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The diagnosis and prediction methods used for estimating the health conditions of the bearing are of great significance in modern petrochemical industries. This paper aims to discuss the accuracy and stability of improved empirical mode decomposition (EMD) algorithm in bearing fault diagnosis.

This paper adopts the improved adaptive complementary ensemble empirical mode decomposition (ICEEMD) to process the nonlinear and nonstationary signals. Two data sets including a multistage centrifugal fan data set from the laboratory and a motor bearing data set from the Case Western Reserve University are used to perform experiments. Furthermore, the proposed fault diagnosis method, combined with intelligent methods, is evaluated by using two data sets. The proposed method achieved accuracies of 99.62% and 99.17%. Through the experiment of two data, it can be seen that the proposed algorithm has excellent performance in the accuracy and stability of diagnosis.

According to the review papers, as one of the effective decomposition methods to deal with nonlinear nonstationary signals, the method based on EMD has been widely used in bearing fault diagnosis. However, EMD is often used to figure out the nonlinear nonstationarity of fault data, but the traditional EMD is prone to modal confusion, and the white noise in signal reconstruction is difficult to eliminate.

In this paper only the top three optimal intrinsic mode functions (IMFs) are selected, but IMFs with less correlation cannot completely deny their value. Considering the actual working conditions of petrochemical units, the feasibility of this method in compound fault diagnosis needs to be studied.

Different from traditional methods, ICEEMD not only does not need human intervention and setting but also improves the extraction efficiency of feature information. Then, it is combined with a data-driven approach to complete the data preprocessing, and further carries out the fault identification and classification with the optimized convolutional neural network.

This study investigates the impact of geopolitical risk (GPR) on foreign remittances (FRs) for the top remittance-receiving countries.

The sample includes Mexico, France, Egypt, China, the Philippines, India, Vietnam, Ukraine, Germany and Belgium for the annual period of 1998–2022 using the nonlinear panel autoregressive distributed lag (ARDL) model to determine the asymmetry in the relationship.

The results suggest that, in the short term, positive GPR shocks have a positive and significant impact on FRs received. On the other hand, the long-run results suggest that adverse GPR shocks negatively affect FRs received in the sampled countries. Additionally, the study confirms the asymmetric impact of GPR on top remittances received in countries.

The policymakers, migrants and recipients should consider the asymmetric nature of GPR while making decisions regarding policies and the transfer of remittances. This information can be used to create more effective policies for controlling and reducing the effects of GPR on overseas remittances, such as assisting migrant workers and developing methods to lessen the volatility of these flows.

Acknowledging the potential fluctuations and uncertainties associated with GPR is crucial to make informed choices regarding remittance-related matters.

This study aims to propose the use of time series autoregressive integrated moving average (ARIMA) models to predict gas path performance in aero engines. The gas path is a critical component of an aero engine and its performance is essential for safe and efficient operation of the engine.

The study analyzes a data set of gas path performance parameters obtained from a fleet of aero engines. The data is preprocessed and then fitted to ARIMA models to predict the future values of the gas path performance parameters. The performance of the ARIMA models is evaluated using various statistical metrics such as mean absolute error, mean squared error and root mean squared error. The results show that the ARIMA models can accurately predict the gas path performance parameters in aero engines.

The proposed methodology can be used for real-time monitoring and controlling the gas path performance parameters in aero engines, which can improve the safety and efficiency of the engines. Both the Box-Ljung test and the residual analysis were used to demonstrate that the models for both time series were adequate.

To determine whether or not the two series were stationary, the Augmented Dickey–Fuller unit root test was used in this study. The first-order ARIMA models were selected based on the observed autocorrelation function and partial autocorrelation function.

Further, the authors find that the trend of predicted values and original values are similar and the error between them is small.

Considering the inherent relationship between environmental degradation and the process of economic development, the latter is particularly reliant on the accumulation of human capital, which also emerges as one of the fundamental principles underlying green growth. However, this relationship tends to overlook varying levels of human capital. Hence, the purpose of this study is to examine the enduring associations between the stock of high human capital and green economies in terms of environmental sustainability among the key countries in the Asia Pacific region, namely Australia, Japan, Singapore, and South Korea, spanning the period from 1990 to 2022.

This paper employs second-generation techniques. The long-term relationships were estimated using two constantly updated models - fully modified and bias corrected, CUP-FM and CUP-BC, respectively, to guarantee the robustness of our conclusions for the presence of cross-sectional dependency.

There is a long-term relationship between the stock of high human capital and the sustainability of the environment, in the same way that we have also found the same relationship between the development of socioeconomic practices of green economies. Finally, we conclude that, in the same way as the environmental Kuznets curve, the countries in our sample incur less environmental pollution as their level of income increases. This relationship may be motivated by a process of technological substitution and investment in the development of new techniques and technology to improve the efficiency of productivity with respect to the environment.

