Abstract
This paper studies a real world dedicated parallel machine scheduling problem with sequence dependent setups, different machine release dates and additional resources (PMSR). To solve this problem, two previously proposed models have been adapted and a novel objective function, the minimisation of the sum of the machine completion times, is proposed to reflect the real conditions of the manufacturing environment that motivates this work. One model follows the strip-packing approach and the other is time-indexed. The solutions obtained show that the new objective function provides a compact production schedule that allows the simultaneous minimisation of machine idle times and setup times. In conclusion, this study provides valuable insights into the effectiveness of different models for solving PMSR problems in real-world contexts and gives directions for future research in this area using complementary approaches such as matheuristics.

Abstract
Process Mining offers a powerful framework for uncovering, analyzing, and optimizing real-world business processes. Petri nets provide a versatile means of modeling process behavior. However, traditional methods often struggle to effectively compare complex Petri nets, hindering their potential for process enhancement. To address this challenge, we introduce PetriNet2Vec, an unsupervised methodology inspired by Doc2Vec. This approach converts Petri nets into embedding vectors, facilitating the comparison, clustering, and classification of process models. We validated our approach using the PDC Dataset, comprising 96 diverse Petri net models. The results demonstrate that PetriNet2Vec effectively captures the structural properties of process models, enabling accurate process classification and efficient process retrieval. Specifically, our findings highlight the utility of the learned embeddings in two key downstream tasks: process classification and process retrieval. In process classification, the embeddings allowed for accurate categorization of process models based on their structural properties. In process retrieval, the embeddings enabled efficient retrieval of similar process models using cosine distance. These results demonstrate the potential of PetriNet2Vec to significantly enhance process mining capabilities.

Abstract
Historically, programming language performance focused on fast execution times. With the advent of cloud and edge computing, and the significant energy consumption of large data centers, energy efficiency has become a critical concern both for computer manufacturers and software developers. Despite the considerable efforts of the green software community in developing techniques and tools for analysing and optimising software energy consumption, there has been limited research on how imposing hardware-level energy constraints affects software energy efficiency. Moreover, prior research has demonstrated that the choice of programming language can significantly impact a program’s energy efficiency. This paper investigates the impact of CPU power capping on the energy consumption and execution time of programs written in Haskell, Java, and Python. Our preliminary results analysing well-established benchmarks indicate that while power capping does reduce energy consumption across all benchmarks, it also substantially increases execution time. These findings highlight the trade-offs between energy efficiency and runtime performance, offering insights for optimising software under energy constraints. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Abstract

Abstract
Various relevant aspects of our lives relate to the places we visit and our daily activities. The movement of individuals between regular places, such as work, school, or other important personal locations is getting increasing attention due to the pervasiveness of geolocation devices and the amount of data they generate. This work presents an approach for location prediction using a probabilistic model and data mining techniques over mobility data streams. We evaluate the method over 5 real-world datasets. The results show the usefulness of the proposal in comparison with other-well-known approaches.

Abstract
The dowry payment system is used in the cultural context and tradition of certain financial transactions related to marriages and engagement. However, disputes, fraud, and financial gaps in exploitation occur in these systems, which affect user confidence. This study uses an exploratory approach to identify the main weaknesses of current traditional dowry payment systems and analyses the benefits that blockchain technology and smart contracts can provide. The proposed data security framework combines blockchain security features such as decentralisation, cryptography, and automatic verification through smart contracts to ensure the integrity and reliability of dowry payment transactions. In this study, we adopt the Design Science Research (DSR) methodology to propose producing and developing artefacts that support solving problems in the existing dowry payment system more efficiently. We will disseminate new ideas or concepts developed to indigenous communities in Timor-Leste using the Diffusion of Innovation (DOI) and Technology Acceptance Model (TAM) frameworks to ensure that the technological framework developed can be used safely and efficiently.