The intraocular pressure response to dehydration: a pilot study

This study was designed to determine the Intraocular Pressure (IOP) response to differing levels of dehydration. Seven males participated in a 90 minute treadmill walk (5 km/h and 1 % grade) in both a cool (22 °C) and hot (43 °C) climate. At Baseline and at 30 minute intervals measurements of IOP, by tonometery, and indicators of hydration status (nude weight and plasma osmolality (Posm)) were taken. Body temperature and heart rate were also measured at these time points. Statistically significant interactions (time point (4) by trial (2)) were observed for IOP (F = 10.747, p = 0.009) and body weight loss (F = 50.083, p < 0.001) to decrease, and Posm (F = 34.867, p < 0.001) to increase, by a significantly greater amount during the hot trial compared to the cool. A univariate general linear model showed a significant relationship between IOP and body weight loss (F = 37.63, p < 0.001) and Posm (F = 38.53, p < 0.001). A significant interaction was observed for body temperature (F = 20.908, p < 0.001) and heart rate (F = 25.487, p < 0.001) between the trials and time points, but there was negligible association between these variables and IOP (Pearson correlation coefficient < ±0.5). The present study provides evidence to suggest that IOP is influenced by hydration status

Modification of the CAB Model for Air-Assist Atomization of Food Sprays

The Cascade Atomization and Drop Breakup (CAB) model has been originally developed for pressure atomizers. In this study, the CAB model is modified to accommodate the atomization of low-pressure, air-assist atomizers. The modifications include the first breakup which is modeled by estimating theWeber number due to the increased liquid-gas relative velocity caused by the air flow. This breakup depends on whether the Weber number is in the catastrophic, stripping or bag breakup regime. The second modification includes a change in the product drop distributions, namely, instead of a uniform distribution, as used in the original CAB model, a X-squared distribution with the same average drop size is assumed. The model changes are validated with experimental data obtained by means of two different air-assist atomizers using an oil-in-water emulsion. The simulations are performed with a modified version of the KIVA-3 CFD code; they show good agreement with the experiments

Treatment of relative permeabilities for application in hydrocarbon reservoir simulation model

Mjerenje relativnih propusnosti i njihova analiza i modifikacija pomoću odgovarajućih modela relativnih propusnosti predstavljaju jedan od temelja za izradu simulacijskih modela ležišta i njihovu verifikaciju. Uz to je vezan pažljiv izbor tih krivulja uz uvjet da postoji statistički dovoljno relevantan skup sličnih krivulja za odabrano ležište ili za pojedine tipove stijena unutar istog ležišta. Da bi se te krivulje mogle pravilno primijeniti pri simulaciji ležišta, prethodno je potrebno dobro poznavanje svih parametara ležišne stijene, tj. šupljikavosti, apsolutnih i efektivnih propusnosti, zasićenja, itd. Svrha ovog rada je prikaz postupaka mjerenja krivulja relativnih propusnosti i metoda analize eksperimentom dobivenih podataka pomoću različitih modela koji opisuju te podatke da bi se mogli što kvalitetnije primijeniti kod numeričke simulacije ležišta. Vezano uz to, u završnom dijelu ovog rada je kroz konkretan primjer izložen postupak analize i obrade podataka relativnih propusnosti u svrhu pripreme za model ležišta ugljikovodika.Measurements of relative permeabilities and their analysis and modification by means of appropriate relative permeability models represent one of the bases for development and verification of reservoir simulation models. It requires careful selection of these curves providing that a statistically sufficiently relevant groups of similar curves are available for the selected reservoir or for individual rock types within the same reservoir. In order to be able to correctly apply these curves in reservoir simulations, previous knowledge of all reservoir rock parameters is required, i.e. porosity, absolute and effective permeability, saturation, etc. Objective of the paper is to present procedures of relative permeability curve measurements and analysis methods of data experimentally obtained from different models which describe such data, to be able to apply them with high degree of quality in numerical reservoir simulations. In that regard, the final part of the paper through a concrete example shows the procedure of relative permeability data analysis and their processing for application in hydrocarbon reservoir model construction

Zeitgemäßes Sprachenlernen. Herausgforderungen beim Einsatz neuer digitaler Technologien

Die Zukunft der Sprachlernkurse wirkt düster: Berufstätige haben oft nicht mehr die Zeit und Muße, sich auf den langjährigen Prozess des Fremdsprachenerwerbs einzulassen. Endgültig überlebt zu haben scheinen sich manche wöchentlichen Präsenzkurse sowie jahrzehntelang gepflegte didaktisch-methodische Ansätze. Was können neue digitale Medien und Technologien hier leisten? Welche Entwicklungen dürfen Bildungseinrichtungen nicht verpassen, welche Potenziale nicht übersehen? Der vorliegende Beitrag beschreibt und diskutiert Entwicklungschancen mobilen und digitalen Sprachenlernens vor dem Hintergrund veränderter Kommunikationsformen und gleichbleibender Rahmenbedingungen eines gelingenden Sprachenerwerbs. Ausgelotet werden vor allem die Möglichkeiten von Blended-Learning-Angeboten und das Lernen mit Online-Plattformen. Der Beitrag bietet zudem Hinweise auf nützliche digitale Tools und Technologien. Den Abschluss bilden Überlegungen zu den Herausforderungen, die Unterrichtende antreffen, wenn es um ein zeitgemäßes Sprachenlernen geht. (DIPF/Orig.)The future of language learning courses appears gloomy: Employed people often no longer have the time and leisure to embark on the lengthy process of foreign language acquisition. Traditional and popular course formats are out of date and some teaching and learning approaches do not work any longer. What can new digital media and technologies accomplish in this case? What developments should educational institutions not miss, and which potentials should not be overlooked? This article describes and discusses development potentials of mobile and digital language learning against the backdrop of changed forms of communication and unchanged conditions for successful language acquisition. Above all, the possibilities of blended learning course and online platforms were tested. The article also offers advice on useful digital tools and technologies. It concludes by considering the challenges that instructors face with up-do-date language learning. (DIPF/Orig.

