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Impact of Machine Intelligence on Clinical Disease Outbreak Prediction
This research paper examines the utilization of Artificial Intelligence (AI) in disease outbreak prediction and its importance in public health. It explores the hurdles associated with predicting disease outbreaks, including data quality and accessibility, ethical considerations, algorithmic bias, and integration and interpretability challenges. The paper presents an overview of AI techniques applied in healthcare and their relevance to forecasting disease outbreaks. Case studies demonstrate the efficacy of AI -based models in predicting infectious diseases, vector-borne diseases, and epidemics/pandemics, employing diverse data sources. The limitations and future prospects of AI in disease outbreak prediction are addressed, accompanied by recommendations for enhancement. In conclusion, the paper highlights AI's potential to revolutionize disease outbreak prediction, leading to proactive public health interventions and improved response strategies. 2023 IEEE. -
Fractional MooreGibsonThomson thermoelastic analysis of nonlocal nanobeams under moving heat source with machine learning-assisted predictive modeling
This study presents a comprehensive investigation of thermoelastic wave propagation in nonlocal nanobeams subjected to a moving heat source within the framework of fractional MooreGibsonThomson (MGT) heat conduction theory. The model incorporates nonlocal elasticity to capture size-dependent mechanical behavior and employs a fractional-order formulation to account for thermal memory and finite-speed heat propagation. The coupled governing equations are derived and solved analytically using Laplace transform techniques to obtain the temperature, displacement, and stress distributions. A detailed parametric analysis is performed to examine the effects of fractional order, nonlocal parameter, thermal relaxation time, and source velocity on the thermoelastic response. The results reveal significant modifications in wave attenuation, temperature evolution, and stress distribution due to the combined influence of nonlocality and fractional thermal effects, particularly under moving thermal loads. To enhance computational efficiency and enable rapid prediction of system responses, a machine learning-based surrogate framework is developed using an artificial neural network (ANN). The network is trained on data generated from the present analytical model and is shown to accurately predict thermoelastic fields across a wide range of governing parameters. The ANN predictions exhibit excellent agreement with analytical results, demonstrating its capability as a reliable reduced-order modeling tool. The proposed hybrid analyticalcomputational approach provides new insights into thermoelastic behavior at the nanoscale and offers an efficient predictive framework for heat transfer applications involving moving thermal loads. This study is motivated by the need to address unresolved challenges in modeling thermoelastic behavior at the nanoscale, particularly the simultaneous incorporation of fractional heat conduction, nonlocal elasticity, and moving thermal loads within a unified framework. 2026 Published by Elsevier Ltd. -
Wavefield analysis of nano-scale surface/interface effects on dynamic stress response in biphasic laminated media with circular defects
The dynamic stress concentration around a nanoscale circular hole located at the centre of a two-phase circular laminated medium subjected to localized anti-plane SH-wave loading is examined in this paper. The model (used in this paper) is developed using the complex variable function method combined with wavefield superposition and multipolar expansion. In this framework, GurtinMurdoch (GM) surface and interface elasticity is incorporated at both the material interface and the free surface of the nanohole, resulting in a coupled two-surface formulation that has not been previously reported for biphasic geometries. This leads to non-classical traction-jump conditions and modified stress-free boundary conditions. The resulting infinite system of linear equations is then solved through series truncation. Numerical results reveal that nanoscale surface and interface effects significantly reduce the dynamic stress concentration factor (DSCF) around the hole, with the most substantial attenuation occurring at low wavenumber ratios and low shear modulus ratios. Conversely, the stress reaches its maximum amplification under long-wavelength excitation or when the outer layer is relatively soft. Overall, these findings offer new insights into nanoscale toughening mechanisms in realistic multilayered systems, providing a solid foundation for defect detection, lifetime prediction, and the damage-tolerant design of laminated nanocomposites and coreshell nanostructures. The Author(s), under exclusive licence to Springer Nature B.V. 2026. -
Analytical investigation of heat transfer in multilayer human eye based on dual-phase-lag thermoelastic theory
