Prediction Of Physical Properties Research Articles

Prediction of soil properties using imaging spectroscopy: Considering fractional vegetation cover to improve accuracy

Spectroscopic techniques have become attractive to assess soil properties because they are fast, require little labor and may reduce the amount of laboratory waste produced when compared to conventional methods. Imaging spectroscopy (IS) can have further advantages compared to laboratory or field proximal spectroscopic approaches such as providing spatially continuous information with a high density. However, the accuracy of IS derived predictions decreases when the spectral mixture of soil with other targets occurs. This paper evaluates the use of spectral data obtained by an airborne hyperspectral sensor (ProSpecTIR-VS – Aisa dual sensor) for prediction of physical and chemical properties of Brazilian highly weathered soils (i.e., Oxisols). A methodology to assess the soil spectral mixture is adapted and a progressive spectral dataset selection procedure, based on bare soil fractional cover, is proposed and tested. Satisfactory performances are obtained specially for the quantification of clay, sand and CEC using airborne sensor data (R2 of 0.77, 0.79 and 0.54; RPD of 2.14, 2.22 and 1.50, respectively), after spectral data selection is performed; although results obtained for laboratory data are more accurate (R2 of 0.92, 0.85 and 0.75; RPD of 3.52, 2.62 and 2.04, for clay, sand and CEC, respectively). Most importantly, predictions based on airborne-derived spectra for which the bare soil fractional cover is not taken into account show considerable lower accuracy, for example for clay, sand and CEC (RPD of 1.52, 1.64 and 1.16, respectively). Therefore, hyperspectral remotely sensed data can be used to predict topsoil properties of highly weathered soils, although spectral mixture of bare soil with vegetation must be considered in order to achieve an improved prediction accuracy.

Role of Input Selection Prediction of Physical Properties of Degradable Composites Using ANFIS

Role of Input Selection Prediction of Physical Properties of Degradable Composites Using ANFIS

Evaluation of machine learning interpolation techniques for prediction of physical properties

A knowledge of the physical properties of materials as a function of temperature, composition, applied external stresses, etc. is an important consideration in materials and process design. For new systems, such properties may be unknown and hard to measure or estimate from numerical simulations such as molecular dynamics. Engineers rely on machine learning to employ existing data in order to predict properties for new systems. Several techniques are currently used for such purposes. These include neural network, polynomial interpolation and Gaussian processes as well as the more recent dynamic trees and scalable Gaussian processes. In this paper we compare these approaches for three sets of materials sciences data: molar volume, electrical conductivity and Martensite start temperature. We make recommendations depending on the nature of the data. We demonstrate that a thorough knowledge of the problem beforehand is critical in selecting the most successful machine learning technique. Our findings show that the Gaussian process regression technique gives very good predictions for all three sets of tested data. Typically, Gaussian process is very slow with a computational complexity of typically n3 where n is the number of data points. In this paper, we found that the scalable Gaussian process approach was able to maintain the high accuracy of the predictions while improving speed considerably, make on-line learning possible.

Physical Property Prediction for Waste Cooking Oil Biodiesel

This paper focuses on the physical property prediction of waste cooking oil biodiesel and examines the accuracy of different methods to estimate reliable basic physical properties, including density, viscosity and surface tension of waste cooking oil biodiesel in a wide range of temperature based on its fatty acid methyl ester component. A program for the physical property prediction of the biodiesel was developed and experimental measurements for the density, viscosity and surface tension of the biodiesel were performed to validate the chosen methods. The results show that the modified Rackett equation and the Orrick-Erbar method have the high estimation accuracy for the prediction of the density and the viscosity, respectively. The Sastri-Rao method and the Pizer method have the accuracy enough to predict the surface tension.

