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The growing ubiquity of digital platforms has enabled unprecedented participation in large-scale group decision-making processes. Nevertheless, integrating subjective linguistically expressed opinions into structured decision protocols remains a significant challenge. This paper presents a novel framework that leverages the semantic and affective capabilities of large language models to support large-scale group decision-making tasks by extracting and quantifying experts’ communicative traits—specifically clarity and trust—from natural language input. Based on these traits, participants are clustered into behavioural groups, each of which is assigned a representative preference structure and a weight reflecting its internal cohesion and communicative quality. A sentiment-informed consensus mechanism then aggregates these group-level matrices to form a collective decision outcome. The method enhances scalability and interpretability while preserving the richness of human expression. The results suggest that incorporating behavioural dimensions into large-scale group decision-making via large language models fosters fairer, more balanced, and semantically grounded decisions, offering a promising avenue for next-generation decision-support systems.

Recommender systems are tools that help users in the decision-making process of choosing items that may be relevant for them among a vast amount of other items. One of the main problems of recommender systems is the cold start problem, which occurs when either new items or new users are added to the system and, therefore, there is no previous information about them. This article presents a multi-source dataset optimized for the study and the alleviation of the cold start problem. This dataset contains info about the users, the items (movies), and ratings with some contextual information. The article also presents an example user behavior-driven algorithm using the introduced dataset for creating recommendations under the cold start situation. In order to create these recommendations, a mixed method using collaborative filtering and user-item classification has been proposed. The results show recommendations with high accuracy and prove the dataset to be a very good asset for future research in the field of recommender systems in general and with the cold start problem in particular.

Completing construction projects in time requires highly integrated contractor selection processes. Selecting the ‘best’ contractor is a multi-criteria and multi-group hard decision-making problem. The decision makers (DMs) usually do not have a joint interest in achieving agreement on choosing the best contractor. Traditionally, consensus on a decision does not mean a full and unanimous agreement on the selection criteria. Because the criteria expressed by quantitative and/or qualitative data are generally conflicting, an improvement in one often results in declining the others. Therefore, DMs base their judgments upon huge-size, high-variety and conflicting data which refer to Big Data. Hence, massive amount of data are analyzed in an iterative and time-sensitive manner for the crucial success of organizations. This study aims to integrate the contractor selection approaches for the formulation of decision problems using fuzzy and crisp data. Fuzzy AHP approach was employed for determining the criteria weights, and fuzzy TOPSIS method was used to find out the performance of contractors. Fuzzy extension of AHP enables the pair-wise comparison of criteria using synthetic global scores based on the data of a single expert. However, in this study, we used the data of multiple DMs and averaged the aggregated findings in the pair-wise comparison table; hence, seven contractors were evaluated based on the Big Data. The results showed that these methodologies are able to assess contractors’ Big Data in a more scientific and practical way. The suggested approach helped to select the best contractor or share the projects between equally strong contractors.

Smart cities represent the convergence of information and communication technologies (ICT) with urban management to improve the quality of life of city dwellers. In this context, recommender systems, tools that offer personalised suggestions to city dwellers, have emerged as key contributors to this convergence. Their successful application in various areas of city life and their ability to process massive amounts of data generated in urban environments has expedited their status as a crucial technology in the evolution of city planning. Our methodology included reviewing the Web of Science database, resulting in 130 articles that, filtered for relevancy, were reduced to 86. The first stage consisted of carrying out a bibliometric analysis with the objective of analysing structural aspects with the SciMAT tool. Secondly, a systematic literature review was undertaken using the PRISMA 2020 statement. The results illustrated the different processes by which recommendations are filtered in areas such as tourism, health, mobility, and transport. This research is seen as a significant breakthrough that can drive the evolution and efficiency of smart cities, establishing a solid framework for future research in this dynamic field.

This paper describes a new proposal for interactive television which is an answer to a continuous change in the traditional television as consequence of the inclusion and evolution of the digital social networks, the Internet and the different elements of the digital age. The digital evolution has encourage the interaction of the viewers with the content and also increases the need to evolved the content, the methods, formats, tools and architectures to adapt the content to the sentiment expressed by the viewer while watching a show. The present paper contains the following objectives: The first objective is to create guidelines that can be used to construct adaptive contents for television, which can be modified in real time by the production team or the director of the show. The second objective is to develop applications that allows to obtain, collect and analyze the sentiment inside of the expressions, data or opinions of the viewers, who interact with the show through social networks or communication channels as: Facebook, Twitter, Instagram and WhatsApp. The third objective is to develop a machine learning to predict the preferences of the viewers, generating options and changes in the sequence of the scenes of the TV show that will be broadcasted in real time. All the objectives explained above are applied to two TV shows which are different in the content but share the live condition. During the broadcasting of the show, the guidelines are applied, the results are obtained, analyzed and the final result is more participation of the viewers and a better perception of the content. As a result of the research and the application in real life of the proposal, this paper contributes with an alternative solution for interactive TV where a viewer can interact with the show and the production team can modify the content according to what the viewers express and expect to watch based on an analysis of sentiment of data using a machine learning.

The rapid advances in Web technologies are promoting the development of new pedagogic models based on virtual teaching. In this framework, personalized services are necessary. Recommender systems can be used in an academic environment to assist users in their teaching-learning processes. In this paper, we present a trust based recommender system, adopting a fuzzy linguistic modeling, that provides personalized activities to students in order to reinforce their education, and applied it in the field of oral surgery and implantology. We don’t take into account users with similar ratings history but users in which each user can trust and we provide a method to aggregate the trust information. This system can be used in order to aid professors to provide students with a personalized monitoring of their studies with less effort. The results obtained in the experiments proved to be satisfactory.

