Abstract
Data-driven decision models based on Artificial Intelligence (AI) have been widely used in the public and private sectors. These models present challenges and are intended to be fair, effective and transparent in public interest areas. Bias, fairness and government transparency are aspects that significantly impact the functioning of a democratic society. They shape the government's and its citizens' relationship, influencing trust, accountability, and the equitable treatment of individuals and groups. Data-driven decision models can be biased at several process stages, contributing to injustices. Our research purpose is to understand fairness in the use of causal discovery for public procurement. By analysing Portuguese public contracts data, we aim i) to predict the place of execution of public contracts using the PC algorithm with sp-mi, smc-chi(2) and mc-chi(2) conditional independence tests; ii) to analyse and compare the fairness in those scenarios using Predictive Parity Rate, Proportional Parity, Demographic Parity and Accuracy Parity metrics. By addressing fairness concerns, we pursue to enhance responsible data-driven decision models. We conclude that, in our case, fairness metrics make an assessment more local than global due to causality pathways. We also observe that the Proportional Parity metric is the one with the lowest variance among all metrics and one with the highest precision, and this reinforces the observation that the Agency category is the one that is furthest apart in terms of the proportion of the groups.

Abstract
The Internet of Things (IoT) enables everyday objects to connect and communicate remotely, transforming areas such as smart homes and industrial automation. IoT systems can be standalone or interconnected in a System of Systems, where multiple devices work together towards a common goal. A key application is Energy Monitoring Systems (EMS), which track energy use within communities, using energy production and consumption. Designing this type of IoT systems remains complex and requires careful consideration of heterogeneous devices, their limitations, software, communication protocols, data management, and security. This paper presents a design approach for EMS communities, with a focus on house-level IoT systems. We introduce a model-driven development methodology, a holistic and flexible framework for designing IoT systems across the development and operations lifecycle. Especially, the concept of projectors enables an easy shift between domain assets and provide automation support. The approach is validated with a real-life use case, for which an analysis phase was developed, showing the benefits of using our approach for managing EMS and the automation of the analysis configuration. © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

Abstract
Debugging and repairing faults when programs fail to formally verify can be complex and time-consuming. Automated Program Repair (APR) can ease this burden by automatically identifying and fixing faults. However, traditional APR techniques often rely on test suites for validation, but these may not capture all possible scenarios. In contrast, formal specifications provide strong correctness criteria, enabling more effective automated repair. In this paper, we present an APR tool for Dafny, a verification-aware programming language that uses formal specifications — including pre-conditions, post-conditions, and invariants — as oracles for fault localization and repair. Assuming the correctness of the specifications and focusing on arithmetic bugs, we localize faults through a series of steps, which include using Hoare logic to determine the state of each statement within the program, and applying Large Language Models (LLMs) to synthesize candidate fixes. The models considered are GPT-4o mini, Llama 3, Mistral 7B, and Llemma 7B. We evaluate our approach using DafnyBench, a benchmark of real-world Dafny programs. Our tool achieves 89.7% fault localization success rate and GPT-4o mini yields the highest repair success rate of 74.18%. These results highlight the potential of combining formal reasoning with LLM-based program synthesis for automated program repair.

Abstract
The transition to the water circular economy (WCE) requires several stakeholders' awareness, articulation, and action involving complex governance concerns. As a participatory approach to identifying problems, designing solutions, and implementing strategic actions, the co-creation process should support stakeholder involvement to adjust existing institutional arrangements to foster the WCE. This article designs and applies a co-creation process to analyse the perception of key stakeholders about institutional challenges for water reuse and explore their contributions to innovate policy, planning, and governance for the implementation of new water reuse technology in Almendralejo (Spain), Lecce (Italy), Omis (Croatia), and Eilat (Israel). The findings indicate that implementing a new water loop encounters complex institutional and production-related obstacles, which different stakeholders address in varying ways. Moreover, the proposed solutions to the on-site issues identified emphasise the need for actions that foster engagement and collaboration, particularly to enhance awareness, training, and regulation. Addressing these challenges associated with adopting new water loops, even when technical, may depend on non-technical solutions regarding the institutional framework. The co-creation processes highlight the importance of focusing on institutional arrangements and stakeholder awareness while implementing new water loops to ensure and promote symbiotic territories that consider the policy, producers', and users' strategies.

Abstract
The textile industry is a complex and dynamic system where structured decision-making processes are essential for efficient supply chain management. In this context, mathematical programming models offer a powerful tool for modeling and optimizing the textile supply chain. This systematic review explores the application of mathematical programming models, including linear programming, nonlinear programming, stochastic programming, robust optimization, fuzzy programming, and multi-objective programming, in optimizing the textile supply chain. The review categorizes and analyzes 163 studies across the textile manufacturing stages, from fiber production to integrated supply chains. Key results reveal the utility of these models in solving a wide range of decision-making problems, such as blending fibers, production planning, scheduling orders, cutting patterns, transportation optimization, network design, and supplier selection, considering the challenges found in the textile sector. Analyzing those models, we point out that sustainability considerations, such as environmental and social aspects, remain underexplored and present significant opportunities for future research. In addition, this study emphasizes the importance of incorporating multi-objective approaches and addressing uncertainties in decision-making to advance sustainable and efficient textile supply chain management.

Abstract
The rise of environmental crimes has become a major concern globally as they cause significant damage to ecosystems, public health and result in economic losses. The availability of vast sensor data provides an opportunity to analyze environmental data proactively. This helps to detect irregularities and uncover potential criminal activities. This paper highlights the critical role played by machine learning (ML) and remote sensing technologies in the continuously evolving scenarios of environmental crime. By examining some case studies on detecting illegal fishing, illegal oil spills, illegal landfills, and illegal logging, we delve into the practical implementation of data-driven approaches for environmental crime detection. Our goal with this study is to provide an overview of the existing research in this area and foster the use of ML and data science techniques to enhance environmental crime detection.