Comparison between HF radar current data and moored ADCP currentmeter

A preliminary assessment of accuracy of a two-sites shore-based HF Radar network along the Venice Lagoon littoral was attempted by means of comparison with a 57.5 day-long ADCP current time series for the period September- October 2002. Results showed that radar measurements were accurate (< 7 cm/s) in more than 50% of the times, since more than 50% of the differences between both E-W (U)and N-S (V )comp onents were under 7 cm/s, and more than 50% of direction differences were under 35◦. The main differences between the HF radar and surface ADCP currents can be explained in terms of random errors affecting the measurement technique and the daily sea breeze forcing, since low-pass filtering of current time series significantly improved the correlation and decreased the RMS of the differences between the two measured data set. Comparison of the semidiurnal (M2, S2)tidal band suggested good agreement between tidal ellipse amplitudes. Wind forcing on a daily time-scale (sea-breeze)w as associated with larger differences between radar and ADCP currents at a diurnal band due to the presence of a vertical shear in the surface layer

The importance of physiological data variability in wearable devices for digital health applications

This paper aims at characterizing the variability of physiological data collected through a wearable device (Empatica E4), given that both intra- and inter-subject variability play a pivotal role in digital health applications, where Artificial Intelligence (AI) techniques have become popular. Inter-beat intervals (IBIs), ElectroDermal Activity (EDA) and Skin Temperature (SKT) signals have been considered and variability has been evaluated in terms of general statistics (mean and standard deviation) and coefficient of variation. Results show that both intra- and inter-subject variability values are significant, especially when considering those parameters describing how the signals vary over time. Moreover, EDA seems to be the signal characterized by the highest variability, followed by IBIs, contrary to SKT that results more stable. This variability could affect AI algorithms in classifying signals according to particular discriminants (e.g. emotions, daily activities, etc.), taking into account the dual role of variability: hindering a net distinction between classes, but also making algorithms more robust for deep learning purposes thanks to the consideration of a wide test population. Indeed, it is worthy to note that variability plays a fundamental role in the whole measurement chain, characterizing data reliability and impacting on the final results accuracy and consequently on decision-making processes

Assessment of Domestic Well-Being: From Perception to Measurement

Nowadays, there are plenty of sensing devices that enable the measurement of physiological, environmental, and behavioral parameters of people 24 hours a day, seven days a week and provide huge quantities of different data. Data and signals coming from sensing devices, installed in indoor or outdoor environments or often worn by the users, generate heterogeneous and complex structured datasets, most of the time not uniformly structured. The artificial intelligence (AI) algorithms applied to these sets of data have demonstrated capabilities to infer indices related to a subject's status and well-being [1]. Well-being is a key parameter in the World Health Organization (WHO) definition of health, considering its physical, mental, and social spheres. Quantitatively assessing a subject's well-being is of paramount importance if we want to assess the whole status of a person, which is particularly useful in the case of ageing people living alone. Assessment allows for continuous remote monitoring to improve people's quality of life (QoL) according to their perceptions, needs, and preferences. Technology undoubtedly plays a pivotal role in this regard, providing us new tools to support the objective evaluation of a subject's status, including her/his perception of the living environment. Its potential is huge, also in terms of support to the healthcare system and ageing people; however, there are several engineering challenges to consider, especially in terms of sensors integrability, connectivity, and metrological performance, in order to obtain reliable and accurate measurement systems

Wearable Devices and Diagnostic Apps: Beyond the Borders of Traditional Medicine, but What about Their Accuracy and Reliability?

Nowadays people are willing to self-monitor their health status, and when they do not feel well, they tend to ask Dr. Google for a diagnosis (over a third of adults go online to analyze or look for information about a health condition [1]). People trust technology, often more than physicians; smartphone and Artificial Intelligence (AI) technologies are undoubtedly making innovative monitoring and diagnostic devices rapidly progress, so much that it seems that the future of medicine is in smartphones, where apps may run and to which devices can be connected, hence supporting mobile health (m-Health) [2]. In addition to smartwatches and wrist-worn devices that are surely the most common wearable devices [3], [4], there are also connected wearable clothes [5], socks [6], rings, or glasses-type wearables [7]

