Browse Items (11810 total)
Sort by:
-
Modified carbon based electrodes for electrochemical sensing of biomolecules
Now-a-days a large variety of biological analytes are detected by highly sensitive newlineelectrochemical sensors which are inexpensive and simple as compared to conventional methods such as UV-vis absorption spectroscopy, spectrofluorometry, HPLC and gas chromatography. Electrochemical analysis is exceptional owing to its economical, low energy consuming and unique approach in the method design, and high sensitivity for the analyte determination. Carbon based electrochemical sensors are commonly used because of their low cost, good electron newlinetransfer kinetics, good chemical stability, and biocompatibility. Recently electrochemical properties of pencil graphite electrodes (PGEs) have been explored in the analysis of various organic compounds. High electrochemical reactivity, easy modification, commercial accessibility, fine rigidity, disposability and low-cost of PGE make it ideal to be used as an effective working electrode. The thesis presented explains different modified PGEs have been employed in the electrocatalytic determination biomolecules such as cholesterol, cortisol, Vitamin B6 and morin. newlineThe modified electrodes are effectively used for the ultra-level sensing of these biomolecules in real samples. The electroactive surface area and the conduvtivity of bare PGE is enhanced newlinedifferent electrode modifiers such as and#946;-CD, graphene, conducting polymer, metal oxides and metal nanoparticles. The modified electrodes are found to exhibit good electrocatalytic behavior towards the target biomolecules. Cyclic voltammetric (CV) studies and electrochemical impedance spectroscopic (EIS) technique were used to investigate the electrochemical properties of the modified sensing platform. The newlinemorphology and step wise fabrication process of the modified electrodes were characterized byvii Ramana spectroscopy, X-ray photoelectron spectroscopy (XPS),scanning electron microscopy (SEM), Transmission electron microscopy (TEM), X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR). -
Non-enzymatic electrochemical determination of salivary cortisol using ZnO-graphene nanocomposites
Electrochemically deposited ZnO nanoparticles on a pencil graphite electrode (PGE) coated with graphene generate a noteworthy conductive and selective electrochemical sensing electrode for the estimation of cortisol. Electrochemical techniques such as cyclic voltammetry (CV) analysis and electrochemical impedance spectroscopic (EIS) tests were adopted to analyze and understand the nature of the modified sensor. Surface morphological analysis was done using various spectroscopic and microscopic techniques like X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), and scanning electron microscopy (SEM). Structural characterization was conducted by X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR). The effect of scan rate, concentration, and cycle numbers was optimized and reported. Differential pulse voltammetric (DPV) analysis reveals that the linear range for the detection of cortisol is 5 10-10M - 115 10-10 M with a very low-level limit of detection value (0.15 nM). The demonstrated methodology has been excellently functional for the determination of salivary cortisol non-enzymatically at low-level concentration with enhanced selectivity despite the presence of interfering substances. The Royal Society of Chemistry. -
Pt Nanospheres Decorated Graphene-?-CD Modified Pencil Graphite Electrode for the Electrochemical Determination of Vitamin B6
An electrochemical sensor for Vitamin B6 determination has been prepared by the electrochemical deposition of Pt nanospheres on graphene-?-CD coated Pencil Graphite Electrode (PGE). Cyclic voltammetric (CV) and electrochemical impedance spectroscopic (EIS) studies were employed to explore the electrochemical properties of the modified electrode. The physicochemical properties of the modified electrodes were characterized by X-ray photoelectron spectroscopy (XPS), Scanning electron microscopy (SEM), Transmission electron microscopy (TEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) and optical profilometric studies. The experimental conditions such as effect of scan rate, concentration and pH were optimized. The linear dynamic range for the determination of Vitamin B6 was found to be 5nM to 205nM. The low level of detection limit (1.2nM) implies the high sensitivity of the process. The suggested method was effectively employed for the electrocatalytic evaluation of Vitamin B6 in different juice samples. Graphical Abstract: [Figure not available: see fulltext.] 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
?-Cyclodextrin-PANI decorated pencil graphite electrode for the electrochemical sensing of morin in almonds and mulberry leaves
