRIC VEHICLE USERS
The present study leverages the Stimulus-Organism-Behavior-Consequence (SOBC) framework to investigate how green transparency influences green brand loyalty and repurchase intention among electric vehicle consumers. Specifically, it examines the mediating roles of brand image and brand credibility in the relationships between green transparency, green brand loyalty, andrepurchase intention. Data collected from 386 electric vehicle users were analyzed using Partial Least Squares-Structural Equation Modeling (PLS-SEM). Results reveal that green transparency positively impacts brand image and brand credibility, which subsequently enhances green brand loyalty and repurchase intention. Mediation analysis further highlights brand image and brand credibility as critical mechanisms linking green transparency to green brand loyalty. This study extends the SOBC framework to green marketing, offering theoretical and practical insights into fostering sustainable consumer behavior. By emphasizing the role of green transparency in building credible and compelling brand narratives, the findings guide marketers in cultivating consumer trust and loyalty while supporting policymakers in formulating transparency regulations for a sustainable marketplace. 2025 Journal of Applied Structural Equation Modeling. -
Which is the green generation? Amultigroup analysis of millennials and Generation Zs green consumerism
Purpose This study aimed to investigate how components of green marketing mix (GMM), green product (GPD), green price (GPC), green place (GPL) and green promotion (GPM) influence consumer attitudes (ATT), subjective norms (SNM), perceived behavioural control (PBC) and purchase intention (PI) and finally green consumerism (GCM). Design/methodology/approach Using Smart PLS 4 software and PLS-SEM approach, data were analysed for structural relationships among the components of GMM, ATT, SNM, PBC, PI and GCM. The model evaluates hypotheses linking GPD, GPC, GPL and GPM to ATT, SNM and PBC and examines how ATT, SNM and PBC affect PI and GCM. Findings The study revealed that GMM, as a higher-order construct, positively impacts ATT, SNM and PBC, while ATT, SNM and PBC partially mediate the relation between GMM and PI. PI then ultimately results in GCM. The multigroup analysis indicated there is no significant difference between the age groups examined. Research limitations/implications The study may not generalize to all industries or regions. Future research could explore additional factors like cultural or technological influences, and longitudinal studies may be conducted. Practical implications As environmental concerns grow, marketers should focus on consumer attitudes towards green products. Aligning green attributes with consumer values, transparent pricing and multi-channel communication can enhance ATT, SNM and PBC over green purchases, fostering acceptance and intention. Social implications While the findings promote GCM, their broader impact is contingent on genuine environmental practices. Without systemic changes in production and policy, GCM risks perpetuating superficial sustainability narratives. Originality/value This study advances the field by investigating how GMM influences purchase intentions (PI) among Indias urban Millennials and Generation Z, two generations pivotal to shaping sustainable consumption trends in a high-pollution economy. 2025 Emerald Publishing Limited