We suggest that investing in education and promoting green economies can be powerful tools in the fight against climate change and promoting environmental sustainability. By prioritizing investments in renewable energy and sustainable technologies, policymakers can promote long-term economic and environmental health. Moreover, the findings suggest that promoting education in countries with high levels of environmental pollution can develop the knowledge and skills needed to implement sustainable practices and technologies. Ultimately, these efforts can contribute to improving income, productivity, and society's living conditions while reducing the environmental impact.

This research studies for the first time the load capacity curve hypothesis in determining the effects of the stock of high human capital and green economies on the environment. Consequently, limited papers have used the load capacity factor in the study of the relationships that we propose, especially that of human capital, which has scarcely been studied in relation to its contribution to the environmental fight.

The purpose of this study is developing a new buy-back coordination contract in the symbiotic supply chain. In this new contract, the goal of the supply chain members (profit maximization) is realized.

This paper encourages the manufacturer to order products optimally by presenting a new buy-back coordination contract, and in return, the supplier undertakes to buy the unsold products from the manufacturer at the buy-back price. By using data-driven decision-making and multiobjective decision-making and considering the existing conditions in the symbiosis industry, a contract has been presented that guarantees the profits of supply chain members.

In this paper, it was found out how the authors can determine the order quantity, buy-back price and wholesale price in a symbiotic supply chain in such a way that it makes a profit for both the supplier and the manufacturer. In other words, how to determine these variables to encourage the manufacturer to order more quantity to the supplier so that both will benefit.

To the best of the authors’ knowledge, this is the first paper that defines a new buy-back coordination contract in the symbiotic supply chain by considering uncertain demand and a multiobjective model. Due to the importance of environmental issues, the sharing of resources by companies and organizations with each other, and the necessity of their cooperation, industries are moving toward a symbiosis industry.

This research investigates the complex relationship between economic policy uncertainty (EPU), energy consumption and institutional factors in the Gulf region. The purpose of this study is to examine how institutional factors moderate the impact of EPU on energy consumption in Gulf countries.

This paper uses the dynamic panel autoregressive distributed lag (PARDL) method, over a period stretching from 1996 to 2021 in the Gulf countries.

The results show that, only in the long term, EPU has a positive and significant impact on energy consumption, suggesting that increased EPU leads to increased energy use. Furthermore, this study found that, only in the long term, government effectiveness and regulatory quality have positive and significant effect on energy consumption. Accordingly, the two institutional factors play a moderating role in the EPU−energy consumption nexus.

This study highlights the importance of considering the time dimension when formulating energy and economic policies in Gulf countries. Policymakers should take into consideration the nature of these relationships to make informed decisions that promote energy efficiency and economic stability in the region.

To the best of the authors’ knowledge, this is the first study examining the relationship between EPU and energy consumption in the Gulf countries while incorporating the role of institutional factors as potential mediators.

The goal of the study is to offer important insights into the dynamics of the cryptocurrency market by analyzing pricing data for Bitcoin. Using quantitative analytic methods, the study makes use of a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and an Autoregressive Integrated Moving Average (ARIMA). The study looks at how predictable Bitcoin price swings and market volatility will be between 2021 and 2023.

The data used in this study are the daily closing prices of Bitcoin from Jan 17th, 2021 to Dec 17th, 2023, which corresponds to a total of 1065 observations. The estimation process is run using 3 years of data (2021–2023), while the remaining (Jan 1st 2024 to Jan 17th 2024) is used for forecasting. The ARIMA-GARCH method is a robust framework for forecasting time series data with non-seasonal components. The model was selected based on the Akaike Information Criteria corrected (AICc) minimum values and maximum log-likelihood. Model adequacy was checked using plots of residuals and the Ljung–Box test.

Using the Box–Jenkins method, various AR and MA lags were tested to determine the most optimal lags. ARIMA (12,1,12) is the most appropriate model obtained from the various models using AIC. As financial time series, such as Bitcoin returns, can be volatile, an attempt is made to model this volatility using GARCH (1,1).

The study used partially processed secondary data to fit for time series analysis using the ARIMA (12,1,12)-GARCH(1,1) model and hence reliable and conclusive results.

The contributions of gender equality to trade and the moderating impact of institutional quality on the trade-gender link have been understudied in contemporary literature. Therefore, this paper aims to use the augmented gravity model to investigate the impacts of gender equality and institutional quality on trade, and the moderating impact of institutional quality on the trade-gender link.

The panel data is comprised of bilateral trade flows of Vietnam and 40 major trading partners in the 2002–2021 period. Estimation methods include combined fixed effect, random effect, system generalized method of moments two-step (SGMM-II) and Poisson-pseudo maximum likelihood (PPML) which allow the treatment of heterogeneity, endogeneity and heteroskedasticity in the research models.

The exporting country’s gender equality has an inconclusive impact on trade in SGMM-II and PPML estimations. However, female labor force participation in the exporting country increases trade. Importing country’s gender equality and female labor force participation increase trade. The direct impact of institutional quality on trade is inconclusive, which is dependent on estimation methods. Most noticeably, the institutional quality of exporting and importing countries facilitates the hampering effects of gender equality and female labor force participation on trade.

