1,928 research outputs found

    Evaluación de diferentes métodos de modelización para la estimación de la altura total del árbol de la Región Mediterránea de Turquía

    Get PDF
    Efficient management of timber resources and wood utilization practices require accurate and versatile information about important characteristics of forest resources for evaluating the numerous management and utilization alternatives for timber resources. Tree height is considered one of the most useful variables along with stocking and diameter at breast height, in estimating forest stand wood volumes and productivity. Six nonlinear growth functions were fitted to tree height-diameter data of three major tree species in Western Mediterranean Region’s forests of Turkey. The generalized regression neural network (GRNN) technique has been applied for tree height prediction, as well, due to its ability to fit complex nonlinear models. The performance of the models was compared and evaluated. Further, equivalence tests of the selected models were conducted. Validation showed the appropriatness of all models to predict tree height. According to the model performance criteria, the six nonlinear growth functions were able to capture the height-diameter relationships and fitted the data almost equally well, while the constructed generalized regression neural network (GRNN) models were found to be superior to all nonlinear regression models, in terms of their predictive ability. La gestión eficiente de los recursos forestales y la de utilización de la madera requieren de información precisa y versátil acerca de las características importantes de los recursos forestales para la evaluación de la gestión y de las alternativas de utilización de los recursos forestales. La altura del árbol es considerada como una de las variables más útiles, junto con la densidad, y el diámetro a la altura del pecho, en la estimación de volúmenes de madera y la productividad de masas forestales. Se ajustaron seis modelos de altura total-diámetro y se compararon con el fin de estimar con precisión la altura total del árbol de las tres principales especies de árboles en los bosques de la Región Occidental Mediterráneo de Turquía. La regresión generalizada de redes neuronales (GRNN) se presenta como una técnica alternativa de red neuronal a la técnica de regresión no lineal para estimar la altura total de los árboles debido a su capacidad para adaptarse a modelos complejos no lineales. Se compararon y evaluaron los modelos. Se llevaron a cabo otras pruebas, como la equivalencia de los modelos seleccionados. De acuerdo con los criterios del rendimiento de los modelos, las seis funciones no lineales de crecimiento fueron capaces de capturar las relaciones altura-diámetro y ajustaron los datos casi igual de bien, mientras que las construidas mediante modelos de regresión generalizados de redes neuronales (GRNN) resultaron ser superiores a todos los modelos de regresión no lineal, en términos de su capacidad predictiva

    A time delay artificial neural network approach for flow routing in a river system

    No full text
    International audienceRiver flow routing provides basic information on a wide range of problems related to the design and operation of river systems. In this paper, three layer cascade correlation Time Delay Artificial Neural Network (TDANN) models have been developed to forecast the one day ahead daily flow at Ilarionas station on the Aliakmon river, in Northern Greece. The networks are time lagged feed-formatted with delayed memory processing elements at the input layer. The network topology is using multiple inputs, which include the time lagged daily flow values further up at Siatista station on the Aliakmon river and at Grevena station on the Venetikos river, which is a tributary to the Aliakmon river and a single output, which are the daily flow values at Ilarionas station. The choice of the input variables introduced to the input layer was based on the cross-correlation. The use of cross-correlation between the ith input series and the output provides a short cut to the problem of the delayed memory determination. Kalman's learning rule was used to modify the artificial neural network weights. The networks are designed by putting weights between neurons, by using the hyperbolic-tangent function for training. The number of nodes in the hidden layer was determined based on the maximum value of the correlation coefficient. The results show a good performance of the TDANN approach for forecasting the daily flow values, at Ilarionas station and demonstrate its adequacy and potential for river flow routing. The TDANN approach introduced in this study is sufficiently general and has great potential to be applicable to many hydrological and environmental applications

    DOWNSCALING OUTPUTS OF THE GENERAL CIRCULATION MODELS FOR THE PREDICTION OF THE MONTHLY PRECIPITATION IN A STATION BY USING NEURAL NETWORKS

