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RayleighBard Convection in a Bi-viscous Bingham Fluid with Weak Vertical Harmonic Oscillations: Linear and Non-linear Analyses
Linear and weakly non-linear stability analyses of RayleighBard convection in a bi-viscous Bingham fluid layer are performed in the presence of vertical harmonic vibrations. In the linear analysis, expression is obtained for the correction Rayleigh-number arising due to the vibrations. The non-linear-analysis based on the GinzburgLandau equation is used to compute the Nusselt-number in terms of the correction Rayleigh-number. The mean-Nusselt-number is then obtained as a function of the scaled-Rayleigh-number, the frequency and the amplitude of modulation, the Prandtl number, and the bi-viscous Bingham fluid parameter. The non-autonomous amplitude-equation is numerically solved using the RungeKuttaFehlberg45 method. It is found that the influence of increasing the amplitude of modulation is to result in a delayed-onset situation and thereby to an enhanced-heat-transport situation. For small and moderate frequencies, the influence of increasing the frequency of oscillations is to decrease the critical Rayleigh-number. However, the mean-Nusselt-number decreases with increase in the frequency of oscillations only in the case of small frequencies. An increase in the value of the bi-viscous Bingham fluid parameter leads to advanced-onset and thereby to an enhanced-heat-transport situation. At very large frequencies, the effect of modulation on onset and heat-transport ceases. 2023, The Author(s), under exclusive licence to Springer Nature India Private Limited. -
Green Growth Nexus: Analysing Environmental Performance and GDP Trends in OECD Economies
Subject and Purpose of the Work: Over the past two decades, OECD countries such as Italy, Germany, France, and the United Kingdom have experienced consistent economic growth, averaging 2% annually in GDP. This upward trend has been driven by various factors, including government spending, investment rates, and favourable global conditions. Recently, environmental performance has emerged as a critical factor influencing economic development. This study aims to examine the relationship between environmental performance indicators and GDP growth in selected OECD countries, focusing on the growing emphasis on environmental sustainability. Materials and Methods: The analysis uses panel data from the OECD and World Bank, spanning 25 years (20002024), for four OECD nations. The study employs a Panel Autoregressive Distributed Lag (ARDL) model, which allows for the estimation of both short-run and long-run dynamics. GDP growth is the dependent variable, while the independent variables include environmental tax revenue (TAX), greenhouse gas emissions (EMI), air quality (QUA), government expenditure on environmental protection (EXP), and the share of renewable energy in total energy supply (REN). Results: The empirical findings indicate that TAX and EXP have minimal positive impact on GDP growth, suggesting potential inefficiencies in the allocation or effectiveness of environmental funds. In contrast, other indicators such as air quality and renewable energy share show a stronger link with economic growth. Conclusion: The study highlights the growing significance of environmental performance in shaping economic outcomes. It contributes to the sustainable development literature by demonstrating that targeted environmental efforts can positively influence long-term economic growth. 2025 K Keerthana et al., published by John Paul II University of Applied Sciences. -
Detection of picric acid in industrial effluents using multifunctional green fluorescent B/N-carbon quantum dots /
Journal of Environmental Chemical Engineering, Vol.10, Issue 2, ISSN No: 2213-3437.
