Effect of stocking density of fish on water quality and growth performance of European Carp and leafy vegetables in a low-tech aquaponic system

Aquaponics (AP) is a semi-closed system of food production that combines aquaculture and hydroponics and represents a new agricultural system integrating producers and consumers. The aim of this study was to test the effect of stocking densities (APL, 2.5 kg m-3; APH, 4.6 kg m-3) on water quality, growth performance of the European Carp (Cyprinus carpio L.), and yield of leafy vegetables (catalogna, lettuce, and Swiss Chard) in a low-technology AP pilot system compared to a hydroponic cultivation. The AP daily consumption of water due to evapotranspiration was not different among treatments with an average value of 8.2 L d-1, equal to 1.37% of the total water content of the system. Dissolved oxygen was significantly (p < 0.05) different among treatments with the lowest median value recorded with the highest stocking density of fish (5.6 mg L-1) and the highest median value in the hydroponic control (8.7 mg L-1). Marketable yield of the vegetables was significantly different among treatments with the highest production in the hydroponic control for catalogna (1.2 kg m-2) and in the APL treatment for Swiss Chard (5.3 kg m-2). The yield of lettuce did not differ significantly between hydroponic control and APL system (4.0 kg m-2 on average). The lowest production of vegetables was obtained in the APH system. The final weight (515 g vs. 413 g for APL and APH, respectively), specific growth rate (0.79% d-1 vs. 0.68% d-1), and feed conversion (1.55 vs. 1.86) of European Carp decreased when stocking density increased, whereas total yield of biomass was higher in the APH system (4.45 kg m-3 vs. 6.88 kg m-3). A low mortality (3% on average) was observed in both AP treatments. Overall, the results showed that a low initial stocking density at 2.5 kg m-3 improved the production of European Carp and of leafy vegetables by maintaining a better water quality in the tested AP system

Passenger-Oriented Enhanced Metrics

We report on a project building the first European ATM simulation combining flight and passenger trip data. New propagation-centric and passenger-centric performance metrics are described. The new metrics will be compared with existing, classical metrics, to compare their respective intelligibility, sensitivity and consistency. The trade-offs in performance across the metrics under a range of flight and passenger prioritisation scenarios will be examined. The corresponding regulatory and socio-political contexts are described. Complexity science techniques demonstrate the need to extend flight-centric network representations to include the passenger perspective

Delay propagation – new metrics, new insights

Network delay propagation is intimately linked with the challenges of managing passenger itineraries and corresponding connections. Airline decision-making governing these processes is driven by operational and regulatory factors. Using the first European network simulation model with explicit passenger itineraries and full delay cost estimations, we explore these factors through various flight and passenger prioritisation rules, assessing the performance impacts. Delay propagation is further characterised under the different prioritisation rules using complexity science techniques such as percolation theory and network attack. The relative effects of randomised and targeted disruption are compared

New perspectives for air transport performance

The average delays of flights and passengers are not the same. The air transport industry is lacking passenger-centric metrics; its reporting is flight-centric. We report on the first European network simulation model with explicit passenger itineraries and full delay cost estimations. Trade-offs in performance are assessed using passenger-centric and flight-centric metrics, under a range of novel flight and passenger prioritisation scenarios. The need for passenger-centric metrics is established. Delay propagation is characterised under the scenarios using, inter alia, Granger causality techniques

NonpModelCheck: An R Package for Nonparametric Lack-of-Fit Testing and Variable Selection

We describe the R package NonpModelCheck for hypothesis testing and variable selection in nonparametric regression. This package implements functions to perform hypothesis testing for the significance of a predictor or a group of predictors in a fully nonparametric heteroscedastic regression model using high-dimensional one-way ANOVA. Based on the p values from the test of each covariate, three different algorithms allow the user to perform variable selection using false discovery rate corrections. A function for classical local polynomial regression is implemented for the multivariate context, where the degree of the polynomial can be as large as needed and bandwidth selection strategies are built in