Abstract
Explainable AI (xAI) emerged as one of the ways of addressing the interpretability issues of the so-called black-box models. Most of the xAI artifacts proposed so far were designed, as expected, for human users. In this work, we posit that such artifacts can also be used by computer systems. Specifically, we propose a set of metrics derived from LIME explanations, that can eventually be used to ascertain the quality of each output of an underlying image classification model. We validate these metrics against quantitative human feedback, and identify 4 potentially interesting metrics for this purpose. This research is particularly useful in concept drift scenarios, in which models are deployed into production and there is no new labelled data to continuously evaluate them, becoming impossible to know the current performance of the model.

Abstract
With the proliferation of ODR service providers, there is a critical necessity to establish mechanisms supporting their functioning, particularly while designing ODR processes. This article aims to examine the impact of process modelling using BPMN, and of its relevance in the integration of AI into ODR processes within the EU. BPMN allows a meticulous depiction of all the ODR process steps, stakeholders, and underlying data in structured formats that are readable and interpretable by both humans and AI, which enables its integration. The advantages include predictive analysis, identification of opportunities for continuous improvement, operational efficiency, cost and time reduction, and enhanced accessibility for self-represented litigants. Additionally, the transparency afforded by explicitly incorporating AI in BPMN notation fosters a clearer comprehension of processes, facilitating management and informed decision-making. Nevertheless, it remains imperative to address ethical concerns such as algorithmic bias, fairness, and privacy.

Abstract
Water scarcity is currently a concerning problem and is likely to worsen in the future. To address this issue, it is essential that water used in human activities is treated before being reused or returned to nature. Wastewater is processed in wastewater treatment plants (WWTPs), which are complex structures that consume a considerable amount of resources and need to operate optimally. Many authors have proposed computational methodologies to optimize WWTPs, and each work has different approaches and characteristics, but most have in common the lack of concern with maximizing sustainability, in its broadest definition. Furthermore, even when sustainability is considered, it is typically addressed in an indirect or superficial manner, rather than being treated as a central objective. This paper provides a critical literature review of computational methodologies that, in some way, focus on improving the sustainability of WWTPs. Considering the target of the paper, this review aims to answer the following main questions: (1) What are the general objectives of the proposed works? (2) In which locations/phases of the treatment process are the proposed techniques applied? (3) What are the main methodologies and performance metrics used in the proposed techniques? The review identifies a strong focus on optimizing aeration in biological reactors, limited holistic and real-time optimization across WWTP stages, and sparse integration of sustainability metrics, especially for environmental and social impacts. Future research should prioritize the development of real-time, multi-objective optimization frameworks that encompass all WWTP stages and fully integrate economic, environmental, and social sustainability dimensions.

Abstract
The evaluation of industrial process efficiency is essential for resource optimization, enabling the identification of bottlenecks, waste, and improvement opportunities while promoting the rational use of resources and enhancing the sustainability of operations. The Multi-layer Stream Mapping (MSM) method is a tool for assessing the efficiency of complex production processes, which identifies the efficiencies and inefficiencies based on reference values. However, its limitation lies in using reference values that often fail to reflect process evolution or distinct operational regimes. This work proposes a new dynamic ecoefficiency assessment methodology, the Evolving Fuzzy Multilayer Stream Mapping System (eFuMSM), based on MSM and an evolving fuzzy system, to provide dynamic reference values, allowing more accurate eco-efficiency assessments considering the process evolution and different regions of operation. The proposed eFuMSM was applied to the primary clarifier of a wastewater treatment plant, evaluating efficiency in removing total suspended solids. Results revealed that systems previously undervalued under the traditional MSM demonstrated improved efficiency when assessed using the eFuMSM system, aligning more accurately with their operational regimes.

Abstract
This paper studies the process of cutting steel bars in a truck suspension factory with the objective of reducing its inventory costs and material losses. A mathematical model is presented that focuses on decisions for a medium-term horizon (4 periods of 2 months). This approach addresses the one-dimensional 3-level integrated lot sizing and cutting stock problem, considering demand, inventory costs and stock level limits for bars (objects-level 1), springs (items-level 2) and spring bundles (final products-level 3), as well as the acquisition of bars as a decision variable. The solution to the proposed mathematical model is reached through an optimization package, using column generation along with a method for achieving integer solutions. The results obtained with real data demonstrate that the method provides significantly better solutions than those carried out at the company, whilst using reduced computational time. Additionally, the application of tests with random data enabled the analysis of both the effect of varying parameters in the solution, which provides managerial insights, and the overall performance of the method.

Abstract
Photovoltaic (PV) panels serve as a standard solution for the collection of solar energy. The flat photovoltaic solar plate design has been the most adopted by the market for its ease of installation. However, this design faces limitations due to geometric constraints and the sun's trajectory through the day. Inspiration was drawn from nature to overcome these limitations by utilizing the tridimensional hexagonal shape observed in honeycomb structures. The used approach aimed to explore a novel design that can reduce the constraints of flat PV panels while maximizing energy output. The unique 3D arrangement of the hexagonal pyramid enables the installation of mirrors inside to ease the reflection of photons and to increase energy production compared to flat panels. Furthermore, this design presents an opportunity to incorporate a water capture and heating system, thereby increasing the system's overall usage.