Stringification of Chiral Dynamics: Wess-Zumino interaction

The QCD hadronic string is supplemented with the reparameterization-invariant boundary interaction to background chiral fields associated with pions in a way compatible with the conformal symmetry. It allows the full reconstruction of the P-even part of the Chiral Lagrangian in a good agreement with the phenomenology of P-even meson interactions. The modification of boundary interaction necessary to induce the parity-odd Chiral Dynamics (WZW action) is outlined.Comment: 5 pages, misprints removed, Talks at the 1th Workshop on Hadron Structure and QCD, 18-22.May 2004, St.Petersburg, Russia, at the Bogolubov Conference on Problems of Theoretical and Mathematical Physics, 2-6.September 2004, Dubna, Russia and at the 6th Conference on Quark Confinement and the Hadron Spectrum, 21-25.September 2004, Villasimius, Sardegna, Ital

The use of the Gompertz model in its differential form for weed emergence modelling

Los modelos empíricos que se utilizan para describir y predecir las emergencias de las malas hierbas basados en temperatura y humedad del suelo presentan dos puntos débiles: la ambigüedad que supone tomar como instante inicial de acumulación de grados termales o hidrotermales el día del laboreo y la necesidad de su validación posterior. La utilización de estos modelos en su forma diferencial haría innecesario establecer un día concreto de inicio de la acumulación de grados y podría aportar predicciones sin necesidad de validaciones. Para comprobar estas hipótesis, la ecuación de Gompertz, uno de los modelos más utilizados para describir emergencias, se ha aplicado en su forma diferencial a 35 conjuntos de datos de malas hierbas de diferentes localidades. El ajuste obtenido reproduce de forma satisfactoria la dinámica de emergencias observada en el campo y permite determinar con precisión el momento de aplicación de una medida de control.The Gompertz model, in its differential form, is used for weed emergence modelling. Empirical models that are used to describe the emergence patterns of weeds based on temperature and soil moisture have two weaknesses: the uncertainty of when to start counting and the need for validation. The use of these models in their differential form avoids setting up an ambiguous starting date and also avoids model validation. In order to check these hypotheses, the Gompertz equation, one of the models used most frequently for weed emergence, was verified in 35 data sets of weed species emergence from different areas within the Iberian Peninsula. In all cases, the emergence pattern and forecast for ideal weed control timing was sufficiently accurate

Pilot3: A crew multi-criteria decision support tool – Estimating performance indicators and uncertainty for tactical trajectory management

During a flight, when a change in the operational conditions arises (e.g., new updated weather forecast, delay at reaching a given waypoint), different alternative trajectories can be computed with dedicated optimisation or prediction systems. These systems usually produce trajectories with trade-offs between expected fuel usage and delay. The pilot, or the dispatcher, considers these expected values in order to decide how to tactically operate the aircraft. This approach has two main challenges. Firstly, it requires the translation of arrival delay into parameters which are relevant for the airlines, such as on-time performance and cost of delay. Secondly, uncertainties in the system need to be estimated, such as holding time at arrival, or taxi-in time. Both of these estimations (airlines performance indicators and uncertainty) rely on the airline staff expertise. Finally, the crew faces a multi-criteria decision process as different objectives (cost, on-time performance) and constraints need to be considered. The use of prior to the flight estimations, such as the cost index of the operational flight plan, might not be relevant at the moment of reassessing the flight, as the situation has evolved (for example, the number of passengers who can potentially miss their connections will depend on the status of the fleet of the airline). In other cases, this expected cost of delay could be estimated by the crew or the dispatchers, but generally it is difficult to internalise the dynamics of cost due to IROPS on passengers, or even to estimate the cost of a potential curfew at the end of the day. Uncertainties such as the expected holding delay, distance flown at the arrival TMA, or taxi-in time, might lead to sub-optimal decisions, such as recovering delay, using extra fuel, which does not translate into economic benefit, as larger holding than anticipated might lead to passengers still missing their connection; or shorter distances flown in the TMA means that speed-ups performed during the cruise were unnecessary. Pilot3, a Clean Sky 2 Research and Innovation action, sets out to overcome these issues by developing a multi-criteria support decision tool, which combines explicit estimation of key performance indicators and estimation of ATM operational parameters. These estimators will be developed incrementally, from simple heuristics to machine learning models. Pilot3 prototype comprises five sub-systems: * An Alternatives Generator, which will compute the different alternatives to be considered by the pilot; fed by two independent sub-systems: * Performance Indicators Estimator, which provides the Alternatives Generator with information on how to estimate the impact of each solution for the different performance indicators; * Operational ATM Estimator, which provides the Alternative Generator with information on how to estimate some operational aspects such as tactical route amendments, expected arrival procedure, holding time in terminal airspace, distance flown (or flight time spent) in terminal airspace due to arrival sequencing and merging operations, or taxi-in time; * Performance Assessment Module, which, considering the expected results for each alternative on the different KPIs, is able to filter and rank the alternatives considering airlines and pilots preferences; and * Human Machine Interface, which will present these alternatives to the pilot and allow them to interact with the system. Pilot3 is led by the University of Westminster with the Universitat Politecnica de Catalunya, Innaxis and PACE Aerospace Engineering and Information Technology as partners. The Topic Manager is Thales AVS France SAS. With support from the Advisory Board, Pilot3 has already identified the key operational performance indicators that crew should consider when tactically adjusting their trajectories (on-time performance and total cost, including fuel, IROPs and others); and a literature review and filtering process on multi-criteria decision making techniques has been conducted to select the most suitable method for the different phases of the optimisation process (trajectory generation, filtering and ranking of alternatives)

