A **group** **contribution** **method** was developed for prediction of pure hydrocarbon vapor pressure in the reduced temperature range from 0.45 up to near critical point (0.95 T c ). Experimental vapor pressure data of totally 456 hydrocarbon compounds were collected and used to obtain model parameters. The developed model employs the combinatorial and the residual UNIFAC terms and fugacity of pure hydrocarbons. The proposed modified model is incomparable accuracy to existing similar models that shows an excellent agreement between estimated and experimental data.

Mercury is not only a toxic pollutant in the envi- ronment, but it is also a corrosive element to process- ing equipment. Understanding mercury pathways in an ecosystem or its distribution in process facilities re- quires a model that is able to predict its thermodynamic behavior in a wide variety of conditions and solvents. In this work, we parameterize a **group** **contribution** **method** to estimate the temperature dependent binary interac- tion parameters between elemental mercury and com- pounds composed of CH, CH 2 , CH 3 , OH, H 2 O, ACH

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Use of a cubic EoS requires appropriate values for the above mentioned empirical parameters. Different methods have been suggested in the literature to calculate binary interaction parameters, but the interaction parameters calculated using those methods may fail to predict the complex phase behavior at high pressures [19]. The motivation behind this study was to estimate temperature dependent interaction parameters for systems of hydrocarbons and carbon dioxide using a **group** **contribution** **method**.

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for estimating the critical properties and acentric factors of paraffins, naphtenes and aromatics with emphasis on extrapolating to very heavy compounds. They predicted the properties with average deviation between 0.6 and 6.5%. Marrero et al. [7] predicted critical properties by using GCM. They showed the new **method** making significant improvements both in accuracy and applicability. Coutsikos et al. [8] presented a **group**-**contribution** model for the prediction of vapor pressures of organic solids. The obtained predictions by them are not very accurate. Moller et al. [9] presented a new **group** **contribution** **method** for the prediction of liquid vapor pressures for non-electrolyte organic compounds. They found the relative error of 5% in pressure. Nannoolal et al. [10] proposed a **group** **contribution** **method** for the estimation of the normal boiling point of nonelectrolyte organic compounds. Ceriani et al. [11] proposed a **group** **contribution** **method** for the estimation of vapor pressure and heat of vaporization of organic liquids. This **method** improved the prediction of normal boiling point.

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The mathematical gnostics was used to analyze the experimental data and recommend one data set for each IL. The recommended data were used to establish the **group** **contribution** model (5399 data points for correlation and 2522 data points for pure prediction). It shows a good agreement with a RAAD better than 0.31%, which proves that the **group** **contribution** model proposed in this paper is able to evaluate the volumetric properties of ILs by any random cation−anion combination accurately. By comparison with literature models, our **method** shows the lowest global RAAD% in terms of the high-pressure density calculations. Through comparison, our **method** also shows an excellent performance in the density estimation of binary IL mixtures, within 0.25%. Finally, this methodology allows the determination of the mechanical coeﬃcients within an accuracy better than 10%.

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The purpose of this work is to extend the LIR equation of state to long chain organic compounds by **group** **contribution** **method**. In the LIR equation of state which has been derived on the basis of the concept of the effective pair potential, the mathematical form of the average effective pair potential function is the same as that for the isolated pair as LJ (12, 6), with the exception that of the effective pair potential parameters in dense region ( ρ > ρ B where ρ B is Boyle density) depend on temperature. Since this potential function is appropriate for spherical-symmetric particles, then, nonsymmetric molecules such as long chain organic compounds show some deviations from the linear behavior of the LIR equation of state, see Table 1. But if we assume each organic compound as a hypothetical mixture of their constituent groups, in which the interaction potential of each pair of these groups is the AEPP. However, in such a hypothetical mixture both the covalent and van der

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The work has delivered important thermodynamic data of the vapor density and second virial coefficient of the environmentally friendly system of R152a and R365mfc. The vapor phase behavior of the mixture was investigated using the Virial equation of state combined with **group** **contribution** **method** of modified UNIFAC. The Virial-Unifac model was chosen, since it is needed for providing reliable results for new refrigerants as well as a large range of applicability in the vapor phase. The second Virial coefficients by these models were compared with data obtained by the high precision fundamental equations of state by McLinden et.al from National Institute of Standard and Technology (NIST). The simulation results achieved good agreement with this model, particularly for the pure fluids.

