Do people want to be vaccinated against COVID-19? Herd immunity is dependent on individuals’ willingness to be vaccinated since vaccination is not mandatory. Our main goal was to investigate people’s intention to be vaccinated and their intentions to vaccinate their children. Moreover, we were interested in understanding the role of the personal characteristics, psychological factors, and the lockdown context on that decision. Therefore, we conducted an online survey during the lockdown in Portugal (15 January 2021 until 14 March 2021). Participants completed a socio-demographic questionnaire, questions about their intentions of being vaccinated, concerns about the vaccine, a COVID-19 attitudes and beliefs scale, a COVID-19 vaccine attitudes and beliefs scale, and the Domain-Specific Risk-Taking (DOSPERT) Scale. Our results showed that from the 649 participants, 63% of the participants reported being very likely to have the vaccine, while 60% reported being very likely to vaccinate their children. We conducted two linear regression models, explaining 65% of the variance for personal vaccination and 56% of the variance for children vaccination. We found that the COVID-19 vaccine general beliefs and attitudes were the main determinants of vaccination intention. Additionally, our proposed artificial neural network model was able to predict with 85% accuracy vaccination intention. Thus, our results suggest that psychological factors are an essential determinant of vaccination intention. Thus, public policy decision makers may use these insights for predicting vaccine hesitancy and designing effective vaccination communication strategies.

Logistics is significantly impacted by quality/quantity issues associated with data collection and data sharing restrictions. Nonetheless, public data from national entities and internet-of-things (IoT) solutions enable the development of integrated tools for performance analysis and real-time optimization of logistics networks. This study proposes a three-module data-driven system architecture that covers (a) logistics data collection tools, (b) logistics services performance evaluation, and (c) the transition to synchromodal systems. Module 1 integrates multisource data from national logistics platforms and embedded devices placed within intermodal containers. A multigraph representation of the problem is conceived. Environmental, economic, and operational data are generated and injected into a digital twin. Thus, key performance indicators (KPIs) are computed by simulation or direct transformation of the collected data. Module 2 uses Multi-directional Efficiency Analysis, an optimization algorithm that benchmarks multimodal transportation routes of containers using prior KPIs. Outputs are a technical performance index relevant to logistics clients and improvement measures for logistics service providers. A real case study application of the solution proposed for Module 2 is presented. Module 3 provides real-time scheduling and assignment models using CP-sat solvers, accommodating varying system dynamics and resource availability, minimizing makespan and operational costs.

Integration of blockchain systems into industrial applications show promise in increasing security, trust, and transparency along the value-chain during product and process tracking. However, current solutions suffer performance deficiencies, or often disregard legacy devices still in operation. We propose a blockchain system built upon an IoT architecture that is secure, modular, easily scalable, and deployable for fast certification of manufacturing data, compatible with current industrial landscapes. First, the proposed architecture is presented along with elements required to manage network functions. Second, easing integration with existing manufacturing solutions, custom APIs are created and subsequently explained. This grants the platform plug-and-play capabilities, requiring minimal hardware and software configuration for deployment. Lastly, a prototype is designed to validate the solution, concluding the viability of the proposed architecture as a fast and secure certification method of manufacturing data.

Decision Trees (DTs) remain one of the most important algorithms in machine learning for their simplicity, interpretability, and often satisfactory performance. Furthermore, they are critical foundational components for more performant models such as Random Forests (RFs) and Gradient Boosted Trees. Central to DTs is the splitting process, where data is partitioned according to criteria traditionally based on information-theoretic measures such as Shannon entropy or Gini index. In this article, we propose a novel parametric entropy-based information gain criterion designed to generalize and extend classical entropic measures to improve classification performance in DTs and RFs. We introduce a five-parameter entropy formulation capable of replicating and extending known entropy measures. This new criterion was incorporated into DT and RF classifiers and evaluated on a collection of 18 benchmarking datasets, including both synthetic and real-world data retrieved from publicly available repositories. Performance was assessed using 5-fold cross-validation and optimized via Bayesian hyperparameter search, with weighted F1-score as the primary metric. Compared to splitting criteria based on existing entropy/purity measures ( e.g ., Gini, Shannon, Rényi, and Tsallis), our method yielded statistically significant improvements in classification performance across most datasets. On multiclass and imbalanced datasets, such as the Wine Quality dataset, F1-score improvements exceeded 40% using RF algorithms. Bayesian signed-rank tests confirmed the robustness of our method, which never underperformed relative to standard approaches. The proposed entropy-based splitting criterion offers a flexible and effective alternative to classical information-theoretic measures, delivering improvements in classification performance.