Thermal damage to ocular tissues is a critical medical concern because even small temperature elevations can impair corneal endothelial function, accelerate cataract formation, and disrupt retinal metabolism. This issue is particularly relevant in regions with intense thermal environments, such as Saudi Arabia, where preventive health care and advanced biomedicalfacilities are required. This study develops a predictive framework for estimating temperature distributions within the human eye under external heat exposure. A dual-phase-lag (DPL) bioheat transfer model incorporating two thermal relaxation times is formulated to capture finite speed thermal wave propagation in the multilayer structure of the eye, and closed-form analytical solutions are obtained using a normal mode approach. A mechanics-informed machine learning surrogate model is then constructed using data generated from the analytical DPL solutions, enabling rapid prediction of intraocular temperature across the parameter space. Parametric investigations examine the effects of ambient temperature, evaporation rate, tissue porosity, and blood perfusion on the thermal response of the six ocular layers. Comparisons with the LordShulman and classical Fourier models reveal important differences in predicted temperature behavior under non-Fourier heat transfer. Additional analysesincluding thermal safety mapping, sensitivity assessment, and response surface visualizationprovide further insight into the combined influence of environmental and physiological parameters. The results show that non-Fourier thermal effects significantly influence peak intraocular temperature, while ambient temperature and evaporation dominate anterior eye heating and perfusion primarily affects deeper tissues. The present model assumes axisymmetric geometry and temperature-independent material properties, which may be extended in future studies using three-dimensional or patient-specific models. Overall, the proposed hybrid analyticalmachine learning framework provides an efficient tool for ocular thermal risk assessment and supports the development of preventive strategies for populations exposed to extreme thermal environments. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2026. -
Dual-Phase-Lag Bioheat Analysis of Non-Fourier Thermal Wave Propagation in Multilayer Ocular Tissues
This study presents an advanced analytical framework for predicting thermal wave propagation in a multilayer ocular structure using the dual-phase-lag (DPL) bioheat formulation. The results confirm that non-Fourier thermal transport mechanisms are essential for accurately capturing transient heat behavior in biological tissues, particularly under external thermal exposure. Compared with classical Fourier and LordShulman models, the DPL model predicts smoother temperature gradients and lower peak thermal loads, thereby providing more physiologically realistic temperature distributions. The model validity regime analysis demonstrates clear operational boundaries where classical diffusion-based formulations fail and non-Fourier effects dominate thermal response. Sensitivity analysis reveals that ambient temperature and evaporation primarily control anterior ocular thermal behavior, while tissue porosity and blood perfusion significantly influence deeper layers such as the retina and sclera. Transient thermal comparisons confirm that classical models overpredict early-time heating due to the absence of relaxation effects. Multi-parameter response surface and thermal safety mapping highlight strong nonlinear coupling between environmental and physiological transport mechanisms, enabling quantitative identification of safe exposure limits. Additionally, surrogate modeling demonstrates high prediction accuracy relative to full DPL solutions while significantly reducing computational cost, enabling real-time thermal prediction and parametric optimization. Overall, the proposed hybrid analyticalcomputational framework establishes a robust platform for ocular thermal safety assessment, biomedical treatment planning, and environmental exposure risk evaluation. The findings also provide a generalized foundation for studying non-Fourier heat transport in layered porous biological media and support the development of next-generation predictive thermal modeling tools. 2026 Wiley Periodicals LLC. -
Analysis of SH and anti-plane SH wave signals for nanosensor applications using two distinct models of piezoelectric materials lead zirconate titanate
The primary goal of the current study is to examine the effects of wave propagation on the performance of surface acoustic wave (SAW) macro- and nano-sensors. Therefore, shear horizontal waves (SH) in an orthotropic piezoelectric layer laid on top of an elastic framework (Model I), a piezoelectric substrate, and an orthotropic piezoelectric substrate (Model II) are studied using the surface piezoelectricity theory. The study used a variable-separable methodology. Theoretical forms are developed and used to show the wavenumber of surface waves in any direction of the piezoelectric medium based on the differential equations and matrix formulation. A piezoelectric material half-space with a nano substrate and an orthotropic piezoelectric material layer over an elastic framework are the two configurations that are investigated. Frequency equations are expressed analytically for both symmetric and anti-symmetric waves. The study looks into how phase velocity is affected by surface density, anisotropic piezoelectric constant, surface elastic constants, and symmetric and antisymmetric modes. The study is limited to the propagation of linear waves. Furthermore, the analysis is predicated on the material's surface characteristics and idealized material qualities. Surface effect study is the novelty, which is conducted in the piezoelectric model and its applications in sensors. The findings of this research may be useful in designing SAW devices. 2025 Wiley-VCH GmbH. -