Variability of currents in front of the Venice Lagoon, Northern Adriatic Sea

Time scales and modes of variability of the flow in the water column in the Northern Adriatic Sea for late summer 2002 are described based on current record from a single bottom-mounted ADCP in the shallow-water area in front of the Venice Lagoon. &lt;br&gt;&lt;br&gt; The time averaged flow was directed 277&amp;deg; E (CCW), roughly aligned with the coastline, with typical magnitudes in the range 4&amp;ndash;6 cm/s and a limited, not significant clockwise veering with depth. Tidal forcing was weak and mainly concentrated in the semidiurnal frequency band, with a barotropic (depth-independent) structure. On a diurnal time scale, tidal signal was biased by the sea-breeze regime and was characterized by a clockwise veering with depth according to the Ekman spiral. &lt;br&gt;&lt;br&gt; A complex EOF analysis on the velocity profile time series extracted two dominant spatial modes of variability, which explained more than 90% of the total variance in the current field. More than 78% of the total variance was accounted for by the first EOF mode, with a barotropic structure that contained the low-frequency components and the barotropic tidal signal at semidiurnal and diurnal frequencies. The second mode had a baroclinic structure with a zero-crossing at mid-depth, which was related with the response of the water column to the high-frequency wind-driven diurnal sea breeze variability. &lt;br&gt;&lt;br&gt; The response of low-passed non-tidal currents to local wind stress was fast and immediate, with negligible temporal lag up to mid-depth. Currents vectors were pointing to the right of wind stress, as expected from the surface Ekman veering, but with angles smaller than the expected ones. A time lag in the range 10 to 11 h was found below 8 m depth, with current vectors pointing to the left of wind stress and a counterclockwise veering towards the bottom. The delay was consistent with the frictional adjustment time scale describing the dynamics of a frictionally dominated flow in shallow water, thus suggesting the importance of bottom friction on the motion over the entire water column

Wearable Devices and Diagnostic Apps: Beyond the Borders of Traditional Medicine, but What about Their Accuracy and Reliability?

Nowadays people are willing to self-monitor their health status, and when they do not feel well, they tend to ask Dr. Google for a diagnosis (over a third of adults go online to analyze or look for information about a health condition [1]). People trust technology, often more than physicians; smartphone and Artificial Intelligence (AI) technologies are undoubtedly making innovative monitoring and diagnostic devices rapidly progress, so much that it seems that the future of medicine is in smartphones, where apps may run and to which devices can be connected, hence supporting mobile health (m-Health) [2]. In addition to smartwatches and wrist-worn devices that are surely the most common wearable devices [3], [4], there are also connected wearable clothes [5], socks [6], rings, or glasses-type wearables [7]

Hydrogen sulphide removal from biogas by zeolite adsorption: part II. A MD simulation

Coupled Grand Canonical-Canonical Monte Carlo and molecular dynamics (MD) simulation techniques have been used to investigate in details the adsorption of low-pressure hydrogen sulfide (H2S) in zeolites, and the selective adsorption behavior towards carbon dioxide and methane, the main biogas constituents. Results from Monte Carlo (MC) simulations indicated, among many others, zeolite NaY as the best option for H2S removal. Afterwards, deterministic simulations have been performed to investigate hydrogen sulfide pathway inside NaY, with respect to other adsorbed molecules (methane and carbon dioxide), as a function of zeolite loading and H2S partial pressure (i.e., biogas composition). Thermodynamic evaluations for 2D molecular dynamic simulations in terms of binding energy evolution vs. time confirm and reinforce the results obtained from Monte Carlo simulations, testifying the greater affinity for H2S to NaY zeolite framework. Results give also new quantitative insights in terms of pathways, binding energies, and equilibration time inside zeolite pores for stabilization

Hydrogen sulphide removal from biogas by zeolite adsorption: part I. GCMC molecular simulations

In this work Grand Canonical Monte Carlo (GCMC) simulations have been used to study hydrogen sulfide (H2S) removal from biogas streams by different zeolites such as FAU (Faujasite, NaX and NaY), LTA (zeolite A (Lynde division, Union Carbide)) and MFI (Zeolite Socony Mobil \u2013 five). Additionally, quantum mechanics (QM) molecular simulations have been performed to obtain structures and partial charges of some sorbates. The computational procedure adopted has been validated by comparison with experimental data available for H2S removal in atmospheric environment by zeolite NaY. In order to obtain a priority list in terms of both H2S isotherms and adsorption selectivity, adsorption simulations for pure H2S at low pressures and for a prototype biogas mixture (i.e., CO2, CH4, and H2S) have been performed and compared. The adsorption mechanisms and competition for accessible adsorption sites in terms of thermodynamic behavior have been also examined. Overall, the results obtained in this work could be routinely applied to different case studies, thus yielding deeper qualitative and quantitative insights into adsorption pollutant removal processes in environmental fields