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Sensitivity Analysis of Heat Transport in Nanofluids with Marangoni Convection
Crystal growth, soap flm stabilization, coating processes, and growth of silicon newlinewafers involve Marangoni convective and#64258;ows. In microgravity situations, Marangoni effect is more prominent than gravity-induced buoyancy forces. In such situations, the convective and#64258;ows in the and#64258;uids will be driven by surface tension gradients. Moreover, the control of heat transport in the hydromagnetic semiconductor crystals involves Marangoni convection. Therefore, the heat transport rate in Marangoni convective and#64258;ow of nanoand#64258;uids is optimized in this research work. The thermal, thermo-solutal, mixed thermo-solutal Marangoni convection problems are explored in the presence of an external magnetic feld. The thermal phenomenon is scrutinized by including thermal radiation. Diand#64256;erent external eand#64256;ects are included in the problems and a detailed parametric analysis is carried out by using graphical visualizations. The newlinegoverning equations are constructed by utilizing the conservation equations of mass, newlinemomentum, energy and concentration. Realistic nanoand#64258;uid models are chosen which are validated with experimental data. Finite-diand#64256;erence-based and Runge-Kuttabased solving methodologies are adopted. The optimization of the heat (and mass) transport is carried out using the Response Surface Methodology (RSM). The facecentered central composite design is used for optimization. The quadratic empirical models obtained are further explored by estimating the sensitivity. The problem studied in each chapter is given below: Thermal Marangoni and#64258;ow of a nanoand#64258;uid with nanoparticle aggregation newlineA study of magnetohydrodynamic thermal Marangoni convection of ethylene glycol (EG) based titania (TiO2) nanoand#64258;uid is carried out by considering the eand#64256;ect of nanoparticle aggregation. The heat transport phenomenon is scrutinized with thermal radiation. The eand#64256;ective thermal conductivity and viscosity with aggregation are modeled by using the Maxwell-Bruggeman and Krieger-Dougherty models. -
Sensitivity analysis of heat transfer in nanoliquid with inclined magnetic field, exponential space-based heat source, convective heating, and slip effects
Sensitivity analysis of the rate of heat transport in the flow of nanoliquids over an elongated sheet using the response surface methodology (RSM) in combination with the face-centered central composite design. The flow is driven due to the velocity slip and the inclined magnetic field effects. Thermal analysis includes aspects of convective heating, Joule heating, viscous heating, and a space-dependent exponential heat source. The nanoliquid model consists of thermophoresis and random motion mechanisms. A set of coupled partial differential governance equations is rehabilitated into a set of ordinary differential equations using the appropriate transformation. Subsequent nonlinear problem is tackled numerically by utilizing finite difference code that employs the formula of four-stage Lobatto IIIa. The rate of heat transport is scrutinized by adopting RSM for three effectual parameters, namely magnetic field parameter ((Formula presented.)), angle of inclination ((Formula presented.)), and suction parameter (Formula presented.)). The velocity and temperature fields were found to be a decreasing function of an angle of inclination of the magnetic field. The velocity range is inversely related to the suction and flow aspects of velocity. Furthermore, the rate of heat transport is more sensitive to the suction parameter than to the magnetic field and to the angle of inclination of the magnetic field. 2020 Wiley Periodicals LLC -
Sensitive crop leaf disease prediction based on computer vision techniques with handcrafted features
Agricultural production is considered the primary source of the economy of many countries. Tomato and Potatoes are the most sensitive and consumable vegetables worldwide. However, during the growth of these crops, they suffer from many leaf diseases, which lead to loss of productivity and economy of the farmers. Many farmers detect and find plant diseases that are more time-consuming, expensive, and require expert decisions following the naked eye method. Therefore, early and accurate diagnosis of Tomato and Potato crops leaf diseases plays a vital role in sustainable agriculture. So, this research paper proposes an efficient leaf disease classification model based on computer vision techniques. The proposed Adaptive Deep Neural Network (ADNN) leaf disease classification method is a hybrid model which combines an optimized long short-term memory (OLSTM) and convolution neural network (CNN). The weight values