Abstract
Weather change plays an important role in work-related accidents, it impairs people's cognitive abilities, increasing the risk of injuries and accidents. Furthermore, weather conditions can cause an increase or decrease in daily sales in the retail sector by influencing individual behaviors. The increase in transactions, in turn, leads employees to fatigue and overload, which can also increase the risk of injuries and accidents. This work aims to conduct a case study in a company in the retail sector to verify whether the transactions records in stores and the weather conditions of each district in mainland Portugal impact the occurrence of work accidents, as well as to perform predictive analysis of the occurrence or non-occurrence of work accidents in each district using these data and comparing different machine learning techniques. The correlation analysis of the occurrence or non-occurrence of work accidents with weather conditions and some transactions pointed out the nonexistence of correlation between the data. Evaluating the precision and the confusion matrix of the predictive models, the study indicates a predisposition of the models to predict the non-occurrence of work accidents to the detriment of the ability to predict the occurrence of work accidents.

Abstract
Purpose: This study aims to propose an architecture and presents the implementation of a unified chatbot that faces the challenges of heterogeneous communication channels. This approach enables the interaction with the chatbot to be carried out over multiple communication media on a single platform. Design/methodology/approach: The chatbot was embedded in a unified communications framework. Furthermore, it has been developed and tested using the information and communications technology (ICT)Core platform. Three test scenarios have been considered in the context of a digital marketing company, which include the use of multiple channels such as text, audio and e-mail. Usability and empirical tests were performed to collect both qualitative and quantitative data. Findings: The results indicate that the proposed model improves the completion rate and enables the chatbot to interact with the customer by capturing information over multiple channels. The findings also reveal that digital marketing organizations can use a unified chatbot in their marketing campaigns, which contributes to improving the quality of customer interaction, message personalization and continuous learning throughout the process. Originality/value: While the use of a chatbot is a relatively common practice among companies, its integration into unified communications networks is an emerging topic. Proposals for integration into a unified communication channel have mainly focused on access to the same account and conversations from multiple devices or access platforms. This approach, while useful, does not allow for the integration of information from multiple sources. Alternatively, an integrated architecture is suggested in which a chatbot obtains knowledge from multiple sources and uses it to increase the quality of communication with the customer. © 2024, Emerald Publishing Limited.

Abstract
Currently, the transmission system operators (TSOs) from Portugal and Spain do not procure a frequency containment reserve (FCR) through market mechanisms. In this context, a virtual power plant (VPP) that aggregates sources, such as wind and solar power and hydrogen electrolyzers (HEs), would benefit from future participation in this ancillary service market. The methodology proposed in this paper allows for quantifying the revenues of a VPP that aggregates wind and solar power and HEs, considering the opportunity costs of these units when reserving power for FCR participation. The results were produced using real data from past FCR market sessions. Using market data from 2022, a VPP that aggregates half of the HEs and is expected to be connected in the country by 2025 will have revenues over EUR 800k, of which EUR 90k will be HEs revenues.

Abstract
TPS621 Background: Breast cancer treatments often pose challenges in balancing efficacy with quality of life. The CINDERELLA Project pioneers an artificial intelligence (AI)-driven approach (CINDERELLA APP) for shared decision-making process, aiming to harmonise locoregional therapeutic interventions with breast cancer patients' expectations about aesthetic outcomes. The CINDERELLA clinical trial aims to establish a new standard in patient-centred care by bridging the gap between clinical treatment options and patient expectations through innovative technology. The trial focuses on evaluating the effectiveness of the CINDERELLA APP in improving patient satisfaction regarding locoregional treatment aesthetic outcomes, aligning patient expectations with real-world results, and assessing its impact on overall quality of life and psychological well-being. Methods: Trial design and statistical methods: This international multicentric interventional randomised controlled open-label clinical trial will recruit and randomise patients into two groups: one receiving standard treatment information and the other using the AI-powered CINDERELLA APP. The primary objective is to assess the levels of agreement among patients' expectations regarding the aesthetic outcome before and 12 months after locoregional treatment. The trial will also evaluate the aesthetic outcome level of agreement between the AI evaluation tool and self-evaluation. The impact of the intervention on aligning expectations with outcomes will be evaluated using the Wilcoxon signed-rank test. The improvement in classification of aesthetic results post-intervention will be measured by calculating the Weighted Cohen's kappa. Outcomes across different groups will be compared using statistical tests and bootstrap methods. CANKADO functions as the base system, allowing doctors to supervise APP content for patients and handle data gathering, while upholding principles of privacy, data security, and ethical AI practices. Intervention planned: Using the CINDERELLA APP, the patient will have access to supervised medical information approved by breast cancer experts, and the AI system will match patient's information to pictures showing the potential aesthetic outcome (spectrum of good-poor) according to different locoregional approach. Major eligibility criteria: Non-metastatic breast cancer patients eligible for either breast-conserving surgery or mastectomy with immediate reconstruction. Current enrollment: Recruitment is currently open at six study sites. The recruitment started on 8 August 2023, aiming to enroll at least 515 patients/arm. As of January 26, 2024, clinical study sites have successfully randomised 177 patients. Clinical trial information: NCT05196269 .

Abstract
Research have been driven by the increased demand for delivery and pick-up services to develop new formulations and algorithms for solving Vehicle Routing Problems (VRP). The main objective is to create algorithms that can identify paths considering execution time in real-world scenarios. This study focused on using the Guided Local Search (GLS) metaheuristic available in OR-Tools to solve the Capacitated Vehicle Routing Problem with Time Windows using the Solomons instances. The execution time was used as a stop criterion, with short runs ranging from 1 to 10 s and a long run of 360 s for comparison. The results showed that the GLS metaheuristic from OR-Tools is applicable for achieving high performance in finding the shortest path and optimizing routes within constrained execution times. It outperformed the best-known solutions from the literature in longer execution times and even provided a close-to-optimal solution within 10 s. These findings suggest the potential application of this tool for dynamic VRP scenarios that require faster algorithms.

Abstract
Purpose - This study, grounded in social cognitive career theory, aims to investigate the effects of the change to remote work during the COVID-19 pandemic on job security and job quality in Portugal. Design/methodology/approach - It adopts a quantitative methodology by conducting a nationwide geographical study. The sample consists of 2,001 employees working in companies registered in Portugal. It explores the impact of the change to remote work on job quality and job security. In addition, it explores the relevance of demographic, organizational and social factors to explain this relationship. Findings - The fi ndings reveal that the change to remote work has influenced the perception of job quality but not job security. Furthermore, demographic, organizational and social variables are factors that influence this perception. Research limitations/implications - Implications that digitalization can have on job security and quality, especially among the population with lower levels of education and more precarious working conditions, should be explored. It is also important to replicate this study in other countries, especially in emerging economies. Practical implications - By investigating job security, the study offers insights into the stability and predictability of employment during crises and disruptive events. By examining job quality, it delves into the multifaceted nature of work satisfaction, including factors like work-life balance, autonomy and fulfilment. Practically, the study provides valuable guidance for policymakers, organizations and individuals navigating remote work environments. Social implications - Understanding the implications for job security allows policymakers to design supportive policies and interventions to mitigate potential negative impacts on employment stability.Originality/value - This study uses a sufficiently comprehensive national sample to determine the impact of COVID-19 on employment. It offers both theoretical and practical contributions to increase knowledge about the phenomenon and provides a relevant guide for policymakers to adopt measures to mitigate the effects of the transition to remote work.