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A ratiometric fluorescent sensor based on dual-emissive carbon dot for the selective detection of Cd2+
Cadmium (Cd2+), a heavy metal ion used in numerous industries, has toxic adverse effects on the environment; it is crucial to develop a quick and reliable method for Cd2+ determination. Fluorescent biomass-derived carbon quantum dots (CD) with rich carboxyl groups on the surface were synthesized using water amaranth leaves by hydrothermal method with a 12.1% quantum yield. The surface of CD was further modified with 1-pyrene carboxaldehyde (PC) to synthesize pyrene carboxaldehyde-carbon quantum dots (PC-CD). This study developed a fluorescent ratiometric nanosensor using a covalently functionalized CD with pyrene derivative and demonstrates highly selective identification capability towards Cd2+ over competing metal ions. The Nano sensor has significant selectivity towards Cd2+ in an excellent linear range of 0-70 ?M with a detection limit as low as 15 nM and demonstrates excellent water solubility and biocompatibility. Transmission electron spectroscopy (TEM), Fourier Transform infrared spectroscopy (FT-IR), and X-ray photon spectroscopy (XPS) were used to identify the surface functionalization of PC-CD. Finally, the developed ratiometric sensor was used for detecting Cd2+ metal ions from various water effluents. 2023 Elsevier Ltd. -
Fluorescein Based Fluorescence Sensors for the Selective Sensing of Various Analytes
Fluorescein molecules are extensively used to develop fluorescent probes for various analytes due to their excellent photophysical properties and the spirocyclic structure. The main structural modification of fluorescein occurs at the carboxyl group where different groups can be easily introduced to produce the spirolactam structure which is non-fluorescent. The spirolactam ring opening accounts for the fluorescence and the dual sensing of analytes using fluorescent sensors is still a topic of high interest. There is an increase in the number of dual sensors developed in the past five years and quite a good number of fluorescein derivatives were also reported based on reversible mechanisms. This review analyses environmentally and biologically important cations such as Cu2+, Hg2+, Fe3+, Pd2+, Zn2+, Cd2+, and Mg2+; anions (F?, OCl?) and small molecules (thiols, CO and H2S). Structural modifications, binding mechanisms, different strategies and a comparative study for selected cations, anions and molecules are outlined in the article. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Weakly Non-linear Stability Analysis of Triple-Diffusive Convection in a Bi-viscous Bingham Fluid Layer with Cross-Diffusion Effects
The paper investigates the impact of cross-diffusion on triple-diffusive convection in a bi-viscous Bingham fluid layer. Non-linear stability analysis is performed, and the expression of the critical-Rayleigh-number is obtained, resulting in an analytical solution of the Ginzburg-Landau model (GLM). The coefficients in the GLM involve the scaled Rayleigh-number, the solutal Rayleigh-numbers, the solutal diffusivity rates, the bi-viscous Bingham fluid parameter, and the cross-diffusion parameters. The solutal Rayleigh-numbers, the solutal diffusivity rates, and the bi-viscous Bingham fluid parameter alone determine the critical-Rayleigh-number, which provides the condition for the stationary onset. The neutral curves for the stationary mode are examined. It is found that the solutal diffusivities and bi-viscous Bingham fluid parameter advance the onset of convection, whereas the solutal Rayleigh-numbers delay it. The Nusselt number, Nu, and the Sherwood numbers, Sh1 and Sh2, determine the heat- and mass-transfer rates obtained for the convection system. We see that Nu, Sh1 and Sh2 increase with an increase in the values of the bi-viscous Bingham fluid parameter. Also, we observe that increase in the Prandtl number effect increases them, and the same is true of the solutal Rayleigh-numbers, whereas the opposite impact on Nu, Sh1 and Sh2 is seen for solutal diffusivities, Soret and cross-diffusion parameters. In general, we observe that mass-transfer is more than the heat-transfer (Sh1>Sh2>Nu) depending on the value of diffusivities. The Author(s), under exclusive licence to Springer Nature India Private Limited 2024. -
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. -
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. -
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. -
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. -
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. -
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] -
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] -
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. -
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. -
Experimental investigation of tribocorrosion
This chapter discusses various techniques available for evaluation of tribocorrosion behavior of industrial components, their applications, and limitations. Numerous influential factors of tribocorrosion, their mechanisms, and their characteristics have been discussed at length. Further, a case study of tribocorrosion behavior of aluminum-based in situ metal matrix composites have been deliberated comprehensively. 2021 Elsevier Inc. All rights reserved. -
Friction and wear behaviour of copper reinforced acrylonitrile butadiene styrene based polymer composite developed by fused deposition modelling process