supplied in the LSTM classifier are optimally selected using the Adaptive Raindrop Optimization algorithm. The handcrafted features are extracted from the segmented image and fused with the hybrid deep neural network to improve the classifier performance. The ADNN method consists of five steps: preprocessing, feature extraction, segmentation, handcrafted feature extraction, and classification. At first, the images are given to the preprocessing stage to remove the noise from leaf images. Then, the image-affected portion is segmented using an enhanced radial basis function neural network. After the segmentation process, the segmented image is given as an input to the adaptive deep neural network (ADNN) that classifies various types of diseases in the Potato and Tomato leaves. The efficiency of the ADNN model based on the OLSTM-CNN approach is determined concerning multiple metrics, namely Accuracy, Precision, Recall, F-measure, Specificity, and Sensitivity. The ADNN model achieved the best Accuracy of 98.02% for Tomatoes and 98% for Potatoes. The ADNN is compared with existing state-of-the-art CNN, LSTM, ResNet50, and MobileNet techniques. The performance analysis proved that the ADNN model improved efficiency in terms of all metrics and methods. 2023, The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden. -
Sense of humour and work culture: A study based in the luxury hotels in Bangalore, India
Employee well-being has been a focus area for Human Resource managers as well as top management alike. The belief that healthy, happy employees would be more efficient, and consequently contribute more, has verily driven the research to understand the implications of different aspects of an employees health and happiness. Based on the work of key humour researcher Dr. Paul McGhee, it has been established that humour does play a key role in ensuring a happy and healthy workforce. The current study attempts to evaluate the benefits of the use of humour at the workplace, primarily in terms of influencing the work culture. The data for the study has been collected from the hotel sector and has been analysed to understand the use of humour and its influence on the work culture. The findings suggest that the presence and use of humour has a strong positive impact on work culture. The researchers also found that, contrary to previous literature, the use of humour did not depend on demographic variables like age, tenure in the current organization or total work experience. Furthermore, the study also attempts to understand how the use of humour would impact the work culture. In this regard, the researchers found that certain dimensions of humour at the workplace, had a stronger impact on the culture and it is expected that the findings would guide the behaviour of leaders and managers in the creation of a mutually beneficial workplace. 2020, Universidade de Aveiro. All rights reserved. -
SemKnowNews: A Semantically Inclined Knowledge Driven Approach for Multi-source Aggregation and Recommendation of News with a Focus on Personalization
The availability of digital devices has increased throughout the world exponentially owing to which the average reader has shifted from offline media to online sources. There are a lot of online sources which aggregate and provide news from various outlets but due to the abundance of content there is an overload to the user. Personalization is therefore necessary to deliver interesting content to the user and alleviate excessive information. In this paper, we propose a novel semantically inclined knowledge driven approach for multi-source aggregation and recommendation of news with a focus on personalization to address the aforementioned issues. The proposed approach surpasses the existing work and yields an accuracy of 96.62% 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Semilinear fractional elliptic equations with combined nonlinearities and measure data
This study focuses on semilinear fractional elliptic problems with concave-convex type nonlinearities and measures as data. Suitable iteration techniques and embedding results are employed to ensure the existence and multiplicity of solutions. 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG. -
Semi automated silkworm cocoon cutting machine /
Patent Number: 202141027195, Applicant: Dr. Jyothi Thomas.
Our Invention Semi Automated Silkworm Cocoon Cutting Machine is a cocoon cutting machine with 10 cutting blades is used to separate cocoon from the pupa without killing it. The inventive device includes a main frame with vibrating hopper and a vibrating table and in between there are 10 numbers of bobbin to carry the cocoon and 10 nos. of cutting disc blades mounted on a motor at 45° to cut the cocoon. -
Semi automated silkworm cocoon cutting machine /
Patent Number: 202141027195, Applicant: Dr. Jyothi Thomas.
