This study presents a thorough bibliometric analysis of Neuroinformatics over the past 20 years, offering insights into the journal’s evolution at the intersection of neuroscience and computational science. Using advanced tools such as VOS viewer and methodologies like co-citation analysis, bibliographic coupling, and keyword co-occurrence, we examine trends in publication, citation patterns, and the journal’s influence. Our analysis reveals enduring research themes like neuroimaging, data sharing, machine learning, and functional connectivity, which form the core of Neuroinformatics. These themes highlight the journal’s role in addressing key challenges in neuroscience through computational methods. Emerging topics like deep learning, neuron reconstruction, and reproducibility further showcase the journal’s responsiveness to technological advances. We also track the journal’s rising impact, marked by a substantial growth in publications and citations, especially over the last decade. This growth underscores the relevance of computational approaches in neuroscience and the high-quality research the journal attracts. Key bibliometric indicators, such as publication counts, citation analysis, and the h-index, spotlight contributions from leading authors, papers, and institutions worldwide, particularly from the USA, China, and Europe. These metrics provide a clear view of the scientific landscape and collaboration patterns driving progress. This analysis not only celebrates Neuroinformatics’s rich history but also offers strategic insights for future research, ensuring the journal remains a leader in innovation and advances both neuroscience and computational science.

Pay-What-You-Want (PWYW), is a pricing strategy increasingly applied in many different industries, both profitable and not. This study aims to identify influential cited works in PWYW research, determine the current status, and indicate the extent to which influential works have shaped the field addressing this concern, a set of bibliometric analyses conducted in this paper. The analysis was carried out on 136 research papers published between 2009 and 2022 have been analyzed based on Web of Science Core Collection (WoS) results. In order to identify the most cited authors and works, the co-citation analysis was applied. To scrutinize the intellectual structure of the field, bibliometric coupling was applied, to show the network structure of the themes, co-word analysis was applied. Building upon the results, this study suggests future research paths.

The ordered weighted averaging (OWA) operator is one of the most used techniques in the operator’s aggregation procedure. This paper proposes a new assignment algorithm by using the OWA operator and different extensions of it in the Branch-and-bound algorithm. The process is based on the use of the ordered weighted average distance operator (OWAD) and the induced OWAD operator (IOWAD). We present it as the Branch-and-bound algorithm with the OWAD operator (BBAOWAD) and the Branch-and-bound algorithm with the IOWAD operator (BBAIOWAD). The main advantage of this approach is that we can obtain more detailed information by obtaining a parameterized family of aggregation operators. The application of the new algorithm is developed in a consumer decision-making model in the city of Barcelona regarding the selection of groceries by districts that best suit their needs. We rely on the opinion of local commerce experts in the city. The key advantage of this approach is that we can consider different sources of information independent of each other.

Urban mobility stands as a fundamental element worthy of consideration by both society and its leaders. Often, decisions in this realm are made by governing figures without duly factoring in the preferences and needs of citizens. In our study, we delve into the changes that have unfolded within Barcelona from the standpoint of its users. The primary aim of this article is to observe the preferences that these users hold regarding the array of mobility options available to them in the urban environment. To this end, we’ve incorporated various aspects of significant relevance and contemporary presence in today’s society, such as mobility and sustainability, focusing specifically on the two most frequently used types of commercial passenger vehicles (CPV) within urban contexts: taxis and ride-hailing services. To gather the necessary sample data, a survey was conducted with a significance level of 95%. Following an exhaustive examination of the existing literature surrounding these concepts, we proceeded with the analysis of the sample using neural networks. The outcomes garnered encompass user receptiveness to the technological evolution shaping the sector, the utilization of mobile applications, a predisposition to opt for fixed tariffs, and the pivotal role of new blockchain-based technologies (NFTs and fan tokens) in influencing user decision-making.