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Control of chaos and intermittent periodic motions in Rayleigh-Bard convection using a feedback controller
Control of regular convective motion, chaos and periodic motion in the Rayleigh-Bard system is studied by considering a feedback control mechanism that considers the dependence of the heating (cooling) of the two boundary plates on one another. This set up ensures that the different flow regimes (convective, chaotic and periodic) in the system have no mechanical interference and the control remains an external mechanism. The rheostatic influence of feedback control on these flows is demonstrated by investigating in detail the critical Rayleigh number in the case of regular convective motion and the Hopf-Rayleigh number in the case of chaotic motion. For mild coupling between lower and upper boundary temperatures, periodic motions are intermittently observed in an otherwise chaotic regime at times when the system arrives at a situation (fuelling zone) wherein it needs to conserve energy in order to sustain chaos at subsequent times. For strong coupling between the boundary temperatures, an interesting situation arises wherein chaos makes a delayed and brief appearance and gives way to a prolonged spell of periodic motion. Features of the classical Rayleigh-Bard system are retained but each regime makes a delayed appearance. The Author(s), under exclusive licence to Springer Nature B.V. 2024. -
Control of chaos in Darcy-Bard axisymmetric convection in a cylindrical enclosure using a uniform vertical cross-flow
The linear and weakly nonlinear stability analyses of Darcy-Bard convection of a Newtonian fluid experiencing a uniform vertical cross-flow is investigated in the paper for various aspect ratios. Making use of the Maclaurin series representation, an expression for axial eigenfunctions is obtained with the radial eigenfunction being a Bessel function of first kind. These eigenfunctions are influenced by the Peclet number, Pe, the non-dimensional number that signifies the rate of vertical cross-flow. The modified-Vadasz-Lorenz model obtained in this paper has newly defined non-dimensional parameters that capture the influence of vertical cross-flow. From the linear stability analysis, it is found that the effect of introducing vertical cross-flow is to stabilize the system. Using a weakly nonlinear stability analysis, the closed-form expression of the Hopf-Rayleigh number as a function of Pe is obtained. Furthermore, the behavior of the modified-Vadasz-Lorenz model is analyzed using the largest Lyapunov exponent and the bifurcation diagram. This gives information about the intensity of chaos and occurrence of the periodic motion. We observe that the influence of vertical cross-flow is to increase the value of the Hopf-Rayleigh number and thereby to delay the onset of chaos. Furthermore, the appearance of the first periodic point is preponed due to the vertical cross-flow. As the rate of vertical cross-flow increases, the intensity of chaos decreases, thereby indicating that the effect of introducing vertical cross-flow is to suppress chaos. 2024 Author(s). -
Influence of Two-Frequency Rotational Modulation on the Dynamics of the Rayleigh-Bard Convection in Water-Based Nanoliquids with Either AA7072 or AA7075 Nanoparticles
The effect of time-periodic two-frequency rotation modulation on Rayleigh-Bard convection in water with either AA7072 or AA7075 nanoparticles is investigated. The single-phase description of the Khanafer-Vafai-Lightstone model is used for modeling the nanoliquids. An asymptotic expansion procedure is adopted in the case of the linear stability to obtain the correction (due to modulation) to the Rayleigh number at marginal stability of unmodulated convection. A nonlinear regime of convection is considered with a nonautonomous generalized Lorenz model as the governing equation. The method of multiscales is then employed to obtain the coupled nonautonomous Ginzburg-Landau equations with cubic nonlinearity from the Lorenz model. These equations are presented in the phase-amplitude form and the amplitude is used to quantify the heat transport. The modulation amplitude is considered to be small (of order less than unity) and moderate frequencies of modulation are considered. We found that there is a threshold frequency beyond which the system behavior reverses. At frequencies below the threshold, the mean Nusselt number increases with an increase in the amplitude of modulation while an opposite influence is seen for values above the threshold. Such a behavior is a consequence of what is analogously seen in the case of the critical Rayleigh number. The influence of two-frequency modulation is more pronounced on the results of the linear and nonlinear regimes compared to that of the single-frequency one. The heat transport is enhanced due to the presence of dilute concentration of suspended nanoparticles (either AA7072 or AA7075 nanoalloys) in water. The influence of nanoparticles is to modify the threshold values generating chaos but it does not qualitatively alter the dynamical behavior of the system. The plots of Lyapunov exponents reveal that there is no possibility of hyper-chaos in the generalized Lorenz model when there is a rotational modulation. 2024 World Scientific Publishing Company. -
