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This study focused on the exposure evaluation of the 2D spatial distribution of the E-field in a one-floor apartment when a WiFi source is placed in uncertain position. An innovative approach that combines a Principal Component Analysis and Kriging model in order to build space-dependent surrogate models was applied and validated. Results showed the feasibility of the approach.

International audience; ELFSTAT project, founded by the French ANSES (2015-2019, Grant agreement n. 2015/1/202), aims at characterizing children's exposure to extremely low frequency magnetic fields (ELF-MF) in real exposure scenarios using stochastic approaches. In this paper, a stochastic approach for extracting information from dataset of recorded ELF-MF signals is presented. The aim is to obtain a better characterization and description of the phenomenon and to investigate on possible correlations or different features between various data subsets.

International audience; This study focused on the evaluation of the electric field induced in the brain grey matter of a 5 years child when exposed to uniform magnetic field at 50 Hz with uncertain orientation. An innovative approach that combines Principal Component Analysis (PCA) and Gaussian process regression (Kriging method) in order to build space-dependent surrogate models was applied and validated. Preliminary results showed the feasibility of the approach.

Risultati sperimentali del prototipo in termini di quantità di dati raccolti, percentuale di funzionamento delle varie apparecchiature sensoristiche.

Nella sezione 1 viene descritto il processo per la realizzazione di lastre sottili di materiale piezoelettrico, da inserire in convertitori con architettura diversa da quelli precedentemente testati. Vengono poi presentate le prove sperimentali con convertitori a due e quattro lamine. Segue l'analisi delle prestazioni del convertitore autoprodotto e delle prove comparative con i risultati precedenti sia con segnale armonico alla frequenza di risonanza che nelle reali condizioni di lavoro. In questo documento e? descritta la fase successiva dello sviluppo, test di laboratorio e collaudo dei vari sistemi di monitoraggio che caratterizzano l'ambiente e-CABIN. In particolare, sono descritte le fasi di sviluppo, realizzazione e misura delle tecnologie sviluppate per il recupero dell'energia, per i sistemi di monitoraggio ambientale e delle condizioni fisiologiche e attivita? dei passeggeri. Per quanto concerne i sistemi per il monitoraggio della qualita? dell'aria e del rumore ambientale in cabina, la Sezione 2 descrive in dettaglio la seconda fase delle prove sperimentali sui dispostivi realizzati, svolte sulla cabina di prova presso l'Universita? di Trieste, nonche? i risultati ottenuti. Per quanto concerne nello specifico il monitoraggio del sonno, vengono descritte le fasi finali della realizzazione e prove sperimentali sui dispositivi realizzati svolte sulla cabina di prova presso l'Universita? di Trieste e i risultati conseguiti. Per quanto concerne il monitoraggio del movimento mediante microonde, la Sezione 3 descrive le attivita? di prova sperimentali che si sono svolte seconda meta? di aprile, presso l'Universita? di Trieste. Inoltre, per il sistema di monitoraggio delle condizioni fisiologiche e di attivita?, nella Sezione 3 si descrive il completamento delle funzionalita? del sistema con particolare riferimento all'integrazione del modulo smartwatch ed all'integrazione con la piattaforma middleware di raccolta dati. Si descrive inoltre l'esito dei test effettuati in laboratorio e presso la cabina di test.