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. -
Mechanics of love-type surface wave energy transmission in viscous liquid-coated piezomagnetic plate
Purpose: This study investigates Love-type wave propagation in a multilayered structure composed of a viscous liquid (VL) layer, a piezomagnetic (PM) layer, and a heterogeneous half-space (HHS). It considers two models: Model 1 (Terfenol-D) and Model 2 (Cobalt Ferrite). Wave behaviour is analysed under magnetically open (MO) and short (MS) circuit conditions. Methods: The dispersion relation for Love-type waves was derived analytically, and phase velocity graphs were displayed and analysed in Mathematica. A thorough analysis was conducted to establish the impact of critical variables on phase velocity, including material heterogeneity, piezomagnetic coupling, and viscous liquid layer thickness. Findings: Both models show significant effects of VL and PM coupling on phase velocity. Terfenol-D (Model 1) displays higher sensitivity to piezomagnetic effects, while Cobalt Ferrite (Model 2) shows steadier trends. MO and MS conditions yield comparable results, indicating minor boundary effects. Research limitations: The model only considers linear wave transmission and excludes nonlinear effects. Furthermore, the technique is predicated on idealised material properties that account for heterogeneity. Practical Implications: The studys findings can be used to design and develop energy harvesters, sensors, and wave manipulation instruments using PM with viscous liquid coatings. Understanding the behaviour of surface waves, including phase velocity, is essential for efficient application in these frameworks. The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2025. -
Machine learning-enhanced heat and mass transfer study of elliptic motion in piezoelectric thermoelastic plates using Green-Naghdi III and three-phase-lag theories
Rayleigh-type surface waves in piezoelectric (PE) solids are pivotal for acoustic sensors, microelectromechanical systems (MEMS), and non-destructive evaluation. However, classical thermoelastic models fail under high heat flux due to the assumption of infinite thermal signal speeds, which limits their accuracy in coupled thermo-mechanical systems. To capture finite-speed and memory-dependent thermal effects, the Rayleigh wave propagation in a transversely isotropic (TI) PE half-space using generalized theories (such as Green-Naghdi type III (GN-III) and three-phase-lag (TPL)) is studied in this paper. The analytical formulation under varied electrical and thermal boundary conditions has been obtained. Secular equations are derived to characterize phase velocity, attenuation, and specific energy loss. A regression-based machine learning (ML) surrogate model is trained by using an analytical dataset to provide rapid predictions of wave parameters. Additionally, a confusion matrix classifier is applied to identify boundary conditions from simulated wave response features. The results demonstrated that the phase velocity increases with inclination angle and stabilizes with wave number, whereas attenuation and specific loss vary strongly by boundary condition (e.g., minimal in shorted-isothermal cases). The ML surrogate successfully reconstructed analytical predictions with minimal residual error, and the confusion matrix demonstrates accurate classification performance and validates the diagnostic potential of the framework. The novelty of this paper lies in integrating dual thermoelastic theories with machine learning, merging mechanics, heat transfer, and intelligent computing. These findings enable enhanced SAW sensor designs for precise gas/chemical detection, low-loss NDE tools for aerospace composite defect identification, and real-time diagnostics in biomedical ultrasonics for clearer imaging and efficient energy harvesting. 2026 Elsevier Ltd -
Hybrid fractional thermoelasticmachine learning framework for heat and mass transfer in skin tissue: Enhanced simulations using AtanganaBaleanu, CattaneoVernotte models, and KNNSVM classifiers
This study presents a hybrid computational framework that couples advanced fractional thermoelastic modeling with machine-learning-based safety classification for heat and mass transfer in skin tissue. The classical CattaneoVernotte (CV) non-Fourier heat conduction law is extended through the AtanganaBaleanu (AB) fractional operator to capture memory-driven thermal responses, finite thermal wave propagation, and nonlocal biological effects more accurately than traditional Fourier-type formulations. Closed-form expressions are derived using Laplace transforms and inverted numerically to obtain transient temperature, displacement, dilation, and stress fields within the tissue. The AB fractional model demonstrates smoother thermal evolution, reduced overshoot, and lower stress concentrations relative to the CV model, reflecting improved