Abstract
Purpose The purpose of this paper is to assess the impact of a maintenance philosophy, Total Productive Maintenance (TPM), on the operational performance of the Portuguese industry, identifying how it enables the systematic reduction of waste in maintenance. Design/methodology/approach A structured questionnaire was constructed and sent to 472 Portuguese enterprises, having obtained a sample constituted of 84 valid answers. With a five-point Likert scale, it was possible to assess the impact of the TPM on five operational performance dimensions, being them: quality, flexibility, productivity, safety and costs. Findings It was found that the planned maintenance, together with education and training are the practices with the highest degree of implementation in the Portuguese industry, exceeding 70% for both. The productivity is the dimension with a higher degree of impact from the implementation of TPM and costs the dimension that suffered a lesser impact. Practical implications This paper shows and analyses the current state of TPM implementation in the Portuguese industry and it will be useful for maintenance professionals, researchers and others concerned with maintenance, in order to understand the effects of TPM implementation on the operational performance of the Portuguese industries. Originality/value The findings from this paper will be valuable for professionals who desire and are looking forward to implement an effective maintenance approach in the maintenance management system, in order to achieve the excellence in maintenance.

Abstract
The Internet revolution in 1990, followed by the data-driven and information revolution, has transformed the world as we know it. Nowadays, what seam to be 10 to 20 years ago, a science fiction idea (i.e., machines dominating the world) is seen as possible. This revolution also brought a need for new regulatory practices where user trust and artificial Intelligence (AI) discourse has a central role. This work aims to clarify some misconceptions about user trust in AI discourse and fight the tendency to design vulnerable interactions that lead to further breaches of trust, both real and perceived. Findings illustrate the lack of clarity in understanding user trust and its effects on computer science, especially in measuring user trust characteristics. It argues for clarifying those notions to avoid possible trust gaps and misinterpretations in AI adoption and appropriation.

Abstract
In this paper, it is presented a field of view analysis of a time of flight sensor, that will be applied in a mobile robotics application. The sensor was configured in order to obtain a tradeoff between reactiveness and accuracy. It was used a microcontroller development board to acquire data and a manipulator to perform the movements, assuring repeatability and accuracy in the data acquisition process. The results of this paper will be used as an input to a simulation, in order to assist in the development of a mobile robotics application and also to be applied in educational contexts. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
Pressure ulcers are skin injuries that develop mainly over bony areas as the result of prolonged pressure caused by the immobility of bedridden patients. They constitute not only a source of additional suffering for these patients but also contribute to the burnout of healthcare professionals who must maintain continuous monitoring of these patients. Data from countries such as the UK or the USA allows the cost of this problem to be estimated to be, respectively, near 2 pound billion and $80 billion. In this article, we describe the SensoMatt approach to pressure ulcer prevention and management, which is being developed as a research project that includes partners from industry, healthcare, and academia. The SensoMatt solution is centered on a pressure sheet that is placed under the patient's mattress, complemented by an online management portal and a mobile app. These provide patients and healthcare providers with an unparalleled set of services that include personalized analysis, prevention warnings and recommendations.

Abstract
In this study, we evaluated the use of small pre-trained 3D Convolutional Neural Networks (CNN) on land use and land cover (LULC) slide-window-based classification. We pre-trained the small models in a dataset with origin in the Eurosat dataset and evaluated the benefits of the transfer-learning plus fine-tuning for four different regions using Sentinel-2 L1C imagery (bands of 10 and 20m of spatial resolution), comparing the results to pre-trained models and trained from scratch. The models achieved an F1 Score of between 0.69-0.80 without significative change when pre-training the model. However, for small datasets, pre-training the model improved the classification by up to 3%.

Abstract
During a pandemic, public discussion and decision-making may be required in face of limited evidence. Data-grounded analysis can support decision-makers in such contexts, contributing to inform public policies. We present an empirical analysis method based on regression modelling and hypotheses testing to assess events for the possibility of occurrence of superspreading contagion with geographically heterogeneous impacts. We demonstrate the method by evaluating the case of the May 1st, 2020 Demonstration in Lisbon, Portugal, on regional growth patterns of COVID-19 cases. The methodology enabled concluding that the counties associated with the change in the growth pattern were those where likely means of travel to the demonstration were chartered buses or private cars, rather than subway or trains. Consequently, superspreading was likely due to travelling to/from the event, not from participating in it. The method is straightforward, prescribing systematic steps. Its application to events subject to media controversy enables extracting well founded conclusions, contributing to informed public discussion and decision-making, within a short time frame of the event occurring.