In questo documento è descritta la fase di integrazione dei vari sistemi di monitoraggio che caratterizzano l'ambiente e-Cabin. In particolare, sono descritte le fasi di integrazione per i sistemi di monitoraggio ambientale, le attuazioni personalizzate, e il monitoraggio delle condizioni fisiologiche e attività dei passeggeri. Il sistema e-Cabin è stato installato nella cabina test sita presso l'Università di Trieste e una dimostrazione dell'intero sistema funzionante in ogni suo aspetto è stata fatta alla presenza di Fincantieri il 4 Ottobre 2018. In questo deliverable, dedicato all'integrazione delle varie componenti di e-Cabin, manca la descrizione dell'integrazione di due applicazioni, in quanto di fatto sono stand-alone. Le applicazioni sono: 1) Mobile Social Networking, e 2) e-Smart CruiseAR, che include le due App Virtual Tour e Orientation Queste app nè prendono dati dal sistema e-Cabin né ne forniscono. Mobile Social Networking è' una applicazione che, da un punto di vista tecnico, funziona indipendentemente dal sistema e-Cabin. Applicazioni comuni di social networking, come Facebook o Twitter, non sono usabili sul sistema nave poichè potrebbero non essere disponibili a bordo; l'applicazione di Mobile Social Networking, ampiamente descritta nei Deliverable D4.2, D4.3 e D4.4, costituisce pertanto una valida alternativa ai classici social. L'applicazione e-SmartCruiseAR integra al suo interno le applicazioni e-Orientation e e-VirtualTour, descritte nei deliverable D4.2, D4.3 e D4.4, e attraverso l'utilizzo di una interfaccia grafica permette all'utente di scegliere quale servizi utilizzare. Tornado invece alle integrazioni, nel Capitolo 1 sono brevemente riportate alcune informazioni salienti sulla piattaforma di comunicazione (il middleware) del sistema e-Cabin, che di fatto costituisce il mezzo di integrazione tra le varie componenti del sistema. Il lettore è invitato a leggere i deliverable D3.3 e D3.4, ampiamente dedicati all'architettura del middleware e alle sue istanze. Il Capitolo 2 è dedicato alla descrizione dell'integrazione dei sensori usati in e-Cabin, che usano protocolli di comunicazione ZWave, ZigBee, Coap e Bluethooth, quest'ultimo usato per comunicare con i beacon installati. In particolare, sono descritte le integrazioni dei sensori ambientali, di monitoraggio della qualità del sonno, della qualità dell'aria, di rumore, e di localizzazione. I Capitolo 3 e 4 presentano come alcune delle App sviluppate per e-Cabin si integrano nel sistema; in particolare le app di Smart Cabin e AroundMe (Cap. 3) e Personal Well Being (cap. 4). Il Capitolo 5 riporta il ruolo essenziale delle ontologie nel sistema di reasoning di e-Cabin, mentre il Capitolo 6 riassume l'integrazione del sistema di recupero dell'energia sia da vibrazioni che termico. Le conclusioni sono tracciate nel Capitolo 7.

The exposure of a 8-years child to a 4G LTE femtocell, both (child and source) freely located in random positions in a realistic indoor environment was assessed by sparse low rank tensor approximation method (sparse LRA) for developing stochastic dosimetry by surrogate models. These latters were used for fast estimating the Specific Absorption Rate (SAR) in all the possible positions of femtocell and child and to assess the compliance with the International Commission of Non-Ionizing Radiation Protection (ICNIRP) guidelines for general public exposure. Results showed that, for all the possible positions in the room, the exposure values were significantly below the ICNIRP guidelines and that the probability of reaching SAR values higher than 1% of the ICNIRP guidelines value was lower than 0.006. The variation of the distance between femtocell and child influenced greatly the exposure, resulting in Quartile Coefficient of Dispersion values always higher than 48 %.

The exposure of an 8-year-old child to a femtocell operating at 2600 MHz, both (child and source) freely located in random positions in an indoor environment, was assessed. In order to develop surrogate models of the exposure, stochastic dosimetry based on sparse low-rank tensor approximation method (sparse LRA) was used. The surrogate models were used for fastly estimating the specific absorption rate (SAR) in all the possible positions of femtocell and child. Results showed that, for all the possible positions in the room, the exposure values were significantly below the International Commission of Non-Ionizing Radiation Protection (ICNIRP) guidelines for general public and that the probability of reaching SAR values higher than 1% of the ICNIRP guidelines value was lower than 0.006. The variation of the distance between femtocell and child influenced greatly the exposure, resulting in quartile coefficient of dispersion values always higher than 48%.

