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Most academic studies of supply chain integration (SCI) have taken large-scale manufacturing environments as their source of empirical data. This paper aims to find the applicability of the dimensions of SCI in the context of project manufacturing environments.

This paper uses a single-case-study approach to examine the network relationship among the constructor of an offshore wind farm, the provider of the main subsystems and two other companies providing subsystems and services in the project used as the empirical setting.

The findings suggest that although some of the dimensions of SCI in large-scale manufacturing environments might have similar applications in projects, some others might be unique to the context of large construction endeavors, e.g. relationship management, information integration and information sharing. An explanation of the possible causes for these differences is provided.

The findings cannot be generalized to the overall context of project manufacturing environments, as only one industry, the construction sector, was studied.

Contrary to suggestions to relax the formal aspect of the partnership with the aim of improving the collaborative relationships, keeping the formality by means of contracts and nondisclosure agreements seems to be necessary for projects that have a considerable amount of resources and intellectual capital at stake.

To the best of the author’s knowledge, this paper is one of the first case studies specifically designed to explore the applicability of the dimensions of SCI in project manufacturing environments. It aims to fill the gap of research carried out in these manufacturing environments, which represent a significant share of the world’s economic output.

As well known, in the finite element method, the calculation and the location of the elements of the matrix C of the coefficients requires a lot of calculation times and memory employment especially for 3D problems. Besides, once the matrix C is properly filled, the solution of the system of linear equations is computationally expensive.

The paper consists of two parts. In the first part, to quickly calculate and store only the non‐null terms of the matrix of the system, a geometrical analysis on three‐dimensional domains has been carried out. The second part of the paper deals with the solution of the system of linear equations and proposes a procedure for increasing the solution speed: the traditional method of the conjugate gradient is hybridized with an adequate genetic algorithm (Genetic Conjugate Gradient).

The proposed geometrical procedure allows us to calculate the non‐null terms and their location within the matrix C by simple recursive formulas. The results concerning the genetic conjugate gradient show that the convergence to the solution of the linear system is obtained in a much smaller number iterations and the calculation time is also significantly decreased.

The approach proposed to analyze the geometrical space has been turned out to be very useful in terms of memory saving and computational cost. The genetic conjugate gradient is an original hybrid method to solve large scale problems quicker than the traditional conjugate gradient. An application of the method has been shown for current fields generated by grounding electrodes.

During the last two decades the share of foreign-born residents in Italy has grown considerably, from just over 1 percent to about 8 percent. This chapter seeks to clarify the status of immigrants in Italy by examining the evolution of their economic situation and, in particular, the presence of economic hardship. Poverty is measured by considering not only the usual income-based indicators but also others that take into account households’ real and financial wealth. The picture that emerges is one of a higher incidence of economic hardship among immigrant households that strongly affects the dynamics of poverty nationwide. The economic gap with respect to natives appears to increase in the years considered, but the condition of poverty is not more persistent for immigrants than for Italians.

This paper aims to propose a reliable local search algorithm having steepest descent pivot rule for computationally expensive optimization problems. In particular, an application to the design of Permanent Magnet Synchronous Motor (PMSM) drives is shown.

A surrogate assisted Hooke‐Jeeves algorithm (SAHJA) is proposed. The SAHJA is a local search algorithm with the structure of the Hooke‐Jeeves algorithm, which employs a local surrogate model dynamically constructed during the exploratory move at each step of the optimization process.

Several numerical experiments have been designed. These experiments are carried out both on the simulation model (off‐line) and at the actual plant (on‐line). Moreover, the off‐line experiments have been considered in non‐noisy and noisy cases. The numerical results show that use of the SAHJA leads to a saving in terms of computational cost without requiring any extra hardware components.

The surrogate approach in the design of electric drives is novel. In addition, implementation of the proposed surrogate model allows the algorithm not only to reduce computational cost but also to filter noise caused by the sensors and measurement devices.

