Search results

This paper aims to apply the non-linear impedance boundary condition (IBC) for a linear piecewise B–H curve in frequency domain simulations to find the equivalent impedance of a simple induction heating system model.

An electromagnetic description of the inductor system is performed to substitute the effects of the induction load, for a mathematical condition, the so-called IBC. This is suitable to be used in electromagnetic systems involving high conductive materials at medium frequencies, as it occurs in an induction heating system.

A reduction of the computational cost of electromagnetic simulation through the application of the IBC. The model based on linear piecewise B–H curve simplifies the electromagnetic description, and it can facilitate the identification of the induction load characteristics from experimental measurements.

This work is performed to assess the feasibility of using the non-linear boundary impedance condition of materials with linear piecewise B–H curve to simulate in the frequency domain with a reduced computational cost compared to time domain simulations.

In this paper, the use of the non-linear boundary impedance condition to describe materials with B–H curve by segments, which can approximate any dependence without hysteresis, has been studied. The results are compared with computationally more expensive time domain simulations.

Depression is a mental health problem characterized by a persistent sense of sadness and loss of interest. EEG signals are regarded as the most appropriate instruments for diagnosing depression because they reflect the operating status of the human brain. The purpose of this study is the early detection of depression among people using EEG signals.

(i) Artifacts are removed by filtering and linear and non-linear features are extracted; (ii) feature scaling is done using a standard scalar while principal component analysis (PCA) is used for feature reduction; (iii) the linear, non-linear and combination of both (only for those whose accuracy is highest) are taken for further analysis where some ML and DL classifiers are applied for the classification of depression; and (iv) in this study, total 15 distinct ML and DL methods, including KNN, SVM, bagging SVM, RF, GB, Extreme Gradient Boosting, MNB, Adaboost, Bagging RF, BootAgg, Gaussian NB, RNN, 1DCNN, RBFNN and LSTM, that have been effectively utilized as classifiers to handle a variety of real-world issues.

1. Among all, alpha, alpha asymmetry, gamma and gamma asymmetry give the best results in linear features, while RWE, DFA, CD and AE give the best results in non-linear feature. 2. In the linear features, gamma and alpha asymmetry have given 99.98% accuracy for Bagging RF, while gamma asymmetry has given 99.98% accuracy for BootAgg. 3. For non-linear features, it has been shown 99.84% of accuracy for RWE and DFA in RF, 99.97% accuracy for DFA in XGBoost and 99.94% accuracy for RWE in BootAgg. 4. By using DL, in linear features, gamma asymmetry has given more than 96% accuracy in RNN and 91% accuracy in LSTM and for non-linear features, 89% accuracy has been achieved for CD and AE in LSTM. 5. By combining linear and non-linear features, the highest accuracy was achieved in Bagging RF (98.50%) gamma asymmetry + RWE. In DL, Alpha + RWE, Gamma asymmetry + CD and gamma asymmetry + RWE have achieved 98% accuracy in LSTM.

A novel dataset was collected from the Central Institute of Psychiatry (CIP), Ranchi which was recorded using a 128-channels whereas major previous studies used fewer channels; the details of the study participants are summarized and a model is developed for statistical analysis using N-way ANOVA; artifacts are removed by high and low pass filtering of epoch data followed by re-referencing and independent component analysis for noise removal; linear features, namely, band power and interhemispheric asymmetry and non-linear features, namely, relative wavelet energy, wavelet entropy, Approximate entropy, sample entropy, detrended fluctuation analysis and correlation dimension are extracted; this model utilizes Epoch (213,072) for 5 s EEG data, which allows the model to train for longer, thereby increasing the efficiency of classifiers. Features scaling is done using a standard scalar rather than normalization because it helps increase the accuracy of the models (especially for deep learning algorithms) while PCA is used for feature reduction; the linear, non-linear and combination of both features are taken for extensive analysis in conjunction with ML and DL classifiers for the classification of depression. The combination of linear and non-linear features (only for those whose accuracy is highest) is used for the best detection results.

This study aims to validate the linear flow theory with computational fluid dynamics (CFD) simulations and to propose a novel shape for the airfoil that will improve supersonic aerodynamic performance compared to the National Advisory Committee for Aeronautics (NACA) 64a210 airfoil.

