Blur identification by multilayer neural network based on multivalued neurons.

A multilayer neural network based on multivalued neurons (MLMVN) is a neural network with a traditional feedforward architecture. At the same time, this network has a number of specific different features. Its backpropagation learning algorithm is derivative-free. The functionality of MLMVN is superior to that of the traditional feedforward neural networks and of a variety kernel-based networks. Its higher flexibility and faster adaptation to the target mapping enables to model complex problems using simpler networks. In this paper, the MLMVN is used to identify both type and parameters of the point spread function, whose precise identification is of crucial importance for the image deblurring. The simulation results show the high efficiency of the proposed approach. It is confirmed that the MLMVN is a powerful tool for solving classification problems, especially multiclass ones.     

Influence of Feed Composition and Drying Parameters on the Surface Composition of a Spray-Dried Multicomponent Particle

Surface properties of multicomponent particles produced in spray drying can be controlled by selective accumulation of specific components, which are present in the liquid feed, on the particle surface. Such modification of the surface composition can take place only before a solid shell forms on the particle surface. In this contribution, the influence of the concentration of surface active component on modifications of the surface composition is discussed. Based on results of single-droplet drying simulations, changes in the concentration of the surface active component at the solution-air interface are related to the composition of spray-dried particles.     

Does academic collaboration equally benefit impact of research across topics? The case of agricultural,resource, environmental and ecological economics

Collaboration is a major factor in the knowledge and innovation creation in emerging science-driven industries where the technology is rapidly changing and constantly evolving, such as nanotechnology. The objective of this work is to investigate the role of individual scientists and their collaborations in enhancing the knowledge flows, and consequently the scientific production. The methodology involves two main phases. First, the data on all the nanotechnology journal publications in Canada was extracted from the SCOPUS database to create the co-authorship network, and then employ statistical data mining techniques to analyze the scientists’ research performance and partnership history. Also, a questionnaire was sent directly to the researchers selected from our database seeking the predominant properties that make a scientist sufficiently attractive to be selected as a research partner. In the second phase, an agent-based model using Netlogo has been developed to study the network in its dynamic context where several factors could be controlled. It was found that scientists in centralized positions in such networks have a considerable positive impact on the knowledge flows, while loyalty and strong connections within a dense local research group negatively affect the knowledge transmission. Star scientists appear to play a substitutive role in the network and are selected when the usual collaborators, i.e., most famous, and trustable partners are scarce or missing.     

Band-like transport, high electron mobility and high photoconductivity in all-inorganic nanocrystal arrays

Flexible, thin-film electronic and optoelectronic devices typically involve a trade-off between performance and fabrication cost. For example, solution-based deposition allows semiconductors to be patterned onto large-area substrates to make solar cells and displays, but the electron mobility in solution-deposited semiconductor layers is much lower than in semiconductors grown at high temperatures from the gas phase. Here, we report band-like electron transport in arrays of colloidal cadmium selenide nanocrystals capped with the molecular metal chalcogenide complex In(2)Se(4)(2-), and measure electron mobilities as high as 16 cm(2) V(-1) s(-1), which is about an order of magnitude higher than in the best solution-processed organic and nanocrystal devices so far. We also use CdSe/CdS core-shell nanoparticles with In(2)Se(4)(2-) ligands to build photodetectors with normalized detectivity D* > 1 × 10(13) Jones (I Jones = 1 cm Hz(1/2) W(-1)), which is a record for II-VI nanocrystals. Our approach does not require high processing temperatures, and can be extended to different nanocrystals and inorganic surface ligands.     