In 2020, household incomes were severely hit by the lockdowns imposed across the world in response to the COVID-19 pandemic. Using data from the last available wave of the euro-area harmonized Household Finance and Consumption Survey for 2016, this chapter documents European households’ financial resilience to this shock, based on pre-shock balance sheets, potential exposure to COVID-19, and in the absence of government interventions. The results highlight that there are large and similar shares of the population across European countries that are likely to suffer from the economic fallout of containment measures – albeit through different channels.

This paper surveys the literature on supply chain integration (SCI) to identify the state of research in the various types of studied industries and manufacturing environments. The purpose of this paper is to identify academic discoveries that could provide offshore wind projects with means to overcome their current supply chain challenges.

A comprehensive literature review was conducted involving 162 articles published in 29 peer-reviewed journals. The papers were analyzed in terms of the dimensions of SCI, research methodology, unit of analysis, level of analysis, type of industry and manufacturing environment being studied, integrative practices, integrative barriers and the link between SCI and performance.

While SCI has been evolving to become an influential topic in the field of supply chain management, scholars have overlooked industrial contingencies by ignoring the differences between the studied industrial contexts, especially project-based manufacturing environments. The present review also reveals that no study of SCI has been conducted on the construction of renewable energy projects. Another finding is that case studies and research articles using networks as a unit of analysis are underrepresented.

This is the first work to advocate for an industrial contingency approach in the analysis of SCI. Thus, it proposes the offshore wind farm-construction industry as a potential study subject to broaden the knowledge in SCI in project manufacturing environments.

This paper aims to design an algorithm able to locate all the possible dangerous areas generated by the leaking of a fault current in a grounding system (i.e. the areas where the limits of the technical standards are not respected) and thus locate, inside each area, the point which takes locally the maximum value of touch voltage.

A fast evolutionary‐deterministic algorithm to solve constrained multimodal optimization problems is proposed. The algorithm is composed by three algorithmic blocks: a Quasi Genetic Algorithm to find a population of feasible solutions, a Fitness Sharing Selection to choose a subpopulation of feasible and fitter solutions having high diversity, a Hooke‐Jeeves Algorithm to find all the global and local feasible maxima.

The proposed algorithm has been successfully applied to various current field (i.e. to many shapes of grounding grids) problems to find the dangerous values of touch voltages generated by various grounding systems having any shape and it has turned out to be fast and reliable.

For this kind of problems, in fact, there is a lack, in literature, of multimodal optimization methods under safety constraints and the application of classical methods (e.g. genetic algorithms or deterministic methods) would be often inadequate since these methods are made so as to converge towards a single maximum point and so they unavoidably lose the information related to all the other possible maxima. On the contrary, a good application of the proposed allows the overcoming of these limits.

The purpose of this paper is to evaluate the association between food consumption classified by the degree of processing and cardiometabolic risk factors in a population at risk of cardiovascular disease.

A cross-sectional study conducted with 325 adults and elderlies who present a cardiovascular risk factor. The food consumption was evaluated by a 24 h dietary recall, and it was classified according to the NOVA classification.

Individuals who presented a higher consumption of processed and ultra-processed food had a higher prevalence of abdominal obesity, waist/hip ratio (PR = 1.005; p-value = 0.049), waist circumference (PR = 1.003; p-value = 0.02) and high total cholesterol (PR = 1.008; p-value = 0.047), while ultra-processed had a higher prevalence of excess weight (PR = 1.004; p-value = 0.04), and abdominal obesity, waist/hip ratio (PR = 1.005; p-value = 0.04), waist circumference (PR = 1.004; p-value = 0.004) and waist/height ratio (PR = 1.003; p-value = 0.03).

An association was found between the degree of food processing and cardiometabolic risk factors, even in a population that already has a risk factor for cardiovascular disease, reinforcing the importance of personalized nutrition orientation that considers the profile of the target population as well as types of meals.

Food processing in itself can influence cardiometabolic risk and, as far as is known, no study has evaluated food processing in individuals who already have some type of cardiovascular risk. Also, consumption was assessed by the degree of processing between meals.