https://www.selleckchem.com/products/cia1.html The flexible device has been demonstrated to monitor a variety of human activities and physical stimuli. The assembled sensor array can accurately and reliably detect the pressure distribution.Adulteration of milk poses a severe health hazard, and it is crucial to develop adulterant-detection techniques that are scalable and easy to use. Water and urea are two of the most common adulterants in commercial milk. Detection of these adulterants is both challenging and costly in urban and rural areas. Here we report on an evaporation-based low-cost technique for the detection of added water and urea in milk. The evaporative deposition is shown to be affected by the presence of adulterants in milk. We observe a specific pattern formation of nonvolatile milk solids deposited at the end of the evaporation of a droplet of unadulterated milk. These patterns alter with the addition of water and urea. The evaporative deposits are dependent on the concentrations of water and urea added. The sensitivity of detection of urea in milk improves with the dilution of milk with water. We show that our method can be used to detect a urea concentration as low as 0.4% in milk. Based on the detection level of urea, we present a regime map that shows the concentration of urea that can be detected at different extents of dilution of milk.Three modeling techniques, namely, a radial basis function neural network (RBFNN), a comprehensive kinetic with genetic algorithm (CKGA), and a response surface methodology (RSM), were used to study the kinetics of Fischer-Tropsch (FT) synthesis. Using a 29 × 37 (4 independent process parameters as inputs and corresponding 36 responses as outputs) matrix with total 1073 data sets for data training through RBFNN, the established model is capable of predicting hydrocarbon product distribution i.e., the paraffin formation rate (C2-C15) and the olefin to paraffin ratio (OPR) within acceptable uncertainties. With addi