In-Flight Cost Index Optimisation Upon Weather Forecast Updates

This paper presents an optimisation framework to compute the altitude and speed profiles of a trajectory in the execution phase of the flight, such that the expected total cost (ETC) of the operation is minimised (i.e., modelling the expected cost of delay and fuel – including arrival uncertainties – at the arrival gate). This is achieved with a two-stage optimisation strategy: a trajectory optimiser that minimises a generalised direct operating cost function, for a given cost index; and an upper-level optimiser, which obtains the best cost index that minimises the ETC. Several case studies are presented for different departure delays, while considering the impact of two different weather forecast updates too: a region with relative high head-winds appearing half way across the flight; and a cold atmosphere scenario, with a tropopause altitude lower than standard conditions

On gonihedric loops and quantum gravity

We present an analysis of the gonihedric loop model, a reformulation of the two dimensional gonihedric spin model, using two different techniques. First, the usual regular lattice statistical physics problem is mapped onto a height model and studied analytically. Second, the gravitational version of this loop model is studied via matrix models techniques. Both methods lead to the conclusion that the model has $c_{matter}=0$ for all values of the parameters of the model. In this way it is possible to understand the absence of a continuous transition

Considering TMA holding uncertinaty into in-flight trajectory optimisation

Aircraft crew are aware of the delay they have experienced at departure. However, uncertainties ahead, and in particular holdings at arrival, can have an impact on the final performance of their operations. When optimising a trajectory the expected cost at the arrival gate should be considered. Consequently, taking into account potential congestion and extra delay at the arrival airspace is paramount to avoid taking sub-optimal decisions at the early stages of the flight. This paper presents a framework to optimise trajectories in the execution phase of the flight considering expected delays at arrival. A flight from Athens (LGAV) to London Heathrow (EGLL) is used as illustrative example, systematically exploring a range of departure delays and expected holdings at arrival

Hydrological and erosion response at micro-plot to -catchment scale following forest wildfire, north-central Portugal

Wildfires can have important impacts on hydrological and soil erosion processes, due to the destruction of vegetation cover and changes to soil properties. According to Shakesby and Doerr (2006), these wildfire effects are: i) much better known at small spatial scales (especially erosion plots) than at the scale of catchments; ii) much better studied with respect to overland flow and streamflow (and, then, especially peak discharges) than to soil erosion. Following up on a precursor project studying runoff generation and the associated soil losses from micro-plot to slope-scale in Portuguese eucalypt forests, the EROSFIRE-II project addresses the connectivity of these processes across hillslopes as well as within the channel network. This is done in the Colmeal study area in central Portugal, where the outlet of an entirely burnt catchment of roughly 10 ha was instrumented with a gauging station continuously recording water level and tubidity, and five slopes were each equipped with 4 runoff plots of < 0,5 m2 (“micro-plot”) and 4 slope-scale plots as well as 1 slope-scale sediment fence. Starting one month after the August 2008 wildfire, the plots were monitored at 1- to 2-weekly intervals, depending on the occurrence of rainfall. The gauging station became operational at the end of November 2008, since the in-situ construction of an H-flume required several weeks. A preliminary analysis of the data collected till the end of 2008, focusing on two slopes with contrasting slope lengths as well as the gauging station: revealed clear differences in runoff and erosion between: (i) the micro-plot and slope-scale plots on the same hillslope; (ii) the two slopes; (iii) an initial dry period and a subsequent much wetter period; (iv) the slopes and the catchment-scale, also depending on the sampling period. These results suggest that the different processes govern the hydrological and erosion response at different spatial scales as well as for different periods, with soil water repellency playing a role during the initial post-fire period. The current presentation will review these preliminary results based on the data collected during the first year after the wildfire

Application of multivariate image analysis for on-line monitoring of a freeze-drying process for pharmaceutical products in vials

