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Reconstructability analysis (RA) is a method for detecting and analyzing the structure of multivariate categorical data. While Jones and his colleagues extended the original variable‐based formulation of RA to encompass models defined in terms of system states, their focus was the analysis and approximation of real‐valued functions. In this paper, we separate two ideas that Jones had merged together: the “g to k” transformation and state‐based modeling. We relate the idea of state‐based modeling to established variable‐based RA concepts and methods, including structure lattices, search strategies, metrics of model quality, and the statistical evaluation of model fit for analyses based on sample data. We also discuss the interpretation of state‐based modeling results for both neutral and directed systems, and address the practical question of how state‐based approaches can be used in conjunction with established variable‐based methods.

The purpose of this paper is to compare four life data models, namely the exponential and the Weibull models, and their corresponding generalized versions, q-exponential and q-Weibull models, by means of one practical application.

Application of the models to a practical example (a welding station), with estimation of parameters by the use of the least squares method, and the Akaike Information Criterion (AIC).

The data of the example considered in this paper is divided into three regimes, decreasing, constant and increasing failure rate, and the q-Weibull model describes the bathtub curve displayed by the data with a single set of parameters.

The simplicity and flexibility of the q-Weibull model may be very useful for practitioners of reliability analysis, and its benefits surpasses the inconvenience of the additional parameter, as AIC shows.

The q-Weibull model is compared in detail with other three models, through the analysis of one example that clearly exhibits a bathtub curve, and it is shown that it can describe the whole time range with a single set of parameters.

Automating warehouse operations can be a very alluring proposition. However, it is vital that a decision to progress with such substantial investment is taken only after the deliverable benefits are thoroughly understood. This is especially important when the business environment is changing rapidly. Describes the approach taken by B&Q to decide on the appropriate level of automation; explains how, through a process of establishing good performance through manual processes, it has created a sound base against which it can evaluate automation, and how its new supply chain strategy has exposed further opportunities to improve business performance. Also describes how B&Q Distribution has renewed its focus on serving the customer (the B&Q retail store), and how this experience has affected the automation project. Finally, covers B&Q’s approach to supplier selection.

Fourier methods used in two‐ and three‐dimensional image reconstruction can be used also in reconstructability analysis (RA). These methods maximize a variance‐type measure instead of information‐theoretic uncertainty, but the two measures are roughly collinear and the Fourier approach yields results close to that of standard RA. The Fourier method, however, does not require iterative calculations for models with loops. Moreover, the error in Fourier RA models can be assessed without actually generating the full probability distributions of the models; calculations scale with the size of the data rather than the state space. State‐based modeling using the Fourier approach is also readily implemented. Fourier methods may thus enhance the power of RA for data analysis and data mining.

The purpose of this paper is to compare k‐ω shear‐stress transport (SST) and large eddy simulation (LES) turbulence model application effect on numerical computation of flow pattern and heat exchange characteristics through the neutron beam window region for European spallation source setup model.

Transient hydrodynamic and thermal calculations with appropriate heat sources are performed using both turbulence models and typical discrepancies in flow and thermal patterns are discussed, as well as, simulation results are qualitatively compared with experimental data for heat transfer coefficient distribution α at the window surface.

Contribution of greater k‐energy field obtained with LES calculation leads to prediction of more intensive heat transfer in comparison to k‐ω SST.

The paper illustrates discrepancies of thermal patterns caused by application of k‐ω SST and LES turbulent models.

In today’s public transport market where competition is keener than ever, railway operators are in desperate need of a customised total quality management approach to enhance performance and outcompete their counterparts. This paper outlines the work related to the establishment and implementation of such a total management approach, an integrated management system evolved from the ISO 9000‐based quality management system, in the Mass Transit Railway Corporation (MTR Corporation), Hong Kong, and how this system serves to enhance the corporation’s competitiveness by successfully addressing its rapidly changing needs.

The purpose of this paper is to investigate the volatility clustering in the return of land markets through both theoretical and empirical approaches.

Using extensive monthly panel data at the provincial level from 1986 to 2013, the authors identify the existence of time-correlated and time-varying returns in Canadian land markets.

Consistent with the proposed theory, volatility clustering in land markets tends to be observed in more populated areas.

The result has significant implications for portfolio management, economic theory and government policy by revealing the systematic pattern of volatility clustering in land markets.

This article examines the concepts of “deglobalization” and “decoupling” from the perspectives of developing and developed nations. It also assesses the short-term impacts of globalization, particularly in the context of the COVID-19 pandemic and predicts the long-term effects on global trade and cooperation between nations.

Panel data from 85 countries (2000–2022) were utilized. Poisson Pseudo-Maximum Likelihood (PPML) regression analysis was conducted to analyze pre- and post-COVID-19 globalization levels. The analysis focuses on trade patterns and trends, specifically comparing the effects on developing and developed nations.

First, there was a slight decline in global trade in 2020 due to COVID-19, followed by recovery in 2021–2022. Second, developing nations experienced more significant trade declines than did developed nations. Third, while US? China trade decreased slightly, China-India and US-India trade increased during the pandemic. These findings suggest that while there may be short-term disruptions, long-term trends indicate resilience in global trade patterns, with shifts in output and new partnerships emerging.

This study contributes to the understanding of deglobalization and decoupling by providing empirical evidence on pre- and post-COVID-19 trade patterns. The findings suggest that while globalization may have short-term effects, it is likely to lead to post-pandemic recovery and strengthened cooperation between developing and developed nations. This research also highlights the importance of developing strategies to manage uncertainty and external shocks in global trade, emphasizing the role of lockdown measures, national security considerations, and trade policies in shaping the future of globalization and decoupling.

The study examined the effect of fiscal policy on poverty in sub-Saharan Africa (SSA) while accounting asymmetric (captured by economic downturns) and spillover effects.

The study used a fixed effect (within regression) IV model to account for country-specific characteristics. The study also adopts a cross-sectional and spatial dependence-consistent model to account for the potential cross-sectional and temporal dependence in panel data modeling.

The study discovered that the effect of fiscal policy on poverty is dependent on the state of the economy. Specifically, we find that fiscal policy helps to reduce the level of poverty during an economic downturn, more than at any other time. More specifically, the findings indicate that the fiscal policy lowers the rate of poverty in SSA, following macroeconomic shocks (captured by the COVID-19 epidemic, the Global Financial Crisis, and the commodity terms of trade shocks). Our findings suggest that fiscal policy is an important policy tool to mitigate the effects of macroeconomic shocks in SSA. Further, the findings also demonstrate that there is a spillover effect of poverty in the region. This implies coordinated, constructive actions by the regional governments can help to lessen the detrimental effects of extreme poverty.

The study examined the effectiveness of fiscal policy to reduce poverty in the event of an economic downturn.

The aim of this chapter is to show the limits of the European policy model and to support the existence, through straightforward empirical analysis, of an inverse relationship both in the short run and in the long run between trust in institutions and unemployment. The empirical methodology relies on dynamic panel data techniques allowing measuring in a single equation both the long-run relationship and the short-run speed of adjustment among variables. This connection appears to be valid both in the Eurozone considered as a whole and in particular in peripheral countries, where the macroeconomic dynamics have been, under this respect, much more divergent from the average. This outcome allows proofing that to consolidate the European process of integration in the long run, institutions should have as main objective not only inflation but especially unemployment.