Hydrophobicities of poly(ε-caprolactone) oligomers functionalized with different succinic anhydrides

Different succinic anhydrides were used for modification of hydrophobicities of linear and star-shaped poly (ε-caprolactone) oligomers with different molecular weights. Hydroxyl-terminated poly(ε-caprolactone) oligomers were acid-functionalized either with succinic anhydride (SAH) or with alkenylsuccinic anhydrides (ASAs) containing 8 or 18 carbons in their alkenyl chains. Hydrophobicities of the linear and corresponding star-shaped oligomers were investigated by immersion studies and by water contact angle measurements. In comparison with SAH functionalized oligomers, alkenyl chain containing oligomers showed lower thermal transitions and higher hydrophobicities. In addition, oligomers with 18 carbons alkenyl chain showed considerably higher hydrophobicities than corresponding oligomers with 8 carbon alkenyl chain.     

Performance of multicomponent self-organizing regression (MCSOR) in QSAR, QSPR, and multivariate calibration: comparison with partial least-squares (PLS) and validation with large external data sets

A novel method for underdetermined regression problems, multicomponent self-organizing regression (MCSOR), has been recently introduced. Here, its performance is compared with partial least-squares (PLS), which is perhaps the most widely adopted multivariate method in chemometrics. A potpourri of models is presented, and MCSOR appears to provide highly predictive models that are comparable with or better than the corresponding PLS models in large internal (leave-one-out, LOO) and pseudo-external (leave-many-out, LMO) validation tests. The "blind" external predictive ability of MCSOR and PLS is demonstrated employing large melting point, factor Xa, log P and log S data sets. In a nutshell, MCSOR is fast, conceptually simple (employing multiple linear regression, MLR, as a statistical tool), and applicable to all kinds of multivariate problems with single Y-variable.     

Fast Growing Solutions of Linear Differential Equations in the Unit Disc

For $n \in \mathbb{N}$ , the n-order of an analytic function f in the unit disc D is defined by $$\sigma _{{{M,n}}} (f) = {\mathop {\lim \sup }\limits_{r \to 1^{ - } } }\frac{{\log ^{ + }_{{n + 1}} M(r,f)}} {{ - \log (1 - r)}},$$ where log⁺ x  =  max{log x, 0}, log ⁺ ₁ x  =  log ⁺ x, log ⁺ _n+1 x  =  log ⁺ log ⁺ _n x, and M(r, f) is the maximum modulus of f on the circle of radius r centered at the origin. It is shown, for example, that the solutions f of the complex linear differential equation $$f^{{(k)}} + a_{{k - 1}} (z)f^{{(k - 1)}} + \cdots + a_{1} (z)f^{\prime} + a_{0} (z)f = 0,\quad \quad \quad (\dag)$$ where the coefficients are analytic in D, satisfy σ _M,n+1(f)  ≤  α if and only if σ _M,n (a _j )  ≤  α for all j  =  0, ..., k ? 1. Moreover, if q ∈{0, ..., k ? 1} is the largest index for which $\sigma _{M,n} ( a_{q}) = {\mathop {\max }\limits_{0 \leq j \leq k - 1} }{\left\{ {\sigma _{{M,n}} {\left( {a_{j} } \right)}} \right\}}$ , then there are at least k ? q linearly independent solutions f of ( $\dag$ ) such that σ _M,n+1(f) = σ _M,n (a _q ). Some refinements of these results in terms of the n-type of an analytic function in D are also given.     

Resource allocation for performance improvement

In this paper the problem of allocating resources among Decision Making Units is considered. This study covers the case in which several homogeneous units are operating under the supervision of a central unit. The resource allocation is carried out by the DM (central unit) in such a way that the overall performance of the system is improved. Performance is defined by means of a convex combination of the ratio of the efficiencies before and after the resource allocation. It is assumed that each unit is allowed to modify its resources within the current production possibility set. A novel model is proposed which aims at achieving the best performance of the system. The method is capable of dealing with some additional constraints, imposed by the DM. The model is illustrated by a simple numerical example and a real application.     

Efficiency analysis to incorporate interval-scale data

We develop an approach to efficiency analysis to enable us to incorporate interval-scale data in addition to ratio-scale data. Our approach introduces a measure of inefficiency and identifies efficient units as is done in Data Envelopment Analysis. The basic idea in our approach is to find the “best” hyperplane separating the units that are better and worse than each unit. “Best” is defined in such a way that the number of not-better units is maximal. The efficiency measure is defined as a proportion of not-better units to all units. The results are invariant under a strictly increasing linear re-scaling of any input- or output-variables. Thus zeroes or negative values do not cause problems for the analysis. The approach is used to analyze the data of the research evaluation exercise recently carried out at the University of Joensuu, Finland.     

1H NMR and IR spectra of methyl dichloropropanoates and butanoates

Methyl dichloropropanoates and butanoates were synthesized and their ¹H NMR and IR spectra were studied. Comparisons were made with the spectral characteristics of corresponding monochlorocompounds. Special attention was given to the elucidation of the characteristic spectral features associated with particular chlorine substitution patterns. The ¹H NMR of erythro and threo methyl 2,3-dichlorobutanoates were analyzed using a computer program MAOCON, a modified LAOCOON 3 program. In the spectrum of the threo form the order of the chemical shifts of protons on asymmetric carbons seems to be opposite to that reported in earlier literature. Also vicinal coupling constants J₂₃ were found to be greater than those reported earlier.     

Evaluation of Cost Efficiency in Finnish Electricity Distribution

This paper describes the process of developing an approach to evaluating the cost-efficiency of Finnish electricity distribution companies based on Data Envelopment Analysis (DEA). The process consisted of three main stages (1) describing of the problem and exploring the key factors, (2) finding measurable quantities for the most essential factors, and (3) selecting the final model to be used in the regulation. Especially the selection of environmental variables, which allow the comparison of distribution companies operating in different environments, played a key role in the process. The selected model is used as a part of the regulation of distribution pricing.     

Improving the performance of SOMFA by use of standard multivariate methods

Self-Organizing Molecular Field Analysis (SOMFA) comes with a built-in regression methodology, the Self-Organizing Regression (SOR), instead of relying on external methods such as PLS. In this article we present a proof of the equivalence between SOR and SIMPLS with one principal component. Thus, the modest performance of SOMFA on complex datasets can be primarily attributed to the low performance of the SOMFA regression methodology. A multi-component extension of the original SOR methodology (MCSOR) is introduced, and the performances of SOR, MCSOR and SIMPLS are compared using several datasets. The results indicate that in general the performance of SOMFA models is greatly improved if SOR is replaced with a more sophisticated regression method. The results obtained for the Cramer (CBG) dataset further underline the fact that it is a very poor benchmark dataset and should not be used to evaluate the performance of QSAR techniques.     

Choice behavior in interactive multiple-criteria decision making

Choice behavior in an interactive multiple-criteria decision making environment is examined experimentally. A free search discrete visual interactive reference direction approach was used on a microcomputer by management students to solve two realistic and relevant multiple-criteria decision problems. The results revealed persistent patterns of intransitive choice behavior, and an unexpectedly rapid degree of convergence of the reference direction approach on a preferred solution. The results can be explained using Tversky' [20] additive utility difference model and Kahneman-Tversky's [5] prospect theory. The implications of the results for the design of interactive multiple-criteria decision procedures are discussed.