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DISCOURSE OF DISSENT: LANGUAGING RESISTANCE AND CONSCIOUSNESS IN SUBALTERN LITERATURES DALIT AND BLACK
The paper highlights the pivotal role of language in Afro-American and Dalit movements, emphasizing identity affirmation and resistance to dominant aesthetic structures. It examines languages dynamic role in shaping subaltern experiences and fuelling revolutionary movements. While there is some analysis of the significance of literary trends and intellectual current in these parallel movements, a few scholarly inquiriesintegratethelinguisticandstylisticaspectscomprehensively. Thestudyaddresses this critical gap by comparing and contrasting the selected study of these two movements to see their convergences and divergences. We employ the theoretical framework of Subaltern Studies and Distributed Language (DL) to understand socio-political motifs of pre- and post-production of a particular kind of language. The selected poems are closely read and analysed through Critical Discourse Analysis, with close reading as a key technique. It allows for an exploration of the intricate relationship between the linguistic structure, use of lexical items, emotive use of language, connotational significations, and compositional semantics. While selected Black literature poems experimented with internal morpho-syntax and everyday language, Dalit literature bluntly presented harsh facts using multilingualism, a unique Indian linguistic trait, and everyday vocabulary. Copyright 2024 Chandan Kumar, Nivea Thomas K. -
Influence of Te doping on the dielectric and optical properties of InBi crystals grown by directional freezing
Stoichiometric pure and tellurium (Te) doped indium bismuthide (InBi) were grown using the directional freezing technique in a fabricated furnace. The X-ray diffraction profiles identified the crystallinity and phase composition. The surface topographical features were observed by scanning electron microscopy and atomic force microscopy. The energy dispersive analysis by X-rays was performed to identify the atomic proportion of elements. Studies on the temperature dependence of dielectric constant (?), loss tangent (tan?), and AC conductivity (?ac) reveal the existence of a ferroelectric phase transition in the doped material at 403 K. When InBi is doped with tellurium (4.04 at%), a band gap of 0.20 eV can be achieved, and this is confirmed using Fourier transform infrared studies. The results thus show the conversion of semimetallic InBi to a semiconductor with the optical properties suitable for use in infrared detectors. 2014 University of Science and Technology Beijing and Springer-Verlag Berlin Heidelberg. -
A homotopy-based computational scheme for two-dimensional fractional cable equation
In this paper, we examine the time-dependent two-dimensional cable equation of fractional order in terms of the Caputo fractional derivative. This cable equation plays a vital role in diverse areas of electrophysiology and modeling neuronal dynamics. This paper conveys a precise semi-analytical method called the q-homotopy analysis transform method to solve the fractional cable equation. The proposed method is based on the conjunction of the q-homotopy analysis method and Laplace transform. We explained the uniqueness of the solution produced by the suggested method with the help of Banach's fixed-point theory. The results obtained through the considered method are in the form of a series solution, and they converge rapidly. The obtained outcomes were in good agreement with the exact solution and are discussed through the 3D plots and graphs that express the physical representation of the considered equation. It shows that the proposed technique used here is reliable, well-organized and effective in analyzing the considered non-homogeneous fractional differential equations arising in various branches of science and engineering. 2024 World Scientific Publishing Company. -
Grey Wolf optimization-Elman neural network model for stock price prediction
Over the past two decades, assessing future price of stock market has been a very active area of research in financial world. Stock price always fluctuates due to many variables. Thus, an accurate prediction of stock price can be considered as a tough task. This study intends to design an efficient model for predicting future price of stock market using technical indicators derived from historical data and natural inspired algorithm. The model adopts Elman neural network (ENN) because of its ability to memorize the past information, which is suitable for solving stock problems. Trial and error-based method is widely used to determine the parameters of ENN. It is a time-consuming task. To address such an issue, this study employs Grey Wolf optimization (GWO) algorithm to optimize the parameters of ENN. Optimized ENN is utilized to predict the future price of stock data in 1day advance. To evaluate the prediction efficiency, proposed model is tested on NYSE and NASDAQ stock data. The efficacy of the proposed model is compared with other benchmark models such as FPA-ELM, PSO-MLP, PSOElman,CSO-ARMA and GA-LSTM to prove its superiority. Results demonstrated that the GWO-ENN model provides accurate prediction for 1day ahead prediction and outperforms the benchmark models taken for comparison. 2020, Springer-Verlag GmbH Germany, part of Springer Nature. -