WOW Skin Science: strategic adaptation for responsible advertising
Learning outcomes: After completing this case study, students will be able to understand the issues firms, brands and influencers face due to sponsorship disclosure regulation and the impact of self-regulation on firms engaging in influencer marketing, explain the challenges regulators face in ensuring compliance in an emerging market, explain Advertising Standard Council of India (ASCI)s challenges in adopting influencer guidelines from emerged markets and recommend ethical theory (or theories) and strategies to firms engaged in influencer marketing. Case overview/synopsis: This case study centers on Mr Manish Chowdhary, co-founder of WOW Skin Science, who started the beauty and personal care business with his brother Karan Chowdhary in 2015 in Bangalore, India. The company successfully built its brand through influencer marketing but faced challenges after the ASCI implemented new influencer guidelines. On May 31, 2021, he expressed disagreement with ASCI guidelines during an interview with Akansha Nagar from Buzz in Content, particularly the requirement to label every product or service received by influencers as an advertisement. He expressed concern about certain rules, fearing they might harm organic content and reduce viewership and followers. Subsequently, ASCI registered noncompliance cases against the company and communicated with them about complaints regarding influencer guideline violations. In this situation, Manish needed to evaluate his decision on noncompliance with regulation and required an action plan to strategically manage its influencer marketing campaign by incorporating ASCIs guidelines. Overall, this case study highlights the journey of WOW Skin Science and its challenges with self-regulatory authorities over its influencer marketing strategy in an emerging market. Additionally, students can gain insight into the marketing communication ethics of a startup operating in an emerging market by embodying the protagonists role. Complexity academic level: This case study is suitable for postgraduate level students pursuing a Master of Business Administration program. The difficulty level ranges from moderate to complex. It fits well into integrated marketing communication and marketing strategy courses. This case study discusses marketing ethics, advertising and promotion regulation. Supplementary materials: Teaching notes are available for educators only. Subject code: CSS 8: Marketing. 2024, Emerald Publishing Limited. -
A conceptual framework for consumer engagement in social media influencer posts
Influencer marketing has received significant attention and is considered as the best way to build consumer engagement with the brand. However, research on Influencer marketing is burgeoning, and it is important to study the consumer behaviour associated with influencer marketing. Therefore, this study proposes a logical conceptual framework by integrating various construct such as ad recognition, informativeness, deceptiveness, irritation, entertainment, ad content value, and consumer engagement from various theories and provides implications for marketers to frame an effective marketing campaign and policymakers to formulate policies to protect consumers from deceptive advertising practice. 2024, IGI Global. All rights reserved. -
Influencer Marketing and Consumer Behaviour: A Systematic Literature Review
Influencer marketing is an emerging area in the field of marketing. Specifically, this topic grabs the attention of several academicians and practitioners because of the key role played by influencers to stimulate consumer behaviour. Significantly, a systematic way of analysing and summarising the works of literature in this nascent area is necessary. A systematic literature review provides a comprehensive overview of the whole literature studied. From this knowledge, a systematic literature review has been undertaken from the period of 2016 to 2021 based on 65 articles from the ABDC journal to fetch relevant research themes, methodology, theories, variables, antecedents and consequences and potential research gaps. From this information, this research proposed an integrative framework which depicts the role of social media influencers in activating consumer behaviour. Future research direction has presented comprising of knowledge gap in the existing literature from key areas such as theory, methodology and settings. This research provides implications for both theories and practice. 2022 Management Development Institute. -