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named QSPR methods (Quantitative Structure Property Relationship). One subgroup of these models is the **group** **contribution** **method**. The idea behind this **method** is to divide the value of a property of the complete molecule into its contributions based on the chemical groups or other molecular subunit. **Group** **contribution** models have been successfully applied to a wide variety of prop- erties including density [1, 2], critical properties [3–5], enthalpy of vaporization [6], normal boiling points [7, 8], water–octanol partition coefficients [9–11], infinite dilu- tion activity coefficients [12] and many more. Also, from Gibbs excess energy models [13–15] and equations of states [16–19] they provide an approach that allows wid- ening their application range to molecules composed of the same chemical groups relatively easily.

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Journalism modules typically teach up to 90 students, grouped into workshop classes of around 20. These weekly workshops can involve a variety of teaching methods and activities over two or three hours. At the end of each semester, marks for the **contribution** element are currently awarded to each student on an impressionistic basis with reference to attendance figures.

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In [19], (GNN) Granular Neural Network for granular data classification was developed.Hyperbox fuzzy set is used to describe granulardata [19]. 2n input nodes are used here, therefore,it can handle granules in theform of hyperboxes. It can classifydifferent size granules more accurately thanGFMM **method** [19]. Its performancegives better results than the previous **method**. But as thevalue of expansion parameter increases granularity of this **method** decreases.

In wireless packet access networks, increasing demands for data services with high rate requirements have imposed serious challenges on system design and link budgets [2]. Orthogonal Frequency Division Multiplexing (OFDM) is a crucial modulation technique to fulfil these requirements thanks to its ability to combat multipath fading, Inter-Symbol Interferences (ISI), and time-varying injurious effects in wireless environments [1],[7]. Moreover, Multiple Input Multiple Output (MIMO) is another promising technology that permits to achieve spectral efficiency as it employs multiple antennas at both the transmitter and the receiver to exploit spatial diversity [5]. Therefore, MIMO-OFDMA is considered as the most efficient **method** to achieve a high performance transmission in advanced wireless networks [11], [12].

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two facts. The dependence of any observable on the subtraction scale µ vanishes, µ dF/dµ = 0 and ultraviolet divergences in QFT are local. In renormalizable QFT leading logarithms can be described by a running coupling. This can be proven directly from the renormalization **group**, but relies on the fact that in a renormalizable theory counter-terms are of the same form at every order. It implies that the leading logarithms are calculable by a one-loop calculation. The counter-terms for an effective low- energy theory, e.g. ChPT, differ at every order. However, Weinberg [29] pointed out that the two-loop leading logarithms can still be calculated from a one-loop calculation. The all order generalization was proven using beta-functions [30] and diagrams [31] . The main underlying observation is that at n-loop order all divergences must cancel. Using dimensional regularization with d = 4 − w the coefficients of {1/w n , log µ/w n−1 , log 2 µ/w n−2 , . . . , log n−1 /w}; {1/w n−1 , log µ/w n−2 , . . . , log n−2 µ/2}; . . . (8) must cancel. The first set of conditions allows to prove that all leading logs can be determined from one-loop diagrams, the second set that the subleading logs can be had from two-loop diagrams, etc..

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The climate is thermomediterranean [1], and the pre- dominant vegetation consists of phrygana and maquis. Human presence has been intensive and continuous for over 2000 years on Kastellorizo alone, which was histori- cally one of the safest ports in the Eastern Mediterranean basin. The few hundred present-day inhabitants work in tourism, agriculture and livestock farming. Due to their environmental characteristics, all the Greek islands in the Kastellorizo **group** have been placed within the “Natura 2000” network.

The primary problem with subtracting library spectra in the areas in which they would be most effective (i.e. areas with good counting statistics) is that in all but the most simple spectra the area with favorable counting statistics will contain contributions from other sources of radiation besides the library up for subtraction. The **method** of spectral stripping relied on subtracting a library until a chosen peak was essentially reduced to zero counts, with the residual spectrum representing the spectrum less the contributions across all channels of the removed library. This **method** will clearly not be sufficient if other contributors exist in the range of channels used to determine if the subtracting library has been removed; the magnitude of the subtraction will be far greater than the library’s actual **contribution** to the spectrum. Therefore, a different metric for library removal needs to be defined.