Future research should apply the framework of this paper to sectoral trade, which allows more sector-specific policy implications to be delivered. Moreover, gender development, gender inequality and institutional quality should be included in the SGMM estimations as endogenous variables for robustness checking purposes in future research.

The paper has justified the integration of gender-specific issues in trade policies, which aligns trade with sustainable development agenda. The explored moderating impact of institutional quality of the exporting country has implied the trade-off relationship between gender equality and export growth in the effort to improve institutional quality. Reversely, in the case of importing countries with higher institutional quality, improved gender equality may mitigate the trade deficit by hampering import growth.

Investigating the impact of gender equality on trade is the prominent contribution of this paper. Gender equality is considered by three component indicators which include gender development, gender inequality and female labor force participation. New measurement approach to the institutional quality level is also introduced. Furthermore, the explored moderating impacts of institutional quality on the trade-gender link are novel in the literature on sustainable development.

This paper aims to compare the efficiency of spatial and nonspatial hedonic price models in capturing housing submarkets dynamics for cities in developing countries. This study expects to contribute to a better understanding of the housing price determinants from both nonspatial and spatial perspectives. In addition, this paper fills a gap in the literature on the study of housing prices from a spatial perspective in Latin American cities.

This study uses a comparative analysis between an ordinary least squares regression and a geographical weighted regression, GWR. The study also assesses the performance of two distinct data sources: the city’s cadastral records and a real estate sales web portal.

The results suggest that compared to the traditional regression model, the spatial regression models are more effective at capturing housing market variations on a fine scale. Moreover, they reveal interesting findings on the spatial varying, sometimes contradictory effects of some housing attributes on housing prices in different areas of the city, suggesting the potential impact from segregation.

The availability of data on housing prices and characteristics in Latin American cities is fragmented and complex. The level of detail, granularity and coverage is not consistent over time. For this reason, this study combines and compares data sets from official and unofficial sources in an effort to close this gap. Likewise, the socioeconomic variables that come from the census must be carefully analyzed, knowing the historical context in which they were constructed, what they represent and their interpretation.

This paper suggests that despite the improvement on the spatial models, the selection of a specific one should always be based on the diagnosis of it as it highly depends on the data used and the objectives of the study.

This study enriches the limited body of literature on spatial hedonic price models of housing in Latin American cities. It also shed light on the importance of spatial approaches to identify complex housing submarkets.

The study aims to determine the long and short-term causal relationships between the variables associated with the adjustment of monetary policy and the stock market in India in the presence of structural breaks.

The study employed the autoregressive distributed lag (ARDL) bounds test and the Error Correction Model to assess long- and short-term causal relationships. The study also used non-frequentist Bayesian inferences for the validity of estimation robustness. The Bai–Perron test is used to identify breakpoint dates for the Indian stock market index, and the Granger Causality test is employed to ascertain the direction of causality.

The F-bounds test reveals cointegration among the variables throughout the examined period. Specifically, the weighted average call money rate (WACR), inflation (WPI), currency exchange rate (EXE), and broad money supply (M3) exhibit statistical significance with precise signs. Furthermore, the study identifies the negative impact of the COVID-19 outbreak in March 2020 on the Indian stock market.

Although the study provides significant insights, it is not exempt from constraints. A significant limitation is selecting a relatively limited time period, specifically from April 2008 to September 2023. The limited time frame of this study may restrict the applicability of the results to more comprehensive economic settings, as dynamics between the monetary policy and the stock market can be influenced by multiple factors over varying time periods. Furthermore, the utilisation of the Weighted Average Call Money Rate (WACR) rather than policy rates such as the Repo rate presents an additional constraint as it may not comprehensively account for the impacts of particular policy initiatives, thereby disregarding essential complexities in the connection between monetary policy variables and financial markets.

The findings of the study suggest that investors and portfolio managers should consider economic issues while developing long-term investing plans. Reserve Bank of India should exercise prudence to prevent any discretionary measures that may lead to a rise in interest rates since this adversely affects the stock market. To mitigate risk, investors should closely monitor the adjustment of monetary policy variables.

The study has important social implications, especially regarding the lower levels of financial literacy among investors in India. Considering the complex nature of the study’s emphasis on monetary policy adjustments and their impact on the stock market. Investors face the risk of significant losses due to unexpected adjustments in monetary policy. Many individuals may need help understanding how policy changes impact their investments. Therefore, RBI must consider both price and financial stability when formulating monetary policies. Furthermore, market participants should consider the potential impact of fluctuating monetary policy variables when devising their long-term investment strategies. Given that adjustments in interest rates can markedly affect stock market dynamics, investors must carefully assess the implications of monetary policy decisions on their portfolios.

The study uses dummy variables in the ARDL model to represent structural breaks that emerged from the COVID-19 pandemic (as determined by the Bai–Perron multiple breakpoint test). The study also used the Perron unit root test to find out the stationary of the series in the presence of structural breaks. Additionally, the study also employed Bayesian inferences to affirm the robustness of the estimates.