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Study of generator shaft behaviour during subsynchronous resonance using finite element method
Scientific research in electric power stations includes various online monitoring and control of equipments. Turbine and generator plays a key role in generating power. Frequency response analysis of the shaft which connects turbine and generator is used to detect the steady state response. It will enable the user to understand and design the system in such a way that it can withstand resonance, fatigue and other vibrations. Subsynchronous resonance which arises during line compensation by series capacitors increases oscillations in the turbine generator shaft system. The oscillations developed at low frequency causes physical damage to the shaft. There are several real time monitoring of the rotor shaft and turbine shaft misalignment by using laser technologies. The aim of this research paper is to use frequency response and modal analysis technique to detect the stress in the shaft and improve the design of it. A viscous damper is designed in the 3D model at the point of highly stressed area to control the resonance effect caused by series capacitors. 2020, Levrotto and Bella. All rights reserved. -
Determination of stress on turbine generator shaft due to subsynchronous resonance using finite element method
Power Capacitors plays a vital role in reactive power compensation. When the capacitors are connected to the transmission line, it improves the reactive power. Although the reactive power is improved, there is a possibility for sub synchronous resonance created by this capacitors in the transmission line which can impact the generator frequency. The sub synchronous resonance causes electro-mechanical stress in the generator shaft which ultimately leads to malfunction of the entire power generating unit. It is necessary to find out operating modes of the generator and turbine when the line is compensated with capacitors. Once the operating modes are clear, it is possible to damp the sub synchronous resonance. In this paper, three phase generator is coupled with a prime mover and capacitors are connected before the load. The stress on the turbine is analysed based on the torque of two rotating machines. Finite element method is used to estimate the stress in the turbine generator shaft system. 2006-2019 Asian Research Publishing Network (ARPN). -
Analysis of multimode oscillations caused by subsynchronous resonance on generator shaft
Series capacitors are installed in high voltage alternating current transmission lines to counteract the inductive reactance of the line. The resonance caused by series capacitors between electric system and mechanical system at frequencies less than the synchronous speed, leads to torsional oscillations. Undamped oscillations ma y cause a severe fatigue in the turbine generator shaft system. Rotating component undergoes various modes of oscillations when it is subjected to resonance. Rotor oscillate in different modes such as swing mode, super synchronous mode, electromechanical mode and torsional mode. Rotor dynamics of rotating structure depends on several factors like Coriolis Effect, moment of inertia and stiffness coefficient. Modal analysis using finite element method gives the natural frequency and mode shapes of any rotating structures. In this paper, a two mass rotating system which is analogous to turbine generator is subjected to resonance by adding series capacitors and its dynamic behavior is studied using finite element method. 2018 Lavoisier. -
Impact on cardioprotective effect of Psidium guajava leaves extract in streptozotocin-induced Wistar mice with molecular in silico analysis
Cardiovascular disease (CVD) and its complications have been regarded as the leading cause of morbidity and mortality. The drugs available in the market are effective to treat CVD, but with many adverse reactions. Nowadays, herbal products are the attention of researchers because of their less adverse effects. In this study, the cardioprotective effects of ethanolic leaves extract of Psidium guajava Linn. (Guava) (P. guajava) were evaluated in streptozotocin (STZ)-treated animal models. Mice acquired for the study were divided into five groups, each consisting of six mice. The toxin-induced mice were treated with the ethanolic leaves extract of P. guajava (300 mg/ kg body weight [b.w.]). The results were compared to the standard drug (glibenclamide)-treated mice (3 mg/kg b.w.). The following parameters were considered for further investigations: creatine kinase-muscle brain (CK-MB), creatine kinase (CK), troponin, lysosomal, and mitochondrial enzymes. Then the docking study was accomplished. The levels of cardiac marker enzymes and lysosomal enzymes increased significantly in the toxin-induced mice, while the level of mitochondrial enzyme decreased significantly. During treatment with the ethanolic leaves extract of P. guajava, the levels of all parameters were notably reversed to normal range (P < 0.05). Further, in docking analysis, the interaction of compounds, such as alpha-terpineol, cyclopentanecarboxamide, guaiol (a sesquiterpenoid alcohol), 1H-cyclopropanaphthalene, tetracyclotridecan-9-ol, dormin/abscisic acid, and epiglobulol, with the respective protein molecules, evidenced the cardioprotective effect of P. guajava leaves. Hence, it was concluded that the ethanolic leaves extract of P. guajava leaves have a cardioprotective effect. 2023 Codon Publications. -
