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Rayleigh-Type Surface Waves in Piezo-Thermoelastic Materials: A Comparative Study Using GreenNaghdi III and Three-Phase-Lag Models with Machine-Learning Surrogates
Abstract: In this work, the GreenNaghdi type III (GN-III) and Three-Phase-Lag (TPL) thermoelastic theories are used to investigate Rayleigh-type surface wave propagation in a transversely isotropic piezo-thermoelastic half-space. Phase velocity, attenuation, and specific loss may be thoroughly evaluated thanks to the derivation of secular equations for electrically open/shorted and thermally insulated/isothermal boundary conditions. The findings indicate that attenuation and loss show a substantial dependency on boundary restrictions and the chosen thermoelastic model, but phase velocity increases with inclination angle and stabilises for high wave numbers. The TPL framework predicts somewhat greater velocities and damping because of thermal relaxation effects, while electrically shorted isothermal surfaces produce the lowest dissipation. By explicitly incorporating governing equations into its loss function, a Physics-Informed Neural Network (PINN) is utilised to overcome the computational burden of solving difficult transcendental equations. The PINN provides an effective stand-in for optimisation and real-time diagnostics in SAW sensors, ultrasonic devices, and smart piezoelectric materials by precisely reconstructing dispersion trends from sparse analytical data. Pleiades Publishing, Ltd. 2025. -
Dispersion Analysis of Love-Type Waves in a Multilayered Piezomagnetic-Heterogeneous Structure with a Viscous Liquid Layer
Abstract: Purpose. This paper examines Love-type wave transmission in a multilayered piezomagnetic (PM) and heterogeneous half-space (HHS) structure with a viscoelastic layer (VL) on top. Wave transmission behaviour is examined in magnetically open (MO) and magnetically closed (MS) circuit boundary conditions. The main study focuses on the dispersion behaviour of phase velocity of a Love-type wave influenced by the combination of VL, PM and HHS. Methods. : The dispersion relation for Love-type waves was determined analytically, and phase velocity graphs were plotted and analysed using numerical simulations with Mathematica software. A comprehensive study was conducted to acquire the effects of significant variables on phase velocity, including material heterogeneity, piezomagnetic coupling, and viscoelastic layer thickness. Findings. : The research results show the attenuation properties of the VL, PM, and HHS materials in MO and MS situations. Graphical comparisons show that piezomagnetic coupling caused the phase velocity curves to alter regularly, indicating its importance in wave propagation. The open and short circuit situations had nearly identical phase velocity, demonstrating that boundary limitations have little effect on how waves propagate. Research limitations. : The model is limited to linear wave transmission and ignores nonlinear effects. Furthermore, the approach is based on idealized material qualities, which account for heterogeneity. Practical Implications. : The studys findings can help build and improve energy harvesters, sensors, and wave manipulation instruments that use PM with viscoelastic coatings. Understanding the behaviour of surface waves is required for effective use in these structures. Novelty. : This article investigates Love-type surface wave transmission in a VL-PM-HHS composite structure that includes a viscoelastic layer, piezomagnetic material, and a heterogeneous half-space. Itexplores how material heterogeneity, piezomagnetic coupling, and viscoelastic attenuation affect phase velocity under magnetic circumstances. Pleiades Publishing, Ltd. 2025. -
Analytical and AINN-based investigation of surface wave propagation in dry long bones with initial stress, magnetic field, and rotation effects
This study presents a comprehensive analytical and artificial intelligence neural network (AINN)-based investigation of wave propagation in dry long bones, modeled as an orthotropic hollow cylindrical structure. The proposed model incorporates the combined effects of initial stress, magnetic field, and rotational motion to capture the complex behavior of bone-like media under coupled physical influences. The governing equations are formulated within a continuum mechanics framework and solved analytically using displacement potential methods, yielding solutions in terms of Bessel functions that satisfy the cylindrical geometry and boundary conditions. Two distinct cases, corresponding to the absence and presence of rotation, are examined to assess the influence of rotational effects on wave dynamics. A detailed parametric analysis is carried out to evaluate the variation of phase velocity and frequency with respect to wave number, initial stress, magnetic parameter, density, and geometric ratios. The analytical results indicate that initial stress enhances wave propagation, while magnetic effects introduce damping and rotation significantly modifies dispersion characteristics. To improve computational efficiency and predictive capability, an AINN model is developed and trained using analytically generated data. The AINN predictions show excellent agreement with the analytical results, as confirmed through parity plots, error analysis, residual distribution, and loss convergence behavior. The novelty of the study lies in the unified analytical-AINN framework that integrates mechanical, electromagnetic, and rotational effects within a single model. The findings provide important insights for wave-based characterization of bone structures and have potential applications in non-destructive evaluation, ultrasonic diagnostics, and advanced biomedical sensing systems. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2026. -