biomedical safety margins during rapid thermal exposure. To enable real-time risk assessment, synthetic datasets generated from the thermoelastic simulations are used to train Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers. The ML models reliably distinguish safe and risky thermal regimes, with SVM offering superior generalization and KNN capturing localized variations. The novelty of this work lies in directly integrating fractional physics-based modeling with machine-learning classification for thermal safety diagnosticsestablishing a unified paradigm for predictive biomedical heat transfer. The framework advances thermal therapy planning, burn-injury prevention, implant design, and smart clinical monitoring. While the current study is based on idealized geometry and simulated data, future extensions will incorporate in-vivo tissue characteristics, complex skin layers, and deep learning models to further enhance clinical applicability. 2025 Elsevier Ltd -
Non-Fourier thermal transport analysis in the human eye using a dual-phase-lag bioheat framework under environmental exposure
Understanding how heat propagates inside the human eye is important for preventing thermal damage during environmental exposure, laser treatments, and biomedical procedures, particularly in hot climates where ocular tissues are vulnerable to temperature rise. Conventional bioheat models based on Fourier heat conduction assume instantaneous heat transfer and may therefore fail to capture delayed thermal responses occurring in heterogeneous biological tissues. The aim of this study is to develop and analytically investigate a dual-phase-lag bioheat model capable of accurately predicting intraocular temperature evolution under combined environmental and physiological thermal loading. Motivated by the need for a more realistic and computationally efficient framework for ocular thermal safety assessmentaligned with Saudi Arabias Vision 2030 goals in healthcare innovation and preventive medicinethis study develops a dual-phase-lag (DPL) bioheat model to analyze heat transport in a multilayer human eye under combined environmental and physiological loading. Closed-form analytical solutions are obtained using normal-mode analysis for all six ocular layers while accounting for convection, evaporation, blood perfusion, and tissue porosity. Results show that the DPL model predicts lower and smoother temperature distributions compared with Fourier and LordShulman models, indicating more physiologically realistic thermal behavior. Ambient temperature and evaporation primarily control heating in anterior eye regions, whereas perfusion and tissue porosity dominate thermal regulation in deeper layers. Sensitivity analysis and thermal-safety maps identify critical combinations of exposure conditions that may increase thermal risk. A surrogate-based reduced-order model is further developed and validated, enabling rapid prediction of intraocular temperature with high accuracy. The study demonstrates that incorporating non-Fourier thermal effects significantly improves prediction of ocular temperature dynamics and provides a practical framework for thermal safety assessment, ophthalmic treatment planning, and climate-adaptive healthcare technologies. 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
A dual-phase-lag mathematical framework with mechanics-informed machine learning for predicting ocular thermal risk under environmental heating
Thermal damage to ocular tissues is a significant medical issue, as even minor increases in temperature can compromise corneal endothelial function, hasten cataract development, and disturb retinal metabolism. The aim of this study is to create a dependable model for forecasting temperature distributions in the human eye during external heat exposure, thereby facilitating safer therapeutic interventions, refined clinical risk evaluation, and improved environmental health surveillance. A dual-phase-lag (DPL) bioheat transfer framework with two relaxation times is created to capture the behavior of thermal waves that travel at a finite speed. Normal-mode analysis is then used to find closed-form analytical solutions for all six ocular layers. Parametric investigations measure the effects of things like temperature, evaporation, porosity, and perfusion. When compared to the LordShulman and Fourier models, DPL is clearly better at predicting thermal responses that are realistic for the body. Complementary thermal-safety mapping, sensitivity analysis, surrogate-model validation, and response-surface visualization offer enhanced engineering insights and expedited predictive capabilities. The study reveals that non-Fourier effects are essential in regulating peak temperatures, and tissue-level parameters substantially affect intraocular thermal loads. The model's limitations consist of axisymmetric geometry and temperature-independent material properties, which could be rectified in forthcoming three-dimensional or patient-specific investigations. This work offers a medically pertinent and computationally efficient methodology for ocular thermal safety, enhancing healthcare modeling, precision diagnostics, and protective measures for populations subjected to extreme thermal conditions. 2026 Wiley-VCH GmbH. -
Sustainable Finance and ESG Investing: A Blueprint for Future Growth in Indias Banking Sector