Our Invention Semi Automated Silkworm Cocoon Cutting Machine is a cocoon cutting machine with 10 cutting blades is used to separate cocoon from the pupa without killing it. The inventive device includes a main frame with vibrating hopper and a vibrating table and in between there are 10 numbers of bobbin to carry the cocoon and 10 nos. of cutting disc blades mounted on a motor at 45° to cut the cocoon. -
Semantic image annotation using convolutional neural network and WordNet ontology
Images are a major source of content on the web. The increase in mobile phones and digital cameras have led to huge amount of non-textual data being generated which is mostly images. Accurate annotation is critical for efficient image search and retrieval. Semantic image annotation refers to adding meaningful meta-data to an image which can be used to infer additional knowledge from an image. It enables users to perform complex queries and retrieve accurate image results. This paper proposes an image annotation technique that uses deep learning and semantic labeling. A convolutional neural network is used to classify images and the predicted class labels are mapped to semantic concepts. The results shows that combining semantic class labeling with image classification can help in polishing the results and finding common concepts and themes. 2018 Jaison Saji Chacko, Tulasi B. -
Semantic Analysis and Topic Modelling of Web-Scrapped COVID-19 Tweet Corpora through Data Mining Methodologies
The evolution of the coronavirus (COVID-19) disease took a toll on the social, healthcare, economic, and psychological prosperity of human beings. In the past couple of months, many organizations, individuals, and governments have adopted Twitter to convey their sentiments on COVID-19, the lockdown, the pandemic, and hashtags. This paper aims to analyze the psychological reactions and discourse of Twitter users related to COVID-19. In this experiment, Latent Dirichlet Allocation (LDA) has been used for topic modeling. In addition, a Bidirectional Long Short-Term Memory (BiLSTM) model and various classification techniques such as random forest, support vector machine, logistic regression, naive Bayes, decision tree, logistic regression with stochastic gradient descent optimizer, and majority voting classifier have been adapted for analyzing the polarity of sentiment. The effectiveness of the aforesaid approaches along with LDA modeling has been tested, validated, and compared with several benchmark datasets and on a newly generated dataset for analysis. To achieve better results, a dual dataset approach has been incorporated to determine the frequency of positive and negative tweets and word clouds, which helps to identify the most effective model for analyzing the corpora. The experimental result shows that the BiLSTM approach outperforms the other approaches with an accuracy of 96.7%. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
Selfipendant and Extremal Pendant Graphs
[No abstract available] -
Selfie Segmentation in Video Using N-Frames Ensemble
Many camera apps and online video conference solutions support instant selfie segmentation or virtual background function for entertainment, aesthetic, privacy, and security reasons. A good number of studies show that Deep-Learning based segmentation model (DSM) is a reasonable choice for selfie segmentation, and the ensemble of multiple DSMs can improve the precision of the segmentation result. However, it is not fit well when we apply these approaches directly to the image segmentation in a video. This paper proposes an N-Frames (NF) ensemble approach for a selfie segmentation in a video using an ensemble of multiple DSMs to achieve a high-performance automatic segmentation. Unlike the N-Models (NM) ensemble which executes multiple DSMs at once for every single video frame, the proposed NF ensemble executes only one DSM upon a current video frame and combines segmentation results of previous frames to produce the final result. For the experiment, we use four state-of-the-art image segmentation models to make an ensemble. We evaluated the proposed approach using 81 videos dataset with a single-person view collected from publicly available websites. To measure the performance of segmentation models, Intersection over Union (IoU), IoU standard deviation, false prediction rate, Memory Efficiency Rate and Computing power Efficiency Rate parameters were considered. The average IoU values of the Two-Models NM ensemble, Two-Frames NF ensemble, Three-Models NM ensemble and Three-Frames NF ensemble were 95.1868%, 95.1253%, 95.3667% and 95.1734% each, whereas the average IoU value of single models was 92.9653%. The result shows that the proposed NF ensemble approach improves the accuracy of selfie segmentation by more than 2% on average. The result of cost efficiency measurement shows that the proposed method consumes less computing power like single models. 2021 IEEE. -
