Binary fuzzy goal programming

Goal programming is an important technique for solving many decision/management problems. Fuzzy goal programming involves applying the fuzzy set theory to goal programming, thus allowing the model to take into account the vague aspirations of a decision-maker. Using preference-based membership functions, we can define the fuzzy problem through natural language terms or vague phenomena. In fact, decision-making involves the achievement of fuzzy goals, some of them are met and some not because these goals are subject to the function of environment/resource constraints. Thus, binary fuzzy goal programming is employed where the problem cannot be solved by conventional goal programming approaches. This paper proposes a new idea of how to program the binary fuzzy goal programming model. The binary fuzzy goal programming model can then be solved using the integer programming method. Finally, an illustrative example is included to demonstrate the correctness and usefulness of the proposed model.     

A NEW FAMILY OF BINARY SEQUENCES WITH OPTIMAL AUTOCORRELATION VALUES AND NICE PSEUDO-RANDOM PROPERTIES

In this paper we present a series of binary sequences which is a generalization of GMW sequences constructed by Scholtz and Welch. Our sequences have optimal autocorrelation values as m -sequences, and nice pseudo-random property. The number of such sequences is counted and the linear span of such sequences is evaluated.     

Generating 2-Gray codes for ballot sequences in constant amortized time

We present a simple algorithm that generates a cyclic 2-Gray code for ballot sequences. The algorithm generates each ballot sequence in constant amortized time using a linear amount of space. This is the first known cyclic 2-Gray code for ballot sequences that achieves this time bound. In addition, the algorithm can be easily modified to output ballot sequences in binary reflected Gray code order in constant amortized time per string using a linear amount of space.     

Evaluation of an activated carbon from olive stones used as an adsorbent for heavy metal removal from aqueous phases

The performance of a microporous activated carbon prepared chemically from olive stones for removing Cu(II), Cd(II) and Pb(II) from single and binary aqueous solutions was investigated via the batch technique. The activated carbon sample was characterized using N₂ adsorption–desorption isotherms, SEM, XRD, FTIR, and Boehm titration. The effect of initial pH and contact time were studied. Adsorption kinetic rates were found to be fast and kinetic experimental data fitted very well the pseudo-second-order equation. The adsorption isotherms fit the Redlich–Peterson model very well and maximum adsorption amounts of single metal ions solutions follow the trend Pb(II) > Cd(II) > Cu(II). The adsorption behavior of binary solution systems shows a relatively high affinity to Cu(II) at the activated carbon surface of the mixture with Cd(II) or Pb(II). An antagonistic competitive adsorption phenomenon was observed. Desorption experiments indicated that about 59.5% of Cu(II) and 23% of Cd(II) were desorbed using a diluted sulfuric acid solution.     

A novel particle tracking algorithm using polar coordinate system similarity

A new algorithm using polar coordinate system similarity (PCSS) for tracking particle in particle tracking velocimetry (PTV) is proposed. The essence of the algorithm is to consider simultaneously the changes of the distance and angle of surrounding particles relative to the object particle. Monte Carlo simulations of a solid body rotational flow and a parallel shearing flow are used to investigate flows measurable by PCSS and the influences of experimental parameters on the implementation of the new algorithm. The results indicate that the PCSS algorithm can be applied to flows subjected to strong rotation and is not sensitive to experimental parameters in comparison with the conventional binary image cross-correlation (BICC) algorithm. Finally, PCSS is applied to images of a real experiment. The project supported by the National Natural Science Foundation of China (50206019) The English text was polished by Yunming Chen.     

Intermolecular hydrogen bonding in the binary mixture [(C2H5)2CO + CH3OH] probed by polarized Raman measurements and DFT calculations

