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Objective: This study investigates the applicability of systematic methods in the identification and evaluation of studies on sustainable urban mobility, providing subsidies to guide managers and policymakers in the development of efficient and environmentally responsible public policies.   Method: The methodology adopted for this research comprises a Systematic Literature Review (SLR) associated with the Fuzzy-TOPSIS method, a multi-criteria model capable of evaluating and prioritizing studies considering the imprecision inherent in decision-making processes. The PICO technique was used to define the analysis criteria, and the PRISMA protocol ensured the transparency and replicability of the results. Six criteria were established in the qualitative analyses for treatment in the Fuzzy-TOPSIS method.   Results and Discussion: The proposed approach proved effective in selecting the most relevant studies. The discussion points to the need to integrate Fuzzy-TOPSIS with complementary methods, such as DEMATEL and Social Network Analysis (SNA), in order to improve the modeling of causal relationships and strengthen the reliability of prioritization.   Research Implications: The results offer important insights for urban planning and the formulation of public policies, contributing to energy efficiency, reducing GHG emissions and improving the quality of public transport.   Originality/Value: The innovation of this study lies in the combination of quantitative and qualitative approaches to the analysis of sustainable mobility, providing a robust benchmark that can positively influence practices and strategies in urban management.

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In real-world scenarios, numerous phenomena generate a series of events that occur in continuous time. Point processes provide a natural mathematical framework for modeling these event sequences. In this comprehensive survey, we aim to explore probabilistic models that capture the dynamics of event sequences through temporal processes. We revise the notion of event modeling and provide the mathematical foundations that underpin the existing literature on this topic. To structure our survey effectively, we introduce an ontology that categorizes the existing approaches considering three horizontal axes: modeling, inference and estimation, and application. We conduct a systematic review of the existing approaches, with a particular focus on those leveraging deep learning techniques. Finally, we delve into the practical applications where these proposed techniques can be harnessed to address real-world problems related to event modeling. Additionally, we provide a selection of benchmark datasets that can be employed to validate the approaches for point processes.

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Evaluating and documenting the robustness of forecasting models to different input conditions is important for their responsible deployment in real-world applications. Time series forecasting models often exhibit degraded performance in the form of unusually large errors, high uncertainty, or hubris (high errors coupled with low uncertainty). Traditional stress testing approaches rely on manually designed adverse scenarios that fail to systematically identify unknown stress factors, in which data characteristics indicate potential issues. To overcome this limitation, this paper introduces MAST (Meta-learning and data Augmentation for Stress Testing), a novel method for stress testing forecasting models. MAST leverages model outputs (error scores and prediction intervals) to automatically identify and characterize input conditions that induce stress. Specifically, MAST is a binary probabilistic classifier that predicts the likelihood of forecasting model stress based on time series features. An additional contribution is a novel time series data augmentation approach based on oversampling or synthetic time series generation, that improves the information about stress factors in the input space, resulting in increased stress classification performance. Experiments were conducted using 6 benchmark datasets containing a total of 97.829 time series. We demonstrate how MAST is able to identify and explain input conditions that lead to manifestations of stress, namely large errors, high uncertainty, or hubris.

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This chapter proposes a day-ahead scheduling framework in an energy hub (EH), integrating different energy conversion and storage technologies to efficaciously fulfill various types of load demands. The mentioned EH is capable of synchronously managing electrical, cooling, and heat load demands. The system is equipped with a combined heat and power (CHP) generating unit that efficiently supplies both heat and electricity. Furthermore, there are an electric heat pump and a boiler that also supply the heating load, while the heater is specifically employed for direct heating usage. The system includes an absorption chiller to supply a cooling load. This chiller absorbs waste heat from the CHP unit, resulting in improved energy efficiency. Battery storage systems enable the efficient use of energy by storing surplus power during times of low demand for future consumption. In addition, solar photovoltaic panels are included to capture renewable energy, therefore decreasing reliance on traditional energy sources and mitigating environmental consequences. The EH also includes a saltwater desalination technology operating together with the energy network to ensure the supply of freshwater, which is especially vital in dry areas. The desalination process is fueled by both renewable and produced thermal energy, thus maximizing resource use and reducing operating costs. The presented scheduling model has been formulated within a mixed-integer linear programming framework, implemented in GAMS, and solved by using the CPLEX solver to ensure optimal operation and minimum computational burden. This chapter provides a broad guideline of how the integrated systems operate. © 2025 Elsevier B.V., All rights reserved.

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