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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-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-Organizing Micro Service Composition for IoT Ecosystem
The Internet of Things (IoT) has become the central focus in many computing applications, with smart devices seamlessly integrated to meet user needs by providing services that reflect their functionalities. Service composition, the process of integrating multiple services to deliver unified functionality, is crucial in this context. However, traditional service composition techniques fall short in highly dynamic and open environments such as the IoT ecosystem, necessitating decentralized models that can effectively support service composition in such settings. The self-organizing microservice composition model for IoT addresses this need by leveraging decentralized, localized interactions that utilize bio-inspired mechanisms. These mechanisms enable the system to autonomously form complex service compositions with minimal human intervention through emergent behaviour, enhancing the systems flexibility, adaptability, and overall performance. This paper presents a model specifically designed for the IoT ecosystem, focusing on healthcare applications. The model dynamically responds to changing conditions, such as varying patient needs, device availability, and network conditions, making it highly suitable for critical healthcare environments. By providing a robust framework for managing the complexities inherent in healthcare IoT, this model has the potential to revolutionize the delivery and management of healthcare services. 2025 IEEE. -
Self-Organizing Microservice Composition for IoT-Enabled Neonatal Monitoring
The fast-paced growth of the Internet of Things (IoT) has revolutionized several industries, including the delivery of healthcare applications, due to its ability to support realtime and automated applications. However, several key challenges are associated with the dynamic and complex nature of IoT environments, such as scalability, increased system complexity, and network instability. This paper presents a self-organizing microservice Composition Model created especially for healthcare systems using IoT to address these issues. The Model employs decentralized, local interactions inspired by bio-inspired mechanisms. These mechanisms enable the system to autonomously form complex service composites with minimal human intervention through emergent behaviour. The model leverages an ant agent to optimize bio-inspired mechanisms and a deep learning agent for autonomous service choreography, enhancing the system's flexibility, adaptability, and overall performance. A continuous neonatal monitoring case study illustrates how the model works in composing services in a self-organizing manner. This research contributes to the advancement of smart healthcare infrastructures by proposing an innovative framework that adapts to the evolving needs of IoT-driven healthcare applications. 2025 IEEE. -
Self-Organizing Microservice Composition for IoT-Enabled Neonatal Monitoring
The fast-paced growth of the Internet of Things (IoT) has revolutionized several industries, including the delivery of healthcare applications, due to its ability to support realtime and automated applications. However, several key challenges are associated with the dynamic and complex nature of IoT environments, such as scalability, increased system complexity, and network instability. This paper presents a self-organizing microservice Composition Model created especially for healthcare systems using IoT to address these issues. The Model employs decentralized, local interactions inspired by bio-inspired mechanisms. These mechanisms enable the system to autonomously form complex service composites with minimal human intervention through emergent behaviour. The model leverages an ant agent to optimize bio-inspired mechanisms and a deep learning agent for autonomous service choreography, enhancing the system's flexibility, adaptability, and overall performance. A continuous neonatal monitoring case study illustrates how the model works in composing services in a self-organizing manner. This research contributes to the advancement of smart healthcare infrastructures by proposing an innovative framework that adapts to the evolving needs of IoT-driven healthcare applications. 2025 IEEE. -
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-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-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-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. -
Selfipendant and Extremal Pendant Graphs
[No abstract available] -
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. -
Semantic segmentation for data validation in unmanned robotic vehicles
Semantic segmentation is a vital aspect of computer vision, widely used in fields such as autonomous driving, medical imaging, and industrial automation. Maintaining high-quality datasets is crucial for enhancing model accuracy and minimizing real-world errors. This paper focuses on developing a comprehensive data validation pipeline for semantic segmentation using OpenCV. The proposed framework integrates automated integrity checks, preprocessing techniques, and consistency verification to manage large-scale datasets effectively. Key validation processes include image quality assessment (detection of blurriness and noise), verification of annotation accuracy, class distribution analysis, and identification of anomalies. Additionally, OpenCV-powered preprocessing steps, such as image resizing, normalization, contrast optimization, and data augmentation, are applied to refine dataset quality for segmentation models. This paper also addresses scalability concerns associated with processing extensive datasets, introducing optimized batch handling and parallel validation techniques. By implementing a structured validation workflow, this research enhances the reliability, robustness, and overall effectiveness of semantic segmentation models, ensuring high-quality training data for deep learning applications. 2026, Intelektual Pustaka Media Utama. All rights reserved. -
Semantic-Contextual Automation of Scriptless BDD Testing for Intelligent Test Coverage Enhancement
This work proposes a framework to improve test automation for Android applications using Behavior-Driven Development (BDD). It addresses the challenges posed by dynamic user interfaces, complex view hierarchies, and unstable locators by capturing user interactions through browser-mirrored Android screens. The framework integrates AI-based widget classification, image-based object detection, and dynamic XPath generation to enhance locator reliability. Test scenarios-including positive, negative, and boundary cases are structured in JSON and automatically converted into BDD feature files, increasing test coverage and minimizing redundancy. Automation of script generation and locator healing reduces manual effort while improving scalability, accuracy, and efficiency in test case management. The optimized validation pipeline supports comprehensive scenario generation and accelerates functional testing, thereby improving software quality in dynamic Android environments. 2025 IEEE. -
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. -
Semi-analytical framework for dynamic stress concentration in semi-elliptical notches of thin walled piezoelectric media under SH-wave excitation and KNN
This study develops a semi-analytical framework to investigate the dynamic response of semi-elliptical notches in piezoelectric half-spaces subjected to shear-horizontal (SH) wave excitation. By employing wave function expansions in elliptical coordinates and Mathieu functions, the model efficiently solves boundary value problems in electromechanically coupled media and demonstrates greater versatility compared to conventional techniques. The analysis highlights how notch depth, wave incidence angle, and excitation frequency govern surface displacement and stress amplification. In particular, deeper notches under high-frequency excitation yield pronounced dynamic stress concentration, which raises concerns regarding the structural integrity of piezoelectric devices. Comparative results further reveal that materials with stronger piezoelectric coupling, such as PZT-5H, exhibit more severe stress localization than PZT-6B or BaTiO?. The study also examines the role of weak interfaces and nanoscale surface effects. Weak interfaces are shown to reduce stiffness in phonon and phason fields while increasing stiffness in the electric field for Rayleigh waves, with such effects becoming most prominent under strongly dispersive conditions. At the nanoscale, surface and interface influences effectively mitigate dynamic stress concentration, with diffraction stress concentration factor (DSCF) decreasing monotonically as the nano-influence factor increases, eventually tending to vanish in the limit of diminishing defect size. To complement the analytical formulation, a K-Nearest Neighbors (KNN) machine learning (ML) model was implemented using the analytical DSCF dataset. The classifier achieved nearly 90% accuracy in distinguishing between low and high stress concentration regimes. Decision maps highlighted frequencygeometry combinations most prone to defect amplification, while the confusion matrix confirmed reliable detection of critical hot-spots. This integration of ML provides a rapid surrogate framework that complements the semi-analytical method, enabling efficient prediction, defect screening, and design optimization in advanced piezoelectric systems. The Author(s), under exclusive licence to Springer Nature B.V. 2025. -
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.