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Bearing friction moments are important factors that affect the vibrations of rotor systems. The bearing friction moments are related to the dimension parameters, lubrication conditions and manufacturing errors of support bearings. This work studies the effects of the bearing friction moments on the vibrations of rotor systems.

The rotor is separated into several shaft elements for formulating a flexible rotor. The time-varying friction moment (TFM) is affected by the time-varying contact loads. The vibrations of FRS from the TFM and Palmgren's friction moment (PFM) calculation methods are compared. Moreover, the effects of the rotor offset and radial clearance on the frequency-amplitude characteristics of FRS are studied.

The TFM method is more consistent with the actual operation mechanisms. The rotor offset and radial clearance can significantly affect the nonlinear vibrations of FRS. This work provides a new reference and research method for the vibration analysis of rotor systems considering the friction effects.

The elastohydrodynamic lubrication (EHL), elastic hysteresis and differential sliding are considered. A flexible rotor system (FRS) dynamic model considering the TFM is proposed. The vibrations of FRS from the TFM calculation method and empirical calculation formula are compared. The effects of the rotor offset and radial clearance on the frequency–amplitude characteristics of FRS are studied.

The purpose of this paper is to optimize the simultaneous flight performance of a hexarotor unmanned aerial vehicle (UAV) by using simultaneous perturbation stochastic approximation (i.e. SPSA), deep neural network and proportional integral derivative (i.e. PID) according to varying arm length (i.e. morphing).

In this paper, proper PID gain coefficients and morphing ratio were obtained using the stochastic optimization method, also known as SPSA to maximize flight efficiency. Because it is difficult to establish an analytical connection between the morphing ratio and hexarotor moments of inertia, the deep neural network was used to obtain the moments of inertia according to the morphing ratio. By using SPSA and deep neural network, the best performance indexes were obtained and both longitudinal and lateral flight simulations were performed with the obtained data.

With SPSA, the best PID coefficients and morphing ratio are obtained for both longitudinal and lateral flight. Because the hexarotor solid body model changes according to the morphing ratio, the moment of inertia values used in the simulations also change. According to the morphing ratio, the moment of inertia values was obtained with the deep neural network over a created data set.

It takes a long time to obtain the morphing ratio suitable for the hexarotor model and the PID gain coefficients suitable for this morphing ratio. However, this situation can be overcome with the proposed SPSA. In addition, it takes a long time to obtain the appropriate moments of inertia according to the morphing ratio. However, in this case, it was overcome using the deep neural network.

Determining the morphing ratio and PID gain coefficients using the optimization method, as well as determining the moments of inertia using the deep neural network, is very useful as it can increase the efficiency of hexarotor flight and flight efficiently with different arm lengths. With the proposed method, the hexarotor design performance criteria (i.e. rise time, settling time and overshoot) values were significantly improved compared to similar studies.

Determining the hexarotor flight parameters using SPSA and deep neural network provides advantages in terms of time, cost and applicability.

The hexarotor flight efficiency is improved with the proposed SPSA and deep neural network approaches. In addition, the desired flight parameters can be obtained more quickly and reliably with the proposed approaches. The design performance criteria were also improved, enabling the hexarotor UAV to follow the given trajectory in the best way and providing convenience for end users. SPSA was preferred because it converged faster than other methods. While other methods perform 2n operations per iteration, SPSA only performs two operations. To obtain the moment of inertia, many physical parameter values of the UAV are required in the existing methods. In the proposed method, by creating a date set, only arm length and moment of inertia were estimated without the need to obtain physical parameters with the deep neural network structure.

Online ride-hailing platforms match drivers with passengers by receiving ride requests from passengers and forwarding them to the nearest driver. In this context, the low acceptance rate of offers by drivers leads to friction in the process of driver and passenger matching. What policies by the platform may increase the acceptance rate and by how much? What factors influence drivers' decisions to accept or reject offers and how much? Are drivers more likely to turn down a ride offer because they know that by rejecting it, they can quickly receive another offer, or do they reject offers due to the availability of outside options? This paper aims to answer such questions using a novel dataset from Tapsi, a ride-hailing platform located in Iran.

The authors specify a structural discrete dynamic programming model to evaluate how drivers decide whether to accept or reject a ride offer. Using this model, the authors quantitatively measure the effect of different policies that increase the acceptance rate. In this model, drivers compare the value of each ride offer with the value of outside options and the value of waiting for better offers before making a decision. The authors use the simulated method of moments (SMM) method to match the dynamic model with the data from Tapsi and estimate the model's parameters.

