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Real-Time State of Charge Prediction Model for Electric Two-Wheeler
To maximise the efficiency and performance of electric vehicles, traction battery State of Charge (SoC) must be accurately predicted. In this work, a prediction model for traction battery State of Charge estimation is developed in real time. The traction battery powers an electric two-wheeler through a predetermined drive cycle. To produce accurate state-of-charge forecasts, the predictive model considers several input characteristics, such as temperature, voltage, and current. This research is crucial for fostering effective energy management and improving the safety and dependability of electric two-wheelers. Open-circuit voltage (OCV) and coulomb counting are two commonly utilised techniques used to evaluate the state of charge prediction model. These techniques act as standards for assessing the developed Neural Network model prediction, the model's dependability and accuracy. The model's usefulness and its potential to outperform the current State of Charge estimating techniques are demonstrated by comparing the state-of-charge predictions from the model with these standard methods. 2024 IEEE. -
Impact of COVID-19 on Gig Workers with Special Reference to Food Delivery Executives
The gig economy, popularly known as shared economy or collaborative work, has acquired a public and academic interest in the twenty-first century. The concept stresses entering into short-term contracts and does not promise any permanent job. The employees in the Gig economy with temporary work relationships are also referred to as gig workers. Any unexpected change in the pulse of the economy has an impact on the gig economy. The COVID-19 pandemic has affected the economy and the gig workers over the last few months. This book chapter provides a timely intervention into the gig workers, particularly the food delivery executives in India. Their remunerations, benefits, and difficulties from a pandemic perspective are included in the chapter. The authors try to bring in the employees views of the work they do and the differences, which have been brought in as an aftermath of the pandemic. The chapter also throws light on the recent government initiatives to benefit gig workers in the economy. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
A study of diesel and Pongamia Pinnata biodiesel combustion in compression ignition engines using zero-dimensional modelling and experimental methods
The present study develops a single-zone zero-dimensional progressive combustion simulation model using Python programming language for compression ignition engines. The model is capable of predicting in-cylinder pressure, heat release rate, engine performance, and emissions characteristics. The numerical model is experimentally validated using resuts from engine testing for diesel, Pongamia Pinnata biodiesel and diesel-biodiesel blends. The chemical composition of fuel is identified using Gas Chromatography-Mass Spectrometry. The values of power output, mean effective pressure and exhaust oxygen concentration are independently obtained from the numerical model and from the experiments. The engine performance is not significantly affected for biodiesel to diesel blending ratio of up to 30%. A higher oxygen concentration in the exhaust gas is observed with increase in blending ratio. The developed numerical model would be useful in studying the performance and emission characteristics for any alternative fuel with known calorific value and chemical composition. 2020 Informa UK Limited, trading as Taylor & Francis Group. -
Phytochemical analysis, anti-oxidant properties and pass assisted prediction of biological activity of sargassum wightti j. Agardh, sargassum ilicifolium (turner) C. Agardh and sargassum lanceolatum J. Agardh
The present study was carried out to determine the phytoconstituents and anti-oxidant properties present in the Sargassum wightti, Sargassum ilicifolium and Sargassum lanceolatum using GC-MS analysis and predict the biological activity by PASS prediction. Gas chromatography/mass spectrometry (GC/MS) analysis was performed on Shimadzu GC interfaced with mass spectrometry using SH-RxiTM-5Sil (Shimadzu) column. The spectra of the phytoconstituents were obtained by the PASS version (http:// www.way2drug.com/passonline). The prediction was based on an analysis of the structure-activity relationships (SAR) in the training set containing information on the structure. GC-MS analysis revealed 39 compounds in S. wightti, 60 compounds in S. lanceolatum and 15 compounds in S. ilicifolium. Common compounds found in these three Sargassum species were 1s,4R,7R,11R-1,3,4,7-Tetramethyltricyclo [5.3.1.0(4,11)] undec-2-en-8-one,2,4-Di-tert-butyl phenol, 1-Decanol, 2-hexyl, Hexatriacontyl-trifluoroacetate, Nonyltetradecyl ether, Neophytadiene, Hexadecane. Sargassum species are rich in anti-oxidant, anti-inflammatory, anti-bacterial and anti-viral properties. The present study proved the presence of bioactive compounds and phytochemical compounds in three species of Sargassum available in Indian coastal regions. 2021, Agri Bio Research Publishers. All rights reserved. -
