Abstract

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
To address the challenges of self-care in oncology, gamification emerges as an innovative strategy to enhance health literacy and self-care among individuals with oncological disease. This study aims to explore and map how gamification can promote health literacy for self-care of oncological diseases. A scoping review was conducted following the Joanna Briggs Institute guidelines and the PRISMA-ScR Checklist developed for scoping reviews. A comprehensive search strategy was employed across MEDLINE (R), CINAHL (R), Scopus (R), and Web of Science (R) databases, with keywords focusing on oncological patients and gamification tools applied to self-management, from inception to December 2023. Thirty studies published between 2011 and 2023 were included, with a total of 1,118 reported participants. Most interventions (n = 21) focused on the development of mobile applications. The most frequent gamification elements included customizable avatars, rewards, social interaction, quizzes, and personalized feedback. The interventions primarily targeted health literacy and patient education, symptom monitoring, management of side effects, pain control, and adherence to medication and nutrition regimens. The integration of gamification elements into digital health solutions for oncology is expanding and holds promises for supporting health literacy and self-care. Further studies, preferably longitudinal, are needed to assess the effectiveness and impact of these interventions across different oncological populations and clinical settings.

Abstract
Planning Energy communities (ECs) requires engaging members, designing business models and governance rules, and sizing distributed energy resources (DERs) for a cost-effective investment. Meanwhile, the growing share of non-dispatchable renewable generation demands more flexible energy systems. Local flexibility markets (LFMs) are emerging as effective mechanisms to procure this flexibility, granting ECs a new revenue stream. Since sizing with flexibility becomes a highly complex problem, we propose a 2-stage methodology for estimating DERs size in an EC with collective self-consumption, flexibility provision and cross-sector (CS) assets such as thermal loads and electric vehicles (EVs). The first stage computes the optimal DER capacities to be installed for each member without flexibility provision. The second stage departs from the first stage capacities to assess how to modify the initial capacities to profit from providing flexibility. The impact of data clustering and flexibility provision are assessed through a case study.

Abstract
Heterogeneous CPU-FPGA C/C++ applications may rely on High-level Synthesis (HLS) tools to generate hardware for critical code regions. As typical HLS tools have several restrictions in terms of supported language features, to increase the size and variety of offloaded regions, we propose several code transformations to improve synthesizability. Such code transformations include: struct and array flattening; moving dynamic memory allocations out of a region; transforming dynamic memory allocations into static; and asynchronously executing host functions, e.g., printf(). We evaluate the impact of these transformations on code region size using three realworld applications whose critical regions are limited by nonsynthesizable C/C++ language features.

Abstract
Among transportation researchers, pedestrian issues are highly significant, and various solutions have been proposed to address these challenges. These approaches include Multi-Criteria Decision Analysis (MCDA) and machine learning (ML) techniques, often categorized into two primary types. While previous studies have addressed diverse methods and transportation issues, this research integrates pedestrian modeling with MCDA and ML approaches. This paper examines how MCDA and ML can be combined to enhance decision-making in pedestrian dynamics. Drawing on a review of 1574 papers published from 1999 to 2023, this study identifies prevalent themes and methodologies in MCDA, ML, and pedestrian modeling. The MCDA methods are categorized into weighting and ranking techniques, with an emphasis on their application to complex transportation challenges involving both qualitative and quantitative criteria. The findings suggest that hybrid MCDA algorithms can effectively evaluate ML performance, addressing the limitations of traditional methods. By synthesizing the insights from the existing literature, this review outlines key methodologies and provides a roadmap for future research in integrating MCDA and ML in pedestrian dynamics. This research aims to deepen the understanding of how informed decision-making can enhance urban environments and improve pedestrian safety.