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.     

Analysis of stepwise charging limits and its implementation for efficiency improvement in switched capacitor DC–DC converters

In this work we analysed the stepwise charging technique to find the limits from which it is beneficial in terms of load capacitance and charge–discharge frequency. We included in the analysis practical limitations such as the consumption of auxiliary logic needed to implement the technique and the minimum size of auxiliary switches imposed by the technology. We proposed an ultra-low-power logic block to push these limits and to obtain benefits from this technique in small capacitances. Finally, we proposed to use a stepwise driver in the driving of the gate capacitance of power switches in switched-capacitor (SC) DC–DC converters. We designed and manufactured, in a 130 nm process, a SC DC–DC converter and measured a 29% energy reduction in the gate-drive losses of the converter. This accounts for an improvement of 4% (from 69 to 73%) in the overall converter efficiency.

     

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.

     

Physicochemical and technological properties of beef burger as influenced by the addition of pea fibre

This study aimed to evaluate the physicochemical characteristics and sensory attributes of beef burgers with the addition of pea fibre as a partial substitute of meat or fat. Three formulations were prepared: control (CON) – similar to the commercial formulation; fibre/less meat (FLM)—5% meat reduction and addition of 1% pea fibre; fibre/less fat (FLF)—7% fat reduction and addition of 1% pea fibre. Non-significant differences were obtained for pH, colour parameters (L* and b*), texture profile, cooking loss and size reduction among formulations. Moreover, sensory analysis with consumers of beef burgers did not indicate differences among the formulations for all the analysed attributes. Therefore, pea fibre is a promising partial replacer for meat and fat in beef burgers due to the preservation of technological parameters and sensory acceptance.     

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...