Introduction ELFSTAT project, founded by the French ANSES (2015-2019, Grant agreement n. 2015/1/202), aims at characterizing children's exposure to Extremely Low Frequency Magnetic Fields (ELF-MF) in real exposure scenarios using stochastic approaches. The present paper gives details about the first step of the project: this step aims at developing stochastic models to model personal exposure from a dataset of recorded ELF-MF signals. These recordings are coming from the ARIMMORA project [1]: 331 children has worn an EMDEX and their exposition were measured during about 3 days. The stochastic models will then be used to construct realistic simulations of ELF-MF time series. Figure 1 gives an example of ELF-MF 24 hours-signal. The majority of ELF-MF time series are characterized by abrupt changes in structure, such as sudden jumps in mean level or in dispersion around a mean level. The developed model consists in detecting these changes and in modeling the signal between two consecutive changes by a stationary process. These stationary processes are described by parametric models. We chose Auto-Regressive model because of the possibility of characterizing mean effects, variance effects and time-correlation effects with a weak number of parameters. The full-model is obtained by modeling the distribution of the parameters from the whole dataset of 331 recordings. Figure 2 gives a schematic of the full approach. Data sources The 331 individuals of the database are distributed according the geographical location (Switzerland or Italy) and the time season (Winter or Summer), as shown in the following Table: For each individual, we consider a full day of measurements (0h-24h). As the interval between two successive observations is 30 seconds, 2880 values per recording are considered. Change-point detection and segment modeling We consider changepoints (or equivalently breakpoints) to be the time points that divide a data set into distinct homogeneous segments (or equivalently stationary segments); the boundaries of segments can be interpreted as changes in the physical system. The problem of changepoints estimation has attracted significant attention and is required in a number of applications including financial data [2], climate data [3], biomedical data [4] and signal processing [5]. Different authors propose various approaches to the problem of changepoints detection or segmentation of time series. This issue is thoroughly surveyed in [6], where different methods are proposed and an exhaustive list of references is given. Some authors study the estimation of a single changepoint problem [7], while others extend it to multiple changepoints problem. In this last case, the number of changepoints is unknown and it is a challenge to jointly estimate the number of changepoints, their location, and also provide an estimation of the model representing each segment. In this work we consider the [2] procedure because of its ability to jointly estimate the number of changepoints, their location and the AR model parameter of each segment. To resume, the segmentation aims to: (1) find the periods of stability and homogeneity in the behaviour of the time series; (2) identify the locations of change, called changepoints; (3) represent the regularities and features of each segment (estimate the model of each segment by a parameter set like changepoints location, segment amplitude, segment duration, segment regularity). Figure 1 gives the segmentation results for the given example. Statistical characterization of model parameters In order to produce a completely model of the time series, the marginal and joint probability distributions of all model parameters are required. The set of parameters related to piecewise autoregressive model are, at time t: duration ?t and coefficient of the AR model (amplitude or mean ?t, noise variance ?t2 and the autoregressive coefficients ?t). These 4 parameters can be modeled separately (whether by a parametric probability distribution or not) as well as jointly (rather in a nonparametric way because of the difficulty to find a multidimensional probability distribution that efficiently models the parameters set). Figure 3 shows that parameters are correlated and thus cannot be modeled separately. We used the Multivariate Kernel Density Estimation [9] that is a nonparametric and classical method. The advantage is that once we have an estimate of this density, we can simulate realistic realizations of these parameters. The figure 3 also gives the Multivariate KDE contours « Congrès National de Radioprotection » SFRP - LILLE - 7, 8 & 9 juin 2017 and 5000 simulations of these parameters via the Multivariate KDE. Concerning the fitted distributions, first result shows no significant difference between the summer database and the winter database. Finally, in the figure 4, we present an example simulated of ELF-MF exposure time series based in piecewise AR model.

In this paper, the influence of conductivity values applied to the foetal tissues at 3, 7 and 9 months gestational age on the electric field distribution induced by a 50 Hz uniform magnetic field was explored by means of stochastic dosimetry performed by making use of polynomial chaos theory. The maximum variations of induced electric field peak can reach up to 12 % in foetal fat.

