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Atman's awakening: Bhagavad Gita's Path to Moksha through Karma Yoga and Atmabodha
Indian psychology is characterized by its diverse and rich traditions that have evolved over several centuries. This chapter tries to fulfill four objectives: 1) To provide a brief overview of the concept of self in Bhagavad Gita; 2) to give a brief overview of the two frameworks for moksha given in the Bhagavad Gita with the help of empirical evidence of current research; 3) to propose a conceptual model using Triguna Framework and Trimarg Framework; and 4) to provide the implications of the proposed model. The chapter begins with an explanation of the Indian philosophical understanding of self from the lens of Bhagavad Gita. In the second section, an effort has been made to compare and contrast the two frameworks given in Bhagavad Gita for Moksha. The last section introduces a conceptual model to enhance sattva guna and reduce the rajas and tamas gunas to attain atmabodha that can have positive psychological implications in modern times. 2024, IGI Global. All rights reserved. -
Exploring perceptions of psychology students in Delhi-NCR Region towards using mental health apps to promote resilience: a qualitative study
Background: Mental health apps (MHapps) have the potential to become an essential constituent for addressing mental health disparities and influencing the psychological outcomes of students in India. Though lauded as a practical approach to preventing various mental health issues, there are concerns that developing and utilizing MHapps standardized on Western populations produce ineffective results for the natives of Asian countries such as India due to a wide range of cultural differences. This research was conducted on psychology students living in the Delhi-NCR region of the Indian subcontinent. The study explored psychology students perceptions, needs, and preferences regarding mental health apps that promote resilience, identified barriers and facilitators for developing effective mental health apps, and explored the cultural relevance of the development of MHapps in India. Methods: This was an exploratory study utilizing focus group discussions among psychology students. Psychology students were sampled using snowball sampling from Delhi-NCR region colleges to participate in FGDs. We conducted six focus groups, which included a representation of 30 psychology students from full-time UG/PG courses. The study used a reflexive thematic analysis framework using the six-step Braun and Clarke process to develop themes. Results: Psychology students valued MHapps for their easy accessibility, 24*7 functionality, affordable costs, highly engaging features, and the option of being anonymous. However, students preferred the apps based on established psychological frameworks with strong empirical evidence and the availability of remote mental health professionals with relevant qualifications and training. The main barriers to using MHapps identified by students included difficulties in differentiating between real and fake MHapps, lack of progress tracking of the users due to minimal human interactions, and ethical and data privacy concerns. Students also emphasized the cultural relevance of MHapps. The interpretation of our findings indicates that students demanded transparency regarding the authenticity of MHapps. Conclusion: The findings of this exploratory investigation offer a better understanding of how college students perceive the usage of MHapps to improve resilience. This study highlights that further research should explore the specific needs and preferences of university students for developing and implementing effective MHapps for different contexts. The Author(s) 2024. -
Navigating the dynamic interplay of fear of failure and social cognition in the digital era
This chapter delves into the intricate relationship between fear of failure and our ability to perceive, interpret, and respond to social cues (i.e., social cognition). This chapter will examine the theoretical foundations of fear of failure and how it manifests across cognitive, emotional, behavioral, and social dimensions. Drawing from empirical research, it will provide real-world insights into how this fear can profoundly affect social interactions. The chapter highlights interventions, such as CBT and mindfulness practices, designed to address the fear of failure and enhance social cognition. It will further explore the dynamic interplay between fear of failure, social cognition, and the evolving landscape of online interventions. As the digital realm shapes our social interactions, understanding how fear of failure influences social cognition in the online context and how online interventions can mitigate its impact is of paramount importance. This chapter seeks to present a thorough summary of these interconnected variables. 2024, IGI Global. -
Analyzing Technology Ecosystem Business Models: A Predictive Modelling Approach
