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Neuromarketing, consumer neuroscience and neuroaesthetics are a broad research area of neuroscience with an extensive background in scientific publications. Thus, the present study aims to identify the highly cited papers (HCPs) in this research field, to deliver a summary of the academic work produced during the last decade in this area, and to show patterns, features, and trends that define the past, present, and future of this specific area of knowledge. The HCPs show a perspective of those documents that, historically, have attracted great interest from a research community and that could be considered as the basis of the research field. In this study, we retrieved 907 documents and analyzed, through H-Classics methodology, 50 HCPs identified in the Web of Science (WoS) during the period 2010–2019. The H-Classic approach offers an objective method to identify core knowledge in neuroscience disciplines such as neuromarketing, consumer neuroscience, and neuroaesthetics. To accomplish this study, we used Bibliometrix R Package and SciMAT software. This analysis provides results that give us a useful insight into the development of this field of research, revealing those scientific actors who have made the greatest contribution to its development: authors, institutions, sources, countries as well as documents and references. [ABSTRACT FROM AUTHOR]

• This study presents a social network consensus-reaching process with minimum adjustment distance. • This study further presents an opinion evolution-based social network consensus-reaching process. • The proposed consensus reaching process is integrated into FMEA to address the divergent opinions. • Some theoretical analyses, a case study, and a detailed comparative analysis are presented to verify the validity of the proposal. As a forward-looking reliability-management engineering technique, failure mode and effect analysis (FMEA) has been widely utilized to improve the reliability of products, processes, systems, and services. In practice, multiple responsible parties for FMEA implementation have different backgrounds, knowledge levels, and opinions. Integrating consensus into FMEA has some notable merits: the connections between FMEA participants can be strengthened, and a collective solution with a high degree of acceptability to the FMEA problem can be yielded. Meanwhile, the social network relationship among FMEA participants should be an essential element in FMEA because the participants' opinions are subject to influence by each other and likely to evolve due to their social network interactions. Thus, this study first proposes a social network consensus model with minimum adjustment distance to assist FMEA participants in attaining a consensus, in which participants utilize a linguistic distribution assessment approach to represent their opinions. Second, an opinion evolution-based social network consensus model with minimum adjustment distance is further presented by considering the phenomenon of opinion evolution. Finally, some theoretical analyses, a case study, and a detailed comparative analysis are presented to verify the validity of the proposed FMEA approach. [ABSTRACT FROM AUTHOR]

• A novel granular fuzzy pay-off method for real option valuation. • Granular fuzzy pay-off method is founded on the principle of justifiable granularity. • Interval type-2 fuzzy sets are used for better dealing with uncertainty. In the last decade, the fuzzy pay-off method has emerged as a widely used alternative approach for real option valuation thanks to its simplicity that makes it easily approachable by practitioners from various application domains. In this study, a new direction for real option valuation is pursued by proposing a granular fuzzy pay-off method. We motivate the proposal by discussing how the extension of the original approach with granular representation can further improve the fuzzy pay-off method. The design of the granular fuzzy pay-off method is founded on the principle of justifiable granularity, which in turn relies on numeric data to build information granules that are semantically sound and experimentally justified. To illustrate the method, a case study in R&D investment in manufacturing is worked out. The extended granular fuzzy pay-off method improves performance and usability in cases with uncertainty. [ABSTRACT FROM AUTHOR]

Nowadays, wine has become a very popular item to purchase. There are a lot of brands and a lot of different types of wines that have different prices and characteristics. Since there is a lot of options, it is easy for buyers to feel lost among the high number of possibilities. Therefore, there is a need for computational tools that help buyers to decide which is the wine that better fits their necessities. In this article, a decision support system built over a fuzzy ontology has been designed for helping people to select a wine. Two different possible architecture implementation designs are presented. Furthermore, imprecise information is used to design a comfortable way of providing information to the system. Users can use this comfortable communication system to express their preferences and provide their opinion about the selected products. Moreover, mechanisms to carry out a constant update of the fuzzy ontology are exposed. [ABSTRACT FROM AUTHOR]

Interval-valued intuitionistic multiplicative variables (IVIMVs) can conveniently and effectively represent the uncertain multiplicative preferred and non-preferred judgements of decision makers, this paper studies group decision making (GDM) with interval-valued intuitionistic multiplicative preference relations (IVIMPRs). To calculate the interval-valued intuitionistic multiplicative priority weight vector reasonably, a new consistency concept is introduced that satisfies robustness and upper triangular property. Following this concept, models to judge the consistency and obtain consistent IVIMPRs from inconsistent ones are constructed, respectively. To address the problem of incomplete preferences, consistency-based model to determine missing values is built. Furthermore, we study the consensus for GDM with IVIMPRs and provide a new consensus approach. When an acceptable consensus level is not achieved, an interactive and automatic adjustment method is applied to reach a better consensus level. Following discussion about consistency and consensus, an algorithm for GDM is offered that can address inconsistent and incomplete IVIMPRs. Finally, a practical problem about selecting the steel supplier is selected to show the application of the new method. [ABSTRACT FROM AUTHOR]

The development of innovative solutions that allow the aging population to remain healthier and independent longer is essential to alleviate the burden that this increasing segment of the population supposes for the long term sustainability of the public health systems. It has been claimed that promoting physical activity could prevent functional decline. However, given the vulnerability of this population, the activity prescription requires to be tailored to the individual's physical condition. We propose mobile Senior Fitness Test (m-SFT), a novel m-health system, that allows the health practitioner to determine the elderly physical condition by implementing a smartphone-based version of the senior fitness test (SFT). The technical reliability of m-SFT has been tested by carrying out a comparative study in seven volunteers (53–61 years) between the original SFT and the proposed m-health system obtaining high agreement (intra-class correlation coefficient (ICC) between 0.93 and 0.99). The system usability has been evaluated by 34 independent health experts (mean = 36.64 years; standard deviation = 6.26 years) by means of the System Usability Scale (SUS) obtaining an average SUS score of 84.4 out of 100. Both results point out that m-SFT is a reliable and easy to use m-health system for the evaluation of the elderly physical condition, also useful in intervention programs to follow-up the patient's evolution. [ABSTRACT FROM AUTHOR]

Location recommendation in a city district plays an essential role for people city experience. Most existing studies consider user mobility data as implicit feedback, and adopt collaborative filtering frameworks to make recommendations. However, most of them treat all unvisited locations as negative examples. This method fails to provide details about users preferences, such as graded ratings in explicit feedback. To cope with this problem, we define geographically similar friends, and propose a Complementing Preference Based Collaborative Filtering method (CPBCF). Specifically, we first build a geosimilarity network for a user and identify the possible locations that he or she could visit. Limiting the study to just these locations alleviates data sparsity caused by a huge number of unrelated locations. Then, we complement the user–location preference matrix using the information from geographically similar friends and adopt a weighted regularized matrix factorization model to make recommendations. We evaluate our method on real-world dataset and the experimental results demonstrate the effectiveness of our methods. • Complement user's preference to cope with incomplete user preference information in location recommendation. • Verify the feasibility of complementing user's preferences with geographically similar friends' locations. • Propose a method to complement the user–location preference matrix, considering personalization. • The experimental results show the effectiveness of the work. [ABSTRACT FROM AUTHOR]

Introduction. In this paper we propose a multi-agent Selective Dissemination of Information service to improve the research community's access to digital library resources. The service also provides a new recommendation approach to satisfy researchers' specific information requirements. [ABSTRACT FROM AUTHOR]