Carbon quantum dots have recently gained widespread attention due to their excellent physicochemical features. The rapid escalation in the dumping of hazardous chemicals into water, spurred demand for developing efficient and selective sensors for toxic chemicals. Herein, we have developed a novel fluorescence sensor for picric acid which is a major pollutant in industrial effluents. The new strategy exploits the development of a fluorescence sensor based on N-doped carbon quantum dots (N-CQDs) followed by boron functionalization. The N-CQDs were synthesized in a rapid single-step microwave technique by employing L-serine and citric acid. -
Pharmaceutical Waste-Derived Carbon Quantum Dots via Microwave Method for Selective Au3+ Ion Detection
Heavy metals released in various ways into water bodies is a key concern for environmental protection. Their nonbiodegradability and the health risks associated with these heavy metals in the environment exacerbate the problem. Scientists worldwide are addressing the issue through various approaches. Among them, the fluorescence approach is unique in its simplicity and rapid results. In this study, carbon quantum dots (CQDs) were synthesized from an expired vitamin B12 tablet using a simple microwave-assisted approach. The resulting CQDs showed sustainability, good photostability with quantum yield (30.08%), water solubility, prolonged storage stability, and produced a brilliant blue emission when exposed to UV light. These CQDs exhibited stable photoluminescence attributes across a wide range of ionic strengths and pH levels. Au3+ ions effectively quenched the PL intensity of CQDs in a linear, selective, and sensitive approach. We demonstrated Au3+ sensing in aqueous conditions utilizing CQDs as the fluorescent probe, with a limit of detection of 41nM. This work has been demonstrated to create an efficient and cost-effective method for detecting auric ions in wastewater effluents. 2025 Wiley-VCH GmbH. -
Analytical study of triple diffusive convection in a bi-viscous Bingham fluid layer using Ginzburg-Landau model
In this paper, considering bi-viscous Bingham as the base fluid, we study the thermophysical-properties (such as density, specific heat, thermal conductivity, thermal diffusivity, and thermal expansion) with different combinations of salts among NaCl, KCl, CaCl2, and NaCl2 of triple diffusive convection in a bi-viscous Bingham fluid layer with heat as one of the diffusing components. A weakly non-linear case is formulated to facilitate a solution to the problem using a series solution Ginzburg-Landau model. With regard to single, double, and triple diffusive convection, the tables are made to record the actual values of thermophysical-properties together with the critical Rayleigh-number for each combination of aqueous-salt solutions. This computation calculates the mean Nusselt and Sherwood numbers to quantify the systems heat- and mass-transfers for various aqueous-solutions. The effect of the bi-viscous Bingham fluid parameter, for small and large values, for different aqueous-solutions, in single, double, and triple diffusive convection has been captured via 2-dimensional (2D) and 3-dimensional (3D) figures and the results are recorded and compared. The investigation reveals that the heat- and mass-transfers increase with an increase or decrease in the bi-viscous Bingham fluid parameter, which in turn depends on the values of (Formula presented.) and (Formula presented.) The results confirm that the heat- and mass-transfers are least for the combination of KCl with CaCl2 and maximum for the combination of NaCl with other salts. 2024 Taylor & Francis Group, LLC. -
Clinical Intelligence: Deep Reinforcement Learning for Healthcare and Biomedical Advancements
Deep reinforcement learning (DRL) is showing a remarkable impact in the healthcare and biomedical domains, leveraging its ability to learn complex decision-making policies from raw data through trial-and-error interactions. DRL can effectively extract the characteristic information in the environment, propose effective behavior strategies, and correct errors that occurred during the training process. Targeted toward healthcare professionals, researchers, and technology enthusiasts, this chapter begins with notable applications of DRL in healthcare, including personalized treatment recommendations, clinical trial optimization, disease diagnosis, robotic surgery and assistance, mental health support systems, chronic disease management and scheduling, and a few more. It also delves on challenges such as data privacy, interpretability, regulatory compliance, validation, and the need for domain expertise to ensure safe and effective deployment. Next, the chapter seamlessly transitions into DRL algorithms contributing to the biomedical field which are gaining traction due to their potential to provide timely and personalized interventions. Over time, the research community has proposed several methods and algorithms within the field of deep reinforcement learning that help agents learn optimal policies from rich data. Healthcare data is often complex, high-dimensional, and