Tuning variegated characteristics of NiO thin films via 50keV nitrogen ion beam irradiation
In this study, a systematic analysis of the changes brought about by low-energy ion beam irradiation in NiO thin films has been carried out. NiO thin films, deposited on glass substrates by RF magnetron sputtering method have been irradiated with 50keV Nitrogen ions (N+) at varied ion fluence values. With N+ irradiation, the intensity of diffraction peak corresponding to (440) decreases up to ion fluence of 1 1016 ion/cm2 due to the irradiation-induced lattice damage. Furthermore, at the highest fluence (5 1016 ions/cm2), the dominancy of (400) is lost and the crystal structure is reoriented to (440) alignment. The low energy ion irradiation has caused a mitigation in thin film transmittance by 25% compared to unirradiated sample. A decrease in the 1LO mode observed from Raman spectroscopy accounts for the formation of Ni vacancy defects at the highest fluence. Ion beam irradiation is seen to tune the material bandgap. The observed reduction in bandgap with an increase in ion fluence can be correlated to the formation of shallow levels near the conduction band of the host material with ion fluence. Bigger grains of pristine NiO thin film are broken into smaller fragments at fluences 5 1015 and 1 1016 ions/cm2. AFM analysis revealed the smoothening of thin film surfaces due to the atomic diffusion arising from ion beam irradiation. The correlated results from structural and morphological analysis support the deposition of subsequent amounts of energy to the lattice and the consequent modifications in the thin film properties. NiO films can thus be tailored with different ion fluences, making them suitable for optical as well as energy storage applications. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Post-millennials: Psychosocial Characteristics, Determinants of Health and Well-Being, Preventive and Promotive Strategies
The post-millennial generation plays a significant role in the progress and development of every nation. The health and well-being of this generation needs critical focus. Post-millennial are unique, possesses an egalitarian worldview, global and open mind-set, commitment to the environment, society, and others. They are called digital natives due to their familiarity with social media and technology. They are also called snowflake generation due to their characteristics of being gentle and unique. Double income households and well-educated parents and their assistance make them a distinguished population. However, these characteristics are less explored while addressing the health and wellness concerns of this cohort. The present chapter discusses the psychosocial characteristics of the post-millennials and their implications exclusively in the mental health realm. It also presents the strength-based strategies to address the concerns of post-millennials and the significance of evidence-based practices in mental health and sensitizing mental health practitioners about the changing scenario. The Editor(s) (if applicable) and The Author(s), under exclusive license to Taylor and Francis Pte Ltd. 2022. -
Sentiment analysis with NLP: A catalyst for sales in analyzing the impact of social media ads and psychological factors online
This chapter explores the role of sentiment analysis, powered by NLP, in boosting sales amidst "Intersection of AI and Business Intelligence in Data-Driven DecisionMaking." It analyzes how social media ads and psychological factors shape online shopping behavior, demonstrating how sentiment analysis drives digital commerce sales. Sentiments from platforms like Twitter, Facebook, and Instagram are categorized into positive, negative, or neutral using advanced NLP algorithms. The chapter delves into psychological factors such as trust, credibility, brand perception, and emotional responses triggered by social media ads. Through sentiment analysis, patterns and correlations between sentiment expressions and consumer actions are revealed, illuminating the impact of social media advertising on online shopping behavior. This insight aids marketers in optimizing digital strategies, developing effective campaigns to enhance sales performance, and engaging customers in the online shopping domain. 2024, IGI Global. All rights reserved. -
Fabrication of silver nanoparticle decorated graphene oxide membranes for water purification, antifouling and antibacterial applications
