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Two-dimensional Ti3C2 MXene for photocatalytic hydrogen production: A review
This study focuses on the utilization of two-dimensional Ti3C2 MXene as a catalyst for photocatalytic hydrogen production. MXenes, a class of transition metal carbides/nitrides, exhibit exceptional properties conducive to enhancing photocatalytic reactions. This research explores the performance of Ti3C2 MXene as a cocatalyst in photocatalytic systems, aiming to improve charge separation, inhibit recombination, and facilitate efficient hydrogen evolution from water under light irradiation. The synthesis methods, catalyst-loading strategies, and overall photocatalytic mechanisms are investigated, shedding light on the potential of Ti3C2 MXene as a promising material for advancing hydrogen production through sustainable means. 2023 Korean Chemical Society, Seoul & Wiley-VCH GmbH. -
Two-dimensional CR2C MXENE decorated with COFE204 nanoparticles for high-performance supercapacitor application /
Patent Number: 202241046374, Applicant: B Shalini Reghunath.
The current innovation shows the cobalt ferrite (CoFe204) decorated on O2C MXene binary composite. This is used as a high-efficiency electrocatalyst for supercapacitor applications in alkaline media. The O2C MXene/CoFe204 binary composite is prepared by etching the O2AIC MAX phase with hydrofluoric acid for 30 min at room temperature, followed by a solvothermal technique using cobalt ferrite nanoparticles. The interlayer spaces of O2C MXene/CoFe204 electrocatalyst improves on introducing CoFe204 nanoparticles between the O2C MXene layers, thereby boosting the capacitance of the composite. -
Two-dimensional CR2C MXENE decorated with COFE204 nanoparticles for high-performance supercapacitor application /
Patent Number: 202241046374, Applicant: B Shalini Reghunath.
The current innovation shows the cobalt ferrite (CoFe204) decorated on O2C MXene binary composite. This is used as a high-efficiency electrocatalyst for supercapacitor applications in alkaline media. The O2C MXene/CoFe204 binary composite is prepared by etching the O2AIC MAX phase with hydrofluoric acid for 30 min at room temperature, followed by a solvothermal technique using cobalt ferrite nanoparticles. The interlayer spaces of O2C MXene/CoFe204 electrocatalyst improves on introducing CoFe204 nanoparticles between the O2C MXene layers, thereby boosting the capacitance of the composite. -
Two inventory models for growing items under different payment policies with deterioration
Industries of growing items show an upward trend in the production as well as in consumption. Poultry and livestock are good examples of growing items which are both deteriorating and ameliorating in nature. In this study apart from these specific features of growing items, one of the real-world business policies, permission of delay in payment is also considered. Present paper proposed two inventory models, one with the permission of delay in payment and another without it. Concavity of the profit functions with respect to decision variables are discussed analytically for both the models. Solution procedure and numerical examples are provided in order to get the managerial insights. The numerical analysis growth in weight is approximated by Richard's growth function. The numerical analysis predicts that net profit and the initial purchase quantity both increases under the permissible delay payment policy compared to without it. Sensitivity analysis provides important managerial insights. Copyright 2022 Inderscience Enterprises Ltd. -
Two distance forcing number of a graph
Motivated from the graph parameters namely zero forcing number, k-forcing number and the connected k-forcing number, in this article, we introduce a new parameter known as the 2-distance forcing number. Assume that each vertex of a graph G = (V (G), E(G)) is colored as either white or black. Consider the set Z2d of black colored vertices of the graph G. The color change rule changes the color of a white vertex v to black if the white vertex v is the only 2-distance white neighbor of a black vertex u. The set Z2d is called a two distance forcing set of G if all vertices of the graph G will be turned black after limited applications of the color change rule. The 2-distance forcing number of G, denoted by Z2d (G), is the minimum of | Z2d | over all 2-distance forcing sets Z2d ? V (G). This manuscript is intended to study the 2-distance forcing number of some graphs. We find the exact value of the 2-distance forcing number of graphs such as the pineapple graph, gear graph, jelly fish graph, helm graph, sunflower graph, comet graph and the n-pan graph. 2020 the author(s). -
Two dimensional fuzzy context-free languages and tiling patterns
