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Design and performance analysis of braking system in an electric vehicle using adaptive neural networks
Research article emphasizes on the impact of braking concepts considering regenerative braking system and energy consumption aspects in electric vehicles through a new perspective. The electric vehicle system is modeled and simulated using the MATLAB/Simulink software. A dataset is developed using the virtual simulation environment created by co-simulation using the MATLAB/Simulink and the IPG Carmaker software. This dataset is also used in a neural network model based on adaptive neuro fuzzy logic and the system performance is analyzed. Parameters considered for training the neural network are the brake pedal displacement, braking change rate and the need for brake application. The highlight of this study is the focus on a front wheel driven electric vehicle, which uses a standard drive cycle input to validate the model. The significant parameters evaluated in this study include the braking effects, kinetic energy, regenerative braking torque, battery state of the charge and the motor torque. The torque generation and its intended braking force requirements based on the acceleration, deceleration and braking conditions are the notable observations. The regenerative capability of this proposed system design is also illustrated along with the surface plots based on the training dataset. Investigation and analysis reveal that, the battery state of charge could be revived throughout the drive with a steady and stable increase. Transitions of motor torques between tractive and regenerative phases are also illustrated and explained for clarity and brevity. 2023 Elsevier Ltd -
Biosynthesis of ZnFe2O4@Ag hybrid nanocomposites for degradation of 2,4-Dichlorophenoxyacetic acid herbicide
This work demonstrates recent advancements in the phytosynthetic and environmentally friendly method of preparing ZnFe2O4 and ZnFe2O4@Ag hybrid nanocomposites using Pedalium murex L leaf extract as a stabilizing and reducing agent. The synthesized nanocomposite was characterized with UVvis, FTIR, TGA/DSC, XRD, FE-SEM, and EDX to investigate the electronic as well as morphological properties. Moreover, the photocatalytic behaviour of ZnFe2O4 and ZnFe2O4@Ag hybrid nanocomposites was evaluated with a breakdown of 2,4-dichlorophenoxyacetic acid (2,4-DPA) by exposing to UVVis light. The results obtained suggest that ZnFe2O4@Ag hybrid nanocomposite exhibited photocatalytic activity for the degradation of 2,4-DPA by approximately 94% in 60 min compared to ZnFe2O4. The hybrid nanostructure of ZnFe2O4@Ag significantly promoted charge transfer and prevented electron and hole recombination resulting in the enhancement of photocatalytic activity. Furthermore, ZnFe2O4@Ag nanocomposite showed the fair recyclable capacity for up to five catalytic cycles with an acceptable degradation percentage of 2,4-DPA. The findings of this study identify efficient charge transfer factor as a major contributor to the catalytic activity, with promising possibilities for the design of environmental remediation nanocomposite for harmful contaminants. 2023 The Author(s) -
Analysis of the Thomson and Troian velocity slip for the flow of ternary nanofluid past a stretching sheet
In this article, the flow of ternary nanofluid is analysed past a stretching sheet subjected to Thomson and Troian slip condition along with the temperature jump. The ternary nanofluid is formed by suspending three different types of nanoparticles namely TiO 2, Cu and Ag into water which acts as a base fluid and leads to the motion of nanoparticles. The high thermal conductivity and chemical stability of silver was the main cause for its suspension as the third nanoparticle into the hybrid nanofluid Cu-TiO 2/ H 2O. Thus, forming the ternary nanofluid Ag-Cu-TiO 2/ H 2O. The sheet is assumed to be vertically stretching where the gravitational force will have its impact in the form of free convection. Furthermore, the presence of radiation and heat source/sink is assumed so that the energy equation thus formed will be similar to most of the real life applications. The assumption mentioned here leads to the mathematical model framed using partial differential equations (PDE) which are further transformed to ordinary differential equations (ODE) using suitable similarity transformations. Thus, obtained system of equations is solved by incorporating the RKF-45 numerical technique. The results indicated that the increase in the suspension of silver nanoparticles enhanced the temperature and due to density, the velocity of the flow is reduced. The slip in the velocity decreased the flow speed while the temperature of the nanofluid was observed to be increasing. 2023, The Author(s). -
