Identifying Anomalies in past en-route Trajectories with Clustering and Anomaly Detection Methods

International audienceThis paper presents a framework to identify and characterise anomalies in past en-route Mode S trajectories. The technique builds upon two previous contributions introduced in 2018: it combines a trajectory-clustering method to obtain the main flows in an airspace with autoencoding artificial neural networks to perform anomaly detection in flown trajectories. The combination of these two well-known Machine Learning techniques (ML) provides a useful reading grid associating cluster analysis with quantified level of abnormality. The methodology is applied to a sector of the French Bordeaux Area Control Center (ACC) during its 385 hours of operation over seven months of ADS-B traffic. The results provide a good taxonomy of deconfliction measures and weather-related ATC actions. The application of this work is manyfold, ranging from safety studies estimating risks of midair collision, to complexity and workload assessments of traffic when a sector is operated, or to the constitution of a database of ATC actions ensuring aircraft separation. This database could be used to train further ML techniques aimed at improving the state of the art of deconfliction algorithms

Recent Advances in Anomaly Detection Methods Applied to Aviation

International audienceAnomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. In particular, we cover unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance

Data-driven airport management enabled by operational milestones derived from ADS-B messages

Standardized, collaborative decision-making processes have already been implemented at some network-relevant airports, and these can be further enhanced through data-driven approaches (e.g., data analytics, predictions). New cost-effective implementations will also enable the appropriate integration of small and medium-sized airports into the aviation network. The required data can increasingly be gathered and processed by the airports themselves. For example, Automatic Dependent Surveillance-Broadcast (ADS-B) messages are sent by arriving and departing aircraft and enable a data-driven analysis of aircraft movements, taking into account local constraints (e.g., weather or capacity). Analytical and model-based approaches that leverage these data also offer deeper insights into the complex and interdependent airport operations. This includes systematic monitoring of relevant operational milestones as well as a corresponding predictive analysis to estimate future system states. In fact, local ADS-B receivers can be purchased, installed, and maintained at low cost, providing both very good coverage of the airport apron operations (runway, taxi system, parking positions) and communication of current airport performance to the network management. To prevent every small and medium-sized airport from having to develop its own monitoring system, we present a basic concept with our approach. We demonstrate that appropriate processing of ADS-B messages leads to improved situational awareness. Our concept is aligned with the operational milestones of Eurocontrol’s Airport Collaborative Decision Making (A-CDM) framework. Therefore, we analyze the A-CDM airport London–Gatwick Airport as it allows us to validate our concept against the data from the A-CDM implementation at a later stage. Finally, with our research, we also make a decisive contribution to the open-data and scientific community

Detecting Controllers' Actions in Past Mode S Data by Autoencoder-Based Anomaly Detection

International audienceThe preparation and execution of training simulations for Air Traffic Control (ATC) and pilots requires a significant commitment of operational experts. Such a mobilisation could be alleviated by a decision support tool trained to generate a realistic environment based on historical data. Prior to studying methods able to learn from a dataset of traffic patterns and ATC orders observed in the past, we focus here on the constitution of such a database from a history of trajectories: the difficulty lies in the fact that past flown trajectories are properly regulated, that observed situations may depend on a wide range of potentially unknown factors and that ownership rules apply on parts of the data. We present here a method to analyse flight trajectories, detect unusual flight behaviours and infer ATC actions. When an anomaly is detected, we place the trajectory in context, then assess whether such anomaly could correspond to an ATC action. The trajectory outlier detection method is based on autoencoder Machine Learning models. It determines trajectory outliers and quantifies a level of abnormality, therefore giving hints about the nature of the detected situations. Results obtained on three different scenarios, with Mode S flight data collected over one year, show that this method is well suited to efficiently detect anomalous situations, ranging from classic air traffic controllers orders to more significant deviations. Detecting such situations is not only a necessary milestone for the generation of ATC orders in a realistic environment; this methodology could also be useful in safety studies for anomaly detection and estimation of probabilities of rare events; and in complexity and performance analyses for detecting actions in neighbouring sectors or estimating ATC workload

Factors associated with the health and wellbeing of older people in a rural African setting

Background South Africa is experiencing a massive HIV epidemic that together with the new epidemic of non-communicable diseases is directly affecting the health and wellbeing of older people. For policy makers, there is a crucial need for information on how this dual epidemic is evolving and how this may affect older people's health, mortality and health care needs. 2. Aims To better understand factors that influence the health, wellbeing and survival of older people, and their need for care in rural South Africa at a time of a growing dual epidemic of chronic diseases (non-communicable and communicable). To provide information which may assist in the planning of health services for older people. 3. Methods Applying the WHO Study on Global Ageing and Adult Health (SAGE) and a study on HIV and non-communicable diseases (NCD), we investigated the health, wellbeing and mortality of the population 50 years and older in the Agincourt sub-district in north-east South Africa which is underpinned by health and demographic surveillance. A random sample of the population 50 years and older was selected for the SAGE survey. A random sample of the population 15 years and older was selected for the HIV and NCD study. All available adults 50 plus were invited to participate in the SAGE module in the 2006 census round. We assessed self-reported health, anthropometric measures, blood pressure and HIV status using dried blood spots. Statistical analysis included simple frequencies, univariate and multivariate analysis and Cox proportional hazard models. 4. Findings The usual pattern of mortality, of increasing death rates with age, is not observed in this population, where those in their 50s have higher mortality compared to older age groups. The high prevalence of HIV in this age group (50 to 59) appears to be the main explanation for the observed pattern. Hypertension affects two thirds of this older population and, although there are no differences by gender, women are more aware of their condition. This is reflected in more women attending primary health care services. Reporting lower quality of life and greater disability are associated with higher likelihood of death. We observed gender differences in the process of ageing with women reporting higher prevalence of mortality risk factors but living longer than men, a phenomenon known as the "survival paradox"