Fuzzy context-free languages are powerful compared to fuzzy regular languages as they are generated by fuzzy context-free grammars and fuzzy pushdown automata, which follow an enhanced computational mechanism. A two dimensional language (picture language) is a collection of two dimensional words, which are a rectangular array of symbols made up of finite alphabets. Two dimensional automata can recognize two dimensional languages that could not be recognized by one dimensional automata. In this paper, we introduce two dimensional fuzzy context-free languages generated by the two dimensional fuzzy context-free grammars and accepted by the two dimensional fuzzy pushdown automata in order to deal with the vagueness that arises in two dimensional context-free languages. We can construct a two dimensional fuzzy context free grammar from the given two dimensional fuzzy pushdown automata and vice versa. In addition, we prove that two dimensional fuzzy context-free languages are closed under union, column concatenation, column star, homomorphism, inverse homomorphism, reflection about right-most vertical, reflection about base, conjugation and half-turn and also show that two dimensional fuzzy context-free languages are not closed under matrix homomorphism, quarter-turn and transpose. Further, we have given the applications and the uses of closure properties in the formation of tiling patterns. 2024 Elsevier B.V. -
Twitter sentiment for analysing different types of crimes
Online social media like a twitter play a vital role as it helps to track the Spatialoral on social media data with respect crime rate. With the very fast evolving of users in social media, sentimental analysis has become an excellent source of information in decision making. Twitter is one of the most popular social networking site for communication and a primary source of information. More than 150 million users publish above 500 million 140 character TWEETS each day. Tweets have become a basis for product recommendation using sentimental analysis. This paper explains the approach for analyzing the sentiments of the users about a particular crime event tweets posted by the active users. The results so obtained will let you know about the change in the public opinion about the crime events whether it's positive or negative and to find out emotions on different types of crimes. 2018 IEEE. -
Twitter Sentiment Analysis using Machine Learning Techniques: A Case Study of ChatGPT
ChatGPT is a powerful AI bot developed by OpenAI. This technology has the potential to generate a humanlike response. ChatGPT is a pre-trained system capable of generating chat and understanding human speech. This paper identified the responses of ChatGPT users through related tweets with the help of natural language processing and machine learning techniques. This paper uses textBlob, VADER and human annotation to find the sentiment of each tweet; countvectorizer is used for feature extraction and different machine learning algorithms to classify them into different classes. LeXmo is used to identify the various sentiment analyses, and it is observed that positive and trust emotions are higher than other sentiments. SVM with 10-fold cross-validation shows better results than other techniques. 2023 IEEE. -
Twitter sentiment analysis on online food services based on elephant herd optimization with hybrid deep learning technique
Twitter is a social media stage, making it a valuable resource for learning about peoples opinions, feelings, and thoughts. For this reason, experts came up with methods to analyse the tone of tweets and determine whether they were favourable or negative. This article aims to assist businesses, and especially app-based meal delivery businesses, in conducting competitive research on social broadcasting and transforming social broadcasting data into data production for decision-makers. In this analysis, we compared Swiggy, Zomato, and UberEats. Customers tweets about all these brands are obtained using R-Studio, and a deep learning-based sentiment examination approach is functional on the retrieved tweets. The pseudo-inverse learning autoencoder is able to provide feature extraction in the form of an analytic solution after pre-processing, without resorting to many iterations. In this research, we suggest framework for combining the Convolutional Neural Network (CNN) and Bi-directional Long Short Term Memory (Bi-LSTM) models. ConvBiLSTM is used, which is a word embedding model that uses numerical values to represent tweets. The CNN layer takes the feature implanting as input and outputs lower features. In this instance, elephant herd optimization is used to fine-tune the Bi-LSTM weights. Among the three firms, the results indicate that Zomato got the most positive feedback (29%), followed by Swiggy (26%), and UberEats (25%). Zomato also had fewer bad reviews than Swiggy and UberEats, with only 11% of users having a poor experience. In addition, tweets were evaluated for unfavourable views against all three meal delivery services, and suggestions for improvement were offered. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
