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"Kumar, R"
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Predicting the thermal distribution in a convective wavy fin using a novel training physics-informed neural network method
Fins are widely used in many industrial applications, including heat exchangers. They benefit from a relatively economical design cost, are lightweight, and are quite miniature. Thus, this study investigates the influence of a wavy fin structure subjected to convective effects with internal heat generation. The thermal distribution, considered a steady condition in one dimension, is described by a unique implementation of a physics-informed neural network (PINN) as part of machine-learning intelligent strategies for analyzing heat transfer in a convective wavy fin. This novel research explores the use of PINNs to examine the effect of the nonlinearity of temperature equation and boundary conditions by altering the hyperparameters of the architecture. The non-linear ordinary differential equation (ODE) involved with heat transfer is reduced into a dimensionless form utilizing the non-dimensional variables to simplify the problem. Furthermore, Runge–Kutta Fehlberg’s fourth–fifth order (RKF-45) approach is implemented to evaluate the simplified equations numerically. To predict the wavy fin's heat transfer properties, an advanced neural network model is created without using a traditional data-driven approach, the ability to solve ODEs explicitly by incorporating a mean squared error-based loss function. The obtained results divulge that an increase in the thermal conductivity variable upsurges the thermal distribution. In contrast, a decrease in temperature profile is caused due to the augmentation in the convective-conductive variable values.
Journal Article
Political parties, party manifestos and elections in India, 1909-2014
In the parliamentary system of government, manifestos constitute and represent an important aspect of the democratic electoral politics as statements of a party's ideology, response and policy. This book offers an examination of election manifestos of different political parties in India at the national level. It explores the manifesto as an input to the policy process and presents a comparative perspective and understanding on the issues and approaches of the national political parties on key affairs.
Book
Negative local resistance caused by viscous electron backflow in graphene
Graphene hosts a unique electron system in which electron-phonon scattering is extremely weak but electron-electron collisions are sufficiently frequent to provide local equilibrium above the temperature of liquid nitrogen. Under these conditions, electrons can behave as a viscous liquid and exhibit hydrodynamic phenomena similar to classical liquids. Here we report strong evidence for this transport regime. We found that doped graphene exhibits an anomalous (negative) voltage drop near current-injection contacts, which is attributed to the formation of submicrometer-size whirlpools in the electron flow. The viscosity of graphene's electron liquid is found to be ~0.1 square meters per second, an order of magnitude higher than that of honey, in agreement with many-body theory. Our work demonstrates the possibility of studying electron hydrodynamics using high-quality graphene.
Journal Article
Global innovation and economic value
This book attempts to capture innovation outcomes. The intent is on a holistic assessment of value creation by innovation the societal value that it delivers to humanity, the economic value that it has the potential to endow to nations, and the monetary value that it provides to innovating firms. With a range of anecdotal examples and empirical analysis, the book endeavours to answer the question: Have investments in innovation paid off data and analytics underpin the development of the book material. The coverage is truly global, accentuating the economic value created by innovation in the technology and pharmaceutical sectors, the two largest bastions of innovation. In addition, it includes numerous examples of successful innovation in global companies while analyzing its economic/financial impact.
Book
Prevalence and severity of secondary traumatic stress and optimism in Indian health care professionals during COVID-19 lockdown
The COVID-19 pandemic has brought to light the lacunae in the preparedness of healthcare systems across the globe. This preparedness also includes the safety of healthcare providers (HCPs) at various levels. Sudden spread of COVID-19 infection has created threatening and vulnerable conditions for the HCPs. The current pandemic situation has not only affected physical health of HCPs but also their mental health.
This study aims to understand the prevalence and severity of secondary traumatic stress, optimism parameters, along with states of mood experienced by the HCPs, viz., doctors, nurses and allied healthcare professionals (including Physiotherapist, Lab technicians, Phlebotomist, dieticians, administrative staff and clinical pharmacist), during the COVID-19 lockdown in India.
The assessment of level of secondary traumatic stress (STS), optimism/pessimism (via Life Orientation Test-Revised) and current mood states experienced by Indian HCPs in the present COVID-19 pandemic situation was done using a primary data of 2,008 HCPs from India during the first lockdown during April-May 2020. Data was collected through snow-ball sampling technique, reaching out to various medical health care professionals through social media platforms.
Amongst the study sample 88.2% of doctors, 79.2 of nurses and 58.6% of allied HCPs were found to have STS in varying severity. There was a female preponderance in the category of Severe STS. Higher optimism on the LOTR scale was observed among doctors at 39.3% followed by nurses at 26.7% and allied health care professionals 22.8%. The mood visual analogue scale which measures the \"mood\" during the survey indicated moderate mood states without any gender bias in the study sample.
The current investigation sheds light on the magnitude of the STSS experienced by the HCPs in the Indian Subcontinent during the pandemic. This hitherto undiagnosed and unaddressed issue, calls for a dire need of creating better and accessible mental health programmes and facilities for the health care providers in India.