Fusion model of wavelet transform and adaptive neuro fuzzy inference system for stock market prediction
Stock market prediction is one of the most important financial subjects that have drawn researchers attention for many years. Several factors affecting the stock market make stock market forecasting highly complicated and a difficult task. The successful prediction of a stock market may promise attractive benefits. Various data mining methods such as artificial neural network (ANN), fuzzy system (FS), and adaptive neuro-fuzzy inference system (ANFIS) etc are being widely used for predicting stock prices. The goal of this paper is to find out an efficient soft computing technique for stock prediction. In this paper, time series prediction model of closing price via fusion of wavelet-adaptive network-based fuzzy inference system (WANFIS) is formulated, which is capable of predicting stock market. The data used in this study were collected from the internet sources. The fusion forecasting model uses the discrete wavelet transform (DWT) to decompose the financial time series data. The obtained approximation and detailed coefficients after decomposition of the original time series data are used as input variables of ANFIS to forecast the closing stock prices. The proposed model is applied on four different companies previous data such as opening price, lowest price, highest price and total volume share traded. The day end closing price of stock is the outcome of WANFIS model. Numerical illustration is provided to demonstrate the efficiency of the proposed model and is compared with the existing techniques namely ANN and hybrid of ANN and wavelet to prove its effectiveness. The experimental results reveal that the proposed fusion model achieves better forecasting accuracy than either of the models used separately. From the results, it is suggested that the fusion model WANFIS provides a promising alternative for stock market prediction and can be a useful tool for practitioners and economists dealing with the prediction of stock market. 2019, Springer-Verlag GmbH Germany, part of Springer Nature. -
Stock price prediction based on technical indicators with soft computing models
Stock market prediction is a very tough task in the finance world. Since stock prices are dynamic, noisy, non-scalable, non-linear, non-parametric and complicated. In recent years, soft computing techniques are used for developing stock prediction model. The main focus of this study is to develop and compare the efficiency of the three different soft computing techniques for predicting the intraday price of individual stocks. The proposed models are based on Time Delay Neural Network (TDNN), Radial Basis Function Neural Network (RBFNN) and Back Propagation Neural Network (BPNN). The predictive models are developed using technical indicators. Sixteen technical indicators were calculated from the historical price and used as inputs of the developed models. Historical prices from 01/01/2018 to 28/02/2018, where the time interval between samples is one minute, are utilized for developing models. The performance of the proposed models is evaluated by measuring some metrics. Also, this study compares the results with other existing models. The experimental result revealed that the BPNN outperforms TDNN, RBFNN as well as other existing models considered for comparison. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. -
Cat Swarm Optimization Algorithm Tuned Multilayer Perceptron for Stock Price Prediction
Due to the nonlinear and dynamic nature of stock data, prediction is one of the most challenging tasks in the financial market. Nowadays, soft and bio-inspired computing algorithms are used to forecast the stock price. This article assesses the efficiency of the hybrid stock prediction model using the multilayer perceptron (MLP) and cat swarm optimization (CSO) algorithm. The CSO algorithm is a bio-inspired algorithm inspired by the behavior traits of cats. CSO is employed to find the appropriate value of MLP parameters. Technical indicators calculated from historical data are used as input variables for the proposed model. The model's performance is validated using historical data not used for training. The model's prediction efficiency is evaluated in terms of MSE, MAPE, RMSE and MAE. The model's results are compared with other models optimized by various bio-inspired algorithms presented in the literature to prove its efficiency. The empirical findings confirm that the proposed CSO-MLP prediction model provides the best performance compared to other models taken for analysis. 2022 Polish Academy of Sciences. All rights reserved. -
