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This paper aims to address workplace learning in terms of investigating school leadership development in an inter-professional team (the team) in which principals, administrators and researchers work together on a local school improvement project. The purpose is to provide an enriched understanding of how school leadership development evolves in a team during two years as the team works on different problem-spaces and the implications for leadership in schools.

The paper is based on a larger study with a qualitative research design with longitudinal, interventional, interactional and multiple-time level approaches. Empirically, the paper draws on tools, video and audio data from the teams’ work. By using cultural–historical activity theory (CHAT), school leadership development is examined as an object-oriented and tool-mediated activity. CHAT allows analyses of activities across timescales and workplaces. It examines leadership development by tracing objects in tool-mediated work and the ways in which they evolved. The object refers to what motivates and directs activity.

The findings suggest that the objects evolved both within and across episodes and the two-year trajectory of the team. Longitudinal trajectories of tools, schools and universities seem to intersect with episodes of leadership development. Some episodes seem to be conducive for changes in the principals’ schools during the collaboration.

There is a need for a broader study that includes more cases in other contexts, thus expanding the existing knowledge.

By switching lenses of zooming, it has been possible to examine leadership development in a way that is not possible through surveys and interviews.

This paper aims to examine the impact of the mandatory adoption of (International Financial Reporting Standards [IFRS] 9) on loan provisions, nonperforming loans (NPL) and impairment loan loss in Gulf banks. This study also investigates potential variations in outcomes compared to prior models and explores the use of the Callaway and Sant’Anna (2021) estimator for difference-in-differences (DiD) with multiple time periods.

The research is based on a sample of 53 Gulf banks covering the period from 2012 to 2020. The study analyzes the changes in loan provisions, impairment loss and NPL following the implementation of IFRS 9. It uses statistical analysis and the DiD method to compare the outcomes between the experimental group (treated by IFRS 9) and the control group (not treated).

The findings reveal a statistically insignificant increase in loan provisions, impairment loss and NPL after the adoption of IFRS 9. These results align with previous studies and suggest that Gulf banks were proactive in anticipating and mitigating the impact of the new standard. The study also observes a synchronization of provisioning practices across Gulf countries and a certain level of consistency in recognizing loan losses.

The practical implications of this study suggest that Gulf banks have successfully absorbed the impact of IFRS 9 and have implemented collaborative approaches.

The study offers some new sight into IFRS9 outcomes in developing countries and opens the door for implementing a novel DiD estimation in future research studies.

The purpose of this paper is to empirically examine the long run and causal relationship between energy consumption, carbon emissions and economic growth in India over the period 1971-2009 within multivariate framework.

The study uses the Johansen cointegration test to examine the possible long-run equilibrium relationship followed by Granger causality test based on vector error correction model to explore short- and long-run causality between energy consumption, carbon emissions and economic growth in India.

Cointegration result indicates the long-run equilibrium relationship between economic growth, energy consumption and carbon emissions. Further causality results suggest unidirectional causality running from energy consumption and carbon emissions to economic growth in long run, energy consumption to carbon emissions, carbon emissions to economic growth and economic growth to energy consumption in short run.

There is urgent need of policy development toward boosting energy efficiency, developing alternative carbon-free energy sources like nuclear, renewables and expansion of affordable energy for faster, sustainable and more inclusive growth for India in upcoming years.

India, an energy-dependent economy needs to effectively implement energy efficiency measures, super critical technologies in power plants, and investment in renewable energy resources in order to minimize the dependence on fossil fuels and carbon emissions for faster, more inclusive and sustainable growth.

COVID-19 evolved from a local health crisis to a pandemic and affected countries worldwide accordingly. Similarly, the impacts of the pandemic on the performance of global stock markets could be time-varying. This study applies a dynamic network analysis approaches to evaluate the evolution over time of the impact of COVID-19 on the stock markets' network.

Daily closing prices of 55 global stock markets from August 1, 2019 to September 10, 2020 were retrieved. This sample period was further divided into nine subsample periods for dynamic analysis purpose. Distance matrix based on long-range correlations was calculated, using rolling window's length of 100 trading days, rolled forward at an interval of one month's working days. These distance matrices than used to construct nine minimum spanning trees (MSTs). Network characteristics were figured out, community detection and network rewiring techniques were also used for extracting meaningful from these MSTs.

The findings are, with the evolution of COVID-19, a change in co-movements amongst stock markets' indices occurred. On the 100th day from the date of reporting of the first cluster of cases, the co-movement amongst the stock markets become 100% positively correlated. However, the international investor can still get better portfolio performance with such temporal correlation structure either avoiding risk or pursuing profits. A little change is observed in the importance of authoritative node; however, this central node changed multiple times with change of epicenters. During COVID-19 substantial clustering and less stable network structure is observed.

It is confirmed that this work is original and has been neither published elsewhere, nor it is currently under consideration for publication elsewhere.