The authors find that the low driver acceptance rate is mainly due to the availability of a variety of outside options. Therefore, even hiding information from or imposing fines on drivers who reject ride offers cannot motivate drivers to accept more offers and does not affect drivers' welfare by a large amount. The results show that by hiding the information, the average acceptance rate increases by about 1.81 percentage point; while, it is 4.5 percentage points if there were no outside options. Moreover, results show that the imposition of a 10-min delay penalty increases acceptance rate by only 0.07 percentage points.

To answer the questions of the paper, the authors use a novel and new dataset from a ride-hailing company, Tapsi, located in a Middle East country, Iran and specify a structural discrete dynamic programming model to evaluate how drivers decide whether to accept or reject a ride offer. Using this model, the authors quantitatively measure the effect of different policies that could potentially increase the acceptance rate.

While science has researched the impact of air pollution on human health, the economic dimension of it has been less researched so far. Renewable energy consumption is an important factor in determining the level of life expectancy and reducing health expenditure. Thus, this study aims to investigate the impact of renewable energy, carbon emissions, health expenditure and urbanization on life expectancy in G-7 countries over the period of 2000–2019.

This study has adopted a novel Method of Moments Quantile Regression (MMQR). Furthermore, as a robustness check for MMQR, the fully modified ordinary least square, dynamic ordinary least squares and fixed effect ordinary least square estimators have been used.

The results indicated that renewable energy consumption, health expenditure and urbanization lead to an increase in life expectancy across all quantiles (5th to 95th), whereas higher carbon dioxide emissions reduce life expectancy at birth across all the quantiles (5th to 95th).

The empirical findings conclude that governments should recognize their potential in renewable energy sources and devise policies such as tax-related regulations, or relevant incentives to encourage further investments in this field.

This paper in comparison to the other research studies used MMQR to investigate the impact of factors affecting life expectancy. Also, to the best of the authors’ knowledge, so far no study has investigated the impact of renewable energy on life expectancy in G-7 countries.

The study aims to evaluate the influence of capital structure decisions and asset structure on firms' performance for East African listed nonfinancial firms.

The research is descriptive and employs secondary data from the East African capital markets' websites. The generalized method of moments approach is used to estimate the relationship due to its ability to account for endogeneity problems.

The result shows that capital structure decisions and asset structure strongly influence the firms' performance. When long-term debts, short-term debts and tangible fixed assets increase, the return on total assets increases. An increase in the total debt ratio raises the return on equity (ROE). However, the increase in long-term debt lowers the ROE.

The results will help investors and potential investors decide on a financing policy that maximizes performance. Likewise, governments and other policymakers review the capital markets' frameworks to attract institutional and individual investors to the markets for financial availability and to increase profitability.

The research provides evidence on the influence of capital structure decisions and asset structure on firms' performance. Furthermore, its results contribute to firms' financing policy formulation and the corporate finance literature.

The purpose of this paper is to investigate the influence of macroeconomic cyclical indicators and country governance on bank non-performing loans in African countries.

Data was collected from the 53 African countries covering 2005–2021. The paper develops an empirical model to examine the impact of country governance in reducing macroeconomic cycle-induced adverse effects on bank credit risk. This research estimates Random Effects models and the General Method of Moment to examine the link between microeconomic and governance factors on bank non-performing loans. Stata version 15.1 was used to conduct panel regression analysis.

The findings of the study revealed that the generalized method of moments findings contributes valuable insights into the persistence of NPLs over time and the specific effects of variables on NPL levels. The study findings highlight that the debt-to-GDP ratio, unemployment, regulatory quality, government effectiveness and inflation have significant relationships with NPLs, shedding light on their specific contributions to credit risk dynamics.

The focus on a specific set of determinants for NPLs, which may not capture all the factors that influence NPL levels. Thus, the study did not consider the impact of macroeconomic shocks, such as natural disasters or global economic crises, which can have a significant impact on NPLs.

Policymakers should prioritize maintaining sustainable debt levels, promoting employment growth and controlling inflation rates to mitigate credit risk and reduce nonperforming loans. Also, enhancing regulatory quality and government effectiveness is crucial in ensuring financial stability and minimizing non-performing loans in Africa.

This paper provides a new possible solution to minimise bank non-performing loans risk by examining interactions of country governance regarding the macroeconomic cycle behaviour.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2022-0729

The purpose of this study is to investigate the relationship between fiscal policy (tax revenues and government expenditure) and economic happiness. The panel data are used from 2012 to 2016 for 18 countries of the Middle East and North Africa (MENA) region.

