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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. -
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-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-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. -
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. -
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. -
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. -
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. -
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. -
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. -
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 -
Sensitivity analysis of Marangoni convection in TiO2EG nanoliquid with nanoparticle aggregation and temperature-dependent surface tension
The sensitivity analysis of the magnetohydrodynamic thermal Marangoni convection of ethylene glycol (EG)-based titania (TiO2) nanoliquid is carried out by considering the effect of nanoparticle aggregation. The rate of heat transfer is explored by utilizing response surface methodology and estimating the sensitivity of the heat transfer rate toward the effective parameters: radiation parameter (1 ? R ? 3), magnetic parameter (1 ? M ? 3) and nanoparticle volume fraction (1 % ? ?? 5 %). The heat transfer phenomenon is scrutinized with thermal radiation and variable temperature at the surface. The effective thermal conductivity and viscosity with aggregation are modeled by using the MaxwellBruggeman and KriegerDougherty models. The governing equations are solved by using the apposite similarity transformations. It is found that when the effect of aggregation is considered, the velocity profile is lower. A positive sensitivity of the Nusselt number toward thermal radiation is observed. Further, a negative sensitivity of the heat transfer rate is observed toward the magnetic field and nanoparticle volume fraction. 2020, Akadiai Kiad Budapest, Hungary. -
Sensitivity analysis of nonlinear radiated heat transport of hybrid nanoliquid in an annulus subjected to the nonlinear Boussinesq approximation
The main emphasis of the current study is to analyze the novel feature of the quadratic convective and nonlinear radiative flow of MHD hybrid nanoliquid (CuAl2O3H2O) in an annulus with sensitivity analysis. The significance of exponential space-related heat source, movement of annuli and a new radiation parameter corresponding to an asymptotic nature are also comprehended in the existing study. The dimensionless governing nonlinear equations are treated numerically by employing shooting technique. Impact of effective parameters on the flow and heat transport features has been scrutinized. The optimization procedure is implemented to analyze the influence of three effective parameters (1.5?Rf?5.5,1?QE?3and1%??Cu?3%) on skin friction and Nusselt number by utilizing response surface methodology and sensitivity analysis. The obtained results portray that the nonlinear convection parameter is more favorable for the skin friction coefficient. Further, a comparison of sensitivity depicts that the skin friction coefficient is more sensitive to Rf and QE, whereas Nusselt number is more sensitive to ?Cu. 2020, Akadiai Kiad Budapest, Hungary. -
Sensitivity Analysis of Operational Parameters of a High Temperature-Proton Exchange Membrane Fuel Cell
The lack of widespread commercialization of High-Temperature Proton Exchange Membrane Fuel Cells (HT-PEMFC) is primarily due to their poor performance and durability. Various factors impact the performance of fuel cells, one such crucial factor being the operational parameters. Suitable operating conditions not only enhance the output cell performance but also extend a fuel cell's life. Current research on the impact of operational factors on HT-PEMFC performance is largely qualitative in nature, with no quantitative indication of affecting the sensitivity of these parameters. In the present work, a three-dimensional, non-isothermal HT-PEMFC model developed earlier is used to investigate the influential sensitivities of five crucial operating parameters, each with four different levels, and is analyzed quantitatively using six evaluation indexes. The orthogonal/Taguchi method L16(45) is implemented to investigate the impact of operating parameters quantitatively. Further, the effect of each operating parameter on evaluation indexes under different operational current density regimes is investigated. The findings show that, of the parameters chosen, the working temperature and cathode pressure are the most sensitive to cell voltage and cathode overpotential distribution under all operating current density regimes. The findings would provide more precise recommendations for experimental research targeted at improving cell performance by optimizing operational parameters. 2023 The Electrochemical Society ("ECS"). Published on behalf of ECS by IOP Publishing Limited.; Highlights Influential sensitivities of crucial operating parameters are investigated. The orthogonal method is employed to investigate the impact of operating parameters. Impact of operational parameters on uniformity of species distribution is examined. The sensitivity of each operating parameter on evaluation indexes is investigated. The varying trends of operating parameters under different current density regimes is studied. 2023 The Electrochemical Society ("ECS"). Published on behalf of ECS by IOP Publishing Limited. -