Analysis of chromosomal aberrations and micronuclei in type 2 diabetes mellitus patients
Introduction: Type 2 diabetes mellitus is a metabolic disorder characterized by insulin resistance and disrupted insulin secretion. It is often linked to injuries, malfunction and failure of several organs in the long term. The elevated chromosomal disruptions and genetic complications in diabetic patients are due to the increased production of reactive oxygen species. Materials and Methods: The current study used chromosomal aberration assay and micronucleus assay to analyze the extent of abnormalities in the subjects. Results: The results showed increase in frequency of chromosomal aberrations in diabetic patients when compared to the control group (2.761.65 and 0.470.75 respectively). They also showed higher levels of micronuclei formation than the control participants (13.288.63 and 4.128.89 respectively). The correlation analysis indicated positive relationship between total aberrations and duration of diabetes. Conclusion: These results indicate that diabetes is associated with genomic instability and studies at a genetic level can be employed for early detection. 2020, West Asia Organization for Cancer Prevention. -
Antecedents of consumer attitude and purchase intention towards counterfeit products
This paper aims to investigate the effect of information reliability, risk, and value consciousness on hedonic behaviour, attitude towards counterfeit products, and genuine store trustworthiness. Using a structured survey instrument, this paper gathered data from 449 respondents from three cities major cities (Kochi, Bangalore, and Chennai) in southern part of India. The hierarchical regression render support that: 1) information reliability is positively related to hedonic behaviour and attitude towards counterfeit products; 2) risk is negatively related to hedonic behaviour and attitude towards counterfeit products; 3) value consciousness is positively related to hedonic behaviour and genuine store trustworthiness; 4) hedonic behaviour and attitude towards products are related to purchase intention. In addition, the results also support the moderation hypotheses of materialism, value consciousness and social status. The study suggests that marketers need to understand the importance of non-deceptive counterfeiting is useful and consumers have tendency to prefer to use these products once they are satisfied with their utility. The conceptual model developed and tested in this research enables the marketing managers to understand the antecedents of consumers purchase intention of counterfeit products. Copyright 2024 Inderscience Enterprises Ltd. -
Perception of Entrepreneurial Ecosystem: Testing the ActorObserver Bias
Entrepreneurial ecosystem is the interacting socio-economic environment that facilitates entrepreneurs to start and develop their enterprises. A vibrant and supportive entrepreneurial ecosystem is necessary for the start-up and growth of an enterprise. The entrepreneurial action would largely depend on the perception of entrepreneurs about the ecosystem. In this context, a study was designed to understand the perceptions of actors (entrepreneurs) and observers (non-entrepreneurs) on various components of the entrepreneurial ecosystem. Data for this study were collected from 296 entrepreneurs and 315 non-entrepreneurs from India, who responded to a 77-item questionnaire by giving their ratings of various aspects of the ecosystem on a 5-point scale. Findings of the study showed that perceptions of the entrepreneurial ecosystem were significantly different for most of the subgroups. Most notable among these differences was those between entrepreneurs and non-entrepreneurs, where the mean scores on all dimensions were found to be significantly higher for non-entrepreneurs than for entrepreneurs except for entrepreneurial capability which was found to be higher for entrepreneurs. Hence, the hypothesis of actorobserver bias in the perceptions of entrepreneurs and non-entrepreneurs is supported. 2019 SAGE Publications. -
A comprehensive novel model for network speech anomaly detection system using deep learning approach
