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Steer-by-wire (SBW) system mainly relies on sensors, controllers and motors to replace the traditionally mechanical transmission mechanism to realize steering functions. However, the sensors in the SBW system are particularly vulnerable to external influences, which can cause systemic faults, leading to poor steering performance and even system instability. Therefore, this paper aims to adopt a fault-tolerant control method to solve the safety problem of the SBW system caused by sensors failure.

This paper proposes an active fault-tolerant control framework to deal with sensors failure in the SBW system by hierarchically introducing fault observer, fault estimator, fault reconstructor. Firstly, the fault observer is used to obtain the observation output of the SBW system and then obtain the residual between the observation output and the SBW system output. And then judge whether the SBW system fails according to the residual. Secondly, dependent on the residual obtained by the fault observer, a fault estimator is designed using bounded real lemma and regional pole configuration to estimate the amplitude and time-varying characteristics of the faulty sensor. Eventually, a fault reconstructor is designed based on the estimation value of sensors fault obtained by the fault estimator and SBW system output to tolerate the faulty sensor.

The numerical analysis shows that the fault observer can be rapidly activated to detect the fault while the sensors fault occurs. Moreover, the estimation accuracy of the fault estimator can reach to 98%, and the fault reconstructor can make the faulty SBW system to retain the steering characteristics, comparing to those of the fault-free SBW system. In addition, it was verified for the feasibility and effectiveness of the proposed control framework.

As the SBW fault diagnosis and fault-tolerant control in this paper only carry out numerical simulation research on sensors faults in matrix and laboratory/Simulink, the subsequent hardware in the loop test is needed for further verification.

Aiming at the SBW system with parameter perturbation and sensors failure, this paper proposes an active fault-tolerant control framework, which integrates fault observer, fault estimator and fault reconstructor so that the steering performance of SBW system with sensors faults is basically consistent with that of the fault-free SBW system.

The theoretical debate of corruption's impact on economic growth remains unsettled, making it an empirical question. This study aims to investigate corruption's effect on BRICS countries' economic growth.

A panel dataset on BRICS countries spanning 1996 to 2020 was used. Bias-corrected estimators in small dynamic panels were employed to estimate a growth model as a linear-quadratic function of corruption that accounts for cross-sectional dependence, endogeneity and unobserved heterogeneity due to country and time-specific characteristics.

The results indicate that corruption is detrimental to economic growth in BRICS countries; the quadratic relationship implies corruption is less prevalent in some countries than others. Thus, governments of BRICS countries are encouraged to embark on anti-corruption policies to boost their economic performance.

An important limitation of corruption studies is the difficulty in measuring real corruption experiences due to the secretive nature of corruption and the fact that corruption is known not to leave a paper trail. For the uncertainty of the index estimates, the analysis used a continuous corruption composite score measuring the standard deviation of the extent to which public power is exercised for public gain. Furthermore, estimation and inference are robust to small dynamic panels with a general form of cross-sectional dependence.

This study aims to identify the ability of gold and cryptocurrency (Cryptocurrency Uncertainty Index (UCRY) Price) as safe haven assets (SHA) for stocks and bonds in both conventional (i.e. stock indices and government bonds) and Islamic markets (i.e. Islamic stock indices and Islamic bonds (IB)).

The authors employed the nonadditive panel quantile regression model by Powell (2016). It measured the safe haven characteristics of gold and UCRY Price for stock indices, government bonds, Islamic stocks, and IB under gold circumstances and level of cryptocurrency uncertainty, respectively. The period spanned from 11 March 2020 to 31 December 2021.

This study discovered three findings, including: (1) gold is a strong safe haven for stocks and bonds in conventional and Islamic markets under bearish conditions; (2) UCRY Price is a strong safe haven for conventional stocks and bonds but only a weak safe haven for Islamic stocks under high crypto uncertainty; and (3) gold offers a safe haven in both emerging and developed countries, while UCRY Price provides a better safe haven in developed than in emerging countries.

Gold always wins big for safe haven properties during unstable economy. It can also win over investors who consider shariah compliant products. Therefore, it should be included in an investor's portfolio. Meanwhile, cryptocurrencies are more common for developed countries. Thus, the governments and regulators of emerging countries need to provide more guidance around cryptocurrency so that the societies have better literacy. On top of that, the investors can consider crypto to mitigate risks but with limited safe haven functions.

The originality aspects of this study include: (1) four chosen assets from conventional and Islamic markets altogether (i.e. stock indices, government bonds, Islamic stock indices and IB); (2) indicator countries selected based on the most used and owned cryptocurrencies for the SHA study; and (3) the utilization of UCRY Price as a crypto indicator and a further examination of the SHA study toward four financial assets.

Given increasing customer expectations and disturbances to product returns management, capabilities such as supply chain resilience (SCR) can complement service recovery strategies in retail supply chains. This study utilizes procedural justice theory (PJT) to conceptualize service recovery resilience as a capability that allows firms to meet customer requirements when dealing with disruptions, and empirically investigates its impact on procedural and interactional justice and customer outcomes (i.e. satisfaction and loyalty) in the context of product replacement.

This research employs two scenario-based experiments using a sample of 368 customers to explore the outcomes associated with service recovery resilience.

The investigation shows more satisfied and loyal customers when a retail supply chain can overcome service recovery challenges through SCR. The study shows that customers evaluate not only the process itself, but also their interactions with the retailer. Specifically, procedural justice and interactional justice have a significant influence on these relationships.