This paper focuses on the development of copper filled Acrylonitrile Butadiene Styrene (ABS) composites by fused deposition modelling (FDM) and to characterize its friction and wear behaviour. Twin screw extrusion technique was employed to extract copper-ABS composite filament. Three different materials were tested, i.e. pure ABS, ABS+2.5wt% Cu and ABS+5wt% Cu. Friction and wear characteristics of pure ABS and copper filled ABS composites were tested under various loads and sliding velocities. Addition of Copper powder has significantly improved the friction and wear properties of the developed composites. Further, it is also observed that friction and wear behaviour increased with increase in copper content in ABS. Worn out surfaces were subjected to scanning electron microscopy studies to analyse and identify the possible wear mechanisms involved. Faculty of Mechanical Engineering, Belgrade. -
Bioinformatics Research Challenges and Opportunities in Machine Learning
This research work has studied about the utilization of machine learning algorithms in bioinformatics. The primary purpose of studying this is to understand bioinformatics and different machine algorithms which are used to analyze the biological data present with us. This research study discusses about different machine learning approaches like supervised, unsupervised, and reinforcement which play an essential role in understanding and analyzing biological data. Machine learning is helping us to solve a wide range of bioinformatics problems by describing a wide range of genomics sequences and analyzing vast amounts of genomic data. One of the biggest real-world problems is that machine learning is helping us to identify cancer with a given gene expression, which is done using a support vector machine. In addition, this study discusses about the classification of molecular data, which will help find out minor diseases. With the advancement of machine learning in healthcare and other related applications, data collection becomes a tedious process. This article also focuses on some of the research problems in machine learning domain. The uses of machine learning algorithms in bioinformatics have been extensively studied. These objectives will help to understand bioinformatics and different machine algorithms that are used to analyze the biological data. This research study presents different machine learning approaches like supervised, unsupervised, and reinforcement, which play an important role in understanding and analyzing biological data. Machine learning helps to solve a wide range of bioinformatics related challenges by describing a wide range of genomics sequences and analyzing huge amounts of genomic data. One of the biggest real-time challenges is that the machine learning is helping to identify cancer with a given gene expression, and this is done by using a support vector machine. Finally, this research study has discussed about the classification of molecular data, which will be helpful in finding out minor diseases. 2022 IEEE. -
A Comparative Performance Analysis of Convolution W/O OpenCL on a Standalone System
Initial approach of this paper is to provide a deep understanding of OpenCL architecture. Secondly, it proposes an implementation of a matrix and image convolution implemented in C (Serial Programming) and OpenCL (Parallel Programming), to describe detailed OpenCL programming flow and to provide a comparative performance analysis. The implementation is being carried on AMD A10 APU and various algebraic scenarios are created, to observe the performance improvement achieved on a single system when using Parallel Programming. In the related works authors have worked on AMDAPPSDK samples such as N-body & SimpleGL to understand the concept of vector data types in OpenCL and OpenCL-GL interoperability, have also implemented 3-D particle bouncing concept in OpenCL & 3D-Mesh rendering using OpenCL. Lastly, authors have also illuminated about their future work, where they intend to implement a novel algorithm for mesh segmentation using OpenCL, for which they have tried to form a strong knowledge base through this work. 2015 IEEE. -
Videoconferencing-delivered psychological intervention for the treatment of COVID-19 related psychological distress in University students: study protocol for a randomised controlled trial in India
Background: The mental health impacts of the COVID-19 pandemic have been profound. This paper outlines the study protocol for a trial that tests the efficacy of a brief group-based psychological intervention (Coping with COVID; CWC), relative to Supportive Counselling, to reduce distress associated with COVID-19 in a young adult population in Bangalore, India. Methods: A single-blind, parallel, randomized controlled trial will be carried out via video conferencing in a small group format. Following informed consent, adults that screen positive for levels of psychological distress (Kessler 10 (K-10 score ? 20) and have access to a videoconferencing platform will be randomised to an adapted version of CWC (n = 90) or Supportive Counselling (SC) (n = 90). The primary outcome will be reduction in psychological distress including anxiety and depression at 2-months post treatment. Secondary outcomes include worry, positive wellbeing, and stress in relation to COVID-19. Discussion: This treatment trial will assess whether CWC will result in reduced distress relative to Supportive Counselling in a young adult population in Bangalore, India. This study will yield important insights into the role of nonspecific factors versus the interventions components in impacting COVID-19 related distress. Trial registration: This trial was prospectively registered on the Australian New Zealand Clinical Trials Registry (ACTRN12621001064897). Ethics and dissemination: Ethics approval has been obtained from the participating institution, CHRIST University in Bangalore. Results of the trial will be submittedfor publication in peer reviewed journals and findings presented at scientific conferences and to key service providers and policy makers. 2022, The Author(s).