Network Intrusion Detection System (NIDS) is the key technology for information security, and it plays significant role for classifying various attacks in the networks accurately. An NIDS gains an understanding of normal and anomalous behavior by examining the network traffic and can identify unknown and new attacks. Analyzing and Identifying unfamiliar attacks are one of the big challenges in Network IDS research. A huge response has been given to deep learning over the past several years and novelty in deep learning techniques are also improved regularly. Deep learning based Network Intrusion Detection approach is highly essential for improved performance. Nowadays, Machine learning algorithms made a revolution in the area of human computer interaction and achieved significant advancement in imitating human brain exactly. Convolutional Neural Network (CNN) is a powerful learning algorithm in deep learning model for improving the machine learning ability in order to achieve high attack classification accuracy and low false alarm rate. In this article, an overview of deep learning methodologies for commonly used NIDS such as Auto Encoder (AE), Deep Belief Network (DBN), Deep Neural Network (DNN), Restricted Boltzmann Machine (RBN). Moreover, the article introduces the most recent work on network anomaly detection using deep learning techniques for better understanding to choose appropriate method while implementing NIDS through widespread literature analysis. The experimental results designate that the accuracy, false alarm rate, and timeliness of the proposed CNN-NIDS model are superior than the traditional algorithms. 2020, Springer Science+Business Media, LLC, part of Springer Nature. -
Investigating and analyzing the causality amid tourism, environment, economy, energy consumption, and carbon emissions using TodaYamamoto approach for Himachal Pradesh, India
Himachal Pradesh is a preferred tourist destination with a Compound Annual Growth Rate (CAGR) of 10.76% between 201112 and 202021. The increasing trend of CAGR has boosted the tourism economy in the state while impacting the local environment. The negative impacts have recently increased due to changes in climatic patterns and increased tourism influx during the post-pandemic period. In this context, the present study analyzed the impact of tourism on the environment, economy, and energy consumption using the Environmental Kuznets Curve (EKC) hypothesis. The novelty of this study is to the existing literature on sustainable tourism development through investigating the interrelationship between tourism, environment, economy, energy consumption, and carbon emissions by employing the TodaYamamoto (TY) technique. This study will be a pioneering scientific investigation with quantitative results in the western Himalayan states of India, encompassing Jammu & Kashmir, Uttarakhand, and Himachal Pradesh. The annual data for each variable, such as per capita carbon emission (CEP), per capita Gross State Domestic Product (GSDP), per capita GSDP square, per capita energy consumption (ECP), and per capita tourism receipts (TRP), was collected from 2010 to 2021. This study exhibited an inverted-U EKC in the state, signifying the initial stage of economic development and extensive exploitation of natural resources for tourism. The TY results indicated an inter-causal relationship and feedback association among the variables in the study area. Thus, increased TRP would lead to an upsurge in energy consumption affecting the environmental quality due to increased carbon emissions. Such environmental degradation in the state would negatively impact the tourism sector in the long run. The research findings would guide planners and policymakers in promoting sustainable tourism. 2023, The Author(s), under exclusive licence to Springer Nature B.V. -
Mental health professionals insights on developing and implementing a Mental Health Awareness and Destigmatisation program (MHAD) for adolescents
This study examined mental health professionals insights on developing and implementing a Mental Health Awareness and Destigmatisation (MHAD) program for adolescents aged 1418 years in Bangalore. Qualitative Interviews with 17 professionals revealed three main themes: 1. Awareness and Destigmatisation Programs: A Boon 2. Key Ingredients: Program Content and Delivery Style 3. Shaping Program Success: Key Drivers and Challenges. Professionals recommend interactive programs that promote open discussions and educate them on symptom recognition, online behavior, and healthy relationships. They identified key enablers and challenges, emphasizing the programs ability to empower adolescents, parents, and educators to create a supportive, stigma-free environment. 2024 Taylor & Francis Group, LLC. -
Knowledge, Attitude, and Stigma Among Adolescents: Effect of Mental Health Awareness and Destigmatisation (MHAD) Program