A conceptual study on the impact of COVID-19 awareness campaigns by social media influencers on brand awareness
Covid 19 has severally affected various people across the world. Undoubtedly, a campaign on forming awareness of the pandemic among people is very important in order to curb the transmission from individual to individual. In this regard, social media influencers have played a significant role in many awareness campaigns organized by companies and the World Health Organization (WHO) because of their popularity and influence among the audience. Social media influencer campaigns on awareness of the pandemic conducted during this period have generated audience engagement. Moreover, it is vital to study the crucial factors that determine the creation of brand awareness among consumers related to influencer marketing campaigns on covid 19 awareness. This study has proposed a conceptual framework by integrating the variables from various theories. This research has incorporated variables such as awareness of covid 19, consumer engagement, physical health, mental health, and brand awareness. This study provides both theoretical and managerial implications. 2024, IGI Global. -
Python Driven Keyword Analysis for SEO Optimization
Every word or string of words a user types into a search engine has meaning. For example, a user might search for a 'hotel' or a 'hotel in New York City.' Keywords are the standard focus of search engine optimization (SEO), which offers a useful method of gauging demand for specific queries and aiding in a better understanding of how users look for goods, services, businesses, and, eventually, solutions. Any effective SEO strategy must include keyword research, and Python is a strong language that can be used to automate and accelerate the process. This project presents a Python-based keyword research tool that works on real-time data to identify the top searches over a user-specified domain to identify trends and customer needs. It does this by utilizing multiple Python libraries and Google Autocomplete. The Google Autocomplete results for the user-specified domain are first parsed by the tool before it can function. After that, unnecessary keywords are eliminated by filtering and cleaning the results. Subsequently, the remaining keywords are arranged for search volume and domain relevancy. The tool looks for trends by comparing the current keyword rankings with previous data. Thanks to this, users can see which keywords are growing in popularity. By identifying the most commonly asked questions and issues, the tool also offers insights into the needs of its users. The tool is simple and adaptable to each user's unique requirements. It can be used to create keyword lists for content marketing, SEO, and product development, among other uses. 2024 IEEE. -
Enhancing Sustainable Urban Energy Management through Short-Term Wind Power Forecasting Using LSTM Neural Network
Integrating wind energy forecasting into urban city energy management systems offers significant potential for optimizing energy usage, reducing the carbon footprint, and improving overall energy efficiency. This article focuses on developing a wind power forecasting model using cutting-edge technologies to enhance urban city energy management systems. To effectively manage wind energy availability, a strategy is proposed to curtail energy consumption during periods of low wind energy availability and boost consumption during periods of high wind energy availability. For this purpose, an LSTM-based model is employed to forecast short-term wind power, leveraging a publicly available dataset. The LSTM model is trained with 27,310 instances and 10 wind energy system attributes, which were selected using the Pearson correlation feature selection method to identify crucial features. The evaluation of the LSTM-based forecasting model yields an impressive R2 score of 0.9107. The models performance metrics attest to its high accuracy, explaining a substantial proportion of the variance in the test data. This study not only contributes to advancing wind power forecasting, but also holds promise for sustainable urban energy management, enabling cities to make informed decisions in optimizing energy consumption and promoting a greener, more resilient future. 2023 by the authors. -
A Dynamic Anomaly Detection Approach for Fault Detection on Fire Alarm System Based on Fuzzy-PSO-CNN Approach
