Fiber optics passive monitoring of groundwater temperature reveals three-dimensional structures in heterogeneous aquifers.

Alluvial aquifers often exhibit highly conductive embedded formations that can act as preferential pathways for the transport of solutes. In this context, a detailed subsurface characterization becomes crucial for an effective monitoring of groundwater quality and early detection of contaminants. However, small-scale heterogeneities are seldom detected by traditional nondestructive investigations. Heat propagation in porous media can be a relatively inexpensive tracer for groundwater flow, potentially offering valuable information in various applications. In this study, we applied passive Fiber Optics Distributed Temperature Sensing (FO-DTS) to a group of observation wells in a highly heterogeneous phreatic aquifer to uncover structures with different hydraulic conductivity, relying on their response to temperature fluctuations triggered by natural and anthropogenic forcings. A comprehensive data analysis approach, combining statistical methods and physics-based numerical modeling, allowed for a three-dimensional characterization of the subsurface at the experimental site with unprecedentedly high resolution.

Distributed fiber optic shape sensing with simultaneous interrogation of multiple fibers based on Rayleigh-signature domain multiplexing.

We propose a method for shape sensing that employs Rayleigh-signature domain multiplexing to simultaneously probe the fibers or cores of a shape sensing setup with a single optical frequency-domain reflectometry scan. The technique enables incrementing the measurement speed by a factor equal to the number of multiplexed fibers at the expense of an increased noise floor in accordance with the Cramér-Rao lower bound. Nonetheless, we verify that the shape reconstruction performance of the proposed method is in very good agreement with that of conventional sequential core interrogation.

Rayleigh-Based Distributed Optical Fiber Sensing.

Distributed optical fiber sensing is a unique technology that offers unprecedented advantages and performance, especially in those experimental fields where requirements such as high spatial resolution, the large spatial extension of the monitored area, and the harshness of the environment limit the applicability of standard sensors. In this paper, we focus on one of the scattering mechanisms, which take place in fibers, upon which distributed sensing may rely, i.e., the Rayleigh scattering. One of the main advantages of Rayleigh scattering is its higher efficiency, which leads to higher SNR in the measurement; this enables measurements on long ranges, higher spatial resolution, and, most importantly, relatively high measurement rates. The first part of the paper describes a comprehensive theoretical model of Rayleigh scattering, accounting for both multimode propagation and double scattering. The second part reviews the main application of this class of sensors.

Mathematical modelling of SigE regulatory network reveals new insights into bistability of mycobacterial stress response.

BACKGROUND: The ability to rapidly adapt to adverse environmental conditions represents the key of success of many pathogens and, in particular, of Mycobacterium tuberculosis. Upon exposition to heat shock, antibiotics or other sources of stress, appropriate responses in terms of genes transcription and proteins activity are activated leading part of a genetically identical bacterial population to express a different phenotype, namely to develop persistence. When the stress response network is mathematically described by an ordinary differential equations model, development of persistence in the bacterial population is associated with bistability of the model, since different emerging phenotypes are represented by different stable steady states. RESULTS: In this work, we develop a mathematical model of SigE stress response network that incorporates interactions not considered in mathematical models currently available in the literature. We provide, through involved analytical computations, accurate approximations of the system's nullclines, and exploit the obtained expressions to determine, in a reliable though computationally efficient way, the number of equilibrium points of the system. CONCLUSIONS: Theoretical analysis and perturbation experiments point out the crucial role played by the degradation pathway involving RseA, the anti-sigma factor of SigE, for coexistence of two stable equilibria and the emergence of bistability. Our results also indicate that a fine control on RseA concentration is a necessary requirement in order for the system to exhibit bistability.

Smart testing and selective quarantine for the control of epidemics.