The study adopted the Barro (1990) model of endogeneity growth to characterize the relationship between fiscal policy and economic happiness. The study estimated the model by using the pooled ordinary least squares method, the fixed effects method and the random-effects method. In addition, the study used the dynamic estimate of this relationship rather than the conventional static estimate through the generalized method of moments’ method. This leads to overcoming the endogeneity problem between the dependent variable and the independent variables.

The main findings indicated that there is a negative and statistically significant relationship between nondistortionary taxes and economic happiness. Also, there is no relationship between public expenditure and economic happiness, whether productive or nonproductive. The results confirmed a positive and significant relationship between other revenues and economic happiness. The current study recommended the diversification of other public revenue sources to increase its contribution to public expenditure financing and the restructuring of the tax system, particularly nondistortionary taxes. These taxes must be replaced by other revenues or by distortionary taxes to increase economic happiness.

The research represents a strong starting base that can help researchers to conduct more studies on economic happiness by using different measures and comparing their results to find out the determinants of happiness. The relationship between economic happiness and fiscal policy with its different aspects requires more studies, especially the relationship between taxes and economic happiness in our region. The study of the relationship between public expenditure and economic happiness according to economic activities can guide decision-makers to direct the expenditure toward economic activities that achieve the happiness of their citizens. Enriching this study requires the availability of fiscal data for the entire MENA region for longer periods, which allow us to divide the countries of the region into petroleum and nonpetroleum countries, but the scarcity of data is one of the limitations of the study.

The governments of MENA countries should diversify other public revenue sources to increase the financing public expenditure by the expense of tax revenues, especially nondistortionary taxes, which would increase the economic happiness of their citizens.

This study is one of the rare studies that investigate the relationship between fiscal policy and economic happiness at the global level. This study contributed to filling the gap of this issue in the MENA region and enriching global literature through the experience of the MENA region. Moreover, this study investigated all aspects of fiscal policy, in contrast to other studies that focused on one of its aspects. The weakness in these studies is because of the lack of correlation between the sources of revenues and the face of their spending.

The purpose of this paper is to present a fast and bare bones implementation of a numerical method for quickly simulating turbulent thermal flows on GPUs. The work also validates earlier research showing that the lattice Boltzmann method (LBM) method is suitable for complex thermal flows.

A dual lattice hydrodynamic (D3Q27) thermal (D3Q7) multiple-relaxation time LBM model capable of thermal DNS calculations is implemented in CUDA.

The model has the same computational performance compared to earlier publications of similar LBM solvers. The solver is validated against three benchmark cases for turbulent thermal flow with available data and is shown to be in excellent agreement.

The combination of a D3Q27 and D3Q7 stencil for a multiple relaxation time -LBM has, to the authors’ knowledge, not been used for simulations of thermal flows. The code is made available in a public repository under a free license.

The performance of financial markets is significantly influenced by the political environment during general elections. This study investigates the effect of general elections on stock market performance in selected African markets.

Prior studies have been inconsistent in determining whether electioneering events negatively or positively influence stock market performance. The study utilized panel data set with annual observations from 1990 to 2020. The generalized method of moments (GMM) is employed to investigate the effect of electioneering and change in government on key stock market performance indicators, including stock market capitalization, stock market turnover ratio and the value of stock traded.

The study finds that electioneering activities generally have a positive impact on the performance of the stock market, whereas a change in government has a negative impact. As a result, the study recommends that stakeholders of the stock market remain vigilant and actively monitor electioneering events to devise and implement effective policies aimed at mitigating political risks during general elections. By adopting these measures, investor confidence can be significantly enhanced, fostering a more robust and secure investment environment.

The study investigates a neglected section of the literature by highlighting not only the effect of elections on stock market indicators but also possible change in government during elections.

This study aims to find the relationship of stock market returns and selected financial market variables (market capitalization, earnings per share, price-earnings multiples, dividend yield and trading volume) of Malaysia grounded by the arbitrage pricing theories.

This study empirically examines the effects of selected financial market variables on stock market returns using 64 companies listed in Malaysia's stock market with data spanning from 2005 to 2018. A systematic empirical study based on the Generalized Method of Moments following Arellano and Bond (1991) has been taken to estimate the effect.

The regression result of the financial market variables and stock market return shows that, except for trading volume, all selected financial market variables play significant roles in the stock market returns. Furthermore, market capitalization, earnings per share, price-earnings ratio, dividend yield and trading volume have a positive impact on stock market returns.

The outcome of this study can contribute by helping domestic and global investors devise strategies to minimize their risks. Also, policy administrators can use the outcomes of this study to inform the micro- and macro-level policy formulation.

This study will contribute to filling the gap in knowledge concerning the new release of factors affecting the stock market returns of Malaysia.