    Get PDF
    Η πρόβλεψη της κλιματικής αλλαγής βασίζεται κυρίως στις εκτιμήσεις των μοντέλων γενικής κυκλοφορίας (General Circulation Models) (GCMs). Οι εκτιμήσεις αυτές αναφέρονται σε μεγάλη χωρική ανάλυση και είναι επιβεβλημένη η εφαρμογή διαδικασιών για τον υποβιβασμό κλίμακας (downscaling) σε κλίμακα είτε τοπική είτε σταθμού. Στην εργασία αυτή ο υποβιβασμός περιγράφει τη σχέση μεταξύ μετεωρολογικών μεταβλητών μεγάλης κλίμακας που προσομοιώνονται από GCMs μοντέλα όπως είναι η βροχόπτωση, η θερμοκρασία, η υγρασία κ.λπ. και της μηνιαίας βροχόπτωσης ενός σταθμού και γίνεται με την εφαρμογή τεχνητών νευρωνικών δικτύων (Artificial Neural Networks) (ANNs) σε συνδυασμό με ανάλυση κυρίων συνιστωσών (PCA). Από την ανάλυση των αποτελεσμάτων συμπεραίνεται η καταλληλότητα των ANNs μοντέλων για τον υποβιβασμό κλίμακας μετεωρολογικών μεταβλητών όπως η βροχόπτωση, η θερμοκρασία, η εξατμισοδιαπνοή κ.λπ.Climate change predictions are generally based on the estimations by general circulation models (GCMs). The GCMs outputs are usually at resolution that is too coarse for many climate change impact studies. Hence, there is a great need to develop tools for downscaling GCM predictions of climate change to regional and local or station scales. This paper examines the potential of the Artificial Neural Network models (ANNs) in combination with Principal Component Analysis (PCA) to describe the relationship between large-scale atmospheric variables such as precipitation, temperature, humidity, pressure, geopotential height etc., and monthly precipitation for a station. It was concluded that ANN-based downscaling models are reliable and can be applied to atmospheric variables downscaling, like precipitation, temperature, evapotranspiration etc

    Preliminary results of high resolution paleoceanography and paleoclimatology during sapropel S1 deposition (South Limnos Basin, North Aegean Sea).

    Get PDF
    Οι παλαιοπεριβαλλοντικές συνθήκες κατά τη διάρκεια απόθεσης του σαπροπηλού S1 στο Βόρειο Αιγαίο (πυρήνας βαρύτητας Μ-4, μήκους 2,53 m, λεκάνης νότιας Λήμνου) προσδιορίζονται με βάση την ποσοτική ανάλυση μικροπαλαιοντολογικών (βενθονικά και πλαγκτονικά τρηματοφόρα) και γεωχημικών (OC, δ13Corg) δεικτών. Χαρακτηριστικό του πυρήνα Μ-4 είναι η μεγάλη εμφάνιση του S1 που φτάνει το πάχος των 96 cm. Η μελέτη κατέδειξε ότι, το κατώτερο σαπροπηλικό στρώμα S1a αποτέθηκε σε θερμότερες συνθήκες, εντονότερης δυσοξίας, σε σχέση με το ανώτερο σαπροπηλικό στρώμα S1b.. Αύξηση της παραγωγικότητας και καλύτερη διατήρηση του οργανικού υλικού πιστοποιήθηκαν στο κατώτερο τμήμα του S1. Η διακοπή των σαπροπηλικών συνθηκών στα 8,0 Ka BP που χαρακτηρίζεται κυρίως από την αύξηση της σχετικής συχνότητας των συμφυρματοπαγών μορφών των βενθονικών τρηματοφόρων υποστηρίζει συνθήκες υψηλής οξυγόνωσης του πυθμένα και εισροή γλυκών υδάτων.The paleoenviromental conditions during the depositional interval of sapropel S1 in the northeastern Aegean (gravity core M-4, length 2.53 m; south Limnos basin) are studied based on quantitative micropaleontological (benthic and planktonic foraminifera) and geochemical (OC, δ13Corg) analyses. Special feature of core M-4 is the thickness of S1 layer (96 cm). Our study points that sapropelic layer S1a has been deposited in more dysoxic and warmer conditions in respect to S1b. Both primary productivity and preservation of organic material are more intense during the lower part of S1. An interruption of the sapropelic conditions at 8.0 Ka BP which is mainly characterized by the increase of agglutinated foraminiferal forms confirms both higher oxygen bottom conditions and freshwater input