This paper is based on the observation that, during Covid-19 epidemic, the choice of which individuals should be tested has an important impact on the effectiveness of selective confinement measures. This decision problem is closely related to the problem of optimal sensor selection, which is a very active research subject in control engineering. The goal of this paper is to propose a policy to smartly select the individuals to be tested. The main idea is to model the epidemics as a stochastic dynamic system and to select the individual to be tested accordingly to some optimality criteria, e.g. to minimize the probability of undetected asymptomatic cases. Every day, the probability of infection of the different individuals is updated making use of the stochastic model of the phenomenon and of the information collected in the previous days. Simulations for a closed community of 10'000 individuals show that the proposed technique, coupled with a selective confinement policy, can reduce the spread of the disease while limiting the number of individuals confined if compared to the simple contact tracing of positive and to an off-line test selection strategy based on the number of contacts.

On the effect of the number of tests and their time of application in tracing policies against COVID-19.

In this paper we explore the effect of the number of daily tests on an epidemics control policy purely based on testing and selective quarantine, and the impact of these actions depending on the time their application starts. We introduce a general model incorporating a stochastic disease evolution, a particular weighted graph representing the population, and an optimal contact tracing strategy to allocate available tests. Simulations on a community of 50'000 individuals show that the evolution of the epidemic produces a clear non-linear response to the variation of the number of tests used and to the starting time of their application. These results suggest that not only a minimum number of tests is necessary to obtain a positive outcome from the tracing strategy but also that there exists a saturation on the contribution of additional tests. The results also show that the timing in the application of the measures is as important as the measures themselves and that an excessive delay can be hardly overcome.

Distributed Multi-Agent Gaussian Regression via Finite-Dimensional Approximations.

We consider the problem of distributedly estimating Gaussian processes in multi-agent frameworks. Each agent collects few measurements and aims to collaboratively reconstruct a common estimate based on all data. Agents are assumed with limited computational and communication capabilities and to gather $M$M noisy measurements in total on input locations independently drawn from a known common probability density. The optimal solution would require agents to exchange all the $M$M input locations and measurements and then invert an $M \times M$M×M matrix, a non-scalable task. Differently, we propose two suboptimal approaches using the first $E$E orthonormal eigenfunctions obtained from the Karhunen-Loève (KL) expansion of the chosen kernel, where typically $E\ll M$E≪M. The benefits are that the computation and communication complexities scale with $E$E and not with $M$M, and computing the required statistics can be performed via standard average consensus algorithms. We obtain probabilistic non-asymptotic bounds that determine a priori the desired level of estimation accuracy, and new distributed strategies relying on Stein's unbiased risk estimate (SURE) paradigms for tuning the regularization parameters and applicable to generic basis functions (thus not necessarily kernel eigenfunctions) and that can again be implemented via average consensus. The proposed estimators and bounds are finally tested on both synthetic and real field data.

Hands-On Experience of Crowdsourcing for Flood Risks. An Android Mobile Application Tested in Frederikssund, Denmark.

There is evidence that the toll of death and destruction caused by natural hazards is rising. This is often ascribed to the impact of climate change that resulted in an increased frequency of extreme meteorological events. As a consequence, it is realistic to expect that the casualties and damages caused by floods will increase in the near future. Advanced weather forecast is a fundamental tool to predict the occurrence of floods and structural mitigation measures are crucial for flood protection. However, these strategies should be associate with tools to promote and increase natural-disaster awareness and nonstructural mitigation measures in the exposed population. To bridge this gap, we coupled innovative, ICT-based technologies with crowdsourcing. The idea is to exploit geospatial data gathered by citizens and volunteers with their own devices such as mobile phones to provide authorities with relevant information in case of flood emergencies. This paper describes the design and testing of an Android application named MAppERS (Mobile Applications for Emergency Response and Support), thought to enhance active participation and response of the population in territorial and flood-risk mitigation in Frederikssund, Denmark. The results of the piloting fully validate MAppERS as an effective tool to support the decision-making process during a crisis and to improve the awareness of the community and their disaster resilience.

Distributed optical fibre sensing for early detection of shallow landslides triggering.

