Ultrasonic sensor for predicting sugar concentration using multivariate calibration

This paper presents a multivariate regression method for the prediction of maltose concentration in aqueous solutions. For this purpose, time and frequency domain of ultrasonic signals are analyzed. It is shown, that the prediction of concentration at different temperatures is possible by using several multivariate regression models for individual temperature points. Combining these models by a linear approximation of each coefficient over temperature results in a unified solution, which takes temperature effects into account. The benefit of the proposed method is the low processing time required for analyzing online signals as well as the non-invasive sensor setup which can be used in pipelines. Also the ultrasonic signal sections used in the presented investigation were extracted out of buffer reflections which remain primarily unaffected by bubble and particle interferences.     

Levels and transitions in126Te

The decay of¹²⁶Sb to¹²⁶Te is studied by use of Ge(Li) detectors in singles and coincidence arrangements. The gamma-gamma directional correlations of six cascades are measured. A level scheme is constructed where all transitions observed are located. The characteristics of several of the levels are assigned, on basis of the logft values deduced and the directional correlation results. Mixing ratios are deduced for three transitions. From the comparison of the relative intensities ofE1 andE2 transitions depopulating the same levels, threeE1 transitions are concluded to be strongly retarded.