Self-supervised learning based anomaly detection in online social media
Online Social Media (OSM) produce enormous data related to the human behaviours based on their interactions. One such data is the opinions expressed and posted for any specific issue addressed in the OSM. Majority of the opinions posted would be categorized as positive, negative and neutral. The lighter group's opinions are termed anomalous as it is not conforming the regular opinions posted by other users. Though, lot of conventional classification and clustering based learning algorithms works well under supervised and un-supervised environment, due to the inherent ambiguity in the tweeted data, anomaly detection poses a bigger challenge in text mining. Though the data is un-supervised, for the learning purpose it is treated as Supervised Learning by assigning class labels for the training data. This paper attempts to give an insight into various anomalies of OSM and identify behavioural anomalies for a Twitter Dataset on user's opinions on demonetization policy in India. Through Self-Supervised learning, it is observed that 86% of the user's opinions did agree to the demonetization policy and the remaining have posted negative opinions for the policy implemented. 2020, Intelligent Network and Systems Society. -
Self-silencing and Attitude Towards Women in the Contemporary World: A Millennial Generation Perspective
Society, including governments and other social agents, advocates for gender equality professionally and personally. Social media, online trends, music, the manufacturing industry, and many more such agencies create inequality in various unorganized forms. Inequality is experienced by people globally, irrespective of their gender, religion, caste, color, socio-economic status, culture, country of origin, sexual preferences, language, food preferences, political ideologies, and so on. By and large, women, irrespective of their gender roles, become the victims, and society develops a stereotype about them, but they keep themselves silent. From developmental aspects, mankind is a part of modern society but still holds a more traditional and conservative attitude towards women. The matter of surprise is the woman herself finds own self trapped in those conventional social norms and chooses to be silent. Being into multiple gender roles, she never externalizes self-perception (as she tends to judge herself by how she thinks other people see her); perceives care as self-sacrifice (putting the other persons needs in front of her own); silences the self (dont speak about own feelings in an intimate relationship when she knows they will cause disagreement) and has divided self (as she finds it harder to be herself when she is in a close relationship than when she is on her own). Considering the same, the current study focuses on understanding the association between self-silencing and attitudes toward women in contemporary society. For this purpose, correlational design was followed, and standardized tools about self-silencing and attitudes towards women were administered to a sample of 101 emerging adults. The present statistical outcomes revealed that younger millennials (born between 1991 and 96) hold a more pro-feminist attitude, whereas older millennials (born between 1981 to 85) still hold more conventional attitudes towards women (t = -3.58; p <.001). It was also found that older millennials were more likely to self-express than younger ones, who prefer to hide their feelings (t = -1.94; <.05). 2024 selection and editorial matter, Dr. Sundeep Katevarapu, Dr. Anand Pratap Singh, Dr. Priyanka Tiwari, Ms. Akriti Varshney, Ms. Priya Lanka, Ms. Aankur Pradhan, Dr. Neeraj Panwar, Dr. Kumud Sapru Wangnue; individual chapters, the contributors. -
Self-regulating fermentation device /
Patent Number: 202211013682, Applicant: Javin Harpal Singh Kaundal.
