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The purpose of this paper is to investigate the effect of nano-alumina sealant sealing treatment on corrosion behavior of the Fe-based amorphous coatings deposited on 304 stainless steel plates by atmospheric plasma spraying (APS) with different hydrogen flow rates.

The surface morphology and microstructure of the unsealed and sealed coatings were characterized by scanning electron microscopy and X-ray diffraction. The corrosion resistance of the coatings was investigated by potentiodynamic polarization test and electrochemical impedance spectroscopy experiment in 3.5 Wt.% NaCl solution.

Results show that a few microcracks and pores exist in the as-sprayed Fe-based amorphous coatings. The pores on the surface of the coatings after sealing treatment have been filled with nano-alumina sealant, which can effectively prevent corrosive medium from entering into coatings. Electrochemical tests results show that the corrosion resistance of the coatings before sealing treatment decreases with the increase of hydrogen flow rate and is significantly improved by sealing treatment.

The effect of nano-alumina sealant sealing treatment on corrosion resistance of APS-sprayed Fe-based amorphous coatings is revealed. The corrosion resistance of the as-sprayed Fe-based amorphous coating can be significantly improved by nano-alumina sealant sealing treatment because of the blocking effect of nano-alumina sealant on corrosive medium, which confirms that the application of nano-alumina sealant sealing treatment is of a practical option to improve corrosion resistance of as-sprayed thermal sprayed coatings.

This paper reviews the literature on zombie firms to provide a holistic view by delineating their formation, impact, widespread nature, prevention and policy implications.

This paper uses a systematic literature review methodology, in which 76 papers published in journals ranked on the Australian Business Deans Council (ABDC) 2022 list were reviewed. The study period was from 2000 to 2022.

Among the main findings, the widespread problems of zombie firms were evident. The authors found that consistent support, either in the form of government grants or a weak financial framework, was responsible for their formation. The suboptimal performance of factors of production, depressed job creation, low innovation and overall negative impact on economic activity are the consequences of zombification. This can be controlled by ensuring better bankruptcy codes, focused on government assistance, technology use and better due diligence by banks.

This review serves as a reference point for future researchers as a cohesive and holistic study presenting a full picture of the problem, so that the proposed solutions are robust and tenable.

This review is among the initial attempts to comprehensively study published work on zombie firms in terms of analyzing their region-specific nature, with an emphasis on definition, causes, impact and prevention.

Most spatial econometrics work focuses on spatial dependence in the regressand or disturbances. However, Lesage and Pace (2009) as well as Pace and LeSage2009 showed that the bias in β from applying OLS to a regressand generated from a spatial autoregressive process was exacerbated by spatial dependence in the regressor. Also, the marginal likelihood function or restricted maximum likelihood (REML) function includes a determinant term involving the regressors. Therefore, high dependence in the regressor may affect the likelihood through this term. In addition, Bowden and Turkington (1984) showed that regressor temporal autocorrelation had a non-monotonic effect on instrumental variable estimators.

We provide empirical evidence that many common economic variables used as regressors (e.g., income, race, and employment) exhibit high levels of spatial dependence. Based on this observation, we conduct a Monte Carlo study of maximum likelihood (ML), REML and two instrumental variable specifications for spatial autoregressive (SAR) and spatial Durbin models (SDM) in the presence of spatially correlated regressors.

Findings indicate that as spatial dependence in the regressor rises, REML outperforms ML and that performance of the instrumental variable methods suffer. The combination of correlated regressors and the SDM specification provides a challenging environment for instrumental variable techniques.

We also examine estimates of marginal effects and show that these behave better than estimates of the underlying model parameters used to construct marginal effects estimates. Suggestions for improving design of Monte Carlo experiments are provided.

The need to integrate environmental management into supply chains has been recognized recently. Yet, there is a lack of theoretical ground and conceptual framework guiding such efforts to leverage resources and capabilities across supply chain partners. Grounded on stakeholder and resource orchestration theories, the purpose of this paper is to map the emerging practices, develops a theoretical framework, and proposes future research for understanding an emerging best-practice called “green supply chain integration” (GSCI).

A systematic literature review of 142 academic articles is conducted to ensure the process of framework development is auditable and repeatable. The article selection criteria are aligned with the review question ensuring that related theories and practices are identified and evaluated.

The paper illustrates how stakeholder and resource orchestration theories can be used to explain an integrative approach of environmental management in supply chains. The paper identifies four GSCI practices – internal, supplier, customer, and stakeholder GSCI. A theoretical framework and proposition also provide for new directions of research.

The results of this paper are drawn from an extensive review of the existing literature and novel practices that have not been revealed and could have been missed. The emerging practices and theoretical framework can be used for further empirical investigation.

This paper integrates theoretical concepts and empirical findings from the disparate literature and identifies four emerging practices of environmental management by developing a theoretical framework and proposition for future research.

Using bibliometric techniques, the author analyzes a dataset of 276 articles on cross-border mergers and acquisitions (CBMAs) published in 13 management and international business journals. The author assesses the scientific impact and visualizes the intellectual landscape of research on CBMAs by analyzing publication and citation data and interconnections between publications. First, the author assesses annual publication trends and identifies highly cited articles and productive journals in the dataset that have significantly contributed to our understanding of CBMAs. Second, the author identifies main themes in recent research on CBMAs by focusing on frequently used keywords in publications. Third, the author identifies clusters of related research and explores their interrelationships to outline emerging trends, new perspectives, and directions for future research on CBMAs. Overall, this chapter contributes to the understanding of CBMAs by documenting the progress made to date and providing important insights for future research.