Background: Stigma against mental health problems is a common issue for adolescents aged 1418 years. However, comprehensive programs that simultaneously address awareness and stigma reduction tailored to the specific needs of this age group are lacking. Method: This study investigated the effectiveness of the Mental Health Awareness and Destigmatisation Program (MHAD) in reducing stigma and improving knowledge and attitudes towards peers with mental health problems. A quasi-experimental pre-post design was employed among adolescents aged 1418 years from an educational institution in Bangalore. After excluding those with high baseline mental health symptoms (PSC-17 > 20), a preassessment was conducted on adolescents' knowledge, attitude, and stigma (n = 52) using the Mental Health Knowledge Schedule, Self-structured Case Vignettes, and Peer Mental Health Stigmatization Scale. After completing the 6-week program, three participants were excluded from the post-assessment, as their attendance was less than 50%. A total of 49 (mean age = 16 years) adolescents were included in the post-assessment. Results: The paired sample t-test revealed significant improvements in all stigma scores. The Wilcoxon signed-rank test indicated a significant improvement in Recognition of Mental Illness scores. Conclusion: Findings showed that MHAD, an education-based program, was effective in reducing adolescents' stigma towards peers with mental health problems and improving their overall recognition of mental health symptoms. Research across larger adolescent populations is essential to enhance these interventions' long-term impact and sustainability. By closely monitoring and expanding research efforts, we can gain deeper insights into how these programs foster self-awareness, a crucial factor in recognizing mental health needs, challenging stigma, and promoting help-seeking behaviors among adolescents. 2024 Wiley Periodicals LLC. -
Stock market prediction employing ensemble methods: the Nifty50 index
Accurately forecasting stock fluctuations can yield high investment returns while minimizing risk. However, market volatility makes these projections unlikely. As a result, stock market data analysis is significant for research. Analysts and researchers have developed various stock price prediction systems to help investors make informed judgments. Extensive studies show that machine learning can anticipate markets by examining stock data. This article proposed and evaluated different ensemble learning techniques such as max voting, bagging, boosting, and stacking to forecast the Nifty50 index efficiently. In addition, an embedded feature selection is performed to choose an optimal set of fundamental indicators as input to the model, and extensive hyperparameter tuning is applied using grid search to each base regressor to enhance performance. Our findings suggest the bagging and stacking ensemble models with random forest (RF) feature selection offer lower error rates. The bagging and stacking regressor model 2 outperformed all other models with the lowest root mean square error (RMSE) of 0.0084 and 0.0085, respectively, showing a better fit of ensemble regressors. Finally, the findings show that machine learning algorithms can help fundamental analyses make stock investment decisions. 2024, Institute of Advanced Engineering and Science. All rights reserved. -
Analysis of Nifty 50 index stock market trends using hybrid machine learning model in quantum finance
Predicting equities market trends is one of the most challenging tasks for market participants. This study aims to apply machine learning algorithms to aid in accurate Nifty 50 index trend predictions. The paper compares and contrasts four forecasting methods: artificial neural networks (ANN), support vector machines (SVM), naive bayes (NB), and random forest (RF). In this study, the eight technical indicators are used, and then the deterministic trend layer is used to translate the indications into trend signals. The principal component analysis (PCA) method is then applied to this deterministic trend signal. This study's main influence is using the PCA technique to find the essential components from multiple technical indicators affecting stock prices to reduce data dimensionality and improve model performance. As a result, a PCA-machine learning (ML) hybrid forecasting model was proposed. The experimental findings suggest that the technical factors are signified as trend signals and that the PCA approach combined with ML models outperforms the comparative models in prediction performance. Utilizing the first three principal components (percentage of explained variance=80%), experiments on the Nifty 50 index show that support vector classifier (SVC) with radial basis function (RBF) kernel achieves good accuracy of (0.9968) and F1-score (0.9969), and the RF model achieves an accuracy of (0.9969) and F1-Score (0.9968). In area under the curve (AUC) performance, SVC (RBF and Linear kernels) and RF have AUC scores of 1. 2023 Institute of Advanced Engineering and Science. All rights reserved. -
Deep Learning for Stock Market Index Price Movement Forecasting Using Improved Technical Analysis