unstructured, such as medical images, genomics data, and patient records. The healthcare-suitable DRL algorithms such as Q-learning, SARSA, Bayesian, actor-critic, reinforcement learning (RL), Deep-Q-Networks (DQN), and Monte Carlo Tree Search (MCTS) are highlighted. In addition, the section offers guidelines for the application of DRL to healthcare and biomedical problems, aiming at providing indications to the designer of new applications in order to choose among different RL methods. Furthermore, a case study is included to fully realize the revolutionary benefits of DRL in healthcare environments, aiming to bridge the gap between theory and practice. The case study presents a remarkable impact on categories such as precision medicine, dynamic treatment regime, medical imaging, diagnostic systems, control systems, chat-bots and advanced interfaces, and healthcare management systems. 2024 Scrivener Publishing LLC. -
Landholding size, indebtedness, and crop insurance in India: A macro-level quantitative assessment
Keerthikumara SM, Saikia B, Hiremath C. 2025. Landholding size, indebtedness, and crop insurance in India: A macro-level quantitative assessment. Asian J Agric 9: 377-390. Indian farmers continue to face structural distress driven by low income, high indebtedness, and inadequate risk protection. This study investigates the interrelationship between landholding size, agricultural credit, indebtedness, and crop insurance uptake using state-level secondary data from 2016 to 2023, drawn from Agricultural Statistics at a Glance, PMFBY/RWBCIS dashboards, and NCRB reports. Using descriptive statistics, linear regression, and paired t-tests, we identify key macro-level trends across 20 major Indian states. Results show that marginal and small farmers (less than 2 hectares) account for over 62.7% of all indebted farm households, but receive only 38.5% of total institutional agricultural credit. A bivariate regression reveals that a ?1,000 increase in monthly farm income is associated with a reduction of 1,314 indebted households (?=-0.445, p=0.016). Despite substantial credit disbursal in high-debt states like Andhra Pradesh and Telangana, farmer debt remains elevated, underscoring that credit alone does not reduce vulnerability. Crop insurance enrollment increased after the 2020 policy shift from mandatory to optional participation among loanee farmers, yet the change was not statistically significant (p=0.099). Actuarial analysis reveals that in several states, claim settlement ratios remain below 50%, with high premiums and delayed payouts fueling distrust. The study recommends fully subsidized premiums for marginal farmers, region-specific pricing, improved claim transparency, and financial literacy integration with agricultural extension. Effective risk mitigation in agriculture must go beyond insurance and integrate income and credit reforms to ensure equitable protection for Indias most vulnerable farmers. 2025, Smujo International. All rights reserved. -
Formula One Race Analysis Using Machine Learning
Formula One (also known as Formula 1 or F1) is the highest class of international auto-racing for single-seater formula racing cars sanctioned by the Fation International de automobile (FIA). The World Drivers Championship, which became the FIA Formula One World Championship in 1981, has been one of the premier forms of racing around the world since its inaugural season in 1950. This article looks at cost-effective alternatives for Formula 1 racing teams interested in data prediction software. In Formula 1 racing, research was undertaken on the current state of data gathering, data analysis or prediction, and data interpretation. It was discovered that a big portion of the leagues racing firms require a cheap, effective, and automated data interpretation solution. As the need for faster and more powerful software grows in Formula 1, so does the need for faster and more powerful software. Racing teams benefit from brand exposure, and the more they win, the more publicity they get. The papers purpose is to address the problem of data prediction. It starts with an overview of Formula 1s current situation and the billion-dollar industrys history. Racing organizations that want to save money might consider using Python into their data prediction to improve their chances of winning and climbing in the rankings. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Reinforcement Q Learning for Terrain-Energy-Aware Lunar Rover Navigation
Effective lunar navigation is difficult in rough terrain and scarce energy resources. Classical path-planning has difficulty with terrain adaptation and energy optimization. This work introduces a Reinforcement Learning (RL)-based solution for energy-optimal lunar rover navigation based on NavCam data from Chandrayaan-3. A Q-learning framework translates terrain characteristics - elevation, slope, and hazards - into a reward scheme, balancing safe travel, minimal energy consumption, and mission effectiveness. The RL agent learns to respond to varying conditions, punishing dangerous regions such as craters and slopes. Simulations on lunar grids demonstrate better energy efficiency and accuracy than traditional approaches. This research pushes autonomous planetary exploration forward, optimizing rover navigation with actual mission imagery for future lunar missions. 2025 IEEE. -