The paper focuses on sustainability transformation in banking, notably through environmental, social, and governance (ESG) investing. Today, completely profit-focused banks are gradually incorporating sustainability into their basic functions. This is consistent with global initiatives, namely the Paris Agreement, besides the United Nations Sustainable Development Goals. This reflects a deeper understanding that financial activity must be environmentally friendly and socially responsible. With that, banks can now appraise if the banks investment is profitable, good, and sustainable. Reserve Bank of India, being the regulatory authority in India, with the help of the Securities and Exchange Board of India, has also been supportive by laying certain rules that enforce transparency of ESG practice, encouraging green bond issues. Thus, regulations are seen as bringing Indian banks close to world standards and promoting this sustainability culture. The paper also draws upon the importance of integrating ESG factors into risk management systems to mitigate any financial risks that could stem from environmental and social concerns. It further argues that although ESG practices will be beneficial in improving financial performance, their impact does differ by component and should, therefore, be adopted while working in emerging economies such as India. 2026 by Nova Science Publishers, Inc. -
Whale Optimization and AutoML for Precise Phishing Detection
Online fraud and social engineering tactics frequently use phishing websites as platforms. Phishers often modify the source code of the web pages they exploit in their attacks to create the illusion that alterations were made to authentic websites. A solitary response is insufficient to mitigate phishing due to the many methods employed in its execution. This study examines machine learning algorithms and evaluates their efficacy when trained on datasets including attributes that differentiate secure websites from phishing sites. Automated algorithms facilitate real-time fraud protection by swiftly detecting suspicious URLs, domain names, and website content. This study aims to identify the optimal method for detecting a prevalent category of cyberattacks. This would enhance the security and privacy of all internet users by facilitating the identification and blocking of malicious websites. Nonetheless, there is an urgent desire for automated models that provide rapid and precise detection. This research introduces a regression-based assessment method for phishing detection to address this demand. Our approach employs a whale optimization algorithm for feature selection. An AutoML framework subsequently utilizes the selected feature subsets as input. The model showed good accuracy in its predictions with very small errors on the test data, shown by an RMSE of 0.1079, an MSE of 0.0116, and an R2 value of 0.9534. These results demonstrate the reliability of our feature selection and modeling methods. 2025 River Publishers -
Quantum Algorithms for Enhancing Cybersecurity in Computational Intelligence in Healthcare
This book explores the exciting field of quantum computing, which is changing how we approach computation. It covers the basics, cybersecurity aspects, advanced machine learning techniques, and the many ways quantum computing can be used. Quantum computing is much more powerful than traditional computing. The book starts by explaining the core concepts like qubits, quantum gates, superposition, entanglement, quantum memory, and quantum parallelism. One important area is how quantum computing can improve machine learning for cybersecurity. It can handle huge amounts of data and find complex patterns faster than regular computers. This is especially useful for finding cyber threats in real time, such as spotting unusual activity in healthcare networks that might mean a security breach. Quantum machine learning can help healthcare organizations better defend against advanced cyberattacks that try to steal patient data. The book also looks at how quantum computing is changing cybersecurity itself. It discusses quantum cryptography, post-quantum cryptography, and secure communication, explaining how quantum computing is leading to new ways of encrypting data, detecting threats, and protecting information. Beyond cybersecurity, the book shows how quantum computing impacts many other fields, such as medicine, finance, materials science, and logistics. It is poised to revolutionize artificial intelligence (AI) in healthcare and many other sectors. Because quantum computing is constantly developing, with discoveries and new applications happening all the time, this book brings together researchers from universities and industries to share their latest findings. It aims to help shape the future of this technology. The book offers a solid foundation, detailed explanations of advanced techniques, and a fascinating look at how quantum computing is being used in the real world. As quantum computing becomes easier to access through new tools and cloud platforms, this book hopes to inspire new research in AI and spark innovative applications that were previously thought impossible. 2026 selection and editorial matter, Prateek Singhal, Pramod Kumar Mishra, and Mokhtar Mohammed Hasan; individual chapters, the contributors. All rights reserved. -