FRACTAL PARAMETERS OF PORE SPACE FROM CT IMAGES OF SOILS UNDER CONTRASTING MANAGEMENT PRACTICES

Soil structure plays an important role in flow and transport phenomena, and a quantitative characterization of the spatial heterogeneity of the pore space geometry is beneficial for prediction of soil physical properties. Morphological features such as pore-size distribution, pore space volume or pore–solid surface can be altered by different soil management practices. Irregularity of these features and their changes can be described using fractal geometry. In this study, we focus primarily on the characterization of soil pore space as a 3D geometrical shape by fractal analysis and on the ability of fractal dimensions to differentiate between two a priori different soil structures. We analyze X-ray computed tomography (CT) images of soils samples from two nearby areas with contrasting management practices. Within these two different soil systems, samples were collected from three depths. Fractal dimensions of the pore-size distributions were different depending on soil use and averaged values also differed at each depth. Fractal dimensions of the volume and surface of the pore space were lower in the tilled soil than in the natural soil but their standard deviations were higher in the former as compared to the latter. Also, it was observed that soil use was a factor that had a statistically significant effect on fractal parameters. Fractal parameters provide useful complementary information about changes in soil structure due to changes in soil management.

Atomistic force field for pyridinium-based ionic liquids: reliable transport properties.

Reliable force field (FF) is a central issue in successful prediction of physical chemical properties via computer simulations. This work introduces refined FF parameters for six popular ionic liquids (ILs) of the pyridinium family (butylpyridinium tetrafluoroborate, bis(trifluoromethanesulfonyl)imide, dicyanamide, hexafluorophosphate, triflate, chloride). We elaborate a systematic procedure, which allows accounting for specific cation-anion interactions in the liquid phase. Once these interactions are described accurately, all experimentally determined transport properties can be reproduced. We prove that three parameters per interaction site (atom diameter, depth of potential well, point electrostatic charge) provide a sufficient basis to predict thermodynamics (heat of vaporization, density), structure (radial distributions), and transport (diffusion, viscosity, conductivity) of ILs at room conditions and elevated temperature. The developed atomistic models provide a systematic refinement upon the well-known Canongia Lopes-Pádua (CL&P) FF. Together with the original CL&P parameters the present models foster a computational investigation of ionic liquids.

Determination and prediction of physical properties of cellulose nanocrystals from dynamic light scattering measurements

Cellulose nanocrystal (CNC) has attracted increasing interest due to their biocompatibility, rigidity, and potential applications in biomedicine and cosmetics. A parameter estimation technique was used to calculate the average dimension of CNC with different aspect ratios, and their dynamic physical parameters denoted by the translational (Dt) and rotational diffusion (Θ) coefficients. For CNC with L/d ratio of 17, the experimental Dt and Θ values produced calculated length (L) and diameter (d) values that deviated from the experimental results by 0.22 and 0.27 % after 1,000 iterations, respectively. The calculated translational and rotational coefficients converged to an asymptotic value of 5.048 × 10−12 m2 s−1 and 551.9 s−1, with the latter requiring a larger number of iterations to achieve convergence. Close agreement between experimentally obtained and calculated dimensions and dynamics (L, d, Dt, and Θ) for various types of CNCs was observed using this technique. By combining the theoretical model formulated by Broersma and the computational method utilizing a Nelder–Mead simplex direct search algorithm, reliable predictions of the average sizes determined from dynamic light scattering of a CNC sample was achieved; yielding an average L = 253.5 nm and d = 15.7 nm. The proposed approach provides a convenient, simple, and robust technique to determine the length and diameter of rod-like nanoparticles, such as CNC from light scattering measurements.

Semi-empirical prediction of physical properties of (B3) TlP compound

The principal goal of this work is the prediction only by means of some emperical formulas and two other physical quantities (the bond length and the bulk modulus calculated initially from first-principle calculations) of the refractive index, the exciton Bohr parameter, the electronic polarizability, the plasmon energy, the force constants, the optical band gap energy, the homopolar and heteropolar energies , the bonding-antibonding energy gap, the ionicity, the dielectric constant, and the optical susceptibility of (B3) thallium phosphide (TlP) compound. In addition, the Debye temperature, the melting temperature, the thermal conductivity and finally the linear thermal expansion of this compound are also obtained. Our results are in general in good agreement with the previous results of the literature. Keywords : Optical Properties, Thermal Properties, TlP Compound.

