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.     

Effect of Drosophila suzukii on Blueberry VOCs: Chemical Cues for a Pupal Parasitoid,Trichopria anastrephae

Journal of Chemical Ecology - Biocontrol agents such as parasitic wasps use long-range volatiles and host-associated cues from lower trophic levels to find their hosts. However, this chemical...     

Porous Co3O4 column as a high-performance Lithium anode material

Journal of Porous Materials - Co3O4 has been widely investigated as a promising candidate anode material for lithium-ion batteries. We report on the porous Co3O4 column synthesized via a simple...     

A cheating immune (k, n) visual cryptography scheme by using the rotation of shares

Multimedia Tools and Applications - Since Noar and Shamir introduced visual cryptography scheme (VCS), the cheating problem of VCS has absorbed much attention of scholars. The current researches on...     

Influence of Continuous Wave Interference on the Efficiency of the Non-Threshold Search Procedure for a Noise-Like Signal by Delay Time with Transition to the Frequency Domain

Journal of Communications Technology and Electronics - A statistical study of the effectiveness of the non-threshold search procedure for a noise-like phase-shift keyed signal by the delay time is...     

A novel strategy based on salp swarm algorithm for extracting the maximum power of proton exchange membrane fuel cell

Cell temperature and water content of the membrane have a significant effect on the performance of fuel cells. The current-power curve of the fuel cell has a maximum power point (MPP) that is needed to be tracked. This study presents a novel strategy based on a salp swarm algorithm (SSA) for extracting the maximum power of proton-exchange membrane fuel cell (PEMFC). At first, a new formula is derived to estimate the optimal voltage of PEMFC corresponding to MPP. Then the error between the estimated voltage at MPP and the actual terminal voltage of the fuel cell is fed to a proportional-integral-derivative controller (PID). The output of the PID controller tunes the duty cycle of a boost converter to maximize the harvested power from the PEMFC. SSA determines the optimal gains of PID. Sensitivity analysis is performed with the operating fuel cell at different cell temperature and water content of the membrane. The obtained results through the proposed strategy are compared with other programmed approaches of incremental resistance method, Fuzzy-Logic, grey antlion optimizer, wolf optimizer, and mine-blast algorithm. The obtained results demonstrated high reliability and efficiency of the proposed strategy in extracting the maximum power of the PEMFC.     

The effect of alkali metal oxide on the properties of borosilicate fireproof glass: Structure,thermal properties,viscosity, chemical stability

The purpose of the current work was to research the effect of alkali metal oxide on the structure, thermal properties, viscosity and chemical stability in the glass system (R₂O–CaO–B₂O₃–SiO₂) systematically. Because the glass would emulsify when Li₂O was added to the glass batch, this article did not discuss Li₂O. The results showed that when the amount of Na₂O was less than 4 mol.%, there was a higher interconnectivity of borate and silicate sub-networks in glass, as more mixed Si–O–B bonds were present in glass. The glass samples exhibited excellent thermal properties and chemical stabilities. As the amount of Na₂O exceeded 4 mol.%, the interconnectivity of borate and silicate sub-networks was weakened. The thermal properties and chemical stabilities of the glass samples were reduced. The connectivity of the silicate sub-network was weakened slightly as the Na/K ratio varied, and the coefficient of thermal expansion (CTE) of the glass samples gradually increased, and the resistance to thermal shock (RTS) value gradually decreased. Moreover, the viscosity of the glass samples decreased with the ratio of Na/Si and Na/K increased.     

5G-oriented Virtual Augmented Reality Scene Construction and Business Information Flow Demonstration

Mobile Networks and Applications - Aiming at the problems of large data volume, long calculation time, and information feedback speed in traditional virtual augmented reality-based scenes, this...     

Thermal prehistory,structure and high-temperature thermodynamic properties of Y2O3-CeO2 and Y2O3-ZrO2-CeO2 solid solutions

Ceria-based solid solutions are important materials for high- and medium-temperature electrochemical applications. However, the stabilities of both binary and ternary ceria-based solid solutions are insufficient at elevated temperatures, which limits their application as solid electrolytes or SOFC cathodes. Data on the high-temperature stability of ceria-based ceramics are unavailable in the literature. In the present study, we report a thermodynamic stability investigation of Y₂O₃-CeO₂ and Y₂O₃-ZrO₂-CeO₂ solid solutions. The thermal prehistories of binary and ternary systems were investigated using STA, XRD, and ESCA techniques. The vaporization processes were investigated in the temperature range of 1577–2227°С via the Knudsen effusion mass spectrometry technique. Using data on the component activity in solid-phase thermodynamic properties of Y₂O₃-CeO₂ solid solutions, which is represented as the Gibbs energy, the excess Gibbs energy was calculated as a function of the ceria mol. %. It was shown that the reduction of Ce⁴⁺ to Ce³⁺ in Y₂O₃-CeO₂ and Y₂O₃-ZrO₂-CeO₂ solid solutions corresponds to less-negative Gibbs energy compared to ZrO₂-CeO₂ solid solutions.