Performance comparison of artificial neural network techniques for foreign exchange rate forecasting
Artificial Neural Networks is one of the promising techniques for forecasting financial time series markets and business. In this paper, Radial Basis Function is used to forecast the daily foreign exchange rate of USD in terms of Indian rupees in India during the period 2009-2014. Here, seven technical indicators like simple moving average of one week, Two week, Momentum, Price rate of change, Disparity 7, Disparity 14, Price oscillator are proposed as inputs for forecasting the time series. In addition, this study compares the four models namely Pattern Recognition Networks, Feed Forward Back Propagation Networks, Feed Forward Networks with no feedback, and Radial Basis Function Network to forecast the daily currency exchange rate during the period. The performance of all these models are analysed from accuracy measures namely Mean Square Error, Mean Absolute Error, Sum Square Error and Root Mean Square Error. From the simulation results, the average performance of Radial Basis Function network was found considerably better than the other networks. Research India Publications. -
Forecasting of foreign currency exchange rate using neural network
Foreign exchange market is the largest and the most important one in the world. Foreign exchange transaction is the simultaneous selling of one currency and buying of another currency. It is essential for currency trading in the international market. In this paper, we have investigated Artificial Neural Networks based prediction modelling of foreign exchange rates using five different training algorithms. The model was trained using historical data to predict four foreign currency exchange rates against Indian Rupee. The forecasting performance of the proposed system is evaluated by using statistical metric and compared. From the results, it is confirmed that the new approach provided an improve technique to forecast foreign exchange rate. It is also an effective tool and significantly close prediction can be made using simple structure. Among the five models, Levenberg-Marquardt based model outperforms than other models and attains comparable results. It also demonstrates the power of the proposed approach and produces more accurate prediction. In conclusion, the proposed scheme can improve the forecasting performance significantly when measured on three commonly used metrics. -
Crude oil prediction using a hybrid radial basis function network
In the recent years, the crude oil is one of the most important commodities worldwide. This paper discusses the prediction of crude oil using artificial neural networks techniques. The research data used in this study is from 1st Jan 2000- 31st April 2014. Normally, Crude oil is related with other commodities. Hence, in this study, the commodities like historical datas of gold prices, Standard & Poors 500 stock index (S & P 500) index and foreign exchange rate are considered as inputs for the network. A radial basis function is better than the back propagation network in terms of classification and learning speed. When creating a radial basis functions, the factors like number of radial basis neurons, radial layers spread constant are taken into an account. The spread constant is determined using a bio inspired particle swarm optimization algorithm. A hybrid Radial Basis Function is proposed for forecasting the crude oil prices. The accuracy measures like Mean Square Error, Mean Absolute Error, Sum Square Error and Root Mean Square Error are used to access the performance. From the results, it is clear that hybrid radial basis function outperforms the other models. 2005 - 2015 JATIT & LLS. All rights reserved. -
Nanocarbon assisted green hydrogen production: Development and recent trends
The increasing consumption of energy and consequent fast depletion of fossil fuels and associated environmental challenges necessitate transformative innovations in the field of energy conversion. Owing to its exceptional energy density and zero emissions during combustion, Hydrogen is hailed as a promising source of clean and renewable energy that can replace fossil fuels in future energy conversion systems. Since Hydrogen is not readily available in the atmosphere, a variety of pathways have been followed for the evolution of Hydrogen from water and organic materials, which requires the involvement of catalysts to accelerate the reactions. Currently, noble metals and their alloys represent state-of-the-art materials for HER (Hydrogen Evolution Reaction), and the scarcity and high expense of such materials impose significant constraints on their widespread implementation in hydrogen production. In this context, nanocarbons and their composites for HER are worth exploring owing to their abundance, cost-effectiveness, eco-friendliness, exceptionally large surface-to-volume ratio, and excellent electrical and charge transfer properties. Here, three leading hydrogen production methods - biological, electrochemical, and photo-driven- are analyzed based on their characteristics, effectiveness, and limitations w.r.t. different nanocarbon materials. 2023 Hydrogen Energy Publications LLC -
