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Cosmic bounce in boundary-corrected symmetric teleparallel gravity
This study delves into modified f(Q,B) gravity, with a primary emphasis on solving field equations within the FLRW metric framework. It investigates bouncing scenarios by scrutinizing two nonlinear cosmological models and assesses energy conditions to authenticate bouncing cosmologies as viable alternatives to the conventional Big Bang theory. Furthermore, this analysis extends to examining geometrical parameters to shed light on the accelerating universe, providing significant insights into the implications of modified gravity for our comprehension of cosmic evolution. In addition, a perturbative analysis is performed for both models, showing that a nearly scale-invariant scalar spectral index ns and a suppressed tensor-to-scalar ratio r can be achieved for suitable parameter ranges, thus supporting the observational viability of the proposed bouncing framework. 2025 World Scientific Publishing Company. -
Cosmic acceleration from topological fluctuations of quantum spacetime
The observed late-time acceleration of the Universe challenges our understanding of gravity and the quantum vacuum. We investigate the framework of Topological Dark Energy, where cosmic acceleration emerges not from a fundamental cosmological constant or exotic fields, but from the topological structure of quantum spacetime. In this picture, the vacuum is a dynamic spacetime foam in which gravitational instantons-non-perturbative configurations in Euclidean quantum gravity-nucleate and alter global topology. The cumulative effect of these processes, coupled to macroscopic gravity through the Gauss-Bonnet invariant, yields a dynamical effective cosmological constant, ?eff. Its sign and magnitude are determined by the statistical ensemble of instantons, allowing the dark energy equation of state to evolve across the phantom divide. By embedding the Topological Dark Energy in a FriedmannLemareRobertsonWalker background, we derive its background and linear perturbative cosmological dynamics and confront the model with DESI DR2 BAO, Union3 supernova, and Planck CMB data. The results show that Topological Dark Energy is observationally viable, reproducing key features of the expansion history while offering a theoretically motivated alternative to ?CDM. 2026 Elsevier B.V. -
Coset component signed graph of a group
In this paper, the notion of a newly derived signed graph called a coset component graph, based on cosets of subgroups of a group is introduced. Let G be a group and H be its subgroup. Then, the coset component graph of H in G, denoted by ?cc, is a simple graph with the vertex set consisting of elements of G and two vertices say, a, b ? ?cc are adjacent if either aH = bH or Ha = Hb. A coset component signed graph of H in G is a signed graph whose edges get the sign in accordance with their inclusion in the edge set of the corresponding coset component graph. The structure and important properties of the coset component signed graphs are determined in this paper. 2024 World Scientific Publishing Company. -
Corrosion studies on low-cost solid lubricant coated stainless steel specimen
AISI 304 stainless steel is widely used in industries owing to its many desirable qualities like excellent formability, drawability and resistance to corrosion. However, AISI 304 stainless steel corrodes when exposed to halide environment such as chloride and fluoride. This study is primarily focused to assess the anti-corrosion properties of AISI 304 steel when coated with CaF2 solid lubricant. CaF2 solid lubricant was synthesized from the discarded egg-shells by ion exchange method by treating the egg-shell powder with hydrogen fluoride solution. Thermal spray coating method was used to coat the synthesized CaF2 solid lubricant on the AISI 304 stainless steel specimen. Slurry erosion test and electrochemical impedance spectroscopy test were conducted on the coated and uncoated specimen to assess the corrosion resistance. From the experimental results, the corrosion rate of the coated specimen was found to be very effective compared to the uncoated specimen. 2023 Elsevier Ltd. All rights reserved. -
Corrosion mitigation performance of disodium EDTA functionalized chitosan biomacromolecule - Experimental and theoretical approach
