Assessment of the N- and P-Fertilization Effect of Black Soldier Fly (Diptera: Stratiomyidae) By-Products on Maize.

To meet the growing demand for an alternative animal protein source, the Black Soldier Fly (BSF) (Hermetia illucens) industry is expanding. Thus, the valuation of its byproducts, foremost BSF frass, is getting more economic and ecological weight. Three different residues, BSF frass, larval skins, and dead adult flies, were compared with a mineral and an organic commercial fertilizer in a pot trial with maize (Zea mays L., [Poales: Poaceae]). byproducts were applied in three nutrient-based application rates (180; 215 kg N/ha; 75 kg P2O5/ha), and plant nutrients, physiological and yield parameters were measured at harvest date. Ground flies had the highest N-fertilizing effect of all byproducts, similar to commercial mineral and organic fertilizers used as controls, whereas its proportion of the BSF production systems' output is low. Frass as the abundant byproduct showed comparably low N-fertilization effects. Its low N availability was attributed to volatilization losses, mainly driven by high pH and ammonium contents. BSF frass as the main byproduct output is more suited as a basic fertilizer or potting substrate amendment than as a short-term organic fertilizer. Postprocessing of frass seems reasonable. For a profound assessment of frass as fertilizer, several aspects (e.g., the overall impact of postprocessing, plant strengthening and plant protection potential, effects on microbial processes) must be clarified.

Object class segmentation of RGB-D video using recurrent convolutional neural networks.

Object class segmentation is a computer vision task which requires labeling each pixel of an image with the class of the object it belongs to. Deep convolutional neural networks (DNN) are able to learn and take advantage of local spatial correlations required for this task. They are, however, restricted by their small, fixed-sized filters, which limits their ability to learn long-range dependencies. Recurrent Neural Networks (RNN), on the other hand, do not suffer from this restriction. Their iterative interpretation allows them to model long-range dependencies by propagating activity. This property is especially useful when labeling video sequences, where both spatial and temporal long-range dependencies occur. In this work, a novel RNN architecture for object class segmentation is presented. We investigate several ways to train such a network. We evaluate our models on the challenging NYU Depth v2 dataset for object class segmentation and obtain competitive results.

Two-layer contractive encodings for learning stable nonlinear features.

Unsupervised learning of feature hierarchies is often a good strategy to initialize deep architectures for supervised learning. Most existing deep learning methods build these feature hierarchies layer by layer in a greedy fashion using either auto-encoders or restricted Boltzmann machines. Both yield encoders which compute linear projections of input followed by a smooth thresholding function. In this work, we demonstrate that these encoders fail to find stable features when the required computation is in the exclusive-or class. To overcome this limitation, we propose a two-layer encoder which is less restricted in the type of features it can learn. The proposed encoder is regularized by an extension of previous work on contractive regularization. This proposed two-layer contractive encoder potentially poses a more difficult optimization problem, and we further propose to linearly transform hidden neurons of the encoder to make learning easier. We demonstrate the advantages of the two-layer encoders qualitatively on artificially constructed datasets as well as commonly used benchmark datasets. We also conduct experiments on a semi-supervised learning task and show the benefits of the proposed two-layer encoders trained with the linear transformation of perceptrons.

Mechanisms of the water-gas shift reaction catalyzed by ruthenium pentacarbonyl: a density functional theory study.

The mechanism of the water-gas shift reaction catalyzed by Ru(CO)5 is analyzed using density functional methods in solution within the conductor-like screening model. Four different mechanistic pathways have been considered. It turned out that the incorporation of solvent effects is very important for a reasonable comparison among the mechanistic alternatives. The explicit inclusion of a water solvent molecule significantly changes the barriers of those steps which involve proton transfer in the transition state. The corresponding barriers are either lowered or increased, depending on the structure of the corresponding cyclic transition states. The results show that protolysis steps become competitive due to solution effects. The formation of formic acid as an intermediate in another, alternative pathway is also found to be competitive.

Preparation and characterization of ultrathin [Ru(CO)3Cl2]2 and [BMIM][Tf2N] films on Al2O3/NiAl(110) under UHV conditions.

Towards a better understanding of the interface chemistry of ionic liquid (IL) thin film catalytic systems we have applied a rigorous surface science model approach. For the first time, a model homogeneous catalyst has been prepared under ultrahigh vacuum conditions. The catalyst, di-µ-chlorobis(chlorotricarbonylruthenium) [Ru(CO)(3)Cl(2)](2), and the solvent, the IL 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide [BMIM][Tf(2)N], have been deposited by physical vapor deposition onto an alumina model support [Al(2)O(3)/NiAl(110)]. First, the interaction between thin films of [Ru(CO)(3)Cl(2)](2) and the support is investigated. Then, the ruthenium complex is co-deposited with the IL and the influence of the solvent on the catalyst is discussed. D(2)O, which is a model reactant, is further added. Growth, surface interactions, and mutual interactions in the thin films are studied with IRAS in combination with density functional (DFT) calculations. At 105 K, molecular adsorption of [Ru(CO)(3)Cl(2)](2) is observed on Al(2)O(3)/NiAl(110). The IRAS spectra of the binary [Ru(CO)(3)Cl(2)](2) + [BMIM][Tf(2)N] and ternary [Ru(CO)(3)Cl(2)](2) + [BMIM][Tf(2)N] + D(2)O show every characteristic band of the individual components. Above 223 K, partial decomposition of the ruthenium complex leads to species of molecular nature attributed to Ru(CO) and Ru(CO)(2) surface species. Formation of metallic ruthenium clusters occurs above 300 K and the model catalyst decomposes further at higher temperatures. Neither the presence of the IL nor of D(2)O prevents this partial decomposition of [Ru(CO)(3)Cl(2)](2) on alumina.