A field-measurements-based LoRa network planning tool

Long range (LoRa) transmission technology enables energy-constrained devices such as the tiny sensor systems used in internet-of-things applications that are distributed over wide areas while still being able to establish appropriate connectivity. This has resulted in the development of an exponentially increasing number of different solutions and services based on LoRa, be they dedicated to the long-term monitoring of distributed plants and infrastructures or to human-centred applications such as safety-oriented sensor systems for use in the workplace. In dense LoRa networks, predicting the number of supported nodes in relation to their position and the propagation environment is essential for ensuring reliable and stable communication and minimising costs. In this paper, after comparing different path loss models based on a field measurement campaign for LoRa received signal strength indicator values within a university campus, two main modifications of the LoRa simulator tool were implemented. These were aimed at improving the accuracy of the prediction of the number of sustainable nodes in relation to the target data extraction rate set. The simulations based on field measurements demonstrated that through an improved path loss evaluation and the use of three gateways, the number of nodes could be increased theoretically from around 100 to around 6,000

IR Kuiper Belt Constraints

We compute the temperature and IR signal of particles of radius $a$ and albedo $\alpha$ at heliocentric distance $R$, taking into account the emissivity effect, and give an interpolating formula for the result. We compare with analyses of COBE DIRBE data by others (including recent detection of the cosmic IR background) for various values of heliocentric distance, $R$, particle radius, $a$, and particle albedo, $\alpha$. We then apply these results to a recently-developed picture of the Kuiper belt as a two-sector disk with a nearby, low-density sector (40<R<50-90 AU) and a more distant sector with a higher density. We consider the case in which passage through a molecular cloud essentially cleans the Solar System of dust. We apply a simple model of dust production by comet collisions and removal by the Poynting-Robertson effect to find limits on total and dust masses in the near and far sectors as a function of time since such a passage. Finally we compare Kuiper belt IR spectra for various parameter values.Comment: 34 pages, LaTeX, uses aasms4.sty, 11 PostScript figures not embedded. A number of substantive comments by a particularly thoughtful referee have been addresse

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

Predictions for neutrino structure functions.

The first measurements of xF3 are higher than current theoretical predictions. We investigate the sensitivity of these theoretical predictions upon a variety of factors including: renormalization scheme and scale, quark mass effects, higher twist, isospin violation, and PDF uncertainties

Heavy Quark Production and PDF's Subgroup Report

We present a status report of a variety of projects related to heavy quark production and parton distributions for the Tevatron Run II.Comment: Latex. 8 pages, 7 eps figures. Contribution to the Physics at Run II Workshops: QCD and Weak Boson Physic

Heartbeat detection by laser doppler vibrometry and machine learning

Background: Heartbeat detection is a crucial step in several clinical fields. Laser Doppler Vibrometer (LDV) is a promising non-contact measurement for heartbeat detection. The aim of this work is to assess whether machine learning can be used for detecting heartbeat from the carotid LDV signal. Methods: The performances of Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and K-Nearest Neighbor (KNN) were compared using the leave-one-subject-out cross-validation as the testing protocol in an LDV dataset collected from 28 subjects. The classification was conducted on LDV signal windows, which were labeled as beat, if containing a beat, or no-beat, otherwise. The labeling procedure was performed using electrocardiography as the gold standard. Results: For the beat class, the f1-score (f 1) values were 0.93, 0.93, 0.95, 0.96 for RF, DT, KNN and SVM, respectively. No statistical differences were found between the classifiers. When testing the SVM on the full-length (10 min long) LDV signals, to simulate a real-world application, we achieved a median macro-f 1 of 0.76. Conclusions: Using machine learning for heartbeat detection from carotid LDV signals showed encouraging results, representing a promising step in the field of contactless cardiovascular signal analysis

De novo 1q21.3q22 duplication revaluation in a “cold” complex neuropsychiatric case with syndromic intellectual disability

Syndromic intellectual disability often obtains a genetic diagnosis due to the combination of first and next generation sequencing techniques, although their interpretation may require revaluation over the years. Here we report on a composite neuropsychiatric case whose phenotype includes moderate intellectual disability, spastic paraparesis, movement disorder, and bipolar disorder, harboring a 1.802 Mb de novo 1q21.3q22 duplication. The role of this duplication has been reconsidered in the light of negativity of many other genetic exams, and of the possible pathogenic role of many genes included in this duplication, potentially configuring a contiguous gene-duplication syndrome

Summary of the Very Large Hadron Collider Physics and Detector Workshop

One of the options for an accelerator beyond the LHC is a hadron collider with higher energy. Work is going on to explore accelerator technologies that would make such a machine feasible. This workshop concentrated on the physics and detector issues associated with a hadron collider with an energy in the center of mass of the order of 100 to 200 TeV