Disodium ethylenediaminetetraacetate salt is known for its excellent coordinating properties with the metal ions. The present study deals with the investigation of the prepared Disodium EDTA functionalized chitosan in corrosion inhibition for mild steel in 1 M HCl. The modified chitosan was characterized by spectral studies, thermal analysis, and Zeta potential studies. The corrosion inhibition efficiency (%) was evaluated using the gravimetric method and electrochemical studies. The electrochemical studies included potentiodynamic polarization, linear polarization resistance, and electrochemical impedance methods. The modified chitosan polymer showed an inhibition efficiency of 96.63% for 500 ppm at 303 K. Adsorption process obeyed Langmuir isotherm. Experimental results and theoretical calculations endorsed initial physisorption followed by a chemisorption process. Surface characterization studies supported the formation of a protective film that enabled the inhibition process. Density functional theory, Monte Carlo studies, and molecular dynamics simulation studies show a good agreement with the experimental results. Two-way Analysis of Variance was performed to test the influence of immersion period and inhibitor concentration on the corrosion rate using the statistical software IBM SPSS 20.0. A quartic model was generated as the best fit with the highest R2 value of 0.973. Design Expert software was employed for statistical modeling fit. 2021 -
Corrosion inhibition of mild steel using eco-friendly porous nanocarbon derived from waste mango kernels: a step towards sustainability
The pervasive corrosion of mild steel in acidic media poses a significant challenge in various industrial applications. While existing synthetic corrosion inhibitors are effective, their high cost and environmental toxicity necessitate the development of more sustainable alternatives. In this study, we present a novel approach to corrosion mitigation employing a porous nanocarbon synthesized from mango kernels, a sustainable source of agricultural waste. The CNS inhibitor was synthesized via pyrolysis at 800 C, yielding a high surface area (1090.2 m2 g?1) as confirmed by BET analysis. FE-SEM revealed a well-developed spherical morphology with an average particle size of 6070 nm. The corrosion inhibition efficiency of CNS was evaluated for mild steel in 1 M HCl using a combination of electrochemical techniques, including open circuit potential, potentiodynamic polarization (PDP), and electrochemical impedance spectroscopy. The CNS derived from waste mango kernels, exhibited excellent inhibition performance, achieving an efficiency of up to 87.1% at 800 ppm. PDP results revealed a mixed-type inhibition mechanism with suppression in both anodic and cathodic reactions. The thermodynamic parameter, adsorption free energy () of about ?20.0 kJ mol?1, indicates a spontaneous process and predominantly physical adsorption. Adsorption behavior was consistent with the Langmuir isotherm model. Surface analyses using SEM, EDS, optical profilometry, and water contact angle measurements corroborated the formation of a protective inhibitor film on the steel surface. These findings highlight the potential of bio-waste-derived materials as a sustainable and environmentally benign corrosion inhibitor for mild steel in acidic environments. This journal is The Royal Society of Chemistry, 2026 -
Corrosion Characterization of Friction Stir Weld Dissimilar Aluminium Alloy Joints
The course of contact mix welding is quick acquiring conspicuousness in aviation, marine and car industry because of its benefits as far as mechanical strength, effect and hardness characteristics. There is as yet a requirement for sure fire consideration from the exploration local area to erosion in grating mix welding zones, hence the work introduced here centres around the consumption portrayal of the grinding mix weld divergent aluminium composite. This study looks into friction stir welding under various parametric settings and shows how corrosion happens in a sodium chloride electrolytic media under potentio-dynamic conditions. The friction stir weld joints of dissimilar alloys aluminium are constructed using three sets of parameters. Straight cylinder, taper cylinder, and straight triangular tool profiles; tool rotational speeds of 800, 1000, and 1200 rpm; tool feed rates of 100, 120, and 140 mm/min; and tool offsets of 0.5, 0 mm, and-1.5 mm. The corrosion current (Icorr) reduces as tool rotating speed increases up to 1200 rpm, after which it slightly increases due to the creation of ridges all around the periphery of the friction stir weld area. 2022, Books and Journals Private Ltd.. All rights reserved. -
Corrosion behaviour in friction stir processed and welded materials
This chapter presents a comprehensive study on the influence of friction stir processing/welding (FSW/FSP) on corrosion behaviour. It briefly discusses the different aspects of corrosion including corrosion types, measurement techniques and data analysis. The corrosion behaviour of a wide range of friction stir processed materials, including light weight metals such as magnesium and aluminum alloys, as well as high strength metals such as steel, has been discussed in detail. The influence of FSP parameters on the microstructural evolution, comprising grain-size and precipitate refinement along with its correlation with the corrosion properties, has been described for different materials. 2014 The authors and contributors. All rights reserved. -