Hybrid fractional thermoelasticmachine learning (KNN, CNN and SVM classifier) framework for heat and mass transfer: A computational mechanics approach
The main goal of this study is to create a single fractional thermoelasticmachine learning framework that can accurately model how heat and stress move through skin tissue over time and automatically sort thermal regimes into safe and dangerous ones. The proposed method combines the AtanganaBaleanu fractional operator with the CattaneoVernotte heat flux law and data-driven classifiers (KNN, SVM, and CNN), and Laplace Transforms techniques to derive generalized thermoelastic formulations capable of capturing finite-speed thermal propagation, memory effects, and nonlocal stress relaxation. This connects strict analytical modeling with smart thermal safety prediction. Closed-form expressions for temperature, displacement, dilation, and stress fields are obtained in the Laplace domain and numerically inverted to evaluate transient responses under thermal shock. All fractional thermoelastic simulations and Laplace inversions were executed in MATLAB R2023a, whereas the machine-learning models (KNN, SVM, CNN) were implemented in Python 3.10 using scikit-learn and TensorFlow. To extend the predictive capacity of the analytical models, simulation-derived datasets are used to train three machine learning classifiersK-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). Comparative analyses through confusion matrices, dispersion maps, ROC curves, residual maps, and bar charts demonstrate that CNN achieves superior nonlinear feature extraction and generalization, SVM provides stable global decision boundaries, and KNN efficiently identifies localized thermalmechanical anomalies. The AB fractional model is shown to suppress temperature overshoot and reduce stress concentration relative to CV, offering safer predictions for biological tissues. The combined fractionalML framework enables rapid classification of safe and risky heating regimes, with potential applications in hyperthermia therapy, burn injury prevention, dermatological laser treatments, and thermal hotspot detection in engineered composites. This study establishes a unified pathway where fractional thermoelastic modeling, deep learning, and classical machine learning synergistically addresses complex biomedical and material thermal interactions. A synthetic dataset generated from fractional ABCV thermoelastic simulations was used for training the ML classifiers. 2026 Elsevier Ltd -
Rheology-dependent surface wave characteristics in an advanced geomaterial flexoelectric plate with viscoelastic coating
This study investigates the transmission of seismic surface waves in a composite framework comprising a viscoelastic layer overlying a flexoelectric material. The study focuses on understanding the impact of different viscoelastic models (Maxwell, Newtonian, and Kelvin-Voigt) and interface conditions (smooth and welded contact) on the damping and dispersion characteristics of these waves. To achieve this, the study employs a variable-separable technique and appropriate boundary conditions to derive complex frequency relations for electrically open and short circuits scenarios. These relations are subsequently divided into real and imaginary parts to examine the dispersion and dampening properties, respectively. Numerical simulations are conducted to analyze the response of flexoelectric coefficient, viscoelastic layer thickness, and bonding parameter on phase velocity and dampening coefficient. The research findings indicate that the attenuation properties of the Maxwell and Newtonian models are lower compared to the Kelvin-Voigt model. Graphical comparisons highlight the influence of viscoelastic models and interface characteristics on wave propagation. This research can help in the development of sensors, energy harvesters, and wave manipulation devices that employ flexoelectric materials with viscoelastic coatings. Knowledge of surface wave dynamics in these structures is vital for their optimal performance. 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved. -
Elucidating the interplay of PPAR gamma inhibition and energy demand in adriamycin-induced cardiomyopathy: In Vitro and In Vivo perspective
Adriamycin is an anticancer anthracycline drug that inhibits the progression of topoisomerase II activity and causes apoptosis. The effective clinical application of the drug is very much limited by its adverse drug reactions on various tissues. Most importantly, Adriamycin causes cardiomyopathy, one of the life-threatening complications of the drug. Altered expression of PPAR? in adipocytes inhibited the glucose and fatty acids uptake by down regulating GLUT4 and CD36 expression and causes cardiotoxicity. Therefore, the influence of Adriamycinin cardiac ailments was investigated in vivo and in vitro. Adriamycin treated rats showed altered ECG profile, arrhythmic heartbeat with the elevated levels of CRP and LDH. Dysregulated lipid profiles with elevated levels of cholesterol and triglycerides were also observed. Possibilities of cardiac problems due to cardiomyopathy were analyzed through histopathology. Adriamycin treated rats showed no signs for atheromatous plaque formation in aorta but disorganized cardiomyocytes with myofibrillar loss and inflammation in heart tissue, indicative of cardiomyopathy. Reduced levels of antioxidant enzymes confirmed the incidence of oxidative stress. Adriamycin treatment significantly reduced glucose and insulin levels, creating energy demand due to decreased glucose and insulin levels with increased fatty acid accumulation, ultimately resulting in oxidative stress mediated cardiomyopathy. Since PPARs play a vital role in regulating oxidative stress, the effect of Adriamycin on PPAR? was analyzed by western blot. Adriamycin downregulated PPAR? in a dose-dependent manner in H9C2 cells in vitro. Overall, our study suggests that Adriamycin alters glucose and lipid metabolism via PPAR? inhibition that leads to oxidative stress and cardiomyopathy that necessitates a different therapeutic approach. 2024 Wiley Periodicals LLC. -