From maximum force to the field equations of general relativity and implications
There are at least two ways to deduce Einstein's field equations from the principle of maximum force c4/4G or from the equivalent principle of maximum power c5/4G. Tests in gravitational wave astronomy, cosmology, and numerical gravitation confirm the two principles. Apparent paradoxes about the limits can all be resolved. Several related bounds arise. The limits illuminate the beauty, consistency and simplicity of general relativity from an unusual perspective. 2022 World Scientific Publishing Company. -
Physics of Gravitational Waves: Sources and Detection Methods
[No abstract available] -
Performing Arts Teaching Pedagogies and Models Evolved During COVID-19
As academicians and teachers at global institutions were scrambling to handle challenges in the wake of COVID-19, online tools such as Zoom, Webex, and Teams along with course management systems like Moodle and Blackboard were adept in meeting the effective synchronous and asynchronous teaching-learning processes in schools in different parts of the world. Meanwhile, the performing arts discipline coped with the situation through some innovative performance projects and pedagogies. This chapter explores those innovative and hybrid pedagogies introduced and experimented by different professors at the Department of Performing Arts, Music and Theatre at Christ University and related institutions in Bangalore, India. Several faculty members are interviewed to find out the innovative pedagogies and strategies they have designed and implemented, along with their plans to use those pedagogic models in the post-pandemic scenario. These new insights and models would contribute to the body of knowledge, especially to teaching-learning processes in the performing arts discipline. 2025 selection and editorial matter, Kennedy Andrew Thomas and Joseph Varghese Kureethara; individuals, the contributors. -
Crisis management, destination recovery and sustainability: Tourism at a crossroads
The COVID-19 pandemic brought travel to a halt and the global tourism industry has been one of the sectors hit hardest during the pandemic. This book looks at how the tourism industry can enhance its resilience and prepare for future crises more effectively. The book provides insights into the economic, social, geopolitical and environmental implications of the COVID-19 pandemic on the tourism and hospitality industries and the responses in diverse international contexts. It highlights key concepts and includes cases with real-life applications. The book also discusses future research directions in a post-pandemic scenario. This book will be an invaluable resource for practitioners in the areas of tourism and crisis management and for readers to compare and contrast tourism destination recovery and crisis management practices through different research methodologies and settings. 2023 selection and editorial matter, James Kennell, Priyakrushna Mohanty, Anukrati Sharma and Azizul Hassan. All rights reserved. -
Introduction: Tourism at a crossroads
[No abstract available] -
Bridging Travel and Learning: Can Ecotourism Be a Resource for Learning Sustainability?
This study explores the role of ecotourism as an educational tool for catering to experiential learning and sustainability awareness. Using a qualitative imaginary narrative method, two ecotourism scenarios are constructed based on insights from two expert environmentalists and existing research. The constructed scenarios were exclusive to undergraduate students from the life sciences and humanities. Using framework analysis, the obtained data were mapped to the SDG framework to understand students' engagement with nature- based learning and sustainability- focused activities, highlighting their emotional connections to nature and behavioural shifts toward sustainability. The framework analysis mapped the scenarios to relevant SDGs, analysing how they cater to ESD. The findings align with SDG 4 (Quality Education), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action), emphasising ecotourism's potential in education. This research contributes to discussions on integrating sustainability learning into educational frameworks. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Facial Recognition Model Using Custom Designed Deep Learning Architecture