Twitter Sentiment Analysis Based on Neural Network Techniques
Our whole world is changing everyday due to the present pace of innovation. One such innovation was the Internet which has become a vital part of our lives and is being utilized everywhere. With the increasing demand to connected and relevant, we can see a rapid increase in the number of different social networking sites, where people shape and voice their opinions regarding daily issues. Aggregating and analysing these opinions regarding buying products and services, news, and so on are vital for todays businesses. Sentiment analysis otherwise called opinion mining is the task to detect the sentiment behind an opinion. Today, analysing the sentiment of different topics like products, services, movies, daily social issues has become very important for businesses as it helps them understand their users. Twitter is the most popular microblogging platform where users put voice to their opinions. Sentiment analysis of Twitter data is a field that has gained a lot of interest over the past decade. This requires breaking up tweets to detect the sentiment of the user. This paper delves into various classification techniques to analyse Twitter data and get their sentiments. Here, different features like unigrams and bigrams are also extracted to compare the accuracies of the techniques. Additionally, different features are represented in dense and sparse vector representation where sparse vector representation is divided into presence and frequency feature type which are also used to do the same. This paper compares the accuracies of Nae Bayes, decision tree, SVM, multilayer perceptron (MLP), recurrent neural network (RNN), convolutional neural network (CNN), and their validation accuracies ranging from 67.88 to 84.06 for different classification techniques and neural network techniques. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Twitter Sentiment Analysis and Emotion Detection Using NLTK and TextBlob
On an average, approximately 7000 tweets are communicated each second and in total it piles up to around 300 billion tweets every year. Society are free to contribute their opinions on public platform and hence it acts as a reliable interface to assess society ongoing viewpoint and attitude over any matter or event. Consumers very often make use of social media to exchange their views about anything. Business may get domain for enhancement and smooth interpretation of the behavior of people regarding various facts through opinion mining. Thus to carry out this mining of opinions on social media interface, textual categorization with language analysis is of great help. With the help of NLP token tool, phrases can be divided into various word series after dropping stop phrases. Larger tweets tokenizing and classifying into distinct labels is a concern. Thus, the main objective of this framework is to process the tweets based on specific keywords given by user, categorize these phrases into negative, positive and neutral ones. TextBlob module assists users and developers to interpret user sentiments about a news. This research tries to give suggestion a textual opinion assessment on social media samples utilizing the NLTK and TextBlob modules. 2023 IEEE. -
Twitter Hate Speech Detection using Stacked Weighted Ensemble (SWE) Model
Online Social Media has expanded the freedom of expression in the internet, which has become a disturbing problem if it has an impact on the situation or the interest of a country. Hate speech refers to the use of hostile, abusive or offensive language, directed at a certain group of people who share common property, whether it is their gender, ethnicity or race (i.e. racism), faith and religion. Therefore, auto detection of hate speeches has an increased importance in Online Social Media for filtering any message that has hatred language before posting it to the network. In this paper, a Stacked Weighted Ensemble (SWE) model is proposed for the detection of hate speeches. The model ensembles five standalone classifiers: Linear Regression, Nae Bayes', Random Forest, Hard Voting and Soft Voting. The experimental results on a Twitter dataset has shown an accuracy of 95.54% in binary classification of tweets into hateful speech and an improved performance is noted compared to the standalone classifiers. 2020 IEEE. -
Twitter data analysis using hadoop ecosystems and apache zeppelin