Sensoriamento remoto na determinação de atributos de um nitossolo sob aplicação de vinhaça

O sensoriamento remoto vem sendo utilizado na avaliação de características químicas e físicas dos solos, como fonte de informação rápida, não destrutiva e de baixo custo, podendo assim auxiliar no gerenciamento de passivos ambientais. Nesse sentido, este trabalho teve por objetivo verificar o potencial da utilização de dados hiperespectrais na determinação de algumas propriedades de um solo submetido a diferentes aplicações de doses de vinhaça, utilizando dados espectrais oriundos de amostras em condições de campo e de terra fina seca em estufa. O experimento, delineado em blocos ao acaso constou de seis tratamentos e quatro repetições, sendo os tratamentos: 1, sem vinhaça; 2, fertirrigado com químicos; e 3, 4, 5 e 6, com aplicação de, respectivamente, 150, 300, 600 e 900 m³ ha-1 de vinhaça. Foram gerados modelos para predição de alguns atributos químicos e físicos do solo para os dois tipos de amostras, a partir de curvas espectrais na região do visível e infravermelho próximo. Para determinação da granulometria, os modelos não foram influenciados pelo tipo de amostra utilizada e foram classificados como "aceitáveis" a "bons" (R² entre 0,7 e 0,9). Em relação aos atributos químicos, foram gerados modelos com capacidade de diferenciação apenas entre altas e baixas concentrações (R² entre 0,50 e 0,65) para os atributos pH, matéria orgânica, H+Al e capacidade de troca catiônica, sendo os gerados para terra fina seca em estufa em alguns casos melhores que os para amostras em condições de campo.

Evaluation of soil structural quality using VIS–NIR spectra

Application of visible (VIS) and near-infrared (NIR) spectroscopy for prediction of soil properties may offer a cost and time effective approach for evaluation of soil structural quality. Spectral data are often suitable for estimation of biochemical soil quality indicators such as soil organic carbon (SOC), total nitrogen and microbial biomass, while contradictory results have been reported for prediction of soil physical properties that are directly associated with soil structure. The aims of this study were to relate soil structural quality to overall indicators of soil quality, and to assess the efficiency of spectral data for the evaluation of soil structural quality. The study was conducted using 40 sites in Ireland under arable (n=20) and grassland (n=20) management systems. At each site five subplots were selected for soil sampling and twenty-one chemical, biological and physical properties were measured using standard methods as indicators of soil quality. The visual evaluation of soil structure (VESS) was performed to evaluate and classify soil structural quality. Soil properties that were significantly different (P<0.05) between soil structural quality classes were considered for further analysis, and principal component analysis was used to determine the key indicators as a minimum data set (MDS). VIS and NIR spectra were then measured and partial least-squares regression used to predict soil quality indicators associated with soil structural quality. SOC, penetration resistance, magnesium (Mg), aggregate size distribution and CN ratio comprised the MDS. An excellent model was achieved for SOC (RPD>4, R2=0.94; RMSE=0.42). A good model was obtained for Mg, CN (RPD from 2 to 2.5, R2≥0.7), and moderate capability for prediction of aggregate size distribution, and penetration resistance (RPD from 1.5 to 1.99, R2≥0.64). Soil structural quality classes were found to be associated with a number of biochemical and physical soil properties. Soil spectra produced acceptable models for predicting relevant soil structural indicators, and the mean soil spectra were different between soil structural classes. Therefore, a combination of spectroscopic and chemometric techniques can be applied as a practical, rapid, low cost and quantitative approach for evaluating soil structural quality under arable and grassland management systems in Ireland.