Psychological capital and innovative work behaviour: The role of mastery orientation and creative self-efficacy
Continuous innovation is what helps companies survive the highly discontinuous competition. Securing innovative work behaviour from employees has drawn the attention of businesses and researchers alike. The current work draws on broaden-and-build theory and goal orientation theory to propose how an individual's psychological capital, which is malleable, helps in achieving innovative work behaviour from employees. The study has been conducted in the context of three-star hotels located in and around New Delhi, the capital of India. The data was collected using standard scales from a dyad of 229 employees and their managers. The present study enriches the innovative work behavior literature by combining different perspectives in a coherent framework and demonstrates the partially mediated positive relationship of psychological capital and innovative work behavior via mastery orientation. Also, the study reveals that the partially mediated indirect effect varies among employees based on their level of CSE. 2022 Elsevier Ltd -
Agriculture 4.0 and smart farming: Imperatives of scaling up innovation and farmer capabilities for sustainable business
Smart agriculture adoption during industry 4.0 is creating new scenarios to farmers across the world. Smart farming promotes not only an increase in the agricultural productivity and incomes, but also building resilience to climate change. Small business farmers had to look at all possible means to cope with the technology applications for implementation of agro-transformation agendas for improved production and business performance. Smart farmers have to make use of several technology applications like drones and satellites, IoT (Internet of Things) based sensors, block chain and big data, biotech, farm maintenance technology (optimising water usage, production, and innovation technology) for better agricultural practices. Though such aggrotech opportunities have demonstrated business improvements, how far such smart farming revolution is well received by the agribusiness owners are less researched into. Henceforth, the purpose of this research is to establish the relationship between aggrotech innovation capabilities and farmer's capabilities associated with agriculture firms and its contributions to business performance. Following cross-sectional descriptive study design, and purposive sampling, the study addressed 3 direct and 2 indirect relationships in the model, on 212 farmers. The data was collected from Selangor state of Malaysia. The study applied Smart PLS SEM to analyse the data. The results show that the innovation (technology) capability and farmer's (people) capability have a positive relationship on business performance. The study also shows the partial mediation effect of technology change on innovation capability and business performance as well as employee capability and business performance. The study is novel in its form by applying Resource Based View theory on Smart agriculture, extending possibilities of generalization agriculture sector. 2021 Ecological Society of India. All rights reserved. -
Design of body wearable antenna for medical monitoring devices
In this research work, an inset fed microstrip patch antenna, an analysis of its effect on human body and human body influence on antenna performance are presented. Polystyrene substrate (?r= 2.6) with a 1 mm thickness is used to create the proposed antenna. Use of HFSS Ver. 18.2 is made for simulations. The simulated antenna exhibits S11 of -37.4 dB in the absence of human arm and -28.39 dB in the presence. Similar to this, the SAR findings showed that the Specific Absorption Rate (SAR) value obtained is 1.28 W/kg, which is significantly less than the allowed standard of 2 W/kg, when the suggested antenna is set at an offset of 2 mm off the body's surface. Hence the proposed antenna can be suitable for integrating with medicalmonitoring devices. 2024 Author(s). -
Antenna Array with Non-Uniform Excitation and DNG Hybrid Metasurface for Next Generation Communication Equipment
This paper presents an approach for designing a hybrid metasurface array with nonuniform excitation. The proposed design features a unique feed network with minimal use of Quarter Wave Transformers (QWT's) to reduce the form-factor. The impedance matching between the feed network and the patch is achieved by adjusting the inset position and the gap between the patch and the feed. The metasurface consists of a hybrid metamaterial unit cell with five Split Ring Resonators (SRRs) on the bottom and a hexagonal ring made of six triangles on the top surface improves the bandwidth, gain and directivity of the proposed design. Equivalent circuit of the proposed array is modeled using ADS software. A prototype 1x4 array with metasurface is designed for a resonant frequency of 2.4 GHz and fabricated. A high gain of 9.46 dB with a -10 dB impedance bandwidth of 110 MHz is achieved, which amounts to an improvement of 16.36% gain and 31.58% bandwidth compared to conventional uniform excitation array. In terms of overall size, the proposed array antenna is reduced by 38.05% compared to traditional 1x4 microstrip array. 2021 IAMOT. All Rights Reserved. -
