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An extended-reality (XR) platform for real-time monitoring of patients' health during surgical procedures is proposed. The proposed system provides real-time access to a comprehensive set of patients' information, which are made promptly available to the surgical team in the operating room (OR). In particular, the XR platform supports the medical staff by automatically acquiring the patient's vitals from the operating room instrumentation and displaying them in real-time directly on an XR headset. Furthermore, information regarding the patient clinical record is also shown upon request. Finally, the XR-based monitoring platform also allows displaying in XR the video stream coming directly from the endoscope. The innovative aspect of the proposed XR-based monitoring platform lies in the comprehensiveness of the available information, in its modularity and flexibility (in terms of adaption to different sources of data), ease of use, and most importantly, in a reliable communication, which are critical requirements for the healthcare field. To validate the proposed system, experimental tests were conducted using instrumentation typically available in the operating room (i.e., a respiratory ventilator, a patient monitor for intensive care, and an endoscope). The overall results showed (i) an accuracy of the data communication greater than 99 %, along with (ii) an average time response below ms, and (iii) satisfying feedback from the SUS questionnaires filled out by the physicians after intensive use. [ABSTRACT FROM AUTHOR]

In this work, a SAW resonator is characterized in terms of admittance (Y-) parameters in the temperature range spanning from 0 °C to 100 °C, with the aim of highlighting how its physical properties are affected by the temperature change. A lumped-element equivalent-circuit model is used to represent the device under test at the considered temperature conditions and a parameters extraction process based on a Lorentzian fitting is developed for the determination of the equivalent-circuit elements in the investigated temperature range. A very good agreement is observed between the performed measurements and the model simulations. The characterization process and the subsequent equivalent-circuit parameters extraction at different temperature values are described and discussed. [ABSTRACT FROM AUTHOR]

Hydrogen is a valuable fuel for the next energy scenario. Unfortunately, hydrogen is highly flammable at concentrations higher than 4% in air. This aspect makes the monitoring of H2 leaks an essential issue for safety reasons, especially in the transportation field. In this paper, nanocomposites based on Pt-doped TiO2/multiwalled carbon nanotubes (MWCNTs) have been introduced as sensitive materials for H2 at low temperatures. Pt-TiO2/MWNTs nanocomposites with different composition have been prepared by a simple wet chemical procedure and their morphological, microstructural and electrical properties were investigated. Resistive thick-film devices have been fabricated printing the hybrid nanocomposites on alumina substrates provided with Pt interdigitated electrodes. Electrical tests in air have shown that embedding MWCNTs in the TiO2 matrix modify markedly the electrical conductivity, providing a means to decrease the resistance of the sensing layer. Pt acts as a catalytic additive. Pt-TiO2/MWNTs-based sensors were found to be sensitive to hydrogen at concentrations between 0.5 and 3% in air, satisfying the requisites for practical applications in hydrogen leak detection devices. [ABSTRACT FROM AUTHOR]

Pure, mixed and doped metal oxides (MOX) have attracted great interest for the development of electrical and electrochemical sensors since they are cheaper, faster, easier to operate and capable of online analysis and real-time identification. This review focuses on highly sensitive chemoresistive type sensors based on doped-SnO2, RhO, ZnO-Ca, Smx-CoFe2−xO4 semiconductors used to detect toxic gases (H2, CO, NO2) and volatile organic compounds (VOCs) (e.g., acetone, ethanol) in monitoring of gaseous markers in the breath of patients with specific pathologies and for environmental pollution control. Interesting results about the monitoring of biochemical substances as dopamine, epinephrine, serotonin and glucose have been also reported using electrochemical sensors based on hybrid MOX nanocomposite modified glassy carbon and screen-printed carbon electrodes. The fundamental sensing mechanisms and commercial limitations of the MOX-based electrical and electrochemical sensors are discussed providing research directions to bridge the existing gap between new sensing concepts and real-world analytical applications. [ABSTRACT FROM AUTHOR]

In the present work, a spar-buoy scaled model was designed and built through a "Lab-on-Sea" unit, equipped with an energy harvesting system. Such a system is based on deformable bands, which are loyal to the unit, to convert wave motion energy into electricity by means of piezo patch transducers. In a preliminary stage, the scaled model, suitable for tests in a controlled ripples-type wave motion channel, was tested in order to verify the "fixed-point" assumption in pitch and roll motions and, consequently, to optimize energy harvesting. A special type of structure was designed, numerically simulated, and experimentally verified. The proposed solution represents an advantageous compromise between the lightness of the used materials and the amount of recoverable energy. The energy, which was obtained from the piezo patch transducers during the simulations in the laboratory, was found to be enough to self-sustain the feasible on-board sensors and the remote data transmission system. [ABSTRACT FROM AUTHOR]

The studied sensor consists of a microstrip interdigital capacitor covered by a gas sensing layer made of titanium dioxide (TiO2). To explore the gas sensing properties of the developed sensor, oxygen detection is considered as a case study. The sensor is electrically characterized using the complex scattering parameters measured with a vector network analyzer (VNA). The experimental investigation is performed over a frequency range of 1.5 GHz to 2.9 GHz by placing the sensor inside a polytetrafluoroethylene (PTFE) test chamber with a binary gas mixture composed of oxygen and nitrogen. The frequency-dependent response of the sensor is investigated in detail and further modelled using an artificial neural network (ANN) approach. The proposed modelling procedure allows mimicking the measured sensor performance over the whole range of oxygen concentration, going from 0% to 100%, and predicting the behavior of the resonant frequencies that can be used as sensing parameters. [ABSTRACT FROM AUTHOR]