Quantitative Prediction of Physical Properties of Imidazolium Based Room Temperature Ionic Liquids through Determination of Condensed Phase Site Charges: A Refined Force Field

Quantitative prediction of physical properties of room temperature ionic liquids through nonpolarizable force field based molecular dynamics simulations is a challenging task. The challenge lies in the fact that mean ion charges in the condensed phase can be less than unity due to polarization and charge transfer effects whose magnitude cannot be fully captured through quantum chemical calculations conducted in the gas phase. The present work employed the density-derived electrostatic and chemical (DDEC/c3) charge partitioning method to calculate site charges of ions using electronic charge densities obtained from periodic density functional theory (DFT) calculations of their crystalline phases. The total ion charges obtained thus range between -0.6e for chloride and -0.8e for the PF6 ion. The mean value of the ion charges obtained from DFT calculations of an ionic liquid closely matches that obtained from the corresponding crystal thus confirming the suitability of using crystal site charges in simulations of liquids. These partial charges were deployed within the well-established force field developed by Lopes et al., and consequently, parameters of its nonbonded and torsional interactions were refined to ensure that they reproduced quantum potential energy scans for ion pairs in the gas phase. The refined force field was employed in simulations of seven ionic liquids with six different anions. Nearly quantitative agreement with experimental measurements was obtained for the density, surface tension, enthalpy of vaporization, and ion diffusion coefficients.

UV curable urethane acrylate coatings formulation: experimental design approach

Purpose – The purpose of this work was to perform a systematic study on the effect of formulation on the physical and mechanical properties of ultaviolet (UV) curable urethane acrylate resins. In addition, the authors wanted to derive mathematical formula for the prediction of physical and mechanical properties for the aforementioned system. Design/methodology/approach – The experiments were carried out based on mixture experimental design to determine the effect of different multifunctional acrylates (i.e. 1,6-hexanediol diacrylate (HDDA), tripropylene glycol diacrylate (TPGDA), trimethyolpropane triactylate (TMPTA)) concentration on the physical and mechanical properties of a UV curable polyurethane acrylate system. The urethane oligomer was synthesized and characterized by the research team. Microhardness, adhesion strength and scratch resistance of the cured films were evaluated as the physical and mechanical properties. Findings – The results revealed that the resin and TMPTA concentrations had the most significant effects on the microhardness property. Adhesion strength of the films showed a linear trend with respect to all variables. Moreover, all components also had a significant and complex influence on the scratch resistance of the cured systems. In addition, mathematical equations proposed by mixture experimental design were derived for all the mentioned properties. Research limitations/implications – Other multifunctional acrylate monomers (i.e. more than three functional) can be used in the formulations. The kinetics of the curing can affect on the network formation and consequently on the properties of the cured films. Practical implications – The obtained results can be used by the researchers who are active in the field of structure-property relationship of polymers and surface coatings. The reported data and the mathematical equations can also be used for the formulating of an appropriate formulation based on a specific application. Originality/value – A systematic and statistical-based approach, i.e. mixture experimental design, was used to evaluate the effect of formulation on some of the properties of a UV curable polyurethane acrylate system. A urethane oligomer and three different multifunctional acrylate monomers as reactive diluents were used in the formulations. Noteworthy to mention that several mathematical models were derived by using analysis of variance for the prediction of the properties studied in this system.

Densities, Viscosities, and Conductivities of Aqueous Solutions of Tetrabutylphosphonium Bromide and Ethyltributylphosphonium Bromide at Different Temperatures

The simple equations developed for prediction of physical properties of multicomponent aqueous solutions of traditional salts were investigated to see if they still work when ionic liquids were used (as solutes) in place of traditional salts. The densities, conductivities, and viscosities of the ternary system [P4,4,4,4]Br (tetrabutylphosphonium bromide) + [P2,4,4,4]Br (ethyltributylphosphonium bromide) + H2O and its binary subsystems [P4,4,4,4]Br + H2O and [P2,4,4,4]Br + H2O were measured at 298.15 K, 303.15 K, and 308.15 K, respectively. The measured values were compared with the values predicted by the above-mentioned equations. The comparison results indicate that with the use of these equations the densities, conductivities, and viscosities of [P4,4,4,4]Br + [P2,4,4,4]Br + H2O can be predicted from the corresponding data of [P4,4,4,4]Br + H2O and [P2,4,4,4]Br + H2O under the condition of identical ionic strength.