Journal Article
Rapid efficient synthesis and characterization of silver, gold, and bimetallic nanoparticles from the medicinal plant Plumbago zeylanica and their application in biofilm control
Nanoparticles (NPs) have gained significance in medical fields due to their high surface-area-to-volume ratio. In this study, we synthesized NPs from a medicinally important plant - Plumbago zeylanica.
Aqueous root extract of P. zeylanica (PZRE) was analyzed for the presence of flavonoids, sugars, and organic acids using high-performance thin-layer chromatography (HPTLC), gas chromatography-time of flight-mass spectrometry (GC-TOF-MS), and biochemical methods. The silver NPs (AgNPs), gold NPs (AuNPs), and bimetallic NPs (AgAuNPs) were synthesized from root extract and characterized using ultraviolet-visible spectra, X-ray diffraction (XRD), energy-dispersive spectrometry (EDS), transmission electron microscopy (TEM), and dynamic light scattering (DLS). The effects of these NPs on Acinetobacter baumannii, Staphylococcus aureus, and Escherichia coli biofilms were studied using quantitative biofilm inhibition and disruption assays, as well as using fluorescence, scanning electron microscopy, and atomic force microscopy.
PZRE showed the presence of phenolics, such as plumbagin, and flavonoids, in addition to citric acid, sucrose, glucose, fructose, and starch, using HPTLC, GC-TOF-MS, and quantitative analysis. Bioreduction of silver nitrate (AgNO₃) and chloroauric acid (HAuCl₄) were confirmed at absorbances of 440 nm (AgNPs), 570 nm (AuNPs), and 540 nm (AgAuNPs), respectively. The maximum rate of synthesis at 50°C was achieved with 5 mM AgNO₃ within 4.5 hours for AgNPs; and with 0.7 mM HAuCl4 within 5 hours for AuNPs. The synthesis of AgAuNPs, which completed within 90 minutes with 0.7 mM AgNO₃ and HAuCl₄, was found to be the fastest. Fourier-transform infrared spectroscopy confirmed bioreduction, while EDS and XRD patterns confirmed purity and the crystalline nature of the NPs, respectively. TEM micrographs and DLS showed about 60 nm monodispersed Ag nanospheres, 20-30 nm Au nanospheres adhering to form Au nanotriangles, and about 90 nm hexagonal blunt-ended AgAuNPs. These NPs also showed antimicrobial and antibiofilm activity against E. coli, A. baumannii, S. aureus, and a mixed culture of A. baumannii and S. aureus. AgNPs inhibited biofilm in the range of 96%-99% and AgAuNPs from 93% to 98% in single-culture biofilms. AuNPs also showed biofilm inhibition, with the highest of 98% in S. aureus. AgNPs also showed good biofilm disruption, with the highest of 88% in A. baumannii.
This is the first report on rapid and efficient synthesis of AgNPs, AuNPs and AgAuNPs from P. zeylanica and their effect on quantitative inhibition and disruption of bacterial biofilms.
Journal Article
Machine learning in healthcare : fundamentals and recent applications
\"Machine Learning in Healthcare discusses how to build various ML algorithms and how they can be applied to improve healthcare systems. It covers fundamental concepts including mathematical requisites and traditional machine-learning framework followed by advanced machine-learning methods and their applications in medical fields\"-- Provided by publisher.
Book
Machine learning-based energy management and power forecasting in grid-connected microgrids with multiple distributed energy sources
The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance the efficiency and reliability of these systems. The proposed SVR algorithm leverages comprehensive historical energy production data, detailed weather patterns, and dynamic grid conditions to accurately forecast power generation. Our model demonstrated significantly lower error metrics compared to traditional linear regression models, achieving a Mean Squared Error of 2.002 for solar PV and 3.059 for wind power forecasting. The Mean Absolute Error was reduced to 0.547 for solar PV and 0.825 for wind scenarios, and the Root Mean Squared Error (RMSE) was 1.415 for solar PV and 1.749 for wind power, showcasing the model’s superior accuracy. Enhanced predictive accuracy directly contributes to optimized resource allocation, enabling more precise control of energy generation schedules and reducing the reliance on external power sources. The application of our SVR model resulted in an 8.4% reduction in overall operating costs, highlighting its effectiveness in improving energy management efficiency. Furthermore, the system’s ability to predict fluctuations in energy output allowed for adaptive real-time energy management, reducing grid stress and enhancing system stability. This approach led to a 10% improvement in the balance between supply and demand, a 15% reduction in peak load demand, and a 12% increase in the utilization of renewable energy sources. Our approach enhances grid stability by better balancing supply and demand, mitigating the variability and intermittency of renewable energy sources. These advancements promote a more sustainable integration of renewable energy into the microgrid, contributing to a cleaner, more resilient, and efficient energy infrastructure. The findings of this research provide valuable insights into the development of intelligent energy systems capable of adapting to changing conditions, paving the way for future innovations in energy management. Additionally, this work underscores the potential of machine learning to revolutionize energy management practices by providing more accurate, reliable, and cost-effective solutions for integrating renewable energy into existing grid infrastructures.
Journal Article