Seasonality in the alpine water logistic system on a regional basis

International audienceIn this study the water logistic system is defined as the interaction of the subsystems water resources, water supply and water demand in terms of water flow. The analysis of a water balance in alpine regions is strongly influenced by both temporal and spatial seasonal fluctuations within these elements, the latter due to the vertical dimension of mountainous areas. Therefore the determination of different seasons plays a key role within the assessment of alpine water logistic systems. In most studies a water balance for a certain region is generated on an annual, monthly or classic 4-seasonal basis. This paper presents a GIS-based multi criteria method to determine an optimal winter and summer period, taking into account different water demand stakeholders, alpine hydrology and the characteristic present day water supply infrastructure of the Alps. Technical snow-making and (winter) tourism were identified as the two major seasonal water demand stakeholders in the study area, which is the Kitzbueheler region in the Austrian Alps. Based upon the geographical datasets mean snow cover start and end date, winter was defined as the period from December to March, and summer as the period from April to November

Measuring Accuracy of Triples in Knowledge Graphs

An increasing amount of large-scale knowledge graphs have been constructed in recent years. Those graphs are often created from text-based extraction, which could be very noisy. So far, cleaning knowledge graphs are often carried out by human experts and thus very inefficient. It is necessary to explore automatic methods for identifying and eliminating erroneous information. In order to achieve this, previous approaches primarily rely on internal information i.e. the knowledge graph itself. In this paper, we introduce an automatic approach, Triples Accuracy Assessment (TAA), for validating RDF triples (source triples) in a knowledge graph by finding consensus of matched triples (among target triples) from other knowledge graphs. TAA uses knowledge graph interlinks to find identical resources and apply different matching methods between the predicates of source triples and target triples. Then based on the matched triples, TAA calculates a confidence score to indicate the correctness of a source triple. In addition, we present an evaluation of our approach using the FactBench dataset for fact validation. Our findings show promising results for distinguishing between correct and wrong triples

Trichlorethilene poisoning

Autori prikazuju suvremene nazore o patologiji i klinici otrovanja trikloretilenom. Pritom se detaljno iznose najnoviji rezultati proučavanja metabolizma te izlučivanje pojedinih metabolita, a naročito trikloroctene kiseline.The authors discuss the modern concepts regarding pathology and clinical aspects of trichlorethylene poisoning. The results are given of the most recent investigations of the metabolism and the secretion of metabolites, especially of trichloroacetic acid

Acute poisoning due to trichloroethylene ingestion

Opisan je slučaj akutnog otrovanja trikloretilenorn putem ingestije. Bolesnica je zabunom popila 50 ml trikloretilena umjesto rakije. Glavni simptom otrovanja bila je narkoza. Povodom ovog slučaja autori raspravljaju 31 slučaj otrovanja ingestijom iz stručne literature.A case of acute trichloroethylene poisoning due to accidental ingestion of approximately 50 ml of the toxic agent is presented. General anaesthaesia was the outstanding feature in the clinical course of the intoxication. The authors review the cases from the literature adding their case to the 31 already reported. Discussing the laboratory findings in the presented case the authors express their opinion that the metabolism of thrichloroethylene when ingested does not essentially differ from the metabolism of the same substance when inhaled

Unmet needs in patients with first-episode schizophrenia: a longitudinal perspective

Background This study aimed to identify the course of unmet needs by patients with a first episode of schizophrenia and to determine associated variables. Method We investigated baseline assessments in the European First Episode Schizophrenia Trial (EUFEST) and also follow-up interviews at 6 and 12 months. Latent class growth analysis was used to identify patient groups based on individual differences in the development of unmet needs. Multinomial logistic regression determined the predictors of group membership. Results Four classes were identified. Three differed in their baseline levels of unmet needs whereas the fourth had a marked decrease in such needs. Main predictors of class membership were prognosis and depression at baseline, and the quality of life and psychosocial intervention at follow-up. Depression at follow-up did not vary among classes. Conclusions We identified subtypes of patients with different courses of unmet needs. Prognosis of clinical improvement was a better predictor for the decline in unmet needs than was psychopathology. Needs concerning social relationships were particularly persistent in patients who remained high in their unmet needs and who lacked additional psychosocial treatmen

