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Aims to introduce a self‐adjusting robotic painting process for automotive fuel containers, capable of predicting the required correction action to avoid further defect production.

Presents the development, testing and on‐site implementation of a robotic thermal machine vision system designed for evaluating coat thickness and coverage attributes. Computer simulation is used to study the effect of the painting robot's program on the film build‐up.

Effective technique for the real‐time detection of anti‐corrosive coat's pinholes and pop‐ups. A systematic study for this paint deposition scheme.

The presented detection system and the simulation program methodology could be further studied and modified for other painting applications.

Provides insights validated with on‐site results and systematic study for the automated or the manual adjustments of the robotic painting parameters.

Introduces a novel application of thermal imaging for evaluating coated surfaces. In addition, a first reported case study of automotive fuel container's painting process. Presents potential application to reduce the defects generation thus, improving quality, and reducing production cost.

The COVID-19 pandemic has created a crisis for healthcare systems worldwide. There have been significant challenges to managing public and private health care and related services systems’ capacity to cope with testing, treatment and containment of the virus. Drawing on the foundational research by Frow et al. (2019), the paper explores how adopting a service ecosystem perspective provides insight into the complexity of healthcare systems during times of extreme stress and uncertainty.

A healthcare framework based on a review of the service ecosystem literature is developed, and the COVID-19 crisis in Australia provides an illustrative case.

The study demonstrates how the service ecosystem perspective provides new insight into the dynamics and multilayered nature of a healthcare system during a pandemic. Three propositions are developed that offer directions for future research and managerial applications.

The research provides an understanding of the relevance of managerial flexibility, innovation, learning and knowledge sharing, which offers opportunities leading to greater resilience in the healthcare system. In particular, the research addresses how service providers in the service ecosystem learn from this pandemic to inform future practices.

The service ecosystem perspective for health care offers fresh thinking and an understanding of how a shared worldview, institutional practices and supportive and disruptive factors influence the systems’ overall well-being during a crisis such as COVID-19.

The controversy between Hayek and Keynes in the 1930s is probably one of the best‐known disputes in economics and several comments have been made on this episode (Hicks, 1967; Machlup, 1977; Fletcher, 1987). In the assessments little attention has been paid to the development of the ideas of the two economists, to the views they had in common and to the influence they had on each other. These aspects will be taken into consideration in this contribution with the aim of extending our knowledge of the fundamental points of disagreement between them. The crucial question is how it came about that Hayek and Keynes, who for some time studied very similar monetary problems, ended as such fierce opponents on the question of how a modern capitalist system works. Keynes went as far as denying that the market system is self‐adjusting, whereas Hayek, especially in his later writings, propounded the view that markets constitute an efficient mechanism for the satisfaction of human needs.

Kalecki's theory of the business cycle is rightly renowned for various reasons: in particular, besides itself providing an original contribution, it set the framework for Kalecki's ideas on effective demand, for his anticipation of a number of Keynesian elements, and for the development of Kalecki's related themes such as income determination and distribution. Although the secondary literature (both technical and descriptive) on this subject is immense, a specific aspect seems to deserve further reflection.

This paper considers the part played by entropy in systems pertaining to discontinuous flow industries. The main principle is that parts or the whole of the economy can be considered to be cybernetic systems. Various assumptions are then made concerning the relationships between industrial cybernetic systems and entropy. It is thus found that in discontinuous flow industries the main objective is a reduction in entropy engendered by the relations between the component systems. The author notes the close connection between entropy and management decisions, and draws some conclusions about optimizing industrial systems.

The purpose of this paper is to propose two operators for diversity and mutation control in artificial immune systems (AISs).

The proposed operators are applied in substitution to the suppression and mutation operators used in AISs. The proposed mechanisms were tested in the opt-aiNet, a continuous optimization algorithm inspired in the theories of immunology. The traditional opt-aiNet uses a suppression operator based on the immune network principles to remove similar cells and add random ones to control the diversity of the population. This procedure is computationally expensive, as the Euclidean distances between every possible pair of candidate solutions must be computed. This work proposes a self-organizing suppression mechanism inspired by the self-organizing criticality (SOC) phenomenon, which is less dependent on parameter selection. This work also proposes the use of the q-Gaussian mutation, which allows controlling the form of the mutation distribution during the optimization process. The algorithms were tested in a well-known benchmark for continuous optimization and in a bioinformatics problem: the rigid docking of proteins.

The proposed suppression operator presented some limitations in unimodal functions, but some interesting results were found in some highly multimodal functions. The proposed q-Gaussian mutation presented good performance in most of the test cases of the benchmark, and also in the docking problem.

First, the self-organizing suppression operator was able to reduce the complexity of the suppression stage in the opt-aiNet. Second, the use of q-Gaussian mutation in AISs presented better compromise between exploitation and exploration of the search space and, as a consequence, a better performance when compared to the traditional Gaussian mutation.