This study proposes service recovery resilience as a concept that bridges service recovery theory with supply chain strategy in the unique context of product replacement. Further, this study also notes how information enhances customer satisfaction with the retailer's effort to address disturbances in the recovery process. Finally, this study informs managers on the capabilities needed to face new customers' needs.

This study aims to examine the impact of market competition, and capital regulation on the cost of financial intermediation of banks of the Bangladesh banking industry.

This study has used a balanced panel dataset comprised of 340 firm-year observations for 34 commercial banks in the Bangladesh banking industry from 2011 to 2020. The Prais Winsten panel estimator has been used to assess the impact of market competition and capital regulation on the cost of financial intermediation of banks.

Based on the regression results, this study has documented that greater market competition results in a lower cost of financial intermediation for banks. Similarly, an increase in the regulatory capital of banks increases the cost of financial intermediation of banks. The main findings of this study are found robust by using alternative proxies for the cost of financial intermediation, market competition and capital regulation. The regression results also suggest that private commercial banks tend to have a higher cost of financial intermediation than state-owned commercial banks.

The regulatory reforms should aim to foster sustainable and optimal market competition for the Bangladesh banking industry to regulate the market power of banks to reduce the cost of financial intermediation. The regulatory authority of Bangladesh should find the optimal policy measures for implementing the capital regulation in the banking industry which would reduce the cost of financial intermediation margin of banks.

Unlike previous studies which have used structural market competition measures, this study has used non-structural market competition measures to assess the relationship between market competition and cost of financial intermediation in the Bangladesh banking industry.

Since its introduction by Chari, Kehoe, and McGrattan (2007), Business Cycle Accounting (BCA) exercises have become widespread. Much attention has been devoted to the results of such exercises and to methodological departures from the baseline methodology. Little attention has been paid to identification issues within these classes of models. In this chapter, the authors investigate whether such issues are of concern in the original methodology and in an extension proposed by Šustek (2011) called Monetary Business Cycle Accounting. The authors resort to two types of identification tests in population. One concerns strict identification as theorized by Komunjer and Ng (2011) while the other deals both with strict and weak identification as in Iskrev (2010). Most importantly, the authors explore the extent to which these weak identification problems affect the main economic takeaways and find that the identification deficiencies are not relevant for the standard BCA model. Finally, the authors compute some statistics of interest to practitioners of the BCA methodology.

This study aims to examine the ability of clean energy stocks to provide cover for investors against market risks related to climate change and disturbances in the oil market.

The study adopts the feasible quasi generalized least squares technique to estimate a predictive model based on Westerlund and Narayan’s (2015) approach to evaluating the hedging effectiveness of clean energy stocks. The out-of-sample forecast evaluations of the oil risk-based and climate risk-based clean energy predictive models are explored using Clark and West’s model (2007) and a modified Diebold & Mariano forecast evaluation test for nested and non-nested models, respectively.

The study finds ample evidence that clean energy stocks may hedge against oil market risks. This result is robust to alternative measures of oil risk and holds when applied to data from the COVID-19 pandemic. In contrast, the hedging effectiveness of clean energy against climate risks is limited to 4 of the 6 clean energy indices and restricted to climate risk measured with climate policy uncertainty.

The study contributes to the literature by providing extensive analysis of hedging effectiveness of several clean energy indices (global, the United States (US), Europe and Asia) and sectoral clean energy indices (solar and wind) against oil market and climate risks using various measures of oil risk (WTI (West Texas intermediate) and Brent volatility) and climate risk (climate policy uncertainty and energy and environmental regulation) as predictors. It also conducts forecast evaluations of the clean energy predictive models for nested and non-nested models.

This study aims to investigate the influences of global uncertainty indicators volatility on the domestic socioeconomic and environmental vulnerability in a sample of 54 developing countries.

The two-step system generalized method of moments estimator is recruited to deal with autoregression and endogeneity matter in our dynamic panel data. Seven different global uncertainty indicators (US trade uncertainty; world trade uncertainty; economic policy uncertainty; world commodities and oil prices; the geopolitical risk index and the world uncertainty index) have been mobilized and compared for their empirical impact on the economic (growth and GDP), social (the misery index and income inequality) and environmental (CO₂ emissions) vulnerabilities of nations.

Our empirical estimations suggest that the socioeconomic and environmental vulnerability cannot be solved through the same pattern: all decrease of a particular aspect will necessarily have a cost and an opposite influence on at least one of the other aspects of the nations' vulnerability.

The originality of this article is to combine these three dimensions of vulnerability in the same investigation. To our knowledge, our research is one of the few providing a joint analysis of the influence of global uncertainty on the economic and socioenvironmental countries' vulnerabilities – given the fact social, economic and environmental aspects are at the heart of the UN sustainable goals, our study can be seen as an investigation of the nations' capabilities to work proactively on meaningful sustainable goals in an increasingly uncertain world.

While there exist many surveys on the use stochastic frontier analysis (SFA), many important issues and techniques in SFA were not well elaborated in the previous surveys, namely, regular models, copula modeling, nonparametric estimation by Grenander’s method of sieves, empirical likelihood and causality issues in SFA using regression discontinuity design (RDD) (sharp and fuzzy RDD). The purpose of this paper is to encourage more research in these directions.

A literature survey.

While there are many useful applications of SFA to econometrics, there are also many important open problems.

This is the first survey of SFA in econometrics that emphasizes important issues and techniques such as copulas.