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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). -
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. -
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. -
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. -
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 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 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. -
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 -
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 -
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. -
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. -
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 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 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 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. -
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. -
Cost Effective and Energy Efficient Drip Irrigation System for IoT Enabled Smart Agriculture
The conventional methods of smart farming consume a significant percentage of the resources such as water, electricity, and manpower. This approach demands more time, money, and effort. The state of the art drip irrigation methods make use of the solenoid valve to control the water flow. The problem with such a system is reflected in its power consumption which is a significant factor for large-scale demands. The method proposed in this paper addresses this problem by developing an automated drip irrigation system that replaces components used in conventional methods with its economical counterparts in the market. A system using Node MCU, DC submersible motor, and soil moisture sensor is developed to automate the irrigation process ensuring efficient water and energy consumption. Since the proposed system utilizes economically cheaper components, it provides an upper edge over other systems in terms of expenditure and in turn economically feasible for large-scale demands. A mobile application is also developed to control, monitor, and schedule irrigation processes. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Cost effective porous areca nut carbon nanospheres for adsorptive removal of dyes and their binary mixtures
Novel porous nanospheres from areca nuts (ACNPs) were synthesized via one-step pyrolysis without the use of any chemical treatment and the materials were used as adsorbents for the removal of cationic methylene blue (MB) and anionic methyl orange (MO) as well as their binary mixtures. Around, 67 tonnes of areca nut biowaste is generated every year which are then burnt due to their slow rate of decomposition resulting in higher carbon footprints. Biosorbents are generally a preferable alternative for dye adsorption but involve chemical modification for surface enhancement and complex sample treatment. In this work, ACNPs, were investigated for their efficiency in the raw form and were characterized by SEM, EDS, FTIR, XRD, and BET techniques before and after subjecting to the dye adsorption studies. The BET analysis of the adsorbents showed a high specific surface area of 693.8 m2/g when prepared at 1000 C, while the N2 adsorption-desorption plot showed type-IV isotherm, suggesting the microporous nature of the carbon matrix. Batch equilibrium studies showed the removal efficiency of >95% for both the dyes and their binary mixtures under the optimum conditions of 0.15 g/L dosage, 10 ?M concentration and contact time of 70 min. Due to the synergistic effects of the binary dyes, higher removal efficiency of MB compared to MO was observed in the binary mixture. Adsorption results were tested using Langmuir, Freundlich, Temkin, Redlich-Peterson, and Elovich isotherms to assess the best fit of the models. The qm value of MB was found to be 97.37 mg/g, while that of MO was 71.22 mg/g which is higher compared to individual dye components having lower values of 86.12 mg/g and 50.35 mg/g, respectively. Extended Langmuir and Jain and Snoeyink isotherms were used for binary data interpretation. The kinetic results showed good agreement with the Pseudo-second order equation, indicating internal diffusion. The possible mechanism involved electrostatic and ?-? interactions between the dye molecules and ACNPs. This approach is comprehensible and cost effective and can be utilized for dye removal in textile industries. 2023 Elsevier Inc. -
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Cost Effective Synthesis of Carbon Nanoparticles and Exploring the Fluorescence and Electrochemical Applications
Graphene-based materials and composites for sensing are a fascinating field in material science research that is experiencing rapid advancement. But the applications of graphene-based materials were often hampered by their high production cost, low yield, expensive and scarce precursors, harmful processing techniques, etc. Coal is made up of islands of nanometer-sized crystalline carbon domains linked by a 3D network of amorphous aliphatic carbon and polymerized aromatic hydrocarbons that can be extracted using mild oxidizing agents. In this context, the present study reports the successful usage of low-grade coal, lignite as an ideal precursor for the production of carbon nanostructures for various sensing applications. This research is divided into three parts where value addition to coal is being done along with finding solutions to three major environmental issues: fluorescence sensing of copper ion; noninvasive glucose fluorescence sensing; simultaneous electrochemical sensing of heavy newlinemetal ions cadmium and lead. In the first study, carbon nanostructures were synthesized from lignite by a simple, scalable, and economical technique and the as-prepared carbon nanostructures, namely LC1, LC2 and LC3, demonstrated excellent fluorescence characteristics. LC3 exhibited remarkable copper ion sensing with a dual linear range with limits of detection (LOD) as low as 1.32 pM and 2.35 pM, with limits of quantification (LOQ) 4 pM and 7.14 pM respectively. The accuracy of the manufactured sensor was shown by the recovery rates of copper ions, which varied from 98.18% to 101.2% with Relative newlineStandard Deviations (RSDs) below 0.4%. The results are captivating, implying that newlinethese lignite derived carbon nanostructures could be employed to efficiently and newlineeconomically detect low concentrations of copper ions in water. In the second study, carbon nanoribbons and nanosheets with superior fluorescence were synthesized from lignite, using a facile chemical oxidation process.