Fusion excitation function revisited

We report on a comprehensive systematics of fusion-evaporation and/or fusion-fission cross sections for a very large variety of systems over an energy range 4-155 A.MeV. Scaled by the reaction cross sections, fusion cross sections do not show a universal behavior valid for all systems although a high degree of correlation is present when data are ordered by the system mass asymmetry.For the rather light and close to mass-symmetric systems the main characteristics of the complete and incomplete fusion excitation functions can be precisely determined. Despite an evident lack of data above 15A.MeV for all heavy systems the available data suggests that geometrical effects could explain the persistence of incomplete fusion at incident energies as high as 155A.MeV.Comment: 8 pages, 5 figures, contribution to the NN2012 Proceeding

Azot-monoksid- biomarker u dijagnostici tiroidnih nodusa

Definisanje malignog potencijala tiroidnih nodusa (TN) je najznačajnija odrednica u njihovoj evaluaciji. Primena imidžing metoda, kao i biohemijskih, citoloških i molekularno bioloških alata doprinosi razlikovanju benignih od malignih TN i tako delimično smanjuje broj nepotrebno tiroidektomsanih pacijenata. Jasno je da idealni biomarker ili metoda za definisanje prirode TN ne postoji, ali smo svedoci nastojanja kliničara da detektuju biomarker ili biomarkere koji bi samostalno ili u kombinaciji sa drugim alatima omogućili još kvalitetniju regrutaciju ispitanika kojima je tiroidektomija zaista potrebna. Studija Samardžića i aut. je analizirala biomarkere u ispirku bioptata TN i pokazala da nivo tiroglobulina u ispirku (TGw) pozitivno koreliše sa Bethesda kategorijom citološkog nalaza bioptata TN, dok nivo NOw pozitivno koreliše sa EU-TIRADS kategorijama. Tako se kod četvoro od petoro tiroidektomisanih ispitanika registrovala pripadnost EU-TIRADS kategorijama 4 i 5. Potencijal NOw i TGw kao pomoćnog alata za preciziranje prirode TN je nedvosmislen, te u budućim kliničkim studijama treba analizirati njihovu pojedinačnu i pridruženu prediktivnost u definisanju malignih TN na većoj populaciji ispitanika.7. Srpski kongres o štitastoj žlezdi : Finalni program i Zbornik sažetaka : Oktobar 13-14, 2022, Beogra

Adaptation and Validation of QUick, Easy, New, CHEap, and Reproducible (QUENCHER) Antioxidant Capacity Assays in Model Products Obtained from Residual Wine Pomace

Evaluation of the total antioxidant capacity of solid matrices without extraction steps is a very interesting alternative for food researchers and also for food industries. These methodologies have been denominated QUENCHER from QUick, Easy, New, CHEap, and Reproducible assays. To demonstrate and highlight the validity of QUENCHER (Q) methods, values of Q-method validation were showed for the first time, and they were tested with products of well-known different chemical properties. Furthermore, new QUENCHER assays to measure scavenging capacity against superoxide, hydroxyl, and lipid peroxyl radicals were developed. Calibration models showed good linearity (R2 > 0.995), proportionality and precision (CV < 6.5%), and acceptable detection limits (<20.4 nmol Trolox equiv). The presence of ethanol in the reaction medium gave antioxidant capacity values significantly different from those obtained with water. The dilution of samples with powdered cellulose was discouraged because possible interferences with some of the matrices analyzed may take place.The autonomous government of Castilla y León (Project BU268A11-2

A differential evolution approach to dimensionality reduction for classification needs

The feature selection problem often occurs in pattern recognition and, more specifically, classification. Although these patterns could contain a large number of features, some of them could prove to be irrelevant, redundant or even detrimental to classification accuracy. Thus, it is important to remove these kinds of features, which in turn leads to problem dimensionality reduction and could eventually improve the classification accuracy. In this paper an approach to dimensionality reduction based on differential evolution which represents a wrapper and explores the solution space is presented. The solutions, subsets of the whole feature set, are evaluated using the k-nearest neighbour algorithm. High quality solutions found during execution of the differential evolution fill the archive. A final solution is obtained by conducting k-fold cross-validation on the archive solutions and selecting the best one. Experimental analysis is conducted on several standard test sets. The classification accuracy of the k-nearest neighbour algorithm using the full feature set and the accuracy of the same algorithm using only the subset provided by the proposed approach and some other optimization algorithms which were used as wrappers are compared. The analysis shows that the proposed approach successfully determines good feature subsets which may increase the classification accuracy