Abstract
The evolution of urban morphology and urban mobility reveals a complex and multidimensional relation that has been historically linked to the evolution of technology and its influence on people's behaviour, desires, and needs. The increasing level of digitalization of human interactions in both social and work environments has created a new paradigm for urban mobility. Alongside, sustainability concerns are also accelerating the design of new policies for improving citizens' quality of life in urban areas. To address this new paradigm, municipalities need to consider new methodologies encompassing the different dimensions of the urban environment. This can be achieved if key stakeholders participate in co-creating and co-designing new solutions for urban mobility. In this paper we propose a multidisciplinary approach to these problems, supported by service-dominant logic concepts. The approach was used to design the CoDUMIS framework that brings together four dimensions of urban areas (social, urban, technological, and organizational). The application of the framework to four distinct cases, in Portuguese municipalities, resulted in a set of guidelines that help municipalities to improve their services and policies in a participatory environment, involving all the stakeholders.

Abstract
Lot-sizing and scheduling in a job shop environment is a fundamental problem that appears in many industrial settings. The problem is very complex, and solutions are often needed fast. Although many solution methods have been proposed, with increasingly better results, their computational times are not suitable for decision-makers who want solutions instantly. Therefore, we propose a novel greedy heuristic to efficiently generate production plans and schedules of good quality. The main innovation of our approach represents the incorporation of a simulation-based technique, which directly generates schedules while simultaneously determining lot sizes. By utilising priority rules, this unique feature enables us to address the complexity of job shop scheduling environments and ensures the feasibility of the resulting schedules. Using a selection of well-known rules from the literature, experiments on a variety of shop configurations and complexities showed that the proposed heuristic is able to obtain solutions with an average gap to Cplex of 4.12%. To further improve the proposed heuristic, a cooperative coevolutionary genetic programming-based hyper-heuristic has been developed. The average gap to Cplex was reduced up to 1.92%. These solutions are generated in a small fraction of a second, regardless of the size of the instance.

Abstract
Setting labor standards is an important topic to operational and strategic planning which requires the time studies establishment. This paper applies the statistical method for the definition of a sample size in order to define a reliable cycle time for a real industrial process. For the case study it is considered a welding process performed by a single operator that does the load and unload of components in 4 different welding machines. In order to perform the time studies, it is necessary to collect continuously data in the production line by measuring the time taken for the operator to perform the task. In order to facilitate the measurements, the task is divided into small elements with visible start and end points, called Measurement Points, in which the measurement process is applied. Afterwards, the statistical method enables to determine the sample size of observations to calculate the reliable cycle time. For the welding process presented, it is stated that the sample size defined through the statistical method is 20. Thus, these time observations of the task are continuously collected in order to obtain a reliable cycle time for this welding process. This time study can be implemented in similar way in other industrial processes.

Abstract
Since digital media has become increasingly popular, video processing has expanded in recent years. Video processing systems require high levels of processing, which is one of the challenges in this field. Various approaches, such as hardware upgrades, algorithmic optimizations, and removing unnecessary information, have been suggested to solve this problem. This study proposes a video saliency map based method that identifies the critical parts of the video and improves the system's overall performance. Using an image registration algorithm, the proposed method first removes the camera's motion. Subsequently, each video frame's color, edge, and gradient information are used to obtain a spatial saliency map. Combining spatial saliency with motion information derived from optical flow and color-based segmentation can produce a saliency map containing both motion and spatial data. A nonlinear function is suggested to properly combine the temporal and spatial saliency maps, which was optimized using a multi-objective genetic algorithm. The proposed saliency map method was added as a preprocessing step in several Human Action Recognition (HAR) systems based on deep learning, and its performance was evaluated. Furthermore, the proposed method was compared with similar methods based on saliency maps, and the superiority of the proposed method was confirmed. The results show that the proposed method can improve HAR efficiency by up to 6.5% relative to HAR methods with no preprocessing step and 3.9% compared to the HAR method containing a temporal saliency map.

Abstract
In the health sector, the implementation of qualitative data research is very important to improve overall services. However, the use of these methods remains relatively unexplored when compared to quantitative analyses. This article describes the qualitative data analysis process that is based on the description, analysis and interpretation of data. It also describes a practical case study and the use of NVivo software to assist in the development of a theory-based qualitative analysis process. This article intends to be a step forward in the use of qualitatively based methodologies in future research in the health sector.

Abstract
Societies throughout today’s global village are increasingly aware of the social injustices that minorities face, and immigrants are no exception. Combined with the lack of adaptation resources and the prejudice of non-migrant residents, immigrants may feel powerless in foreign places as they try to find comfort and security in new and unfamiliar environments. It is increasingly urgent to address immigrant issues, considering the crucial role of enhancing diversity, combating prejudice, and raising awareness of minority experiences. This systematic literature review investigates the innovative use of gamification in exploring and addressing the experiences and issues immigrants face. The review follows the PRISMA statement guidelines and checklist. Scopus, CINAHL, and Medline databases were searched, resulting in 17 relevant articles that were carefully analyzed. This research highlights the diverse applications of gamification in studying immigrant experiences via role-playing, interactive storytelling, and empathy-building simulations. This work explores the potential of gamified interventions in addressing pressing issues immigrants face and assesses their effectiveness in fostering empathy and intercultural communication. It also identifies gaps in the existing information sciences literature and proposes directions for future research. In conclusion, this review sheds light on the emerging field of gamification in immigration studies and games studies in the information sciences, providing valuable insights for scholars, policymakers, and practitioners working with immigrant communities worldwide.