The quality of portable water is adversely affected by inadequate wastewater treatment, increase in domestic & industrial waste, and microbial contamination of surface water sources. Purification techniques such as sedimentation, precipitation, filtration, and ion exchange can be employed to recover clean water from various impurities. Among these, membrane-based purification methods have become more appealing in recent years due to its cost-effective and energy-saving features. However, fouling is a ubiquitous problem in membrane-based purification technologies, which leads to reduced water permeation and quality. Present study embodies the development of silver decorated graphene oxide coated nylon membrane with remarkable antibacterial and antifouling properties. Antibacterial analysis of bacteria Staphylococcus aureus and Escherichia coli, validates that higher concentration of silver in GO (GO A500) composites hinder the growth of bacteria. The antifouling properties of GO A500 membrane showed flux recovery ratio of 96 % with irreversible fouling ratio of 3 % during the filtration of BSA (Bovine serum albumin) protein. Further fabricated composite membrane exhibited pure water flux of 46.7 L m?2 h?1 with dye removal rate of 95 %, 88 % and 85 % for Congo red, Rhodamine-B and Methylene blue respectively. Catalytic studies conducted on GO A500 membrane demonstrated the efficacy of their antifouling properties. The investigations revealed that the composite (GO A500) membrane has excellent antibacterial and antifouling properties, making it a suitable option for wastewater treatment applications. 2023 Elsevier B.V. -
Synthesis and characterization of graphene oxide and reduced graphene oxide membranes for water purification applications
Graphene oxide and reduced graphene oxide-based laminar membranes have been receiving increased attention for its novel filtration applications. In the present work, graphene oxide (GO) solution is synthesized by modified Hummers method and coated on cellulose nitrate by vacuum filtration technique. Further, reduced graphene oxide (rGO) membranes are fabricated by the controlled reduction of GO membrane using vitamin C solution at 60 C. The formation of GO and rGO is confirmed from Raman, FTIR, and UVVis spectroscopy studies. Morphology and thickness of the membranes are investigated using surface and cross-sectional FESEM images. The filtration study showed that rGO membrane has higher water flux (52 L m?2h?1) than GO (40 L m?2h?1) membrane at differential pressure of 0.3MPa. Moreover, both membranes show congo red rejection of 96%. Prior studies showed that water flux decreased upon reduction of GO membrane which hindered the filtration properties of the membrane. Current work indicates that controlled reduction of GO leads to an enhancement in water flux, maintaining the dye rejection ratio. Graphical Abstract: [Figure not available: see fulltext.] 2023, Qatar University and Springer Nature Switzerland AG. -
A Review on Advanced Nanomaterials for Antibacterial Applications
The management of infectious diseases is one of the major public health challenges of the 21st century. Mutation of the microbes, biofilm formation, and other structural-morpholo-gical behaviors have resulted in pathogens acquiring multi-drug resistance. The development of advanced materials that can provide long-lasting and effective protection against harmful microbes is becoming a need of the hour. Biocompatibility, efficient microbial inactivation, thermal and chemical stability of nanomaterials help to reduce the excessive use of antibiotics and, thus, to overcome antimicrobial resistance. Metal and metal oxide nanostructures, graphene, carbon dots, and other two-dimensional materials exhibit excellent antimicrobial properties. This review provides a comprehensive overview of antibacterial mechanisms and factors that help to inactivate the bacteria by nanomaterials. It also points out the enhanced antibacterial behaviors of the modified nanomaterials for future research concerns. 2023 Bentham Science Publishers. -
Study of mineral and nutritional composition of some seaweeds found along the coast of Gulf of Mannar, India