Computational Study of MHD Nanofluid Flow with Effects of Variable Viscosity and Non-uniform Heat Generation
The thermodynamic study of an unsteady two-dimensional nanofluid flow through a permeable stretched sheet is looked at. Water is used as the primary fluid, along with four different nanoparticles, including copper (Cu), titanium dioxide (TiO2), copper oxide (CuO), and aluminium oxide (Al2O3). The heat transfer phenomenon is explained by an outside source. Additionally considered are the impacts of heat generation and absorption. A similarity transformation is used to convert the considered set of mathematical equations into a system of ODEs. The BVP4C method is then mathematically applied, coupled with shooting fashion. The results are given for cases involving copper nanoparticles. The effects of various physical parameters on the profiles of the dimensionless flow field, temperature, average entropy generation function, skin friction, and the Nusselt number are examined with illustrations and thorough explanations. As exceptional circumstances of the current inquiry, there is a strong agreement between the current conclusion and the findings of the other researchers. The average entropy generation number, temperature, and velocity profiles are shown to be strongly influenced by regulating factors. The authors conclude that the average entropy production number decreased in the existence of a temperature- and space-dependent heat source/sink, but it increased with increasing the viscosity parameter. 2023, The Author(s), under exclusive licence to Springer Nature India Private Limited. -
An Efficient and Robust Explainable Artificial Intelligence for Securing Smart Healthcare System
The advent of IoT technologies has a tremendous impact on the healthcare sector enabling efficient monitoring of patients and utilizing the data for better analytics. Since every activity related to a patients health is monitored, the focus on smart healthcare applications has significantly transferred from service provision to a security perspective. As most healthcare applications are automated security plays a vital role. The technique of machine learning has been widely used in securing smart healthcare systems. The major challenge is that these applications require high-quality labeled images, which are difficult to acquire from real-time security applications. Further, it highly time-consuming and cost-expensive process. To address these constraints, in this paper, we define an efficient and robust explainable artificial intelligence technique that takes a small quantity of labeled data to train and de-ploy the security countermeasure for targeted healthcare applications. The proposed approach enhances the security measure through the detection of drifting samples with explainability. It is observed that the proposed approach improved accuracy, high fidelity, and explanation measures. Also, this approach is proven to be considerably resistant against numerous security threats. 2023 IEEE. -
Leveraging Big Data and AI for Optimizing Health Insurance Claims and Risk Assessment in Healthcare Financing
This research elucidates the transformative potential of big data analytics and artificial intelligence in optimizing health insurance claims and risk assessment by employing an empirically robust framework encompassing reliability and validity metrics, Heterotrait-Monotrait Ratio (HTMT) analysis, and bootstrapping to unravel the intricate interdependencies among constructs such as AI model accuracy, claims processing efficiency, cost efficiency, data quality, fraud detection accuracy, system usability, and user trust interface, thereby advancing a comprehensive understanding of the systemic synergies that enhance predictive precision, operational scalability, and equitable resource allocation within the healthcare financing paradigm. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Religious Freedom in the Age of AI: A Constitutional Law Perspective
This chapter examines the developing linkage between religious freedom and artificial intelligence (AI) in the field of constitutional law. As AI systems play a growing role in filtering access to information, censoring speech, and shaping governance, they pose remarkable opportunities while also constituting major challenges to the freedom of religion or belief (FoRB). The chapter considers how AI can improve religious expression and inter- religious dialogue, and then it identifies concerns, such as algorithmic bias, digital censorship, and surveillance of religious groups. It also examines constitutional structures and case laws, with special attention to the balance between technological innovation and fundamental rights protection. By promoting ethically designed AI systems, inclusive governance and effective legal protections, this chapter suggests elements of such a normative blueprint to ensure that religious freedom continues to be affirmed in the digital era. It ends with future- oriented approaches and how AI regulation could be aligned with FoRB standards globally. 2026 by IGI Global Scientific Publishing. All rights reserved. -