Examination of community and consumer nutrition, tobacco and physical activity environments at food and tobacco retail stores in three diverse North Carolina communities

To advance our understanding of multiple health-related dimensions of the built environment, this study examined associations among nutrition, tobacco, and physical activity community and consumer environments. Community environment measures included supermarket access, tobacco outlet density, and physical activity resource density in store neighborhoods. Cross-sectional observations of the nutrition, tobacco and physical activity environments were conducted in 2011 at and around 303 food stores that sold tobacco products in three North Carolina counties. Pearson correlation coefficients and multiple linear regression were used to examine associations between community and consumer environments. Correlations between community nutrition, tobacco, and physical activity environments ranged from slight to fair (-. 0.35 to 0.20) and from poor to fair (-. 0.01 to -. 0.38) between consumer environments. Significant relationships between consumer tobacco and nutrition environments were found after controlling for store and neighborhood characteristics. For example, stores with higher amounts of interior tobacco marketing had higher healthy food availability (p. =. 0.001), while stores with higher amounts of exterior tobacco marketing had lower healthy food availability (p. =. 0.02). Community and consumer environments for nutrition, tobacco, and physical activity were interrelated. Measures that assess single aspects of community or consumer environments could miss characteristics that may influence customer purchasing. Even chain supermarkets, typically regarded as healthful food sources compared to smaller food stores, may expose customers to tobacco marketing inside. Future research could explore combining efforts to reduce obesity and tobacco use by addressing tobacco marketing, healthy food availability and physical activity opportunities at retail food outlets

Classifying Candidate Axioms via Dimensionality Reduction Techniques

We assess the role of similarity measures and learning methods in classifying candidate axioms for automated schema induction through kernel-based learning algorithms. The evaluation is based on (i) three different similarity measures between axioms, and (ii) two alternative dimensionality reduction techniques to check the extent to which the considered similarities allow to separate true axioms from false axioms. The result of the dimensionality reduction process is subsequently fed to several learning algorithms, comparing the accuracy of all combinations of similarity, dimensionality reduction technique, and classification method. As a result, it is observed that it is not necessary to use sophisticated semantics-based similarity measures to obtain accurate predictions, and furthermore that classification performance only marginally depends on the choice of the learning method. Our results open the way to implementing efficient surrogate models for axiom scoring to speed up ontology learning and schema induction methods

Effects of aripiprazole once-monthly on domains of personal and social performance: Results from 2 multicenter, randomized, double-blind studies

Objective: To assess the effects of maintenance therapy with aripiprazole once-monthly 400 mg on personal and social functioning. Methods: Data were analyzed from 2 randomized, double-blind trials of patients with schizophrenia requiring chronic antipsychotic treatment. One study was a 52-week trial of aripiprazole once-monthly 400 mg versus placebo; the other was a 38-week trial of aripiprazole once-monthly 400 mg, oral aripiprazole (10-30 mg daily), and aripiprazole once-monthly 50 mg (subtherapeutic dose to test assay sensitivity). Functioning was assessed using the Personal and Social Performance (PSP) scale, comprising 4 domain subscales. Results: In the 52-week study, 403 patients stabilized on aripiprazole once-monthly 400 mg were randomized to receive aripiprazole once-monthly 400 mg (n=269) or placebo (n=134). In the 38-week study, 662 patients stabilized on oral aripiprazole were randomized to receive aripiprazole once-monthly 400 mg (n=265), oral aripiprazole (n=266), or aripiprazole once-monthly 50 mg (subtherapeutic dose; n=131). In the 52-week study, mean changes from baseline were significantly worsened with placebo compared with aripiprazole once-monthly 400 mg for PSP total score (

Canonicalizing Knowledge Base Literals

Ontology-based knowledge bases (KBs) like DBpedia are very valuable resources, but their usefulness and usability is limited by various quality issues. One such issue is the use of string literals instead of semantically typed entities. In this paper we study the automated canonicalization of such literals, i.e., replacing the literal with an existing entity from the KB or with a new entity that is typed using classes from the KB. We propose a framework that combines both reasoning and machine learning in order to predict the relevant entities and types, and we evaluate this framework against state-of-the-art baselines for both semantic typing and entity matching