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...     

Comparison of the Parameters of UWB Signals for Comparing the Characteristics of the Human Visual System and Matrix Receivers

Journal of Communications Technology and Electronics - A quantitative comparison of the spectral characteristics of the human visual system and matrix photodetectors is carried out. Criteria for a...     

Towards an integrated neonatal brain and cardiac examination capability at 7 T: electromagnetic field simulations and early phantom experiments using an 8-channel dipole array

Objective

Neonatal brain and cardiac imaging would benefit from the increased signal-to-noise ratio levels at 7 T compared to lower field. Optimal performance might be achieved using purpose designed RF coil arrays. In this study, we introduce an 8-channel dipole array and investigate, using simulations, its RF performances for neonatal applications at 7 T.

Methods

The 8-channel dipole array was designed and evaluated for neonatal brain/cardiac configurations in terms of SAR efficiency (ratio between transmit-field and maximum specific-absorption-rate level) using adjusted dielectric properties for neonate. A birdcage coil operating in circularly polarized mode was simulated for comparison. Validation of the simulation model was performed on phantom for the coil array.

Results

The 8-channel dipole array demonstrated up to 46% higher SAR efficiency levels compared to the birdcage coil in neonatal configurations, as the specific-absorption-rate levels were alleviated. An averaged normalized root-mean-square-error of 6.7% was found between measured and simulated transmit field maps on phantom.

Conclusion

The 8-channel dipole array design integrated for neonatal brain and cardiac MR was successfully demonstrated, in simulation with coverage of the baby and increased SAR efficiency levels compared to the birdcage. We conclude that the 8Tx-dipole array promises safe operating procedures for MR imaging of neonatal brain and heart at 7 T.

     

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...     

Polysorbate identity and quantity dictate the extensional flow properties of protein-excipient solutions

While protein medications are promising for treatment of cancer and autoimmune diseases, challenges persist in terms of development and injection stability of high-concentration formulations. Here, the extensional flow properties of protein-excipient solutions are examined via dripping-onto-substrate extensional rheology, using a model ovalbumin (OVA) protein and biocompatible excipients polysorbate 20 (PS20) and 80 (PS80). Despite similar PS structures, differences in extensional flow are observed based on PS identity in two regimes: at moderate total concentrations where surface tension differences drive changes in extensional flow behavior, and at small PS:OVA ratios, which impact the onset of weakly elastic flow behavior. Undesirable elasticity is observed in ultra-concentrated formulations, independent of PS identity; higher PS contents are required to observe these effects than in analogous polymeric excipient solutions. These studies reveal novel extensional flow behaviors in protein-excipient solutions, and provide a straightforward methodology for assessing the extensional flow stability of new protein-excipient formulations.     

Optimal Water Allocation of Surface and Ground Water Resources Under Climate Change with WEAP and IWOA Modeling

Water Resources Management - The water evaluation and planning (WEAP) approach and the invasive weed optimization algorithm (IWOA) are herein employed to determine the optimal operating policies in...