Moving from medical to health systems classifications of deaths : extending verbal autopsy to collect information on the circumstances of mortality

Acknowledgements The authors would also like to acknowledge the field staff at the MRC, SA/Wits Agincourt Unit, particularly Sizzy Ngobeni. The authors also acknowledge Drs Malin Eriksson and Edward Fottrell at Umeå Centre for Global Health Research *UCGHR) who contributed to the development of the SF-VA indicators, Dr Nawi Ng at UCGHR who advised on the cause of death categories, and Dr Kerstin Edin at UCGHR who provided comments on the manuscript categories, and Dr Kerstin Edin who provided comments on the manuscript. Funding A Health Systems Research Initiative Development Grant from the UK Department for International Development (DFID), Economic and Social Research Council (ESRC), Medical Research Council (MRC (and the Wellcome Trust (MR/N005597/1) funds the research presented in this paper. Support for the Agincourt HDSS including verbal autopsies was provided by The Wellcome Trust, UK (grants 058893/Z/99/A; 069683/Z/02/Z; 085477/Z/08/Z; 085477/B/08/Z), and the University of the Witwatersrand and Medical Research Council, South Africa.Peer reviewedPublisher PD

Removal of Waterborne Viruses by Tetrahymena pyriformis Is Virus-Specific and Coincides with Changes in Protist Swimming Speed

Biological treatment of waterborne viruses, specifically grazing of viruses by protists, can enhance microbial water quality while avoiding the production of toxic byproducts and high energy costs. However, tangible applications are limited by the lack of understanding of the underlying mechanisms. Here, we examined the feeding behavior of Tetrahymena pyriformis ciliates on 13 viruses, including bacteriophages, enteric viruses, and respiratory viruses. Significant differences in virus removal by T. pyriformis were observed, ranging from no removal (Qbeta, coxsackievirus B5) to ≥2.7 log10 (JC polyomavirus) after 48 h of co-incubation of the protist with the virus. Removal rates were conserved even when protists were co-incubated with multiple viruses simultaneously. Video analysis revealed that the extent of virus removal was correlated with an increase in the protists' swimming speed, a behavioral trait consistent with the protists' response to the availability of food. Protistan feeding may be driven by a virus' hydrophobicity but was independent of virus size or the presence of a lipid envelope. Keywords: biological water treatment; enveloped virus; grazing; protists; swimming speed; waterborne virus

Deep Drainage Lowers Methane and Nitrous Oxide Emissions from Rice Fields in a Semi-Arid Environment in Rwanda

Few studies have explored greenhouse gas (GHG) emissions from arable land in sub-Saharan Africa (SSA), and particularly from rice paddy fields, which can be a major source of methane (CH4) and nitrous oxide (N2O) emissions. This study examined the effect of drainage on CH4 and N2O emissions from rice fields in Rwanda under shallow drainage to 0.6 m, with the drain weir open four times per week, and deep drainage to 1.2 m with the weir open four times or two times per week. CH4 and N2O fluxes from the soil surface were measured on nine occasions during rice flowering and ripening, using a closed chamber method. Measured fluxes made only a minor contribution to total GHG emissions from rice fields. However, drainage depth had significant effects on CH4 emissions, with shallow drainage treatment giving significantly higher emissions (~0.8 kg ha−1 or ~26 kg CO2-equivalents ha−1) than deep drainage (0.0 kg) over the 44-day measurement period. No treatment effect was observed for N2O fluxes, which ranged from low uptake to low release, and were generally not significantly different from zero, probably due to low nitrogen (N) availability in soil resulting from low N fertilization rate (in the region). Overall, the results suggest that deep drainage can mitigate CH4 emissions compared with traditional shallow drainage, while not simultaneously increasing N2O emissions

Modification of optical and electrical properties of zinc oxide-coated porous silicon nanostructures induced by swift heavy ion

Morphological and optical characteristics of radio frequency-sputtered zinc aluminum oxide over porous silicon (PS) substrates were studied before and after irradiating composite films with 130 MeV of nickel ions at different fluences varying from 1 × 10(12) to 3 × 10(13) ions/cm(2). The effect of irradiation on the composite structure was investigated by scanning electron microscopy, X-ray diffraction (XRD), photoluminescence (PL), and cathodoluminescence spectroscopy. Current–voltage characteristics of ZnO-PS heterojunctions were also measured. As compared to the granular crystallites of zinc oxide layer, Al-doped zinc oxide (ZnO) layer showed a flaky structure. The PL spectrum of the pristine composite structure consists of the emission from the ZnO layer as well as the near-infrared emission from the PS substrate. Due to an increase in the number of deep-level defects, possibly oxygen vacancies after swift ion irradiation, PS-Al-doped ZnO nanocomposites formed with high-porosity PS are shown to demonstrate a broadening in the PL emission band, leading to the white light emission. The broadening effect is found to increase with an increase in the ion fluence and porosity. XRD study revealed the relative resistance of the film against the irradiation, i.e., the irradiation of the structure failed to completely amorphize the structure, suggesting its possible application in optoelectronics and sensing applications under harsh radiation conditions