Automatic derivation of land-use from topographic data

The paper presents an approach for the reclassification and generalization of land-use information from topographic information. Based on a given transformation matrix describing the transition from topographic data to land-use data, a semantic and geometry based generalization of too small features for the target scale is performed. The challenges of the problem are as follows: (1) identification and reclassification of heterogeneous feature classes by local interpretation, (2) presence of concave, narrow or very elongated features, (3) processing of very large data sets. The approach is composed of several steps consisting of aggregation, feature partitioning, identification of mixed feature classes and simplification of feature outlines. The workflow will be presented with examples for generating CORINE Land Cover (CLC) features from German Authoritative Topographic Cartographic Information System (ATKIS) data for the whole are of Germany. The results will be discussed in detail, including runtimes as well as dependency of the result on the parameter setting

Building generalization using deep learning

Cartographic generalization is a problem, which poses interesting challenges to automation. Whereas plenty of algorithms have been developed for the different sub-problems of generalization (e.g. simplification, displacement, aggregation), there are still cases, which are not generalized adequately or in a satisfactory way. The main problem is the interplay between different operators. In those cases the benchmark is the human operator, who is able to design an aesthetic and correct representation of the physical reality. Deep Learning methods have shown tremendous success for interpretation problems for which algorithmic methods have deficits. A prominent example is the classification and interpretation of images, where deep learning approaches outperform the traditional computer vision methods. In both domains – computer vision and cartography – humans are able to produce a solution; a prerequisite for this is, that there is the possibility to generate many training examples for the different cases. Thus, the idea in this paper is to employ Deep Learning for cartographic generalizations tasks, especially for the task of building generalization. An advantage of this task is the fact that many training data sets are available from given map series. The approach is a first attempt using an existing network. In the paper, the details of the implementation will be reported, together with an in depth analysis of the results. An outlook on future work will be given

Learning cartographic building generalization with deep convolutional neural networks

Cartographic generalization is a problem, which poses interesting challenges to automation. Whereas plenty of algorithms have been developed for the different sub-problems of generalization (e.g., simplification, displacement, aggregation), there are still cases, which are not generalized adequately or in a satisfactory way. The main problem is the interplay between different operators. In those cases the human operator is the benchmark, who is able to design an aesthetic and correct representation of the physical reality. Deep learning methods have shown tremendous success for interpretation problems for which algorithmic methods have deficits. A prominent example is the classification and interpretation of images, where deep learning approaches outperform traditional computer vision methods. In both domains-computer vision and cartography-humans are able to produce good solutions. A prerequisite for the application of deep learning is the availability of many representative training examples for the situation to be learned. As this is given in cartography (there are many existing map series), the idea in this paper is to employ deep convolutional neural networks (DCNNs) for cartographic generalizations tasks, especially for the task of building generalization. Three network architectures, namely U-net, residual U-net and generative adversarial network (GAN), are evaluated both quantitatively and qualitatively in this paper. They are compared based on their performance on this task at target map scales 1:10,000, 1:15,000 and 1:25,000, respectively. The results indicate that deep learning models can successfully learn cartographic generalization operations in one single model in an implicit way. The residual U-net outperforms the others and achieved the best generalization performance

Recertification and Reentry to Practice for Nurse Anesthetists: Determining Core Competencies and Evaluating Performance via High-Fidelity Simulation Technology

Introduction The National Board of Certification and Recertification for Nurse Anesthetistsaddressed a barrier to return to practice of uncertified practitioners by replacing required direct patient care experiences with high-fidelity simulation. Objectives The aims of this study were to: (a) validate a set of clinical activities for their relevance to reentry and determine if they could be replicated using simulation, (b) evaluate the content validity of an existing simulation scenario containing the proposed clinical activities and determine its substitutability for a clinical practicum, and (c) evaluate the validity of two methods to assess simulation performance. Methods A modified Delphi method incorporating an autonomous, anonymous, three-round online survey process using three unique expert certified registered nurse anesthetists groups was used to address each study aim. Results Twenty-seven clinical activities gained consensus as necessary to be assessed in the simulation. All 14 survey questions used to determine simulation content validity exceeded the minimum content validity index (CVI) value of 0.78, with a mean CVI of 0.99. The global rating scale CVI and the competency checklist CVI were 0.83 and 1.0, respectively. Conclusion The findings add to the existing literature supporting the utility of simulation for high-stakes provider assessment and certification

Generator-collector voltammetry at dual-plate gold-gold microtrench electrodes as diagnostic tool in ionic liquids