Memes as multimodal ensemble
Memes have now become a common medium of communication. There are multiple ways memes are considered in academia. Semiotics offers information on how the media and modes that memes consist of can be interpreted and how the characteristics of semiotic resources apply to memes. Drawing from a pool of memes collected during the Kerala assembly election in 2021, this research argues that certain memes need to be categorised as multimodal ensemble. Different modalities play different roles meaning construction, and they also collaborate with each other for a uniform purpose. By comparing existing memes defined in academia and multiple methodologies to analyse memes, the paper puts forth a framework to analyse memes. 2023 De Gruyter Mouton. All rights reserved. -
Machine Learning-Enabled NIR Spectroscopy. Part 3: Hyperparameter by Design (HyD) Based ANN-MLP Optimization, Model Generalizability, and Model Transferability
Data variations, library changes, and poorly tuned hyperparameters can cause failures in data-driven modelling. In such scenarios, model drift, a gradual shift in model performance, can lead to inaccurate predictions. Monitoring and mitigating drift are vital to maintain model effectiveness. USFDA and ICH regulate pharmaceutical variation with scientific risk-based approaches. In this study, the hyperparameter optimization for the Artificial Neural Network Multilayer Perceptron (ANN-MLP) was investigated using open-source data. The design of experiments (DoE) approach in combination with target drift prediction and statistical process control (SPC) was employed to achieve this objective. First, pre-screening and optimization DoEs were conducted on lab-scale data, serving as internal validation data, to identify the design space and control space. The regression performance metrics were carefully monitored to ensure the right set of hyperparameters was selected, optimizing the modelling time and storage requirements. Before extending the analysis to external validation data, a drift analysis on the target variable was performed. This aimed to determine if the external data fell within the studied range or required retraining of the model. Although a drift was observed, the external data remained well within the range of the internal validation data. Subsequently, trend analysis and process monitoring for the mean absolute error of the active content were conducted. The combined use of DoE, drift analysis, and SPC enabled trend analysis, ensuring that both current and external validation data met acceptance criteria. Out-of-specification and process control limits were determined, providing valuable insights into the models performance and overall reliability. This comprehensive approach allowed for robust hyperparameter optimization and effective management of model lifecycle, crucial in achieving accurate and dependable predictions in various real-world applications. Graphical Abstract: [Figure not available: see fulltext.]. 2023, The Author(s). -
Potential of banana based cellulose materials for advanced applications: A review on properties and technical challenges
Biocompatibility, biodegradability, and toxicity issues of synthetic polymers have propelled the search for environmentally friendly and non-toxic alternatives. In this context, biobased materials have gained much popularity due to their non-toxic, biodegradable, and sustainable nature. Bananas are considered as one of such natural material which fulfil the requirements to be tailored as a biocompatible biopolymer. Banana derived wastes can be used for extraction of commercially important biopolymers like starch, cellulose, nanocellulose and their subsequent utilization in wide variety of applications. Banana derived biopolymers and their bio composites and widely used for medical applications such as wound healing, fabrication of bone plates, cellulose based gate dielectrics, and capacitors for insulin pumps, and pacemakers. In addition, banana based nanocellulose can be used in tissue engineering, biosensing, drug delivery, bioimaging, wound healing, enzyme immobilization and preparation of tablets for oral administration. Moreover, banana-based polymers can be employed in applications such as food packaging, biofuel production, and production of multilayered papers. Considering the potential applications of banana-based nanomaterials, this review work is framed to understand the process of extraction of starch, cellulose, nanocellulose and biopolymers from banana derived wastes with specific emphasis on their extraction methods and composite preparation methods. In addition, it discusses in detail the promising and potential applications of the derived materials in health and environmental sectors. The presented review is a comprehensive discussion on banana-based waste conversion strategies to produce value added products useful in medical and environmental applications. 2023 The Author(s) -