Corrosion behavior of AlCuFeMn alloy in aqueous sodium chloride solution
Medium Entropy Alloy AlCuFeMn possesses high room temperature strength and oxidation endurance. In present work, the aqueous corrosion resistance of the as-cast as well as low temperature oxidized AlCuFeMn alloy in 3.5 wt% NaCl solution, is explored. Equimolar proportions of high purity copper, manganese, iron, and aluminum were arc melted and cast in a copper mold. The alloy primarily consists of a face-centered cubic and a body-centered cubic phase. Potentiodynamic polarization tests on the alloy after low temperature surface oxidation reveal an aqueous corrosion resistance comparable to AISI 304 steel and CoCrFeMnNi high entropy alloy. The X-ray photoelectron spectroscopic studies confirmed that the free surface in the as-cast alloy is in partially oxidized state. The same completely oxidizes after low-temperature surface oxidation. Such low temperature surface oxidation improves pitting corrosion resistance in AlCuFeMn alloy due to increased metal/oxide layer resistance. The electrochemical impedance spectroscopy tests coupled with microscopy confirmed that the principal corrosion mechanisms in the alloy are of the uniform and pitting type. The energy dispersive spectroscopy experiments indicate that a copper oxide enriched layer is formed on the surface oxidized specimen during corrosion. 2021 Elsevier B.V. -
Corroboration of skin diseases: Melanoma, vitiligo vascular tumor using transfer learning
The precise identification of skin disease is an exigent process even for more experienced doctors and dermatologists because there is a small variation between surrounding skin and lesions, a visual affinity between different skin diseases. Transfer learning is the approach which stores acquired knowledge while solving one problem and apply that knowledge to similar problems. It is a type of machine learning task where a model proposed for a task can be used again. Transfer learning is used in various areas like image processing and gaming simulation. Image processing is an evolving field in the diagnosis of various kinds of skin diseases. Here transfer learning is used to identify three skin diseases such as melanoma, vitiligo, and vascular tumors. The inception V3 model was used as a base model. Networks were pre-trained and then fine-tuned. Considerable growth of training accuracy and testing accuracy were achieved. 2021 IEEE. -
Corrigendum to Implementation of modified Buongiorno's model for the investigation of chemically reacting rGO-Fe3O4-TiO2-H2O ternary nanofluid jet flow in the presence of bio-active mixers [Chemical Physics Letters, 786, 2022, 139194]
A few typographical errors have been identified in our paper titled Implementation of modified Buongiorno's model for the investigation of chemically reacting rGO-Fe3O4-TiO2-H2O ternary nanofluid jet flow in the presence of bio-active mixers. This corrigendum addresses those errors however, these errors have no impact on the obtained results. 2022 -
Corrigendum to Computational simulation of surface tension and gravitation-induced convective flow of a nanoliquid with cross-diffusion: An optimization procedure [applied mathematics and computation 425 (2022) 127108]
This corrigendum addresses both the physical configuration and certain typographical errors in [1] to improve clarity. These corrections do not impact the originality, results, or mathematical validity of [1]. 2024 -
Correlation of temperature, velocity and perforation location in a flat unglazed transpired solar collector (Utc) due to air flow
An unglazed transpired solar collector is a system that can leverage the abundant solar energy for various purposes. The solar collector is available in flat or corrugated form and is seen to be installed as an exterior layer of building facades. The cladding thus made absorbs radiation from the sun and heats up air being sucked by fan and flowing through perforations. In this paper, the focus has been to understand the correlation of plate temperature, exit temperature, the velocity distribution in the chamber and perforation location when air flows past a flat unglazed transpired solar collector (UTC). The establishment of correlations was carried out in the dataset of flow variables obtained after solving the problem using Navier-Stokes (NS) equations along with the standard k-? turbulence model and shear stress transport (SST) k-? model. An attempt has also been made to compute Pearsons correlation coefficient of any two flow variables to understand their strong and weak correlations. A linear regression analysis has been done to predict the response variables against the response obtained in CFD solver by using an open source software Rstudio . A strong correlation among cavity vertical velocity, perforation location and temperature has been established. From the study, it is noted that the location of a perforation has a strong correlation with the cavity vertical velocity and a weak correlation exists with temperature and plate temperature. 2020, Pushpa Publishing House. All rights reserved. -
