Abstract
High-impedance faults (HIFs) in medium-voltage distribution networks represent an ongoing challenge for protection systems, accounting for 5% to 10% of ground faults and posing risks to both electrical infrastructure integrity and public safety. Despite more than four decades of research, HIF detection remains problematic, particularly in modern networks integrating inverter-based resources (IBR), where fault current variability can be mistaken for normal fluctuations or other system events. Conventional overcurrent relays have demonstrated less than 20% effectiveness in detecting these faults. This paper addresses the modelling of HIFs in real-time simulators, explicitly using the OPAL-RT platform, and proposes an optimised model that incorporates the random nature of resistances and DC sources associated with HIFs. A comparative analysis between real-time simulation and offline simulation in Simulink is presented, demonstrating the consistency and accuracy of the implemented model. The results confirm that the optimised model not only accurately replicates theoretical behaviours but also meets real-time execution requirements, thereby contributing to the improvement of protection schemes and fault detection studies in modern distribution systems.

Abstract
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
Widespread fleet electrification is concentrating electricity demand at commercial depots that face volatile prices, tight feeder limits and scarce chargers. This paper proposes a forecast-aware mixed-integer linear program (MILP) that co-optimises vehicle charging, battery-energy-storage dispatch and photovoltaic self-consumption. The model minimises energy cost plus state-of-charge (SOC) penalties, while enforcing charger exclusivity, battery-health bounds and continuous priority weights. It is evaluated on a 48-interval weekday data set comprising 20 electric vehicles, two 11?kW chargers, half-hourly solar forecasts, factory-load predictions and Iberian day-ahead prices. Relative to an uncontrolled first-come/first-served baseline, the optimiser cuts total charging expenditure by 49?%, inceases SOC compliance from 35?% to 65?%, increases PV self-consumption from 33.4?% to 35.5?% and lowers grid-attributed CO2 emissions by 66?%. A modest rise in instantaneous demand is held within transformer limits through strategic battery discharge. These results confirm that predictive scheduling transforms depot charging from a passive load into a cost-optimal, carbon-aware asset and motivate future extensions that embed stochastic forecasts, vehicle-to-grid services. route-energy coupling and Keywords. EV fleet charging; mixed-integer linear programming; battery energy self-consumption; predictive scheduling

Abstract
This document addresses and develops a framework tool to solve reliability issues in the calculation of processing times for components, using their dimensions. This framework was implemented in a real industrial setting, specifically in a multinational company that manufactures highly customizable electric motors according to customer requirements. After identifying the most critical components and their respective process diagrams, a prototype of the proposed framework was developed to calculate production time. Additionally, another prototype was developed to aid in visualizing the company’s workload. As a result of this work, various improvements were observed in the company, including a 42% reduction in the time required to create workflows and an increase in the reliability and dependability of process times. The framework significantly enhanced operational efficiency, streamlined production processes, and provided a robust solution for managing the complexities of custom manufacturing, demonstrating its effectiveness in a real-world industrial environment. Furthermore, this approach has the potential to be adapted for use in other industries facing similar challenges. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
The growing integration of Inverter-Based Distributed Generation (IIDG) in distribution networks poses significant challenges for protection systems, as it alters the usual short-circuit patterns and impacts their effectiveness. International standards such as IEEE 1547-2018 and the German network code VDE-AR-N 410 for distribution networks, along with the IEEE 2800-2021 standard for transmission systems, have set criteria for the connection of IIDGs and their behavior under fault conditions, including the injection of reactive current and current limiting. These standards have driven the development of new control models for fault response: the conventional model, according to IEEE 1547-2018, requires IIDGs to inject only balanced positive sequence currents to provide voltage support to the network, while the advanced model, in accordance with VDE-AR-N 410 and IEEE 2800-2021, demands the injection of both positive and negative sequence currents to enhance voltage support during unbalanced faults. This article explores how these fault response models affect the efficiency of traditional protection schemes, including overcurrent and directional elements, and develops a methodology for modeling the inverter's response to faults. This approach enables the replication and application of international standards for the design of new protection schemes, facilitating their adoption by researchers in the field.

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.