Morin (3,2,4,5,7-pentahydroxyflavone) is one of the natural flavonoids which is present in a variety of fruits and herbs. ?-cyclodextrin (?-CD) and polyaniline (PANI) decorated Pencil graphite electrode (PGE) has been successfully used as a sensitive and conducting electrode for the determination of morin. The hydroxyl groups of ?-CD attract the analyte towards the modified electrode through hydrogen bonding. Cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) techniques were employed to study the electrochemical properties of the modified electrodes. The enhanced surface roughness of ?-CD-PANI/PGE has resulted in the increase of electrocatalytic activity of electrode towards the analyte. Opitical profilometric studies were performed to evaluate the surface roughness of electrodes and differential pulse votammetry (DPV) was used for the quantitative analysis of morin. Scanning electron microscopy (SEM), Raman spectroscopy and Fourier transform infrared (FTIR) spectroscopy were carried out to know the physicochemical characteristics of the modified electrodes. The experimental conditions such as scan rate, pH and concentration were optimized. The electrochemical process was found to be adsorption controlled and irreversible from scan rate studies. Under optimal conditions, the linear dynamic range for the quantification of morin was found to be 1.1732nM. The low detection limit (0.38nM) indicates ultrasensitivity of the proposed method. The suggested method has been effectively employed for the determination of morin in almonds and mulberry leaves. 2020, Springer Nature Switzerland AG. -
MnO2 Nanoclusters Decorated on GrapheneModified Pencil Graphite Electrode for Non-Enzymatic Determination of Cholesterol
Electrochemically deposited MnO2 on graphene coated Pencil Graphite Electrode (PGE) has been used to develop a facile electrochemical sensor for the determination of Cholesterol. Cyclic voltammetric (CV) studies and electrochemical impedance spectroscopic (EIS) technique were used to investigate the electrochemical properties of the modified sensing platform. The physicochemical properties of the modified electrodes were characterized by X-ray photoelectron spectroscopy (XPS), Scanning electron microscopy (SEM), Transmission electron microscopy (TEM), X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR). The experimental conditions such as effect of scan rate, concentration and pH were optimized. The linear dynamic range for the determination of Cholesterol was found to be 120?10 M2400?10 M under optimum conditions. The ultralow level of detection limit (0.42 nM) demonstrates the high sensitivity of the proposed method. The developed method was successfully applied for the non-enzymatic determination of Cholesterol in human blood samples at ultralow levels. 2020 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim -
Data: A Key to HR Analytics for Talent Management
The past few years have witnessed a significant rise in job openings across various industries worldwide. This trend has highlighted the need for companies to plan and recruit better talent to keep up with the demand for skilled employees. As a result, Human Resource (HR) professionals are now using workforce planning and HR analytics to address the challenges of finding and retaining the right employees. With the help of technological advancements in HR systems, businesses are leveraging data and insights to understand workplace dynamics better. Workforce planning has thus become crucial for organizations of all sizes to ensure they have the necessary talent to achieve their goals in the present and future. This chapter delves deeper and examines the importance of workforce planning and how HR analytics can help companies achieve their strategic objectives. Talent Management is about analyzing the workforce skill requirements of the organization. It needs a strategic plan to ensure the appropriate people are in the right roles at the right times. Talent Management is a crucial element of every businesss performance. In this process, data play a pivotal role in evaluating the existing workforce and planning for future workforce needs. Based on this, a strategy is developed to fill gaps or prospective shortages. Organizations can achieve their goals by using talent planning and collecting data about upcoming projects and skill requirements based on market needs. For example, talent planning is essential in the healthcare sector to guarantee that hospitals and clinics have enough doctors, nurses, and other healthcare workers to fulfill the rising demand for healthcare services. HR analytics is the key to talent management, enabling organizations to stay competitive, enhance productivity, and achieve long-term strategic objectives. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Implementation of AI in manufacturing industries a case study
Artificial intelligence (AI) is getting progressively integrated into nearly every facet of our existence. Its applications are ubiquitous and ever-evolving, spanning fields such as autonomous vehicles, geology, medicine, and art. AI has, however, posed as many questions as it has answered. These include the definition and application of the technology (viz., assisted, augmented, or independent intellect), the question of whether computers are thinking machines similarly to humans, the wider implications of the impact of automation on society, and the unexpected moral and principled quandaries. This chapter provides an overview of artificial intelligence in manufacturing intended for executives in manufacturing and industrial companies who want to integrate AI into their business. Its main objective is to apply AI to the engineering, testing, and production stages of the manufacturing value chain. The goal is to discuss business applications that technology, data, and automated processes can support, and how the appropriate personnel, organizational structure, and culture can support them. This article discusses current advancements, poses problems, asks questions, and attempts to bring cutting-edge concepts and research closer to business. 2025 Mohamed Arezki Mellal. All rights reserved. -