A comparative study of leadership styles between public and private sector
This dissertation project is a descriptive research, it focuses on the differences in leadership styles between public and private sector, also examining whether leaders learn more towards being people oriented or task oriented. Task-oriented leaders prioritize towards goal achievement and tend to adopt more hands off approach when it comes to managing people. In contrast, people oriented leaders demonstrate concern for subordinates exhibiting warmth and support, but are less involved in task management. The project starts with the study of three banks of each public and private sector and taking interview given by 5 leaders from each bank to know about what kind of leadership style they possess based on which it could be determined how close they are with people and how much importance they give to the achievement of task. 2025, IGI Global Scientific Publishing. -
Are expensive decisions impulsive? Young adults impulsive housing and real estate buying behavior in India
Purpose: The purpose of the study is to determine website quality, materialism, psychological factors, hedonic value and social media as factors that influence the young adults impulsive housing and real estate buying behavior in India. In addition, this study also measures the mediating effects of social media influence between psychological factors and hedonic value and young adults impulsive housing and real estate buying behavior. Design/methodology/approach: Related literature, quantifiable variables with a five-point Likert Scale, hypothesis testing and mediators are used to study the model. A systematic questionnaire that was divided into six sections was used. A total of 385 valid responses were collected and analyzed through a structural equation model. Findings: The results suggest that materialism, psychological factors and social media have a considerable impact on young adults impulsive housing and real estate buying behavior. The findings also ascertained that website quality and hedonic value do not have a considerable impact on young adults impulsive housing and real estate buying behavior. Research limitations/implications: This study is limited to the responses of young consumers from a limited number of brokers and regions in India. Future studies could be more widespread across the globe. Originality/value: As per the review of existing literature, this research is the first, to the best of the authors knowledge, to determine the factors affecting the impulse buying decision mainly in the housing and real estate sector with the target consumers being young. 2022, Emerald Publishing Limited. -
Diabetic Retinopathy Diagnosis Using Retinal Fundus Images through MobileNetV3
Diabetic Retinopathy (DR) is a major disease throughoutthe world. Diagnosis of diabetes at an early stage is so critical and could help save several lifestyles. One out of two individuals experiencing diabetes has been determined to have some phase of DR. Recognition of DR symptoms in time can turn away the vision weakness inmost the cases, nonetheless, such disclosure is troublesome with present devices and strategies. Existingmethods for determining whether a person is suffering from diabetes or maybe the chances of acquiring diabetesrely heavily on examiners. Most of the time, it can be treated if caught during the early stages. There is a need for creating models that are efficient and robust to detect DR holistically. In recent times the advent of Deep learning models has been used extensively in various Bio medical applications. In this work, we utilize a Hyper parameter tuned MobileNet-V3 model based on a multi-stage Convolutional Neural Network (CNN) to efficiently classify images from the IRDID dataset. A Multiclass classification model involving images collated from various sources were trained, validated and tested for classification accuracy. The network was evaluated based on parameters and the network was able to achieve an accuracy of 88.6% 2023 IEEE. -
Reversing Cell Aging - Understanding the Causes and Potential Remedies, a Current Perspective
To retain vigor, vitality and youthfulness has always been cherished as the dream of mankind from time immemorial. Scientists have been on the quest to find out the reasons for aging so that remedial measures can be thought of. With the current pace of developments in this domain, the possibility of extending one's expiry date is not so far off in the future. Through reverse cell aging, the concept of everlasting youth might no longer be a fantasy. Aging is the net result of reduction in the efficiency of physiological activities in our body essentially due to wear and tear. At the molecular level, the reasons for this include telomere shortening, oxidative stress, glycation, protein aggregation, mutation etc. Progressive damage due to these underlying causes result in pathological manifestation resulting in cell aging. The current review focuses on the causes of cellular aging, both genetic and epigenetic and discusses various approaches like telomere reactivation, epigenetic age reversal and hyperbaric oxygen treatment to reverse that condition. 2022 World Research Association. All rights reserved. -
Artificial Intelligence in Green Transportation: A Conceptual Review
