Search results for "Neural"
showing 10 items of 2783 documents
Insight into the noble crayfish morphological diversity: a geometric morphometric approach.
2022
The noble crayfish (Astacus astacus), a keystone species of high ecological, economic, and cultural importance in Europe, is threatened due to a long-term population decline caused by anthropogenic pressure on its habitats, the presence of non-indigenous invasive crayfish species and climate change. Since the effective protection of the remaining populations requires conservation measures based on the comprehensive knowledge of the species, including good understanding of its genetic and morphological variability, our aim was to study morphological features of the noble crayfish in Croatia using geometric morphometrics for the first time. We applied two-dimensional geometric morphometrics t…
Traitement de données RGB et Lidar à extrêmement haute résolution: retombées de la compétition de fusion de données 2015 de l'IEEE GRSS - Partie A / …
2016
International audience; In this paper, we discuss the scientific outcomes of the 2015 data fusion contest organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (IEEE GRSS). As for previous years, the IADF TC organized a data fusion contest aiming at fostering new ideas and solutions for multisource studies. The 2015 edition of the contest proposed a multiresolution and multisensorial challenge involving extremely high-resolution RGB images and a three-dimensional (3-D) LiDAR point cloud. The competition was framed in two parallel tracks, considering 2-D and 3-D products, respectively. In this paper, we discuss the sci…
Air pollution in European countries and life expectancy—modelling with the use of neural network
2019
Abstract The present paper discusses a novel methodology based on neural network to determine air pollutants’ correlation with life expectancy in European countries. The models were developed using historical data from the period 1992–2016, for a set of 20 European countries. The subject of the analysis included the input variables of the following air pollutants: sulphur oxides, nitrogen oxides, carbon monoxide, particulate matters, polycyclic aromatic hydrocarbons and non-methane volatile organic compounds. Our main findings indicate that all the variables significantly affect life expectancy. Sensitivity of constructed neural networks to pollutants proved to be particularly important in …
A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1
2020
The hydroxyl radical (OH) plays critical roles within the troposphere, such as determining the lifetime of methane (CH4), yet is challenging to model due to its fast cycling and dependence on a multitude of sources and sinks. As a result, the reasons for variations in OH and the resulting methane lifetime (τCH4), both between models and in time, are difficult to diagnose. We apply a neural network (NN) approach to address this issue within a group of models that participated in the Chemistry-Climate Model Initiative (CCMI). Analysis of the historical specified dynamics simulations performed for CCMI indicates that the primary drivers of τCH4 differences among 10 models are the flux of UV li…
Neural networks for analysing the relevance of input variables in the prediction of tropospheric ozone concentration
2006
Abstract This paper deals with tropospheric ozone modelling by using Artificial Neural Networks (ANNs). In this study, ambient ozone concentrations are estimated using surface meteorological variables and vehicle emission variables as predictors. The work is especially focused on analysing the importance of the input variables used by these models. This analysis is carried out in different time windows: all the time of study (April of 1997, 1999 and 2000), one month (April 1999), and finally, an hourly analysis. All the information extracted from these analyses can determine the most important factors in tropospheric ozone formation, thus achieving a qualitative model from the quantitative …
Two-days ahead prediction of daily maximum concentrations of SO2, O3, PM10, NO2, CO in the urban area of Palermo, Italy
2007
Abstract Artificial neural networks are functional alternative techniques in modelling the intricate vehicular exhaust emission dispersion phenomenon. Pollutant predictions are notoriously complex when using either deterministic or stochastic models, which explains why this model was developed using a neural network. Neural networks have the ability to learn about non-linear relationships between the used variables. In this paper a recurrent neural network (Elman model) based forecaster for the prediction of daily maximum concentrations of SO2, O3, PM10, NO2, CO in the city of Palermo is proposed. The effectiveness of the presented forecaster was tested using a time series recorded between …
The electron affinity of astatine
2020
One of the most important properties influencing the chemical behavior of an element is the electron affinity (EA). Among the remaining elements with unknown EA is astatine, where one of its isotopes, 211At, is remarkably well suited for targeted radionuclide therapy of cancer. With the At− anion being involved in many aspects of current astatine labeling protocols, the knowledge of the electron affinity of this element is of prime importance. Here we report the measured value of the EA of astatine to be 2.41578(7) eV. This result is compared to state-of-the-art relativistic quantum mechanical calculations that incorporate both the Breit and the quantum electrodynamics (QED) corrections and…
Non-adrenergic non-cholinergic nerve-mediated inhibitory control of pigeon oesophageal muscle.
1996
Pigeon oesophageal smooth muscle in vitro has spontaneous electromechanical activity. In the presence of atropine and guanethidine, electrical field stimulation evokes a transient TTX-sensitive response comprising inhibition of electric bursting activity and muscular relaxation. This NANC inhibitory response was analysed using the K+ channel blockers TEA and apamin, TEA perfusion (0.1-5 mM) induced a concentration-dependent reduction in amplitude of EFS-evoked relaxation. Responses to higher stimulation frequencies were more sensitive to TEA than those to lower ones. The maximum reduction in amplitude (29% of control) was obtained on 30 Hz EFS evoked responses during 5 mM TEA perfusion. In …
Electrophysiological and microiontophoretic analysis of the habenulo-hippocampal circuit.
1991
In the cat, the effects of lateral habenula stimulation, at different ranges of frequency, on hippocampal units were studied. Habenular stimulation at low frequency excited, while at high frequency inhibited the greater part of hippocampal units. Moreover, in order to clarify the possible pathway involved in the habenulo-hippocampal circuit, the effects of iontophoretic acetylcholine and serotonin on hippocampal units were compared with those of habenular stimulation. Iontophoretic acetylcholine induced both excitatory and inhibitory responses while serotonin induced only inhibitory responses. Iontophoretic atropine blocked the effects of acetylcholine ejection but did not antagonize stimul…
Enabling Real-Time Computation of Psycho-Acoustic Parameters in Acoustic Sensors Using Convolutional Neural Networks
2020
Sensor networks have become an extremely useful tool for monitoring and analysing many aspects of our daily lives. Noise pollution levels are very important today, especially in cities where the number of inhabitants and disturbing sounds are constantly increasing. Psycho-acoustic parameters are a fundamental tool for assessing the degree of discomfort produced by different sounds and, combined with wireless acoustic sensor networks (WASNs), could enable, for example, the efficient implementation of acoustic discomfort maps within smart cities. However, the continuous monitoring of psycho-acoustic parameters to create time-dependent discomfort maps requires a high computational demand that …