Design of Deep Convolution Neural Networks for categorical signature classification of raw panchromatic satellite images

Multimedia Tools and Applications - Remote Sensing categorical signature classification has gained significant implications on spatial resolution image analysis due to differences in the...     

Influence of Titanium Excess in a Ti–Si Reaction Mixture on the Synthesis of a Composite in a Combustion Wave

Theoretical Foundations of Chemical Engineering - On the basis of the classic concepts of the theory of solid-phase combustion, for the first time, a model with a detailed scheme of chemical...     

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...     

The structure,vacancy characteristics,and magnetic properties of GdMn1-xCrxO3 ceramics

To investigate the evolution of the structural and enhanced magnetic properties of GdMnO₃ systems induced by the substitution of Mn with Cr, polycrystalline GdMn_1-xCr_xO₃ samples were synthesized via solid-state reactions. XRD characterization shows that all GdMn_1-xCr_xO₃ compounds with single-phase structures crystallize well and that Cr³⁺ ions entering the lattice sites of GdMnO₃ induce structural distortion. SEM results indicate that the grain size of the synthesized samples (a few microns) decreases as the Cr substitution concentration increases. Positron annihilation lifetime spectroscopy reveals that vacancy-type defects occur in GdMn_1-xCr_xO₃ ceramics and that the vacancy size and concentration clearly change with the Cr content. The temperature and field dependence of the magnetization curves show that Cr substitution significantly influences the magnetic ordering of the gadolinium sublattice, improving the weak ferromagnetic transition temperature and magnetization of GdMn_1-xCr_xO₃. The enhanced magnetization of GdMn_1-xCr_xO₃ is closely related to the vacancy defect concentration.     

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.     

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.     

Interfacial microstructure and mechanical properties of Ti3SiC2 ceramic and TC11 alloy joint diffusion bonded with a Cu interlayer

Reliable joints of Ti₃SiC₂ ceramic and TC11 alloy were diffusion bonded with a 50 μm thick Cu interlayer. The typical interfacial structure of the diffusion boned joint, which was dependent on the interdiffusion and chemical reactions between Al, Si and Ti atoms from the base materials and Cu interlayer, was TC11/α-Ti + β-Ti + Ti₂Cu + TiCu/Ti₅Si₄ + TiSiCu/Cu(s, s)/Ti₃SiC₂. The influence of bonding temperature and time on the interfacial structure and mechanical properties of Ti₃SiC₂/Cu/TC11 joint was analyzed. With the increase of bonding temperature and time, the joint shear strength was gradually increased due to enhanced atomic diffusion. However, the thickness of Ti₅Si₄ and TiSiCu layers with high microhardness increased for a long holding time, resulting in the reduction of bonding strength. The maximum shear strength of 251 ± 6 MPa was obtained for the joint diffusion bonded at 850 °C for 60 min, and fracture primarily occurred at the diffusion layer adjacent to the Ti₃SiC₂ substrate. This work provided an economical and convenient solution for broadening the engineering application of Ti₃SiC₂ ceramic.