Ionic liquids provide high viscosity solvent environments with interesting voltammetric characteristics and new electrochemical mechanisms. Here, a gold-gold dual-plate microtrench electrode is employed in generator-collector mode to enhance viscosity-limited currents in ionic liquids due to fast feedback within small inter-electrode gaps (5μm inter-electrode gap, 27μm microtrench depth) and to provide a mechanistic diagnosis tool. Three redox systems in the ionic liquid BMIm+BF4- are investigated: (i) ferrocene oxidation, (ii) oxygen reduction, and (iii) 2-phenyl-naphthyl-1,4-dione reduction. Both transient and steady state voltammetric responses are compared. Asymmetric diffusion processes, reaction intermediates, and solubility changes in the ionic liquid are revealed.</p

Operator Spin Foam Models

The goal of this paper is to introduce a systematic approach to spin foams. We define operator spin foams, that is foams labelled by group representations and operators, as the main tool. An equivalence relation we impose in the set of the operator spin foams allows to split the faces and the edges of the foams. The consistency with that relation requires introduction of the (familiar for the BF theory) face amplitude. The operator spin foam models are defined quite generally. Imposing a maximal symmetry leads to a family we call natural operator spin foam models. This symmetry, combined with demanding consistency with splitting the edges, determines a complete characterization of a general natural model. It can be obtained by applying arbitrary (quantum) constraints on an arbitrary BF spin foam model. In particular, imposing suitable constraints on Spin(4) BF spin foam model is exactly the way we tend to view 4d quantum gravity, starting with the BC model and continuing with the EPRL or FK models. That makes our framework directly applicable to those models. Specifically, our operator spin foam framework can be translated into the language of spin foams and partition functions. We discuss the examples: BF spin foam model, the BC model, and the model obtained by application of our framework to the EPRL intertwiners.Comment: 19 pages, 11 figures, RevTex4.

Elevated faecal ovotransferrin concentrations are indicative for intestinal barrier failure in broiler chickens

Intestinal health is critically important for the welfare and performance of poultry. Enteric diseases that cause gut barrier failure result in high economic losses. Up till now there is no reliable faecal marker to measure gut barrier failure under field conditions. Therefore, the aim of the present study was to identify a faecal protein marker for diminished intestinal barrier function due to enteric diseases in broilers. To assess this, experimental necrotic enteritis and coccidiosis in broilers were used as models for gut barrier failure. Ovotransferrin was identified as a marker for gut barrier failure using a proteomics approach on samples from chickens with necrotic enteritis. These results were confirmed via ELISA on samples derived from both necrotic enteritis and coccidiosis trials, where faecal ovotransferrin levels were significantly correlated with the severity of gut barrier failure caused by either coccidiosis or necrotic enteritis. This indicates that faecal ovotransferrin quantification may represent a valuable tool to measure gut barrier failure caused by enteric pathogens

Protein truncating variants of colA in clostridium perfringens type G strains

Extracellular matrix (ECM) degrading enzymes produced by Clostridium perfringens may play an important role during the initial phases of avian necrotic enteritis by facilitating toxin entry in the intestinal mucosa and destruction of the tissue. C. perfringens is known to produce several ECM-degrading proteases, such as kappa toxin, an extracellular collagenase that is encoded by the colA gene. In this study, the colA gene sequence of a collection of 48 C. perfringens strains, including pathogenic (i.e. toxinotype G) and commensal (i.e. toxinotype A) chicken derived strains and strains originating from other host species, was analyzed. Although the colA gene showed a high level of conservation (>96% nucleotide sequence identity), several gene variants carrying different nonsense mutations in the colA gene were identified, leading to the definition of four truncated collagenase variant types (I-IV). Collagenase variant types I, III and IV have a (nearly) complete collagenase unit but lack parts of the C-terminal recruitment domains, whereas collagenase variant types II misses the N-terminal part of collagenase unit. Gene fragments encoding a truncated collagenase were mainly linked with necrotic enteritis associated C. perfringens type G strains with collagenase variant types I and II being the most prevalent types. Gelatin zymography revealed that both recombinant full-length and variant type I collagenase have active auto-cleavage products. Moreover, both recombinant fragments were capable of degrading type I as well as type IV collagen, although variant type I collagenase showed a higher relative activity against collagen type IV as compared to full-length collagenase. Consequently, these smaller truncated collagenases might be able to break down collagen type IV in the epithelial basement membrane of the intestinal villi and so contribute to the initiation of the pathological process leading to necrotic enteritis

Dissociation in a polymerization model of homochirality

A fully self-contained model of homochirality is presented that contains the effects of both polymerization and dissociation. The dissociation fragments are assumed to replenish the substrate from which new monomers can grow and undergo new polymerization. The mean length of isotactic polymers is found to grow slowly with the normalized total number of corresponding building blocks. Alternatively, if one assumes that the dissociation fragments themselves can polymerize further, then this corresponds to a strong source of short polymers, and an unrealistically short average length of only 3. By contrast, without dissociation, isotactic polymers becomes infinitely long.Comment: 16 pages, 6 figures, submitted to Orig. Life Evol. Biosp