Real-Time Implementation of Deep Learning Model for Polyp Classification and Segmentation in Medical Imaging
Real-time deep learning models for polyp identification and segmentation in medical imaging. Recognising the limits of current database systems for real-time applications, the research focusses on creating a deep learning model capable of recognising crucial picture components to aid in precise polyp categorisation. The suggested methodology is intended for realtime, practical healthcare and diagnostic applications that need quick polyp detection via preliminary colonoscopy testing. Performance investigation demonstrates that ResNet50 and EfficientNet B2 outperform other models, implying that they are suitable for real-world application and optimal outcomes. 2025 Bharati Vidyapeeth, New Delhi. -
Bridging the Divide: Innovative Pathways to Gender Equality in the Workplace
Gender inequality in the workplace is a global issue, despite several initiatives to close the gap. While some regions have made progress, women continue to face inequities in possibilities for employment, earnings, and leadership responsibilities across sectors. Drawing on important research, including reviews of corporate board quotas and structural reasons of gender imbalance, this chapter investigates the root causes of workplace inequality. It identifies major market and social factors that maintain gender disparities and investigates how organizational policies and public actions might affect systemic change. The chapter proposes tangible solutions to minimize gender disparity and enhance fairness and opportunity for women in the workplace. 2026, IGI Global Scientific Publishing. All rights reserved. -
Navigating uncertainty: how do parenting and grit influence career indecisiveness among Indian emerging adults?
This paper examined how perceived parenting styles (permissive, authoritative, and authoritarian) influenced career indecisiveness among Indian emerging adults, with grit as a mediating factor. A total of 420 Indian emerging adults (18-25 years) were administered self-reported questionnaires. Both PROCESS mediation analyses and path analysis using SPSS AMOS were computed. Both parents authoritative and authoritarian parenting styles predicted career indecisiveness. The passion component of grit emerged as a significant mediator, as supported by the PROCESS mediation analysis. These findings offer new methodological and practical insights, highlighting the significance of culturally relevant career counseling interventions, integrating both familial influences and noncognitive traits. The Author(s), under exclusive licence to Springer Nature B.V. 2025. -
Can Tradition and Ambition Coexist? Unpacking Career and Collective Identity Integration Among Indian Emerging Adults
Developing a meaningful identity requires integrating lived experiences by coordinating the past, present and future identities, integrating multiple personally meaningful identity domains, or aligning ones identities with ones culture to form a coherent sense of self. However, there is a dearth of studies on identity integration across multiple identity domains in the Indian sociocultural context. Even in the West, identity integration is studied using survey methods predominantly in minority populations and on identity domains like ethnicity/religion and religion/sexuality. However, this focus risks overlooking the complexity and nuance of identity experiences, which are often deeply shaped by personally salient and central domains such as career and family. Thus, this paper expands the understanding of identity integration across career and collective identity, considering its relevance among Indian emerging adults using a qualitative approach. Ten emerging adults (1825 years) were purposively selected, and their interviews were analysed using inductive thematic analysis. The narrative accounts revealed career and collective identity integration as a bidirectional phenomenon where family dynamics influence career identities, which, in turn, influence family relations and the reshaping of their worth and communication dynamics. A unique configuration of struggling to balance between family and career emerged as emerging adults negotiated between their desire to stay with family and career pursuits. The impact of financial independence on career identity and parental relationships emerged as another significant aspect. The results discuss theoretical and practical implications for identity research in light of urban Indias unique sociocultural context. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025. -
Artificial intelligence based system and method for management, recommendation, mapping of skill /
Patent Number: 202111054501, Applicant: Durgansh Sharma.Artificial intelligence based system (100) and method for management, recommendation, mapping of skill comprising EmpNet (101), recommender system (102), automated machine learning system (103), skillset dataset (104), optimization system (105), industry interface system (106). The method for management, recommendation, mapping of skill comprising the steps of: a) capturing the required skillset personal data by the panchayat system (701); b) verify the skillset (702).