Modeling of laser-induced breakdown spectroscopic data analysis by an automatic classifier

Laser-induced breakdown spectroscopy (LIBS) is a multi-elemental and real-time analytical technique with simultaneous detection of all the elements in any type of sample matrix including solid, liquid, gas, and aerosol. LIBS produces vast amount of data which contains information on elemental composition of the material among others. Classification and discrimination of spectra produced during the LIBS process are crucial to analyze the elements for both qualitative and quantitative analysis. This work reports the design and modeling of optimal classifier for LIBS data classification and discrimination using the apparatus of statistical theory of detection. We analyzed the noise sources associated during the LIBS process and created a linear model of an echelle spectrograph system. We validated our model based on assumptions through statistical analysis of “dark signal” and laser-induced breakdown spectra from the database of National Institute of Science and Technology. The results obtained from our model suggested that the quadratic classifier provides optimal performance if the spectroscopy signal and noise can be considered Gaussian

Evaluation of non-Gaussian statistical properties in virtual breast phantoms

Images derived from a "virtual phantom" can be useful in characterizing the performance of imaging systems. This has driven the development of virtual breast phantoms implemented in simulation environments. In breast imaging, several such phantoms have been proposed. We analyze the non-Gaussian statistical properties from three classes of virtual breast phantoms and compare them to similar statistics from a database of breast images. These include clustered-blob lumpy backgrounds (CBLBs), truncated binary textures, and the UPenn virtual breast phantoms. We use Laplacian fractional entropy (LFE) as a measure of the non-Gaussian statistical properties of each simulation procedure. Our results show that, despite similar power spectra, the simulation approaches differ considerably in LFE with very low scores for the CBLB to high values for the UPenn phantom at certain frequencies. These results suggest that LFE may have value in developing and tuning virtual phantom simulation procedures

The Breast Size Satisfaction Survey (BSSS)

The Breast Size Satisfaction Survey (BSSS) was established to assess women’s breast size dissatisfaction and breasted experiences from a cross-national perspective. A total of 18,541 women were recruited from 61 research sites across 40 nations and completed measures of current-ideal breast size discrepancy, as well as measures of theorised antecedents (personality, Western and local media exposure, and proxies of socioeconomic status) and outcomes (weight and appearance dissatisfaction, breast awareness, and psychological well-being). In the total dataset, 47.5% of women wanted larger breasts than they currently had, 23.2% wanted smaller breasts, and 29.3% were satisfied with their current breast size. There were significant cross-national differences in mean ideal breast size and absolute breast size dissatisfaction, but effect sizes were small (η2 = .02-.03). The results of multilevel modelling showed that greater Neuroticism, lower Conscientiousness, lower Western media exposure, greater local media exposure, lower financial security, and younger age were associated with greater breast size dissatisfaction across nations. In addition, greater absolute breast size dissatisfaction was associated with greater weight and appearance dissatisfaction, poorer breast awareness, and poorer psychological well-being across nations. These results indicate that breast size dissatisfaction is a global public health concern linked to women’s psychological and physical well-being