In the rapidly changing landscape of technology, companies are devoting an increasing amount of their resources to developing product ecosystems that collaborate to deliver enhanced consumer experiences and strengthen their business models. As opposed to traditional standalone solutions, these ecosystems are intended to facilitate everyday tasks, increase user engagement, and provide seamless integration, all of which ensure a steady stream of revenue and dedicated customer base. This analysis provides an overview of the many ecosystem models that are now transforming the technology industry. An examination of ecosystems that help businesses maintain long-term revenue sustainability and high customer retention rates is provided by the model analysis, along with insights into how ecosystems may enhance user experience by being more connected, straightforward, and user-friendly. Technology ecosystems' quantitative effects are lacking, which makes it difficult to comprehend how they affect long-term revenue sustainability and customer retention. It is challenging to understand how technological ecosystems impact long-term revenue sustainability and customer retention due to the lack of measurable consequences. Through the use of multiple linear regression, this study illustrates the ecosystem business models' long-term revenue and customer retention. The study visualized the relationships of the technology ecosystem with an accuracy of 90-99%. This shows how to measure ecosystem impact and gives firms data-driven insights to improve their ecosystem initiatives. 2025 IEEE. -
K-Nearest Neighbor Optimization of Silver-Graphene Fiber Optic Sensor for Lung Cancer Detection
At nearly 1.8 million deaths annually, lung cancer is among the world's top causes of mortality. Cancer is curable up to a point, after which recovery is extremely challenging. Preventing cancer requires early cancer detection, which localized surface plasmon resonance (LSPR)-based sensors high sensitivity. The phenomenon known as localized surface plasmon resonance (LSPR) occurs when nanoparticles resonate with light at certain wavelengths, leading to the development of characteristics including quick reaction times, adjustable resonance, high sensitivity, and localized light-matter interaction. Since silver-graphene has qualities that make it perfect for cancer detection, it is selected as the material composition. The silver-graphene sensor is utilized for detecting CL1-5 and A549 cell lines, for which the peak of the extinction coefficients was found to be 2.7169 and 1.8592, with a sensitivity of 107 RIU. The Silver-Graphene LSPR sensor interaction with cell lines generated a novel dataset, for which K-Nearest Neighbor Regression has been chosen due to its adaptability and robustness to outliers and has been used to improve the functionality of the sensor by optimizing sensor design, improving sensor sensitivity, and reducing experimental time. With a prediction rate of 99%, KNN and the Silver-Graphene LSPR sensor are an excellent combination for early lung cancer diagnosis. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Lung Cancer Classification from CT-Scan Images Using an Enhanced VGG16 Model
Lung cancer has been one of the most common and deadly types of cancer around the globe, for which early detection is quite crucial for patient survival. In this research work, a deep learning-based method for four-class classification of chest CT-scan images, such as Squamous Cell Carcinoma, Large Cell Carcinoma, Adenocarcinoma, and Normal, is presented. With a modified VGG16 architecture, adding Squeeze-and-Excitation (SE) blocks and residual connections, the enhanced SERES_VGG16 model enhances feature representation and classification accuracy. The dataset we used here contains preprocessed chest CT-scan images divided into a training set, validation set, and test set. It is trained with augmentation techniques in the data to improve generalization. Its performance is evaluated using measures of standard performances, such as F1-score, recall, precision, accuracy and confusion matrices. The model achieved over 95% accuracy, class-wise precision ranging from 94 to 99%, recall ranging from 88 to 99%, F1-score from 93 to 96%. The presented approach reached over 95% accuracy on the test set and can be a trusted second opinion for radiologists to assist with early and accurate lung cancer subtype classification. However, this study is constrained by the small size of the dataset and the lack of other clinical parameters like genetic information. Future studies will concentrate on expanding the dataset and integrating multi-modal clinical information for enhanced robustness. This work in this study justifies the importance of deep learning in the classification of the medical images and points out further ways toward improving automated diagnostic systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Design of Triple Tuned Passive Harmonic Power Filter - A Novel Approach
Nowadays, there is a race between active and passive harmonic filters and still ambiguity persists. It is a proven fact that active harmonic filters (AHFs) are costly solutions though have proved better than passive harmonic filters. Except sizing and resonance problems, tuned passive harmonic filters (TPHFs) are proved to give economical solutions with little compromise on their performance. The accurate design of TPHFs gives a greater impact on its performance. The triple-TPHF (TTPHF) is essential to alleviate first three dominant ac side current harmonics simultaneously at the high voltage direct current (HVdc) converters and it is proved better than the single and double TPHFs. Existing equivalent methods of TTPHF design failed to give satisfactory performance under dynamic conditions. Hence, this article introduces a novel parametric method-based design of TTPHF, which will give better performance under static and dynamic loading conditions. The results also reveal that the proposed TTPHF design method will perform better than the existing methods. 2021 IEEE. -