Facial Recognition is widely used in some applications such as attendance tracking, phone unlocking, and security systems. An extensive study of methodologies and techniques used in face recognition systems has already been suggested, but it doesn't remain easy in the real-world domain. Preprocessing steps are mentioned in this, including data collection, normalization, and feature extraction. Different classification algorithms such as Support Vector Machines (SVM), Nae Bayes, and Convolutional Neural Networks (CNN) are examined deeply, along with their implementation in different research studies. Moreover, encryption schemes and custom-designed deep learning architecture, particularly designed for face recognition, are also covered. A methodology involving training data preprocessing, dimensionality reduction using Principal Component Analysis, and training multiple classifiers is further proposed in this paper. It has been analyzed that a recognition accuracy of 91% is achieved after thorough experimentation. The performance of the trained models on the test dataset is evaluated using metrics such as accuracy and confusion matrix. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Interacting Dark Energy and Its Implications for Unified Dark Sector
Alternative dark energy models were proposed to address the limitation of the standard concordance model. Though different phenomenological considerations of such models are widely studied, scenarios where they interact with each other remain unexplored. In this context, we study interacting dark energy scenarios (IDEs), incorporating alternative dark energy models. The three models that are considered in this study are time-varying ?, Generalized Chaplygin Gas (GCG), and K-essence. Each model includes an interaction rate ? to quantify energy density transfer between dark energy and matter. Among them, GCG coupled with an interaction term shows promising agreement with the observed TT power spectrum, particularly for ?<70, when ? falls within a specific range. The K-essence model (??0.1) is more sensitive to ? due to its non-canonical kinetic term, while GCG (??1.02) and the time-varying ? (??0.01) models are less sensitive, as they involve different parameterizations. We then derive a general condition when the non-canonical scalar field ? (with a kinetic term Xn) interacts with GCG. This has not been investigated in general form before. We find that current observational constraints on IDEs suggest a unified scalar field with a balanced regime, where it mimics quintessence behavior at n<1 and phantom behavior at n>1. We outline a strong need to consider alternative explanations and fewer parameter dependencies while addressing potential interactions in the dark sector. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Conformal Invariance and Phase Transitions: Implications for Stable Black Hole Horizons?
Abstract: The behavior of black hole horizons under extreme conditionssuch as near collapse or phase transitionsremains less understood, particularly in the context of soft hair and Aretakis instabilities. We show that the breakdown of conformal symmetry during the balding phase induces a topological reorganization of the horizon, leading to divergent entropy corrections and emergent pressure terms. These corrections exhibit universal scaling laws, analogous to quantum phase transitions in condensed matter systems, with extremal limits functioning as quantum critical points. Interestingly, by employing quasi-equilibrium boundary conditions, one could stabilize horizon dynamics without explicitly introducing ad hoc higher-order corrections, further limiting the universal applicability of conformal invariance in black hole physics. Pleiades Publishing, Ltd. 2025. -
QuintessenceChameleon transitions in anisotropic Kiselev model of neutron stars
We investigate a chameleon scalar field dynamically interacting with a Kiselev-type metric, where the static anisotropic fluid part of the metric is replaced by a density-dependent scalar field nonminimally coupled to curvature. This construction enables a transition from screened behavior in high-density regions where the scalar acquires an effective mass m???1?2 to unscreened quintessence dynamics at large scales, characterized by a critical screening radius rcrit?m??1. By solving the modified TolmanOppenheimerVolkoff equations under spherical symmetry, we show that radial scalar gradients (?r?) induce pressure anisotropies (?p?r?1) in neutron star envelopes, while deviations from general relativity (GR) are suppressed deep in the core (r -
Parametric effect on machining characteristics of laser machined Al7075TiB2 in-situ composite
The effect of laser parameters on the machining characteristics of an Al7075 based in-situ metal matrix composite reinforced with Titanium diboride(TiB2) is investigated. The cutting speed (at 10001200 m/hr), stand-off distance (SOD) (0.30.5 mm), and gas pressure (0.50.7 bar) were studied. Scanning electron microscopy (SEM) was used to validate the machining behaviour of in-situ composites. Surface roughness and dimensional error decrease as the SOD increases up to 0.4 mm, but both increases as the SOD increases to 0.5 mm. whereas the volumetric material removal rate (VMRR) increases up to 0.4 mm SOD and then decreases as SOD increases (0.5 mm). Surface roughness, VMRR, and dimensional error were all found to increase with laser speed. Surface roughness and dimensional error increase as gas pressure increase up to 0.5 bar, then decreases. The VMRR, on the other hand, increased continuously as the assist gas pressure increased. Copyright 2021 Inderscience Enterprises Ltd.