[No abstract available] -
Transformational innovation technologies for regenerative bioeconomy: Case study on green initiatives for tourism logistics service providers
The tourism industry has negative environmental consequences, overshadowing the regenerative bioeconomy. Climate change, land degradation, and resource depletion are significant challenges. Excessive use of non-biodegradable resources threatens the planet, requiring bio-based resources. It is critical to transition to and reuse bio-based resources. In this chapter, the regenerative bioeconomy has a wide- reaching impact on accomplishing SDG 6, 8, 11, and 12, with a focus on the circular economy's involvement in tourism logistics. Investments in talent development, digital technologies, and partnerships are needed to realise bioeconomy potential. Engaging local communities and implementing sustainable business practices can reduce energy use and environmental impact. Digital transformation requires technological advancements, foreign investment, and active participation from all stakeholders. This chapter tries to explain the complicated interplay between the regenerative bioeconomy, tourism logistics, and sustainable practises intertwining. 2023, IGI Global. -
2-(6-Chloro-2,3,4,9-tetrahydro-1H-carbazol-1-yl-idene)propanedinitrile
The molecular conformation of the title compound, C15H 10ClN3, is stabilized by an intramolecular N-H?N hydrogen bond with an S(7) ring motif. The crystal packing is controlled by N-H?N and C-H?N intermolecular interactions. One of the methylene groups of the cyclohexene ring is disordered over two positions with refined occupancies of 0.457 (12) and 0.543 (12). -
Hybrid sparse and block-based compressive sensing algorithm for industry based applications
Image reconstructions are a challenging task in MRI images. The performance of the MRI image can be measure by following parameters like mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). Compromising the above parameters and reconstructing the MRI image leads to false diagnosing. To avoid the false diagnosis, we have combined sparse based compressive sensing and block-based compressive sensing algorithm, and we introduced the hybrid sparse and block-based compressive sensing algorithm (HSBCS). In compressive stage, however, image reconstruction performance is decreased, hence, in the image reconstruction module, we have introduced convex relaxation algorithm. This proposed algorithm is obtained by relaxing some of the constraints of the original problem and meanwhile extending the objective function to the larger space. The performance is compared with the existing algorithm, block-based compressive sensing algorithm (BCS), BCS based on discrete wavelet transform (DWT), and sparse based compress-sensing algorithm (SCS). The experimentation is carried out using BRATS dataset, and the performance of image compression HSBCS evaluated based on SSIM, and PSNR, which attained 56.19 dB, and 0.9812. Copyright 2024 Inderscience Enterprises Ltd. -
Teleneuropsychometry Solution in Resource-Constrained Setting An Initial Experience in Adults With Brain Tumors
Background. Teleneuropsychometry constitutes a sophisticated technological innovation that enhances access to specialized neuropsychological services for patients situated in geographically remote or resource-limited contexts. When optimally utilized, teleneuropsychometry emerges as an advanced modality for bridging the gap between patient and neuropsychologists, facilitating timely preoperative cognitive evaluations. Methods. The study delineates two case reports of brain tumor patients who underwent teleneuropsychometric assessment prior to surgical interventions while also critically analyzing the complexities inherent in establishing such a service. Results. Both patients successfully completed the assessments with minimal assistance, providing valuable insights into their cognitive abilities. These insights enabled the medical team to customize surgical planning and anticipate potential risks. Conclusion. The findings reinforce the growing body of evidence supporting the feasibility of teleneuropsychometric assessments in a resource-constrained environment and highlights their broader applicability within the domains of neuro-oncology. 2025. Sekar et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC-BY). -
Student and Teacher Perceptions on AI Integration in Indian Higher Education: A Qualitative Stakeholder Study
This study aimed to explore the perceptions of students and educators in Indian universities regarding the integration of Artificial Intelligence (AI) into higher education, focusing on its role in learning and assignments. The research examined the attitudes and beliefs about AI, particularly in terms of its impact on pedagogical approaches and student learning outcomes. Specifically, the study explored the ethical implications, perceived benefits, and challenges associated with AI usage in the Indian educational context. A phenomenological approach was employed, with data collected through semi-structured interviews and focus group discussions involving students and educators across diverse academic disciplines. Thematic analysis identified key patterns in participants' experiences, revealing both enthusiasm for AI's potential to personalize and enhance education and concerns over its implications for critical thinking, privacy, and educational equity. The findings offer insights for the development of AI-related educational policies in India. 2026, IGI Global Scientific Publishing. All rights reserved.