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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. -
Genetic Algorithms for Graph Theoretic Problems
[No abstract available] -
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. -
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. -
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. -
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. -
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. -
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 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. -
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. -
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. -
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. -
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. -
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. -
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. -
ASSESSMENT OF WATER QUALITY IMPACTS ON CROP PRODUCTIVITY IN SALINE SOILS: INTEGRATING HYDRO CHEMICAL ANALYSIS AND CROP PERFORMANCE
This study aims to address the effect of water quality on crop productivity on saline soils. Water quality parameters to be studied include salinity level, pH, oxygen concentration, nutrient, and heavy metal level. The study will particularly focus on irrigation water sources that are situated in regions characterised by saline soils. Moreover, there are intended growth experiments for the evaluation of various specific crops such as rice, wheat or maize with different levels of water quality. The study will incorporate sophisticated analytical tools including ion chromatography, atomic absorption spectroscopy (AAS), and electric field mapping (EMI) to shed light on the current water quality and soil conditions of the selected area. The experiments that we plan to conduct will involve studying the growth parameters, yield, water use efficiency and the percentage of uptake of nutrients under several water quality scenarios. The data collected in this work will ascertain the link between the values of water quality parameters, soil salinity extent and productivity of crops and will provide the basis for the creation of innovative saline soil agriculture irrigation management practices. 2024, Scibulcom Ltd.. All rights reserved. -
Volatility Spillover Effects in Cryptocurrencies
Cryptocurrencies' growing use has increased investors and decision- makers interest. Cryptocurrencies' volatility and how it impacts others is most intriguing. Arguments include speculative pressures, valuation uncertainty, and lack of regulation. These traits cannot fully explain cryptocurrency volatility and volatility spillovers, suggesting other relevant factors. In this study, currency volatility and spillovers, as well as their relationship with the sentiment of global investors, were investigated. The study analysed 22 cryptocurrencies from 01/01/2018 to 31/12/2022. The study used FIGARCH and FIEGARCH, a GARCH family model to analyse the long-memory and leverage effects on cryptocurrency volatility, the ADCC-GARCH framework, and the Diebold-Yilmaz spillover index to analyse cryptocurrency volatility spillovers. The long-memory and leverage impacts on bitcoin volatility were analysed using the FIGARCH and FIEGARCH models from the GARCH family. Both the Chow-test and the Pai-Berron Test found structural breaks in the cryptocurrencies. Cryptocurrencies such as Adacordono, Aertinity, ARK, BAT, BCH, BNT, BTC, Dogecoin, Ethereum, Funtoken, ICON, KMD, KNC, NEO, PIVX, QTUM, SNT, TRX, ZCASH, have positive (difference) FIGARCH coefficient values. It indicates a long memory in currencies, and volatility shocks affect future volatility. On the other hand, the FIGARCH coefficient of BTG cryptocurrency (difference) is negative (-0.035), which suggests that the individual has a short memory. In this scenario, the effects of volatility shocks are only temporary. When extreme volatility is promptly followed by low volatility or vice versa, this indicates anti- persistence. The study also found that both positive and negative news has a significant impact on the volatility of specific cryptocurrencies such as BCH (0.015), BNT (0.0016), BTG (0.01972), DOGE (0.2296), EOS (0.0112), KNC (0.0366), PIVX (0.0021), TRX (0.0013), Adacordono (- 0.027), Aertinity (-0.0393), ARK (-0.0377), BAT (-0.028058), and BTC (-0.0665). Ethereum has the largest spillover (4.09), followed by QTUM (4.06), EOS (4.05), Adacordono (4.05), and Dogecoin (2.4). All cryptocurrencies show fundamental instabilities (P-values less than 0.05). Hence the alternative hypothesis is accepted, and the null hypothesis is rejected. The hill estimator tail index value is ? > 0, fat tail or heavy tail; high chance of catastrophic event which is observed in all the 22 cryptocurrencies. Both investors and speculators can use sentiment analysis to forecast market volatility and generate gains. Policymakers can also utilize this information to establish laws that reduce market volatility. As a result, the study contributes to the ongoing discussion on the factors that cause bitcoin's volatility.3 -
Strategic Integration of HR, Organizational Management, Big Data, IoT, and AI: A Comprehensive Framework for Future-Ready Enterprises
This exploration paper proposes a comprehensive frame aimed at fostering unborn-ready enterprises through the strategic integration of Human coffers(HR), Organizational Management, Big Data, the Internet of Things (IoT), and Artificial Intelligence(AI). By synthesizing these critical factors, the frame seeks to optimize organizational effectiveness, enhance decision-making processes, and acclimatize proactively to evolving request dynamics. Through a methodical review of being literature and empirical substantiation, the paper delineates the interconnectedness of these rudiments and elucidates their collaborative impact on organizational performance and dexterity. likewise, it explores perpetration strategies and implicit challenges associated with espousing such an intertwined approach. This paper not only contributes to the theoretical understanding of strategic operation but also provides practical perceptivity for directors and directors seeking to navigate the complications of the contemporary business geography and place their associations for sustained success in a decreasingly digitized and competitive terrain. 2024 IEEE. -
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Design of a Multi Camera Enabled Scrutinizing Framework for Smart Cities
This research paper presents a multi-camera surveillance system tailored for the demands of smart cities. By integrating edge computing, the system decentralizes processing, reducing latency and alleviating network bandwidth strain. The architecture includes layers for data collection, edge processing, centralized storage, AI-driven analysis, synchronization, and user visualization. Cameras capture and preprocess data locally to identify anomalies and minimize unnecessary transmission. AI algorithms handle tasks like object tracking, behavior analysis, and event detection with precision. Synchronization ensures seamless temporal alignment across video streams for accurate event reconstruction. User-friendly dashboards provide actionable insights for urban planning and public safety. By leveraging edge computing, AI, and robust synchronization, this system addresses scalability, latency, and privacy concerns, offering enhanced safety, optimized traffic flow, and better urban planning. 2025 IEEE.