Abstract
The grape moth is a common pest that affects grapevines by consuming both fruit and foliage, rendering grapes deformed and unsellable. Integrated pest management for the grape moth heavily relies on pheromone traps, which serve a crucial function by identifying and tracking adult moth populations. This information is then used to determine the most appropriate time and method for implementing other control techniques. This study aims to find the best method for detecting small insects. We evaluate the following recent YOLO models: v5, v6, v7, and v8 for detecting and counting grape moths in insect traps. The best performance was achieved by YOLOv8, with an average precision of 92.4% and a counting error of 8.1%.

Abstract
The digital transformation of manufacturing activities is expected to bring large societal benefits in terms of productivity and sustainability. However, uptake of digital technologies is slower than desirable. As a result, governments are taking action to try to overcome some of the barriers to adoption. However, the mechanisms through which government may act are quite diverse. In this paper, we compare the national strategies across the 27 countries members of the European Union. We map each country's initiative to 14 barriers to the adoption of digital technologies in manufacturing observed in the literature. We observe that most institutional efforts focus on providing funding, developing new regulatory frameworks related to data privacy and security, and creating human capital. Some known barriers to adoption observed at the firm level, such as the lack of off-the-shelf solutions, or the need for retrofitting old equipment, are largely overlooked. We do not find any relationship between the number of initiatives proposed by each country, and the country's existing level of digitalization. We conclude by proposing several policy recommendations, as well as directions for future research.

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
In recent years, the way that machine learning is used has undergone a paradigm shift driven by distributed and collaborative learning. Several approaches have emerged to enable pervasive computing and distributed learning in ubiquitous Internet of Things (IoT) systems. Numerous decentralized strategies have been proposed to deal with the limitations of centralized learning, including privacy and latency due to sharing local data, while utilizing distributed computations as a promising substitute to centralized learning. However, such distributed learning schemes come with new security and privacy concerns that should be addressed. Thus, in this paper, we first provide an overview for the emerging paradigms developed for distributed learning. Then, we performed a comprehensive survey for the privacy and security challenges associated with distributed learning along with the presented solutions to overcome them. Furthermore, we highlight key challenges and open future research directions toward implementing more robust distributed systems.