Early detection is crucial due to the catastrophic threats to life and property that are involved with fires. Sensory systems used in fire alarms are prone to false alerts and breakdowns, endangering lives and property. Therefore, it is essential to check the functionality of smoke detectors often. Traditional plans for such systems have included periodic maintenance; however, because they don't account for the condition of the fire alarm sensors, they are sometimes carried out not when necessary but rather on a predefined conservative timeframe. They describe a data-driven online anomaly detection of smoke detectors, which analyzes the behavior of these devices over time and looks for aberrant patterns that may imply a failure, to aid in the development of a predictive maintenance approach. The suggested procedure begins with three steps: preprocessing, segmentation, and model training. A pre-processing unit can enhance data quality by compensating for sensor drifts, sample-to-sample volatility, and disturbances (noise). The proposed approach normalizes the data in preparation. The smoke source can be detected by using segmentation to differentiate it from the background. Following segmentation, Fuzzy-PSO-CNN is used to train the models. CNN and PSO, two of the most used alternatives, are both outperformed by the proposed method. 2023 IEEE. -
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Enhancement of resilience an quality of life using strength based counselling and the mediating role of parental bonding in adolescents with type 1 diabetes
The current study was an attempt to understand the socio-demographic profile of newlineadolescents with type 1diabetes, the relationship between variables such as resilience, quality of life, parental bonding and the mediating role of parental bonding. It was also aimed to understand parent s perception of the adolescent s resilience and quality of life and their newlineexperiences. In phase 1, 100 adolescents/ newlinechildren (M=40, F=60, Age 10-18years) with an newlineexisting diagnosis of type 1 diabetes and their parents were enrolled from two hospitals and one clinic in Bangalore. The adolescents were administered Paediatric Quality Of Life for Diabetics (PedsQL) (Child and Adolescent Form), The Resilience Scale for Youth (CYRM), The Parental Bonding Instrument PBI (Father and Mother Form ). Parents were administered the consent form, demographic data sheet and parent version of resilience and quality of life.All scales were translated and back translated. In Phase 2 of the study, an intervention model based on the principles of strength newlinebased counselling to enhance resilience and quality of life (SBCTD1) was developed and newlineused. Qualitative interviews were conducted to understand the experiences of parents of newlinechildren/adolescents having type 1 diabetes. These interviews were tape recorded. 50 newlineadolescents from Phase1, were randomly assigned to the intervention group (n=25) and the control group (n=25). For the intervention group after going through the sessions the Resilience Scales and The Paediatric Quality of Life scales were administered after one and three months respectively. HbA1c values were also collected again after three months of newlineintervention. Control group received regular care and treatment at the center and were not newlineexposed to the model developed for the study. -
Exploring the Impact of Disengagement on the Burnout Among ICU Nurses of Indian Private Hospitals: The Influence of Perceived Organization Support
Among healthcare workers, the nursing workforce has a high intensity of burnout which occurs as a response to the pressure they face at their workplace. Burnout in nursing professionalsis a widespread phenomenon characterized by a reduction in nurses energy that manifests in emotional exhaustion, lack of encouragement, and feelings of frustration and may lead to a decrease in work efficacy. This is a significant issue in human services that needs to be addressed.Several studies have been done on nurses; however, burnout in Indian ICU nurses has not received much attention. Intensive Care Units are specialized hospital areas requiring constant attention and vigorous patient care. Studies state that ICU nurses have to be more alert due to the severity of the illness in patients. Nurses in ICUs face many challenges at work which could lead to burnout. Disengagement is one such factor that might lead to burnout in nurses. When people experience disengagement, they feel detached from their workplace. Previous studies indicate that due to pandemic experiences, nurses are disengaging at a rate twice that of other healthcare staff. The challenging demands of their work have significantly affected the nurse's psychological wellness and overall health. Limited studies have been done on ICU nurse disengagement and its effect on burnout in Indian settings. Therefore, the study aims to examine how nurse disengagement affects the burnout they experience. Healthcare organizations must prioritize the support to be extended and care for the nurses well-being to prevent further disengagement. Nurses may become more committed to the organization when they feel supported by hospital administrators and have lighter workloads. This