A molecular approach to ascertain the success of "in situ" AM fungi inoculation in the revegetation of a semiarid, degraded land

The positive effect of arbuscular mycorrhizal fungi inoculation on plant establishment under field conditions has been shown. However, whether this effect is related to the survival of the AMF and how the AMF inoculum affects the colonisation of plant roots by the native AMF remain uncertain.In this study, we assessed the AMF community composition in O. europaea roots inoculated “in situ” with three types of AMF inoculum: Glomus sp., G. intraradices and a mixture of Glomus sp. and G. intraradices. All the AMF isolates inoculated showed a good percentage of persistence. Mycorrhizal inoculation with the mixture of Glomus sp. and G. intraradices was the most effective treatment for increasing the AMF diversity in roots 14 months after plantation. Plant growth was increased significantly by all the inocula tested, although plant biomass was not correlated with variation in the AMF diversity or with the AMF inoculum persistence. Thus, it seems that this positive effect was mediated by the interaction between the AMF inoculum and the natural colonisation. Therefore, the application of native AMF isolates in the planting hole may be considered a good strategy for the revegetation of semiarid, degraded soils, in order to reactivate the indigenous AMF populations and improve the performance of O. europaea seedlings, particularly when mixtures of native isolates are used.     

A Multirobot System for Distributed Area Coverage and Signal Searching in Large Outdoor Scenarios*

This work presents a complete multirobot solution for signal searching tasks in large outdoor scenarios. An evaluation of two different coverage path‐planning strategies according to field size and shape is presented. A signal location system developed to simulate mines or chemical source detections is also described. The solution presented is a pioneer in evaluating multimaster robotics operative system architectures with a fleet of robots in real scenarios. This solution minimizes the use of communications bandwidth required for full operation. Finally, field results are provided, and the advantages of the implemented solution are analyzed.     

Order Barrier for Low-Storage DIRK Methods with Positive Weights

In this paper we study an order barrier for low-storage diagonally implicit Runge–Kutta (DIRK) methods with positive weights. The Butcher matrix for these schemes, that can be implemented with only two memory registers in the van der Houwen implementation, has a special structure that restricts the number of free parameters of the method. We prove that third order low-storage DIRK methods must contain negative weights, obtaining the order barrier \(p\le 2\) for these schemes. This result extends the well known one for symplectic DIRK methods, which are a particular case of low-storage DIRK methods. Some other properties of second order low-storage DIRK methods are given.     

Face recognition using a permutation coding neural classifier

Face recognition is an important security task. We propose a high-level method to solve this problem: a permutation coding neural classifier (PCNC). A PCNC with a special feature extractor for face image recognition systems is a relatively new method that has been tested with good results to classify real environment images (such as larvae of various types and handmade elements). As baseline methods, a support vector machine (SVM) and the iterative closest point (ICP) method are selected for comparison. We applied these methods to gray-level images from the FRAV3D face database. Fifteen experiments were performed to examine a large set of training and testing conditions. As a general result, it was observed that errors are lower with the PCNC than with the SVM and the ICP classifier. We aggregated various distortions for the initial images to improve the PCNC. We analyze and discuss the obtained results.     

Production of bacterial nanocellulose from wine industry residues: Importance of fermentation time on pellicle characteristics

Bacterial nanocellulose (BNC) was produced by Gluconacetobacter xylinus under static conditions using grape pomace extract (the most abundant residue of the wine industry) as a carbon source and corn steep liquor (a byproduct of corn wet‐milling) as the main nitrogen source. Carbon and nitrogen source concentrations, as well as inocula size, fermentation time, and temperature, were all considered in order to maximize BNC production by the use of statistically designed experiments and the response surface methodology. At optimum production conditions, the effect of fermentation time on morphology, solids content, chemical structure, crystallinity, thermal decomposition pattern, and storage modulus of dried BNC pellicles was analyzed. The results evidenced that dried BNC pellicles that were incubated for longer times showed higher thermal stability, higher crystallinity, and higher storage modulus, resulting from a denser nanoribbons network. All of these characteristics will certainly play a role in the performance of BNC in practical applications. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43109.