This article reviews some of the key debates in the area of just‐in‐time operation and implementation, and identifies 13 research issues. These issues include: information systems for JIT; organisational structures; performance measurement; the management of the supply‐manufacturer‐customer chain; location policy; purchasing and distribution strategy and the social implications of JIT.

The purpose of this paper is to apply a hybrid neuro-fuzzy paradigm called self-organizing neuro-fuzzy multilayered classifier (SONeFMUC) to classify the operating faults of a hydraulic system. The main motivation behind the use of SONeFMUC is to attest the capabilities of neuro-fuzzy classifier for handling the difficulties associated with fault diagnosis of hydraulic circuits.

In the proposed methodology, first, the neuro-fuzzy nodes at each layer of the SONeFMUC are trained separately using two well-known bio-inspired algorithms, i.e. a semi deterministic method with random walks called co-variance matrix adaptation evolutionary strategy (CMA-ES) and a swarm-based explorer with adaptive fuzzified parameters (SBEAFP). Thereafter, a revised version of the group method data handling (GMDH) policy that uses the Darwinian concepts such as truncation selection and elitism is engaged to connect the nodes of different layers in an effective manner.

Based on comparative numerical experiments, the authors conclude that integration of neuro-fuzzy method and bio-inspired supervisor results in a really powerful classification tool beneficial for uncertain environments. It is proved that the method outperforms some well-known classifiers such as support vector machine (SVM) and particle swarm optimization-based SVM (PSO-SVM). Besides, it is indicated that an efficient bio-inspired method can effectively adjust the constructive parameters of the multi-layered neuro-fuzzy classifier. For the case, it is observed that designing a fuzzy controller for PSO predisposes it to effectively balance the exploration/exploitation capabilities, and consequently optimize the structure of SONeFMUC.

The originality of the paper can be considered from both numerical and practical points of view. The signals obtained through the data acquisition possess six different features in order for the hydraulic system to undergo four types of faults, i.e. cylinder fault, pump fault, valve leakage fault and rupture of the piping system. Besides, to elaborate on the authenticity and efficacy of the proposed method, its performance is compared with well-known rival techniques.

Business cycle theory is normally described as having evolved out of a previous tradition of writers focusing exclusively on crises. In this account, the turning point is seen as residing in Clément Juglar's contribution on commercial crises and their periodicity. It is well known that the champion of this view is Schumpeter, who propagated it on several occasions. The same author, however, pointed to a number of other writers who, before and at the same time as Juglar, stressed one or another of the aspects for which Juglar is credited primacy, including the recognition of periodicity and the identification of endogenous elements enabling the recognition of crises as a self-generating phenomenon. There is indeed a vast literature, both primary and secondary, relating to the debates on crises and fluctuations around the middle of the nineteenth century, from which it is apparent that Juglar's book Des Crises Commerciales et de leur Retour Périodique en France, en Angleterre et aux États-Unis (originally published in 1862 and very much revised and enlarged in 1889) did not come out of the blue but was one of the products of an intellectual climate inducing the thinking of crises not as unrelated events but as part of a more complex phenomenon consisting of recurring crises related to the development of the commercial world – an interpretation corroborated by the almost regular occurrence of crises at about 10-year intervals.

This study aims to examine the impact of Covid19 on service ecosystem self-adjustment (SES_SA) and additionally to explore the mediating role of SES_SA on the relationship between the Covid19 pandemic and the development of digital service capability (DD_SC).

Data were drawn from 384 business people with the help of a survey questionnaire. The interrelation of the model was examined with the help of structural equation modeling (SEM) using bootstrapping measures in Smart-partial least square (PLS). Three constructs (Covid19, DD_SC and SES_SA) were found with the help of exploratory factor analysis (EFA). Convergent and discriminant validity were obtained through confirmatory factor analysis (CFA) using statistical package for the social sciences-analysis of a moment structures (SPSS-AMOS)-V.23.

There is a substantial impact of Covid19 on SES_SA and DD_SC. The investigation also discovered that SES_SA significantly impact DD_SC, whereas, Covid19 impact DD_SC indirectly through SES_SA. Age has a significant favorable influence on fear of Covid19.

There is scant literature linking SES_SA and the DD_SC.

The study promotes understanding of the contribution of Covid19 and SES_SA in the DD_SC among business people to enhance value co-creation. Capitalizing on DD_SC will enhance customer experience, assist customers in decision-making, and foster digital economic growth.

It enlightens on the digital capabilities needed for creating and co-creating value. Most studies in this area are qualitative/conceptually based and have not studied this kind of interrelation. Hence, it’s the only quantitative study that has examined the inter-relations among Covid19, SES_SA and DD_SC using SEM. This study also offers comprehension of all theories used in this context by relating Covid19 effects to DD_SC.