paper focuses on exploring the consequences of disengagement on burnout experienced by ICU nurses and the influence of Perceived Organizational Support (POS) between them. The sample used for the study was 449 nurses working in the ICU divisions of private hospitals in India. This study employed a simple random sampling technique. A survey was utilized and distributed to the nursing professionals working in the ICU divisions. The commonly recognized aspects of burnout are emotional exhaustion, depersonalization, and reduced personal achievement. The study results indicated that disengagement has no impact on the emotional exhaustion experienced by ICU nurses. However, it influences depersonalization and nurses achievement. The study results state that POS moderated the relationship between disengagement and the two burnout aspects: depersonalization and personal achievement. From the findings, it may be concluded that perceived organizational support is crucial in encouraging greater personal accomplishment, reduced depersonalization, and bringing about positive change for disengaged ICU nurses. Better organizational support for ICU nurses becomes a critical responsibility of nursing administration. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Synthesis and Characterization of WO3 Nanostructures by the Solvothermal Method for Electrochromic Applications
In this study, a tungsten trioxide (WO3) thin film was deposited by direct current (DC) sputtering onto a fluorine-doped tin oxide (FTO) substrate as the seed layer at an oxygen partial pressure of 8 10?4mbar. A simple solvothermal method involving tungsten hexacarbonyl (W(CO)6), ethanol (C2H5OH), and hydrochloric acid (HCl) was used to synthesize vertically stacked nanoscale WO3 hierarchical structures on WO3 seed-layered FTO. After the deposition process, the FTO samples with nanostructures were subjected to annealing in air at 400C for 4 h. After annealing, the surface morphology, structural characteristics, and optical and electrochromic properties of the grown nanostructures were investigated using scanning electron microscopy (SEM), x-ray diffraction (XRD), Raman spectroscopy, UVvisible spectroscopy, and electrochemical analysis. From the XRD analysis, all the diffraction patterns were ascribed to a monoclinic phase. The SEM analysis showed that films grown with 5?L HCl had a nanoflower structure compared to the films grown with 0?L HCl and 20?L HCl. The nanoflower-structured films showed a higher cathodic peak current (?2.22mA), diffusion coefficient (5.43 10?9 cm2/s), and coloration efficiency (23.6 cm2/C). The increased electrochromic characteristics were attributed to the nanostructured films, which enhanced the diffusion of H+ ions by providing a large surface area during the charge transfer process. The Minerals, Metals & Materials Society 2024. -
Detection of Lung Cancer with a Deep Learning Hybrid Classifier
This article presents a deep learning framework combining a convolutional neural network (CNN) and a support vector machine (SVM) for lung cancer diagnosis. The model uses data divided into six groups: 250 images in the training set and 150 images in the test set. The work includes preliminary data and development using the Keras image data generator, VGG-16 architecture, high-level rules, and SVM classifier training with labels and vectors. The model achieves 90% accuracy with 85% selection impact and 75% cross-validation flexibility using VGG-16 and SVM hybrid classifier. This study finally revealed the classification of the model by multi-class ROC curve analysis and confusion matrix. 2024 IEEE. -
Sentiment Analysis on Amazon Product Review
Users throughout the world may now access massive amounts of data thanks to the internet and social media platforms. [5] In every facet of human existence, electronic commerce (e-commerce) plays a crucial role. E-commerce is a marketing approach that enables businesses and consumers to buy and sell things via the internet. When buyers look for product information and compare alternatives online, they generally have access to dozens or hundreds of product reviews from alternative shoppers. Machine learning is the most appropriate approach to training a neural network in today's age of practical artificial intelligence. So implementing a model to polarize those reviews and learn from them would make passing hundreds of comments a lot easier. [24] The interpretation will be a very basic product with positive, neutral, and negative polarization. The product is checked. This study suggests a sentiment evaluation model for shopper reviews based on the object and emotive word mining for emotional level analysis using machine learning approaches. 2022 IEEE. -
Evaluating the usability of mhealth applications on type 2 diabetes mellitus using various mcdm models