Sensitivity analysis of radiative heat transfer in Casson and nano fluids under diffusion-thermo and heat absorption effects
The exact analysis of the magnetohydrodynamic flow of a Newtonian nanofluid past an inclined plate through a porous medium is carried out. The flows of a Newtonian nanofluid and a non-Newtonian Casson fluid are juxtaposed. The heat transport phenomenon is analyzed in the presence of Dufour and heat absorption effects. The Darcy model and Rosseland approximation are employed to simulate the effects of porous media and radiative heat. The exact solutions are obtained by using the Laplace transform method. The effects of different physical parameters on the velocity, temperature and concentration profiles are scrutinized using graphs. Statistical techniques, like the slope of data points, coefficient of correlation, probable error, and multiple linear regression, are employed to analyze the rate of heat transfer and skin friction coefficient. Further, the sensitivity of the skin friction coefficient and Nusselt number are analyzed using the Response Surface Methodology (RSM). The Nusselt number has a positive sensitivity towards thermal radiation, and it is negatively sensitive towards nanoparticle volume fraction and Dufour number. 2019, SocietItaliana di Fisica / Springer-Verlag GmbH Germany, part of Springer Nature. -
Sensitivity analysis of thermal optimisation within conical gap between the cone and the surface of disk with particle deposition
This work examines the thermal and flow characteristics of TiO2+AgBr+GO/EG trihybrid nanofluid in the conical gap that exists between a disc and a cone. Effect of thermophoresis and particle deposition are examined to perceive the mass dissipation change on the surface. The governing equations of the problem are in the form of partial differential equations which are converted to nonlinear ordinary differential equations by applying proper scaling similarity transformations, and then the resultant equations are approximated numerically by using RKF45 technique. The interesting part of this research is to discuss the impact of various pertinent parameters on three cases namely: (1) rotating cone/disk (2) rotating cone/stationary disk and (3) stationary cone/rotating disk. The flow field, heat and mass transfer rates were analysed using graphical representations. Additionally, sensitivity analysis is performed on derived rate of heat transfer as a response function for input factors for different parameters. From the graph, it is perceived that flow field increases significantly with increase in the values of Reynolds numbers for both cone and disk rotations. Also, it is seen that temperature upsurges significantly for ascendent values of solid volume fraction of nanoparticles. It is also noticed that the sensitivity of the Nusselt number towards n is more for all the values of source/sink and for middle level values of n. Akadiai KiadZrt 2024. -
Sensitivity computation of nonlinear convective heat transfer in hybrid nanomaterial between two concentric cylinders with irregular heat sources
Heat exchangers, hot rolling, heat storage systems, and nuclear power plants utilize hybrid nanoliquid flow through an annulus for heat transport. The linear Boussinesq approximation is no longer suitable as these devices work at both moderate and extremely high temperatures. Hence, the salient features of quadratic convection on the hybrid nanoliquid flow in an inclined porous annulus are analyzed. The heat transport phenomenon is examined with an exponential space-related heat source (ESHS), the convective boundary conditions, and temperature-related heat source (THS). The significance of various shapes of nanoparticles (blades, spherical, platelets, bricks, and cylinders) on the heat and fluid flow characteristics has been explored. The complicated governing equations are solved numerically. Additionally, a statistical study (response surface methodology (RSM) and sensitivity analysis) is performed. The consequence of key parameters on the non-dimensional velocity, skin friction coefficient, temperature, and Nusselt number fields are presented through two-dimensional and surface plots. The irregular heat sources increase the magnitude of velocity and temperature fields. The quadratic and mixed convection mechanism favors the flow structure. The temperature and velocity fields are greater for platelet-shaped nanoparticles followed by cylinder, brick, and spherical-shaped nanoparticles. Further, the Nusselt number is more influenced by THS and less by the total nanoparticle volume fraction 2021 Elsevier Ltd -
Sensory processing sensitivity in relation to coping strategies: exploring the mediating role of depression, anxiety and stress
