Introduction of snow and geomorphic disturbance variables into predictive models of alpine plant distribution in the Western Swiss Alps

Indirect topographic variables have been used successfully as surrogates for disturbance processes in plant species distribution models (SDM) in mountain environments. However, no SDM studies have directly tested the performance of disturbance variables. In this study, we developed two disturbance variables: a geomorphic index (GEO) and an index of snow redistribution by wind (SNOW). These were developed in order to assess how they improved both the fit and predictive power of presenceabsence SDM based on commonly used topoclimatic (TC) variables for 91 plants in the Western Swiss Alps. The individual contribution of the disturbance variables was compared to TC variables. Maps of models were prepared to spatially test the effect of disturbance variables. On average, disturbance variables significantly improved the fit but not the predictive power of the TC models and their individual contribution was weak (5.6% for GEO and 3.3% for SNOW). However their maximum individual contribution was important (24.7% and 20.7%). Finally, maps including disturbance variables (i) were significantly divergent from TC models in terms of predicted suitable surfaces and connectivity between potential habitats, and (ii) were interpreted as more ecologically relevant. Disturbance variables did not improve the transferability of models at the local scale in a complex mountain system, and the performance and contribution of these variables were highly species-specific. However, improved spatial projections and change in connectivity are important issues when preparing projections under climate change because the future range size of the species will determine the sensitivity to changing conditions.

Variation in biomass related variables of reed canary grass

Selostus: Ruokohelven biomassan tuotantoon vaikuttavien ominaisuuksien vaihtelu

Scour Around Bridge Piers and Abutments, HR-30 and Iowa Highway Research Board Bulletin No. 4, 1956

Man’s never-ending search for better materials and construction methods and for techniques of analysis and design has overcome most of the early difficulties of bridge building. Scour of the stream bed, however, has remained a major cause of bridge failures ever since man learned to place piers and abutments in the stream in order to cross wide rivers. Considering the overall complexity of field conditions, it is not surprising that no generally accepted principles (not even rules of thumb) for the prediction of scour around bridge piers and abutments have evolved from field experience alone. The flow of individual streams exhibits a manifold variation, and great disparity exists among different rivers. The alignment, cross section, discharge, and slope of a stream must all be correlated with the scour phenomenon, and this in turn must be correlated with the characteristics of the bed material ranging from clays and fine silts to gravels and boulders. Finally, the effect of the shape of the obstruction itself-the pier or abutment-must be assessed. Since several of these factors are likely to vary with time to some degree, and since the scour phenomenon as well is inherently unsteady, sorting out the influence of each of the various factors is virtually impossible from field evidence alone. The experimental approach was chosen as the investigative method for this study, but with due recognition of the importance of field measurements and with the realization that the results must be interpreted so as to be compatible with the present-day theories of fluid mechanics and sediment transportation. This approach was chosen because, on the one hand, the factors affecting the scour phenomenon can be controlled in the laboratory to an extent that is not possible in the field, and, on the other hand, the model technique can be used to circumvent the present inadequate understanding of the phenomenon of the movement of sediment by flowing water. In order to obtain optimum results from the laboratory study, the program was arranged at the outset to include a related set of variables in each of several phases into which the whole problem was divided. The phases thus selected were : 1. Geometry of piers and abutments, 2. Hydraulics of the stream, 3. Characteristics of the sediment, 4. Geometry of channel shape and alignment.

Trust region versus line search for computing the optical flow

We consider the numerical treatment of the optical flow problem by evaluating the performance of the trust region method versus the line search method. To the best of our knowledge, the trust region method is studied here for the first time for variational optical flow computation. Four different optical flow models are used to test the performance of the proposed algorithm combining linear and nonlinear data terms with quadratic and TV regularization. We show that trust region often performs better than line search; especially in the presence of non-linearity and non-convexity in the model.

Summer ozone cycles in the Lisbon metropolitan area and its relation to some meteorological variables

The aim of this work is to study the tropospheric ozone concentrations and daily peak cycles in the Lisbon MetropolitanArea (LMA) during the summer season (June, July and August, JJA) covering the 4-yr study period 2002-2005. Theresults show that all the stations have the same pattern: a minimum in the early morning followed by an increase at 1000UTC reaching to a peak at 1300-1400 UTC, dropped again to minimum values 1800 UTC but with different concentrationsdue to regional and local wind circulations and complex dynamic interactions. We identified in Lisbon city the ozone “weekendeffect”. Finally, we studied an episode of very high levels of tropospheric ozone and related daily ozone concentrationswith some meteorological variables.

Neural networks complemented with genetic algorithms and fuzzy systems for predicting nitrogenous effluent variables in wastewater treatment plants

This work focuses on the prediction of the two main nitrogenous variables that describe the water quality at the effluent of a Wastewater Treatment Plant. We have developed two kind of Neural Networks architectures based on considering only one output or, in the other hand, the usual five effluent variables that define the water quality: suspended solids, biochemical organic matter, chemical organic matter, total nitrogen and total Kjedhal nitrogen. Two learning techniques based on a classical adaptative gradient and a Kalman filter have been implemented. In order to try to improve generalization and performance we have selected variables by means genetic algorithms and fuzzy systems. The training, testing and validation sets show that the final networks are able to learn enough well the simulated available data specially for the total nitrogen

Staging Bipolar Disorder.

