Search results for "forecasting"

Neurotrophin secretion: current facts and future prospects

2003

The proteins of the mammalian neurotrophin family (nerve growth factor (NGF), brain-derived neurotrophic factor (BDNF), neurotrophin-3 (NT-3) and neurotrophin-4/5 (NT-4/5)) were originally identified as neuronal survival factors. During the last decade, evidence has accumulated implicating them (especially BDNF) in addition in the regulation of synaptic transmission and synaptogenesis in the CNS. However, a detailed understanding of the secretion of neurotrophins from neurons is required to delineate their role in regulating synaptic function. Some crucial questions that need to be addressed include the sites of neurotrophin secretion (i.e. axonal versus dendritic; synaptic versus extrasyna…

Morphological parameters as predictors of successful correction of Class III malocclusion

2001

The aim of the study was to assess pre-treatment cephalometric parameters and measurements of the size of the apical bases as predictors of successful orthodontic correction of Class III malocclusions. Pre- and post-treatment lateral cephalograms and study models of 80 completed Class III subjects were examined to obtain 23 cephalometric parameters taken mainly from the analyses of McNamara and Schwarz, and to measure the size of the apical bases. Success of occlusal correction was evaluated as the percentage change of peer assessment rating score during treatment, which was used as the dependent variable in multivariate statistical analyses testing the predictive value of the parameters as…

Preliminary Analysis on Correlations between Spatial Distribution of Chlorophyll-a and Experimental Data of Biomass in the Strait of Sicily

2010

This study, using both remotely sensed and measured in situ data, is directed to the analysis of the correlations between the chlorophyll-a concentration and the biomass of sardines and anchovies acoustically evaluated in the Strait of Sicily. This work, inter alia, shows the usefulness of remote observation of seas in determining possible relationships between fish stocks and some oceanographic parameters (Sea Surface Temperature, Chlorophyll-a, Zooplankton).

2017

Abstract. Polycyclic aromatic hydrocarbons (PAHs) are hazardous pollutants, with increasing emissions in pace with economic development in East Asia, but their distribution and fate in the atmosphere are not yet well understood. We extended the regional atmospheric chemistry model WRF-Chem (Weather Research Forecast model with Chemistry module) to comprehensively study the atmospheric distribution and the fate of low-concentration, slowly degrading semivolatile compounds. The WRF-Chem-PAH model reflects the state-of-the-art understanding of current PAHs studies with several new or updated features. It was applied for PAHs covering a wide range of volatility and hydrophobicity, i.e. phenanth…

Stochastic models for wind speed forecasting

2011

Abstract This paper is concerned with the problem of developing a general class of stochastic models for hourly average wind speed time series. The proposed approach has been applied to the time series recorded during 4 years in two sites of Sicily, a region of Italy, and it has attained valuable results in terms both of modelling and forecasting. Moreover, the 24 h predictions obtained employing only 1-month time series are quite similar to those provided by a feed-forward artificial neural network trained on 2 years data.

Day-ahead forecasting for photovoltaic power using artificial neural networks ensembles

2016

Solar photovoltaic plants power output forecasting using machine learning techniques can be of a great advantage to energy producers when they are implemented with day-ahead energy market data. In this work a model was developed using a supervised learning algorithm of multilayer perceptron feedforward artificial neural network to predict the next twenty-four hours (day-ahead) power of a solar facility using fetched weather forecast of the following day. Each set of tested network configuration was trained by the historical power output of the plant as a target. For each configuration, one hundred networks ensembles was averaged to give the ability to generalize a better forecast. The train…

Unreliable predictions about COVID‐19 infections and hospitalizations make people worry: The case of Italy

2021

Methodological considerations for interrupted time series analysis in radiation epidemiology: an overview

2021

Interrupted time series analysis (ITSA) is a method that can be applied to evaluate health outcomes in populations exposed to ionizing radiation following major radiological events. Using aggregated time series data, ITSA evaluates whether the time trend of a health indicator shows a change associated with the radiological event. That is, ITSA checks whether there is a statistically significant discrepancy between the projection of a pre-event trend and the data empirically observed after the event. Conducting ITSA requires one to consider specific methodological issues due to unique threats to internal validity that make ITSA prone to bias. We here discuss the strengths and limitations of …

ADME Prediction with KNIME: Development and Validation of a Publicly Available Workflow for the Prediction of Human Oral Bioavailability.

2020

In silico prediction of human oral bioavailability is a relevant tool for the selection of potential drug candidates and for the rejection of those molecules with less probability of success during the early stages of drug discovery and development. However, the high variability and complexity of oral bioavailability and the limited experimental data in the public domain have mainly restricted the development of reliable in silico models to predict this property from the chemical structure. In this study we present a KNIME automated workflow to predict human oral bioavailability of new drug and drug-like molecules based on five machine learning approaches combined into an ensemble model. Th…

Predicting perceived visual complexity of abstract patterns using computational measures: The influence of mirror symmetry on complexity perception

2017

Visual complexity is relevant for many areas ranging from improving usability of technical displays or websites up to understanding aesthetic experiences. Therefore, many attempts have been made to relate objective properties of images to perceived complexity in artworks and other images. It has been argued that visual complexity is a multidimensional construct mainly consisting of two dimensions: A quantitative dimension that increases complexity through number of elements, and a structural dimension representing order negatively related to complexity. The objective of this work is to study human perception of visual complexity utilizing two large independent sets of abstract patterns. A w…