Designing multimodal tracks for mobile users in unfamiliar urban environments

While there have been many studies of navigation design guidelines for integrating maps in mobile devices, we argue that the research focus should turn more towards the design of multimodal navigation aids, which would reflect the impact of typical human wayfinding behaviour. Therefore, we suggest an interdisciplinary design approach, i.e. building on urban design practice that focuses on supporting the navigation of pedestrians in urban areas. The design implications are explored in this study with the subjects being pedestrians accomplishing wayfinding tasks in an unfamiliar, urban area. The main areas that this paper contributes to are: investigating the design implications of multimodal navigation aids; evaluating these in the context of mobile wayfinding tasks; and reflecting the results according to the user's wayfinding strategies and spatial anxiety. It is concluded that through designing multimodal tracks into mobile navigation applications we can help users to find their way in unfamiliar, urban environments.     

ALCHEMIST: A General Purpose Transformation Generator

ALCHEMIST is a general purpose transformation generating environment, which supports specification, generation and execution of data transformations. ALCHEMIST allows an abstract specification of the transformation through a window-based interface and supports the generation and compilation of transformation program code from these specifications. Unlike compiler-compilers, ALCHEMIST is intended to automate building transformations between two complex representation formats and is thus especially suitable for constructing transformations between database tools, CASE tools, graphical editors or text formatters. In this paper we describe the design principles and the structure of ALCHEMIST, and demonstrate its use. We also discuss our experiences with several example transformations and present a real-life case study of using ALCHEMIST for interfacing two software development environments.     

Neural networks and genetic algorithms for bankruptcy predictions

We are focusing on three alternative techniques-linear discriminant analysis, logit analysis and genetic algorithms-that can be used to empirically select predictors for neural networks in failure prediction. The selected techniques all have different assumptions about the relationships between the independent variables. Linear discriminant analysis is based on linear combination of independent variables, logit analysis uses the logistical cumulative function and genetic algorithms is a global search procedure based on the mechanics of natural selection and natural genetics. In an empirical test all three selection methods chose different bankruptcy prediction variables. The best prediction results were achieved when using genetic algorithms.     

Microbial content of house dust samples determined with qPCR

This study was designed to produce information about microbial concentrations using qPCR and their variation in different seasons and home environments with analyses of two types of house dust samples. Also the correlations between the two types of samples and the reproducibility of the parallel subsamples were studied. Two types of vacuumed house dust samples, rug dust and vacuum cleaner bag dust, were collected in 5 normal urban homes in four different seasons (N = 20 + 20). From all dust samples, five parallel subsamples were subjected to qPCR analyses of 17 microbial species or assay groups of microbes. The highest fungal concentrations were found for the Penicillium/Aspergillus/Paecilomyces variotii group, and for the species Aspergillus penicillioides, Aureobasidium pullulans, Cladosporium cladosporioides and Cladosporium herbarum. These species/groups were present in almost all samples. The two types of dust samples gave similar results for most microbial species or groups analyzed, but in general, concentrations were slightly higher in rug dust than in dust from vacuum cleaner bag. Microbial concentrations varied significantly between different seasons and hence the similarity of samples within home was mainly low. The concentrations varied significantly also between different home environments. The reproducibility of the parallel subsamples was good or moderate for most of the analyzed species or assay groups. However, further studies are needed to fully understand the factors causing variation in these methods. Nevertheless, in order to show actual differences in fungal concentrations between urban homes with no known microbial sources, all dust samples to be compared should be taken during the same season.     

