Abstract
The aim of this study was to conduct a qualitative analysis research that will support the internal and external analysis, as well as the strategic options to be developed for a Marketing Plan for the Polytechnic Institute of Viana do Castelo. The methodology used was through exploratory interviews using a script based on nine key dimensions for creating a marketing plan. The script was applied through face-to-face interviews with the president of the Polytechnic Institute of Viana do Castelo. We obtained six interviews that could be analyzed in the nine dimensions. It was possible to understand that the elements suggest some changes in the different dimensions of the script. One of the points with the most observations for improvement was communication, mostly internal. However, all the improvements and growth that the institute has achieved over the years were acknowledged. It was also possible to realize that the six people interviewed have the same perspective and make similar observations on the different questions asked. With the interviews, it will be possible to outline the best structure and path for the strategic marketing plan. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Abstract

Abstract
The presence of missing data poses a common challenge for time series analysis in general since the most usual requirement is that the data is equally spaced in time and therefore imputation methods are required. For time series of counts, the usual imputation methods which usually produce real valued observations, are not adequate. This work employs Bayesian principles for handling missing data within time series of counts, based on first-order integer-valued autoregressive (INAR) models, namely Approximate Bayesian Computation (ABC) and Gibbs sampler with Data Augmentation (GDA) algorithms. The methodologies are illustrated with synthetic and real data and the results indicate that the estimates are consistent and present less bias when the percentage of missing observations decreases, as expected. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
The footwear industry, a sub-sector of textile industrial sector, faces increased pressures towards higher levels of sustainability and circularity along all the value chain. Along the last decades, shoe products have become more complex products, integrating a greater number of components, materials diversity and often long supply-chains related to cost reduction and production or sourcing delocalization strategies. Full value-chain digitalization, as a cornerstone of Industry 4.0 paradigm, plays a key role for leveraging more sustainable and circular products, namely by traceability operationalization and forthcoming instruments such as Digital Product Passport. This research studied, via a state-of-art framing of the challenges followed by qualitative approach, how Industry 4.0 technologies can support the development of new services that contribute to sustainable and circular practices in footwear companies. An interview-based survey was conducted to 6 footwear companies, to map the adoption level of Industry 4.0 technologies and cross-linking to circular services business models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
In the field of gaming artificial intelligence, selecting the appropriate machine learning approach is essential for improving decision-making and automation. This paper examines the effectiveness of deep reinforcement learning (DRL) within interactive gaming environments, focusing on complex decision-making tasks. Utilizing the Unity engine, we conducted experiments to evaluate DRL methodologies in simulating realistic and adaptive agent behavior. A vehicle driving game is implemented, in which the goal is to reach a certain target within a small number of steps, while respecting the boundaries of the roads. Our study compares Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) in terms of learning efficiency, decision-making accuracy, and adaptability. The results demonstrate that PPO successfully learns to reach the target, achieving higher and more stable cumulative rewards. Conversely, SAC struggles to reach the target, displaying significant variability and lower performance. These findings highlight the effectiveness of PPO in this context and indicate the need for further development, adaptation, and tuning of SAC. This research contributes to developing innovative approaches in how ML can improve how player agents adapt and react to their environments, thereby enhancing realism and dynamics in gaming experiences. Additionally, this work emphasizes the utility of using games to evolve such models, preparing them for real-world applications, namely in the field of vehicles' autonomous driving and optimal route calculation.

Abstract
In the era of Industry 4.0, fault tolerance is essential for maintaining the robustness and resilience of industrial systems facing unforeseen or undesirable disturbances. Current methodologies for fault tolerance stages namely, detection, diagnosis, and recovery, do not correspond with the accelerated technological evolution pace over the past two decades. Driven by the advent of digital technologies such as Internet of Things, cloud and edge computing, and artificial intelligence, associated with enhanced computational processing and communication capabilities, local or monolithic centralized fault tolerance methodologies are out of sync with contemporary and future systems. Consequently, these methodologies are limited in achieving the maximum benefits enabled by the integration of these technologies, such as accuracy and performance improvements. Accordingly, in this paper, a collaborative fault tolerance methodology for cyber-physical systems, named Collaborative Fault * (CF*), is proposed. The proposed methodology takes advantage of the inherent data analysis and communication capabilities of cyber-physical components. The proposed methodology is based on multi-agent system principles, where key components are self-fault tolerant, and adopts collaborative and distributed intelligence behavior when necessary to improve its fault tolerance capabilities. Experiments were conducted focusing on the fault detection stage for temperature and humidity sensors in warehouse racks. The experimental results confirmed the accuracy and performance improvements under CF* compared with the local methodology and competitiveness when compared with a centralized approach.