Iron(II) Metallomesogens Exhibiting Coupled Spin State and Liquid Crystal Phase Transitions near Room Temperature

Reaction of the ligand 2,2,2‐tris(2‐aza‐3‐((5‐akloxy)(6‐methyl)(2‐pyridyl))prop‐2‐enyl)ethane (C_n‐tameMe) with Fe(X)₂ · sH₂O salts afforded a series of spin crossover metallomesogens with general formula [Fe(C_n‐tame)](X)₂ · sH₂O (s ≥ 0), with n = 6, 10, 12, 14, 16, 17, 18, or 20 and X = ClO₄^? or BF₄^?. Single crystal X‐ray measurements have been performed on the [Fe(C₆‐tameMe)](ClO₄)₂ ( C ₆ ‐1 ) derivative at 100 K. The complex C ₆ ‐1 crystallizes in the triclinic system and adopts the Pbca space group. The iron(II) ion is in a distorted octahedral environment shaped by three imine and three pyridine nitrogen atoms of the C_n‐tameMe ligand. The average Fe–N^im and Fe–N^py bond distance is 1.918(2) Å and 2.084(2) Å, respectively, which are characteristic for the Fe(II) ion in the LS state. Neighbor molecules are packed in a head‐to‐head fashion forming a layered microsegregated structure. Ionic layer is composed of cationic spin crossover head‐groups and perchlorate anions, whereas alkyl tails are arranged into a non‐polar hydrocarbon layer. The complex [Fe(C₁₈‐tameMe)](ClO₄)₂ ( C ₁₈ ‐1 ) and the series [Fe(C_n‐tameMe)](BF₄)₂ (n = 10, 12, 14, 16, 17, 18, 20) ( C _n ‐2 ) series show thermally driven spin crossover and mesomorphism above 300 K. The thermotropic behavior of compounds C _n ‐2 depends on the chain length defined by n. A common structural feature among all homologues of the series is a similar layered structure and adoption on melting of a smectic mesophase at ca. 350–375 K. Formation of the mesophase is due to the melting of the alkyl chains rather than a rearrangement of the ionic bilayers composed of polar SCO cations and BF₄^? or ClO₄^? anions. This gives rise to a mesophase with molten alkyl chains but with restricted movement through the ionic layer. Compounds C _n ‐2 show thermochromic properties, they are dark violet in the LS state (T < 275 K, Cr) and become red in the HS state (T > 275 K, Cr, S_X). These bi‐functional materials combine spin state change and mesomorphic behavior in a narrow interval of temperatures, 300–375 K.     

Mapping land cover and land use from object-based classification: an example from a complex agricultural landscape

From its inception, land-use and land-cover mapping have been major themes in remote-sensing research and applications. Although frequently considered together, land use and land cover (LULC) are defined differently, with land use referring to the economic function of the Earth’s surface and land cover to its natural or engineered biophysical cover. Land cover can be observed directly using remote sensing, but land use must be inferred from the cover type. In this study, we test whether object-based image analysis (OBIA) can improve the land-cover and land-use classification in a complex agricultural landscape located along the border between Poland and Ukraine. We quantitatively compared the results of OBIA-based versus per-pixel classifications for both land cover and land use, respectively. Our results show that land-cover classification was not significantly improved when OBIA-based methods were used. Although overall classification accuracy was modest, land-use classification was significantly improved when OBIA-based methods were applied using both spectral and spatial/geometric features of image objects, but not when spectral or spatial/geometric features were used independently. Our results suggest that in anthropogenically altered landscapes where the geometry and arrangement of surface spatial structure may convey land-use information, use of OBIA-based techniques may provide a powerful tool for improving classification.     

Robust extended Kalman filter of discrete-time Markovian jump nonlinear system under uncertain noise

This paper examines the problem of robust extended Kalman filter design for discrete-time Markovian jump nonlinear systems with noise uncertainty. Because of the existence of stochastic Markovian switching, the state and measurement equations of underlying system are subject to uncertain noise whose covariance matrices are time-varying or un-measurable instead of stationary. First, based on the expression of filtering performance deviation, admissible uncertainty of noise covariance matrix is given. Secondly, two forms of noise uncertainty are taken into account: Non-Structural and Structural. It is proved by applying game theory that this filter design is a robust mini-max filter. A numerical example shows the validity of the method.