Correlation of Surface Properties and Catalytic Activity of Metal Aluminophosphates
Alumina and its modified forms have gained significant interest in recent years as potential material for a wide variety of industrial applications. Aluminophosphates were reported as one of the promising catalytic materials in organic transformations. The catalytic activity of a material is always associated with the nature of active centers available on the surface of a catalyst. The incorporation of metal ions into aluminophosphates is of particular interest for the design of the novel catalysts. The post and pretreatment methods followed for the synthesis of the material determines the structural and textural properties of the material. The transition metal loading over aluminophosphates play a significant role in the generation of surface-active sites. This chapter deals with simple ecofriendly synthesis, physico-chemical characterization and catalytic activity of metal aluminophosphates. The chapter mainly explains the correlation of surface properties and catalytic activity of metal aluminophosphates in industrially important organic transformations. 2023 selection and editorial matter, Anitha Varghese and Gurumurthy Hegde; individual chapters, the contributors. -
Correlation Between Evaluative Beliefs of Patients, Reminder and Medication Adherence
Patients often fail to comply with the instructions given by their physicians. They miss the timing, forget, neglect, or procrastinate taking their medication. This deteriorates the health and causes financial burden to the patient and family. Reminders have been successfully used in many phases of day-to-day activities, increasing the efficiency and productivity. This paper tries to identify the relationship between reminder and the perception of importance of medication based on 15 different factors. These factors have been further assessed to find their relationship with adherence of medication. Hence, with a two-way approach, the studies use exploratory factor analysis method to identify the latent factors, and these latent factors have been used to find the correlation between reminder and adherence through confirmatory factor analysis. It was found that there is positive and significant correlation between reminder and the latent factors and also between the latent factors and adherence. 2022 IGI Global. All rights reserved. -
Correlation based ADALINE neural network for commodity trading
Commodity trading is one of the most popular resources owning to its eminent predictable return on investment to earn money through trading. The trading includes all kinds of commodities like agricultural goods such as wheat, coffee, cocoa etc. and hard products like gold, rubber, crude oils etc.,. The investment decision can be made very easily with the help of the proposed model. The proposed model correlation based multi layer perceptron feed forward adaline neural network is an integrated method to forecast the future values of all commodity trading. The correlation based adaline neuron is used as an optimized predictor in the multi layer perceptron feed forward neural network. The correlation is used for feature selection before building the predictive model. The aim of the paper is to build the predictive model for commodity trading. The model is created using correlation based feature selection and adaline neural network to prognosticate all future values of commodities. The adaptive linear neuron is formed with the help of linear regression. To implement the proposed model the live data is captured from mcxindia. The mcxindia is considered as one the popular website for doing commodities and derivatives in India. To train the proposed model, few random samples are used and the model is evaluated with the help of few test samples from the same data set. 2015 Chandra, J., M. Nachamai and Anitha S. Pillai. -
Correlated variability of the reflection fraction with the X-ray flux and spectral index for Mrk 478