A Slotted Circular Patch Antenna with Defected Ground for Sub 6 GHz 5G Communications
In this paper, a slotted circular patch antenna with Defected Ground Structure (DGS) is presented. The slots created on radiating element and the defect introduced on the ground plane shifted the resonance frequency from 2.49 GHz to 1.17 GHz. This corresponds to 53% reduction in size at 1.17 GHz. The proposed antenna is designed on FR-4 substrate (r=4.4) with thickness of 1.6 mm. Simulations are carried out using HFSS Ver. 18.2. The simulated reflection coefficient of Circular Patch Antenna (CPA) at 2.49 GHz, Slotted Circular Patch antenna (SCPA) at 2.34 GHz and Slotted Circular Patch antenna with Defected Ground Structure (SCPA-DGS) at 1.17 GHz are - 28.7 dB, -31.33 dB and -11.03 dB respectively. For validating the simulated design, SCPA-DGS is fabricated and measured its reflection coefficient and VSWR using Vector Network Analyzer (Anritrsu S820E). The measured and simulated values are very well matched with each other. Therefore the proposed antennas may be used in sub 6 GHz 5G communication applications. 2022 IEEE. -
A miniaturized antenna array for direct air-to-ground communication of aircrafts
In this paper, a miniaturized, high directivity low-cost antenna array is presented. The uniqueness of the proposed array (PA) exists in the feed mechanism designed using Dolph-Chebyshev non-uniform excitations. Authors simulated the designed antenna array using ANSYS EM 18.2 (HFSS) software and characterization is carried out in a fully established anechoic chamber. The simulated array antenna is operating at 2.4 GHz with a gain of 8.12 dB and a reflection coefficient of -28.45 dB having a bandwidth of 110 MHz. On contrast with the traditional array (TA), PA exhibits enhanced resonance characteristics by maintaining the same radiation characteristics. The bandwidth is increased by 37.5%, maintaining the same gain of 8.12 dB. In contrast, there is a remarkable reduction in the size compared to the traditional corporate feed array antenna with non-uniform excitation. The overall size of the PA antenna is 242.5 mm 58.8 mm, which is 33.73% less compared to the TA. Published under licence by IOP Publishing Ltd. -
Performance analysis of optimized corporate-fed microstrip array for ISM band applications
This paper presents a low cost high gain corporate feed rectangular microstrip patch antenna array of two elements having cuttings at the corners, with detailed steps of design process, operates in Industrial Scientific Medical (ISM) band (2.4 GHz). The proposed antenna structures are designed using FR4 dielectric substrate having permittivity ?r= 44 and substrate thickness of 1.6 mm. The gain of these simulated antennas are obtained as 2.4819 dB with return loss of -17.779 dB for a single element patch and 6.3128 dB with return loss of -15.8320 dB for an array of two elements. The simulations have been carried out by using Antenna simulator HFSS version 15.0.0 to obtain the VSWR, return loss and radiation pattern. 2017 IEEE. -
Reflector Backed Conical Dielectric Resonator Antenna with Enhanced Gain
This paper reports a wideband, high gain, slot coupled reflector backed conical dielectric resonator antenna (DRA). The key findings of the work are as follows; i) the antenna operates over 7.73-8.3 GHz, with peak gain of 10.32 dBi, ii) an gain enhancement > 5dBi achieved by placing a reflector below the ground plane, iii) the measured results best matches with their measured counter parts, iv) the antenna deals with many advantages, including performance, volume, and fabrication feasibility. From application point of view the developed model can be successfully used for X-band wireless communication. 2018 IEEE. -
Inculcation of On- Campus Pet Companionship as an Animal- Assisted Therapeutic Intervention for the Psychological Well- Being of College Students
Research on developing coping strategies and therapeutic interventions is crucial for college students due to the seemingly unavoidable stressors they face as young adults. This chapter proposes the inclusion of on- campus pet companionship in higher educational institutions as an intervention to enhance distress tolerance, psychological resilience, and better- coping strategies among college students. It acknowledges existing research on pet companionship's positive effects on well- being while addressing the concerns about potential negative impacts. The study aims to explore the potential effects of pet companionship on college students, discuss methods for introducing on- campus pet companionship, and identify cost- effective and feasible approaches for implementation. 2024 by IGI Global. All rights reserved.