Edible oil adulteration is a main concern for consumers. This paper presents a study on the use of smartphone, coupled with image processing and chemometrics, to quantify adulterant levels in extra virgin olive oil. A sequence of light with varying colours is generated on the phone screen, which is used to illuminate oil samples.Videos are recorded to capture the colour changes on sample surface and are subsequently converted into spectral data for analysis. To evaluate the performance of this video approach, partial least squares regression models constructed from such video data as well as near-infrared, ultraviolet-visible and digital imaging data are compared in the task of quantifying the level of vegetable oil in extra virgin olive oil in the range 5%−50% (v/v).The results show that the video approach (R 2 = 0.98 and RMSE = 0.02) yields comparable performance to baseline spectroscopy techniques and outperforms computer vision system approach. Since the smartphone-based sensor system is low-cost and easy to operate, it has high potential to become a consumer-oriented solution for detecting edible oil adulteration.
The factors that affect productivity are a major focus in construction. This article proposes a machine learning–based approach to predict task productivity by using a subjective measure (compatibility of personality), together with external and site conditions, and other workers' characteristics. The approach integrates K‐nearest neighbor (KNN), deep neural network (DNN), logistic regression, support vector machine (SVM), and ResNet18 to discover the mapping between input and output variables, alongside rigorous statistical analyses to interpret data. A database including 1977 productivity measures is utilized to train, test, and validate the approach. Results test rules in the masonry industry, which do not seem to have been tested before: Small crews are more productive than large crews; higher compatibility results in higher productivity in easy but not in difficult tasks; the relevance of experience to task productivity may depend on the difficulty of the task.
We present a study on the radial profiles of the D4000, luminosity-weighted stellar ages τ L, and luminosity-weighted stellar metallicities [Z/H]L of 3654 nearby galaxies (0.01 < z < 0.15) using the IFU spectroscopic data from the MaNGA survey available in the SDSS DR15, in an effort to explore the connection between median stellar population radial gradients (i.e., ∇D4000, ∇τ L, ∇[Z/H]L) out to ∼1.5 R e and various galaxy properties, including stellar mass (M ⋆), specific star formation rate (sSFR), morphologies, and local environment. We find that M ⋆ is the single most predictive physical property for ∇D4000 and ∇[Z/H]L. The most predictive properties for ∇τ L are sSFR and, to a lesser degree, M ⋆. The environmental parameters, including local galaxy overdensities and central–satellite division, have virtually no correlation with stellar population radial profiles for the whole sample, but the ∇D4000 of star-forming satellite galaxies with M ⋆ ≲ 1010 M ⊙ exhibit a significant positive correlation with galaxy overdensities. Galaxies with lower sSFR have on average steeper negative stellar population gradients, and this sSFR dependence is stronger for more massive star-forming galaxies. The negative correlation between the median stellar population gradients and M ⋆ are best described largely as segmented relationships, whereby median gradients of galaxies with log M ⋆ ≲ 10.0 (with the exact value depending on sSFR) have much weaker mass dependence than galaxies with higher M ⋆. While the dependence of the radial gradients of ages and metallicities on T-Types and central stellar mass surface densities are generally not significant, galaxies with later T-Types or lower central mass densities tend to have significantly lower D4000, younger τ L, and lower [Z/H]L across the radial ranges probed in this study.
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