The recent developments in the IT world have brought several changes in the medical industry. This research work focuses on few mHealth applications that work on the management of type 2 diabetes mellitus (T2DM) by the patients on their own. Looking into the present doctor-to?patient ratio in our country (1:1700 as per a Times of India report in 2021), it is very essential to develop self?management mHealth applications. Thus, there is a need to ensure simple and user-friendly mHealth applications to improve customer satisfaction. The goal of this study is to assess and appraise the usability and effectiveness of existing T2DM?focused mHealth applications. TOP? SIS, VIKOR, and PROMETHEE II are three multi?criteria decision?making (MCDM) approaches considered in the proposed work for the evaluation of the usability of five existing T2DM mHealth applications, which include Glucose Buddy, mySugr, Diabetes: M, Blood Glucose Tracker, and OneTouch Reveal. The methodology used in the research work is a questionnaire?based evaluation that focuses on certain attributes and sub?attributes, identified based on the features of mHealth applications. CRITIC methodology is used for obtaining the attribute weights, which give the pri-ority of the attributes. The resulting analysis signifies our proposed research by ranking the mHealth applications based on usability and customer satisfaction. 2021 by the authors. Licensee MDPI, Basel, Switzerland. -
TSM: A Cloud Computing Task Scheduling Model
Cloud offers online-based runtime computing services through virtualized resources, ensuring scalability and efficient resource utilization on demand. Resource allocation in the dynamic cloud environment poses challenges for providers due to fluctuating user demand and resource availability. Cloud service providers must dynamically and economically allocate substantial resources among dispersed users worldwide. Users, in turn, expect reliable and cost-effective computing services, requiring the establishment of Service Level Agreements (SLAs). Resource distribution uncertainty arises in view of the dynamicity of the cloud, where VMs, memory capacity requirement, processing power, and networking are allocated to user applications using virtualization technology. Resource allocation strategies must address issues such as insufficient provisioning, scarcity, competition, resources fragmentation. CPU scheduler plays a crucial role in task completion, by selecting job from queue considering specific requirements. The Task Scheduling Model (TSM) algorithm improves scheduling by considering expected execution time, standard deviation, and resource completion time, aiming to address resource imbalances and task waiting times. The research discusses previous work, presents experimental findings, describes the experimental setup and results, and concludes with future research directions. 2023 IEEE. -
Comparative Analysis of Maize Leaf Disease Detection using Convolutional Neural Networks
Worldwide, maize is a significant cereal crop for crop productivity, identifying diseases in the plant's leaves is essential to raise a good crop. Deep learning methods that have been used in recent years to precisely identify and categorize these serious diseases, offering a non-destructive and effective way to find maize leaf ailments. In order to detect maize leaf disease, this paper suggests using three well-liked deep learning models: VGG16, Inception V3, and EfficientNet. The models were trained and assessed using a datasets of 4000 images of three distinct maize leaf diseases and a healthy class. All three models had high accuracy rates, according to the results, though EfficientNet outperformed the other two models. The suggested method can detect and track diseases in maize crops with high accuracy and can be applied practically. It can accurately classify various diseases. The study also demonstrates that deep learning models can offer a trustworthy and effective solution for detecting crop diseases, which can aid in lowering crop losses, raising crop yields, and enhancing food security. 2023 IEEE. -
A Multi Objective Artificial Eco-System Based Optimization Technique Integrating Solar Photovoltaic System In Distribution Network
Agricultural sector contributes 6.4% of total economic generation across the world. Notably, the utilization of technology to improve the yield and economy is rapidly increasing. To provide continuous supply to the residential customers, the agricultural feeder grid-dependency has to be integrated with Solar Photo Voltaic (SPV) systems. In this paper, an Artificial Eco-System based Optimization (AEO) algorithm is proposed for simultaneously identifying the locations and quantifying the sizes of SPV systems. A practical distribution system feeder 'Racheruvu 11kV agricultural feeder' Andhra Pradesh, India is considered for simulation purpose and the performance is compared with the standard IEEE-33 radial distribution system. 2022 IEEE.