Design and Implementation of Low Complexity Multiplier-Less Reconfigurable Band Tuning Filter Structure with Sharp Sub-Bands
Digital flter banks are extensively used for communication purposes for channelization. Reconfgurable non-uniform multi-channels with sharp transition widths are necessary for channelization in digital channelizer and spectrum sensing in wireless communication networks. The aim of this research work is to design reconfgurable flter structures featuring non-uniform and sharp transition newlinewidth channels with reduced number of flter coeffcients. The four different flter structures are proposed in this research for achieving low complexity reconfgurable structure for the design of multiple non-uniform sharp transition width arbitrary bandwidth channels. The foundational newlineelement of this research is centered around the design of a prototype flter. This prototype flter serves as a basis for developing various reconfgurable flter structures. Leveraging the prototype newlineflter s bandwidth characteristics, these structures are categorized into two main groups: narrow band prototype flters and wide band prototype flters. The narrow band prototype flter category comprises structures capable of designing a single fnite impulse response flter with a narrow passband characterized by sharp transition widths. In contrast, the wide band prototype flter category includes structures capable of designing a single FIR flter with a wide passband also characterized by sharp transition widths. A novel flter structures are designed with the help of interpolated newlinefnite impulse response, cosine modulation technique, complex exponential modulation technique and frequency response masking techniques. The proposed method is evaluated using MATLAB R2019b, where the linear phase FIR flter coeffcients are computed based on the Parks-McClellan algorithm. The examples are employed to illustrate the effcient operation of the proposed designs. The results point to the fact that the proposed designs have less multiplier complexity than existing cuttingedge techniques. -
Preprocessed text compression method for Malayalam text files
The increasing importance of Unicode for text files implies an increase in storage space required for data and the time for the transmission of data, with a corresponding need for compression of data. Conventional compressors fair purely on UTF-8 texts, where each character can span multiple bytes. Malayalam which is one among the four major languages of the Dravidian family, is represented by using Unicode characters. The contribution of this paper is a reversible transformation mapping of the input to reduce the actual size of the input file before a general purpose compression method. After the preprocessing, LZW compression achieves more compression to Malayalam text files containing any characters including ASCII characters. This method can be extended to any native language files containing mostly the characters of only one script. BEIESP. -
Evaluation of Virtual Reality Experiential Dimensions using Sentiment Analysis
Experiential technologies like Virtual Reality (VR) are revitalizing the gaming industries through higher immersive and interactive gaming experiences. The immersive technology has a considerable impact on the industry and will evolve simultaneously as the technology continues to update and improve further. Indian tech cities Bangalore, Delhi NCR, Mumbai, Kolkata, Chennai, Pune, and Hyderabad were chosen for the study and the user-generated content was scraped from the top gaming centers of each city. User Generated Content analysis is gaining immense interest among businesses for devising better decision-making and marketing strategies. The study devised an integrated framework comprised of web data scraping, data cleaning, data pre-processing, AI model designing, sentiment analysis, logistic regression model, and support vector machine model. Logistic Regression predicted the sentiment of the text and the Support Vector Machine classified the VR experiential dimensions and helped in understanding the most important dimension for customer satisfaction. The study has found that VR experiences are gaining positive responses among the customers and illusion emerges as the most significant dimension for their satisfaction. 2024 IEEE. -
A Study on Factors Enhancing Immersive Virtual Reality Experiences
The objective of this study is to identify the various influential factors of immersive virtual reality (VR) experiences and examine the relationship between the immersion factors (technology, visuals, sound, interaction, and sound) and virtual reality experiential outcomes (satisfaction and loyalty). The survey comprises 412 participants who experienced VR games at the Orion Mall in Bangalore. The study has identified the prominent factors for enhancing the immersive experience. The factors are technology, visuals, sound, interaction, and sound. It also identified that there exists a positive association between VR experiential satisfaction and technology, visuals, sound, interaction, and sound. The results imply that service providers should focus on elevating immersive experience as it is closely associated with VR experiential satisfaction and VR experiential loyalty. This will increase the revisit intention and spread positive word of mouth about the virtual experiences. This paper provided valuable insights that pay way to analyze the association between immersion factors and VR experiential outcomes. 2024 IEEE. -