Covariate selection with iterative principal component analysis for predicting physical soil properties

Local and regional soil data can be improved by coupling new digital soil mapping techniques with high resolution remote sensing products to quantify both spatial and absolute variation of soil properties. The objective of this research was to advance data-driven digital soil mapping techniques for the prediction of soil physical properties at high spatial resolution using auxiliary data in a semiarid ecosystem in southeastern Arizona, USA. An iterative principal component analysis (iPCA) data reduction routine of reflectance and elevation covariate layers was combined with a conditioned Latin Hypercube field sample design to effectively capture the variability of soil properties across the 6250ha study area. We sampled 52 field sites by genetic horizon to a 30cm depth and determined particle size distribution, percent coarse fragments, Munsell color, and loss on ignition. Comparison of prediction models of surface soil horizons using ordinary kriging and regression kriging indicated that ordinary kriging had greater predictive power; however, regression kriging using principal components of covariate data more effectively captured the spatial patterns of soil property–landscape relationships. Percent silt and soil redness rating had the smallest normalized mean square error and the largest correlation between observed and predicted values, whereas soil coarse fragments were the most difficult to predict. This research demonstrates the efficacy of coupling data reduction, sample design, and geostatistical techniques for effective spatial prediction of soil physical properties in a semiarid ecosystem. The approach applied here is flexible and data-driven, allows incorporation of wide variety of numerically continuous covariates, and provides accurate quantitative prediction of individual soil properties for improved land management decisions and ecosystem and hydrologic models.

Prediction of Building Limestone Physical and Mechanical Properties by Means of Ultrasonic P-Wave Velocity

The aim of this study was to evaluate ultrasonic P-wave velocity as a feature for predicting some physical and mechanical properties that describe the behavior of local building limestone. To this end, both ultrasonic testing and compressive tests were carried out on several limestone specimens and statistical correlation between ultrasonic velocity and density, compressive strength, and modulus of elasticity was studied. The effectiveness of ultrasonic velocity was evaluated by regression, with the aim of observing the coefficient of determination r 2 between ultrasonic velocity and the aforementioned parameters, and the mathematical expressions of the correlations were found and discussed. The strong relations that were established between ultrasonic velocity and limestone properties indicate that these parameters can be reasonably estimated by means of this nondestructive parameter. This may be of great value in a preliminary phase of the diagnosis and inspection of stone masonry conditions, especially when the possibility of sampling material cores is reduced.

Generalization of Abelian Gauge Symmetry, Dark Matter and Cosmological Expansion

A commutative generalization of the U(1) gauge symmetry group is proposed. The two-parametric family of two-connected abelian Lie groups is obtained. The necessity of existence of so-called imaginary charges and electromagnetic fields with negative energy density (dark photons) is derived. The possibilities when the overall Lagrangian represents a sum or difference of two identical Lagrangians for the visible and hidden sectors (i.e. copies of unbroken U(1)) are ruled out by the extended symmetry. The distinction between the two types of fields resides in the fact that for one of them current and electromagnetic kinetic terms in Lagrangians are identical in sign, whereas for another type these terms are opposite in sign. As a consequence, and in contrast to the common case, like imaginary charges attract and unlike charges repel. Some cosmological issues of the proposed hypothesis are discussed. Particles carrying imaginary charges are proposed as one of the components of dark matter. Such a matter would be imaginarily charged on a large scale for the reason that dark atoms carry non-compensated charges. It leads to important predictions for matter distribution, interaction and other physical properties being different from what is observed in dominant dark matter component in the standard model. These effects of imaginary charges depend on their density and could be distinguished in future observations. Dark electromagnetic fields can play crucial dynamical role in the very early universe as they may dominate in the past and violate weak energy condition which provides physical grounds for bouncing cosmological scenarios pouring a light on the problem of origin of the expanding matter flow.

