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.     

κ-Opioid Signaling in the Lateral Hypothalamic Area Modulates Nicotine-Induced Negative Energy Balance

Several studies have reported that nicotine, the main bioactive component of tobacco, exerts a marked negative energy balance. Apart from its anorectic action, nicotine also modulates energy expenditure, by regulating brown adipose tissue (BAT) thermogenesis and white adipose tissue (WAT) browning. These effects are mainly controlled at the central level by modulation of hypothalamic neuropeptide systems and energy sensors, such as AMP-activated protein kinase (AMPK). In this study, we aimed to investigate the kappa opioid receptor (κOR)/dynorphin signaling in the modulation of nicotine’s effects on energy balance. We found that body weight loss after nicotine treatment is associated with a down-regulation of the κOR endogenous ligand dynorphin precursor and with a marked reduction in κOR signaling and the p70 S6 kinase/ribosomal protein S6 (S6K/rpS6) pathway in the lateral hypothalamic area (LHA). The inhibition of these pathways by nicotine was completely blunted in κOR deficient mice, after central pharmacological blockade of κOR, and in rodents where κOR was genetically knocked down specifically in the LHA. Moreover, κOR-mediated nicotine effects on body weight do not depend on orexin. These data unravel a new central regulatory pathway modulating nicotine’s effects on energy balance.     

Tailored-Made Polydopamine Nanoparticles to Induce Ferroptosis in Breast Cancer Cells in Combination with Chemotherapy

Ferroptosis is gaining followers as mechanism of selective killing cancer cells in a non-apoptotic manner, and novel nanosystems capable of inducing this iron-dependent death are being increasingly developed. Among them, polydopamine nanoparticles (PDA NPs) are arousing interest, since they have great capability of chelating iron. In this work, PDA NPs were loaded with Fe³⁺ at different pH values to assess the importance that the pH may have in determining their therapeutic activity and selectivity. In addition, doxorubicin was also loaded to the nanoparticles to achieve a synergist effect. The in vitro assays that were performed with the BT474 and HS5 cell lines showed that, when Fe³⁺ was adsorbed in PDA NPs at pH values close to which Fe(OH)₃ begins to be formed, these nanoparticles had greater antitumor activity and selectivity despite having chelated a smaller amount of Fe³⁺. Otherwise, it was demonstrated that Fe³⁺ could be released in the late endo/lysosomes thanks to their acidic pH and their Ca²⁺ content, and that when Fe³⁺ was co-transported with doxorubicin, the therapeutic activity of PDA NPs was enhanced. Thus, reported PDA NPs loaded with both Fe³⁺ and doxorubicin may constitute a good approach to target breast tumors.     

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.

     

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...