Acetylation of cellulose in LiCl-N,N-dimethylacetamide: first report on the correlation between the reaction efficiency and the aggregation number of dissolved cellulose

The acylation of three cellulose samples by acetic anhydride, Ac(2)O, in the solvent system LiCl/N,N-dimethylacetamide, DMAc (4 h, 110 A degrees C), has been revisited in order to investigate the dependence of the reaction efficiency on the structural characteristics of cellulose, and its aggregation in solution. The cellulose samples employed included microcrystalline, MCC; mercerized cotton linters, M-cotton, and mercerized sisal, M-sisal. The reaction efficiency expresses the relationship between the degree of substitution, DS, of the ester obtained, and the molar ratio Ac(2)O/AGU (anhydroglucose unit of the biopolymer); 100% efficiency means obtaining DS = 3 at Ac(2)O/AGU = 3. For all celluloses, the dependence of DS on Ac(2)O/AGU is described by an exponential decay equation: DS = DS(o) - Ae(-[(Ac2O/AGU)/B]); (A) and (B) are regression coefficients, and DS(o) is the calculated maximum degree of substitution, achieved under the conditions of each experiment. Values of (B) are clearly dependent on the cellulose employed: B((M-cotton)) > B((M-sisal)) > B((MCC)); they correlate qualitatively with the degree of polymerization of cellulose, and linearly with the aggregation number, N(agg), of the dissolved biopolymer, as calculated from static light scattering measurements: (B) = 1.709 + 0.034 N(agg). To our knowledge, this is the first report on the latter correlation; it shows the importance of the physical state of dissolved cellulose, and serves to explain, in part, the need to use distinct reaction conditions for MCC and fibrous celluloses, in particular Ac(2)O/AGU, time, temperature.

Appropriate choice of aggregation operators in fuzzy decision support systems

Fuzzy logic provides a mathematical formalism for a unified treatment of vagueness and imprecision that are ever present in decision support and expert systems in many areas. The choice of aggregation operators is crucial to the behavior of the system that is intended to mimic human decision making. This paper discusses how aggregation operators can be selected and adjusted to fit empirical data—a series of test cases. Both parametric and nonparametric regression are considered and compared. A practical application of the proposed methods to electronic implementation of clinical guidelines is presented

Women's perceptions of familial aspects of breast cancer

Women’s understanding of familial aspects of breast cancer was examined using both focus groups and interviews. The studies covered issues related to perceptions of breast cancer risk factors, perceived breast cancer risk, understanding of risk information, and family history of breast cancer as a risk factor. Study 1 consisted of four focus group discussions with women from the general community. Study 2 comprised ten face-to-face interviews with women who had a family history of breast cancer. The results in combination indicate a fairly high level of awareness of family history as a risk factor for breast cancer. However, the definition of a familial history of breast cancer differed between the groups, with those without a family history being more inclusive than those with such a history. The paper concludes with suggestions for use by those developing resources materials for those with a familial history of breast cancer.

Monotone approximation of aggregation operators using least squares splines

The need for monotone approximation of scattered data often arises in many problems of regression, when the monotonicity is semantically important. One such domain is fuzzy set theory, where membership functions and aggregation operators are order preserving. Least squares polynomial splines provide great flexbility when modeling non-linear functions, but may fail to be monotone. Linear restrictions on spline coefficients provide necessary and sufficient conditions for spline monotonicity. The basis for splines is selected in such a way that these restrictions take an especially simple form. The resulting non-negative least squares problem can be solved by a variety of standard proven techniques. Additional interpolation requirements can also be imposed in the same framework. The method is applied to fuzzy systems, where membership functions and aggregation operators are constructed from empirical data.