1,721,266 research outputs found
Molecular thermodynamic modeling of surface tensions of some fatty acid esters and biodiesels
No full textThis work addresses the molecular thermodynamic and artificial neural network (ANN) modeling of surface tensions of several fatty acid esters and biodiesels. Two biodiesels were considered as pure fluid and the other as a binary mixture. The molecular thermodynamic model is based on the statistical mechanical expression according to Fowler-Kirkwood-Buff approximation. Regarding this, contributions to surface tension from the hard-chain repulsions, Lennard-Jones dispersion forces, and dipolar interactions were considered and assumed to be additive in the model development. The molecular thermodynamic model used three molecular parameters reflecting the hard-core diameter, dispersive energy and segment number as well as the liquid densities for which the values were predicted from perturbed Yukawa-chain equation of state. Further, the model used dipole moment as an adjustable parameter for the accurate calculation of surface tensions. The model could predict 149 surface tension data points for 9 FAEs and 3 biodiesels in 268.6–393 K range with the average absolute relative deviation (AARD) of 1.82%. The degree of accuracy of proposed model has also been compared with some empirical equations. Concerning ANN modeling, a network comprising two hidden layers and 9 neurons for each layer has been trained, according to the constructive approach. The result of the training was quite good, the AARD of the pure fluid dataset of 137 points was found to be 0.44%
Density and viscosity modeling of liquid adipates using neural network approaches
No full textLiquid Dialkylesters of adipic acid (adipates) have achieved prominence as alternative green solvents due to their special properties. To enhance their utilization, accurate thermophysical property data are required, especially for extended pressure ranges. This study presents the application of Artificial Neural Networks (ANNs) for predicting the densities and viscosities of liquid adipates over a wide range of temperatures and pressures. A substantial dataset, including 1145 viscosity and 891 density data points, was utilized across a broad range of temperature and pressure conditions. The dataset used for densities includes data for six different liquid adipates and covers a temperature range of 293.15–403.15 K and pressures up to 140 MPa, while the dataset used for viscosities encompasses data for four different liquid adipates, ranging from 293.15 K to 403.15 K in temperature and up to 65.62 MPa in pressure. Two ANN models were developed and fine-tuned, with two separate models created to predict the properties of liquid adipates. These models exhibited very good performance, achieving an Average Absolute Relative Deviation (AARD) of 0.028 % for density and 0.400 % for viscosity. The results from the ANN models were compared with semi-empirical models based on the equation of state and rough hard-sphere theory, showcasing the technique's potential as a powerful tool for characterizing thermophysical properties. As part of enhancing model reliability and robustness, various tests were conducted to validate the chosen model
A new scaled equation to calculate the surface tension of ketones
No full textThis work presents a new formula to calculate the surface tension of ketones. As a first step, an analysis of the available data of the experimental surface tension data for ketones was made. Experimental data were collected for the following pure fluids: acetone, 2-butanone, 2-pentanone, 3-pentanone, 2-hexanone, 3-heptanone, 4-heptanone, 2-octanone, and 6-undecanone. The data were then regressed with the most reliable semi-empirical correlation methods in the literature based on the corresponding states theory. The final equation proposed is very simple and gives noticeable improvement with respect to existing equations. © 2013 Akadémiai Kiadó, Budapest, Hungary
The QlandQlife tool
No full textIn recent decades, the city and the broader concept of the territory have experienced a metamorphosis: from usable physical resources and controllable, designable space to a new interpretation of the urban system. This system is complex, so the inadequacy of linear planning becomes clear when faced with an increasingly strong need for multiple intelligible responses. The ideal of the city as a “single element” has been substituted by the concept of “system city”, going beyond the model of a city that can be decomposed and simplified to attain an interpretation of the system as a “complex unit”
Density, viscosity and CO2 solubility modeling of deep eutectic solvents from various neural network approaches
No full textBackground: Deep eutectic solvents (DESs) have gained attention as innovative green solvents, but accurate prediction of their thermophysical properties is essential for practical applications. This work explored the potential of different deep learning approaches to model density, viscosity, and CO2 solubility over a wide range of temperature and pressure conditions. Methods: A comprehensive dataset was compiled, consisting of 2218 data points for density, 148 points for viscosity, and 144 points for CO2 solubility, covering a range of DES compositions. Deep neural network (NN) architecture was employed for density prediction, while simpler artificial neural network (ANN) architectures were used for viscosity and CO2 solubility predictions. Significant findings: The deep NN model exhibited an excellent performance in predicting the density, achieving an average absolute relative deviation (AARD%) of 0.13 % and R2 value of 0.9998, indicating high accuracy and robust generalization. The ANN models for viscosity and CO2 solubility also demonstrated promising results, with AARD% values of 1.44 % and 1.11 %, respectively. The comparison with semi-empirical models further highlighted the superiority of NN approaches for characterizing these innovative solvents. This work showcases the capability of deep learning in accurately modeling the thermophysical properties of DESs, providing valuable tools for applications of these green solvents
Surface tension of liquid organic acids: An artificial neural network model
Full text linkAn artificial neural network model is proposed for the surface tension of liquid organic fatty acids covering a wide temperature range. A set of 2051 data collected for 98 acids (including carboxylic, aliphatic, and polyfunctional) was considered for the training, testing, and prediction of the resulting network model. Different architectures were explored, with the final choice giving the best results, in which the input layer has the reduced temperature (temperature divided by the critical point temperature), boiling temperature, and acentric factor as an independent variable, a 41-neuron hidden layer, and an output layer consisting of one neuron. The overall absolute percentage deviation is 1.33%, and the maximum percentage deviation is 14.53%. These results constitute a major improvement over the accuracy obtained using corresponding-states correlations from the literature
Multi-target strategy for Parkinsonian patients: the role of deep brain stimulation in the centromedian-parafascicularis complex.