The day-to-day life of the people doesn't depend only on what they think, but it is affected and influenced by what others think. The advertisements and campaigns of the favourite celebrities and mesmerizing personalities influence the way people think and see the world. People get the news and information at lightning speed than ever before. The growth of textual data on the internet is very fast. People express themselves in various ways on the web every minute. They make use of various platforms to share their views and opinions. A huge amount of data is being generated at every moment on this process. Being one of the most important and well-known social media of the present time, millions of tweets are posted on Twitter every day. These tweets are a source of very important information and it can be made use for business, small industries, creating government policies, and various studies can be performed by using it. This paper focuses on the location from where the tweets are posted and the language in which the tweets are written. These details can be effectively analysed by using Hadoop. Hadoop is a tool that is used to analyze distributed big data, streaming data, timestamp data and text data. With the help of Apache Flume, the tweets can be collected from Twitter and then sink in the HDFS (Hadoop Distributed File System). These raw data then analyzed using Apache Pig and the information available can be made use for social and commercial purposes. The result will be visualized using Apache Zeppelin. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved. -
Twins in diversity: Understanding circumstellar disc evolution in the twin clusters of W5 complex
Young star-forming regions in massive environments are ideal test beds to study the influence of surroundings on the evolution of discs around low-mass stars. We explore two distant young clusters, IC 1848-East and West located in the massive W5 complex. These clusters are unique due to their similar (distance, age and extinction) yet distinct (stellar density and far-ultraviolet radiation fields) physical properties. We use deep multiband photometry in optical, near-infrared and mid-infrared wavelengths complete down to the substellar limit in at least five bands. We trace the spectral energy distribution of the sources to identify the young pre-main sequence members in the region and derive their physical parameters. The disc fraction for the East and West clusters down to 0.1?M was found to be 2 per?cent (N = 184, N = 492) and 1 per?cent (N = 173, N = 814), respectively. While no spatial variation in the disc fraction is observed, these values are lower than those in other nearby young clusters. Investigating the cause of this decrease, we find a correlation with the intense feedback from massive stars throughout the cluster area. We also identified the disc sources undergoing accretion and observed the mass accretion rates to exhibit a positive linear relationship with the stellar host mass and an inverse relationship with stellar age. Our findings suggest that the environment significantly influences the dissipation of discs in both clusters. These distant clusters, characterized by their unique attributes, can serve as templates for future studies in outer galaxy regions, offering insights into the influence of feedback mechanisms on star and planetary formation. 2024 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. -
Twin deficit hypothesis: Some recent evidence from India /
Global Business And Economics Review, Vol.18, Issue 3/4, pp.487 - 495, ISSN: 1097-4954. -
Twin deficit hypothesis: Some recent evidence from India
The purpose of this study is to examine the relationship between budget deficit and trade deficit commonly known as 'twin deficits hypotheses' in Indian economy. We used time series data for the period of 1970 to 2013. The empirical results of this study follow the autoregressive distributed lag (ARDL) cointegration technique for long run and short run estimates and error correction mechanism (ECM). In this study, we check the hypotheses that trade deficit is the determinant of budget deficit with its current values or the lag values. The results of the ARDL model confirm that there is the positive and significant relationship between trade deficit and budget deficit. So twin deficits hypothesis is valid for India. The ARDL results of the short run confirm the hypothesis that trade deficit can determine the budget deficit in the case of India. The results of the long run estimates are also significant. The error correction specification is used to find evidence of long-run causality running from budget deficit to trade deficit and vice versa. The empirical results suggest that trade deficit can determine the budget deficit in case of India. 2016 Inderscience Enterprises Ltd. -