Energy Efficiency Enhancement in Wind-Powered Pumping with TBRC MPPT Integration
This paper introduces a novel strategy to enhance energy efficiency in a Battery and Wind Energy-based Pumping Scheme (BWEPS) by implementing a Test Bench Rapid Control (TBRC) based Maximum Power Point Tracking (MPPT) system within a LabVIEW SPEEDGOAT environment. Wind Energy Conversion Systems (WECS) are inherently challenged by the stochastic nature of wind, which causes frequent fluctuations in output power and reduces overall efficiency if not properly managed. To address these issues, this study applies TBRC as a real-time control framework, enabling faster response, improved adaptability, and more accurate tracking of the maximum power point under dynamic conditions. The integration of battery storage further contributes to stabilizing system performance by mitigating intermittency and ensuring reliable energy availability for pumping operations. The proposed approach not only develops and validates the TBRC-based MPPT algorithm but also optimizes BWEPS operation and benchmarks it against traditional energy storage and control techniques. Experimental validation through real-time simulation demonstrates significant improvements in energy efficiency, reliability, and operational stability. The outcomes highlight the potential of TBRC-based MPPT control as a promising solution for advancing hybrid renewable energy systems, offering an effective pathway for sustainable and resilient water pumping applications. 2025 IEEE. -
Design of a novel shunt active harmonic compensator with AUV-PQ-SRF reference current extraction, OSV-MPC and SMC techniques
Harmonic distortion makes it difficult to maintain good Electrical Power Quality (EPQ) in distribution networks with many nonlinear loads. Three significant advances are combined in this papers innovative Shunt Active Harmonic Compensator (SAHC) design: (i) a new technique for extracting reference currents, called AUV-PQ-SRF, which combines the Unit Vector, PQ, and SRF techniques in a unique way to improve harmonic detection; (ii) an OSV-MPC strategy that improves reference current tracking accuracy by doing away with traditional pulse width modulation; and (iii) a Sliding Mode Controller (SMC) for dynamic and reliable DC link voltage regulation under a range of load conditions. The accuracy, robustness, and response time issues with traditional methods are addressed by the suggested approach. Results from simulations conducted in accordance with IEEE-519-2022 standards show a considerable decrease in total harmonic distortion (THD), along with increased power factor and real and reactive power compensation. This study provides a thorough and useful solution for dynamic power quality issues, setting a new standard in active filtering. The Author(s) 2025. -
Exploring How Gender and Culture Shape the Lived Experiences of Indian Clients with Emotional Abuse: A Social Justice Approach to Counselling
In this study, we have carried out an in-depth, idiographic exploration of how Indian clients describe their experiences of emotional abuse in a parent-adult child context from a social justice lens. This study focused on the contribution of persisting systemic influences, including gender and culture, in maintaining emotional abuse. We collected data from seven participants through a semi-structured interview schedule, and utilized an interpretative phenomenological analysis for the research design and analysis. Findings indicatedvarious cultural and gender norms were responsible for contributing to and maintaining emotional abuse. The five master themes developed included Unmet Emotional Needs, Mental Health Issues due to Impact of Emotional Abuse, Gender and Culture Norms as Backgrounded, Unfair and Oppressive Norms and Attitudes, and Intergenerational Nature of Norms, Beliefs, and Abuse. Implications for counsellors, policymakers, and researchers in the fields of counselling and psychotherapy, social justice, social psychology, and critical psychology are discussed. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Exploring Therapeutic Change in Indian Clients Experiencing Emotional Abuse: A Social Justice Approach to Counselling
Background: This study examined the lived experiences of emotional abuse (EA) in Indian parent-adult child relationships, emphasising the intersection of systemic influences in maintaining EA. Employing a social justice framework, the research explored pathways to foster change at both individual and societal levels to address EA. Methods: Data were collected through semi-structured interviews with ten participants undergoing therapy, and analysed using Interpretative Phenomenological Analysis. Results: Four master themes emerged: State of Lack, Lack of Relatability to Gender and Culture Norms, Therapy as a Catalyst for Regaining Sense of Self and Empowerment, and Cultural Shifts, Therapeutic Integration and Redefining Norms to Address Emotional Abuse. Conclusion: The findings emphasise the contribution of gender and cultural norms in the reinforcement of EA, while highlighting therapy's potential in fostering individual healing while advocating for societal transformation. Our study adds valuable literature to the fields of counselling, social justice research, cultural psychology, social psychology, and feminist psychology, and provides a basis for future research. 2025 British Association for Counselling and Psychotherapy. -