Existing research on sensory processing sensitivity (SPS) focuses majorly on populations involving children, those with Autism Spectrum Disorder, and those belonging to the Western countries. This study aims to contribute in bridging this gap by exploring the mediating role of Depression, Anxiety, Stress on the relationship between SPS and coping strategies in the general population, while also assessing the prevalence of these variables. Data was collected from a convenience sample of 107 participants (mean age = 20.6years, 57.9% females). Participants responses were recorded for the Highly Sensitive Person Scale (HSPS), the Depression, Anxiety, Stress Scale (DASS-21), and the Coping Strategies Inventory-Short Form (CSI-SF). Correlational and mediation analyses of SPS, coping strategies and Depression, Anxiety and Stress were done. In the sample, 31.78% of individuals were found to be Highly Sensitive Persons (HSPs). The findings revealed significant relationships between SPS, Depression, Anxiety, Stress and coping strategies. Depression and Anxiety were observed to be significant mediators. While SPS as a trait may not be inherently modifiable, our results on its association with emotion-focused disengagement coping provide insight into target dysfunctional patterns for effective management of depression, stress, and anxiety. Further research is warranted to enhance the applicability of this study. The Author(s) 2024. -
Sentence Classification Using Attention Model for E-Commerce Product Review
The importance of aspect extraction in text classification, particularly in the e-commerce sector. E-commerce platforms generate vast amounts of textual data, such as comments, product descriptions, and customer reviews, which contain valuable information about various aspects of products or services. Aspect extraction involves identifying and classifying individual traits or aspects mentioned in textual reviews to understand customer opinions, improve products, and enhance the customer experience. The role of product reviews in e-commerce is discussed, emphasizing their value in aiding customers' purchase decisions and guiding businesses in product stocking and marketing strategies. Reviews are essential for boosting sales potential, maintaining a good reputation, and promoting brand recognition. Customers extensively research product reviews from different sources before purchasing, making them vital user-generated content for e-commerce businesses. The current work provided an efficient and novel classification model for sentence classification using the ABNAM model. The automated text classification models available cannot categorize the data into sixteen distinct classes. The technologies applied for the mentioned work contain TF-IDF, N-gram, CNN, linear SVM, random forest, Nae bays, and ABNAM with significant results. The best-performing ML method for the successful classification of a given sentence into one of the sixteen categories is achieved with the proposed model named the based Neural Attention Model (ABNAM), which has the highest accuracy at 97%. The research acclaimed ABNAM as a novel classification model with the highest-class categorizations. 2024 Nagendra N and Chandra J. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
Sentiment Analysis of COVID-19 tweets by Deep Learning ClassifiersA study to show how popularity is affecting accuracy in social media
COVID-19 originally known as Corona VIrus Disease of 2019, has been declared as a pandemic by World Health Organization (WHO) on 11th March 2020. Unprecedented pressures have mounted on each country to make compelling requisites for controlling the population by assessing the cases and properly utilizing available resources. The rapid number of exponential cases globally has become the apprehension of panic, fear and anxiety among people. The mental and physical health of the global population is found to be directly proportional to this pandemic disease. The current situation has reported more than twenty four million people being tested positive worldwide as of 27th August, 2020. Therefore, it is the need of the hour to implement different measures to safeguard the countries by demystifying the pertinent facts and information. This paper aims to bring out the fact that tweets containing all handles related to COVID-19 and WHO have been unsuccessful in guiding people around this pandemic outbreak appositely. This study analyzes two types of tweets gathered during the pandemic times. In one case, around twenty three thousand most re-tweeted tweets within the time span from 1st Jan 2019 to 23rd March 2020 have been analyzed and observation says that the maximum number of the tweets portrays neutral or negative sentiments. On the other hand, a dataset containing 226,668 tweets collected within the time span between December 2019 and May 2020 have been analyzed which contrastingly show that there were a maximum number of positive and neutral tweets tweeted by netizens. The research demonstrates that though people have tweeted mostly positive regarding COVID-19, yet netizens were busy engrossed in re-tweeting the negative tweets and that no useful words could be found in WordCloud or computations using word frequency in tweets. The claims have been validated through a proposed model using deep learning classifiers with admissible accuracy up to 81%. Apart from these the authors have proposed the implementation of a Gaussian membership function based fuzzy rule base to correctly identify sentiments from tweets. The accuracy for the said model yields up to a permissible rate of 79%. 2020 Elsevier B.V.