Non-locality and entropic uncertainty relations in neutrino oscillations

Using the wave-packet approach to neutrino oscillations, we analyze quantum-memory-assisted entropic uncertainty relations and show that uncertainty and the non-local advantage of quantum coherence are anti-correlated. Furthermore, we explore the hierarchy among three different definitions of NAQC, those based on l1-norm, relative entropy and skew information coherence measures, and we find that the coherence content detected by the l1-norm-based NAQC overcomes the other two. The connection between QMA-EUR and NAQC could provide a better understanding of the physical meaning of the results so far obtained and suggest their extension to quantum field theory

An unsupervised behavioral modeling and alerting system based on passive sensing for elderly care

Artificial Intelligence in combination with the Internet of Medical Things enables remote healthcare services through networks of environmental and/or personal sensors. We present a remote healthcare service system which collects real-life data through an environmental sensor package, including binary motion, contact, pressure, and proximity sensors, installed at households of elderly people. Its aim is to keep the caregivers informed of subjects’ health-status progressive trajectory, and alert them of health-related anomalies to enable objective on-demand healthcare service delivery at scale. The system was deployed in 19 households inhabited by an elderly person with post-stroke condition in the Emilia–Romagna region in Italy, with maximal and median observation durations of 98 and 55 weeks. Among these households, 17 were multi-occupancy residences, while the other 2 housed elderly patients living alone. Subjects’ daily behavioral diaries were extracted and registered from raw sensor signals, using rule-based data pre-processing and unsupervised algorithms. Personal behavioral habits were identified and compared to typical patterns reported in behavioral science, as a quality-of-life indicator. We consider the activity patterns extracted across all users as a dictionary, and represent each patient’s behavior as a ‘Bag of Words’, based on which patients can be categorized into sub-groups for precision cohort treatment. Longitudinal trends of the behavioral progressive trajectory and sudden abnormalities of a patient were detected and reported to care providers. Due to the sparse sensor setting and the multi-occupancy living condition, the sleep profile was used as the main indicator in our system. Experimental results demonstrate the ability to report on subjects’ daily activity pattern in terms of sleep, outing, visiting, and health-status trajectories, as well as predicting/detecting 75% hospitalization sessions up to 11 days in advance. 65% of the alerts were confirmed to be semantically meaningful by the users. Furthermore, reduced social interaction (outing and visiting), and lower sleep quality could be observed during the COVID-19 lockdown period across the cohort

Hardware-oriented adaptation of a Particle Swarm Optimization algorithm forobject detection

In this paper we propose a simplified, hardware-oriented algorithm for object detection, based on Particle Swarm Optimization. Starting from an algorithm coded in a highlevel language which has shown to perform well, both in terms of accuracy and of computation efficiency, the simplified version can be implemented on an FPGA. After describing the original algorithm, we describe how it has been simplified for hardware implementation. We show how the intrinsic modularity of the algorithm permits to define a general core, independent of the specific application, which implements object search, along with a simple applicationspecific module, which implements a problem-dependent fitness function. This makes the system easily reconfigurable when switching between different object detection applications. Finally, we show some examples of application of our algorithm and discuss about possible future developments

A video-based fall detector sensor well suited for a data-fusion approach

This paper describes an innovative video fall detector which can be implemented in hardware. The goal was to design a very economic embedded video sensor, based on a computationally light algorithm. Such a sensor can be placed in an environment equipped with an 'ambient intelligence system', to produce a robust detection of fall events, using a data-fusion approach which exploits the correlation of information obtained from this and other sensors (wearable sensors, audio sensors, etc.)