Moist Heat Accelerated Ageing Test of Naturally Aged Paper : Prediction of Initial Physical Properties and Degradation Rate Indicator at Room Temperature by Suspension Method

Moist Heat Accelerated Ageing Test of Naturally Aged Paper : Prediction of Initial Physical Properties and Degradation Rate Indicator at Room Temperature by Suspension Method

Geochemistry of Paleozoic marine oils from the Tarim Basin, NW China. Part 4: Paleobiodegradation and oil charge mixing

Fifty-three oil samples from Paleozoic marine reservoirs of the Tarim Basin were analyzed by GC–FID and GC–MS to elucidate the influence of biodegradation and mixing on molecular compositions. Biodegradation has a major impact on many classes of compounds but the ultimate compositions and physical properties of the oil are influenced strongly by mixing with late oil charge, subsequent to the process of biodegradation. Almost all samples display biodegradation signatures, characterized by variable size of the unresolved complex mixture (UCM) and variable amounts of 25-norhopanes (25-NHs) and 17-nortricyclic terpanes (17-NTs). The ubiquitous occurrence of n-alkanes suggests that the petroleum accumulated in these fields is mainly a mixture of previously biodegraded oils with later migrated fresh oils. The unaltered late charge of oil, which has commingled with the initial biodegraded charge, gives the wide range of oil gravities and asphaltene contents observed.Most commonly used maturity and depositional environment diagnostic parameters in the saturated hydrocarbon fraction are affected by biodegradation. Based on the intensity of 25-NHs and 17-NTs, oils from the Tabei Uplift appear to have experienced a much greater degree of biodegradation than those from the Tazhong Uplift. However, we caution against the simplistic use of molecular indicators to define a specific level of biodegradation in this case study, as it may be somewhat misleading for physical property prediction. A wide range of estimated maturity values from different components provides an important clue for multiple charge and mixing interpretations. The relationship between the absolute concentrations of compound class and peak/hump ratios of the samples can potentially, qualitatively differentiate the pulse of inputs to the mixed oils. The positive correlation between the concentrations of a given compound class and peak/hump ratios may suggest that that class of compounds is most likely derived from the late charge after biodegradation, while the negative correlation may indicate compounds largely inherited from the early charge before biodegradation. Some compounds show almost no correlation between their concentrations and peak/hump ratios, implying more similar intensity of charge inherited from early pulse and amalgamated from later pulses. In some extremely biodegraded cases where UCM hump can also be diminished (not in present case study), the correlation between compound class concentrations and peak/hump ratios will be more complicated.

A benzene chain-based contribution method for prediction of physical properties of aromatic compounds

The physical properties of aromatic and condensed ring especially polybenzene compounds are severely lacking and difficult to obtain from experiment, while the existing estimation methods are hard to be applied to this system. A new benzene chain-based contribution (BC–C) method is proposed on the basis of the three order group contribution method of Marrero and Gani method in this study, several new groups are defined by considering their benzene chain structure. The boiling point and critical properties are estimated using the BC–C and Joback–Reid methods and their average relative deviation are compared. Through the critical properties estimated by the BC–C method, the liquid densities can be calculated using the Riedel and Yamada–Gunn equations. The calculated data agreed well with the literature data with an overall average absolute relative deviation of less than 10%. The BC–C method provides a new and easy approach for prediction of the physical properties of aromatic compounds through defining of the new groups and receives precise predicted results.

A review of near-infrared spectroscopy for monitoring moisture content and density of solid wood

This review article examines past and current research on the application of near-infrared (NIR) reflectance/transmittance spectroscopy (NIRS) for real-time monitoring of moisture content and density of solid wood. Most of the applications of NIRS on solid wood have focussed on the application of multivariate statistics as exploratory tools for the prediction of physical, chemical and mechanical properties, such as moisture content, density, stiffness, cellulose and lignin content. However, very few studies on the development of optical models and the use of NIRS transmittance techniques on solid wood have been reported. NIRS technology has the potential to be used as a rapid tool that could be employed for at-line measurement and monitoring of wood properties in the forest products industry.