There exist situations of decision-making under information overload in the Internet, where people have an overwhelming number of available options to choose from, e.g. products to buy in an e-commerce site, or restaurants to visit in a large city. Recommender systems arose as a data-driven personalized decision support tool to assist users in these situations: they are able to process user-related data, filtering and recommending items based on the users preferences, needs and/or behaviour. Unlike most conventional recommender approaches where items are inanimate entities recommended to the users and success is solely determined upon the end users reaction to the recommendation(s) received, in a Reciprocal Recommender System (RRS) users become the item being recommended to other users. Hence, both the end user and the user being recommended should accept the 'matching' recommendation to yield a successful RRS performance. The operation of an RRS entails not only predicting accurate preference estimates upon user interaction data as classical recommenders do, but also calculating mutual compatibility between (pairs of) users, typically by applying fusion processes on unilateral user-to-user preference information. This paper presents a snapshot-style analysis of the extant literature that summarizes the state-of-the-art RRS research to date, focusing on the algorithms, fusion processes and fundamental characteristics of RRS, both inherited from conventional user-to-item recommendation models and those inherent to this emerging family of approaches. Representative RRS models are likewise highlighted. Following this, we discuss the challenges and opportunities for future research on RRSs, with special focus on (i) fusion strategies to account for reciprocity and (ii) emerging application domains related to social recommendation.

Robotic devices have become increasingly important in upper-limb rehabilitation, as they assist therapists, improve treatment efficiency, and enable personalised therapy. However, the lack of standardised protocols and integrative tools limits their widespread adoption and effectiveness. To address these challenges, a robotic framework was developed for upper-limb rehabilitation in patients with acquired brain injury (ABI). The framework is designed to be adaptable to various ROS-compatible collaborative robots with admittance control and potentially adaptable to other types of control, and also integrates kinematic and electrophysiological (EMG) metrics to monitor patient performance and progress. It combines data acquisition through EMG and robot motion sensors, gamification elements to enhance engagement, and configurable robot control modes within a unified software platform. A pilot evaluation with eight healthy subjects performing upper limb movements on an ROS-compatible robot from the UR family demonstrated the feasibility of the framework’s components, including robot control, EMG acquisition and synchronization, gamified interaction, and synchronised data collection. User performance through all levels remained below the controller limits of force and velocity thresholds even in the most resistive damping. These results support the potential of the proposed framework as a flexible, extensible, and integrative tool for upper-limb rehabilitation, providing a foundation for future clinical studies and multi-platform implementations.

The presence of leukocyte subpopulations in malignant pleural effusions (MPEs) can have a different impact on tumor cell proliferation and vascular leakiness, their analysis can help to understand the metastatic microenvironment. We analyzed the relationship between the leukocyte subpopulation counts per ml of pleural fluid and the tumor cell count, molecular phenotype of lung adenocarcinoma (LAC), time from cancer diagnosis and previous oncologic therapy. We also evaluated the leukocyte composition of MPEs as a biomarker of prognosis. We determined CD4+ T, CD8+ T and CD20+ B cells, monocytes and neutrophils per ml in pleural effusions of 22 LAC and 10 heart failure (HF) patients by flow cytometry. Tumor cells were identified by morphology and CD326 expression. IFNγ, IL-10 and IL-17, and chemokines were determined by ELISAs and migratory response to pleural fluids by transwell assays. MPEs from LAC patients had more CD8+ T lymphocytes and a tendency to more CD4+ T and CD20+ B lymphocytes than HF-related fluids. However, no correlation was found between lymphocytes and tumor cells. In those MPEs which were detected >1 month from LAC diagnosis, there was a negative correlation between pleural tumor cells and CD8+ T lymphocytes. CXCL10 was responsible for the attraction of CD20+ B, CD4+ T and CD8+ T lymphocytes in malignant fluids. Concentrations of IL-17 were higher in MPEs than in HF-related effusions. Survival after MPE diagnosis correlated positively with CD4+ T and CD8+ T lymphocytes, but negatively with neutrophils and IL-17 levels. In conclusion, lymphocyte enrichment in MPEs from LAC patients is mostly due to local migration and increases patient survival.

Environmental microbes harbor an enormous pool of antibiotic and biocide resistance genes that can impact the resistance profiles of animal and human pathogens via horizontal gene transfer. Pseudomonas putida strains are ubiquitous in soil and water but have been seldom isolated from humans. We have established a collection of P. putida strains isolated from in-patients in different hospitals in France. One of the isolated strains (HB3267) kills insects and is resistant to the majority of the antibiotics used in laboratories and hospitals, including aminoglycosides, ß-lactams, cationic peptides, chromoprotein enediyne antibiotics, dihydrofolate reductase inhibitors, fluoroquinolones and quinolones, glycopeptide antibiotics, macrolides, polyketides and sulfonamides. Similar to other P. putida clinical isolates the strain was sensitive to amikacin. To shed light on the broad pattern of antibiotic resistance, which is rarely found in clinical isolates of this species, the genome of this strain was sequenced and analysed. The study revealed that the determinants of multiple resistance are both chromosomally-borne as well as located on the pPC9 plasmid. Further analysis indicated that pPC9 has recruited antibiotic and biocide resistance genes from environmental microorganisms as well as from opportunistic and true human pathogens. The pPC9 plasmid is not self-transmissible, but can be mobilized by other bacterial plasmids making it capable of spreading antibiotic resistant determinants to new hosts.