In this two‐part paper, aeroelastic analysis of turbomachinery blade rows and phase‐lagged boundary conditions used for analysis are described. Part I of the paper describes a study of phase‐lagged boundary condition methods used for non‐zero interblade phase angle analysis. The merits of time‐shifted (direct‐store), Fourier decomposition and multiple passage methods are compared. These methods are implemented in a time marching Euler/Navier‐Stokes solver and are applied to a fan for subsonic and supersonic inflow and to a turbine geometry with supersonic exit flow. Results showed good comparisons with published results and measured data. The time‐shifted and Fourier decomposition methods compared favorably in computational costs with respect to multiple passage analysis despite a slower rate of convergence. The Fourier‐decomposition method was found to be better suited for workstation environment as it required significantly less storage, although at the expense of slightly higher computational cost. The time‐shifted method was found to be better suited for computers where fast input‐output devices are available.

Part II of the two‐part paper describes an aeroelastic analysis program and its application for stability computations of turbomachinery blade rows. Unsteady Euler or Navier‐Stokes equations are solved on dynamically deforming, body fitted, and grid to obtain the aeroelastic characteristics. Blade structural response is modeled using a modal representation of the blade and the work‐per‐cycle method is used to evaluate the stability characteristics. Non‐zero inter‐blade phase angle is modeled using phase‐lagged boundary conditions. Results are presented for a flat plate helical fan, a turbine cascade and a high‐speed fan, to highlight the aeroelastic analysis method, and its capability and accuracy. Obtained results showed good correlation with existing experimental, analytical and numerical results. Numerical analysis also showed that given the computational resources available currently, engineering solutions with good accuracy are possible using higher fidelity analyses.

This paper aims to verify how the intellectual property (IP) box affects firms’ effective tax rate, growth and innovation activity outcomes related to intellectual property rights.

Implementing the innovation box regimes into the tax system intends to encourage firms to engage in more innovative activities. In UK, Italy and Poland, the IP box tax relief was introduced in 2013, 2015 and 2019, respectively. In return, companies may reduce their tax rate to increase their investment and innovativeness. With a panel model approach – system GMM and DiD with multiple time periods – it analyses data from the Orbis database for 2011–2019 of 673 firms from the gaming industry in 11 countries and hand-collected data on intellectual property rights protection. The authors study public and private companies from the gaming sector in leading European markets and all three countries that protect intellectual property rights of software (Japan, South Korea, the USA).

Recent reforms enable gaming companies to use preferential tax treatment for IP-related income and significantly impact a firm’s revenue growth.

Nevertheless, European gaming firms require time to leap the gap to the growth and innovativeness of countries that protect software.

The authors show that the IP box stimulates gaming firms to protect IP via wordmarks, figurative marks, trademarks and software patents that bring effects in five years. Despite the critics against IP box, the authors prove its lagged efficiency, especially in profitable and larger firms.

This study aims to generate different three-dimensional (3D) foam models using computer tomography (CT) scan and solid continuum techniques. The generated foam models were used to study deformation mechanism and the elastic-plastic behaviour with the existing experimental foam behaviour.

CT scan model was generated by combing 2D images of foam in MIMICS software. Afterwards, it was imported in ABAQUS/CAE software. However, solid continuum model was generated in ABAQUS/CAE software by using crushable foam properties. Then, the generated foam models were sets boundary conditions for a compression test.

CT scans capture the actual morphology of foam sample which may directly an image based finite element foam model. The sectional views of both the models were used to observe deformation mechanism on compression. The real compressive behaviour of foam was visualised in CT-Scan foam model. It was observed that CT-scan model was the more accurate modelling method than crushable foam model.

The internal structure of foam is very complex and difficult to analyse. Therefore, CT-scanning may be the accurate method for capturing the macro-level detailing of foam structure. A CT-scan foam model can be used for multiple times for mechanical analysis using a simulation software, which may reduce the manufacturing and the experimental cost and time.

When dealing with simple functional functions, traditional reliability calculation methods, such as the linear second-order moment and quadratic second ordered moment, Monte Carlo simulation method, are powerful. However, when the functional function of the structure shows strong nonlinearity or even implicit, traditional methods often fail to meet the actual needs of engineering in terms of calculation accuracy or efficiency.

To improve the reliability analysis efficiency and calculation accuracy of complex structures, the reliability analysis methods based on parametric and semi-parametric models are analyzed.

This paper proposes a reliability method that combines the Kriging model and the importance sampling method to improve the calculation efficiency of traditional reliability analysis methods.

This method uses an active learning function and introduces an importance sampling method to screen sample points and shift the center of gravity, thereby reducing the sample size and the amount of calculation.

After briefly reviewing the past history of Bayesian econometrics and Alan Greenspan's (2004) recent description of his use of Bayesian methods in managing policy-making risk, some of the issues and needs that he mentions are discussed and linked to past and present Bayesian econometric research. Then a review of some recent Bayesian econometric research and needs is presented. Finally, some thoughts are presented that relate to the future of Bayesian econometrics.