Pea–wheat intercrops in low-input conditions combine high economic performances and low environmental impacts

Intensive agriculture ensures high yields but can cause serious environmental damages. The optimal use of soil and atmospheric sources of nitrogen in cereal–legume mixtures may allow farmers to maintain high production levels and good quality with low external N inputs, and could potentially decrease environmental impacts, particularly through a more efficient energy use. These potential advantages are presented in an overall assessment of cereal–legume systems, accounting for the agronomic, environmental, energetic, and economic performances. Based on a low-input experimental field network including 16 site-years, we found that yields of pea–wheat intercrops (about 4.5 Mg ha−1 whatever the amount of applied fertiliser) were higher than sole pea and close to conventionally managed wheat yields (5.4 Mg ha−1 on average), the intercrop requiring less than half of the nitrogen fertiliser per ton of grain compared to the sole wheat. The land equivalent ratio and a statistical analysis based on the Price\u27s equation showed that the crop mixture was more efficient than sole crops particularly under unfertilised situations. The estimated amount of energy consumed per ton of harvested grains was two to three times higher with conventionally managed wheat than with pea–wheat mixtures (fertilised or not). The intercrops allowed (i) maintaining wheat grain protein concentration and gross margin compared to wheat sole crop and (ii) increased the contribution of N2 fixation to total N accumulation of pea crop in the mixture compared to pea sole crop. They also led to a reduction of (i) pesticide use compared to sole crops and (ii) soil mineral nitrogen after harvest compared to pea sole crop. Our results demonstrate that pea–wheat intercropping is a promising way to produce cereal grains in an efficient, economically sustainable and environmentally friendly way

Using Strategic Movement to Calibrate a Neural Compass: A Spiking Network for Tracking Head Direction in Rats and Robots

The head direction (HD) system in mammals contains neurons that fire to represent the direction the animal is facing in its environment. The ability of these cells to reliably track head direction even after the removal of external sensory cues implies that the HD system is calibrated to function effectively using just internal (proprioceptive and vestibular) inputs. Rat pups and other infant mammals display stereotypical warm-up movements prior to locomotion in novel environments, and similar warm-up movements are seen in adult mammals with certain brain lesion-induced motor impairments. In this study we propose that synaptic learning mechanisms, in conjunction with appropriate movement strategies based on warm-up movements, can calibrate the HD system so that it functions effectively even in darkness. To examine the link between physical embodiment and neural control, and to determine that the system is robust to real-world phenomena, we implemented the synaptic mechanisms in a spiking neural network and tested it on a mobile robot platform. Results show that the combination of the synaptic learning mechanisms and warm-up movements are able to reliably calibrate the HD system so that it accurately tracks real-world head direction, and that calibration breaks down in systematic ways if certain movements are omitted. This work confirms that targeted, embodied behaviour can be used to calibrate neural systems, demonstrates that ‘grounding’ of modelled biological processes in the real world can reveal underlying functional principles (supporting the importance of robotics to biology), and proposes a functional role for stereotypical behaviours seen in infant mammals and those animals with certain motor deficits. We conjecture that these calibration principles may extend to the calibration of other neural systems involved in motion tracking and the representation of space, such as grid cells in entorhinal cortex

Neural network model of the primary visual cortex: From functional architecture to lateral connectivity and back

The role of intrinsic cortical dynamics is a debatable issue. A recent optical imaging study (Kenet et al., 2003) found that activity patterns similar to orientation maps (OMs), emerge in the primary visual cortex (V1) even in the absence of sensory input, suggesting an intrinsic mechanism of OM activation. To better understand these results and shed light on the intrinsic V1 processing, we suggest a neural network model in which OMs are encoded by the intrinsic lateral connections. The proposed connectivity pattern depends on the preferred orientation and, unlike previous models, on the degree of orientation selectivity of the interconnected neurons. We prove that the network has a ring attractor composed of an approximated version of the OMs. Consequently, OMs emerge spontaneously when the network is presented with an unstructured noisy input. Simulations show that the model can be applied to experimental data and generate realistic OMs. We study a variation of the model with spatially restricted connections, and show that it gives rise to states composed of several OMs. We hypothesize that these states can represent local properties of the visual scene

A continuous attractor network model without recurrent excitation: maintenance and integration in the head direction cell system.