Escherichia coli as a Model for the Description of the Antimicrobial Mechanism of a Cationic Polymer Surface: Cellular Target and Bacterial Contrast Response

In this study, we use Escherichia coli as a model to investigate the antimicrobial mechanism of a film made of a copolymer based on monomethylether poly(ethylene glycol), methyl methacrylate, and 2-dimethyl(aminoethyl) methacrylate, whose surface is active towards Gram-negative and Gram-positive bacteria. The polymer contains not quaternized amino groups that can generate a charged surface by protonation when in contact with water. For this purpose, we adopted a dual strategy based on the analysis of cell damage caused by contact with the polymer surface and on the evaluation of the cell response to the surface toxic action. The lithic effect on the protoplasts of E. coli showed that the polymer surface can affect the structure of cytoplasmic membranes, while assays of calcein leakage from large unilamellar vesicles at different phospholipid compositions indicated that action on membranes does not need a functionally active cell. On the other hand, the significant increase in sensitivity to actinomycin D demonstrates that the polymer interferes also with the structure of the outer membrane, modifying its permeability. The study on gene expression, based on the analysis of the transcripts in a temporal window where the contact with the polymer is not lethal and the damage is reversible, showed that some key genes of the synthesis and maintenance of the outer membrane structure (fabR, fadR, fabA, waaA, waaC, kdsA, pldA, and pagP), as well as regulators of cellular response to oxidative stress (soxS), are more expressed when bacteria are exposed to the polymer surface. All together these results identified the outer membrane as the main cellular target of the antimicrobial surface and indicated a specific cellular response to damage, providing more information on the antimicrobial mechanism. In this perspective, data reported here could play a pivotal role in a microbial growth control strategy based not only on the structural improvements of the materials but also on the possibility of intervening on the cellular pathways involved in the contrast reaction to these and other polymers with similar mechanisms

Complete complementarity relations for quantum correlations in neutrino oscillations

We analyze quantum correlations and quantum coherence in neutrino oscillations. To this end, we exploit complete complementarity relations (CCR) that fully characterize the interplay between different correlations encoded in a quantum system both for pure and mixed states. We consider the CCR for neutrino oscillations both in the case of plane-waves (pure state) and of wave packets (mixed state). In this last case we find a complex structure of correlations depending on the mixing angle, and we show the connection with the non local advantage of quantum coherence, a relevant quantifier of coherence

Optimization of the anaerobic denitrification process mediated by Bacillus cereus in a batch reactor

7nononeThe purpose of this work was to study the performances of a facultative denitrifying bacterium, Bacillus cereus, in an anaerobic condition in presence of synthetic wastewater enriched of nitrate. Here, the kinetics of nitrate and nitrite consumption operated by Bacillus cereus in a specifically devised batch bioreactor, in anoxic condition and with acetates and sucrose as the organic substrate, was characterized. In particular, the effect of pH and BOD concentration on the denitrification performances was assessed. A complete denitrification was found in the presence of [BOD] > 4 g/L and with a pH buffered around 7. When the initial concentration of nitrates was in the range 100–200 mg/L, the denitrification process took place in about 30 h with a kinetics described by a zero-order mechanism with respect to nitrate and nitrite concentration. The denitrification process was characterized by calculating the degradation kinetic constants: kd NO3 −=0.5±0.1 mg L−1 min−1 and kd NO2 −=7.3±1.5×10−2 mg L−1 min−1 for nitrate and nitrite ions, respectively. Moreover, a thorough analyses in terms of Monod kinetics yielded a bacterial growth yield Y=5.7±0.2× 10−3 gVSS/mgN−NO3 −, a specific denitrification rate SDR = 89.5 mgN−NO3 −/gVSS h−1 and a maximum bacterial growth rate μmax=0.86± 0.03 h−1. These results allow one to consider Bacillus cereus one of most efficient denitrifying bacilli to be employed in industrial wastewater treatment plants (e.g. dairy farm). © 2019noneZarrella I.; Matrella S.; Fortunato G.; Marchettini N.; Proto A.; Motta O.; Rossi F.Zarrella, I.; Matrella, S.; Fortunato, G.; Marchettini, N.; Proto, A.; Motta, O.; Rossi, F