Extremely low frequency magnetic fields (ELF-MF) have been classified as possibly carcinogenic to humans based on reasonably consistent epidemiological data for childhood leukaemia [1]. Despite the classification and consequent implementation of numerous health risk assessment processes to evaluate possible risk of ELF exposure of children, the real everyday exposure to ELF-MF in Europe is not well known. Indeed, only a few studies analysed children exposure to ELF-MF by collecting personal measurements, correlating the daily exposure patterns to children's movement and behaviour [2-7]. Furthermore, the exposure assessment to MF sources other than power lines has been not yet addressed. Therefore, an improved knowledge of these exposure contributions is needed to better understand biological mechanisms and to interpret previous epidemiological studies as well. A correct assessment of the induced fields in tissues should also be carried out. Indeed, so far, the estimation of induced fields are limited to exposure of a few children's anatomies [8-9] and on foetal exposure [9-13]. Personal exposure measurements and computational dosimetry contribute to provide the picture of the impact of exposure on health. However, due to the high variability of real exposure scenarios, the exposure assessment by means of those tools turns in high time-consuming processes. ELFSTAT project started in November 2015 and is founded by the French ANSES (2015-2019, Grant agreement n. 2015/1/202). The main purpose of ELFSTAT is to characterize children's exposure to low frequency magnetic fields (MF, from 40 to 800 Hz) in real exposure scenarios using stochastic approaches. Both the global exposure at personal level and tissue dosimetry due to far- and near-field sources will be investigated. Finally, prediction of the impact of new technologies (e.g. smart grids, electric vehicles) on children's exposure will be carry out, enlarging the frequency range to the intermediate frequencies (IF). ELFSTAT project aims to develop stochastic models able to provide exposure assessment of children in several exposure conditions and hence considering the high variability of real exposure scenarios, on the base of a relatively few experimental and/or computational data. The project is divided in three Work Packages (WP): WP1-Stochastic Models: the aim is to develop stochastic models to model personal exposure and tissue dosimetry. The exposure will be characterize in terms of magnetic field amplitude at personal level and induced electric field for tissue dosimetry. WP2- Children exposure assessment to ELF-MF: the aim is to characterize children's exposure to low frequency magnetic fields (MF) from 40 to 800 Hz using stochastic models developed in WP1. Furthermore, appropriate indicators to represent children's exposure based on the stochastic exposure assessments will be developed. WP3- Exposure to new technologies ELF devices: the aim is to evaluate the impact of new technologies for energy on children exposure. A systematic literature review will be conducted about research papers on the ELF-MF exposure of new technologies. An estimation of the change of exposure due to these new sources will be done to evaluate the impact on children's exposure of these new technologies. Ongoing work is about: i) modelling of extremely low frequency magnetic field time series obtained from the EU project ARIMMORA [3] and EXPERS database [6-7] by segmenting them into blocks modelled by locally stationary processes [14]; ii) the development of stochastic models of induced electric field in foetal tissues and in the children exposed to MF by means of the polynomial chaos theory; iii) identification of the new technologies that could change the ELF EMF exposure scenario for the young population

In this study, we used stochastic dosimetry, a promising approach that combines electromagnetic computational techniques and statistics, to assess the exposure of a fetus at 9 months of gestational age to a 4G LTE tablet in realistic scenarios characterized by variability. In particular, we analyzed how the exposure changes when moving the tablet in a range of positions representative of realistic exposure scenarios. Polynomial chaos theory, applied to build surrogate models of Specific Absorption Rate (SAR), permitted a fast estimation of the variability of the exposure due to the variation in the tablet position.

Wireless communication systems are spreadly used, even though the concern about the exposure to Radio- Frequency electromagnetic fields (RF-EMF). Also children are increasingly exposed to RF-EMF generated by novel devices. Therefore, an assessment of children's exposure to these sources in real scenarios is needed. In this study an analysis of a 1-year-old child exposure to a 3G femtocell emitting in downlink mode has been carried out. In order to estimate the variability of the exposure due to the position of this device with respect to the baby, stochastic dosimetry, based on the Polynomial Chaos (PC) theory, has been used to build surrogate models of the SAR induced in the child at a low computational cost.

Aim of this study is to characterize the E distributions due to the H1 coil, developed for the Deep Transcranial Magnetic Stimulation, in the brain of two realistic human models by computational electromagnetic techniques. Despite inter-individual differences, our results show that the dorsolateral prefrontal cortex is the region preferentially stimulated by H1 coil when they it is placed in the position on the scalp adopted in this study, with a broad and non-focal distribution. Moreover, the H1 coil spread also both in the prefrontal and medial prefrontal cortex and towards some deeper brain structures.