Tweaking the electrocatalytic ability of Cu-MOF by the inclusion of PTA: a selective electrochemical sensor for resorcinol
Resorcinol (RL) is a phenolic compound that is extensively utilized in the industrial sector, mostly for skin care applications as an antiseptic and disinfectant. However, this chemical has the potential to be very hazardous to people and the environment due to its pernicious nature in the environment owing to its high degree of toxicity and weak degradation capacity. Finding novel analytical techniques to monitor RL is therefore crucial. A facile and superior electrochemical fabrication route was procured to develop the composite of Cu-BTC-MOF/PTA/CFP for the sensitive detection of resorcinol (RL). The modified Cu-BTC-MOF/PTA/CFP (copper benzene-1,3,5-tricarboxylate-poly-3-thiophene acetic acid) electrode displayed improved electron transport features as well as excellent electrocatalytic performance. The developed electrode was characterized using physicochemical and electrochemical techniques. The enhanced electrochemical activity of the Cu-BTC-MOF/PTA/CFP electrode compared to the individual MOF and polymer electrode was examined using electrochemical characterization, which revealed a 10-fold increase in the current response for Cu-BTC-MOF/PTA/CFP (0.004 A) compared to the bare electrode. The cyclic voltammetric analysis of the Cu-BTC-MOF/PTA/CFP electrode in the presence of 120 nM analyte gave an oxidation peak at 0.62 V and a 5.4-fold increase in the current peak compared to the bare CFP electrode suggesting a higher sensitivity in sensing the analyte. The limit of detection for RL under optimal conditions was calculated to be 8 nM with a broad linear range from 0.025 ?M to 350 ?M. In addition, the Cu-MOF/PTAA/CFP electrode was scrutinized for its stability, reproducibility, and selectivity. Real sample analysis was carried out to validate the analytical applications. 2024 RSC. -
Turbulent Flow in Forced Convection Heat Transfer-Numerical Validation
Forced convective heat transfer of airflow through circular pipe with constant heat input and different free stream velocities is numerically validated. The significance of the present work is that the suction flow has been employed in the forced convection set up domain kept in the wind tunnel. From first law of thermodynamics and applying the energy balance equation, experimental heat transfer coefficient is determined. Further correlations are used to validate the experimental results. Although correlations provide reasonable estimates from the point of feasibility and accuracy, computational methods are used to estimate the convective heat transfer coefficient. Hence in this paper experimental, theoretical and computational analysis is carried out. The results reveal that the numerical validation is an effective tool from the point of feasibility and accuracy to determine the convective heat transfer coefficient. 2022. MechAero Foundation for Technical Research & Education Excellence. -
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. -
Tuning the output of the higher plants Circadian Clock
The circadian clock is an ascribed regulator found in the cells of creatures, that keeps biological and behavioral processes in stnc with dailt environmental changes throughout the 24-hour ctcles. When the circadian clock in humans malfunctions or is misaligned with environmental signals, the timing of the sleep-wake ctcle is altered and several circadian rhtthm sleep disorders result. Due to the Earth's rotation on its axis, predictable environmental changes are anticipated bt complex processes. The combined term for these ststems is the circadian clock. The circadian rhtthm regulates photostnthesis and photoperiodism, making it the "primart controller of plant life." The circadian clock is made up of post-translational alterations to core oscillators, epigenetic tweaks to DNA and histones, and auto regulatort feedback loops in transcription. In addition, the circadian clock is cell-autonomous and regulates the circadian rhtthms of distinct organs. Biochemical elements such as photostnthetic products, mineral nutrients, calcium ions, and hormones are used bt the core oscillators to communicate with one another. Arabidopsis is utilized to identift clock-related genes that govern plant growth, germination, pollination, flowering, abiotic and biotic stress responses, and more. The biological ctcles of all species, notablt humans, are undoubtedlt impacted bt other elements, including high altitude and changing ecoststems, in addition to the ones alreadt stated. Although it hasn't tet published ant experimental or scientific evidence to support them, the implication that living things have lives does appear inescapable. Hence, the present studt elaborates on the higher plants related to the circadian clock. The Author(s).