Natural language processing for aviation safety: Extracting knowledge from publicly-available loss of separation reports

Background: The air traffic management (ATM) system has historically coped with a global increase in traffic demand ultimately leading to increased operational complexity. When dealing with the impact of this increasing complexity on system safety it is crucial to automatically analyse the losses of separation (LoSs) using tools able to extract meaningful and actionable information from safety reports. Current research in this field mainly exploits natural language processing (NLP) to categorise the reports,with the limitations that the considered categories need to be manually annotated by experts and that general taxonomies are seldom exploited. Methods: To address the current gaps,authors propose to perform exploratory data analysis on safety reports combining state-of-the-art techniques like topic modelling and clustering and then to develop an algorithm able to extract the Toolkit for ATM Occurrence Investigation (TOKAI) taxonomy factors from the free-text safety reports based on syntactic analysis. TOKAI is a tool for investigation developed by EUROCONTROL and its taxonomy is intended to become a standard and harmonised approach to future investigations. Results: Leveraging on the LoS events reported in the public databases of the Comisión de Estudio y Análisis de Notificaciones de Incidentes de Tránsito Aéreo and the United Kingdom Airprox Board,authors show how their proposal is able to automatically extract meaningful and actionable information from safety reports,other than to classify their content according to the TOKAI taxonomy. The quality of the approach is also indirectly validated by checking the connection between the identified factors and the main contributor of the incidents. Conclusions: Authors' results are a promising first step toward the full automation of a general analysis of LoS reports supported by results on real-world data coming from two different sources. In the future,authors' proposal could be extended to other taxonomies or tailored to identify factors to be included in the safety taxonomies

Storage conditions and aleurone PCD in wheat aged seeds

Storage conditions and aleurone PCD in wheat aged seed

A dynamic, interpretable, and robust hybrid data analytics system for train movements in large-scale railway networks

We investigate the problem of analysing the train movements in large-scale railway networks for the purpose of understanding and predicting their behaviour. We focus on different important aspects: the Running Time of a train between two stations, the Dwell Time of a train in a station, the Train Delay, the Penalty Costs associated to a delay, and the Train Overtaking between two trains which are in the wrong relative position on the railway network. Two main approaches exist in the literature to address these problems. One is based on the knowledge of the network and the experience of the operators. The other one is based on the analysis of the historical data about the network with advanced data analytics methods. In this paper, we will propose a hybrid approach in order to address the limitations of the current solutions. In fact, experience-based models are interpretable and robust but not really able to take into account all the factors which influence train movements resulting in low accuracy. From the other side, data-driven models are usually not easy to interpret nor robust to infrequent events and require a representative amount of data which is not always available if the phenomenon under examination changes too fast. Results on real-world data coming from the Italian railway network will show that the proposed solution outperforms both state-of-the-art experience-based and data-driven systems in terms of interpretability, robustness, ability to handle nonrecurring events and changes in the behaviour of the network, and ability to consider complex and exogenous information

Data-Driven Methods for Aviation Safety: From Data to Knowledge

Demand upon the future Air Traffic Management (ATM) systems is expected to grow to possibly exceed available system capacity, pushing forward the need for automation and digitisation to maintain safety while increasing efficiency. This work focuses on a manifestation of ATM safety, the Loss of Separation (LoS), exploiting safety reports and ATM-system data (e.g., flights information, radar tracks, and Air Traffic Control events). Current research on Data-Driven Models (DDMs) is rarely able to support safety practitioners in the process of investigation of an incident after it happened. Furthermore, integration between different sources of data (i.e., free-text reports and structured ATM data) is almost never exploited. To fill these gaps, the authors propose (i) to automatically extract information from Safety Reports and (ii) to develop a DDM able to automatically assess if the Pilots or the Air Traffic Controller (ATCo) or both contributed to the incident, as soon as the LoS happens. The LoSs’ reported in the public database of the Comisión de Estudio y Análisis de Notificaciones de Incidentes de Tránsito Aéreo (CEANITA) support the authors’ proposal

Traffic Characterization for a Dynamic and Adaptive Trajectory Prediction Data-Driven Approach