An Outlook on Sustainable Business Practices through Virtual Reality Marketing
Technologies and businesses blend progressively and work towards creating a sustainable future through the company's marketing strategies. The purpose of the study is to find out the various sustainable outcomes of Virtual Reality Marketing (VRM). The exploratory research identified immersive experience, experiential economy, positive image creation, positive travel decisions, and repeat purchase as the constructs of VRM, and a total of 418 people were surveyed to analyze those constructs. The data were analyzed through statistical tests such as t-test, One-way ANOVA, and Chi-square with the help of SPSS software. The study shows a positive relationship between customers and virtual reality marketing. The results predict that businesses that have incorporated VRM tend to likely have a high-profit margin and more sustainable returns compared to their peer competitors. 2024 IEEE. -
Neural Network based Student Grade Prediction Model
Student final grade GPA is the collective efforts of their previous and ongoing efforts of each semester examination may predict accurately using the neural network which receives the input weight of each matrix element of variables to next neuron. The GPA prediction based on regular class performance and previous grades with background variables were found much significant. This research tries to explore the model comparison and evaluate student grade prediction using various neural network models. The single-layer half i.e., successful student model predicts 90 total accuracies than the single layer with five hidden layer neurons (88.5 percent). The multi-layer with two hidden layers (7,3) is 84 percent accuracy is less than one percent accuracy than multilayer with three hidden layers. Similarly, the multilayered with four hidden layered 25,12,7,3 model predicts the least accuracy (77 percent accuracy) for student grade. Similarly, the passed student prediction model has less accuracy than both students' 86 percent. 2022 IEEE. -
Flares during eclipses of high-mass X-ray binary systems Vela X-1, 4U 1700?37, and LMC X-4
In eclipsing X-ray binary systems, the direct X-ray emission is blocked by the companion star during the eclipse. We observe only reprocessed emission that contains clues about the environment of the compact object and its chemical composition, ionization levels, etc. We have found flares in some X-ray binaries during their eclipses. The study of eclipse flares provides additional clues regarding the size of the reprocessing region and helps distinguish between different components of the X-ray spectrum observed during the eclipse. In the archival data, we searched for flares during eclipses of high-mass X-ray binaries and found flares in three sources: Vela X-1, LMC X-4, and 4U 1700?37. Comparing spectral properties of the eclipse flare and non-flare data, we found changes in the power-law photon index in all three sources and multiple emission lines in Vela X-1 and 4U 1700-37. The fluxes of prominent emission lines showed a similar increase as the overall X-ray flux during the eclipse flare, suggesting the lines originate in the binary environment and not in the interstellar medium. We also observed a soft excess in 4U 1700-37 that remains unchanged during both eclipse flare and non-flare states. Our analysis suggests that this emission originates from the extremely thin shell of the stellar wind surrounding the photosphere of its companion star. The detection of short (100200 s) count-rate doubling time-scale in 4U 1700?37 and LMC X-4 indicates that the eclipse reprocessing occurs in a region larger than, but comparable to the size of the companion star. 2024 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. -
Discovery of quasi-periodic oscillations in the persistent X-ray emission of accreting binary X-ray pulsar LMC X-4
We report the discovery of quasi-periodic oscillations (QPOs) in the high-mass X-ray binary (HMXB) pulsar LMC X-4 in its non-flaring (persistent) state using observations with XMM-Newton. In addition to the 74 mHz coherent pulsations, the persistent emission light curve shows a QPO feature in the frequency range of 20-30 mHz. Quasi-periodic flares have been previously observed from LMC X-4 in observations made with Rossi X-ray Timing Explorer (RXTE). However, this is the first time QPOs have been observed in the persistent emission observations of LMC X-4. QPOs in X-ray binaries are generally thought to be related to the rotation of the inhomogeneous matter distribution in the inner accretion disc. In HMXBs such as LMC X-4 where the compact object is a neutron star with a high magnetic field, the radius of the inner accretion disc is determined by the mass accretion rate and the magnetic moment of the neutron star. In such systems, the QPO feature, along with the pulse period and X-ray luminosity measurement, helps us to constrain the magnetic field strength of the neutron star. We use considerations of magnetospheric accretion to have an approximate value of the magnetic field strength of the neutron star in LMC X-4. 2022 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