Internet of Things Enabled Smart Hand Gesture Virtual Mouse System
This research is aim to focus on IoT based hand gesture model. Mouse is one of the most important input devices of a computer. It works as a pointing device and allows the user to move the pointer as needed by the user. In the early days, a wired mechanical mouse was used for this purpose. In mechanical mouse a ball is fixed underneath the mouse, which rotates as the user moves the mouse. This movement of the ball is used to move the mouse pointer on the screen. Now mostly we use optical mouse which can be wired or wireless. An optical mouse has a high-power laser below it, which takes more than thousand pictures of the surface below the mouse. An image comparator compares the images and sends the signal to move the mouse pointer as the texture of the image changes. Both the types of mouse works based on old technology. As technology leaps to greater heights, the need for simplicity also increases. With the invention of different kind of sensors, microcontrollers and other electronics, we can eliminate the mouse as an input device and instead use our hands to do the work of a mouse. This prototype is an embedded system which runs with the help of an arduino microcontroller. Flex sensors are used to capture the hand gestures. The proposed IoT based hand gesture model is providing high accuracy rate compare to the regular model. The proposed model is analyzed with accuracy level, the average accuracy level of proposed model is more than 90%. 2025 IEEE. -
Facile synthesis of Bi2WO6-NiO nanocomposite for supercapacitor application
In order to prepare for future high-power storage-related applications, a tremendous amount of studies have been conducted on the manufacturing of high-performance supercapacitor electrodes. The hydrothermal technique was used to synthesize Bi2WO6NiO nanocomposite (NC), which was examined using FTIR, XRD, HR-TEM, EDX, FESEM, and XPS techniques. Furthermore, the Bi2WO6-NiO NC performs with an elevated specific capacity of 398.2C/g at 10 mV/s. The charge transfer resistance (Rct) and solution resistance (Rs) of Bi2WO6-NiO NC were determined as 0.81 and 0.23 ? using electrochemical impedance spectra (EIS). Bi2WO6-NiO NC extended the chargedischarge time and rate capacities, as shown by the galvanostatic chargedischarge (GCD) analysis. Even after 2000 cycles, Bi2WO6-NiO NC cyclic stability was superior with a capacitive retention of 89.3 %. A power density of 6750 W/kg resulted from the constructed asymmetric supercapacitor (ASC) device based on Bi2WO6-NiO/AC, exhibiting an energy density of 32.5 Wh/kg. Additionally, the ASC maintains high cyclic stability with 90.8 % of initial capacity, even after 2000 chargedischarge cycles in a row. 2024 Elsevier B.V. -
Genetic Algorithms for Graph Theoretic Problems
[No abstract available] -
Algorithms for the metric dimension of a simple graph
Let G = (V, E) be a connected, simple graph with n vertices and m edges. Let v1, v2 $$\in$$ V, d(v1, v2) is the number of edges in the shortest path from v1 to v2. A vertex v is said to distinguish two vertices x and y if d(v, x) and d(v, y) are different. D(v) as the set of all vertex pairs which are distinguished by v. A subset of V, S is a metric generator of the graph G if every pair of vertices from V is distinguished by some element of S. Trivially, the whole vertex set V is a metric generator of G. A metric generator with minimum cardinality is called a metric basis of the graph G. The cardinality of metric basis is called the metric dimension of G. In this paper, we develop algorithms to find the metric dimension and a metric basis of a simple graph. These algorithms have the worst-case complexity of O(nm). The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Parallel Algorithm to find Integer k where a given Well-Distributed Graph is k-Metric Dimensional
Networks are very important in the world. In signal processing, the towers are modeled as nodes (vertices) and if two towers communicate, then they have an arc (edge) between them or precisely, they are adjacent. The least number of nodes in a network that can uniquely locate every node in the network is known in the network theory as the resolving set of a network. One of the properties that is used in determining the resolving set is the distance between the nodes. Two nodes are at a distance one if there is a single arc can link them whereas the distance between any two random nodes in the network is the least number of distinct arcs that can link them. We propose two algorithms in this paper with the proofs of correctness. The first one is in lines with the BFS that find distance between a designated node to every other node in the network. This algorithm runs in O(log n). The second algorithm is to identify the integer k, such that the given graph is k-metric dimensional. This can be implemented in O(log n) time with O(n2) processors in a CRCW PRAM. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Facile synthesis of Bi2WO6-NiO nanocomposite for supercapacitor application