[EN] A new Process Analytical Technology (PAT) has been developed and tested for on-line process monitoring of a vacuum freeze-drying process. The sensor uses an infrared camera to obtain thermal images of the ongoing process and multivariate image analysis (MIA) to extract the information. A reference model was built and different kind of anomalous events were simulated to test the capacity of the system to promptly identify them. Two different data structures and two different algorithms for the imputation of the missing information have been tested and compared. Results show that the MIA-based PAT system is able to efficiently detect on-line undesired events occurring during the vacuum freeze-drying process.The authors would like to acknowledge Elena Lietta for her support in the experimental investigation. This research work was partially supported by the Spanish Ministry of Economy, Industry and Competitiveness under the project DPI2017-82896-C2-1-R.Colucci, D.; Prats-Montalbán, JM.; Fisore, D.; Ferrer, A. (2019). Application of multivariate image analysis for on-line monitoring of a freeze-drying process for pharmaceutical products in vials. Chemometrics and Intelligent Laboratory Systems. 187:19-27. https://doi.org/10.1016/j.chemolab.2019.02.004S192718

Dispatcher3: Innovative processing for flight practices

An experienced dispatcher will have a good understanding on the differences between planned and executed flight plans. These differences will be driven by uncertainty factors such as, which runway is the one that will be used at arrival, what is the actual weather that the flight will experience, or how much delay will the flight experience as holding at the arrival. Besides safety aspects, dispatchers will consider, among other parameters, the operational environment and constraints (such as flight date and time, network congestion or route availability), the situation of the airline fleet (e.g., delays), airline policies and performance indicators (cost and on-time performance) to select the most suitable flight plan: route, profile and cost index. Some of these aspects can be automatised by using advanced flight dispatching and planning tools, but having a good understanding of the expected discrepancies between planned and realised, and of the key driving factors for these variations is key to produce robust and efficient solutions. Dispatcher3, an Innovative Action within the frame of CleanSky 2 ITD System, will provide a data infrastructure for levering on historical data and machine learning techniques to systematically estimate the variability between planned and executed flight plans. The project is led by the University of Westminster, with Innaxis, the Universitat Politecnica de Catalunya, Vueling Airlines, PACE Aerospace Engineering and Information Technology and skeyes as partners. The Topic Manager is Thales AVS France SAS. Dispatcher3 focuses on the activities prior to departure and aims at supporting dispatchers, pilots and the strategic scheduling process. * Dispatchers will benefit from * predictions of the expected actual performance of a flight, * advice on the flight plan design and selection process, and * identification of the key driving factors for the variations between planning and execution. * The flight crew will obtain * information on the expected variance between the flight plan and the flight execution, and * qualitative advice on some flight operations. * Schedule planners will count with an infrastructure able to identify which flights are systematically prone to variations between schedules and execution blocks requiring the need of further assessment. Dispatcher3 is organised in three layers: * Data infrastructure: Powered by DataBeacon (a multi-sided, open source, data storage and processing platform). It provides private environments to perform analytical and modelling tasks, secure data fusion, and a cloud computing scalable infrastructure; * Predictive capabilities; with two different modules: * Data acquisition and preparation: with a first phase of data wrangling and a second step of descriptive analytics. * Predictive modelling: following the standard machine learning pipeline of target variable labelling, feature engineering, and finally training, testing and validation of machine learning models. * Advice capabilities: relying on the output of the predictive layer and producing specific advise to users: dispatchers, pilots and schedule planners. Dispatcher3 will consider datasets available within airlines, but also analyse which datasets are currently not accessible but could benefit these predictive capabilities. This quantification on the predictive improvement will help identify which multi-stakeholders collaborations should be established

A software engine for multi-criteria decision support in flight management - Use of dynamic cost functions - Architecture and first results

Tactical trajectory optimisation should consider the total expected cost of the flight (fuel and delay). The cost of fuel can be estimated from the expected fuel usage. The cost of delay can be approximated by simple non-linear functions but, we propose a methodology to explicitly consider its different components: passenger related (regulation 261, duty of care, missed connections and soft costs), crew and maintenance, and reactionary costs (delay and curfew). This explicit modelling captures the non-continuous aspects of the cost function, which can significantly impact the optimisation profile, e.g. ensure that missed connections are reduced. The cost of delay, dependent on the arrival time at the gate, can be subject to uncertainties which are inherent (e.g. if a passenger will or not miss a connection) and external (e.g. taxi-in or holding times). Therefore, the optimisation framework should estimate the arrival time to the gate (not the runway) while considering these associated uncertainties. The described architecture models the processes affecting the cost (e.g. considering probabilities of missed connections or explicit propagation of delay) and operational aspects at arrival which impact the realisation of the planned optimised trajectory (holding time, sequencing and merging distance (tromboning), and taxi-in time). The consideration of the operational uncertainties enables the estimation of the probability of achieving the flight on-time performance. All these operational uncertainties are integrated into the cost function producing a total expected cost as a function of arrival to FL100 during the descent at the arrival airport. The trajectory is then optimised in its vertical and speed profile finding the cost index which is expected to minimise the total costs with a simulated annealing framework. The first results presented describe how the cost functions are generated, uncertainties considered and trajectories optimised for a flight in the LEDM-EDDF route