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.

Abstract
Industry 5.0 complements the Industry 4.0 approach by enabling the transition of industry digitization to a sustainable, human-centered and resilient paradigm. This paper delves into the exploration of enabling technologies that facilitate both Industry 4.0 and Industry 5.0 in the context of supporting supply chain (SC) logistics. The paper defines the principles of Logistics 5.0, which focuses on smart logistics systems for customized distribution, transportation, inventory management and warehousing by emphasizing interconnectivity, digitization, and optimization across SC operations. The traditional logistics framework requires innovative solutions grounded in emerging Industry 5.0 technologies capable of capturing and processing extensive datasets to empower decision-making based on information and knowledge. A comprehensive research has enabled to critically analyze enabling Industry 5.0 technologies by assessing their application status through real-case scenarios within SC Logistics 5.0. Furthermore, the paper identifies research gaps in the reviewed technologies by outlining promising areas for each Industry 4.0 technology. This guidance aims to direct future studies toward the practical application of technologies in supporting Logistics 5.0.

Abstract
Indoor air quality (IAQ) problems in school environments are very common and have significant impacts on students' performance, development and health. Indoor air conditions depend on the adopted ventilation practices, which in Mediterranean countries are essentially based on natural ventilation controlled through manual window opening. Citizen science projects directed to school communities are effective strategies to promote awareness and knowledge acquirement on IAQ and adequate ventilation management. Our multidisciplinary research team has developed a framework-SchoolAIR-based on low-cost sensors and a scalable IoT system architecture to support the improvement of IAQ in schools. The SchoolAIR framework is based on do-it-yourself sensors that continuously monitor air temperature, relative humidity, concentrations of carbon dioxide and particulate matter in school environments. The framework was tested in the classrooms of University Fernando Pessoa, and its deployment and proof of concept took place in a high school in the north of Portugal. The results obtained reveal that CO2 concentrations frequently exceed reference values during classes, and that higher concentrations of particulate matter in the outdoor air affect IAQ. These results highlight the importance of real-time monitoring of IAQ and outdoor air pollution levels to support decision-making in ventilation management and assure adequate IAQ. The proposed approach encourages the transfer of scientific knowledge from universities to society in a dynamic and active process of social responsibility based on a citizen science approach, promoting scientific literacy of the younger generation and enhancing healthier, resilient and sustainable indoor environments.

Abstract
Optical tweezers are an interesting tool to enable single cell analysis, especially when coupled with optical sensing and advanced computational methods. Nevertheless, such approaches are still hindered by system operation variability, and reduced amount of data, resulting in performance degradation when addressing new data sets. In this manuscript, we describe the deployment of an automatic and intelligent optical tweezers setup, capable of trapping, manipulating, and analyzing the physical properties of individual microscopic particles in an automatic and autonomous manner, at a rate of 4 particle per min, without user intervention. Reproducibility of particle identification with the help of machine learning algorithms is tested both for manual and automatic operation. The forward scattered signal of the trapped PMMA and PS particles was acquired over two days and used to train and test models based on the random forest classifier. With manual operation the system could initially distinguish between PMMA and PS with 90% accuracy. However, when using test datasets acquired on a different day it suffered a loss of accuracy around 24%. On the other hand, the automatic system could classify four types of particles with 79% accuracy maintaining performance (around 1% variation) even when tested with different datasets. Overall, the automated system shows an increased reproducibility and stability of the acquired signals allowing for the confirmation of the proportionality relationship expected between the particle size and its friction coefficient. These results demonstrate that this approach may support the development of future systems with increased throughput and reliability, for biosciences applications.

Abstract