Modeling health potential and quality of fresh-cut strawberries after washing-disinfection with peracetic acid

En este trabajo se propone cuantificar y modelar los cambios en el potencial saludable, atributos fisicoquímicos y reducción de microorganismos aerobios mesófilos de dos variedades de frutillas frescas cortadas (Camarosa y Selva) cuando se someten al lavado-­desinfección con soluciones de ácido peracético (APA), variando la concentración (0 ? 100 mgL-­‐1), el tiempo de contacto (10 ? 120 s) y la temperatura de la solución de lavado (4 ? 40°C). Para el diseño y análisis de los resultados se aplicó la Metodología de Superficie de Respuesta, siendo las respuestas: porcentajes de retención de acido ascórbico (RAA) y vitamina C (RVit C), antocianinas totales (RAnt T), fenoles totales (RFT), capacidad antioxidante (RCA), y sólidos solubles(RSS), cambios de pH (CpH), acidez total (CAT), y de los parámetros de color:L*, a*, b*, Cab* y hab; y reducción de microorganismos aerobios mesófilos (RedFAM). Los modelos de retenciones de AA, Ant T, CA y FT se vieron afectados por las variables del proceso, principalmente por la concentración de APA y el tiempo, no observándose diferencias entre ambas variedades. La retención de VitC y los cambios de color no sufrieron modificaciones debido a las variables de procesamiento para el cultivar Camarosa. Sin embargo, para la variedad Selva, los modelos predictivos de estos parámetros si se vieron afectados por las variables de la operación. La retención de SS, y los cambios de pH y AT no fueron afectados por el lavado, y por lo tanto no pudieron modelarse. Por otra parte, la Red FAM fue afectada por las variables del proceso obteniéndose modelos predictivos para cada cultivar. Este trabajo demuestra el comportamiento diferente de ambas variedades de frutillas ante un mismo proceso de lavado-desinfección y provee herramientas predictivas sencillas para cuantificar dicho efectoThe aim of this work was to quantify and model changes in bioactive compounds and antioxidant capacity content, physicochemical attributes and aerobic mesophilic microorganisms, of two freshFcut strawberries varieties (Camarosa and Selva) with peracetic acid washingFdisinfection (APA) at different concentrations (0 F 100 mg LF1 ), contact times (10 F 120 s) and temperatures (4 F 40°C). Response surface methodology was employed for the design and analysis of results. The studied responses were: ascorbic acid (RAA), vitamin C (RVit C), total anthocyanins (RAnt T), total phenols (RFT), antioxidant capacity (RCA), soluble solids (RSS) retention percentages, and changes on pH (CpH), total acidity (CAT), and color parameters: L*, a*, b*, Cab* and hab. Reduction of aerobic mesophilic microorganisms (FAM) was also evaluated. The retention of AA, Ant T, AC and TP were affected by the process washingFdisinfection variables, mainly by the concentration of PAA and time, and there were no differences between both varieties. Vit C retention and color changes were not affected by processing variables for Camarosa cultivar. However, latter parameters were affected by the process variables in Selva strawberries. Retention of SS, and changes in pH and TA were not affected by the washing process, and therefore could not be modeled. FAM reduction was affected by the washingFdisinfection in both cultivars differentially, and predictive models for each of them were obtained. This work demonstrates the different behavior of two strawberry varieties after the same washingFdisinfection process and provides simple predictive tools to quantify this effectFil: Van de Velde, Franco. Universidad Nacional del Litoral. Facultad de Ingeniería Química. Instituto de Tecnología de los Alimentos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Piagentini, Andrea. Universidad Nacional del Litoral. Facultad de Ingeniería Química. Instituto de Tecnología de los Alimentos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Guemes, Daniel. Universidad Nacional del Litoral. Facultad de Ingeniería Química. Instituto de Tecnología de los Alimentos; ArgentinaFil: Salsi, Maria Sara. Universidad Nacional del Litoral. Facultad de Ingeniería Química. Instituto de Tecnología de los Alimentos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Tiburzi, Maria del Carmen. Universidad Nacional del Litoral. Facultad de Ingeniería Química. Instituto de Tecnología de los Alimentos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Moguilevsky, María. Universidad Nacional del Litoral. Facultad de Ingeniería Química. Instituto de Tecnología de los Alimentos; ArgentinaFil: Pirovani, Maria Elida. Universidad Nacional del Litoral. Facultad de Ingeniería Química. Instituto de Tecnología de los Alimentos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Data-driven uncertainty quantification and propagation for probabilistic trajectory planning