The recent advancements in the design of TMS coils to reach specific cortical and subcortical regions have allowed the treatment of various neuropsychiatric disorders, whose prevalence increases with age. This could be also due to the anatomical and morphological changes with age of the brain tissues, such as the atrophy that characterizes the elderly cortex. This study provides a description of the electric field distribution induced in the cerebral tissues, considered as one of the main engine of the stimulation. That was performed by comparing, making use of computational electromagnetic techniques, the E distributions in two human models of different ages (34 and 84 years-old males), the older one showing cortical atrophy in the prefrontal lobe. The analysis of the parameters describing the spread of the electric field distribution shows that H1 coil is able to induce in the prefrontal cortex an E amplitude higher than the neural threshold and with a widespread distribution in both models, with a slight prevalence on the younger one. On the contrary, the maximum E penetration depth and the consequent capability to reach deeper targets in the brain, is slightly higher for the elderly model.

In the last few years, cerebellar transcranial direct current stimulation (ctDCS) and transcutaneous spinal direct current stimulation (tsDCS), have been successfully applied to modulate and restore some cerebellar and spinal functions. Of particular interest is the possible use of these techniques in paediatric age, since many pathologies and injuries, which affect the cerebellar cortex as well as spinal cord circuits, are diffuse in adults as well as in children. To this purpose, there is still a lack of information about the safety issues as well as the appropriate dose to be used during the treatment. In order to fill these gap in knowledge, this work wants to estimate, through computational techniques, the electric field distributions induced in the target tissues during the application of cTDCS and tsDCS administered to differently aged and gender children models.

Recently, a procedure to personalize the electrode to stimulate specific cortical regions by transcranial electric stimulations (tES) has been proposed. Results of these studies documented that the cortical excitability was differently modulated depending on where the personalized electrode was positioned, thus refining previous evidences that it is possible to focus the effects of tES by properly shaping and positioning the electrodes to target a region of interest of the cerebral cortex. This study aims to assess, through computational modelling techniques, the distribution of the electric field (E) induced by tES via the regional personalized (RePE) and the non-personalized (ReNPE) electrode.

ELFSTAT Project started in November 2015 and is founded by the French ANSES (2015-2018, Grant agreement n. 2015/1/202). The main purpose of ELFSTAT is to characterize children's exposure to low frequency magnetic fields (MF, from 40 to 800 Hz) in real exposure scenarios using stochastic approaches. Both the global exposure at personal level and tissue dosimetry due to far- and near-field sources will be investigated. Finally, prediction of the impact of new technologies (e.g. smart grids, electric vehicles) on children's exposure will be carry out, enlarging the frequency range to the intermediate frequencies (IF).

International audience; ELFSTAT Project started in November 2015 and is founded by the French ANSES (2015-2018, Grant agreement n. 2015/1/202). The main purpose of ELFSTAT is to characterize children’s exposure to low frequency magnetic fields (MF, from 40 to 800 Hz) in real exposure scenarios using stochastic approaches. Both the global exposure at personal level and tissue dosimetry due to far- and near-field sources will be investigated. Finally, prediction of the impact of new technologies (e.g. smart grids, electric vehicles) on children’s exposure will be carry out, enlarging the frequency range to the intermediate frequencies (IF).

There is an increasing interest in the possible use of the cerebellar transcranial direct current stimulation also in pediatric age. However, there is still little information about its safety in children. This paper provides a characterization of the electric field distributions due to cerebellar tDCS in pediatric brain by making use of computational electromagnetics techniques applied to more models of children of different ages and gender.

