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Parameter identification is a technique which aims at determining material or other process parameters based on a combination of experimental and numerical techniques. In recent years, heuristic approaches, such as genetic algorithms (GAs), have been proposed as possible alternatives to classical identification procedures. The present work shows that particle swarm optimization (PSO), as an example of such methods, is also appropriate to identification of inelastic parameters. The paper aims to discuss these issues.

PSO is a class of swarm intelligence algorithms which attempts to reproduce the social behaviour of a generic population. In parameter identification, each individual particle is associated to hyper-coordinates in the search space, corresponding to a set of material parameters, upon which velocity operators with random components are applied, leading the particles to cluster together at convergence.

PSO has proved to be a viable alternative to identification of inelastic parameters owing to its robustness (achieving the global minimum with high tolerance for variations of the population size and control parameters), and, contrasting to GAs, higher convergence rate and small number of control variables.

PSO has been mostly applied to electrical and industrial engineering. This paper extends the field of application of the method to identification of inelastic material parameters.

The Behavioural Finance contests the modern financial theory statements, specially the rationality conception of the market as well as the agent behaviour. For the Behavioural Finance, the human being is susceptible to make mistakes and often acts under “irrational” and passional impulses. This article describes, comparatively, the Behavioural Finance and the modern finance theory investigating precisely the aversion feeling to loss under the investor view. The comprehension of the aversion feeling of loss is deepened from psychoanalytical theory contribution. As the aversion feeling to loss constitutes an aspect of the human subjectivity and cannot be explained just through quantification, the qualitative methodology was used. It was investigated about the influence meanings, experienced by the investors.

The purpose of this paper is twofold: to analyze the computational complexity of the cogeneration design problem; to present an expert system to solve the proposed problem, comparing such an approach with the traditional searching methods available.

The complexity of the cogeneration problem is analyzed through the transformation of the well-known knapsack problem. Both problems are formulated as decision problems and it is proven that the cogeneration problem is np-complete. Thus, several searching approaches, such as population heuristics and dynamic programming, could be used to solve the problem. Alternatively, a knowledge-based approach is proposed by presenting an expert system and its knowledge representation scheme.

The expert system is executed considering two case-studies. First, a cogeneration plant should meet power, steam, chilled water and hot water demands. The expert system presented two different solutions based on high complexity thermodynamic cycles. In the second case-study the plant should meet just power and steam demands. The system presents three different solutions, and one of them was never considered before by our consultant expert.

The expert system approach is not a “blind” method, i.e. it generates solutions based on actual engineering knowledge instead of the searching strategies from traditional methods. It means that the system is able to explain its choices, making available the design rationale for each solution. This is the main advantage of the expert system approach over the traditional search methods. On the other hand, the expert system quite likely does not provide an actual optimal solution. All it can provide is one or more acceptable solutions.

This paper seeks to develop experimental laboratory biological techniques for approximation of existing road networks, optimizing transport links, and designing alternative optimal solutions to current transport problems. It studies how slime mould of Physarum polycephalum approximate highway networks of Brazil.

The 21 most populous urban areas in Brazil are considered and represented with source of nutrients placed in the positions of slime mould growing substrate corresponding to the areas. At the beginning of each experiment slime mould is inoculated in São Paulo area. Slime mould exhibits foraging behavior and spans sources of nutrients (which represent urban areas) with a network of protoplasmic tubes (which approximate vehicular transport networks). The structure of transport networks developed by slime mould are analyzed and compared with families of known proximity graphs. The paper also imitates slime‐mould response to simulated disaster.

It was found that the plasmodium of P. polycephalum develops a minimal approximation of a transport network spanning urban areas. Physarum‐developed network matches man‐made highway network very well. The high degree of similarity is preserved even when high‐demand constraints are placed on repeatability of links in the experiments. Physarum approximates almost all major transport links. In response to a sudden disaster, gradually spreading from its epicenter, the Physarum transport networks react by abandoning transport links affected by disaster zone, enhancement of those unaffected directly by the disaster, massive sprouting from the epicenter, and increase of scouting activity in the regions distant to the epicenter of the disaster.

Experimental methods and computer analysis techniques presented in the paper lay a foundation of novel biological laboratory approaches to imitation and prognostication of socio‐economical developments.

This paper aims to examine the relationship between the main decisions for designing distribution centers (DCs) and the contextual characteristics of the distribution networks.

Experts were surveyed and responses analyzed quantitatively through multivariate data techniques. This study considered four contextual characteristics that were deemed as influential for DC design: types of routes in the distribution network, quantity of DCs, distribution network levels and company size.

This paper evidenced which decisions are affected by each contextual characteristic encompassed in this study. This paper identified that the characteristic types of route in the distribution network must be carefully considered, as it had the greatest amount of associations with the decisions for designing a DC.