One of the main objectives of Trajectory-Based Operations (TBO) is to increase the predictability of the aircraft behavior within the Air Traffic Management (ATM) system. However, most systems involved in TBO (such as flight planning systems) focus on proposing deterministic trajectories in the strategic phase, not taking into account the uncertainty factors that affect the trajectory prediction process in the tactical phase. Consequently, there is an increased frequency of updates and modifications to trajectories in later planning phases, which leads to degraded stability, resulting in an overall decrease of the performance of the ATM network. In this presentation, a data-driven methodology will be introduced for characterizing the uncertainties affecting the development of an aircraft trajectory, together with their integration into a stochastic trajectory predictor for obtaining robust sets of probabilistic trajectories from an initial flight plan. Additionally, this methodology employs data assimilation models that capture updated information from the air traffic system to reduce the present uncertainty. First, the main sources of uncertainty for aircraft trajectories will be identified and quantified using historical flight instances for a full year of pan-European air traffic. After quantifying these sources of uncertainty, it will be possible to evaluate the potential variations for a flight plan given the probability distributions for uncertain factors affecting the flight. Instead of applying computationally demanding methods, such as Monte Carlo simulations, for calculating all possible trajectories, a stochastic trajectory predictor is proposed that makes use of the characterization of trajectory uncertainty to compute probabilistic trajectories given an initial flight plan. The stochastic trajectory predictor uses arbitrary Polynomial Chaos Expansion (PCE) theory and the point collocation method to find polynomials describing the aircraft trajectory for the initial flight plan as a function of the identified uncertain factors. Therefore, the quantified uncertainty sources can be fitted in the polynomials to find a reduced set of probabilistic trajectories that are robust and resilient to potential variations in the tactical phase. Complementing this, a set of advanced data-assimilation models based on machine learning techniques are integrated to provide accurate estimations for some of the uncertain factors based on the last available status of the air traffic system. These estimates reduce the uncertainty spectrum for important variables in the trajectory prediction process and help adapting the resulting probabilistic trajectories to the current system status. Finally, a study case is introduced in which the proposed methodology is implemented. This study includes the results of analyzing the probabilistic trajectories for one city-pair and supports the idea of integrating probabilistic trajectories as a key enabler for envisioned TBO concepts and modern airline operations plannin

Data-driven methodology for uncertainty quantification of aircraft trajectory predictions

One of the main objectives of the so-called trajectory-based operations (TBO) concept is to increase the predictability of the aircraft behavior within the air traffic management (ATM) system, thus reducing inefficiencies and increasing the robustness and resiliency of operations. Most systems involved in TBO, such as flight planning systems or on-ground trajectory predictors, focus on proposing deterministic trajectories in the strategic phase and do not take into account the uncertain factors that affect the trajectory prediction process. While TBO is enabled by the automated updating of trajectories in reaction to developing uncertainties, an excessive frequency of trajectory updates in later planning and tactical phases could lead to degraded stability, resulting in an overall decrease of the performance of the ATM network. The use of probabilistic trajectories instead of deterministic ones would allow to reduce the frequency of these updates, as well as increasing to increase the situational awareness of the ATM system. Nonetheless, the analysis of the uncertainty affecting the prediction of a flight is a complex problem that has been tackled in the literature. The main difficulty regarding aircraft trajectory uncertainty is that it cannot be estimated in a post-processing study based on the comparison between the predicted and the actual trajectories. This is because the uncertainty is represented by the estimation of those potential deviations in an a priori phase, based on the identification and quantification of the possible sources impacting that uncertainty and the propagation of the joint effect of those sources to obtain the probability distribution describing the potential trajectory

Towards a Stable and resilient ATM by integrating Robust airline operations into the network - Scientific Progress during the 1st year of START project.

