Abstract
This study explores models for synthetic data generation of time series. In order to improve the achieved results, i.e., the data generated, new ways of improvement are explored and different models of synthetic data generation are compared. The model addressed in this work is the Generative Adversarial Networks (GANs), known for generating data similar to the original basis data through the training of a generator. The GANs are applied using the datasets of Quinta de Santa Bárbara and the Pinhão region, with the main variables being the Average temperature, Wind direction, Average wind speed, Maximum instantaneous wind speed and Solar radiation. The model allowed to generate missing data in a given period and, in turn, enables to analyze the results and compare them with those of a multiple linear regression method, being able to evaluate the effectiveness of the generated data. In this way, through the study and analysis of the GANs we can see if the model presents effectiveness and accuracy in the synthetic generation of meteorological data. With the proper conclusions of the results, this information can be used in order to improve the search for different models and the ability to generate synthetic time series data, which is representative of the real, original, data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

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Lot-sizing and scheduling in a job shop environment is a fundamental problem that appears in many industrial settings. The problem is very complex, and solutions are often needed fast. Although many solution methods have been proposed, with increasingly better results, their computational times are not suitable for decision-makers who want solutions instantly. Therefore, we propose a novel greedy heuristic to efficiently generate production plans and schedules of good quality. The main innovation of our approach represents the incorporation of a simulation-based technique, which directly generates schedules while simultaneously determining lot sizes. By utilising priority rules, this unique feature enables us to address the complexity of job shop scheduling environments and ensures the feasibility of the resulting schedules. Using a selection of well-known rules from the literature, experiments on a variety of shop configurations and complexities showed that the proposed heuristic is able to obtain solutions with an average gap to Cplex of 4.12%. To further improve the proposed heuristic, a cooperative coevolutionary genetic programming-based hyper-heuristic has been developed. The average gap to Cplex was reduced up to 1.92%. These solutions are generated in a small fraction of a second, regardless of the size of the instance.

Abstract
Vicarious experiences in tourism possess significant marketing implications. While numerous studies have explored how various forms of vicarious experiences can impact an individual, the role of different time spans as a key factor determining the extent of said impact has been neglected in prior research. To address this gap, the present study thus bridges environmental psychology with the context of tourism and applies the theory of mental representations. An experiment (n = 359) was designed to examine differences in select mental representation dimensions (cognitive, affective, conative, and sensorial) among male and female Chinese college students who have zero/medium/maximum durations of constant vicarious experiences related to European destinations in their home environment. The results indicate that the medium duration of constant vicarious experiences leads to the most positive changes in cognitive and conative dimensions, while the longest constant vicarious experiences produce desirable affective dimension outcomes. Moreover, male college students seem to be more susceptible to the influences of such constant vicarious experiences.

Abstract
The trend for an increasingly ubiquitous and cyber-physical world has been leveraging the use and importance of microcontrollers (mu C) to unprecedented levels. Therefore, microcontroller programming (mu CP) becomes a paramount skill for electrical and computer engineering students. However, mu CP poses significant challenges for undergraduate students, given the need to master low-level programming languages and several algorithmic strategies that are not usual in generic programming. Moreover, mu CP can be time-consuming and complex even when using high-level languages. This article samples the current state of mu CP education in Portugal and unveils the potential support of natural language processing (NLP) tools (such as chatGPT). Our analysis of mu CP curricular units from seven representative Portuguese engineering schools highlights a predominant use of AVR 8-bit mu C and project-based learning. While NLP tools emerge as strong candidates as students' mu C companion, their application and impact on the learning process and outcomes deserve to be understood. This study compares the most prominent NLP tools, analyzing their benefits and drawbacks for mu CP education, building on both hands-on tests and literature reviews. By providing automatic code generation and explanation of concepts, NLP tools can assist students in their learning process, allowing them to focus on software design and real-world tasks that the mu C is designed to handle, rather than on low-level coding. We also analyzed the specific impact of chatGTP in the context of a mu CP course at ISEP, confirming most of our expectations, but with a few curiosities. Overall, this work establishes the foundations for future research on the effective integration of NLP tools in mu CP courses.