Humic-like substances from urban waste as auxiliaries for photo-Fenton treatment: a fluorescence EEM-PARAFAC study

In this work, analysis of excitation-emission-matrices (EEM) has been employed to gain further insight into the characterization of humic like substances (HLS) obtained from urban wastes (soluble bio-organic substances, SBOs). In particular, complexation of these substances with iron and changes along a photo-Fenton process have been studied. Recorded EEMs were decomposed by using parallel factor analysis (PARAFAC). Three fluorescent components were identified by PARAFAC modeling of the entire set of SBO solutions studied. The EEM peak locations (λex/λem) of these components were 310?330 nm/400?420 nm (C1), 340?360 nm/450?500 nm (C2), and 285 nm/335?380 nm (C3). Slight variations of the maximum position of each component with the solution pH were observed. The interaction of SBO with Fe(III) was characterized by determining the stability constants of the components with Fe(III) at different pH values, which were in the order of magnitude of the ones reported for humic substances and reached their highest values at pH = 5. Photochemical experiments employing SBO and Fe(III), with and without H2O2, showed pH-dependent trends for the evolution of the modeled components, which exhibited a strong correlation with the efficiency reported for the photo-Fenton processes in the presence of SBO at different pH values.Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicada

Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge

Consumer wearables and sensors are a rich source of data about patients' daily disease and symptom burden, particularly in the case of movement disorders like Parkinson's disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95)

A Comprehensive Case Study of Data-Driven Methods for Robust Aircraft Sensor Fault Isolation

Recent catastrophic events in aviation have shown that current fault diagnosis schemes may not be enough to ensure a reliable and prompt sensor fault diagnosis. This paper describes a comparative analysis of consolidated data-driven sensor Fault Isolation (FI) and Fault Estimation (FE) techniques using flight data. Linear regression models, identified from data, are derived to build primary and transformed residuals. These residuals are then implemented to develop fault isolation schemes for 14 sensors of a semi-autonomous aircraft. Specifically, directional Mahalanobis distance-based and fault reconstruction-based techniques are compared in terms of their FI and FE performance. Then, a bank of Bayesian filters is proposed to compute, in flight, the fault belief for each sensor. Both the training and the validation of the schemes are performed using data from multiple flights. Artificial faults are injected into the fault-free sensor measurements to reproduce the occurrence of failures. A detailed evaluation of the techniques in terms of FI and FE performance is presented for failures on the air-data sensors, with special emphasis on the True Air Speed (TAS), Angle of Attack (AoA), and Angle of Sideslip (AoS) sensors

A Comprehensive Case Study of Data-Driven Methods for Robust Aircraft Sensor Fault Isolation

Recent catastrophic events in aviation have shown that current fault diagnosis schemes may not be enough to ensure a reliable and prompt sensor fault diagnosis. This paper describes a comparative analysis of consolidated data-driven sensor Fault Isolation (FI) and Fault Estimation (FE) techniques using flight data. Linear regression models, identified from data, are derived to build primary and transformed residuals. These residuals are then implemented to develop fault isolation schemes for 14 sensors of a semi-autonomous aircraft. Specifically, directional Mahalanobis distance-based and fault reconstruction-based techniques are compared in terms of their FI and FE performance. Then, a bank of Bayesian filters is proposed to compute, in flight, the fault belief for each sensor. Both the training and the validation of the schemes are performed using data from multiple flights. Artificial faults are injected into the fault-free sensor measurements to reproduce the occurrence of failures. A detailed evaluation of the techniques in terms of FI and FE performance is presented for failures on the air-data sensors, with special emphasis on the True Air Speed (TAS), Angle of Attack (AoA), and Angle of Sideslip (AoS) sensors

Monocular Reactive Collision Avoidance Based on Force Fields for Enhancing the Teleoperation of MAVs

The teleoperation of aerial vehicles can be onerous for naive operators unless the robot is endowed with some autonomy, including sense-and-avoid capabilities. This ensures a safe and smooth navigation even in case of users' lack of experience or distraction. In this paper, we propose a reactive collision avoidance strategy that allows a micro aerial vehicle (MAV) to autonomously avoid obstacles while being steered by an operator. We assume that the only available measurements come from an onboard RGB camera and we adopt a collision avoidance strategy based on virtual force fields. A U-Net is used to estimate the depth map starting from RGB images. Simulations conducted in several different outdoor environments validate the proposed approach

From intrusion detection to software design

I believe the single most important reason why we are so helpless against cyber-attackers is that present systems are not super- visable. This opinion is developed in years spent working on network in- trusion detection, both as academic and entrepreneur. I believe we need to start writing software and systems that are supervisable by design; in particular, we should do this for embedded devices. In this paper, I present a personal view on the field of intrusion detection, and conclude with some consideration on software design