Abstract
In this paper, all-optical logic functions, implemented with a single SOA-based Mach-Zehnder interferometer (SOA-MZI), are demonstrated experimentally and through numerical simulations. The proposed optical configuration is capable to carry out four logic operations, using simultaneously both output ports of the SOA-MZI. This may reduce the power cost and make possible to obtain simultaneously multi logic functions. The performance of such an architecture is assessed measuring the obtained extinction ratio (ER) for each Boolean function. The potential of integration makes the proposed scheme attractive to perform optical signal processing operations in next generation photonic transmission systems.

Abstract
The present paper describes the state of the art related to IIoT Devices and Cyber-Physical systems and presents a use case related to predictive maintenance. Industry 4.0 is the boost for smart manufacturing and demands flexibility and adaptability of all devices/machines in the shop floor. The machines must become smart and interact with other machines inside and outside the industries/factories. The predictive maintenance is a key topic in this industrial revolution. The reason is based on the idea that smart machines must be capable to automatically identify and predict possible faults and actuate before they occur. Vibrations can be problematic in electrical motors. For this reason, we address an experimental study associated with an automatic classification procedure, Based on two distinct architectures to detect anomalies: National Instruments acquisition board and an Arduino board with an EtherCAT Shield. With the implementation of a supervised learning model, both approaches corroborate the applicability and usefulness of this machine learning algorithm to predict vibration faults.

Abstract
This study presents a dataset containing three layers of data that are useful for body position classification and all uses related to it. The PoPu dataset contains simultaneously collected data from two different sensor sheets-one placed over and one placed under a mattress; furthermore, a segmentation data layer was added where different body parts are identified using the pressure data from the sensors over the mattress. The data included were gathered from 60 healthy volunteers distributed among the different gathered characteristics: namely sex, weight, and height. This dataset can be used for position classification, assessing the viability of sensors placed under a mattress, and in applications regarding bedded or lying people or sleep related disorders. Dataset The dataset is available on GitHub: https://github.com/rdionisio1403/PoPu/. Dataset License The dataset is available under Creative Commons (CC0).

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
Efficient use of resources is a critical factor in almond crops. Technological solutions can significantly contribute to this purpose. The VeraTech project aims to explore the integration of sensors and cloud-based technologies in almond crops for efficient use of resources and reduction of environmental impact. It also makes available a set of relevant and impactful performance indicators in agricultural activity, which promote productivity gains supported by efficient use of resources. The proposed solution includes a sensor network in the almond crops, the transmission of data and its integration in the cloud, making this data available to be consumed, processed, and presented in the monitoring and alerts dashboard. In the current state of the development, several data are collected by sensors, transmitted over LoRaWAN, integrated using AWS IoT Core, and monitored and analysed through a cloud business analytics service. This project is implemented on a farm located in the Beira-Baixa region of Portugal and involves a partnership between Vera Cruz (owner of the farm), Veratech, and the Polytechnic Institute of Castelo Branco.

Abstract
This study presents the SENSOMATT sensor sheet, a novel, non-invasive pressure monitoring technology intended for placement beneath a mattress. The development and design process of the sheet, which includes a novel sensor arrangement, material selection, and incorporation of an elastic rubber sheet, is investigated in depth. Highlighted features include the ability to adjust to varied mattress sizes and the incorporation of AI technology for pressure mapping. A comparison with conventional piezoelectric contact sensor sheets demonstrates the better accuracy of the SENSOMATT sensor for monitoring pressures beneath a mattress. The report highlights the sensor network's cost-effectiveness, durability, and enhanced data measurement, alongside the problems experienced in its design. Evaluations of performance under diverse settings contribute to a full understanding of its potential pressure injury prediction and patient care applications. Proposed future paths for the SENSOMATT sensor sheet include clinical validation, more cost and performance improvement, wireless connection possibilities, and improved long-term monitoring data analysis. The study concludes that the SENSOMATT sensor sheet has the potential to transform pressure injury prevention techniques in healthcare.