Abstract
The fourth industrial revolution promotes Industrial Cyber Physical Systems (ICPS) as the key to achieve smart, efficient, flexible and self-organizing production plants. In a shop floor there are heterogeneous physical and logical assets that form the ICPS. But without proper communication and composition techniques the integration of these assets in ICPS is compromised. Component Based Software Engineering (CBSE) is a discipline of growing relevance for ICPS because integration and composition issues have been extensively researched in the software domain. Under the Reference Architecture for Industry 4.0 (RAMI 4.0), the Industry 4.0 Component Model inherits aspects of CBSE to specify how several industrial plant assets can form an ICPS. The technological aspects for physical assets digitalization and integration have been explored, but the I4.0 Component model lacks proposals and use cases for dealing with industrial software components. In this work we discuss the development of the Smart Component Model as a proposal for integration of software components in ICPS. Furthermore, we focus on how prediction and monitoring applications could be converted in I4.0 Components and integrated in ICPS. To sustain our proposals, we describe a real industrial case study where these developments are being applied. © 2020 IEEE.

Abstract

Abstract
Pressure ulcers are associated with significant morbidity, resulting in a decreased quality of life for the patient, and contributing to healthcare professional burnout, as well as an increase of health service costs. Their prompt diagnosis and treatment are important, and several studies have proposed solutions to help healthcare professionals in this process. This work analyzes studies that use machine-learning algorithms for risk assessment and management of preventive treatments for pressure ulcers. More specifically, it focuses on the use of machine-learning algorithms that combine information from intrinsic and extrinsic pressure-ulcer predisposing factors to produce recommendations/alerts to healthcare professionals. The review includes articles published from January 2010 to June 2021. From 60 records screened, seven articles were analyzed in full-text form. The results show that most of the proposed algorithms do not use information related to both intrinsic and extrinsic predisposing factors and that many of the approaches separately address one of the following three components: data acquisition; data analysis, and production of complementary support to well-informed clinical decision-making. Additionally, only a few studies describe in detail the outputs of the algorithm, such as alerts and recommendations, without assessing their impacts on healthcare professionals' activities.

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This experimental study focuses on the comparison between two different sensors for vibration signals: a magnetoresistive sensor and an accelerometer as a calibrated reference. The vibrations are collected from a variable speed inductor motor setup, coupled to a ball bearing load with adjustable misalignments. To evaluate the performance of the magnetoresistive sensor against the accelerometer, several vibration measurements are performed in three different axes: axial, horizontal and vertical. Vibration velocity measurements from both sensors were collected and analyzed based on spectral decomposition of the signals. The high cross-correlation coefficient between spectrum vibration signatures in all experimental measurements shows good agreement between the proposed magnetoresistive sensor and the reference accelerometer performances. The results demonstrate the potential of this type of innovative and non-contact approach to vibration data collection and a prospective use of magnetoresistive sensors for predictive maintenance models for inductive motors in Industry 4.0 applications.

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This article presents the contribution of the Polytechnic Institute of Castelo Branco (IPCB) within the scope of PPS1 of the PRODUTECH SIF program - Solutions for the Future Industry, in terms of the definition of modules for scalability, adaptation, plug-and-play, with interoperability between processes and technologies inter/intra industrial plants. More specifically, its contribution for the definition, implementation and evaluation of OPC- UA device discovery services. Adopting the CompactRIO platform developed on PPS2 as a physical basis for a Smartbox, its development was extended during the present task with a view to incorporating and validating the adopted service discovery mechanisms in the program.

Abstract
Although previous research on the e-learning system acceptance has been conducted usingUTAUT, no study followed the longitudinal approach. Accordingly, this research examines the engineering students' (N = 291) e-learning system acceptance by three years of study. The structural equation modelling analysis confirmed UTAUT relationships in each year. Effort expectancy and social influence resulted as significant predictors of behavioural intention in all three years. In contrast, performance expectancy influence got lower in later usage. Altogether, our longitudinal study presented that the UTAUT model has weakened over time. Therefore, we propose extending the UTAUT model in future research to better understand user satisfaction and positively contribute to system acceptance. Our research findings can be used for university leaders to investigate and evaluate any implemented information system acceptance through the years.