The Raman spectra of neat (C₂H₅)₂CO (pentanone) and its binary mixtures with hydrogen donor solvent (CH₃OH), [(C₂H₅)₂CO + CH₃OH] having different mole fractions of the reference system, (C₂H₅)₂CO in the range 0.1-0.9 at a regular interval of 0.1 were recorded in the CO stretching region. In neat liquid, the Raman peak appears asymmetric. The asymmetric nature of the peak has been attributed to the CO stretching mode of the two conformers of (C₂H₅)₂CO having C₂ and C_2v point groups and the corresponding bands at ∼1711 and ∼1718 cm⁻¹, respectively. A careful analysis of the I_iso (isotropic component of the Raman scattered intensity) at different concentrations reveals that upon dilution with methanol, at mole fraction C = 0.6, an additional peak in the CO stretching region is observed at ∼1703 cm⁻¹ which is attributed to the hydrogen bonding with methanol. A peculiar feature in this study is that upon dilution, the peak at ∼1718 cm⁻¹ shows a minimum at C = 0.6, but on further dilution it shows a blue shift. However, the other peak at ∼1711 cm⁻¹ shows a continuous red shift with dilution as well as a maximum at C = 0.7 in the linewidth vs. concentration plot, which is essentially due to competition between motional narrowing and diffusion phenomena. A significant amount of narrowing in the Raman band at ∼1718 cm⁻¹ can be understood in terms of caging effect of the reference molecule by the solvent molecules at high dilution. A density functional theoretic (DFT) calculation on optimized geometries and vibrational frequencies of two conformers of neat (C₂H₅)₂CO in C₂ ad C_2v forms and the complexes with one and two CH₃OH molecules with both the conformers was performed. The experimental results and theoretical calculations together indicate a co-existence of two conformers as well as hydrogen bonded complex with methanol in the binary mixture, [(C₂H₅)₂CO + CH₃OH] at intermediate concentrations.     

High-dimensional generation of Bernoulli random vectors

The objective of this paper is to explore different modeling strategies to generate high-dimensional Bernoulli vectors. We discuss the multivariate Bernoulli (MB) distribution, probe its properties and examine three models for generating random vectors. A latent multivariate normal model whose bivariate distributions are approximated with Plackett distributions with univariate normal distributions is presented. A conditional mean model is examined where the conditional probability of success depends on previous history of successes. A mixture of beta distributions is also presented that expresses the probability of the MB vector as a product of correlated binary random variables. Each method has a domain of effectiveness. The latent model offers unpatterned correlation structures while the conditional mean and the mixture model provide computational feasibility for high-dimensional generation of MB vectors.     

Molecular dynamics comparative study of methane–nitrogen and methane–nitrogen–ethane systems

This work concerns the site–site interaction study of 256 particles using the Buckingham potential model. We have calculated the new parameters of the Buckingham potential using an iterative algorithm with a mean square method. This adapted model allows determining the characteristics for each state point. We have applied this model to study the liquefied natural gas LNG properties for methane-nitrogen and methane–nitrogen–ethane mixtures by molecular dynamics. We have calculated the thermodynamic, dynamic and structural properties for both the microcanonical NVT and the isothermal-isobaric NPT ensembles of binary and ternary systems from the SP₁ to SP₉ points. Then, we have compared the results between binary and ternary systems. We have obtained a good prediction on transport properties. From the calculated values of self-diffusion coefficient and viscosity, we have confirmed the liquid state of the liquefied natural gas LNG system.     

Decomposition tree of a lexicographic product of binary structures

Given a set S and a positive integer k, a binary structure is a function . The set S is denoted by V(B) and the integer k is denoted by . With each subset X of V(B) associate the binary substructure B[X] of B induced by X defined by B[X](x,y)=B(x,y) for any x≠y∈X. A subset X of V(B) is a clan of B if for any x,y∈X and v∈V(B)?X, B(x,v)=B(y,v) and B(v,x)=B(v,y). A subset X of V(B) is a hyperclan of B if X is a clan of B satisfying: for every clan Y of B, if X∩Y≠0?, then X⊆Y or Y⊆X. With each binary structure B associate the family Π(B) of the maximal proper and nonempty hyperclans under inclusion of B. The decomposition tree of a binary structure B is constituted by the hyperclans X of B such that Π(B[X])≠0? and by the elements of Π(B[X]). Given binary structures B and C such that , the lexicographic product B⌊C⌋ of C by B is defined on V(B)×V(C) as follows. For any (x,y)≠(x^′,y^′)∈V(B)×V(C), B⌊C⌋((x,x^′),(y,y^′))=B(x,y) if x≠y and B⌊C⌋((x,x^′),(y,y^′))=C(x^′,y^′) if x=y. The decomposition tree of the lexicographic product B⌊C⌋ is described from the decomposition trees of B and C.     

A comparative study of the leading machine learning techniques and two new optimization algorithms

We present here a computational study comparing the performance of leading machine learning techniques to that of recently developed graph-based combinatorial optimization algorithms (SNC and KSNC). The surprising result of this study is that SNC and KSNC consistently show the best or close to best performance in terms of their F₁-scores, accuracy, and recall. Furthermore, the performance of SNC and KSNC is considerably more robust than that of the other algorithms; the others may perform well on average but tend to vary greatly across data sets. This demonstrates that combinatorial optimization techniques can be competitive as compared to state-of-the-art machine learning techniques. The code developed for SNC and KSNC is publicly available.