The presence of Algae on the Earth is ubiquitous. The industry that widely uses algae is food industry, where the algae are used as a food supplement and also as an addition to the nutrient rich food. This study emphasizes on the mineral and nutritional composition of the selected fourteen algal species which are abundantly found along the coast of the Gulf of Mannar. The selected species of algae belong to different algal families such as Chlorophyta, Phaeophyta and Rhodophyta. The amount of minerals such as Ca, Zn, Fe, K, Mg, Mn, and Cu were estimated by employing the method of acid digestion followed by atomic absorption spectroscopy. We estimated the nutritional content based on the assessment of total protein, carbohydrate, phenol, ash and moisture contents of the algal species. The results based on the analysis of the mineral content in the algal seaweeds depicted that the seaweeds comprised of high amount of the macro minerals and trace minerals. Estimation of nutritional composition revealed that these algal species are rich in protein and carbohydrate. The ash contents were found to be very high in Jania rubens (86.66%), Padina boergesenii (85%) and Valoniopsis pachynema (84%). Based on the present study we infer that the algal seaweeds contained high amount of the nutritional compounds, which might pave the way for a higher standard of nutritional supply to the humans in the future. Jose & Xavier (2020). This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited (https://creativecommons.org/licenses/by/4.0/). -
Seasonal Variation of Physicochemical Parameters and Their Impact on the Algal Flora of Chimmony Wildlife Sanctuary
Background and Objective: The lack of biodiversity knowledge and biodiversity loss are the two inevitable truths around us. Algae are the most crucial organism in our entire biodiversity. The seasonal variation of algal diversity can monitor the environmental changes of the freshwater ecosystem. The present study was conducted because the seasonal changes of algal diversity in Chimmony Wildlife Sanctuary were utterly unknown. Materials and Methods: The algal samples were collected and preserved from ten stations for three seasons (pre-monsoon, monsoon, post-monsoon). The physicochemical parameters of water like temperature, pH, total dissolved solids, total dissolved oxygen, total alkalinity and light intensity of the sampling stations were recorded. Results: The study revealed that the seasonal variation of physicochemical parameters provoked a change in the diversity of Algae. The Chimmony Wildlife Sanctuary has its highest algal diversity during pre-monsoon season. The Chlorophyceae Algae were dominant during the pre-monsoon season, while the Cyanophycean Algae were dominant during monsoon season. The ANOVA (two-way) analysis showed no significant difference between stations and there is a considerable difference between seasons for dissolved oxygen, alkalinity, temperature and total dissolved solids. While for pH, it showed no significant difference between seasons and stations but for light intensity, it showed a substantial difference between stations and seasons. A negative correlation was observed between algal species and seasons. The temperature and dissolved oxygen showed a negative correlation. Conclusion: The physicochemical parameters were changed according to the seasonal variation. Since Algae act as a biological pollution indicator for all the water resources, the study of algal flora according to the seasonal variation is crucial. 2022 Joel Jose and Jobi Xavier. -
The study of algal diversity from fresh water bodies of Chimmony Wildlife Sanctuary, Kerala, India
The algal diversity of the freshwater ecosystem is very significant because they are the primary energy producers in the food web. The study for the algal diversity was conducted at Chimmony Wildlife Sanctuary, Thrissur, Kerala, India, from selected sampling sites (Pookoyil thodu, Kidakkapara thodu, Viraku thodu, Nellipara thodu, Anaporu thodu, Kodakallu thodu, Odan thodu, Mullapara thodu, Payampara thodu, Chimmony dam). The identified algal species belong to four different classes: Chlorophyceae, Euglenineae, Rhodophyceae, and Cyanophyceae. Sixty-one algal species were identified, represented by 37 genera, 22 families, and 14 orders. Among the four, Chlorophyceae was the dominant class. Jose & Xavier 2022. Creative Commons Attribution 4.0 International License. JoTT allows unrestricted use, reproduction, and distribution of this article in any medium by providing adequate credit to the author(s) and the source of publication. -
Dimensionally engineered ternary nanocomposite of reduced graphene oxide/multiwalled carbon nanotubes/zirconium oxide for supercapacitors
Three dimensional (3D) hybrid nanoarchitecture of two-dimensional (2D) reduced graphene oxide/one dimensional (1D) multiwalled carbon nanotube and zero-dimensional (0D) zirconium oxide (ZrO2) nanoparticles (rGO/MWCNT/ZrO2) was synthesised by a simple hydrolysis method for high performance supercapacitors. To unlock the properties of individual materials to the maximum, binaries of ZrO2 with GO and MWCNT were also synthesised. The increased wettability, integrated structure, and the synergistic effect of rGO, MWCNT, and ZrO2 in rGO/MWCNT/ZrO2 (GMZ) offer a capacitance of 357 F g?1 at 1 A g?1 with excellent capacitance retention of 98% across 5000 cycles. 1D structure of MWCNT creates an exceptional conductive network with rGO due to the confinement of electrons and ions without disturbing its electronic structure. The intriguing supercapacitor performance of differently dimensioned framework with ZrO2 emphasises the engineered orientation and tuning of a designed environment for its appropriateness, uniqueness, and sensitivity to push up enhanced performance. 2021 Elsevier B.V. -