Neural network techniques for object orientation detection.Solution by optimal feedforward network and learning vector quantizationapproaches

The computer-vision problem of determining object orientation from the consensus of orientations of individual symbols or marks is examined. The problem arises in automatic inspection where orientation can be detected from printed text but there is no knowledge of the content of the text. This is a high-dimensional classification problem, and there is a requirement for highly accurate detection and rapid processing. The typical multilayer threshold networks are seen as unsuitable, and the optimal Bayesian detector is derived and found to have the highly parallel structure of a feedforward network. The learning vector quantization neural network method of T. Kohonen (1988) is also applied. Experimental results, comparisons, and a complete implementation are described     

On Discriminative Bayesian Network Classifiers and Logistic Regression

Discriminative learning of the parameters in the naive Bayes model is known to be equivalent to a logistic regression problem. Here we show that the same fact holds for much more general Bayesian network models, as long as the corresponding network structure satisfies a certain graph-theoretic property. The property holds for naive Bayes but also for more complex structures such as tree-augmented naive Bayes (TAN) as well as for mixed diagnostic-discriminative structures. Our results imply that for networks satisfying our property, the conditional likelihood cannot have local maxima so that the global maximum can be found by simple local optimization methods. We also show that if this property does not hold, then in general the conditional likelihood can have local, non-global maxima. We illustrate our theoretical results by empirical experiments with local optimization in a conditional naive Bayes model. Furthermore, we provide a heuristic strategy for pruning the number of parameters and relevant features in such models. For many data sets, we obtain good results with heavily pruned submodels containing many fewer parameters than the original naive Bayes model.Editors: Pedro Larrañaga, Jose A. Lozano, Jose M. Peña and Iñaki Inza     

In Vitro and In Vivo Pharmaco-Toxicological Characterization of 1-Cyclohexyl-x-methoxybenzene Derivatives in Mice: Comparison with Tramadol and PCP

1-cyclohexyl-x-methoxybenzene is a novel psychoactive substance (NPS), first discovered in Europe in 2012 as unknown racemic mixture of its three stereoisomers: ortho, meta and para. Each of these has structural similarities with the analgesic tramadol and the dissociative anesthetic phencyclidine. In light of these structural analogies, and based on the fact that both tramadol and phencyclidine are substances that cause toxic effects in humans, the aim of this study was to investigate the in vitro and in vivo pharmacodynamic profile of these molecules, and to compare them with those caused by tramadol and phencyclidine. In vitro studies demonstrated that tramadol, ortho, meta and para were inactive at mu, kappa and delta opioid receptors. Systemic administration of the three stereoisomers impairs sensorimotor responses, modulates spontaneous motor activity, induces modest analgesia, and alters thermoregulation and cardiorespiratory responses in the mouse in some cases, with a similar profile to that of tramadol and phencyclidine. Naloxone partially prevents only the visual sensorimotor impairments caused by three stereoisomers, without preventing other effects. The present data show that 1-cyclohexyl-x-methoxybenzene derivatives cause pharmaco-toxicological effects by activating both opioid and non-opioid mechanisms and suggest that their use could potentially lead to abuse and bodily harm.     

Immunological Memory of Mountain Birches: Effects of Phenolics on Performance of the Autumnal Moth Depend on Herbivory History of Trees

Plants have been suggested to have an immunological memory comparable to animals. The evidence for this, however, is scarce. In our study with the mountain birch—Epirrita autumnata system, we demonstrated that birches exposed as long as 5 yr to feeding of E. autumnata larvae (delayed induced resistance, DIR), responded more strongly to a new challenge than trees without an herbivory history. Pupal weights remained lower, and the duration of the larval period was prolonged in the DIR trees, although immunity, measured as an encapsulation rate, was not affected. We further demonstrated that the effects of birch phenolics on performance of E. autumnata were different in the exposed (DIR) trees from naive control trees, although we found only one significant change in chemistry. The quercetin:kaemferol ratio was increased in DIR trees, suggesting that herbivory caused oxidative stress in birches. In DIR trees, phenolics, especially hydrolyzable tannins (HTs), affected pupal weights negatively, whereas in control trees, the effects were either nonsignificant or positive. HTs also prolonged the duration of the larval period of females, whereas peroxidase (POD) activity prolonged that of males. We suggest that the causal explanation for the induced resistance was an enhanced oxidation of phenolic compounds from the DIR trees in the larval digestive tract. Phenolic oxidation produces semiquinones, quinones, free radicals, and ROS, which may have toxic, antinutritive, and/or repellent properties against herbivores.