Nanomaterial - Based Electrochemical Sensor for Monitoring Potential Biomakers of Chronic Disorders
Detecting various biomarkers in the health industry and the biomedical sector has been newlinesignificant due to their crucial role in diagnosing, assessing, exposing, and treating disorders. This work reports electrochemical sensors for detecting biomarkers using different modifications (2D materials and nanomaterials) on carbon fiber paper electrode-based (CFPE) sensors. Adopting these modifications on the CFPE electrode greatly intensified the oxidation and reduction of peak current values. The physio-chemical characterizations of the designed electrodes were examined employing Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), Electron Diffraction X-ray (EDX), X-Rayv Photoelectron spectroscopy (XPS), Fourier Transform Infrared Spectroscopy (FTIR), and Raman Spectroscopy. Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) newlineassisted in optimizing the electrochemical properties via Nyquist plots, sensing performance, scan rate effect, and pH effect. Both electro-activity studies and Nyquist plots confirmed the enhancement in the electroanalytical performance of the fabricated electrodes. Real sample newlinestudies were successfully analyzed using developed electrodes, producing good recovery newlinepercentages. Overall, all the works conducted have been established to be facile and selective, with novelty in the fabrication of ultrasensitive voltammetric-based sensors to quantify different biomarkers. -
Acetylcholine esterase inhibition activity of leaf extract of Saraca asoca using zebrafish as model organism
Alzheimers disease, also called as Senile Dementia, is a progressive neurogenerative disease that slowly destroys important mental functions like memory, reasoning and thinking. A plethora of factors including genetics, lifestyle, environment, age etc. play a part in determining its incidence. One of the commonly used techniques to slow down the progression of Alzheimers is to reduce the functioning of Acetylcholinesterase (AChE) enzyme which breaks down the neurotransmitter acetylcholine. Plants have been found to be natural sources of AChE inhibitors. Hence the present investigation was an attempt to screen Ashoka plant (Saraca asoca) for such inhibitors. Zebrafish (Danio rerio) was used as a model organism due to its genetic similarities with humans. Both in vivo and in vitro analyses using zebrafish indicated inhibitory action of the leaf extract on AChE. Gas Chromatography- Mass spectrometry (GCMS) analysis of the methanolic leaf extract and further docking studies of prominent phytochemicals revealed the AChE inhibitory potential of molecules like Stigmasterol, ?-sitosterol, Vitamin E etc. Hence these molecules can be thought of as targets in the therapy of Alzheimers disease. 2020 World Research Association. All rights reserved. -
Breaking News Recognition Using OCR
Identifying and recognition of breaking news in most of the TV channels in different backgrounds with varying positions from a static image plays a significant role in journalism and multimedia image processing. Now a days its very challenging to isolate only breaking news from headlines due to overlapping of many categories of news, keeping all this in mind, a novel methodology is proposed in this paper for detecting specific text as a breaking news from a given multimedia image. Basic digital image processing techniques are used to detect text from the images. The methods like MSER (Maximally Stable Extremal Regions) and SWT (Stroke Width Transform) are used for text detection. The proposed work focuses on extraction of text in breaking news images also discusses the different methods to overcome existing challenges in text detection along with different types of breaking news datasets collected from various news channels are used to identify text from images and comparative study of different text detection methods. The comparative study proves that MSER and SWT is a better technique to detect text in images. Finally using OCR (Optical Character Recognition) technique to extract the breaking news text from the detected regions will help in easy indexing and analysis for journalism and common people. Extensive experiments are carried out to demonstrate the effectiveness of the proposed approach. 2019, Springer Nature Singapore Pte Ltd. -
-
An Analysis of Sentiment Using Aspect-Based Perspective
Opinions play a major role in almost every human practice. Finding product and service reviews is made easy online. Product reviews are readily available in huge quantities. Considering each review and making a concise decision about a product is not feasible or even possible. Aspect-based sentiment analysis (ABSA) is one of the best solutions to this problem. Summary and online reviews analysis is delivered in this paper. ABSA has made extensive use of machine learning techniques. Recent years have seen deep learning take off due to the growth of computer processing power and digitalization. When applied to various deep learning techniques, numerous NLP tasks produced futuristic results. An overview of various deep learning models used in the field of ABSA is presented in this chapter after an introduction to ABSA. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.