No full textBrain Res Bull. 2009 Feb 16;78(2-3):113-8. doi: 10.1016/j.brainresbull.2008.08.007. Epub 2008 Sep 21.
Multi-target strategy for Parkinsonian patients: the role of deep brain stimulation in the centromedian-parafascicularis complex.
Stefani A, Peppe A, Pierantozzi M, Galati S, Moschella V, Stanzione P, Mazzone P.
SourceIRCCS Fondazione S. Lucia, Roma, Italy. [email protected]
Abstract
The intra-laminar (IL) thalamic complex, composed of centromedian (CM) and parafascicular (Pf) nucleus, is a strategic crossroad for the activity of the basal ganglia and is recently regaining its position has a putative neurosurgical target for Parkinsonian syndromes. The multi-target approach we have encouraged since the late nineties has allowed the combined implantation of a standard target (the subthalamic nucleus-STN or the internal pallidus-GPi) plus an innovative one (CM/Pf) in well-identified Parkinson's disease (PD) patients; hence, it is possible to study, in the same PD patients, the specific target-mediated effects on different clinical signs. Here, we focus on the potential usefulness of implanting the CM/Pf complex when required in the management of contra-lateral tremor (resistant to standard deep brain stimulation-DBS - in STN - , n=2) and disabling involuntary movements, partially responsive to GPi-DBS (n=6). When considering global UPDRS scores, CM/Pf-DBS ameliorate extra-pyramidal symptoms but not as strongly as STN (or GPi) does. Yet, CM/Pf acts very powerfully on tremor and contributes to the long-term management of l-Dopa-induced involuntary movements. The lack of cognitive deficits and psychic impairment associated with the improvement of their quality of life, in our small cohort of CM/Pf implanted patients, reinforces the notion of CM/Pf as a safe and attractive area for surgical treatment of advanced PD, possibly affecting not only motor but also associative functions
Environmental quality index in hospital patient's rooms rapid assessment tool for the verification and design
No full textThe research proposes an environmental quality system assessment in hospital patient's rooms, based on a series of spreadsheets that define a global index, resulting from measurement of spatial, technological and perceptive parameters
Verifica degli impatti socioeconomici ed ambientali conseguenza dei mutamenti climatici in atto nel bacino del Mediterraneo, attraverso l’uso del telerilevamento satellitare
Full text linkOggi l’uomo è pericolosamente prossimo ad un punto di non ritorno, in cui può decidere di
cambiare rotta od accettare di vedere avverarsi ciò che più temeva l'economista statunitense Premio
Nobel Paul Robin Krugmani, il quale sosteneva che le future generazioni non avrebbero più potuto
contare, come avevano sempre fatto le precedenti, su di un tenore di vita migliore rispetto a quello
di padri e nonni. Questo potrebbe avvenire perché è stato ormai sollevato il problema dell’assoluta
carenza di risorse naturali, questione che per lungo tempo è stata risolta dai paesi più industrializzati
con l’esclusione della maggioranza della popolazione mondiale dai consumi di massa propri
dell’Occidente. Ma la problematica riguardante questa scarsità segue, ogni giorno di più, quella
altrettanto drammatica che investe il clima e per la comunità scientifica è ormai ovvio che entrambi
sono due facce di una stessa medaglia. Sempre più spesso, dunque, siamo chiamati a confrontarci
non solo con la miseria, in preoccupante aumento in ogni Paese dopo la crisi economica globale
iniziata nel 2008 e non ancora conclusasi, ma, anche, con tematiche come calamità naturali, eventi
estremi, squilibri ecologici, avvenimenti pure essi in apparente, decisa crescita. Privazioni e
mutamenti climatici hanno un costo economico e sociale altissimo e l’unico metodo per risolvere
queste difficoltà parrebbe quello di ricondurre la dottrina al significato primigenio della parola
economia, “oikos nomos”, che circa 2700 anni fa significava gestione della casa comune, ovvero la
Terra, pianeta in cui viviamo. In questo contesto è da inquadrare il programma di studio
interministeriale che ha per titolo “Modello integrato per l’evoluzione degli ecosistemi naturali ed
agricoli in relazione ai cambiamenti climatici nell’area mediterranea (FISR-MICENA)”; il lavoro
che qui si presenta è parte integrante dell’attività di ricerca n.19 di questo progetto
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