Despite its importance, most studies on design of DCs disregard the effect of the context in which DCs are inserted. This research provides arguments to support decision-making process of DCs design, increasing assertiveness of their planning. This work fulfills a literature gap by empirically examining the effect of contextual variables on the decisions related to DC design. Regarding practice, this paper addressed a fundamental issue for managers looking to design a DC, as it evidenced how contextual characteristics impact the decision-making.

The purpose of this paper is to introduce a chaotic harmony search (CHS) approach based on the chaotic Zaslavskii map to parameters identification of Jiles-Atherton vector hysteresis model.

In laminated magnetic cores when the magnetic flux rotates in the lamination plane, one observes an increase in the magnetic losses. The magnetization in these regions is very complex needing a vector model to analyze and predict its behavior. The vector Jiles-Atherton hysteresis model can be employed in rotational flux modeling. The vector Jiles-Atherton model needs a set of five parameters for each space direction taken into account. In this context, a significant amount of research has already been undertaken to investigate the application of metaheuristics in solving difficult engineering optimization problems. Harmony search (HS) is a derivative-free real parameter optimization metaheuristic algorithm, and it draws inspiration from the musical improvisation process of searching for a perfect state of harmony. In this paper, a CHS approach based on the chaotic Zaslavskii map is proposed and evaluated.

The proposed CHS presents an efficient strategy to improve the search performance in preventing premature convergence to local minima when compared with the classical HS algorithm. Numerical comparisons with results using classical HS, genetic algorithms (GAs), particle swarm optimization (PSO), and evolution strategies (ES) demonstrated that the performance of the CHS is promising in parameters identification of Jiles-Atherton vector hysteresis model.

This paper presents an efficient CHS approach applied to parameters identification of Jiles-Atherton vector hysteresis model.

This paper presents a review of the state of the art on the application of blockchain and smart contracts to the peer-review process of scientific papers. The paper seeks to analyse how the main characteristics of the existing blockchain solutions in this field to detect opportunities for the improvement of future applications.

A systematic review of the literature on the subject was carried out in three databases recognized by the research community (IEEE Xplore, Scopus and Web of Science) and the Frontiers in Blockchain journal. A total of 1,967 articles were initially found, and after the exclusion process, the 26 remaining articles were classified according to the following dimensions: System Type, Open Access, Review Type, Reviewer Incentive, Token Economy, Blockchain Access, Blockchain Identification, Blockchain Used, Paper Storage, Anonymity and Maturity of the solution.

Results show that the solutions are normally concerned on offering incentives to the reviewers' work (often monetary). Other common general preferences among the solutions are the adoption of open reviews, the use of Ethereum, the implementation of publishing ecosystems and the use of InterPlanetary File System to the storage of the papers.

There are currently no studies covering the main aspects of blockchain solutions in the field of scientific peer review. The present study provides an overall review of the topic, summarizing important information on the current research and helping new adopters to develop solutions grounded on the existing literature.

Edible insects are increasingly recognized as great food resources of the future. Entomophagy, the practice of eating insects, has a predicted market by 2030 of nearly $8 billion. But this market could grow even more with the acceptance of insects as food by Western consumers, as an alternative to conventional protein sources. In this context, the authors aim to collect and analyze information about consumers' perception of insects as food in Brazil, through the word association task.

In total, 751 participants agreed to complete the word association task in relation to entomophagy through an online questionnaire. They had to write down the first five words or terms that came to their minds when they thought of eating insects.

Individual mentions were categorized according to their similarities and/or proximity. Based on this grouping the authors identified six dimensions, with emphasis on “hedonic attitudes and feelings”, “cultural aspects” and “exotic”. The study concludes that Brazilians see insect consumption as belonging to another culture, not Western.

Few studies have explored the association of words in relation to entomophagy, especially in Brazil. This article aims to fill this gap, using this methodology applied to the Brazilian public.

This study aims to apply for the first time in literature a new multi-objective sensor selection and placement optimization methodology based on the multi-objective Lichtenberg algorithm and test the sensors' configuration found in a delamination identification case study.

This work aims to study the damage identification in an aircraft wing using the Lichtenberg and multi-objective Lichtenberg algorithms. The former is used to identify damages, while the last is associated with feature selection techniques to perform the first sensor placement optimization (SPO) methodology with variable sensor number. It is applied aiming for the largest amount of information about using the most used modal metrics in the literature and the smallest sensor number at the same time.

The proposed method was not only able to find a sensor configuration for each sensor number and modal metric but also found one that had full accuracy in identifying delamination location and severity considering triaxial modal displacements and minimal sensor number for all wing sections.

This study demonstrates for the first time in the literature how the most used modal metrics vary with the sensor number for an aircraft wing using a new multi-objective sensor selection and placement optimization methodology based on the multi-objective Lichtenberg algorithm.