To design the new airfoil shape, this study uses a convex optimization approach to obtain a global optimal shape for an airfoil. First, modeling is conducted using linear flow theory, and then numerical verification is done by CFD simulations using ANSYS Fluent. The optimization process ensures that the new airfoil maintains the same cross-sectional area and thickness as the NACA 64a210 airfoil. This study found that an efficient way to obtain the ideal airfoil shape is by using linear flow theory, and the numerical simulations supported the assumptions inherent in the linear flow theory.

This study’s findings show notable improvements (from 4% to 200%) in the aerodynamic performance of the airfoil, especially in the supersonic range, which points to the suggested airfoil as a potential option for several fighter aircraft. Under various supersonic conditions, the optimized airfoil exhibits improved lift-over-drag ratios, leading to improved flight performance and lower fuel consumption.

This study was conducted mainly for supersonic flow, whereas the subsonic flow is tested for a Mach number of 0.7. This study would be extended for both subsonic and supersonic flights.

Convex optimization and linear flow theory are combined in this work to create an airfoil that performs better in supersonic conditions than the NACA 64a210. By closely matching the CFD results, the linear flow theory's robustness is confirmed. This means that the initial design phase no longer requires extensive CFD simulations, and the linear flow theory can be used quickly and efficiently to obtain optimal airfoil shapes.

The proposed airfoil can be used in different fighter aircraft to enhance performance and reduce fuel consumption. Thus, lower carbon emission is expected.

The unique aspect of this work is how convex optimization and linear flow theory were combined to create an airfoil that performs better in supersonic conditions than the NACA 64a210. Comprehensive CFD simulations were used for validation, highlighting the optimization approach's strength and usefulness in aerospace engineering.

India’s historical fiscal performance has featured elevated deficit levels. Driven by the imperative need for fiscal stimulus measures in response to the crisis, efforts toward fiscal consolidation from 2003 to 2008 were reversed in 2008–2009 due to the financial crisis. These stimulus actions are believed to have wielded a notable influence on inflation dynamics. Presumably, a high inflation rate hinders growth and inflicts severe welfare costs. Accordingly, the principal objective of this paper is to scrutinise the threshold effects of fiscal deficit on inflation within the context of the Indian economy.

We employed the Smooth Transition Autoregressive (STAR) Model, a robust tool for capturing non-linear relationships, to discern the specific threshold level of fiscal deficit. Our analysis encompasses annual data spanning from 1971 to 2020. Additionally, we have leveraged the Toda-Yamamoto causality test to establish the existence and direction of a causal connection between fiscal deficit and inflation in the Indian economy.

Our analysis pinpointed a critical threshold level of 3.40% for fiscal deficit, a value beyond which inflation dynamics in India undergo a marked transition, signifying the presence of significant non-linear effects. Moreover, the results derived from the Toda-Yamamoto causality test offer substantiating evidence of a causal relationship originating from the fiscal deficit and leading to inflation within the Indian economic framework.

The findings of our study carry significant implications, particularly for the formulation and execution of both fiscal and monetary policies. Understanding the threshold effects of fiscal deficit on inflation in India provides policymakers with valuable insights into achieving a harmonious balance between these two critical economic variables.

To the best of our knowledge, this study is the first of its kind to empirically investigate threshold effects of fiscal deficit on inflation in India from a non-linear perspective using the Smooth Transition Autoregression (STAR) model.

The objective of this research is to examine the accelerated adoption mechanisms of emerging technologies in information systems. Its goal is to comprehend the drivers behind the prompt assimilation of technology trends such as TikTok, ChatGPT, mobile payment schemes, cryptocurrency and VR.

The study follows the systematic literature review methodology (using the PRISMA protocol to guide the selection of scholarly materials from Google Scholar, Scopus and Springer). Specifically, the research draws on identified literature on the adoption trajectories of technologies (ChatGPT, TikTok, cryptocurrency, mobile payment systems, and virtual reality) to systematically assess pertinent insights, and draws on theoretical lenses of Disruptive Innovation Theory to reach interpretations.

The study indicates that the prompt assimilation of technology is shaped by several variables such as user-centered design, network effects, content powered through algorithms, viral trends, ease-of-use and accessibility features, engagement levels and retention rates.

The selection of specific platforms may limit the generalizability of findings.