Introduction: Recently we developed a new procedure to personal- ize the electrode to selectively stimulate specific cortical regions by transcranial electric stimulations (tES).This study aims to assess by computational approach the distribution of the intra-cerebral elec- tric field induced by tES through the personalized in comparison with a non-personalized electrode in human models. Methods: We used two realistic human models and 40 tissue con- ductivities.We targeted bilateral primary motor (M1) and somatosensory cortex (S1) alternatively with the personalized and non-personalized electrode, with the reference on the occipital area. We estimated the distribution of the electric field across the brain structures. Results: The personalized electrode was able to modulate more deeply and strongly the area of the central sulcus than the non- personalized one. This trend was more evident in the regions along the central sulcus more far away from Cz, where the two electrodes are less overlapped and for both targets (M1 and S1). Moreover, the personalized electrode used to target S1 more selectively modulated the postcentral gyrus, whereas the personalized electrode used to target M1 broadened its effects over both the pre and postcentral gyrus. Discussions: This work strengthens the notion that tES can be focused by properly shaping and positioning the stimulating elec- trodes according to the target regions. doi:10.1016/j.clinph.2015.11.419

This paper investigates how normal variations in anatomy may affect the current flow through the brain due to transcranial direct current stimulation. This was done by comparing the electric field and current density distributions within the tissues obtained in human models of different age and sex.

Summary The change of the orientation of the B-field vector respect to the human body is a parameter to take into account for the estimation of the variability of the human exposure. The deterministic dosimetry would be too much time consuming to perform this analysis. In this paper the stochastic dosimetry is applied to the analysis of the pregnant woman exposure at 7 months of gestational age, to build-up a statistical meta-model of the induced electric field for each fetal tissue and in the fetal whole-body by means of the Polynomial Chaos expansion as a function of the B-field orientation, considering a uniform exposure at 50 Hz.

At the current time the new Directive 2013/35/EU suggests limits for the occupational exposure to ELF-MF. Pregnant woman exposure to ELF-MF is a concern because the woman and the fetus should be prevented to possible health hazards at workplaces. In this work the compliance of the pregnant woman exposure to the EU Directive 2013 was assessed, by means of advanced high resolution numerical pregnant woman models at 3, 7 and 9 months of gestational ages, exposed to differently polarized uniform MF of 1 and 6 mT at 50 Hz. exposure at 50 Hz.

This work estimates the distribution of the current density on the spinal cord due to transcutaneous spinal direct current stimulation. We used computation method applied to realistic human model of different age and sex and we compared three different electrode montages. Data showed that the current density tends to be primarily directed longitudinally along the spinal cord with the region of higher amplitude influenced by the reference electrode position, while on transversal sections the J amplitude distributions were quite uniform.

In this paper, fetal exposure to uniform magnetic fields (MF) with different polarizations is quantified at 50 Hz. Numerical computations were performed on high-resolution pregnant models at 3, 7 and 9 months of gestational age (GA), that distinguish a high number of fetal tissues. Fetal whole-body and tissue-specific induced electric fields (E) and current densities (J) are analyzed as a function of both the extremely low frequency magnetic field (ELF-MF) polarization and GA. Additionally, the induced field variations due to changes in fetal position has been analyzed by means of two new pregnant models. The uncertainty budget due to the grid resolution has been also calculated. Finally, the compliance of the fetal exposure to the ICNIRP Guidelines has been checked. A fetal exposure matrix has been built at 50 Hz, which could be used to further investigate possible interaction mechanisms between ELF-MF and health risk. Some specific findings are: 1) the induced fields increase with GA; 2) the maxima E were found in skin and fat tissues at each GA; 3) fetal tissue-specific exposure is modified as a function of GA and of the polarization; 4) the change of the fetal position in woman's womb modifies significantly the induced E in some fetal tissues; 5) the induced fields are in compliance to the ICNIRP Guidelines and result quite below the permitted threshold limit.

This paper investigates the induced electric field distributions in a realistic human head model due to 16 coil configurations. We used the scalar potential finite element method differentiating the brain structures. We found that, despite the presence of a depth-focality tradeoff, some coils are able to reach subcortical white matter tracts at effective electric field level.

The exposure to WLAN systems has been little investigated until now. However, their recent broad diffusion makes particularly urgent the assessment of EMF exposure in typical everyday scenarios. These include different subjects with variable positions close to the antenna of the client unit, which is also often close to the head of the user. In particular, the specific assessment of brain tissues exposure, where the absorption of Wi-Fi energy could be relatively large, is highly valuable to integrate the process of health risk assessment necessary to reduce population concern.