Motivated by experimental observations of the head direction system, we study a three population network model that operates as a continuous attractor network. This network is able to store in a short-term memory an angular variable (the head direction) as a spatial profile of activity across neurons in the absence of selective external inputs, and to accurately update this variable on the basis of angular velocity inputs. The network is composed of one excitatory population and two inhibitory populations, with inter-connections between populations but no connections within the neurons of a same population. In particular, there are no excitatory-to-excitatory connections. Angular velocity signals are represented as inputs in one inhibitory population (clockwise turns) or the other (counterclockwise turns). The system is studied using a combination of analytical and numerical methods. Analysis of a simplified model composed of threshold-linear neurons gives the conditions on the connectivity for (i) the emergence of the spatially selective profile, (ii) reliable integration of angular velocity inputs, and (iii) the range of angular velocities that can be accurately integrated by the model. Numerical simulations allow us to study the proposed scenario in a large network of spiking neurons and compare their dynamics with that of head direction cells recorded in the rat limbic system. In particular, we show that the directional representation encoded by the attractor network can be rapidly updated by external cues, consistent with the very short update latencies observed experimentally by Zugaro et al. (2003) in thalamic head direction cells

ADVANCES IN MULTI-SENSOR SCANNING AND VISUALIZATION OF COMPLEX PLANTS: THE UTMOST CASE OF A REACTOR BUILDING

In a context of increased maintenance operations and workers generational renewal, a nuclear owner and operator like Electricité de France (EDF) is interested in the scaling up of tools and methods of “as-built virtual reality” for larger buildings and wider audiences. However, acquisition and sharing of as-built data on a large scale (large and complex multi-floored buildings) challenge current scientific and technical capacities. In this paper, we first present a state of the art of scanning tools and methods for industrial plants with very complex architecture. Then, we introduce the inner characteristics of the multi-sensor scanning and visualization of the interior of the most complex building of a power plant: a nuclear reactor building. We introduce several developments that made possible a first complete survey of such a large building, from acquisition, processing and fusion of multiple data sources (3D laser scans, total-station survey, RGB panoramic, 2D floor plans, 3D CAD as-built models). In addition, we present the concepts of a smart application developed for the painless exploration of the whole dataset. The goal of this application is to help professionals, unfamiliar with the manipulation of such datasets, to take into account spatial constraints induced by the building complexity while preparing maintenance operations. Finally, we discuss the main feedbacks of this large experiment, the remaining issues for the generalization of such large scale surveys and the future technical and scientific challenges in the field of industrial “virtual reality”

Quality measures of reconstruction filters for stereoscopic volume rendering

In direct volume rendering (DVR), the choice of reconstruction filter can have a significant effect on the visual appearance of the images produced and thus, on the perceived quality of a DVR rendered scene. This paper presents the results of a subjective experiment where participants stereoscopically viewed DVR rendered scenes and rated their subjective quality. The statistical analysis of the results focuses on the relationship between the quality of the stereoscopic scene and properties of the filters such as post-aliasing and smoothing, as well as the relationship between the quality of the stereoscopic scene and properties of the rendered images such as shape compactness. The experiment evaluated five reconstruction filters on four different volumetric datasets. Participants rated the stereoscopic scenes on four quality measures: depth quality, depth layout, lack of jaggyness, and sharpness. The results show that the correlation between the quality measures and post-aliasing and smoothing, which are properties associated with each reconstruction filter, is moderate and statistically insignificant. On the other hand, the correlation between the quality measures and compactness, which is a property specific to each rendered image, is strong and statistically significant