Accurate and reliable trajectory prediction (TP) is a fundamental requirement to support trajectory-based operations (TBOs). Particularly, the mismatch between planned and flown trajectories (caused by operational uncertainties from airports, Air Traffic Control interventions, Airspace Users behaviour and changes in flight plan data) act as a driver for shortcomings in flow and capacity management (e.g. congestion and suboptimal decision making) and as a precursor for potential safety conflicts. Therefore, enhanced traffic forecasts (whkh integrate uncertainty assessment and include different sources of relevant flight information) may enable improved demand-capacity balancing and conflict detection and resolution (CD&R) models. Moreover, new methodological approaches, as the exploitation of historical data by means of machine-learning techniques is expected to boost TP performance. This paper presents the data-driven, dynamic and adaptive TP framework achieved within DIAPasON project, considering adaptation to different Airspace Users' characteristics and strategies. The main target is the development of a methodology for TP and traffic forecasting in a pre-tactical phase (one day to six days before the day of operations), when few or no flight plans are available. This is able to be adjusted to different time scales (planning horizons), taking into account the level of predictability of each of them

Natural Language Processing and Data-Driven Methods for Aviation Safety and Resilience: from Extant Knowledge to Potential Precursors

Demand upon the future Air Traffic Management (ATM) system will possibly grow to exceed available system capacity, pushing forward the need for automation and digitisation to maintain safety while increasing efficiency. This work focuses on a manifestation of ATM safety, the loss of separation (LoS), and its analysis via Natural Language Processing (NLP) and Data-Driven Methods (DDMs), able to extract meaningful and actionable information from the LoS-related data. These data are, primarily, safety reports and ATM-system data (e.g, flights information, radar tracks, and Air Traffic Control events). Current research in this field mainly exploits NLP to categorise the reports and DDMs to predict safety events. The limitation of current NLP-based approaches is that the considered categories need to be manually annotated by experts and general taxonomies are seldom exploited. At the same time, current DDMs are rarely able to support safety practitioners in the process of investigation of an incident after it happened. To fill these gaps, the authors propose to (i) perform Exploratory Data Analysis on safety reports combining state-of-the-art techniques like topic modelling and clustering, then to (ii) develop an algorithm able to extract the recent Toolkit for ATM Occurrence Investigation (TOKAI) taxonomy factors from the free-text safety reports based on Syntactic Analysis, and finally to (iii) develop a DDM able to automatically assess if the Pilots or the Air Traffic Controller (ATCo) or both contributed to the incident, almost immediately after the LoS. The results on LoSs reported in the public database of the Comisión de Estudio y Análisis de Notificaciones de Incidentes de Tránsito Aéreo (CEANITA) support the authors' proposal

THYROID FUNCTION AND EXPOSURE TO STYRENE

BACKGROUND: Many natural substances and drugs have long been known to cause goiter or thyroid dysfunction. More recently, several environmental pollutants, such as pesticides and industrial compounds, have been investigated for their thyroid-disrupting activity and related adverse effects on human health. The aim of this study was to evaluate the effects of styrene on the thyroid axis in occupationally exposed workers. METHODS: Thirty-eight exposed (E) and 123 nonexposed (NE) male workers (controls) were assessed. Serum concentrations of thyrotropin (TSH; basal and after thyrotropin-releasing hormone [TRH] administration.), free thyroxine (FT(4)), free triiodothyronine (FT(3)), anti-thyroglobulin, thyroid peroxidase antibody, and calcitonin were measured. Thyroid ultrasound examination was also performed. In E workers, urinary creatinine, mandelic acid (MA), and phenylglyoxylic acid (PGA) were also measured. RESULTS: No significant differences between E and NE workers were demonstrated, as far as thyroid volume, nodularity, serum thyroid antibodies, and calcitonin were analyzed. However, in the E group a positive correlation between duration of exposure and thyroid volume was detected. After exclusion of subjects with nodular or autoimmune thyroid diseases, serum concentrations of FT(4), FT(3), and TSH did not differ between the two groups. In E workers there was a positive correlation between the urinary concentrations of styrene metabolites (MA plus PGA) and FT(4) or FT(4)/FT(3) ratio (p < 0.05; r = 0.45 and p < 0.005; r = 0.61, respectively), while no correlation was observed between urinary concentrations of MA plus PGA and serum TSH (either basal and stimulated). CONCLUSIONS: Chronic exposure to styrene is not associated with an increase in nodular or autoimmune thyroid diseases. However, styrene could interfere with peripheral metabolism of thyroid hormones by inhibiting T(4) to T(3) conversion. Whether this is a direct effect on iodothyronine deiodinases or a consequence of a general distress, such as in nonthyroidal illnesses, remains to be established. Further studies in a larger population of exposed workers are needed to confirm these preliminary observations