The computational model of nanofluid considering heat transfer and entropy generation across a curved and flat surface
The entropy generation analysis for the nanofluid flowing over a stretching/shrinking curved region is performed in the existence of the cross-diffusion effect. The surface is also subjected to second-order velocity slip under the effect of mixed convection. The Joule heating that contributes significantly to the heat transfer properties of nanofluid is incorporated along with the heat source/sink. Furthermore, the flow is assumed to be governed by an exterior magnetic field that aids in gaining control over the flow speed. With these frameworks, the mathematical model that describes the flow with such characteristics and assumptions is framed using partial differential equations (PDEs). The bvp4c solver is used to numerically solve the system of non-linear ordinary differential equations (ODEs) that are created from these equations. The solutions of obtained through this technique are verified with the available articles and the comparison is tabulated. Meanwhile, the interpretation of the results of this study is delivered through graphs. The findings showed that the Bejan number was decreased by increasing Brinkman number values whereas it enhanced the entropy generation. Also, as the curvature parameter goes higher, the speed of the nanofluid flow diminishes. Furthermore, the increase in the Soret and Dufour effects have enhanced the thermal conduction and the mass transfer of the nanofluid. 2023, The Author(s). -
Aging Bodies and Necropower: A Critical Study of Geronticide in Tamil Films K.D. and Thalaikoothal
This paper analyzes the act of thalaikoothal, performed on ill and infirm older people in the southern part of the state of Tamil Nadu, India, with reference to two Tamil films, K.D. (2019) and Thalaikoothal (2023), which portray the horrors of the practice. A kind of involuntary euthanasia, the ritual continues to have covert social acceptance as exemplified in the films, favoring the common stereotype that marks aging bodies as unproductive and unworthy of living. Therefore, this article explores the aging bodies in the films as sites of power, controlled by death enforced through Thalaikoothal, employing Mbembes concept of necropower. It also examines the effects of the practice on the elderly, who are compelled to prepare themselves for a forced death. The study further identifies the underlying factors contributing to the prolonged existence of Thalaikoothal even in the present times. 2023, University of Zadar. All rights reserved. -
Theorizing the Phenomenon of Women Empowerment in a Course to Discover the Purpose of Life for Marginalized Women in IndiaEvidence from Phool
The present study attempts to theorize the phenomenon of empowerment of marginalized women in the context of social enterprises involved in sustainable business practices. To extract the deeper meaning of empowerment of such women, an inductive process using Gioias method was employed by interviewing 13 marginalized women working in the social enterprise Phool. The venture is involved in recycling sacred floral waste into incense sticks, organic fertilizers, and other sustainable packaging solutions. The findings of the study were built on the Social Identity Theory, which emphasizes the fundamental need to be a part of social groups. Our findings suggest that for marginalized women, empowerment manifests in dignity and honour, economic sovereignty and social admittance and embracement. The study contributes to the extant literature on womens empowerment by intersecting with the phenomenon of marginalization in the context of social enterprise and explains how marginalized women experience empowerment at work. 2023 Birla Institute of Management Technology. -
The Universal Dimensions of Change: A Systematic Review of Couple Techniques