A distributed optical fibre sensing system is used to measure landslide-induced strains on an optical fibre buried in a large scale physical model of a slope. The fibre sensing cable is deployed at the predefined failure surface and interrogated by means of optical frequency domain reflectometry. The strain evolution is measured with centimetre spatial resolution until the occurrence of the slope failure. Standard legacy sensors measuring soil moisture and pore water pressure are installed at different depths and positions along the slope for comparison and validation. The evolution of the strain field is related to landslide dynamics with unprecedented resolution and insight. In fact, the results of the experiment clearly identify several phases within the evolution of the landslide and show that optical fibres can detect precursory signs of failure well before the collapse, paving the way for the development of more effective early warning systems.

Finding potential support vectors in separable classification problems.

This paper considers the classification problem using support vector (SV) machines and investigates how to maximally reduce the size of the training set without losing information. Under separable data set assumptions, we derive the exact conditions stating which observations can be discarded without diminishing the overall information content. For this purpose, we introduce the concept of potential SVs, i.e., those data that can become SVs when future data become available. To complement this, we also characterize the set of discardable vectors (DVs), i.e., those data that, given the current data set, can never become SVs. Thus, these vectors are useless for future training purposes and can eventually be removed without loss of information. Then, we provide an efficient algorithm based on linear programming that returns the potential and DVs by constructing a simplex tableau. Finally, we compare it with alternative algorithms available in the literature on some synthetic data as well as on data sets from standard repositories.

Characterization of a novel dual-core elliptical hollow optical fiber with wavelength decreasing differential group delay.

The modal distribution of a novel elliptical hollow optical fiber is experimentally and numerically characterized. The fiber has a central elliptical air hole surrounded by a germanosilicate lanceolate ring core. Experiments reveal that the fiber behaves like a dual core waveguide and it is found that the differential group delay of each core decreases with wavelength with a PMD coefficient slope of ~10(-2) ps/m/THz. Experimental results are also compared with numerical modeling based on scanning electron microscopy images.

The "Wireless Sensor Networks for City-Wide Ambient Intelligence (WISE-WAI)" Project.

This paper gives a detailed technical overview of some of the activities carried out in the context of the "Wireless Sensor networks for city-Wide Ambient Intelligence (WISE-WAI)" project, funded by the Cassa di Risparmio di Padova e Rovigo Foundation, Italy. The main aim of the project is to demonstrate the feasibility of large-scale wireless sensor network deployments, whereby tiny objects integrating one or more environmental sensors (humidity, temperature, light intensity), a microcontroller and a wireless transceiver are deployed over a large area, which in this case involves the buildings of the Department of Information Engineering at the University of Padova. We will describe how the network is organized to provide full-scale automated functions, and which services and applications it is configured to provide. These applications include long-term environmental monitoring, alarm event detection and propagation, single-sensor interrogation, localization and tracking of objects, assisted navigation, as well as fast data dissemination services to be used, e.g., to rapidly re-program all sensors over-the-air. The organization of such a large testbed requires notable efforts in terms of communication protocols and strategies, whose design must pursue scalability, energy efficiency (while sensors are connected through USB cables for logging and debugging purposes, most of them will be battery-operated), as well as the capability to support applications with diverse requirements. These efforts, the description of a subset of the results obtained so far, and of the final objectives to be met are the scope of the present paper.

Reflectometric measurement of birefringence rotation in single-mode optical fibers.

We present a novel reflectometric technique for the measurement of orientation and modulus of the linear birefringence vector in single-mode optical fibers. The technique provides information also on circular birefringence, although this component, if present, appears as a rotation of the linear birefringence. A detailed theoretical analysis is reported and validated by experimental results.

Influence of the birefringence autocorrelation function on the polarization mode dispersion of constantly spun fibers.

We show that the polarization mode dispersion of a constantly spun, single-mode fiber is strongly influenced by the autocorrelation function of its birefringence. In particular, under probable conditions, the mean square differential group delay of the spun fiber may even be higher than the delay that the same fiber would have if it were not spun.

Simplified phenomenological model for randomly birefringent strongly spun fibers.

A simplified phenomenological model for the description of randomly birefringent, strongly spun fibers is proposed. It is shown that the spinning, besides causing an apparent reduction of the linear random birefringence, may also induce an apparent deterministic circular birefringence.