The emergence of new technologies is causing a shift in societal behaviors and norms, which has significant social implications. While platforms such as TikTok offer opportunities for community-building, there are concerns regarding digital divide and privacy issues that need to be addressed. So understanding the impact of these changes becomes vital for achieving fairness in access and making technology's potential transformation practicalized effectively.

This research enhances the current body of literature by presenting a thorough examination of the non-linear patterns involved in adopting advanced technologies. By combining knowledge from numerous fields, this study delivers an integrated comprehension regarding what factors prompt swift adoption.

Previous studies have mostly estimated there to be a symmetric effect in the Foreign direct investment (FDI) inflow regarding the economic growth of Central, East and Southeast European (CESEE) countries. However, for the CESEE countries, as well as for the majority of countries around the world, there has been no study that has estimated the symmetric and asymmetric effect of outward FDI on economic growth. The main objective of this study is to estimate whether the relation between outward FDI and economic growth in CESEE countries is symmetric or asymmetric.

This study includes a sample based on eight CESEE countries. The authors used the linear and non-linear autoregressive distributed lag (ARDL) model and annual data for the period from 1990 to 2020.

In the long run, in the linear ARDL model, a significant symmetrical effect due to OFDI on the economic growth of Romania and Slovenia was found, while in the non-linear ARDL model, a significant asymmetric effect of OFDI on the economic growth of Bulgaria, Poland, Romania, Russia, Slovenia and Slovakia was found. In six out of the eight countries, asymmetry was found while symmetry was found in the other two. Poorer symmetry results can be ascribed to the lack of linear model neglecting the asymmetric behaviour of the positive and negative change decomposition as part of the OFDI movement, which leads to the wrong conclusion.

This is the first study to evaluate the asymmetric effect of outward FDI on the economic growth of eight CESEE countries.

The successful completion of linear infrastructure construction projects such as railroads, roads, tunnels, and pipelines relies heavily on decision-making processes during planning phase. Professionals in the construction industry emphasize that determining the starting point of a linear infrastructure construction project is one of the most important decisions to be made in the planning phase. However, the existing literature does not specifically focus on selection of the starting point of the segments to be constructed. Therefore, it is of utmost importance to develop a multi-criteria decision-making (MCDM) model to support selection of the starting point of the segments to be constructed in linear infrastructure construction projects.

Based on the characteristics of the railroad projects and insights gathered from expert interviews, the appropriate criteria for the model were determined. Once the criteria were determined, a decision hierarchy was developed and the weights of the criteria (w_i) were calculated using DEcision MAking Trial and Evaluation Laboratory (DEMATEL) method. Then, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), COmplex PRoportional Assessment (COPRAS), and evaluation based on distance from average solution (EDAS) methods were used. The alternatives were ranked in terms of their priority with TOPSIS method based on relative closeness (Ci) of each alternative to the ideal solution, COPRAS method based on quantitative utility (Ui) for each alternative and EDAS method based on evaluation score (ASi) for all alternatives. The results were compared with each other.

The study reveals the effects of all criteria on the proposed model. The results of DEMATEL method indicated that quantity of aggregate (w_i = 0.075), ballast (w_i = 0.071), and sub-ballast (w_i = 0.069) are the most important criteria in starting location selection for railroads, where earthquake (w_i = 0.046), excavation cost (w_i = 0.054), and longest distance from borrow pit (w_i = 0.055) were found to be less important criteria. The starting location alternatives were ranked based on TOPSIS, COPRAS and EDAS methods. The A-1 alternative was selected as the most appropriate alternative (Ci = 0.64; Ui = 100%; ASi = 0.81), followed by A-6 alternative (Ci = 0.61; Ui = 97%; ASi = 0.73) and A-7 alternative (Ci = 0.59; Ui = 94%; ASi = 0.60). Even tough different methods were used, they provided compatible results where the same ranking was achieved except three alternatives.

This study identifies novel criteria for the starting location selection of railroad construction based on the data of a railroad project. This study uses different methods for selecting the starting location. Considering the project type and its scope, the model can be used by decision-makers in linear infrastructure projects for which efficient planning and effective location selection are critical for successful operations.

