Abstract

Abstract
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.

Abstract
With the serious danger to nature and humanity that forest fires are, taken into consideration, this work aims to develop an artificial intelligence model capable of accurately predicting the forest fire risk in a certain region based on four different factors: temperature, wind speed, rain and humidity. Thus, three models were created using three different approaches: Artificial Neural Networks (ANN), Random Forest (RF), and K-Nearest Neighbor (KNN), and making use of an Algerian forest fire dataset. The ANN and RF both achieved high accuracy results of 97%, while the KNN achieved a slightly lower average of 91%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Europe's position in the current cloud market needs to be improved. This market is currently dominated by non-European players by 75%, shaping the way that Europe is deploying and using cloud services. Although these players are bound to laws and regulations of foreign powers, such as PR China and USA, generating legitimate concerns for the EU, its businesses and citizens. EU's digital future resides on having installed secure, high-quality data processing capacity. This can only be offered by cloud services both centrally and at the edge. In this context NOUS's ambition is completely in line with the European Strategy for data as aims to create the foundations for a European Cloud Service which exploits the HPC network and tackles specific-to-the-EU-economy requirements as well as leverages different data spaces (Mobility, Energy, Green Deal and Manufacturing).