Equity market forecasting is difficult due to the high explosive nature of stock data and its impact on investor's stock investment and finance. The stock market serves as an indicator for forecasting the growth of the economy. Because of the nonlinear nature, it becomes a difficult job to predict the equity market. But the use of different methods of deep learning has become a vital source of prediction. These approaches employ time-series stock data for deep learning algorithm training and help to predict their future behavior. In this research, deep learning methods are evaluated on the India NIFTY 50 index, a benchmark Indian equity market, by performing a technical data augmentation approach. This paper presents a Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and the three variants of Gated Recurrent Unit (GRU) to analyze the model results. The proposed three GRU variants technique is evaluated on two sets of technical indicator datasets of the NIFTY 50 index (namely TA1 and TA2) and compared to the RNN and LSTM models. The experimental outcomes show that the GRU variant1 (GRU1) with TA1 provided the lowest value of Mean Square Error (MSE=0.023) and Root Mean Square Error (RMSE= 0.152) compared with existing methods. In conclusion, the NIFTY 50 index experiments with technical indicator datasetTA1 were more efficient by GRU. Hence, TA1 can be used to construct a robust predictive model in forecasting the stock index movements. 2021. All Rights Reserved. -
Generalized Vertex Induced Connected Subsets of a Graph
Vol.2 (May), 61-68 -
On Πk – connectivity of some product graphs
Vol. 21, No.2, 70 - 79 ISSN 13105132 -
Polypyrrole functionalized MoS2 for sensitive and simultaneous determination of heavy metal ions in water
Assessing heavy metal ion (HMI) contamination to sustain drinking water hygiene is a challenge. Conventional approaches are appealing for the detection of HMIs but electrochemical approaches can resolve the limitations of these approaches, such as tedious sample preparation, high cost, time consuming and the need for trained professionals. Here, an electrochemical approach is developed using a nano-sphered polypyrrole (PPy) functionalized with MoS2 (PPy/MoS2) by square wave anodic stripping voltammetry for the detection of HMIs. The developed sensor can detect Pb2+ with a limit of detection of 0.03 nM and a sensitivity of 36.42 ?A nM?1. Additionally, the PPy/MoS2 sensor was employed for the simultaneous detection of HMIs of Cd2+, Pb2+, Cu2+ and Hg2+. The reproducibility, stability and anti-interference studies confirm that the sensor can be used to monitor HMI contamination of water. 2025 The Royal Society of Chemistry. -
Molecular docking study, and ADMET analysis for the synthesized novel Zn(II) complexes as potential SARS-CoV-2 inhibitors
A new SARS-CoV-2 virus and its variants including omicron created a pandemic situation and caused more deaths in worldwide prompted many researchers to explore potential drug candidates. In this connection, we explored the first-of-its-kind report on computational studies such as molecular docking, and ADMET properties of Zn(II) complexes. The studies revealed the novel zinc complexes have high binding affinities with the SARS-CoV-2 spike glycoprotein (6vxx) alpha variant (7EKF), beta variant (7ekg), gamma variant (7EKC), delta variant (7V8B), and the omicron variant (7T9J). Molecular docking results of RMSD for SARS-CoV-2 beta variant (7ekg) and gamma variant (7EKC) are within excellent chemical stability in their protein-ligand complex state and should be effective in the biological system. ADME studies provided the better results with no adverse effect of toxicity related AMES along with absence of hepatotoxicity and skin sensitization when compared to Molnupiravir drug and it has a greater hepatotoxicity. This study could open further exploration of these novel zinc complexes for SARS-CoV-2 inhibition. (2024) DergiPark. -
Heat transfer enhancement in the boundary layer flow of hybrid nanofluids due to variable viscosity and natural convection
The aim of the current work is to explore how heat transfer can be enhanced by variations in the basic properties of fluids in the presence of free convection with the aid of suspended hybrid nanofluids. Also, the influence of the Laurentz force on the flow is considered. The mathematical equations are converted into a pair of self-similarity equations by applying appropriate transformations. The reduced similarity equivalences are then solved numerically by Runge-Kutta-Fehlberg 45 th -order method. To gain better perception of the problem, the flow and energy transfer characteristics are explored for distinct values of significant factors such as variable viscosity, convection, magnetic field, and volume fraction. The results acquired are in good agreement with previously published results. The noteworthy finding is that the thermal conductivity is greater in hybrid nanofluid than that of a regular nanofluid in the presence of specified factors. The boundary layer thickness of both hybrid nanofluid and normal nanofluid diminishes due to decrease in variable viscosity. The fluid flow and temperature of the hybrid nanofluid and normal nanofluid increases as there is a rise in volume fraction. 2019