One can find a rich set of empirically evaluated techniques across different schools in couple therapy over its evolution of five decades. Though there are multiple systematic reviews and analyses of couple intervention studies, none focus on reviewing the universal dimensions of change across therapeutic techniques. Understanding the common areas of change would enable integrated learning across therapy modalities for novice therapists. Therefore, the aim is to identify the techniques employed in couple intervention research and categorize their change dimensions. We examined 40 articles on couple interventions published across 16 journals and identified 111 techniques. The five therapeutic change dimensions, namely behavior, cognition, emotion, attachment, and holistic, were categorized based on the common factor integration of techniques. The identified techniques were further classified under the five dimensions using the voting procedure to validate the universality of change dimensions. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Exploring the Influence Dynamism of Economic Factors on Fluctuation of Exchange Rate-An Empirical Investigation for India Using ARDL Model
The Indian Foreign Exchange Market has experienced significant changes over the past decade, due to high degree of instability of the Indian Rupee leading to its devaluation against major global currencies. Exchange rate is considered as one of crucial indicators to determine the economic growth. Volatility of exchange rate of each day is influenced by various factors such as demand and supply, Gross Domestic Product, Interest rate, employment rate, public debt, balance of payments, inflation etc. Though there are multiple causes to determine the movement of exchange rate, but still the accurate level of causation is unpredictable. Keeping this in mind, this paper tries to attempt the relationship that exists between the exchange rate and select macroeconomic factors. To analyse the extent of influence of the selected variables on the exchange rate, the research paper uses 10 years of data spanning from Jan 2013 to Nov 2022. Further, the study uses monthly data of above-mentioned variables to bring out the analysis to meet the objectives. Descriptive statistics is used to find the characteristics of the data, correlation analysis and Ordinary Least Square method is used to find the relationship and impact level select macroeconomic factors on exchange rate. Autoregressive Distributed Lag (ARDL) model is used to find if any short run and long run association exists between the variables and the exchange rate. 2023, ASERS Publishing House. All rights reserved. -
Did Russia's Invasion of Ukraine Induce Herding Behavior in the Indian Stock Market?
This study empirically examines the herding behavior of the Indian stock market investors during the heightened geopolitical tensions between Russia and Ukraine in 2022. An intensified Russia-Ukraine geopolitical event window was constructed, and the high-frequency trading data (intraday) of the Nifty index was analyzed using Multifractal Detrended Fluctuation Analysis (MFDFA) to compute the 5th-order Hurst exponent (Hq (5)) that detects herding behavior. The study's empirical results revealed the presence of profound herding behavior during the intensified Russia-Ukraine geopolitical event window. The study contributes to the existing literature on herding behavior by examining the impact of a geopolitical event on the Indian stock market. Additionally, the study utilizes MFDFA to compute Hurst exponents, a relatively new approach to detecting herding behavior in financial markets. The findings of this study may assist investors and policymakers in understanding the impact of geopolitical events on financial markets and the potential for herding behavior among investors during times of heightened uncertainty. The study's results demonstrate the interconnectedness of global events and financial markets, highlighting the need for policymakers to consider the potential social and economic consequences of geopolitical events. 2023 The Author(s). -
Lesion detection in women breasts dynamic contrast-enhanced magnetic resonance imaging using deep learning
Breast cancer is one of the most common cancers in women and the second foremost cause of cancer death in women after lung cancer. Recent technological advances in breast cancer treatment offer hope to millions of women in the world. Segmentation of the breasts Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is one of the necessary tasks in the diagnosis and detection of breast cancer. Currently, a popular deep learning model, U-Net is extensively used in biomedical image segmentation. This article aims to advance the state of the art and conduct a more in-depth analysis with a focus on the use of various U-Net models in lesion detection in womens breast DCE-MRI. In this article, we perform an empirical study of the effectiveness and efficiency of U-Net and its derived deep learning models including ResUNet, Dense UNet, DUNet, Attention U-Net, UNet++, MultiResUNet, RAUNet, Inception U-Net and U-Net GAN for lesion detection in breast DCE-MRI. All the models are applied to the benchmarked 100 Sagittal T2-Weighted fat-suppressed DCE-MRI