An electrochemical sensor for nanomolar detection of caffeine based on nicotinic acid hydrazide anchored on graphene oxide (NAHGO)
A simple modified sensor was developed with nicotinic acid hydrazide anchored on graphene oxide (NAHGO), by ultrasonic-assisted chemical route, using hydroxy benzotriazole as a mediator. Structural and morphologies of NAHGO samples were investigated in detail by Fourier-Transform Infrared spectroscopy (FT-IR), Powder X-ray diffraction (P-XRD), Raman spectroscopy, Scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), and Thermogravimetric analysis (TGA). The detailed morphological examination and electrochemical studies revealed the delaminated sheet with the tube-like structure of NAHGO provided the route for more electroactive surface which influenced the electrooxidation of caffeine with increased current. The electrochemical behaviour of NAHGO on a glassy carbon electrode (GCE) for caffeine detection was demonstrated by employing voltammetric techniques. The influence of scan rate, pH, and concentration on caffeine's peak current was also studied. The NAHGO sensor was employed for the determination of caffeine in imol plus and energy drinks. The detection limit determined was 8.7 109M, and the best value was reported so far. The results show that NAHGO modified electrodes are one of the best preferences to establish new, efficient, and reliable analytical tools for the detection of caffeine. 2021, The Author(s). -
Spectrochemical and theoretical approaches for acylhydrazone-based fluoride sensors
Abstract: Acylhydrazone derivatives N?-[1-(2-fluorophenyl)ethylidene]pyridine-3-carbohydrazide (R1) and N?-[2-fluorobenzylidene]benzohydrazide (R2) were synthesized from their corresponding hydrazides and characterized by spectroscopic methods. The response of these acylhydrazones towards different anions was studied by colorimetric and spectrofluorometric methods in acetonitrile. The receptors exhibited a specific response towards fluoride ion. The binding affinity of the receptors with fluoride anion was studied by fluorescence spectroscopic techniques and abinitio density functional theory calculations with Beckers three-parameter LeeYangPar (B3LYP) exchange functional with 6-311G basis set. Graphical abstract: [Figure not available: see fulltext.]. 2018, Springer Nature B.V. -
MADeGen: Multi-Agent based Deep Reinforcement Learning for Sequential Keyphrase Generation
Keyphrase generation is an essential tool in the field of natural language processing for information retrieval, document summarization, and text recommendation applications, extracting succinct and representative phrases from the text document. Traditional keyphrase extraction methods applied the supervised or unsupervised learning fail to capture the sequential keyphrase generation in a dynamic environment. The keyphrase generation approaches lack focus on explicitly discriminating the present and absent keyphrases, leading to the inadequate generation of semantically rich absent keyphrases. Hence, this work utilizes the potential benefits of reinforcement learning with the design of a distinguished reward function for present and absent keyphrases for sequential decision-making in the keyphrase generation. Thus, this work presents a novel keyphrase generation system, MADeGen, utilizing Multi- Agent Deep Reinforcement Learning (MADRL). In particular, a multi-agent reinforcement system collaboratively enables the generation of representative and coherent keyphrases by the evaluation metric-aware cooperative reward function analysis and adaptively training the agents. The proposed MADeGen incorporates two major phases, such as multi-agent modelling and actor critic-based policy optimization towards accurate keyphrase generation. In the first phase, the proposed approach designs two learning agents, including the extraction agent and generation agent, with the incorporation of a pre-trained language model. In the multi-agent system, the generation agent is the finetuned version of the extraction agent with the integration of the Wikipedia source. Secondly, the evaluation-aware adaptive reward function is designed to evaluate each agent's generated keyphrases with reference to ground-truth keyphrases. In subsequence, the cooperative reward analysis triggers the actor critic-based policy optimization for the generation agent in the multi-agent system to precisely generate the semantically relevant keyphrases with the assistance of an external web source. Experimental results on several benchmark datasets, such as Inspec, PubMed, and wiki20, illustrate the effectiveness of the proposed MADeGen compared to the existing keyphrase extraction models, yielding state-of-the-art performance in keyphrase extraction tasks. The proposed MADeGen proves its higher performance in the present as well as absent keyphrase extraction as 0.367 and 0.438 F1-score, respectively, while testing on the Inspec dataset. (2024), (Intelligent Network and Systems Society). All Rights Reserved. -