The X-ray spectrum of Mrk 478 is known to be dominated by a strong soft excess that can be described using relativistic blurred reflection. Using observations from XMM-Newton, AstroSat, and Swift, we show that for the long-term (?years) and intermediate-term (days to months) variability, the reflection fraction is anticorrelated with the flux and spectral index, which implies that the variability is due to the hard X-ray producing corona moving closer to and further from the black hole. Using flux-resolved spectroscopy of the XMM-Newton data, we show that the reflection fraction has the same behaviour with flux and index on short time-scales of hours. The results indicate that both the long- and short-term variability of the source is determined by the same physical mechanism of strong gravitational light bending causing enhanced reflection and low flux as the corona moves closer to the black hole. 2022 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
Corrected Score Estimation of Regression with Autocorrelation Under Measurement Errors
In regression models with autocorrelated errors, measurement errors in the covariates can lead to biased and inconsistent estimation of the regression coefficients. Measurement error refers to the discrepancy between the observed values of a variable and its true values. It occurs during the data collection process due to various factors such as inaccuracies in measurement instruments, limitations in the measuring process, human errors, or environmental influences. These errors can introduce bias into collected data, impacting the reliability and validity of statistical analyses and model outcomes. Recognizing the importance of time dependencies, our study extends to time series regression models in the presence of measurement error. A score function is the derivative of the log-likelihood function with respect to the parameters. In this paper, a correction for score function in regression with autocorrelated errors is considered to account for the impact of measurement errors on parameter estimation, and it attempts to provide a two-step estimation procedure to resolve the bias caused by both these challenges. Further, the efficiency of these estimates is compared with least square estimates by carrying out a simulation study for finite samples, and we conclude the proposed methodology provides more accurate results. The applicability of the proposed model has been illustrated using the Phillips Curve dataset and it was found that in the presence of measurement error, corrected score estimation gives more accurate estimates than OLS estimates. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Corpus based sentimenal movie review analysis using auto encoder convolutional neural network
In natural language processing, most prominent branch is sentiment analysis. Peoples emotions and attitudes are analyzed using this sentiment analysis towards service, some product, etc. In prediction of the future scope of a product, some benefits are given by sentiment analysis. However, manual analysis of such a huge amount of documents is a highly tedious task, especially with limited time. Hence, for solving this problem, various attempts are made in literature and proposed different sentiment analysis methods. However, in generation of lexicon, popular NLP tools has some drawbacks. The accuracy of lexicons based on humans is less and they are limited too. On the other hand, lexicons based on dictionary are highly general and they are domain specific. So, a technique called Corpus Integrated Autoencoder Convolutional Neural Network based Sentiment Analysis (CI-AECNN) is proposed in this work for solving this issue. The sentiment lexicon generation based on corpus is performed in this work. Candidates sentiment orientation are computed using this and seed lexicon are added with recognized sentiment words and from seed lexicon, words with incorrect sentiment are removed. The long short-term memory (LSTM) is used for performing Word Sense Disambiguation. Conditional random fields are used for extracting features. At last, auto-encoder, convolutional neural network is used for performing classification. In MATLAB simulation environment, conducted this research works overall analysis and it indicates that better results are produced by proposed technique when compared with available techniques. 2021 Taru Publications. -
Corporate social responsibility: Myth and reality
Companies nowadays strive to be socially conscious in the way they do business by taking up corporate social responsibility (CSR) activities besides maintaining profitability. Similarly consumers modulate their purchase choices to be made up of products that have been produced and marketed through socially responsible processes. But the congruence between achieving gain and being responsible to the community has ethical contradictions due to the presence of self interest. This paper proposes to examine the dimensions of this conflict and towards the end suggest a new orientation that foregrounds social responsibility relative to profit or gain. 2013 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (Dharmaram Vidya Kshetram, Bangalore).