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537 (see Table 3) which were classified as malignant (class 1) by a strong majority of trees but with a significant num- ber of trees expressing an opposite view. Figure 5 presents feature contributions for these two instances (grey and light grey bars) against the median values for class 1 (black bars). The largest difference can be seen on the **contribution** of feature F23: it is highly negative for two instances under consideration compared to a large positive value commonly found in instances of class 1. Recall that a negative value contributes towards the classification in class 0. There are also three new significant attributes (F2, F17 and F22) that contribute towards the correct classification. Feature F22 is judged as moderately important by both of the variable im- portance methods in Figure 3. However, features F2 and F17 are located towards the bottom of both panels. It is, therefore, surprising to note that the **contribution** of these three new features was instrumental in correctly classifying instances 195 and 537 as malignant. This highlights the fact that features which may not generally be important for the model may, nonetheless, be important for classifying spe- cific instances. The approach presented in this paper is able to identify such features, whilst the standard variable im- portance measures for random forest cannot.

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The methodologies used for generating climate change scenarios for our **contribution** to the Fourth National Communication came from Working **Group** I and Working **Group** II of the Fourth Assessment Report (AR4), and mainly from the updated methodology published in June 2007 by the IPCC’s Task **Group** on Data and Scenario Support for Impact and Climate Assessment (TGICA) entitled General Guidelines on the Use of Scenario Data for Climate Impact and Adaptation Assessment Version 2 (IPCC-TGICA, 2007). The main reason for using such methodology is that it is proposed by the IPCC for the specific objective of providing guidelines for the generation of climate change scenarios for impact and adaptation assessments (which is the central purpose of the studies conducted for Climate Action Plans at the state-level in México, for example). This TGICA proposal takes into account the advances in both the WGI and the WGII of the AR4.

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To statistically measure the effect of the cosmic environment on the DM halo mass of galaxies we use weak gravitational lensing (WL), currently the only **method** that provides a direct measure of the mass of a system without any assumptions on its dynamical state. More specifically, we use galaxy–galaxy lensing (GGL; see e.g. Brainerd, Blandford & Smail 1996; Hoekstra, Yee & Gladders 2004; Mandelbaum et al. 2006): the coherent tangential distortion of background galaxy images due to the gravitational deflection of light by the mass of a sample of foreground galaxies and their haloes. To select foreground galaxies, we use the spectroscopic Galaxy And Mass Assembly survey (Driver et al. 2011), whereas the images of the background galaxies are taken from the photometric Kilo- Degree Survey (de Jong et al. 2013). This combination of data sets was also employed by the GGL studies of Viola et al. (2015) to measure the masses of galaxy groups, Sif´on et al. (2015) to study **group** satellites, and van Uitert et al. (2016) to measure the stellar- to-halo mass relation. To infer the mass of the haloes associated with the lens galaxies, we employ a simple halo model on the measured GGL signals.

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Indeed, using a multi-level mesh, the **contribution** on the main period is computed with the maximal accuracy using the fast **method** developed in the case of a finite domains. Then, using the scale independance of the demagnetizing operator, we compute a coarser approximation of the stray field on a mesh whose cells ares twice bigger in each periodic direction. The process is repeated several times and the combination of the approximations on each level gives an approximation of the total field on the whole space (see fig. 1). The main advantage of the **method** is that we compute the **contribution** of the stray field on each level, from the more accurate to the coarser, with the same fast **method**, and the same matrix (see fig. 2 for the convergence rate and [4] for details).

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places for the analysis of the meteorological data in terms of comfort index class has been proposed which has ad- vantages over the conventional **method** that represents only the comfortable hours. The proposed **method** incor- porates both the non comfortable hours and comfortable hours in its analysis. It sums up the weighted contribu- tion from different hours categorized in different classes, namely cool, warm, comfortable, uncomfortably hot and severe danger due to heat. Some hours contribute nega- tively whereas some positively towards the quantification of total comfortability for each city. The new **method** has the benefits of incorporating complete data set against the conventional **method** that is based on comfortable hours alone and considering the entire spectrum of comfort indices that is demonstrated for the examined five cities in India through the estimated Effective Comfortability and its comparison with the conventional **method**. The proposed **method** also depicts Effective Uncomfortability (EU) to get in relative terms a qualitative indicator for the estimation of the cooling load requirement for each city and for each month. Various methodologies are dis- cussed and it is concluded that approximations and mer- its and demerits for each of these will affect the outcome in similar manner and hence relative rankings proposed here are representative. The methodology using comfort index proposed and demonstrated here could be applied to any other kind of thermal stress index and a way for- ward to initiate cumulative representation of comfort indices of cities for various societal applications such as tourism or energy demand planning.

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Chapter Four: Include project methodology and design whole methodology in general, in addition start to design the algorithms of two methods (Histogram Shifting, Spread Spectrum), as result, we designed algorithms based on literature survey (embedding and extracting )for each **method** in order to improve and achieve the aim of the research.

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