Neural Networks for CollaborativeFiltering

Recommender systems are an integral part of almost all modern e-commerce companies. They contribute significantly to the overall customer satisfaction by helping the user discover new and relevant items, which consequently leads to higher sales and stronger customer retention. It is, therefore, not surprising that large e-commerce shops like Amazon or streaming platforms like Netflix and Spotify even use multiple recommender systems to further increase user engagement. Finding the most relevant items for each user is a difficult task that is critically dependent on the available user feedback information. However, most users typically interact with products only through noisy implicit feedback, such as clicks or purchases, rather than providing explicit information about their preferences, such as product ratings. This usually makes large amounts of behavioural user data necessary to infer accurate user preferences. One popular approach to make the most use of both forms of feedback is called collaborative filtering. Here, the main idea is to compare individual user behaviour with the behaviour of all known users. Although there are many different collaborative filtering techniques, matrix factorization models are among the most successful ones. In contrast, while neural networks are nowadays the state-of-the-art method for tasks such as image recognition or natural language processing, they are still not very popular for collaborative filtering tasks. Therefore, the main focus of this thesis is the derivation of multiple wide neural network architectures to mimic and extend matrix factorization models for various collaborative filtering problems and to gain insights into the connection between these models. The basics of the proposed architecture are wide and shallow feedforward neural networks, which will be established for rating prediction tasks on explicit feedback datasets. These networks consist of large input and output layers, which allow them to capture user and item representation similar to matrix factorization models. By deriving all weight updates and comparing the structure of both models, it is proven that a simplified version of the proposed network can mimic common matrix factorization models: a result that has not been shown, as far as we know, in this form before. Additionally, various extensions are thoroughly evaluated. The new findings of this evaluation can also easily be transferred to other matrix factorization models. This neural network architecture can be extended to be used for personalized ranking tasks on implicit feedback datasets. For these problems, it is necessary to rank products according to individual preferences using only the provided implicit feedback. One of the most successful and influential approaches for personalized ranking tasks is Bayesian Personalized Ranking, which attempts to learn pairwise item rankings and can also be used in combination with matrix factorization models. It is shown, how the introduction of an additional ranking layer forces the network to learn pairwise item rankings. In addition, similarities between this novel neural network architecture and a matrix factorization model trained with Bayesian Personalized Ranking are proven. To the best of our knowledge, this is the first time that these connections have been shown. The state-of-the-art performance of this network is demonstrated in a detailed evaluation. The most comprehensive feedback datasets consist of a mixture of explicit as well as implicit feedback information. Here, the goal is to predict if a user will like an item, similar to rating prediction tasks, even if this user has never given any explicit feedback at all: a problem, that has not been covered by the collaborative filtering literature yet. The network to solve this task is composed out of two networks: one for the explicit and one for the implicit feedback. Additional item features are learned using the implicit feedback, which capture all information necessary to rank items. Afterwards, these features are used to improve the explicit feedback prediction. Both parts of this combined network have different optimization goals, are trained simultaneously and, therefore, influence each other. A detailed evaluation shows that this approach is helpful to improve the network's overall predictive performance especially for ranking metrics

The direct synthesis of crosslinked polymeric azomethines

Char yields of synthesized crosslinked polymeric azomethine

Polymeric Schiff bases. 17 - Azomethine copolymers

Chemical synthesis of azomethine copolymers by melt polymerization techniques - polymeric Schiff base

Microbial responses to changes in land use

Background/Question/Methods&#xd;&#xa;Land use change is one of the greatest threats to biodiversity worldwide. This is especially true for land use change that results in the destruction of intact forest, or &#x22;deforestation&#x201d;. Deforestation is causing a loss of biological diversity on an unprecedented scale, especially in the Tropics. It is unclear how the majority of the biodiversity on Earth &#x2013; microbial biodiversity &#x2013; is responding to these extraordinary rates of deforestation. I will provide an overview of our current understanding of microbial responses to deforestation. I will focus, as an example, on our current research regarding the effects of deforestation on the diversity of arbuscular mycorrhizal fungi (AMF), bacteria and archaea within Amazon Rainforest soils. This study takes advantage of an established chronosequence of primary rainforest, pastures of various ages, and secondary rainforest to determine the effect of deforestation on the taxonomic, phylogenetic and functional diversity of soil microorganisms, assayed using culture-independent methods.&#xd;&#xa;&#xd;&#xa;Results/Conclusions&#xd;&#xa;There is increasing evidence that deforestation significantly affects microbial diversity, and that &#x201c;recovery&#x201d; of microbial diversity in secondary forest soils is incomplete. For example, rarefaction curves suggest that the accumulation of AMF taxa is higher for Amazon primary forest soil relative to secondary forest soil. In addition, the community composition varies with land use; three AMF taxa were shared between primary and secondary forests, seven were found only in primary forest, and three were found exclusively in secondary forest soil. We also observed that the phylogenetic diversity of AMF is more reduced in secondary forest soils than expected given the regional pool of AMF taxa.&#xd;&#xa;&#xd;&#xa;*The audio track for talks in this symposium may be obtained at the following web address:*&#xd;&#xa;&#xd;&#xa;*https://sites.google.com/site/esa2010symposium13audiocontent/esa2010-symposium13-audio-content