The main focus is to provide a non-similar solution for the magnetohydrodynamic (MHD) flow of Casson fluid over a curved stretching surface through the novel technique of the artificial intelligence (AI)-based Lavenberg–Marquardt scheme of an artificial neural network (ANN). The effects of joule heating, viscous dissipation and non-linear thermal radiation are discussed in relation to the thermal behavior of Casson fluid.

The non-linear coupled boundary layer equations are transformed into a non-linear dimensionless Partial Differential Equation (PDE) by using a non-similar transformation. The local non-similar technique is utilized to truncate the non-similar dimensionless system up to 2nd order, which is treated as coupled ordinary differential equations (ODEs). The coupled system of ODEs is solved numerically via bvp4c. The data sets are constructed numerically and then implemented by the ANN.

The results indicate that the non-linear radiation parameter increases the fluid temperature. The Casson parameter reduces the fluid velocity as well as the temperature. The mean squared error (MSE), regression plot, error histogram, error analysis of skin friction, and local Nusselt number are presented. Furthermore, the regression values of skin friction and local Nusselt number are obtained as 0.99993 and 0.99997, respectively. The ANN predicted values of skin friction and the local Nusselt number show stability and convergence with high accuracy.

AI-based ANNs have not been applied to non-similar solutions of curved stretching surfaces with Casson fluid model, with viscous dissipation. Moreover, the authors of this study employed Levenberg–Marquardt supervised learning to investigate the non-similar solution of the MHD Casson fluid model over a curved stretching surface with non-linear thermal radiation and joule heating. The governing boundary layer equations are transformed into a non-linear, dimensionless PDE by using a non-similar transformation. The local non-similar technique is utilized to truncate the non-similar dimensionless system up to 2nd order, which is treated as coupled ODEs. The coupled system of ODEs is solved numerically via bvp4c. The data sets are constructed numerically and then implemented by the ANN.

Standard economic theory predicts that any increase in public spending is accompanied by a rise in inflation in an economy. This paper presents empirical proof that prices do not always rise with an increase in public expenditure but only up to a certain threshold level. The primary aim of this paper is to unearth the government size-inflation nexus in India for the period from 1971 to 2019.

The logistic STAR (smooth transition autoregression) model is employed to unravel the government size-inflation nexus for the Indian economy from a non-linear perspective.

The finding of our study confirm the non-linear relationship between the size of the government and inflation in India. The estimated threshold level for government size is precisely found to be 9.27%. The size of the government exerts a negative influence on inflation until it reaches the optimal or threshold level. Any further increase in the size of government beyond this threshold level would result in a rise in inflation.

The findings have implications for the conduct of fiscal policy. Policymakers can increase government spending in a regime of small government size without having any inflationary impacts by generating revenues from taxes and other sources instead of relying much on the central bank. In the regime of a large-sized government, adhering strictly to the discipline in the conduct of fiscal and monetary policies would help curb inflation and enhance growth synchronously, hence alleviating any loss of welfare.

To the best of the authors’ knowledge, this study is an attempt to revisit the government size-inflation nexus in India from a non-linear perspective using the Smooth Transition Autoregression (STAR) model for the first time.

This paper aims to contribute to the current debate between the mainstream and the non-mainstream literature on the effect of the growth of finance on the level of income inequality, for which the empirical evidence has also been providing mixed results.

We estimate a linear model and a non-linear model by employing a panel autoregressive distributed lag approach and relying on the dynamic fixed-effects estimator because of the existence of variables that are stationary in levels and stationary in the first differences.

Our findings confirm that finance, economic growth, educational attainment and degree of trade openness have a positive long-term effect on the level of income inequality in the European Union countries, whilst government spending has a negative impact in the short term.

Our findings imply that policy makers should rethink the functioning of the financial system in order to restore a supportive relationship between finance and income inequality and adopt public policies that are more in favour of the poor in order to constrain the growth of income inequality in the European Union countries.

To the best of our knowledge, this is the first paper that, simultaneously, focuses on the European Union countries, assesses the nexus between finance and income inequality, uses three different variables as proxies for the level of income inequality (the Gini coefficient, the top 1% income share and the top 10% income share), measures the variables that are proxies for the level of income inequality in terms of pre-tax and pre-transfer values and as post-tax and post-transfer values, takes into account four different variables as proxies for the role of finance (credit, credit-to-deposit ratio, liquid liabilities and stock market capitalisation) and identifies the long-term and short-term determinants of income inequality.