Marketed pain relief drugs with one to three biologically active components, as well as mixtures of these ingredients, were qualitatively and quantitatively analyzed in an undergraduate student lab using a compact, low-field 1H NMR spectrometer. The students successfully analyzed more than 50 self-made sample mixtures with two or three components as well as the two marketed tablet formulations containing acetylsalicylic acid/l-ascorbic acid, or acetylsalicylic acid/paracetamol (acetaminophen)/caffeine. The NMR-based quantification is an attractive application of the technique, as well as a helpful introduction to NMR spectroscopic applications in life sciences. Problem-based learning on NMR techniques on commonly known drugs provided students the opportunity to develop and improve their skills in solving 1H NMR problems.

In this paper new semilogarithmic quantizer for Laplacian distribution is presented. It is simpler than classic A-law semilogarithmic quantizer since it has unit gain around zero. Also, it gives for 2.97 dB higher signal-to-quantization noise-ratio (SQNR) for referent variance in relation to A-law, and therefore it is more suitable for adaptation. Forward adaptation of this quantizer is done on frame-by-frame basis. In this way G.712 standard is satisfied with 7 bits/sample, which is not possible with classic A-law. Inside each frame subframes are formed and lossless encoder is applied on subframes. In that way, double adaptation is done: adaptation on variance within frames and adaptation on amplitude within subframes. Joined design of quantizer and lossless encoder is done, which gives better performances. As a result, standard G.712 is satisfied with only 6.43 bits/sample. Experimental results, obtained by applying this model on speech signal, are presented. It is shown that experimental and theoretical results are matched very well (difference is less than 1.5%). Models presented in this paper can be applied for speech signal and any other signal with Laplacian distribution.

Background of mathematical model for prediction of road traffic noise called NAISS model is shown in this paper. The model has been created by extracting function relation among the equivalent noise levels and the traffic parameters collected by systematic traffic noise monitoring in urban areas of the city of Nis. Based on the analysis of three different variants of traffic noise prediction model, the model with three input parameters (the number of passenger vehicles, freight vehicles and buses) and one output (Leq) and two separate equations for two ranges of noise levels has been proposed as rather correctly for using in the urban areas of the city of Nis. In order to examine validity of formed model, it is carried out the comparative analysis of NAISS model and the other models available in literature and the verification of NAISS model based on data collected by traffic noise monitoring in urban areas of the city of Nis during the years 2008-2010. The good results obtained in the comparison with other prediction methods have been confirmed in the verification process of NAISS model. Scatter plot for model verification shown in this paper as well as the results of statistical analysis of the differences between measured and calculated data show the validity and enforceability of the NAISS model for traffic noise prediction in urban areas of the city of Nis.

This paper deals with the procedure of long-term road traffic noise monitoring in urban areas. For the purpose of strategic noise mapping and assessment of harmful effects of environmental noise it is necessary to determine the annual value of noise indicators, mainly by long-term measurements, which can be realized by permanent and semi-permanent noise monitoring. The permanent noise monitoring includes noise measurements of 24 hours a day, 365 days a year, while the semi-permanent monitoring typically ranges from a few days up to several weeks or months. The research described in this paper was conducted with the aim to determine adequate duration of semi-permanent monitoring of road traffic noise in the city of Nis to enable cost-effective monitoring of road traffic noise and determination of noise indicators at multiple locations with only a few noise monitoring stations. Based on the presented sets of measurement results at three locations, it can be concluded that road traffic noise monitoring with a duration of one month yields very acceptable and repeatable values for road traffic noise assessment for the three observed locations. Likewise, the semi-permanent road traffic noise monitoring with a duration of one week or only during workdays yields very usable values for the determination of annual value of noise indicators, which can be used for road traffic noise survey.

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