Designing optical glasses by machine learning coupled with a genetic algorithm

Engineering new glass compositions have experienced a sturdy tendency to move forward from (educated) trial-and-error to data- and simulation-driven strategies. In this work, we developed a computer program that combines data-driven predictive models (in this case, neural networks) with a genetic algorithm to design glass compositions with desired combinations of properties. First, we induced predictive models for the glass transition temperature (T_g) using a dataset of 45,302 compositions with 39 different chemical elements, and for the refractive index (n_d) using a dataset of 41,225 compositions with 38 different chemical elements. Then, we searched for relevant glass compositions using a genetic algorithm informed by a design trend of glasses having high n_d (1.7 or more) and low T_g (500 °C or less). Two candidate compositions suggested by the combined algorithms were selected and produced in the laboratory. These compositions are significantly different from those in the datasets used to induce the predictive models, showing that the used method is indeed capable of exploration. Both glasses met the constraints of the work, which supports the proposed framework. Therefore, this new tool can be immediately used for accelerating the design of new glasses. These results are a stepping stone in the pathway of machine learning-guided design of novel glasses.     

Microwave hybrid fast sintering of red clay ceramics

This work aimed to examine the performance of the hybrid sintering of clay ceramic in a microwave furnace, compared to the sintering process in a conventional furnace. The raw materials were subjected to X-ray fluorescence, loss on ignition (LOI), X-ray diffraction, particle size distribution, real specific mass, and thermogravimetric analyses. The red clay ceramic mass was prepared, extruded, pre-sintered in a conventional furnace at 600°C/60 min, and sintered at temperatures between 700 °C and 1100 °C. The sintering conventional (resistive oven) was carried out for 60 min with a heating rate of 10°C/min. In the microwave furnace, the sintering times were 5, 10, and 15 min, with a heating rate of 50°C/min, with a sintering chamber coated with silicon carbide (susceptor). The sintered specimens were characterized according to linear shrinkage, water absorption, apparent porosity, apparent specific mass, X-ray diffraction, Raman spectroscopy analysis, spectroscopy analysis in the ultraviolet and visible regions, microhardness, and scanning electron microscopy. The results showed that microwave sintering promoted an increase in the microhardness and apparent specific mass, and reduction in water absorption and apparent porosity values, due to greater densification in the microstructure. The best results occurred for specimens sintered at 1100°C.     

Methodology for Estimating Streamflow by Water Balance and Rating Curve Methods Based on Logistic Regression

Both water balance (WB) and rating curve (RC) are methods for estimating streamflow. The first is mostly used to estimate reservoir outflows, while the second is usually adopted in hydrometeorological network streamflow gauges. While WB uses hourly collected data, the RC estimates streamflow using current water level and extrapolation techniques. The objective of this study was to analyze variations in the reservoir’s hourly outflow at Queimado Hydroelectric Power Plant (HPP Queimado) and to propose a method to evaluate whether the estimate of the daily outflows, obtained by the WB method, is similar to the flow values obtained at a conventional station. The logistic regression (LR) model was used because it is a method that adopts binary, categorically dependent variables to identify the event of interest. The results showed that the values of streamflow, obtained from an average of two daily readings, were a good representation of the flows in the region. The LR was able to identify atypical data, especially in the rainy season. This means that data consistency analysis can be faster and safer, when adequately employed and considering the proposed conditions, contributing to both management policies and the management of water resources.

     

Fat Distribution and Lipid Profile of Young Adults with Congenital Adrenal Hyperplasia Due to 21-Hydroxylase Enzyme Deficiency

We aimed to compare detailed fat distribution and lipid profile between young adults with congenital adrenal hyperplasia due to 21-hydroxylase enzyme deficiency and a control group. We also verified independent associations of treatment duration and daily hydrocortisone dose equivalent (HDE) with lipid profile within patients. This case–control study included 23 patients (7 male and 16 female) matched by an age range of young adults (18–31 years) with 20 control subjects (8 male and 12 female). Dual energy X-ray absorptiometry was used to measure the fat distribution. Male patients demonstrated elevated indices of fat mass for total (7.7 ± 2.1 vs. 4.5 ± 1.3 kg/m², p = 0.003), trunk (4.0 ± 1.2 vs. 2.2 ± 0.8 kg/m², p = 0.005), android (0.63 ± 0.24 vs. 0.32 ± 0.15 kg/m², p = 0.008), gynoid (1.34 ± 0.43 vs. 0.74 ± 0.24 kg/m², p = 0.005), arm (0.65 ± 0.16 vs. 0.39 ± 0.10 kg/m², p = 0.009), and leg regions (2.7 ± 0.8 vs. 1.6 ± 0.4 kg/m², p = 0.005) than the control group, but not in females. However, female patients demonstrated elevated ratio of low-density lipoprotein cholesterol to high-density lipoprotein cholesterol (1.90 ± 0.46 vs. 1.39 ± 0.47, p = 0.009) than the control group, but not in males. Total fat mass was inversely correlated with total testosterone (r = −0.64, p = 0.014) and positively correlated with leptin in males (r = 0.75, p = 0.002). An elevated daily HDE (β = 0.43, p = 0.038 and β = 0.47, p = 0.033) and trunk to total fat mass ratio (β = 0.46, p = 0.025, and β = 0.45, p = 0.037) were independently correlated with impaired lipid profile markers. Although there is no altered lipid profile, male patients demonstrated an increased fat distribution. However, female patients presented with an impaired lipid profile marker but demonstrated close values of normal fat distribution. Interestingly, the dose of glucocorticoid therapy can have some role in the lipid mechanisms.     

Pressure drop and optimization meta-models for arbitrary low-height pleated filter shapes and flowrates

Journal of Mechanical Science and Technology - This study delivers equations useful for low-height pleated fibrous filter design: two pressure drop equations and one set of optimum design equations...     

Optimization of quinacridone magenta,Cu-phthalocyanine cyan,and arylide yellow ink films formulated for maximum color gamut

Investigation of ink formulation options with the purpose to obtain color-gamut-optimal set of Cyan Magenta and Yellow CMY inks is reported. Implementation of the thickness dependent Kubelka-Munk model on multiple ink layers having different and well-defined thicknesses, provides characteristic absorption and scattering (K, S ) spectra of the ink ingredients. These data enable accurate computation of the reflectance spectrum and thus the L*a*b* color coordinates for any given ink thickness or substrate. Pigment materials investigated are quinacridone as magenta, copper-phthalocyanine as cyan, and arylide yellow. Scaling the peak of the absorption band to the number of molecules per unit area for the specific pigments studied in this article provides the molar extinction coefficients, 1.21 × 10⁴ , 4.7 × 10⁴ , and 3.3 × 10⁴ cm²/millimole respectively, regardless of the different ink formulations used, in accord with Avogadro's principle. Having a set of three pairs of K, S spectra is used to compute the color gamut of any CMY color combination in the L*a*b* space as a function of ink layer thickness and formulation. Using an iterative algorithm, a color-gamut-optimal set of CMY inks is obtained.