    Deregulation of methylation of transcribed-ultra conserved regions in colorectal cancer and their value for detection of adenomas and adenocarcinomas

    No full text
    Expression of Transcribed Ultraconserved Regions (T-UCRs) is often deregulated in cancer. The present study assesses the expression and methylation of three T-UCRs (Uc160, Uc283 and Uc346) in colorectal cancer (CRC) and explores the potential of T-UCR methylation in circulating DNA for the detection of adenomas and adenocarcinomas. Expression levels of Uc160, Uc283 and Uc346 were lower in neoplastic tissues from 64 CRC patients (statistically significant for Uc160, p<0.001), compared to non-malignant tissues, while methylation levels displayed the inverse pattern (p<0.001, p=0.001 and p=0.004 respectively). In colon cancer cell lines, overexpression of Uc160 and Uc346 led to increased proliferation and migration rates. Methylation levels of Uc160 in plasma of 50 CRC, 59 adenoma patients, 40 healthy subjects and 12 patients with colon inflammation or diverticulosis predicted the presence of CRC with 35% sensitivity and 89% specificity (p=0.016), while methylation levels of the combination of all three T-UCRs resulted in 45% sensitivity and 74.3% specificity (p=0.013). In conclusion, studied T-UCRs’ expression and methylation status are deregulated in CRC while Uc160 and Uc346 appear to have a complicated role in CRC progression. Moreover their methylation status appears a promising non-invasive screening test for CRC, provided that the sensitivity of the assay is improved

    Wavelength-dependent effects of artificial light at night on phytoplankton growth and community structure

    Get PDF
    Artificial light at night (ALAN) is a disruptive form of pollution, impacting physiological and behavioural processes that may scale up to population and community levels. Evidence from terrestrial habitats show that the severity and type of impact depend on the wavelength and intensity of ALAN; however, research on marine organisms is still limited. Here, we experimentally investigated the effect of different ALAN colours on marine primary producers. We tested the effect of green (525 nm), red (624 nm) and broad-spectrum white LED ALAN, compared to a dark control, on the green microalgae Tetraselmis suesica and a diatom assemblage. We show that green ALAN boosted chlorophyll production and abundance in T. suesica. All ALAN wavelengths affected assemblage biomass and diversity, with red and green ALAN having the strongest effects, leading to higher overall abundance and selective dominance of specific diatom species, some known to cause harmful algal blooms. Our findings show that green and red ALAN should be used with caution as alternative LED colours in coastal areas, where there might be a need to strike a balance between the effects of green and red light on marine primary producers with the benefit they appear to bring to other organisms

    The Neurocognitive Architecture of Individual Differences in Math Anxiety in Typical Children

    Get PDF
    Math Anxiety (MA) is characterized by a negative emotional response when facing math-related situations. MA is distinct from general anxiety and can emerge during primary education. Prior studies typically comprise adults and comparisons between high- versus low-MA, where neuroimaging work has focused on differences in network activation between groups when completing numerical tasks. The present study used voxel-based morphometry (VBM) to identify the structural brain correlates of MA in a sample of 79 healthy children aged 7–12 years. Given that MA is thought to develop in later primary education, the study focused on the level of MA, rather than categorically defining its presence. Using a battery of cognitive- and numerical-function tasks, we identified that increased MA was associated with reduced attention, working memory and math achievement. VBM highlighted that increased MA was associated with reduced grey matter in the left anterior intraparietal sulcus. This region was also associated with attention, suggesting that baseline differences in morphology may underpin attentional differences. Future studies should clarify whether poorer attentional capacity due to reduced grey matter density results in the later emergence of MA. Further, our data highlight the role of working memory in propagating reduced math achievement in children with higher MA

    Long-Baseline Neutrino Facility (LBNF) and Deep Underground Neutrino Experiment (DUNE) Conceptual Design Report Volume 2: The Physics Program for DUNE at LBNF

    Full text link
    The Physics Program for the Deep Underground Neutrino Experiment (DUNE) at the Fermilab Long-Baseline Neutrino Facility (LBNF) is described
    corecore