Abstract
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.

Abstract
We introduce Fed-VFDT, a federated adaptation of the Very Fast Decision Tree (VFDT) algorithm for classification over streaming data. While VFDT is a widely adopted online learning algorithm, its sequential and order-sensitive nature poses challenges in federated settings, marked by statistical heterogeneity and communication constraints. Fed-VFDT addresses these issues by having each client incrementally train a local VFDT and report split statistics to a central server when a leaf satisfies the Hoeffding criterion. The server selects a global splitting feature by aggregating clients' proposals according to a configurable strategy: quorum, merit-based selection, or majority voting. Once a feature is selected, it is broadcast to all clients, which apply the split at the corresponding tree path using their locally computed thresholds. We evaluate Fed-VFDT against its centralized counterpart using predictive and structural metrics, demonstrating that it maintains comparable performance while reducing communication and preserving synchronized tree growth.

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This chapter describes the IMPACTOUR co-creation method, which is developed to enhance the impact of cultural tourism in various destinations. The method utilizes effective strategies and actions to monitor and increase the impact of cultural tourism. The primary objective of the IMPACTOUR technique is to support decision-makers in improving the sustainability and competitiveness of cultural tourists in their destinations. The method involves collecting and analyzing data from diverse sources, including tourism stakeholders and specifically local communities to create a comprehensive decision-making system. The resulting recommendations aim to promote the positive impacts of cultural tourism while minimizing negative effects and fostering long-term development. Ultimately, the IMPACTOUR method seeks to assist destinations and attractions in becoming more competitive and attractive to cultural visitors, while ensuring their long-term sustainability.

Abstract
Background: In modern, high-speed work settings, the significance of mental health disorders is increasingly acknowledged as a pressing health issue, with potential adverse consequences for organizations, including reduced productivity and increased absenteeism. Over the past few years, various mental health management solutions, such as biofeedback applications, have surfaced as promising avenues to improve employees' mental well-being. However, most studies on these interventions have been conducted in controlled laboratory settings. Objective: This review aimedtosystematicallyidentify and analyzestudies that implementedbiofeedback-based interventions in real-world occupational settings, focusing on their effectiveness in improving psychological well-being and mental health. Methods: A systematic review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched PubMed and EBSCO databases for studies published between 2012 and 2024. Inclusion criteria were original peer-reviewed studies that focused on employees and used biofeedback interventions to improve mental health or prevent mental illness. Exclusion criteria included nonemployee samples, lack of a description of the intervention, and low methodological quality (assessed using the Physiotherapy Evidence Database [PEDro] checklist). Data were extracted on study characteristics, intervention type, physiological and self-reported outcomes, and follow-up measures. Risk of bias was assessed, and VOSviewer was used to visualize the distribution of research topics. Results: A total of 9 studies met the inclusion criteria. The interventions used a range of delivery methods, including traditional biofeedback, mobile apps, mindfulness techniques, virtual reality, and cerebral blood flow monitoring. Most studies focused on breathing techniques to regulate physiological responses (eg, heart rate variability and respiratory sinus arrhythmia) and showed reductions in stress, anxiety, and depressive symptoms. Mobile and app-directed interventions appeared particularly promising for improving resilience and facilitating recovery after stress. Of the 9 studies, 8 (89%) reported positive outcomes, with 1 (11%) study showing initial increases in stress due to logistical limitations in biofeedback access. Sample sizes were generally small, and long-term follow-up data were limited. Conclusions:Biofeedback interventions in workplace settings show promising short-term results in reducing stress and improving mental health, particularly when incorporating breathing techniques and user-friendly delivery methods such as mobile apps. However, the field remains underexplored in occupational contexts. Future research should address adherence challenges, scalability, cost-effectiveness, and long-term outcomesto support broader implementation of biofeedback as a sustainable workplace mental health strategy.