Femtosecond laser ablation of dentin and enamel for fast and more precise dental cavity preparation.

PURPOSE: The purpose of the present work was to achieve fast and more precise ablation in dentin and enamel by using a commercial femtosecond laser system with high repetition rate, whilst avoiding any collateral irreversible damages in the hard tissue and pulp area. METHODS: We used fluence of the incident laser pulses which was marginally higher than the ablation threshold for dentin and enamel. The study was based on the hypothesis that femtosecond laser operating with a repetition rate in the range of 100-500 kHz can controllably ablate dental tissue obtaining sufficiently high removal rate whilst avoiding any collateral irreversible damages in the hard tissue and pulp area. RESULTS: The ablation yielded the formation of 1 mm3 craters with well-defined precise vertical cavity sides and edges. Advantageous high porosity and numerous interconnected pores were introduced in the ablated zones. Thermal load and hence collateral thermo-mechanical damages were avoided and the crystalline structure of the tooth constituent hydroxyapatite was preserved. CONCLUSION: The ultrafast femtosecond laser used in our work hold the promise of a significant drilling ability without collateral thermomechanical effects. It achieves high processing efficiency, overcomes disadvantages of other laser systems reported and can be used to develop an instrument for cavity preparation based on fast and precise ablation. Our further aim is to exceed the speed of traditional drilling instruments and thus to reduce the treatment time which in turn will bring comfort to the patient.

Recovering incomplete data using Statistical Multiple Imputations (SMI): a case study in environmental chemistry.

This paper presents a statistical technique that can be applied to environmental chemistry data where missing values and limit of detection levels prevent the application of statistics. A working example is taken from an environmental leaching study that was set up to determine if there were significant differences in levels of leached arsenic (As), chromium (Cr) and copper (Cu) between lysimeters containing preservative treated wood waste and those containing untreated wood. Fourteen lysimeters were setup and left in natural conditions for 21 weeks. The resultant leachate was analysed by ICP-OES to determine the As, Cr and Cu concentrations. However, due to the variation inherent in each lysimeter combined with the limits of detection offered by ICP-OES, the collected quantitative data was somewhat incomplete. Initial data analysis was hampered by the number of 'missing values' in the data. To recover the dataset, the statistical tool of Statistical Multiple Imputation (SMI) was applied, and the data was re-analysed successfully. It was demonstrated that using SMI did not affect the variance in the data, but facilitated analysis of the complete dataset.

Combining self-modeling curve resolution methods and partial least squares to develop a quantitative reaction monitoring method with minimal reference data.

An example of combining self-modeling curve resolution (SMCR) methods and partial least squares (PLS) to construct a quantitative model using minimal reference data is presented. The objective was to construct a quantitative calibration model to allow real-time in situ ultraviolet-attenuated total reflection (UV/ATR) measurements to determine the end-point during a chlorination reaction. Time restrictions for development combined with difficult reaction sampling conditions required the method to be developed using only a few key reference measurements. Utilizing evolving factor analysis (EFA) and the orthogonal projection approach (OPA), initial estimates of the concentration and spectral profiles for the intermediate and product were obtained. Further optimization by multivariate curve resolution-alternating least squares (MCR-ALS) led to refined estimates of the concentration profiles. A PLS2 model was then constructed using the calculated concentration profiles and the preprocessed UV spectra. Using a standard PLS model compatible with the spectrometer's standard process software facilitated real-time predictions for new batches. This method was applied to five 45 liter batches in a large-scale laboratory facility. The method successfully predicted the product concentration of batch 1 but exhibited larger prediction error for subsequent batches. The largest prediction error was attained during batch 3, for which a final concentration of 0.22 mole L(-1) was predicted, while the true measured value was 0.271 mole L(-1) (an error of 18.8%). However, the qualitative real-time profiles proved to be extremely useful as they allowed the end-point to be determined without sampling or performing off-line analysis. Furthermore, the concentration profile of the intermediate species, which could not be observed by the offline method, could also be observed in real-time and gave further confidence that the process was approaching the end-point. Another benefit of real-time reaction profiles was encountered during the manufacture when the formation of product in batch 3 appeared to be progressing slower than was observed in previous batches. This prompted a check of the batch temperature and it was found to be 10 degrees C lower than the required set-point. The temperature was corrected and the batch successfully reached completion in the expected time.

The automatic recognition and counting of cough.

BACKGROUND: Cough recordings have been undertaken for many years but the analysis of cough frequency and the temporal relation to trigger factors have proven problematic. Because cough is episodic, data collection over many hours is required, along with real-time aural analysis which is equally time-consuming. A method has been developed for the automatic recognition and counting of coughs in sound recordings. METHODS: The Hull Automatic Cough Counter (HACC) is a program developed for the analysis of digital audio recordings. HACC uses digital signal processing (DSP) to calculate characteristic spectral coefficients of sound events, which are then classified into cough and non-cough events by the use of a probabilistic neural network (PNN). Parameters such as the total number of coughs and cough frequency as a function of time can be calculated from the results of the audio processing. Thirty three smoking subjects, 20 male and 13 female aged between 20 and 54 with a chronic troublesome cough were studied in the hour after rising using audio recordings. RESULTS: Using the graphical user interface (GUI), counting the number of coughs identified by HACC in an hour long recording, took an average of 1 minute 35 seconds, a 97.5% reduction in counting time. HACC achieved a sensitivity of 80% and a specificity of 96%. Reproducibility of repeated HACC analysis is 100%. CONCLUSION: An automated system for the analysis of sound files containing coughs and other non-cough events has been developed, with a high robustness and good degree of accuracy towards the number of actual coughs in the audio recording.