In order to prepare for future high-power storage-related applications, a tremendous amount of studies have been conducted on the manufacturing of high-performance supercapacitor electrodes. The hydrothermal technique was used to synthesize Bi2WO6NiO nanocomposite (NC), which was examined using FTIR, XRD, HR-TEM, EDX, FESEM, and XPS techniques. Furthermore, the Bi2WO6-NiO NC performs with an elevated specific capacity of 398.2C/g at 10 mV/s. The charge transfer resistance (Rct) and solution resistance (Rs) of Bi2WO6-NiO NC were determined as 0.81 and 0.23 ? using electrochemical impedance spectra (EIS). Bi2WO6-NiO NC extended the chargedischarge time and rate capacities, as shown by the galvanostatic chargedischarge (GCD) analysis. Even after 2000 cycles, Bi2WO6-NiO NC cyclic stability was superior with a capacitive retention of 89.3 %. A power density of 6750 W/kg resulted from the constructed asymmetric supercapacitor (ASC) device based on Bi2WO6-NiO/AC, exhibiting an energy density of 32.5 Wh/kg. Additionally, the ASC maintains high cyclic stability with 90.8 % of initial capacity, even after 2000 chargedischarge cycles in a row. 2024 Elsevier B.V. -
Patients trust in the Indian healthcare system and its impact on the intention to use artificial intelligence-based healthcare chatbots
Purpose: Indian patients have different medicine systems available at the service that alter their healthseeking behaviour (HSB). This study aims to examine the beliefs and behaviour of patients in India towards the healthcare system and how it affects their intention to use healthcare chatbots. Design/methodology/approach: A survey instrument was developed from standard scales and validated by experts. The data was collected from 397 respondents in an urban area and tested using a structural equation model in SAS JMP software. Findings: The study found that awareness and perception of chatbots and distrust on doctors and health systems impact trust in a chatbot. The results show that trust in chatbots influences the intention to use chatbots. The belief in alternative medicine systems and HSB also influence the intention to use chatbots. The study findings also imply that health-care chatbots should cater to HSB and the belief in alternative medicine. Research limitations/implications: The study was conducted only among the urban population because services based on technology are more available in metro cities. Bengaluru is considered the representative population of urban India. Practical implications: The level of disruption that chatbots can provide to the healthcare system makes this study significant. The study findings will help to manage the factors that can enable chatbot inclusivity, as the current system is inaccessible to many patients. Originality/value: This paper addresses an identified need to study patients trust in the Indian healthcare system and their intention to use chatbots. The level of disruptions these chatbots can cause in the health-care system is undeniable and patients trust in these chatbots will eventually transform the health-care sector. 2024, Emerald Publishing Limited. -
An Overview of Augmenting AI Application in Healthcare
Artificial intelligence (AI) is showing a paradigm shift in all spheres of the world by mimicking human cognitive behavior. The application of AI in healthcare is noteworthy because of availability of voluminous data and mushrooming analytics techniques. The various applications of AI, especially, machine learning and neural networks are used across different areas in the healthcare industry. Healthcare disruptors are leveraging this opportunity and are innovating in various fields such as drug discovery, robotic surgery, medical imaging, and the like. The authors have discussed the application of AI techniques in a few areas like diagnosis, prediction, personal care, and surgeries. Usage of AI is noteworthy in this COVID-19 pandemic situation too where it assists physicians in resource allocation, predicting death rate, patient tracing, and life expectancy of patients. The other side of the coin is the ethical issues faced while using this technology like data transparency, bias, security, and privacy of data becomes unanswered. This can be handled better if strict policy measures are imposed for safe handling of data and educating the public about how treatment can be improved by using this technology which will tend to build trust factor in near future. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Antecedent Factors in Adolescents Consumer Socialization Process Through Social Media
The research paper attempts to find the antecedent factors that influence in adolescents consumer socialization process through social media and its impact on family purchase. Consumer socialization of adolescents through social media has become a key indicator in the area of marketing because of predominant online interaction of consumer. Socialization process framework is adopted to investigate among 254 respondents. The results show there is positive influence of antecedent variables like age, social media and peer identification on Purchase Intention and the variable social media also influences Product Involvement in family decision making. The outcome of this research benefits the academicians and marketers to explore the impact of social media on adolescent in their family decision making. Springer Nature Switzerland AG 2020.