GWebPositionRank: Unsupervised Graph and Web-based Keyphrase Extraction form BERT Embeddings
Automatic keyphrase extraction is considered a preliminary task in many Natural Language Processing (NLP) applications that attempt to extract the descriptive phrases representing the main content of a document. Owing to the need for a large amount of labelled training data, an unsupervised approach is highly appropriate for keyphrase extraction and ranking. Keyphrase Extraction with BERT Transformers (KeyBERT) leverages the BERT embeddings that utilize the cosine similarity to rank the candidate keyphrases. However, extracting keyphrases based on the fundamental cosine similarity measure does not consider the spatial dimension locally and globally. Hence, this work focuses on enhancing the KeyBERT-based method with a Graph-based WebPositionRank (GWebPositionRank) design. The proposed unsupervised GWebPositionRank is the composition of graph-based ranking, referring to local analysis and web-based ranking, referring to the global analysis. To spatially examine the keyphrases, the proposed approach conducts the keyphrase position analysis at the document level through graph-based ranking and the web level using the WebPositionRank algorithm. Initially, the proposed approach extracts the coarse-grained keyphrases from the KeyBERT model and ranks the extracted keyphrases, the modelling of quality and fine-tuned keyphrases. In the GWebPositionRank method, the quality keyphrase ranking involves the document-level position analysis and four different graph centrality measures in a constructed textual graph for each text document, whereas the fine-tuned keyphrase ranking involves the web-level position analysis and diversity computation for the quality keyphrases extracted from the graph-based ranking method. Thus, the proposed approach extracts a set of potential keyphrases for each document through the advantage of the GWebPositionRank algorithm. The experimental results illustrate that the proposed unsupervised algorithm yielded superior results than the comparative baseline models while testing on the SemEval2017 dataset. 2024 IEEE. -
Photophysical and Electrochemical Studies of Anchored Chromium (III) Complex on Reduced Graphene Oxide via Diazonium Chemistry
Covalently anchored chromium complex on reduced graphene oxide (rGO-Cr) is successfully synthesised through trimethoxy silyl propanamine (TMSPA) and phenyl azo salicylaldehyde (PAS) coupling. The rGO-Cr is characterised by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), electron dispersive analysis of X-rays (EDAX), Raman spectroscopy, scanning electron microscopy (SEM) and high resolution transmission electron microscopy (HRTEM). Absorption and emission properties of rGO-TMSPA-PAS are studied by excitation dependent photoluminescence emissions at room temperature. Electrochemical sensing activity of rGO-Cr is monitored for paracetamol using modified glassy carbon electrode. Cyclic voltammetry measurements indicated that rGO-Cr substantially enhance the eletrochemical response of paracetamol. The experimental factors are investigated and optimized. 2019 John Wiley & Sons, Ltd. -
Machine Learning Algorithms for Prediction of Mobile Phone Prices
The drastic growth of technology helps us to reduce the man work in our day-to-day life. Especially mobile technology has a vital role in all areas of our lives today. This work focused on a data-driven method to estimate the price of a new smartphone by utilizing historical data on smartphone pricing, and key feature sets to build a model. Our goal was to forecast the cost of the phone by using a dataset with 21 characteristics related to price prediction. Logistic regression (LR), decision tree (DT), support vector machine (SVM), Naive Bayes algorithm (NB), K-nearest neighbor (KNN) algorithm, XGBoost, and AdaBoost are only a few of the popular machine learning techniques used for the prediction. The support vector machine achieved the highest accuracy (97%) compared to the other four classifiers we tested. K-nearest neighbors 94% accuracy was close to that of the support vector machine. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.