A self-regulating fermentation device (100) comprising an outer shell (102) housing a container (104); a lid (106); a sensor (108) configured to sense a temperature of the liquid received within the container (104); a coil (110) configured to heat the liquid placed inside the container (104); a cooling element (112) attached to the lid (106) that is configured to cool down the liquid placed inside the container (104); a controller (114) configured to receive temperature of the liquid from the sensor (108). -
Self-Powered Dynamic Glazing Based on Nematic Liquid Crystals and Organic Photovoltaic Layers for Smart Window Applications
Dynamic windows allow monitoring of in-door solar radiation and thus improve user comfort and energy efficiency in buildings and vehicles. Existing technologies are, however, hampered by limitations in switching speed, energy efficiency, user control, or production costs. Here, we introduce a new concept for self-powered switchable glazing that combines a nematic liquid crystal, as an electro-optic active layer, with an organic photovoltaic material. The latter aligns the liquid crystal molecules and generates, under illumination, an electric field that changes the molecular orientation and thereby the device transmittance in the visible and near-infrared region. Small-area devices can be switched from clear to dark in hundreds of milliseconds without an external power supply. The drop in transmittance can be adjusted using a variable resistor and is shown to be reversible and stable for more than 5 h. First solution-processed large-area (15 cm2) devices are presented, and prospects for smart window applications are discussed. 2023 American Chemical Society. -
Self-Induced Versus Structured Corporate Social Responsibility: The Indian Context
Adoption of Corporate Social Responsibility (CSR) ranks among the top priorities of the corporates in contemporary times. It is treated as a core business practice across the corporate globe. In the year 2013, the Ministry of Corporate Affairs, Government of India enacted mandatory CSR rules under the Companies Act, 2013 and imposed statutory obligations on the companies operating in India to implement CSR activities. With this, India became one of the first countries in the world to legislate minimum regulatory spends on CSR practices. This chapter aims to evaluate the response of this legislation since the introduction of mandatory CSR rules in India. It looks into the important trends in corporate social responsibility spending of companies in India and also maps the CSR expenditure with various Sustainable Development Goals (SDGs). This chapter forms a case for deliberation for policymakers, practitioners, scholars and business organization to understand the implications of mandatory CSR as well as how Indian companies have responded to this CSR rule. The findings also provide important insights for the other countries promulgating statutory approaches to implement CSR in their own countries. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Self-esteem, eudemonic well-being and flow at work among managers in banking sector
The present research tries to establish a link among well-being, flow at work and self-esteem among managers working in banking sector. The present study aimed to investigate the gender differences in self-esteem eudemonic well-being and flow at work among managers in banking sector, and ascertain the role of self-esteem and eudemonic well-being in predicting flow at work. The present study employs an ex-post facto research design and uses purposive sampling technique to select the respondents (N=100 male and 100 female managers working in the private banks). The data was first checked for normality and then t- test and stepwise multiple regression analysis was used to analyze it. There are significant gender differences on self-esteem, employee well-being and flow at work. Different set of predictors emerged for flow at work for males and females. Studying self-esteem, eudemonic well-being and flow at work has implications not only for the individual but also for the organizations as well, as employees with better well-being and having high self-esteem will eventually help the organization to achieve its goals and objectives. 2021 Ecological Society of India. All rights reserved. -
Self-esteem and self-efficacy among HIV-positive adolescents: an intervention study
Introduction: The aim of the present study was to understand the impact of comprehensive intervention program on self-esteem and self-efficacy among human immunodeficiency virus (HIV)-positive adolescents. Material and methods: Participants of the research were perinatally HIV-infected adolescent boys and girls, currently living in HIV care and support center. The study adopts a quasi-experimental non-equivalent control group design. Sample consisted of 97 adolescents (47 boys and 50 girls). Self-esteem was assessed using Morris Rosenbergs (1965) self-esteem scale, and self-efficacy was assessed using general self-efficacy scale (GSE) (1995) by Ralf Schwarzer & Matthias Jerusalem. It was hypothesized that there would be a significant improvement in the level of self-esteem and self-efficacy among participants of experimental group and no such improvement would be noticed in control group. Group intervention was conducted for experimental group focusing on four domains physical, cognitive, affective, and social, for 44 hours spread over 6 months. Comprehensive intervention was implemented through innovative expressive strategies. Participants were assessed pre and post-intervention. Results were analyzed using correlated t-test for self-esteem and Wilcoxon signed-rank test for self-efficacy scores. Results: There is a significant improvement in the level of self-esteem (t = 21.154; p < 0.001) and self-efficacy (z = 6.036; p < 0.001) post-intervention in the experimental group, and no such improvement was observed on both the variables in control group. Conclusions: The current study reveal that post-intervention there is a significant improvement in the level of self-esteem and self-efficacy among HIV-positive adolescents. 2022 Termedia Publishing House Ltd.. All rights reserved.