This paper aims to devise a robust controller for the non-linear aircraft model using output feedback control topology in the presence of uncertain aerodynamic parameters.

Feedback linearization-based state feedback (SFB) controller is considered along with a robust outer loop control which is designed using Lyapunov’s second method. A high-gain observer (HGO) in accordance with the separation principle is used to implement the output feedback (OFB) control scheme. The robustness of the controller and observer is assessed by introducing uncertain aerodynamics coefficients in the dynamic model. The proposed scheme is validated using MATLAB/SIMULINK.

The efficacy of the proposed scheme is authenticated with the simulation results which show that HGO-based OFB control achieves the SFB control performance for a small value of the high-gain parameter in the presence of uncertain aerodynamic parameters.

A HGO for the non-linear model of aircraft with uncertain parameters is a novel contribution which could be further used for the unmanned aerial vehicles autopilot, flight trajectory tracking and path following.

The operations research method of data envelopment analysis (DEA) shows promise for assessing radiotherapy treatment plan quality. The purpose of this paper is to consider the technical requirements for using DEA for plan assessment.

In total, 41 prostate treatment plans were retrospectively analysed using the DEA method. The authors investigate the impact of DEA weight restrictions with reference to the ability to differentiate plan performance at a level of clinical significance. Patient geometry influences plan quality and the authors compare differing approaches for managing patient geometry within the DEA method.

The input-oriented DEA method is the method of choice when performing plan analysis using the key undesirable plan metrics as the DEA inputs. When considering multiple inputs, it is necessary to constrain the DEA input weights in order to identify potential plan improvements at a level of clinical significance. All tested approaches for the consideration of patient geometry yielded consistent results.

This work is based on prostate plans and individual recommendations would therefore need to be validated for other treatment sites. Notwithstanding, the method that requires both optimised DEA weights according to clinical significance and appropriate accounting for patient geometric factors is universally applicable.

DEA can potentially be used during treatment plan development to guide the planning process or alternatively used retrospectively for treatment plan quality audit.

DEA is independent of the planning system platform and therefore has the potential to be used for multi-institutional quality audit.

To the authors’ knowledge, this is the first published examination of the optimal approach in the use of DEA for radiotherapy treatment plan assessment.

The purpose of this paper is to study the evolution of financial contagion between Eurozone banks, observing the credit default swaps (CDSs) market during the period 2009–2017.

The authors use a dynamic spatial Durbin model that enables to explore the direct and indirect effects over the short and long run and the transmission channels of the contagion.

The results show how contagion emerges through physical and financial market links between banks. This finding implies that a bank can fail because people expect other related financial institutions to fail as well (self-fulfilling crisis). The study provides statistically significant evidence of the presence of credit risk spillovers in CDS markets. The findings show that equity market dynamics of “neighbouring” banks are important factors in risk transmission.

The research provides a new contribution to the analysis of EZ banking risk contagion, studying CDS spread determinants both under a temporal and spatial dimension. Considering the cross-dependence of credit spreads, the study allowed to verify the non-linearity between the probability of default of a debtor and the observed credit spreads (credit spread puzzle). The authors provide information on the transmission mechanism of contagion and, on the effects among the largest banks. In fact, through the study of short- and long-term impacts, direct and indirect, the paper classify banks of systemic importance according to their effect on the financial system.

The most used spatial regression models for binary-dependent variable consider a symmetric link function, such as the logistic or the probit models. When the dependent variable represents a rare event, a symmetric link function can underestimate the probability that the rare event occurs. Following Calabrese and Osmetti (2013), we suggest the quantile function of the generalized extreme value (GEV) distribution as link function in a spatial generalized linear model and we call this model the spatial GEV (SGEV) regression model. To estimate the parameters of such model, a modified version of the Gibbs sampling method of Wang and Dey (2010) is proposed. We analyze the performance of our model by Monte Carlo simulations and evaluate the prediction accuracy in empirical data on state failure.

The purpose of this paper is to find optimal reef parameters to minimize the maximum instantaneous opening load for a reefed parachute with geometry and environmental parameters given in the model.

The dynamic model Drop Test Vehicle Simulation (DTVSim) is used to model the inflation and descent of the reefed parachute system. It is solved by the fourth-order Runge–Kutta method, and the opening load values are thereby obtained. A parallel genetic algorithm (GA) code is developed to optimize the reefed parachute. A penalty scheme is used to have the maximum dynamic pressure restricted within a certain range.

The simulation results from DTVSim fit well with experimental data from drop tests, showing that the simulator has high accuracy. The one-stage and two-stage reefed parachute systems are optimized by GA and their maximum opening loads are decreased by 43 and 25 per cent, respectively. With the optimal reef parameters, two of the peaks in the opening load curve are almost equal. The velocity, loiter time and flight path angle of the parachute system all change, but these changes have no negative effect on the parachute’s operational performance.

An optimization method for reefed parachute design is proposed for the first time. This methodology can be used in the preliminary design phase for a reefed parachute system and significantly improve design efficiency.