On Diffusion Limited Deposition

We propose a simple model of columnar growth through {\it diffusion limited aggregation} (DLA). Consider a graph G_N\times\realmathbb{N}, where the basis has $N$ vertices $G_N:=\{1,\dots,N\}$, and two vertices $(x,h)$ and $(x',h')$ are adjacent if $|h-h'|\le 1$. Consider there a simple random walk {\it coming from infinity} which {\it deposits} on a growing cluster as follows: the cluster is a collection of columns, and the height of the column first hit by the walk immediately grows by one unit. Thus, columns do not grow laterally. We prove that there is a critical time scale $N/\log(N)$ for the maximal height of the piles, i.e., there exist constants $\alpha<\beta$ such that the maximal pile height at time $\alpha N/\log(N)$ is of order $\log(N)$, while at time $\beta N/\log(N)$ is larger than $N^\chi$ for some positive $\chi$. This suggests that a \emph{monopolistic regime} starts at such a time and only the highest pile goes on growing. If we rather consider a walk whose height-component goes down deterministically, the resulting \emph{ballistic deposition} has maximal height of order $\log(N)$ at time $N$. These two deposition models, diffusive and ballistic, are also compared with uniform random allocation and Polya's urn

The Rise and Fall of Social Democracy, 1918-2017

We describe the electoral history of one of Europe’s most successful party families over the past 100 years in 31 countries. With a unique and newly collected dataset of national election results, and a large number of economic and social variables mea- sured for each country-election observation, we find that two main factors drive the electoral performance of social democratic parties: public sector spending, and the size of the manufacturing sector. Our findings suggest that most of the fall in support for social democratic parties in recent years is correlated with a decline in the number of industrial workers as well as a reduction in the propensity of social democratic parties’ core supporters (industrial workers and public sector employees) to vote for them

A Wearable SSVEP BCI for AR-based, Real-time Monitoring Applications

A real-time monitoring system based on Augmented Reality (AR) and highly wearable Brain-Computer Interface (BCI) for hands-free visualization of patient's health in Operating Room (OR) is proposed. The system is designed to allow the anesthetist to monitor hands-free and in real-time the patient's vital signs collected from the electromedical equipment available in OR. After the analysis of the requirements in a typical Health 4.0 scenario, the conceptual design, implementation and experimental validation of the proposed system are described in detail. The effectiveness of the proposed AR-BCI-based real-time monitoring system was demonstrated through an experimental activity was carried out at the University Hospital Federico II (Naples, Italy), using operating room equipment

A Wearable AR-based BCI for Robot Control in ADHD Treatment: Preliminary Evaluation of Adherence to Therapy

A wearable, single-channel Brain-Computer Interface (BCI) based on Augmented Reality (AR) and Steady-State Visually Evoked Potentials (SSVEPs) for robot control is proposed as an innovative therapy for robot-based Attention Deficit Hyperactivity Disorder (ADHD) rehabilitation of children. The system manages to overcome the challenges regarding immersivity and wearability, providing a direct path between human brain and social robots, already successfully employed for ADHD treatment. Through the proposed system, even without training, the user can drive a robot, in real-time, by brain signals. A preliminary evaluation of the children adherence to the therapy was conducted as a case study on 18 subjects, at an accredited rehabilitation center. After investigating the children acceptance of the proposed system, different tasks were assigned to the volunteers aiming to observe their level of involvement. The experimental activity showed encouraging results, where almost all the participants were satisfied with the experience and keen to repeat it again in the future

Evaluation of the Effectiveness of a Wearable, {AR}-based {BCI} for Robot Control in {ADHD} Treatment

A highly wearable, single-channel Brain-Computer Interface based on Augmented Reality and Steady-State Visually Evoked Potentials is proposed as a therapy for Attention Deficit Hyperactivity Disorder (ADHD) rehabilitation of children. Through the proposed system, the user can drive a social robot, in real-time, by simply looking at the flickering icons rendered on the Augmented Reality (AR) smart glasses. The social robot is already successfully employed for ADHD treatment. After a preliminary evaluation of the children adherence to the therapy (involving 18 subjects), a one-month therapy was conducted on 7 participants. During the tests, different tasks were assigned to the children depending on their level of involvement. The obtained results, based on the Italian Battery for ADHD, highlight that for all the participants, an improvement in the various tests proposed could be observed, even with a low number of sessions

Performance and Usability Evaluation of an Extended Reality Platform to Monitor Patient’s Health during Surgical Procedures

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

Role of MRI Features as a Prognostic Index in Cervical Spondilogenetic Myelopathy

Introduction The importance of a correct preoperative radiological diagnosis in patients with cervical myelopathy has been widely demonstrated. Indeed, few studies still exist about the correlation between postoperative radiographic and clinical modifications. Materials and Methods The authors present a prospective study of 54 patients with cervical spondilogenetic myelopathy, who underwent surgery for corpectomy and anterior fusion with mesh in a period between January 2005 and August 2013. Images of cervical RMN were studied pre-and postoperatively and attention has been focused on alterations of intramedullary signal on T1- and T2-weighted sequences. Pre-and postoperative changes were correlated with clinical data (obtained by means of a Nurick scales and JOA classification—modified by Benzel). In relation to cervical RM-based studies, patients were divided into 3 groups: (A) no intramedullary signal alteration; (B) alterations in T2-weighted sequences; (C) alterations of the signal in both T1- and T2-weighted sequences. Results In all patients, decompression of the cervical spinal cord has been demonstrated by extension of the anteroposterior diameter of the spinal canal and by increase in the thickness of the subarachnoid space. In group A patients, no intramedullary signal changes were highlighted postoperatively. Patients in group B showed improvement on the base of hyperintensity disappearance on T2-weighted MRI, correlating with an improvement in the clinical quadro. Patients of group C have not been showing changes in the intramedullary MRI signal despite spinal cord decompression. Conclusions Signal alterations in T1 are an unfavorable prognostic index and proved to be irreversible. They correlate with a lack of clinical improvement of the patient. Patients in group B are those with the greatest clinical benefit after surgery and in whom clinical improvement correlates clearly with the radiological outcome

Adoption of Machine Learning Techniques to Enhance Classification Performance in Reactive Brain-Computer Interfaces

This paper proposes the adoption of an innovative algorithm to enhance the performance of highly wearable, reactive Brain-Computer Interfaces (BCIs), which exploit the Steady-State Visually Evoked Potential (SSVEP) paradigm. In particular, a combined time-domain/frequency-domain processing is performed in order to reduce the number of features of the brain signals acquired. Successively, these features are classified by means of an Artificial Neural Network (ANN) with a learnable activation function. In this way, the user intention can be translated into commands for external devices. The proposed algorithm was initially tested on a benchmark data set, composed by 35 subjects and 40 simultaneous flickering stimuli, obtaining performance comparable with the state of the art. Successively, the algorithm was also applied to a data set realized with highly wearable BCI equipment. In particular, (i) Augmented Reality (AR) smart glasses were used to generate the flickering stimuli necessary to the SSVEPs elicitation, and (ii) a single-channel EEG acquisition was conducted for each volunteer. The obtained results showed that the proposed strategy provides a significant enhancement in SSVEPs classification with respect to other state-of-the-art algorithms. This can contribute to improve reliability and usability of brain computer interfaces, thus favoring the adoption of this technology also in daily-life applications