slices of 20 patients and their performance is compared. Also, a comparative study has been conducted with V-Net, W-Net, and DeepLabV3+. Non-parametric statistical test Wilcoxon Signed Rank Test is used to analyze the significance of the quantitative results. Furthermore, Multi-Criteria Decision Analysis (MCDA) is used to evaluate overall performance focused on accuracy, precision, sensitivity, F 1 -score, specificity, Geometric-Mean, DSC, and false-positive rate. The RAUNet segmentation model achieved a high accuracy of 99.76%, sensitivity of 85.04%, precision of 90.21%, and Dice Similarity Coefficient (DSC) of 85.04% whereas ResNet achieved 99.62% accuracy, 62.26% sensitivity, 99.56% precision, and 72.86% DSC. ResUNet is found to be the most effective model based on MCDA. On the other hand, U-Net GAN takes the least computational time to perform the segmentation task. Both quantitative and qualitative results demonstrate that the ResNet model performs better than other models in segmenting the images and lesion detection, though computational time in achieving the objectives varies. 2023, The Author(s). -
High performance computational method for fractional model of solid tumour invasion
The behaviour of the solid tumour invasion system in the sense of Caputo fractional with time ? and space x is analyzed by the high performance computational method: q-Homotopy Analysis Transform method (q-HATM). The existence of the solutions has been verified with the assist of fixed point theorem and derived numerical solution for different values of ?,?,h. The novel simulation for all cases is explained through figures. We derived that the method is very efficient for analyzing the behaviour of the epidemiological system. 2023 THE AUTHORS -
Recycled Surgical Mask Waste as a Resource Material in Sustainable Geopolymer Bricks
With the advent of the COVID-19 pandemic, the global consumption of single-use surgical masks has risen immensely, and it is expected to grow in the coming years. Simultaneously, the disposal of surgical masks in the environment has caused plastic pollution, and therefore, it is exigent to find innovative ways to handle this problem. In this study, surgical masks were processed in a laboratory using the mechanical grinding method to obtain recycled surgical masks (RSM). The RSM was added in doses of 0%, 1%, 2%, 3%, and 4% by volume of geopolymer bricks, which were synthesized with ground granulated blast furnace slag (GGBS), rice husk ash (RHA), sand, and sodium silicate (Na2SiO3) at ambient conditions for a duration of 28 days. The developed bricks were tested for compressive strength, flexural strength, density, water absorption, efflorescence, and drying shrinkage. The results of the study reveal that compressive strength and flexural strength improved with the inclusion of RSM in the bricks. The highest values of compressive strength and flexural strength were 5.97 MPa and 1.62 MPa for bricks with 4% RSM, respectively. Further, a reduction in the self-weight of the bricks was noticed with an increase in RSM. There was no pronounced effect of RSM on the water absorption and efflorescence properties. However, the RSM played a role in reducing the drying shrinkage of the bricks. The sustainability analysis divulges the catalytic role of RSM in improving material performance, thereby proving to be a potential candidate for low-carbon material in the construction industry. 2023 by the authors. -
An adept approach to ascertain and elude probable social bots attacks on twitter and twitch employing machine learning approach
There has been a tremendous increase in the popularity of social media such as blogs, Instagram, twitter, online websites etc. The increasing utilization of these platforms have enabled the users to share information on a regular basis and also publicize social events. Nevertheless, most of the multimedia events are filled with social bots which raise concerns on the authenticity of the information shared in these events. With the increasing advancements of social bots, the complexity of detecting and fact-checking is also increasing. This is mainly due to the similarity between authorized users and social bots. Several researchers have introduced different models for detecting social bots and fact checking. However, these models suffer from various challenges. In most of the cases, these bots become indistinguishable from existing users and it is challenging to extract relevant attributes of the bots. In addition, it is also challenging to collect large scale data and label them for training the bot detection models. The performance of existing traditional classifiers used for bot detection processes is not satisfactory. This paper presents: A machine learning based adaptive fuzzy neuro model integrated with a hist gradient boosting (HGB) classifier for identifying the persisting pattern of social bots for fake news detection. And Harris Hawk optimization with Bi-LSTM for social bot prediction. Results validate the efficacy of the HGB classifier which achieves a phenomenal accuracy of 95.64 % for twitter bot and 98.98 % for twitch bot dataset. 2023 -