The purpose of this study was to analyze the evidence supporting a staging model for bipolar disorder. The authors conducted an extensive Medline and Pubmed search of the published literature using a variety of search terms (staging, bipolar disorder, early intervention) to find relevant articles, which were reviewed in detail. Only recently specific proposals have been made to apply clinical staging to bipolar disorder. The staging model in bipolar disorder suggests a progression from prodromal (at-risk) to more severe and refractory presentations (Stage IV). A staging model implies a longitudinal appraisal of different aspects: clinical variables, such as number of episodes and subsyndromal symptoms, functional and cognitive impairment, comorbidity, biomarkers, and neuroanatomical changes. Staging models are based on the fact that response to treatment is generally better when it is introduced early in the course of the illness. It assumes that earlier stages have better prognosis and require simpler therapeutic regimens. Staging may assist in bipolar disorder treatment planning and prognosis, and emphasize the importance of early intervention. Further research is required in this exciting and novel area.

Major variables influencing correct antibiotic prescription by primary care physicians in acute pharyngitis: a cross-sectional study

Objectives:To analyse which are the main variables that influence primary care professionals, in the prescription of antibiotics in patients with acute pharyngitis.To analyse which is the diagnosis pattern used by primary care professionals towards cutepharyngitis. To recognize the clinical and analytical criteria that primary care professionals use, to determine antibiotic treatment in acute pharyngitis.To identify the main clinical variables related with the prescription of antibiotics by primary care professionals, in acute pharyngitis treatment. Design: Cross-­‐sectional study Participants:165 primary care professionals from the Sanitary Region of Girona not attending paediatric patients and randomly selected from 29 ABS managed by two of the main health care providers: Insitut Català de la Salut (ICS) and Institut d’Assistència Sanitària (IAS) Main outcome measures: Each participant will fill out a questionnaire with personal and workplace questions, as well as about knowledge and attitude in front of the acute pharyngitis caused by group A streptococci. They will also answer 4 clinical questions about correct treatment and diagnosis of acute pharyngitis caused by group A streptococci

Amino acid composition of soybean seeds as affected by climatic variables

The objective of this work was to perform a quantitative analysis of the amino acid composition of soybean seeds as affected by climatic variables during seed filling. Amino acids were determined from seed samples taken at harvest in 31 multi-environment field trials carried out in Argentina. Total amino acids ranged from 31.69 to 49.14%, and total essential and nonessential amino acids varied from 12.83 to 19.02% and from 18.86 to 31.15%, respectively. Variance components expressed as the percentage of total variation showed that the environment was the most important source of variation for all traits, followed by the genotype x environment interaction. Significant explanatory linear regressions were detected for amino acid content regarding: average daily mean air temperature and cumulative solar radiation, during seed filling; precipitation minus potential evapotranspiration, during the whole reproductive period; and the combinations of these climatic variables. Each amino acid behaves differently according to environmental conditions, indicating compensatory effects among them.

Variables físicas que explican la variabilidad de suelo aluvial y su comportamiento espacial

El objetivo de este trabajo fue utilizar el análisis de componentes principales y de semivarianza para seleccionar variables físicas que explicaran la variabilidad de un suelo aluvial, y establecer el comportamiento espacial de las variables seleccionadas, con el fin de definir técnicamente la localización de parcelas experimentales para estudiar los efectos de la abrasividad del suelo sobre el desgaste de herramientas agrícolas. Las pruebas de campo se realizaron en 2008, en un lote plano de 6.000 m² con suelos de textura media a pesada (Vertic Haplustepts). Se hizo un muestreo intensivo en cuadrícula de 10x14 m. Las variables que mayor peso tuvieron en los tres primeros componentes principales fueron los contenidos de limo, arena fina y media, gravilla media, la humedad a capacidad de campo y el coeficiente higroscópico. A excepción de la arena media y la capacidad de campo, las demás propiedades presentaron alta dependencia espacial y su distribución mostró que en el lote experimental se encuentran tres sectores de acumulación diferencial de limo y de arena fina. La combinación de los análisis de componentes principales y geoestadística permitió definir las propiedades del suelo involucradas en el desgaste de herramientas, su patrón espacial y la manera más adecuada de distribuir parcelas experimentales, para estudiar la abrasividad del suelo.

Dry matter production of Tanzania grass as a function of agrometeorological variables

The objective of this work was to develop and validate linear regression models to estimate the production of dry matter by Tanzania grass (Megathyrsus maximus, cultivar Tanzania) as a function of agrometeorological variables. For this purpose, data on the growth of this forage grass from 2000 to 2005, under dry‑field conditions in São Carlos, SP, Brazil, were correlated to the following climatic parameters: minimum and mean temperatures, degree‑days, and potential and actual evapotranspiration. Simple linear regressions were performed between agrometeorological variables (independent) and the dry matter accumulation rate (dependent). The estimates were validated with independent data obtained in São Carlos and Piracicaba, SP, Brazil. The best statistical results in the development and validation of the models were obtained with the agrometeorological parameters that consider thermal and water availability effects together, such as actual evapotranspiration, accumulation of degree‑days corrected by water availability, and the climatic growth index, based on average temperature, solar radiation, and water availability. These variables can be used in simulations and models to predict the production of Tanzania grass.