Trajectory-based operations (TBO) is one of the cornerstones of a modernised air traffic-management (ATM) system. The TBO operation concept takes into account the trajectory of every aircraft during all phases of the flight and manages their interactions to achieve the optimum system outcome, with minimal deviation from the user requested flight trajectry, whenever possbile. However, as TBO is based on a constant exchange of information about trajectories between the ground and air systems, uncertainties inherent in the ATM system sometimes lead to a degradation of its performance when disruptions occur. The EU-funded START project aims to design, apply and verify optimised algorithms that will enable a robust ATM system not only for conventional air traffic but resilient in disrupted circumstances as well

Simulation Exercises for robust Flight dispatching solution under thunderstorm disruptions

The development, implementation and validation of optimisation algorithms for robust airline operations that result in stable and resilient Air Traffic Management (ATM) performance even in disturbed scenarios are the overall goals of START. This presentation focusses on the validation part. The validation of the START robust airline operations is performed by comparing the performance of a reference and a resilient scenario under disturbed and undisturbed conditions. The reference scenario is derived from the traffic demand for two days in 2018, June 7th and June 10th with strong convective weather phenomena. The resilient scenario is built on the reference scenario but is prepared for more frequent planning updates due to changing forecasts of capacity shortfalls mainly caused by weather impacts. Resiliency refers to the intrinsic ability of a system to adjust its functioning prior to, during, or following changes and disturbances. Within the validation trials performed, disturbances are included by means of convective weather areas which are handled as No-Fly-Zones (NFZ). Validation of the START results is performed threefold. First, reference and resilient scenarios are compared, mainly focussing on expected duration of overall conflict hours of aircraft with other aircraft and convective weather zones. Second, real life departure uncertainties are added by means of Monte-Carlo simulations with different distributions. Finally, scenarios are resolved with conflict resolution algorithms above FL150 as far as possible. The presentation gives an overview of the validation results, showing an overall low but stable benefit for the adapted aircraft fleet (Star Alliance) of the resilient scenario, with no negative effects for the global scenario

Three-Dimensional Molecular Cartography of the Caribbean Reef-Building Coral Orbicella faveolata

All organisms host a diversity of associated viruses, bacteria, and protists, collectively defined as the holobiont. While scientific advancements have enhanced the understanding of the functional roles played by various components of the holobiont, there is a growing need to integrate multiple types of molecular data into spatially and temporally resolved frameworks. To that end, we mapped 16S and 18S rDNA metabarcoding, metatranscriptomics, and metabolomic data onto three-dimensional reconstructions of coral colonies to examine microbial diversity, microbial gene expression, and biochemistry on two colonies of the ecologically important, reef-building coral, Orbicella faveolata and their competitors (i.e., adjacent organisms interacting with the corals: fleshy algae, turf algae, hydrozoans, and other corals). Overall, no statistically significant spatial patterns were observed among the samples for any of the data types; instead, strong signatures of the macroorganismal hosts (e.g., coral, algae, hydrozoa) were detected, in the microbiome, the transcriptome, and the metabolome. The 16S rDNA analysis demonstrated higher abundance of Firmicutes in the coral microbiome than in its competitors. A single bacterial amplicon sequence variant from the genus Clostridium was found exclusively in all O. faveolata samples. In contrast to microbial taxa, a portion of the functionally annotated bacterial RNA transcripts (6.86%) and metabolites (1.95%) were ubiquitous in all coral and competitor samples. Machine learning analysis of microbial transcripts revealed elevated T7-like cyanophage-encoded photosystem II transcripts in O. faveolata samples, while sequences involved in bacterial cell division were elevated in turf algal and interface samples. Similar analysis of metabolites revealed that bacterial-produced antimicrobial and antifungal compounds were highly enriched in coral samples. This study provides insight into the spatial and biological patterning of the coral microbiome, transcriptome, and metabolome