Lithium photodisintegration with linearly polarized photons at astrophysical energies
We present here a model-independent theoretical discussion of differential cross-sections in photodisintegration of lithium with unpolarized and linearly polarized photons. In recent years, experimental measurements are being carried out on the photodisintegration of lithium in the reaction channel 7Li(?, n)6Li to study the angular dependence of cross-section. In this regard, we have studied the spin structure of amplitudes in 7Li(?, n)6Li by expressing the differential cross-section in terms of Legendre polynomials. 2023 Oxford University Press. All rights reserved. -
Solution of boundary eigenvalue problems and IBVP involving a system of PDEs using the successive linearization method
The paper illustrates a numerical technique to solve a system of three partial differential equationsthat govern the problem of Rayleigh-Bard-Brinkman convection in a two-dimensional porous rectangular box. As a result of linear and weakly nonlinear stability analyses of the system a boundary eigenvalue problem (BEVP) and an initial boundary value problem (IBVP) arise. Spatial information on the periodicity of the convection cells is first used in the system of PDEs to make it possible for the successive linearization method (SLM) to be applied. The resulting much-simplified versions of BEVP and the IVP are then solved by direct and time multi-stepping versions of SLM, respectively. The SLM solution of the BEVP is compared with that obtained through MATLAB routine bvp4c and the multi-stepping-SLM solution of the IVP is validated with that of the Runge-Kutta-Fehlberg (RKF45) method (using MATLAB routine ode45). The present numerical technique is found to have quadratic convergence for any desiredaccuracy. 2023 Wiley-VCH GmbH. -
Furaldehyde-based magnetic supported palladium nanoparticles as an efficient heterogeneous catalyst for Mizoroki-Heck cross-coupling reaction
A novel palladium functionalised magnetic nanocatalyst (Fe3O4@SiO2-FA-Pd) was prepared using silica-coated iron oxide and furaldehyde Schiff base as a supported material. FT-IR, VSM, TEM, FESEM, XPS, EDAX and TGA were used to analyse the morphological structure and chemical composition of Fe3O4@SiO2-FA-Pd NPs. Thus, the prepared Fe3O4@SiO2-FA-Pd NPs as a magnetic nanocatalyst were used in the Mizoroki-Heck reaction of arylbromide and terminal alkenes, exhibiting effective catalysis with good yields. The magnetic nanocatalyst can be reused up to 6 times without significantly losing catalytic activity and is easily separable from the reaction mixture. This new catalytic system has significant and potentially useful applications in the pharmaceutical industry. 2024 The Royal Society of Chemistry. -
Facile synthesis of aluminum oxyhydroxide-PVA films and its adsorptive evaluation for the removal of methylene blue dye from water: kinetics, optimization studies and mechanism
Aluminum oxyhydroxide-polyvinyl alcohol films were synthesized by the solgel method with varying metal-polymer ratios. The results of the characterization confirmed the formation of aluminum oxyhydroxideand the incorporation of metal into the polymer. The adsorption isotherms of the film exhibited type IV isotherms indicating the mesoporous nature with a non-uniform pore size distribution. The optical profilometric studies confirmed the surface roughness of the film. The adsorptive nature of the film was tested for the removal of methylene blue dye from the aqueous solution. Optimization studies were conducted to investigate the effect of various parameters on the adsorption process. The adsorption isotherms of methylene blue fit with the Langmuir isotherm model and follow a pseudo-second-order equation. A suitable reaction mechanism was proposed, which confirms the adsorptive nature of the film is due to the electrostatic attraction. The synergetic effect of the metal-polymer blend, surface roughness and pore size of the film, which ranges from 0.01 to 0.025nm,enhances the adsorption of methylene blue. The comparison of results obtained in the current study with earlier reports confirms that the aluminum oxyhydroxide-polyvinyl alcohol films can be considered an eco-friendly, cost-effective adsorbent for removing methylene blue. 2023, The Polymer Society, Taipei.